Metabolic Indices in Patients with Polycystic Ovary Syndrome

Metabolic Indices in Patients with Polycystic Ovary Syndrome | 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 Metabolic Indices in Patients with Polycystic Ovary Syndrome Emre Uysal, Omer Tammo, Esra Soylemez, Mehmet Incebıyık, Dilber Filiz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4445299/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Aug, 2024 Read the published version in BMC Endocrine Disorders → Version 1 posted 18 You are reading this latest preprint version Abstract Background: Polycystic ovary syndrome (PCOS) is a prevalent hormonal disorder affecting 5-15% of women of reproductive age, characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovarian morphology. PCOS is associated with metabolic disturbances such as dyslipidemia, insulin resistance (IR), and an increased risk of type 2 diabetes (T2DM) and cardiovascular disease (CVD). Objective: This study aimed to evaluate the relationships between new anthropometric indices (BAI, VAI, LAP, BRI, ABSI) and atherogenic indices (Castelli index-I, Castelli index-II, AIP, AC, LCI, TG/HDL-C ratio, METS-IR, TyG index, TyG-BMI index, TyG-WC index) with glucose and insulin profiles in women with PCOS. Methods: A retrospective analysis was conducted on 248 women diagnosed with PCOS based on the 2003 Rotterdam criteria. Anthropometric measurements, biochemical parameters, and atherogenic indices were collected from patient records. Statistical analyses were performed using SPSS software version 28.0. Results: Significant correlations were found between fasting glucose and various anthropometric indices, such as BMI, WHtR, and BAI, indicating a link between adiposity and glucose metabolism in PCOS. Atherogenic indices like Castelli's risk indices, AIP, and AC showed positive correlations with glucose and insulin levels, reinforcing their role in assessing cardiovascular risk. Novel indices such as METS-IR and TyG demonstrated strong correlations with both glucose and insulin profiles, highlighting their potential as reliable markers for IR and cardiometabolic risk. Conclusion: The study underscores the importance of using a range of anthropometric and atherogenic indices for comprehensive metabolic assessment in women with PCOS. Indices like METS-IR and TyG offer valuable insights into insulin sensitivity and cardiovascular risk, potentially aiding in better management and prognosis of PCOS. Polycystic ovary syndrome Insulin resistance Metabolic indices Atherogenic indices Cardiometabolic risk Anthropometric measurements Introduction Polycystic ovary syndrome (PCOS) is a common and complex hormonal disorder affecting women of reproductive age, with a prevalence of approximately 5–15% ( 1 ). It is characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovarian morphology. In addition, PCOS can lead to dyslipidemia, hyperinsulinemia, insulin resistance (IR), impaired glucose metabolism, type 2 diabetes (T2DM), metabolic syndrome, infertility, and oxidative stress disorders ( 2 – 4 ). The pathogenesis of PCOS is complex and its etiology remains incompletely understood. Numerous studies have investigated the mechanisms of metabolic dysregulation (glucose and lipids) and inflammatory mechanisms in the pathogenesis of PCOS. IR is now a well-known feature of PCOS and, along with hypertension and dyslipidemia, increases the risk of cardiovascular (CV) and cerebrovascular events. These risk factors are further exacerbated by central obesity and metabolic syndrome, which are present in most women with PCOS ( 5 , 6 ). Many studies have shown that women with PCOS have significantly higher levels of triglyceride to high-density lipoprotein (HDL) cholesterol ratio, indicating an important association with IR and cardiometabolic risk factors. Lipid indices are highly correlated with impaired insulin metabolism and hyperandrogenemia ( 7 , 8 ). A study by Zheng et al. indicated that new metabolic lipid indices (TyG index - triglyceride glucose index; TyG-BMI index - triglyceride-body mass index; TyG-WC index - triglyceride-waist circumference index) are useful in the early identification of prediabetes risk ( 9 ). Given that adipose tissues secrete adipokines, inflammatory cytokines, and reactive oxygen species, leading to various metabolic disorders, such indices may be better predictors of IR than the TyG index alone ( 10 , 11 ). Additionally, a new non-insulin-based score, METS-IR (Metabolic Score for Insulin Resistance), may be useful in assessing insulin sensitivity and detecting IR in patients at risk of developing T2DM ( 12 ). Therefore, METS-IR, along with the lipoprotein combined index (LCI), a new risk determinant for coronary artery disease (CAD), are promising scores for evaluating cardiometabolic risk in women with PCOS. Adiposity plays a significant role in the prevention and management of PCOS. Anthropometric indices such as waist-hip ratio (WHR), waist-height ratio (WHtR), visceral adiposity index (VAI), body adiposity index (BAI), lipid accumulation product (LAP), body roundness index (BRI), and a body shape index (ABSI) may serve as indicators of adipose tissue abnormalities and CVD risk in patients with PCOS ( 13 , 14 ). The aim of this study is to apply new anthropometric indices (BAI, VAI, LAP, BRI, ABSI) and new atherogenic indices [Castelli index-I, Castelli index-II, atherogenic risk of plasma (AIP), atherogenic coefficient (AC), lipoprotein combined index (LCI), triglycerides to HDL-Cholesterol ratio (TG/HDL-C ratio), metabolic score of insulin resistance (METS-IR), triglyceride glucose index (TyG index), triglyceride glucose-body mass index (TyG-BMI index), triglyceride glucose-waist circumference index (TyG-WC index)] to patients with PCOS. Materials and Methods Study Group This study was conducted retrospectively at Mardin Training and Research Hospital, reviewing patient records from 2022 to 2023. A total of 248 women diagnosed with PCOS according to the 2003 Rotterdam criteria were included in the study. The sample size was calculated using the formula recommended in a paper by Hajian-Tilaki (15). 2003 Rotterdam Criteria Oligo-ovulation or anovulation Clinical and/or biochemical hyperandrogenism Ultrasonda görülen polikistik overler (2-9 mm boyutunda 12 veya daha fazla follikül) PCOS was diagnosed if at least two of these three criteria were met. Exclusion Criteria: The exclusion criteria were as follows: refusal to participate in the study; pregnancy; hemolyzed samples; use of hormonal contraceptives, glucocorticosteroids, oral steroids, lipid-lowering medications, or drugs affecting glucose metabolism; previously diagnosed and treated diabetes; decompensated thyroid disorders; diseases associated with androgen excess (congenital or late-onset congenital adrenal hyperplasia, hyperprolactinemia, Cushing's disease/syndrome, androgen-secreting tumors, idiopathic hirsutism); depressive disorders and treatment for depression. Laboratory Measurements and Indices Patient records from 2022 to 2023 were retrospectively reviewed. Demographic data (height, weight, waist circumference, hip circumference) and laboratory results (fasting glucose, fasting insulin, HDL-C, triglycerides, LDL-C (low-density lipoprotein cholesterol)) were collected from patient records. For this study, no additional blood was drawn from patients beyond what was required for their diagnosis and follow-up. The value of the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index was calculated using the following formula: HOMA-IR = fasting insulin (mU/mL) × fasting glucose (mmol/L) / 22.5 (16). The value of METS-IR (Metabolic Score for Insulin Resistance) was calculated as follows: METS-IR = (In ((2 x fasting glucose) (mg/dL) + TG (mg/dL) x BMI)/(ln (HDL-C) (mg/dL)) (17). The TyG index was calculated using the following formula: ln[fasting glucose (mg/dL) x TG (mg/dL)/2] (16). The TyG-BMI index is defined as follows: ln[fasting glucose (mg/dL) x fasting triglycerides (mg/dL)/2] x BMI (18) TyG-WC is defined as: ln[fasting glucose (mg/dL) x TG (mg/dL)/2] x WC (18) LCI was calculated using the following formula: ((TC (mmol/L) × TG (mmol/L) × LDL-C (mmol/L))/HDL-C (mmol/L)) (19) Castelli risk index-I was calculated according to the formula = (TC/HDL-C) (7). Castelli risk index-II was calculated according to the formula = (LDL-C/HDL-C) (7). Atherogenic coefficient (AC) was calculated according to the formula = ((TC-HDL-C)/HDL-C) (7). The atherogenic index (AIP) of plasma was calculated according to the formula = (log(TG/HDL-C)) (7). The ratio of triglyceride to HDL-cholesterol was calculated according to the formula = (TG/HDL-C) (7). Anthropometric parameters were measured using standard methods. These measurements included body weight [kg], height [cm], waist circumference [cm], and hip circumference [cm]. Body mass index (BMI) is calculated according to the following formula: BMI = body weight [kg]/height [m]2 (20) Waist-to-hip ratio (WHR) was calculated according to the following formula: WHR = waist circumference [cm]/hip circumference [cm] (21) Waist-height ratio (WHtR) was calculated according to the following formula: WHtR = waist circumference [cm]/height [cm] (21) Body adiposity index (BAI) was calculated according to the following formula: BAI = ((hip circumference [cm]/height [m]1.5)-18) (22) Visceral Adiposity Index (VAI) was calculated according to the following formula: VAI = [waist circumference [cm]/(36.58 + (1.89 × BMI))] × (triglyceride concentration [mmol/L]/0.81) × (1.52/ HDL concentration [mmol/L]) (16) Lipid Accumulation Product (LAP) was calculated according to the following formula: LAP = (waist circumference [cm] – 58) × (triglyceride concentration [mmol/L]) (16) Body Roundness Index (BRI) was calculated according to the following formula: BRI = 365.2 − 365.5 × √(1 − (((WC/2π)2)/[(0.5 × height)]2) (23) A Body Shape Index (ABSI) was calculated according to the following formula: ABSI = WC[m]/[(BMI)2/3) × (height [m])1/2)] (23) Statistical Analysis The data obtained in the study will be analyzed using SPSS software version 28.0. The Shapiro-Wilk test was used to assess the distribution of the data. Continuous variables are expressed as mean ± standard deviation (for normally distributed data) or median and interquartile range (for non-normally distributed data). For normally distributed data, the Student's t-test will be used, while the Mann-Whitney U test will be used for non-parametric data. Correlation between variables will be evaluated using the Pearson correlation coefficient (for normally distributed data) and Spearman's rank correlation coefficient (for non-normally distributed data). A p -value of less than 0.05 will be considered statistically significant. Results The anthropometric measurements, biochemical parameters, and atherogenic index results of women diagnosed with PCOS are presented in Table 1 . Table 1 Anthropometric Measurements, Biochemical Parameters, and Atherogenic Index Results of Women Diagnosed with PCOS N x̄±SS Age 248 22.72 ± 4.07 Anthropometric Measurements N x̄±SS Weight (kg) 248 77.30 ± 17.81 Height (cm) 248 162.54 ± 4.95 Waist Circumference (cm) 248 83.91 ± 12.80 Hip Circumference (cm) 248 109.26 ± 15.01 BMI (kg/m 2 ) 248 29.24 ± 6.72 WHtR 248 0.52 ± 0.08 BAI (%) 248 34.85 ± 7.88 BRI 248 3.81 ± 1.59 N Median (25th-75th Quarter) WHR 248 0.76 (0.74–0.78) VAI 247 3.85 (2.65–5.97) LAP 248 5325 (3074–7176) ABSI 247 0.07 (0.07–0.07) Biochemical Parameters N x̄±SS OGTT After 120 Minutes (mmol/L) 248 141.97 ± 23.35 Insulin OGTT After 60 Minutes (pmol/L) 248 111.31 ± 19.66 HbA1c (%) 248 5.39 ± 0.59 Total Cholesterol (mmol/L) 248 171.19 ± 30.22 HDL Cholesterol (mmol/L) 248 55.68 ± 14.43 LDL Cholesterol (mmol/L) 248 95.92 ± 25.95 N Median (25th-75th Quarter) Fasting Glucose (mmol/L) 248 89 (84–95) Fasting Insulin (pmol/L) 248 12.30 (8.05–15.64) OGTT After 60 Minutes (mmol/L) 248 89 (85–94) Triglycerides (mmol/L) 248 94.50 (71.50–131.00) HOMA-IR Index 248 47.36 (32.72–67.21) Atherogenic Indexes N x̄±SS Castelli's Risk Index-I 248 3.25 ± 0.90 Castelli’s Risk Index-II 248 1.86 ± 0.74 AIP 248 0.26 ± 0.24 AC 248 2.25 ± 0.90 METS-IR 247 41.75 ± 11.03 TyG Index 248 8.38 ± 0.48 TyG-BMI 247 245.54 ± 60.78 TyG-Waist Circumference Index 248 917.80 ± 148.37 N Median (25th-75th Quarter) LCI 248 28481.54 (15771.24–47946.00) TG/HDL-C 248 1.74 (1.16–2.62) BMI: Body mass index, WHtR: Waist-height ratio, BAI: Body adiposity index, BRI: Body Roundness Index, WHR: Waist-to-hip ratio, VAI: Visceral Adiposity Index, LAP: Lipid Accumulation Product, ABSI: A Body Shape Index, OGTT: Oral glucose tolerance test, HbA1c: Hemoglobin A1c, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance, AIP: Atherogenic risk of plasma, AC: atherogenic coefficient, METS-IR: Metabolic score of insulin resistance, TyG Index: triglyceride glucose index, TyG-BMI: triglyceride glucose-body mass index, LCI: lipoprotein combined index, TG/HDL-C: Triglycerides to HDL-cholesterol ratio The relationships between anthropometric measurements, atherogenic indices, and glucose profile in the study group are presented in Table 2 . Table 2 Relationship Between Anthropometric Measurements, Atherogenic Indices, and Glucose Profile in the Study Group N Fasting Glucose (mmol/L) OGTT After 60 Minutes (mmol/L) OGTT After 120 Minutes (mmol/L) r p r p r p Weight (kg) 248 0.265 < 0.001 0.294 < 0.001 0.419 < 0.001 Height (cm) 248 0.267 < 0.001 0.323 < 0.001 0.473 < 0.001 Waist Circumference (cm) 248 0.254 < 0.001 0.321 < 0.001 0.487 < 0.001 Hip Circumference (cm) 248 0.276 < 0.001 0.331 < 0.001 0.428 < 0.001 WHR 248 0.162 0.010 0.195 0.002 0.188 0.003 WHtR 248 0.267 < 0.001 0.335 < 0.001 0.473 < 0.001 BAI (%) 248 0.240 < 0.001 0.322 < 0.001 0.475 < 0.001 VAI 247 0.156 0.014 0.272 < 0.001 0.200 0.002 LAP 248 0.161 0.011 0.299 < 0.001 0.419 < 0.001 BRI 248 0.267 < 0.001 0.335 < 0.001 0.458 < 0.001 ABSI 247 -0.036 0.572 0.013 0.839 0.075 0.242 Castelli’s Risk Index-I 248 0.274 < 0.001 0.267 < 0.001 0.171 0.007 Castelli’s Risk Index-II 248 0.233 < 0.001 0.210 < 0.001 0.173 0.006 AIP 248 0.170 0.007 0.279 < 0.001 0.146 0.022 AC 248 0.274 < 0.001 0.267 < 0.001 0.171 0.007 LCI 248 0.182 0.004 0.259 < 0.001 0.333 < 0.001 TG/HDL-C 248 0.170 0.007 0.279 < 0.001 0.172 0.007 METS-IR 247 0.372 < 0.001 0.410 < 0.001 0.382 < 0.001 TyG Index 248 0.275 < 0.001 0.390 < 0.001 0.290 < 0.001 TyG-BMI 247 0.326 < 0.001 0.399 < 0.001 0.452 < 0.001 TyG- Waist Circumference Index 248 0.308 < 0.001 0.399 < 0.001 0.506 < 0.001 BMI: Body mass index, WHtR: Waist-height ratio, BAI: Body adiposity index, BRI: Body Roundness Index, WHR: Waist-to-hip ratio, VAI: Visceral Adiposity Index, LAP: Lipid Accumulation Product, ABSI: A Body Shape Index, OGTT: Oral glucose tolerance test, HbA1c: Hemoglobin A1c, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance, AIP: Atherogenic risk of plasma, AC: atherogenic coefficient, METS-IR: Metabolic score of insulin resistance, TyG Index: triglyceride glucose index, TyG-BMI: triglyceride glucose-body mass index, LCI: lipoprotein combined index, TG/HDL-C: Triglycerides to HDL-cholesterol ratio Correlation coefficients given in italics were calculated using Spearman's rank correlation test. Upon examining Table 2 , a low positive correlation was found between fasting glucose (mmol/L) and weight, waist circumference, hip circumference, BMI, WHR, WHtR, BAI, VAI, LAP, BRI, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, LCI, TG/HDL-C, and TyG index; a moderate positive correlation was found between fasting glucose and METS-IR, TyG-BMI, and TyG-waist circumference index (p < 0.001, p < 0.01, p 0.05). After 60 minutes, the oral glucose tolerance test (OGTT) (mmol/L) showed a low positive correlation with weight, WHR, VAI, LAP, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, LCI, and TG/HDL-C; and a moderate positive correlation with waist circumference, hip circumference, BMI, WHtR, BAI, BRI, METS-IR, TyG index, TyG-BMI, and TyG-waist circumference index (p < 0.001, p 0.05). After 120 minutes, the OGTT (mmol/L) demonstrated a low positive correlation with WHR, VAI, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, TG/HDL-C, and TyG index; and a moderate positive correlation with weight, waist circumference, hip circumference, BMI, WHtR, BAI, LAP, BRI, LCI, METS-IR, TyG-BMI, and TyG-waist circumference index (p < 0.001, p < 0.01, p 0.05). The relationships between anthropometric measurements, atherogenic indices, and insulin profile in the study group are presented in Table 3 . Table 3 Relationship Between Anthropometric Measurements, Atherogenic Indices, and Insulin Profile in the Study Group N Fasting Insulin (pmol/L) Insulin OGTT After 60 Minutes (pmol/L) r p r p Weight (kg) 248 0.336 < 0.001 0.406 < 0.001 Waist Circumference (cm) 248 0.321 < 0.001 0.475 < 0.001 Hip Circumference (cm) 248 0.312 < 0.001 0.527 < 0.001 BMI (kg/m 2 ) 248 0.345 < 0.001 0.396 < 0.001 WHR 248 0.132 0.038 0.161 0.011 WHtR 248 0.331 < 0.001 0.464 < 0.001 BAI (%) 248 0.313 < 0.001 0.497 < 0.001 VAI 247 0.112 0.079 0.245 < 0.001 LAP 248 0.187 0.003 0.432 < 0.001 BRI 248 0.331 < 0.001 0.456 < 0.001 ABSI 247 -0.015 0.817 0.102 0.109 Castelli’s Risk Index-I 248 0.160 0.012 0.290 < 0.001 Castelli’s Risk Index-II 248 0.134 0.036 0.290 < 0.001 AIP 248 0.116 0.068 0.170 0.007 AC 248 0.160 0.012 0.290 < 0.001 LCI 248 0.129 0.042 0.388 < 0.001 TG/HDL-C 248 0.116 0.068 0.219 < 0.001 METS-IR 247 0.364 < 0.001 0.392 < 0.001 TyG Index 248 0.105 0.099 0.268 < 0.001 TyG-BMI 247 0.342 < 0.001 0.430 < 0.001 TyG- Waist Circumference Index 248 0.292 < 0.001 0.539 < 0.001 BMI: Body mass index, WHtR: Waist-height ratio, BAI: Body adiposity index, BRI: Body Roundness Index, WHR: Waist-to-hip ratio, VAI: Visceral Adiposity Index, LAP: Lipid Accumulation Product, ABSI: A Body Shape Index, OGTT: Oral glucose tolerance test, HDL: High-density lipoprotein, AIP: Atherogenic risk of plasma, AC: atherogenic coefficient, TyG Index: triglyceride glucose index, TyG-BMI: triglyceride glucose-body mass index, LCI: lipoprotein combined index, TG/HDL-C: Triglycerides to HDL-cholesterol ratio Correlation coefficients given in italics were calculated using Spearman's rank correlation test. Upon examining Table 3 , a low positive correlation was found between fasting insulin (pmol/L) and WHR, VAI, LAP, Castelli's risk index-I, Castelli's risk index-II, AC, LCI, TG/HDL-C, TyG index, and TyG-waist circumference index; a moderate positive correlation was found between fasting insulin and weight, waist circumference, hip circumference, BMI, WHtR, BAI, BRI, METS-IR, and TyG-BMI (p < 0.001, p < 0.01, p 0.05). After 60 minutes, the insulin OGTT (pmol/L) showed a low positive correlation with WHR, VAI, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, TG/HDL-C, and TyG index; and a moderate positive correlation with weight, waist circumference, hip circumference, BMI, WHtR, BAI, LAP, BRI, LCI, METS-IR, TyG-BMI, and TyG-waist circumference index (p < 0.001, p < 0.01, p 0.05). The relationships between anthropometric measurements, atherogenic indices, and hemoglobin A1c (HbA1c) and HOMA-IR index in the study group are presented in Table 4 . Table 4 Relationship Between Anthropometric Measurements, Atherogenic Indices, and HbA1c and HOMA-IR Index in the Study Group N HbA1c (%) HOMA-IR Index r p r p Weight (kg) 248 0.340 < 0.001 0.363 < 0.001 Waist Circumference (cm) 248 0.203 0.001 0.308 < 0.001 Hip Circumference (cm) 248 0.207 0.001 0.299 < 0.001 BMI (kg/m 2 ) 248 0.283 < 0.001 0.369 < 0.001 WHR 248 0.132 0.038 0.117 0.065 WHtR 248 0.181 0.004 0.315 < 0.001 BAI (%) 248 0.166 0.009 0.294 < 0.001 VAI 247 0.096 0.133 0.114 0.074 LAP 248 0.144 0.024 0.180 0.005 BRI 248 0.175 0.006 0.315 < 0.001 ABSI 247 -0.198 0.002 -0.086 0.178 Castelli’s Risk Index-I 248 0.204 < 0.001 0.177 0.005 Castelli’s Risk Index-II 248 0.223 < 0.001 0.149 0.019 AIP 248 0.086 0.175 0.128 0.043 AC 248 0.204 0.001 0.177 0.005 LCI 248 0.177 0.005 0.133 0.036 TG/HDL-C 248 0.104 0.101 0.128 0.043 METS-IR 247 0.276 < 0.001 0.398 < 0.001 TyG Index 248 0.175 0.006 0.140 0.027 TyG-BMI 247 0.290 < 0.001 0.373 < 0.001 TyG- Waist Circumference Index 248 0.222 < 0.001 0.296 < 0.001 BMI: Body mass index, WHtR: Waist-height ratio, BAI: Body adiposity index, BRI: Body Roundness Index, WHR: Waist-to-hip ratio, VAI: Visceral Adiposity Index, LAP: Lipid Accumulation Product, ABSI: A Body Shape Index, OGTT: Oral glucose tolerance test, HbA1c: Hemoglobin A1c, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance, AIP: Atherogenic risk of plasma, AC: atherogenic coefficient, METS-IR: Metabolic score of insulin resistance, TyG Index: triglyceride glucose index, TyG-BMI: triglyceride glucose-body mass index, LCI: lipoprotein combined index, TG/HDL-C: Triglycerides to HDL-cholesterol ratio Correlation coefficients given in italics were calculated using Spearman's rank correlation test Upon examining Table 4 , a low positive correlation was found between HbA1c (%) and waist circumference, hip circumference, BMI, WHR, WHtR, BAI, LAP, BRI, Castelli's risk index-I, Castelli's risk index-II, AC, LCI, TG/HDL-C, METS-IR, TyG index, TyG-BMI, and TyG-waist circumference index; a moderate positive correlation was found with weight; and a low negative correlation was found with ABSI (p < 0.001, p < 0.01, p 0.05). For the HOMA-IR index, a low positive correlation was found with hip circumference, BAI, LAP, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, LCI, TG/HDL-C, TyG index, and TyG-waist circumference index; a moderate positive correlation was found with weight, waist circumference, BMI, WHtR, BRI, and METS-IR (p < 0.001, p < 0.01, p 0.05). Discussion The findings of this study provide valuable insights into the relationships between various anthropometric, biochemical, and atherogenic indices and their relevance to glucose and insulin profiles in women with PCOS. This study reinforces the importance of comprehensive metabolic assessment in managing PCOS and highlights the potential utility of newer indices like METS-IR, TyG, and LCI in clinical practice. Anthropometric Indices and Glucose Profile The positive correlations between fasting glucose and anthropometric indices such as BMI, WHtR, and BAI indicate that central and overall adiposity are significant contributors to glucose dysregulation in PCOS patients. This aligns with previous studies that have shown a strong link between obesity and impaired glucose metabolism in PCOS ( 1 , 5 ). The lack of significant correlation with ABSI suggests that this index may not be as sensitive in detecting metabolic disturbances associated with PCOS compared to other measures of adiposity. Atherogenic Indices and Cardiometabolic Risk Atherogenic indices such as Castelli's risk index-I and II, AIP, and AC showed positive correlations with both fasting glucose and insulin levels, reinforcing their role in assessing cardiovascular risk in PCOS patients ( 7 ). These findings are consistent with the understanding that dyslipidemia is a common feature in PCOS and contributes to the heightened cardiovascular risk ( 2 , 3 ). The novel indices, particularly the TyG index and its derivatives (TyG-BMI, TyG-WC), demonstrated robust correlations with glucose and insulin parameters, underscoring their utility as predictors of insulin resistance and cardiometabolic risk ( 9 , 16 ). Insulin Resistance and Metabolic Indices The significant correlations between METS-IR and both glucose and insulin profiles highlight its potential as a reliable marker for insulin resistance in PCOS. METS-IR, which incorporates both metabolic and anthropometric parameters, provides a comprehensive assessment of insulin sensitivity ( 12 ). This is particularly valuable in PCOS, where insulin resistance plays a central role in the pathophysiology and contributes to the risk of developing T2DM and cardiovascular disease ( 6 , 10 ). Clinical Implications The results of this study suggest that incorporating a range of anthropometric and atherogenic indices into routine clinical assessment could enhance the identification and management of metabolic and cardiovascular risks in women with PCOS. Indices such as the TyG and METS-IR, which are simple to calculate and do not require complex testing, could be particularly useful in resource-limited settings. Limitations and Future Research This study has several limitations, including its retrospective design and reliance on existing patient records, which may introduce selection bias and limit the generalizability of the findings. Additionally, the study population was limited to a single geographic region, and further research is needed to validate these findings in more diverse populations. Prospective studies with larger sample sizes and longitudinal follow-up are warranted to establish the predictive value of these indices for long-term metabolic and cardiovascular outcomes in PCOS. Conclusion In conclusion, this study highlights the significant relationships between various metabolic indices and glucose and insulin profiles in women with PCOS. The findings support the use of newer indices such as METS-IR, TyG, and LCI as effective tools for assessing metabolic and cardiovascular risk in PCOS. Incorporating these indices into clinical practice could improve the management and prognosis of PCOS, ultimately leading to better health outcomes for affected women. Abbreviations ABSI A Body Shape Index AC Atherogenic Coefficient AIP Atherogenic Risk of Plasma BAI Body Adiposity Index BMI Body Mass Index BRI Body Roundness Index CAD Coronary Artery Disease CV Cardiovascular CVD Cardiovascular Disease HbA1c Hemoglobin A1c HDL High-Density Lipoprotein HOMA-IR Homeostatic Model Assessment for Insulin Resistance IR Insulin Resistance LAP Lipid Accumulation Product LCI Lipoprotein Combined Index LDL Low-Density Lipoprotein METS-IR Metabolic Score for Insulin Resistance OGTT Oral Glucose Tolerance Test PCOS Polycystic Ovary Syndrome SPSS Statistical Package for the Social Sciences T2DM Type 2 Diabetes Mellitus TC Total Cholesterol TG/HDL-C Triglycerides to High-Density Lipoprotein Cholesterol Ratio TyG Triglyceride Glucose Index TyG-BMI Triglyceride Glucose-Body Mass Index TyG-WC Triglyceride Glucose-Waist Circumference Index VAI Visceral Adiposity Index WC Waist Circumference WHR Waist-to-Hip Ratio WHtR Waist-Height Ratio Declarations Acknowledgements Not applicable. Authors’ contributions Study conception and design: EU Designed and prepared the text messages: EU, MI and MA Performed the analysis: EU Wrote the Manuscript: EU and MA Collected the data: OT, ES and DF All of the authors reviewed the results and approved the final version of the manuscript. Funding No specifc funding for this research Availability of data and materials The datasets used in this study are available from the corresponding author upon request. Ethics approval and consent to participate The objectives of the study were explained in detail to the study participants. The collection of demographic information and the data obtained from all patients participating in this research were done after obtaining informed consent and willingness to participate in the study. Also, all the information remained confidential and the results were published anonymously and only statistically. Ethical approval for the study was obtained from the Ethics Committee of Mardin Artuklu University (Decision number: 2024/3-21). The authors confirm that all experiments were performed following the relevant Declaration of Helsinki. Consent for publication Written informed consent for the publication of this case report was obtained from the patient. Conflict of interest The authors declare no conflict of interest. References March WA, Moore VM, Willson KJ, Phillips DI, Norman RJ, Davies MJ. The prevalence of polycystic ovary syndrome in a community sample assessed under contrasting diagnostic criteria. Human reproduction. 2010;25(2):544-51. Liu Q, Xie Y-j, Qu L-h, Zhang M-x, Mo Z-c. Dyslipidemia involvement in the development of polycystic ovary syndrome. Taiwanese journal of obstetrics and gynecology. 2019;58(4):447-53. Lathia T, Joshi A, Behl A, Dhingra A, Kalra B, Dua C, et al. A Practitioner’s Toolkit for Polycystic Ovary Syndrome Counselling. Indian Journal of Endocrinology and Metabolism. 2022;26(1):17-25. Sachdeva G, Gainder S, Suri V, Sachdeva N, Chopra S. Comparison of the different PCOS phenotypes based on clinical metabolic, and hormonal profile, and their response to clomiphene. Indian journal of endocrinology and metabolism. 2019;23(3):326-31. Tao L-C, Xu J-n, Wang T-t, Hua F, Li J-J. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovascular Diabetology. 2022;21(1):68. Guo F, Gong Z, Fernando T, Zhang L, Zhu X, Shi Y. The lipid profiles in different characteristics of women with PCOS and the interaction between dyslipidemia and metabolic disorder states: a retrospective study in Chinese population. Frontiers in Endocrinology. 2022;13:892125. Kamoru AA, Japhet OM, Adetunji AD, Musa MA, Hammed OO, Akinlawon AA, et al. Castelli risk index, atherogenic index of plasma, and atherogenic coefficient: emerging risk predictors of cardiovascular disease in HIV-treated patients. Saudi J Med Pharm Sci. 2017;4929:1101-10. Liu Z, He H, Dai Y, Yang L, Liao S, An Z, Li S. Comparison of the diagnostic value between triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio in metabolic-associated fatty liver disease patients: a retrospective cross-sectional study. Lipids in health and disease. 2022;21(1):55. Zheng S, Shi S, Ren X, Han T, Li Y, Chen Y, et al. Triglyceride glucose-waist circumference, a novel and effective predictor of diabetes in first-degree relatives of type 2 diabetes patients: cross-sectional and prospective cohort study. Journal of translational medicine. 2016;14:1-10. Lee J, Kim B, Kim W, Ahn C, Choi HY, Kim JG, et al. Lipid indices as simple and clinically useful surrogate markers for insulin resistance in the US population. Scientific reports. 2021;11(1):2366. Klöting N, Fasshauer M, Dietrich A, Kovacs P, Schön MR, Kern M, et al. Insulin-sensitive obesity. American Journal of Physiology-Endocrinology and Metabolism. 2010;299(3):E506-E15. Bello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. European journal of endocrinology. 2018;178(5):533-44. Naghshband Z, Kumar L, Mandappa S, Murthy ASN, Malini SS. Visceral adiposity index and lipid accumulation product as diagnostic markers of metabolic syndrome in south indians with polycystic ovary syndrome. Journal of Human Reproductive Sciences. 2021;14(3):234-43. Gönülalan G, Saçkan F. The importance of new anthropometric measurements in detecting cardio metabolic risk and insulin resistance in patients with polycystic ovary syndrome: Single center experience. Türkiye Diyabet ve Obezite Dergisi. 2021;5(1):25-32. Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. Journal of biomedical informatics. 2014;48:193-204. Ahn N, Baumeister SE, Amann U, Rathmann W, Peters A, Huth C, et al. Visceral adiposity index (VAI), lipid accumulation product (LAP), and product of triglycerides and glucose (TyG) to discriminate prediabetes and diabetes. Scientific Reports. 2019;9(1):9693. Yoon J, Jung D, Lee Y, Park B. The metabolic score for insulin resistance (METS-IR) as a predictor of incident ischemic heart disease: a longitudinal study among Korean without diabetes. Journal of personalized medicine. 2021;11(8):742. Khamseh ME, Malek M, Abbasi R, Taheri H, Lahouti M, Alaei-Shahmiri F. Triglyceride glucose index and related parameters (triglyceride glucose-body mass index and triglyceride glucose-waist circumference) identify nonalcoholic fatty liver and liver fibrosis in individuals with overweight/obesity. Metabolic syndrome and related disorders. 2021;19(3):167-73. Si Y, Liu J, Han C, Wang R, Liu T, Sun L. The correlation of retinol-binding protein-4 and lipoprotein combine index with the prevalence and diagnosis of acute coronary syndrome. Heart and Vessels. 2020;35:1494-501. Weir CB, Jan A. BMI classification percentile and cut off points. 2019. Consultation WE. Waist circumference and waist-hip ratio. Report of a WHO Expert Consultation Geneva: World Health Organization. 2008;2008:8-11. Bergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG, et al. A better index of body adiposity. Obesity. 2011;19(5):1083-9. Głuszek S, Ciesla E, Głuszek-Osuch M, Kozieł D, Kiebzak W, Wypchło Ł, Suliga E. Anthropometric indices and cut-off points in the diagnosis of metabolic disorders. PLoS One. 2020;15(6):e0235121. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Aug, 2024 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 26 Jun, 2024 Reviews received at journal 22 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviews received at journal 17 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviews received at journal 14 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers agreed at journal 12 Jun, 2024 Reviewers agreed at journal 11 Jun, 2024 Reviews received at journal 06 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers invited by journal 29 May, 2024 Editor invited by journal 24 May, 2024 Editor assigned by journal 22 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 19 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4445299","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308993131,"identity":"810f1b18-5e0e-48ee-b67f-4141361b4f6b","order_by":0,"name":"Emre Uysal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDADPvnjBx8AaR4+orWwSfAkG4C0sJGghcFMAswgpJJf7OzDx7xtdfJs0g1plV9z7GTYGJgfPrqBR4vk7HRjY962w4ZtMgeP3Zbdlgx0GJuxcQ4eLQa309ikedsOMLYxJKTdltzGDNTCwyaNT4v97TT230CH2QO1mBVLbqsnrMVAOo2NmbeNObFNIsGM8eO2w4S1SNxOY5acc+5wchvPmWRpxm3HediYCfiFf3Ya44c3ZXW2/eztBz/+3FZtz8/e/PAxPi1gwAiNC2YeMElIORj8gWr9QZTqUTAKRsEoGGkAAIW3QDq5cyQzAAAAAElFTkSuQmCC","orcid":"","institution":"Yusufeli State Hospital","correspondingAuthor":true,"prefix":"","firstName":"Emre","middleName":"","lastName":"Uysal","suffix":""},{"id":308993132,"identity":"ef170bd6-9e77-423b-af92-97d3e2982010","order_by":1,"name":"Omer Tammo","email":"","orcid":"","institution":"Mardin Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Omer","middleName":"","lastName":"Tammo","suffix":""},{"id":308993133,"identity":"86bd0e20-3e7e-456e-98b8-b03d251dc91f","order_by":2,"name":"Esra Soylemez","email":"","orcid":"","institution":"Mardin Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Esra","middleName":"","lastName":"Soylemez","suffix":""},{"id":308993134,"identity":"366ccedb-ebfb-4182-af32-3b69ecd72737","order_by":3,"name":"Mehmet Incebıyık","email":"","orcid":"","institution":"Sanlıurfa Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"","lastName":"Incebıyık","suffix":""},{"id":308993135,"identity":"e8be6f75-4ef9-4635-9106-723d2a70fbb7","order_by":4,"name":"Dilber Filiz","email":"","orcid":"","institution":"Mardin Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dilber","middleName":"","lastName":"Filiz","suffix":""},{"id":308993136,"identity":"cd704c6e-9f29-44f0-99ef-6856e4a29860","order_by":5,"name":"Mesut Alcı","email":"","orcid":"","institution":"Giresun Gynecology and Pediatrics Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mesut","middleName":"","lastName":"Alcı","suffix":""}],"badges":[],"createdAt":"2024-05-19 17:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4445299/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4445299/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12902-024-01701-6","type":"published","date":"2024-08-28T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63821453,"identity":"3f11834d-0d40-4dd1-807c-c5a8abd09669","added_by":"auto","created_at":"2024-09-02 16:13:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":912961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4445299/v1/4965ce84-6dd3-4da8-b8bc-0877ba7f0a70.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic Indices in Patients with Polycystic Ovary Syndrome","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is a common and complex hormonal disorder affecting women of reproductive age, with a prevalence of approximately 5\u0026ndash;15% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovarian morphology. In addition, PCOS can lead to dyslipidemia, hyperinsulinemia, insulin resistance (IR), impaired glucose metabolism, type 2 diabetes (T2DM), metabolic syndrome, infertility, and oxidative stress disorders (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pathogenesis of PCOS is complex and its etiology remains incompletely understood. Numerous studies have investigated the mechanisms of metabolic dysregulation (glucose and lipids) and inflammatory mechanisms in the pathogenesis of PCOS. IR is now a well-known feature of PCOS and, along with hypertension and dyslipidemia, increases the risk of cardiovascular (CV) and cerebrovascular events. These risk factors are further exacerbated by central obesity and metabolic syndrome, which are present in most women with PCOS (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany studies have shown that women with PCOS have significantly higher levels of triglyceride to high-density lipoprotein (HDL) cholesterol ratio, indicating an important association with IR and cardiometabolic risk factors. Lipid indices are highly correlated with impaired insulin metabolism and hyperandrogenemia (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A study by Zheng et al. indicated that new metabolic lipid indices (TyG index - triglyceride glucose index; TyG-BMI index - triglyceride-body mass index; TyG-WC index - triglyceride-waist circumference index) are useful in the early identification of prediabetes risk (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Given that adipose tissues secrete adipokines, inflammatory cytokines, and reactive oxygen species, leading to various metabolic disorders, such indices may be better predictors of IR than the TyG index alone (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Additionally, a new non-insulin-based score, METS-IR (Metabolic Score for Insulin Resistance), may be useful in assessing insulin sensitivity and detecting IR in patients at risk of developing T2DM (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, METS-IR, along with the lipoprotein combined index (LCI), a new risk determinant for coronary artery disease (CAD), are promising scores for evaluating cardiometabolic risk in women with PCOS.\u003c/p\u003e \u003cp\u003eAdiposity plays a significant role in the prevention and management of PCOS. Anthropometric indices such as waist-hip ratio (WHR), waist-height ratio (WHtR), visceral adiposity index (VAI), body adiposity index (BAI), lipid accumulation product (LAP), body roundness index (BRI), and a body shape index (ABSI) may serve as indicators of adipose tissue abnormalities and CVD risk in patients with PCOS (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of this study is to apply new anthropometric indices (BAI, VAI, LAP, BRI, ABSI) and new atherogenic indices [Castelli index-I, Castelli index-II, atherogenic risk of plasma (AIP), atherogenic coefficient (AC), lipoprotein combined index (LCI), triglycerides to HDL-Cholesterol ratio (TG/HDL-C ratio), metabolic score of insulin resistance (METS-IR), triglyceride glucose index (TyG index), triglyceride glucose-body mass index (TyG-BMI index), triglyceride glucose-waist circumference index (TyG-WC index)] to patients with PCOS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Group\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted retrospectively at Mardin Training and Research Hospital, reviewing patient records from 2022 to 2023. A total of 248 women diagnosed with PCOS according to the 2003 Rotterdam criteria were included in the study. The sample size was calculated using the formula recommended in a paper by Hajian-Tilaki\u0026nbsp;(15).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2003 Rotterdam Criteria\u003c/em\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cem\u003eOligo-ovulation or anovulation\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eClinical and/or biochemical hyperandrogenism\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eUltrasonda g\u0026ouml;r\u0026uuml;len polikistik overler (2-9 mm boyutunda 12 veya daha fazla follik\u0026uuml;l)\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePCOS was diagnosed if at least two of these three criteria were met.\u003c/p\u003e\n\u003cp\u003eExclusion Criteria: The exclusion criteria were as follows: refusal to participate in the study; pregnancy; hemolyzed samples; use of hormonal contraceptives, glucocorticosteroids, oral steroids, lipid-lowering medications, or drugs affecting glucose metabolism; previously diagnosed and treated diabetes; decompensated thyroid disorders; diseases associated with androgen excess (congenital or late-onset congenital adrenal hyperplasia, hyperprolactinemia, Cushing\u0026apos;s disease/syndrome, androgen-secreting tumors, idiopathic hirsutism); depressive disorders and treatment for depression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLaboratory Measurements and Indices\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient records from 2022 to 2023 were retrospectively reviewed. Demographic data (height, weight, waist circumference, hip circumference) and laboratory results (fasting glucose, fasting insulin, HDL-C, triglycerides, LDL-C (low-density lipoprotein cholesterol)) were collected from patient records. For this study, no additional blood was drawn from patients beyond what was required for their diagnosis and follow-up.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe value of the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index was calculated using the following formula:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHOMA-IR = fasting insulin (mU/mL) \u0026times; fasting glucose (mmol/L) / 22.5\u0026nbsp;(16).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe value of METS-IR (Metabolic Score for Insulin Resistance) was calculated as follows:\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMETS-IR = (In ((2 x fasting glucose) (mg/dL) + TG (mg/dL) x BMI)/(ln (HDL-C) (mg/dL))\u0026nbsp;(17).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe TyG index was calculated using the following formula:\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eln[fasting glucose\u0026nbsp;(mg/dL) x TG (mg/dL)/2]\u0026nbsp;(16).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe TyG-BMI index is defined as follows:\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eln[fasting glucose\u0026nbsp;(mg/dL) x fasting triglycerides (mg/dL)/2] x BMI\u0026nbsp;(18)\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eTyG-WC is defined as:\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eln[fasting glucose\u0026nbsp;(mg/dL) x TG (mg/dL)/2] x WC\u0026nbsp;(18)\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eLCI was calculated using the following formula:\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e((TC (mmol/L) \u0026times; TG (mmol/L) \u0026times; LDL-C (mmol/L))/HDL-C (mmol/L))\u0026nbsp;(19)\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCastelli risk index-I was calculated according to the formula = (TC/HDL-C)\u0026nbsp;(7).\u003c/li\u003e\n \u003cli\u003eCastelli risk index-II was calculated according to the formula = (LDL-C/HDL-C)\u0026nbsp;(7).\u003c/li\u003e\n \u003cli\u003eAtherogenic coefficient (AC) was calculated according to the formula = ((TC-HDL-C)/HDL-C)\u0026nbsp;(7).\u003c/li\u003e\n \u003cli\u003eThe atherogenic index (AIP) of plasma was calculated according to the formula = (log(TG/HDL-C))\u0026nbsp;(7).\u003c/li\u003e\n \u003cli\u003eThe ratio of triglyceride to HDL-cholesterol was calculated according to the formula = (TG/HDL-C)\u0026nbsp;(7).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAnthropometric parameters were measured using standard methods. These measurements included body weight [kg], height [cm], waist circumference [cm], and hip circumference [cm].\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eBody mass index\u0026nbsp;(BMI) is calculated according to the following formula: BMI = body weight [kg]/height [m]2\u0026nbsp;(20)\u003c/li\u003e\n \u003cli\u003eWaist-to-hip ratio (WHR) was calculated according to the following formula: WHR = waist circumference [cm]/hip circumference [cm]\u0026nbsp;(21)\u003c/li\u003e\n \u003cli\u003eWaist-height ratio\u0026nbsp;(WHtR) was calculated according to the following formula: WHtR = waist circumference [cm]/height [cm]\u0026nbsp;(21)\u003c/li\u003e\n \u003cli\u003eBody adiposity index (BAI) was calculated according to the following formula: BAI = ((hip circumference [cm]/height [m]1.5)-18)\u0026nbsp;(22)\u003c/li\u003e\n \u003cli\u003eVisceral Adiposity Index (VAI) was calculated according to the following formula: VAI = [waist circumference [cm]/(36.58 + (1.89 \u0026times; BMI))] \u0026times; (triglyceride concentration [mmol/L]/0.81) \u0026times; (1.52/ HDL concentration [mmol/L])\u0026nbsp;(16)\u003c/li\u003e\n \u003cli\u003eLipid Accumulation Product (LAP) was calculated according to the following formula: LAP = (waist circumference [cm] \u0026ndash; 58) \u0026times; (triglyceride concentration [mmol/L])\u0026nbsp;(16)\u003c/li\u003e\n \u003cli\u003eBody Roundness Index (BRI) was calculated according to the following formula: BRI = 365.2 \u0026minus; 365.5 \u0026times; \u0026radic;(1 \u0026minus; (((WC/2\u0026pi;)2)/[(0.5 \u0026times; height)]2)\u0026nbsp;(23)\u003c/li\u003e\n \u003cli\u003eA Body Shape Index (ABSI) was calculated according to the following formula: ABSI = WC[m]/[(BMI)2/3) \u0026times; (height [m])1/2)] (23)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data obtained in the study will be analyzed using SPSS software version 28.0. The Shapiro-Wilk test was used to assess the distribution of the data. Continuous variables are expressed as mean \u0026plusmn; standard deviation (for normally distributed data) or median and interquartile range (for non-normally distributed data). For normally distributed data, the Student\u0026apos;s t-test will be used, while the Mann-Whitney U test will be used for non-parametric data. Correlation between variables will be evaluated using the Pearson correlation coefficient (for normally distributed data) and Spearman\u0026apos;s rank correlation coefficient (for non-normally distributed data). A \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 will be considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe anthropometric measurements, biochemical parameters, and atherogenic index results of women diagnosed with PCOS are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric Measurements, Biochemical Parameters, and Atherogenic Index Results of Women Diagnosed with PCOS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex̄\u0026plusmn;SS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnthropometric Measurements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ex̄\u0026plusmn;SS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.30\u0026thinsp;\u0026plusmn;\u0026thinsp;17.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.91\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.26\u0026thinsp;\u0026plusmn;\u0026thinsp;15.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.24\u0026thinsp;\u0026plusmn;\u0026thinsp;6.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian (25th-75th Quarter)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.74\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.85 (2.65\u0026ndash;5.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5325 (3074\u0026ndash;7176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.07\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ex̄\u0026plusmn;SS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGTT After 120 Minutes (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.97\u0026thinsp;\u0026plusmn;\u0026thinsp;23.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin OGTT After 60 Minutes (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.31\u0026thinsp;\u0026plusmn;\u0026thinsp;19.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171.19\u0026thinsp;\u0026plusmn;\u0026thinsp;30.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL Cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.68\u0026thinsp;\u0026plusmn;\u0026thinsp;14.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL Cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.92\u0026thinsp;\u0026plusmn;\u0026thinsp;25.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian (25th-75th Quarter)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (84\u0026ndash;95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Insulin (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.30 (8.05\u0026ndash;15.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGTT After 60 Minutes (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (85\u0026ndash;94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.50 (71.50\u0026ndash;131.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.36 (32.72\u0026ndash;67.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAtherogenic Indexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ex̄\u0026plusmn;SS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCastelli's Risk Index-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCastelli\u0026rsquo;s Risk Index-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETS-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.75\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245.54\u0026thinsp;\u0026plusmn;\u0026thinsp;60.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-Waist Circumference Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e917.80\u0026thinsp;\u0026plusmn;\u0026thinsp;148.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian (25th-75th Quarter)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28481.54 (15771.24\u0026ndash;47946.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74 (1.16\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBMI: Body mass index, WHtR: Waist-height ratio, BAI: Body adiposity index, BRI: Body Roundness Index, WHR: Waist-to-hip ratio, VAI: Visceral Adiposity Index, LAP: Lipid Accumulation Product, ABSI: A Body Shape Index, OGTT: Oral glucose tolerance test, HbA1c: Hemoglobin A1c, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance, AIP: Atherogenic risk of plasma, AC: atherogenic coefficient, METS-IR: Metabolic score of insulin resistance, TyG Index: triglyceride glucose index, TyG-BMI: triglyceride glucose-body mass index, LCI: lipoprotein combined index, TG/HDL-C: Triglycerides to HDL-cholesterol ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relationships between anthropometric measurements, atherogenic indices, and glucose profile in the study group are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship Between Anthropometric Measurements, Atherogenic Indices, and Glucose Profile in the Study Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFasting Glucose (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eOGTT After 60 Minutes (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eOGTT After 120 Minutes (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.265\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.294\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.267\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.323\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.254\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.321\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.276\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.331\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.162\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.195\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.188\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.267\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.335\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.240\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.322\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.156\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.272\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.200\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.161\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.299\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.419\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.267\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.335\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.036\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.075\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCastelli\u0026rsquo;s Risk Index-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.274\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.267\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCastelli\u0026rsquo;s Risk Index-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.233\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.210\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.170\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.279\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.274\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.267\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.182\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.259\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.333\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.170\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.279\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.172\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETS-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.372\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.410\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.275\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.390\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.326\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.399\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG- Waist Circumference Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.308\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.399\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBMI: Body mass index, WHtR: Waist-height ratio, BAI: Body adiposity index, BRI: Body Roundness Index, WHR: Waist-to-hip ratio, VAI: Visceral Adiposity Index, LAP: Lipid Accumulation Product, ABSI: A Body Shape Index, OGTT: Oral glucose tolerance test, HbA1c: Hemoglobin A1c, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance, AIP: Atherogenic risk of plasma, AC: atherogenic coefficient, METS-IR: Metabolic score of insulin resistance, TyG Index: triglyceride glucose index, TyG-BMI: triglyceride glucose-body mass index, LCI: lipoprotein combined index, TG/HDL-C: Triglycerides to HDL-cholesterol ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCorrelation coefficients given in italics were calculated using Spearman's rank correlation test.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eUpon examining Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a low positive correlation was found between fasting glucose (mmol/L) and weight, waist circumference, hip circumference, BMI, WHR, WHtR, BAI, VAI, LAP, BRI, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, LCI, TG/HDL-C, and TyG index; a moderate positive correlation was found between fasting glucose and METS-IR, TyG-BMI, and TyG-waist circumference index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant relationship observed with ABSI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAfter 60 minutes, the oral glucose tolerance test (OGTT) (mmol/L) showed a low positive correlation with weight, WHR, VAI, LAP, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, LCI, and TG/HDL-C; and a moderate positive correlation with waist circumference, hip circumference, BMI, WHtR, BAI, BRI, METS-IR, TyG index, TyG-BMI, and TyG-waist circumference index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with no significant relationship observed with ABSI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAfter 120 minutes, the OGTT (mmol/L) demonstrated a low positive correlation with WHR, VAI, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, TG/HDL-C, and TyG index; and a moderate positive correlation with weight, waist circumference, hip circumference, BMI, WHtR, BAI, LAP, BRI, LCI, METS-IR, TyG-BMI, and TyG-waist circumference index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant relationship observed with ABSI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe relationships between anthropometric measurements, atherogenic indices, and insulin profile in the study group are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship Between Anthropometric Measurements, Atherogenic Indices, and Insulin Profile in the Study Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFasting Insulin (pmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eInsulin OGTT After 60 Minutes (pmol/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.336\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.321\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.312\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.345\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.132\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.161\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.331\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.313\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.112\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.245\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.187\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.432\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.331\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.015\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.102\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCastelli\u0026rsquo;s Risk Index-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.160\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCastelli\u0026rsquo;s Risk Index-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.134\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.116\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.160\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.129\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.388\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.116\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.219\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETS-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.364\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.105\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.342\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG- Waist Circumference Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.292\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBMI: Body mass index, WHtR: Waist-height ratio, BAI: Body adiposity index, BRI: Body Roundness Index, WHR: Waist-to-hip ratio, VAI: Visceral Adiposity Index, LAP: Lipid Accumulation Product, ABSI: A Body Shape Index, OGTT: Oral glucose tolerance test, HDL: High-density lipoprotein, AIP: Atherogenic risk of plasma, AC: atherogenic coefficient, TyG Index: triglyceride glucose index, TyG-BMI: triglyceride glucose-body mass index, LCI: lipoprotein combined index, TG/HDL-C: Triglycerides to HDL-cholesterol ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCorrelation coefficients given in italics were calculated using Spearman's rank correlation test.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eUpon examining Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a low positive correlation was found between fasting insulin (pmol/L) and WHR, VAI, LAP, Castelli's risk index-I, Castelli's risk index-II, AC, LCI, TG/HDL-C, TyG index, and TyG-waist circumference index; a moderate positive correlation was found between fasting insulin and weight, waist circumference, hip circumference, BMI, WHtR, BAI, BRI, METS-IR, and TyG-BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant relationship was observed with ABSI and AIP (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAfter 60 minutes, the insulin OGTT (pmol/L) showed a low positive correlation with WHR, VAI, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, TG/HDL-C, and TyG index; and a moderate positive correlation with weight, waist circumference, hip circumference, BMI, WHtR, BAI, LAP, BRI, LCI, METS-IR, TyG-BMI, and TyG-waist circumference index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant relationship was observed with ABSI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe relationships between anthropometric measurements, atherogenic indices, and hemoglobin A1c (HbA1c) and HOMA-IR index in the study group are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship Between Anthropometric Measurements, Atherogenic Indices, and HbA1c and HOMA-IR Index in the Study Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHOMA-IR Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.363\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.308\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.299\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.369\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.132\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.117\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.315\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.294\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.096\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.114\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.144\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.180\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.315\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e-0.198\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-0.086\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCastelli\u0026rsquo;s Risk Index-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.177\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCastelli\u0026rsquo;s Risk Index-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.149\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.128\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.177\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.177\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.133\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.104\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.128\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETS-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.398\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.140\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.373\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG- Waist Circumference Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.296\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBMI: Body mass index, WHtR: Waist-height ratio, BAI: Body adiposity index, BRI: Body Roundness Index, WHR: Waist-to-hip ratio, VAI: Visceral Adiposity Index, LAP: Lipid Accumulation Product, ABSI: A Body Shape Index, OGTT: Oral glucose tolerance test, HbA1c: Hemoglobin A1c, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance, AIP: Atherogenic risk of plasma, AC: atherogenic coefficient, METS-IR: Metabolic score of insulin resistance, TyG Index: triglyceride glucose index, TyG-BMI: triglyceride glucose-body mass index, LCI: lipoprotein combined index, TG/HDL-C: Triglycerides to HDL-cholesterol ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation coefficients given in italics were calculated using Spearman's rank correlation test\u003c/h2\u003e \u003cp\u003eUpon examining Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a low positive correlation was found between HbA1c (%) and waist circumference, hip circumference, BMI, WHR, WHtR, BAI, LAP, BRI, Castelli's risk index-I, Castelli's risk index-II, AC, LCI, TG/HDL-C, METS-IR, TyG index, TyG-BMI, and TyG-waist circumference index; a moderate positive correlation was found with weight; and a low negative correlation was found with ABSI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant relationship was observed with VAI, AIP, and TG/HDL-C (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFor the HOMA-IR index, a low positive correlation was found with hip circumference, BAI, LAP, Castelli's risk index-I, Castelli's risk index-II, AIP, AC, LCI, TG/HDL-C, TyG index, and TyG-waist circumference index; a moderate positive correlation was found with weight, waist circumference, BMI, WHtR, BRI, and METS-IR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant relationship was observed with WHR, VAI, and ABSI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study provide valuable insights into the relationships between various anthropometric, biochemical, and atherogenic indices and their relevance to glucose and insulin profiles in women with PCOS. This study reinforces the importance of comprehensive metabolic assessment in managing PCOS and highlights the potential utility of newer indices like METS-IR, TyG, and LCI in clinical practice.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric Indices and Glucose Profile\u003c/h2\u003e \u003cp\u003eThe positive correlations between fasting glucose and anthropometric indices such as BMI, WHtR, and BAI indicate that central and overall adiposity are significant contributors to glucose dysregulation in PCOS patients. This aligns with previous studies that have shown a strong link between obesity and impaired glucose metabolism in PCOS (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The lack of significant correlation with ABSI suggests that this index may not be as sensitive in detecting metabolic disturbances associated with PCOS compared to other measures of adiposity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAtherogenic Indices and Cardiometabolic Risk\u003c/h2\u003e \u003cp\u003eAtherogenic indices such as Castelli's risk index-I and II, AIP, and AC showed positive correlations with both fasting glucose and insulin levels, reinforcing their role in assessing cardiovascular risk in PCOS patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These findings are consistent with the understanding that dyslipidemia is a common feature in PCOS and contributes to the heightened cardiovascular risk (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The novel indices, particularly the TyG index and its derivatives (TyG-BMI, TyG-WC), demonstrated robust correlations with glucose and insulin parameters, underscoring their utility as predictors of insulin resistance and cardiometabolic risk (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInsulin Resistance and Metabolic Indices\u003c/h2\u003e \u003cp\u003eThe significant correlations between METS-IR and both glucose and insulin profiles highlight its potential as a reliable marker for insulin resistance in PCOS. METS-IR, which incorporates both metabolic and anthropometric parameters, provides a comprehensive assessment of insulin sensitivity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This is particularly valuable in PCOS, where insulin resistance plays a central role in the pathophysiology and contributes to the risk of developing T2DM and cardiovascular disease (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eThe results of this study suggest that incorporating a range of anthropometric and atherogenic indices into routine clinical assessment could enhance the identification and management of metabolic and cardiovascular risks in women with PCOS. Indices such as the TyG and METS-IR, which are simple to calculate and do not require complex testing, could be particularly useful in resource-limited settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Research\u003c/h2\u003e \u003cp\u003eThis study has several limitations, including its retrospective design and reliance on existing patient records, which may introduce selection bias and limit the generalizability of the findings. Additionally, the study population was limited to a single geographic region, and further research is needed to validate these findings in more diverse populations. Prospective studies with larger sample sizes and longitudinal follow-up are warranted to establish the predictive value of these indices for long-term metabolic and cardiovascular outcomes in PCOS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study highlights the significant relationships between various metabolic indices and glucose and insulin profiles in women with PCOS. The findings support the use of newer indices such as METS-IR, TyG, and LCI as effective tools for assessing metabolic and cardiovascular risk in PCOS. Incorporating these indices into clinical practice could improve the management and prognosis of PCOS, ultimately leading to better health outcomes for affected women.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABSI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;A Body Shape Index\u003c/p\u003e\n\u003cp\u003eAC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Atherogenic Coefficient\u003c/p\u003e\n\u003cp\u003eAIP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Atherogenic Risk of Plasma\u003c/p\u003e\n\u003cp\u003eBAI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body Adiposity Index\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body Mass Index\u003c/p\u003e\n\u003cp\u003eBRI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body Roundness Index\u003c/p\u003e\n\u003cp\u003eCAD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Coronary Artery Disease\u003c/p\u003e\n\u003cp\u003eCV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardiovascular\u003c/p\u003e\n\u003cp\u003eCVD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003eHbA1c\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eHDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;High-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eHOMA-IR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Homeostatic Model Assessment for Insulin Resistance\u003c/p\u003e\n\u003cp\u003eIR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Insulin Resistance\u003c/p\u003e\n\u003cp\u003eLAP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lipid Accumulation Product\u003c/p\u003e\n\u003cp\u003eLCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lipoprotein Combined Index\u003c/p\u003e\n\u003cp\u003eLDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eMETS-IR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Metabolic Score for Insulin Resistance\u003c/p\u003e\n\u003cp\u003eOGTT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Oral Glucose Tolerance Test\u003c/p\u003e\n\u003cp\u003ePCOS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Polycystic Ovary Syndrome\u003c/p\u003e\n\u003cp\u003eSPSS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eT2DM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eTC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total Cholesterol\u003c/p\u003e\n\u003cp\u003eTG/HDL-C\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Triglycerides to High-Density Lipoprotein Cholesterol Ratio\u003c/p\u003e\n\u003cp\u003eTyG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride Glucose Index\u003c/p\u003e\n\u003cp\u003eTyG-BMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Triglyceride Glucose-Body Mass Index\u003c/p\u003e\n\u003cp\u003eTyG-WC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride Glucose-Waist Circumference Index\u003c/p\u003e\n\u003cp\u003eVAI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Visceral Adiposity Index\u003c/p\u003e\n\u003cp\u003eWC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Waist Circumference\u003c/p\u003e\n\u003cp\u003eWHR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Waist-to-Hip Ratio\u003c/p\u003e\n\u003cp\u003eWHtR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Waist-Height Ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception and design: EU Designed and prepared the text messages: EU, MI and MA Performed the analysis: EU Wrote the Manuscript: EU and MA Collected the data: OT, ES and DF All of the authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specifc funding for this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe objectives of the study were explained in detail to the study participants. The collection of demographic information and the data obtained from all patients participating in this research were done after obtaining informed consent and willingness to participate in the study. Also, all the information remained confidential and the results were published anonymously and only statistically. Ethical approval for the study was obtained from the Ethics Committee of Mardin Artuklu University (Decision number: 2024/3-21). The authors confirm that all experiments were performed following the relevant Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for the publication of this case report was obtained from the patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarch WA, Moore VM, Willson KJ, Phillips DI, Norman RJ, Davies MJ. The prevalence of polycystic ovary syndrome in a community sample assessed under contrasting diagnostic criteria. Human reproduction. 2010;25(2):544-51.\u003c/li\u003e\n\u003cli\u003eLiu Q, Xie Y-j, Qu L-h, Zhang M-x, Mo Z-c. Dyslipidemia involvement in the development of polycystic ovary syndrome. Taiwanese journal of obstetrics and gynecology. 2019;58(4):447-53.\u003c/li\u003e\n\u003cli\u003eLathia T, Joshi A, Behl A, Dhingra A, Kalra B, Dua C, et al. A Practitioner\u0026rsquo;s Toolkit for Polycystic Ovary Syndrome Counselling. Indian Journal of Endocrinology and Metabolism. 2022;26(1):17-25.\u003c/li\u003e\n\u003cli\u003eSachdeva G, Gainder S, Suri V, Sachdeva N, Chopra S. Comparison of the different PCOS phenotypes based on clinical metabolic, and hormonal profile, and their response to clomiphene. Indian journal of endocrinology and metabolism. 2019;23(3):326-31.\u003c/li\u003e\n\u003cli\u003eTao L-C, Xu J-n, Wang T-t, Hua F, Li J-J. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovascular Diabetology. 2022;21(1):68.\u003c/li\u003e\n\u003cli\u003eGuo F, Gong Z, Fernando T, Zhang L, Zhu X, Shi Y. The lipid profiles in different characteristics of women with PCOS and the interaction between dyslipidemia and metabolic disorder states: a retrospective study in Chinese population. Frontiers in Endocrinology. 2022;13:892125.\u003c/li\u003e\n\u003cli\u003eKamoru AA, Japhet OM, Adetunji AD, Musa MA, Hammed OO, Akinlawon AA, et al. Castelli risk index, atherogenic index of plasma, and atherogenic coefficient: emerging risk predictors of cardiovascular disease in HIV-treated patients. Saudi J Med Pharm Sci. 2017;4929:1101-10.\u003c/li\u003e\n\u003cli\u003eLiu Z, He H, Dai Y, Yang L, Liao S, An Z, Li S. Comparison of the diagnostic value between triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio in metabolic-associated fatty liver disease patients: a retrospective cross-sectional study. Lipids in health and disease. 2022;21(1):55.\u003c/li\u003e\n\u003cli\u003eZheng S, Shi S, Ren X, Han T, Li Y, Chen Y, et al. Triglyceride glucose-waist circumference, a novel and effective predictor of diabetes in first-degree relatives of type 2 diabetes patients: cross-sectional and prospective cohort study. Journal of translational medicine. 2016;14:1-10.\u003c/li\u003e\n\u003cli\u003eLee J, Kim B, Kim W, Ahn C, Choi HY, Kim JG, et al. Lipid indices as simple and clinically useful surrogate markers for insulin resistance in the US population. Scientific reports. 2021;11(1):2366.\u003c/li\u003e\n\u003cli\u003eKl\u0026ouml;ting N, Fasshauer M, Dietrich A, Kovacs P, Sch\u0026ouml;n MR, Kern M, et al. Insulin-sensitive obesity. American Journal of Physiology-Endocrinology and Metabolism. 2010;299(3):E506-E15.\u003c/li\u003e\n\u003cli\u003eBello-Chavolla OY, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A, et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. European journal of endocrinology. 2018;178(5):533-44.\u003c/li\u003e\n\u003cli\u003eNaghshband Z, Kumar L, Mandappa S, Murthy ASN, Malini SS. Visceral adiposity index and lipid accumulation product as diagnostic markers of metabolic syndrome in south indians with polycystic ovary syndrome. Journal of Human Reproductive Sciences. 2021;14(3):234-43.\u003c/li\u003e\n\u003cli\u003eG\u0026ouml;n\u0026uuml;lalan G, Sa\u0026ccedil;kan F. The importance of new anthropometric measurements in detecting cardio metabolic risk and insulin resistance in patients with polycystic ovary syndrome: Single center experience. T\u0026uuml;rkiye Diyabet ve Obezite Dergisi. 2021;5(1):25-32.\u003c/li\u003e\n\u003cli\u003eHajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. Journal of biomedical informatics. 2014;48:193-204.\u003c/li\u003e\n\u003cli\u003eAhn N, Baumeister SE, Amann U, Rathmann W, Peters A, Huth C, et al. Visceral adiposity index (VAI), lipid accumulation product (LAP), and product of triglycerides and glucose (TyG) to discriminate prediabetes and diabetes. Scientific Reports. 2019;9(1):9693.\u003c/li\u003e\n\u003cli\u003eYoon J, Jung D, Lee Y, Park B. The metabolic score for insulin resistance (METS-IR) as a predictor of incident ischemic heart disease: a longitudinal study among Korean without diabetes. Journal of personalized medicine. 2021;11(8):742.\u003c/li\u003e\n\u003cli\u003eKhamseh ME, Malek M, Abbasi R, Taheri H, Lahouti M, Alaei-Shahmiri F. Triglyceride glucose index and related parameters (triglyceride glucose-body mass index and triglyceride glucose-waist circumference) identify nonalcoholic fatty liver and liver fibrosis in individuals with overweight/obesity. Metabolic syndrome and related disorders. 2021;19(3):167-73.\u003c/li\u003e\n\u003cli\u003eSi Y, Liu J, Han C, Wang R, Liu T, Sun L. The correlation of retinol-binding protein-4 and lipoprotein combine index with the prevalence and diagnosis of acute coronary syndrome. Heart and Vessels. 2020;35:1494-501.\u003c/li\u003e\n\u003cli\u003eWeir CB, Jan A. BMI classification percentile and cut off points. 2019.\u003c/li\u003e\n\u003cli\u003eConsultation WE. Waist circumference and waist-hip ratio. Report of a WHO Expert Consultation Geneva: World Health Organization. 2008;2008:8-11.\u003c/li\u003e\n\u003cli\u003eBergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG, et al. A better index of body adiposity. Obesity. 2011;19(5):1083-9.\u003c/li\u003e\n\u003cli\u003eGłuszek S, Ciesla E, Głuszek-Osuch M, Kozieł D, Kiebzak W, Wypchło Ł, Suliga E. Anthropometric indices and cut-off points in the diagnosis of metabolic disorders. PLoS One. 2020;15(6):e0235121.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-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":"Polycystic ovary syndrome, Insulin resistance, Metabolic indices, Atherogenic indices, Cardiometabolic risk, Anthropometric measurements","lastPublishedDoi":"10.21203/rs.3.rs-4445299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4445299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePolycystic ovary syndrome (PCOS) is a prevalent hormonal disorder affecting 5-15% of women of reproductive age, characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovarian morphology. PCOS is associated with metabolic disturbances such as dyslipidemia, insulin resistance (IR), and an increased risk of type 2 diabetes (T2DM) and cardiovascular disease (CVD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to evaluate the relationships between new anthropometric indices (BAI, VAI, LAP, BRI, ABSI) and atherogenic indices (Castelli index-I, Castelli index-II, AIP, AC, LCI, TG/HDL-C ratio, METS-IR, TyG index, TyG-BMI index, TyG-WC index) with glucose and insulin profiles in women with PCOS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective analysis was conducted on 248 women diagnosed with PCOS based on the 2003 Rotterdam criteria. Anthropometric measurements, biochemical parameters, and atherogenic indices were collected from patient records. Statistical analyses were performed using SPSS software version 28.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSignificant correlations were found between fasting glucose and various anthropometric indices, such as BMI, WHtR, and BAI, indicating a link between adiposity and glucose metabolism in PCOS. Atherogenic indices like Castelli's risk indices, AIP, and AC showed positive correlations with glucose and insulin levels, reinforcing their role in assessing cardiovascular risk. Novel indices such as METS-IR and TyG demonstrated strong correlations with both glucose and insulin profiles, highlighting their potential as reliable markers for IR and cardiometabolic risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe study underscores the importance of using a range of anthropometric and atherogenic indices for comprehensive metabolic assessment in women with PCOS. Indices like METS-IR and TyG offer valuable insights into insulin sensitivity and cardiovascular risk, potentially aiding in better management and prognosis of PCOS.\u003c/p\u003e","manuscriptTitle":"Metabolic Indices in Patients with Polycystic Ovary Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 19:04:12","doi":"10.21203/rs.3.rs-4445299/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-26T05:23:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-22T11:39:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-21T17:17:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-17T10:36:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48755126608666121374672010152787218817","date":"2024-06-17T07:00:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-14T16:14:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301190592035988555048022170728167715590","date":"2024-06-12T10:54:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199960276372655193021179693519472538996","date":"2024-06-12T05:49:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251028572483223606202857399352484862507","date":"2024-06-12T02:41:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-06T04:56:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117330430379272877412202991482176977443","date":"2024-05-29T22:24:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17429454587785083324708392107323991204","date":"2024-05-29T20:57:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225214619090139585742907201429309082654","date":"2024-05-29T16:37:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-29T14:39:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-24T07:32:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-22T07:21:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-22T07:21:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2024-05-19T17:43:36+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":"0d5367d2-a0ca-4160-8c92-cabf90426c90","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-02T16:08:02+00:00","versionOfRecord":{"articleIdentity":"rs-4445299","link":"https://doi.org/10.1186/s12902-024-01701-6","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2024-08-28 15:57:46","publishedOnDateReadable":"August 28th, 2024"},"versionCreatedAt":"2024-06-03 19:04:12","video":"","vorDoi":"10.1186/s12902-024-01701-6","vorDoiUrl":"https://doi.org/10.1186/s12902-024-01701-6","workflowStages":[]},"version":"v1","identity":"rs-4445299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4445299","identity":"rs-4445299","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}