Association Between Obesity-Related Indicators and Peripheral Neuropathy in Type 2 Diabetes Mellitus

Association Between Obesity-Related Indicators and Peripheral Neuropathy in Type 2 Diabetes Mellitus | 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 Association Between Obesity-Related Indicators and Peripheral Neuropathy in Type 2 Diabetes Mellitus Huizhen Ji, Jiahui Cui, Yiyi Zhang, Qiran Ma, Xiang Hu, Huihui Deng, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8230770/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 Objective This study investigated the association between obesity-related indices [Lipid Accumulation Product (LAP), Visceral Adipose Index (VAI), Body Roundness Index (BRI)] and diabetic peripheral neuropathy (DPN) in Chinese patients with type 2 diabetes mellitus. Methods 1098 participants with type 2 diabetes were enrolled. The correlation between and DPN as well as abnormal vibration perception threshold (VPT) were explored by logistic regression and multiple linear regression analysis. Results VAI was significantly correlated with higher likelihood of having DPN (OR 1.175, 95% CI 1.012–1.363, P = 0.034). LAP and VAI were associated with abnormal VPT (OR 1.024, 95%CI 1.011–1.038, P < 0.001 for LAP; OR 1.203, 95%CI 1.044–1.386, P = 0.011 for VAI). Multiple linear regression analysis showed that LAP and VAI were associated with VPT (LAP, β = 0.038, 95% CI 0.014–0.061, P = 0.002; VAI, β = 0.313, 95% CI 0.037–0.589, P = 0.026). Conclusions Our study shows that VAI was significantly associated with DPN, and LAP as well as VAI were significantly correlated with abnormal VPT in Chinese patients with type 2 diabetes. Type 2 diabetes mellitus Diabetic peripheral neuropathy Lipid accumulation product Visceral adipose index Vibration perception threshold Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Type 2 diabetes mellitus is known as a common chronic disease lowering the quality of people's lives and generating enormous economic and social burdens. The 11th edition of the International Diabetes Federation (IDF) shows that there are 589 million adults (aged 20–79 years) are living with diabetes mellitus worldwide, among which approximately 90% are type 2 diabetes mellitus. Notably, the number of diabetic patients in China has reached 148 million, ranking first in the world ( https://diabetesatlas.org ). Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of type 2 diabetes mellitus, characterized by peripheral nerve dysfunction and unfavorable prognosis. DPN plays a key role in the occurrence and development of diabetic foot ulcers, leading to a poor prognosis such as amputation or even death, and contributing to the high recurrence rate of diabetic foot ulcers after treatment [ 1 , 2 , 3 ] . Poor glycemic control is regarded as one of the major risk factors for DPN. Prolonged hyperglycemia can cause endoneurial microangiopathy, which may impair the synthesis and secretion of neurotrophic factors, and induce oxidative stress. These factors collectively contribute to nerve cell damage, thereby disrupting signal conduction and ultimately resulting in neurological dysfunction [ 4 ] . Single blood glucose control is still unable to effectively delay the progression of DPN. Therefore, early intervention in high-risk individuals to mitigate risk factors is crucial for reducing the incidence of DPN. Studies have shown that optimal glycemic control can effectively prevent the development of peripheral neuropathy and autonomic neuropathy in patients with type 1 diabetes mellitus. However, the benefits of glycemic control in preventing DPN are less pronounced in type 2 diabetes mellitus patients [ 5 , 6 ] . In the Veterans Affairs Diabetes Trial (VADT), the intensive glycemic control group achieved a significantly lower final glycosylated hemoglobin A1c (HbA1c) level compared to the standard control group (6.9% vs. 8.4%). Despite the difference in glycemic control, there was no significant difference in the cumulative incidence of any type of neuropathy between the two groups [ 7 ] . Similarly, the United Kingdom Prospective Diabetes Study (UKPDS) reported that there was no significant difference in the loss of ankle reflex between the intensive and standard glycemic control groups in patients with type 2 diabetes mellitus (35% in the intensive group vs. 37% in the standard group) [ 8 ] . These results suggest that factors beyond glycemic control may contribute to the onset and progression of DPN in patients with type 2 diabetes mellitus. Diabetes duration, age, hypertension, smoking, alcohol abuse, and body mass index (BMI) were also considered as major predictors of DPN [ 9 ] . One study investigated the correlation between neuropathy subtypes and serological parameters and found that the predominant neuropathy type in type 1 diabetes mellitus was significantly associated with poor glycemic control and loss of nerve conduction, whereas in type 2 diabetes mellitus, it was linked to alterations in lipid metabolism [ 10 ] . This implies that lipid metabolism may also play a role in the development and progression of DPN in patients with type 2 diabetes mellitus. Studies have shown that alterations in lipid metabolism are closely associated with peripheral nerve dysfunction, and lipid-lowering therapy can delay the onset and progression of DPN [ 11 , 12 ] . A meta-analysis revealed that higher levels of triglycerides (TG) and lower levels of high-density lipoprotein cholesterol (HDL-C) are associated with an increased risk of DPN, suggesting that lipid levels should be explored as routine laboratory markers for predicting the risk of DPN [ 13 ] . Obesity is an independent risk factor for DPN [ 14 , 15 ] . Abdominal obesity is closely related with type 2 diabetes mellitus, diabetic kidney disease (DKD), and diabetic retinopathy (DR) [ 16 , 17 ] , as well as DPN among individuals with type 2 diabetes [ 18 , 19 , 20 ] . Dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI) are standard reference methods for assessing abdominal obesity [ 21 ] . However, these methods are time-consuming and costly, making them unsuitable as screening and tracking tools. In recent years, novel obesity-related indices such as the Lipid Accumulation Product (LAP), Visceral Adiposity Index (VAI), and Body Roundness Index (BRI) have been proposed, which are cost-effective, easily obtainable, and better reflect fat distribution compared to BMI and waist circumference. LAP was calculated based on waist circumference and TG, showing good predictive performance for metabolic syndrome and cardiovascular diseases, and was significantly correlated with the severity of chronic kidney disease, type 2 diabetes and diabetic retinopathy [ 22 , 23 ] . VAI is an abdominal obesity indicator based on waist circumference, BMI, TG and HDL-C. Studies have shown that VAI is closely related to cardiometabolic risk [ 24 ] , and is also correlated with metabolic syndrome, type 2 diabetes and chronic kidney disease [ 25 ] . BRI calculates body roundness based on an elliptical model of the human body shape and uses eccentricity as an indicator to estimate the percentage of visceral fat and total body fat [ 26 ] . Studies have shown that BRI is associated with all-cause mortality and cardiovascular disease-specific mortality [ 27 , 28 ] . However, the correlation between the above-mentioned new obesity-related indicators and DPN has not been reported so far. Therefore, this study aims to explore the correlation between the forementioned obesity-related indicators and DPN, with the expectation of providing early screening indicators for insidious DPN. RESERCH DESIGN AND METHODS Study Subjects The participants were recruited from the First Affiliated Hospital of Wenzhou Medical University in Zhejiang Province, China, from 2017 to 2019. Information on demographics (age, gender), lifestyle factors (smoking, drinking) and duration of diabetes were collected through a standardized questionnaire. Anthropometric parameters such as height, weight, waist circumference, and blood pressure were measured, and neurological dysfunction were evaluated. In addition, biochemical indicators such as HbA1c, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), HDL-C, TG, renal function and liver function were measured for each participant. All patients were diagnosed with type 2 diabetes mellitus according to the 1999 WHO criteria ( www.who.int/entity/diabetes/currentpublications/en ), and all patients were aged ≥ 18 years. The exclusion criteria were as follows: 1) Type 1 diabetes mellitus, special types of diabetes mellitus, gestational diabetes mellitus; 2) Lumbar disc herniation, lumbar tumors and other secondary lower extremity neuropathy; 3) Osteoarthritis of lower limb, rheumatoid arthritis, joint effusion, abscess and other osteoarthropathy; 4) Patients with a history of cerebral infarction and walking disabilities of lower limbs; 5) Active plantar ulcer exists; 6) History of severe chronic complications of diabetes such as retinal blindness, end-stage renal disease or lower limb amputation; 7) Acute complications of diabetes; 8) The disease history or drug use history that causes peripheral neuropathy; 9) History of malignant tumor; 10) There was fever, surgical trauma and other stress history in the past 2 weeks during hospitalization. The current study was approved by the Institutional Review Board of the First Affiliated Hospital of Wenzhou Medical University and informed consent was obtained from each participant. Calculation of Obesity-Related Indices BMI, LAP, VAI and BRI of each participant were calculated according to the corresponding formulas. (1) BMI = weight (kg) / height (m 2 ) (2) LAP: Males: LAP = (WC (cm) - 65) * TG (mmol/L) . Females: LAP = (WC (cm) − 58) * TG (mmol/L) . (3) VAI: Males: VAI = [WC (cm) / (39.68 + 1.88 * BMI)] * (TG (mmol/L) / 1.03) * (1.31 / HDL-C (mmol/L) ). Females: VAI = [WC (cm) / 36.58 + 1.89 * BMI] * (TG (mmol/L) / 0.81) * (1.52 / HDL-C (mmol/L) ). (4) BRI = 364.2–365.6 * [1 - (WC (m) / 2π) 2 / (0.5 * height (m) ) 2 ] 1/2 Definition of DPN Clinician-diagnosed DPN was adopted in this study. Patients could be clinically diagnosed with DPN when they presented with both neuropathic symptoms and one or more positive signs, or exhibited two or more positive signs in the absence of symptoms [ 29 , 30 ] . Neuropathic symptoms were documented based on self-reports from all participants and included numbness, hypoesthesia, tingling, burning sensations, or pain, predominantly in the toes, feet, or legs. Neuropathic signs, including abnormalities in vibration sensation, pressure sensation, temperature sensation, pain sensation and ankle reflexes, were evaluated by a specialized foot examination utilizing 128-Hz tuning fork, 10-g monofilament, Tip-Therm, pinprick test and percussion hammer. Measurement of Vibration Perception Threshold (VPT) VPT was measured by trained technicians using a neurothesiometer (Model: A100; Beijing Laxons Technology Co., LTD., Beijing, China), The test was performed on the distal pulp of the participant's big toe on each side. The voltage intensity was gradually increased from zero until the participant begins to feel the vibration and the voltage intensity is recorded at this point. The preceding operations was repeated for three times, and the average value of VPT was used for analysis for each side. VPT values of 15 V or higher were defined as abnormal vibration perception, while VPT values below 15 V were considered normal vibration perception [ 31 ] . Statistical Analyses Grouping of subjects (1) Participants were divided into DPN group and non-DPN group based on the occurrence of peripheral neuropathy (DPN Group vs. no-DPN groups). (2) Participants were divided into normal VPT group and abnormal VPT group based on whether VPT was abnormal (VPT < 15 V vs. VPT ≥ 15 V). Statistical Method (1) Continuous variables were summarized as medians with inter-quartile ranges, and categorical variables were expressed as counts and percentages. For continuous variables with skewed distribution and categorical variables, Mann-Whitney U test and chi-squared tests were performed to identify the difference of characteristics between different groups, respectively. (2) Multivariable logistic regression was applied to examine the association between LAP, VAI, BRI and DPN or abnormal vibration perception, adjusting for potential confounding factors including age, gender, diabetes duration, smoking, drinking, BMI, waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), HbA1c, TC, HDL-C, LDL-L and TG. (3) Multivariable linear regression model was used to assess the relationship between each index and VPT, with adjustment for the forementioned confounding factors. (4) Subgroup analyses were conducted based on age, gender, diabetes duration, BMI, HbA1c, smoking and drinking, and the interactions of these covariates with LAP or VAI were also evaluated. (5) All statistical analyses were conducted using SPSS software (version 26.0; IBM, Armonk, New York), and a two-tailed P value of < 0.05 was considered statistically significant. RESULTS A total of 1098 subjects diagnosed with type 2 diabetes mellitus were included in this study, with a mean age of 58.4 years, ranging from 22 to 88 years. A total of 659 males and 439 females were enrolled, of which 753 patients were Clinician-diagnosed DPN and 225 patients were with abnormal vibration perception (VPT ≥ 15 V). Baseline Analysis of the DPN Group and the Non-DPN Group Table 1. Baseline characteristics of participants stratified by the presence of DPN. Characteristic Non-DPN DPN P value N 345 753 - Gender (Males/Females) 223/122 436/317 0.035 Age (years) 55 (46, 62) 60 (54, 68) <0.001 Diabetes duration (years) 7 (2, 10) 10 (5,17) <0.001 Smoking [n (%)] 122 (32.50%) 233 (30.90%) 0.614 Drinking [n (%)] 100 (29.00%) 201 (26.70%) 0.429 BMI 25.13 (22.48, 27.17) 24.5 (22.5, 26.73) 0.209 WC (cm) 90 (85, 97) 90 (85, 96) 0.600 SBP (mmHg) 134 (121, 147) 135 (121, 151) 0.044 DBP (mmHg) 80 (73, 88) 79 (72, 87) 0.154 HbA1c (%) 9.4 (7.9, 11.3) 9.5 (8, 11) 0.669 TC (mmol/L) 4.85 (3.94, 5.73) 4.68 (3.93, 5.605) 0.196 LDL-C (mmol/L) 2.57 (1.95, 3.29) 2.49 (1.87, 3.14) 0.192 HDL-C (mmol/L) 0.97 (0.85, 1.14) 1.00 (0.85, 1.17) 0.173 TG (mmol/L) 1.52 (1.07, 2.47) 1.47 (1.05, 2.16) 0.089 LAP 44.02 (27.2, 78.03) 40.96 (25.48, 66.5) 0.081 VAI 2.52 (1.56, 4.52) 2.31 (1.47, 4.01) 0.113 BRI 4.18 (3.47, 5.07) 4.30 (3.48, 5.18) 0.463 VPT 8.60 (6.60, 11.65) 12.2 (9.40, 15.55) <0.001 Data are summarized as n (%) or median (interquartile range). P values < 0.05 are shown in bold. VPT, Vibration perception threshold; BMI, body mass index; WC, waist circumference; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LAP, Lipid Accumulation Product; VAI, Visceral Adipose Index; BRI, Body roundness index. Table 1 shows the general characteristics of the participants in DPN and non-DPN group. Participants with DPN were more likely to be older, women, longer diabetes duration and higher systolic blood pressure (all P < 0.05). The median level of VPT was higher among individuals with DPN versus without DPN ( P < 0.05). Baseline Aanalysis of VPT < 15V Group and VPT ≥ 15V Group Table 2. Baseline characteristics of participants stratified by the presence of abnormal vibration perception. Characteristic VPT<15V VPT≥15V P value N 873 225 - Gender (males/Females) 520/353 139/86 0.546 Age (years) 57 (50, 64) 66 (59, 71) <0.001 Diabetes duration (years) 10 (4, 13) 10 (8, 20) <0.001 Smoking [n (%)] 273 (31.30%) 72 (30.00%) 0.834 Drinking [n (%)] 246 (28.20%) 55 (24.40%) 0.263 BMI 24.64 (22.49, 26.84) 24.61 (22.48, 27.04) 0.856 WC (cm) 90 (85, 96) 90.15 (85, 97.5) 0.372 SP (mmHg) 134 (120, 149) 139 (125, 155.5) < 0.001 DP (mmHg) 80 (73, 88) 78 (71, 87) 0.227 HbA1c (%) 9.50 (7.90, 11.18) 9.40 (8.00, 10.90) 0.543 TC (mmol/L) 4.75 (3.96, 5.64) 4.72 (3.80, 5.62) 0.470 LDL-C (mmol/L) 2.52 (1.93, 3.18) 2.47 (1.79, 3.17) 0.403 HDL-C (mmol/L) 0.99 (0.86, 1.16) 0.97 (0.83, 1.15) 0.352 TG (mmol/L) 1.50 (1.08, 2.27) 1.39 (1.00, 2.01) 0.050 LAP 42.24 (26.66, 71.13) 40.00 (22.84, 66.12) 0.103 VAI 2.40 (1.56, 4.18) 2.26 (1.40, 3.78) 0.124 BRI 4.22 (3.49,5.07) 4.30 (3.41,5.37) 0.411 Data are summarized as n (%) or median (interquartile range). P values<0.05 are shown in bold. VPT, Vibration perception threshold; BMI, body mass index; WC, waist circumference; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LAP, Lipid Accumulation Product; VAI, Visceral Adipose Index; BRI, Body roundness index. Table 2 shows that age, diabetes duration, SBP were more likely to be higher in subjects with abnormal vibration perception than those with normal vibration perception ( P < 0.05). Association Between Obesity-Related Indicators and DPN Table 3 Logistic regression analysis of obesity-related indicators and the presence of DPN in patients with type 2 diabetes. Variables Model1 Model2 OR (95%CI) P value OR (95%CI) P value LAP 0.999 (0.997, 1.001) 0.402 1.002 (0.992, 1.012) 0.680 VAI 0.999 (0.965, 1.035) 0.974 1.175 (1.012, 1.363) 0.034 BRI 1.004 (0.980, 1.029) 0.734 1.011 (0.944, 1.083) 0.753 †Data are showed as OR (95% CIs). P values < 0.05 are shown in bold. Model 1 was adjusted for gender and age. Model 2 was further adjusted for diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL, HDL, TG. OR, Odds Ratio; CI, Confidence Interval; LAP, Lipid Accumulation Product; VAI, Visceral Adipose Index; BRI, Body roundness index; Multivariable logistic regression was conducted to analyze the correlation between obesity-related indicators and DPN. Results showed that higher VAI was associated with higher likelihood of having DPN after adjustment for age, gender, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL-C, HDL-C and TG (OR 1.175, 95%CI 1.012–1.363, P = 0.034, Table 3 ). Association Between Obesity-Related Indicators and Abnormal Vibration Perception Table 4 Logistic regression analysis of obesity and lipid-related indicators and abnormal vibration perception in patients with type 2 diabetes. Variables Model1 Model2 OR (95%CI) P value OR (95%CI) P value LAP 1.001 (0.998, 1.004) 0.382 1.024 (1.011, 1.038) < 0.001 VAI 1.023 (0.977, 1.071) 0.337 1.203 (1.044, 1.386) 0.011 BRI 1.010 (0.983, 1.039) 0.468 1.012 (0.977, 1.047) 0.101 Data are showed as OR (95% CIs). P values < 0.05 are shown in bold. Model 1 was adjusted for gender and age. Model 2 was further adjusted for diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL, HDL, TG. OR, Odds Ratio; CI, Confidence Interval; LAP, Lipid Accumulation Product; VAI, Visceral Adipose Index; BRI, Body roundness index; Multivariable logistic regression was used to assess the association between LAP, VAI, BRI and abnormal vibration perception, respectively. The results showed that LAP and VAI were significantly associated with abnormal vibration perception (Table 4 ). Higher LAP and VAI were associated with higher likelihood of having abnormal vibration perception in patients with type 2 diabetes after adjustment for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TG, HDL-C, LDL-L, TC (OR 1.024, 95% CI 1.011–1.038, P < 0.001 for LAP; OR 1.203, 95% CI 1.044–1.386, P = 0.011 for VAI, Table 4 ). Association Between Obesity-Related Indicators and LAP Table 5 Linear regression analysis of lipid accumulation product (LAP) and VPT in patients with type 2 diabetes. Variables β S.E t (95%CI) P value Intercept -0.784 3.159 -0.248 (-6.982, 5.415) 0.804 Gender 0.789 0.424 1.862 (-0.042, 1.621) 0.063 Age 0.233 0.016 14.25 (0.201, 0.265) < 0.001 Diabetes duration 0.118 0.024 4.891 (0.071, 0.166) < 0.001 Smoking 0.468 0.446 1.050 (-0.406, 1.342) 0.294 Drinking -0.209 0.433 -0.482 (-1.059, 0.641) 0.630 BMI -0.094 0.064 -1.482 (-0.219, 0.031) 0.139 WC -0.058 0.033 -1.779 (-0.122, 0.006) 0.076 SP 0.020 0.010 2.078 (0.001, 0.039) 0.038 DP 0.010 0.017 0.574 (-0.024, 0.044) 0.566 HbA1c 0.218 0.076 2.867 (0.069, 0.367) 0.004 TC -0.010 0.008 -1.224 (-0.026, 0.006) 0.221 LDL 0.219 0.144 1.517 (-0.064, 0.502) 0.130 HDL -0.761 0.607 -1.253 (-1.963, 0.431) 0.210 TG -1.176 0.355 -3.309 (-1.873, -0.479) 0.001 LAP 0.038 0.012 3.172 (0.014, 0.061) 0.002 Data are showed as β (95% CIs). P values < 0.05 are shown in bold. S.E, Standard Error. BMI, body mass index; WC, waist circumference; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LAP, Lipid Accumulation Product. Association Between Obesity-Related Indicators and VAI Table 6 Linear regression analysis of Visceral Adipose Index (VAI) and VPT in patients with type 2 diabetes. Variables β S.E t (95%CI) P value Intercept -7.956 2.471 -3.222 (-12.80, -3.111) 0.001 Gender 0.818 0.447 1.825 (-0.061, 1.696) 0.068 Age 0.233 0.016 14.236 (0.201, 0.265) < 0.001 Diabetes duration 0.114 0.024 4.698 (0.066, 0.161) < 0.001 Smoking 0.432 0.447 0.966 (-0.445, 1.309) 0.334 Drinking -0.253 0.434 -0.582 (-1.104, 0.599) 0.560 BMI -0.071 0.065 -1.093 (-0.199, 0.057) 0.275 WC 0.007 0.022 0.306 (-0.038, 0.052) 0.760 SP 0.020 0.010 2.087 (0.001, 0.039) 0.037 DP 0.008 0.017 0.468 (-0.026, 0.042) 0.640 HbA1c 0.199 0.076 2.606 (-0.049, 0.348) 0.009 TC -0.010 0.008 -1.238 (-0.026, 0.006) 0.216 LDL 0.278 0.145 1.920 (-0.006, 0.562) 0.055 HDL 0.200 0.692 0.289 (-1.158, 1.559) 0.773 TG -0.671 0.277 -2.417 (-1.215, -0.126) 0.016 VAI 0.313 0.141 2.228 (0.037, 0.589) 0.026 Data are showed as β (95% CIs). P values < 0.05 are shown in bold. S.E, Standard Error. BMI, body mass index; WC, waist circumference; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; VAI, Visceral Adipose Index; In addition, multivariable linear regression model was used to assess the relationship between each index and VPT. Significant evidence of association with VPT was shown for LAP (β=0.038; 95% CI 0.014-0.061; P =0.002, Table 5) as well as for VAI (β=0.313; 95% CI 00.037-0.589; P =0.026, Table 6) in patients with type 2 diabetes, with adjustment for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TG, HDL-C, LDL-L, TC. However, no significant association with VPT was found for BRI (Table S1) Subgroup Analyses Subgroup analyses were performed to further evaluate the association between VAI and DPN as well as the association between LAP, VAI and abnormal vibration perception in patients with type 2 diabetes according to gender (female or male), age (< 65 or ≥ 65 years), diabetes duration (< 10 years or ≥ 10 years), BMI (< 24 kg/m 2 or ≥ 24 kg/m 2 ), HbA1c (< 7% or ≥ 7%), smoking and drinking. Models were adjusted for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL-C, HDL-C, TG. Subgroup variables were excluded from the model. OR, odds ratio; CI, confidence interval; BMI, body mass index; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; VAI, Visceral Adipose Index. As shown in Fig. 1 , in the subgroup analyses, significant association between VAI and DPN were observed in participants with HbA1c ≥ 7%( P = 0.043). No significant interactions were detected between VAI and gender, age, diabetes duration, BMI, HbA1c, smoking or drinking. Models were adjusted for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL-C, HDL-C, TG. Subgroup variables were excluded from the model. OR, odds ratio; CI, confidence interval; BMI, body mass index; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LAP, Lipid Accumulation Product. As shown in Fig. 2 , significant associations between LAP and abnormal VPT were consistently observed in subgroups of males, non-elderly patients, diabetes duration ≥ 10 years and HbA1c < 7%. There was a significant association between LAP and abnormal VPT, regardless of whether they were overweight (BMI < 24 kg/m2 or ≥ 24 kg/m2), and whether they had a history of smoking or drinking. No subgroup variables showed significant interactions with LAP. Models were adjusted for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL-C, HDL-C, TG. Subgroup variables were excluded from the model. OR, odds ratio; CI, confidence interval; BMI, body mass index; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; VAI, Visceral Adipose Index. As shown in Fig. 3 , in subgroups with diabetes duration < 10 years, BMI < 24 kg/m², HbA1c ≥ 7%, smoking history, or drinking history, VAI remained significantly associated with abnormal VPT. No significant interactions were found between VAI and any subgroup variables. DISCUSSION The present study investigated the associations between novel obesity-related indices and DPN as well as abnormal vibration perception, and found that VAI was significantly associated with DPN, while both LAP and VAI showed significant correlations with abnormal vibration perception. These associations remained consistent in subgroup analyses. This indicates that lipids might promote the pathogenesis of DPN, and LAP and VAI may serve as potential markers for assess the existence risk of DPN and abnormal vibration perception in patients with type 2 diabetes mellitus Obesity is considered as an independent risk factor for DPN [ 32 , 33 ] . A large observational cohort study conducted in the Danish population demonstrated that obesity markers (body weight, waist circumference and BMI) are potential risk factors for DPN [ 34 ] . An observational study conducted in Korea among newly diagnosed patients with type 2 diabetes mellitus revealed significant associations between DPN and BMI, waist circumference, as well as visceral fat area (VFA) assessed by bioelectrical impedance analysis [ 35 ] . Furthermore, prevention of weight gain and moderate weight reduction can decrease the incidence of diabetes-related complications, including DPN, in patients with type 2 diabetes mellitus [ 36 ] . An interventional study involving overweight or obese patients with type 2 diabetes showed that subjects who underwent 2–3 years of weight management had a lower incidence of DPN and exhibited significant improvement in light touch sensation [ 37 ] . Abdominal obesity can lead to chronic low-grade inflammation and various metabolic disturbances, and is closely associated with insulin resistance, type 2 diabetes mellitus, non-alcoholic fatty liver disease (NAFLD), as well as cardiovascular diseases and even certain cancers [ 38 ] . Studies have shown that abdominal obesity is also a risk factor for diabetic chronic complications, including diabetic kidney disease (DKD) and diabetic retinopathy (DR) [ 17 , 18 ] . It was reported that high triglyceride level is a strong DPN risk factor [ 39 ] . Furthermore, patients with hypertriglyceridemia exhibited faster DPN progression [ 40 ] , suggesting that dyslipidemia may play an important role in DPN development and progression. However, research findings remain inconsistent. A cross-sectional study from Taiwan, China, found no association between either hyperlipidemia or lipid-lowering medications and DPN [ 41 ] . LAP, calculated from both TG and waist circumference, is a novel indicator of lipid overaccumulation. It has been recognized as a cost-effective method for assessing the risk of several chronic diseases, including type2 diabetes mellitus, hypertension, and cardiovascular diseases [ 42 , 43 ] . LAP was associated with oxidative stress biomarkers, suggesting the elevation of LAP could identify an imbalance in the redox status [ 44 ] . VAI, derived from a composite calculation of BMI, waist circumference, TG, and HDL-C, serves as a marker of cardiometabolic risk [ 45 ] . Compared to traditional indicators such as BMI, waist circumference, and lipid levels, both LAP and VAI may represent stronger risk indicators for type 2 diabetes mellitus in women [ 46 ] . LAP and VAI are novel visceral adiposity indices derived from a combination of anthropometric and lipid parameters, providing a more accurate reflection of body fat distribution than BMI or waist circumference alone. VPT, as a sensitive indicator of peripheral neuropathy, can be easily measured and calculated, making it particularly valuable for clinical practice and large-scale population studies [ 47 ] . Our study demonstrated significant associations between LAP, VAI and VPT in patients with type 2 diabetes mellitus. However, we only identified an association between VAI and DPN in patients with type 2 diabetes mellitus, while no such relationship was observed between LAP and DPN. Furthermore, BRI showed no significant correlations with either DPN or VPT. Given the relatively small sample size of this study, larger-scale investigations and prospective studies are warranted to further elucidate the relationships between these indices and DPN as well as VPT. This study is the first to investigate associations between novel obesity-related indices (LAP and VAI) and DPN as well as abnormal VPT in Chinese patients with type 2 diabetes mellitus. Our findings indicate that VAI shows significant correlation with DPN, while both LAP and VAI are significantly associated with abnormal vibration perception. All anthropometric measurements and questionnaire surveys were carried out by a single trained research team, which ensured the high-quality of data collection. However, several limitations should be noted. Firstly, as a cross-sectional study, this research cannot establish a causal relationship between LAP, VAI, and DPN or abnormal vibration perception. Further prospective studies are needed to clarify these associations. Secondly, this was a single-center study, and the findings should be validated through multicenter research to enhance generalizability. Third, the study did not include quantitative electromyography (EMG) in the assessment of participants. Future studies should incorporate nerve conduction studies or detailed EMG evaluations to further explore the relationship between LAP, VAI, and DPN. Finally, data on potential confounding factors such as lipid-lowering therapy, dietary habits, and physical activity levels were not collected. Subsequent research should include these variables to improve the robustness of the analysis. CONCLUSION The present study indicated that VAI was significantly associated with DPN, while both LAP and VAI showed significant correlations with abnormal vibration perception in Chinese patients with type 2 diabetes mellitus. These findings suggest that LAP and VAI may serve as potential biomarkers for early screening of DPN in patients with type 2 diabetes mellitus. Declarations Ethics approval and consent to participate This study was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University, China (KY2024-R170), date of approval: 22 July, 2024. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding The investigators are grateful to all the dedicated participants and all research staff of the study. This work was supported by National Science and Technology Major Project grant (2024ZD0523300) and the Open Research Project of Shanghai Key Laboratory of Diabetes Mellitus [ SHKLD-KF-2401]. Author Contribution The study was conceived and supervised by Weihui Yu (Corresponding Author). Huizhen Ji led the design of the work, interpretation of data, and statistical analysis, and played a central role in manuscript writing and revision. Jiahui Cui contributed to interpretation of data and statistical analysis, and was instrumental in interpreting the results and drafting the manuscript. Yiyi Zhang was responsible for data collection and preliminary data analysis. Qiran Ma assisted with data acquisition and provided support for statistical analysis. Xiang Hu managed patient recruitment, data collection, and dataset quality control. Huihui Deng performed data entry and data cleaning. Shuoping Chen provided clinical expertise, contributed to the development of the study protocol, and reviewed the manuscript. Qi Zhou supported data collection and assisted in manuscript editing. Wei Pan participated in the statistical analysis. Jing Hong and Tingting Ye jointly carried out patient recruitment and data organization. Feixia Shen and Hong Zhu were responsible for ethical approval and regulatory compliance. All authors have approved the final version of the manuscript for submission. Acknowledgements The investigators are grateful to all the dedicated participants and all research staff of the study. Data Availability The datasets analysed during the current study are not publicly available due to privacy protection but are available from the corresponding author on reasonable request. References Armstrong DG, Boulton AJM, Bus SA. Diabetic foot ulcers and their recurrence. N Engl J Med. 2017;376(24):2367–75. 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Elevated triglycerides correlate with progression of diabetic neuropathy. Diabetes. 2009;58(7):1634–40. Chang KC, Pai YW, Lin CH, et al. The association between hyperlipidemia, lipid-lowering drugs and diabetic peripheral neuropathy in patients with type 2 diabetes mellitus. PLoS ONE. 2023;18(6):e0287373. Wakabayashi I, Daimon T. A strong association between lipid accumulation product and diabetes mellitus in japanese women and men. J Atheroscler Thromb. 2014;21(3):282–8. Khanmohammadi S, Tavolinejad H, Aminorroaya A, et al. Association of lipid accumulation product with type 2 diabetes mellitus, hypertension, and mortality: a systematic review and meta-analysis. J Diabetes Metab Disord. 2022;21(2):1943–73. Lugo R, Avila-Nava A, Pech-Aguilar AG, et al. Relationship between lipid accumulation product and oxidative biomarkers by gender in adults from Yucatan. Mexico Sci Rep. 2022;12(1):14338. Amato MC, Giordano C. (2014). Visceral adiposity index: an indicator of adipose tissue dysfunction. Int J Endocrinol. 2014:2014:730827. Brahimaj A, Rivadeneira F, Muka T et al. (2019). Novel metabolic indices and incident type 2 diabetes among women and men: the Rotterdam Study. Diabetologia.62(9):1581-90. Martin CL, Waberski BH, Pop-Busui R, et al. Vibration perception threshold as a measure of distal symmetrical peripheral neuropathy in type 1 diabetes: results from the DCCT/EDIC study. Diabetes Care. 2010;33(12):2635–41. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 06 Jan, 2026 Editor invited by journal 11 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 28 Nov, 2025 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-8230770","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571890025,"identity":"30b2501a-dc62-46f9-9648-f066c4a1c294","order_by":0,"name":"Huizhen Ji","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huizhen","middleName":"","lastName":"Ji","suffix":""},{"id":571890026,"identity":"9f2e86a5-6a58-4656-8e0b-3212b6d390b9","order_by":1,"name":"Jiahui Cui","email":"","orcid":"","institution":"First Affiliated Hospital of Wenzhou Medical 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2","display":"","copyAsset":false,"role":"figure","size":316015,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between LAP and abnormal vibration perception in patients with type 2 diabetes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8230770/v1/d84beadce5560f9b1168e1c8.png"},{"id":100094141,"identity":"512312ee-c18b-4947-bbba-1b6fa957b078","added_by":"auto","created_at":"2026-01-13 01:28:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":234375,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between VAI and abnormal vibration perception in patients with type 2 diabetes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8230770/v1/29c768b7949a4e70d07111eb.png"},{"id":100382180,"identity":"b035b6f7-a831-4050-8397-cbd201376370","added_by":"auto","created_at":"2026-01-16 10:41:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1836139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8230770/v1/233ce520-e65a-4224-b89c-0b5aa77a9f0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Obesity-Related Indicators and Peripheral Neuropathy in Type 2 Diabetes Mellitus","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eType 2 diabetes mellitus is known as a common chronic disease lowering the quality of people's lives and generating enormous economic and social burdens. The 11th edition of the International Diabetes Federation (IDF) shows that there are 589\u0026nbsp;million adults (aged 20\u0026ndash;79 years) are living with diabetes mellitus worldwide, among which approximately 90% are type 2 diabetes mellitus. Notably, the number of diabetic patients in China has reached 148\u0026nbsp;million, ranking first in the world (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://diabetesatlas.org\u003c/span\u003e\u003cspan address=\"https://diabetesatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of type 2 diabetes mellitus, characterized by peripheral nerve dysfunction and unfavorable prognosis. DPN plays a key role in the occurrence and development of diabetic foot ulcers, leading to a poor prognosis such as amputation or even death, and contributing to the high recurrence rate of diabetic foot ulcers after treatment\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePoor glycemic control is regarded as one of the major risk factors for DPN. Prolonged hyperglycemia can cause endoneurial microangiopathy, which may impair the synthesis and secretion of neurotrophic factors, and induce oxidative stress. These factors collectively contribute to nerve cell damage, thereby disrupting signal conduction and ultimately resulting in neurological dysfunction\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Single blood glucose control is still unable to effectively delay the progression of DPN. Therefore, early intervention in high-risk individuals to mitigate risk factors is crucial for reducing the incidence of DPN.\u003c/p\u003e \u003cp\u003eStudies have shown that optimal glycemic control can effectively prevent the development of peripheral neuropathy and autonomic neuropathy in patients with type 1 diabetes mellitus. However, the benefits of glycemic control in preventing DPN are less pronounced in type 2 diabetes mellitus patients\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In the Veterans Affairs Diabetes Trial (VADT), the intensive glycemic control group achieved a significantly lower final glycosylated hemoglobin A1c (HbA1c) level compared to the standard control group (6.9% vs. 8.4%). Despite the difference in glycemic control, there was no significant difference in the cumulative incidence of any type of neuropathy between the two groups\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Similarly, the United Kingdom Prospective Diabetes Study (UKPDS) reported that there was no significant difference in the loss of ankle reflex between the intensive and standard glycemic control groups in patients with type 2 diabetes mellitus (35% in the intensive group vs. 37% in the standard group)\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. These results suggest that factors beyond glycemic control may contribute to the onset and progression of DPN in patients with type 2 diabetes mellitus. Diabetes duration, age, hypertension, smoking, alcohol abuse, and body mass index (BMI) were also considered as major predictors of DPN\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. One study investigated the correlation between neuropathy subtypes and serological parameters and found that the predominant neuropathy type in type 1 diabetes mellitus was significantly associated with poor glycemic control and loss of nerve conduction, whereas in type 2 diabetes mellitus, it was linked to alterations in lipid metabolism\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. This implies that lipid metabolism may also play a role in the development and progression of DPN in patients with type 2 diabetes mellitus. Studies have shown that alterations in lipid metabolism are closely associated with peripheral nerve dysfunction, and lipid-lowering therapy can delay the onset and progression of DPN\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis revealed that higher levels of triglycerides (TG) and lower levels of high-density lipoprotein cholesterol (HDL-C) are associated with an increased risk of DPN, suggesting that lipid levels should be explored as routine laboratory markers for predicting the risk of DPN\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eObesity is an independent risk factor for DPN\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Abdominal obesity is closely related with type 2 diabetes mellitus, diabetic kidney disease (DKD), and diabetic retinopathy (DR)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, as well as DPN among individuals with type 2 diabetes \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI) are standard reference methods for assessing abdominal obesity\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, these methods are time-consuming and costly, making them unsuitable as screening and tracking tools.\u003c/p\u003e \u003cp\u003eIn recent years, novel obesity-related indices such as the Lipid Accumulation Product (LAP), Visceral Adiposity Index (VAI), and Body Roundness Index (BRI) have been proposed, which are cost-effective, easily obtainable, and better reflect fat distribution compared to BMI and waist circumference. LAP was calculated based on waist circumference and TG, showing good predictive performance for metabolic syndrome and cardiovascular diseases, and was significantly correlated with the severity of chronic kidney disease, type 2 diabetes and diabetic retinopathy\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. VAI is an abdominal obesity indicator based on waist circumference, BMI, TG and HDL-C. Studies have shown that VAI is closely related to cardiometabolic risk\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, and is also correlated with metabolic syndrome, type 2 diabetes and chronic kidney disease\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. BRI calculates body roundness based on an elliptical model of the human body shape and uses eccentricity as an indicator to estimate the percentage of visceral fat and total body fat\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that BRI is associated with all-cause mortality and cardiovascular disease-specific mortality\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. However, the correlation between the above-mentioned new obesity-related indicators and DPN has not been reported so far. Therefore, this study aims to explore the correlation between the forementioned obesity-related indicators and DPN, with the expectation of providing early screening indicators for insidious DPN.\u003c/p\u003e"},{"header":"RESERCH DESIGN AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy Subjects\u003c/h2\u003e\n\u003cp\u003eThe participants were recruited from the First Affiliated Hospital of Wenzhou Medical University in Zhejiang Province, China, from 2017 to 2019. Information on demographics (age, gender), lifestyle factors (smoking, drinking) and duration of diabetes were collected through a standardized questionnaire. Anthropometric parameters such as height, weight, waist circumference, and blood pressure were measured, and neurological dysfunction were evaluated. In addition, biochemical indicators such as HbA1c, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), HDL-C, TG, renal function and liver function were measured for each participant.\u003c/p\u003e\n\u003cp\u003eAll patients were diagnosed with type 2 diabetes mellitus according to the 1999 WHO criteria (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.who.int/entity/diabetes/currentpublications/en\u003c/span\u003e\u003c/span\u003e), and all patients were aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years. The exclusion criteria were as follows: 1) Type 1 diabetes mellitus, special types of diabetes mellitus, gestational diabetes mellitus; 2) Lumbar disc herniation, lumbar tumors and other secondary lower extremity neuropathy; 3) Osteoarthritis of lower limb, rheumatoid arthritis, joint effusion, abscess and other osteoarthropathy; 4) Patients with a history of cerebral infarction and walking disabilities of lower limbs; 5) Active plantar ulcer exists; 6) History of severe chronic complications of diabetes such as retinal blindness, end-stage renal disease or lower limb amputation; 7) Acute complications of diabetes; 8) The disease history or drug use history that causes peripheral neuropathy; 9) History of malignant tumor; 10) There was fever, surgical trauma and other stress history in the past 2 weeks during hospitalization.\u003c/p\u003e\n\u003cp\u003eThe current study was approved by the Institutional Review Board of the First Affiliated Hospital of Wenzhou Medical University and informed consent was obtained from each participant.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eCalculation of Obesity-Related Indices\u003c/h3\u003e\n\u003cp\u003eBMI, LAP, VAI and BRI of each participant were calculated according to the corresponding formulas.\u003c/p\u003e\n\u003cp\u003e(1) BMI\u0026thinsp;=\u0026thinsp;weight \u003csub\u003e(kg)\u003c/sub\u003e / height \u003csub\u003e(m\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e)\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e(2) LAP:\u003c/p\u003e\n\u003cp\u003eMales: LAP = (WC \u003csub\u003e(cm)\u003c/sub\u003e- 65) * TG \u003csub\u003e(mmol/L)\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eFemales: LAP = (WC \u003csub\u003e(cm)\u003c/sub\u003e \u0026minus;\u0026thinsp;58) * TG \u003csub\u003e(mmol/L)\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e(3) VAI:\u003c/p\u003e\n\u003cp\u003eMales: VAI = [WC \u003csub\u003e(cm)\u003c/sub\u003e / (39.68\u0026thinsp;+\u0026thinsp;1.88 * BMI)] * (TG \u003csub\u003e(mmol/L)\u003c/sub\u003e / 1.03) * (1.31 / HDL-C \u003csub\u003e(mmol/L)\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eFemales: VAI = [WC \u003csub\u003e(cm)\u003c/sub\u003e / 36.58\u0026thinsp;+\u0026thinsp;1.89 * BMI] * (TG \u003csub\u003e(mmol/L)\u003c/sub\u003e / 0.81) * (1.52 / HDL-C \u003csub\u003e(mmol/L)\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003e(4) BRI\u0026thinsp;=\u0026thinsp;364.2\u0026ndash;365.6 * [1 - (WC\u003csub\u003e(m)\u003c/sub\u003e / 2\u0026pi;)\u003csup\u003e2\u003c/sup\u003e / (0.5 * height\u003csub\u003e(m)\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e]\u003csup\u003e1/2\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eDefinition of DPN\u003c/h3\u003e\n\u003cp\u003eClinician-diagnosed DPN was adopted in this study. Patients could be clinically diagnosed with DPN when they presented with both neuropathic symptoms and one or more positive signs, or exhibited two or more positive signs in the absence of symptoms\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Neuropathic symptoms were documented based on self-reports from all participants and included numbness, hypoesthesia, tingling, burning sensations, or pain, predominantly in the toes, feet, or legs. Neuropathic signs, including abnormalities in vibration sensation, pressure sensation, temperature sensation, pain sensation and ankle reflexes, were evaluated by a specialized foot examination utilizing 128-Hz tuning fork, 10-g monofilament, Tip-Therm, pinprick test and percussion hammer.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eMeasurement of Vibration Perception Threshold (VPT)\u003c/h2\u003e\n\u003cp\u003eVPT was measured by trained technicians using a neurothesiometer (Model: A100; Beijing Laxons Technology Co., LTD., Beijing, China), The test was performed on the distal pulp of the participant's big toe on each side. The voltage intensity was gradually increased from zero until the participant begins to feel the vibration and the voltage intensity is recorded at this point. The preceding operations was repeated for three times, and the average value of VPT was used for analysis for each side. VPT values of 15 V or higher were defined as abnormal vibration perception, while VPT values below 15 V were considered normal vibration perception\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eGrouping of subjects\u003c/strong\u003e (1) Participants were divided into DPN group and non-DPN group based on the occurrence of peripheral neuropathy (DPN Group vs. no-DPN groups). (2) Participants were divided into normal VPT group and abnormal VPT group based on whether VPT was abnormal (VPT\u0026thinsp;\u0026lt;\u0026thinsp;15 V vs. VPT\u0026thinsp;\u0026ge;\u0026thinsp;15 V).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Method\u003c/strong\u003e (1) Continuous variables were summarized as medians with inter-quartile ranges, and categorical variables were expressed as counts and percentages. For continuous variables with skewed distribution and categorical variables, Mann-Whitney U test and chi-squared tests were performed to identify the difference of characteristics between different groups, respectively. (2) Multivariable logistic regression was applied to examine the association between LAP, VAI, BRI and DPN or abnormal vibration perception, adjusting for potential confounding factors including age, gender, diabetes duration, smoking, drinking, BMI, waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), HbA1c, TC, HDL-C, LDL-L and TG. (3) Multivariable linear regression model was used to assess the relationship between each index and VPT, with adjustment for the forementioned confounding factors. (4) Subgroup analyses were conducted based on age, gender, diabetes duration, BMI, HbA1c, smoking and drinking, and the interactions of these covariates with LAP or VAI were also evaluated. (5) All statistical analyses were conducted using SPSS software (version 26.0; IBM, Armonk, New York), and a two-tailed \u003cem\u003eP\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 1098 subjects diagnosed with type 2 diabetes mellitus were included in this study, with a mean age of 58.4 years, ranging from 22 to 88 years. A total of 659 males and 439 females were enrolled, of which 753 patients were Clinician-diagnosed DPN and 225 patients were with abnormal vibration perception (VPT\u0026thinsp;\u0026ge;\u0026thinsp;15 V).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Analysis of the DPN Group and the Non-DPN Group\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan style=\"text-align: inherit;\"\u003eTable 1. Baseline characteristics of participants stratified by the presence of DPN.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable style=\"width:418.8pt;margin-left:4.55pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 19.65pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eCharacteristic \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-DPN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;DPN \u003cem\u003eP\u003c/em\u003e value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 345 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;753 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eGender (Males/Females) 223/122 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;436/317 \u003cstrong\u003e0.035\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eAge (years) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 55 (46, 62) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;60 (54, 68) \u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eDiabetes duration (years) 7 (2, 10) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 10 (5,17) \u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eSmoking [n (%)] \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;122 (32.50%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;233 (30.90%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.614\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eDrinking [n (%)] \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;100 (29.00%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;201 (26.70%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.429\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;25.13 (22.48, 27.17) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;24.5 (22.5, 26.73) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.209\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eWC (cm) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;90 (85, 97) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;90 (85, 96) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.600\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eSBP (mmHg) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;134 (121, 147) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 135 (121, 151) \u003cstrong\u003e0.044\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 17.25pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eDBP (mmHg) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 80 (73, 88) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;79 (72, 87) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.154\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 21.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eHbA1c (%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;9.4 (7.9, 11.3) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;9.5 (8, 11) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.669\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eTC (mmol/L) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;4.85 (3.94, 5.73) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.68 (3.93, 5.605) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.196\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eLDL-C (mmol/L) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.57 (1.95, 3.29) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.49 (1.87, 3.14) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.192\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eHDL-C (mmol/L) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.97 (0.85, 1.14) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.00 (0.85, 1.17) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.173\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eTG (mmol/L) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1.52 (1.07, 2.47) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.47 (1.05, 2.16) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.089\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eLAP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 44.02 (27.2, 78.03) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;40.96 (25.48, 66.5) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.081\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eVAI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.52 (1.56, 4.52) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.31 (1.47, 4.01) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.113\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eBRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;4.18 (3.47, 5.07) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.30 (3.48, 5.18) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.463\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 10.75pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eVPT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.60 (6.60, 11.65) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 12.2 (9.40, 15.55) \u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eData are summarized as n (%) or median (interquartile range).\u0026nbsp;\u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are shown in bold. VPT, Vibration perception threshold; BMI, body mass index; WC, waist circumference; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LAP, Lipid Accumulation Product; VAI, Visceral Adipose Index; BRI, Body roundness index.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003eTable 1 shows the general characteristics of the participants in DPN and non-DPN group. Participants with DPN were more likely to be older, women, longer diabetes duration and higher systolic blood pressure (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The median level of VPT was higher among individuals with DPN versus without DPN (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003eBaseline Aanalysis of VPT\u0026thinsp;\u0026lt;\u0026thinsp;15V Group and VPT\u0026thinsp;\u0026ge;\u0026thinsp;15V Group\u003c/h2\u003e\n \u003c/div\u003e\n \u003cp\u003eTable 2. Baseline characteristics of participants stratified by the presence of abnormal vibration perception.\u003c/p\u003e\n \u003ctable style=\"width:418.8pt;margin-left:4.55pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 19.6pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eCharacteristic\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;VPT\u0026lt;15V\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;VPT\u0026ge;15V \u003cem\u003eP\u003c/em\u003e value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;873\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;225\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eGender (males/Females) 520/353\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;139/86\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.546\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eAge (years)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;57 (50, 64)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;66 (59, 71) \u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eDiabetes duration (years) 10 (4, 13)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;10 (8, 20) \u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eSmoking [n (%)]\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;273 (31.30%)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;72 (30.00%)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;0.834\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eDrinking [n (%)]\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;246 (28.20%)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;55 (24.40%)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;0.263\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;24.64 (22.49, 26.84)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;24.61 (22.48, 27.04)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;0.856\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eWC (cm)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;90 (85, 96)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;90.15 (85, 97.5)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.372\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eSP (mmHg)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;134 (120, 149)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;139 (125, 155.5)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eDP (mmHg)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;80 (73, 88)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;78 (71, 87)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;0.227\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eHbA1c (%)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;9.50 (7.90, 11.18)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;9.40 (8.00, 10.90)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.543\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eTC (mmol/L)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;4.75 (3.96, 5.64)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;4.72 (3.80, 5.62)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.470\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eLDL-C (mmol/L)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;2.52 (1.93, 3.18)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;2.47 (1.79, 3.17)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.403\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eHDL-C (mmol/L)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.99 (0.86, 1.16)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;0.97 (0.83, 1.15)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.352\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eTG (mmol/L)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;1.50 (1.08, 2.27)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;1.39 (1.00, 2.01)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.050\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eLAP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;42.24 (26.66, 71.13)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;40.00 (22.84, 66.12)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;0.103\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border: none;padding: 0in 5.4pt;height: 20.8pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eVAI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.40 (1.56, 4.18)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;2.26 (1.40, 3.78)\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.124\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 418.8pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;height: 13.25pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-size:16px;font-family:\"Calibri\",sans-serif;'\u003eBRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.22 (3.49,5.07) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.30 (3.41,5.37) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.411\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eData are summarized as n (%) or median (interquartile range).\u0026nbsp;\u003cem\u003eP\u003c/em\u003e values\u0026lt;0.05 are shown in bold. VPT, Vibration perception threshold; BMI, body mass index; WC, waist circumference; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LAP, Lipid Accumulation Product; VAI, Visceral Adipose Index; BRI, Body roundness index.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows that age, diabetes duration, SBP were more likely to be higher in subjects with abnormal vibration perception than those with normal vibration perception (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation Between Obesity-Related Indicators and DPN\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic regression analysis of obesity-related indicators and the presence of DPN in patients with type 2 diabetes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999 (0.997, 1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.002 (0.992, 1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999 (0.965, 1.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.175 (1.012, 1.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004 (0.980, 1.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.011 (0.944, 1.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003e\u0026dagger;Data are showed as OR (95% CIs). \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are shown in bold. Model 1 was adjusted for gender and age. Model 2 was further adjusted for diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL, HDL, TG. OR, Odds Ratio; CI, Confidence Interval; LAP, Lipid Accumulation Product; VAI, Visceral Adipose Index; BRI, Body roundness index;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eMultivariable logistic regression was conducted to analyze the correlation between obesity-related indicators and DPN. Results showed that higher VAI was associated with higher likelihood of having DPN after adjustment for age, gender, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL-C, HDL-C and TG (OR 1.175, 95%CI 1.012\u0026ndash;1.363, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation Between Obesity-Related Indicators and Abnormal Vibration Perception\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic regression analysis of obesity and lipid-related indicators and abnormal vibration perception in patients with type 2 diabetes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001 (0.998, 1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.024 (1.011, 1.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.023 (0.977, 1.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.203 (1.044, 1.386)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.010 (0.983, 1.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.012 (0.977, 1.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eData are showed as OR (95% CIs). \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are shown in bold. Model 1 was adjusted for gender and age. Model 2 was further adjusted for diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL, HDL, TG. OR, Odds Ratio; CI, Confidence Interval; LAP, Lipid Accumulation Product; VAI, Visceral Adipose Index; BRI, Body roundness index;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eMultivariable logistic regression was used to assess the association between LAP, VAI, BRI and abnormal vibration perception, respectively. The results showed that LAP and VAI were significantly associated with abnormal vibration perception (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Higher LAP and VAI were associated with higher likelihood of having abnormal vibration perception in patients with type 2 diabetes after adjustment for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TG, HDL-C, LDL-L, TC (OR 1.024, 95% CI 1.011\u0026ndash;1.038, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for LAP; OR 1.203, 95% CI 1.044\u0026ndash;1.386, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011 for VAI, Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation Between Obesity-Related Indicators and LAP\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear regression analysis of lipid accumulation product (LAP) and VPT in patients with type 2 diabetes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-6.982, 5.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.042, 1.621)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.201, 0.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.071, 0.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.406, 1.342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.059, 0.641)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.219, 0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.122, 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001, 0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.024, 0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.069, 0.367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.026, 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.064, 0.502)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.963, 0.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.873, -0.479)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014, 0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eData are showed as \u0026beta; (95% CIs). \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are shown in bold. S.E, Standard Error. BMI, body mass index; WC, waist circumference; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LAP, Lipid Accumulation Product.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssociation Between Obesity-Related Indicators and VAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" style=\"width: 1046px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear regression analysis of Visceral Adipose Index (VAI) and VPT in patients with type 2 diabetes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003cth style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e-7.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e2.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e-3.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-12.80, -3.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e1.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-0.061, 1.696)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e14.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(0.201, 0.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eDiabetes duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e4.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(0.066, 0.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-0.445, 1.309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e-0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e-0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-1.104, 0.599)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e-1.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-0.199, 0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-0.038, 0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e2.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(0.001, 0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-0.026, 0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e2.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-0.049, 0.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e-1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-0.026, 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e1.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-0.006, 0.562)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-1.158, 1.559)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e-0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e-2.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(-1.215, -0.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 274.488px;\" align=\"left\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 115.512px;\" align=\"left\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 101px;\" align=\"left\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 122px;\" align=\"left\"\u003e\n \u003cp\u003e2.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 258px;\" align=\"left\"\u003e\n \u003cp\u003e(0.037, 0.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 139px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 61.0258px;\"\u003e\n \u003ctd style=\"height: 61.0258px; width: 1010px;\" colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eData are showed as \u0026beta; (95% CIs). \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are shown in bold. S.E, Standard Error. BMI, body mass index; WC, waist circumference; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; VAI, Visceral Adipose Index;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003cp\u003eIn addition, multivariable linear regression model was used to assess the relationship between each index and VPT. Significant evidence of association with VPT was shown for LAP (\u0026beta;=0.038; 95% CI 0.014-0.061; \u003cem\u003eP\u003c/em\u003e=0.002, Table 5) as well as for VAI (\u0026beta;=0.313; 95% CI 00.037-0.589; \u003cem\u003eP\u003c/em\u003e=0.026, Table 6) in patients with type 2 diabetes, with adjustment for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TG, HDL-C, LDL-L, TC. However, no significant association with VPT was found for BRI (Table S1)\u003c/p\u003e\n \u003ch2\u003eSubgroup Analyses\u003c/h2\u003e\n \u003cp\u003eSubgroup analyses were performed to further evaluate the association between VAI and DPN as well as the association between LAP, VAI and abnormal vibration perception in patients with type 2 diabetes according to gender (female or male), age (\u0026lt;\u0026thinsp;65 or \u0026ge;\u0026thinsp;65 years), diabetes duration (\u0026lt;\u0026thinsp;10 years or \u0026ge;\u0026thinsp;10 years), BMI (\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e or \u0026ge;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e), HbA1c (\u0026lt;\u0026thinsp;7% or \u0026ge;\u0026thinsp;7%), smoking and drinking.\u003c/p\u003e\n \u003cp\u003eModels were adjusted for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL-C, HDL-C, TG. Subgroup variables were excluded from the model. OR, odds ratio; CI, confidence interval; BMI, body mass index; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; VAI, Visceral Adipose Index.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, in the subgroup analyses, significant association between VAI and DPN were observed in participants with HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7%(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043). No significant interactions were detected between VAI and gender, age, diabetes duration, BMI, HbA1c, smoking or drinking.\u003c/p\u003e\n \u003cp\u003eModels were adjusted for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL-C, HDL-C, TG. Subgroup variables were excluded from the model. OR, odds ratio; CI, confidence interval; BMI, body mass index; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LAP, Lipid Accumulation Product.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, significant associations between LAP and abnormal VPT were consistently observed in subgroups of males, non-elderly patients, diabetes duration\u0026thinsp;\u0026ge;\u0026thinsp;10 years and HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7%. There was a significant association between LAP and abnormal VPT, regardless of whether they were overweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m2 or \u0026ge;\u0026thinsp;24 kg/m2), and whether they had a history of smoking or drinking. No subgroup variables showed significant interactions with LAP.\u003c/p\u003e\n \u003cp\u003eModels were adjusted for gender, age, diabetes duration, smoking, drinking, BMI, waist circumference, SP, DP, HbA1c, TC, LDL-C, HDL-C, TG. Subgroup variables were excluded from the model. OR, odds ratio; CI, confidence interval; BMI, body mass index; SP, systolic blood pressure; DP, diastolic blood pressure; HbA1c, glycosylated hemoglobin; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; VAI, Visceral Adipose Index.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, in subgroups with diabetes duration\u0026thinsp;\u0026lt;\u0026thinsp;10 years, BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u0026sup2;, HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7%, smoking history, or drinking history, VAI remained significantly associated with abnormal VPT. No significant interactions were found between VAI and any subgroup variables.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study investigated the associations between novel obesity-related indices and DPN as well as abnormal vibration perception, and found that VAI was significantly associated with DPN, while both LAP and VAI showed significant correlations with abnormal vibration perception. These associations remained consistent in subgroup analyses. This indicates that lipids might promote the pathogenesis of DPN, and LAP and VAI may serve as potential markers for assess the existence risk of DPN and abnormal vibration perception in patients with type 2 diabetes mellitus\u003c/p\u003e \u003cp\u003eObesity is considered as an independent risk factor for DPN\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. A large observational cohort study conducted in the Danish population demonstrated that obesity markers (body weight, waist circumference and BMI) are potential risk factors for DPN\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. An observational study conducted in Korea among newly diagnosed patients with type 2 diabetes mellitus revealed significant associations between DPN and BMI, waist circumference, as well as visceral fat area (VFA) assessed by bioelectrical impedance analysis\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Furthermore, prevention of weight gain and moderate weight reduction can decrease the incidence of diabetes-related complications, including DPN, in patients with type 2 diabetes mellitus\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. An interventional study involving overweight or obese patients with type 2 diabetes showed that subjects who underwent 2\u0026ndash;3 years of weight management had a lower incidence of DPN and exhibited significant improvement in light touch sensation\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAbdominal obesity can lead to chronic low-grade inflammation and various metabolic disturbances, and is closely associated with insulin resistance, type 2 diabetes mellitus, non-alcoholic fatty liver disease (NAFLD), as well as cardiovascular diseases and even certain cancers\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that abdominal obesity is also a risk factor for diabetic chronic complications, including diabetic kidney disease (DKD) and diabetic retinopathy (DR)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt was reported that high triglyceride level is a strong DPN risk factor\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Furthermore, patients with hypertriglyceridemia exhibited faster DPN progression\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, suggesting that dyslipidemia may play an important role in DPN development and progression. However, research findings remain inconsistent. A cross-sectional study from Taiwan, China, found no association between either hyperlipidemia or lipid-lowering medications and DPN\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLAP, calculated from both TG and waist circumference, is a novel indicator of lipid overaccumulation. It has been recognized as a cost-effective method for assessing the risk of several chronic diseases, including type2 diabetes mellitus, hypertension, and cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. LAP was associated with oxidative stress biomarkers, suggesting the elevation of LAP could identify an imbalance in the redox status\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. VAI, derived from a composite calculation of BMI, waist circumference, TG, and HDL-C, serves as a marker of cardiometabolic risk\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Compared to traditional indicators such as BMI, waist circumference, and lipid levels, both LAP and VAI may represent stronger risk indicators for type 2 diabetes mellitus in women\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. LAP and VAI are novel visceral adiposity indices derived from a combination of anthropometric and lipid parameters, providing a more accurate reflection of body fat distribution than BMI or waist circumference alone. VPT, as a sensitive indicator of peripheral neuropathy, can be easily measured and calculated, making it particularly valuable for clinical practice and large-scale population studies\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Our study demonstrated significant associations between LAP, VAI and VPT in patients with type 2 diabetes mellitus. However, we only identified an association between VAI and DPN in patients with type 2 diabetes mellitus, while no such relationship was observed between LAP and DPN. Furthermore, BRI showed no significant correlations with either DPN or VPT. Given the relatively small sample size of this study, larger-scale investigations and prospective studies are warranted to further elucidate the relationships between these indices and DPN as well as VPT.\u003c/p\u003e \u003cp\u003eThis study is the first to investigate associations between novel obesity-related indices (LAP and VAI) and DPN as well as abnormal VPT in Chinese patients with type 2 diabetes mellitus. Our findings indicate that VAI shows significant correlation with DPN, while both LAP and VAI are significantly associated with abnormal vibration perception. All anthropometric measurements and questionnaire surveys were carried out by a single trained research team, which ensured the high-quality of data collection. However, several limitations should be noted. Firstly, as a cross-sectional study, this research cannot establish a causal relationship between LAP, VAI, and DPN or abnormal vibration perception. Further prospective studies are needed to clarify these associations. Secondly, this was a single-center study, and the findings should be validated through multicenter research to enhance generalizability. Third, the study did not include quantitative electromyography (EMG) in the assessment of participants. Future studies should incorporate nerve conduction studies or detailed EMG evaluations to further explore the relationship between LAP, VAI, and DPN. Finally, data on potential confounding factors such as lipid-lowering therapy, dietary habits, and physical activity levels were not collected. Subsequent research should include these variables to improve the robustness of the analysis.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe present study indicated that VAI was significantly associated with DPN, while both LAP and VAI showed significant correlations with abnormal vibration perception in Chinese patients with type 2 diabetes mellitus. These findings suggest that LAP and VAI may serve as potential biomarkers for early screening of DPN in patients with type 2 diabetes mellitus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University, China (KY2024-R170), date of approval: 22 July, 2024.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe investigators are grateful to all the dedicated participants and all research staff of the study. This work was supported by National Science and Technology Major Project grant (2024ZD0523300) and the Open Research Project of Shanghai Key Laboratory of Diabetes Mellitus [ SHKLD-KF-2401].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe study was conceived and supervised by Weihui Yu (Corresponding Author). Huizhen Ji led the design of the work, interpretation of data, and statistical analysis, and played a central role in manuscript writing and revision. Jiahui Cui contributed to interpretation of data and statistical analysis, and was instrumental in interpreting the results and drafting the manuscript. Yiyi Zhang was responsible for data collection and preliminary data analysis. Qiran Ma assisted with data acquisition and provided support for statistical analysis. Xiang Hu managed patient recruitment, data collection, and dataset quality control. Huihui Deng performed data entry and data cleaning. Shuoping Chen provided clinical expertise, contributed to the development of the study protocol, and reviewed the manuscript. Qi Zhou supported data collection and assisted in manuscript editing. Wei Pan participated in the statistical analysis. Jing Hong and Tingting Ye jointly carried out patient recruitment and data organization. Feixia Shen and Hong Zhu were responsible for ethical approval and regulatory compliance. All authors have approved the final version of the manuscript for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe investigators are grateful to all the dedicated participants and all research staff of the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analysed during the current study are not publicly available due to privacy protection but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArmstrong DG, Boulton AJM, Bus SA. Diabetic foot ulcers and their recurrence. N Engl J Med. 2017;376(24):2367\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang ZH, Li SQ, Kou Y, Huang L, et al. Risk factors for the recurrence of diabetic foot ulcers among diabetic patients: a meta-analysis. 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J Atheroscler Thromb. 2014;21(3):282\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanmohammadi S, Tavolinejad H, Aminorroaya A, et al. Association of lipid accumulation product with type 2 diabetes mellitus, hypertension, and mortality: a systematic review and meta-analysis. J Diabetes Metab Disord. 2022;21(2):1943\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLugo R, Avila-Nava A, Pech-Aguilar AG, et al. Relationship between lipid accumulation product and oxidative biomarkers by gender in adults from Yucatan. Mexico Sci Rep. 2022;12(1):14338.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmato MC, Giordano C. (2014). Visceral adiposity index: an indicator of adipose tissue dysfunction. Int J Endocrinol. 2014:2014:730827.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrahimaj A, Rivadeneira F, Muka T et al. (2019). Novel metabolic indices and incident type 2 diabetes among women and men: the Rotterdam Study. Diabetologia.62(9):1581-90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin CL, Waberski BH, Pop-Busui R, et al. Vibration perception threshold as a measure of distal symmetrical peripheral neuropathy in type 1 diabetes: results from the DCCT/EDIC study. Diabetes Care. 2010;33(12):2635\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, Diabetic peripheral neuropathy, Lipid accumulation product, Visceral adipose index, Vibration perception threshold","lastPublishedDoi":"10.21203/rs.3.rs-8230770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8230770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study investigated the association between obesity-related indices [Lipid Accumulation Product (LAP), Visceral Adipose Index (VAI), Body Roundness Index (BRI)] and diabetic peripheral neuropathy (DPN) in Chinese patients with type 2 diabetes mellitus.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e1098 participants with type 2 diabetes were enrolled. The correlation between and DPN as well as abnormal vibration perception threshold (VPT) were explored by logistic regression and multiple linear regression analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eVAI was significantly correlated with higher likelihood of having DPN (OR 1.175, 95% CI 1.012\u0026ndash;1.363, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). LAP and VAI were associated with abnormal VPT (OR 1.024, 95%CI 1.011\u0026ndash;1.038, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for LAP; OR 1.203, 95%CI 1.044\u0026ndash;1.386, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011 for VAI). Multiple linear regression analysis showed that LAP and VAI were associated with VPT (LAP, β\u0026thinsp;=\u0026thinsp;0.038, 95% CI 0.014\u0026ndash;0.061, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; VAI, β\u0026thinsp;=\u0026thinsp;0.313, 95% CI 0.037\u0026ndash;0.589, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study shows that VAI was significantly associated with DPN, and LAP as well as VAI were significantly correlated with abnormal VPT in Chinese patients with type 2 diabetes.\u003c/p\u003e","manuscriptTitle":"Association Between Obesity-Related Indicators and Peripheral Neuropathy in Type 2 Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 01:28:41","doi":"10.21203/rs.3.rs-8230770/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-13T01:08:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283904122727552066832830687988519752527","date":"2026-01-07T06:06:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-06T19:56:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-11T09:31:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T14:04:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T14:00:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-11-28T13:06:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94ab3b1f-6da6-4fe8-8475-5289a4af8cb4","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T01:28:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 01:28:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8230770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8230770","identity":"rs-8230770","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}