Triglycerides to total cholesterol ratio: an early screening tool for NAFLD in Chinese populations

Triglycerides to total cholesterol ratio: an early screening tool for NAFLD in Chinese populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Triglycerides to total cholesterol ratio: an early screening tool for NAFLD in Chinese populations Jingyuan Chen, Yiping Yang, Qize Yang, Jiangang Wang, Zhiheng Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-35196/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Non-alcoholic fatty liver disease(NAFLD) has a high prevalence in the general population worldwide. Both triglycerides (TG) and total cholesterol (TC) are correlated with the prevalence of NAFLD. The study purpose is to determine whether TG/TC is an effective method to screen NAFLD in the general Chinese population. Methods 93,449 subjects with average age of 43.7 ± 14.3 years were included in this cross-sectional study. Multiple logistic regressions and receiver operator characteristic curve (ROC) analyses were performed. Results Among these subjects, 16,138 (42.7%) men and 9,591 (17.2%) women were diagnosed with NAFLD. Subjects in the higher quartiles of TG/TC had a higher prevalence of NAFLD. After adjusting multiple confounding factors, the odds ratio (OR) for NAFLD in the highest compared with the lowest quartile was 4.08 (95%CI 3.64, 4.57) in men and 4.65 (95%CI 4.14, 5.21) in women. Moreover, ROC analyses suggested TG/TC showed high diagnostic ability for detecting NAFLD, and the areas under the curves (AUC) in men and women were 0.920 (95%CI 0.917, 0.923) and 0.863 (95%CI 0.859, 0.867), respectively. Furthermore, the diagnostic ability was significantly higher in younger age groups. The AUC in 18–34 and 35–44 years group were 0.943 (95%CI 0.939, 0.946) and 0.921 (95%CI 0.917, 0.925), respectively. Besides, the women and the young people had a greater negative predictive values (90.84% for women, 93.13% for aged 18–34). Conclusion TG/TC shows a high diagnostic accuracy for identifying NAFLD in young women, providing an important clue to establish a new tool to screen NAFLD particularly in Chinese young females. Nutrition & Dietetics TG/TC Non-alcoholic fatty liver disease gender age Figures Figure 1 Figure 2 Introduction Non-alcoholic fatty liver disease (NAFLD) is an expanding global health problem, with a high prevalence in the general population. Worldwide, the prevalence of NAFLD diagnosed by imaging varies between 14% and 32% [ 1 ]. In China, NAFLD influences over 25% of the population, and its prevalence is rapidly increasing because of the considerable alteration in lifestyle and population age structure[ 2 , 3 ]. NAFLD can increased the risk of cirrhosis, hepatocellular carcinoma, and death[ 4 , 5 ]. Furthermore, considering the growing burden of NAFLD, early detection or screening of the disease are of utmost importance. Currently, liver biopsy is the gold standard for the diagnosis of NAFLD, but there are well-known limitations including sampling errors, invasiveness, high cost and severe complications, such as mortality, bleeding, and pain[ 6 ]. The imaging diagnosis of NAFLD, such as ultrasonography and magnetic resonance elastography, is more safe but still expensive and highly dependent on experienced imaging doctors[ 7 ]. Therefore, simple, inexpensive and noninvasive methods are urgently needed to be used for diagnosing NAFLD. Numerous epidemiological studies suggest that dyslipidemia is closely related to NAFLD[ 8 , 9 ]. Previous studies demonstrated that triglycerides (TG) and cholesterol were important risk factors of NAFLD and associated with its pathogenesis in animal experiments[ 10 – 14 ]. It’s well established that the accumulation of TG within hepatocytes could lead to NAFLD[ 10 ]. Besides, among NAFLD patients, cholesterol metabolism was significantly altered reflected by increased cholesterol synthesis and diminished absorption[ 11 ]. In animal experiments, high cholesterol diets and free cholesterol accumulation in hepatic stellate cells were toxic to livers of rats and mice[ 12 – 14 ]. However, the diagnostic ability based on a single lipid parameter was not enough[ 15 ]. Recent studies based on ratio, as such triglyceride to high-density lipoprotein cholesterol (TG/HDL-C)[ 15 ], total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C)[ 16 ], and apolipoprotein B/AI (ApoB/A I)[ 17 ], explored the diagnostic value for NAFLD, and the AUC of TG/HDL-C is significantly higher than that based on a single lipid parameter. To date, there are limited evidence of the indexes on the ability of identifying NAFLD. Furthermore, among these indexes, the highest AUC of those was only 0.85[ 15 , 16 , 17 ]. It is utmost important to set a new index for accurately identifying individuals at high risk of NAFLD in clinical practice. As triglycerides and total cholesterol are both available on traditional lipid panels and requires no additional cost, we performed a large scale of cross-sectional analysis to investigate the association between triglycerides to total cholesterol ratio (TG/TC) and prevalence of NAFLD and evaluate the accuracy of TG/TC as a marker for NAFLD. Methods Study population The cross-sectional study population comprised individuals who visited the Health Management Center in the Third Xiangya Hospital of Central South University (Changsha), the largest medical institution in central China, between 2012 and 2018. A total of 686,264 individuals aged ≥18 years, who performed abdominal ultrasonography examination agreed to be included in this cross-sectional study. The study protocol was approved by the Medical Ethics Committee of the Third Xiangya Hospital., and were conducted according to the guidelines from the Helsinki Declaration. All the subjects has signed an informed written consent. Subjects who met the following criteria were excluded (Fig 1): 1) excessive alcohol consumption , which was defined as an average consumption of alcohol ≥140 g/week for males and ≥70 g/week for females,[18] and no available data on alcohol drinking (n =332,014) ; 2) viral hepatitis, schistosomiasis liver disease or other chronic liver diseases(n =87,609); 3) a history of taking lipid-lowering medications (n=2,393)；4) no available data on TC or TG, (n =546); 5）no available data on liver ultrasonography examination（n=170,253）. Data collection and measurements All participants underwent an interview by trained interviewers and complete questionnaires. Age, sex, alcohol drinking, smoking history, exercise, and medical history. For the analyses, cigarette smoking was recorded as smoking （smoking currently and smoking before）or never smoking. Participants were considered physically active when reporting every-day exercise. Waist circumference (WC) was measured at the umbilical level using an un-stretched tape without applying pressure to the body surface. Blood pressure (BP) was measured on the right arm in the sitting position using a corrected mercury sphygmomanometer after at least 10 min rest. Systolic BP and diastolic BP were each measured twice with a 30s interval, and the mean of the two readings was considered the participant’s BP. If the two readings differed by >5 mmHg, a third measurement was performed and the average of all three readings was applied. All measurement methods of blood samples collected from the antecubital vein, including fasting blood glucose (FBG), alanine transaminase (ALT), aspartate aminotransferase (AST), total bilirubin(TBIL), serum uric acid, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and creatinine, were described in previous study[19]. Assessment of non-alcoholic fatty liver disease The Assessment of non-alcoholic fatty liver disease has been described previously[19]. The diagnosis of NAFLD was based on liver ultrasound for the presence of liver steatosis, excluding acute or chronic liver disease and secondary hepatic fat accumulation, including excessive alcohol drinking and taking steatogenic medication[20, 21]. Experienced and trained radiologists performed the liver ultrasonography, who were blinded to the subjects’ clinical diagnosis and biochemical tests. The ultrasonographic criteria of hepatic steatosis included: diffusely increased liver near field ultrasound echo (‘bright liver’), liver echo greater than kidney, vascular blurring and the gradual attenuation of far field ultrasound echo. Participants with Two or more of the abnormal findings listed above were diagnosed with hepatic steatosis. Statistical analysis Basic features of the study participants were presented as the mean ± standard deviation (SD) for continuous variables and as numbers with percentages for categorical variables. Comparisons of basic characteristics between the NAFLD and non-NAFLD groups were tested by using Student t tests for continuous variables and Pearson’s χ2 test for categorical variables. To explore the association between the levels of TG/TC and the prevalence of NAFLD, The study subjects was divided into 4 groups according to TG/TC quartiles (Q1,Q2,Q3,Q4). Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using logistic regression analysis to determine the risk of NAFLD in each TG/TC quartile and using the lowest quartile as the reference. The area under the receiver operating characteristic curve (AUROC) was used to describe the diagnostic accuracy of TG/TC. The AUROCs were tested using a nonparametric approach. The sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) for relevant cut-offs were also displayed. All statistical analyses were conducted using Stata version 16.0 (Stata Corp, College Station, TX) and P values of <0.05 were considered statistically significant. Results Characteristics of the study subjects Totally 93,449 subjects, 37,752 men and 55,697 women with average age of 43.7 ± 14.3years, were included(Table 1 , Fig. 1 ). Among these subjects, 16,138 (42.7%) men and 9,591 (17.2%) women were diagnosed with NAFLD. Characteristics of the NAFLD and non-NAFLD groups were shown in Table 1 .The NAFLD group had a higher level of TG, TC, and LDL-C. Meanwhile, the patients with NAFLD had higher WC, SBP, DBP, FBG, ALT, AST, uric acid, creatinine and reduced TBIL, HDL-C levels than those without NAFLD. Besides, the subjects with no-smoking and exercising every day had a lower prevalence of NAFLD . Table 1 Clinical and biochemical characteristics of the study subjects with or without NAFLD ALL Non-NAFLD NAFLD P value N 93449 67720 25729 Age (years) 43.7 ± 14.3 41.6 ± 14.0 49.0 ± 13.8 ༜0.001 gender ༜0.001 Male,% 37752 21614(57.3) 16138(42.7) Female,% 55697 46106(82.8) 9591(17.2) waist(cm) 79.1 ± 10.0 75.6 ± 7.9 88.3 ± 9.3 ༜0.001 Physical activity ༜0.001 Occasionally,% 55697 42147(72.3) 16969(28.7) Everyday,% 34333 25573(74.5) 8760(25.5) smoke ༜0.001 Yes,% 64452 45854(71.1) 18598(28.9) No,% 28997 21866(75.4) 7131(24.6) SBP (mmHg) 119.6 ± 16.2 116.5 ± 15.1 128.0 ± 15.9 ༜0.001 DBP (mmHg) 72.9 ± 10.4 70.9 ± 9.7 78.3 ± 10.5 ༜0.001 FBG (mmol/L) 5.3 ± 1.1 5.2 ± 0.8 5.8 ± 1.5 ༜0.001 ALT (U/L) 25.3 ± 21.0 21.3 ± 16.8 35.9 ± 26.6 ༜0.001 AST(U/L) 22.2 ± 10.4 21.2 ± 10.2 24.1 ± 10.4 ༜0.001 TBIL (µmol/L) 16.1 ± 5.9 16.3 ± 5.9 15.7 ± 5.8 ༜0.001 Uric acid (µmol/L) 308.9 ± 85.2 288.7 ± 76.2 362.1 ± 84.8 ༜0.001 TG (mmol/L) 1.5 ± 1.2 1.2 ± 0.8 2.3 ± 1.7 ༜0.001 TC (mmol/L) 5.0 ± 1.0 4.9 ± 0.9 5.3 ± 1.0 ༜0.001 HDL-C (mmol/L) 1.4 ± 0.3 1.5 ± 0.3 1.3 ± 0.3 ༜0.001 LDL-C(mmol/L) 2.8 ± 0.8 2.8 ± 0.8 3.0 ± 0.9 ༜0.001 Creatinine (µmol/L) 84.6 ± 429.8 80.3 ± 389.5 95.7 ± 521.3 ༜0.001 Abbreviations: WC waist circumference, SBP systolic blood pressure, DBP diastolic blood pressure, FBG fasting blood glucose, ALT alanine transaminase, AST: aspartate aminotransferase, TBIL total bilirubin, TG triglyceride, TC total cholesterol, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol Continuous variables were presented as means ± SD Association between TG/TC and the prevalence of non-alcoholic fatty liver disease As was shown in Table 2 , higher quartiles of TG/TC was more associated with the increased risk of NAFLD in both men and women. In men, After adjusting for age and waist (Model2), the OR for NAFLD in the comparison between the highest and lowest quartiles of TG/TC was 5.14(95% CI 4.64, 5.69). After further adjusting smoking, physical activity, SBP, DBP, FBG, TBIL, UA, LDLC, HDLC (Model4), the OR (4.08,95%CI 3.64,4.57) in the highest compared with the lowest quartile of TG/TC was still significant. Furthermore, subgroup analysis indicated that the association between TG/TC and NAFLD was more stronger in women. In adjusted Model4, the OR (4.65, 95% CI 4.14, 5.21) in the highest compared with the lowest quartile of TG/TC was higher than that in men. When TG/TC is considered as a continuous exposure variable, the results was (per SD increment, Table 2 ). Table 2 Odds ratios for non-alcoholic fatty liver disease according to quartiles of TG/TC Quartiles of TG/TC Q1 Q2 Q3 Q4 per SD increment P for trend MAN N 14698 10535 7666 4853 Model1 1(Ref) 2.15(1.95,2.36) 4.19(3.83,4.59) 11.51(10.53,12.58) 69.42(60.43,79.74) < 0.001 Model2 1(Ref) 1.53(1.37,1.72) 2.43(2.19,2.70) 5.14(4.64,5.69) 15.77(13.65,18.21) < 0.001 Model3 1(Ref) 1.53(1.37,1.71) 2.43(2.19,2.70) 5.13(4.64,5.68) 15.73(13.62,18.10) < 0.001 Model4 1(Ref) 1.40(1.25,1.57) 2.07(1.86,2.31) 4.08(3.64,4.57) 14.49(12.19,17.20) < 0.001 WOMAN N 8667 12827 15693 18510 Model1 1(Ref) 2.46( 2.25,2.69) 5.52( 5.07, 6.01) 13.70(12.57, 14.94) 139.70(117.30,166.30) < 0.001 Model2 1(Ref) 1.91( 1.72,2.11) 3.30( 3.00, 3.64) 7.09(6.43, 7.81) 32.28(26.85,38.80) < 0.001 Model3 1(Ref) 1.91( 1.73,2.11) 3.31( 3.01, 3.64) 7.10(6.44, 7.83) 32.34(26.90,38.80) < 0.001 Model4 1(Ref) 1.63(1.47,1.81) 2.50(2.25,2.77) 4.65(4.14,5.21) 15.20(12.22,18.90) < 0.001 Model1:adjusted for age Model2:further adjusted for waist Model3:further for adjusted for smoke ,physical activity Model4:further adjusted for SBP,DBP,FBG,TBIL,UA,LDLC,HDLC Per SD increment: per SD increment of log TG/TC Accuracy Of Tg/tc For Diagnosing Non-alcoholic Fatty Liver Disease To investigate the accuracy of TG/TC for detecting NAFLD, the ROC of TG/TC was analyzed. As shown in Fig. 2 , the area under the curve (AUC) of TG/TC was 0.920 (0.917,0.923) in women and 0.863(0.859,0.867) in men, after adjusting multiple confounding factors. The sensitivity, specificity, positive predictive values (PPVs) and negative predictive values (NPVs) for both gender were shown in Supplementary Table 1, Additional File 1. Assessment of the diagnosing accuracy of TG/TC in different age groups The AUC for TG/TC as a diagnosing index of NAFLD in different age groups was showed in Table 3 . The diagnostic ability of TG/TC was significantly better in young age groups, and the AUC of TG/TC in 18–34 years group and 35–44 years group were 0.943(95%CI 0.939,0.946) and 0.921(95%CI 0.917,0.925), with NPV 0f 93.13% and 89.27%, respectively. The sensitivity, specificity, PPVs and NPVs of each age groups were shown in Table 3 . Table 3 Comparison of areas under the ROC curves of TG/TC in different ages groups Age,years N AUC(95%CI) P value sensitivity specificity PPV NPV 18–34 29470 0.943(0.939,0.946) < 0.001 59.53% 96.45% 74.64% 93.13% 35–44 21249 0.921(0.917,0.925) < 0.001 66.14% 92.77% 75.08% 89.27% 45–54 19731 0.876(0.871,0.880) < 0.001 67.60% 87.10% 75.05% 82.41% 55–64 10401 0.848(0.840,0.855) < 0.001 69.99% 82.32% 74.76% 78.57% 65–75 5690 0.830(0.820,0.841) < 0.001 67.43% 80.56% 71.86% 77.06% 75+ 3134 0.824(0.810,0.838) < 0.001 60.85% 84.07% 69.62% 78.17% Abbreviations: PPV positive predictive values;NPV negative predictive value Discussion It is already established that dyslipidaemia played the important role in NAFLD. However, the diagnostic ability of current indexes based lipids was limited. Surprisingly, in our study, the TG/TC ratio had a high diagnostic ability for identifying NAFLD than before, especially in young females. Furthermore, TG/TC had a high NPV(> 90%) in women and aged 18–34 groups, which means it could be used to exclude subjects with NAFLD for these population in clinical settings. The characteristics of TG/TC made it become a strong surrogate for screening NAFLD particularly in Chinese young females. Over the last decades, the prevalence has risen in young adults, which was often unrecognized[ 22 ]. A recent cross-sectional analysis, which evaluated the prevalence of NAFLD in subjects aged 18–35 years, suggest that the prevalence in young adults has increased almost 2.5 times over three decades, ranging from 9.6% in 1988–1994 to 24% in 2005–2010, and over one half of morbidly obese young adults had NAFLD (57.4%)[ 23 ]. Besides, some risk factors, such as obesities, type 2 diabetes, smoking, and unhealthy diets, were increasingly prevalent among young adults, which led to a higher risk for NAFLD[ 24 – 27 ]. This trend of NAFLD prompted us to explore the diagnostic ability of TG/TC in different age groups. Interestingly, our study showed that the diagnostic accuracy of TG/TC was significantly better in young age groups, in which we found an AUC of 0.943 in 18–34 years group and 0.921 in 35–44 years group. Our results supported that TG/TC was a powerful index that could be applied to screen NAFLD, especially in young population. Furthermore, the diagnostic ability of TG/TC was significantly higher than other indexes based on lipid parameters, with AUROC of 0.920 in women and 0.863 in men. Fan and his colleague analyzed the association between different serum lipids (TG, TC, LDL-C, HDL-C,) and NAFLD in a cross-sectional study, with AUROC of 0.84,0.65,0.65,0.77 respectively. Meanwhile, they found the AUC of TG/HDL-C in women and men was 0.85 (0.84–0.86) and 0.79 (0.78–0.80), respectively.[ 15 ]. A perspective cohort study including 3374 Chinese adults investigated the association between nonHDL-C/HDL-C with NAFLD, with AUROC of 0.717 in women and 0.682 in men[ 28 ]. The Jinchang Cohort study consisting of 32,121 subjects, evaluated the ability of TC/HDL-C for identifying NAFLD, and the results suggested the AUC of TC/HDL-C was 0.645[ 16 ]. Therefore, TG/TC was more powerful for identifying NAFLD. The underlying mechanism about the association between TG/TC and NAFLD has not been clarified. The association between TG/TC could be partly explained by elevated plasma triglycerides. Our study showed people with NAFLD had a higher triglycerides levels, which is consistent with previous study [ 8 , 29 ]. Triglyceride molecules represent the major form of storage and transport of fatty acids within cells and in the plasma[ 10 ]. Steatosis develops when fatty acid (FA) input rate (uptake and synthesis and subsequent esterification to TG) was greater than fatty acid output rate (oxidation and secretion), steatosis develops [ 30 ]. Besides, insulin resistance may play a role in the association between TG/TC and NAFLD. Insulin resistance accelerates NAFLD by inducing lipolysis of TG in adipose tissue and de novo synthesis of TG in the liver[ 31 ]. Furthermore, IR could be accompanied by systemic inflammation, which played an important role in hepatic steatosis[ 32 ]. Some inflammation cells activation, such as Kupfer cells[ 33 ], stellate cells [ 34 ] and circulating mononuclear cells that infiltrate the liver[ 35 ], could trigger TG accumulation in the liver. However, because of the lack data of serum insulin level, our study couldn’t investigate the relationship between TG/TC and insulin resistance. The advantages of our study included that it was a large-scale cross-sectional study and first investigated the association between TG/TC and NAFLD. Furthermore, as a novel index for diagnosing NAFLD, TG/TC was radiation-free, simple, cheap, and easy to perform, compared with CT and MRI. However, some limitations of our study were existed. First, our study was cross-sectional, and the effects of TG/TC for identifying NAFLD should be test in prospective observational studies. Second, the diagnosis of NAFLD in our study was based on ultrasonic examination. The diagnosis of ultrasonography examination could also be influenced by interobserver variation, even if the radiologist were very experienced. However, the gold standard for diagnosis of NAFLD was liver biopsy, which was not appropriate as a screening tool for a population-based epidemiological study[ 36 ]. Conclusion In conclusion, TG/TC ratio is significantly associated with the prevalence of NAFLD, and the diagnostic accuracy of TG/TC is especially high among young woman. TG/TC ratio may be a potential early screening tool for NAFLD in Chinese young females. Abbreviations ALT: alanine transaminase, AST:aspartate aminotransferase, AUC:area under the curves, DBP:diastolic blood pressure, FBG:fasting blood glucose, HDL-C high-density lipoprotein cholesterol, LDL-C:low-density lipoprotein cholesterol, NAFLD:Non-alcoholic fatty liver disease, NPV:negative predictive value, PPV:positive predictive value, ROC:receiver operator characteristic curve, SBP:systolic blood pressure, TC:total cholesterol, TG:triglycerides, WC:waist circumference Declarations Ethics approval and consent to participate The study protocol was approved by the Medical Ethics Committee of the Third Xiangya Hospital and all participants signed a written consent form. Consent for publication Not applicable Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the National Natural Science Foundation of China (81800393, 81673520), National Key Research and Development Program of China(2019YFF0216300), Sub-project of National Key Research and Development Program of China (2018YFC1311302), Hunan Youth Talent Project (2019RS2014) and the Natural Science Foundation of Hunan Province (2018JJ3783), Outstanding Young Investigator of Hunan Province(2020JJ2057), Fundamental Research Funds for Central Universities of Central South University (2019zzts1058). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Authors’ contributions Study design: HY, YL; Acquisition of the data: JW, ZC; Data analysis and interpretation of the data: YY, QY, JC; Drafting the manuscript or revising it critically for important intellectual content: JC. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64:73–84. Fan JG. Epidemiology of alcoholic and nonalcoholic fatty liver disease in China. J Gastroenterol Hepatol. 2013;28(Suppl 1):11–7. Li Z, Xue J, Chen P, Chen L, Yan S, Liu L. Prevalence of nonalcoholic fatty liver disease in mainland of China: a meta-analysis of published studies. J Gastroenterol Hepatol. 2014;29:42–51. 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The natural history of nonalcoholic fatty liver disease: a population-based cohort study. Gastroenterology. 2005;129:113–21. Fabbrini E, Sullivan S, Klein S. Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology. 2010;51:679–89. Choi SH, Ginsberg HN. Increased very low density lipoprotein (VLDL) secretion, hepatic steatosis, and insulin resistance. Trends Endocrinol Metab. 2011;22:353–63. Shoelson SE, Herrero L, Naaz A. Obesity, inflammation, and insulin resistance. Gastroenterology. 2007;132:2169–80. Huang W, Metlakunta A, Dedousis N, Zhang P, Sipula I, Dube JJ, Scott DK, O'Doherty RM. Depletion of liver Kupffer cells prevents the development of diet-induced hepatic steatosis and insulin resistance. Diabetes. 2010;59:347–57. Mollica MP, Lionetti L, Putti R, Cavaliere G, Gaita M, Barletta A. From chronic overfeeding to hepatic injury: role of endoplasmic reticulum stress and inflammation. Nutr Metab Cardiovasc Dis. 2011;21:222–30. Obstfeld AE, Sugaru E, Thearle M, Francisco AM, Gayet C, Ginsberg HN, Ables EV, Ferrante AW Jr. C-C chemokine receptor 2 (CCR2) regulates the hepatic recruitment of myeloid cells that promote obesity-induced hepatic steatosis. Diabetes. 2010;59:916–25. Rinella ME, Sanyal AJ. Management of NAFLD: a stage-based approach. Nat Rev Gastroenterol Hepatol. 2016;13:196–205. Supplementary Files AdditionalFile1.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-35196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":708133,"identity":"d8c6e459-45a7-4324-8098-99b80af2ba19","order_by":0,"name":"Jingyuan Chen","email":"","orcid":"","institution":"Central South University Third Xiangya Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingyuan","middleName":"","lastName":"Chen","suffix":""},{"id":708134,"identity":"326fc592-86d3-46cd-826c-d4b04dc2d3fe","order_by":1,"name":"Yiping Yang","email":"","orcid":"","institution":"Central South University Third Xiangya Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Yang","suffix":""},{"id":708135,"identity":"62a8a48e-cee1-4f94-b8c8-519d3b066ba1","order_by":2,"name":"Qize Yang","email":"","orcid":"","institution":"Suzhou University of Science and Technology Town Foreign Language school","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qize","middleName":"","lastName":"Yang","suffix":""},{"id":708136,"identity":"e9ade488-af0a-489f-b900-8bd3bb3e6134","order_by":3,"name":"Jiangang Wang","email":"","orcid":"","institution":"Central South University Third Xiangya Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiangang","middleName":"","lastName":"Wang","suffix":""},{"id":708137,"identity":"420fa7c0-e965-4d3d-8103-36a55d11a999","order_by":4,"name":"Zhiheng Chen","email":"","orcid":"","institution":"Central South University Third Xiangya Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiheng","middleName":"","lastName":"Chen","suffix":""},{"id":708138,"identity":"9200bd49-1466-43b8-b96e-0017f4e7566e","order_by":5,"name":"Hong Yuan","email":"","orcid":"","institution":"Central South University Third Xiangya Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Yuan","suffix":""},{"id":708139,"identity":"dfcde01d-b1b1-4236-a2ac-5f963fef4bce","order_by":6,"name":"Yao Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYJACZhDBxt7//cMHAxs74rXw8RwwY5xRkJZMvBY5iQQzZp4PhxgbCCk3uJH87HFBxR27NomEtMc2BgeYGdgPH92AX0uaufGMM8+S23geHDfOMbjDx8CTlnYDv5YEM2netsPJbOyJDdI5Bs+YGSR4zAhoSf8G0cKQzCBtYXCYsYGwlhywLXZsHGls0gzEaJE886ZMmufM4QQ2njPMhj0GaclshPzCdzx9mzRPxWF7+fYexgc//tjY8bMfPoZXi8IBCJ3YABNhw6ccBOShSu0JKRwFo2AUjIIRDAC8KEpkRx3tlAAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2020-06-12 04:49:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-35196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-35196/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1406944,"identity":"ead65072-b1f1-4795-bb0d-a40e639b7c7d","added_by":"auto","created_at":"2020-06-24 14:40:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70343,"visible":true,"origin":"","legend":"ROC curve of TG/TC in women (a) and men (b). a. The areas under the ROC curve for the TG/TC ratio in women was 0.920. b. The areas under the ROC curve for the TG/TC ratio in men was 0.863.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-35196/v1/Figure2.jpg"},{"id":1406943,"identity":"362309d6-9505-4450-8c0a-724d9622f671","added_by":"auto","created_at":"2020-06-24 14:40:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125774,"visible":true,"origin":"","legend":"Flowchart of participants.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-35196/v1/Figure1.jpg"},{"id":13544173,"identity":"be32ef8c-c4ae-49ab-83e4-bde5d77b0b93","added_by":"auto","created_at":"2021-09-17 02:01:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":476361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-35196/v1/d308d245-fe22-4b85-9bfb-140abae64c4d.pdf"},{"id":1406946,"identity":"bbb6679a-054c-449c-b6af-de64ad9ee4ba","added_by":"auto","created_at":"2020-06-24 14:40:38","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":59392,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.doc","url":"https://assets-eu.researchsquare.com/files/rs-35196/v1/AdditionalFile1.doc"}],"financialInterests":"","formattedTitle":"Triglycerides to total cholesterol ratio: an early screening tool for NAFLD in Chinese populations","fulltext":[{"header":"Introduction","content":" \u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is an expanding global health problem, with a high prevalence in the general population. Worldwide, the prevalence of NAFLD diagnosed by imaging varies between 14% and 32% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, NAFLD influences over 25% of the population, and its prevalence is rapidly increasing because of the considerable alteration in lifestyle and population age structure[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. NAFLD can increased the risk of cirrhosis, hepatocellular carcinoma, and death[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, considering the growing burden of NAFLD, early detection or screening of the disease are of utmost importance. Currently, liver biopsy is the gold standard for the diagnosis of NAFLD, but there are well-known limitations including sampling errors, invasiveness, high cost and severe complications, such as mortality, bleeding, and pain[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The imaging diagnosis of NAFLD, such as ultrasonography and magnetic resonance elastography, is more safe but still expensive and highly dependent on experienced imaging doctors[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, simple, inexpensive and noninvasive methods are urgently needed to be used for diagnosing NAFLD.\u003c/p\u003e \u003cp\u003eNumerous epidemiological studies suggest that dyslipidemia is closely related to NAFLD[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous studies demonstrated that triglycerides (TG) and cholesterol were important risk factors of NAFLD and associated with its pathogenesis in animal experiments[\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It\u0026rsquo;s well established that the accumulation of TG within hepatocytes could lead to NAFLD[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Besides, among NAFLD patients, cholesterol metabolism was significantly altered reflected by increased cholesterol synthesis and diminished absorption[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In animal experiments, high cholesterol diets and free cholesterol accumulation in hepatic stellate cells were toxic to livers of rats and mice[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the diagnostic ability based on a single lipid parameter was not enough[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recent studies based on ratio, as such triglyceride to high-density lipoprotein cholesterol (TG/HDL-C)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and apolipoprotein B/AI (ApoB/A I)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], explored the diagnostic value for NAFLD, and the AUC of TG/HDL-C is significantly higher than that based on a single lipid parameter.\u003c/p\u003e \u003cp\u003eTo date, there are limited evidence of the indexes on the ability of identifying NAFLD. Furthermore, among these indexes, the highest AUC of those was only 0.85[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is utmost important to set a new index for accurately identifying individuals at high risk of NAFLD in clinical practice. As triglycerides and total cholesterol are both available on traditional lipid panels and requires no additional cost, we performed a large scale of cross-sectional analysis to investigate the association between triglycerides to total cholesterol ratio (TG/TC) and prevalence of NAFLD and evaluate the accuracy of TG/TC as a marker for NAFLD.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cross-sectional study population comprised individuals who visited the Health Management Center in the Third Xiangya Hospital of Central South University (Changsha), the largest medical institution in central China, between 2012 and 2018. A total of 686,264 individuals aged \u0026ge;18 years, who performed abdominal ultrasonography examination agreed to be included in this cross-sectional study. The study protocol was approved by the Medical Ethics Committee of the Third Xiangya Hospital., and were conducted according to the guidelines from the Helsinki Declaration. All the subjects has signed an informed written consent. Subjects who met the following criteria were excluded (Fig 1): 1) excessive alcohol consumption , which was defined as an average consumption of alcohol \u0026ge;140 g/week for males and \u0026ge;70 g/week for females,[18] and no available data on alcohol drinking (n =332,014) ; 2) viral hepatitis, schistosomiasis liver disease or other chronic liver diseases(n =87,609); 3) a history of taking lipid-lowering medications (n=2,393)；4) no available data on TC or TG, (n =546); 5）no available data on liver ultrasonography examination（n=170,253）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants underwent an interview by trained interviewers and complete questionnaires. Age, sex, alcohol drinking, smoking history, exercise, and medical history. For the analyses, cigarette smoking was recorded as smoking （smoking currently and smoking before）or never smoking. Participants were considered physically active when reporting every-day exercise.\u003c/p\u003e\n\u003cp\u003eWaist circumference (WC) was measured at the umbilical level using an un-stretched tape without applying pressure to the body surface. Blood pressure (BP) was measured on the right arm in the sitting position using a corrected mercury sphygmomanometer after at least 10 min rest. Systolic BP and diastolic BP were each measured twice with a 30s interval, and the mean of the two readings was considered the participant\u0026rsquo;s BP. If the two readings differed by \u0026gt;5 mmHg, a third measurement was performed and the average of all three readings was applied.\u003c/p\u003e\n\u003cp\u003eAll measurement methods of blood samples collected from the antecubital vein, including fasting blood glucose (FBG), alanine transaminase (ALT), aspartate aminotransferase (AST), total bilirubin(TBIL), serum uric acid, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and creatinine, were described in previous study[19].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of non-alcoholic fatty liver disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Assessment of non-alcoholic fatty liver disease has been described previously[19]. The diagnosis of NAFLD was based on liver ultrasound for the presence of liver steatosis, excluding acute or chronic liver disease and secondary hepatic fat accumulation, including excessive alcohol drinking and taking steatogenic medication[20, 21]. Experienced and trained radiologists performed the liver ultrasonography, who were blinded to the subjects\u0026rsquo; clinical diagnosis and biochemical tests. The ultrasonographic criteria of hepatic steatosis included: diffusely increased liver near field ultrasound echo (\u0026lsquo;bright liver\u0026rsquo;), liver echo greater than kidney, vascular blurring and the gradual attenuation of far field ultrasound echo. Participants with Two or more of the abnormal findings listed above were diagnosed with hepatic steatosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasic features of the study participants were presented as the mean \u0026plusmn; standard deviation (SD) for continuous variables and as numbers with percentages for categorical variables. Comparisons of basic characteristics between the NAFLD and non-NAFLD groups were tested by using Student t tests for continuous variables and Pearson\u0026rsquo;s \u0026chi;2 test for categorical variables. To explore the association between the levels of TG/TC and the prevalence of NAFLD, The study subjects was divided into 4 groups according to TG/TC quartiles (Q1,Q2,Q3,Q4). Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using logistic regression analysis to determine the risk of NAFLD in each TG/TC quartile and using the lowest quartile as the reference. The area under the receiver operating characteristic curve (AUROC) was used to describe the diagnostic accuracy of TG/TC. The AUROCs were tested using a nonparametric approach. The sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) for relevant cut-offs were also displayed. All statistical analyses were conducted using Stata version 16.0 (Stata Corp, College Station, TX) and P values of \u0026lt;0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study subjects\u003c/h2\u003e \u003cp\u003eTotally 93,449 subjects, 37,752 men and 55,697 women with average age of 43.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3years, were included(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among these subjects, 16,138 (42.7%) men and 9,591 (17.2%) women were diagnosed with NAFLD. Characteristics of the NAFLD and non-NAFLD groups were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.The NAFLD group had a higher level of TG, TC, and LDL-C. Meanwhile, the patients with NAFLD had higher WC, SBP, DBP, FBG, ALT, AST, uric acid, creatinine and reduced TBIL, HDL-C levels than those without NAFLD. Besides, the subjects with no-smoking and exercising every day had a lower prevalence of NAFLD .\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\u003eClinical and biochemical characteristics of the study subjects with or without NAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-NAFLD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25729\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21614(57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16138(42.7)\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\u003eFemale,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46106(82.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9591(17.2)\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\u003ewaist(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\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༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42147(72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16969(28.7)\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\u003eEveryday,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25573(74.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8760(25.5)\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\u003esmoke\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༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45854(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18598(28.9)\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\u003eNo,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21866(75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7131(24.6)\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\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308.9\u0026thinsp;\u0026plusmn;\u0026thinsp;85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288.7\u0026thinsp;\u0026plusmn;\u0026thinsp;76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e362.1\u0026thinsp;\u0026plusmn;\u0026thinsp;84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.6\u0026thinsp;\u0026plusmn;\u0026thinsp;429.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.3\u0026thinsp;\u0026plusmn;\u0026thinsp;389.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.7\u0026thinsp;\u0026plusmn;\u0026thinsp;521.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e༜0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: WC waist circumference, SBP systolic blood pressure, DBP diastolic blood pressure, FBG fasting blood glucose, ALT alanine transaminase, AST: aspartate aminotransferase, TBIL total bilirubin, TG triglyceride, TC total cholesterol, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation between TG/TC and the prevalence of non-alcoholic fatty liver disease\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs was shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, higher quartiles of TG/TC was more associated with the increased risk of NAFLD in both men and women. In men, After adjusting for age and waist (Model2), the OR for NAFLD in the comparison between the highest and lowest quartiles of TG/TC was 5.14(95% CI 4.64, 5.69). After further adjusting smoking, physical activity, SBP, DBP, FBG, TBIL, UA, LDLC, HDLC (Model4), the OR (4.08,95%CI 3.64,4.57) in the highest compared with the lowest quartile of TG/TC was still significant. Furthermore, subgroup analysis indicated that the association between TG/TC and NAFLD was more stronger in women. In adjusted Model4, the OR (4.65, 95% CI 4.14, 5.21) in the highest compared with the lowest quartile of TG/TC was higher than that in men. When TG/TC is considered as a continuous exposure variable, the results was (per SD increment, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdds ratios for non-alcoholic fatty liver disease according to quartiles of TG/TC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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\u003eQuartiles of TG/TC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eper SD increment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAN\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\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4853\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.15(1.95,2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19(3.83,4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.51(10.53,12.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.42(60.43,79.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53(1.37,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.43(2.19,2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.14(4.64,5.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.77(13.65,18.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53(1.37,1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.43(2.19,2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.13(4.64,5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.73(13.62,18.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40(1.25,1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07(1.86,2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.08(3.64,4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.49(12.19,17.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWOMAN\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\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18510\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46( 2.25,2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.52( 5.07, 6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.70(12.57, 14.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139.70(117.30,166.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91( 1.72,2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.30( 3.00, 3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.09(6.43, 7.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.28(26.85,38.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91( 1.73,2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31( 3.01, 3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.10(6.44, 7.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.34(26.90,38.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63(1.47,1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50(2.25,2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.65(4.14,5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.20(12.22,18.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel1:adjusted for age\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel2:further adjusted for waist\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel3:further for adjusted for smoke ,physical activity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel4:further adjusted for SBP,DBP,FBG,TBIL,UA,LDLC,HDLC\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePer SD increment: per SD increment of log TG/TC\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003ch2\u003eAccuracy Of Tg/tc For Diagnosing Non-alcoholic Fatty Liver Disease\u003c/h2\u003e \u003cp\u003eTo investigate the accuracy of TG/TC for detecting NAFLD, the ROC of TG/TC was analyzed. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the area under the curve (AUC) of TG/TC was 0.920 (0.917,0.923) in women and 0.863(0.859,0.867) in men, after adjusting multiple confounding factors. The sensitivity, specificity, positive predictive values (PPVs) and negative predictive values (NPVs) for both gender were shown in Supplementary Table\u0026nbsp;1, Additional File 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of the diagnosing accuracy of TG/TC in different age groups\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe AUC for TG/TC as a diagnosing index of NAFLD in different age groups was showed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The diagnostic ability of TG/TC was significantly better in young age groups, and the AUC of TG/TC in 18\u0026ndash;34\u0026nbsp;years group and 35\u0026ndash;44\u0026nbsp;years group were 0.943(95%CI 0.939,0.946) and 0.921(95%CI 0.917,0.925), with NPV 0f 93.13% and 89.27%, respectively. The sensitivity, specificity, PPVs and NPVs of each age groups were shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of areas under the ROC curves of TG/TC in different ages groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003esensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003especificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.943(0.939,0.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.921(0.917,0.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89.27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.876(0.871,0.880)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848(0.840,0.855)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.830(0.820,0.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.824(0.810,0.838)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78.17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: PPV positive predictive values;NPV negative predictive value\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eIt is already established that dyslipidaemia played the important role in NAFLD. However, the diagnostic ability of current indexes based lipids was limited. Surprisingly, in our study, the TG/TC ratio had a high diagnostic ability for identifying NAFLD than before, especially in young females. Furthermore, TG/TC had a high NPV(\u0026gt;\u0026thinsp;90%) in women and aged 18\u0026ndash;34 groups, which means it could be used to exclude subjects with NAFLD for these population in clinical settings. The characteristics of TG/TC made it become a strong surrogate for screening NAFLD particularly in Chinese young females.\u003c/p\u003e \u003cp\u003eOver the last decades, the prevalence has risen in young adults, which was often unrecognized[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A recent cross-sectional analysis, which evaluated the prevalence of NAFLD in subjects aged 18\u0026ndash;35 years, suggest that the prevalence in young adults has increased almost 2.5 times over three decades, ranging from 9.6% in 1988\u0026ndash;1994 to 24% in 2005\u0026ndash;2010, and over one half of morbidly obese young adults had NAFLD (57.4%)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Besides, some risk factors, such as obesities, type 2 diabetes, smoking, and unhealthy diets, were increasingly prevalent among young adults, which led to a higher risk for NAFLD[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This trend of NAFLD prompted us to explore the diagnostic ability of TG/TC in different age groups. Interestingly, our study showed that the diagnostic accuracy of TG/TC was significantly better in young age groups, in which we found an AUC of 0.943 in 18\u0026ndash;34\u0026nbsp;years group and 0.921 in 35\u0026ndash;44\u0026nbsp;years group. Our results supported that TG/TC was a powerful index that could be applied to screen NAFLD, especially in young population.\u003c/p\u003e \u003cp\u003eFurthermore, the diagnostic ability of TG/TC was significantly higher than other indexes based on lipid parameters, with AUROC of 0.920 in women and 0.863 in men. Fan and his colleague analyzed the association between different serum lipids (TG, TC, LDL-C, HDL-C,) and NAFLD in a cross-sectional study, with AUROC of 0.84,0.65,0.65,0.77 respectively. Meanwhile, they found the AUC of TG/HDL-C in women and men was 0.85 (0.84\u0026ndash;0.86) and 0.79 (0.78\u0026ndash;0.80), respectively.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A perspective cohort study including 3374 Chinese adults investigated the association between nonHDL-C/HDL-C with NAFLD, with AUROC of 0.717 in women and 0.682 in men[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The Jinchang Cohort study consisting of 32,121 subjects, evaluated the ability of TC/HDL-C for identifying NAFLD, and the results suggested the AUC of TC/HDL-C was 0.645[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, TG/TC was more powerful for identifying NAFLD.\u003c/p\u003e \u003cp\u003eThe underlying mechanism about the association between TG/TC and NAFLD has not been clarified. The association between TG/TC could be partly explained by elevated plasma triglycerides. Our study showed people with NAFLD had a higher triglycerides levels, which is consistent with previous study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Triglyceride molecules represent the major form of storage and transport of fatty acids within cells and in the plasma[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Steatosis develops when fatty acid (FA) input rate (uptake and synthesis and subsequent esterification to TG) was greater than fatty acid output rate (oxidation and secretion), steatosis develops [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Besides, insulin resistance may play a role in the association between TG/TC and NAFLD. Insulin resistance accelerates NAFLD by inducing lipolysis of TG in adipose tissue and de novo synthesis of TG in the liver[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, IR could be accompanied by systemic inflammation, which played an important role in hepatic steatosis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Some inflammation cells activation, such as Kupfer cells[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], stellate cells [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and circulating mononuclear cells that infiltrate the liver[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], could trigger TG accumulation in the liver. However, because of the lack data of serum insulin level, our study couldn\u0026rsquo;t investigate the relationship between TG/TC and insulin resistance.\u003c/p\u003e \u003cp\u003eThe advantages of our study included that it was a large-scale cross-sectional study and first investigated the association between TG/TC and NAFLD. Furthermore, as a novel index for diagnosing NAFLD, TG/TC was radiation-free, simple, cheap, and easy to perform, compared with CT and MRI. However, some limitations of our study were existed. First, our study was cross-sectional, and the effects of TG/TC for identifying NAFLD should be test in prospective observational studies. Second, the diagnosis of NAFLD in our study was based on ultrasonic examination. The diagnosis of ultrasonography examination could also be influenced by interobserver variation, even if the radiologist were very experienced. However, the gold standard for diagnosis of NAFLD was liver biopsy, which was not appropriate as a screening tool for a population-based epidemiological study[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eIn conclusion, TG/TC ratio is significantly associated with the prevalence of NAFLD, and the diagnostic accuracy of TG/TC is especially high among young woman. TG/TC ratio may be a potential early screening tool for NAFLD in Chinese young females.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eALT: alanine transaminase, AST:aspartate aminotransferase, AUC:area under the curves, DBP:diastolic blood pressure, FBG:fasting blood glucose, HDL-C high-density lipoprotein cholesterol, LDL-C:low-density lipoprotein cholesterol, NAFLD:Non-alcoholic fatty liver disease, NPV:negative predictive value, PPV:positive predictive value, ROC:receiver operator characteristic curve, SBP:systolic blood pressure, TC:total cholesterol, TG:triglycerides, WC:waist circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Medical Ethics Committee of the Third Xiangya Hospital and all participants signed a written consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the National Natural Science Foundation of China (81800393, 81673520), National Key Research and Development Program of China(2019YFF0216300), Sub-project of National Key Research and Development Program of China (2018YFC1311302), Hunan Youth Talent Project (2019RS2014) and the Natural Science Foundation of Hunan Province (2018JJ3783), Outstanding Young Investigator of Hunan Province(2020JJ2057), Fundamental Research Funds for Central Universities of Central South University (2019zzts1058). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design: HY, YL; Acquisition of the data: JW, ZC; Data analysis and interpretation of the data: YY, QY, JC; Drafting the manuscript or revising it critically for important intellectual content: JC. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eYounossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64:73\u0026ndash;84.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eFan JG. Epidemiology of alcoholic and nonalcoholic fatty liver disease in China. J Gastroenterol Hepatol. 2013;28(Suppl 1):11\u0026ndash;7.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLi Z, Xue J, Chen P, Chen L, Yan S, Liu L. Prevalence of nonalcoholic fatty liver disease in mainland of China: a meta-analysis of published studies. J Gastroenterol Hepatol. 2014;29:42\u0026ndash;51.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eYounossi ZM, Blissett D, Blissett R, Henry L, Stepanova M, Younossi Y, Racila A, Hunt S, Beckerman R. The economic and clinical burden of nonalcoholic fatty liver disease in the United States and Europe. Hepatology. 2016;64:1577\u0026ndash;86.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eYounossi Z, Henry L. 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Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology. 2010;51:1979\u0026ndash;87.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eVan Rooyen DM, Larter CZ, Haigh WG, Yeh MM, Ioannou G, Kuver R, Lee SP, Teoh NC, Farrell GC. Hepatic free cholesterol accumulates in obese, diabetic mice and causes nonalcoholic steatohepatitis. Gastroenterology. 2011;141:1393\u0026ndash;403. 1403 e1391-1395.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eAlves-Bezerra M, Cohen DE. Triglyceride Metabolism in the Liver. Compr Physiol. 2017;8:1\u0026ndash;8.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSimonen P, Kotronen A, Hallikainen M, Sevastianova K, Makkonen J, Hakkarainen A, Lundbom N, Miettinen TA, Gylling H, Yki-Jarvinen H. Cholesterol synthesis is increased and absorption decreased in non-alcoholic fatty liver disease independent of obesity. J Hepatol. 2011;54:153\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChan J, Sharkey FE, Kushwaha RS, VandeBerg JF, VandeBerg JL. Steatohepatitis in laboratory opossums exhibiting a high lipemic response to dietary cholesterol and fat. Am J Physiol Gastrointest Liver Physiol. 2012;303:G12\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBass NM. Lipidomic dissection of nonalcoholic steatohepatitis: moving beyond foie gras to fat traffic. Hepatology. 2010;51:4\u0026ndash;7.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eTomita K, Teratani T, Suzuki T, Shimizu M, Sato H, Narimatsu K, Okada Y, Kurihara C, Irie R, Yokoyama H, et al. Free cholesterol accumulation in hepatic stellate cells: mechanism of liver fibrosis aggravation in nonalcoholic steatohepatitis in mice. Hepatology. 2014;59:154\u0026ndash;69.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eFan N, Peng L, Xia Z, Zhang L, Song Z, Wang Y, Peng Y. Triglycerides to high-density lipoprotein cholesterol ratio as a surrogate for nonalcoholic fatty liver disease: a cross-sectional study. Lipids Health Dis. 2019;18:39.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRen XY, Shi D, Ding J, Cheng ZY, Li HY, Li JS, Pu HQ, Yang AM, He CL, Zhang JP, et al. Total cholesterol to high-density lipoprotein cholesterol ratio is a significant predictor of nonalcoholic fatty liver: Jinchang cohort study. Lipids Health Dis. 2019;18:47.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChoe YG, Jin W, Cho YK, Chung WG, Kim HJ, Jeon WK, Kim BI. Apolipoprotein B/AI ratio is independently associated with non-alcoholic fatty liver disease in nondiabetic subjects. J Gastroenterol Hepatol. 2013;28:678\u0026ndash;83.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLee YJ, Lee HR, Shim JY, Moon BS, Lee JH, Kim JK. Relationship between white blood cell count and nonalcoholic fatty liver disease. Dig Liver Dis. 2010;42:888\u0026ndash;94.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eDai H, Wang W, Chen R, Chen Z, Lu Y, Yuan H. Lipid accumulation product is a powerful tool to predict non-alcoholic fatty liver disease in Chinese adults. Nutr Metab (Lond). 2017;14:49.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChalasani N, Younossi Z, Lavine JE, Diehl AM, Brunt EM, Cusi K, Charlton M, Sanyal AJ: The diagnosis and management of non-alcoholic fatty liver disease: practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology 2012, 55:2005\u0026ndash;2023.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eFan JG, Jia JD, Li YM, Wang BY, Lu LG, Shi JP, Chan LY, Chinese Association for the Study of Liver D: Guidelines for the diagnosis and management of nonalcoholic fatty liver disease: update 2010: (published in Chinese on Chinese Journal of Hepatology 2010; 18:163\u0026ndash;166). J Dig Dis 2011, 12:38\u0026ndash;44.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eDoycheva I, Watt KD, Alkhouri N. Nonalcoholic fatty liver disease in adolescents and young adults: The next frontier in the epidemic. Hepatology. 2017;65:2100\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMrad RA, Merjaneh N, Mubarak G, Lopez R, Zein NN, Alkhouri N. The increasing burden of nonalcoholic fatty liver disease among young adults in the United States: A growing epidemic. 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Diabetes. 2010;59:347\u0026ndash;57.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMollica MP, Lionetti L, Putti R, Cavaliere G, Gaita M, Barletta A. From chronic overfeeding to hepatic injury: role of endoplasmic reticulum stress and inflammation. Nutr Metab Cardiovasc Dis. 2011;21:222\u0026ndash;30.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eObstfeld AE, Sugaru E, Thearle M, Francisco AM, Gayet C, Ginsberg HN, Ables EV, Ferrante AW Jr. C-C chemokine receptor 2 (CCR2) regulates the hepatic recruitment of myeloid cells that promote obesity-induced hepatic steatosis. Diabetes. 2010;59:916\u0026ndash;25.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRinella ME, Sanyal AJ. Management of NAFLD: a stage-based approach. Nat Rev Gastroenterol Hepatol. 2016;13:196\u0026ndash;205.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TG/TC, Non-alcoholic fatty liver disease, gender, age","lastPublishedDoi":"10.21203/rs.3.rs-35196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-35196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-alcoholic fatty liver disease(NAFLD) has a high prevalence in the general population worldwide. Both triglycerides (TG) and total cholesterol (TC) are correlated with the prevalence of NAFLD. The study purpose is to determine whether TG/TC is an effective method to screen NAFLD in the general Chinese population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e93,449 subjects with average age of 43.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u0026nbsp;years were included in this cross-sectional study. Multiple logistic regressions and receiver operator characteristic curve (ROC) analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong these subjects, 16,138 (42.7%) men and 9,591 (17.2%) women were diagnosed with NAFLD. Subjects in the higher quartiles of TG/TC had a higher prevalence of NAFLD. After adjusting multiple confounding factors, the odds ratio (OR) for NAFLD in the highest compared with the lowest quartile was 4.08 (95%CI 3.64, 4.57) in men and 4.65 (95%CI 4.14, 5.21) in women. Moreover, ROC analyses suggested TG/TC showed high diagnostic ability for detecting NAFLD, and the areas under the curves (AUC) in men and women were 0.920 (95%CI 0.917, 0.923) and 0.863 (95%CI 0.859, 0.867), respectively. Furthermore, the diagnostic ability was significantly higher in younger age groups. The AUC in 18\u0026ndash;34 and 35\u0026ndash;44\u0026nbsp;years group were 0.943 (95%CI 0.939, 0.946) and 0.921 (95%CI 0.917, 0.925), respectively. Besides, the women and the young people had a greater negative predictive values (90.84% for women, 93.13% for aged 18\u0026ndash;34).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTG/TC shows a high diagnostic accuracy for identifying NAFLD in young women, providing an important clue to establish a new tool to screen NAFLD particularly in Chinese young females.\u003c/p\u003e","manuscriptTitle":"Triglycerides to total cholesterol ratio: an early screening tool for NAFLD in Chinese populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-06-24 14:40:37","doi":"10.21203/rs.3.rs-35196/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da595663-53c6-420a-9357-d6fd7f57e97e","owner":[],"postedDate":"June 24th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":129694,"name":"Nutrition \u0026 Dietetics"}],"tags":[],"updatedAt":"2020-07-02T12:19:04+00:00","versionOfRecord":[],"versionCreatedAt":"2020-06-24 14:40:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-35196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-35196","identity":"rs-35196","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}