Comparison of the Incidence and Diagnostic Value of Insulin Resistance Indicators in the Prevalence of Metabolic Syndrome in Southeast China

Comparison of the Incidence and Diagnostic Value of Insulin Resistance Indicators in the Prevalence of Metabolic Syndrome in Southeast China | 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 Article Comparison of the Incidence and Diagnostic Value of Insulin Resistance Indicators in the Prevalence of Metabolic Syndrome in Southeast China Xinxin Yang, Qingquan Chen, Haiping Hu, Huanhuan Shi, Yuanyu She, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3909069/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Metabolic syndrome (MetS) is a risk factor for cardiovascular diseases and cancer, and its pre-stage is as well. The incidence of MetS is increasing annually, but currently, there is no unified diagnostic criterion, and the diagnostic conditions are complex, posing challenges for primary healthcare professionals. Insulin resistance indicators are widely used for MetS screening, but there is limited research on their discriminatory ability for preMetS. Objective To assess the prevalence of preMetS in adults in Southeast China and the differences among three MetS standards. Additionally, to compare the differences in the correlation and diagnostic value of six insulin resistance indicators with preMetS. Methods A total of 9,399 individuals participating in health examinations in five communities in Fuzhou City were selected for questionnaire surveys, physical examinations, and laboratory tests. Binary logistic regression was used to analyze the correlation between each indicator and preMetS, and a restricted cubic spline model was used to analyze the dose-response relationship between the two. The diagnostic abilities of each indicator were compared using the area under the receiver operating characteristic curve. A nomogram model combining various indicators and age was established to improve and reassess diagnostic capabilities. Results The overall prevalence of preMetS ranged from 10.63–49.68%. Regardless of gender, the kappa values between the revised ATP III and JCDCG ranged from 0.700 to 0.820, while those with IDF ranged from 0.316 to 0.377. In the ATP and JCDCG standards, the TyG index was the best screening indicator, with maximum AUC values of 0.731 (95% CI: 0.718–0.744) and 0.724 (95% CI: 0.712–0.737), and optimal cutoff values of 7.736 and 7.739, respectively. Additionally, WHtR showed consistent performance with TyG in the JCDCG standard, with AUC and cutoff values of (95% CI: 0.698–0.725) and 0.503. In the normal weight population, in the revised ATP III, there was no significant difference in screening abilities between TG/HDL and TyG. The nomogram model combining age with TG/HDL or TyG showed better screening abilities for preMetS compared to other indicators, but the model with age and TG/HDL had a better fit. Conclusion The consistency between the revised ATP III and JCDCG in MetS tri-classification is good. TyG has the best identification ability for preMetS (revised ATP III and JCDCG). Additionally, WHtR has equally good identification ability for preMetS (JCDCG). The nomogram model with TG/HDL has the best identification ability. In conclusion, the consistency of MetS tri-classification is better in the revised ATP III and JCDCG. TyG is an effective indicator for identifying preMetS in adults in Southeast China. WHtR is a non-invasive indicator for screening preMetS (JCDCG). The diagnostic capabilities are improved with the inclusion of age and TG/HDL in the nomogram model, with less error. Insulin Resistance-Related indicators preMetabolic syndrome Normal-weight Screening Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Metabolic Syndrome (MetS) is a cluster of cardiovascular metabolic risks, including central obesity, elevated blood pressure, abnormal glucose tolerance, and abnormal lipid levels, which can increase the risk of cardiovascular diseases. One or two components of MetS constitute the pre-stage of MetS (pre-MS), indicating early signs of MetS. Compared to MetS patients, individuals with preMetS have a lower risk of various diseases but still face increased risks of gastrointestinal diseases 1 , cardiovascular diseases (CVD) 2 , cancer 3, 4 , and dementia. PreMetS lacks a clear definition, and some studies use different criteria to identify this entity, namely having fewer than the required number of MetS components 5–8 . Over the past few decades, various international organizations have provided definitions for MetS 9–11 . Due to the use of different MetS definitions, estimates of preMetS prevalence vary globally, leading to confusion and a lack of comparability between studies 12–14 . Therefore, it is necessary to explore the prevalence and characteristics of preMetS based on different standards (IDF, revised ATP III, and JCDCG standards), which may help researchers better understand MetS and formulate a more scientific definition. Previous studies have indicated that some anthropometric measurements and insulin resistance indicators perform well in MetS screening 15–18 . Further exploration of the performance of these indicators in preMetS screening among non-MetS populations is warranted. Studies suggest that BMI and WHtR cannot distinguish between the distribution of fat and muscle tissue, but they show good performance in assessing cardiovascular metabolic risks in middle-aged Koreans 19 . Additionally, some emerging anthropometric indices, such as the ABSI 19 , perform well in MetS screening. Insulin resistance, considered a core mechanism in the pathogenesis of MetS, is one of the main underlying causes of MetS and its components 20 . Therefore, insulin resistance is an effective method for screening MetS. Although assessing high insulin-normal glucose status is crucial for early MetS diagnosis, its determination often requires complex and invasive methods 21 , making it impractical for large-scale community screening and routine clinical practice. While anthropometric indicators are correlated to some extent with insulin resistance 22–24 , the correlation may not be significant enough to comprehensively assess the degree of insulin resistance. Considering these challenges, alternative markers assessing insulin resistance, such as the Triglyceride-Glucose Index (TYG) and its derivatives 25 , as well as TG/HDL 26 , have been proposed. The cardiovascular metabolic risks in individuals with normal BMI are often overlooked 27 . Research indicates that individuals with a normal weight but unhealthy metabolism have an increased risk of diabetes and cardiovascular diseases by 1.5 to 2 times 28, 29 . Additionally, WHO recommends excluding individuals already diagnosed with type 2 diabetes or CVD from the MetS definition because MetS cannot be used for primary prevention in this population 30 . Therefore, using simple and efficient indicators to screen for preMetS in different populations is of greater public health significance. However, there are currently no relevant reports on the identification capabilities of the above indicators for adult preMetS. Hence, this study aims to describe the prevalence of preMetS in adults in the Fuzhou region, compare the identification capabilities of different indicators, and calculate critical values. Finally, we establish a nomogram model combining the best indicators with age to further enhance the discriminatory abilities of the indicators. 2. Methods 2.1 Study participants This was a retrospective cross-sectional survey. Between June and December 2022, we randomly recruited patients with type 2 diabetes from one community in each of the six urban areas of Fuzhou City. All participants underwent a face-to-face survey using a homemade uniform questionnaire and took a physical examination, which were both conducted by trained primary care professionals. A multistage stratified cluster random sampling method was employed to recruit residents aged ≥ 18 years (residing in the area for ≥ 6 months), resulting in 9,399 participants with a response rate of 95.75%. After excluding individuals with missing components of metabolic syndrome and insulin resistance indicators, 8,997 individuals met the criteria. The study received ethical approval from the Fuzhou City CDC Ethics Review Committee (Approval Number: 2022002), and all participants provided informed consent. All experimental protocols involving human data were in accordance with the Helsinki Declaration. 2.2 Data measurements A self-designed unified questionnaire was used for face-to-face interviews conducted by trained professionals from grassroots medical institutions. The survey covered personal health status, medical history, and lifestyle behaviors (exercise, smoking, alcohol consumption, and sleep patterns). Physical examinations included height, weight, waist circumference (measured twice, averaged), and blood pressure measured using a validated combination diagnostic system (UR-9000F) with three measurements. Laboratory biochemical tests collected venous blood samples from all participants in a fasting state. Serum TC levels were determined by enzymatic colorimetry, and serum LDL-C, HDL-C, and TG were measured by colorimetry. Serum FPG was measured using the hexokinase method, and serum uric acid levels were determined by colorimetry (all using the Hitachi 7100 fully automatic biochemical analyzer). 2.3 Description of variables ( 1 ) Non-smokers were classified as low risk 31 . Regular physical activity was defined as at least 150 minutes of moderate-intensity activity per week or 75 minutes of vigorous activity per week (or an equivalent combination) 31 . Food frequency questionnaire assessed various food intake over the past year 32 . Low-risk alcohol consumption was defined as moderate drinking (females: 5–15 grams/day; males: 5–30 grams/day) 33 . Healthy weight was defined as a BMI of 18.5–24.0 kg/m² 31 . Adequate sleep time (7–8 hours/day) was classified as low risk 34 . ( 2 )Insulin Resistance-Related Indicators 27, 35 :BMI: weight(kg)/height²(m). WHtR: WC(cm)/height(m). ABSI: WC/(BMI^(2/3) * height^(1/2)). TYG: LN(TG(mg/dL) * FPG(mg/dL)/2). TG/HDL: TG/HDL-C. ( 3 )Metabolic Syndrome Diagnostic Criteria:( 1 ) IDF Standard (2005) 36 :Waist circumference ≥ 90 cm for males and ≥ 85 cm for females. Meeting the criteria but not reaching the Metabolic Syndrome (MetS) standard can be defined as preMetS.( 2 ) Revised ATPIII Standard (2005) 36 : 1)Waist circumference ≥ 90 cm for Chinese males and ≥ 85 cm for Chinese females (based on population and country/region-specific definitions).2)TG levels ≥ 1.7 mmol/L or receiving treatment. 3)HDL-C levels < 1.03 mmol/L for males, 130 mmHg and/or DBP > 85 mmHg, or diagnosed with hypertension.5) FPG ≥ 5.6 mmol/L or diagnosed with diabetes. ( 3 ) JCDCG Standard (2007) 37 : 1)Waist circumference ≥ 90 cm for males and ≥ 85 cm for females.2)HDL-C 130 mmHg and/or DBP > 85 mmHg or diagnosed with hypertension.4)Fasting TG ≥ 1.70 mmol/L.5)FPG ≥ 6.1 mmol/L, or 2-hour postprandial glucose (2hPG) ≥ 7.8 mmol/L, or diagnosed with diabetes. PreMetS was defined as meeting one to two conditions. 2.4 Statistical methods Descriptive statistics were used for continuous and categorical variables. Group comparisons for continuous and categorical variables were conducted using independent sample t-tests and chi-square tests, respectively. Z-scores were used for insulin resistance-related indicators. Binary logistic regression analyzed the correlation between preMetS and various indicators, adjusting for age, education level, occupation, marital status, smoking, and alcohol habits. The diagnostic abilities of each indicator were evaluated using the area under the receiver operating characteristic curve. Sensitivity, specificity, Youden's index, and critical values were calculated for each indicator. The best two variables were selected based on the determined indicators. Multiple logistic regression was used to select statistically significant variables such as gender, age, residence, marital status, education level, smoking, and alcohol habits, and a nomogram model was constructed (as other variables had a weak impact in the nomogram model, only age was included). The backward LR method (inclusion criterion: P 0.1) was employed to include indicators and age in the nomogram model. The nomogram model was constructed and the calibration curve was drawn to assess its calibration. All statistical analyses were performed using SPSS 26.0 and R software (version 4.2.0) 3. Results 3.1 Consensus on Definitions of PreMetS We applied three different definitions to categorize the entire population into three MetS groups. The prevalence and characteristics of preMetS and MetS varied based on the definitions used in the Chinese population. With the three different standards, the overall prevalence of preMetS ranged from 10.63–49.68%, and the total prevalence of MetS ranged from 21.41–31.07%. Notably, in the male population, the Revised ATP III criteria exhibited the highest preMetS prevalence, while in females, the JCDCG criteria had the highest preMetS prevalence, with the opposite pattern observed for MetS prevalence (Table 1 ). Table 1 Prevalence of preMetS and MetS According to Different Definitions Normal preMetS MetS All(n = 8997) IDF 6115(67.97%) 956(10.63%) 1926(21.41%) Revised ATP III 1732(19.25%) 4470(49.68%) 2795(31.07%) JCDCG 1844(20.5%) 4466(49.64%) 2687(29.87%) Male(n = 3925) IDF 2784(70.93%) 384(9.78%) 757(19.29%) Revised ATP III 748(19.06%) 2053(52.31%) 1124(28.64%) JCDCG 712(18.14%) 1993(50.78%) 1220(31.08%) Female(n = 5072) IDF 3331(65.67%) 572(11.28%) 1169(23.05%) Revised ATP III 984(19.4%) 2417(47.65%) 1671(32.95%) JCDCG 1132(22.32%) 2473(48.76%) 1467(28.92%) Notes: IDF: International Diabetes Federation;Revised ATP III: Adult Treatment Panel III of the National Cholesterol Education Program, Revised;JCDCG: Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia. 3.2 Consistency and Differences in Diagnoses The consistency and differences in diagnoses using IDF, Revised ATP III, and JCDCG standards are summarized in the table. Irrespective of gender, there was better consistency between the Revised ATP III and JCDCG standards, with kappa values ranging from 0.700 to 0.820. However, the consistency with the IDF standard was lower, with kappa values ranging from 0.316 to 0.377. Notably, the consistency of preMetS definitions between JCDCG and Revised ATP III criteria in males (kappa = 0.820) was higher than in females (kappa = 0.700) (Table 2 ). Table 2 Consistency among various definitions of preMetS and MetS. Revised ATP III JCDCG kappa 95%CI kappa 95%CI All(n = 8997) IDF 0.350 (0.340–0.361) 0.310 (0.300-0.321) Revised ATP III - - 0.752 (0.740–0.764) Male(n = 3925) IDF 0.316 (0.362–0.392) 0.274 (0.258–0.289) Revised ATP III - - 0.820 (0.804–0.836) Female(n = 5072) IDF 0.377 (0.362–0.392) 0.341 (0.326–0.357) Revised ATP III - - 0.700 (0.683–0.718) Notes:CI: Confidence Interval; IDF: International Diabetes Federation;Revised ATP III: Adult Treatment Panel III of the National Cholesterol Education Program, Revised;JCDCG: Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia. 3.2 Basic Characteristics of the Study Population Due to the better performance in consistency between JCDCG and the revised ATP III (kappa = 0.752), we employed these two definitions for subsequent analyses. A total of 6202 and 6310 subjects participated in this study when preMetS was defined using the Revised ATP III and JCDCG criteria, respectively. The age, weight, WC, BMI, WHtR, ABSI, TyG (Triglyceride-glucose index), TG/HDL, blood pressure, and lipid indices (expected HDL-C) in the preMetS group were all significantly higher than those in the non-preMetS group ( P < 0.001) (Table 3 ). Table 3 Baseline Characteristics of All Participants Variables preMetS(n = 6202) Revised ATPIII preMetS (n = 6310) JCDCG Normal(n = 1732) preMetS(n = 4470) p Normal(n = 1844) preMetS(n = 4466) p Abdominal obesity < 0.001 < 0.001 YES 0 (0) 956 (21.4) 0 (0) 993 (22.2) NO 1732 (100) 3514 (78.6) 1844 (100) 3473 (77.8) Hypertension < 0.001 < 0.001 YES 0 (0) 2288 (51.3) 0 (0) 2311 (51.9) NO 1732 (100) 2168 (48.7) 1844 (100) 2142 (48.1) Abnormal blood sugar < 0.001 < 0.001 YES 0 (0) 1361 (30.5) 0 (0) 1752 (41.0) NO 1732 (100) 3094 (69.5) 1844 (100) 2518 (59.0) Low HDL-C < 0.001 < 0.001 YES 0 (0) 1106 (24.7) 0 (0) 475 (10.6) NO 1732 (100) 3364 (75.3) 1844 (100) 3991 (89.4) High TG < 0.001 < 0.001 YES 0 (0) 950 (21.3) 0 (0) 1005 (22.5) NO 1732 (100) 3520 (78.7) 1844 (100) 3461 (77.5) Gender 0.055 < 0.001 Male 748 (43.2) 2053 (45.9) 712 (38.6) 1993 (44.6) Female 984 (56.8) 2417 (54.1) 1132 (61.4) 2473 (55.4) Age (years) 50.19 ± 14.72 58.08 ± 13.98 < 0.001 49.5 ± 14.76 58.54 ± 13.64 < 0.001 Residence < 0.001 < 0.001 Urban 1142 (65.9) 2686 (60.1) 1251 (67.8) 2671 (59.8) Rural 590 (34.1) 1784 (39.9) 593 (32.2) 1795 (40.2) Education level < 0.001 < 0.001 High school and above 700 (40.5) 1165 (26.1) 774 (42) 1126 (25.2) Below high school 1030 (59.5) 3304 (73.9) 1068 (58) 3339 (74.8) Marital status 0.049 0.014 Married 1471 (85.1) 3889 (87) 1560 (84.7) 3886 (87.1) Separated/divorced 258 (14.9) 580 (13) 282 (15.3) 577 (12.9) Non-smoking 1496 (86.4) 3764 (84.2) 0.039 1621 (87.9) 3781 (84.7) 0.001 Non or moderate alcohol consumption 1672 (96.5) 4279 (95.7) 0.168 1788 (97) 4265 (95.5) 0.009 Moderate to high-intensity exercise 379 (21.9) 844 (18.9) 0.009 408 (22.1) 833 (18.7) 0.002 Adequate sleep duration 875 (50.5) 1951 (43.7) < 0.001 954 (51.8) 1912 (42.9) < 0.001 Healthy dietary habits 797 (46) 1726 (38.6) < 0.001 873 (47.3) 1699 (38) < 0.001 SBP (mmHg) 114.65 ± 9.72 128.24 ± 18 < 0.001 114.79 ± 9.57 128.32 ± 18.32 < 0.001 DBP (mmHg) 72.97 ± 6.44 79.4 ± 10.29 < 0.001 73.02 ± 6.36 79.35 ± 10.24 < 0.001 FBG(mmol/L) 4.8 ± 0.47 5.43 ± 1.83 < 0.001 4.86 ± 0.54 5.49 ± 1.89 < 0.001 PBG(mmol/L) 6.26 ± 1.69 7.24 ± 3.15 < 0.001 5.82 ± 1.06 7.3 ± 3.25 < 0.001 TC(mmol/L) 4.8 ± 0.92 5.08 ± 1.33 < 0.001 4.72 ± 0.91 5.12 ± 1.42 < 0.001 TG (mmol/L) 0.98 ± 0.31 1.41 ± 0.92 < 0.001 0.99 ± 0.32 1.42 ± 0.95 < 0.001 HDL-C (mmol/L) 1.55 ± 0.37 1.39 ± 0.39 < 0.001 1.5 ± 0.37 1.4 ± 0.39 < 0.001 LDL-C (mmol/L) 2.68 ± 0.77 2.95 ± 0.87 < 0.001 2.64 ± 0.78 2.97 ± 0.85 < 0.001 Uric acid (mmol/L) 302.43 ± 79.6 333.44 ± 95.05 < 0.001 300.3 ± 78.86 333.1 ± 94.76 < 0.001 Creatinine (mmol/L) 66.5 ± 18.3 69.81 ± 21.84 < 0.001 66.12 ± 17.53 69.47 ± 21.75 < 0.001 BUN(mmol/L) 4.99 ± 2.02 5.34 ± 2.94 < 0.001 4.95 ± 2.19 5.4 ± 3.33 < 0.001 WHtR 0.47 ± 0.04 0.51 ± 0.07 < 0.001 0.47 ± 0.04 0.51 ± 0.06 < 0.001 BMI (kg/m2) 21.94 ± 3.15 23.5 ± 3.96 < 0.001 22 ± 3.08 23.51 ± 4 < 0.001 WC(cm) 76.42 ± 6.64 81.61 ± 11.47 < 0.001 76.44 ± 6.44 81.54 ± 9.83 < 0.001 TYG 7.34 ± 0.36 7.73 ± 0.51 < 0.001 7.36 ± 0.37 7.75 ± 0.53 < 0.001 TG/HDL 0.66 ± 0.26 1.09 ± 0.93 < 0.001 0.7 ± 0.28 1.11 ± 1.02 < 0.001 ABSI 7.71 ± 0.5 7.89 ± 0.79 < 0.001 7.7 ± 0.49 7.89 ± 0.64 < 0.001 Notes:IDF, International Diabetes Federation;Revised ATP III, Adult Treatment Panel III of the National Cholesterol Education Program, Revised;JCDCG, Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia. SBP, Systolic Blood Pressure;DBP, Diastolic Blood Pressure;FBG, Fasting Blood Glucose;PBG, Postprandial blood Glucose; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; BUN, Blood Urea Nitrogen; TyG: Triglyceride-glucose index; TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio; WHtR: Waist-to-height ratio; BMI: Body mass index; ABSI: A body shape index; WC: Waist circumference. 3.3 Binary Logistic Regression of Relevant Indicators and preMetS Controlling for gender, age, education level, marital status, smoking, alcohol consumption, sleep duration, dietary habits, exercise time, cholesterol, low-density lipoprotein cholesterol, uric acid, creatinine, and blood urea nitrogen, the odds ratios (ORs) and 95% confidence intervals (CIs) were analyzed through logistic regression with related Z-scores. All indicators showed independent associations with preMetS. The strongest correlation was observed between TG/HDL and preMetS defined by the revised ATP III criteria (OR = 10.48, 95% CI: 8.51–12.90). The correlation between TG/HDL and preMetS defined by JCDCG showed a smaller difference (TG/HDL: OR = 6.03, 95% CI: 5.03–7.23), as did TyG (OR = 5.24, 95% CI: 4.50–6.11). Additionally, WC showed the weakest correlation with preMetS defined by both criteria (OR = 1.09, 95% CI: 1.08–1.10) (Table 4 ). Table 4 Binary Logistic Regression of Insulin Resistance-Related Indicators and preMetS Index Revised ATP III JCDCG OR (95%CI) a OR (95%CI) b OR (95%CI) c OR (95%CI) a OR (95%CI) b OR (95%CI) c TYG 6.85(5.92–7.93) 6.72(5.79–7.79) 6.53(5.56–7.69) 6.29(5.47–7.23) 6.16(5.35–7.10) 5.24(4.50–6.11) TG/HDL 10.71(8.81–13.02) 11.69(9.57–14.28) 10.48(8.51–12.90) 6.91(5.83–8.20) 7.16(6.02–8.53) 6.03(5.03–7.23) ABSI 1.93(1.72–2.16) 1.66(1.48–1.87) 1.62(1.44–1.83) 2.10(1.87–2.35) 1.76(1.56–1.97) 1.71(1.52–1.92) WC 1.09(1.08–1.10) 1.10(1.09–1.11) 1.09(1.08–1.10) 1.09(1.08–1.10) 1.10(1.09–1.10) 1.09(1.08–1.10) BMI 1.24(1.21–1.27) 1.26(1.23–1.29) 1.23(1.20–1.26) 1.23(1.20–1.25) 1.24(1.22–1.27) 1.22(1.19–1.25) WHtR 5.02(4.38–5.74) 4.62(4.03–5.29) 4.20(3.64–4.84) 5.11(4.48–5.84) 4.76(4.16–5.45) 4.19(3.64–4.83) Notes: a No adjusted b Adjusted for gender, age c Adjusted for gender, age, education level, marital status, smoking, alcohol consumption, sleep duration, dietary habits, exercise time, cholesterol, low-density lipoprotein cholesterol, uric acid, creatinine, and blood urea nitrogen.。TyG: Triglyceride-glucose index; TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio; WHtR: Waist-to-height ratio; BMI: Body mass index; ABSI: A body shape index; WC: Waist circumference. Utilizing Restricted Cubic Spline (RCS) analysis to elucidate the risk of preMetS associated with TyG and its related parameters. (Figs. 1 and 2 ) 3.4 Screening Ability of Insulin Resistance-Related Indicators for preMetS and Normal Weight preMetS According to Two Criteria The ROC curves depicting the predictive ability of insulin resistance-related parameters and traditional indicators for preMetS risk are presented below. The AUC values for TyG and TG/HDL, along with their related parameters, surpass those of traditional indicators. For the Revised ATP III criteria, TyG's maximum AUC value is 0.731 (95% CI: 0.718–0.744), significantly larger than the AUC values of other indicators (P_delong < 0.05). The optimal cutoff value for TyG, based on a Youden index of 0.352 (sensitivity = 0.867, specificity = 0.485), is determined to be 7.736. In the case of the JCDCG criteria, TyG's maximum AUC value is 0.724 (95% CI: 0.712–0.737). Based on a Youden index of 0.340 (sensitivity = 0.844, specificity = 0.496), the optimal cutoff value for TyG is 7.739. However, the AUC value for WHtR is 0.712 (95% CI: 0.698–0.725), with no significant difference in AUC compared to TyG (P_delong = 0.145). The best cutoff value for WHtR, based on a Youden index of 0.327 (sensitivity = 0.768, specificity = 0.559), is determined to be 0.503 (Fig. 3 and Table 5 ). The screening abilities of insulin resistance-related indicators for normal weight preMetS according to the two criteria are presented in Table 6 . Table 5 ROC Analysis of Insulin Resistance Indices for Screening Different Criteria of preMetS Definitions Variables Cut-off Sensitivity Specificity Youden’s index AUC (95%CI) P Revised ATP III TYG 7.736 0.867 0.485 0.352 0.731(0.718–0.744) - TG/HDL 0.899 0.822 0.507 0.329 0.722(0.709–0.735) 0.023 WHtR 0.503 0.763 0.546 0.309 0.705(0.692–0.719) 0.004 BMI 22.492 0.601 0.631 0.232 0.662(0.648–0.677) < 0.001 ABSI 7.794 0.573 0.584 0.157 0.601(0.586–0.617) < 0.001 WC 81.625 0.782 0.488 0.270 0.682(0.668–0.696) < 0.001 TYG 7.739 0.844 0.496 0.340 0.724(0.712–0.737) - JCDCG TG/HDL 0.966 0.828 0.461 0.289 0.694(0.680–0.707) < 0.001 WHtR 0.503 0.768 0.559 0.327 0.712(0.698–0.725) 0.145 BMI 22.918 0.662 0.562 0.224 0.655(0.641–0.670) < 0.001 ABSI 7.870 0.653 0.523 0.176 0.615(0.600–0.630) < 0.001 WC 81.325 0.783 0.504 0.287 0.686(0.673-0.700) < 0.001 Notes: * indicates the P value from the DeLong test comparing the AUC with that of TyG. TyG: Triglyceride-glucose index, TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio, WHtR: Waist-to-height ratio, BMI: Body mass index, ABSI: A body shape index, WC: Waist circumference. Table 6 ROC Analysis of Insulin Resistance Indices for Screening Normal Weight preMetS Under Different Criteria Definitions Variables Cut-off Sensitivity Specificity Youden’s index AUC (95%CI) P Revised ATP III TYG 7.730 0.864 0.488 0.352 0.727(0.711–0.743) - TG/HDL 0.899 0.831 0.509 0.340 0.718(0.702–0.734) 0.084 WHtR 0.493 0.731 0.489 0.221 0.643(0.625–0.661) < 0.001 BMI 20.893 0.388 0.756 0.144 0.589(0.570–0.608) < 0.001 ABSI 7.794 0.537 0.621 0.158 0.600(0.581–0.618) < 0.001 WC 79.025 0.702 0.462 0.164 0.612(0.593–0.630) < 0.001 JCDCG TYG 7.739 0.844 0.496 0.340 0.724(0.712–0.737) - TG/HDL 0.966 0.828 0.461 0.289 0.694(0.680–0.707) < 0.001 WHtR 0.503 0.768 0.559 0.327 0.712(0.698–0.725) 0.145 BMI 22.918 0.662 0.562 0.224 0.655(0.641–0.670) < 0.001 ABSI 7.870 0.653 0.523 0.176 0.615(0.600–0.630) < 0.001 WC 81.325 0.783 0.504 0.287 0.686(0.673-0.700) < 0.001 Notes: * indicates the P values from the DeLong test comparing the AUCs of the column charts for TyG and age. TyG: Triglyceride-glucose index, TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio, WHtR: Waist-to-height ratio, BMI: Body mass index, ABSI: A body shape index, WC: Waist circumference. 3.5 Screening Ability of an Ensemble Column Chart Model of Insulin Resistance-Related Indicators for preMetS After variable selection through univariate and multivariate logistic regression, and excluding less influential variables, an ensemble column chart model was established. Calibration curves, ROC curves, and column charts for the ensemble column chart model were plotted. In both criteria, the diagnostic ability of the TG/HDL and TyG ensemble column chart models surpassed that of all insulin resistance indicators. For the Revised ATP III criteria, the AUCs (95% CI) of the TG/HDL and TyG ensemble column chart models were 0.766 (0.754–0.779) and 0.771 (0.759–0.784), respectively, with comparable screening abilities (P_delong > 0.05). However, for the JCDCG criteria, the AUCs (95% CI) of the TG/HDL and TyG ensemble column chart models were 0.766 (0.754–0.778) and 0.771 (0.759–0.784), respectively. Yet, in the Revised ATP III criteria, the TG/HDL ensemble column chart model demonstrated a better fit, with R2 (TG/HDL) = 0.267 > R2 (TYG) = 0.239, and smaller errors, MAE (TG/HDL) = 0.005 > MAE (TYG) = 0.025. Consistently, in the JCDCG criteria, MAE (TG/HDL) = 0.006 > MAE (TYG) = 0.015 (Figs. 4 and 5 , Table 7 ). Table 7 ROC Analysis of Insulin Resistance Index Ensemble Column Chart Models for Screening Different Criteria of preMetS Definitions Variables Cut-off AUC (95%CI) P R 2 MAE MSE Revised TYG + age 71.965 0.766(0.754–0.779) - 0.239 0.025 9.90E-04 ATP III TG/HDL + age 8.056 0.771(0.759–0.784) 0.129 0.267 0.005 3.00E-05 WHtR + age 17.753 0.730(0.716–0.743) < 0.001 0.182 0.017 4.10E-04 BMI + age 14.755 0.719(0.705–0.733) < 0.001 0.162 0.01 1.40E-04 ABSI + age 30.501 0.664(0.649–0.679) < 0.001 0.094 0.002 1.00E-05 WC + age 20.506 0.724(0.710–0.737) < 0.001 0.174 0.017 4.20E-04 JCDCG TYG + age 74.485 0.771(0.759–0.784) - 0.257 0.015 3.50E-04 TG/HDL + age 9.535 0.766(0.754–0.778) 0.07 0.26 0.006 5.00E-05 WHtR + age 21.509 0.747(0.734–0.760) < 0.001 0.21 0.02 5.00E-05 BMI + age 15.584 0.731(0.717–0.744) < 0.001 0.185 0.007 6.00E-05 ABSI + age 35.631 0.688(0.674–0.702) < 0.001 0.125 0.003 1.00E-05 WC + age 21.469 0.741(0.728–0.754) < 0.001 0.202 0.017 1.70E-02 Notes: * indicates the P values from the DeLong test comparing the AUCs of the column charts for TyG and age. TyG: Triglyceride-glucose index, TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio, WHtR: Waist-to-height ratio, BMI: Body mass index, ABSI: A body shape index, WC: Waist circumference. 4. Discussion This study compared the prevalence and consistency of preMetS among three commonly used definitions of MetS, including the revised ATP III 11 , IDF 36 , and JCDCG definitions 37 . According to these definitions, the prevalence of preMetS in the same study population in Fuzhou City ranged from 10.63–49.68%. Regardless of gender, the consistency of JCDCG with the revised ATP III for MetS and preMetS was superior to the IDF standard, and it was more consistent in males but relatively poorer in females.The study also compared the diagnostic capabilities of six insulin resistance-related indices, traditional indicators, and an ensemble column chart model combined with age for preMetS. The results indicated that TYG had the best discriminatory ability for preMetS (revised ATP III) (P delong < 0.001, Table 5 ). Additionally, TYG and WHtR had similar discriminatory abilities for preMetS (JCDCG) (P delong 0.05, Table 6 ). Including age and related indicators in the ensemble column chart model, it was found that the TG/HDL ensemble column chart model had a better fit for preMetS than TYG (R 2 (TG/HDL) > R 2 (TYG), Table 7 ). Until now, the concept of PreMetS (Pre-Metabolic Syndrome) has been sparingly employed in studies on disease prevalence, especially in-depth investigations into the varied risks associated with preMetS. In prior studies, preMetS prevalence ranged from 12.1% 14 to 24% 38 , whereas in our study, it reached a staggering 50%. The primary reason for this contrast may lie in the exclusion of individuals with diabetes and cardiovascular diseases in those earlier studies. However, given that these studies were conducted a decade ago and considering the current ominous surge in chronic diseases, further research is imperative, particularly regarding preMetS as a low-risk yet highly prevalent group.Earlier research suggests a noteworthy increase in baseline activated platelets among preMetS individuals, and this baseline platelet activation is positively correlated with hyperlipidemia and liver damage 39 . Furthermore, in chronic hepatitis B patients with concurrent preMetS, there is a delayed clearance of hepatitis B e antigen (HBeAg), and there is a heightened risk of advanced liver fibrosis 40 . Studies also indicate that preMetS elevates the risk of atherosclerotic cardiovascular diseases and ischemic heart disease by 1.5–2.3 times in both genders 41 . This is attributed to lower antioxidant defense levels in PreMetS patients, where adiponectin plays a crucial role in regulating the adipose-vascular axis 42 , thereby increasing the risk of insulin resistance and complications associated with atherosclerosis 43 . Additionally, compared to the normal population, individuals with preMetS exhibit early axonal loss in brain white matter, thereby increasing the risk of dementia 44 . Given the lack of clear thresholds in defining Metabolic Syndrome (MetS), insulin resistance appears to be the most important and directly reflective indicator; hence, further research is deemed necessary. Participants identified under different definitions of Metabolic Syndrome (MetS) exhibit significant variations. The use of different criteria to define preMetS, particularly differences in parameters for increased waist circumference and other pathological conditions (such as abnormal blood glucose and lipid levels), underscores the need for standardized definitions to ensure comparability of results across countries and different time periods 45–47 . While MetS is considered more prevalent in the elderly population 48 , it has garnered widespread attention as an early deteriorating state before the onset of diseases in younger individuals 49 . Our study specifically aims to include younger populations to understand how different definitions and components manifest in this demographic.Previous research among Chinese adults has explored differences between definitions and conducted parallel studies on MetS using IDF, ATP III, or JCDCG criteria 37 . However, there has been limited analysis of preMetS in further studies. In our research, the prevalence of preMetS defined by IDF was considerably lower than the other two definitions. This difference arises because the preMetS prevalence in IDF corresponds to the non-MetS abdominal obesity prevalence. Therefore, ATP III and JCDCG may be more suitable for the Chinese population. The primary distinction between ATP III and JCDCG lies in the relatively low overlap in rates of abnormal blood glucose and low HDL-c prevalence. This study, for the first time, explores and compares four obesity indicators—general obesity (BMI), abdominal obesity (WC, WHtR), and visceral obesity (ABSI)—as well as two insulin resistance indicators (TyG and TG/HDL) in terms of their screening capabilities for preMetS. After adjusting for confounding factors, all these indicators show a significant association with preMetS. Our research indicates that the TyG index demonstrates the strongest identification ability for preMetS, while age significantly enhances the diagnostic capability of TG/HDL. After adjusting for confounding factors, both TyG and TG/HDL exhibit the strongest correlation with preMetS defined by the two criteria.TyG and TG/HDL serve as indices measuring insulin resistance, suggesting a certain degree of insulin resistance in preMetS 50 . It is noteworthy that previous research 51 suggests high blood glucose as a major risk factor for preMetS and a primary contributor to the progression of metabolic impairment. Therefore, patients with higher blood glucose levels are more likely to be identified.In the changing trends within the three MetS categories, PreMetS is characterized as a state of aseptic inflammation, possibly involving insulin resistance, activation of inflammatory signaling pathways, abnormal cytokine production, and increased acute-phase response 52 . Compared to the normal population, preMetS individuals exhibit higher white blood cell counts, C-reactive protein levels, and interleukin-6 concentrations. Moreover, these inflammatory markers become imbalanced earlier than the manifestation of MetS, indicating a more pronounced change in the preMetS stage 53 . Hence, it is necessary to identify preMetS patients to guide further anti-inflammatory treatments and lifestyle interventions 54 . All the indicators used in this study are easy to obtain, providing benefits in large-scale community screenings by requiring only two or three indicators compared to the complexity of acquiring five indicators. Firstly, the multifaceted diagnosis of preMetS and MetS, due to the necessity of intricate physical examinations and clinical metrics 55 , is challenging to widely implement in community screenings. Secondly, the use of different instruments for the complex assessment of five risk factors can lead to significant measurement errors and inefficiencies 56, 57 , especially in large-scale screening projects. Additionally, we found that WHtR may be a crucial non-invasive screening indicator for preMetS (JCDCG), with its identification ability showing no significant difference compared to TYG (P < 0.001). Therefore, in primary healthcare clinical practices, TYG and WHtR respectively serve as simple, cost-effective, and efficient indicators for screening preMetS according to revised ATP and JCDCG standards. Our study also separately assessed adults with normal weight in the Fuzhou area. The cardiovascular risk increases in individuals with normal weight but with metabolic risks, emphasizing the importance of metabolic risk screening in this population 58 . Additionally, due to having a normal BMI, individuals often overlook their metabolic risks. Therefore, it is necessary to conduct metabolic risk screening for those with a normal weight.Previous research 59 indicates that WHtR is one of the cost-effective and non-invasive screening indicators for Metabolic Syndrome (MetS) in non-obese individuals, aligning with the findings in our study where WHtR emerged as the optimal indicator for preMetS screening based on JCDCG criteria. The consistent screening ability of preMetS in both the general population and individuals with normal weight suggests that obesity is not synonymous with preMetS, and the metabolic benign obesity associated with preMetS may be attributed to insulin resistance. 5. Clinical and public health potential Firstly, in the three classifications of MetS, the IDF criteria are deemed inappropriate, as they overly emphasize abdominal obesity and overlook individuals with unhealthy metabolism. Secondly, following MetS screening, the remaining "healthy" population can still be differentiated using the retained screening indicators, thereby further enhancing the utility of these indicators. Lastly, we have found that WHtR may be the only non-invasive and effective indicator for preMetS (JCDCG) screening. 6. Strengths and Limitations The strength of this study lies in including a sufficient number of samples and incorporated certain indicators for MetS screening, further exploring the additional effects of these indicators. However, the study has some limitations. Firstly, although the AUC in our study was greater than 0.7, it did not exceed 0.8, indicating that these screening indicators are considered good but not excellent. Secondly, we observed significant numerical changes in age in the column chart, while variables related to gender and lifestyle habits showed smaller variations. However, it remains unknown whether these numerical changes might be influenced by different ethnicities or regions. Thirdly, the participants in our study were all residents of coastal areas in China, and the results may not be suitable for extrapolation to other countries or inland regions. Further prospective cohort studies with larger sample sizes and more detailed data are needed to assess the identification value of each indicator in different populations. 7. Conclusion The consistency between the revised ATP III and JCDCG in MetS tri-classification is good. TyG has the best identification ability for preMetS (revised ATP III and JCDCG). Additionally, WHtR has equally good identification ability for preMetS (JCDCG). The nomogram model with TG/HDL has the best identification ability. In conclusion, the consistency of MetS tri-classification is better in the revised ATP III and JCDCG. TyG is an effective indicator for identifying preMetS in adults in Southeast China. WHtR is a non-invasive indicator for screening preMetS (JCDCG). The diagnostic capabilities are improved with the inclusion of age and TG/HDL in the nomogram model, with less error. Abbreviations IDF, International Diabetes Federation;Revised ATP III, Adult Treatment Panel III of the National Cholesterol Education Program, Revised;JCDCG, Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia.SBP, Systolic Blood Pressure;DBP, Diastolic Blood Pressure;FBG, Fasting Blood Glucose;PBG, Postprandial blood Glucose; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; BUN, Blood Urea Nitrogen; TYG: Triglyceride-glucose index; TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio; WHtR: Waist-to-height ratio; BMI: Body mass index; ABSI: A body shape index; WC: Waist circumference. AUC: area under the curve. Declarations Conflict of interest statement: The authors declare no potential conflicts of interest. Author Contributions: Conceptualization and methodology, Xiaoyang Zhang and Youqiong Xu; writing—original draft preparation, Xinfeng Huang and Qingquan Chen; writing—review and editing, Xinxin Yang, Haiping Hu, Hong Li and Ruoming Huang; validation, Yuanyu She and Huanhuan Shi; formal analysis, Xiangyu Cao. All authors have read and agreed to the published version of the manuscript. Ethics approval and consent This study was approved by the Ethics Committee of the Fuzhou Center for Disease Control and Prevention (approval number: 2022002). Informed consent was obtained from all participants and/or their legal guardians for this study. There is no conflict of interest in this study. Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Funding This study was supported by the Fuzhou Science and Technology Program (No. 2022-S-032). Declaration of Interest Statement The authors declare no potential conflicts of interest. Acknowledgments None. References Niigaki M, Adachi K, Hirakawa K, Furuta K, Kinoshita Y. Association between metabolic syndrome and prevalence of gastroesophageal reflux disease in a health screening facility in Japan. J Gastroenterol. Apr 2013;48(4):463-72. doi:10.1007/s00535-012-0671-3 Kim TE, Kim H, Sung J, et al. The association between metabolic syndrome and heart failure in middle-aged male and female: Korean population-based study of 2 million individuals. Epidemiol Health. 2022;44:e2022078. doi:10.4178/epih.e2022078 Miyashita Y, Hitsumoto T, Fukuda H, et al. Metabolic syndrome is linked to the incidence of pancreatic cancer. EClinicalMedicine. Jan 2024;67:102353. doi:10.1016/j.eclinm.2023.102353 Lee J, Lee KS, Kim H, et al. 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Combined effect of obesity and cardio-metabolic abnormality on the risk of cardiovascular disease: a meta-analysis of prospective cohort studies. Int J Cardiol. Oct 12 2013;168(5):4761-8. doi:10.1016/j.ijcard.2013.07.230 Adil SO, Musa KI, Uddin F, Shafique K, Khan A, Islam MA. Role of anthropometric indices as a screening tool for predicting metabolic syndrome among apparently healthy individuals of Karachi, Pakistan. Front Endocrinol (Lausanne). 2023;14:1223424. doi:10.3389/fendo.2023.1223424 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-3909069","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":271342167,"identity":"a3ad26c6-297f-49de-bcca-53c4f9aaeaa6","order_by":0,"name":"Xinxin Yang","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Yang","suffix":""},{"id":271342168,"identity":"7e319163-81ff-4246-8b6a-8e8916123359","order_by":1,"name":"Qingquan Chen","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingquan","middleName":"","lastName":"Chen","suffix":""},{"id":271342169,"identity":"3ff61c75-6933-4ecc-8413-cbce155b83a5","order_by":2,"name":"Haiping Hu","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haiping","middleName":"","lastName":"Hu","suffix":""},{"id":271342170,"identity":"8353f6ca-877f-44ef-97d6-69e1fb138756","order_by":3,"name":"Huanhuan Shi","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huanhuan","middleName":"","lastName":"Shi","suffix":""},{"id":271342171,"identity":"f463a031-f77e-4e38-9251-4ef2549b0656","order_by":4,"name":"Yuanyu She","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyu","middleName":"","lastName":"She","suffix":""},{"id":271342172,"identity":"788159db-57c3-4d4e-944c-16a5d318ccbd","order_by":5,"name":"Hong Li","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Li","suffix":""},{"id":271342173,"identity":"95b71c12-e060-4103-a0b7-ff63139e89ff","order_by":6,"name":"Ruoming Huang","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruoming","middleName":"","lastName":"Huang","suffix":""},{"id":271342174,"identity":"50a29e30-04b1-4d87-a076-57d55c125643","order_by":7,"name":"Xiangyu Cao","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Cao","suffix":""},{"id":271342175,"identity":"91463438-88d7-4df7-a63d-e388ae62cb49","order_by":8,"name":"Xiaoyang Zhang","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyang","middleName":"","lastName":"Zhang","suffix":""},{"id":271342176,"identity":"4659c4de-bdbe-4b94-9e68-701def56d7b2","order_by":9,"name":"Youqiong Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACxgYog5+Z+eAD0rRItrMlG5BmncF5HjMBolQytzc/e/Cjwk7O+DCDGQNDjU00YYf1HDM37DmTbGx2mCHtAcOxtNwGglpmJJhJM7YdSNx2mOG4AWPDYWK0pH8Da9nczNgmQaSWHIgtG5iZ2YjU0nOmTBLkF4nDbMwGCcT4xbC9fZsEKMT4+89/fPChxoYILSgqEggpBwF5YhSNglEwCkbBCAcAm+E7tsklGE0AAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Youqiong","middleName":"","lastName":"Xu","suffix":""},{"id":271342177,"identity":"818bc04b-9868-4c4e-b0bd-f8ded7fcbba9","order_by":10,"name":"Xinfeng Huang","email":"","orcid":"","institution":"The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinfeng","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-01-29 14:00:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3909069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3909069/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50924148,"identity":"c3d26abb-1458-4c58-a5ca-36ccab3a4788","added_by":"auto","created_at":"2024-02-09 17:03:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":380573,"visible":true,"origin":"","legend":"\u003cp\u003ethe association between preMetS (Revised ATP III) and six insulin resistance indicators using Restricted Cubic Spline modeling.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3909069/v1/cad41b1b417fc860ba43a348.png"},{"id":50926458,"identity":"8aaf5590-db6a-4ff3-a0e7-c5d4e6a4f394","added_by":"auto","created_at":"2024-02-09 17:11:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":395400,"visible":true,"origin":"","legend":"\u003cp\u003ethe association between preMetS (JCDCG) and six insulin resistance indicators using Restricted Cubic Spline modeling.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3909069/v1/684c5c36db28941299bbfc2c.png"},{"id":50924147,"identity":"fac0e75e-0d1e-4671-9f78-caae76813658","added_by":"auto","created_at":"2024-02-09 17:03:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":444772,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves for screening preMetS based on different definitions. The left panel corresponds to the Revised ATP III criteria, while the right panel corresponds to JCDCG criteria.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3909069/v1/a9dcf57c78d64e99c8ba0a53.png"},{"id":50924145,"identity":"df6a9f07-b781-4d22-96e5-8d653717be9d","added_by":"auto","created_at":"2024-02-09 17:03:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":538239,"visible":true,"origin":"","legend":"\u003cp\u003ethe risk ensemble column charts, calibration curves, and ROC curves used to estimate the risk of preMetS in men and women (Revised ATP III criteria). The top-left corresponds to TG/HDL, and the top-right corresponds to TYG. TyG: Triglyceride-glucose index, TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio, WHtR: Waist-to-height ratio, BMI: Body mass index, ABSI: A body shape index, WC: Waist circumference.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3909069/v1/cdc755f5caecf2e36c69d4fd.png"},{"id":50924149,"identity":"e4601b30-4588-486e-aed3-d75db215748a","added_by":"auto","created_at":"2024-02-09 17:03:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":553909,"visible":true,"origin":"","legend":"\u003cp\u003ethe risk ensemble column charts, calibration curves, and ROC curves used to estimate the risk of preMetS in men and women (Revised ATP III criteria). The top-left corresponds to TG/HDL, and the top-right corresponds to TYG. TyG: Triglyceride-glucose index, TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio, WHtR: Waist-to-height ratio, BMI: Body mass index, ABSI: A body shape index, WC: Waist circumference.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3909069/v1/679497e42031172b89f48f5d.png"},{"id":59267234,"identity":"f9df3b5f-f986-4a4f-94dd-a3ddc7e69f46","added_by":"auto","created_at":"2024-06-28 11:32:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3493244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909069/v1/b27431e4-8a04-4ae0-a905-8435c18f2d7c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of the Incidence and Diagnostic Value of Insulin Resistance Indicators in the Prevalence of Metabolic Syndrome in Southeast China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic Syndrome (MetS) is a cluster of cardiovascular metabolic risks, including central obesity, elevated blood pressure, abnormal glucose tolerance, and abnormal lipid levels, which can increase the risk of cardiovascular diseases. One or two components of MetS constitute the pre-stage of MetS (pre-MS), indicating early signs of MetS. Compared to MetS patients, individuals with preMetS have a lower risk of various diseases but still face increased risks of gastrointestinal diseases\u003csup\u003e1\u003c/sup\u003e, cardiovascular diseases (CVD)\u003csup\u003e2\u003c/sup\u003e, cancer\u003csup\u003e3, 4\u003c/sup\u003e, and dementia.\u003c/p\u003e \u003cp\u003ePreMetS lacks a clear definition, and some studies use different criteria to identify this entity, namely having fewer than the required number of MetS components\u003csup\u003e5\u0026ndash;8\u003c/sup\u003e. Over the past few decades, various international organizations have provided definitions for MetS\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. Due to the use of different MetS definitions, estimates of preMetS prevalence vary globally, leading to confusion and a lack of comparability between studies\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e. Therefore, it is necessary to explore the prevalence and characteristics of preMetS based on different standards (IDF, revised ATP III, and JCDCG standards), which may help researchers better understand MetS and formulate a more scientific definition.\u003c/p\u003e \u003cp\u003ePrevious studies have indicated that some anthropometric measurements and insulin resistance indicators perform well in MetS screening\u003csup\u003e15\u0026ndash;18\u003c/sup\u003e. Further exploration of the performance of these indicators in preMetS screening among non-MetS populations is warranted. Studies suggest that BMI and WHtR cannot distinguish between the distribution of fat and muscle tissue, but they show good performance in assessing cardiovascular metabolic risks in middle-aged Koreans\u003csup\u003e19\u003c/sup\u003e. Additionally, some emerging anthropometric indices, such as the ABSI\u003csup\u003e19\u003c/sup\u003e, perform well in MetS screening. Insulin resistance, considered a core mechanism in the pathogenesis of MetS, is one of the main underlying causes of MetS and its components\u003csup\u003e20\u003c/sup\u003e. Therefore, insulin resistance is an effective method for screening MetS. Although assessing high insulin-normal glucose status is crucial for early MetS diagnosis, its determination often requires complex and invasive methods\u003csup\u003e21\u003c/sup\u003e, making it impractical for large-scale community screening and routine clinical practice. While anthropometric indicators are correlated to some extent with insulin resistance\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e, the correlation may not be significant enough to comprehensively assess the degree of insulin resistance. Considering these challenges, alternative markers assessing insulin resistance, such as the Triglyceride-Glucose Index (TYG) and its derivatives\u003csup\u003e25\u003c/sup\u003e, as well as TG/HDL\u003csup\u003e26\u003c/sup\u003e, have been proposed.\u003c/p\u003e \u003cp\u003eThe cardiovascular metabolic risks in individuals with normal BMI are often overlooked\u003csup\u003e27\u003c/sup\u003e. Research indicates that individuals with a normal weight but unhealthy metabolism have an increased risk of diabetes and cardiovascular diseases by 1.5 to 2 times\u003csup\u003e28, 29\u003c/sup\u003e. Additionally, WHO recommends excluding individuals already diagnosed with type 2 diabetes or CVD from the MetS definition because MetS cannot be used for primary prevention in this population\u003csup\u003e30\u003c/sup\u003e. Therefore, using simple and efficient indicators to screen for preMetS in different populations is of greater public health significance. However, there are currently no relevant reports on the identification capabilities of the above indicators for adult preMetS. Hence, this study aims to describe the prevalence of preMetS in adults in the Fuzhou region, compare the identification capabilities of different indicators, and calculate critical values. Finally, we establish a nomogram model combining the best indicators with age to further enhance the discriminatory abilities of the indicators.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study participants\u003c/h2\u003e \u003cp\u003eThis was a retrospective cross-sectional survey. Between June and December 2022, we randomly recruited patients with type 2 diabetes from one community in each of the six urban areas of Fuzhou City. All participants underwent a face-to-face survey using a homemade uniform questionnaire and took a physical examination, which were both conducted by trained primary care professionals.\u003c/p\u003e \u003cp\u003e A multistage stratified cluster random sampling method was employed to recruit residents aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years (residing in the area for \u0026ge;\u0026thinsp;6 months), resulting in 9,399 participants with a response rate of 95.75%. After excluding individuals with missing components of metabolic syndrome and insulin resistance indicators, 8,997 individuals met the criteria.\u003c/p\u003e \u003cp\u003e The study received ethical approval from the Fuzhou City CDC Ethics Review Committee (Approval Number: 2022002), and all participants provided informed consent. All experimental protocols involving human data were in accordance with the Helsinki Declaration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data measurements\u003c/h2\u003e \u003cp\u003eA self-designed unified questionnaire was used for face-to-face interviews conducted by trained professionals from grassroots medical institutions. The survey covered personal health status, medical history, and lifestyle behaviors (exercise, smoking, alcohol consumption, and sleep patterns).\u003c/p\u003e \u003cp\u003ePhysical examinations included height, weight, waist circumference (measured twice, averaged), and blood pressure measured using a validated combination diagnostic system (UR-9000F) with three measurements. Laboratory biochemical tests collected venous blood samples from all participants in a fasting state. Serum TC levels were determined by enzymatic colorimetry, and serum LDL-C, HDL-C, and TG were measured by colorimetry. Serum FPG was measured using the hexokinase method, and serum uric acid levels were determined by colorimetry (all using the Hitachi 7100 fully automatic biochemical analyzer).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Description of variables\u003c/h2\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Non-smokers were classified as low risk\u003csup\u003e31\u003c/sup\u003e. Regular physical activity was defined as at least 150 minutes of moderate-intensity activity per week or 75 minutes of vigorous activity per week (or an equivalent combination)\u003csup\u003e31\u003c/sup\u003e. Food frequency questionnaire assessed various food intake over the past year\u003csup\u003e32\u003c/sup\u003e. Low-risk alcohol consumption was defined as moderate drinking (females: 5\u0026ndash;15 grams/day; males: 5\u0026ndash;30 grams/day)\u003csup\u003e33\u003c/sup\u003e. Healthy weight was defined as a BMI of 18.5\u0026ndash;24.0 kg/m\u0026sup2;\u003csup\u003e31\u003c/sup\u003e. Adequate sleep time (7\u0026ndash;8 hours/day) was classified as low risk\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)Insulin Resistance-Related Indicators\u003csup\u003e27, 35\u003c/sup\u003e:BMI: weight(kg)/height\u0026sup2;(m). WHtR: WC(cm)/height(m). ABSI: WC/(BMI^(2/3) * height^(1/2)). TYG: LN(TG(mg/dL) * FPG(mg/dL)/2). TG/HDL: TG/HDL-C.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)Metabolic Syndrome Diagnostic Criteria:(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) IDF Standard (2005)\u003csup\u003e36\u003c/sup\u003e:Waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for males and \u0026ge;\u0026thinsp;85 cm for females. Meeting the criteria but not reaching the Metabolic Syndrome (MetS) standard can be defined as preMetS.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Revised ATPIII Standard (2005)\u003csup\u003e36\u003c/sup\u003e: 1)Waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for Chinese males and \u0026ge;\u0026thinsp;85 cm for Chinese females (based on population and country/region-specific definitions).2)TG levels\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L or receiving treatment. 3)HDL-C levels\u0026thinsp;\u0026lt;\u0026thinsp;1.03 mmol/L for males, \u0026lt; 1.29 mmol/L for females, or receiving treatment.4)SBP\u0026thinsp;\u0026gt;\u0026thinsp;130 mmHg and/or DBP\u0026thinsp;\u0026gt;\u0026thinsp;85 mmHg, or diagnosed with hypertension.5) FPG\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/L or diagnosed with diabetes. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) JCDCG Standard (2007)\u003csup\u003e37\u003c/sup\u003e: 1)Waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for males and \u0026ge;\u0026thinsp;85 cm for females.2)HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.03 mmol/L.3)SBP\u0026thinsp;\u0026gt;\u0026thinsp;130 mmHg and/or DBP\u0026thinsp;\u0026gt;\u0026thinsp;85 mmHg or diagnosed with hypertension.4)Fasting TG\u0026thinsp;\u0026ge;\u0026thinsp;1.70 mmol/L.5)FPG\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L, or 2-hour postprandial glucose (2hPG)\u0026thinsp;\u0026ge;\u0026thinsp;7.8 mmol/L, or diagnosed with diabetes.\u003c/p\u003e \u003cp\u003ePreMetS was defined as meeting one to two conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical methods\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used for continuous and categorical variables. Group comparisons for continuous and categorical variables were conducted using independent sample t-tests and chi-square tests, respectively. Z-scores were used for insulin resistance-related indicators. Binary logistic regression analyzed the correlation between preMetS and various indicators, adjusting for age, education level, occupation, marital status, smoking, and alcohol habits. The diagnostic abilities of each indicator were evaluated using the area under the receiver operating characteristic curve. Sensitivity, specificity, Youden's index, and critical values were calculated for each indicator. The best two variables were selected based on the determined indicators. Multiple logistic regression was used to select statistically significant variables such as gender, age, residence, marital status, education level, smoking, and alcohol habits, and a nomogram model was constructed (as other variables had a weak impact in the nomogram model, only age was included). The backward LR method (inclusion criterion: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, exclusion criterion: P\u0026thinsp;\u0026gt;\u0026thinsp;0.1) was employed to include indicators and age in the nomogram model. The nomogram model was constructed and the calibration curve was drawn to assess its calibration. All statistical analyses were performed using SPSS 26.0 and R software (version 4.2.0)\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Consensus on Definitions of PreMetS\u003c/h2\u003e \u003cp\u003eWe applied three different definitions to categorize the entire population into three MetS groups. The prevalence and characteristics of preMetS and MetS varied based on the definitions used in the Chinese population. With the three different standards, the overall prevalence of preMetS ranged from 10.63\u0026ndash;49.68%, and the total prevalence of MetS ranged from 21.41\u0026ndash;31.07%. Notably, in the male population, the Revised ATP III criteria exhibited the highest preMetS prevalence, while in females, the JCDCG criteria had the highest preMetS prevalence, with the opposite pattern observed for MetS prevalence (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\u003ePrevalence of preMetS and MetS According to Different Definitions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epreMetS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll(n\u0026thinsp;=\u0026thinsp;8997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6115(67.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e956(10.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1926(21.41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1732(19.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4470(49.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2795(31.07%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJCDCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1844(20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4466(49.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2687(29.87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(n\u0026thinsp;=\u0026thinsp;3925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2784(70.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e384(9.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e757(19.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e748(19.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2053(52.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1124(28.64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJCDCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e712(18.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1993(50.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1220(31.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale(n\u0026thinsp;=\u0026thinsp;5072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3331(65.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e572(11.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1169(23.05%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e984(19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2417(47.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1671(32.95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJCDCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1132(22.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2473(48.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1467(28.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: IDF: International Diabetes Federation;Revised ATP III: Adult Treatment Panel III of the National Cholesterol Education Program, Revised;JCDCG: Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Consistency and Differences in Diagnoses\u003c/h2\u003e \u003cp\u003eThe consistency and differences in diagnoses using IDF, Revised ATP III, and JCDCG standards are summarized in the table. Irrespective of gender, there was better consistency between the Revised ATP III and JCDCG standards, with kappa values ranging from 0.700 to 0.820. However, the consistency with the IDF standard was lower, with kappa values ranging from 0.316 to 0.377. Notably, the consistency of preMetS definitions between JCDCG and Revised ATP III criteria in males (kappa\u0026thinsp;=\u0026thinsp;0.820) was higher than in females (kappa\u0026thinsp;=\u0026thinsp;0.700) (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\u003eConsistency among various definitions of preMetS and MetS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eJCDCG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekappa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekappa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll(n\u0026thinsp;=\u0026thinsp;8997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.340\u0026ndash;0.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.300-0.321)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.740\u0026ndash;0.764)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(n\u0026thinsp;=\u0026thinsp;3925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.362\u0026ndash;0.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.258\u0026ndash;0.289)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.804\u0026ndash;0.836)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale(n\u0026thinsp;=\u0026thinsp;5072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.362\u0026ndash;0.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.326\u0026ndash;0.357)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.683\u0026ndash;0.718)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes:CI: Confidence Interval; IDF: International Diabetes Federation;Revised ATP III: Adult Treatment Panel III of the National Cholesterol Education Program, Revised;JCDCG: Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Basic Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eDue to the better performance in consistency between JCDCG and the revised ATP III (kappa\u0026thinsp;=\u0026thinsp;0.752), we employed these two definitions for subsequent analyses. A total of 6202 and 6310 subjects participated in this study when preMetS was defined using the Revised ATP III and JCDCG criteria, respectively. The age, weight, WC, BMI, WHtR, ABSI, TyG (Triglyceride-glucose index), TG/HDL, blood pressure, and lipid indices (expected HDL-C) in the preMetS group were all significantly higher than those in the non-preMetS group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (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\u003eBaseline Characteristics of All Participants\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003epreMetS(n\u0026thinsp;=\u0026thinsp;6202) Revised ATPIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003epreMetS (n\u0026thinsp;=\u0026thinsp;6310) JCDCG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal(n\u0026thinsp;=\u0026thinsp;1732)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epreMetS(n\u0026thinsp;=\u0026thinsp;4470)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNormal(n\u0026thinsp;=\u0026thinsp;1844)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epreMetS(n\u0026thinsp;=\u0026thinsp;4466)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e956 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e993 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1732 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3514 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1844 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3473 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2288 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2311 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1732 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2168 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1844 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2142 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal blood sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1361 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1752 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1732 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3094 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1844 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2518 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1106 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e475 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1732 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3364 (75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1844 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3991 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e950 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1005 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1732 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3520 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1844 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3461 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e748 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2053 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e712 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1993 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e984 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2417 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1132 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2473 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.19\u0026thinsp;\u0026plusmn;\u0026thinsp;14.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.08\u0026thinsp;\u0026plusmn;\u0026thinsp;13.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.54\u0026thinsp;\u0026plusmn;\u0026thinsp;13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1142 (65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2686 (60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1251 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2671 (59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e590 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1784 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e593 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1795 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eHigh school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e700 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1165 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e774 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1126 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1030 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3304 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1068 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3339 (74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1471 (85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3889 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1560 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3886 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e580 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e577 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1496 (86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3764 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1621 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3781 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon or moderate alcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1672 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4279 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1788 (97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4265 (95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate to high-intensity exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e844 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e408 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e833 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate sleep duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e875 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1951 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e954 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1912 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eHealthy dietary habits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e797 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1726 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e873 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1699 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.65\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.24\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114.79\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128.32\u0026thinsp;\u0026plusmn;\u0026thinsp;18.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.97\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.02\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.35\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eFBG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003ePBG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eTC(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eUric acid (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302.43\u0026thinsp;\u0026plusmn;\u0026thinsp;79.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.44\u0026thinsp;\u0026plusmn;\u0026thinsp;95.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300.3\u0026thinsp;\u0026plusmn;\u0026thinsp;78.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e333.1\u0026thinsp;\u0026plusmn;\u0026thinsp;94.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eCreatinine (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.81\u0026thinsp;\u0026plusmn;\u0026thinsp;21.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.12\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.47\u0026thinsp;\u0026plusmn;\u0026thinsp;21.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eBUN(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eWC(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.61\u0026thinsp;\u0026plusmn;\u0026thinsp;11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.54\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes:IDF, International Diabetes Federation;Revised ATP III, Adult Treatment Panel III of the National Cholesterol Education Program, Revised;JCDCG, Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSBP, Systolic Blood Pressure;DBP, Diastolic Blood Pressure;FBG, Fasting Blood Glucose;PBG, Postprandial blood Glucose; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; BUN, Blood Urea Nitrogen; TyG: Triglyceride-glucose index; TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio; WHtR: Waist-to-height ratio; BMI: Body mass index; ABSI: A body shape index; WC: Waist circumference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Binary Logistic Regression of Relevant Indicators and preMetS\u003c/h2\u003e \u003cp\u003eControlling for gender, age, education level, marital status, smoking, alcohol consumption, sleep duration, dietary habits, exercise time, cholesterol, low-density lipoprotein cholesterol, uric acid, creatinine, and blood urea nitrogen, the odds ratios (ORs) and 95% confidence intervals (CIs) were analyzed through logistic regression with related Z-scores. All indicators showed independent associations with preMetS. The strongest correlation was observed between TG/HDL and preMetS defined by the revised ATP III criteria (OR\u0026thinsp;=\u0026thinsp;10.48, 95% CI: 8.51\u0026ndash;12.90). The correlation between TG/HDL and preMetS defined by JCDCG showed a smaller difference (TG/HDL: OR\u0026thinsp;=\u0026thinsp;6.03, 95% CI: 5.03\u0026ndash;7.23), as did TyG (OR\u0026thinsp;=\u0026thinsp;5.24, 95% CI: 4.50\u0026ndash;6.11). Additionally, WC showed the weakest correlation with preMetS defined by both criteria (OR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 1.08\u0026ndash;1.10) (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\u003eBinary Logistic Regression of Insulin Resistance-Related Indicators and preMetS\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eJCDCG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%CI)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%CI)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%CI)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%CI)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.85(5.92\u0026ndash;7.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.72(5.79\u0026ndash;7.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.53(5.56\u0026ndash;7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.29(5.47\u0026ndash;7.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.16(5.35\u0026ndash;7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.24(4.50\u0026ndash;6.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.71(8.81\u0026ndash;13.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.69(9.57\u0026ndash;14.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.48(8.51\u0026ndash;12.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.91(5.83\u0026ndash;8.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.16(6.02\u0026ndash;8.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.03(5.03\u0026ndash;7.23)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.93(1.72\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66(1.48\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.62(1.44\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.10(1.87\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.76(1.56\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.71(1.52\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09(1.08\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10(1.09\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09(1.08\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09(1.08\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.10(1.09\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09(1.08\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24(1.21\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26(1.23\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23(1.20\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.23(1.20\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.24(1.22\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.22(1.19\u0026ndash;1.25)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.02(4.38\u0026ndash;5.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.62(4.03\u0026ndash;5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.20(3.64\u0026ndash;4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.11(4.48\u0026ndash;5.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.76(4.16\u0026ndash;5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.19(3.64\u0026ndash;4.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes:\u003csup\u003ea\u003c/sup\u003eNo adjusted \u003csup\u003eb\u003c/sup\u003eAdjusted for gender, age \u003csup\u003ec\u003c/sup\u003eAdjusted for gender, age, education level, marital status, smoking, alcohol consumption, sleep duration, dietary habits, exercise time, cholesterol, low-density lipoprotein cholesterol, uric acid, creatinine, and blood urea nitrogen.。TyG: Triglyceride-glucose index; TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio; WHtR: Waist-to-height ratio; BMI: Body mass index; ABSI: A body shape index; WC: Waist circumference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUtilizing Restricted Cubic Spline (RCS) analysis to elucidate the risk of preMetS associated with TyG and its related parameters. (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 Screening Ability of Insulin Resistance-Related Indicators for preMetS and Normal Weight preMetS According to Two Criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe ROC curves depicting the predictive ability of insulin resistance-related parameters and traditional indicators for preMetS risk are presented below. The AUC values for TyG and TG/HDL, along with their related parameters, surpass those of traditional indicators. For the Revised ATP III criteria, TyG's maximum AUC value is 0.731 (95% CI: 0.718\u0026ndash;0.744), significantly larger than the AUC values of other indicators (P_delong\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The optimal cutoff value for TyG, based on a Youden index of 0.352 (sensitivity\u0026thinsp;=\u0026thinsp;0.867, specificity\u0026thinsp;=\u0026thinsp;0.485), is determined to be 7.736. In the case of the JCDCG criteria, TyG's maximum AUC value is 0.724 (95% CI: 0.712\u0026ndash;0.737). Based on a Youden index of 0.340 (sensitivity\u0026thinsp;=\u0026thinsp;0.844, specificity\u0026thinsp;=\u0026thinsp;0.496), the optimal cutoff value for TyG is 7.739. However, the AUC value for WHtR is 0.712 (95% CI: 0.698\u0026ndash;0.725), with no significant difference in AUC compared to TyG (P_delong\u0026thinsp;=\u0026thinsp;0.145). The best cutoff value for WHtR, based on a Youden index of 0.327 (sensitivity\u0026thinsp;=\u0026thinsp;0.768, specificity\u0026thinsp;=\u0026thinsp;0.559), is determined to be 0.503 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe screening abilities of insulin resistance-related indicators for normal weight preMetS according to the two criteria are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC Analysis of Insulin Resistance Indices for Screening Different Criteria of preMetS\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden\u0026rsquo;s index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.731(0.718\u0026ndash;0.744)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.722(0.709\u0026ndash;0.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.023\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\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.705(0.692\u0026ndash;0.719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.662(0.648\u0026ndash;0.677)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.601(0.586\u0026ndash;0.617)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.682(0.668\u0026ndash;0.696)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.724(0.712\u0026ndash;0.737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJCDCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.694(0.680\u0026ndash;0.707)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.712(0.698\u0026ndash;0.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.145\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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.655(0.641\u0026ndash;0.670)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.615(0.600\u0026ndash;0.630)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.686(0.673-0.700)\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes:\u003csup\u003e*\u003c/sup\u003eindicates the P value from the DeLong test comparing the AUC with that of TyG. TyG: Triglyceride-glucose index, TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio, WHtR: Waist-to-height ratio, BMI: Body mass index, ABSI: A body shape index, WC: Waist circumference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC Analysis of Insulin Resistance Indices for Screening Normal Weight preMetS Under Different Criteria\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden\u0026rsquo;s index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRevised ATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.727(0.711\u0026ndash;0.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.718(0.702\u0026ndash;0.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.084\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\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.643(0.625\u0026ndash;0.661)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.589(0.570\u0026ndash;0.608)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.600(0.581\u0026ndash;0.618)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.612(0.593\u0026ndash;0.630)\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\u003eJCDCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.724(0.712\u0026ndash;0.737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\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\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.694(0.680\u0026ndash;0.707)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.712(0.698\u0026ndash;0.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.145\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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.655(0.641\u0026ndash;0.670)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.615(0.600\u0026ndash;0.630)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.686(0.673-0.700)\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes:\u003csup\u003e*\u003c/sup\u003eindicates the P values from the DeLong test comparing the AUCs of the column charts for TyG and age. TyG: Triglyceride-glucose index, TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio, WHtR: Waist-to-height ratio, BMI: Body mass index, ABSI: A body shape index, WC: Waist circumference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Screening Ability of an Ensemble Column Chart Model of Insulin Resistance-Related Indicators for preMetS\u003c/h2\u003e \u003cp\u003eAfter variable selection through univariate and multivariate logistic regression, and excluding less influential variables, an ensemble column chart model was established. Calibration curves, ROC curves, and column charts for the ensemble column chart model were plotted. In both criteria, the diagnostic ability of the TG/HDL and TyG ensemble column chart models surpassed that of all insulin resistance indicators. For the Revised ATP III criteria, the AUCs (95% CI) of the TG/HDL and TyG ensemble column chart models were 0.766 (0.754\u0026ndash;0.779) and 0.771 (0.759\u0026ndash;0.784), respectively, with comparable screening abilities (P_delong\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, for the JCDCG criteria, the AUCs (95% CI) of the TG/HDL and TyG ensemble column chart models were 0.766 (0.754\u0026ndash;0.778) and 0.771 (0.759\u0026ndash;0.784), respectively. Yet, in the Revised ATP III criteria, the TG/HDL ensemble column chart model demonstrated a better fit, with R2 (TG/HDL)\u0026thinsp;=\u0026thinsp;0.267\u0026thinsp;\u0026gt;\u0026thinsp;R2 (TYG)\u0026thinsp;=\u0026thinsp;0.239, and smaller errors, MAE (TG/HDL)\u0026thinsp;=\u0026thinsp;0.005\u0026thinsp;\u0026gt;\u0026thinsp;MAE (TYG)\u0026thinsp;=\u0026thinsp;0.025. Consistently, in the JCDCG criteria, MAE (TG/HDL)\u0026thinsp;=\u0026thinsp;0.006\u0026thinsp;\u0026gt;\u0026thinsp;MAE (TYG)\u0026thinsp;=\u0026thinsp;0.015 (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC Analysis of Insulin Resistance Index Ensemble Column Chart Models for Screening Different Criteria of preMetS\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYG\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.766(0.754\u0026ndash;0.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.90E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATP III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG/HDL\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.771(0.759\u0026ndash;0.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.00E-05\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\u003eWHtR\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.730(0.716\u0026ndash;0.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.10E-04\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\u003eBMI\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.719(0.705\u0026ndash;0.733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40E-04\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\u003eABSI\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.664(0.649\u0026ndash;0.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00E-05\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\u003eWC\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.724(0.710\u0026ndash;0.737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.20E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJCDCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYG\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.771(0.759\u0026ndash;0.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.50E-04\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\u003eTG/HDL\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.766(0.754\u0026ndash;0.778)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.00E-05\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\u003eWHtR\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.747(0.734\u0026ndash;0.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.00E-05\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\u003eBMI\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731(0.717\u0026ndash;0.744)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.00E-05\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\u003eABSI\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688(0.674\u0026ndash;0.702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00E-05\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\u003eWC\u0026thinsp;+\u0026thinsp;age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.741(0.728\u0026ndash;0.754)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.70E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes:\u003csup\u003e*\u003c/sup\u003eindicates the P values from the DeLong test comparing the AUCs of the column charts for TyG and age. TyG: Triglyceride-glucose index, TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio, WHtR: Waist-to-height ratio, BMI: Body mass index, ABSI: A body shape index, WC: Waist circumference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study compared the prevalence and consistency of preMetS among three commonly used definitions of MetS, including the revised ATP III\u003csup\u003e11\u003c/sup\u003e, IDF\u003csup\u003e36\u003c/sup\u003e, and JCDCG definitions\u003csup\u003e37\u003c/sup\u003e. According to these definitions, the prevalence of preMetS in the same study population in Fuzhou City ranged from 10.63\u0026ndash;49.68%. Regardless of gender, the consistency of JCDCG with the revised ATP III for MetS and preMetS was superior to the IDF standard, and it was more consistent in males but relatively poorer in females.The study also compared the diagnostic capabilities of six insulin resistance-related indices, traditional indicators, and an ensemble column chart model combined with age for preMetS. The results indicated that TYG had the best discriminatory ability for preMetS (revised ATP III) (P\u003csub\u003edelong\u003c/sub\u003e \u0026lt; 0.001, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, TYG and WHtR had similar discriminatory abilities for preMetS (JCDCG) (P\u003csub\u003edelong\u003c/sub\u003e \u0026lt; 0.001, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and this was also observed in the normal-weight population (P\u003csub\u003edelong\u003c/sub\u003e \u0026gt; 0.05, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Including age and related indicators in the ensemble column chart model, it was found that the TG/HDL ensemble column chart model had a better fit for preMetS than TYG (R\u003csup\u003e2\u003c/sup\u003e (TG/HDL)\u0026thinsp;\u0026gt;\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e (TYG), Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUntil now, the concept of PreMetS (Pre-Metabolic Syndrome) has been sparingly employed in studies on disease prevalence, especially in-depth investigations into the varied risks associated with preMetS. In prior studies, preMetS prevalence ranged from 12.1%\u003csup\u003e14\u003c/sup\u003e to 24%\u003csup\u003e38\u003c/sup\u003e, whereas in our study, it reached a staggering 50%. The primary reason for this contrast may lie in the exclusion of individuals with diabetes and cardiovascular diseases in those earlier studies. However, given that these studies were conducted a decade ago and considering the current ominous surge in chronic diseases, further research is imperative, particularly regarding preMetS as a low-risk yet highly prevalent group.Earlier research suggests a noteworthy increase in baseline activated platelets among preMetS individuals, and this baseline platelet activation is positively correlated with hyperlipidemia and liver damage\u003csup\u003e39\u003c/sup\u003e. Furthermore, in chronic hepatitis B patients with concurrent preMetS, there is a delayed clearance of hepatitis B e antigen (HBeAg), and there is a heightened risk of advanced liver fibrosis\u003csup\u003e40\u003c/sup\u003e. Studies also indicate that preMetS elevates the risk of atherosclerotic cardiovascular diseases and ischemic heart disease by 1.5\u0026ndash;2.3 times in both genders\u003csup\u003e41\u003c/sup\u003e. This is attributed to lower antioxidant defense levels in PreMetS patients, where adiponectin plays a crucial role in regulating the adipose-vascular axis\u003csup\u003e42\u003c/sup\u003e, thereby increasing the risk of insulin resistance and complications associated with atherosclerosis\u003csup\u003e43\u003c/sup\u003e. Additionally, compared to the normal population, individuals with preMetS exhibit early axonal loss in brain white matter, thereby increasing the risk of dementia\u003csup\u003e44\u003c/sup\u003e. Given the lack of clear thresholds in defining Metabolic Syndrome (MetS), insulin resistance appears to be the most important and directly reflective indicator; hence, further research is deemed necessary.\u003c/p\u003e \u003cp\u003eParticipants identified under different definitions of Metabolic Syndrome (MetS) exhibit significant variations. The use of different criteria to define preMetS, particularly differences in parameters for increased waist circumference and other pathological conditions (such as abnormal blood glucose and lipid levels), underscores the need for standardized definitions to ensure comparability of results across countries and different time periods\u003csup\u003e45\u0026ndash;47\u003c/sup\u003e. While MetS is considered more prevalent in the elderly population\u003csup\u003e48\u003c/sup\u003e, it has garnered widespread attention as an early deteriorating state before the onset of diseases in younger individuals\u003csup\u003e49\u003c/sup\u003e. Our study specifically aims to include younger populations to understand how different definitions and components manifest in this demographic.Previous research among Chinese adults has explored differences between definitions and conducted parallel studies on MetS using IDF, ATP III, or JCDCG criteria\u003csup\u003e37\u003c/sup\u003e. However, there has been limited analysis of preMetS in further studies. In our research, the prevalence of preMetS defined by IDF was considerably lower than the other two definitions. This difference arises because the preMetS prevalence in IDF corresponds to the non-MetS abdominal obesity prevalence. Therefore, ATP III and JCDCG may be more suitable for the Chinese population. The primary distinction between ATP III and JCDCG lies in the relatively low overlap in rates of abnormal blood glucose and low HDL-c prevalence.\u003c/p\u003e \u003cp\u003eThis study, for the first time, explores and compares four obesity indicators\u0026mdash;general obesity (BMI), abdominal obesity (WC, WHtR), and visceral obesity (ABSI)\u0026mdash;as well as two insulin resistance indicators (TyG and TG/HDL) in terms of their screening capabilities for preMetS. After adjusting for confounding factors, all these indicators show a significant association with preMetS. Our research indicates that the TyG index demonstrates the strongest identification ability for preMetS, while age significantly enhances the diagnostic capability of TG/HDL. After adjusting for confounding factors, both TyG and TG/HDL exhibit the strongest correlation with preMetS defined by the two criteria.TyG and TG/HDL serve as indices measuring insulin resistance, suggesting a certain degree of insulin resistance in preMetS\u003csup\u003e50\u003c/sup\u003e. It is noteworthy that previous research\u003csup\u003e51\u003c/sup\u003e suggests high blood glucose as a major risk factor for preMetS and a primary contributor to the progression of metabolic impairment. Therefore, patients with higher blood glucose levels are more likely to be identified.In the changing trends within the three MetS categories, PreMetS is characterized as a state of aseptic inflammation, possibly involving insulin resistance, activation of inflammatory signaling pathways, abnormal cytokine production, and increased acute-phase response\u003csup\u003e52\u003c/sup\u003e. Compared to the normal population, preMetS individuals exhibit higher white blood cell counts, C-reactive protein levels, and interleukin-6 concentrations. Moreover, these inflammatory markers become imbalanced earlier than the manifestation of MetS, indicating a more pronounced change in the preMetS stage\u003csup\u003e53\u003c/sup\u003e. Hence, it is necessary to identify preMetS patients to guide further anti-inflammatory treatments and lifestyle interventions\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll the indicators used in this study are easy to obtain, providing benefits in large-scale community screenings by requiring only two or three indicators compared to the complexity of acquiring five indicators. Firstly, the multifaceted diagnosis of preMetS and MetS, due to the necessity of intricate physical examinations and clinical metrics\u003csup\u003e55\u003c/sup\u003e, is challenging to widely implement in community screenings. Secondly, the use of different instruments for the complex assessment of five risk factors can lead to significant measurement errors and inefficiencies\u003csup\u003e56, 57\u003c/sup\u003e, especially in large-scale screening projects. Additionally, we found that WHtR may be a crucial non-invasive screening indicator for preMetS (JCDCG), with its identification ability showing no significant difference compared to TYG (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, in primary healthcare clinical practices, TYG and WHtR respectively serve as simple, cost-effective, and efficient indicators for screening preMetS according to revised ATP and JCDCG standards.\u003c/p\u003e \u003cp\u003eOur study also separately assessed adults with normal weight in the Fuzhou area. The cardiovascular risk increases in individuals with normal weight but with metabolic risks, emphasizing the importance of metabolic risk screening in this population\u003csup\u003e58\u003c/sup\u003e. Additionally, due to having a normal BMI, individuals often overlook their metabolic risks. Therefore, it is necessary to conduct metabolic risk screening for those with a normal weight.Previous research\u003csup\u003e59\u003c/sup\u003e indicates that WHtR is one of the cost-effective and non-invasive screening indicators for Metabolic Syndrome (MetS) in non-obese individuals, aligning with the findings in our study where WHtR emerged as the optimal indicator for preMetS screening based on JCDCG criteria. The consistent screening ability of preMetS in both the general population and individuals with normal weight suggests that obesity is not synonymous with preMetS, and the metabolic benign obesity associated with preMetS may be attributed to insulin resistance.\u003c/p\u003e"},{"header":"5. Clinical and public health potential","content":"\u003cp\u003eFirstly, in the three classifications of MetS, the IDF criteria are deemed inappropriate, as they overly emphasize abdominal obesity and overlook individuals with unhealthy metabolism. Secondly, following MetS screening, the remaining \"healthy\" population can still be differentiated using the retained screening indicators, thereby further enhancing the utility of these indicators. Lastly, we have found that WHtR may be the only non-invasive and effective indicator for preMetS (JCDCG) screening.\u003c/p\u003e"},{"header":"6. Strengths and Limitations","content":"\u003cp\u003eThe strength of this study lies in including a sufficient number of samples and incorporated certain indicators for MetS screening, further exploring the additional effects of these indicators. However, the study has some limitations. Firstly, although the AUC in our study was greater than 0.7, it did not exceed 0.8, indicating that these screening indicators are considered good but not excellent. Secondly, we observed significant numerical changes in age in the column chart, while variables related to gender and lifestyle habits showed smaller variations. However, it remains unknown whether these numerical changes might be influenced by different ethnicities or regions. Thirdly, the participants in our study were all residents of coastal areas in China, and the results may not be suitable for extrapolation to other countries or inland regions. Further prospective cohort studies with larger sample sizes and more detailed data are needed to assess the identification value of each indicator in different populations.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThe consistency between the revised ATP III and JCDCG in MetS tri-classification is good. TyG has the best identification ability for preMetS (revised ATP III and JCDCG). Additionally, WHtR has equally good identification ability for preMetS (JCDCG). The nomogram model with TG/HDL has the best identification ability. In conclusion, the consistency of MetS tri-classification is better in the revised ATP III and JCDCG. TyG is an effective indicator for identifying preMetS in adults in Southeast China. WHtR is a non-invasive indicator for screening preMetS (JCDCG). The diagnostic capabilities are improved with the inclusion of age and TG/HDL in the nomogram model, with less error.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eIDF, International Diabetes Federation;Revised ATP III, Adult Treatment Panel III of the National Cholesterol Education Program, Revised;JCDCG, Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia.SBP, Systolic Blood Pressure;DBP, Diastolic Blood Pressure;FBG, Fasting Blood Glucose;PBG, Postprandial blood Glucose; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; BUN, Blood Urea Nitrogen; TYG: Triglyceride-glucose index; TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol ratio; WHtR: Waist-to-height ratio; BMI: Body mass index; ABSI: A body shape index; WC: Waist circumference. AUC: area under the curve.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and methodology, Xiaoyang Zhang and Youqiong Xu; writing\u0026mdash;original draft preparation, Xinfeng Huang and Qingquan Chen; writing\u0026mdash;review and editing, Xinxin Yang, Haiping Hu, Hong Li and Ruoming Huang; validation, Yuanyu She and Huanhuan Shi; formal analysis, Xiangyu Cao. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Fuzhou Center for Disease Control and Prevention (approval number: 2022002). Informed consent was obtained from all participants and/or their legal guardians for this study. There is no conflict of interest in this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Fuzhou Science and Technology Program (No. 2022-S-032).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNiigaki M, Adachi K, Hirakawa K, Furuta K, Kinoshita Y. 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Front Endocrinol (Lausanne). 2023;14:1223424. doi:10.3389/fendo.2023.1223424\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Insulin Resistance-Related indicators, preMetabolic syndrome, Normal-weight, Screening","lastPublishedDoi":"10.21203/rs.3.rs-3909069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3909069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolic syndrome (MetS) is a risk factor for cardiovascular diseases and cancer, and its pre-stage is as well. The incidence of MetS is increasing annually, but currently, there is no unified diagnostic criterion, and the diagnostic conditions are complex, posing challenges for primary healthcare professionals. Insulin resistance indicators are widely used for MetS screening, but there is limited research on their discriminatory ability for preMetS.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo assess the prevalence of preMetS in adults in Southeast China and the differences among three MetS standards. Additionally, to compare the differences in the correlation and diagnostic value of six insulin resistance indicators with preMetS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 9,399 individuals participating in health examinations in five communities in Fuzhou City were selected for questionnaire surveys, physical examinations, and laboratory tests. Binary logistic regression was used to analyze the correlation between each indicator and preMetS, and a restricted cubic spline model was used to analyze the dose-response relationship between the two. The diagnostic abilities of each indicator were compared using the area under the receiver operating characteristic curve. A nomogram model combining various indicators and age was established to improve and reassess diagnostic capabilities.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall prevalence of preMetS ranged from 10.63\u0026ndash;49.68%. Regardless of gender, the kappa values between the revised ATP III and JCDCG ranged from 0.700 to 0.820, while those with IDF ranged from 0.316 to 0.377. In the ATP and JCDCG standards, the TyG index was the best screening indicator, with maximum AUC values of 0.731 (95% CI: 0.718\u0026ndash;0.744) and 0.724 (95% CI: 0.712\u0026ndash;0.737), and optimal cutoff values of 7.736 and 7.739, respectively. Additionally, WHtR showed consistent performance with TyG in the JCDCG standard, with AUC and cutoff values of (95% CI: 0.698\u0026ndash;0.725) and 0.503. In the normal weight population, in the revised ATP III, there was no significant difference in screening abilities between TG/HDL and TyG. The nomogram model combining age with TG/HDL or TyG showed better screening abilities for preMetS compared to other indicators, but the model with age and TG/HDL had a better fit.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe consistency between the revised ATP III and JCDCG in MetS tri-classification is good. TyG has the best identification ability for preMetS (revised ATP III and JCDCG). Additionally, WHtR has equally good identification ability for preMetS (JCDCG). The nomogram model with TG/HDL has the best identification ability. In conclusion, the consistency of MetS tri-classification is better in the revised ATP III and JCDCG. TyG is an effective indicator for identifying preMetS in adults in Southeast China. WHtR is a non-invasive indicator for screening preMetS (JCDCG). The diagnostic capabilities are improved with the inclusion of age and TG/HDL in the nomogram model, with less error.\u003c/p\u003e","manuscriptTitle":"Comparison of the Incidence and Diagnostic Value of Insulin Resistance Indicators in the Prevalence of Metabolic Syndrome in Southeast China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-09 17:03:11","doi":"10.21203/rs.3.rs-3909069/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"841747b1-4613-41cc-b0e2-c67e787922bd","owner":[],"postedDate":"February 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-28T11:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-09 17:03:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3909069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3909069","identity":"rs-3909069","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}