Prediction of insulin resistance and non-alcoholic fatty liver disease using serum uric acid and related markers in children and adolescents

Prediction of insulin resistance and non-alcoholic fatty liver disease using serum uric acid and related markers in children and adolescents | 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 Prediction of insulin resistance and non-alcoholic fatty liver disease using serum uric acid and related markers in children and adolescents Kyungchul Song, Youngha Choi, Hyejin Yang, Soyoung Jeon, Kyoung Won Cho, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3890639/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jul, 2024 Read the published version in European Journal of Clinical Nutrition → Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the relationship between serum uric acid (Uacid) and related parameters with insulin resistance (IR) and non-alcoholic fatty liver disease (NAFLD)and their potential as predictors of IR and NAFLD in children and adolescents Methods The data of 1,648 participants aged 10–18 years was analyzed using nationwide survey. Logistic regression analysis was performed with IR and NAFLD as dependent variables, and odds ratios (ORs) and 95% confidence intervals (CIs) were computed for tertiles 2 and 3 of each parameter in comparison to tertile 1, which served as the reference group. Receiver operating characteristic (ROC) curves were generated to assess predictability of the parameters for IR and NAFLD. Results Hyperuricemia, IR, and NAFLD were significantly associated each other. All Uacid and related markers showed continuous increase in ORs and 95% CIs across the tertiles for IR and NAFLD (all p < 0.001). In ROC curve, all Uacid and related markers demonstrated significant predictability for IR and NAFLD. Overall, Uacid combined with obesity indices showed higher ORs and AUC compared to Uacid alone. Uacid-body mass index (BMI) standard deviation score presented the largest AUC for IR. For NAFLD, Uacid-BMI and Uacid-waist-to-height ratio showed the largest AUC. Conclusions Uacid combined with obesity indices are robust markers for prediction of IR and NAFLD in children and adolescents, which was superior to Uacid. Uacid and related markers have potential as simple markers which does not require fasting for screening of IR and NAFLD in children and adolescents Health sciences/Endocrinology/Endocrine system and metabolic diseases/Metabolic syndrome Health sciences/Diseases/Endocrine system and metabolic diseases Health sciences/Risk factors Figures Figure 1 Figure 2 Introduction Non-alcoholic fatty liver disease (NAFLD) is a chronic liver condition characterized by the accumulation of excessive fat in the liver, often accompanied by elevated hepatic enzyme levels [ 1 ]. It is recognized as a significant contributor to liver fibrosis, advanced liver disease, and is closely linked to various cardio-metabolic risk factors such as obesity, dyslipidemia, and insulin resistance (IR). A global meta-analysis showed that the prevalence of pediatric NAFLD increased from 4.62% in 2000 to 9.02% in 2017 [ 2 ]. For screening of pediatric NAFLD, although alanine aminotransferase (ALT) is suggested, but it has limitation due to sensitivity and specificity [ 1 ]. Although various biomarkers for NAFLD are used in adults, investigations on biomarkers for pediatrics NAFLD is limited [ 3 ]. IR has key role in the development of metabolic diseases including NAFLD [ 4 ]. IR leads to increase in serum level of insulin, glucose, and fatty acids, which in turn promote the accumulation of fatty acids and triglycerides in the liver [ 1 ]. The glucose clamp technique, a classical method for measuring IR, is highly invasive, involving intravenous catheters and continuous monitoring, making it uncomfortable for patients and impractical for large-scale studies [ 5 ]. Its complexity and cost also restrict its widespread application in clinical practice and research. Therefore, an alternative approach, the homeostasis model assessment of IR (HOMA-IR) index, has been introduced as a reliable method for quantifying IR [ 6 , 7 ]. However, HOMA-IR relies on fasting blood samples which can be clinically relevant for some individuals. Moreover, it's important to note that there is no standardized protocol for measuring insulin levels, and such measurements are not routinely conducted in children. Therefore, investigations on development of simple markers for IR are required. Meanwhile, uric acid (Uacid) has emerged as a potentially valuable biomarker for predicting and understanding metabolic conditions including NAFLD in adults, which does not require fasting for test [ 8 , 9 ]. This connection is stem from Uacid's role in promoting IR, a central factor in the pathogenesis of NAFLD, as well as its correlation with other metabolic risk factors such as obesity and dyslipidemia [ 8 ]. A systematic review reported that odds ratio (OR) of NAFLD was 1.92 times higher when comparing individuals with the highest serum Uacid levels to those with the lowest levels [ 9 ]. Moreover, a population-based study reported that serum Uacid level is increasing recently among Korean children and adolescents. However, research exploring the link between Uacid levels, IR, and NAFLD in children and adolescents is currently limited. Therefore, we aimed to investigate the association of Uacid and related markers with IR and NAFLD in the youth by analyzing data from the Korea National Health and Nutrition Examination Survey (KNHANES). Our objectives were to: 1) examine the association between hyperuricemia and IR and NAFLD, 2) compare Uacid and related markers for prediction of IR and NAFLD, 3) establish optimal cutoff value of Uacid and related marker for prediction of IR and NAFLD. Methods We retrospectively evaluated data from 1,648 children and adolescents aged 10–18 years who participated in the KNHANES conducted between 2019 and 2021. Figure 1 illustrates the study design and workflow. KNHANES is a nationally representative survey carried out in Korea, employing a complex, stratified, multistage probability sampling method to select participants from the entire population. The survey is administered by the Korea Centers for Disease Control and Prevention and encompasses health surveys, medical examinations, and nutrition assessments. These datasets offer a wide range of insights into individuals' health status, behaviors, socio-economic characteristics, and laboratory test results. To ensure accuracy, sample weights were applied to address variations in selection probabilities and non-response rates, and these weighted data were subsequently adjusted to accurately reflect the demographics of the Korean population by sex and age groups [ 10 ]. KNHANES is approved by the Korea Disease Control and Prevention Agency. Participants' weights were determined with a scale (Giant 150N, HANA, Seoul, South Korea) accurate to the nearest 0.1 kg, while their heights and waist circumferences (WC) were measured using a stadiometer (range: 850–2060 mm; Seriter, Holtain Ltd., Crymych, UK) with precision to the nearest 0.1 cm. Body Mass Index (BMI) was computed as the weight in kilograms divided by the square of the height in meters. The height, weight, and BMI were then expressed as standard deviation score (SDS) values, referencing the 2017 Korean National Growth Charts [ 11 ]. Children were categorized based on their BMI into three groups: those with a BMI < 85th percentile were considered normal weight, those with a BMI ranging from the 85th to the 95th percentile were classified as overweight, and those with a BMI ≥ 95th percentile were considered obese. The measurement of WC was taken at the midpoint between the costal margin and iliac crest during a normal exhalation. Waist-to-height ratio (WHtR) was calculated by dividing the waist circumference (cm) by the height (cm). Central obesity was defined as having a waist circumference exceeding the 90th percentile based on Korean waist reference standards [ 12 ]. Blood samples were obtained from the antecubital vein after an overnight fast of 8 hours. These samples were then processed and promptly stored in a refrigerator. The levels of aspartate aminotransferase and ALT in the serum were determined using commercially available kits (Pureauto S ALT, Daiichi Pure Chemicals, Tokyo, Japan) without employing the pyridoxal-5-phosphate method, relying instead on ultraviolet light measurement. Serum insulin levels were assessed using the Wizard 1470 gamma counter (Perkin-Elmer, Turku, Finland). Plasma concentrations of fasting glucose, Uacid, total cholesterol, high-density lipoprotein cholesterol (HDL), and triglycerides were measured using the Hitachi Automatic Analyzer 7600/7600 − 210 (Hitachi, Tokyo, Japan). Serum creatinine (Cr) was measured using Cobas c702 (Roche Diagnostics, Mannheim, Germany). Low-density lipoprotein cholesterol (LDL) concentrations were determined using the Friedewald formula, which is computed as LDL equals the total cholesterol minus the sum of HDL-C and triglycerides divided by five (LDL = total cholesterol − [HDL + (triglycerides/5)] [ 13 ]. Non-HDL levels were calculated by subtracting HDL from the total cholesterol. The HOMA-IR was calculated as fasting insulin (mg/dL) multiplied by fasting glucose (mg/dL) and then divided by 22.5, and IR was defined as having a HOMA-IR value exceeding the 95th percentile for each gender and age group, as determined by Korean HOMA-IR reference data. [ 14 ] NAFLD was defined as having ALT levels higher than 26 IU/L for males and greater than 22 IU/L for females, provided there was no concurrent hepatitis B viral infection [ 15 , 16 ]. Hyperuricemia was defined as higher uric acid levels based on age-specific reference value [ 17 ]. Uacid related markers were defined and calculated as follows: Uacid divided by Cr (Uacid/Cr), Uacid divided by HDL (Uacid/HDL), Uacid × BMI (Uacid-BMI), Uacid × BMI SDS (Uacid-BMI SDS), Uacid × WC (Uacid-WC), Uacid × WHtR (Uacid-WHtR) [ 18 – 20 ]. All categorical variables are presented as numbers and weighted percentages, and continuous variables are presented as weighted means and standard errors. Student’s t -test was used to compare the mean values of the continuous variables. The Rao-Scott-chi-square test was used to compare categorical variables. Logistic regression analyses were performed with IR and NAFLD as dependent variables to investigate relationship among obesity, cental obesity, hyperuricemia, IR, and NAFLD. ORs and their corresponding 95% confidence intervals (CIs) were calculated for tertiles 2 and 3 of each parameter, with tertile 1 serving as the reference point for comparison. Sensitivity and specificity were determined as the optimal cutoff values for the markers, employing Youden's index. Receiver operating characteristic (ROC) curves were generated to assess and compare the relative diagnostic effectiveness of these parameters in identifying IR and NAFLD. Pairwise comparisons of the parameters' area under the curve (AUC) values were carried out using the bootstrap method. Statistical significance was established at a p-value of less than 0.05. All statistical analyses were conducted using SAS version 9.4 (SAS Inc., Cary, NC) and R version 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria), accounting for the complex survey design with clustering, stratification, and unequal weighting of the KNHANES sample. Results Table 1 presents a comparison of the clinical characteristic of the participants with or without IR and NAFLD. Obesity indicators including BMI, BMI SDS, WC, and WHtR and serum level of total cholesterol, LDL, non-HDL, triglycerides, glucose, insulin, HOMA-IR, AST, and ALT and proportion of participants with hyperuricemia were higher in the IR and NAFLD groups compared to those in the non-IR and non- NAFLD groups, respectively (total cholesterol and glucose in NAFLD/non-NAFLD, p = 0.002 and p = 0.011, respectively; for other comparisons, p < 0.001). In contrast, HDL level was lower in in the IR and NAFLD groups compared to those in the non-IR and non-NAFLD groups (all p < 0.001). Overall, the IR and NAFLD groups had higher values of Uacid and Uacid related markers than those in the non-IR and non-NAFLD groups (all p < 0.001). Table 1 Baseline characteristics of participants with respect to IR and NAFLD Variables Total (n = 1,648) IR (n = 381) non-IR (n = 1,267) *p value NAFLD (n = 251) non-NAFLD (n = 1,397) *p value Age, year 14.13 (0.07) 13.78 (0.17) 14.23 (0.08) 0.018 14.61 (0.19) 14.05 (0.08) 0.008 Sex, female, % 47.28 (1.36) 36.12 (2.78) 50.54 (1.55) < 0.001 26.19 (3.06) 51.00 (1.47) < 0.001 Height SDS 0.35 (0.03) 0.58 (0.06) 0.28 (0.04) < 0.001 0.47 (0.10) 0.33 (0.04) 0.182 Weight SDS 0.31 (0.04) 1.55 (0.08) -0.05 (0.04) < 0.001 1.49 (0.10) 0.10 (0.04) < 0.001 BMI, kg/m 2 21.39(0.15) 25.89(0.38) 20.60(0.14) < 0.001 20.17(0.11) 25.57(0.34) < 0.001 BMI SDS 0.18 (0.05) 1.60 (0.10) -0.24 (0.04) < 0.001 1.57 (0.12) -0.07 (0.05) < 0.001 BMI percentile, < 0.001 < 0.001 Normal, % 74.48 (1.47) 33.77 (3.12) 86.37 (1.06) 35.74 (3.56) 81.30 (1.33) Overweight, % 9.25 (0.79) 17.41 (2.13) 6.86 (0.79) 14.48 (2.46) 8.33 (0.76) Obesity, % 16.27 (1.24) 48.82 (3.25) 6.77 (0.80) 49.78 (3.77) 10.37 (1.11) WC, cm 72.26 (0.39) 83.26 (0.86) 69.04 (0.31) < 0.001 84.99 (0.93) 70.02 (0.35) < 0.001 Central obesity, % 22.14 (1.35) 62.25 (3.15) 10.42 (0.93) < 0.001 60.15 (3.67) 15.45 (1.22) < 0.001 WHtR 0.45 (0.00) 0.51 (0.00) 0.43 (0.00) < 0.001 0.51 (0.01) 0.43 (0.00) < 0.001 Total cholesterol, mg/dL 163.69 (0.86) 170.04 (1.74) 161.83 (0.91) < 0.001 170.71 (2.51) 162.45 (0.89) 0.002 LDL, mg/dL 94.86 (0.74) 101.08 (1.61) 93.05 (0.77) < 0.001 103.03 (2.12) 93.43 (0.76) < 0.001 HDL, mg/dL 51.95 (0.32) 46.88 (0.60) 53.43 (0.34) < 0.001 47.44 (0.66) 52.74 (0.35) < 0.001 non-HDL, mg/dL 111.74 (0.83) 123.16 (1.68) 108.41 (0.83) < 0.001 123.26 (2.33) 109.71 (0.84) < 0.001 Cr, mg/dL 0.67 (0.00) 0.66 (0.01) 0.67 (0.01) 0.280 0.71 (0.01) 0.66 (0.01) < 0.001 Triglycerides, mg/dL 87.77 (1.60) 120.50 (3.83) 78.21 (1.40) < 0.001 109.63 (4.46) 83.93 (1.59) < 0.001 Glucose, mg/dL 92.09 (0.22) 96.26 (0.45) 90.87 (0.23) < 0.001 93.43 (0.57) 91.85 (0.24) 0.011 Insulin, µIU/mL 15.37 (0.39) 31.05 (1.21) 10.79 (0.14) < 0.001 25.00 (1.59) 13.68 (0.31) < 0.001 HOMA-IR 3.57 (0.10) 7.42 (0.31) 2.44 (0.03) < 0.001 5.88 (0.39) 3.16 (0.08) < 0.001 IR†, % 22.61 (1.35) 51.14 (3.57) 17.59 (1.31) < 0.001 AST, mg/dL 21.53 (0.30) 24.47 (0.73) 20.68 (0.30) < 0.001 34.03 (1.38) 19.33 (0.17) < 0.001 ALT, mg/dL 17.93 (0.51) 28.96 (1.58) 14.71 (0.37) < 0.001 48.39 (2.08) 12.56 (0.15) < 0.001 NAFLD, % 14.97 (1.05) 33.86 (2.70) 9.45 (0.98) < 0.001 Uacid, mg/dL 5.49 (0.05) 6.15 (0.09) 5.30 (0.05) < 0.001 6.56 (0.11) 5.30 (0.04) < 0.001 Hyperuricemia, % 14.79 (1.18) 33.48 (2.89) 9.35 (1.06) < 0.001 35.15 (3.65) 11.21 (1.15) < 0.001 Uacid/Cr 8.43 (0.07) 9.63 (0.15) 8.08 (0.07) < 0.001 9.54 (0.16) 8.24 (0.07) < 0.001 Uacid/HDL 0.11 (0.00) 0.14 (0.00) 0.10 (0.00) < 0.001 0.14 (0.00) 0.11 (0.00) < 0.001 Uacid-BMI 120.45 (1.73) 161.27 (4.34) 108.58 (1.36) < 0.001 173.17 (4.31) 111.18 (1.57) < 0.001 Uacid-BMI SDS 1.81 (0.32) 10.84 (0.78) -0.82 (0.21) < 0.001 11.17 (0.84) 0.16 (0.29) < 0.001 Uacid-WC 405.74 (5.30) 523.72 (12.75) 371.41 (4.52) < 0.001 567.27 (12.99) 377.32 (4.83) < 0.001 Uacid-WHtR 2.48 (0.03) 3.18 (0.07) 2.28 (0.02) < 0.001 3.40 (0.07) 2.32 (0.03) < 0.001 Note: Values are presented as mean (standard error), and categorical data as percentages (standard error). * P value is assessed using Student’s t-test and Rao-scott-Chi-square test. †IR was defined as the HOMA-IR of above the 95th percentile for each gender and age using Korean HOMA-IR reference data. Abbreviations: IR, insulin resistance; NAFLD, non-alcoholic fatty liver disease; SDS, standard deviation score; BMI, body mass index; WC, waist circumference; 90p, 90th percentile; WHtR, waist-to-height ratio; TC , total cholesterol; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol; Cr, creatinine; HOMA-IR, homeostasis model assessment of insulin resistance; AST, aspartate aminotransferase; ALT, alanine aminotransferase; Uacid , uric acid. In logistic regression analyses with IR and NAFLD as dependent variables, ORs of generalized and abdominal obesity for IR were 4.38 (95% CI, 2.23–8.63; p < 0.001) and 3.16 (95% CI, 1.70–5.86; p < 0.001), and the corresponding values for NAFLD were 4.00 (95% CI, 2.01–7.94; p < 0.01) and 1.75 (95% CI, 0.95–3.22; p 0.073), respectively (Table 2 ). The hyperuricemia group had OR value of 1.91 (95% CI, 1.33–2.76; p < 0.01) for IR and 1.81 (95% CI, 1.04–3.17; p 0.037) for NAFLD. In addition, OR of IR for NAFLD was 1.63 (95% CI, 1.13–2.34; p = 0.009) Table 2 Odds ratio of IR and NAFLD according to each parameter IR NAFLD OR (95% Cl) p value* OR (95% Cl) p value* BMI percentile Normal Reference Reference Overweight 2.90 (1.69–4.97) 0.13 2.17 (1.20–3.89) 0.729 Obesity 4.38 (2.23–8.63) < 0.001 4.00 (2.01–7.94) < 0.001 Central Obesity 3.16 (1.70–5.86) < 0.001 1.75 (0.95–3.22) 0.073 Hyperuricemia 1.91 (1.33–2.76) < 0.001 1.81 (1.04–3.17) 0.037 IR 1.63 (1.13–2.34) 0.009 NAFLD 1.65 (1.15–2.36) 0.006 Note: Logistic regression analyses were performed with IR and NAFLD as dependent variables. Abbreviations: IR , insulin resistance; NAFLD , non-alcoholic fatty liver disease; OR , odds ratio; CI , confidence interval.. The ORs and 95% CIs for IR and NAFLD progressively increased across tertiles of each variable including Uacid, Uacid-Cr, Uacid-HDL, Uacid-BMI, Uacid-BMI SDS, Uacid-WC, Uacid-WHtR, total cholesterol, triglycerides, LDL, non-HDL among the total subjects (Table 3 ). Uacid combined with obesity indices exhibited ORs of tertile 3 ranging as 8.17–18.74 compared with those of tertile 1, while the Uacid, Uacid-Cr, and Uacid-HDL exhibited ORs of tertile 3 as 4.39. 4.94, and 5.23 compared with those of tertile 1 for IR in all individuals. For NAFLD, Uacid combined with obesity indices exhibited ORs of tertile 3 ranging as 11.73–19.22 compared with those of tertile 1, while the Uacid, Uacid/Cr, and Uacid/HDL exhibited ORs of tertile 3 as 9.86, 3.83, and 6.95 compared with those of tertile 1 in all individuals. Among the indices, Uacid-BMI SDS presented the highest ORs and 95% CIs for IR in the total subjects (OR, 18.74), and Uacid-WHtR for NAFLD (OR, 19.22), respectively. Overall, the Uacid combined with obesity indices presented higher ORs and 95% CIs than the Uacid alone, Uacid/Cr, Uacid/HDL, lipid parameters for IR and the Uacid alone, Uacid/Cr, Uacid/HDL, lipid profiles, insulin, and HOMA-IR for NAFLD. Table 3 Odds ratio for IR and NAFLD according to tertiles of each parameter IR NAFLD OR (95% Cl) p value* OR (95% Cl) p value* Uacid, mg/dL T2 1.46 (1.00-2.13) 0.013 3.31 (1.94–5.67) 0.787 T3 4.39 (3.04–6.35) < 0.001 9.86 (6.24–15.60) < 0.001 Uacid/Cr T2 2.04 (1.40–2.95) 0.564 1.73 (1.11–2.69) 0.474 T3 4.94 (3.37–7.25) < 0.001 3.83 (2.50–5.88) < 0.001 Uacid/HDL T2 1.52 (1.04–2.20) 0.007 1.65 (1.03–2.67) 0.015 T3 5.23 (3.62–7.55) < 0.001 6.95 (4.64–10.42) < 0.001 Uacid-BMI T2 2.37 (1.56–3.62) 0.153 2.40 (1.36–4.24) 0.040 T3 8.91 (5.94–13.37) < 0.001 13.64 (8.32–22.34) < 0.001 Uacid-BMI SDS T2 2.59 (1.59–4.20) 0.004 1.87 (1.05–3.33) 0.005 T3 18.74 (11.44–30.69) < 0.001 11.73 (7.13–19.30) < 0.001 Uacid-WC T2 2.66 (1.75–4.04) 0.635 3.39 (1.88–6.11) 0.500 T3 8.17 (5.44–12.26) < 0.001 15.11 (8.83–25.87) < 0.001 Uacid-WHtR T2 2.54 (1.67–3.87) 0.167 4.59 (2.55–8.26) 0.824 T3 9.89 (6.54–14.95) < 0.001 19.22 (11.43–32.31) < 0.001 Total cholesterol, mg/dL T2 1.43 (1.03–1.99) 0.867 1.07 (0.70–1.65) 0.184 T3 1.95 (1.43–2.67) < 0.001 1.86 (1.19–2.91) 0.002 Triglycerides, mg/dL T2 2.34 (1.56–3.52) 0.552 1.50 (0.96–2.35) 0.445 T3 6.59 (4.50–9.65) < 0.001 2.93 (1.87–4.58) < 0.001 HDL, mg/dL T2 0.39 (0.28–0.53) 0.189 0.41 (0.28–0.59) 0.096 T3 0.22 (0.16–0.31) < 0.001 0.30 (0.20–0.45) < 0.001 LDL, mg/dL T2 1.37 (0.99–1.90) 0.527 1.76 (1.14–2.72) 0.683 T3 2.23 (1.60–3.10) < 0.001 2.67 (1.73–4.13) < 0.001 non-HDL, mg/dL T2 1.63 (1.15–2.30) 0.399 2.31 (1.48–3.58) 0.349 T3 3.38 (2.42–4.72) < 0.001 3.82 (2.42–6.04) < 0.001 Insulin, µIU/mL T2 2.18 (1.35–3.53) 0.254 T3 7.29 (4.72–11.26) < 0.001 HOMA-IR T2 2.42 (1.45–4.05) 0.679 T3 6.97 (4.49–10.82) < 0.001 Note: ORs and 95% CIs of tertiles 2 and 3 for each parameter were calculated and compared with tertile 1 as a reference. * p value is assessed using logistic regression. Abbreviations: IR , insulin resistance; NAFLD , non-alcoholic fatty liver disease; OR , odds ratio; CI , confidence interval; T , Tertitle; Uacid , uric acid; Cr , creatinine; HDL , high-density lipoprotein cholesterol; BMI , body mass index; WC , waist circumference; WHtR , waist-to-height ratio; TC , total cholesterol; HDL , high-density lipoprotein cholesterol; LDL , low-density lipoprotein cholesterol; HOMA-IR , homeostasis model assessment of insulin resistance Table 4 and Fig. 2 summarize the results of ROC curve analyses and AUCs with the corresponding 95% CIs for Uacid and Uacid related markers to predict IR and NAFLD. All variables predicted IR and NAFLD significantly (all p = 0.001). The cut-off values for IR prediction were 5.95, 9.13, 0.11, 139.89, 3.54, 438.38, and 2.56 for Uacid, Uacid-Cr, Uacid-HDL, Uacid-BMI, Uacid-BMI SDS, Uacid-WC, Uacid-WHtR, respectively. All Uacid combined with obesity indices showed higher AUC compared to Uacid, Uacid/Cr, Uacid/HDL for IR. Uacid-BMI SDS showed the largest AUC for IR detection with 0.837. In addition, Uacid/HDL presented higher AUC compared to Uacid. The cutoff values for NAFLD prediction were 5.55, 9.17, 0.12, 137.88, 3.76, 421.86, 2.49, 15.45, and 3.04 for Uacid, Uacid/Cr, Uacid/HDL, Uacid-BMI, Uacid-BMI SDS, Uacid-WC, Uacid-WHtR, insulin, and HOMA-IR, respectively. All Uacid combined with obesity indices showed higher AUC compared to Uacid, Uacid/Cr, Uacid/HDL, insulin, and HOMA-IR for NAFLD. Uacid-WHtR showed the largest AUC for NAFLD detection with 0.803. Uacid-BMI SDS presented significantly higher AUC values and 95% CIs than all other parameters for IR prediction (all p < 0.001) (Supplementary Table S1). For NAFLD prediction, all Uacid combined with obesity indices presented higher AUC compared to Uacid, Uacid/Cr, Uacid/HDL, insulin, and HOMA-IR. In addition, Uacid-BMI and Uacid- WHtR had highest AUC among the parameters. Table 4 Cutoff values and areas under the receiver operating characteristic curves for each parameter for predicting IR and NAFLD IR NAFLD Variables Cutoff Sensitivity Specificity AUC (95% CI) p -value Cutoff Sensitivity Specificity AUC (95% CI) p -value Uacid, mg/dL 5.95 53.56 (48.54–58.58) 73.95 (71.54–76.37) 0.67 (0.63–0.70) < 0.001 5.55 73.60 (68.14–79.06) 62.03 (59.49–64.58) 0.73 (0.70–0.77) < 0.001 Uacid/Cr 9.13 57.78 (52.81–62.76) 71.59 (69.10-74.07) 0.69 (0.66–0.72) < 0.001 9.17 56.00 (49.85–62.15) 69.13 (66.70-71.55) 0.66 (0.63–0.70) < 0.001 Uacid/HDL 0.11 72.03 (67.51–76.55) 63.30 (60.65–65.95) 0.71 (0.68–0.74) < 0.001 0.12 65.60 (59.71–71.49) 72.85 (70.52–75.18) 0.74 (0.71–0.77) < 0.001 Uacid-BMI 139.89 56.20 (51.21–61.20) 83.50 (81.46–85.55) 0.76 (0.73–0.79) < 0.001 137.88 68.80 (63.06–74.54) 80.87 (78.81–82.94) 0.80 (0.76–0.83) < 0.001 Uacid-BMI SDS 3.54 74.67 (70.29–79.05) 78.77 (76.52–81.02) 0.84 (0.81–0.86) < 0.001 3.76 73.20 (67.71–78.69) 75.00 (72.73–77.27) 0.79 (0.76–0.83) < 0.001 Uacid-WC 438.38 60.16 (55.23–65.09) 75.77 (73.41–78.13) 0.74 (0.71–0.77) < 0.001 421.86 74.40 (68.99–79.81) 70.70 (68.31–73.09) 0.79 (0.76–0.82) < 0.001 Uacid-WHtR 2.56 67.81 (63.11–72.51) 71.59 (69.10-74.07) 0.76 (0.73–0.79) < 0.001 2.49 80.40 (75.48–85.32) 66.55 (64.07–69.02) 0.80 (0.77–0.83) < 0.001 Insulin, µIU/mL 15.45 66.53 (60.70-72.37) 70.08 (67.68–72.48) 0.74 (0.70–0.77) < 0.001 HOMA-IR 3.04 75.30 (69.96–80.63) 60.84 (58.29–63.40) 0.73 (0.70–0.77) < 0.001 Abbreviations: IR , insulin resistance; NAFLD , non-alcoholic fatty liver disease; AUC , area under the curve; CI , confidence interval; Uacid , uric acid; Cr , creatinine; BMI , body mass index; WC , waist circumference; WHtR , waist-to-height ratio; HOMA-IR , homeostasis model assessment of insulin resistance Discussion This study demonstrated that Uacid combined with obesity indices can be useful predictors for IR and NAFLD in children and adolescents. These parameters were superior to Uacid, Uacid/Cr, and Uacid/HDL for prediction of IR and NAFLD. Moreover, Uacid combined with obesity indices was superior to insulin level and HOMA-IR for NAFLD prediction although insulin and HOMA-IR were also useful for NAFLD prediction. In addition, hyperuricemia, IR, and NAFLD were closely related each other. In our study, Uacid was related with IR and NAFLD as well as hyperuricemia, IR, and NAFLD exhibited strong interconnections. A retrospective study reported that HOMA-IR was positively associated with Uacid among youth with obesity [ 21 ]. Another retrospective study reported that serum serum Uacid level was related to non-alcoholic steatohepatitis among children with obesity. [ 22 ] Relationship between Uacid and IR and NAFLD can be explained by followings: 1) hyperuricemia decrease nitric oxide synthase activity which induce IR [ 23 ]; 2) Intracellular Uacid stimulates adenosine monophosphate dehydrogenase enzyme activity while inhibiting adenosine monophosphate kinase enzyme activity, and adenosine monophosphate dehydrogenase, in turn, stimulates hepatic gluconeogenesis while intracellular adenosine monophosphate kinase inhibits this process [ 24 ]; 3) Endothelial dysfunction, oxidative stress, inflammation, and IR induced by hyperuricemia contribute to development of NAFLD [ 9 ]. Uacid combined with obesity indices was more powerful than Uacid alone, Uacid/HDL, and Uacid/Cr for IR prediction in our study. This is because IR is closely associated with obesity and central obesity [ 5 , 25 ]. Adipose tissue generates hormones, cytokines, and adipokines such as leptin and adiponectin, and an imbalance in these molecules, frequently seen in obesity, can trigger inflammation and IR [ 26 ]. Moreover, in obesity, increased free fatty acids can enter the liver through the portal circulation, leading to increase in hepatic lipid synthesis, gluconeogenesis, and the development of IR within the liver [ 27 , 28 ]. Among the Uacid related markers, Uacid-BMI SDS was the most powerful predictor for IR prediction in our study. In children, obesity is defined as having a BMI at or above the 95th percentile of sex- and age-specific reference values, making BMI SDS generally more appropriate than BMI alone for assessment [ 29 ]. In our previous study, triglyceride glucose index was more powerful for IR prediction when combined with BMI SDS compared to BMI [ 5 ]. For NAFLD, Uacid combined with obesity indices was more powerful than Uacid alone for prediction like IR. Obesity induces excessive fat tissue throughout the body, which can be stored in the liver, resulting in hepatic steatosis, which is a key characteristic of NAFLD [ 30 ]. Moreover, the excess fat in obesity, especially visceral fat, produces pro-inflammatory cytokines that can promote inflammation in the liver, worsening NAFLD [ 27 ]. In addition, IR induced by obesity accelerate NAFLD progression [ 1 , 31 ]. In a pediatric study, OR for children with BMI SDS above 3 was 2.56 compared to those with 2 ≤ BMI-SDS < 3 [ 32 ]. In a nationwide study, OR per BMI SDS unit was 4.67 for NAFLD risk among children with obesity [ 33 ]. Especially, Uacid-WHtR was powerful predictor for NAFLD in our study. Accumulation of visceral abdominal fat leads to an increase in circulating free fatty acids, contributing to the onset of NAFLD, which is more closely associated with WC rather than BMI [ 31 , 34 ]. A systematic review reported that WHtR showed a significant increase in NAFLD patients compared to the control group, with a mean difference of 0.073 [ 35 ]. A cross-sectional study reported that individuals with overweight had central adiposity or IR faced a higher risk of NAFLD compared to those with milder central adiposity or IR, even if their obesity status was similar. Uacid/HDL significantly associated with both IR and NAFLD as well as combination with HDL added incremental value to predictability if Uacid for IR in our study. The mechanism regarding negative relationship between HDL and IR can be explained by followings: 1) Ceramide, a sphingolipid positively associated with IR may be taken up by HDL from adipose tissue [ 36 ]. Thus, lower HDL levels could indicate higher tissue ceramide concentrations, which related to IR; 2) In individuals with IR, HDL level decrease due to increased inflammatory cytokines, which are elevated due to the visceral obesity typically seen in the metabolic syndrome [ 37 , 38 ]. In addition, association of HDL with obesity and IR might contribute to relationship between HDL and NAFLD [ 1 , 39 , 40 ]. A cross-sectional study reported that Uacid/HDL related to HOMA-IR in adults with type 2 diabetes [ 41 ]. A cohort study reported that Uacid/HDL was significantly related to NAFLD in adults with normal serum Uacid [ 39 ]. Further studies are required to clarify robustness of Uacid/HDL as predictors for IR and NAFLD among children. Our research has some limitations. Firstly, it was a cross-sectional investigation limited to the Korean population. Secondly, we were not able to consider factors like pubertal status, dietary habits, and physical activity. Thirdly, imaging studies or liver biopsies for NAFLD diagnosis were not taken into account in our analysis. Lastly, we were not able to consider measuring both lean and fat body mass in our study. However, this study assessed Uacid and related markers as predictors of IR and NAFLD across a large number of children and adolescents. In addition, we proposed new parameters, Uacid combined with obesity indices as predictive markers for IR and NAFLD in youth. In conclusion, this study demonstrates the value of combining Uacid with obesity indices as robust predictors of IR and NAFLD as well as intricate interplay among hyperuricemia, IR, and NAFLD in children and adolescents. These combined parameters outperform individual Uacid alone and other related markers, in predicting both IR and NAFLD. Notably, they also exhibit superior predictive power compared to insulin levels and HOMA-IR for NAFLD. Combination of Uacid with obesity indices does not require fasting, making it a convenient tool for early detection and prevention strategies in the pediatric population. Therefore, this research underscores the potential of Uacid combined with obesity indices for screening of IR and NAFLD among children and adolescents. Declarations Acknowledgments We would like to thank Editage (www.editage.co.kr) for providing assistance in English language editing. Author Contributions Youngha Choi was responsible for conceptualization, methodology, formal analysis, investigation and writing - original draft. Hyejin Yang was responsible for resources, data curation, formal analysis and writing - original draft. Soyoung Jeon was responsible for resources, data curation, formal analysis and writing - original draft. Kyoung Won Cho, Seo Jung Kim and Sujin Kim was responsible for conceptualization, methodology and writing - review & editing. Myeongseob Lee was responsible for methodology, investigation and writing - review & editing. Junghwan Suh was responsible for conceptualization, methodology and writing - review & editing. Hyun Wook Chae was responsible for conceptualization, methodology and writing - review & editing. Ho-Seong Kim was responsible for supervision and writing - review & editing. Kyungchul song was responsible for conceptualization, methodology, formal analysis, investigation and writing - review & editing. Competing Interests All authors declare no conflicts of interest. Data Availability Statement Data can be made available upon request to the corresponding author. Source of Funding: This study was supported by a faculty research grant from Yonsei University College of Medicine (6-2021-0150). References Song K, Kim H-S, Chae HW. Nonalcoholic fatty liver disease and insulin resistance in children. Clin. Exp. Pediatr. 2023:1–29. Li J, Ha A, Rui F, Zou B, Yang H, Xue Q, et al. 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Additional Declarations There is NO conflict of interest to disclose Supplementary Files SupplementaryTable1.docx Supplementary Table S1 Cite Share Download PDF Status: Published Journal Publication published 26 Jul, 2024 Read the published version in European Journal of Clinical Nutrition → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3890639","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":270005560,"identity":"f1046b6d-25de-44c0-85c4-eeafec5b8321","order_by":0,"name":"Kyungchul 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online\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKNHANES\u003c/em\u003e, Korea National Health and Nutrition Examination Survey; \u003cem\u003eAST,\u003c/em\u003e aspartate transaminase; \u003cem\u003eALT,\u003c/em\u003e alanine transaminase; \u003cem\u003eIR,\u003c/em\u003einsulin resistance;\u003cem\u003e NAFLD,\u003c/em\u003e non-alcoholic fatty liver disease\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3890639/v1/d97fef63f606e31dfd989711.png"},{"id":54450247,"identity":"ef75e9b8-f77f-4dc5-a8bc-f70a159b75e5","added_by":"auto","created_at":"2024-04-10 17:43:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1096497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve for each parameter to predict IR and NAFLD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves to predict IR in (A) and NAFLD in (B)\u003c/p\u003e\n\u003cp\u003eThe dot on the curves represented the position of cutoff point in ROC curve.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eROC\u003c/em\u003e, Receiver operating characteristic;\u003cem\u003e IR,\u003c/em\u003e insulin resistance; \u003cem\u003eNAFLD\u003c/em\u003e, non-alcoholic fatty liver disease; \u003cem\u003eUacid\u003c/em\u003e, uric acid; \u003cem\u003eCr, \u003c/em\u003ecreatinine; \u003cem\u003eBMI,\u003c/em\u003ebody mass index; \u003cem\u003eWC,\u003c/em\u003e waist circumference; \u003cem\u003eWHtR,\u003c/em\u003e waist-to-height ratio; \u003cem\u003eHOMA-IR,\u003c/em\u003e homeostasis model assessment of insulin resistance.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3890639/v1/9f0e69738bd448973cba62d8.png"},{"id":61210792,"identity":"836cf662-3888-4bb0-9cdc-e40a934f5d75","added_by":"auto","created_at":"2024-07-27 07:07:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2083745,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3890639/v1/aa3c0ca5-c19d-4625-913e-b1cf5c88a429.pdf"},{"id":54450244,"identity":"4ef5a70f-10d4-4071-821f-08aa24831e71","added_by":"auto","created_at":"2024-04-10 17:43:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23140,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S1\u003c/p\u003e","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3890639/v1/3dbf1a363af70616bbd462f5.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Prediction of insulin resistance and non-alcoholic fatty liver disease using serum uric acid and related markers in children and adolescents","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is a chronic liver condition characterized by the accumulation of excessive fat in the liver, often accompanied by elevated hepatic enzyme levels [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is recognized as a significant contributor to liver fibrosis, advanced liver disease, and is closely linked to various cardio-metabolic risk factors such as obesity, dyslipidemia, and insulin resistance (IR). A global meta-analysis showed that the prevalence of pediatric NAFLD increased from 4.62% in 2000 to 9.02% in 2017 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For screening of pediatric NAFLD, although alanine aminotransferase (ALT) is suggested, but it has limitation due to sensitivity and specificity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although various biomarkers for NAFLD are used in adults, investigations on biomarkers for pediatrics NAFLD is limited [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIR has key role in the development of metabolic diseases including NAFLD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. IR leads to increase in serum level of insulin, glucose, and fatty acids, which in turn promote the accumulation of fatty acids and triglycerides in the liver [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The glucose clamp technique, a classical method for measuring IR, is highly invasive, involving intravenous catheters and continuous monitoring, making it uncomfortable for patients and impractical for large-scale studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its complexity and cost also restrict its widespread application in clinical practice and research. Therefore, an alternative approach, the homeostasis model assessment of IR (HOMA-IR) index, has been introduced as a reliable method for quantifying IR [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, HOMA-IR relies on fasting blood samples which can be clinically relevant for some individuals. Moreover, it's important to note that there is no standardized protocol for measuring insulin levels, and such measurements are not routinely conducted in children. Therefore, investigations on development of simple markers for IR are required.\u003c/p\u003e \u003cp\u003eMeanwhile, uric acid (Uacid) has emerged as a potentially valuable biomarker for predicting and understanding metabolic conditions including NAFLD in adults, which does not require fasting for test [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This connection is stem from Uacid's role in promoting IR, a central factor in the pathogenesis of NAFLD, as well as its correlation with other metabolic risk factors such as obesity and dyslipidemia [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A systematic review reported that odds ratio (OR) of NAFLD was 1.92 times higher when comparing individuals with the highest serum Uacid levels to those with the lowest levels [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, a population-based study reported that serum Uacid level is increasing recently among Korean children and adolescents. However, research exploring the link between Uacid levels, IR, and NAFLD in children and adolescents is currently limited.\u003c/p\u003e \u003cp\u003eTherefore, we aimed to investigate the association of Uacid and related markers with IR and NAFLD in the youth by analyzing data from the Korea National Health and Nutrition Examination Survey (KNHANES). Our objectives were to: 1) examine the association between hyperuricemia and IR and NAFLD, 2) compare Uacid and related markers for prediction of IR and NAFLD, 3) establish optimal cutoff value of Uacid and related marker for prediction of IR and NAFLD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e We retrospectively evaluated data from 1,648 children and adolescents aged 10\u0026ndash;18 years who participated in the KNHANES conducted between 2019 and 2021. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the study design and workflow. KNHANES is a nationally representative survey carried out in Korea, employing a complex, stratified, multistage probability sampling method to select participants from the entire population. The survey is administered by the Korea Centers for Disease Control and Prevention and encompasses health surveys, medical examinations, and nutrition assessments. These datasets offer a wide range of insights into individuals' health status, behaviors, socio-economic characteristics, and laboratory test results. To ensure accuracy, sample weights were applied to address variations in selection probabilities and non-response rates, and these weighted data were subsequently adjusted to accurately reflect the demographics of the Korean population by sex and age groups [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. KNHANES is approved by the Korea Disease Control and Prevention Agency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParticipants' weights were determined with a scale (Giant 150N, HANA, Seoul, South Korea) accurate to the nearest 0.1 kg, while their heights and waist circumferences (WC) were measured using a stadiometer (range: 850\u0026ndash;2060 mm; Seriter, Holtain Ltd., Crymych, UK) with precision to the nearest 0.1 cm. Body Mass Index (BMI) was computed as the weight in kilograms divided by the square of the height in meters. The height, weight, and BMI were then expressed as standard deviation score (SDS) values, referencing the 2017 Korean National Growth Charts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Children were categorized based on their BMI into three groups: those with a BMI\u0026thinsp;\u0026lt;\u0026thinsp;85th percentile were considered normal weight, those with a BMI ranging from the 85th to the 95th percentile were classified as overweight, and those with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;95th percentile were considered obese. The measurement of WC was taken at the midpoint between the costal margin and iliac crest during a normal exhalation. Waist-to-height ratio (WHtR) was calculated by dividing the waist circumference (cm) by the height (cm). Central obesity was defined as having a waist circumference exceeding the 90th percentile based on Korean waist reference standards [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBlood samples were obtained from the antecubital vein after an overnight fast of 8 hours. These samples were then processed and promptly stored in a refrigerator. The levels of aspartate aminotransferase and ALT in the serum were determined using commercially available kits (Pureauto S ALT, Daiichi Pure Chemicals, Tokyo, Japan) without employing the pyridoxal-5-phosphate method, relying instead on ultraviolet light measurement. Serum insulin levels were assessed using the Wizard 1470 gamma counter (Perkin-Elmer, Turku, Finland). Plasma concentrations of fasting glucose, Uacid, total cholesterol, high-density lipoprotein cholesterol (HDL), and triglycerides were measured using the Hitachi Automatic Analyzer 7600/7600\u0026thinsp;\u0026minus;\u0026thinsp;210 (Hitachi, Tokyo, Japan). Serum creatinine (Cr) was measured using Cobas c702 (Roche Diagnostics, Mannheim, Germany).\u003c/p\u003e \u003cp\u003eLow-density lipoprotein cholesterol (LDL) concentrations were determined using the Friedewald formula, which is computed as LDL equals the total cholesterol minus the sum of HDL-C and triglycerides divided by five (LDL\u0026thinsp;=\u0026thinsp;total cholesterol \u0026minus; [HDL + (triglycerides/5)] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Non-HDL levels were calculated by subtracting HDL from the total cholesterol. The HOMA-IR was calculated as fasting insulin (mg/dL) multiplied by fasting glucose (mg/dL) and then divided by 22.5, and IR was defined as having a HOMA-IR value exceeding the 95th percentile for each gender and age group, as determined by Korean HOMA-IR reference data. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] NAFLD was defined as having ALT levels higher than 26 IU/L for males and greater than 22 IU/L for females, provided there was no concurrent hepatitis B viral infection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Hyperuricemia was defined as higher uric acid levels based on age-specific reference value [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Uacid related markers were defined and calculated as follows: Uacid divided by Cr (Uacid/Cr), Uacid divided by HDL (Uacid/HDL), Uacid \u0026times; BMI (Uacid-BMI), Uacid \u0026times; BMI SDS (Uacid-BMI SDS), Uacid \u0026times; WC (Uacid-WC), Uacid \u0026times; WHtR (Uacid-WHtR) [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll categorical variables are presented as numbers and weighted percentages, and continuous variables are presented as weighted means and standard errors. Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used to compare the mean values of the continuous variables. The Rao-Scott-chi-square test was used to compare categorical variables. Logistic regression analyses were performed with IR and NAFLD as dependent variables to investigate relationship among obesity, cental obesity, hyperuricemia, IR, and NAFLD. ORs and their corresponding 95% confidence intervals (CIs) were calculated for tertiles 2 and 3 of each parameter, with tertile 1 serving as the reference point for comparison. Sensitivity and specificity were determined as the optimal cutoff values for the markers, employing Youden's index. Receiver operating characteristic (ROC) curves were generated to assess and compare the relative diagnostic effectiveness of these parameters in identifying IR and NAFLD. Pairwise comparisons of the parameters' area under the curve (AUC) values were carried out using the bootstrap method. Statistical significance was established at a p-value of less than 0.05. All statistical analyses were conducted using SAS version 9.4 (SAS Inc., Cary, NC) and R version 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria), accounting for the complex survey design with clustering, stratification, and unequal weighting of the KNHANES sample.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a comparison of the clinical characteristic of the participants with or without IR and NAFLD. Obesity indicators including BMI, BMI SDS, WC, and WHtR and serum level of total cholesterol, LDL, non-HDL, triglycerides, glucose, insulin, HOMA-IR, AST, and ALT and proportion of participants with hyperuricemia were higher in the IR and NAFLD groups compared to those in the non-IR and non- NAFLD groups, respectively (total cholesterol and glucose in NAFLD/non-NAFLD, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, respectively; for other comparisons, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, HDL level was lower in in the IR and NAFLD groups compared to those in the non-IR and non-NAFLD groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, the IR and NAFLD groups had higher values of Uacid and Uacid related markers than those in the non-IR and non-NAFLD groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eBaseline characteristics of participants with respect to IR and NAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" fcolname=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1,648)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;381)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enon-IR (n\u0026thinsp;=\u0026thinsp;1,267)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e*p\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNAFLD (n\u0026thinsp;=\u0026thinsp;251)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003enon-NAFLD (n\u0026thinsp;=\u0026thinsp;1,397)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e*p\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.13 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.78 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.23 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.61 (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.05 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, female, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.28 (1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.12 (2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.54 (1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.19 (3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.00 (1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.47 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.05 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.49 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.39(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.89(0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.60(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.17(0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.57(0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eBMI SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.24 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.57 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.07 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eBMI percentile,\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eNormal, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.48 (1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.77 (3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.37 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.74 (3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e81.30 (1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.25 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.41 (2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.86 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.48 (2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.33 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.27 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.82 (3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.77 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.78 (3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.37 (1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.26 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.26 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.04 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.99 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.02 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eCentral obesity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.14 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.25 (3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.42 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.15 (3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.45 (1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.51 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eTotal cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163.69 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170.04 (1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161.83 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e170.71 (2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e162.45 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.86 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101.08 (1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.05 (0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e103.03 (2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.43 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eHDL, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.95 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.88 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.43 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.44 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52.74 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003enon-HDL, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111.74 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123.16 (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108.41 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e123.26 (2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e109.71 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eCr, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eTriglycerides, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.77 (1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.50 (3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.21 (1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109.63 (4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83.93 (1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eGlucose, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.09 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.26 (0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.87 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.43 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.85 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin, \u0026micro;IU/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.37 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.05 (1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.79 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.00 (1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.68 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.57 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.42 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.44 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.88 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.16 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eIR\u0026dagger;, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.61 (1.35)\u003c/p\u003e \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 \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.14 (3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.59 (1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eAST, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.53 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.47 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.68 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.03 (1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.33 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eALT, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.93 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.96 (1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.71 (0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.39 (2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.56 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eNAFLD, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.97 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.86 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.45 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUacid, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.49 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.15 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.30 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.56 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.30 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eHyperuricemia, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.79 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.48 (2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.35 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.15 (3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.21 (1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eUacid/Cr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.43 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.63 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.08 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.54 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.24 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eUacid/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eUacid-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120.45 (1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161.27 (4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108.58 (1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e173.17 (4.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e111.18 (1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eUacid-BMI SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.84 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.82 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.17 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eUacid-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e405.74 (5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e523.72 (12.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e371.41 (4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e567.27 (12.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e377.32 (4.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003eUacid-WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.48 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.18 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.28 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.40 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.32 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\"\u003eNote: Values are presented as mean (standard error), and categorical data as percentages (standard error).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*\u003cem\u003eP\u003c/em\u003e value is assessed using Student\u0026rsquo;s t-test and Rao-scott-Chi-square test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u0026dagger;IR was defined as the HOMA-IR of above the 95th percentile for each gender and age using Korean HOMA-IR reference data.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003eAbbreviations: \u003cem\u003eIR,\u003c/em\u003e insulin resistance;\u003cem\u003e\u0026nbsp;NAFLD,\u003c/em\u003e non-alcoholic fatty liver disease; \u003cem\u003eSDS,\u003c/em\u003e standard deviation score; \u003cem\u003eBMI,\u003c/em\u003e body mass index; \u003cem\u003eWC,\u003c/em\u003e waist circumference; 90p, 90th percentile; \u003cem\u003eWHtR,\u003c/em\u003e waist-to-height ratio; \u003cem\u003eTC\u003c/em\u003e, total cholesterol; \u003cem\u003eLDL,\u003c/em\u003e low-density lipoprotein cholesterol; \u003cem\u003eHDL,\u003c/em\u003e high-density lipoprotein cholesterol; \u003cem\u003eCr,\u0026nbsp;\u003c/em\u003ecreatinine;\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eHOMA-IR,\u003c/em\u003e homeostasis model assessment of insulin resistance; \u003cem\u003eAST,\u003c/em\u003e aspartate aminotransferase; \u003cem\u003eALT,\u003c/em\u003e alanine aminotransferase; \u003cem\u003eUacid\u003c/em\u003e, uric acid.\u003c/p\u003e\n \u003cp\u003eIn logistic regression analyses with IR and NAFLD as dependent variables, ORs of generalized and abdominal obesity for IR were 4.38 (95% CI, 2.23\u0026ndash;8.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 3.16 (95% CI, 1.70\u0026ndash;5.86; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the corresponding values for NAFLD were 4.00 (95% CI, 2.01\u0026ndash;7.94; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 1.75 (95% CI, 0.95\u0026ndash;3.22; p 0.073), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The hyperuricemia group had OR value of 1.91 (95% CI, 1.33\u0026ndash;2.76; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for IR and 1.81 (95% CI, 1.04\u0026ndash;3.17; p 0.037) for NAFLD. In addition, OR of IR for NAFLD was 1.63 (95% CI, 1.13\u0026ndash;2.34; p\u0026thinsp;=\u0026thinsp;0.009)\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\u003eOdds ratio of IR and NAFLD according to each parameter\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\u003eIR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNAFLD\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\u003eOR\u0026nbsp;(95%\u0026nbsp;Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u0026nbsp;(95%\u0026nbsp;Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI percentile\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\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.90 (1.69\u0026ndash;4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17 (1.20\u0026ndash;3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.38 (2.23\u0026ndash;8.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 (2.01\u0026ndash;7.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eCentral Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.16 (1.70\u0026ndash;5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75 (0.95\u0026ndash;3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91 (1.33\u0026ndash;2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81 (1.04\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIR\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 \u003cp\u003e1.63 (1.13\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65 (1.15\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Logistic regression analyses were performed with IR and NAFLD as dependent variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: \u003cem\u003eIR\u003c/em\u003e, insulin resistance; \u003cem\u003eNAFLD\u003c/em\u003e, non-alcoholic fatty liver disease; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval..\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ORs and 95% CIs for IR and NAFLD progressively increased across tertiles of each variable including Uacid, Uacid-Cr, Uacid-HDL, Uacid-BMI, Uacid-BMI SDS, Uacid-WC, Uacid-WHtR, total cholesterol, triglycerides, LDL, non-HDL among the total subjects (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Uacid combined with obesity indices exhibited ORs of tertile 3 ranging as 8.17\u0026ndash;18.74 compared with those of tertile 1, while the Uacid, Uacid-Cr, and Uacid-HDL exhibited ORs of tertile 3 as 4.39. 4.94, and 5.23 compared with those of tertile 1 for IR in all individuals. For NAFLD, Uacid combined with obesity indices exhibited ORs of tertile 3 ranging as 11.73\u0026ndash;19.22 compared with those of tertile 1, while the Uacid, Uacid/Cr, and Uacid/HDL exhibited ORs of tertile 3 as 9.86, 3.83, and 6.95 compared with those of tertile 1 in all individuals. Among the indices, Uacid-BMI SDS presented the highest ORs and 95% CIs for IR in the total subjects (OR, 18.74), and Uacid-WHtR for NAFLD (OR, 19.22), respectively. Overall, the Uacid combined with obesity indices presented higher ORs and 95% CIs than the Uacid alone, Uacid/Cr, Uacid/HDL, lipid parameters for IR and the Uacid alone, Uacid/Cr, Uacid/HDL, lipid profiles, insulin, and HOMA-IR for NAFLD.\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\u003eOdds ratio for IR and NAFLD according to tertiles of each parameter\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\u003eIR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNAFLD\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\u003eOR\u0026nbsp;(95%\u0026nbsp;Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u0026nbsp;(95%\u0026nbsp;Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUacid, mg/dL\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46 (1.00-2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31 (1.94\u0026ndash;5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.39 (3.04\u0026ndash;6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.86 (6.24\u0026ndash;15.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eUacid/Cr\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04 (1.40\u0026ndash;2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73 (1.11\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.94 (3.37\u0026ndash;7.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.83 (2.50\u0026ndash;5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eUacid/HDL\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (1.04\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65 (1.03\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.23 (3.62\u0026ndash;7.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.95 (4.64\u0026ndash;10.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eUacid-BMI\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.37 (1.56\u0026ndash;3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40 (1.36\u0026ndash;4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.91 (5.94\u0026ndash;13.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.64 (8.32\u0026ndash;22.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eUacid-BMI SDS\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.59 (1.59\u0026ndash;4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87 (1.05\u0026ndash;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.74 (11.44\u0026ndash;30.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.73 (7.13\u0026ndash;19.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eUacid-WC\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.66 (1.75\u0026ndash;4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.39 (1.88\u0026ndash;6.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.17 (5.44\u0026ndash;12.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.11 (8.83\u0026ndash;25.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eUacid-WHtR\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54 (1.67\u0026ndash;3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.59 (2.55\u0026ndash;8.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.89 (6.54\u0026ndash;14.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.22 (11.43\u0026ndash;32.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eTotal cholesterol, mg/dL\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43 (1.03\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.70\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95 (1.43\u0026ndash;2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86 (1.19\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dL\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34 (1.56\u0026ndash;3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50 (0.96\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.59 (4.50\u0026ndash;9.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.93 (1.87\u0026ndash;4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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, mg/dL\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.28\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.28\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.16\u0026ndash;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.20\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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, mg/dL\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 (0.99\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76 (1.14\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23 (1.60\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.67 (1.73\u0026ndash;4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003enon-HDL, mg/dL\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63 (1.15\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31 (1.48\u0026ndash;3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.38 (2.42\u0026ndash;4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.82 (2.42\u0026ndash;6.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eInsulin, \u0026micro;IU/mL\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\u003eT2\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 \u003cp\u003e2.18 (1.35\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\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 \u003cp\u003e7.29 (4.72\u0026ndash;11.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003eT2\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 \u003cp\u003e2.42 (1.45\u0026ndash;4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\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 \u003cp\u003e6.97 (4.49\u0026ndash;10.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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=\"5\"\u003eNote: ORs and 95% CIs of tertiles 2 and 3 for each parameter were calculated and compared with tertile 1 as a reference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*\u003cem\u003ep\u003c/em\u003e value is assessed using logistic regression.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: \u003cem\u003eIR\u003c/em\u003e, insulin resistance; \u003cem\u003eNAFLD\u003c/em\u003e, non-alcoholic fatty liver disease; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eT\u003c/em\u003e, Tertitle; \u003cem\u003eUacid\u003c/em\u003e, uric acid; \u003cem\u003eCr\u003c/em\u003e, creatinine; \u003cem\u003eHDL\u003c/em\u003e, high-density lipoprotein cholesterol; \u003cem\u003eBMI\u003c/em\u003e, body mass index; \u003cem\u003eWC\u003c/em\u003e, waist circumference; \u003cem\u003eWHtR\u003c/em\u003e, waist-to-height ratio; \u003cem\u003eTC\u003c/em\u003e, total cholesterol; \u003cem\u003eHDL\u003c/em\u003e, high-density lipoprotein cholesterol; \u003cem\u003eLDL\u003c/em\u003e, low-density lipoprotein cholesterol; \u003cem\u003eHOMA-IR\u003c/em\u003e, homeostasis model assessment of insulin resistance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarize the results of ROC curve analyses and AUCs with the corresponding 95% CIs for Uacid and Uacid related markers to predict IR and NAFLD. All variables predicted IR and NAFLD significantly (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). The cut-off values for IR prediction were 5.95, 9.13, 0.11, 139.89, 3.54, 438.38, and 2.56 for Uacid, Uacid-Cr, Uacid-HDL, Uacid-BMI, Uacid-BMI SDS, Uacid-WC, Uacid-WHtR, respectively. All Uacid combined with obesity indices showed higher AUC compared to Uacid, Uacid/Cr, Uacid/HDL for IR. Uacid-BMI SDS showed the largest AUC for IR detection with 0.837. In addition, Uacid/HDL presented higher AUC compared to Uacid. The cutoff values for NAFLD prediction were 5.55, 9.17, 0.12, 137.88, 3.76, 421.86, 2.49, 15.45, and 3.04 for Uacid, Uacid/Cr, Uacid/HDL, Uacid-BMI, Uacid-BMI SDS, Uacid-WC, Uacid-WHtR, insulin, and HOMA-IR, respectively. All Uacid combined with obesity indices showed higher AUC compared to Uacid, Uacid/Cr, Uacid/HDL, insulin, and HOMA-IR for NAFLD. Uacid-WHtR showed the largest AUC for NAFLD detection with 0.803. Uacid-BMI SDS presented significantly higher AUC values and 95% CIs than all other parameters for IR prediction (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Table S1). For NAFLD prediction, all Uacid combined with obesity indices presented higher AUC compared to Uacid, Uacid/Cr, Uacid/HDL, insulin, and HOMA-IR. In addition, Uacid-BMI and Uacid- WHtR had highest AUC among the parameters.\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\u003eCutoff values and areas under the receiver operating characteristic curves for each parameter for predicting IR and NAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUacid, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.56\u003c/p\u003e \u003cp\u003e(48.54\u0026ndash;58.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.95\u003c/p\u003e \u003cp\u003e(71.54\u0026ndash;76.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003cp\u003e(0.63\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e73.60\u003c/p\u003e \u003cp\u003e(68.14\u0026ndash;79.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e62.03\u003c/p\u003e \u003cp\u003e(59.49\u0026ndash;64.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.70\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\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\u003eUacid/Cr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.78\u003c/p\u003e \u003cp\u003e(52.81\u0026ndash;62.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.59\u003c/p\u003e \u003cp\u003e(69.10-74.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003cp\u003e(0.66\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e56.00\u003c/p\u003e \u003cp\u003e(49.85\u0026ndash;62.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e69.13\u003c/p\u003e \u003cp\u003e(66.70-71.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003cp\u003e(0.63\u0026ndash;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\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\u003eUacid/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.03\u003c/p\u003e \u003cp\u003e(67.51\u0026ndash;76.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.30\u003c/p\u003e \u003cp\u003e(60.65\u0026ndash;65.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e(0.68\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e65.60\u003c/p\u003e \u003cp\u003e(59.71\u0026ndash;71.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e72.85\u003c/p\u003e \u003cp\u003e(70.52\u0026ndash;75.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003cp\u003e(0.71\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\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\u003eUacid-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.20\u003c/p\u003e \u003cp\u003e(51.21\u0026ndash;61.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.50\u003c/p\u003e \u003cp\u003e(81.46\u0026ndash;85.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003cp\u003e(0.73\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e137.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e68.80\u003c/p\u003e \u003cp\u003e(63.06\u0026ndash;74.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e80.87\u003c/p\u003e \u003cp\u003e(78.81\u0026ndash;82.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.76\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\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\u003eUacid-BMI SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.67\u003c/p\u003e \u003cp\u003e(70.29\u0026ndash;79.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.77\u003c/p\u003e \u003cp\u003e(76.52\u0026ndash;81.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e(0.81\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e73.20\u003c/p\u003e \u003cp\u003e(67.71\u0026ndash;78.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003cp\u003e(72.73\u0026ndash;77.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003cp\u003e(0.76\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\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\u003eUacid-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e438.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.16\u003c/p\u003e \u003cp\u003e(55.23\u0026ndash;65.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.77\u003c/p\u003e \u003cp\u003e(73.41\u0026ndash;78.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003cp\u003e(0.71\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e421.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e74.40\u003c/p\u003e \u003cp\u003e(68.99\u0026ndash;79.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e70.70\u003c/p\u003e \u003cp\u003e(68.31\u0026ndash;73.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003cp\u003e(0.76\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\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\u003eUacid-WHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.81\u003c/p\u003e \u003cp\u003e(63.11\u0026ndash;72.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.59\u003c/p\u003e \u003cp\u003e(69.10-74.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003cp\u003e(0.73\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e80.40\u003c/p\u003e \u003cp\u003e(75.48\u0026ndash;85.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e66.55\u003c/p\u003e \u003cp\u003e(64.07\u0026ndash;69.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.77\u0026ndash;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\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\u003eInsulin, \u0026micro;IU/mL\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e15.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e66.53\u003c/p\u003e \u003cp\u003e(60.70-72.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e70.08\u003c/p\u003e \u003cp\u003e(67.68\u0026ndash;72.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003cp\u003e(0.70\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.30\u003c/p\u003e \u003cp\u003e(69.96\u0026ndash;80.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e60.84\u003c/p\u003e \u003cp\u003e(58.29\u0026ndash;63.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003cp\u003e(0.70\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\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=\"13\"\u003eAbbreviations: \u003cem\u003eIR\u003c/em\u003e, insulin resistance; \u003cem\u003eNAFLD\u003c/em\u003e, non-alcoholic fatty liver disease; \u003cem\u003eAUC\u003c/em\u003e, area under the curve; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eUacid\u003c/em\u003e, uric acid; \u003cem\u003eCr\u003c/em\u003e, creatinine; \u003cem\u003eBMI\u003c/em\u003e, body mass index; \u003cem\u003eWC\u003c/em\u003e, waist circumference; \u003cem\u003eWHtR\u003c/em\u003e, waist-to-height ratio; \u003cem\u003eHOMA-IR\u003c/em\u003e, homeostasis model assessment of insulin resistance\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that Uacid combined with obesity indices can be useful predictors for IR and NAFLD in children and adolescents. These parameters were superior to Uacid, Uacid/Cr, and Uacid/HDL for prediction of IR and NAFLD. Moreover, Uacid combined with obesity indices was superior to insulin level and HOMA-IR for NAFLD prediction although insulin and HOMA-IR were also useful for NAFLD prediction. In addition, hyperuricemia, IR, and NAFLD were closely related each other.\u003c/p\u003e \u003cp\u003eIn our study, Uacid was related with IR and NAFLD as well as hyperuricemia, IR, and NAFLD exhibited strong interconnections. A retrospective study reported that HOMA-IR was positively associated with Uacid among youth with obesity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Another retrospective study reported that serum serum Uacid level was related to non-alcoholic steatohepatitis among children with obesity. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Relationship between Uacid and IR and NAFLD can be explained by followings: 1) hyperuricemia decrease nitric oxide synthase activity which induce IR [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; 2) Intracellular Uacid stimulates adenosine monophosphate dehydrogenase enzyme activity while inhibiting adenosine monophosphate kinase enzyme activity, and adenosine monophosphate dehydrogenase, in turn, stimulates hepatic gluconeogenesis while intracellular adenosine monophosphate kinase inhibits this process [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; 3) Endothelial dysfunction, oxidative stress, inflammation, and IR induced by hyperuricemia contribute to development of NAFLD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUacid combined with obesity indices was more powerful than Uacid alone, Uacid/HDL, and Uacid/Cr for IR prediction in our study. This is because IR is closely associated with obesity and central obesity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Adipose tissue generates hormones, cytokines, and adipokines such as leptin and adiponectin, and an imbalance in these molecules, frequently seen in obesity, can trigger inflammation and IR [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, in obesity, increased free fatty acids can enter the liver through the portal circulation, leading to increase in hepatic lipid synthesis, gluconeogenesis, and the development of IR within the liver [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Among the Uacid related markers, Uacid-BMI SDS was the most powerful predictor for IR prediction in our study. In children, obesity is defined as having a BMI at or above the 95th percentile of sex- and age-specific reference values, making BMI SDS generally more appropriate than BMI alone for assessment [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our previous study, triglyceride glucose index was more powerful for IR prediction when combined with BMI SDS compared to BMI [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor NAFLD, Uacid combined with obesity indices was more powerful than Uacid alone for prediction like IR. Obesity induces excessive fat tissue throughout the body, which can be stored in the liver, resulting in hepatic steatosis, which is a key characteristic of NAFLD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, the excess fat in obesity, especially visceral fat, produces pro-inflammatory cytokines that can promote inflammation in the liver, worsening NAFLD [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, IR induced by obesity accelerate NAFLD progression [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In a pediatric study, OR for children with BMI SDS above 3 was 2.56 compared to those with 2\u0026thinsp;\u0026le;\u0026thinsp;BMI-SDS\u0026thinsp;\u0026lt;\u0026thinsp;3 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In a nationwide study, OR per BMI SDS unit was 4.67 for NAFLD risk among children with obesity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Especially, Uacid-WHtR was powerful predictor for NAFLD in our study. Accumulation of visceral abdominal fat leads to an increase in circulating free fatty acids, contributing to the onset of NAFLD, which is more closely associated with WC rather than BMI [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A systematic review reported that WHtR showed a significant increase in NAFLD patients compared to the control group, with a mean difference of 0.073 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A cross-sectional study reported that individuals with overweight had central adiposity or IR faced a higher risk of NAFLD compared to those with milder central adiposity or IR, even if their obesity status was similar.\u003c/p\u003e \u003cp\u003eUacid/HDL significantly associated with both IR and NAFLD as well as combination with HDL added incremental value to predictability if Uacid for IR in our study. The mechanism regarding negative relationship between HDL and IR can be explained by followings: 1) Ceramide, a sphingolipid positively associated with IR may be taken up by HDL from adipose tissue [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, lower HDL levels could indicate higher tissue ceramide concentrations, which related to IR; 2) In individuals with IR, HDL level decrease due to increased inflammatory cytokines, which are elevated due to the visceral obesity typically seen in the metabolic syndrome [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, association of HDL with obesity and IR might contribute to relationship between HDL and NAFLD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A cross-sectional study reported that Uacid/HDL related to HOMA-IR in adults with type 2 diabetes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A cohort study reported that Uacid/HDL was significantly related to NAFLD in adults with normal serum Uacid [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Further studies are required to clarify robustness of Uacid/HDL as predictors for IR and NAFLD among children.\u003c/p\u003e \u003cp\u003eOur research has some limitations. Firstly, it was a cross-sectional investigation limited to the Korean population. Secondly, we were not able to consider factors like pubertal status, dietary habits, and physical activity. Thirdly, imaging studies or liver biopsies for NAFLD diagnosis were not taken into account in our analysis. Lastly, we were not able to consider measuring both lean and fat body mass in our study. However, this study assessed Uacid and related markers as predictors of IR and NAFLD across a large number of children and adolescents. In addition, we proposed new parameters, Uacid combined with obesity indices as predictive markers for IR and NAFLD in youth.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates the value of combining Uacid with obesity indices as robust predictors of IR and NAFLD as well as intricate interplay among hyperuricemia, IR, and NAFLD in children and adolescents. These combined parameters outperform individual Uacid alone and other related markers, in predicting both IR and NAFLD. Notably, they also exhibit superior predictive power compared to insulin levels and HOMA-IR for NAFLD. Combination of Uacid with obesity indices does not require fasting, making it a convenient tool for early detection and prevention strategies in the pediatric population. Therefore, this research underscores the potential of Uacid combined with obesity indices for screening of IR and NAFLD among children and adolescents.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.co.kr) for providing assistance in English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYoungha Choi was responsible for conceptualization, methodology, formal analysis, investigation and writing - original draft. Hyejin Yang was responsible for resources, data curation, formal analysis and writing - original draft. Soyoung Jeon was responsible for resources, data curation, formal analysis and writing - original draft. Kyoung Won Cho, Seo Jung Kim and Sujin Kim was responsible for conceptualization, methodology and writing - review \u0026amp; editing. Myeongseob Lee was responsible for methodology, investigation and writing - review \u0026amp; editing. Junghwan Suh was responsible for conceptualization, methodology and writing - review \u0026amp; editing. Hyun Wook Chae was responsible for conceptualization, methodology and writing - review \u0026amp; editing. Ho-Seong Kim was responsible for supervision and writing - review \u0026amp; editing. Kyungchul song was responsible for conceptualization, methodology, formal analysis, investigation and writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData can be made available upon request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of Funding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a faculty research grant from Yonsei University College of Medicine (6-2021-0150).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSong K, Kim H-S, Chae HW. Nonalcoholic fatty liver disease and insulin resistance in children. Clin. Exp. 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Hepatology. 2008;48:449\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y-N, Wang Q-Q, Chen Y-S, Shen C, Xu C-F. Association between serum uric acid to hdl-cholesterol ratio and nonalcoholic fatty liver disease in lean chinese adults. International journal of endocrinology. 2020;2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu W, Liang A, Shi P, Yuan S, Zhu Y, Fu J, et al. Higher serum uric acid to HDL-cholesterol ratio is associated with onset of non-alcoholic fatty liver disease in a non-obese Chinese population with normal blood lipid levels. BMC gastroenterology. 2022;22:196.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, Xu J. Association between serum uric acid-to‐high‐density lipoprotein cholesterol ratio and insulin resistance in patients with type 2 diabetes mellitus. Journal of Diabetes Investigation. 2023.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3890639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3890639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo investigate the relationship between serum uric acid (Uacid) and related parameters with insulin resistance (IR) and non-alcoholic fatty liver disease (NAFLD)and their potential as predictors of IR and NAFLD in children and adolescents\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThe data of 1,648 participants aged 10–18 years was analyzed using nationwide survey. Logistic regression analysis was performed with IR and NAFLD as dependent variables, and odds ratios (ORs) and 95% confidence intervals (CIs) were computed for tertiles 2 and 3 of each parameter in comparison to tertile 1, which served as the reference group. Receiver operating characteristic (ROC) curves were generated to assess predictability of the parameters for IR and NAFLD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Hyperuricemia, IR, and NAFLD were significantly associated each other. All Uacid and related markers showed continuous increase in ORs and 95% CIs across the tertiles for IR and NAFLD (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). In ROC curve, all Uacid and related markers demonstrated significant predictability for IR and NAFLD. Overall, Uacid combined with obesity indices showed higher ORs and AUC compared to Uacid alone. Uacid-body mass index (BMI) standard deviation score presented the largest AUC for IR. For NAFLD, Uacid-BMI and Uacid-waist-to-height ratio showed the largest AUC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eUacid combined with obesity indices\u003cstrong\u003e \u003c/strong\u003eare robust markers for prediction of IR and NAFLD in children and adolescents, which was superior to Uacid. Uacid and related markers have potential as simple markers which does not require fasting for screening of IR and NAFLD in children and adolescents\u003c/p\u003e","manuscriptTitle":"Prediction of insulin resistance and non-alcoholic fatty liver disease using serum uric acid and related markers in children and adolescents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 17:43:33","doi":"10.21203/rs.3.rs-3890639/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7d209052-3f21-4f3f-be16-7c6845d14b11","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28453381,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Metabolic syndrome"},{"id":28453382,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"},{"id":28453383,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-07-27T07:07:50+00:00","versionOfRecord":{"articleIdentity":"rs-3890639","link":"https://doi.org/10.1038/s41430-024-01475-z","journal":{"identity":"european-journal-of-clinical-nutrition","isVorOnly":false,"title":"European Journal of Clinical Nutrition"},"publishedOn":"2024-07-26 04:00:00","publishedOnDateReadable":"July 26th, 2024"},"versionCreatedAt":"2024-04-10 17:43:33","video":"","vorDoi":"10.1038/s41430-024-01475-z","vorDoiUrl":"https://doi.org/10.1038/s41430-024-01475-z","workflowStages":[]},"version":"v1","identity":"rs-3890639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3890639","identity":"rs-3890639","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}