The association between visceral adipose tissue, hyperuricemia, and hepatic fat deposition: a post-hoc analysis from the Habitual Diet vs. Avocado randomized control trial

The association between visceral adipose tissue, hyperuricemia, and hepatic fat deposition: a post-hoc analysis from the Habitual Diet vs. Avocado randomized control trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association between visceral adipose tissue, hyperuricemia, and hepatic fat deposition: a post-hoc analysis from the Habitual Diet vs. Avocado randomized control trial John FitzGerald, Jieping Yang, David Elashoff, Zhaoping Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8970445/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Background: Hyperuricemia, while associated with, is not part of the metabolic syndrome. We sought to examine the association between central obesity as measured by visceral abdominal tissue (VAT) deposition and serum urate (SU). We further explored the association between SU and hepatic fat deposition (HFD). Methods: Through a post-hoc analysis, using a single study site from the Habitual Diet vs. Avocado (HAT) multi-center randomized control trial, baseline serum, abdominal magnetic resonance imaging (MRI) and responses to questionnaires were available. Means, medians and proportions were used to describe variables. Anova, and Chi-square were used to describe bivariate associations between VAT and SU as well as SU and HFD. Generalized linear models (linear or logistic) were used to describe above associations while controlling for other potential metabolic confounders. Principle component analyses were used to evaluate the fit of SU with the other metabolic variables. Results: The baseline cohort of 223 subjects were mostly female (80%) with mean age of 46 years and BMI of 33, (65% were obese), 25% had hyperuricemia (SU > 360 mmol/L). SU was strongly correlated with VAT deposits (r = 0.31, p 360 μmol/L) were more likely to be male, obese (BMI > 30), morbidly obese (BMI > 40), and have greater amounts of VAT deposits. More than two-thirds (71%) of subjects with hyperuricemia had elevated HFD (HFD > 5%) compared to 41% of subjects with normal SU (p < 0.0001). After controlling for potential confounding covariates, including BMI, the association between VAT and SU was attenuated potentially through mediating factors associated with covariates. Hyperuricemia remained strongly associated with HFD > 5%, (Odds Ratio = 3.0, p = 0.005) after adjusting for BMI and other confounders. Conclusions: For this cohort of subjects with known central adiposity, higher SU levels were associated with greater deposits of VAT strengthening the association between hyperuricemia and metabolic syndrome. Hyperuricemia was also associated with higher proportion of HFD. As a limitation of this post-hoc cross-sectional design, causation (or directionality) cannot be assumed. The parent HAT clinical trial was registered under the approved clinical trial protocol, NCT03528031. Original submission, 04, May 2018. Final version, 17, March, 2023. Trial registration: The parent habitual diet avocado trial was registered with clinical trials.gov with the following protocol, NCT03528031 Uric Acid Metabolic Syndrome Central obesity Hepatic Fat Deposition Figures Figure 1 Figure 2 Figure 3 Background Gout and hyperuricemia are frequently associated with metabolic syndrome as well as the associated comorbidities [ 1 ]. However, neither gout nor hyperuricemia are part of the metabolic syndrome (MetS) criteria [ 2 ]. Gout is frequently referred to as a “fellow traveler” [ 3 ] of MetS. Obesity is a known risk factor for gout and hyperglycemia. Central obesity is associated with metabolic syndrome [ 4 ], insulin resistance [ 5 ], cardiovascular disease [ 6 , 7 ], hypertension [ 8 ], chronic low-grade inflammation (↑ IL-6, TNF-α) [ 9 ], MASLD, and all-cause mortality [ 10 , 11 ]. However, there has been little research examining the association between the distribution of fatty deposits (central versus subcutaneous) with the presence of hyperuricemia or the diagnosis of gout. Two recent articles using 2007–2018 NHANES data reported that higher measures of central obesity were associated with hyperglycemia (> 7 mg/dL in men and > 6 mg/dL in women) and patient reported prevalent gout. In a 2024 publication on this topic, the authors demonstrated that the Body Roundness Index (BRI) and the Weight adjusted Waist Index (WWI) both performed better than BMI for their accuracy in identifying concurrent hyperuricemia and gout [ 12 ]. The BRI uses waste circumference and height while the WWI uses waist circumference and weight. In a 2025 publication, using the same database, separate authors, looked at visceral adiposity index (VAI) [ 13 ]. The VAI is sex-specific index that uses BMI, waist circumference, and includes lipid parameters (triglycerides and HDL). Receiver operator curves (ROC) for all these indices performed in the 0.6 to 0.65 range [ 12 , 13 ]. These findings suggest the importance of fat distribution, specifically central obesity and the presence of hyperuricemia and gout. However, these indices are simple proxies for more direct measurements. Magnetic Resonance Imaging (MRI) provides that direct measurement of Visceral Adipose Tissue (VAT) [ 14 ]. MRI VAT volumes > 1 liter (L) are associated with a moderate increased risk of cardiovascular disease (CVD) and volumes > 3.5 L associated with a high risk [ 15 ]. To evaluate the association between central obesity and hyperuricemia, we conducted a post-hoc analysis on the subjects recruited at UCLA for the Habitual Diet vs. Avocado (HAT) multi-center randomized control trial. Like gout, the metabolic dysfunction-associated steatotic liver disease (MASLD) is not part of the MetS diagnostic criteria, but it is referred to as the “hepatic manifestation” of MetS. “MASLD is defined by the coexistence of hepatic steatosis (HFD > 5%) with at least one of the five typical traits of the metabolic syndrome in the absence of clinically significant alcohol consumption and other secondary causes of steatosis.” [ 16 ] A recent meta-analysis reported that hyperuricemia had an odds ratio of 1.9 for the association with MASLD [ 17 ]. Furthermore, using NHANES 2017–2018 data, authors conducted a Mendelian randomization study to determine that hyperuricemia as a causal role in developing MASLD (OR = 1.2, 95% CI = 1.06–1.5) [ 18 ]. As HFD was concurrently collected with VAT in the HAT study, we further sought to evaluate the association between hyperuricemia and HFD in this analysis. Methods To better understand the role of SU in the MetS in subjects with central obesity, we evaluated SU and components of the metabolic syndrome (insulin resistance, HDL cholesterol, triglycerides, central obesity, and presence of hypertension) using principal component analysis. To better understand the correlation between central obesity and SU as well as SU/hyperuricemia with HFD, for the UCLA cohort, we evaluated SU from stored study serum samples. Patient cohort Subjects with suspected central obesity were solicited to participate in the HAT study. To be eligible to participate, female subjects had to have a waist circumference of at least 35 inches and at least 40 inches for male subjects. A minimum age of 25 years old at screening, a willingness to consume avocados, but not more than 2 per month in their current diet was required [ 19 ]. Data collection Measurement of fat deposition MRI was performed to assess the volume of VAT and HFD. Each Participant underwent a non-contrast 3 Tesla Siemens MRI at week 0 and week 26 per the HAT trial protocol [ 19 ]. A DIXON axial vibe two- echo sequence was used for measuring volume of VAT. Slice coverage was enough to cover 4 cm above the dome of the liver to 7 cm below the top of the iliac crest. Parameters were approximately: slices 96, FOV 400 mm, slice thickness 3.5 mm, TR 5 ms, TE (1.23, 2.46 ms), flip angle 9◦. Subcutaneous fat was manually segmented and excluded using slice Omatic and a watershed algorithm. Fat images were thresholded using an implementation of the Otsu algorithm from ImageJ. For 9 patients, VAT was not able to be calculated. There were no differences between the 9 patients with missing versus the remainder of the cohort for any of the potential confounders listed below, and therefore the nine patients with missing VAT were simply excluded from all VAT analyses. MR spectroscopy was used to measure HFD, using a PRESS sequence. Spectra were processed with LC Model version 6.3 using lipid quantification settings, and T1 and T2 correction were applied [ 20 ]. Data were blinded, anonymized and sent to the central study site for processing. Serum urate assessment SU was measured using Amplex® Red Uric Acid/Uricase Assay Kit supplied by Fischer Scientific, Catalog no. A22181. Other metabolic covariates Patient age, gender and height were collected at baseline visit. Systolic and diastolic blood pressure were collected every 4 weeks. Body weight, serum, plasma and RBCs were collected at Week 0, 12 and 26, sent to a central lab, and analyzed for: fasting total cholesterol, triglyceride, high density lipoprotein-cholesterol (HDL–C), glucose, insulin, (insulin resistance as calculated by HOMA-IR) and high sensitivity C-reactive protein (hsCRP) concentrations, and RBC fatty acid profiles. Low density lipoprotein (LDL)-cholesterol was calculated using the Friedewald equation [ 21 ]. The three adiposity indices were all calculated based on STEPwise Approach to Surveillance [ 22 ]. Subjects completed dietary and quality of life questionnaires at week 0, 12, and 26, that included Healthy Eating Index (HEI2015) [ 23 ] and RAND Health Instruments [ 24 ]. For the UCLA site, subjects’ local Electronic medical records were reviewed for diagnosis of gout, urate lowering medications (including allopurinol and febuxostat), and medications that secondarily raise or lower great levels (such as diuretics or Losartan). Statistical analysis Means with standard deviations and medians with interquartile ranges of continuous variables were described as appropriate based on the distribution of the data, proportions for categorical variables. To evaluate associations between SU and the main metabolic variables, scatterplots and Pearson correlations were evaluated. SU was then categorized into descriptive groups that included hyperuricemia (SU > 360 µmol/L) vs. normouricemic ( ≤ 360 µmol/L). Normouricemic was further dichotomized to low normal (SU ≤ 300 µmol/L) and 300 µmol/L < SU ≤ 360 µmol/L (creating 3 SU categories). Kruskal-Wallace tests were used to compare clinical and demographic variables across the 3 SU categories and Mantel-Haenszel Chi-Square tests were used to compare stratified VAT and HFD across the 3 SU categories. To evaluate the relationship of SU with the other metabolic variables in our data set, we first used principal component analysis for the 12 metabolic variables (excluding SU). Scree plots were created. Principal component pattern plots suggested 3 conceptual themes that were used to organize the correlation matrix into a thematic correlation heat map. We also performed the principal component assay with the 12 metabolic variables in addition to SU to evaluate where SU loaded in relation to the other 12 variables (and the three dominant themes). To further evaluate the independent impact of VAT on SU, we used generalized linear models to evaluate SU as a function of VAT while controlling for age, gender, race, hsCRP, and BMI. Multivariate logistic regression evaluated the adjusted odds ratio of abnormal HFD (> 5%) as a function of hyperuricemia while controlling for the same potential confounders. Ethical Conduct of Research UCLA IRB reviewed and approved the following protocol IRB-18-0181: Habitual diet and avocado trial (HAT). All subjects completed a written informed consent process. Results For the 223 enrolled UCLA subjects, the mean age was 46 years with a mean BMI of 33, and 181 (80%) were female. Sixty-five percent were obese, (BMI > 30) and 12% morbidly obese (BMI > 40). Slightly more than half of the cohort reported good eating habits as evaluated by the Healthy Eating Index 2015 (See Table 1 ). On review of local EMR, no subjects had a diagnosis of gout, and none were using primary urate lowering agents. Fifty-six subjects (25%) had hyperuricemia (defined as SU > 360 µmol/L). (Table 1 .) Subjects with higher SU were more likely to be male (p = 0.003), have higher BMI (p < 0.001) and significantly higher insulin resistance scores (p < 0.0001). Central obesity, serum urate, and metabolic syndrome From the 223 baseline subjects, 9 had missing VAT scores. For the remaining 214 subjects, VAT ranged from 0.8–8.6 L with median (and quartile values) of 2.6 L (1.9 L, 3.4 L). In bivariate analysis, SU was strongly correlated with VAT deposition (r = 0.32 p 360 µmol/L) had higher median VAT deposition than subjects with normal SU levels (3.3 L versus 2.5 L, p = 0.0002) and a greater proportion of subjects with hyperuricemia had deposits of VAT greater than 3.4 L (associated with significantly increased cardiovascular disease, [ 15 ]) compared to subjects with normal SU (48% versus 17%, p = 0.0013, Table 2 a). To further evaluate the association between central obesity and SU multivariant analysis included potential confounders with SU including age, gender and log transformed hsCRP while also controlling for the general measure of obesity (BMI), (Table 3 a). After adjusting for age, gender, race, hsCRP, and BMI, the correlation between central obesity and SU reduced to 0.09 (p = 0.25). From the principal component analysis, for the 12 metabolic variables of interest (not including SU), five principal components were identified within eigenvalues greater than 1. (See Fig. 1 for Scree plots and Supplementary Table 1 for eigenvalues and Supplementary Table 2: Rotated Factor Patterns.) From the 2 dominant factors we created component pattern plots (Fig. 2 a&b). For the 12 metabolic variables, we identified 3 dominant conceptual themes. These themes included metabolic factors, lipids and a composite category of age and blood pressure. (See Fig. 2 a: Clustering of 12 metabolic variables based on the first two principal components.) With subsequent addition of SU to the principal component analysis, SU loaded with the VAT (Supplementary Table 2: Rotated Factor Pattern) and the other metabolic syndrome variables (Fig. 2 b). Serum Urate and Hepatic Fat Deposition For the 223 subjects in the cohort, HFD ranged from 0–54% with median (and quartile values) of 4.7% (2.3%, 15.8%), 107 (48%) subjects had elevated HFD > 5%. Among subjects with hyperuricemia, 71% had elevated HFD (> 5%) compared to subjects with normal SU where only 59% had elevated HFD, p = 0.0015, (Table 2 b). After controlling for potential covariates (age, gender, race, hsCRP, and BMI), the adjusted odds ratio of having increased fatty liver deposits (greater than 5%) was 3.03 (p = 0.005) for patients with hyperuricemia compared to those with normal SU levels. (Table 3 b) Discussion Neither gout nor hyperuricemia are part of the definition for the metabolic syndrome; rather gout has been referred to as a “fellow traveler” [ 3 ]. However, patients with gout have (2- to 4-fold) higher rates of hypertension, chronic kidney disease, obesity, diabetes mellitus, myocardial infarction, heart failure, and stroke [ 1 ]. Meanwhile, the prevalence of gout [ 25 ] and MetS [ 26 ] have both increased based on 2011 to 2018 NHANES data. Central obesity leads to insulin resistance and MetS [ 27 ]. Mendelian randomization studies have examined the association between hyperuricemia and insulin resistance, connecting the association with a proposed causal pathway from insulin resistance to hyperuricemia [ 28 , 29 ]. The association between gout and MASLD has been reported using the 2006–2010 UK Biobank data [ 30 ]. Patients with gout had 2 to 4 times higher odds ratios for risk of MASLD then patient without gout (depending on number of covariant included in the multivariant analysis). The explanation for the increased association could be due to either a common risk factor such as consumption of high fructose corn syrup (HFCS) or through a shared causal pathway, such as insulin resistance [ 16 ]. HFCS is a known risk factor for both gout and MASLD. Fructokinase breaks down fructose through an uninhibited depletion of Adenosine Triphosphate resulting in an abundant production of Adenosine Monophosphate and subsequently urate [ 31 ]. Additionally, fructokinase stimulates production of triglycerides that then deposit in the liver [ 32 ]. Separately, increased urate levels result in mitochondrial dysfunction, a decrease in the activity of aconitase, increase in citrate, which can then function as a substrate for triglyceride formation [ 32 ]. Beyond simply entangling either gout or hyperuricemia with MetS, these associations have important clinical findings as newer therapeutics have pluripotent benefits including the benefits of sodium glucose to transporter inhibitor (glycemic control, reduction of cardiovascular deaths and hospitalization for heart failure and slowing the progression of chronic kidney disease with additional benefits of reduced never with. [ 33 ] and reduced hepatic steatosis/fibrosis [ 34 ]. The GLP-1 receptor antagonists similarly have pluripotent benefits, including reduction in cardiovascular outcomes in patients with type II diabetes [ 35 ], reduction in albuminuria [ 36 ]and similarly improvements in hepatic steatosis/fibrosis [ 37 ] (modulated through improved insulin sensitivity). Finally, a recent small trial of 104 patients with MASLD were randomized to allopurinol (100 mg per day) versus placebo. Patients in the allopurinol arm showed improvement in SU (p-value not reported), alanine transaminase (p = 0.004) and ultrasonic grading (p = 0.001). While there was no significant improvement in the control group for these parameters; there was also no significant difference between the control group and the intervention [ 38 ]. Conclusions Central obesity as measured by MRI measured VAT is associated with hyperuricemia. This association is partially mediated through the insulin resistance pathway. Subjects with hyperuricemia have higher levels of HFD than subjects with normal SU levels. The association between HFD and hyperuricemia persist after adjusting for known potential confounders of HFD including BMI, age, race and inflammatory markers. Though a causal pathway cannot be evaluated in this cross-sectional analysis, given the introduction of newer pluripotent medications, greater attention to the inter-relationship between gout/SU and the other various metabolic pathways is warranted. Abbreviations BMI Body mass index BRI Body Roundness Index EMR Electronic Medical Record FOV Field of view HDL High-density lipoprotein HEI Healthy Eating Index HFD Hepatic Fat Deposition HOMA-IR Homeostatic Model Assessment of Insulin Resistance hsCRP high sensitivity C-reactive protein LDL Low-density lipoprotein MASLD Metabolic dysfunction-associated steatotic liver disease MetS Metabolic Syndrome ms milliseconds NHANES National Health and Nutrition Examination Survey s.d. Standard deviation VAT Visceral Abdominal Tissue VLDL Very low-density lipoprotein WWI Weight-Adjusted Waist Index Declarations Ethics approval and consent to participate UCLA IRB reviewed and approved the following protocol IRB-18-0181: Habitual diet and avocado trial (HAT). All subjects completed a Written informed consent process. The study adhered to the submitted protocol in compliance with the Declaration of Helsinki. Consent for publication All authors consent to publication. Funding The HAT clinical trial was funded by the Avocado Nutrition Center. Departmental funding was used for this post hoc analysis. Author Contribution Conceptualization: (JF,ZL); Methodology: (JF,JP,ZL); Formal analysis: (JF); Data curation: (JF,JP,ZL); Writing - Original Draft: (JF); Writing - Review & Editing: (JP,ZL); Visualization: (JF,JP,ZL); Supervision: (ZL); Project administration: (ZL); Funding acquisition: (ZL) Data Availability The data supporting this article are available from the Hass Avocado Board by request. References Krishnan E. Interaction of inflammation, hyperuricemia, and the prevalence of hypertension among adults free of metabolic syndrome: NHANES 2009–2010. J Am Heart Assoc. 2014;3(2):e000157. Bloomgarden ZT. American Association of Clinical Endocrinologists (AACE) consensus conference on the insulin resistance syndrome: 25–26 August 2002, Washington, DC. Diabetes Care, 2003. 26(3): pp. 933-9. Thottam GE, Krasnokutsky S, Pillinger MH. Gout and Metabolic Syndrome: a Tangled Web. Curr Rheumatol Rep. 2017;19(10):60. Shah RV, et al. 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Tables Tables and Figures Table 1 Baseline descriptors for all subjects and stratified by Serum Urate level. Serum Urate (µmol/L) 360 (n = 56) All (n = 223) p-value across SU Age (years), mean (s.d.) 45.4 (13.1) 48.6 (15.1) 44.9 (13.1) 46.3 (13.8) 0.22 Female, n (%) 84 (88%) 60 (83%) 35 (64%) 181 (80%) 0.003 BMI, mean (s.d.) 31.7 (5.6) 32.2 (5.2) 35.3 (6.1) 32.8 (5.80) 40) 8(9%) 33(35%) 27(29%) 15(16%) 10(11%) 4(6%) 23(32%) 28(39%) 11(15%) 6(8%) 1(2%) 8(15%) 22(40%) 14(25%) 10(18%) 13(6%) 64(29%) 77(35%) 40(18%) 27(12%) 0.02 hsCRP (mg/L), median 4.0 (1.2, 7.9) 3.5 (1.5, 7.3) 5.6 (2.2, 9.3) 4.2 (1.5, 8.3) 0.14 Height (cm), mean 164 (8.7) 165 (8.7) 168 (9.5) 165 (8.9) 0.06 Weight (kg), mean 85 (16.3) 88 (18.4) 99 (19.3) 90 (19) < 0.0001 SBP/DBP, mean 118/75 122/77 121/79 121/77 0.21/0.08 Healthy Eating Index (Proportion Good) 41 (45%) 46 (64%) 29 (53%) 116 (53%) 0.02 HOMA_IR median (quartiles) 2.2 (1.4, 4.1) 2.9 (1.6, 4.3) 4.0 (2.3, 6.5) 2.9 (1.6, 4.6) < 0.0001 Table 2 a: Cross-sectional bivariate correlates between Visceral Adipose Tissue and Serum Urate. Serum Urate (µmol/L) 360 (n = 54) All (n = 214) p-value across SU Visceral Adipose Tissue, L median (quartiles) 2.3 (1.7, 3.2) 2.5 (2.2, 3.0) 3.3 (2.2, 4.4) 2.6 (1.9, 3.4) 0.0002 Lowest quartile, (0-1.9 L) 25th – 75th quartiles, (1.9–3.4 L) 75th-90th percentile (3.4–4.9 L) 90 + percentile (4.9–8.6 L) 3437% 3943% 1012% 78% 11 16% 49 70% 6 8% 4 6% 8 15% 20 37% 15 28% 11 20% 53 24% 108 48% 32 14% 22 10% 0.0009 Table 2 b: Cross-sectional bivariate correlates between Serum Urate and Hepatic Fat Deposition. Serum Urate (µmol/L) 360 (n = 56) All (n = 223) p-value across SU Hepatic Fat Deposition, median (quartiles) 0.04 (0.02, 0.11) 0.04 (0.02, 0.10) 0.12 (0.04, 0.24) 0. 047 (0.023, 0.158) 0.0003 Normal, (< 0.05) 50th-75th percentile 75th-90th percentile 90 + percentile 5760% 1718% 1415% 77% 4258% 1825% 57% 73% 1629% 1527% 1425% 1119% 11652% 5022% 3315% 2511% 0.015 Table 3 a: Multivariate model for Serum Urate as a function of central obesity (n = 211, 3 missing covariate values) Variable Parameter Estimate Standard Error p-value Intercept 311.5 44.6 < .0001 Central obesity (VAT) 6.8 6.0 0.25 Age (years) 0.6 0.5 0.23 Female (vs. Male) -51.5 15.3 0.00 Black (vs. White) -13.7 14.4 0.34 Asian (vs. White) 33.2 17.1 0.05 Other race (vs. White) 7.0 13.3 0.60 BMI (kg/m 2 ) 1.7 1.1 0.13 Log hsCRP (mg/L) -0.9 7.2 0.91 Legend: VAT = visceral adipose tissue, BMI = body mass index, hsCRP = high sensitivity C-reactive protein Table 3 b: Multivariate adjusted odds ratio (OR) for elevated hepatic fatty deposition (> 5%) as a function of hyperuricemia (n = 220, 3 missing covariate values) Parameter OR 95% (LL, UL) p-value Hyperuricemia (vs. SU < 360) 3.03 (1.41, 6.52) 0.005 Age (years) 1.04 (1.01, 1.06) 0.007 Female (vs. Male) 0.96 (0.41, 2.24) 0.92 Black (vs. White) 0.13 (0.05, 0.34) < .0001 Asian (vs. White) 1.77 (0.62, 5.06) 0.04 Other race (vs. White) 1.48 (0.63, 3.46) 0.05 BMI (kg/m 2 ) 1.06 (0.99, 1.13) 0.08 Log hsCRP (mg/L) 2.25 (1.41, 3.59) 0.001 Legend: BMI = body mass index, hsCRP = high sensitivity C-reactive protein Additional Declarations No competing interests reported. Supplementary Files BMCSupplementaryHAT.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Editor invited by journal 04 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 03 Mar, 2026 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. 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FitzGerald","email":"data:image/png;base64,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","orcid":"","institution":"Veterans Health Administration","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"","lastName":"FitzGerald","suffix":""},{"id":609722912,"identity":"1be7095a-d870-4875-9f31-e538bc9f7083","order_by":1,"name":"Jieping Yang","email":"","orcid":"","institution":"University of California","correspondingAuthor":false,"prefix":"","firstName":"Jieping","middleName":"","lastName":"Yang","suffix":""},{"id":609722913,"identity":"d4065a20-0389-4ba5-aa79-d00a2fb303f7","order_by":2,"name":"David Elashoff","email":"","orcid":"","institution":"University of California","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Elashoff","suffix":""},{"id":609722914,"identity":"3c2d6d3e-c209-48ea-90b2-04f42024bca7","order_by":3,"name":"Zhaoping Li","email":"","orcid":"","institution":"Veterans Health Administration","correspondingAuthor":false,"prefix":"","firstName":"Zhaoping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-25 18:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8970445/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8970445/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105282289,"identity":"700c8845-d8cd-41a9-b3cb-6517c3d32b86","added_by":"auto","created_at":"2026-03-24 10:27:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32873,"visible":true,"origin":"","legend":"\u003cp\u003eScree plots from the principal components analysis for the 13 metabolic variables.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8970445/v1/20418fa29b980c9cceb26bb9.jpg"},{"id":105282301,"identity":"e7344cf9-e535-4c45-a2d8-523dbc957e6e","added_by":"auto","created_at":"2026-03-24 10:27:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67342,"visible":true,"origin":"","legend":"\u003cp\u003e2a: Clustering of 12 metabolic variables based on the first two principal components\u003c/p\u003e\n\u003cp\u003e2b: Uric acid loads with the other metabolic syndrome variables\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8970445/v1/c948926d22466def86f7a7b9.jpg"},{"id":105282250,"identity":"ba6762b1-b979-470b-ab43-472da472ce64","added_by":"auto","created_at":"2026-03-24 10:27:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237013,"visible":true,"origin":"","legend":"\u003cp\u003ePearson Correlation Matrix organized by the 3 clusters of covariates based on principal components analysis (plus uric acid).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8970445/v1/6ee69ef059ca4ff3a1fcb7cf.jpg"},{"id":105282449,"identity":"de59a216-0191-4052-b70d-a69195b9e824","added_by":"auto","created_at":"2026-03-24 10:27:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17716,"visible":true,"origin":"","legend":"","description":"","filename":"BMCSupplementaryHAT.docx","url":"https://assets-eu.researchsquare.com/files/rs-8970445/v1/100ff3ce8bedfd8f88f7a6ff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between visceral adipose tissue, hyperuricemia, and hepatic fat deposition: a post-hoc analysis from the Habitual Diet vs. Avocado randomized control trial","fulltext":[{"header":"Background","content":"\u003cp\u003eGout and hyperuricemia are frequently associated with metabolic syndrome as well as the associated comorbidities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, neither gout nor hyperuricemia are part of the metabolic syndrome (MetS) criteria [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Gout is frequently referred to as a \u0026ldquo;fellow traveler\u0026rdquo; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] of MetS. Obesity is a known risk factor for gout and hyperglycemia. Central obesity is associated with metabolic syndrome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], insulin resistance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], cardiovascular disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], hypertension [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], chronic low-grade inflammation (\u0026uarr; IL-6, TNF-α) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], MASLD, and all-cause mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, there has been little research examining the association between the distribution of fatty deposits (central versus subcutaneous) with the presence of hyperuricemia or the diagnosis of gout. Two recent articles using 2007\u0026ndash;2018 NHANES data reported that higher measures of central obesity were associated with hyperglycemia (\u0026gt;\u0026thinsp;7 mg/dL in men and \u0026gt;\u0026thinsp;6 mg/dL in women) and patient reported prevalent gout. In a 2024 publication on this topic, the authors demonstrated that the Body Roundness Index (BRI) and the Weight adjusted Waist Index (WWI) both performed better than BMI for their accuracy in identifying concurrent hyperuricemia and gout [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The BRI uses waste circumference and height while the WWI uses waist circumference and weight. In a 2025 publication, using the same database, separate authors, looked at visceral adiposity index (VAI) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The VAI is sex-specific index that uses BMI, waist circumference, and includes lipid parameters (triglycerides and HDL). Receiver operator curves (ROC) for all these indices performed in the 0.6 to 0.65 range [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings suggest the importance of fat distribution, specifically central obesity and the presence of hyperuricemia and gout. However, these indices are simple proxies for more direct measurements. Magnetic Resonance Imaging (MRI) provides that direct measurement of Visceral Adipose Tissue (VAT) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. MRI VAT volumes\u0026thinsp;\u0026gt;\u0026thinsp;1 liter (L) are associated with a moderate increased risk of cardiovascular disease (CVD) and volumes\u0026thinsp;\u0026gt;\u0026thinsp;3.5 L associated with a high risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To evaluate the association between central obesity and hyperuricemia, we conducted a post-hoc analysis on the subjects recruited at UCLA for the Habitual Diet vs. Avocado (HAT) multi-center randomized control trial.\u003c/p\u003e \u003cp\u003eLike gout, the metabolic dysfunction-associated steatotic liver disease (MASLD) is not part of the MetS diagnostic criteria, but it is referred to as the \u0026ldquo;hepatic manifestation\u0026rdquo; of MetS. \u0026ldquo;MASLD is defined by the coexistence of hepatic steatosis (HFD\u0026thinsp;\u0026gt;\u0026thinsp;5%) with at least one of the five typical traits of the metabolic syndrome in the absence of clinically significant alcohol consumption and other secondary causes of steatosis.\u0026rdquo; [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] A recent meta-analysis reported that hyperuricemia had an odds ratio of 1.9 for the association with MASLD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, using NHANES 2017\u0026ndash;2018 data, authors conducted a Mendelian randomization study to determine that hyperuricemia as a causal role in developing MASLD (OR\u0026thinsp;=\u0026thinsp;1.2, 95% CI\u0026thinsp;=\u0026thinsp;1.06\u0026ndash;1.5) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As HFD was concurrently collected with VAT in the HAT study, we further sought to evaluate the association between hyperuricemia and HFD in this analysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eTo better understand the role of SU in the MetS in subjects with central obesity, we evaluated SU and components of the metabolic syndrome (insulin resistance, HDL cholesterol, triglycerides, central obesity, and presence of hypertension) using principal component analysis. To better understand the correlation between central obesity and SU as well as SU/hyperuricemia with HFD, for the UCLA cohort, we evaluated SU from stored study serum samples.\u003c/p\u003e \u003cp\u003ePatient cohort\u003c/p\u003e \u003cp\u003eSubjects with suspected central obesity were solicited to participate in the HAT study. To be eligible to participate, female subjects had to have a waist circumference of at least 35 inches and at least 40 inches for male subjects. A minimum age of 25 years old at screening, a willingness to consume avocados, but not more than 2 per month in their current diet was required [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eMeasurement of fat deposition\u003c/p\u003e \u003cp\u003eMRI was performed to assess the volume of VAT and HFD. Each Participant underwent a non-contrast 3 Tesla Siemens MRI at week 0 and week 26 per the HAT trial protocol [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A DIXON axial vibe two- echo sequence was used for measuring volume of VAT. Slice coverage was enough to cover 4 cm above the dome of the liver to 7 cm below the top of the iliac crest. Parameters were approximately: slices 96, FOV 400 mm, slice thickness 3.5 mm, TR 5 ms, TE (1.23, 2.46 ms), flip angle 9◦. Subcutaneous fat was manually segmented and excluded using slice Omatic and a watershed algorithm. Fat images were thresholded using an implementation of the Otsu algorithm from ImageJ. For 9 patients, VAT was not able to be calculated. There were no differences between the 9 patients with missing versus the remainder of the cohort for any of the potential confounders listed below, and therefore the nine patients with missing VAT were simply excluded from all VAT analyses.\u003c/p\u003e \u003cp\u003eMR spectroscopy was used to measure HFD, using a PRESS sequence. Spectra were processed with LC Model version 6.3 using lipid quantification settings, and T1 and T2 correction were applied [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Data were blinded, anonymized and sent to the central study site for processing.\u003c/p\u003e \u003cp\u003eSerum urate assessment\u003c/p\u003e \u003cp\u003eSU was measured using Amplex\u0026reg; Red Uric Acid/Uricase Assay Kit supplied by Fischer Scientific, Catalog no. A22181.\u003c/p\u003e \u003cp\u003eOther metabolic covariates\u003c/p\u003e \u003cp\u003ePatient age, gender and height were collected at baseline visit. Systolic and diastolic blood pressure were collected every 4 weeks. Body weight, serum, plasma and RBCs were collected at Week 0, 12 and 26, sent to a central lab, and analyzed for: fasting total cholesterol, triglyceride, high density lipoprotein-cholesterol (HDL\u0026ndash;C), glucose, insulin, (insulin resistance as calculated by HOMA-IR) and high sensitivity C-reactive protein (hsCRP) concentrations, and RBC fatty acid profiles. Low density lipoprotein (LDL)-cholesterol was calculated using the Friedewald equation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The three adiposity indices were all calculated based on STEPwise Approach to Surveillance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubjects completed dietary and quality of life questionnaires at week 0, 12, and 26, that included Healthy Eating Index (HEI2015) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and RAND Health Instruments [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the UCLA site, subjects\u0026rsquo; local Electronic medical records were reviewed for diagnosis of gout, urate lowering medications (including allopurinol and febuxostat), and medications that secondarily raise or lower great levels (such as diuretics or Losartan).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMeans with standard deviations and medians with interquartile ranges of continuous variables were described as appropriate based on the distribution of the data, proportions for categorical variables. To evaluate associations between SU and the main metabolic variables, scatterplots and Pearson correlations were evaluated. SU was then categorized into descriptive groups that included hyperuricemia (SU\u0026thinsp;\u0026gt;\u0026thinsp;360 \u0026micro;mol/L) vs. normouricemic (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;360 \u0026micro;mol/L). Normouricemic was further dichotomized to low normal (SU\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;300 \u0026micro;mol/L) and 300 \u0026micro;mol/L\u0026thinsp;\u0026lt;\u0026thinsp;SU\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;360 \u0026micro;mol/L (creating 3 SU categories). Kruskal-Wallace tests were used to compare clinical and demographic variables across the 3 SU categories and Mantel-Haenszel Chi-Square tests were used to compare stratified VAT and HFD across the 3 SU categories.\u003c/p\u003e \u003cp\u003eTo evaluate the relationship of SU with the other metabolic variables in our data set, we first used principal component analysis for the 12 metabolic variables (excluding SU). Scree plots were created. Principal component pattern plots suggested 3 conceptual themes that were used to organize the correlation matrix into a thematic correlation heat map.\u003c/p\u003e \u003cp\u003eWe also performed the principal component assay with the 12 metabolic variables in addition to SU to evaluate where SU loaded in relation to the other 12 variables (and the three dominant themes).\u003c/p\u003e \u003cp\u003eTo further evaluate the independent impact of VAT on SU, we used generalized linear models to evaluate SU as a function of VAT while controlling for age, gender, race, hsCRP, and BMI.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression evaluated the adjusted odds ratio of abnormal HFD (\u0026gt;\u0026thinsp;5%) as a function of hyperuricemia while controlling for the same potential confounders.\u003c/p\u003e \u003cp\u003eEthical Conduct of Research\u003c/p\u003e \u003cp\u003eUCLA IRB reviewed and approved the following protocol IRB-18-0181: Habitual diet and avocado trial (HAT). All subjects completed a written informed consent process.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFor the 223 enrolled UCLA subjects, the mean age was 46 years with a mean BMI of 33, and 181 (80%) were female. Sixty-five percent were obese, (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30) and 12% morbidly obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;40). Slightly more than half of the cohort reported good eating habits as evaluated by the Healthy Eating Index 2015 (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On review of local EMR, no subjects had a diagnosis of gout, and none were using primary urate lowering agents. Fifty-six subjects (25%) had hyperuricemia (defined as SU\u0026thinsp;\u0026gt;\u0026thinsp;360 \u0026micro;mol/L). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.) Subjects with higher SU were more likely to be male (p\u0026thinsp;=\u0026thinsp;0.003), have higher BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and significantly higher insulin resistance scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eCentral obesity, serum urate, and metabolic syndrome\u003c/p\u003e \u003cp\u003eFrom the 223 baseline subjects, 9 had missing VAT scores. For the remaining 214 subjects, VAT ranged from 0.8\u0026ndash;8.6 L with median (and quartile values) of 2.6 L (1.9 L, 3.4 L). In bivariate analysis, SU was strongly correlated with VAT deposition (r\u0026thinsp;=\u0026thinsp;0.32 p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Subjects with hyperuricemia (SU\u0026thinsp;\u0026gt;\u0026thinsp;360 \u0026micro;mol/L) had higher median VAT deposition than subjects with normal SU levels (3.3 L versus 2.5 L, p\u0026thinsp;=\u0026thinsp;0.0002) and a greater proportion of subjects with hyperuricemia had deposits of VAT greater than 3.4 L (associated with significantly increased cardiovascular disease, [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]) compared to subjects with normal SU (48% versus 17%, p\u0026thinsp;=\u0026thinsp;0.0013, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eTo further evaluate the association between central obesity and SU multivariant analysis included potential confounders with SU including age, gender and log transformed hsCRP while also controlling for the general measure of obesity (BMI), (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). After adjusting for age, gender, race, hsCRP, and BMI, the correlation between central obesity and SU reduced to 0.09 (p\u0026thinsp;=\u0026thinsp;0.25).\u003c/p\u003e \u003cp\u003eFrom the principal component analysis, for the 12 metabolic variables of interest (not including SU), five principal components were identified within eigenvalues greater than 1. (See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for Scree plots and Supplementary Table\u0026nbsp;1 for eigenvalues and Supplementary Table\u0026nbsp;2: Rotated Factor Patterns.) From the 2 dominant factors we created component pattern plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u0026amp;b). For the 12 metabolic variables, we identified 3 dominant conceptual themes. These themes included metabolic factors, lipids and a composite category of age and blood pressure. (See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea: Clustering of 12 metabolic variables based on the first two principal components.) With subsequent addition of SU to the principal component analysis, SU loaded with the VAT (Supplementary Table\u0026nbsp;2: Rotated Factor Pattern) and the other metabolic syndrome variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eSerum Urate and Hepatic Fat Deposition\u003c/p\u003e \u003cp\u003eFor the 223 subjects in the cohort, HFD ranged from 0\u0026ndash;54% with median (and quartile values) of 4.7% (2.3%, 15.8%), 107 (48%) subjects had elevated HFD\u0026thinsp;\u0026gt;\u0026thinsp;5%. Among subjects with hyperuricemia, 71% had elevated HFD (\u0026gt;\u0026thinsp;5%) compared to subjects with normal SU where only 59% had elevated HFD, p\u0026thinsp;=\u0026thinsp;0.0015, (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAfter controlling for potential covariates (age, gender, race, hsCRP, and BMI), the adjusted odds ratio of having increased fatty liver deposits (greater than 5%) was 3.03 (p\u0026thinsp;=\u0026thinsp;0.005) for patients with hyperuricemia compared to those with normal SU levels. (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNeither gout nor hyperuricemia are part of the definition for the metabolic syndrome; rather gout has been referred to as a \u0026ldquo;fellow traveler\u0026rdquo; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, patients with gout have (2- to 4-fold) higher rates of hypertension, chronic kidney disease, obesity, diabetes mellitus, myocardial infarction, heart failure, and stroke [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Meanwhile, the prevalence of gout [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and MetS [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] have both increased based on 2011 to 2018 NHANES data. Central obesity leads to insulin resistance and MetS [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mendelian randomization studies have examined the association between hyperuricemia and insulin resistance, connecting the association with a proposed causal pathway from insulin resistance to hyperuricemia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between gout and MASLD has been reported using the 2006\u0026ndash;2010 UK Biobank data [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Patients with gout had 2 to 4 times higher odds ratios for risk of MASLD then patient without gout (depending on number of covariant included in the multivariant analysis). The explanation for the increased association could be due to either a common risk factor such as consumption of high fructose corn syrup (HFCS) or through a shared causal pathway, such as insulin resistance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHFCS is a known risk factor for both gout and MASLD. Fructokinase breaks down fructose through an uninhibited depletion of Adenosine Triphosphate resulting in an abundant production of Adenosine Monophosphate and subsequently urate [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, fructokinase stimulates production of triglycerides that then deposit in the liver [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Separately, increased urate levels result in mitochondrial dysfunction, a decrease in the activity of aconitase, increase in citrate, which can then function as a substrate for triglyceride formation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond simply entangling either gout or hyperuricemia with MetS, these associations have important clinical findings as newer therapeutics have pluripotent benefits including the benefits of sodium glucose to transporter inhibitor (glycemic control, reduction of cardiovascular deaths and hospitalization for heart failure and slowing the progression of chronic kidney disease with additional benefits of reduced never with. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and reduced hepatic steatosis/fibrosis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GLP-1 receptor antagonists similarly have pluripotent benefits, including reduction in cardiovascular outcomes in patients with type II diabetes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], reduction in albuminuria [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]and similarly improvements in hepatic steatosis/fibrosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] (modulated through improved insulin sensitivity).\u003c/p\u003e \u003cp\u003eFinally, a recent small trial of 104 patients with MASLD were randomized to allopurinol (100 mg per day) versus placebo. Patients in the allopurinol arm showed improvement in SU (p-value not reported), alanine transaminase (p\u0026thinsp;=\u0026thinsp;0.004) and ultrasonic grading (p\u0026thinsp;=\u0026thinsp;0.001). While there was no significant improvement in the control group for these parameters; there was also no significant difference between the control group and the intervention [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCentral obesity as measured by MRI measured VAT is associated with hyperuricemia. This association is partially mediated through the insulin resistance pathway. Subjects with hyperuricemia have higher levels of HFD than subjects with normal SU levels. The association between HFD and hyperuricemia persist after adjusting for known potential confounders of HFD including BMI, age, race and inflammatory markers. Though a causal pathway cannot be evaluated in this cross-sectional analysis, given the introduction of newer pluripotent medications, greater attention to the inter-relationship between gout/SU and the other various metabolic pathways is warranted.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Roundness Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Medical Record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFOV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eField of view\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHEI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthy Eating Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHFD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatic Fat Deposition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHOMA-IR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomeostatic Model Assessment of Insulin Resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehsCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh sensitivity C-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMASLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic dysfunction-associated steatotic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMetS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ems\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emilliseconds\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003es.d.\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVisceral Abdominal Tissue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVery low-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWWI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeight-Adjusted Waist Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eUCLA IRB reviewed and approved the following protocol IRB-18-0181: Habitual diet and avocado trial (HAT). All subjects completed a Written informed consent process. The study adhered to the submitted protocol in compliance with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e All authors consent to publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe HAT clinical trial was funded by the Avocado Nutrition Center. Departmental funding was used for this post hoc analysis.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: (JF,ZL); Methodology: (JF,JP,ZL); Formal analysis: (JF); Data curation: (JF,JP,ZL); Writing - Original Draft: (JF); Writing - Review \u0026amp; Editing: (JP,ZL); Visualization: (JF,JP,ZL); Supervision: (ZL); Project administration: (ZL); Funding acquisition: (ZL)\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting this article are available from the Hass Avocado Board by request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKrishnan E. Interaction of inflammation, hyperuricemia, and the prevalence of hypertension among adults free of metabolic syndrome: NHANES 2009\u0026ndash;2010. 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Sci Rep. 2015;5:18495.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDespr\u0026eacute;s JP, et al. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol. 2008;28(6):1039\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong X, et al. Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur J Clin Nutr. 2013;67(12):1298\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayedi A, et al. Body mass index, abdominal adiposity, weight gain and risk of developing hypertension: a systematic review and dose-response meta-analysis of more than 2.3 million participants. Obes Rev. 2018;19(5):654\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026uuml;ttgers D, et al. Association of food consumption with total volumes of visceral and subcutaneous abdominal adipose tissue in a Northern German population. Br J Nutr. 2015;114(11):1929\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuk JL, et al. Visceral fat is an independent predictor of all-cause mortality in men. Obes (Silver Spring). 2006;14(2):336\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, et al. Abdominal Obesity and the Risk of All-Cause, Cardiovascular, and Cancer Mortality. Circulation. 2008;117(13):1658\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao T, et al. Relationship between gout, hyperuricemia, and obesity\u0026mdash;does central obesity play a significant role?\u0026mdash;a study based on the NHANES database. Diabetol Metab Syndr. 2024;16(1):24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang M, et al. Association between visceral adiposity index and hyperuricemia and gout among US adults: a cross-sectional analysis of NHANES 2007\u0026ndash;2018. Sci Rep. 2025;15(1):22196.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffmann J, et al. A new approach to quantify visceral fat via bioelectrical impedance analysis and ultrasound compared to MRI. Int J Obes. 2024;48(2):209\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlebna PL, et al. Evaluation of the PREVENT risk assessment tool and visceral adiposity: Insights from the UK Biobank. Progress in Cardiovascular Diseases; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTargher G, Valenti L, Byrne CD. Metabolic Dysfunction-Associated Steatotic Liver Disease. N Engl J Med. 2025;393(7):683\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou S, et al. The Correlation Between Serum Uric Acid and Metabolic Dysfunction-Associated Fatty Liver Disease: An Updated Systematic Review and Meta-Analysis. J Gen Intern Med. 2026;41(2):506\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, et al. Serum uric acid and the risk of MASLD in Americans: A cross-sectional study combined with Mendelian randomization and network toxicology analysis. Med (Baltim). 2026;105(1):e46841.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReboussin DM, et al. The design and rationale of a multi-center randomized clinical trial comparing one avocado per day to usual diet: The Habitual Diet and Avocado Trial (HAT). Contemp Clin Trials. 2021;110:106565.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ed'Assignies G, et al. Noninvasive quantitation of human liver steatosis using magnetic resonance and bioassay methods. Eur Radiol. 2009;19(8):2033\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley L, et al. The World Health Organization STEPwise Approach to Noncommunicable Disease Risk-Factor Surveillance: Methods, Challenges, and Opportunities. Am J Public Health. 2016;106(1):74\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrebs-Smith SM, et al. Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHays RD, Sherbourne CD, Mazel RM. The RAND 36-Item Health Survey 1.0. Health Econ. 1993;2(3):217\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYokose C, et al. Trends in Prevalence of Gout Among US Asian Adults, 2011\u0026ndash;2018. JAMA Netw Open. 2023;6(4):e239501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang X, et al. Prevalence of metabolic syndrome in the United States National Health and Nutrition Examination Survey 2011-18. Postgrad Med J. 2023;99(1175):985\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMechanick JI, et al. Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review. J Am Coll Cardiol. 2020;75(5):525\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, et al. Factors Associated with Bone Erosion in Patients with Gout: A Dual-Energy Gemstone Spectral Imaging Computed Tomography Study. Mod Rheumatol; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCormick N, et al. Assessing the Causal Relationships Between Insulin Resistance and Hyperuricemia and Gout Using Bidirectional Mendelian Randomization. Arthritis Rheumatol. 2021;73(11):2096\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, et al. Metabolic dysfunction-associated fatty liver disease and the risk of gout: a UK Biobank prospective cohort study. Eur J Med Res. 2025;30(1):1027.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto T, Moriwaki Y, Takahashi S. Effect of ethanol on metabolism of purine bases (hypoxanthine, xanthine, and uric acid). Clin Chim Acta. 2005;356(1):35\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson R, et al. Sugar, Uric Acid, and the Etiology of Diabetes and Obesity. Diabetes. 2013;62:3307\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYokose C et al. The clinical benefits of sodium-glucose cotransporter type 2 inhibitors in people with gout. Nat Rev Rheumatol, 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee YH, Lim S, Davies MJ. Cardiometabolic and renal benefits of sodium-glucose cotransporter 2 inhibitors. Nat Rev Endocrinol. 2025;21(12):783\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNauck MA et al. Glucagon-like receptor agonists and next-generation incretin-based medications: metabolic, cardiovascular, and renal benefits. Lancet, 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann JFE, et al. Effects of once-weekly subcutaneous semaglutide on kidney function and safety in patients with type 2 diabetes: a post-hoc analysis of the SUSTAIN 1\u0026ndash;7 randomised controlled trials. Lancet Diabetes Endocrinol. 2020;8(11):880\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMashayekhi M, et al. Weight Loss-Independent Effect of Liraglutide on Insulin Sensitivity in Individuals With Obesity and Prediabetes. Diabetes. 2024;73(1):38\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhani E et al. The role of allopurinol in metabolic dysfunction-associated steatotic liver disease: A randomized clinical trial. Arab J Gastroenterol, 2026.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables and Figures\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\u003eBaseline descriptors for all subjects and stratified by Serum Urate level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSerum Urate (\u0026micro;mol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;300\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e301\u0026ndash;360\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;360\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;223)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003cp\u003eacross SU\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean (s.d.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.4 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.6 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.9 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.3 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean (s.d.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.7 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.2 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.3 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.8 (5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal, n (%)\u003c/p\u003e \u003cp\u003eOverweight (BMI 25\u0026ndash;30)\u003c/p\u003e \u003cp\u003eObese (BMI 30\u0026ndash;35)\u003c/p\u003e \u003cp\u003eObese (BMI 35\u0026ndash;40)\u003c/p\u003e \u003cp\u003eMorbidly obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(9%)\u003c/p\u003e \u003cp\u003e33(35%)\u003c/p\u003e \u003cp\u003e27(29%)\u003c/p\u003e \u003cp\u003e15(16%)\u003c/p\u003e \u003cp\u003e10(11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(6%)\u003c/p\u003e \u003cp\u003e23(32%)\u003c/p\u003e \u003cp\u003e28(39%)\u003c/p\u003e \u003cp\u003e11(15%)\u003c/p\u003e \u003cp\u003e6(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2%)\u003c/p\u003e \u003cp\u003e8(15%)\u003c/p\u003e \u003cp\u003e22(40%)\u003c/p\u003e \u003cp\u003e14(25%)\u003c/p\u003e \u003cp\u003e10(18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(6%)\u003c/p\u003e \u003cp\u003e64(29%)\u003c/p\u003e \u003cp\u003e77(35%)\u003c/p\u003e \u003cp\u003e40(18%)\u003c/p\u003e \u003cp\u003e27(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsCRP (mg/L), median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0 (1.2, 7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (1.5, 7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6 (2.2, 9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2 (1.5, 8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm), mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg), mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP/DBP, mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118/75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122/77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121/79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121/77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21/0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy Eating Index\u003c/p\u003e \u003cp\u003e(Proportion Good)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA_IR median (quartiles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 (1.4, 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9 (1.6, 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (2.3, 6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9 (1.6, 4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\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\u003ea: Cross-sectional bivariate correlates between Visceral Adipose Tissue and Serum Urate.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSerum Urate (\u0026micro;mol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;300\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e301\u0026ndash;360\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;360\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003cp\u003eacross SU\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Adipose Tissue, L\u003c/p\u003e \u003cp\u003emedian (quartiles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3 (1.7, 3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (2.2, 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3 (2.2, 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6 (1.9, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest quartile, (0-1.9 L)\u003c/p\u003e \u003cp\u003e25th \u0026ndash; 75th quartiles, (1.9\u0026ndash;3.4 L)\u003c/p\u003e \u003cp\u003e75th-90th percentile (3.4\u0026ndash;4.9 L)\u003c/p\u003e \u003cp\u003e90\u0026thinsp;+\u0026thinsp;percentile (4.9\u0026ndash;8.6 L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3437%\u003c/p\u003e \u003cp\u003e3943%\u003c/p\u003e \u003cp\u003e1012%\u003c/p\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 16%\u003c/p\u003e \u003cp\u003e49 70%\u003c/p\u003e \u003cp\u003e6 8%\u003c/p\u003e \u003cp\u003e4 6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 15%\u003c/p\u003e \u003cp\u003e20 37%\u003c/p\u003e \u003cp\u003e15 28%\u003c/p\u003e \u003cp\u003e11 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 24%\u003c/p\u003e \u003cp\u003e108 48%\u003c/p\u003e \u003cp\u003e32 14%\u003c/p\u003e \u003cp\u003e22 10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: Cross-sectional bivariate correlates between Serum Urate and Hepatic Fat Deposition.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSerum Urate (\u0026micro;mol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;300\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e301\u0026ndash;360\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;360\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;223)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003cp\u003eacross SU\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic Fat Deposition, median (quartiles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.02, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.02, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.04, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 047 (0.023, 0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal, (\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e50th-75th percentile\u003c/p\u003e \u003cp\u003e75th-90th percentile\u003c/p\u003e \u003cp\u003e90\u0026thinsp;+\u0026thinsp;percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5760%\u003c/p\u003e \u003cp\u003e1718%\u003c/p\u003e \u003cp\u003e1415%\u003c/p\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4258%\u003c/p\u003e \u003cp\u003e1825%\u003c/p\u003e \u003cp\u003e57%\u003c/p\u003e \u003cp\u003e73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1629%\u003c/p\u003e \u003cp\u003e1527%\u003c/p\u003e \u003cp\u003e1425%\u003c/p\u003e \u003cp\u003e1119%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11652%\u003c/p\u003e \u003cp\u003e5022%\u003c/p\u003e \u003cp\u003e3315%\u003c/p\u003e \u003cp\u003e2511%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea: Multivariate model for Serum Urate as a function of central obesity (n\u0026thinsp;=\u0026thinsp;211, 3 missing covariate values)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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 \u003cp\u003eParameter\u003c/p\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003cp\u003eError\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\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\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e311.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentral obesity (VAT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale (vs. Male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack (vs. White)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsian (vs. White)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther race (vs. White)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLog hsCRP (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: VAT\u0026thinsp;=\u0026thinsp;visceral adipose tissue, BMI\u0026thinsp;=\u0026thinsp;body mass index, hsCRP\u0026thinsp;=\u0026thinsp;high sensitivity C-reactive protein\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: Multivariate adjusted odds ratio (OR) for elevated hepatic fatty deposition (\u0026gt;\u0026thinsp;5%) as a function of hyperuricemia (n\u0026thinsp;=\u0026thinsp;220, 3 missing covariate values)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% (LL, UL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\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\u003eHyperuricemia (vs. SU\u0026thinsp;\u0026lt;\u0026thinsp;360)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.41, 6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.01, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale (vs. Male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.41, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack (vs. White)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.05, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsian (vs. White)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.62, 5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther race (vs. White)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.63, 3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.99, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLog hsCRP (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.41, 3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: BMI\u0026thinsp;=\u0026thinsp;body mass index, hsCRP\u0026thinsp;=\u0026thinsp;high sensitivity C-reactive protein\u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-rheumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brhm","sideBox":"Learn more about [BMC Rheumatology](http://bmcrheumatol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/brhm/default.aspx","title":"BMC Rheumatology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Uric Acid, Metabolic Syndrome, Central obesity, Hepatic Fat Deposition","lastPublishedDoi":"10.21203/rs.3.rs-8970445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8970445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Hyperuricemia, while associated with, is not part of the metabolic syndrome. We sought to examine the association between central obesity as measured by visceral abdominal tissue (VAT) deposition and serum urate (SU). We further explored the association between SU and hepatic fat deposition (HFD).\u003c/p\u003e\n\u003cp\u003eMethods: Through a post-hoc analysis, using a single study site from the Habitual Diet vs. Avocado (HAT) multi-center randomized control trial, baseline serum, abdominal magnetic resonance imaging (MRI) and responses to questionnaires were available. Means, medians and proportions were used to describe variables. Anova, and Chi-square were used to describe bivariate associations between VAT and SU as well as SU and HFD. Generalized linear models (linear or logistic) were used to describe above associations while controlling for other potential metabolic confounders. Principle component analyses were used to evaluate the fit of SU with the other metabolic variables.\u003c/p\u003e\n\u003cp\u003eResults: The baseline cohort of 223 subjects were mostly female (80%) with mean age of 46 years and BMI of 33, (65% were obese), 25% had hyperuricemia (SU \u0026gt; 360 mmol/L). SU was strongly correlated with VAT deposits (r = 0.31, p\u0026lt;.0001) and HFD (r = 0.23, p =0. 0005).\u003c/p\u003e\n\u003cp\u003eSubjects with hyperuricemia (SU \u0026gt; 360 μmol/L) were more likely to be male, obese (BMI \u0026gt; 30), morbidly obese (BMI \u0026gt; 40), and have greater amounts of VAT deposits. More than two-thirds (71%) of subjects with hyperuricemia had elevated HFD (HFD \u0026gt; 5%) compared to 41% of subjects with normal SU (p \u0026lt; 0.0001).\u003c/p\u003e\n\u003cp\u003eAfter controlling for potential confounding covariates, including BMI, the association between VAT and SU was attenuated potentially through mediating factors associated with covariates. Hyperuricemia remained strongly associated with HFD \u0026gt; 5%, (Odds Ratio = 3.0, p = 0.005) after adjusting for BMI and other confounders.\u003c/p\u003e\n\u003cp\u003eConclusions: For this cohort of subjects with known central adiposity, higher SU levels were associated with greater deposits of VAT strengthening the association between hyperuricemia and metabolic syndrome. Hyperuricemia was also associated with higher proportion of HFD. As a limitation of this post-hoc cross-sectional design, causation (or directionality) cannot be assumed.\u003c/p\u003e\n\u003cp\u003eThe parent HAT clinical trial was registered under the approved clinical trial protocol, NCT03528031. Original submission, 04, May 2018. Final version, 17, March, 2023.\u003c/p\u003e\n\u003cp\u003eTrial registration:\u003c/p\u003e\n\u003cp\u003eThe parent habitual diet avocado trial was registered with clinical trials.gov with the following protocol, NCT03528031\u003c/p\u003e","manuscriptTitle":"The association between visceral adipose tissue, hyperuricemia, and hepatic fat deposition: a post-hoc analysis from the Habitual Diet vs. Avocado randomized control trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 10:27:16","doi":"10.21203/rs.3.rs-8970445/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-01T19:10:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T18:07:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T15:29:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333339543809575240790426684625617584185","date":"2026-03-23T14:51:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262177536681149522247591484904705451021","date":"2026-03-23T12:14:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282639673936330145332825786941509563411","date":"2026-03-21T03:12:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318112807594319751239754141859009029545","date":"2026-03-20T23:13:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T23:50:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52281444688641939615904474219750639230","date":"2026-03-18T23:37:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T15:15:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-13T12:06:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-04T12:40:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T00:27:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Rheumatology","date":"2026-03-03T23:12:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-rheumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brhm","sideBox":"Learn more about [BMC Rheumatology](http://bmcrheumatol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/brhm/default.aspx","title":"BMC Rheumatology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4af65f7-a45f-4c1b-8a32-20ec6776f42d","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T19:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 10:27:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8970445","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8970445","identity":"rs-8970445","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}