Abdominal visceral to subcutaneous fat distribution in dogs: computed tomography accuracy and factors associated with distribution

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S. Turner, S. M. Firestone, F. R. Dunshea, C. S. Mansfield This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5272968/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Abdominal fat distribution, particularly visceral fat, is commonly assessed as a marker of obesity-related and metabolic diseases in people. Whilst this relationship may exist, few studies consider the factors that may influence the relative distribution of visceral and subcutaneous abdominal fat in dogs. This cross-sectional study evaluated associations between visceral and subcutaneous fat distribution (V/SQ), total abdominal adiposity, body condition score (BCS), age, sex, neuter status, and breed conformation in 205 dogs presenting to a tertiary veterinary hospital. The influence of several disease states on abdominal adiposity and fat distribution was also evaluated. Additionally, the study aimed to assess the reliability of computed tomography (CT) measures of abdominal adiposity and fat distribution. Results Total abdominal adiposity increased with age, reaching a plateau around 10 years before gradually decreasing, and was lower in terrier breeds and dogs with neoplasia. The V/SQ fat ratio increased with age and was higher in hounds and terriers, but decreased with increasing BCS, total abdominal adiposity, and thoracic height-width ratio. Additionally, V/SQ was higher in dogs with hyperadrenocorticism. Body condition score was moderately correlated with total abdominal, visceral, and subcutaneous adiposity. Abdominal fat areas measured at L3 overestimated total abdominal and visceral fat percentages but underestimated subcutaneous fat percentages, with increasing bias at higher fat percentages. Linea alba fat measurements were moderately correlated with total abdominal adiposity, but only weakly correlated with abdominal fat distribution. Conclusions This study reinforces the association between abdominal adiposity, age, breed category, and potentially certain diseases like neoplasia. Moreover, it highlights the correlation between V/SQ fat distribution, age, and total adiposity, whilst emphasising the preferential distribution of fat to the visceral compartment in dogs with hyperadrenocorticism. The study also identified a novel association between V/SQ fat distribution, specific breed categories and body conformation (i.e. thoracic height-width ratio). Importantly, CT volumetric measures are more reliable in determining abdominal fat distribution than area and linear measures, instilling confidence in the study’s methodology and its implications for future research and clinical practice. body composition fat distribution visceral fat subcutaneous fat CT dog Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Overweight and obesity (OO) are conditions defined as abnormal or excessive fat accumulation that may impair health, contribute to disease, and shorten lifespan (1–4). Dogs are considered overweight when their body fat exceeds 20–30% and obese when it surpasses 30% of their body weight (4). Overweight and obesity is recognised as one of the most prevalent disorders affecting pet dogs, with a 22–60% prevalence reported (5–10). Increased adiposity has welfare implications as OO is associated with chronic dysregulation in adipokines, increases in proinflammatory markers, reduced quality of life and longevity, insulin resistance, and chronic diseases such as osteoarthritis, urinary tract infections, neoplasia, cardiorespiratory dysfunction and potential hepatic dysfunction (11–14). Further, OO influences the pharmacokinetics of many drugs, commonly affecting the volume of distribution and rate of drug clearance (15, 16). Thus, OO has clinical and research implications, and veterinarians should tailor their treatments to account for dog adiposity. Obesity is predominantly a nutritional disorder (17, 18). However, OO is more complicated than this and should be considered a multifactorial process that includes dog-related (e.g. signalment, genetic factors, exercise volume), diet-related (e.g. amount of food intake, frequency of treats, consumption of table scraps), owner-related (e.g. demographic, socioeconomic situation, ability to exercise); disease-related (e.g. hyperadrenocorticism, hypothyroidism), pharmaceutical-related (e.g. glucocorticoids or anticonvulsants administration), and environment-related (e.g. exposure to obesogens) factors (5, 13). A potential contributor to OO pathophysiology is the relative distribution of fat to the visceral (central) or subcutaneous (peripheral) regions in dogs. Though there is scant and, at times, contradictory research, increased fat distribution to the visceral compartment surrounding the organs may be more inflammatory and more significantly influence adipokine, cardiovascular and metabolic dysregulation than general OO (19–22). Current evidence suggests increased visceral-to-subcutaneous (V/SQ) fat deposition is associated with increasing age, neuter status, and certain dog breeds, but not with specific diets (23–26). Further, preferential visceral fat deposition may be favoured in some disease states, such as hyperadrenocorticism (HAC), though this has not been objectively evaluated in dogs (27, 28). Investigation of these and other associations needs to be established if visceral adiposity's effect on dogs' overall health is to be fully elucidated and early intervention initiated. In humans, the gold standard methods for measuring visceral fat are CT and MRI, though dual-energy X-ray absorptiometry (DXA) is commonly used due to its relatively high accuracy, accessibility and low radiation exposure (29, 30). Visceral fat area (VFA) established from a single transverse slice through the abdomen at the level of the third lumbar vertebrae is widely used in people (29). Ultrasound measurements have also been used to estimate human visceral fat to provide greater access and limit radiation exposure, but reproducibility and accuracy are poor (29). Further, there is growing consensus focused on the abdominal circumference of people, supported by body mass index (BMI), as a more accurate reflector of central obesity and its relationship with metabolic health dysfunction (31). Some of these methods have been replicated in dog studies, with CT-determined VFA at the third lumbar vertebra commonly referenced, and CT volume, MRI and ultrasound methods published (23, 26, 32–35). However, there is limited validation and standardisation of these techniques. Thus, this cross-sectional observational study aimed to assess for an association between abdominal visceral and subcutaneous (SQ) fat distribution, total abdominal adiposity, body condition score (BCS), age, sex, neuter status, and breed conformation in a tertiary veterinary hospital population of dogs. The influence of several disease states on abdominal adiposity and abdominal fat distribution was also evaluated. Finally, the use of single fat area measurements at the third lumbar vertebra and a single measurement of the falciform fat thickness were compared and validated against the abdominal volume measurements of fat. Results Descriptive Statistics A total of 205 abdominal CTs were analysed from the original 448 CTs retrieved from the hospital records between March 2006 and March 2020. Data were 100% complete for breed, age, sex-neuter status and body weight and 80% for BCS. Supplemental 1 (S1) displays frequency cross-tabulations and univariable comparisons for categorical data. There was a bimodal age distribution, with an overall median age of 8.0 years (IQR: 2.7 to 10.8 years; range: 3 months to 16.3 years), with modes at approximately 1–2 years and 10–11 years. Neutered dogs (mean ± SD = 7.7 ± 4.3 years [95% CI of mean: 7.0, 8.3 years]) were significantly older than entire dogs (4.9 ± 4.2 years [95% CI: 3.4, 6.4 years]). The median body weight was 21.0 kg (IQR: 10.1 to 32.8 kg; range: 1.2 to 67.8 kg), with a median body condition score of 5 out of 9 (IQR: 4 to 6; range: 2 to 9). There were 174 purebred and 74 mixed-bred dogs, with predominant breeds including Border collies (n = 13), Labrador retriever (n = 11), Staffordshire terriers (n = 9), golden retrievers (n = 7), German shepherds (n = 7), Jack Russell terriers (n = 6), beagles (n = 5), and pugs (n = 5). No other breed had more than five representatives. When categorised according to ANKC standards, there were 32 working dogs, 24 gundogs, 24 utility dogs, 19 terriers, 11 non-sporting breeds, 11 toy breeds and 10 hounds. Two American and two Australian Bulldogs were considered mixed breeds due to ANKC recognition limitations, resulting in 74 mixed-breed dogs. Additionally, there were 88 mesocephalic, 25 brachycephalic, and 18 dolichocephalic dogs, with 99 non-chondrodystrophic and 32 chondrodystrophic dogs. The HWR varied across breed groups, as shown in Supplemental Table 2 (S2). Specifically, working dogs and non-sporting ANKC breeds, giant breeds, and dolichocephalic dogs had deeper, narrower cranial abdomens, while chondrodystrophic dogs exhibited shallower, wider cranial abdomens. Hounds exhibited deeper chests in this sample, although the observed difference was not statistically significant. Abdominal Fat Distribution The multivariable associations between each risk factor and fat distribution are displayed in Tables 1 and 2 and Supplementals 3 and 4. The CT confounding variables significantly reduced total abdominal, visceral and SQ fat measurements. Specifically, small abdominal organs and an increasing volume of effusion emerged as the main contributors to CT confounding. Small abdominal organs resulted in an increased V/SQ ratio, whilst the other CT confounders did not show a statistically significant effect on the V/SQ ratio. Notably, the greatest impact of effusion and small organs was on SQ fat distribution, as the V/SQ ratio increased when these confounders were not adjusted for total abdominal adiposity. Table 1 Putative causal diagram-guided logistic regression analysis outputs for variables associated with CTV total abdominal fat percentage in a tertiary veterinary hospital population of dogs Variable Adjustment set (total effect) Unadjusted estimates Adjusted estimates p-value Model estimated marginal means for levels Age (years, quadratic) Age, centred CT Confounders, Confirmed Hyperadrenocorticism 1.683 (1.299, 2.067) 1.670 (1.293, 2.046) < 0.001 (Age, centred) 2 Age, centred, CT Confounders, Confirmed Hyperadrenocorticism -0.314 (-0.417, -0.211) -0.222 (-0.312, -0.132) < 0.001 Sex Females Age‡, CT Confounders, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 46.98 (42.74, 51.21) Males -2.112 (-6.054, 1.831) -1.125 (-4.257, 2.007) 0.482 45.85 (41.52, 50.19) Neuter Status Entire Age‡, CT Confounders, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 43.56 (38.01, 49.11) Desexed 7.943 (2.600, 13.286) 3.310 (-1.095, 7.714) 0.142 46.87 (42.86, 50.87) Sex-Neuter Status Female entire Age‡, CT Confounders, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 44.80 (36.04, 53.55) Female spayed 8.539 (-1.669, 18.747) 2.351 (-6.073, 10.775) 0.585 47.15 (42.83, 51.47) Male entire -0.274 (-11.567, 11.018) -1.799 (-10.936, 7.337) 0.700 43.00 (36.89, 49.11) Male neutered 7.075 (-3.054, 17.203) 1.651 (-6.661, 9.964) 0.697 46.45 (42.02, 50.88) ANKC Breed Group Toys Age‡, CT Confounders, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 47.77 (39.94, 55.61) Terriers 0.634 (-10.024, 11.293) -6.996 (-15.417, 1.424) 0.105 40.78 (34.32, 47.24) Gundogs 4.605 (-5.637, 14.848) -1.109 (-9.139, 6.921) 0.787 46.67 (40.75, 52.58) Hounds 0.598 (-11.693, 12.89) -1.460 (-11.064, 8.144) 0.766 46.31 (38.32, 54.31) Working Dogs 1.703 (-8.130, 11.535) -0.976 (-8.690, 6.738) 0.804 46.80 (41.30, 52.30) Utility 6.037 (-4.206, 16.28) 2.178 (-5.870, 10.226) 0.596 49.95 (43.94, 55.96) Non-sporting -0.533 (-12.528, 11.462) 3.358 (-6.081, 12.796) 0.487 51.13 (43.36, 58.91) Mixed breed 3.732 (-5.359, 12.822) -1.805 (-8.965, 5.354) 0.622 45.97 (41.71, 50.23) ANKC Terrier Terriers Age‡, CT Confounders, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 40.66 (34.26, 47.06) Non-terriers 2.517 (-4.212, 9.246) 6.184 (0.838, 11.531) 0.024 46.84 (42.89, 50.80) ANKC Hound Hounds Age‡, CT Confounders, Confirmed Hyperadrenocorticism 0.000 0.000 45.98 (37.96, 54.00) Non-hounds 2.439 (-6.626, 11.503) 0.500 (-6.672, 7.671) 0.892 46.48 (42.48, 50.48) 0.5 (-6.672, 7.671) 0.892 Skull Shape Brachycephalic Age‡, CT Confounders, Confirmed Hyperadrenocorticism 4.797 (-3.839, 13.432) 3.100 (-3.756, 9.955) 0.377 49.33 (43.39, 55.26) Mesocephalic 5.491 (-1.735, 12.718) 0.047 (-5.887, 5.980) 0.988 46.27 (41.67, 50.88) Dolichocephalic 0.000 0.000 Reference category 46.23 (39.68, 52.78) Mixed breed 5.877 (-1.464, 13.219) -0.138 (-6.189, 5.913) 0.964 46.09 (41.79, 50.38) 46.59 (42.01, 51.17) Chondrodystrophy Status Chondrodystrophic Age‡, CT Confounders, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 46.72 (41.20, 52.25) Non-chondrodystrophic 1.827 (-3.865, 7.520) 0.161 (-4.371, 4.694) 0.944 46.88 (42.37, 51.39) Mixed breed 2.654 (-3.268, 8.576) -0.602 (-5.331, 4.127) 0.803 46.12 (41.83, 50.41) HWR Age‡, CT Confounders, Confirmed Hyperadrenocorticism -12.041 (-24.563, 0.480) -9.928 (-20.196, 0.340) 0.060 Size Category (Nominal Categorisation with Cross Breed Excluded) Age‡, CT Confounders, Confirmed Hyperadrenocorticism 1.931 (0.136, 3.725) 0.973 (-0.3612, 2.307) 0.156 Age, CT Confounders, Hyperadrenocorticism Size Category (including Mixed Breed) Extra Small Age‡, CT Confounders, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 51.61 (40.95, 62.27) Small -7.373 (-21.312, 6.566) -7.860 (-18.932, 3.213) 0.166 43.75 (37.26, 50.24) Medium-small 4.690 (-9.988, 19.367) -2.322 (-14.021, 9.377) 0.698 49.29 (41.79, 56.78) Medium-large -2.385 (-15.369, 10.599) -6.775 (-17.104, 3.553) 0.200 44.83 (39.75, 49.92) Large 2.979 (-10.341, 16.298) -4.367 (-15.002, 6.269) 0.422 47.24 (41.70, 52.78) Giant 4.035 (-9.752, 17.822) 0.296 (-10.676, 11.269) 0.958 51.90 (45.56, 58.25) Mixed breed 1.150 (-11.591, 13.891) -5.368 (-15.538, 4.801) 0.302 46.24 (41.99, 50.49) BCS (nominal) Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder 5.764 (4.596, 6.932) 3.908 (2.816, 5.000) < 0.001 BCS (categorical) 2 Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder 0.000 0.000 29.95 (20.87, 39.03) 3 4.771 (-5.521, 15.064) 3.354 (-5.488, 12.195) 0.458 33.31 (27.06, 39.55) 4 13.495 (3.818, 23.171) 9.184 (0.771, 17.596) 0.034 39.14 (34.06, 44.22) 5 17.039 (7.422, 26.656) 11.114 (2.709, 19.519) 0.011 41.07 (35.93, 46.21) 6 22.67 (12.429, 32.91) 14.65 (5.534, 23.766) 0.002 44.60 (38.60, 50.60) 7 29.338 (18.597, 40.078) 19.854 (10.32, 29.389) < 0.001 49.81 (43.23, 56.39) 8 37.876 (24.412, 51.34) 28.425 (16.493, 40.357) < 0.001 58.38 (48.89, 67.87) 9 38.573 (22.851, 54.295) 24.628 (10.676, 38.581) < 0.001 54.58 (42.24, 66.93) Abdominal Adiposity Not applicable CTV Mean Hounsfield Units Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder -0.506 (-0.525, -0.488) -0.494 (-0.518, -0.470) < 0.001 Disease Chronicity Acute (< 3 months) Age‡, Sex-Neuter Status, ANKC Terrier, CT Confounders. 0.000 0.000 Reference category 40.90 (37.41, 44.39) Chronic (≥ 3 months) -6.396 (-10.264, -2.528) -0.705 (-4.002, 2.593) 0.676 40.20 (36.29, 44.11) Disease Category Endocrine Age‡, Sex-Neuter Status, ANKC Terrier, CT Confounders. 0.000 0.000 Reference category 43.31 (37.84, 48.78) Neoplasia -5.392 (-11.430, 0.645) -5.498 (-10.664, -0.332) 0.038 37.81 (33.96, 41.66) Gastroenteric -3.973 (-13.337, 5.392) 0.318 (-7.887, 8.523) 0.940 43.63 (36.46, 50.80) Trauma/Musculoskeletal -11.641 (-27.248, 3.966) -3.370 (-17.034, 10.294) 0.629 39.94 (26.62, 53.26) Congenital Portal Vascular Anomaly -20.914 (-27.814, -14.015) -0.792 (-8.465, 6.882) 0.840 42.52 (36.36, 48.68) Inflammatory -7.630 (-15.043, -0.217) 0.959 (-6.007, 7.925) 0.788 44.27 (38.70, 49.84) Other -5.202 (-13.037, 2.633) -2.473 (-9.26, 4.314) 0.476 40.84 (35.04, 46.63) Confirmed Hyperadrenocorticism No evidence of hyperadrenocorticism Age‡, Sex-Neuter Status, ANKC Terrier, CT Confounder. 0.000 0.000 Reference category 40.61 (37.33, 43.89) Confirmed Hyperadrenocorticism 9.017 (-0.439, 18.473) 4.137 (-3.523, 11.797) 0.291 44.75 (36.50, 52.99) Drug Length No drugs Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder 0.000 0.000 Reference category 42.40 (37.10, 47.70) Acute (< 3 months) -3.756 (-7.972, 0.460) -0.366 (-3.794, 3.062) 0.834 42.03 (36.76, 47.31) Chronic (≥ 3 months) 2.994 (-2.833, 8.821) 3.048 (-1.672, 7.768) 0.207 45.45 (39.17, 51.72) Prednisone /Phenobarbitone No Age‡, Sex-Neuter Status, ANKC Terrier, CT Confounder. 0.000 0.000 Reference category 40.32 (37.03, 43.62) Yes 4.425 (-3.297, 12.147) 5.231 (-0.867, 11.329) 0.094 45.55 (38.97, 52.13) CT Confounding Findings Normal Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 45.40 (39.79, 51.01) Abnormal -6.143 (-10.256, -2.030) -5.445 (-8.842, -2.049) 0.002 39.95 (35.12, 44.79) Presence of Effusion No Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 41.20 (36.25, 46.15) Yes -2.354 (-7.035, 2.326) -3.656 (-7.422, 0.110) 0.059 37.54 (31.74, 43.35) Volume of Effusion Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism -2.225 (-3.923, -0.528) -2.318 (-3.682, -0.954) < 0.001 Presence of Mass No Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 42.41 (37.36, 47.45) Yes 2.154 (-2.017, 6.326) -4.285 (-7.820, -0.749) 0.019 38.12 (32.78, 43.46) Organomegaly No Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 42.40 (37.06, 47.73) Yes -5.040 (-9.237, -0.843) -2.718 (-6.253, 0.816) 0.133 39.68 (34.56, 44.79) Mass and Organomegaly No Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 43.83 (38.45, 49.20) Yes -3.245 (-7.130, 0.640) -4.255 (-7.480, -1.030) 0.010 39.57 (34.64, 44.51) Small organ No Age‡, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 41.88 (36.92, 46.84) Yes -15.901 (-20.677, -11.124) -5.679 (-10.585, -0.773) 0.024 36.20 (29.91, 42.49) ‡ Wherever age was entered into a model it was centred by its mean and entered in quadratic form as age, centred + (age, centred) 2 CTV – Volume computed tomography; Abd. Fat – total abdominal fat percentage; SQ – Subcutaneous abdominal fat percentage; Visc. – Visceral abdominal fat percentage; V/SQ – Visceral-to-Subcutaneous Fat Ratio; LgV/SQ – log 10 (V/SQ) ANKC - Australian National Kennel Council; BCS – body condition score Table 2 Putative causal diagram-guided logistic regression analysis outputs for variables associated with log of CTV abdominal visceral-to-subcutaneous fat distribution (lgV/SQ) in a tertiary veterinary hospital population of dogs. Variable Adjustment set (total effect) Coefficients (β) (exponentiated) Adjusted estimates (exponentiated) p-value (adjusted) Model estimated marginal means for levels (exponentiated) Age (years) [V/SQ % per year] Age CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 1.008 (1.003, 1.013) [0.8% (0.3%, 1.3%)] 1.017 (1.011, 1.022) [1.7% (1.1. 2.2%)] < 0.001 Sex Females Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.000 0.000 Reference category 0.69 (0.60, 0.78) Males 1.025 (0.978, 1.073) 1.029 (0.987, 1.072) 0.184 0.73 (0.64, 0.83) 1.025 (0.984, 1.069) 0.236 Neuter Status Entire Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.000 0.000 Reference category 0.74 (0.63, 0.88) Desexed 0.985 (0.923, 1.050) 0.975 (0.920, 1.035) 0.410 0.70 (0.62, 0.79) Sex-Neuter Status Female entire Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.000 0.000 Reference category 0.63 (0.48, 0.82) Female spayed 1.085 (0.960, 1.226) 1.042 (0.932, 1.165) 0.470 0.69 (0.61, 0.79) Male entire 1.146 (1.001, 1.312) 1.105 (0.980, 1.247) 0.106 0.79 (0.66, 0.95) Male neutered 1.093 (0.969, 1.234) 1.059 (0.949, 1.182) 0.307 0.72 (0.63, 0.82) ANKC Breed Group Toys Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.000 0.000 Reference category 0.66 (0.52, 0.84) Terriers 1.153 (1.023, 1.301) 1.103 (0.989, 1.230) 0.080 0.83 (0.68, 1.00) Gundogs 1.026 (0.914, 1.152) 1.009 (0.909, 1.120) 0.865 0.67 (0.56, 0.81) Hounds 1.121 (0.976, 1.288) 1.129 (0.996, 1.279) 0.059 0.87 (0.69, 1.11) Working Dogs 0.980 (0.877, 1.095) 0.985 (0.891, 1.089) 0.763 0.64 (0.54, 0.75) Utility 1.019 (0.908, 1.144) 1.049 (0.945, 1.165) 0.366 0.74 (0.62, 0.89) Non-sporting 0.915 (0.799, 1.047) 0.945 (0.836, 1.068) 0.366 0.58 (0.46, 0.73) Mixed breed 1.055 (0.952, 1.169) 1.035 (0.943, 1.136) 0.468 0.71 (0.63, 0.81) 0.72 (0.61, 0.85) ANKC Terrier Terriers Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.000 0.000 Reference category 0.83 (0.68, 1.00) Non-terriers 0.889 (0.822, 0.961) 0.928 (0.865, 0.997) 0.041 0.70 (0.62, 0.79) ANKC Hound Hounds Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. Reference category 0.88 (0.69, 1.12) Non-hounds 0.920 (0.827, 1.024) 0.904 (0.823, 0.993) 0.037 0.70 (0.62, 0.79) Skull Shape Brachycephalic Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.942 (0.852, 1.043) 0.943 (0.861, 1.033) 0.208 0.65 (0.54, 0.78) Mesocephalic 1.016 (0.933, 1.106) 0.980 (0.906, 1.060) 0.614 0.71 (0.61, 0.81) Dolichocephalic 0.000 0.000 Reference category 0.74 (0.61, 0.90) Mixed breed 1.028 (0.944, 1.121) 0.983 (0.907, 1.065) 0.669 0.71 (0.62, 0.81) Chondrodystrophy Status Chondrodystrophic Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.000 0.000 Reference category 0.73 (0.61, 0.86) Non-chondrodystrophic 0.965 (0.903, 1.032) 0.979 (0.923, 1.040) 0.496 0.69 (0.60, 0.79) Mixed breed 1.002 (0.935, 1.074) 0.992 (0.932, 1.055) 0.792 0.71 (0.62, 0.81) HWR Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.837 (0.722, 0.969) 0.863 (0.755, 0.988) 0.034 Size Category (Nominal Categorisation with Cross Breed Excluded) Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.983 (0.963, 1.003) 0.994 (0.975, 1.013) 0.512 Size Category Extra Small Age, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity. 0.000 0.000 Reference category 0.73 (0.52, 1.02) Small 1.090 (0.923, 1.287) 1.018 (0.877, 1.183) 0.813 0.76 (0.62, 0.93) Medium-small 1.008 (0.846, 1.201) 0.990 (0.846, 1.159) 0.902 0.71 (0.56, 0.90) Medium-large 0.999 (0.856, 1.167) 0.966 (0.841, 1.111) 0.630 0.67 (0.58, 0.79) Large 1.020 (0.870, 1.196) 0.978 (0.847, 1.129) 0.759 0.69 (0.58, 0.82) Giant 0.963 (0.817, 1.136) 0.978 (0.844, 1.134) 0.771 0.69 (0.57, 0.85) Mixed breed 1.041 (0.894, 1.212) 0.989 (0.862, 1.134) 0.872 0.71 (0.62, 0.81) BCS (nominal) Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder 0.993 (0.975, 1.011) 0.982 (0.964, 1.000) 0.050 BCS (categorical) 2 Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder 0.000 0.000 Reference category 0.92 (0.65, 1.32) 3 1.051 (0.894, 1.234) 1.005 (0.864, 1.169) 0.950 0.93 (0.73, 1.19) 4 1.026 (0.882, 1.194) 0.963 (0.835, 1.111) 0.607 0.85 (0.69, 1.03) 5 0.994 (0.856, 1.156) 0.926 (0.803, 1.068) 0.293 0.77 (0.63, 0.95) 6 1.000 (0.852, 1.174) 0.907 (0.776, 1.059) 0.217 0.73 (0.58, 0.93) 7 0.998 (0.844, 1.181) 0.911 (0.776, 1.071) 0.261 0.74 (0.57, 0.96) 8 1.075 (0.871, 1.327) 0.965 (0.790, 1.179) 0.726 0.85 (0.59, 1.23) 9 0.939 (0.735, 1.201) 0.899 (0.709, 1.140) 0.380 0.72 (0.44, 1.17) Abdominal Adiposity Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder 0.997 (0.996, 0.999) 0.995 (0.993, 0.996) < 0.001 CTV Mean Hounsfield Units Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder 1.001 (1.000, 1.002) 1.002 (1.001, 1.003) < 0.001 Disease Chronicity Acute (< 3 months) Age, Sex-Neuter Status, ANKC Terrier, Abdominal Adiposity, CT Confounder. 0.000 0.000 Reference category 0.63 (0.56, 0.70) Chronic (≥ 3 months) 1.008 (0.962, 1.056) 1.020 (0.976, 1.067) 0.374 0.66 (0.58, 0.74) Disease Category Endocrine Age, Sex-Neuter Status, ANKC Terrier, Abdominal Adiposity, CT Confounders. 0.000 0.000 Reference category 0.72 (0.61, 0.86) Neoplasia 0.977 (0.906, 1.055) 0.949 (0.884, 1.018) 0.145 0.64 (0.57, 0.72) Gastroenteric 0.902 (0.801, 1.015) 0.920 (0.824, 1.027) 0.139 0.60 (0.48, 0.75) Trauma/Musculoskeletal 0.978 (0.803, 1.191) 1.009 (0.839, 1.212) 0.928 0.74 (0.49, 1.12) Congenital Portal Vascular Anomaly 0.942 (0.864, 1.028) 0.971 (0.880, 1.072) 0.561 0.68 (0.56, 0.81) Inflammatory 0.864 (0.787, 0.949) 0.920 (0.838, 1.010) 0.083 0.60 (0.50, 0.71) Other 0.884 (0.801, 0.976) 0.886 (0.809, 0.971) 0.010 0.55 (0.46, 0.66) Confirmed Hyperadrenocorticism No evidence of hyperadrenocorticism Age, Sex-Neuter Status, ANKC Terrier, Abdominal Adiposity, CT Confounder. 0.000 0.000 Reference category 0.63 (0.57, 0.70) Confirmed Hyperadrenocorticism 1.171 (1.048, 1.308) 1.174 (1.061, 1.299) 0.002 0.92 (0.71, 1.18) Drug Length No drugs Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, Abdominal Adiposity, CT Confounder 0.000 0.000 Reference category 0.77 (0.65, 0.90) Acute (< 3 months) 1.009 (0.959, 1.061) 1.004 (0.960, 1.051) 0.848 0.77 (0.66, 0.91) Chronic (≥ 3 months) 0.968 (0.903, 1.038) 0.979 (0.919, 1.042) 0.502 0.73 (0.60, 0.88) Prednisone /Phenobarbitone No Age, Sex-Neuter Status, ANKC Terrier, Abdominal Adiposity, CT Confounder. 0.000 0.000 Reference category 0.64 (0.58, 0.71) Yes 0.945 (0.863, 1.036) 0.983 (0.905, 1.069) 0.693 0.62 (0.50, 0.76) CT Confounding Findings Normal Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism. 0.000 0.000 Reference category 0.74 (0.61, 0.89) Abnormal 1.048 (0.998, 1.101) 1.027 (0.979, 1.077) 0.283 0.78 (0.67, 0.92) 1.027 (0.979, 1.077) 0.283 Presence of Effusion No Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 0.77 (0.65, 0.90) Yes 1.021 (0.966, 1.080) 1.016 (0.963, 1.071) 0.567 0.80 (0.66, 0.96) 0.997 (0.949, 1.048) 0.913 Volume of Effusion Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 1.017 (0.997, 1.038) 1.018 (0.999, 1.038) 0.069 1.018 (0.999, 1.038) 0.069 Presence of Mass No Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 0.77 (0.65, 0.91) Yes 1.036 (0.987, 1.089) 1.003 (0.954, 1.055) 0.893 0.78 (0.65, 0.93) Organomegaly No Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 0.74 (0.63, 0.89) Yes 1.049 (0.998, 1.102) 1.030 (0.980, 1.081) 0.246 0.80 (0.68, 0.94) Mass and Organomegaly No Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 0.74 (0.62, 0.89) Yes 1.058 (1.011, 1.107) 1.026 (0.981, 1.074) 0.266 0.79 (0.67, 0.93) Small organ No Age, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism 0.000 0.000 Reference category 0.75 (0.64, 0.88) Yes 1.018 (0.957, 1.083) 1.077 (1.008, 1.150) 0.029 0.89 (0.73, 1.09) CTV – Volume computed tomography; Abd. Fat – total abdominal fat percentage; SQ – Subcutaneous abdominal fat percentage; Visc. – Visceral abdominal fat percentage; V/SQ – Visceral-to-Subcutaneous Fat Ratio; LgV/SQ – log 10 V/SQ ANKC - Australian National Kennel Council; BCS – body condition score Table 1 : Putative causal diagram-guided logistic regression analysis outputs for variables associated with CTV total abdominal fat percentage in a tertiary veterinary hospital population of dogs. Table 2 : Putative causal diagram-guided logistic regression analysis outputs for variables associated with the log of CTV abdominal visceral-to-subcutaneous fat distribution in a tertiary veterinary hospital population of dogs. The following results are presented with adjustments made for age (linear or quadratic, as stated), total abdominal adiposity, sex-neutered status, ANKC terrier breed, cHAC, and CT confounders, as applicable. Further details regarding the relationship between BCS and the total abdominal adiposity and fat distribution are provided below. After adjusting for confounders, total abdominal adiposity was associated with four variables: age, ANKC terrier breed, BCS, and disease category (see Table 1 ). The relationship between total abdominal adiposity and age was found to be quadratic, with fat accumulation increasing with age, reaching a plateau at around 10 years, and then gradually decreasing with age (j-shaped distribution) (see Fig. 1 ). Additionally, abdominal adiposity was lower in terrier breeds, and it varied with disease categories. On average, dogs with neoplasia had the lowest abdominal adiposity. Abdominal visceral fat percentage was associated with six variables after adjusting for confounders: age, HWR, BCS, total abdominal adiposity, disease category, and cHAC (see S3). The visceral fat percentage increased with increasing total abdominal adiposity. The relationship between abdominal visceral fat percentage and age displayed a limited quadratic pattern, with a gradual plateau around 13 years of age. Based on this analysis and the scatter plot, the visceral fat and age relationship was considered linear for the multivariable analysis (see Fig. 1 ). Deep-chested dogs, or those with an increasing HWR, exhibited decreasing visceral fat percentages. Dogs with cHAC displayed higher visceral fat percentages than dogs without cHAC, and dogs with inflammatory or "other" diseases had lower visceral fat percentages, on average, than other disease categories. After adjusting for confounders, SQ fat was associated with six variables: age, HWR, BCS, total abdominal adiposity, disease category, and cHAC (see S4). The SQ fat percentage increased with increasing total adiposity. The relationship between SQ fat percentage and age was found to be quadratic, with SQ fat accumulation rising with age, reaching a plateau at around 8–10 years, and then gradually decreasing with age (j-shaped distribution) (see Fig. 1 ). Deep-chested dogs, based on increasing HWR, were associated with increasing SQ fat percentages. Dogs with HAC displayed lower SQ fat percentages than dogs without HAC. Dogs with HAC displayed lower SQ fat percentages than dogs without HAC. In contrast, dogs with inflammatory or "other" diseases had the highest average SQ fat percentages After adjusting for confounders, the log(V/SQ) fat ratio showed associations with eight variables: age, ANKC hounds, ANCK terrier, HWR, BCS, total abdominal adiposity, disease category, and cHAC. Increasing BCS and total abdominal adiposity were associated with decreasing V/SQ (see Fig. 2 ), while age showed a gradual linear increase in V/SQ. Deep-chested dogs had lower V/SQ, although this relationship was not evident in ANKC hounds, which had higher V/SQ than non-hounds. Terriers and dogs with cHAC also favoured visceral fat distribution over SQ fat distribution. Dogs with "other" diseases had, on average, the lowest V/SQ ratios of any group, but no clear pattern was identified within this group. The "other" diseases category included respiratory, neurological, urinary, haematological, and non-neoplastic mass conditions. Figure 1 : Scatter plots showing pairwise comparison and quadratic relationship between age and CTV total abdominal fat percentage (A), age and CTV abdominal visceral fat percentage (B), age and CTV abdominal subcutaneous (SQ) fat percentage (C), and age and log of CTV abdominal visceral-to-subcutaneous fat ratio (V/SQ) (D) in a tertiary veterinary hospital population of dogs. Validity of Methods Predicting Abdominal Adiposity and Fat Distribution Tables 3 and 4 and Figs. 5 and 6 display the validation of L3 CTA, mean Hounsfield units (HU), and ultrasound fat indices to predict CTV fat indices. Table 3 Validity of L3 CTA measures of abdominal fat indices relative to their corresponding CTV measures. r r c lower B-A Difference Mean (SD) B-A Difference 95% LOA B-A Percentage Difference Mean (SD) B-A Percentage Difference 95% LOA 95% CL L3 CTA total abdominal fat % relative to CTV total abdominal fat % 0.971 0.919 -3.6 (4.3) -12.0, 4.9 6.5% (10.4) -26.9, 13.8% L3 CTA visceral fat % relative to CTV visceral abdominal fat % 0.931 0.566 -6.9 (4.6) -16.0, 2.2 -32.3% (18.8) -69.1, 4.5% L3 CTA subcutaneous fat % relative to CTV subcutaneous abdominal fat % 0.971 0.900 3.3 (2.4) -1.4, 8.1 15.3% (10.4) -5.1, 35.8% L3 CTA V/SQ relative to CTV V/SQ 0.893 0.425 -0.4 (0.3) -1.1, 0.2 -46.5% (20.6) -86.9, -6.1% r = Pearson's correlation r c = Lin's Concordance Correlation Coefficient B-A Difference mean = mean difference [CTV – CTA values] versus the average of both values B-A Percentage Difference Mean = mean of the percentage difference [100*(CTV – CTA values)/average)] versus the average of both values (SD) = standard deviation 95% LOA = 95% Limits of Agreement 95% CL – lower 95% confidence limit L3 CTA – transverse cross-sectional computed tomographic area at the cranial margin of the third lumbar vertebrae on CT CTV - computed tomographic volume of the abdomen from the cranial margin of the 10th thoracic to the cranial margin of first sacral vertebrae. V/SQ – visceral-to-subcutaneous fat ratio Table 4 Linear regression statistics of mean Hounsfield units and ultrasound measures to estimate CTV measures of abdominal fat. r Regression Equation Gradient 95% Confidence Interval y-intercept 95% Confidence Interval CTV mean HU relative to CTV total abdominal fat % -0.966 y = -1.843x + 73.29 -1.911, -1.774 70.18, 76.40 L3 CTA mean HU relative to CTV total abdominal fat % -0.948 y = -1.954x + 67.01 -2.044, -1.863 62.89, 71.14 TAT relative to CTV total abdominal fat % 0.575 y = 0.639x − 3.666 0.510, 0.767 -9.557, 2.225 VAT relative to CTV visceral abdominal fat % 0.440 y = 0.896x + 2.382 0.637, 1.155 -2.126, 6.890 SAT relative to CTV subcutaneous abdominal fat % 0.444 Y = 0.223x + 1.220 0.159, 0.286 -0.624, 3.064 VAT/SAT relative to CTV V/SQ 0.257 y = 2.473x + 1.428 1.158, 3.787 0.509, 2.346 r = Pearson's correlation 95% LOA = 95% Limits of Agreement HU – Hounsfield units L3 CTA – transverse cross-sectional computed tomographic area at the cranial margin of the third lumbar vertebrae on CT CTV - computed tomographic volume of the abdomen from the cranial margin of the 10th thoracic to the cranial margin of first sacral vertebrae. V/SQ – visceral-to-subcutaneous fat ratio VAT – linear measure of the visceral adipose thickness measured on and perpendicular to the linea alba from the linea alba’s parietal surface to the ventral margin of the most caudal aspect of liver that crosses midline SAT – linear measure of the subcutaneous adipose thickness at the same level of VAT VAT/SAT – VAT-to-SAT ratio TAT – linear measure of the total adipose thickness at the same level of VAT (VAT + SAT) In univariable analysis, BCS had poor to moderate correlation with CTV total abdominal fat percentage (r = 0.604), CTV visceral fat percentage (r = 0.509), CTV SQ fat percentage (r = 0.529), L3 CTA total abdominal fat percentage (r = 0.605), L3 CTA visceral fat percentage (r = 0.514), and L3 CTA SQ fat percentage (r = 0.476). There was marked variability of CTV total abdominal adiposity for every BCS unit, with, at best, 36% of the variation in adiposity being explained by BCS (r 2 = 0.365, [95%CI:0.231,0.529]) and for every unit increase in BCS, the CTV total abdominal adiposity increased by 5.76% (95% CI: 4.60, 6.93) (see Fig. 2 ) and L3 CTA abdominal fat changed by 6.62% (95% CI:5.29, 7.96). Additionally, BCS had a poor, negative correlation with V/SQ (r=-0.066) when not correcting for signalment or hyperadrenocorticism. There was a moderate negative correlation between CTV and L3 mean HU with BCS (r=-0.638 and r=-0.626, respectively). See Tables 1 and 2 and supplementals 3 and 4 for further analyses. Figure 2 : Scatter plots showing pairwise comparison and linear relationship between CT volume (CTV) total abdominal fat percentage and BCS (A), L3 CT area (L3 CTA) total abdominal fat percentage and BCS (B), CTV visceral-to-subcutaneous (CTV V/SQ) fat ratio (CTV V/SQ and BCS (C), and CTV V/SQ and CTV total abdominal fat percentage (D) in a tertiary veterinary hospital population of dogs. Overall, L3 CTA abdominal fat measures showed consistent biases relative to the CTV measures, generally overestimating total abdominal fat and visceral fat percentages but underestimating SQ fat percentages. The variations worsened with increasing fat percentages (drift), most significantly observed in visceral fat percentages. This drift resulted in a more significant disparity in the L3 CTA V/SQ estimation in overweight/obese dogs (see Table 3 and Fig. 3 ). The CTV and the L3 CTA mean HU strongly and negatively correlated with the overall abdominal fat percentage (r=-0.966 and r=-0.948, respectively). Ultimately, the CTV mean HU could explain more of the variance than the L3 CTA mean HU (see Table 4 and Fig. 4 ). The linea alba fat measurements showed poor to moderate correlation with the corresponding CTV fat indices (see Table 4 and Fig. 4 ), whilst TAT provided a moderate correlation with total abdominal adiposity. The poor association of VAT and SAT measurements to CTV visceral and SQ fat measures resulted in a weak correlation between VAT/SAT and V/SQ (r = 0.257). Total adipose thickness (TAT) correlated poorly with BCS (p = 0.42). Figure 3 : The CT cross-sectional area of abdominal, visceral, and subcutaneous fat percentages measured at the cranial margin of L3 compared to the equivalent volumetric CT measurements between the cranial margins of T10 to S1. A – Total abdominal fat. B – Subcutaneous abdominal fat. C – Visceral abdominal fat. D – Visceral-to-subcutaneous fat ratio Figure 4 : A - The mean Hounsfield Units (HU) recorded within the entire abdominal volume (CTV Mean HU) compared to the CT Volume Abdominal Fat Percentage. B - The mean Hounsfield Units (HU) recorded within the entire transverse abdominal area measured at the cranial margin of L3 (L3 Mean HU) compared to the CT Volume Abdominal Fat Percentage. C – Ultrasound total adipose thickness (TAT) compared to CT volume total abdominal fat percentage. D – Ultrasound visceral adipose thickness (VAT) compared to CT volume visceral fat percentage. E - Ultrasound subcutaneous adipose thickness (SAT) compared to CT volume subcutaneous fat percentage. F - Ultrasound VAT/SAT compared to volume CT V/SQ. The abdominal volume was recorded as tissue volume between the cranial margins of T10 to S1. The abdominal volume was measured using a -250 to 2000 HU range, and abdominal fat using a range of -250 to -25 HU. The visceral adipose thickness (VAT) and subcutaneous adipose thickness (SAT) were measured as thickness perpendicular to the linea alba at the most caudal aspect of the liver as it crosses the midline . Reliability of Abdominal Fat Segmentation Fat segmentation using the − 250/-25 HU thresholding extracted fat with a mean of -99.40 HU (SD ± 11.10 HU, [95% CI: -100.90, -97.88HU) and median of -102.00 HU (range: -118.10 to − 67.40 HU), with a slightly positive skew (skewness = 0.79 ± 0.17). There was minimal variation when the CTV measurements were repeated, with a near-perfect intra-observer agreement in the segmentation of total abdominal volume (− 250/-25HU: r c lower 95% CL = 0.998, B-A 95% LOA [− 3.5, 3.9%]); total abdominal fat (− 250/-25HU: r c lower 95% CL = 0.999, B-A 95% LOA [− 4.0, 4.3%]); and visceral abdominal fat (− 250/-25HU: r c lower 95% CL = 0.996, B-A 95% LOA [− 7.2, 2.5%]) when repeated after more than six months of performing the original measurements. The visceral abdominal fat was slightly overestimated on the repeat measurement (bias ~ 2.3%) and resulted in a near-perfect intra-observer agreement in, but a slightly overestimated repeat V/SQ measurement (V/SQ: r c lower 95% CL = 0.990, B-A 95% LOA [− 9.8, 1.5%]). The intra-abdominal CTA measurements varied more than CTV when repeated more than six months after the original measurements. There was near perfect intra-observer agreement in the segmentation of L3 abdominal volume (− 250/-25HU: r c lower 95% CL = 0.999, B-A 95% LOA [− 1.7, 2.0%]) and L3 abdominal fat (− 250/-25HU: r c lower 95% CL = 0.998, B-A 95% LOA [− 5.8, 7.3%]). However, despite a near-perfect intra-observer agreement for L3 visceral fat, there was greater variability (− 250/-25HU: r c lower 95% CL = 0.992, B-A 95% LOA [− 15.8, 13.4%]). This resulted in relatively variable L3 V/SQ calculations (− 250/-25HU: r c lower 95% CL = 0.940, B-A 95% LOA [− 47.0, 32.2%]). Discussion To the authors' knowledge, this is the first study to evaluate the relationship between signalment and disease on CTV abdominal fat distribution in dogs. This study, using abdominal adiposity as a proxy for total adiposity, reinforces previous associations found between adiposity, age, breed and certain diseases (36). Additionally, it confirms the correlation between V/SQ fat distribution and age, as established in earlier research (3, 8, 18, 23, 36). Notably, the findings also introduce a novel relationship between V/SQ, abdominal adiposity (contrary to existing literature) and body conformation (HWR) (23). Our results further support the supposition that dogs with HAC preferentially distribute fat to the visceral space (18, 37). These findings emphasise the importance of shifting our focus from general adiposity to a more nuanced examination of the relationship and causal direction of regional fat distribution on health, metabolic dysfunction, and disease pathogenesis (20). Age Despite the heterogeneity of the data set, abdominal adiposity displayed a quadratic distribution pattern (j-shape) with age, consistent with the trends observed in total adiposity in dogs (5, 38, 39). Notably, abdominal fat increases until around 10 years of age and subsequently decreases (5). Moreover, our study affirms that ageing is also linked to the redistribution pattern of adiposity (23). Specifically, there is a preferential rise in visceral fat compared to the quadratic distribution of SQ fat, resulting in an overall increase in the V/SQ ratio with age (40). This preferential distribution of fat to the visceral space is considered metabolically detrimental. In people, age is one of the leading contributors to visceral redistribution and the development of adipose tissue dysfunction (41, 42). Human age-related adipose tissue dysfunction is a complex interaction between adipose cell differentiation and senescence, immune cell infiltration, and the release of proinflammatory cytokines (41). This intricate interplay ultimately leads to the progressive impairment of the SQ adipose tissues to store lipids ("triglyceride sink"), redistributing fat to visceral and ectopic regions and chronic low-grade inflammation. While there is some debate within the veterinary literature, insulin resistance has been associated with increasing visceral mass, suggesting a similar mechanism may occur in dogs (20, 43–45). Consequently, the pro-inflammatory cytokines are associated with insulin resistance, as well as endothelial, metabolic and inflammatory dysregulation, and ultimately contribute to the pathophysiology of several diseases (20). This increasing V/SQ fat ratio, with decreasing total and SQ fat, may be associated with geriatric-related morbidities and reduced lifespan in dogs, but further evaluation is required (46). Sex and Neuter Status Though no statistical significance was established in our study, neutered dogs tend to have higher abdominal adiposity. This relationship between desexing and OO is well-established, and desexing is also associated with increased SQ fat content, increased food intake, and reduced resting metabolism (47, 48). Fat distribution to the visceral or subcutaneous compartments was not significantly related to the dog's sex or neuter status, similar to our previous publication (23). However, entire male dogs had a higher visceral fat distribution compared to entire female dogs, favouring subcutaneous fat distribution. These trends may parallel the effect of sex hormones on the regulation of body fat distribution and sexual dimorphism of fat distribution in people. The fat distribution patterns in desexed dogs and how they compare to entire dogs offer a valuable opportunity to explore the influence of testosterone and oestrogen on fat distribution, a phenomenon that exhibits significant individual variability in the human population (49, 50). Our study found that MN and FS dogs tended to have similar visceral-to-subcutaneous (V/SQ) fat distribution, falling between intact male and female dogs. This observation aligns with the general understanding that testosterone promotes increased lean muscle mass but tends to deposit fat preferentially in visceral areas. Conversely, oestrogen supports a healthier subcutaneous fat distribution without the bias towards lean muscle mass, and the lack of either hormone would result in an intermediate effect (50). The trend in our study is supported by studies of 3 male dogs and a large cohort of toy breed dogs, where it was noted that desexing resulted in increased subcutaneous fat male dogs a year after castration, and higher V/SQ seen in spayed female toy breeds compared to intact females (5, 24, 40, 48). This consensus occurred despite differences in research methodologies and that our research accounted for the potential confounding effect of total adiposity (24). However, it is crucial to recognise that all studies have had a limited number of intact dogs, hindering a robust comparison between desexed and intact dog cohorts. Further, the age of neutering may influence V/SQ fat distribution, which was not accounted for in our study (24, 40). Thus, further research into the impact of reproductive hormones on dog fat distribution is warranted Breed and Breed Conformation Specific breeds are predisposed to being OO, and it has been recently alluded that certain toy breeds may favour visceral fat distribution compared to SQ fat (5, 24). The influence of specific breed and breed conformations on abdominal adiposity and fat distribution was identified in terriers and with abdominal height-width ratio (HWR) conformation. Terriers had lower abdominal adiposity than other breeds, as previously demonstrated, and favoured a distribution to the visceral over the SQ compartment (8, 51). Hounds appeared to favour distribution to the visceral over the SQ compartment, despite having relatively high HWR. These findings may reflect breed-specific differences, such as an increase in lean muscle mass in terriers that may limit the risk of obesity seen with desexing and may suggest a breed-protective trait from obesity (51, 52). In our study, HWR reflected breed conformation well, and its use in CT is the first time this has been described (9). Dogs with deep, narrow conformations (increasing HWR) typically favoured reducing total adiposity with SQ over visceral fat distribution, though, as stated, this was not reflected in the Hounds group. However, the dolichocephalic dogs and a few Great Danes probably exaggerated this effect and skewed the data. These variations should be evaluated further, as they may be related to breed variations and implicated in obesity-related disorders seen in certain breeds. V/SQ Relative to Adiposity The relationship between V/SQ fat distribution and total abdominal adiposity differed from our previous findings, likely due to a more significant power in this study [22]. This relationship has been noted previously and is consistent with the anticipated physiological orthotopic fat distribution pattern, with a preference for the SQ space. It is crucial to emphasise that the observed relationship or effect is relatively weak, a factor that is likely diminished with advancing age (25, 33, 40, 41). The low contribution of BCS and total abdominal adiposity to the variation in V/SQ demonstrates that focusing on measures of total adiposity is not a sensitive nor specific means of evaluating the effect of fat distribution on health (Figs. 2 and 5 ). This is particularly relevant to BCS, which has a stronger positive relationship with subcutaneous fat mass than visceral fat mass (53). Similarly, in people, there is now a growing consensus to focus on measures of central obesity, specifically visceral obesity, such as waist circumference, more so than metrics of total body obesity, such as BMI (31). These metrics of visceral adiposity have been shown to provide both independent and additive information for predicting morbidity and risk of death in people and are a means of stratifying metabolically unhealthy from metabolic healthy obesity. Additionally, substantial evidence emphasises the importance of considering lean muscle mass in conjunction with adiposity (25, 52). Sarcopenic obesity appears to have a more profound impact than obesity accompanied by adequate muscle mass. This underscores the necessity for research methods that effectively assess fat distribution and lean muscle mass, shedding light on the influence of compartmental fat distribution on overall health. Fat distribution in OO dogs may contribute to the variations in their health metrics seen in the literature (43, 54). Some studies have found that not all OO dogs show clinical metabolic issues, but small subgroups exhibit obesity-related metabolic dysfunction (ORMD) (54–56). Though not evaluated, this heterogenous metabolic outcome related to OO may be attributable to regional fat distribution and relative muscle mass. Subcutaneous fat deposition may be "protective" and, combined with greater muscle mass, as seen in younger OO dogs, may limit metabolic derangement and the manifestation of ORMD (25, 57). Morphological methods of assessing obesity, like BCS, do not assess fat redistribution patterns well and thus limit their applicability in exploring these metabolic subsets in dogs (20, 25). Figure 5 : transverse CT images illustrating various abdominal adiposity patterns and visceral-to-subcutaneous (V/SQ) fat distribution in a tertiary veterinary hospital population of dogs. The images are arranged from left to right, indicating increasing total abdominal fat adiposity, and from bottom to top, representing increasing V/SQ fat ratios. The four patients depicted are as follows: A) 1.5-year-old female-spayed mixed breed with a left divisional intrahepatic portocaval shunt, having 14% total abdominal fat and a V/SQ ratio of 1.35; B) 11-year-old male-neutered mixed breed undergoing staging for soft tissue sarcoma of the left thoracic limb, exhibiting 52% total abdominal fat and a V/SQ ratio of 1.22; C) 1.4-year-old female-spayed Boxer diagnosed with intestinal adenocarcinoma, displaying 28% total abdominal fat and a V/SQ ratio of 0.379; D) 7-year-old male-neutered Golden Retriever undergoing staging for insulinoma, demonstrating 57% total abdominal fat and a V/SQ ratio of 0.287. Disease and Fat Distribution The effect of the hypothalamic-pituitary-adrenal (HPA) axis, particularly HAC, on both generalised and visceral obesity has been described (3, 27, 28). However, to the authors' knowledge, this is the first objective evaluation to demonstrate the preferential distribution of visceral fat in dogs with HAC. Interestingly, HAC was not related to total abdominal adiposity in this study. The predisposition for general obesity in dogs with HAC is likely due to polyphagia and increased caloric intake, coupled with reduced energy expenditure, but is not a strongly recognised component of the disease, which is supported in our findings (27). However, an increase in visceral fat is commonly noted in dogs with HAC, though compounded by the concurrent hepatomegaly and weakening of the abdominal muscles. This is supported by the documented increase in serum leptin concentrations in dogs with HAC, with leptin preferentially released by visceral fat (28). This has potential diagnostic application from a diagnostic imaging perspective, particularly AI-generated radiomics, as it may provide differential diagnosis stratification based on physiological manifestations, such as differentiating functional adrenocortical adenocarcinoma from phaeochromocytomas. This may be further augmented by using other measures of body composition, such as lean tissue mass and distribution. Dogs with neoplasia tended to have lower total adiposity than dogs with other disease categories. However, the effect of diseases on fat distribution was uncertain in our study, apart from HAC. The category of "other" disease was a random assortment of diseases, and no clear pattern emerged. Generally, endocrinopathies tended to favour higher visceral fat and V/SQ distribution than inflammatory and neoplastic diseases, likely skewed by the HAC cases. Further, this may be compounded by organ steatosis, such as hepatic lipidosis, which may increase the visceral adiposity on CT by reducing the Hounsfield units of the liver (58). This was seen in one case of a dog that presented with diabetic ketoacidosis and hepatic lipidosis, which reduced the liver attenuation to -45HU. Thus, with more specific clinical questions, more specific segmentation, and the support of deep neural networking, V/SQ and body composition analysis may be used to provide differential diagnosis stratification (59). Ultimately, body composition and tissue distribution may determine health-associated outcomes in dogs, like in people (41). Congenital portal vascular anomalies formed a large cohort of young dogs and contributed to the bimodal age distribution. This was due to the high intravascular interventional radiology caseload at the hospital. Typically, these dogs had the lowest total, visceral and SQ fat percentages, which resulted in marked variations in V/SQ. However, these findings were not present after correcting for age and the presence of ascites, as seen in cases of portal venous hypertension. Further studies evaluating young dogs without portosystemic shunts should be performed to ensure this was not a significant contributor to the age-related variation in V/SQ seen in this study. Finally, the administration of phenobarbitone and prednisolone did not statistically significantly alter total abdominal adiposity, but there was a trend towards increasing adiposity. This is expected due to the side effect of polyphagia secondary to these medications and should be considered in future investigations (3). Reliability A significant consideration in using CT segmentation is the threshold values set for segmenting specific body tissues and the presence of confounding diseases that may alter the attenuation of the regions of interest. Regarding the CT threshold values, the authors used the previously published ranges of -250/-25 HU for fat (36). This has shown a slightly higher correlation and agreement with DXA measure of fat than Ishoika's -135/-105 HU threshold, and is similar to that used in cat and human thresholds (32). The distribution of fat HU in this paper was, on average higher than that proposed by Ishioka. This might suggest that more accurate measures of fat thresholding may be achieved with a narrower but slightly higher threshold value (97% CI for fat attenuation was − 101/-98 HU) than that used in this study and by Ishioka, but our measurements were not standardised against a CT quality assurance phantom. The CT confounding variables ascites, abdominal masses, organomegaly, and small organs significantly influenced the results. This was addressed and corrected within the multivariable analysis of this paper. However, these confounding variables should be considered when using CT to research body composition. Interestingly, small organs were associated with greatly reduced total, visceral and SQ fat percentages with increased V/SQ and likely reflected the cohort of young dogs with congenital portal vascular anomalies (60). Effusions, abdominal masses, and organomegaly resulted in a decrease in total abdominal adiposity. Surprisingly, these conditions had no impact or even increased the V/SQ. This was attributed to reductions in both visceral and subcutaneous fat percentages, with a more pronounced decrease in subcutaneous fat. Although the precise cause of this phenomenon remains unclear, this relationship suggests that the underlying disease has a more significant influence on fat distribution than the soft tissue alterations that obscure fat on CT scans. This implies that CT scans might offer a reliable method for evaluating body composition and tissue distribution in the presence of significant abdominal pathology, although further research is necessary to understand these findings fully. Body condition scoring was moderately correlated with total abdominal fat and as previously demonstrated, showed a stronger relationship with SQ fat distribution than visceral fat (40, 53). However, the weak negative correlation between total abdominal adiposity and V/SQ was weakly replicated in dogs with increasing BCS. Like human body mass index (BMI), BCS may be a crude marker of dog adiposity (31). This study found that BCS only moderately correlated with volume and CTA L3 total abdominal fat percentages, differing from a study of 38 beagles, which found BCS had a strong correlation with the total adipose area at L3 (r = 0.809) (33). This difference may be due to the 20% of cases missing BCS data in our study, the heterogeneity of our study population, differing methodologies, or breed-specific differences in fat deposition at L3. Further, in our study, the BCS was performed by a non-standardised set of clinicians, and the considerable variation in adiposity for each BCS unit likely highlights the high BCS inter-observer variation, as seen in other studies (4, 61). However, our study validated Laflamme's original estimates that an increase in 5% adiposity occurs with every unit of BCS (4). Most abdominal fat distribution papers use a single slice area of the abdomen at L3 or L5 (20, 24, 25, 32, 40). It has also been shown that visceral fat deposition occurs mainly in the cranial abdomen (L3), while subcutaneous deposition is more caudally distributed (L5) (24, 53). We showed that CTA L3 fat distribution showed more significant variation than CTV measures and that CTA generally overestimated total abdominal and visceral fat while underestimating SQ fat percentages compared to CTV measures. This bias worsened with increasing abdominal adiposity, resulting in marked variation in V/SQ measures. Adolphe's method also identified similar measurements and reduced variation, which used averaged visceral and subcutaneous fat areas over multiple slices from the thirteenth thoracic vertebra to the seventh lumbar vertebra (20, 25). This is expected, as the area measured highly depends on what is within the acquired abdominal CT slice. Conformational variation, relatively mobile organs (e.g. spleen), ectopic organs, and local pathology (e.g., hepatomegaly, ascites, masses) are some factors that may have a more significant influence on the CTA L3 measure compared to CTV measures of adiposity and fat distribution in dogs. For this reason, the authors recommend using volume metrics to assess abdominal adiposity and fat distribution for greater accuracy. However, in doing so, understanding the influence on specific fat distributions, such as peri-gluteal subcutaneous fat deposition, is reduced. Additionally, a potential negative of using CTV methodology as a clinical tool is the time it takes to segment regions of interest and the limited access to semi-automated and automated software assistance. Thus, L3 CTA may still prove a clinically valuable tool for assessing adiposity and fat distribution. Further, the average Hounsfield units were assessed as a marker of overall abdominal adiposity. A radiologist can perform this measure in seconds and provide a reasonable estimate of overall body adiposity (r=-0.967). Further research may show abdominal attenuation values as a simple clinical or radiomic measure of abdominal obesity. As there is limited access to CT in many veterinary clinics, the authors were also interested in further validating their recently introduced ultrasound method of assessing fat distribution (23). This would provide a more accessible tool for assessing dog fat distribution, as no sedation or anaesthesia is required. Unfortunately, though a moderate correlation between TAT and total abdominal fat percentage was identified, only a poor correlation was observed between VAT and SAT relative to CTV visceral and SQ fat, and ultimately, a weak correlation between VAT/SAT and V/SQ. The flattening of the ventral abdomen due to sternal recumbency and variable liver size may have influenced the measurements, but this was likely negligible. Though the falciform ultrasound method may help assess total adiposity, the current methodology is not useful for determining V/SQ. Though the relationship between total body adiposity and total abdominal adiposity was supported by the moderate correlation between total abdominal adiposity and BCS, further validation of abdominal CT and, ideally, whole-body CT metrics of body composition are required. Additionally, this study did not consider the specific regional distribution of fat, such as thoracolumbar, lumbar, inguinal or peri-gluteal SQ fat distribution, or cranial and caudal visceral fat distribution. Further, it did not evaluate organ fat distribution such as intra-cellular (steatosis, e.g. hepatic lipidosis), intra-organ (e.g. intramuscular) or between organs (e.g. intermuscular) fat distribution, nor did it evaluate the relationship between fat and lean muscle mass distribution. All these factors may be related to specific health-associated metrics that must be evaluated. For example, fat distributed around pre-menopausal women's glutei and legs is associated with positive health metrics compared to central obesity in men (41). Further, lean tissue mass favours positive health outcomes compared to sarcopenic obesity (52). Therefore, CT offers a greater means of quantifying body composition metrics and should be evaluated against biomarkers of health, associated morbidities, quality of life and risk of death. The associations made in this study are relatively robust, given the limitations introduced by its retrospective nature and the heterogeneity of the sample population. Ultimately, prospective evaluation of specific factors affecting fat distribution should be performed, but we hope the observational results provide some framework for future investigations. Ideally, greater numbers of different breeds and morphologies would have been evaluated, but this was limited by case availability and the time for data retrieval and tissue segmentation. Some methodological improvements that could be entertained in future research include the potential accuracy and speed of computer-aided segmentation and the reduced variation introduced by standardised protocols, such as the length of fasting prior to the study, size of urinary bladder at the time of the study, and standardised positioning, all of which were optimised for the presenting complaint in this study. Every animal usually undergoes a minimum 12-hour fast before CT, though this was not defined, and there were various degrees of gastric distension within the study population. The gastric volume and the degree of urinary bladder distension may displace and reduce the CTV visceral fat volume. The influence of these physiological variations is unknown but is likely to affect volume-based metrics less than single-area measurements. Sternal recumbency was used in all these cases due to its standard use in clinical practice, as it limits the effect of respiratory movement on organs; however, its effect on body composition assessment is unknown (62). Another methodological restraint for this study was that the same scales used to weigh each dog were not tared daily. Finally, including dogs less than a year in our research sample may have introduced the confounding effect of brown adipose tissue. However, as the distribution, extent, and regression of brown fat in dogs are poorly characterised, young dogs were not excluded from our analysis (63). As brown adipose tissue may appear like fat or soft tissue on CT, further evaluation of brown adipose tissue may require F18-FDG PET-CT, and possibly MRI, to differentiate it from white adipose tissue (64, 65). Conclusions The main finding of this study supports the association between abdominal adiposity, age, breed category and, potentially, certain diseases. Moreover, it highlights the correlation between V/SQ fat distribution, age, and total adiposity, whilst emphasising the preferential distribution of fat to the visceral compartment in dogs with HAC. The study also identified a novel association between V/SQ fat distribution, specific breed categories and body conformation (HWR). Additionally, CT volumetric measures appear more reliable in determining abdominal fat distribution than area and linear measures. Methods Case Selection Medical case records of dogs that underwent abdominal computed tomography (CT) at the U-Vet Werribee Animal Hospital, University of Melbourne, were retrieved from March 2006 to March 2020. Data collated included weight, breed, age, sex, neuter status, 9-point scale BCS and reason for the CT. Abdominal Fat Analysis Non-contrast volume acquisition of the abdomen with dogs in sternal recumbency was performed using a 16-slice CT scanner (Somatom Emotion 16, Siemens, Erlangen, Germany). Proprietary software (Somaris 5 Syngo CT 2014A, Siemens AG, Muenchen, Germany) was used for semi-automated body composition and distribution volume quantification. Based on previously described methods, the total abdominal, visceral, and SQ volumes of interest (VOI) were established between the cranial margin of the 10th thoracic vertebrae to the cranial margin of the first sacral vertebrae and using 3 mm slice thickness for reconstruction (23, 36). The computed tomographic volume (CTV) of all tissue between these regions was calculated by the software using threshold ranges of -250/2000 HU for all tissues (fat, lean tissue and bone) and − 250/-25 HU for fat, as used in a prior study (36). Subcutaneous (SQ) fat included SQ, inter-muscular and intramuscular fat outside the peritoneal cavity. The visceral and SQ fat volumes were used to determine the visceral-to-subcutaneous fat volume ratio (V/SQ). The CT cross-sectional areas (CTA) of total abdominal, visceral, and SQ fat were calculated at the cranial margins of L3 using the same borders and as previously described (34, 42). The mean Hounsfield units (HU) were recorded for the volume and area measurements. The visceral adipose thickness (VAT), subcutaneous adipose thickness (SAT) and total adipose thickness (TAT) measurements were taken perpendicular to the parietal surface of the linea alba, aligning with the most caudal margin of the liver at the midline, an adaption from recent ultrasound methodology (23). The VAT was measured from the linea alba's parietal surface to the ventral surface of the liver. The SAT was measured from the skin surface to the parietal surface of the linea alba. The TAT was calculated as the combined measurement of both VAT and SAT (see Fig. 6 ). Figure 6 : transverse and sagittal CT reconstruction of a dog's abdomen showing the location of the abdominal height and width (hashed lined), the visceral adipose thickness (VAT), and subcutaneous adipose thickness (SAT) (solid lines) measurements. The cranial abdominal height and width were measured at the widest internal diameter of the cranial abdomen constrained within the costal arch and used to determine the cranial abdominal height-width ratio (HWR). The VAT and SAT were performed perpendicular to the parietal surface of the linea alba, aligning with the most caudal margin of the liver at the midline. Abdominal Conformation Standardised measurements from the transverse CT images assessed the abdominal conformation modified from a previously established method (66). The cranial abdominal height and width were measured at the widest internal diameter of the cranial abdomen constrained within the costal arch. The height was measured from the ventral surface of thoracic vertebrae perpendicular to the parietal surface of the linea alba, and the width between the inner surface of the left and right ribs on the same slice of the CT. The height and width were used to determine the height-to-width ratio (HWR). This body conformation was relatively restrained by skeletal conformation and minimally affected by intra-abdominal anatomy and SQ fat deposition. Floating 12th and 13th ribs were not included in the measurements. Data Stratification and Preparation Data cleaning and preparation for analysis were completed by the primary author and statistician (R.T. and S.M.F.). The definition of categories is provided in Supplemental 5 (S5). Dog breeds were categorised based on the Australian National Kennel Council (ANKC) standards ( http://ankc.org.au/Breed/Index/1 ) as: group 1 (toys); group 2 (terriers); group 3 (gundogs); group 4 (hounds); group 5 (working dogs); group 6 (utility); and group 7 (non-sporting) breeds (5). Mixed breeds were categorised separately (group 8). The dogs were further classified on skull shape as dolichocephalic, mesocephalic or brachycephalic; and as either chondrodystrophic or non-chondrodystrophic breeds and as previously described (67–70). Mixed breeds were excluded from these categories. Dogs were also classified into size categories as extra small (< 6.5kg), small (6.5 to < 9kg), medium-small (9 to < 15kg, medium-large (15 to < 30kg), large (30 to 40kg) as previously described (12). The reason for the clinician requesting the CT and final diagnosis was recorded, and the disease was categorised as acute (< 3 months) or chronic (≥ 3 months) (71, 72). The primary diagnoses were categorised as endocrine (including neoplasia), neoplastic (non-endocrine), primary gastroenteric (not neoplasia), trauma/musculoskeletal, congenital portal vasculature anomaly, inflammatory, or other based on the primary diagnosis. The specific endocrinopathy was reported, as was the type and location of the neoplasia. Dogs were defined as having confirmed hyperadrenocorticism (cHAC) if confirmed by low-dose dexamethasone stimulation or ACTH stimulation test and supported by appropriate imaging findings. Presumptive hyperadrenocorticism (pHAC) was recorded if the dog had chronic treatment by glucocorticoids (≥ 3 months) at the time of the CT, or an adrenal nodule/mass was observed on the abdominal CT, and no confirmatory dynamic endocrine testing results were available. Sensitivity analyses assessed the influence of considering HAC status as confirmed, presumptive, or a combination of the two (see supplementary S6). Presumptive HAC diluted the effect of cHAC, and cHAC alone was used in the multivariable analyses. The type and length of medications before the CT were recorded; the length of medication use was categorised as chronic if used for over three months. The first CT was used for dogs that underwent multiple CT examinations, with duplicates excluded from the analysis. No further exclusions were made based on CT confounders, but the data was stratified for analysis as defined below. The presence of potentially confounding pathology on CT was noted (CT confounders), particularly the presence of abdominal and pleural effusion, abdominal masses, organomegaly (including markedly distended urinary bladders or gastrointestinal tract), and small organs. The volume of effusion was semi-quantified as trace (fat stranding to a thin sliver of fluid), mild (≤ 2 pockets, ≤ 2cm depth), moderate (> 2 pockets), and marked (diffuse fluid accumulation) (73). A mass was defined as any focal lesion ≥ 2cm in diameter, and the mass location and diameter were reported (74). Organomegaly or small organs were recorded if it was reported in the original radiology report. The presence of effusion, abdominal masses, organomegaly, and small organs were considered potential confounding variables in abdominal fat measurements. The CT findings were dichotomised into CTs with no confounders and CTs with confounders (effusion, abdominal masses, organomegaly or small organs). The literature was reviewed to explore characteristics associated with fat distribution. A putative causal network was constructed as a directed acyclic graph (DAG) and used to generate appropriate adjustment sets of variables for each exposure and outcome of interest (see Fig. 7 ; S5: Explanatory variables; and definitions S7: Putative causal web model/Directed acyclic graph (DAG) and justification) (75). The association between each risk factor was assessed using univariable analysis (see S1: Descriptive statistics and univariable analysis of categorical variables in a cross-sectional study of CTV abdominal fat distribution in a tertiary veterinary hospital population of dogs). For reporting, exploratory variables were collapsed into seven risk factors for the putative diagram: age, ANKC Terrier Breed, sex-neuter status, total abdominal adiposity, cHAC, and CT confounding pathology on CT. ANKC Hound group was considered too small for analysis. Figure 7 : Putative causal web model/Directed acyclic graph (DAG) Directed acyclic graph showing putative causal paths linking explanatory variables to the measured CTV abdominal visceral-to-subcutaneous fat ratio in an Australian tertiary veterinary hospital population of dogs. A detailed description of each link in the above plot is provided in supplementary materials S2. Power Calculation A power analysis was performed using the data from a previous study (n = 22 dogs), comparing V/SQ to total body fat percentage and age where the model explained nearly half the variation in V/SQ (R 2 = 0.471, R 2 Adjusted = 0.383), though only age was statistically significant (partial η 2 = 0.355) (23). A sample size calculation undertaken in G*Power (version 3.1.9.4) estimated 106 dogs were required, using an f 2 effect size of 0.095, alpha = 0.05, power = 0.80, and testing for statistically significant associations for two predictor variables of 8 total degrees of freedom (76, 77). Thus, a minimum sample size of 120 was sought to allow for expected controlling for subgroup analysis. Statistical Methods Relationships between variables were visualised on scatter plots, and the assumption of normality and group variance were evaluated. V/SQ had a lognormal distribution, which was logarithmically transformed to base 10 for analysis, log(V/SQ), with results back-transformed for interpretation. Independent t-tests were used to compare continuous variables between groups. Multivariable linear regression models were constructed to assess the association between explanatory variables and each outcome variable (total abdominal fat percentage, visceral fat percentage, SQ fat percentage and the log visceral/subcutaneous fat ratio, i.e. log(V/SQ). The directed acyclic graph (DAG, see supplementary materials S7) informed which variables to adjust for based on the exposure and outcome of interest. Model outputs included regression coefficients and their 95% confidence intervals (95% CIs) for both univariable models and models based on the DAG-informed adjustment sets, along with model-estimated marginal means for each level of categorical explanatory variables. When assessing the linearity of associations between age and each of the outcome variables, Lowess (Locally Weighted Scatterplot Smoothed) curves were fit to plots of each outcome variable and age, and the coefficients and Akaike information criterion (AIC) values were compared (78). As this indicated that the relationship was nonlinear and that the most appropriate fit could be achieved by incorporating age into regression models as a quadratic term centred on its mean. However, for visceral fat and log(V/SQ), the relationship did not seriously depart from linear. Method validation and strength-of-agreement of the L3 area and ultrasound measurements of abdominal fat relative to the CT volume indices were performed using Lin's Concordance Correlation Coefficient (r c ) (Lin's Concordance Correlation Coefficient; SPSS Syntax; Garcia-Granero, M.; updated 04/2009, https://gjyp.nl/marta/Lin.sps ) and Bland-Altman (B-A) limits of agreement (LOA) (79). The r c lower 95% confidence limit (CL) was reported as the strength of agreement and described as perfect (r c =1.00), near perfect (> 0.99), substantial (0.95–0.99), moderate (0.90–0.95) and poor agreement (< 0.90) (80). Statistical analysis was performed using GraphPad Prism (GraphPad Prism for Mac OS X, version 9.3.1, GraphPad Software, La Jolla, CA, USA, www.graphpad.com ), Jamovi (The jamovi project (2022). Jamovi (Version 2.3.21.0) [Computer Software]. Retrieved from https://www.jamovi.org ) and the R software package (81). Abbreviations AB abdominal fat volume (as measured by CT) BCS body condition score CL confidence limit CT computed tomography DXA dual-energy x-ray absorptiometry HU Hounsfield units ROI region of interest SQ abdominal subcutaneous fat volume (as measured by CT) VOI volume of interest V/SQ visceral-to-subcutaneous fat ratio as measured by CT Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and material The datasets used and analysed for the study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding Funding information is not applicable. Authors' contributions RT was a major contributor to experimental design, data acquisition, statistical analysis, data interpretation, critical analysis and writing of the manuscript. SF was a major contributor to the experimental design, statistical analysis, data interpretation and critical analysis. CM and FD were major contributors to the manuscript's data interpretation and critical analysis. All authors have read and approved the final manuscript. Acknowledgements I am grateful to my wife for enduring and keeping me grounded through my research endeavours. 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McGrath AP, Hancock L, Stiers CA, Brejda J, Morris EM. Retrospective analysis of dual-energy x-ray absorptiometry data demonstrates body composition changes with age in dogs and cats. American Journal of Veterinary Research. 2024:1-11. Kobayashi T, Koie H, Kusumi A, Kitagawa M, Kanayama K, Otsuji K. Comparative Investigation of Body Composition in Male Dogs Using CT and Body Fat Analysis Software. Journal of Veterinary Medical Science. 2014;76(3):439-46. Malandrino N, Bhat SZ, Alfaraidhy M, Grewal RS, Kalyani RR. Obesity and Aging. Endocrinology and Metabolism Clinics of North America. 2023;52(2):317-39. Müller L, Kollár E, Balogh L, Pöstényi Z, Márián T, Garai I, et al. Body fat distribution and metabolic consequences — Examination opportunities in dogs. Acta Veterinaria Hungarica. 2014;62(2):169-79. McKenzie BA, Chen FL, Gruen ME, Olby NJ. Canine Geriatric Syndrome: A Framework for Advancing Research in Veterinary Geroscience. Frontiers in veterinary science. 2022;9. Lottati M, Kolka CM, Stefanovski D, Kirkman EL, Bergman RN. Greater Omentectomy Improves Insulin Sensitivity in Nonobese Dogs. Obesity. 2009;17(4):674-80. Ryan VH, German AJ, Wood IS, Hunter L, Morris P, Trayhurn P. Adipokine expression and secretion by canine adipocytes: stimulation of inflammatory adipokine production by LPS and TNF alpha. Pflugers Archiv-European Journal of Physiology. 2010;460(3):603-16. Kealy RD, Lawler DF, Ballam JM, Mantz SL, Biery DN, Greeley EH, et al. Effects of diet restriction on life span and age-related changes in dogs. Journal of the American Veterinary Medical Association. 2002;220(9):1315-20. Mugnier A, Morin A, Cellard F, Devaux L, Delmas M, Adib-Lesaux A, et al. Association between birth weight and risk of overweight at adulthood in Labrador dogs. PloS one. 2020;15(12):e0243820. Mugnier A, Cellard F, Morin A, Delmas M, Grellet A, Chastant S. Association between Birth Weight and Subcutaneous Fat Thickness at Adulthood in Dogs. Vet Sci. 2023;10(3). Tchernof A, Brochu D, Maltais-Payette I, Mansour MF, Marchand GB, Carreau A-M, et al. Androgens and the Regulation of Adiposity and Body Fat Distribution in Humans. Comprehensive Physiology. p. 1253-90. Mongraw-Chaffin ML, Anderson CA, Allison MA, Ouyang P, Szklo M, Vaidya D, et al. Association between sex hormones and adiposity: qualitative differences in women and men in the multi-ethnic study of atherosclerosis. J Clin Endocrinol Metab. 2015;100(4):E596-600. McGreevy PD. Prevalence of obesity in dogs examined by Australian veterinary practices and the risk factors involved. Veterinary Record. 2005;156(22):695. Morgan PT, Smeuninx B, Breen L. Exploring the Impact of Obesity on Skeletal Muscle Function in Older Age. Front Nutr. 2020;7:569904. Linder DE, Freeman LM, Sutherland-Smith J. Association between subcutaneous fat thickness measured on thoracic radiographs and body condition score in dogs. American Journal of Veterinary Research. 2013;74(11):1400-3. Palatucci AT, Piantedosi D, Rubino V, Giovazzino A, Guccione J, Pernice V, et al. Circulating regulatory T cells (Treg), leptin and induction of proinflammatory activity in obese Labrador Retriever dogs. Veterinary Immunology and Immunopathology. 2018;202:122-9. Piantedosi D, Di Loria A, Guccione J, De Rosa A, Fabbri S, Cortese L, et al. Serum biochemistry profile, inflammatory cytokines, adipokines and cardiovascular findings in obese dogs. The Veterinary Journal. 2016;216:72-8. Tvarijonaviciute A, Ceron JJ, Holden SL, Cuthbertson DJ, Biourge V, Morris PJ, et al. Obesity-related metabolic dysfunction in dogs: a comparison with human metabolic syndrome. BMC Veterinary Research. 2012;8(1):147. Longo M, Zatterale F, Naderi J, Parrillo L, Formisano P, Raciti GA, et al. Adipose Tissue Dysfunction as Determinant of Obesity-Associated Metabolic Complications. International Journal of Molecular Sciences. 2019;20(9):2358. Carloni A, Paninarova M, Cavina D, Romanucci M, Salda LD, Teodori S, et al. Negative hepatic computed tomographic attenuation pattern in a dog with vacuolar hepatopathy and hepatic fat accumulation secondary to cushing's syndrome. Veterinary Radiology & Ultrasound. 2019;60(5):E54-E7. Lee YS, Hong N, Witanto JN, Choi YR, Park J, Decazes P, et al. Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment. Clin Nutr. 2021;40(8):5038-46. Konstantinidis AO, Patsikas MN, Papazoglou LG, Adamama-Moraitou KK. Congenital Portosystemic Shunts in Dogs and Cats: Classification, Pathophysiology, Clinical Presentation and Diagnosis. Veterinary Sciences. 2023;10(2):160. Gant P, Holden SL, Biourge V, German AJ. Can you estimate body composition in dogs from photographs? BMC Veterinary Research. 2016;12(1):18. Oliveira CR, Henzler MA, Johnson RA, Drees R. Assessment of respiration-induced displacement of canine abdominal organs in dorsal and ventral recumbency using multislice computed tomography. Veterinary Radiology & Ultrasound. 2015;56(2):133-43. Oelkrug R, Polymeropoulos ET, Jastroch M. Brown adipose tissue: physiological function and evolutionary significance. Journal of Comparative Physiology B, Biochemical, Systemic, and Environmental Physiology. 2015;185(6):587-606. Agrawal A, Nair N, Baghel NS. A novel approach for reduction of brown fat uptake on FDG PET. The British journal of radiology. 2009;82(980):626-31. Reddy NL, Jones TA, Wayte SC, Adesanya O, Sankar S, Yeo YC, et al. Identification of brown adipose tissue using MR imaging in a human adult with histological and immunohistochemical confirmation. J Clin Endocrinol Metab. 2014;99(1):E117-21. Glickman LT, Glickman NW, Schellenberg DB, Raghavan M, Lee T. Non-dietary risk factors for gastric dilatation-volvulus in large and giant breed dogs. Journal of the American Veterinary Medical Association. 2000;217(10):1492-9. Pegram C, Raffan E, White E, Ashworth AH, Brodbelt DC, Church DB, et al. Frequency, breed predisposition and demographic risk factors for overweight status in dogs in the UK. Journal of Small Animal Practice. 2021;62(7):521-30. Hermanson JW, DeLahunta A, Evans HE, Evans HE, Miller ME. Miller and Evans' Anatomy of the Dog. Fifth edition. ed: Elsevier; 2020. Edmunds GL, Smalley MJ, Beck S, Errington RJ, Gould S, Winter H, et al. Dog breeds and body conformations with predisposition to osteosarcoma in the UK: a case-control study. Canine Medicine and Genetics. 2021;8(1):2. Batcher K, Dickinson P, Giuffrida M, Sturges B, Vernau K, Knipe M, et al. Phenotypic Effects of FGF4 Retrogenes on Intervertebral Disc Disease in Dogs. Genes. 2019;10(6):435. Studdert VP, Gay CC, Hinchcliff KW. Saunders comprehensive veterinary dictionary. Fifth edition. ed: Elsevier; 2021. Syme HM, Jepson R. 321: Clinical approach and laboratory evaluation of renal disease. In: Stephen J. Ettinger, Edward C. Feldman, Cote E, editors. Textbook of veterinary internal medicine: diseases of the dog and the cat. Eighth edition. ed: Elsevier; 2017. Lo Gullo R, Mishra S, Lira D, Digumarthy S, Stone J, Kalra M, et al. The neglected space: quantifying the third space body fluid with whole body ct and autopsy. Radiological Society of North America 2014 Scientific Assembly and Annual Meeting: Radiological Society of North America; 2014. Yankin I, Nemanic S, Funes S, de Morais H, Gorman E, Ruaux C. Clinical relevance of splenic nodules or heterogeneous splenic parenchyma assessed by cytologic evaluation of fine-needle samples in 125 dogs (2011-2015). Journal of Veterinary Internal Medicine. 2020;34(1):125-31. Textor J, van der Zander B, Gilthorpe MK, Liskiewicz M, Ellison GTH. Robust causal inference using directed acyclic graphs: the R package 'dagitty'. International Journal of Epidemiology 2016;45(6):1887-94. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods. 2009;41:1149-60. Soper DSOS. Multiple Regression Sample Size Calculator [Online Software] https://www.analyticscalculators.com.2021 [Available from: https://www.analyticscalculators.com. Akaike H. Selected papers of Hirotugu Akaike / Emanuel Parzen, Kunio Tanabe, Genshiro Kitagawa, editors. Springer; 1998. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet (London, England). 1986;1(8476):307-10. McBride GB. A proposal for strength-of-agreement criteria for Lin's Concordance Correlation Coefficient. Hamilton, New Zealand: National Institute of Water and Atmospheric Research (NIWA); 2005. Report No.: NIWA Client Report: HAM2005-062. Team RC. R: A Language and Environment for Statistical Computing. In: Computing RFfS, editor. Vienna, Austria: R Foundation for Statistical Computing; 2024. Tables Tables 1 to 4 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files S1DescriptiveStatisticsandUnivariateAnalysis.docx Additional File 1: S1: Descriptive statistics and univariable analysis of categorical variables in a cross-sectional study of volume CT abdominal fat distribution in a tertiary veterinary hospital population of dogs S2HWRDescriptivestatisticscompleted.docx Additional File 2: S2: Descriptive statistics and univariable analysis of the relationship between CT abdominal height-to-width ratio (HWR), breed, and breed-related characteristics in a tertiary veterinary hospital population of dogs S3MultivariateViscFat.docx Additional File 3: S3: Putative causal diagram-guided logistic regression analysis outputs for variables associated with CTV abdominal visceral fat percentage in a tertiary veterinary hospital population of dogs. S4MultivariateSQFat.docx Additional File 4: S4: Putative causal diagram-guided logistic regression analysis outputs for variables associated with CTV abdominal subcutaneous fat percentage in a tertiary veterinary hospital population of dogs. S5Explanatoryvariablesanddefintions.docx Additional File 5: S5: Explanatory variables and definitions S6SensitivityTableforHAC.docx Additional File 6: S6: Sensitivity analysis of putative causal diagram-guided logistic regression analysis outputs for variables associated with CTV abdominal fat distribution in a tertiary veterinary hospital population of dogs, based on whether hyperadrenocorticism was considered based on confirmed, presumptive, or presumptive and confirmed dogs according to the case definitions. S7DAGandjustification.docx Additional File 7: S7: Putative causal web model/Directed acyclic graph (DAG) and justification. T1MultivariateAbdFat.docx T2MultivariatelgVSQ.docx T3ValidityofL3CTA.docx T4ValidityofHounsfieldandLinearVATSAT.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Oct, 2024 Editor assigned by journal 17 Oct, 2024 Submission checks completed at journal 16 Oct, 2024 First submitted to journal 16 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5272968","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367039215,"identity":"656a63ac-88b6-4347-b7a8-31f5d3e0a8e2","order_by":0,"name":"Robert B. S. Turner","email":"data:image/png;base64,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","orcid":"","institution":"University of Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Robert","middleName":"B. S.","lastName":"Turner","suffix":""},{"id":367039217,"identity":"f36ef152-81ea-4011-94af-627eb923e5e2","order_by":1,"name":"S. M. Firestone","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"M.","lastName":"Firestone","suffix":""},{"id":367039218,"identity":"c0bdb531-2be1-4ea1-a0e1-1df0e3f26071","order_by":2,"name":"F. R. Dunshea","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"F.","middleName":"R.","lastName":"Dunshea","suffix":""},{"id":367039222,"identity":"1465d461-e72d-4eee-ac6e-72053213dd52","order_by":3,"name":"C. S. Mansfield","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"C.","middleName":"S.","lastName":"Mansfield","suffix":""}],"badges":[],"createdAt":"2024-10-16 06:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5272968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5272968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68729122,"identity":"2f03cf47-848b-47df-9501-2f50c9df9c25","added_by":"auto","created_at":"2024-11-11 12:07:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1322946,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots showing pairwise comparison and quadratic relationship between age and CTV total abdominal fat percentage (A), age and CTV abdominal visceral fat percentage (B), age and CTV abdominal subcutaneous (SQ) fat percentage (C), and age and log of CTV abdominal visceral-to-subcutaneous fat ratio (V/SQ) (D) in a tertiary veterinary hospital population of dogs.\u003c/p\u003e","description":"","filename":"F1FatversusAgeQuadratic.png","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/44228e0d52bff16dd0a4c0fe.png"},{"id":68728788,"identity":"fd15ca98-75cb-44d1-9553-131f4d221882","added_by":"auto","created_at":"2024-11-11 11:59:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3962593,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots showing pairwise comparison and linear relationship between CT volume (CTV) total abdominal fat percentage and BCS (A), L3 CT area (L3 CTA) total abdominal fat percentage and BCS (B), CTV visceral-to-subcutaneous (CTV V/SQ) fat ratio (CTV V/SQ and BCS (C), and CTV V/SQ and CTV total abdominal fat percentage (D) in a tertiary veterinary hospital population of dogs.\u003c/p\u003e","description":"","filename":"F2FatBCSandVSQ.png","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/7a26f81bb9d16ec8710d1f9b.png"},{"id":68729133,"identity":"7010062a-17ca-484c-8ffa-40f6b51befe0","added_by":"auto","created_at":"2024-11-11 12:07:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5497146,"visible":true,"origin":"","legend":"\u003cp\u003eThe CT cross-sectional area of abdominal, visceral, and subcutaneous fat percentages measured at the cranial margin of L3 compared to the equivalent volumetric CT measurements between the cranial margins of T10 to S1. A – Total abdominal fat. B – Subcutaneous abdominal fat. C – Visceral abdominal fat. D – Visceral-to-subcutaneous fat ratio\u003c/p\u003e","description":"","filename":"F3ValidityofCTAL3measurements.png","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/52a1de51d6ee4c99c4d0e8bf.png"},{"id":68730077,"identity":"8313321b-e030-43a4-8586-59b2dd5502d1","added_by":"auto","created_at":"2024-11-11 12:15:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4351822,"visible":true,"origin":"","legend":"\u003cp\u003eA - The mean Hounsfield Units (HU) recorded within the entire abdominal volume (CTV Mean HU) compared to the CT Volume Abdominal Fat Percentage. B - The mean Hounsfield Units (HU) recorded within the entire transverse abdominal area measured at the cranial margin of L3 (L3 Mean HU) compared to the CT Volume Abdominal Fat Percentage. C – Ultrasound total adipose thickness (TAT) compared to CT volume total abdominal fat percentage. D – Ultrasound visceral adipose thickness (VAT) compared to CT volume visceral fat percentage. E - Ultrasound subcutaneous adipose thickness (SAT) compared to CT volume subcutaneous fat percentage. F - Ultrasound VAT/SAT compared to volume CT V/SQ. The abdominal volume was recorded as tissue volume between the cranial margins of T10 to S1. The abdominal volume was measured using a -250 to 2000 HU range, and abdominal fat using a range of -250 to -25 HU. The visceral adipose thickness (VAT) and subcutaneous adipose thickness (SAT) were measured as thickness perpendicular to the linea alba at the most caudal aspect of the liver as it crosses the midline.\u003c/p\u003e","description":"","filename":"F4ValidityofLinearandHUMeasurements.png","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/aae9ab9eb605877ea6faf805.png"},{"id":68730729,"identity":"d94cd693-84ab-4fd2-9f89-758631917bf2","added_by":"auto","created_at":"2024-11-11 12:23:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2505183,"visible":true,"origin":"","legend":"\u003cp\u003etransverse CT images illustrating various abdominal adiposity patterns and visceral-to-subcutaneous (V/SQ) fat distribution in a tertiary veterinary hospital population of dogs. The images are arranged from left to right, indicating increasing total abdominal fat adiposity, and from bottom to top, representing increasing V/SQ fat ratios. The four patients depicted are as follows: A) 1.5-year-old female-spayed mixed breed with a left divisional intrahepatic portocaval shunt, having 14% total abdominal fat and a V/SQ ratio of 1.35; B) 11-year-old male-neutered mixed breed undergoing staging for soft tissue sarcoma of the left thoracic limb, exhibiting 52% total abdominal fat and a V/SQ ratio of 1.22; C) 1.4-year-old female-spayed Boxer diagnosed with intestinal adenocarcinoma, displaying 28% total abdominal fat and a V/SQ ratio of 0.379; D) 7-year-old male-neutered Golden Retriever undergoing staging for insulinoma, demonstrating 57% total abdominal fat and a V/SQ ratio of 0.287.\u003c/p\u003e","description":"","filename":"F5VSQVariation300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/c6c4fcf3a3e5b28f5cdee2c7.png"},{"id":68729127,"identity":"cf8c8fe8-83c2-44bf-b65d-b0f5fe487103","added_by":"auto","created_at":"2024-11-11 12:07:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2060237,"visible":true,"origin":"","legend":"\u003cp\u003etransverse and sagittal CT reconstruction of a dog's abdomen showing the location of the abdominal height and width (hashed lined), the visceral adipose thickness (VAT), and subcutaneous adipose thickness (SAT) (solid lines) measurements. The cranial abdominal height and width were measured at the widest internal diameter of the cranial abdomen constrained within the costal arch and used to determine the cranial abdominal height-width ratio (HWR). The VAT and SAT were performed perpendicular to the parietal surface of the linea alba, aligning with the most caudal margin of the liver at the midline.\u003c/p\u003e","description":"","filename":"F6FalciformMeasurementsmarkers300dpiVATSATwithHWR.png","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/a52c66a2a77fbbb9245893e6.png"},{"id":68730081,"identity":"b8cff3fe-158c-443c-919c-010bf6879239","added_by":"auto","created_at":"2024-11-11 12:15:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":401460,"visible":true,"origin":"","legend":"\u003cp\u003ePutative causal web model/Directed acyclic graph (DAG)\u003c/p\u003e\n\u003cp\u003eDirected acyclic graph showing putative causal paths linking explanatory variables to the measured CTV abdominal visceral-to-subcutaneous fat ratio in an Australian tertiary veterinary hospital population of dogs. A detailed description of each link in the above plot is provided in supplementary materials S2.\u003c/p\u003e","description":"","filename":"F7DAGcHAC.png","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/627d91794badcea3f600355d.png"},{"id":68731063,"identity":"fb867f80-fd8b-4c1b-bc66-0faa63ccedc8","added_by":"auto","created_at":"2024-11-11 12:32:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24213739,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/79e9d034-514e-479c-a227-9eb461c53906.pdf"},{"id":68728782,"identity":"f561df4b-f61d-408b-bc48-e160a1610a6c","added_by":"auto","created_at":"2024-11-11 11:59:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49502,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 1: \u0026nbsp;S1: Descriptive statistics and univariable analysis of categorical variables in a cross-sectional study of volume CT abdominal fat distribution in a tertiary veterinary hospital population of dogs\u003c/p\u003e","description":"","filename":"S1DescriptiveStatisticsandUnivariateAnalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/4064e6e41cb3120d873a7e30.docx"},{"id":68730079,"identity":"5b9ae5b7-3512-4781-ba54-66531e6471f3","added_by":"auto","created_at":"2024-11-11 12:15:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22814,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 2: \u0026nbsp;S2: Descriptive statistics and univariable analysis of the relationship between CT abdominal height-to-width ratio (HWR), breed, and breed-related characteristics in a tertiary veterinary hospital population of dogs\u003c/p\u003e","description":"","filename":"S2HWRDescriptivestatisticscompleted.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/1544c45bf88b82e1b409c916.docx"},{"id":68728783,"identity":"3fc0d349-3063-485e-9058-6d90c0611a4e","added_by":"auto","created_at":"2024-11-11 11:59:46","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":47703,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 3: S3: Putative causal diagram-guided logistic regression analysis outputs for variables associated with CTV abdominal visceral fat percentage in a tertiary veterinary hospital population of dogs.\u003c/p\u003e","description":"","filename":"S3MultivariateViscFat.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/e3c6bc71966d8ed8037b807c.docx"},{"id":68729123,"identity":"eec605a2-c2b3-41d7-aaa9-5db7d6a8a47f","added_by":"auto","created_at":"2024-11-11 12:07:46","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":49259,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 4: \u0026nbsp;S4: Putative causal diagram-guided logistic regression analysis outputs for variables associated with CTV abdominal subcutaneous fat percentage in a tertiary veterinary hospital population of dogs.\u003c/p\u003e","description":"","filename":"S4MultivariateSQFat.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/b4628cb3ee083f28b303fe36.docx"},{"id":68730731,"identity":"469961cc-49ee-4cd9-89b5-98a701433aba","added_by":"auto","created_at":"2024-11-11 12:23:49","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21441,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 5: S5: Explanatory variables and definitions\u003c/p\u003e","description":"","filename":"S5Explanatoryvariablesanddefintions.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/4b235da61da976d930f3d5de.docx"},{"id":68729131,"identity":"3e7508cb-5603-41a7-a11a-55a521efa418","added_by":"auto","created_at":"2024-11-11 12:07:46","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":20595,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 6: S6: Sensitivity analysis of putative causal diagram-guided logistic regression analysis outputs for variables associated with CTV abdominal fat distribution in a tertiary veterinary hospital population of dogs, based on whether hyperadrenocorticism was considered based on confirmed, presumptive, or presumptive and confirmed dogs according to the case definitions.\u003c/p\u003e","description":"","filename":"S6SensitivityTableforHAC.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/52b88cf7a434f7a38baa12f9.docx"},{"id":68728794,"identity":"5a34dd96-100c-4a94-b089-bb56678c008d","added_by":"auto","created_at":"2024-11-11 11:59:46","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":123862,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 7: S7: Putative causal web model/Directed acyclic graph (DAG) and justification.\u003c/p\u003e","description":"","filename":"S7DAGandjustification.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/c80b0a8e3f54ad7c3672f096.docx"},{"id":68728789,"identity":"10c2c699-4f18-41d3-a177-1fdbdd181b69","added_by":"auto","created_at":"2024-11-11 11:59:46","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":49589,"visible":true,"origin":"","legend":"","description":"","filename":"T1MultivariateAbdFat.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/f9fdfed34190a852e94ff71e.docx"},{"id":68729129,"identity":"1c54d184-5fbf-45e6-bc99-8c4b7d588db9","added_by":"auto","created_at":"2024-11-11 12:07:46","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":50578,"visible":true,"origin":"","legend":"","description":"","filename":"T2MultivariatelgVSQ.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/1a5e1d909122ff78ae18b796.docx"},{"id":68728797,"identity":"7348eb74-e2d7-4f94-9846-3711323148a5","added_by":"auto","created_at":"2024-11-11 11:59:47","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":19220,"visible":true,"origin":"","legend":"","description":"","filename":"T3ValidityofL3CTA.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/dacf9c7871c55f3d653ba0cc.docx"},{"id":68730080,"identity":"5d74c27c-21b0-46a4-95bb-d704e6928826","added_by":"auto","created_at":"2024-11-11 12:15:46","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":16523,"visible":true,"origin":"","legend":"","description":"","filename":"T4ValidityofHounsfieldandLinearVATSAT.docx","url":"https://assets-eu.researchsquare.com/files/rs-5272968/v1/39ffce2a324971db5b5b64f1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abdominal visceral to subcutaneous fat distribution in dogs: computed tomography accuracy and factors associated with distribution","fulltext":[{"header":"Background","content":"\u003cp\u003eOverweight and obesity (OO) are conditions defined as abnormal or excessive fat accumulation that may impair health, contribute to disease, and shorten lifespan (1\u0026ndash;4). Dogs are considered overweight when their body fat exceeds 20\u0026ndash;30% and obese when it surpasses 30% of their body weight (4). Overweight and obesity is recognised as one of the most prevalent disorders affecting pet dogs, with a 22\u0026ndash;60% prevalence reported (5\u0026ndash;10). Increased adiposity has welfare implications as OO is associated with chronic dysregulation in adipokines, increases in proinflammatory markers, reduced quality of life and longevity, insulin resistance, and chronic diseases such as osteoarthritis, urinary tract infections, neoplasia, cardiorespiratory dysfunction and potential hepatic dysfunction (11\u0026ndash;14). Further, OO influences the pharmacokinetics of many drugs, commonly affecting the volume of distribution and rate of drug clearance (15, 16). Thus, OO has clinical and research implications, and veterinarians should tailor their treatments to account for dog adiposity.\u003c/p\u003e \u003cp\u003eObesity is predominantly a nutritional disorder (17, 18). However, OO is more complicated than this and should be considered a multifactorial process that includes dog-related (e.g. signalment, genetic factors, exercise volume), diet-related (e.g. amount of food intake, frequency of treats, consumption of table scraps), owner-related (e.g. demographic, socioeconomic situation, ability to exercise); disease-related (e.g. hyperadrenocorticism, hypothyroidism), pharmaceutical-related (e.g. glucocorticoids or anticonvulsants administration), and environment-related (e.g. exposure to obesogens) factors (5, 13). A potential contributor to OO pathophysiology is the relative distribution of fat to the visceral (central) or subcutaneous (peripheral) regions in dogs. Though there is scant and, at times, contradictory research, increased fat distribution to the visceral compartment surrounding the organs may be more inflammatory and more significantly influence adipokine, cardiovascular and metabolic dysregulation than general OO (19\u0026ndash;22).\u003c/p\u003e \u003cp\u003eCurrent evidence suggests increased visceral-to-subcutaneous (V/SQ) fat deposition is associated with increasing age, neuter status, and certain dog breeds, but not with specific diets (23\u0026ndash;26). Further, preferential visceral fat deposition may be favoured in some disease states, such as hyperadrenocorticism (HAC), though this has not been objectively evaluated in dogs (27, 28). Investigation of these and other associations needs to be established if visceral adiposity's effect on dogs' overall health is to be fully elucidated and early intervention initiated.\u003c/p\u003e \u003cp\u003eIn humans, the gold standard methods for measuring visceral fat are CT and MRI, though dual-energy X-ray absorptiometry (DXA) is commonly used due to its relatively high accuracy, accessibility and low radiation exposure (29, 30). Visceral fat area (VFA) established from a single transverse slice through the abdomen at the level of the third lumbar vertebrae is widely used in people (29). Ultrasound measurements have also been used to estimate human visceral fat to provide greater access and limit radiation exposure, but reproducibility and accuracy are poor (29). Further, there is growing consensus focused on the abdominal circumference of people, supported by body mass index (BMI), as a more accurate reflector of central obesity and its relationship with metabolic health dysfunction (31). Some of these methods have been replicated in dog studies, with CT-determined VFA at the third lumbar vertebra commonly referenced, and CT volume, MRI and ultrasound methods published (23, 26, 32\u0026ndash;35). However, there is limited validation and standardisation of these techniques.\u003c/p\u003e \u003cp\u003eThus, this cross-sectional observational study aimed to assess for an association between abdominal visceral and subcutaneous (SQ) fat distribution, total abdominal adiposity, body condition score (BCS), age, sex, neuter status, and breed conformation in a tertiary veterinary hospital population of dogs. The influence of several disease states on abdominal adiposity and abdominal fat distribution was also evaluated. Finally, the use of single fat area measurements at the third lumbar vertebra and a single measurement of the falciform fat thickness were compared and validated against the abdominal volume measurements of fat.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eA total of 205 abdominal CTs were analysed from the original 448 CTs retrieved from the hospital records between March 2006 and March 2020. Data were 100% complete for breed, age, sex-neuter status and body weight and 80% for BCS.\u003c/p\u003e \u003cp\u003eSupplemental 1 (S1) displays frequency cross-tabulations and univariable comparisons for categorical data. There was a bimodal age distribution, with an overall median age of 8.0 years (IQR: 2.7 to 10.8 years; range: 3 months to 16.3 years), with modes at approximately 1\u0026ndash;2 years and 10\u0026ndash;11 years. Neutered dogs (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026thinsp;=\u0026thinsp;7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 years [95% CI of mean: 7.0, 8.3 years]) were significantly older than entire dogs (4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 years [95% CI: 3.4, 6.4 years]). The median body weight was 21.0 kg (IQR: 10.1 to 32.8 kg; range: 1.2 to 67.8 kg), with a median body condition score of 5 out of 9 (IQR: 4 to 6; range: 2 to 9).\u003c/p\u003e \u003cp\u003eThere were 174 purebred and 74 mixed-bred dogs, with predominant breeds including Border collies (n\u0026thinsp;=\u0026thinsp;13), Labrador retriever (n\u0026thinsp;=\u0026thinsp;11), Staffordshire terriers (n\u0026thinsp;=\u0026thinsp;9), golden retrievers (n\u0026thinsp;=\u0026thinsp;7), German shepherds (n\u0026thinsp;=\u0026thinsp;7), Jack Russell terriers (n\u0026thinsp;=\u0026thinsp;6), beagles (n\u0026thinsp;=\u0026thinsp;5), and pugs (n\u0026thinsp;=\u0026thinsp;5). No other breed had more than five representatives. When categorised according to ANKC standards, there were 32 working dogs, 24 gundogs, 24 utility dogs, 19 terriers, 11 non-sporting breeds, 11 toy breeds and 10 hounds. Two American and two Australian Bulldogs were considered mixed breeds due to ANKC recognition limitations, resulting in 74 mixed-breed dogs. Additionally, there were 88 mesocephalic, 25 brachycephalic, and 18 dolichocephalic dogs, with 99 non-chondrodystrophic and 32 chondrodystrophic dogs. The HWR varied across breed groups, as shown in Supplemental Table\u0026nbsp;2 (S2). Specifically, working dogs and non-sporting ANKC breeds, giant breeds, and dolichocephalic dogs had deeper, narrower cranial abdomens, while chondrodystrophic dogs exhibited shallower, wider cranial abdomens. Hounds exhibited deeper chests in this sample, although the observed difference was not statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAbdominal Fat Distribution\u003c/h3\u003e\n\u003cp\u003eThe multivariable associations between each risk factor and fat distribution are displayed in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementals 3 and 4. The CT confounding variables significantly reduced total abdominal, visceral and SQ fat measurements. Specifically, small abdominal organs and an increasing volume of effusion emerged as the main contributors to CT confounding. Small abdominal organs resulted in an increased V/SQ ratio, whilst the other CT confounders did not show a statistically significant effect on the V/SQ ratio. Notably, the greatest impact of effusion and small organs was on SQ fat distribution, as the V/SQ ratio increased when these confounders were not adjusted for total abdominal adiposity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePutative causal diagram-guided logistic regression analysis outputs for variables associated with CTV total abdominal fat percentage in a tertiary veterinary hospital population of dogs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjustment set\u003c/p\u003e \u003cp\u003e(total effect)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnadjusted estimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted estimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel estimated marginal means for levels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years, quadratic)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, centred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.683 (1.299, 2.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.670 (1.293, 2.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Age, centred)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, centred, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.314 (-0.417, -0.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.222 (-0.312, -0.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.98 (42.74, 51.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.112 (-6.054, 1.831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.125 (-4.257, 2.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.85 (41.52, 50.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeuter Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.56 (38.01, 49.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDesexed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.943 (2.600, 13.286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.310 (-1.095, 7.714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.87 (42.86, 50.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex-Neuter Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale entire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.80 (36.04, 53.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale spayed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.539 (-1.669, 18.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.351 (-6.073, 10.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.15 (42.83, 51.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale entire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.274 (-11.567, 11.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.799 (-10.936, 7.337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.00 (36.89, 49.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale neutered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.075 (-3.054, 17.203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.651 (-6.661, 9.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.45 (42.02, 50.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANKC Breed Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.77 (39.94, 55.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634 (-10.024, 11.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.996 (-15.417, 1.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.78 (34.32, 47.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGundogs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.605 (-5.637, 14.848)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.109 (-9.139, 6.921)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.67 (40.75, 52.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.598 (-11.693, 12.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.460 (-11.064, 8.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.31 (38.32, 54.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorking Dogs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.703 (-8.130, 11.535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.976 (-8.690, 6.738)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.80 (41.30, 52.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUtility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.037 (-4.206, 16.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.178 (-5.870, 10.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.95 (43.94, 55.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-sporting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.533 (-12.528, 11.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.358 (-6.081, 12.796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.13 (43.36, 58.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.732 (-5.359, 12.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.805 (-8.965, 5.354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.97 (41.71, 50.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANKC Terrier\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.66 (34.26, 47.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-terriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.517 (-4.212, 9.246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.184 (0.838, 11.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.84 (42.89, 50.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANKC Hound\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.98 (37.96, 54.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-hounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.439 (-6.626, 11.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.500 (-6.672, 7.671)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.48 (42.48, 50.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5 (-6.672, 7.671)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkull Shape\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrachycephalic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.797 (-3.839, 13.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.100 (-3.756, 9.955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.33 (43.39, 55.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMesocephalic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.491 (-1.735, 12.718)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047 (-5.887, 5.980)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.27 (41.67, 50.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDolichocephalic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.23 (39.68, 52.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.877 (-1.464, 13.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.138 (-6.189, 5.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.09 (41.79, 50.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.59 (42.01, 51.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChondrodystrophy Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChondrodystrophic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.72 (41.20, 52.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-chondrodystrophic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.827 (-3.865, 7.520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.161 (-4.371, 4.694)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.88 (42.37, 51.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.654 (-3.268, 8.576)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.602 (-5.331, 4.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.12 (41.83, 50.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHWR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.041 (-24.563, 0.480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.928 (-20.196, 0.340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize Category (Nominal Categorisation with Cross Breed Excluded)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.931 (0.136, 3.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.973 (-0.3612, 2.307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize Category\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(including Mixed Breed)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra Small\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, CT Confounders, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.61 (40.95, 62.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.373 (-21.312, 6.566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.860 (-18.932, 3.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.75 (37.26, 50.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium-small\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.690 (-9.988, 19.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.322 (-14.021, 9.377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.29 (41.79, 56.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium-large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.385 (-15.369, 10.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.775 (-17.104, 3.553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.83 (39.75, 49.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.979 (-10.341, 16.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.367 (-15.002, 6.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.24 (41.70, 52.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGiant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.035 (-9.752, 17.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.296 (-10.676, 11.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.90 (45.56, 58.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.150 (-11.591, 13.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.368 (-15.538, 4.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.24 (41.99, 50.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCS (nominal)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.764 (4.596, 6.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.908 (2.816, 5.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCS (categorical)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.95 (20.87, 39.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.771 (-5.521, 15.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.354 (-5.488, 12.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.31 (27.06, 39.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.495 (3.818, 23.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.184 (0.771, 17.596)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.14 (34.06, 44.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.039 (7.422, 26.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.114 (2.709, 19.519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.07 (35.93, 46.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.67 (12.429, 32.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.65 (5.534, 23.766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.60 (38.60, 50.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.338 (18.597, 40.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.854 (10.32, 29.389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.81 (43.23, 56.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.876 (24.412, 51.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.425 (16.493, 40.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58.38 (48.89, 67.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.573 (22.851, 54.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.628 (10.676, 38.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.58 (42.24, 66.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbdominal Adiposity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTV Mean Hounsfield Units\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.506 (-0.525, -0.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.494 (-0.518, -0.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease Chronicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute (\u0026lt;\u0026thinsp;3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, CT Confounders.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.90 (37.41, 44.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic (\u0026ge;\u0026thinsp;3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.396 (-10.264, -2.528)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.705 (-4.002, 2.593)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.20 (36.29, 44.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndocrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, CT Confounders.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.31 (37.84, 48.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeoplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.392 (-11.430, 0.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.498 (-10.664, -0.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.81 (33.96, 41.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGastroenteric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.973 (-13.337, 5.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.318 (-7.887, 8.523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.63 (36.46, 50.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrauma/Musculoskeletal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.641 (-27.248, 3.966)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.370 (-17.034, 10.294)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.94 (26.62, 53.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCongenital Portal Vascular Anomaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-20.914 (-27.814, -14.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.792 (-8.465, 6.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.52 (36.36, 48.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.630 (-15.043, -0.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.959 (-6.007, 7.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.27 (38.70, 49.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.202 (-13.037, 2.633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.473 (-9.26, 4.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.84 (35.04, 46.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConfirmed Hyperadrenocorticism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo evidence of hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, CT Confounder.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.61 (37.33, 43.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.017 (-0.439, 18.473)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.137 (-3.523, 11.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.75 (36.50, 52.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrug Length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.40 (37.10, 47.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute (\u0026lt;\u0026thinsp;3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.756 (-7.972, 0.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.366 (-3.794, 3.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.03 (36.76, 47.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic (\u0026ge;\u0026thinsp;3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.994 (-2.833, 8.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.048 (-1.672, 7.768)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.45 (39.17, 51.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrednisone /Phenobarbitone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, CT Confounder.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.32 (37.03, 43.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.425 (-3.297, 12.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.231 (-0.867, 11.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.55 (38.97, 52.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCT Confounding Findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.40 (39.79, 51.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.143 (-10.256, -2.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.445 (-8.842, -2.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.95 (35.12, 44.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresence of Effusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.20 (36.25, 46.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.354 (-7.035, 2.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.656 (-7.422, 0.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37.54 (31.74, 43.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVolume of Effusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.225 (-3.923, -0.528)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.318 (-3.682, -0.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresence of Mass\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.41 (37.36, 47.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.154 (-2.017, 6.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.285 (-7.820, -0.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.12 (32.78, 43.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganomegaly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.40 (37.06, 47.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.040 (-9.237, -0.843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.718 (-6.253, 0.816)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.68 (34.56, 44.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMass and Organomegaly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.83 (38.45, 49.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.245 (-7.130, 0.640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.255 (-7.480, -1.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.57 (34.64, 44.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmall organ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026Dagger;, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.88 (36.92, 46.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-15.901 (-20.677, -11.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.679 (-10.585, -0.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.20 (29.91, 42.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026Dagger; Wherever age was entered into a model it was centred by its mean and entered in quadratic form as age, centred + (age, centred)\u003csup\u003e2\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCTV \u0026ndash; Volume computed tomography; Abd. Fat \u0026ndash; total abdominal fat percentage; SQ \u0026ndash; Subcutaneous abdominal fat percentage; Visc. \u0026ndash; Visceral abdominal fat percentage; V/SQ \u0026ndash; Visceral-to-Subcutaneous Fat Ratio; LgV/SQ \u0026ndash; log\u003csub\u003e10\u003c/sub\u003e(V/SQ)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eANKC - Australian National Kennel Council; BCS \u0026ndash; body condition score\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePutative causal diagram-guided logistic regression analysis outputs for variables associated with log of CTV abdominal visceral-to-subcutaneous fat distribution (lgV/SQ) in a tertiary veterinary hospital population of dogs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjustment set\u003c/p\u003e \u003cp\u003e(total effect)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficients (β) (exponentiated)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted estimates (exponentiated)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value (adjusted)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel estimated marginal means for levels (exponentiated)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e[V/SQ % per year]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008 (1.003, 1.013)\u003c/p\u003e \u003cp\u003e[0.8% (0.3%, 1.3%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.017 (1.011, 1.022)\u003c/p\u003e \u003cp\u003e[1.7% (1.1. 2.2%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69 (0.60, 0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.025 (0.978, 1.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.029 (0.987, 1.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.64, 0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.025 (0.984, 1.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeuter Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.63, 0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDesexed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.985 (0.923, 1.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.975 (0.920, 1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.62, 0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex-Neuter Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale entire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63 (0.48, 0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale spayed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.085 (0.960, 1.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.042 (0.932, 1.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69 (0.61, 0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale entire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.146 (1.001, 1.312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.105 (0.980, 1.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79 (0.66, 0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale neutered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.093 (0.969, 1.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.059 (0.949, 1.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72 (0.63, 0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANKC Breed Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66 (0.52, 0.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.153 (1.023, 1.301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.103 (0.989, 1.230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83 (0.68, 1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGundogs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.026 (0.914, 1.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.009 (0.909, 1.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67 (0.56, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.121 (0.976, 1.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.129 (0.996, 1.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.69, 1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorking Dogs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.980 (0.877, 1.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.985 (0.891, 1.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64 (0.54, 0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUtility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.019 (0.908, 1.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.049 (0.945, 1.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.62, 0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-sporting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.915 (0.799, 1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.945 (0.836, 1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58 (0.46, 0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.055 (0.952, 1.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.035 (0.943, 1.136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.63, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72 (0.61, 0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANKC Terrier\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83 (0.68, 1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-terriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.889 (0.822, 0.961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.928 (0.865, 0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.62, 0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANKC Hound\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88 (0.69, 1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-hounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.920 (0.827, 1.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.904 (0.823, 0.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70 (0.62, 0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkull Shape\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrachycephalic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.942 (0.852, 1.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943 (0.861, 1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65 (0.54, 0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMesocephalic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.016 (0.933, 1.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.980 (0.906, 1.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.61, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDolichocephalic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.61, 0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.028 (0.944, 1.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983 (0.907, 1.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.62, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChondrodystrophy Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChondrodystrophic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.61, 0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-chondrodystrophic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.965 (0.903, 1.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.979 (0.923, 1.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69 (0.60, 0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.002 (0.935, 1.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992 (0.932, 1.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.62, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHWR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.837 (0.722, 0.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.863 (0.755, 0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize Category (Nominal Categorisation with Cross Breed Excluded)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.983 (0.963, 1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.994 (0.975, 1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra Small\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, CT Confounders, Confirmed Hyperadrenocorticism, Abdominal Adiposity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.52, 1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.090 (0.923, 1.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.018 (0.877, 1.183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.62, 0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium-small\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008 (0.846, 1.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.990 (0.846, 1.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.56, 0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium-large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999 (0.856, 1.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.966 (0.841, 1.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67 (0.58, 0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.020 (0.870, 1.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.978 (0.847, 1.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69 (0.58, 0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGiant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.963 (0.817, 1.136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.978 (0.844, 1.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69 (0.57, 0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed breed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.041 (0.894, 1.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.989 (0.862, 1.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.62, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCS (nominal)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.993 (0.975, 1.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.982 (0.964, 1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCS (categorical)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92 (0.65, 1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.051 (0.894, 1.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.005 (0.864, 1.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93 (0.73, 1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.026 (0.882, 1.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.963 (0.835, 1.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.69, 1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994 (0.856, 1.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.926 (0.803, 1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.63, 0.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (0.852, 1.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.907 (0.776, 1.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.58, 0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998 (0.844, 1.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.911 (0.776, 1.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.57, 0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.075 (0.871, 1.327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.965 (0.790, 1.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.59, 1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.939 (0.735, 1.201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.899 (0.709, 1.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72 (0.44, 1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbdominal Adiposity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997 (0.996, 0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.995 (0.993, 0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCTV Mean Hounsfield Units\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.001 (1.000, 1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.002 (1.001, 1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease Chronicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute (\u0026lt;\u0026thinsp;3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Abdominal Adiposity, CT Confounder.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63 (0.56, 0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic (\u0026ge;\u0026thinsp;3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008 (0.962, 1.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.020 (0.976, 1.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66 (0.58, 0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease Category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndocrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Abdominal Adiposity, CT Confounders.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72 (0.61, 0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeoplasia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.977 (0.906, 1.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.949 (0.884, 1.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64 (0.57, 0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGastroenteric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.902 (0.801, 1.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.920 (0.824, 1.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60 (0.48, 0.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrauma/Musculoskeletal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.978 (0.803, 1.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.009 (0.839, 1.212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.49, 1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCongenital Portal Vascular Anomaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.942 (0.864, 1.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.971 (0.880, 1.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68 (0.56, 0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.864 (0.787, 0.949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.920 (0.838, 1.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60 (0.50, 0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884 (0.801, 0.976)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.886 (0.809, 0.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.55 (0.46, 0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConfirmed Hyperadrenocorticism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo evidence of hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Abdominal Adiposity, CT Confounder.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63 (0.57, 0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.171 (1.048, 1.308)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.174 (1.061, 1.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92 (0.71, 1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrug Length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism, Abdominal Adiposity, CT Confounder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.65, 0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute (\u0026lt;\u0026thinsp;3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.009 (0.959, 1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.004 (0.960, 1.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.66, 0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic (\u0026ge;\u0026thinsp;3 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.968 (0.903, 1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.979 (0.919, 1.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.60, 0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrednisone /Phenobarbitone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Abdominal Adiposity, CT Confounder.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64 (0.58, 0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.945 (0.863, 1.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983 (0.905, 1.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62 (0.50, 0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCT Confounding Findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.61, 0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.048 (0.998, 1.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.027 (0.979, 1.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78 (0.67, 0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.027 (0.979, 1.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresence of Effusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.65, 0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.021 (0.966, 1.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.016 (0.963, 1.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.80 (0.66, 0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.997 (0.949, 1.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVolume of Effusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.017 (0.997, 1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.018 (0.999, 1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.018 (0.999, 1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresence of Mass\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.65, 0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.036 (0.987, 1.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.003 (0.954, 1.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78 (0.65, 0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganomegaly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.63, 0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.049 (0.998, 1.102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.030 (0.980, 1.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.80 (0.68, 0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMass and Organomegaly\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.62, 0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.058 (1.011, 1.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.026 (0.981, 1.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79 (0.67, 0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmall organ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, Sex-Neuter Status, ANKC Terrier, Confirmed Hyperadrenocorticism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75 (0.64, 0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.018 (0.957, 1.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.077 (1.008, 1.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89 (0.73, 1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCTV \u0026ndash; Volume computed tomography; Abd. Fat \u0026ndash; total abdominal fat percentage; SQ \u0026ndash; Subcutaneous abdominal fat percentage; Visc. \u0026ndash; Visceral abdominal fat percentage; V/SQ \u0026ndash; Visceral-to-Subcutaneous Fat Ratio; LgV/SQ \u0026ndash; log\u003csub\u003e10\u003c/sub\u003eV/SQ\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eANKC - Australian National Kennel Council; BCS \u0026ndash; body condition score\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003ePutative causal diagram-guided logistic regression analysis outputs for variables associated with CTV total abdominal fat percentage in a tertiary veterinary hospital population of dogs.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003ePutative causal diagram-guided logistic regression analysis outputs for variables associated with the log of CTV abdominal visceral-to-subcutaneous fat distribution in a tertiary veterinary hospital population of dogs.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe following results are presented with adjustments made for age (linear or quadratic, as stated), total abdominal adiposity, sex-neutered status, ANKC terrier breed, cHAC, and CT confounders, as applicable. Further details regarding the relationship between BCS and the total abdominal adiposity and fat distribution are provided below.\u003c/p\u003e \u003cp\u003eAfter adjusting for confounders, total abdominal adiposity was associated with four variables: age, ANKC terrier breed, BCS, and disease category (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The relationship between total abdominal adiposity and age was found to be quadratic, with fat accumulation increasing with age, reaching a plateau at around 10 years, and then gradually decreasing with age (j-shaped distribution) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, abdominal adiposity was lower in terrier breeds, and it varied with disease categories. On average, dogs with neoplasia had the lowest abdominal adiposity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAbdominal visceral fat percentage was associated with six variables after adjusting for confounders: age, HWR, BCS, total abdominal adiposity, disease category, and cHAC (see S3). The visceral fat percentage increased with increasing total abdominal adiposity. The relationship between abdominal visceral fat percentage and age displayed a limited quadratic pattern, with a gradual plateau around 13 years of age. Based on this analysis and the scatter plot, the visceral fat and age relationship was considered linear for the multivariable analysis (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Deep-chested dogs, or those with an increasing HWR, exhibited decreasing visceral fat percentages. Dogs with cHAC displayed higher visceral fat percentages than dogs without cHAC, and dogs with inflammatory or \"other\" diseases had lower visceral fat percentages, on average, than other disease categories.\u003c/p\u003e \u003cp\u003eAfter adjusting for confounders, SQ fat was associated with six variables: age, HWR, BCS, total abdominal adiposity, disease category, and cHAC (see S4). The SQ fat percentage increased with increasing total adiposity. The relationship between SQ fat percentage and age was found to be quadratic, with SQ fat accumulation rising with age, reaching a plateau at around 8\u0026ndash;10 years, and then gradually decreasing with age (j-shaped distribution) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Deep-chested dogs, based on increasing HWR, were associated with increasing SQ fat percentages. Dogs with HAC displayed lower SQ fat percentages than dogs without HAC. Dogs with HAC displayed lower SQ fat percentages than dogs without HAC. In contrast, dogs with inflammatory or \"other\" diseases had the highest average SQ fat percentages\u003c/p\u003e \u003cp\u003eAfter adjusting for confounders, the log(V/SQ) fat ratio showed associations with eight variables: age, ANKC hounds, ANCK terrier, HWR, BCS, total abdominal adiposity, disease category, and cHAC. Increasing BCS and total abdominal adiposity were associated with decreasing V/SQ (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while age showed a gradual linear increase in V/SQ. Deep-chested dogs had lower V/SQ, although this relationship was not evident in ANKC hounds, which had higher V/SQ than non-hounds. Terriers and dogs with cHAC also favoured visceral fat distribution over SQ fat distribution. Dogs with \"other\" diseases had, on average, the lowest V/SQ ratios of any group, but no clear pattern was identified within this group. The \"other\" diseases category included respiratory, neurological, urinary, haematological, and non-neoplastic mass conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eScatter plots showing pairwise comparison and quadratic relationship between age and CTV total abdominal fat percentage (A), age and CTV abdominal visceral fat percentage (B), age and CTV abdominal subcutaneous (SQ) fat percentage (C), and age and log of CTV abdominal visceral-to-subcutaneous fat ratio (V/SQ) (D) in a tertiary veterinary hospital population of dogs.\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eValidity of Methods Predicting Abdominal Adiposity and Fat Distribution\u003c/h3\u003e\n\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e display the validation of L3 CTA, mean Hounsfield units (HU), and ultrasound fat indices to predict CTV fat indices.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidity of L3 CTA measures of abdominal fat indices relative to their corresponding CTV measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003csub\u003ec\u003c/sub\u003e lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB-A Difference\u003c/p\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB-A Difference\u003c/p\u003e \u003cp\u003e95% LOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB-A Percentage Difference\u003c/p\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB-A Percentage Difference 95% LOA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3 CTA total abdominal fat % relative to CTV total abdominal fat %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.6 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-12.0, 4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.5% (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-26.9, 13.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3 CTA visceral fat % relative to CTV visceral abdominal fat %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.9 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-16.0, 2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-32.3% (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-69.1, 4.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3 CTA subcutaneous fat % relative to CTV subcutaneous abdominal fat %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.4, 8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.3% (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.1, 35.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3 CTA V/SQ relative to CTV V/SQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.4 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.1, 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-46.5% (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-86.9, -6.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003er\u0026thinsp;=\u0026thinsp;Pearson's correlation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003er\u003csub\u003ec\u003c/sub\u003e= Lin's Concordance Correlation Coefficient\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eB-A Difference mean\u0026thinsp;=\u0026thinsp;mean difference [CTV \u0026ndash; CTA values] versus the average of both values\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eB-A Percentage Difference Mean\u0026thinsp;=\u0026thinsp;mean of the percentage difference [100*(CTV \u0026ndash; CTA values)/average)] versus the average of both values\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e(SD)\u0026thinsp;=\u0026thinsp;standard deviation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e95% LOA\u0026thinsp;=\u0026thinsp;95% Limits of Agreement\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e95% CL \u0026ndash; lower 95% confidence limit\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eL3 CTA \u0026ndash; transverse cross-sectional computed tomographic area at the cranial margin of the third lumbar vertebrae on CT\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCTV - computed tomographic volume of the abdomen from the cranial margin of the 10th thoracic to the cranial margin of first sacral vertebrae.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eV/SQ \u0026ndash; visceral-to-subcutaneous fat ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression statistics of mean Hounsfield units and ultrasound measures to estimate CTV measures of abdominal fat.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression Equation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGradient\u003c/p\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ey-intercept\u003c/p\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTV mean HU relative to CTV total abdominal fat %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey = -1.843x\u0026thinsp;+\u0026thinsp;73.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.911, -1.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.18, 76.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3 CTA mean HU relative to CTV total abdominal fat %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey = -1.954x\u0026thinsp;+\u0026thinsp;67.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.044, -1.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.89, 71.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAT relative to CTV total abdominal fat %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.639x \u0026minus;\u0026thinsp;3.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.510, 0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.557, 2.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT relative to CTV visceral abdominal fat %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.896x\u0026thinsp;+\u0026thinsp;2.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.637, 1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.126, 6.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT relative to CTV subcutaneous abdominal fat %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.223x\u0026thinsp;+\u0026thinsp;1.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159, 0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.624, 3.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT/SAT relative to CTV V/SQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;2.473x\u0026thinsp;+\u0026thinsp;1.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.158, 3.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.509, 2.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003er\u0026thinsp;=\u0026thinsp;Pearson's correlation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e95% LOA\u0026thinsp;=\u0026thinsp;95% Limits of Agreement\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eHU \u0026ndash; Hounsfield units\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eL3 CTA \u0026ndash; transverse cross-sectional computed tomographic area at the cranial margin of the third lumbar vertebrae on CT\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCTV - computed tomographic volume of the abdomen from the cranial margin of the 10th thoracic to the cranial margin of first sacral vertebrae.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eV/SQ \u0026ndash; visceral-to-subcutaneous fat ratio\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eVAT \u0026ndash; linear measure of the visceral adipose thickness measured on and perpendicular to the linea alba from the linea alba\u0026rsquo;s parietal surface to the ventral margin of the most caudal aspect of liver that crosses midline\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSAT \u0026ndash; linear measure of the subcutaneous adipose thickness at the same level of VAT\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eVAT/SAT \u0026ndash; VAT-to-SAT ratio\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTAT \u0026ndash; linear measure of the total adipose thickness at the same level of VAT (VAT\u0026thinsp;+\u0026thinsp;SAT)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn univariable analysis, BCS had poor to moderate correlation with CTV total abdominal fat percentage (r\u0026thinsp;=\u0026thinsp;0.604), CTV visceral fat percentage (r\u0026thinsp;=\u0026thinsp;0.509), CTV SQ fat percentage (r\u0026thinsp;=\u0026thinsp;0.529), L3 CTA total abdominal fat percentage (r\u0026thinsp;=\u0026thinsp;0.605), L3 CTA visceral fat percentage (r\u0026thinsp;=\u0026thinsp;0.514), and L3 CTA SQ fat percentage (r\u0026thinsp;=\u0026thinsp;0.476). There was marked variability of CTV total abdominal adiposity for every BCS unit, with, at best, 36% of the variation in adiposity being explained by BCS (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.365, [95%CI:0.231,0.529]) and for every unit increase in BCS, the CTV total abdominal adiposity increased by 5.76% (95% CI: 4.60, 6.93) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and L3 CTA abdominal fat changed by 6.62% (95% CI:5.29, 7.96). Additionally, BCS had a poor, negative correlation with V/SQ (r=-0.066) when not correcting for signalment or hyperadrenocorticism. There was a moderate negative correlation between CTV and L3 mean HU with BCS (r=-0.638 and r=-0.626, respectively). See Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and supplementals 3 and 4 for further analyses.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eScatter plots showing pairwise comparison and linear relationship between CT volume (CTV) total abdominal fat percentage and BCS (A), L3 CT area (L3 CTA) total abdominal fat percentage and BCS (B), CTV visceral-to-subcutaneous (CTV V/SQ) fat ratio (CTV V/SQ and BCS (C), and CTV V/SQ and CTV total abdominal fat percentage (D) in a tertiary veterinary hospital population of dogs.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverall, L3 CTA abdominal fat measures showed consistent biases relative to the CTV measures, generally overestimating total abdominal fat and visceral fat percentages but underestimating SQ fat percentages. The variations worsened with increasing fat percentages (drift), most significantly observed in visceral fat percentages. This drift resulted in a more significant disparity in the L3 CTA V/SQ estimation in overweight/obese dogs (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CTV and the L3 CTA mean HU strongly and negatively correlated with the overall abdominal fat percentage (r=-0.966 and r=-0.948, respectively). Ultimately, the CTV mean HU could explain more of the variance than the L3 CTA mean HU (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe linea alba fat measurements showed poor to moderate correlation with the corresponding CTV fat indices (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e), whilst TAT provided a moderate correlation with total abdominal adiposity. The poor association of VAT and SAT measurements to CTV visceral and SQ fat measures resulted in a weak correlation between VAT/SAT and V/SQ (r\u0026thinsp;=\u0026thinsp;0.257). Total adipose thickness (TAT) correlated poorly with BCS (p\u0026thinsp;=\u0026thinsp;0.42).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u003cb\u003eThe CT cross-sectional area of abdominal, visceral, and subcutaneous fat percentages measured at the cranial margin of L3 compared to the equivalent volumetric CT measurements between the cranial margins of T10 to S1. A \u0026ndash; Total abdominal fat. B \u0026ndash; Subcutaneous abdominal fat. C \u0026ndash; Visceral abdominal fat. D \u0026ndash; Visceral-to-subcutaneous fat ratio\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e: \u003cb\u003eA - The mean Hounsfield Units (HU) recorded within the entire abdominal volume (CTV Mean HU) compared to the CT Volume Abdominal Fat Percentage. B - The mean Hounsfield Units (HU) recorded within the entire transverse abdominal area measured at the cranial margin of L3 (L3 Mean HU) compared to the CT Volume Abdominal Fat Percentage. C \u0026ndash; Ultrasound total adipose thickness (TAT) compared to CT volume total abdominal fat percentage. D \u0026ndash; Ultrasound visceral adipose thickness (VAT) compared to CT volume visceral fat percentage. E - Ultrasound subcutaneous adipose thickness (SAT) compared to CT volume subcutaneous fat percentage. F - Ultrasound VAT/SAT compared to volume CT V/SQ. The abdominal volume was recorded as tissue volume between the cranial margins of T10 to S1. The abdominal volume was measured using a -250 to 2000 HU range, and abdominal fat using a range of -250 to -25 HU. The visceral adipose thickness (VAT) and subcutaneous adipose thickness (SAT) were measured as thickness perpendicular to the linea alba at the most caudal aspect of the liver as it crosses the midline\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eReliability of Abdominal Fat Segmentation\u003c/h3\u003e\n\u003cp\u003eFat segmentation using the \u0026minus;\u0026thinsp;250/-25 HU thresholding extracted fat with a mean of -99.40 HU (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;11.10 HU, [95% CI: -100.90, -97.88HU) and median of -102.00 HU (range: -118.10 to \u0026minus;\u0026thinsp;67.40 HU), with a slightly positive skew (skewness\u0026thinsp;=\u0026thinsp;0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17).\u003c/p\u003e \u003cp\u003eThere was minimal variation when the CTV measurements were repeated, with a near-perfect intra-observer agreement in the segmentation of total abdominal volume (\u0026minus;\u0026thinsp;250/-25HU: r\u003csub\u003ec\u003c/sub\u003e lower 95% CL\u0026thinsp;=\u0026thinsp;0.998, B-A 95% LOA [\u0026minus;\u0026thinsp;3.5, 3.9%]); total abdominal fat (\u0026minus;\u0026thinsp;250/-25HU: r\u003csub\u003ec\u003c/sub\u003e lower 95% CL\u0026thinsp;=\u0026thinsp;0.999, B-A 95% LOA [\u0026minus;\u0026thinsp;4.0, 4.3%]); and visceral abdominal fat (\u0026minus;\u0026thinsp;250/-25HU: r\u003csub\u003ec\u003c/sub\u003e lower 95% CL\u0026thinsp;=\u0026thinsp;0.996, B-A 95% LOA [\u0026minus;\u0026thinsp;7.2, 2.5%]) when repeated after more than six months of performing the original measurements. The visceral abdominal fat was slightly overestimated on the repeat measurement (bias\u0026thinsp;~\u0026thinsp;2.3%) and resulted in a near-perfect intra-observer agreement in, but a slightly overestimated repeat V/SQ measurement (V/SQ: r\u003csub\u003ec\u003c/sub\u003e lower 95% CL\u0026thinsp;=\u0026thinsp;0.990, B-A 95% LOA [\u0026minus;\u0026thinsp;9.8, 1.5%]).\u003c/p\u003e \u003cp\u003eThe intra-abdominal CTA measurements varied more than CTV when repeated more than six months after the original measurements. There was near perfect intra-observer agreement in the segmentation of L3 abdominal volume (\u0026minus;\u0026thinsp;250/-25HU: r\u003csub\u003ec\u003c/sub\u003e lower 95% CL\u0026thinsp;=\u0026thinsp;0.999, B-A 95% LOA [\u0026minus;\u0026thinsp;1.7, 2.0%]) and L3 abdominal fat (\u0026minus;\u0026thinsp;250/-25HU: r\u003csub\u003ec\u003c/sub\u003e lower 95% CL\u0026thinsp;=\u0026thinsp;0.998, B-A 95% LOA [\u0026minus;\u0026thinsp;5.8, 7.3%]). However, despite a near-perfect intra-observer agreement for L3 visceral fat, there was greater variability (\u0026minus;\u0026thinsp;250/-25HU: r\u003csub\u003ec\u003c/sub\u003e lower 95% CL\u0026thinsp;=\u0026thinsp;0.992, B-A 95% LOA [\u0026minus;\u0026thinsp;15.8, 13.4%]). This resulted in relatively variable L3 V/SQ calculations (\u0026minus;\u0026thinsp;250/-25HU: r\u003csub\u003ec\u003c/sub\u003e lower 95% CL\u0026thinsp;=\u0026thinsp;0.940, B-A 95% LOA [\u0026minus;\u0026thinsp;47.0, 32.2%]).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the authors' knowledge, this is the first study to evaluate the relationship between signalment and disease on CTV abdominal fat distribution in dogs. This study, using abdominal adiposity as a proxy for total adiposity, reinforces previous associations found between adiposity, age, breed and certain diseases (36). Additionally, it confirms the correlation between V/SQ fat distribution and age, as established in earlier research (3, 8, 18, 23, 36). Notably, the findings also introduce a novel relationship between V/SQ, abdominal adiposity (contrary to existing literature) and body conformation (HWR) (23). Our results further support the supposition that dogs with HAC preferentially distribute fat to the visceral space (18, 37). These findings emphasise the importance of shifting our focus from general adiposity to a more nuanced examination of the relationship and causal direction of regional fat distribution on health, metabolic dysfunction, and disease pathogenesis (20).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAge\u003c/h2\u003e \u003cp\u003eDespite the heterogeneity of the data set, abdominal adiposity displayed a quadratic distribution pattern (j-shape) with age, consistent with the trends observed in total adiposity in dogs (5, 38, 39). Notably, abdominal fat increases until around 10 years of age and subsequently decreases (5). Moreover, our study affirms that ageing is also linked to the redistribution pattern of adiposity (23). Specifically, there is a preferential rise in visceral fat compared to the quadratic distribution of SQ fat, resulting in an overall increase in the V/SQ ratio with age (40). This preferential distribution of fat to the visceral space is considered metabolically detrimental. In people, age is one of the leading contributors to visceral redistribution and the development of adipose tissue dysfunction (41, 42). Human age-related adipose tissue dysfunction is a complex interaction between adipose cell differentiation and senescence, immune cell infiltration, and the release of proinflammatory cytokines (41). This intricate interplay ultimately leads to the progressive impairment of the SQ adipose tissues to store lipids (\"triglyceride sink\"), redistributing fat to visceral and ectopic regions and chronic low-grade inflammation. While there is some debate within the veterinary literature, insulin resistance has been associated with increasing visceral mass, suggesting a similar mechanism may occur in dogs (20, 43\u0026ndash;45). Consequently, the pro-inflammatory cytokines are associated with insulin resistance, as well as endothelial, metabolic and inflammatory dysregulation, and ultimately contribute to the pathophysiology of several diseases (20). This increasing V/SQ fat ratio, with decreasing total and SQ fat, may be associated with geriatric-related morbidities and reduced lifespan in dogs, but further evaluation is required (46).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSex and Neuter Status\u003c/h3\u003e\n\u003cp\u003eThough no statistical significance was established in our study, neutered dogs tend to have higher abdominal adiposity. This relationship between desexing and OO is well-established, and desexing is also associated with increased SQ fat content, increased food intake, and reduced resting metabolism (47, 48). Fat distribution to the visceral or subcutaneous compartments was not significantly related to the dog's sex or neuter status, similar to our previous publication (23). However, entire male dogs had a higher visceral fat distribution compared to entire female dogs, favouring subcutaneous fat distribution.\u003c/p\u003e \u003cp\u003eThese trends may parallel the effect of sex hormones on the regulation of body fat distribution and sexual dimorphism of fat distribution in people. The fat distribution patterns in desexed dogs and how they compare to entire dogs offer a valuable opportunity to explore the influence of testosterone and oestrogen on fat distribution, a phenomenon that exhibits significant individual variability in the human population (49, 50). Our study found that MN and FS dogs tended to have similar visceral-to-subcutaneous (V/SQ) fat distribution, falling between intact male and female dogs. This observation aligns with the general understanding that testosterone promotes increased lean muscle mass but tends to deposit fat preferentially in visceral areas. Conversely, oestrogen supports a healthier subcutaneous fat distribution without the bias towards lean muscle mass, and the lack of either hormone would result in an intermediate effect (50). The trend in our study is supported by studies of 3 male dogs and a large cohort of toy breed dogs, where it was noted that desexing resulted in increased subcutaneous fat male dogs a year after castration, and higher V/SQ seen in spayed female toy breeds compared to intact females (5, 24, 40, 48). This consensus occurred despite differences in research methodologies and that our research accounted for the potential confounding effect of total adiposity (24). However, it is crucial to recognise that all studies have had a limited number of intact dogs, hindering a robust comparison between desexed and intact dog cohorts. Further, the age of neutering may influence V/SQ fat distribution, which was not accounted for in our study (24, 40). Thus, further research into the impact of reproductive hormones on dog fat distribution is warranted\u003c/p\u003e\n\u003ch3\u003eBreed and Breed Conformation\u003c/h3\u003e\n\u003cp\u003eSpecific breeds are predisposed to being OO, and it has been recently alluded that certain toy breeds may favour visceral fat distribution compared to SQ fat (5, 24). The influence of specific breed and breed conformations on abdominal adiposity and fat distribution was identified in terriers and with abdominal height-width ratio (HWR) conformation. Terriers had lower abdominal adiposity than other breeds, as previously demonstrated, and favoured a distribution to the visceral over the SQ compartment (8, 51). Hounds appeared to favour distribution to the visceral over the SQ compartment, despite having relatively high HWR. These findings may reflect breed-specific differences, such as an increase in lean muscle mass in terriers that may limit the risk of obesity seen with desexing and may suggest a breed-protective trait from obesity (51, 52). In our study, HWR reflected breed conformation well, and its use in CT is the first time this has been described (9). Dogs with deep, narrow conformations (increasing HWR) typically favoured reducing total adiposity with SQ over visceral fat distribution, though, as stated, this was not reflected in the Hounds group. However, the dolichocephalic dogs and a few Great Danes probably exaggerated this effect and skewed the data. These variations should be evaluated further, as they may be related to breed variations and implicated in obesity-related disorders seen in certain breeds.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eV/SQ Relative to Adiposity\u003c/h2\u003e \u003cp\u003eThe relationship between V/SQ fat distribution and total abdominal adiposity differed from our previous findings, likely due to a more significant power in this study [22]. This relationship has been noted previously and is consistent with the anticipated physiological orthotopic fat distribution pattern, with a preference for the SQ space. It is crucial to emphasise that the observed relationship or effect is relatively weak, a factor that is likely diminished with advancing age (25, 33, 40, 41). The low contribution of BCS and total abdominal adiposity to the variation in V/SQ demonstrates that focusing on measures of total adiposity is not a sensitive nor specific means of evaluating the effect of fat distribution on health (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This is particularly relevant to BCS, which has a stronger positive relationship with subcutaneous fat mass than visceral fat mass (53).\u003c/p\u003e \u003cp\u003eSimilarly, in people, there is now a growing consensus to focus on measures of central obesity, specifically visceral obesity, such as waist circumference, more so than metrics of total body obesity, such as BMI (31). These metrics of visceral adiposity have been shown to provide both independent and additive information for predicting morbidity and risk of death in people and are a means of stratifying metabolically unhealthy from metabolic healthy obesity. Additionally, substantial evidence emphasises the importance of considering lean muscle mass in conjunction with adiposity (25, 52). Sarcopenic obesity appears to have a more profound impact than obesity accompanied by adequate muscle mass. This underscores the necessity for research methods that effectively assess fat distribution and lean muscle mass, shedding light on the influence of compartmental fat distribution on overall health.\u003c/p\u003e \u003cp\u003eFat distribution in OO dogs may contribute to the variations in their health metrics seen in the literature (43, 54). Some studies have found that not all OO dogs show clinical metabolic issues, but small subgroups exhibit obesity-related metabolic dysfunction (ORMD) (54\u0026ndash;56). Though not evaluated, this heterogenous metabolic outcome related to OO may be attributable to regional fat distribution and relative muscle mass. Subcutaneous fat deposition may be \"protective\" and, combined with greater muscle mass, as seen in younger OO dogs, may limit metabolic derangement and the manifestation of ORMD (25, 57). Morphological methods of assessing obesity, like BCS, do not assess fat redistribution patterns well and thus limit their applicability in exploring these metabolic subsets in dogs (20, 25).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e: \u003cb\u003etransverse CT images illustrating various abdominal adiposity patterns and visceral-to-subcutaneous (V/SQ) fat distribution in a tertiary veterinary hospital population of dogs. The images are arranged from left to right, indicating increasing total abdominal fat adiposity, and from bottom to top, representing increasing V/SQ fat ratios. The four patients depicted are as follows: A) 1.5-year-old female-spayed mixed breed with a left divisional intrahepatic portocaval shunt, having 14% total abdominal fat and a V/SQ ratio of 1.35; B) 11-year-old male-neutered mixed breed undergoing staging for soft tissue sarcoma of the left thoracic limb, exhibiting 52% total abdominal fat and a V/SQ ratio of 1.22; C) 1.4-year-old female-spayed Boxer diagnosed with intestinal adenocarcinoma, displaying 28% total abdominal fat and a V/SQ ratio of 0.379; D) 7-year-old male-neutered Golden Retriever undergoing staging for insulinoma, demonstrating 57% total abdominal fat and a V/SQ ratio of 0.287.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDisease and Fat Distribution\u003c/h2\u003e \u003cp\u003eThe effect of the hypothalamic-pituitary-adrenal (HPA) axis, particularly HAC, on both generalised and visceral obesity has been described (3, 27, 28). However, to the authors' knowledge, this is the first objective evaluation to demonstrate the preferential distribution of visceral fat in dogs with HAC. Interestingly, HAC was not related to total abdominal adiposity in this study. The predisposition for general obesity in dogs with HAC is likely due to polyphagia and increased caloric intake, coupled with reduced energy expenditure, but is not a strongly recognised component of the disease, which is supported in our findings (27). However, an increase in visceral fat is commonly noted in dogs with HAC, though compounded by the concurrent hepatomegaly and weakening of the abdominal muscles. This is supported by the documented increase in serum leptin concentrations in dogs with HAC, with leptin preferentially released by visceral fat (28). This has potential diagnostic application from a diagnostic imaging perspective, particularly AI-generated radiomics, as it may provide differential diagnosis stratification based on physiological manifestations, such as differentiating functional adrenocortical adenocarcinoma from phaeochromocytomas. This may be further augmented by using other measures of body composition, such as lean tissue mass and distribution.\u003c/p\u003e \u003cp\u003eDogs with neoplasia tended to have lower total adiposity than dogs with other disease categories. However, the effect of diseases on fat distribution was uncertain in our study, apart from HAC. The category of \"other\" disease was a random assortment of diseases, and no clear pattern emerged. Generally, endocrinopathies tended to favour higher visceral fat and V/SQ distribution than inflammatory and neoplastic diseases, likely skewed by the HAC cases. Further, this may be compounded by organ steatosis, such as hepatic lipidosis, which may increase the visceral adiposity on CT by reducing the Hounsfield units of the liver (58). This was seen in one case of a dog that presented with diabetic ketoacidosis and hepatic lipidosis, which reduced the liver attenuation to -45HU. Thus, with more specific clinical questions, more specific segmentation, and the support of deep neural networking, V/SQ and body composition analysis may be used to provide differential diagnosis stratification (59). Ultimately, body composition and tissue distribution may determine health-associated outcomes in dogs, like in people (41).\u003c/p\u003e \u003cp\u003eCongenital portal vascular anomalies formed a large cohort of young dogs and contributed to the bimodal age distribution. This was due to the high intravascular interventional radiology caseload at the hospital. Typically, these dogs had the lowest total, visceral and SQ fat percentages, which resulted in marked variations in V/SQ. However, these findings were not present after correcting for age and the presence of ascites, as seen in cases of portal venous hypertension. Further studies evaluating young dogs without portosystemic shunts should be performed to ensure this was not a significant contributor to the age-related variation in V/SQ seen in this study.\u003c/p\u003e \u003cp\u003eFinally, the administration of phenobarbitone and prednisolone did not statistically significantly alter total abdominal adiposity, but there was a trend towards increasing adiposity. This is expected due to the side effect of polyphagia secondary to these medications and should be considered in future investigations (3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReliability\u003c/h2\u003e \u003cp\u003eA significant consideration in using CT segmentation is the threshold values set for segmenting specific body tissues and the presence of confounding diseases that may alter the attenuation of the regions of interest. Regarding the CT threshold values, the authors used the previously published ranges of -250/-25 HU for fat (36). This has shown a slightly higher correlation and agreement with DXA measure of fat than Ishoika's -135/-105 HU threshold, and is similar to that used in cat and human thresholds (32). The distribution of fat HU in this paper was, on average higher than that proposed by Ishioka. This might suggest that more accurate measures of fat thresholding may be achieved with a narrower but slightly higher threshold value (97% CI for fat attenuation was \u0026minus;\u0026thinsp;101/-98 HU) than that used in this study and by Ishioka, but our measurements were not standardised against a CT quality assurance phantom.\u003c/p\u003e \u003cp\u003eThe CT confounding variables ascites, abdominal masses, organomegaly, and small organs significantly influenced the results. This was addressed and corrected within the multivariable analysis of this paper. However, these confounding variables should be considered when using CT to research body composition. Interestingly, small organs were associated with greatly reduced total, visceral and SQ fat percentages with increased V/SQ and likely reflected the cohort of young dogs with congenital portal vascular anomalies (60). Effusions, abdominal masses, and organomegaly resulted in a decrease in total abdominal adiposity. Surprisingly, these conditions had no impact or even increased the V/SQ. This was attributed to reductions in both visceral and subcutaneous fat percentages, with a more pronounced decrease in subcutaneous fat. Although the precise cause of this phenomenon remains unclear, this relationship suggests that the underlying disease has a more significant influence on fat distribution than the soft tissue alterations that obscure fat on CT scans. This implies that CT scans might offer a reliable method for evaluating body composition and tissue distribution in the presence of significant abdominal pathology, although further research is necessary to understand these findings fully.\u003c/p\u003e \u003cp\u003eBody condition scoring was moderately correlated with total abdominal fat and as previously demonstrated, showed a stronger relationship with SQ fat distribution than visceral fat (40, 53). However, the weak negative correlation between total abdominal adiposity and V/SQ was weakly replicated in dogs with increasing BCS. Like human body mass index (BMI), BCS may be a crude marker of dog adiposity (31). This study found that BCS only moderately correlated with volume and CTA L3 total abdominal fat percentages, differing from a study of 38 beagles, which found BCS had a strong correlation with the total adipose area at L3 (r\u0026thinsp;=\u0026thinsp;0.809) (33). This difference may be due to the 20% of cases missing BCS data in our study, the heterogeneity of our study population, differing methodologies, or breed-specific differences in fat deposition at L3. Further, in our study, the BCS was performed by a non-standardised set of clinicians, and the considerable variation in adiposity for each BCS unit likely highlights the high BCS inter-observer variation, as seen in other studies (4, 61). However, our study validated Laflamme's original estimates that an increase in 5% adiposity occurs with every unit of BCS (4).\u003c/p\u003e \u003cp\u003eMost abdominal fat distribution papers use a single slice area of the abdomen at L3 or L5 (20, 24, 25, 32, 40). It has also been shown that visceral fat deposition occurs mainly in the cranial abdomen (L3), while subcutaneous deposition is more caudally distributed (L5) (24, 53). We showed that CTA L3 fat distribution showed more significant variation than CTV measures and that CTA generally overestimated total abdominal and visceral fat while underestimating SQ fat percentages compared to CTV measures. This bias worsened with increasing abdominal adiposity, resulting in marked variation in V/SQ measures. Adolphe's method also identified similar measurements and reduced variation, which used averaged visceral and subcutaneous fat areas over multiple slices from the thirteenth thoracic vertebra to the seventh lumbar vertebra (20, 25). This is expected, as the area measured highly depends on what is within the acquired abdominal CT slice. Conformational variation, relatively mobile organs (e.g. spleen), ectopic organs, and local pathology (e.g., hepatomegaly, ascites, masses) are some factors that may have a more significant influence on the CTA L3 measure compared to CTV measures of adiposity and fat distribution in dogs. For this reason, the authors recommend using volume metrics to assess abdominal adiposity and fat distribution for greater accuracy. However, in doing so, understanding the influence on specific fat distributions, such as peri-gluteal subcutaneous fat deposition, is reduced.\u003c/p\u003e \u003cp\u003eAdditionally, a potential negative of using CTV methodology as a clinical tool is the time it takes to segment regions of interest and the limited access to semi-automated and automated software assistance. Thus, L3 CTA may still prove a clinically valuable tool for assessing adiposity and fat distribution. Further, the average Hounsfield units were assessed as a marker of overall abdominal adiposity. A radiologist can perform this measure in seconds and provide a reasonable estimate of overall body adiposity (r=-0.967). Further research may show abdominal attenuation values as a simple clinical or radiomic measure of abdominal obesity.\u003c/p\u003e \u003cp\u003eAs there is limited access to CT in many veterinary clinics, the authors were also interested in further validating their recently introduced ultrasound method of assessing fat distribution (23). This would provide a more accessible tool for assessing dog fat distribution, as no sedation or anaesthesia is required. Unfortunately, though a moderate correlation between TAT and total abdominal fat percentage was identified, only a poor correlation was observed between VAT and SAT relative to CTV visceral and SQ fat, and ultimately, a weak correlation between VAT/SAT and V/SQ. The flattening of the ventral abdomen due to sternal recumbency and variable liver size may have influenced the measurements, but this was likely negligible. Though the falciform ultrasound method may help assess total adiposity, the current methodology is not useful for determining V/SQ.\u003c/p\u003e \u003cp\u003eThough the relationship between total body adiposity and total abdominal adiposity was supported by the moderate correlation between total abdominal adiposity and BCS, further validation of abdominal CT and, ideally, whole-body CT metrics of body composition are required. Additionally, this study did not consider the specific regional distribution of fat, such as thoracolumbar, lumbar, inguinal or peri-gluteal SQ fat distribution, or cranial and caudal visceral fat distribution. Further, it did not evaluate organ fat distribution such as intra-cellular (steatosis, e.g. hepatic lipidosis), intra-organ (e.g. intramuscular) or between organs (e.g. intermuscular) fat distribution, nor did it evaluate the relationship between fat and lean muscle mass distribution. All these factors may be related to specific health-associated metrics that must be evaluated. For example, fat distributed around pre-menopausal women's glutei and legs is associated with positive health metrics compared to central obesity in men (41). Further, lean tissue mass favours positive health outcomes compared to sarcopenic obesity (52). Therefore, CT offers a greater means of quantifying body composition metrics and should be evaluated against biomarkers of health, associated morbidities, quality of life and risk of death.\u003c/p\u003e \u003cp\u003eThe associations made in this study are relatively robust, given the limitations introduced by its retrospective nature and the heterogeneity of the sample population. Ultimately, prospective evaluation of specific factors affecting fat distribution should be performed, but we hope the observational results provide some framework for future investigations. Ideally, greater numbers of different breeds and morphologies would have been evaluated, but this was limited by case availability and the time for data retrieval and tissue segmentation. Some methodological improvements that could be entertained in future research include the potential accuracy and speed of computer-aided segmentation and the reduced variation introduced by standardised protocols, such as the length of fasting prior to the study, size of urinary bladder at the time of the study, and standardised positioning, all of which were optimised for the presenting complaint in this study. Every animal usually undergoes a minimum 12-hour fast before CT, though this was not defined, and there were various degrees of gastric distension within the study population. The gastric volume and the degree of urinary bladder distension may displace and reduce the CTV visceral fat volume. The influence of these physiological variations is unknown but is likely to affect volume-based metrics less than single-area measurements. Sternal recumbency was used in all these cases due to its standard use in clinical practice, as it limits the effect of respiratory movement on organs; however, its effect on body composition assessment is unknown (62). Another methodological restraint for this study was that the same scales used to weigh each dog were not tared daily. Finally, including dogs less than a year in our research sample may have introduced the confounding effect of brown adipose tissue. However, as the distribution, extent, and regression of brown fat in dogs are poorly characterised, young dogs were not excluded from our analysis (63). As brown adipose tissue may appear like fat or soft tissue on CT, further evaluation of brown adipose tissue may require F18-FDG PET-CT, and possibly MRI, to differentiate it from white adipose tissue (64, 65).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe main finding of this study supports the association between abdominal adiposity, age, breed category and, potentially, certain diseases. Moreover, it highlights the correlation between V/SQ fat distribution, age, and total adiposity, whilst emphasising the preferential distribution of fat to the visceral compartment in dogs with HAC. The study also identified a novel association between V/SQ fat distribution, specific breed categories and body conformation (HWR). Additionally, CT volumetric measures appear more reliable in determining abdominal fat distribution than area and linear measures.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eCase Selection\u003c/h2\u003e\n\u003cp\u003eMedical case records of dogs that underwent abdominal computed tomography (CT) at the U-Vet Werribee Animal Hospital, University of Melbourne, were retrieved from March 2006 to March 2020. Data collated included weight, breed, age, sex, neuter status, 9-point scale BCS and reason for the CT.\u003c/p\u003e\n\u003ch2\u003eAbdominal Fat Analysis\u003c/h2\u003e\n\u003cp\u003eNon-contrast volume acquisition of the abdomen with dogs in sternal recumbency was performed using a 16-slice CT scanner (Somatom Emotion 16, Siemens, Erlangen, Germany). Proprietary software (Somaris 5 Syngo CT 2014A, Siemens AG, Muenchen, Germany) was used for semi-automated body composition and distribution volume quantification.\u003c/p\u003e\n\u003cp\u003eBased on previously described methods, the total abdominal, visceral, and SQ volumes of interest (VOI) were established between the cranial margin of the 10th thoracic vertebrae to the cranial margin of the first sacral vertebrae and using 3 mm slice thickness for reconstruction (23, 36). The computed tomographic volume (CTV) of all tissue between these regions was calculated by the software using threshold ranges of -250/2000 HU for all tissues (fat, lean tissue and bone) and \u0026minus;\u0026thinsp;250/-25 HU for fat, as used in a prior study (36). Subcutaneous (SQ) fat included SQ, inter-muscular and intramuscular fat outside the peritoneal cavity. The visceral and SQ fat volumes were used to determine the visceral-to-subcutaneous fat volume ratio (V/SQ). The CT cross-sectional areas (CTA) of total abdominal, visceral, and SQ fat were calculated at the cranial margins of L3 using the same borders and as previously described (34, 42). The mean Hounsfield units (HU) were recorded for the volume and area measurements.\u003c/p\u003e\n\u003cp\u003eThe visceral adipose thickness (VAT), subcutaneous adipose thickness (SAT) and total adipose thickness (TAT) measurements were taken perpendicular to the parietal surface of the linea alba, aligning with the most caudal margin of the liver at the midline, an adaption from recent ultrasound methodology (23). The VAT was measured from the linea alba\u0026apos;s parietal surface to the ventral surface of the liver. The SAT was measured from the skin surface to the parietal surface of the linea alba. The TAT was calculated as the combined measurement of both VAT and SAT (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e: \u003cstrong\u003etransverse and sagittal CT reconstruction of a dog\u0026apos;s abdomen showing the location of the abdominal height and width (hashed lined), the visceral adipose thickness (VAT), and subcutaneous adipose thickness (SAT) (solid lines) measurements. The cranial abdominal height and width were measured at the widest internal diameter of the cranial abdomen constrained within the costal arch and used to determine the cranial abdominal height-width ratio (HWR). The VAT and SAT were performed perpendicular to the parietal surface of the linea alba, aligning with the most caudal margin of the liver at the midline.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eAbdominal Conformation\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eStandardised\u003c/strong\u003e measurements from the transverse CT images assessed the abdominal conformation modified from a previously established method (66). The cranial abdominal height and width were measured at the widest internal diameter of the cranial abdomen constrained within the costal arch. The height was measured from the ventral surface of thoracic vertebrae perpendicular to the parietal surface of the linea alba, and the width between the inner surface of the left and right ribs on the same slice of the CT. The height and width were used to determine the height-to-width ratio (HWR). This body conformation was relatively restrained by skeletal conformation and minimally affected by intra-abdominal anatomy and SQ fat deposition. Floating 12th and 13th ribs were not included in the measurements.\u003c/p\u003e\n\u003ch2\u003eData Stratification and Preparation\u003c/h2\u003e\n\u003cp\u003eData cleaning and preparation for analysis were completed by the primary author and statistician (R.T. and S.M.F.). The definition of categories is provided in Supplemental 5 (S5).\u003c/p\u003e\n\u003cp\u003eDog breeds were categorised based on the Australian National Kennel Council (ANKC) standards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ankc.org.au/Breed/Index/1\u003c/span\u003e\u003c/span\u003e) as: group 1 (toys); group 2 (terriers); group 3 (gundogs); group 4 (hounds); group 5 (working dogs); group 6 (utility); and group 7 (non-sporting) breeds (5). Mixed breeds were categorised separately (group 8). The dogs were further classified on skull shape as dolichocephalic, mesocephalic or brachycephalic; and as either chondrodystrophic or non-chondrodystrophic breeds and as previously described (67\u0026ndash;70). Mixed breeds were excluded from these categories. Dogs were also classified into size categories as extra small (\u0026lt;\u0026thinsp;6.5kg), small (6.5 to \u0026lt;\u0026thinsp;9kg), medium-small (9 to \u0026lt;\u0026thinsp;15kg, medium-large (15 to \u0026lt;\u0026thinsp;30kg), large (30 to \u0026lt;\u0026thinsp;40kg) and giant (\u0026gt;\u0026thinsp;40kg) as previously described (12).\u003c/p\u003e\n\u003cp\u003eThe reason for the clinician requesting the CT and final diagnosis was recorded, and the disease was categorised as acute (\u0026lt;\u0026thinsp;3 months) or chronic (\u0026ge;\u0026thinsp;3 months) (71, 72). The primary diagnoses were categorised as endocrine (including neoplasia), neoplastic (non-endocrine), primary gastroenteric (not neoplasia), trauma/musculoskeletal, congenital portal vasculature anomaly, inflammatory, or other based on the primary diagnosis. The specific endocrinopathy was reported, as was the type and location of the neoplasia. Dogs were defined as having confirmed hyperadrenocorticism (cHAC) if confirmed by low-dose dexamethasone stimulation or ACTH stimulation test and supported by appropriate imaging findings. Presumptive hyperadrenocorticism (pHAC) was recorded if the dog had chronic treatment by glucocorticoids (\u0026ge;\u0026thinsp;3 months) at the time of the CT, or an adrenal nodule/mass was observed on the abdominal CT, and no confirmatory dynamic endocrine testing results were available. Sensitivity analyses assessed the influence of considering HAC status as confirmed, presumptive, or a combination of the two (see supplementary S6). Presumptive HAC diluted the effect of cHAC, and cHAC alone was used in the multivariable analyses. The type and length of medications before the CT were recorded; the length of medication use was categorised as chronic if used for over three months.\u003c/p\u003e\n\u003cp\u003eThe first CT was used for dogs that underwent multiple CT examinations, with duplicates excluded from the analysis. No further exclusions were made based on CT confounders, but the data was stratified for analysis as defined below.\u003c/p\u003e\n\u003cp\u003eThe presence of potentially confounding pathology on CT was noted (CT confounders), particularly the presence of abdominal and pleural effusion, abdominal masses, organomegaly (including markedly distended urinary bladders or gastrointestinal tract), and small organs. The volume of effusion was semi-quantified as trace (fat stranding to a thin sliver of fluid), mild (\u0026le;\u0026thinsp;2 pockets, \u0026le; 2cm depth), moderate (\u0026gt;\u0026thinsp;2 pockets), and marked (diffuse fluid accumulation) (73). A mass was defined as any focal lesion\u0026thinsp;\u0026ge;\u0026thinsp;2cm in diameter, and the mass location and diameter were reported (74). Organomegaly or small organs were recorded if it was reported in the original radiology report. The presence of effusion, abdominal masses, organomegaly, and small organs were considered potential confounding variables in abdominal fat measurements. The CT findings were dichotomised into CTs with no confounders and CTs with confounders (effusion, abdominal masses, organomegaly or small organs).\u003c/p\u003e\n\u003cp\u003eThe literature was reviewed to explore characteristics associated with fat distribution. A putative causal network was constructed as a directed acyclic graph (DAG) and used to generate appropriate adjustment sets of variables for each exposure and outcome of interest (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; S5: Explanatory variables; and definitions S7: Putative causal web model/Directed acyclic graph (DAG) and justification) (75). The association between each risk factor was assessed using univariable analysis (see S1: Descriptive statistics and univariable analysis of categorical variables in a cross-sectional study of CTV abdominal fat distribution in a tertiary veterinary hospital population of dogs). For reporting, exploratory variables were collapsed into seven risk factors for the putative diagram: age, ANKC Terrier Breed, sex-neuter status, total abdominal adiposity, cHAC, and CT confounding pathology on CT. ANKC Hound group was considered too small for analysis.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e: \u003cstrong\u003ePutative causal web model/Directed acyclic graph (DAG)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDirected acyclic graph showing putative causal paths linking explanatory variables to the measured CTV abdominal visceral-to-subcutaneous fat ratio in an Australian tertiary veterinary hospital population of dogs. A detailed description of each link in the above plot is provided in supplementary materials S2.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003ePower Calculation\u003c/h2\u003e\n\u003cp\u003eA power analysis was performed using the data from a previous study (n\u0026thinsp;=\u0026thinsp;22 dogs), comparing V/SQ to total body fat percentage and age where the model explained nearly half the variation in V/SQ (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.471, R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eAdjusted\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.383), though only age was statistically significant (partial \u0026eta;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.355) (23). A sample size calculation undertaken in G*Power (version 3.1.9.4) estimated 106 dogs were required, using an f\u003csup\u003e2\u003c/sup\u003e effect size of 0.095, alpha\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.80, and testing for statistically significant associations for two predictor variables of 8 total degrees of freedom (76, 77). Thus, a minimum sample size of 120 was sought to allow for expected controlling for subgroup analysis.\u003c/p\u003e\n\u003ch2\u003eStatistical Methods\u003c/h2\u003e\n\u003cp\u003eRelationships between variables were visualised on scatter plots, and the assumption of normality and group variance were evaluated. V/SQ had a lognormal distribution, which was logarithmically transformed to base 10 for analysis, log(V/SQ), with results back-transformed for interpretation.\u003c/p\u003e\n\u003cp\u003eIndependent t-tests were used to compare continuous variables between groups. Multivariable linear regression models were constructed to assess the association between explanatory variables and each outcome variable (total abdominal fat percentage, visceral fat percentage, SQ fat percentage and the log visceral/subcutaneous fat ratio, i.e. log(V/SQ). The directed acyclic graph (DAG, see supplementary materials S7) informed which variables to adjust for based on the exposure and outcome of interest. Model outputs included regression coefficients and their 95% confidence intervals (95% CIs) for both univariable models and models based on the DAG-informed adjustment sets, along with model-estimated marginal means for each level of categorical explanatory variables. When assessing the linearity of associations between age and each of the outcome variables, Lowess (Locally Weighted Scatterplot Smoothed) curves were fit to plots of each outcome variable and age, and the coefficients and Akaike information criterion (AIC) values were compared (78). As this indicated that the relationship was nonlinear and that the most appropriate fit could be achieved by incorporating age into regression models as a quadratic term centred on its mean. However, for visceral fat and log(V/SQ), the relationship did not seriously depart from linear.\u003c/p\u003e\n\u003cp\u003eMethod validation and strength-of-agreement of the L3 area and ultrasound measurements of abdominal fat relative to the CT volume indices were performed using Lin\u0026apos;s Concordance Correlation Coefficient (r\u003csub\u003ec\u003c/sub\u003e) (Lin\u0026apos;s Concordance Correlation Coefficient; SPSS Syntax; Garcia-Granero, M.; updated 04/2009, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gjyp.nl/marta/Lin.sps\u003c/span\u003e\u003c/span\u003e) and Bland-Altman (B-A) limits of agreement (LOA) (79). The r\u003csub\u003ec\u003c/sub\u003e lower 95% confidence limit (CL) was reported as the strength of agreement and described as perfect (r\u003csub\u003ec\u003c/sub\u003e=1.00), near perfect (\u0026gt;\u0026thinsp;0.99), substantial (0.95\u0026ndash;0.99), moderate (0.90\u0026ndash;0.95) and poor agreement (\u0026lt;\u0026thinsp;0.90) (80). Statistical analysis was performed using GraphPad Prism (GraphPad Prism for Mac OS X, version 9.3.1, GraphPad Software, La Jolla, CA, USA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.graphpad.com\u003c/span\u003e\u003c/span\u003e), Jamovi (The jamovi project (2022). \u003cem\u003eJamovi\u003c/em\u003e (Version 2.3.21.0) [Computer Software]. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jamovi.org\u003c/span\u003e\u003c/span\u003e) and the R software package (81).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eabdominal fat volume (as measured by CT)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody condition score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence limit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDXA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edual-energy x-ray absorptiometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHounsfield units\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eregion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eabdominal subcutaneous fat volume (as measured by CT)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVOI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evolume of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eV/SQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evisceral-to-subcutaneous fat ratio as measured by CT\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed for the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eFunding information is not applicable.\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eRT was a major contributor to experimental design, data acquisition, statistical analysis, data interpretation, critical analysis and writing of the manuscript. SF was a major contributor to the experimental design, statistical analysis, data interpretation and critical analysis. CM and FD were major contributors to the manuscript's data interpretation and critical analysis. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eI am grateful to my wife for enduring and keeping me grounded through my research endeavours. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eObesity and overweight World Health Organisation 2021 [Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight \u003c/li\u003e\n\u003cli\u003eLaflamme DP. Understanding and Managing Obesity in Dogs and Cats. Veterinary Clinics of North America: Small Animal Practice. 2006;36(6):1283-95.\u003c/li\u003e\n\u003cli\u003eRamos-Pl\u0026agrave; JJ. Chapter 176: Obesity. In: Stephen J. Ettinger ECF, Etienne Cote, editor. 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Vienna, Austria: R Foundation for Statistical Computing; 2024.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"body composition, fat distribution, visceral fat, subcutaneous fat, CT, dog","lastPublishedDoi":"10.21203/rs.3.rs-5272968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5272968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eAbdominal fat distribution, particularly visceral fat, is commonly assessed as a marker of obesity-related and metabolic diseases in people. Whilst this relationship may exist, few studies consider the factors that may influence the relative distribution of visceral and subcutaneous abdominal fat in dogs. This cross-sectional study evaluated associations between visceral and subcutaneous fat distribution (V/SQ), total abdominal adiposity, body condition score (BCS), age, sex, neuter status, and breed conformation in 205 dogs presenting to a tertiary veterinary hospital. The influence of several disease states on abdominal adiposity and fat distribution was also evaluated. Additionally, the study aimed to assess the reliability of computed tomography (CT) measures of abdominal adiposity and fat distribution.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTotal abdominal adiposity increased with age, reaching a plateau around 10 years before gradually decreasing, and was lower in terrier breeds and dogs with neoplasia. The V/SQ fat ratio increased with age and was higher in hounds and terriers, but decreased with increasing BCS, total abdominal adiposity, and thoracic height-width ratio. Additionally, V/SQ was higher in dogs with hyperadrenocorticism. Body condition score was moderately correlated with total abdominal, visceral, and subcutaneous adiposity. Abdominal fat areas measured at L3 overestimated total abdominal and visceral fat percentages but underestimated subcutaneous fat percentages, with increasing bias at higher fat percentages. Linea alba fat measurements were moderately correlated with total abdominal adiposity, but only weakly correlated with abdominal fat distribution.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study reinforces the association between abdominal adiposity, age, breed category, and potentially certain diseases like neoplasia. Moreover, it highlights the correlation between V/SQ fat distribution, age, and total adiposity, whilst emphasising the preferential distribution of fat to the visceral compartment in dogs with hyperadrenocorticism. The study also identified a novel association between V/SQ fat distribution, specific breed categories and body conformation (i.e. thoracic height-width ratio). Importantly, CT volumetric measures are more reliable in determining abdominal fat distribution than area and linear measures, instilling confidence in the study\u0026rsquo;s methodology and its implications for future research and clinical practice.\u003c/p\u003e","manuscriptTitle":"Abdominal visceral to subcutaneous fat distribution in dogs: computed tomography accuracy and factors associated with distribution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 11:59:41","doi":"10.21203/rs.3.rs-5272968/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-17T04:50:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-17T04:33:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-17T00:43:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2024-10-16T06:03:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"281b04f7-26eb-4c27-b254-0b8cf5f2727c","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T18:09:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-11 11:59:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5272968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5272968","identity":"rs-5272968","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}