Association between skeletal muscle mass to visceral fat area ratio and insulin resistance in type 2 diabetes

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We investigated the Skeletal muscle mass to visceral fat area ratio (SVR), neutrophil/lymphocyte ratio (NLR), and insulin resistance (IR) in 201 patients with T2DMwho treated in the outpatient department and ward of the Department of Endocrinology of the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine between June 2022 and March 2023. Appendicular skeletal muscle mass (ASM), total body fat (TBF), visceral fat area (VFA), and basal metabolic volume were measured using multifrequency bioimpedance analysis method. The percentage of body fat to body mass (TBF%), appendicular skeletal muscle mass index (ASMI), and SVR were calculated. Results Patients were divided equally into three groups (Q1–Q3) according to SVR levels. Compared with the Q3 group, in both Q1 and Q2 groups, waist-hip ratio, neutrophils, NLR, fasting blood glucose, fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides, total cholesterol, free fatty acid, TBF, TBF%, and VFA were all increased ( P <0.005), whereas lymphocytes, insulin sensitivity index (ISI), ASM, ASMI, basal metabolic rate, and SVR were all decreased (P<0.005). Conclusion There is a correlation between SVR and IR in T2DM patients, suggesting that SVR has certain clinical value in the early warning of IR in T2DM. type 2 diabetes skeletal muscle mass to visceral fat area ratio inflammatory factors insulin resistance 1 Introduction The prevalence of type 2 diabetes mellitus (T2DM) and obesity has rapidly increased worldwide [ 1 ]. T2DM is characterized by persistent low-grade inflammation and insulin resistance (IR) [ 2 ]. Obesity, especially visceral obesity (VO), is an important cause of chronic low-grade inflammation, which can aggravate IR and increase the risk of metabolic disorders [ 3 ]. Currently, conventional indicators such as body mass index (BMI) and waist circumference are mostly used in clinical practice to determine obesity; however, these methods have limitations [ 4 ]. For example, waist circumference can visually reflect the level of abdominal subcutaneous fat but not the level of intra-abdominal fat. In addition, BMI is influenced by muscle mass and cannot distinguish between muscle- and fat-related body mass. An important feature of body composition in patients with obesity is the change in body fat and muscle content; an increase in fat mass and decrease in muscle mass, occurring simultaneously, is often observed. Most adipokines are pro-inflammatory and involved in the development of obesity-related metabolic dysfunction. Skeletal muscle, the largest insulin-sensitive tissue, is the main effector organ for insulin-mediated glucose metabolism, and its loss of mass can accelerate insulin resistance. Increased expression of inflammatory cytokines is associated with reduced skeletal muscle mass and visceral fat accumulation [ 5 , 6 ]. Furthermore, reduced skeletal muscle mass to visceral fat area ratio (SVR) reflects a chronic low-grade inflammatory state and can be used as an independent predictor of T2DM [ 7 ]. However, there are few clinical studies on the relationship between SVR and IR. Therefore, this study used a cross-sectional study to observe whether there is a correlation between the SVR, inflammatory factors, and IR by measuring body composition, IR, and inflammatory factors in patients with T2DM. The primary objective of this study was to investigate the relationship between SVR, NLR and IR in T2DM. We pursued two goals. First, we analyzed the correlation between NLR and Appendicular skeletal muscle mass (ASM), appendicular skeletal muscle mass index (ASMI), visceral fat area (VFA), SVR, homeostasis model assessment of insulin resistance (HOMA-IR) and insulin sensitivity index (ISI) to determine whether IR is related to inflammation. Secondly, the correlation between SVR and IR is analyzed to determine whether IR is correlated with SVR. Our objective was to determine whether SVR is associated with IR in T2DM patients. 2 Methods 2.1 Setting and Participants The study enrolled patients with T2DM who visited the Department of Endocrinology of the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine from June 2022 to March 2023 as study participants, all of whom met the medical diagnosis and treatment criteria for diabetes mellitus issued by the American Diabetes Association in 2022 [ 8 ]. The exclusion criteria were as follows: (1) type Ι diabetes mellitus; (2) presence of infections or other systemic diseases such as tumors, severe liver and kidney diseases, and systemic immune diseases; or (3) incomplete data. This study was approved by the Ethics Committee of the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, and all participants signed an informed consent form. 2.2 Variables and Bias Physical examination was performed according to standard operating methods for nutritional evaluation [ 9 ]. The height and weight of the participants were measured after taking off shoes, hats, and outer clothing. Height was measured with the patient in an upright position; feet together; shoulders, head, and heels close to the scale; eyes looking straight ahead; and a right-angle plate close to the top of the head. The measurement standard was accurate to 0.1 cm, and the weight was measured with a measurement standard accurate to 0.1 Kg. The participants were dressed in light clothing and pants, with their bodies upright, abdomens relaxed, and feet placed 40 cm apart. The waist hip circumference measurement positioned at the circumference of the horizontal circumference around the umbilicus and read when the participant exhaled; the hip circumference was measured at the maximum extension of the hips and read after wrapping around the hip horizontally with a leather ruler. The measurement standard was accurate to 0.1 cm. BMI = weight (kg)/height 2 (m 2 ). The waist-hip ratio (WHR) was calculated as waist circumference (cm)/hip circumference (cm), and the results were accurate to two decimal places. The human body composition analyzer(GAIA KIKO,SELVAS Healthcare. Inc.) was used for measurements using a multifrequency bioimpedance analysis method. To reduce measurement error and variability in the measurement conditions, an independent examination room was set up, and the measurement was performed after training by professional personnel. The participants were instructed to void their bladder and bowel, remove their socks, and wipe their hands clean with 75% alcohol gauze before coming in contact with the electrode surface of the analyzer; the hands and feet were naturally separated. The patient's personal information was entered on the body composition analyzer computer and the measurements were recorded. The results included body weight; BMI; waist-to-hip ratio; fat-free mass; ASM; protein and mineral content; total body, intracellular, and extracellular water; TBF;VFA; and basal metabolic rate. TBF%, ASMI, and SVR were calculated using the following formulae: TBF%=TBF/body weight; ASMI (kg/m 2 ) = ASM(kg)/height 2 (m 2 ); SVR = ASM/VFA. 2.3 Clinical biochemical measurement and bias All participants kept a light diet for three days before blood collection, avoiding spicy and stimulating greasy food, such as fatty meat, fried food, animal offal, etc., and were required to refrain from alcohol and maintain a fasting stomach for 10 hours before blood collection.2 mL of elbow venous blood in 2 tubes was collected early in the morning on an empty stomach in the morning. One tube was stored in an EDTA anticoagulation tube for routine blood testing, and the other tube was stored in a serum collection vessel for blood biochemistry testing. The serum was divided and stored at -80°C in a refrigerator, and batches were regularly prepared for testing. Neutrophils (Neu) and lymphocytes (Lym) were detected using a fully automated modular blood fluid analyzer (XN-10 [B4]), and the neutrophil to lymphocyte ratio (NLR) was calculated by flow cytometry and DNA/RNA fluorescence staining ,and the reagents purchased from Sysmex Biotechnology Co. The fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), and lipoprotein were measured using a fully automated biochemical analyzer (Beckman AU5800). The FPG assay was performed using the glucokinase method, and the TC, TG, high-density lipoprotein (HDL), and LDL assays were performed using the enzyme method, and the reagents were purchased from Beckman Co. Fasting insulin (FINS) levels were determined using a fully automated electrochemiluminescence immunoassay system (Roche). All tests were quality-controlled prior to testing, and in the case of loss of control, the specific reasons were analyzed and dealt with accordingly until the specimens were again under control. Steady-state models were used to assess HOMA-IR and ISI. HOMA-IR was calculated using the equation: HOMA-IR = FPG×FINS/22.5, ISI was calculated using the equation: ISI = Ln 1/(FPG×FINS). 2.4 Diagnostic criteria (1) According to the recommendations of the BMI classification for Chinese adults published by the Chinese Working Group on Obesity, BMI < 18.5 kg/m 2 is considered as low body mass, individuals with BMIs between 24 and 28 kg/m 2 as overweight, and those with BMIs ≥ 28 kg/m 2 as obese [ 10 ]. (2) Based on the study by Deurenberg [ 11 ], those with TBF% > 25% among men and > 30% among women were considered to have a high body fat. (3) According to the appropriate waist circumference cut points published by the Chinese Obesity Working Group, men with a waist circumference ≥ 85 cm and/or a WHR ≥ 0.85 and women with a waist circumference ≥ 80 cm and/or a WHR ≥ 0.80 are considered to have excessive accumulation of abdominal fat. (3) According to the Chinese Guidelines for the Prevention and Treatment of Adult Dyslipidemia formulated by the Joint Committee in 2007, the levels of lipid stratification were determined by TC ≥ 6.22 mmol/L, TG ≥ 2.26 mmol/L and LDL ≥ 4.14 mmol/L. Any of the HDL ≤ 1.04 mmol/L is considered as dyslipidemia. (4) According to the Asian Working Group for Sarcopenia criteria, if ASMI < 7.0 kg/m 2 for men and 5.7 kg/m 2 for women, loss of skeletal muscle mass can be judged [ 12 ]. 2.5 Study size This study is a cross-sectional study design, the purpose of this study was to observe the correlation between SVR, NLR and IR in T2DM patients. Since SVR is measurement data, PASS 15 software was used to calculate the sample size by Means-One mean-confidence intervals for one mean. A two-sided test alpha of 0.05, i.e. confidence level (1-Alpha) = 0.95, tolerance error δ of 0.04, predicted standard deviation σ of 0.2, calculated N = 97, and at least 122 subjects were required considering the loss of follow-up rate of 20%. 2.6 Quantitative variables and Statistical methods Stratified sampling was adopted in this study, with 65 years old as the age node for stratification, in order to avoid the influence of senile sarcopenia on SVR results. After stratification, a simple random sampling method was adopted to ensure that every object in the population had an equal opportunity to be sampled. If the subject is missing any data from anthropometric or biochemical tests, it will be marked as unqualified and will be removed from the enrollment queue. Continuous data were expressed as mean ± standard deviation ( \(\:\stackrel{-}{x}\) ±s), and count data were expressed as percentages (%). One-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal–Wallis one-way ANOVA was used for non-normal data. Correlations between the NLR and ASMI, VFA, SVR, HOMA-IR, and ISI were analyzed using Pearson’s (conforming to normality) or Spearman’s (not conforming to normality) correlations. Multiple linear regression analysis was used to analyze the relationship between SVR and IR. All statistical analyses were performed using SPSS 23.0 software(SPSS Inc., Chicago, IL,USA), and P < 0.05 was considered a statistically significant difference. 3 Results 3.1 Participants and Descriptive data A total of 256 T2DM patients were included in this study, 10 of whom were excluded from the cohort due to incomplete clinical biochemical indicators. A total of 246 T2DM patients were included, including 150 males and 96 females, average age 50.370 ± 12.279. Because the objective was to observe the correlation between SVR and insulin resistance, and the SVR was continuous data, three groups were divided according to the SVR quintile. In the male population, Q1: 0.524 kg/cm 2 ; and in the female population, Q1: 0.624 kg/cm 2 . There were 82 patients in each group, comprising 50 males and 32 women. The results showed that in male patients with T2DM, the WHR, Neu, NLR, FPG, FINS, HOMA-IR, TG, TC, and free fatty acid levels were significantly higher in the Q1 and Q2 groups than in Q3 group ( P < 0.05), whereas ISI and Lym were significantly lower ( P 0.05) in the male patients with T2DM among the three groups, as shown in Table 1 . Table 1 Descriptive and clinical characteristics of male study participants ( \(\:\stackrel{-}{x}\) ±s) index Q1 group(n = 82) Q2group(n = 82) Q3 group(n = 82) F -value P- value Age 51.290 ± 11.370 48.710 ± 14.876 42.380 ± 13.376 6.756 0.002 Disease duration 6.487 ± 7.687 5.392 ± 7.370 3.784 ± 5.779 1.528 0.208 Body weight(kg) 80. 824 ± 16.295 77.395 ± 15.401 78.081 ± 17.927 0.503 0.606 BMI(kg/m 2 ) 28.498 ± 5.076 26.999 ± 5.073 26.239 ± 4.996 2.176 0.118 WHR 0.979 ± 0.616 0.926 ± 0.022 0.855 ± 0.727 51.237 < 0.001 Neu 60.910 ± 8.037 58.833 ± 9.952 55.995 ± 8.053 3.356 0.038 Lym 28.943 ± 8.350 30.748 ± 8.512 33.855 ± 7.627 3.882 0.023 NLR 2.391 ± 1.114 2.184 ± 1.117 1.799 ± 0.692 3.833 0.024 FPG(mmol/L) 11.632 ± 4.135 9.681 ± 3.300 8.737 ± 3.005 7.418 0.001 FINS(mU/L) 123.624 ± 95.930 79.472 ± 82.806 62.845 ± 57.107 6.435 0.002 HOMA-IR 11.131 ± 9.402 6.477 ± 6.538 4.943 ± 3.961 4.805 < 0.001 ISI -5.226 ± 0.797 -4.685 ± 0.744 -4.350 ± 0.952 4.679 < 0.001 TG(mmol/L) 2.707 ± 1.747 2.267 ± 1.456 1.789 ± 0.988 4.329 0.015 TC(mmol/L) 5.593 ± 0.823 4.466 ± 0.847 4.526 ± 1.025 20.698 < 0.001 HDL(mmol/L) 1.041 ± 0.022 1.098 ± 0.378 1.063 ± 0.232 0.418 0.659 LDL(mmol/L) 2.911 ± 0.962 2.930 ± 1.045 2.967 ± 1.040 0.033 0.968 FFA(mmol/L) 0.985 ± 1.570 0.515 ± 0.186 0.488 ± 0.177 3.884 0.023 One-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal–Wallis one-way ANOVA was used for non-normal data. BMI, body mass index; WHR, waist-hip ratio; Neu, neutrophils; Lym, lymphocytes; NLR, neutrophil/lymphocyte ratio; FPG, fasting plasma glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment of insulin resistance; ISI, insulin sensitivity index; TG, triglycerides; TC, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; FFA, free fatty acid. In female patients with T2DM in both Q1 and Q2 groups, compared with those in the Q3 group, weight, BMI, WHR, Neu, NLR, FPG, FINS, HOMA-IR, TG, TC, HDL, and FFA were significantly higher ( P < 0.05) and ISI and Lym were significantly lower ( P 0.05), as shown in Table 2 . Table 2 Descriptive and clinical characteristics of female study participants ( \(\:\stackrel{-}{x}\) ±s) index Q1 group(n = 82) Q2 group(n = 82) Q3 group(n = 82) F -value P- value Age 58.000 ± 14.835 56.720 ± 16.079 47.800 ± 16.666 3.061 0.053 Disease duration 5.632 ± 5.770 4.915 ± 5.648 8.994 ± 7.600 2.892 0.062 Body weight(kg) 73.796 ± 19.311 70.568 ± 17.501 54.504 ± 10.652 10.103 < 0.001 BMI(kg/m 2 ) 30.388 ± 5.733 27.932 ± 5.423 22.550 ± 4.348 14.848 < 0.001 WHR 0.905 ± 0.041 0.855 ± 0.032 0.775 ± 0.047 65.382 < 0.001 Neu 65.662 ± 7.831 57.164 ± 7.430 54.144 ± 7.425 15.584 < 0.001 Lym 25.837 ± 5.704 32.647 ± 9.055 36.492 ± 7.277 13.039 < 0.001 NLR 2.706 ± 0.869 2.083 ± 1.351 1.568 ± 0.496 8.610 < 0.001 FPG(mmol/L) 10.262 ± 4.100 9.190 ± 2.913 6.714 ± 2.420 7.973 0.001 FINS(mU/L) 143.854 ± 98.378 98.804 ± 108.715 43.611 ± 36.317 8.286 0.001 HOMA-IR 9.038 ± 7.488 6.100 ± 8.620 2.067 ± 2.066 6.822 0.002 ISI -5.013 ± 0.790 -4.431 ± 1.067 -3.233 ± 1.445 16.046 < 0.001 TG(mmol/L) 2.375 ± 1.054 2.132 ± 1.509 1.346 ± 0.726 5.534 0.006 TC(mmol/L) 5.304 ± 1.246 4.865 ± 0.929 4.327 ± 0.897 5.574 0.006 HDL(mmol/L) 1.164 ± 0.256 1.196 ± 0.284 1.389 ± 0.304 4.648 0.013 LDL(mmol/L) 2.895 ± 0.780 3.015 ± 0.862 3.008 ± 1.184 0.123 0.884 FFA(mmol/L) 0.688 ± 0.193 0.585 ± 0.225 0.395 ± 0.157 14.664 < 0.001 One-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal–Wallis one-way ANOVA was used for non-normal data.BMI, body mass index; WHR, waist-hip ratio; Neu, neutrophils; Lym, lymphocytes; NLR, neutrophil/lymphocyte ratio; FPG, fasting plasma glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment of insulin resistance; ISI, insulin sensitivity index; TG, triglycerides; TC, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; FFA, free fatty acid. 3.2 Comparison of body composition indices in each group by SVR tristimulus grouping In male patients with T2DM in the Q1 and Q2 groups, compared with those in the Q3 group, TBF, TBF%, and VFA were significantly higher ( P < 0.05); SVI, ASM, ASMI, basal metabolic volume, and total cellular water were significantly lower ( P 0.05), as shown in Table 3 . Table 3. Comparison of body composition indices of male participants in each SVR tristimulus group (±s) index Q1 group(n=82) Q2 group(n=82) Q3 group(n=82) F -value P- value TBF(kg) 56.874±8.555 58.431±7.946 61.500±11.020 8.953 <0.001 TBF% 28.236±4.909 24.876±5.006 19.210±7.094 26.359 <0.001 FFM(kg) 23.631±8.254 20.945±8.390 15.895±8.864 2.709 0.071 ASM(kg) 50.162±7.167 54.748±6.794 56.276±11.790 5.394 0.006 Protein content(kg) 11.433±1.529 11.848±1.418 12.855±1.924 8.364 <0.001 Mineral content(kg) 4.537±0.967 4.507±0.855 4.517±0.988 0.011 0.989 Total body water(kg) 41.310±6.177 41.362±7.491 44.762±7.459 3.289 0.041 Intracellular water(kg) 27.217±4.111 27.683±3.794 29.248±4.834 2.608 0.078 Extracellular water(kg) 14.093±2.116 14.392±1.991 15.514±2.715 4.473 0.013 Basal metabolic rate (kcal) 1356.191±190.777 1408.143±202.801 1512.100±226.746 6.159 0.003 VFA(cm 2 ) 163.379±44.450 118.238±15.942 82.457±26.424 70.758 <0.001 ASMI 17.676±2.285 19.007±3.645 19.182±2.286 3.605 0.030 SVR 0.320±0.591 0.466±0.513 0.761±0.324 57.161 <0.001 One-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal–Wallis one-way ANOVA was used for non-normal data.TBF, total body fat; TBF%, percentage of body fat to body mass; FFM, fat-free mass; ASM, appendicular skeletal muscle mass; VFA, visceral fat area; ASMI, appendicular skeletal muscle mass index; SVR, skeletal muscle mass to visceral fat area ratio. In women with T2DM in the Q1 and Q2 groups, compared with those in the Q3 group, TBF, TBF%, and VFA were significantly higher ( P <0.05); SVR, ASM, ASMI, basal metabolic volume, and total cellular water were significantly lower ( P 0.05), as shown in Table 4. Table 4. Comparison of body composition indices in female participants in each SVR tristimulus group (±s) index Q1 group (n=82) Q2 group(n=82) Q3 group (n=82) F -value P- value TBF(kg) 28.060±10.617 23.676±8.949 14.556±5.272 16.134 <0.001 TBF% 37.360±4.311 32.756±4.305 25.188±6.201 37.485 <0.001 FFM(kg) 45.732±9.301 46.892±9.277 41.048±5.239 3.589 0.033 ASM(kg) 37.084±3.292 41.776±6.381 43.464±9.998 5.409 0.006 Protein content(kg) 8.664±1.598 9.128±1.683 8.292±0.928 2.106 0.129 Mineral content(kg) 4.140±1.073 4.008±0.980 3.204±0.562 7.934 0.001 Total body water(kg) 29.548±3.778 32.922±6.705 33.752±6.671 3.585 0.033 Intracellular water(kg) 21.520±4.493 22.088±4.441 19.296±2.475 3.547 0.034 Extracellular water(kg) 11.412±2.237 11.668±2.263 10.252±1.372 3.556 0.034 Basal metabolic rate (kcal) 1064.560±95.516 1106.240±142.627 1212.000±169.735 7.435 0.001 VFA(cm 2 ) 131.720±40.335 87.880±23.682 43.080±14.480 61.452 <0.001 ASMI 15.479±1.864 16.654±2.027 17.692±4.458 3.357 0.040 SVR 0.330±0.660 0.501±0.618 0.962±0.275 95.648 <0.001 One-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal–Wallis one-way ANOVA was used for non-normal data.TBF, total body fat; TBF%, percentage of body fat to body mass; FFM, fat-free mass; ASM, appendicular skeletal muscle mass; VFA, visceral fat area; ASMI, appendicular skeletal muscle mass index; SVR, skeletal muscle mass to visceral fat area ratio. 3.3 Correlation analysis of NLR with body composition indices and IR The results of the correlation analysis of NLR with body composition indices and IR showed that, among body composition indices, TBF, TBF%, VFA, ASM, ASMI, SVR were significantly correlated with NLR, with ASM, ASMI, SVR being significantly negatively correlated with NLR (all P < 0.005); HOMA-IR and NLR were significantly positively correlated, and ISI and NLR were significantly negatively correlated (all P < 0.005; as shown in Table 5 ). Table 5 Correlation analysis of NLR with body composition indices and IR index r -value P -value TBF -0.270 0.016 TBF% 0.261 < 0.001 VFA 0.141 0.046 ASM -0.193 0.006 ASMI -0.213 0.002 SVR -0.295 < 0.001 HOMA-IR 0.385 < 0.001 ISI -0.239 0.001 Using Pearson’s (conforming to normality) or Spearman’s (not conforming to normality) correlations. TBF, total body fat; TBF%, percentage of body fat to body mass; VFA, visceral fat area; ASMI, appendicular skeletal muscle mass index; SVR, skeletal muscle mass to visceral fat area ratio; HOMA-IR, homeostasis model assessment of insulin resistance; ISI, insulin sensitivity index. 3.4 Multiple linear regression analysis of body composition indices and IR The results using multiple linear regression analysis revealed significant results for several variables ( F = 25.584, P < 0.001). VFA ( β =-0.203, P = 0.001) and SVR ( β =-0.242, P = 0.002) significantly negatively predicted and ASMI ( β = 0.323, P = 0.323) significantly positively predicted HOMA-IR. Together, these variables explained 34.3% of the variance in HOMA-IR ,as shown in Table 6 . Table 6 Correlation analysis of body composition indices and HOMA-IR index B -value β- value T- value P- value F- value Adjust R 2 ASMI 1.665 0.323 5.161 < 0.001 25.584 0.343 VFA -0.415 -0.203 -3.379 0.001 TBF% 0.611 0.114 1.538 0.126 SVR -33.045 -0.242 -3.169 0.002 Using multiple linear regression analysis. ASMI, appendicular skeletal muscle mass index; VFA, visceral fat area; VFA, visceral fat area; SVR, skeletal muscle mass to visceral fat area ratio. The results using the multiple linear regression analysis revealed that revealed significant results for several variable ( F = 46.105, P < 0.001). VFA ( β =-0.352, P < 0.001) and TBF% ( β =-0.022, P = 0.001) significantly negatively predicted ISI, and SVR ( β = 0.683, P < 0.001) significantly positively predicted ISI. Together, these variables explained 48.5% of the variance in ISI, as shown in Table 7 . Table 7 Correlation analysis of body composition indices and ISI index B -value β- value T- value P- value F- value Adjust R 2 ASMI 0.036 0.097 1.716 0.088 46.105 0.485 VFA -0.053 -0.352 -6.620 < 0.001 TBF% -0.088 0.022 -3.384 0.001 SVR 3.907 0.683 5.725 < 0.001 Using multiple linear regression analysis. ASMI, appendicular skeletal muscle mass index; VFA, visceral fat area; VFA, visceral fat area; SVR, skeletal muscle mass to visceral fat area ratio; ; ISI, insulin sensitivity index. 4 Discussion 4.1 Key results and Generalisability From 1980 to 2013, globally, the prevalence of overweight or obesity increased by 27.5% in adults, and the prevalence of overweight and obesity in adults in China exceeded 1/3 of the total population,, among which the rates of overweight and obesity among males and females were 71% and 22.78%, respectively, and 5.02% and 5.51%, respectively [ 13 ]. Of the 246 patients with T2DM enrolled in this study, 102 (68.00%) were overweight males and 48 (32.00%) were men with obesity, whereas 66 (68.75%) women were overweight and 30 (31.25%) women were obese. Body fat distribution and adipose tissue dysfunction are key factors in the development of IR associated with obesity and can be a good predictor of an individual's risk of metabolic disease [ 14 ].Among the 246 patients with T2DM enrolled in this study, there were 129 (86.51%) men with a WHR ≥ 0.85 and 56 (37.33%) with a TBF% > 25% among the total of 150 male patients; among the total of 96 female patients with T2DM,78 (81.25%) had a WHR ≥ 0.80 and 58 (60.42%) had a TBF% > 30%. Although both BMI and waist circumference are widely used clinically, they are still considered inaccurate methods for assessing obesity [ 15 ]. A comparison of general information on men with T2DM grouped according to SVR showed no significant difference in weight or BMI, suggesting that the site of fat accumulation seems to be more important than obesity itself. The degree of IR directly correlates with the degree of inflammation [ 16 ]. The present results showed a correlation between NLR and both HOMA-IR and ISI. NLR represents a non-specific inflammatory pathway, and the secretion of pro-inflammatory factors such as nuclear factor-kB and Interleukin-6 from visceral adipose tissue leads to an increase in systemic neutrophils [ 17 ] and promotes the release of reactive oxygen species by neutrophil activation, which aggravates IR [ 18 ]. Comparison of body composition across the SVR groups in this study showed that compared to patients in the Q1 group, patients (both male and female) in the Q2 and Q3 groups had decreased neutrophils, whereas their VFA, TBF, and TBF% were increased. This is consistent with previous findings from the Japanese Obesity Society, which concluded that visceral fat accumulation is associated with a higher risk of metabolic disease, with a combined risk of health impairment of approximately 1.5 times more when the visceral fat area reaches 100 cm 2 [ 19 ]. Lym represents an adaptive immune response, and inadequate IL-2 receptor expression during IR exacerbates the decrease in Lym [ 20 ]; comparison of body composition by SVR trichotomies showed that Lym was elevated in both men and women in the Q2 and Q3 groups compared with those in the Q1 group. Furthermore, NLR is a common inflammatory factor in measured in routine blood tests, and the present results showed a positive correlation between NLR and HOMA-IR [ 21 , 22 ], suggesting that NLR could be used as a predictor of IR. Patients with obesity experience changes in the content and ratio of their body fat and muscle; fat mass is increased whereas muscle mass often decreases simultaneously. In this study, dual bioimpedance analyzers were used to measure VFA, and magnetic resonance imaging and computed tomography (CT) are currently recognized as the most accurate and objective imaging techniques for assessing visceral fat distribution. Epidemiological investigation showed a good correlation between VFA measured using a dual bioimpedance analyzer and abdominal CT ( r = 0.821, P < 0.001) [ 23 ]; this method has the advantages of high safety, low cost, simple operation, and no radiation. The comparison of body composition by SVR grouping in this study showed that VFA, TBF, and TBF% were elevated along with a decrease in ASM and ASMI in men and women in the Q1 group compared with those in the Q3 group. When there is excess fat in the body, fatty acids accumulate in skeletal muscle and result in intramuscular fat (IMAT). The metabolically active component of IMAT can, like visceral adipose tissue, secrete inflammatory cytokines that cause an inflammatory response in muscle and the body as a whole, causing muscle atrophy and attenuating muscle strength production. Vella et al [ 24 ] found that as the area and density of IMAT increased, inflammatory markers increased linearly ( P < 0.01), and an increase in 1 standard deviation (SD) in IMAT area in the abdomen increased IL-6, leptin and C-reactive protein by 21%, 36% and 20%, respectively, and decreased lipocalin by 19% ( P < 0.001). Reduced skeletal muscle mass and visceral fat accumulation are associated with increased expression of inflammatory cytokines [ 25 , 26 ]. Therefore, reduced SVR levels reflect a chronic low-grade inflammatory state, and the results of the current study showed a negative correlation between SVR and NLR (P < 0.001). This suggests that SVR, a new measure of obesity, is closely associated with an increased risk of IR and can be used as a predictor of T2DM and as an IR indicator[ 7 ]. 4.2 Limitations and Interpretation In summary, this study mainly discusses the correlation between SVR and IR in T2DM patients, which is helpful for clinicians to identify high-risk groups of obesity and provide a new entry point for the prevention and treatment of T2DM. However, there are some limitations and shortcomings in this study. First, as this was a cross-sectional study, a causal relationship between SVR and IR could not be inferred and can only be proven in subsequent cohort studies. Second, muscle strength was not taken as a confounding factor in the measurement of muscle content by multi-frequency bio-resistance method, and the ratio of muscle content and fat in the body composition was mainly intended to be reflected in this article. Even so, if muscle strength such as arm strength could be taken into account in subsequent studies, the data results would be more accurate. Among the evaluation methods, insulin resistance was evaluated using HOMA-IR instead of gold standard test, and skeletal muscle mass and dirty fat area were evaluated using multi-frequency bioimpedance method instead of other potentially more accurate methods such as dual-energy X-ray (DEXA), mainly considering the scalability and portability of clinical application. Therefore, although it is not a gold standard evaluation method, it has certain guiding significance for guiding clinical application. Third, this was a single-center study influenced by multiple factors, and the sample size was small. We will further confirm the research results by increasing the sample size, involving multiple centers, and improving follow-up. Abbreviations T2DM type 2 diabetes mellitus IR insulin resistance VO visceral obesity BMI body mass index SVR skeletal muscle mass to visceral fat area ratio WHR waist-hip ratio ASM appendicular skeletal muscle mass TBF total body fat VFA visceral fat area TBF% the percentage of body fat to body mass ASMI appendicular skeletal muscle mass index NLR neutrophil to lymphocyte ratio FPG fasting plasma glucose TG triglycerides TC total cholesterol LDL low-density lipoprotein HDL high-density lipoprotein FINS Fasting insulin ISI insulin sensitivity index ANOVA One-way analysis of variance CT computed tomography IMAT intramuscular fat SD standard deviation Declarations Ethics approval: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine (ethical approval number: 2022-7th-HIRB-004;date of approval January 29,2022). Conflicts of Interest information: The authors declare no conflict of interest. Informed Consent Statement: Not applicable. Funding: This research was funded by the Pudong New Area Health Commission clinical characteristic discipline construction project(PWYts2021-13),and Shanghai University of Traditional Chinese Medicine Curriculum Construction Project(SHUTCM2021KC021). Author Contribution Conceptualization S.T., H,W., and X.L.; methodology S.T. H,W. and J,L.; investigation M.L. and Y.L.; software H.W., M,L. and R.C.; data curation S.T. ,Y.L. and R.C.; Writing-original draft Writing-original draft S.T., H,W. and M.L.; writing-review and editing G.L. and X.L.; funding acquisition S.T. and X.L; supervision G.L. and X.L.. All authors have read and agreed to the published version of the manuscript. Acknowledgement We would like to thank Editage (www.editage.cn) for English language editing. 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Lancet ;2017,390(10113):2627-2642.https://doi:10.1016/ S0140-6736(17)32129-3. Gijs HG. The Metabolic Phenotype in Obesity: Fat Mass, Body Fat Distribution, and Adipose Tissue Function[J]. Obes Facts ;2017,10(3):207-215.https://doi.10.1159/000471488.Epub 2017 Jun 1. Engin A. The Definition and prevalence of obesity and metabolic syndrome[J]. Adv Exp Med Biol ;2017,960:1-17. https://doi.org/10.1007/978-3-319-48382-5_1. Ravindran J, Ravindran R, Dhanasekaran S. Emerging Role of Adipocytokines in Type 2 Diabetes as Mediators of Insulin Resistance and Cardiovascular Disease[J]. Canadian journal of diabetes ;2018,42(4):446-456.e1. https:// 10.1016/j.jcjd.2017.10.040. Mutsert RD, Gast K, Widya R, et al. Associations of Abdominal Subcutaneous and Visceral Fat with Insulin Resistance and Secretion Differ Between Men and Women: The Netherlands Epidemiology of Obesity Study[J]. Metab Syndr Relat Disord ;2018,16(1):54-63.https://doi:10.1089 /met.2017.0128. Epub 2018 Jan 17. Uzun S, Ozari M, Gursu M, et al. Changes in the inflammatory markers with advancing stages of diabetic nephropathy and the role of pentraxin-3[J]. Renal Failure ;2016,38(8):1193-1198. https:// 10.1080/0886022X.2016.1209031. Byrne CD, Targher G.Ectopic fat, insulin resistance, and nonalcoholic fatty liver disease: implications for cardiovascular disease[J]. Arterioscler Thromb Vasc Biol ;2014,34(6):1155-1161. https://doi.org/10.1161/atvbaha.114.303034. Lorenzo C, Hanley AJ, Haffner SM. Differential white cell count and incident type 2 diabetes: the insulin resistance atherosclerosis study[J]. Diabetologia ;2014,57(1):83-92. https://doi.org/10.1007/s00125-013-3080-0. Xu T, Weng ZH, Pei C, et al. The relationship between neutrophil-to-lymphocyte ratio and diabetic peripheral neuropathy in Type 2 diabetes mellitus[J]. Medicine ;2017,96(45):1-6. https://doi.org/10.1097/MD.0000000000008289. Zhou ZW, Chen HM,Sun MZ,Ju HX. Mean Platelet Volume and Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis[J]. J Diabetes Res ;2018,2:1985026. https://doi.10.1155/2018/1985026. Wijarnpreecha K, Panjawatanan P, Aby E, Ahmed A, Kim D. Nonalcoholic fatty liver disease in the over-60s: Impact of sarcopenia and obesity[J]. Maturitas ;2019,124:48-54. https://doi:10.1016/ j.maturitas.2019.03.016.Epub 2019 Mar 25. Vella CA, Allison MA. Associations of abdominal intermuscular adipose tissue and inflammation: the Multi-Ethnic study of Atherosclerosis[J]. Obes Res Clin Pract ;2018,12(6):534-540. https://doi.org/10.1016/j.orcp.2018.08.002. Palau-Rodriguez M, Marco-Ramell A, Casas-Agustench P, et al. Visceral Adipose Tissue Phospholipid Signature of Insulin Sensitivity and Obesity[J]. Journal of Proteome Research ;2021,20(5):2410-2419. https://doi.org/10.1021/acs.jproteome.0c00918. Lee S, Libman I,Hughan K, et al. Effects of Exercise Modality on Insulin Resistance and Ectopic Fat in Adolescents with Overweight and Obesity: A Randomized Clinical Trial.[J].J Pediatr;2019,206:91-98.e1. https://doi.10.1016/j.jpeds.2018.10.059.Epub 2018 Dec 13. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4786661","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343386457,"identity":"4d80af90-2bdc-4e2a-bf0f-a5b407ef37de","order_by":0,"name":"Si-yun Tang","email":"","orcid":"","institution":"Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Si-yun","middleName":"","lastName":"Tang","suffix":""},{"id":343386458,"identity":"4a05e23d-202f-42b0-a8cc-8b008beb1ebd","order_by":1,"name":"Hui Wu","email":"","orcid":"","institution":"Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wu","suffix":""},{"id":343386459,"identity":"894cfc7f-f09e-4dad-8501-b379e3216911","order_by":2,"name":"Meng-ran Liu","email":"","orcid":"","institution":"Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Meng-ran","middleName":"","lastName":"Liu","suffix":""},{"id":343386460,"identity":"b46ae585-d440-4876-8b2e-fa01a74e7734","order_by":3,"name":"Jing Li","email":"","orcid":"","institution":"Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":343386461,"identity":"63dceb12-0b54-40b9-b788-432d64b7d626","order_by":4,"name":"Yi-chen Lu","email":"","orcid":"","institution":"Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yi-chen","middleName":"","lastName":"Lu","suffix":""},{"id":343386462,"identity":"ac54949e-d51f-4d64-a79e-20b1a01da2cb","order_by":5,"name":"Rui-li Cao","email":"","orcid":"","institution":"Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rui-li","middleName":"","lastName":"Cao","suffix":""},{"id":343386463,"identity":"5ba07f8d-e655-417f-a476-83f107aec034","order_by":6,"name":"Gu-qin Lu","email":"","orcid":"","institution":"Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Gu-qin","middleName":"","lastName":"Lu","suffix":""},{"id":343386464,"identity":"df443ac7-acfa-4c48-8b93-e8ed744e4670","order_by":7,"name":"Xiao-hua Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYDACCR4QacPDz99AmpY0GckZB0jTctjGoCGBSB0Gt3sYGN62necxYDjA+OFjDhFaJOecYWCc23abx5y5gVly5jYitPBL5DAw8wK1WDYcYGPmJUYLG0TLOR6DAwlEaoHacoAELZIzchgY55xL5pGccbCZOL8Y3Mh/wPCmzM6en7/54IePxGgBAvYfvGwgmrGBOPVgwPOHBMWjYBSMglEw8gAAHF8vwzK/uTUAAAAASUVORK5CYII=","orcid":"","institution":"Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xiao-hua","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-23 07:52:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4786661/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4786661/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64964194,"identity":"786af850-90e9-49b7-9cee-c5f4a2d31f6a","added_by":"auto","created_at":"2024-09-21 03:39:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":905549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4786661/v1/0f6cf945-ffd6-4301-b2bc-f8b6b3c0e27c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between skeletal muscle mass to visceral fat area ratio and insulin resistance in type 2 diabetes","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe prevalence of type 2 diabetes mellitus (T2DM) and obesity has rapidly increased worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. T2DM is characterized by persistent low-grade inflammation and insulin resistance (IR) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Obesity, especially visceral obesity (VO), is an important cause of chronic low-grade inflammation, which can aggravate IR and increase the risk of metabolic disorders [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, conventional indicators such as body mass index (BMI) and waist circumference are mostly used in clinical practice to determine obesity; however, these methods have limitations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For example, waist circumference can visually reflect the level of abdominal subcutaneous fat but not the level of intra-abdominal fat. In addition, BMI is influenced by muscle mass and cannot distinguish between muscle- and fat-related body mass.\u003c/p\u003e \u003cp\u003eAn important feature of body composition in patients with obesity is the change in body fat and muscle content; an increase in fat mass and decrease in muscle mass, occurring simultaneously, is often observed. Most adipokines are pro-inflammatory and involved in the development of obesity-related metabolic dysfunction. Skeletal muscle, the largest insulin-sensitive tissue, is the main effector organ for insulin-mediated glucose metabolism, and its loss of mass can accelerate insulin resistance. Increased expression of inflammatory cytokines is associated with reduced skeletal muscle mass and visceral fat accumulation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, reduced skeletal muscle mass to visceral fat area ratio (SVR) reflects a chronic low-grade inflammatory state and can be used as an independent predictor of T2DM [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there are few clinical studies on the relationship between SVR and IR. Therefore, this study used a cross-sectional study to observe whether there is a correlation between the SVR, inflammatory factors, and IR by measuring body composition, IR, and inflammatory factors in patients with T2DM.\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to investigate the relationship between SVR, NLR and IR in T2DM. We pursued two goals. First, we analyzed the correlation between NLR and Appendicular skeletal muscle mass (ASM), appendicular skeletal muscle mass index (ASMI), visceral fat area (VFA), SVR, homeostasis model assessment of insulin resistance (HOMA-IR) and insulin sensitivity index (ISI) to determine whether IR is related to inflammation. Secondly, the correlation between SVR and IR is analyzed to determine whether IR is correlated with SVR. Our objective was to determine whether SVR is associated with IR in T2DM patients.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Setting and Participants\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study enrolled patients with T2DM who visited the Department of Endocrinology of the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine from June 2022 to March 2023 as study participants, all of whom met the medical diagnosis and treatment criteria for diabetes mellitus issued by the American Diabetes Association in 2022 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The exclusion criteria were as follows: (1) type Ι diabetes mellitus; (2) presence of infections or other systemic diseases such as tumors, severe liver and kidney diseases, and systemic immune diseases; or (3) incomplete data. This study was approved by the Ethics Committee of the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, and all participants signed an informed consent form.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Variables and Bias\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePhysical examination was performed according to standard operating methods for nutritional evaluation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The height and weight of the participants were measured after taking off shoes, hats, and outer clothing. Height was measured with the patient in an upright position; feet together; shoulders, head, and heels close to the scale; eyes looking straight ahead; and a right-angle plate close to the top of the head. The measurement standard was accurate to 0.1 cm, and the weight was measured with a measurement standard accurate to 0.1 Kg. The participants were dressed in light clothing and pants, with their bodies upright, abdomens relaxed, and feet placed 40 cm apart.\u003c/p\u003e \u003cp\u003eThe waist hip circumference measurement positioned at the circumference of the horizontal circumference around the umbilicus and read when the participant exhaled; the hip circumference was measured at the maximum extension of the hips and read after wrapping around the hip horizontally with a leather ruler. The measurement standard was accurate to 0.1 cm. BMI\u0026thinsp;=\u0026thinsp;weight (kg)/height \u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e2\u003c/sup\u003e). The waist-hip ratio (WHR) was calculated as waist circumference (cm)/hip circumference (cm), and the results were accurate to two decimal places.\u003c/p\u003e \u003cp\u003eThe human body composition analyzer(GAIA KIKO,SELVAS Healthcare. Inc.) was used for measurements using a multifrequency bioimpedance analysis method. To reduce measurement error and variability in the measurement conditions, an independent examination room was set up, and the measurement was performed after training by professional personnel. The participants were instructed to void their bladder and bowel, remove their socks, and wipe their hands clean with 75% alcohol gauze before coming in contact with the electrode surface of the analyzer; the hands and feet were naturally separated. The patient's personal information was entered on the body composition analyzer computer and the measurements were recorded. The results included body weight; BMI; waist-to-hip ratio; fat-free mass; ASM; protein and mineral content; total body, intracellular, and extracellular water; TBF;VFA; and basal metabolic rate. TBF%, ASMI, and SVR were calculated using the following formulae: TBF%=TBF/body weight; ASMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;ASM(kg)/height\u003csup\u003e2\u003c/sup\u003e(m\u003csup\u003e2\u003c/sup\u003e); SVR\u0026thinsp;=\u0026thinsp;ASM/VFA.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Clinical biochemical measurement and bias\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll participants kept a light diet for three days before blood collection, avoiding spicy and stimulating greasy food, such as fatty meat, fried food, animal offal, etc., and were required to refrain from alcohol and maintain a fasting stomach for 10 hours before blood collection.2 mL of elbow venous blood in 2 tubes was collected early in the morning on an empty stomach in the morning. One tube was stored in an EDTA anticoagulation tube for routine blood testing, and the other tube was stored in a serum collection vessel for blood biochemistry testing. The serum was divided and stored at -80\u0026deg;C in a refrigerator, and batches were regularly prepared for testing. Neutrophils (Neu) and lymphocytes (Lym) were detected using a fully automated modular blood fluid analyzer (XN-10 [B4]), and the neutrophil to lymphocyte ratio (NLR) was calculated by flow cytometry and DNA/RNA fluorescence staining ,and the reagents purchased from Sysmex Biotechnology Co. The fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), and lipoprotein were measured using a fully automated biochemical analyzer (Beckman AU5800). The FPG assay was performed using the glucokinase method, and the TC, TG, high-density lipoprotein (HDL), and LDL assays were performed using the enzyme method, and the reagents were purchased from Beckman Co. Fasting insulin (FINS) levels were determined using a fully automated electrochemiluminescence immunoassay system (Roche). All tests were quality-controlled prior to testing, and in the case of loss of control, the specific reasons were analyzed and dealt with accordingly until the specimens were again under control. Steady-state models were used to assess HOMA-IR and ISI. HOMA-IR was calculated using the equation: HOMA-IR\u0026thinsp;=\u0026thinsp;FPG\u0026times;FINS/22.5, ISI was calculated using the equation: ISI\u0026thinsp;=\u0026thinsp;Ln 1/(FPG\u0026times;FINS).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Diagnostic criteria\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e(1) According to the recommendations of the BMI classification for Chinese adults published by the Chinese Working Group on Obesity, BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e is considered as low body mass, individuals with BMIs between 24 and 28 kg/m\u003csup\u003e2\u003c/sup\u003e as overweight, and those with BMIs\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e as obese [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e(2) Based on the study by Deurenberg [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e11\u003c/span\u003e], those with TBF% \u0026gt; 25% among men and \u0026gt;\u0026thinsp;30% among women were considered to have a high body fat. (3) According to the appropriate waist circumference cut points published by the Chinese Obesity Working Group, men with a waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;85 cm and/or a WHR\u0026thinsp;\u0026ge;\u0026thinsp;0.85 and women with a waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;80 cm and/or a WHR\u0026thinsp;\u0026ge;\u0026thinsp;0.80 are considered to have excessive accumulation of abdominal fat.\u003c/p\u003e\u003cp\u003e (3) According to the Chinese Guidelines for the Prevention and Treatment of Adult Dyslipidemia formulated by the Joint Committee in 2007, the levels of lipid stratification were determined by TC\u0026thinsp;\u0026ge;\u0026thinsp;6.22 mmol/L, TG\u0026thinsp;\u0026ge;\u0026thinsp;2.26 mmol/L and LDL\u0026thinsp;\u0026ge;\u0026thinsp;4.14 mmol/L. Any of the HDL\u0026thinsp;\u0026le;\u0026thinsp;1.04 mmol/L is considered as dyslipidemia.\u003c/p\u003e\u003cp\u003e(4) According to the Asian Working Group for Sarcopenia criteria, if ASMI\u0026thinsp;\u0026lt;\u0026thinsp;7.0 kg/m\u003csup\u003e2\u003c/sup\u003e for men and 5.7 kg/m\u003csup\u003e2\u003c/sup\u003e for women, loss of skeletal muscle mass can be judged [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Study size\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study is a cross-sectional study design, the purpose of this study was to observe the correlation between SVR, NLR and IR in T2DM patients. Since SVR is measurement data, PASS 15 software was used to calculate the sample size by Means-One mean-confidence intervals for one mean. A two-sided test alpha of 0.05, i.e. confidence level (1-Alpha)\u0026thinsp;=\u0026thinsp;0.95, tolerance error δ of 0.04, predicted standard deviation σ of 0.2, calculated N\u0026thinsp;=\u0026thinsp;97, and at least 122 subjects were required considering the loss of follow-up rate of 20%.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quantitative variables and Statistical methods\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStratified sampling was adopted in this study, with 65 years old as the age node for stratification, in order to avoid the influence of senile sarcopenia on SVR results. After stratification, a simple random sampling method was adopted to ensure that every object in the population had an equal opportunity to be sampled. If the subject is missing any data from anthropometric or biochemical tests, it will be marked as unqualified and will be removed from the enrollment queue.\u003c/p\u003e \u003cp\u003eContinuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s), and count data were expressed as percentages (%). One-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal\u0026ndash;Wallis one-way ANOVA was used for non-normal data. Correlations between the NLR and ASMI, VFA, SVR, HOMA-IR, and ISI were analyzed using Pearson\u0026rsquo;s (conforming to normality) or Spearman\u0026rsquo;s (not conforming to normality) correlations. Multiple linear regression analysis was used to analyze the relationship between SVR and IR. All statistical analyses were performed using SPSS 23.0 software(SPSS Inc., Chicago, IL,USA), and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered a statistically significant difference.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participants and Descriptive data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA total of 256 T2DM patients were included in this study, 10 of whom were excluded from the cohort due to incomplete clinical biochemical indicators. A total of 246 T2DM patients were included, including 150 males and 96 females, average age 50.370\u0026thinsp;\u0026plusmn;\u0026thinsp;12.279. Because the objective was to observe the correlation between SVR and insulin resistance, and the SVR was continuous data, three groups were divided according to the SVR quintile. In the male population, Q1: \u0026lt;0.384 kg/cm\u003csup\u003e2\u003c/sup\u003e, Q2: 0.384\u0026ndash;0.524 kg/cm\u003csup\u003e2\u003c/sup\u003e, and Q3: \u0026gt;0.524 kg/cm\u003csup\u003e2\u003c/sup\u003e; and in the female population, Q1: \u0026lt;0.420 kg/cm\u003csup\u003e2\u003c/sup\u003e, Q2: 0.420\u0026ndash;0.624 kg/cm\u003csup\u003e2\u003c/sup\u003e, and Q3: \u0026gt;0.624 kg/cm\u003csup\u003e2\u003c/sup\u003e. There were 82 patients in each group, comprising 50 males and 32 women. The results showed that in male patients with T2DM, the WHR, Neu, NLR, FPG, FINS, HOMA-IR, TG, TC, and free fatty acid levels were significantly higher in the Q1 and Q2 groups than in Q3 group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas ISI and Lym were significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences in disease course, body weight, BMI, or HDL or LDL levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the male patients with T2DM among the three groups, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \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\u003eDescriptive and clinical characteristics of male study participants (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s)\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1 group(n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2group(n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3 group(n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e51.290\u0026thinsp;\u0026plusmn;\u0026thinsp;11.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.710\u0026thinsp;\u0026plusmn;\u0026thinsp;14.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e42.380\u0026thinsp;\u0026plusmn;\u0026thinsp;13.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.487\u0026thinsp;\u0026plusmn;\u0026thinsp;7.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.392\u0026thinsp;\u0026plusmn;\u0026thinsp;7.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.784\u0026thinsp;\u0026plusmn;\u0026thinsp;5.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e80. 824\u0026thinsp;\u0026plusmn;\u0026thinsp;16.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e77.395\u0026thinsp;\u0026plusmn;\u0026thinsp;15.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e78.081\u0026thinsp;\u0026plusmn;\u0026thinsp;17.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.498\u0026thinsp;\u0026plusmn;\u0026thinsp;5.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.999\u0026thinsp;\u0026plusmn;\u0026thinsp;5.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e26.239\u0026thinsp;\u0026plusmn;\u0026thinsp;4.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.979\u0026thinsp;\u0026plusmn;\u0026thinsp;0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.926\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.855\u0026thinsp;\u0026plusmn;\u0026thinsp;0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.237\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\u003eNeu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e60.910\u0026thinsp;\u0026plusmn;\u0026thinsp;8.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e58.833\u0026thinsp;\u0026plusmn;\u0026thinsp;9.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e55.995\u0026thinsp;\u0026plusmn;\u0026thinsp;8.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLym\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.943\u0026thinsp;\u0026plusmn;\u0026thinsp;8.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.748\u0026thinsp;\u0026plusmn;\u0026thinsp;8.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e33.855\u0026thinsp;\u0026plusmn;\u0026thinsp;7.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.391\u0026thinsp;\u0026plusmn;\u0026thinsp;1.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.184\u0026thinsp;\u0026plusmn;\u0026thinsp;1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.799\u0026thinsp;\u0026plusmn;\u0026thinsp;0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.632\u0026thinsp;\u0026plusmn;\u0026thinsp;4.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.681\u0026thinsp;\u0026plusmn;\u0026thinsp;3.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.737\u0026thinsp;\u0026plusmn;\u0026thinsp;3.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFINS(mU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e123.624\u0026thinsp;\u0026plusmn;\u0026thinsp;95.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e79.472\u0026thinsp;\u0026plusmn;\u0026thinsp;82.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e62.845\u0026thinsp;\u0026plusmn;\u0026thinsp;57.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.131\u0026thinsp;\u0026plusmn;\u0026thinsp;9.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.477\u0026thinsp;\u0026plusmn;\u0026thinsp;6.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.943\u0026thinsp;\u0026plusmn;\u0026thinsp;3.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.805\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\u003eISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-5.226\u0026thinsp;\u0026plusmn;\u0026thinsp;0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-4.685\u0026thinsp;\u0026plusmn;\u0026thinsp;0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-4.350\u0026thinsp;\u0026plusmn;\u0026thinsp;0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.679\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\u003eTG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.707\u0026thinsp;\u0026plusmn;\u0026thinsp;1.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.267\u0026thinsp;\u0026plusmn;\u0026thinsp;1.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.789\u0026thinsp;\u0026plusmn;\u0026thinsp;0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.329\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 \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.593\u0026thinsp;\u0026plusmn;\u0026thinsp;0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.466\u0026thinsp;\u0026plusmn;\u0026thinsp;0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.526\u0026thinsp;\u0026plusmn;\u0026thinsp;1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.698\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\u003eHDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.041\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.098\u0026thinsp;\u0026plusmn;\u0026thinsp;0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.063\u0026thinsp;\u0026plusmn;\u0026thinsp;0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.911\u0026thinsp;\u0026plusmn;\u0026thinsp;0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.930\u0026thinsp;\u0026plusmn;\u0026thinsp;1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.967\u0026thinsp;\u0026plusmn;\u0026thinsp;1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.985\u0026thinsp;\u0026plusmn;\u0026thinsp;1.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.515\u0026thinsp;\u0026plusmn;\u0026thinsp;0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.488\u0026thinsp;\u0026plusmn;\u0026thinsp;0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\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=\"BlockQuote\"\u003e \u003cp\u003eOne-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal\u0026ndash;Wallis one-way ANOVA was used for non-normal data. BMI, body mass index; WHR, waist-hip ratio; Neu, neutrophils; Lym, lymphocytes; NLR, neutrophil/lymphocyte ratio; FPG, fasting plasma glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment of insulin resistance; ISI, insulin sensitivity index; TG, triglycerides; TC, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; FFA, free fatty acid.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn female patients with T2DM in both Q1 and Q2 groups, compared with those in the Q3 group, weight, BMI, WHR, Neu, NLR, FPG, FINS, HOMA-IR, TG, TC, HDL, and FFA were significantly higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and ISI and Lym were significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Age, disease duration, and LDL were not significantly different in the female patients with T2DM among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eDescriptive and clinical characteristics of female study participants (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s)\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1 group(n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2 group(n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3 group(n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e58.000\u0026thinsp;\u0026plusmn;\u0026thinsp;14.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e56.720\u0026thinsp;\u0026plusmn;\u0026thinsp;16.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e47.800\u0026thinsp;\u0026plusmn;\u0026thinsp;16.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.632\u0026thinsp;\u0026plusmn;\u0026thinsp;5.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.915\u0026thinsp;\u0026plusmn;\u0026thinsp;5.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.994\u0026thinsp;\u0026plusmn;\u0026thinsp;7.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e73.796\u0026thinsp;\u0026plusmn;\u0026thinsp;19.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e70.568\u0026thinsp;\u0026plusmn;\u0026thinsp;17.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e54.504\u0026thinsp;\u0026plusmn;\u0026thinsp;10.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.103\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\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e30.388\u0026thinsp;\u0026plusmn;\u0026thinsp;5.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.932\u0026thinsp;\u0026plusmn;\u0026thinsp;5.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.550\u0026thinsp;\u0026plusmn;\u0026thinsp;4.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.848\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\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.905\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.855\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.775\u0026thinsp;\u0026plusmn;\u0026thinsp;0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.382\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\u003eNeu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65.662\u0026thinsp;\u0026plusmn;\u0026thinsp;7.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e57.164\u0026thinsp;\u0026plusmn;\u0026thinsp;7.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e54.144\u0026thinsp;\u0026plusmn;\u0026thinsp;7.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.584\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\u003eLym\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.837\u0026thinsp;\u0026plusmn;\u0026thinsp;5.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e32.647\u0026thinsp;\u0026plusmn;\u0026thinsp;9.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.492\u0026thinsp;\u0026plusmn;\u0026thinsp;7.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.039\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\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.706\u0026thinsp;\u0026plusmn;\u0026thinsp;0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.083\u0026thinsp;\u0026plusmn;\u0026thinsp;1.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.568\u0026thinsp;\u0026plusmn;\u0026thinsp;0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.610\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\u003eFPG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.262\u0026thinsp;\u0026plusmn;\u0026thinsp;4.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.190\u0026thinsp;\u0026plusmn;\u0026thinsp;2.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.714\u0026thinsp;\u0026plusmn;\u0026thinsp;2.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFINS(mU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e143.854\u0026thinsp;\u0026plusmn;\u0026thinsp;98.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e98.804\u0026thinsp;\u0026plusmn;\u0026thinsp;108.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e43.611\u0026thinsp;\u0026plusmn;\u0026thinsp;36.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.038\u0026thinsp;\u0026plusmn;\u0026thinsp;7.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.100\u0026thinsp;\u0026plusmn;\u0026thinsp;8.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.067\u0026thinsp;\u0026plusmn;\u0026thinsp;2.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-5.013\u0026thinsp;\u0026plusmn;\u0026thinsp;0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e-4.431\u0026thinsp;\u0026plusmn;\u0026thinsp;1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e-3.233\u0026thinsp;\u0026plusmn;\u0026thinsp;1.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.046\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\u003eTG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.375\u0026thinsp;\u0026plusmn;\u0026thinsp;1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.132\u0026thinsp;\u0026plusmn;\u0026thinsp;1.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.346\u0026thinsp;\u0026plusmn;\u0026thinsp;0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.304\u0026thinsp;\u0026plusmn;\u0026thinsp;1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.865\u0026thinsp;\u0026plusmn;\u0026thinsp;0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.327\u0026thinsp;\u0026plusmn;\u0026thinsp;0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.164\u0026thinsp;\u0026plusmn;\u0026thinsp;0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.196\u0026thinsp;\u0026plusmn;\u0026thinsp;0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.895\u0026thinsp;\u0026plusmn;\u0026thinsp;0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.015\u0026thinsp;\u0026plusmn;\u0026thinsp;0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.008\u0026thinsp;\u0026plusmn;\u0026thinsp;1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.688\u0026thinsp;\u0026plusmn;\u0026thinsp;0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.585\u0026thinsp;\u0026plusmn;\u0026thinsp;0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.395\u0026thinsp;\u0026plusmn;\u0026thinsp;0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.664\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOne-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal\u0026ndash;Wallis one-way ANOVA was used for non-normal data.BMI, body mass index; WHR, waist-hip ratio; Neu, neutrophils; Lym, lymphocytes; NLR, neutrophil/lymphocyte ratio; FPG, fasting plasma glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment of insulin resistance; ISI, insulin sensitivity index; TG, triglycerides; TC, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; FFA, free fatty acid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of body composition indices in each group by SVR tristimulus grouping\u003c/h2\u003e \u003cp\u003eIn male patients with T2DM in the Q1 and Q2 groups, compared with those in the Q3 group, TBF, TBF%, and VFA were significantly higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); SVI, ASM, ASMI, basal metabolic volume, and total cellular water were significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); whereas there were no significant differences between the defatted body weight, mineral content, and intracellular water (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Comparison of body composition indices of male participants in each SVR tristimulus group (\u0026plusmn;s)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\"\u003e\n \u003cp\u003e\u003cstrong\u003eindex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1 group(n=82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2 group(n=82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3 group(n=82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eTBF(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e56.874\u0026plusmn;8.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e58.431\u0026plusmn;7.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e61.500\u0026plusmn;11.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e8.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eTBF%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e28.236\u0026plusmn;4.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e24.876\u0026plusmn;5.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e19.210\u0026plusmn;7.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e26.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eFFM(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e23.631\u0026plusmn;8.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e20.945\u0026plusmn;8.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e15.895\u0026plusmn;8.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e2.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eASM(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e50.162\u0026plusmn;7.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e54.748\u0026plusmn;6.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e56.276\u0026plusmn;11.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e5.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eProtein content(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e11.433\u0026plusmn;1.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e11.848\u0026plusmn;1.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e12.855\u0026plusmn;1.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e8.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eMineral content(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e4.537\u0026plusmn;0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e4.507\u0026plusmn;0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e4.517\u0026plusmn;0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eTotal body water(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e41.310\u0026plusmn;6.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e41.362\u0026plusmn;7.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e44.762\u0026plusmn;7.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e3.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eIntracellular water(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e27.217\u0026plusmn;4.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e27.683\u0026plusmn;3.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e29.248\u0026plusmn;4.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e2.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eExtracellular water(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e14.093\u0026plusmn;2.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e14.392\u0026plusmn;1.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e15.514\u0026plusmn;2.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e4.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eBasal metabolic rate (kcal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e1356.191\u0026plusmn;190.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e1408.143\u0026plusmn;202.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e1512.100\u0026plusmn;226.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e6.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eVFA(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e163.379\u0026plusmn;44.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e118.238\u0026plusmn;15.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e82.457\u0026plusmn;26.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e70.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eASMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e17.676\u0026plusmn;2.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e19.007\u0026plusmn;3.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e19.182\u0026plusmn;2.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e3.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eSVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e0.320\u0026plusmn;0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e0.466\u0026plusmn;0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e0.761\u0026plusmn;0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e57.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOne-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal\u0026ndash;Wallis one-way ANOVA was used for non-normal data.TBF, total body fat; TBF%, percentage of body fat to body mass; FFM, fat-free mass; ASM, appendicular skeletal muscle mass; VFA, visceral fat area; ASMI, appendicular skeletal muscle mass index; SVR, skeletal muscle mass to visceral fat area ratio.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn women with T2DM in the Q1 and Q2 groups, compared with those in the Q3 group, TBF, TBF%, and VFA were significantly higher (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); SVR, ASM, ASMI, basal metabolic volume, and total cellular water were significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); whereas there were no significant differences between defatted body weight, intracellular water, and extracellular water (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05), as shown in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Comparison of body composition indices in female participants in each SVR tristimulus group (\u0026plusmn;s)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\"\u003e\n \u003cp\u003e\u003cstrong\u003eindex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1 group (n=82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2 group(n=82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3 group (n=82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eTBF(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e28.060\u0026plusmn;10.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e23.676\u0026plusmn;8.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e14.556\u0026plusmn;5.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e16.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eTBF%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e37.360\u0026plusmn;4.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e32.756\u0026plusmn;4.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e25.188\u0026plusmn;6.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e37.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eFFM(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e45.732\u0026plusmn;9.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e46.892\u0026plusmn;9.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e41.048\u0026plusmn;5.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e3.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eASM(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e37.084\u0026plusmn;3.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e41.776\u0026plusmn;6.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e43.464\u0026plusmn;9.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e5.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.855595667870038%\" valign=\"top\"\u003e\n \u003cp\u003eProtein content(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" valign=\"top\"\u003e\n \u003cp\u003e8.664\u0026plusmn;1.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e9.128\u0026plusmn;1.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e8.292\u0026plusmn;0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e2.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.913357400722022%\" valign=\"top\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eMineral content(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e4.140\u0026plusmn;1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e4.008\u0026plusmn;0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e3.204\u0026plusmn;0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e7.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eTotal body water(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e29.548\u0026plusmn;3.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e32.922\u0026plusmn;6.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e33.752\u0026plusmn;6.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e3.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eIntracellular water(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e21.520\u0026plusmn;4.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e22.088\u0026plusmn;4.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e19.296\u0026plusmn;2.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e3.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eExtracellular water(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e11.412\u0026plusmn;2.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e11.668\u0026plusmn;2.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e10.252\u0026plusmn;1.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e3.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eBasal metabolic rate (kcal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e1064.560\u0026plusmn;95.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e1106.240\u0026plusmn;142.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e1212.000\u0026plusmn;169.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e7.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eVFA(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e131.720\u0026plusmn;40.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e87.880\u0026plusmn;23.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e43.080\u0026plusmn;14.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e61.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eASMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e15.479\u0026plusmn;1.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e16.654\u0026plusmn;2.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e17.692\u0026plusmn;4.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e3.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.81981981981982%\" valign=\"top\"\u003e\n \u003cp\u003eSVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e0.330\u0026plusmn;0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e0.501\u0026plusmn;0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e0.962\u0026plusmn;0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" colspan=\" \" valign=\"top\"\u003e\n \u003cp\u003e95.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.891891891891891%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOne-way analysis of variance (ANOVA) was used to compare normally distributed data among the three groups, and Kruskal\u0026ndash;Wallis one-way ANOVA was used for non-normal data.TBF, total body fat; TBF%, percentage of body fat to body mass; FFM, fat-free mass; ASM, appendicular skeletal muscle mass; VFA, visceral fat area; ASMI, appendicular skeletal muscle mass index; SVR, skeletal muscle mass to visceral fat area ratio.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation analysis of NLR with body composition indices and IR\u003c/h2\u003e \u003cp\u003eThe results of the correlation analysis of NLR with body composition indices and IR showed that, among body composition indices, TBF, TBF%, VFA, ASM, ASMI, SVR were significantly correlated with NLR, with ASM, ASMI, SVR being significantly negatively correlated with NLR (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005); HOMA-IR and NLR were significantly positively correlated, and ISI and NLR were significantly negatively correlated (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005; as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of NLR with body composition indices and IR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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\u003eVFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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\u003eISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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\u003eUsing Pearson\u0026rsquo;s (conforming to normality) or Spearman\u0026rsquo;s (not conforming to normality) correlations. TBF, total body fat; TBF%, percentage of body fat to body mass; VFA, visceral fat area; ASMI, appendicular skeletal muscle mass index; SVR, skeletal muscle mass to visceral fat area ratio; HOMA-IR, homeostasis model assessment of insulin resistance; ISI, insulin sensitivity index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multiple linear regression analysis of body composition indices and IR\u003c/h2\u003e \u003cp\u003eThe results using multiple linear regression analysis revealed significant results for several variables (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25.584, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). VFA (\u003cem\u003eβ\u003c/em\u003e=-0.203, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and SVR (\u003cem\u003eβ\u003c/em\u003e=-0.242, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) significantly negatively predicted and ASMI (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.323, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.323) significantly positively predicted HOMA-IR. Together, these variables explained 34.3% of the variance in HOMA-IR ,as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of body composition indices and HOMA-IR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eβ-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eT-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eF-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eAdjust R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-3.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-33.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-3.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing multiple linear regression analysis. ASMI, appendicular skeletal muscle mass index; VFA, visceral fat area; VFA, visceral fat area; SVR, skeletal muscle mass to visceral fat area ratio.\u003c/p\u003e \u003cp\u003eThe results using the multiple linear regression analysis revealed that revealed significant results for several variable (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46.105, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). VFA (\u003cem\u003eβ\u003c/em\u003e=-0.352, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TBF% (\u003cem\u003eβ\u003c/em\u003e=-0.022, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) significantly negatively predicted ISI, and SVR (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.683, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly positively predicted ISI. Together, these variables explained 48.5% of the variance in ISI, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of body composition indices and ISI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eβ-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eT-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAdjust R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing multiple linear regression analysis. ASMI, appendicular skeletal muscle mass index; VFA, visceral fat area; VFA, visceral fat area; SVR, skeletal muscle mass to visceral fat area ratio; ; ISI, insulin sensitivity index.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Key results and Generalisability\u003c/h2\u003e \u003cp\u003eFrom 1980 to 2013, globally, the prevalence of overweight or obesity increased by 27.5% in adults, and the prevalence of overweight and obesity in adults in China exceeded 1/3 of the total population,, among which the rates of overweight and obesity among males and females were 71% and 22.78%, respectively, and 5.02% and 5.51%, respectively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Of the 246 patients with T2DM enrolled in this study, 102 (68.00%) were overweight males and 48 (32.00%) were men with obesity, whereas 66 (68.75%) women were overweight and 30 (31.25%) women were obese. Body fat distribution and adipose tissue dysfunction are key factors in the development of IR associated with obesity and can be a good predictor of an individual's risk of metabolic disease [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Among the 246 patients with T2DM enrolled in this study, there were 129 (86.51%) men with a WHR\u0026thinsp;\u0026ge;\u0026thinsp;0.85 and 56 (37.33%) with a TBF% \u0026gt; 25% among the total of 150 male patients; among the total of 96 female patients with T2DM,78 (81.25%) had a WHR\u0026thinsp;\u0026ge;\u0026thinsp;0.80 and 58 (60.42%) had a TBF% \u0026gt; 30%. Although both BMI and waist circumference are widely used clinically, they are still considered inaccurate methods for assessing obesity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A comparison of general information on men with T2DM grouped according to SVR showed no significant difference in weight or BMI, suggesting that the site of fat accumulation seems to be more important than obesity itself.\u003c/p\u003e \u003cp\u003eThe degree of IR directly correlates with the degree of inflammation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The present results showed a correlation between NLR and both HOMA-IR and ISI. NLR represents a non-specific inflammatory pathway, and the secretion of pro-inflammatory factors such as nuclear factor-kB and Interleukin-6 from visceral adipose tissue leads to an increase in systemic neutrophils [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and promotes the release of reactive oxygen species by neutrophil activation, which aggravates IR [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Comparison of body composition across the SVR groups in this study showed that compared to patients in the Q1 group, patients (both male and female) in the Q2 and Q3 groups had decreased neutrophils, whereas their VFA, TBF, and TBF% were increased. This is consistent with previous findings from the Japanese Obesity Society, which concluded that visceral fat accumulation is associated with a higher risk of metabolic disease, with a combined risk of health impairment of approximately 1.5 times more when the visceral fat area reaches 100 cm\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Lym represents an adaptive immune response, and inadequate IL-2 receptor expression during IR exacerbates the decrease in Lym [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; comparison of body composition by SVR trichotomies showed that Lym was elevated in both men and women in the Q2 and Q3 groups compared with those in the Q1 group. Furthermore, NLR is a common inflammatory factor in measured in routine blood tests, and the present results showed a positive correlation between NLR and HOMA-IR [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e22\u003c/span\u003e], suggesting that NLR could be used as a predictor of IR.\u003c/p\u003e \u003cp\u003ePatients with obesity experience changes in the content and ratio of their body fat and muscle; fat mass is increased whereas muscle mass often decreases simultaneously. In this study, dual bioimpedance analyzers were used to measure VFA, and magnetic resonance imaging and computed tomography (CT) are currently recognized as the most accurate and objective imaging techniques for assessing visceral fat distribution. Epidemiological investigation showed a good correlation between VFA measured using a dual bioimpedance analyzer and abdominal CT (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.821, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; this method has the advantages of high safety, low cost, simple operation, and no radiation. The comparison of body composition by SVR grouping in this study showed that VFA, TBF, and TBF% were elevated along with a decrease in ASM and ASMI in men and women in the Q1 group compared with those in the Q3 group. When there is excess fat in the body, fatty acids accumulate in skeletal muscle and result in intramuscular fat (IMAT). The metabolically active component of IMAT can, like visceral adipose tissue, secrete inflammatory cytokines that cause an inflammatory response in muscle and the body as a whole, causing muscle atrophy and attenuating muscle strength production. Vella et al [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e24\u003c/span\u003e] found that as the area and density of IMAT increased, inflammatory markers increased linearly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and an increase in 1 standard deviation (SD) in IMAT area in the abdomen increased IL-6, leptin and C-reactive protein by 21%, 36% and 20%, respectively, and decreased lipocalin by 19% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Reduced skeletal muscle mass and visceral fat accumulation are associated with increased expression of inflammatory cytokines [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, reduced SVR levels reflect a chronic low-grade inflammatory state, and the results of the current study showed a negative correlation between SVR and NLR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that SVR, a new measure of obesity, is closely associated with an increased risk of IR and can be used as a predictor of T2DM and as an IR indicator[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Limitations and Interpretation\u003c/h2\u003e \u003cp\u003eIn summary, this study mainly discusses the correlation between SVR and IR in T2DM patients, which is helpful for clinicians to identify high-risk groups of obesity and provide a new entry point for the prevention and treatment of T2DM.\u003c/p\u003e \u003cp\u003eHowever, there are some limitations and shortcomings in this study. First, as this was a cross-sectional study, a causal relationship between SVR and IR could not be inferred and can only be proven in subsequent cohort studies. Second, muscle strength was not taken as a confounding factor in the measurement of muscle content by multi-frequency bio-resistance method, and the ratio of muscle content and fat in the body composition was mainly intended to be reflected in this article. Even so, if muscle strength such as arm strength could be taken into account in subsequent studies, the data results would be more accurate. Among the evaluation methods, insulin resistance was evaluated using HOMA-IR instead of gold standard test, and skeletal muscle mass and dirty fat area were evaluated using multi-frequency bioimpedance method instead of other potentially more accurate methods such as dual-energy X-ray (DEXA), mainly considering the scalability and portability of clinical application. Therefore, although it is not a gold standard evaluation method, it has certain guiding significance for guiding clinical application. Third, this was a single-center study influenced by multiple factors, and the sample size was small. We will further confirm the research results by increasing the sample size, involving multiple centers, and improving follow-up.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003etype 2 diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003einsulin resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003evisceral obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003ebody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eSVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003eskeletal muscle mass to visceral fat area ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003ewaist-hip ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eASM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003eappendicular skeletal muscle mass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eTBF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003etotal body fat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eVFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003evisceral fat area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eTBF%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003ethe percentage of body fat to body mass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eASMI \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003eappendicular skeletal muscle mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003eneutrophil to lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003efasting plasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003etriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003etotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003elow-density lipoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003ehigh-density lipoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eFINS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003eFasting insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eISI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003einsulin sensitivity index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eANOVA \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003eOne-way analysis of variance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eCT \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003ecomputed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eIMAT \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003eintramuscular fat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.380434782608695%\" valign=\"top\"\u003e\n \u003cp\u003eSD \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.6195652173913%\" valign=\"top\"\u003e\n \u003cp\u003estandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval:\u003c/strong\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine (ethical approval number: 2022-7th-HIRB-004;date of approval January 29,2022).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflicts of Interest information:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the Pudong New Area Health Commission clinical characteristic discipline construction project(PWYts2021-13),and Shanghai University of Traditional Chinese Medicine Curriculum Construction Project(SHUTCM2021KC021).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization S.T., H,W., and X.L.; methodology S.T. H,W. and J,L.; investigation M.L. and Y.L.; software H.W., M,L. and R.C.; data curation S.T. ,Y.L. and R.C.; Writing-original draft Writing-original draft S.T., H,W. and M.L.; writing-review and editing G.L. and X.L.; funding acquisition S.T. and X.L; supervision G.L. and X.L.. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Editage (www.editage.cn) for English language editing.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe data presented in this article are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045[J].\u003cem\u003eDiabetes Res Clin Pract\u003c/em\u003e;2022,183:109119. https://doi.org/ 10.1016/j.diabres.2021.109119. \u003c/li\u003e\n\u003cli\u003eRohm TV, Meier DT, Olefsky JM, Donath MY, . Inflammation in obesity, diabetes, and related disorders[J].\u003cem\u003eImmunity\u003c/em\u003e;2022,55(1):31-55. https://: 10.1016/j.immuni.2021.12.013.\u003c/li\u003e\n\u003cli\u003eAhmed B,Sultn. Adipose tissue and insulin resistance in obese [J].\u003cem\u003eBiomed Pharmacother\u003c/em\u003e;2021,3(137):111315. https:// 10.1016/j.biopha.2021.111315. \u003c/li\u003e\n\u003cli\u003eEngin A. The Definition and prevalence of obesity and metabolic syndrome[J].\u003cem\u003eAdv Exp Med Biol\u003c/em\u003e;2017,960:1-17. https://doi.org/10.1007/978-3-319-48382-5_1.\u003c/li\u003e\n\u003cli\u003eBluher M. 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The Metabolic Phenotype in Obesity: Fat Mass, Body Fat Distribution, and Adipose Tissue Function[J].\u003cem\u003eObes Facts\u003c/em\u003e;2017,10(3):207-215.https://doi.10.1159/000471488.Epub 2017 Jun 1.\u003c/li\u003e\n\u003cli\u003eEngin A. The Definition and prevalence of obesity and metabolic syndrome[J].\u003cem\u003eAdv Exp Med Biol\u003c/em\u003e;2017,960:1-17. https://doi.org/10.1007/978-3-319-48382-5_1.\u003c/li\u003e\n\u003cli\u003eRavindran J, Ravindran R, Dhanasekaran S. Emerging Role of Adipocytokines in Type 2 Diabetes as Mediators of Insulin Resistance and Cardiovascular Disease[J].\u003cem\u003eCanadian journal of diabetes\u003c/em\u003e;2018,42(4):446-456.e1. https:// 10.1016/j.jcjd.2017.10.040.\u003c/li\u003e\n\u003cli\u003eMutsert RD, Gast K, Widya R, et al. Associations of Abdominal Subcutaneous and Visceral Fat with Insulin Resistance and Secretion Differ Between Men and Women: The Netherlands Epidemiology of Obesity Study[J].\u003cem\u003eMetab Syndr Relat Disord\u003c/em\u003e;2018,16(1):54-63.https://doi:10.1089 /met.2017.0128. Epub 2018 Jan 17.\u003c/li\u003e\n\u003cli\u003eUzun S, Ozari M, Gursu M, et al. Changes in the inflammatory markers with advancing stages of diabetic nephropathy and the role of pentraxin-3[J].\u003cem\u003eRenal Failure\u003c/em\u003e;2016,38(8):1193-1198. https:// 10.1080/0886022X.2016.1209031.\u003c/li\u003e\n\u003cli\u003eByrne CD, Targher G.Ectopic fat, insulin resistance, and nonalcoholic fatty liver disease: implications for cardiovascular disease[J].\u003cem\u003eArterioscler Thromb Vasc Biol\u003c/em\u003e;2014,34(6):1155-1161. https://doi.org/10.1161/atvbaha.114.303034.\u003c/li\u003e\n\u003cli\u003eLorenzo C, Hanley AJ, Haffner SM. Differential white cell count and incident type 2 diabetes: the insulin resistance atherosclerosis study[J].\u003cem\u003eDiabetologia\u003c/em\u003e;2014,57(1):83-92. https://doi.org/10.1007/s00125-013-3080-0.\u003c/li\u003e\n\u003cli\u003eXu T, Weng ZH, Pei C, et al. The relationship between neutrophil-to-lymphocyte ratio and diabetic peripheral neuropathy in Type 2 diabetes mellitus[J].\u003cem\u003eMedicine\u003c/em\u003e;2017,96(45):1-6. https://doi.org/10.1097/MD.0000000000008289.\u003c/li\u003e\n\u003cli\u003eZhou ZW, Chen HM,Sun MZ,Ju HX. Mean Platelet Volume and Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis[J].\u003cem\u003eJ Diabetes Res\u003c/em\u003e;2018,2:1985026. https://doi.10.1155/2018/1985026. \u003c/li\u003e\n\u003cli\u003eWijarnpreecha K, Panjawatanan P, Aby E, Ahmed A, Kim D. Nonalcoholic fatty liver disease in the over-60s: Impact of sarcopenia and obesity[J].\u003cem\u003eMaturitas\u003c/em\u003e;2019,124:48-54. https://doi:10.1016/ j.maturitas.2019.03.016.Epub 2019 Mar 25.\u003c/li\u003e\n\u003cli\u003eVella CA, Allison MA. Associations of abdominal intermuscular adipose tissue and inflammation: the Multi-Ethnic study of Atherosclerosis[J].\u003cem\u003eObes Res Clin Pract\u003c/em\u003e;2018,12(6):534-540. https://doi.org/10.1016/j.orcp.2018.08.002.\u003c/li\u003e\n\u003cli\u003ePalau-Rodriguez M, Marco-Ramell A, Casas-Agustench P, et al. Visceral Adipose Tissue Phospholipid Signature of Insulin Sensitivity and Obesity[J].\u003cem\u003eJournal of Proteome Research\u003c/em\u003e;2021,20(5):2410-2419. https://doi.org/10.1021/acs.jproteome.0c00918.\u003c/li\u003e\n\u003cli\u003eLee S, Libman I,Hughan K, et al. Effects of Exercise Modality on Insulin Resistance and Ectopic Fat in Adolescents with Overweight and Obesity: A Randomized Clinical Trial.[J].J Pediatr;2019,206:91-98.e1. https://doi.10.1016/j.jpeds.2018.10.059.Epub 2018 Dec 13.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"type 2 diabetes, skeletal muscle mass to visceral fat area ratio, inflammatory factors, insulin resistance","lastPublishedDoi":"10.21203/rs.3.rs-4786661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4786661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e A cross-sectional design was used in this study. We investigated the Skeletal muscle mass to visceral fat area ratio (SVR), neutrophil/lymphocyte ratio (NLR), and insulin resistance (IR) in 201 patients with T2DMwho treated in the outpatient department and ward of the Department of Endocrinology of the Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine between June 2022 and March 2023. Appendicular skeletal muscle mass (ASM), total body fat (TBF), visceral fat area (VFA), and basal metabolic volume were measured using multifrequency bioimpedance analysis method. The percentage of body fat to body mass (TBF%), appendicular skeletal muscle mass index (ASMI), and SVR were calculated.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Patients were divided equally into three groups (Q1–Q3) according to SVR levels. Compared with the Q3 group, in both Q1 and Q2 groups, waist-hip ratio, neutrophils, NLR, fasting blood glucose, fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides, total cholesterol, free fatty acid, TBF, TBF%, and VFA were all increased (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.005), whereas lymphocytes, insulin sensitivity index (ISI), ASM, ASMI, basal metabolic rate, and SVR were all decreased (P\u0026lt;0.005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e There is a correlation between SVR and IR in T2DM patients, suggesting that SVR has certain clinical value in the early warning of IR in T2DM.\u003c/p\u003e","manuscriptTitle":"Association between skeletal muscle mass to visceral fat area ratio and insulin resistance in type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 05:01:47","doi":"10.21203/rs.3.rs-4786661/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68e10c37-e098-4351-b83f-93d42c3716dd","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T12:03:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-22 05:01:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4786661","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4786661","identity":"rs-4786661","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}