The clinical application value of the visceral fat area-to-hip circumference ratio in type 2 diabetes mellitus comorbidities

The clinical application value of the visceral fat area-to-hip circumference ratio in type 2 diabetes mellitus comorbidities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The clinical application value of the visceral fat area-to-hip circumference ratio in type 2 diabetes mellitus comorbidities Huiying Sha, Shan Fan, Xia Deng, Li Zhao, Caifeng Luo, Qiaoyan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8588872/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Purpose: Visceral fat is a crucial factor that elevates the risk of comorbidities associated with type 2 diabetes mellitus (T2DM). Consequently, this study endeavors to investigate the association between the visceral fat area-to-hip circumference ratio (VHR) and T2DM comorbidities, aiming to swiftly assess the risk of comorbidities in patients by measuring VHR. Patients and methods: A total of 2124 T2DM patients visited the Department of Endocrinology at the National Metabolic Management Center of the Affiliated Hospital of Jiangsu University between June 2018 and October 2023, and collected the physical measurement indicators and various blood biochemical indicators of all subjects. The subjects were stratified into four subgroups by the quartiles of VHR levels, and a comparative analysis of the prevalence of T2DM comorbidities among these subgroups was performed. Correlation analysis was carried out to investigate the correlation between VHR and various indicators. Binary logistic regression analysis was utilized to evaluate the relationship between the VHR level and T2DM comorbidities. The ROC curve was employed to determine the diagnostic value of VHR for T2DM comorbidities. Results: The comorbidities of patients with T2DM increase significantly along with the elevation of the VHR level. The VHR level was positively correlated with IR, UA, and TG and negatively correlated with age, disease duration, and HDL-c ( P < 0.001). Binary logistic regression analysis demonstrated that VHR remained an independent influencing factor for T2DM comorbidities after adjusting for multiple confounding factors. ROC curve analysis showed that the areas under the curve for VHR in predicting insulin resistance, hyperlipidemia, and hyperuricemia in patients with T2DM were 0.673, 0.651, and 0.665 respectively ( P < 0.001). Conclusion: VHR is independently associated with multiple diabetic complications, indicating that VHR may serve as a predictor in the onset of comorbidities related to T2DM. type 2 diabetes mellitus visceral fat area hip circumference comorbidities Figures Figure 1 Figure 2 1. Introduction T2DM is a chronic metabolic disorder in which the body experiences insulin resistance or insufficient insulin secretion due to the combined influence of genetic and environmental factors. The International Diabetes Federation (IDF) released the tenth edition of the latest data, which reveals that the number of people with diabetes in China has reached as high as 141 million. Among them, over 90% are suffering from T2DM[ 1 ]. Meanwhile, the proportion of individuals with T2DM accompanied by insulin resistance (IR), lipid metabolism abnormalities, and uric acid metabolism disorders has gradually risen. Owing to alterations in lifestyle, the population of obese individuals has been on the rise, and correspondingly, the incidence of IR exhibits an upward tendency[ 2 ]. IR, in conjunction with excessive accumulation of visceral fat, is capable of triggering overproduction and clearance impairments[ 3 , 4 ]of very low-density lipoprotein (VLDL) and triglyceride (TG). Simultaneously, it can diminish the renal uric acid clearance rate and augment the serum uric acid concentration[ 5 ], thereby leading to aberrations in lipid and uric acid metabolism and precipitating hyperlipidemia and hyperuricemia episodes. Several studies have indicated that between 82% and 95% of patients with T2DM frequently present with two or more comorbidities, such as insulin resistance, hyperlipidemia, and hyperuricemia[ 6 , 7 ]. Comorbidities increase the economic burden on patients and, to a certain extent, impact the selection of glucose-lowering treatment regimens[ 8 , 9 ]. Currently, the diagnosis and evaluation of T2DM comorbidities are mainly based on blood tests, which are invasive and costly despite their good sensitivity and specificity. Therefore, Zhang S et al. [ 10 ] developed a novel obesity index, namely VHR, within the Chinese population. This index was calculated by visceral fat area and hip circumference, enabling the direct assessment of the degree of abdominal obesity without needing to weigh. Meanwhile, the VHR index is simple and fast, allowing for dynamic and non-invasive monitoring, which is beneficial for clinical promotion. The association between VHR and IR has been demonstrated in relevant studies[ 10 ], but few studies have been documented regarding the significance of VHR to the comorbidities of T2DM. Therefore, this study intends to explore the relationship between VHR and the comorbidities of T2DM, with the expectation that clinically, the risk of comorbidities occurring in patients with T2DM can be evaluated promptly by measuring VHR. 2. Methods 2.1 Study population. In this study, patients with T2DM who visited the Department of Endocrinology and Metabolism at the Affiliated Hospital of Jiangsu University between June 2018 and October 2023 were included. All participants satisfied the 1999 WHO diagnostic criteria for type 2 diabetes mellitus[11]. This study also excluded those with type 1 diabetes mellitus, gestational diabetes mellitus, and specific types of diabetes mellitus; individuals with a history of autoimmune diseases and malignant tumours; patients who have schizophrenia and manic-depressive disorder; as well as those with severe cardiovascular and cerebrovascular diseases. To ensure the integrity of the sample size data in this study, cases with missing data on key variables, namely visceral fat area and hip circumference, were excluded. A total of 2,124 patients were finally included. 2.2 Collection of clinical and biochemical information. All subjects' general clinical data and physical measurement parameters, such as gender, age, disease duration, smoking history, drinking history, height, weight, waist circumference(WC), and hip circumference(HC), were collected by trained diabetes nurses following standardized procedures. Body mass index(BMI)= weight (kg)/[height (m)] 2 ; Waist-hip ratio (WHR)= WC (cm)/HC(cm); Visceral fat to hip circumference (VHR)= VFA (cm 2 )/ HC(cm).Insulin resistance was described by the homeostatic model assessment (HOMA): HOMA-IR=Fasting plasma insulin (FIns) × Fasting plasma glucose (FPG)/22.5. If HOMA-IR>2.69, it is considered insulin resistance[12]. All patients fasted for more than 8 to 10 hours. Elbow venous blood was collected in the early morning of the next day. Fasting plasma glucose (FPG) was detected using the glucose oxidase method. Fasting insulin (FIns) was detected by chemiluminescence. Glycated hemoglobin c(HbA1c) was determined by high-performance liquid chromatography. The BEKMAN AU5800 automatic biochemical analyzer was used to detect various indicators of blood lipids and liver and kidney function. The Omron visceral fat detector measured the visceral fat area (VFA) and subcutaneous fat area (SFA). Before the measurements, avoid strenuous activities, empty the bladder and avoid taking barium sulfate contrast or foaming agents. Participants were supine, exposing the skin of ankles, wrists, abdomen, and waist. They took a deep breath and held their breath at the end of the exhalation. The total anatomical area of the abdomen with a flat umbilicus was measured. The electrode belt and clips were placed on the abdomen, hands and feet. They held their breath again at the end of exhalation, and the abdominal VFA was measured. The difference between the total anatomical area of the abdomen and the VFA was taken as the SFA. 2.3 Statistical Methods. All statistical analyses were carried out using SPSS 27.0 and GraphPad Prism software. For non-normally distributed measures, median and interquartile range [M(P25, P75)] were employed to describe them. The Kruskal-Wallis H test was used to compare multiple groups. Categorical variables were expressed as percentages or frequencies, and intergroup comparisons were performed using the χ2 test. Correlation analysis was employed to examine the correlation between VHR and body measurements as well as various blood biochemical indexes. Binary logistic regression analysis was utilized to explore the relationship between VHR and comorbidities of T2DM. Additionally, ROC curves were used to determine the predictive value of VHR for T2DM comorbidities. With a P < 0.05, the difference was considered statistically significant. 3. Results 3.1 Baseline characteristics of all participants. According to the VHR levels, all participants were distributed into four groups. The general clinical data of the four groups of patients are shown in Table 1. With the increase of VHR, BMI, SBP, DBP, WC, HC, WHR, VFA, SFA, TG, aminotransferase levels, and HDL-C gradually increased( P < 0.001, Table 1). There is no significant difference in HbA1c, TC value and LDL-C value among the four groups( P ＞0.05, Table 1). Further data analysis found that as the VHR level increased, the prevalence of IR, hyperlipidemia, and hyperuricemia events gradually increased ( P < 0.001, figure 1). When stratified by different comorbidities, the VHR levels were notably elevated in subjects with concurrent IR, hyperlipidemia, and hyperuricemia compared to those without IR, hyperlipidemia, and hyperuricemia ( P < 0.001, Supplementary figure 2 ). 3.2 The correlation between VHR and physical measurement parameters as well as clinical biochemical indicators. The results of the correlation analysis showed that among all the population with T2DM, the VHR levels were associated positively with DBP, SBP, BMI, NC, WC, HC, WHR, VFA, SFA, IR, ALT, AST, UA, and TG ( P < 0.001). It was negatively correlated with age, duration of disease, and HDL-c ( P 0.05, Table 2）. 3.3 The relationship between VHR and T2DM comorbidities in different models. Binary logistic regression analysis was conducted with the presence or absence of comorbidities in patients with type 2 diabetes mellitus as the dependent variable and VHR as the independent variable. The results demonstrated that VHR was an independent influencing factor for insulin resistance, hyperlipidemia, and hyperuricemia. After adjusting for confounding factors such as gender, age, and ALT, there was still a significant correlation between VHR and IR, hyperlipidemia, and hyperuricemia(Table 3). These results suggested that the risk of developing complications in T2DM increases with the elevation of VHR level. 3.4 The predictive value of VHR for T2DM comorbidities. The analysis of ROC curve 1 demonstrated that the area under the curve of VHR in predicting insulin resistance (IR) among patients with type 2 diabetes mellitus was 0.673 [95% CI = (0.650 - 0.696), P < 0.001]. Subsequently, the optimal cutoff value of VHR for IR prediction was calculated. It was found that when VHR was equal to or greater than 0.81, the sensitivity reached 71.8%, and the specificity was 53.7%. The analysis of ROC curve 2 indicated that the area under the curve of VHR in predicting hyperlipidemia among patients with type 2 diabetes mellitus was 0.651 [95% CI = (0.627 - 0.676), P < 0.001]. Subsequently, the optimal cutoff value of VHR for hyperlipidemia prediction was further computed. It was determined that when VHR was more significant than or equal to 0.97, the sensitivity was 56.6%, and the specificity was 65.2%. The analysis of ROC curve 3 revealed that the area under the curve of VHR for predicting hyperuricemia in patients with type 2 diabetes mellitus was 0.665 [95%CI = (0.622 - 0.709), P < 0.001]. Subsequently, the optimal cutoff value of VHR for hyperuricemia prediction was further calculated. It was found that when VHR was greater than or equal to 1.01, the sensitivity was 62.3%, and the specificity was 62.8%( Fig 3). 4. Discussion In this study, we found for the first time that as the level of VHR increases, the prevalence of comorbidities in type 2 diabetes mellitus shows an upward trend. Meanwhile, the level of VHR is positively correlated with indicators such as IR, uric acid, and low high-density lipoprotein cholesterol (HDL-C). However, it is negatively correlated with age and the course of disease. Studies have shown that among the diabetic population in China, compared with elderly patients, the number of overweight, obese, and hyperlipidemic patients is higher among younger patients, which is consistent with the results of this study, indicating that the probability of obesity in diabetic patients decreases with age. The reason may be due to the development of the disease and the decline in physical function, resulting in the reduction of adipose tissue[ 13 , 14 ]. After adjusting for multiple confounding factors, VHR remained an independent influencing factor for type 2 diabetes mellitus complicated with insulin resistance (IR), hyperlipidemia, and hyperuricemia. Thus, the results of this study suggest that there is a certain correlation between the VHR index and the occurrence of complications in type 2 diabetes mellitus. IR is the main pathophysiological basis for the occurrence and development of T2DM[ 15 ]. With the changes in lifestyle and dietary habits, the body takes in excessive amounts of foods rich in carbohydrates and fats, causing swelling of its fat cells. While inflammatory reactions occur in these cells, inflammatory mediators will permeate into the visceral adipose tissue[ 16 , 17 ]; this leads to local inflammation in the visceral adipose tissue and promotes the occurrence of IR[ 18 ]. Hip circumference can reflect the mass of skeletal muscle tissue in the legs[ 19 ], and skeletal muscle fiber tissue is related to IR. The narrower the hip circumference is, the less the content of leg muscle tissue it represents, which further induces the formation of IR[ 20 ]. The results of this study showed that as the level of VHR increases, the prevalence of IR gradually rises, and VHR is positively correlated with IR. After adjusting for confounding factors, VHR remains an independent influencing factor for IR. Zhang S et al. [ 10 ] separately explored the relationship between VHR and IR among the population with T2DM in different genders. They found that after additionally adjusting for the factor of non-alcoholic fatty liver disease among the male population, there was no apparent linear relationship between the two variables. In contrast, after adjusting for confounding factors, there was a significant positive correlation among the female population. The reason might be the relatively small sample size in Zhang S's study and the differences in hormone secretion among different genders. This study further demonstrates that VHR is closely associated with IR and may be involved in the occurrence of IR. The increase in visceral fat leads to an increase in the body's free fatty acid content, resulting in abnormal lipid synthesis in organs such as the liver and pancreas, and ultimately leading to hyperlipidemia[ 21 ]. The results of this study showed that the prevalence of hyperlipidemia gradually increased with rising VHR levels, and VHR was positively correlated with TG and low HDL-c.In Bisschop CN et al. [ 22 ] study, all male subjects were categorized into four subgroups based on their visceral fat and physical activity levels. The research findings indicated that a higher level of visceral fat was significantly correlated with the risks of hyperlipidemia, abdominal obesity, and diminished levels of HDL-c. Meanwhile, it was also found that compared with men with a low visceral fat level, men with a high visceral fat level had significantly more unhealthy metabolic risk factors and a greater risk of suffering from metabolic syndrome, which was consistent with the results of this study. However, in the Jeon HH et al. [ 23 ] study, the visceral fat area was positively correlated with TC and LDL-c, which was inconsistent with the results of the present study. The reason for this may be due to the difference in sample size, which was included in the Jeon HH study in a larger number of samples than in the present study, and the difference in the disease states also contributed to the difference. Accumulation of visceral fat promotes the occurrence of IR, which will consequently reduce the clearance effect of uric acid by the kidneys in the body, leading to an increase in uric acid content in the serum[ 24 ]. Previous studies have shown[ 25 ] that compared with their peers with normal body weight, the serum uric acid levels of patients with morbid obesity increase significantly. Hikita M et al. [ 26 ] explored the correlation between the visceral fat area and serum uric acid in the population with metabolic syndrome accompanied by visceral fat accumulation (≥ 100 cm²). They showed that the visceral fat area was the most significant influencing factor for serum uric acid. Moreover, as the factors of metabolic syndrome increase, the serum uric acid level keeps rising and is positively correlated with abdominal fat content. Tao M et al. [ 27 ] found that in the population with non-alcoholic fatty liver disease, the serum uric acid level was positively correlated with the visceral fat area, the subcutaneous fat area, and the amount of trunk fat. Meanwhile, after adjusting for multiple confounding factors such as age, gender, and BMI, visceral fat was an independent risk factor for the serum uric acid level. In the present study, we found that the prevalence of hyperuricemia gradually increased with increasing VHR levels, and VHR was positively correlated with blood uric acid. After adjusting for confounding factors, VHR remained an independent influence on hyperuricemia, similar to the results of the above studies. In summary, the greater the level of VHR, the higher the prevalence of T2DM complicated with IR, hyperlipidemia and hyperuricemia. Further analysis showed that VHR was an independent influencing factor for T2DM comorbidities and had a certain predictive value. Therefore, clinical monitoring of VHR levels in patients with T2DM should be performed early to prevent comorbidities at an early stage. There are some limitations in this study: although 2124 study subjects were included, there was a lack of controlled studies in the normal population; at the same time, the causes of T2DM comorbidities are complex, the use of medications is inconsistent, and the degree of variability between individuals is large, so subsequent multicenter and large-sample studies are still needed to conduct validation studies. In addition, this study did not conduct a dynamic observation of the VHR index, and the correlation between the change in the VHR index and the progress of T2DM comorbidities needs to be further explored. Declarations Ethics approval and consent to participate The study was approved by the Biomedical Research Ethics Committee of Affiliated Hospital of Jiangsu University, Zhenjiang, China, and performed in accordance with the Declaration of Helsinki. All participants obtained written informed consent. Consent for publication Not applicable. Availability of data and materials Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Competing interests The authors declare no competing interests. Funding This research was supported by the Social Development of Zhenjiang(SH2023053), the Affiliated Hospital of Jiangsu University Beigu Talent Cultivation Plan Project (BGYCB202206), and the Science and Technology Planning Project—Fundamental Research Fund of Zhenjiang City（JC2004029）. Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by QYL, CFL, XD and LZ. The first draft of the manuscript was written by HYS and SF and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. References Sun H, Saeedi P, Karuranga S, et al. 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Bmc Endocr Disorders. 2023;23. http:dx.doi.org/10.1186/s12902-023-01447-7 . Tables Table 1. Baseline characteristics of all participants. VHR Characteristics VHR1(n=542) VHR2(n=534) VHR3(n=529) VHR4(n=519) P Male/female(N) 31/511 303/231 350/179 393/126 ＜0.001 Ages（years） 55(48,61) 56(47,63) 55(45.5,63) 53(41,62) 0.005 BMI（kg/cm 2 ） 22(20.3,23.7) 24.2（22.7,25.9） 25.6（23.9,27.25） 27.8（25.7,30.4） ＜0.001 Duration of T2DM (M) 53.5(1,126) 41.5(0,124) 28(0,110.5) 15(0,98) ＜0.001 SBP（mmHg） 122(111,134) 125(114,136) 129(118,140) 132(120,144) ＜0.001 DBP（mmHg） 71(65,79) 74(67.75,81) 77(70.5,84) 79(72,87) ＜0.001 Smoking（%） 34.31 31.08 35.92 42.20 0.002 Drinking（%） 31.37 8.1 42.34 40.66 ＜0.001 NC(cm) 36(34,38) 37(35,39) 39(37,41) 41(38,43) ＜0.001 WC(cm) 83（78,88） 89（84,93） 93（89,97） 99（94,106） ＜0.001 HC(cm) 93（89,96） 96（93,100） 98（94,102） 101（96,107） ＜0.001 WHR（cm） 0.89（0.86,0.93） 0.93（0.89,0.96） 0.95（0.92,0.97） 0.98（0.95,1.02） ＜0.001 VFA(cm 2 ) 49（36,58） 78（72,83） 101（94,108） 138（124,158） 0.000 SFA(cm 2 ) 126.5（99,153） 166（141,195） 189（161.5,220.0） 229（188,282） ＜0.001 HbA1c/% 9.8(8,11.6) 9.5(8,11) 9.7(8.1,11.3) 9.6(8.3,11.1) 0.148 FPG(mmol/L) 10.21(7.47,13.25) 7.48(10.05,12.49) 9.98(7.86,12.93) 9.98(8.15,12.76) 0.540 Fins(μIU/mL) 4.75(2.87,8.05) 6.805(4.2,10.83) 7.79(4.91,11.35) 9.31(6.18,14.25) ＜0.001 Comorbidities Hypertension(N) 161 7 285 287 ＜0.001 Hyperlipidemia(N) 90 168 205 242 ＜0.001 Hyperuricemia(N) 0 31 42 65 ＜0.001 NAFLD(N) 92 175 235 281 ＜0.001 HOMA-IR 2.035（1.24,3.36） 2.93（1.91,4.50） 3.37（2.1,5.22） 4.18（2.67,6.29） ＜0.001 ALT(U/L） 17.1(11.4,25.15) 20.5(14.5,32.2) 22.8(15.1,38.9) 31.7(18.2,51.6) ＜0.001 AST(U/L） 15(12.3,20) 17.35(13.5,22.6) 17.9(13.3,17.9) 21.9(16,34.8) ＜0.001 UA(μmol/L) 249(199,307.25) 282(231,340.25) 299(242,351.2) 325(263,391) ＜0.001 TG(mmol/L) 1.4（1.03,2.02） 1.81（1.33,2.74） 2.07（1.41,2.93） 2.26（1.60,3.51） ＜0.001 TC(mmol/L) 4.795（4.15,5.64） 4.9（4.17,5.70） 4.86（4.17,5.59） 4.89（4.32,5.65） 0.23 HDL-c(mmol/L) 1.225（1.01,1.53） 1.08（0.92,1.29） 1.06（0.89,1.24） 0.98（0.84,1.19） ＜0.01 LDL-C(mmol/L) 2.74（2.2,2.38） 2.89（2.28,3.58） 2.79（2.23,3.39） 2.83（2.3,3.43） 0.105 Note: Data are presented as medians(interquartile range), percentages, or frequencies. P values<0.05 were considered statistically significant. VHR: VHR1≤0.69, 0.69＜VHR2≤0.91, 0.91＜VHR3≤1.15，VHR4＞1.15. BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; NC: neck circumference; WC: waist circumference; HC: hip circumference; WHR:waist-to-hip ratio; VFA: visceral fat area; VHR: visceral fat area-to-hip circumference ratio; SFA: subcutaneous fat area; FPG: fasting plasma glucose; Fins: fasting plasma insulin; HbA1c: glycosylated hemoglobin c; NAFLD: non-alcoholic fatty-liver disease; HOMA-IR: homeostasis model assessment-insulin resistance index; ALT: alanine aminotransferase; AST: aspartate aminotransferase; UA: uric acid; TG: triglycerid; TC:total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol. Table 2. The correlation between VHR and physical measurement parameters as well as clinical biochemical indicators. Characteristics R P Ages（years） -0.074 ＜0.001 DBP（mmHg） 0.255 ＜0.001 SBP（mmHg） 0.224 ＜0.001 BMI（kg/m2） 0.656 ＜0.001 Duration of T2DM (M) -0.135 ＜0.001 NC（cm） 0.458 ＜0.001 WC（cm） 0.673 ＜0.001 HC（cm） 0.467 ＜0.001 WHR（cm） 0.566 ＜0.001 VFA（cm 2 ） 0.987 ＜0.001 SFA（cm 2 ） 0.640 ＜0.001 HbA1c/% -0.024 0.266 FPG 0.012 0.587 IR 0.346 ＜0.001 ALT(U/L） 0.292 ＜0.001 AST(U/L） 0.260 ＜0.001 UA(μmol/L) 0.314 ＜0.001 TG(mmol/L) 0.319 ＜0.001 TC(mmol/L) 0.041 0.057 HDL-c(mmol/L) -0.270 ＜0.01 LDL-c(mmol/L) 0.023 0.279 Table 3. The relationship between VHR and T2DM comorbidities in different models. OR 95%CI P value Without vs. With IR Model 1 5.876 4.477-7.713 ＜0.001 Model 2 6.154 4.669-8.126 ＜0.001 Model 3 5.727 4.323-7.586 ＜0.001 Without vs.With Hyperlipidemia Model 1 4.432 3.403-5.772 ＜0.001 Model 2 4.484 3.428-5.865 ＜0.001 Model 3 4.311 3.289-5.651 ＜0.001 Without vs.With Hyperuricemia Model 1 4.274 2.805-6.514 ＜0.001 Model 2 3.508 2.283-5.389 ＜0.001 Model 3 3.428 2.223-5.286 ＜0.001 Model 1：unjusted； Model 2：adjusted for sex and age； Model 3：adjusted for model 2 and ALT Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Editor invited by journal 16 Jan, 2026 Submission checks completed at journal 15 Jan, 2026 First submitted to journal 15 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-8588872","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588051781,"identity":"cfc67db7-078b-49c0-8827-a218f4ad8e21","order_by":0,"name":"Huiying Sha","email":"","orcid":"","institution":"Affiliated Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Huiying","middleName":"","lastName":"Sha","suffix":""},{"id":588051782,"identity":"639862b4-61a9-4b3f-8776-f084d3d56484","order_by":1,"name":"Shan Fan","email":"","orcid":"","institution":"Affiliated Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Fan","suffix":""},{"id":588051783,"identity":"e3cec7e9-0e46-4f77-a90f-4575794456cf","order_by":2,"name":"Xia Deng","email":"","orcid":"","institution":"Affiliated Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Deng","suffix":""},{"id":588051784,"identity":"f457de83-f752-44d4-8620-dee110b5f24c","order_by":3,"name":"Li Zhao","email":"","orcid":"","institution":"Affiliated Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhao","suffix":""},{"id":588051785,"identity":"576bc65d-8404-4ec0-aea9-19bf851bae95","order_by":4,"name":"Caifeng Luo","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Caifeng","middleName":"","lastName":"Luo","suffix":""},{"id":588051786,"identity":"bef292ec-2202-4e73-86ac-265e29e126e7","order_by":5,"name":"Qiaoyan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3RMQrCMBSA4YRAXGLnVwp6hZSCOBRP4CEiBacO3sBKQRfdI3oIJ1crQl10L7jo4lwQnAra1sml6SiYf3y8b0geQjrdD0YJDiLB3R6ehRGCYhQpiNEI99d0NPTIPBb1SIvFni3TA57JPv+MVISC37EYJ6Qp2RO6GWoZicCPkZpQw1wttmBOkWMmglhSTRix1+ecBGiwSQQlTE0A7xL/DpChsZqUz+ccT6RPASgSXEnKT+bCw/PY6ZpTsJenW2hVkXZYnDJ7Fae8XSBz28bR2z+qyFcEymPioC7Id9P6uzqdTvdHvQHQ8ko/qa2RPgAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hospital of Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Qiaoyan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-13 07:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8588872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8588872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102447487,"identity":"98201655-2590-4558-a06e-e8e2f88463ff","added_by":"auto","created_at":"2026-02-11 17:55:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prevalence of T2DM comorbidities among different subgroups. \u003c/strong\u003eAccording to the VHR levels, all participants were distributed into four groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8588872/v1/869282e6079dd15e85b8ac4b.png"},{"id":102447510,"identity":"9622a695-f592-440f-9235-a3e3574fa71d","added_by":"auto","created_at":"2026-02-11 17:55:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3．The predictive value of VHR for T2DM comorbidities\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8588872/v1/70a568d8ffd09694e31a43e2.png"},{"id":102447521,"identity":"edb0c615-b840-49f2-bae5-2c8d7b9feea1","added_by":"auto","created_at":"2026-02-11 17:55:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":987501,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8588872/v1/a1fedb7e-0397-48af-9d56-62100cf5a74a.pdf"},{"id":102447448,"identity":"e47b2c27-4132-45cb-9732-5f263e7f845d","added_by":"auto","created_at":"2026-02-11 17:55:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":41778,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8588872/v1/0b0ac8473be34358a200585d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The clinical application value of the visceral fat area-to-hip circumference ratio in type 2 diabetes mellitus comorbidities","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eT2DM is a chronic metabolic disorder in which the body experiences insulin resistance or insufficient insulin secretion due to the combined influence of genetic and environmental factors. The International Diabetes Federation (IDF) released the tenth edition of the latest data, which reveals that the number of people with diabetes in China has reached as high as 141\u0026nbsp;million. Among them, over 90% are suffering from T2DM[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Meanwhile, the proportion of individuals with T2DM accompanied by insulin resistance (IR), lipid metabolism abnormalities, and uric acid metabolism disorders has gradually risen. Owing to alterations in lifestyle, the population of obese individuals has been on the rise, and correspondingly, the incidence of IR exhibits an upward tendency[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. IR, in conjunction with excessive accumulation of visceral fat, is capable of triggering overproduction and clearance impairments[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]of very low-density lipoprotein (VLDL) and triglyceride (TG). Simultaneously, it can diminish the renal uric acid clearance rate and augment the serum uric acid concentration[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], thereby leading to aberrations in lipid and uric acid metabolism and precipitating hyperlipidemia and hyperuricemia episodes.\u003c/p\u003e \u003cp\u003eSeveral studies have indicated that between 82% and 95% of patients with T2DM frequently present with two or more comorbidities, such as insulin resistance, hyperlipidemia, and hyperuricemia[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Comorbidities increase the economic burden on patients and, to a certain extent, impact the selection of glucose-lowering treatment regimens[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Currently, the diagnosis and evaluation of T2DM comorbidities are mainly based on blood tests, which are invasive and costly despite their good sensitivity and specificity. Therefore, Zhang S et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] developed a novel obesity index, namely VHR, within the Chinese population. This index was calculated by visceral fat area and hip circumference, enabling the direct assessment of the degree of abdominal obesity without needing to weigh.\u003c/p\u003e \u003cp\u003eMeanwhile, the VHR index is simple and fast, allowing for dynamic and non-invasive monitoring, which is beneficial for clinical promotion. The association between VHR and IR has been demonstrated in relevant studies[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], but few studies have been documented regarding the significance of VHR to the comorbidities of T2DM. Therefore, this study intends to explore the relationship between VHR and the comorbidities of T2DM, with the expectation that clinically, the risk of comorbidities occurring in patients with T2DM can be evaluated promptly by measuring VHR.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study population.\u003c/strong\u003e In this study, patients with T2DM who visited the Department of Endocrinology and Metabolism at the Affiliated Hospital of Jiangsu University between June 2018 and October 2023 were included. All participants satisfied the 1999 WHO diagnostic criteria for type 2 diabetes mellitus[11]. This study also excluded those with type 1 diabetes mellitus, gestational diabetes mellitus, and specific types of diabetes mellitus; individuals with a history of autoimmune diseases and malignant tumours; patients who have schizophrenia and manic-depressive disorder; as well as those with severe cardiovascular and cerebrovascular diseases. To ensure the integrity of the sample size data in this study, cases with missing data on key variables, namely visceral fat area and hip circumference, were excluded. A total of 2,124 patients were finally included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Collection of clinical and biochemical information.\u0026nbsp;\u003c/strong\u003eAll subjects' general clinical data and physical measurement parameters, such as gender, age, disease duration, smoking history, drinking history, height, weight, waist circumference(WC), and hip circumference(HC), were collected by trained diabetes nurses following standardized procedures. Body mass index(BMI)= weight (kg)/[height (m)]\u003csup\u003e2\u003c/sup\u003e; Waist-hip ratio (WHR)= WC (cm)/HC(cm); Visceral fat to hip circumference (VHR)= VFA (cm\u003csup\u003e2\u003c/sup\u003e)/ HC(cm).Insulin resistance was described by the homeostatic model assessment (HOMA): HOMA-IR=Fasting plasma insulin (FIns) × Fasting plasma glucose (FPG)/22.5. If HOMA-IR\u0026gt;2.69, it is considered insulin resistance[12].\u003c/p\u003e\n\u003cp\u003eAll patients fasted for more than 8 to 10 hours. Elbow venous blood was collected in the early morning of the next day. Fasting plasma glucose (FPG) was detected using the glucose oxidase method. Fasting insulin (FIns) was detected by chemiluminescence. Glycated hemoglobin c(HbA1c) was determined by high-performance liquid chromatography. The BEKMAN AU5800 automatic biochemical analyzer was used to detect various indicators of blood lipids and liver and kidney function.\u003c/p\u003e\n\u003cp\u003eThe Omron visceral fat detector measured the visceral fat area (VFA) and subcutaneous fat area (SFA). Before the measurements, avoid strenuous activities, empty the bladder and avoid taking barium sulfate contrast or foaming agents. Participants were supine, exposing the skin of ankles, wrists, abdomen, and waist. They took a deep breath and held their breath at the end of the exhalation. The total anatomical area of the abdomen with a flat umbilicus was measured. The electrode belt and clips were placed on the abdomen, hands and feet. They held their breath again at the end of exhalation, and the abdominal VFA was measured. The difference between the total anatomical area of the abdomen and the VFA was taken as the SFA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Statistical Methods.\u0026nbsp;\u003c/strong\u003eAll statistical analyses were carried out using SPSS 27.0 and GraphPad Prism software. For non-normally distributed measures, median and interquartile range [M(P25, P75)] were employed to describe them. The Kruskal-Wallis H test was used to compare multiple groups. Categorical variables were expressed as percentages or frequencies, and intergroup comparisons were performed using the χ2 test. Correlation analysis was employed to examine the correlation between VHR and body measurements as well as various blood biochemical indexes. Binary logistic regression analysis was utilized to explore the relationship between VHR and comorbidities of T2DM. Additionally, ROC curves were used to determine the predictive value of VHR for T2DM comorbidities. With a P \u0026lt; 0.05, the difference was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline characteristics of all participants.\u003c/strong\u003e According to the VHR levels, all participants were distributed into four groups. The general clinical data of the four groups of patients are shown in Table 1. With the increase of VHR, BMI, SBP, DBP, WC, HC, WHR, VFA, SFA, TG, aminotransferase levels, and HDL-C gradually increased(\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, Table 1). There is no significant difference in HbA1c, TC value and LDL-C value among the four groups(\u003cem\u003eP\u003c/em\u003e＞0.05,\u0026nbsp;Table\u0026nbsp;1). Further data analysis found that as the VHR level increased, the prevalence of IR, hyperlipidemia, and hyperuricemia events gradually increased (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001,\u0026nbsp;figure 1). When stratified by different comorbidities, the VHR levels were notably elevated in subjects with concurrent IR, hyperlipidemia, and hyperuricemia compared to those without IR, hyperlipidemia, and hyperuricemia (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, Supplementary\u0026nbsp;figure\u0026nbsp;2 ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 The correlation between VHR and physical measurement parameters as well as clinical biochemical indicators.\u0026nbsp;\u003c/strong\u003eThe results of the correlation analysis showed that among all the population with T2DM, the VHR levels were associated positively with DBP, SBP, BMI, NC, WC, HC, WHR, VFA, SFA, IR, ALT, AST, UA, and TG (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). It was negatively correlated with age, duration of disease, and HDL-c (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and did not correlate with HbA1c, TC, and LDL-c (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05, Table 2）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 The relationship between VHR and T2DM comorbidities in different models.\u0026nbsp;\u003c/strong\u003eBinary logistic regression analysis was conducted with the presence or absence of comorbidities in patients with type 2 diabetes mellitus as the dependent variable and VHR as the independent variable. The results demonstrated that VHR was an independent influencing factor for insulin resistance, hyperlipidemia, and hyperuricemia. After adjusting for confounding factors such as gender, age, and ALT, there was still a significant correlation between VHR and IR, hyperlipidemia, and hyperuricemia(Table 3). These results suggested that the risk of developing complications in T2DM increases with the elevation of VHR level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 The predictive value of VHR for T2DM comorbidities.\u0026nbsp;\u003c/strong\u003eThe analysis of ROC curve 1 demonstrated that the area under the curve of VHR in predicting insulin resistance (IR) among patients with type 2 diabetes mellitus was 0.673 [95% CI = (0.650 - 0.696), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001]. Subsequently, the optimal cutoff value of VHR for IR prediction was calculated. It was found that when VHR was equal to or greater than 0.81, the sensitivity reached 71.8%, and the specificity was 53.7%. The analysis of ROC curve 2 indicated that the area under the curve of VHR in predicting hyperlipidemia among patients with type 2 diabetes mellitus was 0.651 [95% CI = (0.627 - 0.676), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001]. Subsequently, the optimal cutoff value of VHR for hyperlipidemia prediction was further computed. It was determined that when VHR was more significant than or equal to 0.97, the sensitivity was 56.6%, and the specificity was 65.2%.\u0026nbsp;The analysis of ROC curve 3 revealed that the area under the curve of VHR for predicting hyperuricemia in patients with type 2 diabetes mellitus was 0.665 [95%CI = (0.622 - 0.709), \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001]. Subsequently, the optimal cutoff value of VHR for hyperuricemia prediction was further calculated. It was found that when VHR was greater than or equal to 1.01, the sensitivity was 62.3%, and the specificity was 62.8%( Fig 3).\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we found for the first time that as the level of VHR increases, the prevalence of comorbidities in type 2 diabetes mellitus shows an upward trend. Meanwhile, the level of VHR is positively correlated with indicators such as IR, uric acid, and low high-density lipoprotein cholesterol (HDL-C). However, it is negatively correlated with age and the course of disease. Studies have shown that among the diabetic population in China, compared with elderly patients, the number of overweight, obese, and hyperlipidemic patients is higher among younger patients, which is consistent with the results of this study, indicating that the probability of obesity in diabetic patients decreases with age. The reason may be due to the development of the disease and the decline in physical function, resulting in the reduction of adipose tissue[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. After adjusting for multiple confounding factors, VHR remained an independent influencing factor for type 2 diabetes mellitus complicated with insulin resistance (IR), hyperlipidemia, and hyperuricemia. Thus, the results of this study suggest that there is a certain correlation between the VHR index and the occurrence of complications in type 2 diabetes mellitus.\u003c/p\u003e \u003cp\u003eIR is the main pathophysiological basis for the occurrence and development of T2DM[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. With the changes in lifestyle and dietary habits, the body takes in excessive amounts of foods rich in carbohydrates and fats, causing swelling of its fat cells. While inflammatory reactions occur in these cells, inflammatory mediators will permeate into the visceral adipose tissue[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; this leads to local inflammation in the visceral adipose tissue and promotes the occurrence of IR[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Hip circumference can reflect the mass of skeletal muscle tissue in the legs[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and skeletal muscle fiber tissue is related to IR. The narrower the hip circumference is, the less the content of leg muscle tissue it represents, which further induces the formation of IR[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The results of this study showed that as the level of VHR increases, the prevalence of IR gradually rises, and VHR is positively correlated with IR. After adjusting for confounding factors, VHR remains an independent influencing factor for IR. Zhang S et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] separately explored the relationship between VHR and IR among the population with T2DM in different genders. They found that after additionally adjusting for the factor of non-alcoholic fatty liver disease among the male population, there was no apparent linear relationship between the two variables. In contrast, after adjusting for confounding factors, there was a significant positive correlation among the female population. The reason might be the relatively small sample size in Zhang S's study and the differences in hormone secretion among different genders. This study further demonstrates that VHR is closely associated with IR and may be involved in the occurrence of IR.\u003c/p\u003e \u003cp\u003eThe increase in visceral fat leads to an increase in the body's free fatty acid content, resulting in abnormal lipid synthesis in organs such as the liver and pancreas, and ultimately leading to hyperlipidemia[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The results of this study showed that the prevalence of hyperlipidemia gradually increased with rising VHR levels, and VHR was positively correlated with TG and low HDL-c.In Bisschop CN et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] study, all male subjects were categorized into four subgroups based on their visceral fat and physical activity levels. The research findings indicated that a higher level of visceral fat was significantly correlated with the risks of hyperlipidemia, abdominal obesity, and diminished levels of HDL-c. Meanwhile, it was also found that compared with men with a low visceral fat level, men with a high visceral fat level had significantly more unhealthy metabolic risk factors and a greater risk of suffering from metabolic syndrome, which was consistent with the results of this study. However, in the Jeon HH et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] study, the visceral fat area was positively correlated with TC and LDL-c, which was inconsistent with the results of the present study. The reason for this may be due to the difference in sample size, which was included in the Jeon HH study in a larger number of samples than in the present study, and the difference in the disease states also contributed to the difference.\u003c/p\u003e \u003cp\u003eAccumulation of visceral fat promotes the occurrence of IR, which will consequently reduce the clearance effect of uric acid by the kidneys in the body, leading to an increase in uric acid content in the serum[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Previous studies have shown[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] that compared with their peers with normal body weight, the serum uric acid levels of patients with morbid obesity increase significantly. Hikita M et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] explored the correlation between the visceral fat area and serum uric acid in the population with metabolic syndrome accompanied by visceral fat accumulation (\u0026ge;\u0026thinsp;100 cm\u0026sup2;). They showed that the visceral fat area was the most significant influencing factor for serum uric acid. Moreover, as the factors of metabolic syndrome increase, the serum uric acid level keeps rising and is positively correlated with abdominal fat content. Tao M et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] found that in the population with non-alcoholic fatty liver disease, the serum uric acid level was positively correlated with the visceral fat area, the subcutaneous fat area, and the amount of trunk fat. Meanwhile, after adjusting for multiple confounding factors such as age, gender, and BMI, visceral fat was an independent risk factor for the serum uric acid level. In the present study, we found that the prevalence of hyperuricemia gradually increased with increasing VHR levels, and VHR was positively correlated with blood uric acid. After adjusting for confounding factors, VHR remained an independent influence on hyperuricemia, similar to the results of the above studies.\u003c/p\u003e \u003cp\u003eIn summary, the greater the level of VHR, the higher the prevalence of T2DM complicated with IR, hyperlipidemia and hyperuricemia. Further analysis showed that VHR was an independent influencing factor for T2DM comorbidities and had a certain predictive value. Therefore, clinical monitoring of VHR levels in patients with T2DM should be performed early to prevent comorbidities at an early stage. There are some limitations in this study: although 2124 study subjects were included, there was a lack of controlled studies in the normal population; at the same time, the causes of T2DM comorbidities are complex, the use of medications is inconsistent, and the degree of variability between individuals is large, so subsequent multicenter and large-sample studies are still needed to conduct validation studies. In addition, this study did not conduct a dynamic observation of the VHR index, and the correlation between the change in the VHR index and the progress of T2DM comorbidities needs to be further explored.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Biomedical Research Ethics Committee of Affiliated Hospital of Jiangsu University, Zhenjiang, China, and performed in accordance with the Declaration of Helsinki. All participants obtained written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sharing is not applicable to this article as no datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Social Development of Zhenjiang(SH2023053), the Affiliated Hospital of Jiangsu University Beigu Talent Cultivation Plan Project (BGYCB202206), and the Science and Technology Planning Project\u0026mdash;Fundamental Research Fund of Zhenjiang City（JC2004029）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by QYL, CFL, XD and LZ. The first draft of the manuscript was written by HYS and SF and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\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. Diabetes Res Clin Pract. 2022;183:109\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi WP, Chen QF, Xie YK, et al. Prevalence and degree of insulin resistance in Chinese Han women with PCOS: Results from euglycemic-hyperinsulinemic clamps. 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Baseline characteristics of all participants.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"696\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 472px;\"\u003e\n \u003cp\u003eVHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eVHR1(n=542)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eVHR2(n=534)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVHR3(n=529)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eVHR4(n=519)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eMale/female(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e31/511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e303/231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e350/179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e393/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eAges（years）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e55(48,61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e56(47,63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e55(45.5,63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e53(41,62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eBMI（kg/cm\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e22(20.3,23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e24.2（22.7,25.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e25.6（23.9,27.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e27.8（25.7,30.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eDuration of T2DM (M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e53.5(1,126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e41.5(0,124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e28(0,110.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e15(0,98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eSBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e122(111,134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e125(114,136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e129(118,140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e132(120,144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eDBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e71(65,79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e74(67.75,81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e77(70.5,84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e79(72,87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eSmoking（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e34.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e31.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e35.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e42.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eDrinking（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e31.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e42.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e40.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eNC(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e36(34,38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e37(35,39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e39(37,41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e41(38,43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eWC(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e83（78,88）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e89（84,93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e93（89,97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e99（94,106）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eHC(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e93（89,96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e96（93,100）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e98（94,102）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e101（96,107）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eWHR（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0.89（0.86,0.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.93（0.89,0.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.95（0.92,0.97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.98（0.95,1.02）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eVFA(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e49（36,58）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e78（72,83）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e101（94,108）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e138（124,158）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eSFA(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e126.5（99,153）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e166（141,195）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e189（161.5,220.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e229（188,282）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eHbA1c/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e9.8(8,11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e9.5(8,11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.7(8.1,11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e9.6(8.3,11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eFPG(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e10.21(7.47,13.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e7.48(10.05,12.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.98(7.86,12.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e9.98(8.15,12.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eFins(\u0026mu;IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.75(2.87,8.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e6.805(4.2,10.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e7.79(4.91,11.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e9.31(6.18,14.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eHypertension(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eHyperlipidemia(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eHyperuricemia(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eNAFLD(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.035（1.24,3.36）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2.93（1.91,4.50）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e3.37（2.1,5.22）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4.18（2.67,6.29）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eALT(U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e17.1(11.4,25.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e20.5(14.5,32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e22.8(15.1,38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e31.7(18.2,51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eAST(U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e15(12.3,20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e17.35(13.5,22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e17.9(13.3,17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e21.9(16,34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eUA(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e249(199,307.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e282(231,340.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e299(242,351.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e325(263,391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTG(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.4（1.03,2.02）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.81（1.33,2.74）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e2.07（1.41,2.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.26（1.60,3.51）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eTC(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.795（4.15,5.64）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e4.9（4.17,5.70）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e4.86（4.17,5.59）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4.89（4.32,5.65）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eHDL-c(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.225（1.01,1.53）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.08（0.92,1.29）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1.06（0.89,1.24）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.98（0.84,1.19）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2.74（2.2,2.38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2.89（2.28,3.58）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e2.79（2.23,3.39）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.83（2.3,3.43）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data are presented as medians(interquartile range), percentages, or frequencies. P values\u0026lt;0.05 were considered statistically significant. VHR: VHR1\u0026le;0.69, 0.69＜VHR2\u0026le;0.91, 0.91＜VHR3\u0026le;1.15，VHR4＞1.15.\u0026nbsp;BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; NC: neck circumference; WC: waist circumference; HC: hip circumference; WHR:waist-to-hip ratio; VFA: visceral fat area; VHR: visceral fat area-to-hip circumference ratio; SFA: subcutaneous fat area; FPG: fasting plasma glucose; Fins: fasting plasma insulin; HbA1c: glycosylated hemoglobin c; NAFLD: non-alcoholic fatty-liver disease; HOMA-IR: homeostasis model assessment-insulin resistance index; ALT: alanine aminotransferase; AST: aspartate aminotransferase; UA: uric acid; TG: triglycerid; TC:total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. The correlation between VHR and physical measurement parameters as well as clinical biochemical indicators.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eAges（years）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eDBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eSBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eBMI（kg/m2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eDuration of T2DM (M)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eNC（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eWC（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eHC（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eWHR（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eVFA（cm\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eSFA（cm\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eHbA1c/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eALT(U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eAST(U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eUA(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eTG(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eTC(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eHDL-c(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e＜0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eLDL-c(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. The relationship between VHR and T2DM comorbidities in different models.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eWithout vs. With IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e4.477-7.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e4.669-8.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e4.323-7.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eWithout vs.With Hyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3.403-5.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3.428-5.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3.289-5.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eWithout vs.With Hyperuricemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2.805-6.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e3.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2.283-5.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e3.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2.223-5.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1：unjusted；\u003c/p\u003e\n\u003cp\u003eModel 2：adjusted for sex and age；\u003c/p\u003e\n\u003cp\u003eModel 3：adjusted for model 2 and ALT\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"type 2 diabetes mellitus, visceral fat area, hip circumference, comorbidities","lastPublishedDoi":"10.21203/rs.3.rs-8588872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8588872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eVisceral fat is a crucial factor that elevates the risk of comorbidities associated with type 2 diabetes mellitus (T2DM). Consequently, this study endeavors to investigate the association between the visceral fat area-to-hip circumference ratio (VHR) and T2DM comorbidities, aiming to swiftly assess the risk of comorbidities in patients by measuring VHR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients and methods:\u003c/strong\u003e A total of 2124 T2DM patients visited the Department of Endocrinology at the National Metabolic Management Center of the Affiliated Hospital of Jiangsu University between June 2018 and October 2023, and collected the physical measurement indicators and various blood biochemical indicators of all subjects. The subjects were stratified into four subgroups by the quartiles of VHR levels, and a comparative analysis of the prevalence of T2DM comorbidities among these subgroups was performed. Correlation analysis was carried out to investigate the correlation between VHR and various indicators. Binary logistic regression analysis was utilized to evaluate the relationship between the VHR level and T2DM comorbidities. The ROC curve was employed to determine the diagnostic value of VHR for T2DM comorbidities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The comorbidities of patients with T2DM increase significantly along with the elevation of the VHR level. The VHR level was positively correlated with IR, UA, and TG and negatively correlated with age, disease duration, and HDL-c (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Binary logistic regression analysis demonstrated that VHR remained an independent influencing factor for T2DM comorbidities after adjusting for multiple confounding factors. ROC curve analysis showed that the areas under the curve for VHR in predicting insulin resistance, hyperlipidemia, and hyperuricemia in patients with T2DM were 0.673, 0.651, and 0.665 respectively (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e VHR is independently associated with multiple diabetic complications, indicating that VHR may serve as a predictor in the onset of comorbidities related to T2DM.\u003c/p\u003e","manuscriptTitle":"The clinical application value of the visceral fat area-to-hip circumference ratio in type 2 diabetes mellitus comorbidities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 17:54:37","doi":"10.21203/rs.3.rs-8588872/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-28T08:33:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153993601821700776534959335829542173526","date":"2026-02-14T07:44:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T07:33:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T07:14:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-16T05:29:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T03:48:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-01-16T03:42:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5bda6723-7ab7-4c6f-be99-46b8aadf46c7","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T17:54:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 17:54:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8588872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8588872","identity":"rs-8588872","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}