Genetic variation of HSD17B13 is associated with an increased risk of lean nonalcoholic fatty liver disease

Genetic variation of HSD17B13 is associated with an increased risk of lean nonalcoholic fatty liver disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic variation of HSD17B13 is associated with an increased risk of lean nonalcoholic fatty liver disease Hong Fan, Xin Zhang, Pengyan Zhang, Zhenqiu Liu, Xinyu Han, Tingting Shi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1469582/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/purpose: Carriage of HSD17B13 rs72613567:TA is associated with a reduced risk of nonalcoholic fatty liver disease (NAFLD); however, whether this protective effect exists in lean NAFLD remains unknown. Therefore, we compared the association of HSD17B13 rs72613567 and NAFLD between lean and non-lean individuals. Methods: We tested the association of HSD17B13 rs72613567 with NAFLD, cirrhosis, and hepatocellular carcinoma (HCC) in 313,309 individuals from the UK Biobank, including 1464 patients with NAFLD, 1559 with cirrhosis, and 526 with HCC. We calculated the minor allele frequency (MAF) of HSD17B13 rs72613567 and analyzed this SNP using codominant, dominant, and recessive models. Furthermore, we calculated the population-attributable fraction (PAF) and the combined PAF for five SNPs ( HSD17B13 rs72613567, TM6SF2 rs58542926, MBOAT7 rs641738, PNPLA3 rs738409, and GCKR rs1260326) and used multifactor dimensionality reduction (MDR) to analyze the interactions between HSD17B13 and the other four SNPs. Results: The MAF of HSD17B13 rs72613567 was considerably higher in lean NAFLD (32.57%) than in non-lean NAFLD (26.16%). Moreover, homozygosity of the TA allele showed a significant risk effect in the codominant (OR = 1.94; 95% CI: 1.09–3.44) and recessive models (OR = 1.94; 95% CI: 1.12–3.35). By contrast, in the case of cirrhosis, HCC, and non-lean NAFLD, both homozygosity and heterozygosity of the TA allele showed a protective effect. Finally, the MDR analysis did not detect any interaction between HSD17B13 and the other four SNPs. Conclusion: The polymorphism of HSD17B13 rs72613567 is different between lean and non-lean NAFLD, and the TA allele showed a risk effect for lean NAFLD. NAFLD lean NAFLD genetic variation HSD17B13 PNPLA3 TM6SF2 cirrhosis hepatocellular carcinoma population-attributable fraction multifactor dimensionality reduction minor allele frequency Figures Figure 1 Figure 2 Introduction Nonalcoholic fatty liver disease (NAFLD) has emerged as a leading cause of chronic liver disease worldwide in the past few decades. NAFLD is particularly common among patients with obesity, however, NAFLD also occurs in lean individuals [ 1 – 4 ]; the prevalence of lean NAFLD varies across different regions and populations, with the overall prevalence being 19.2% [ 2 ]. Compared with non-lean NAFLD, lean NAFLD presents better metabolic characteristics and a more benign clinical course [ 3 , 5 , 6 ], but higher cumulative mortality [ 7 ], and it also shows characteristics distinct from those of non-lean NAFLD in terms of parameters such as serological factors, intestinal flora, and metabolism [ 6 , 8 – 10 ]. Thus, lean NAFLD has gradually been taken seriously as a distinct entity [ 9 ]. Genetic factors play a key role across the spectrum of NAFLD pathogenesis. Certain loci associated with increased risk of NAFLD have been robustly validated, with the most crucial of these being the loci for patatin-like phospholipase domain-containing 3 ( PNPLA3 ) rs738409, transmembrane 6 superfamily 2 ( TM6SF2 ) rs58542926, membrane-bound O-acyltransferase domain-containing 7 ( MBOAT7 ) rs641738, and glucokinase regulatory protein ( GCKR ) rs1260326. While in 2018, a genetic variant of 17-β-hydroxysteroid dehydrogenase 13 ( HSD17B13 ) rs72613567 was identified as being protective against NAFLD in a genome-wide association study [ 11 ]. HSD17B13 encodes the hepatic lipid-droplet enzyme HSD17B13, while NAFLD is histologically characterized by a massive accumulation of liver lipid droplets [ 12 , 13 ]. The TA allele in HSD17B13 rs72613567 is a loss-of-function variant harboring an indel (insertion of A) that leads to the production of a truncated protein exhibiting diminished enzymatic activity [ 11 , 12 , 14 ]. In 2018, Abul-Husn et al. [ 11 ] first reported that the HSD17B13 variant was associated with reduced levels of alanine transaminase (ALT) and showed a protective effect against chronic liver disease. Subsequent studies revealed that HSD17B13 rs72613567: TA was associated with the prevention of nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and superior prognosis for chronic liver disease [ 15 – 18 ]. In patients with NAFLD, carriage of TA in HSD17B13 rs72613567 was shown to be associated with a lower grade of hepatocyte ballooning [ 19 ]. Previous study showed the frequency of the TA allele of HSD17B13 rs72613567 was markedly decreased in patients with chronic liver disease and HCC patients [ 20 ]. A recent study conducted in 111,612 individuals showed that the ALT-lowering effect of HSD17B13 rs72613567: TA was amplified by increasing adiposity [ 21 ], and a genome-wide interaction study showed that HSD17B13 was modified by the body mass index (BMI) [ 22 ]. In a study employing 356 biopsy-proven NAFLD cases, the protective effect of HSD17B13 rs72613567: TA was found to disappear when BMI was included in the regression model [ 14 ], and the protective effect of HSD17B13 rs72613567: TA against steatohepatitis risk was recently shown to be only relevant among patients with a BMI ≥ 35 kg/m 2 [ 23 ]. These lines of evidence collectively indicate that adiposity might modify the effect of the HSD17B13 variant in NAFLD. However, the potential differences in the effect and frequency of the TA allele of HSD17B13 between lean and non-lean NAFLD remain obscure. An enhanced understanding of the unique characteristics of lean NAFLD could facilitate future prevention of NAFLD among distinct patient subgroups. To disentangle the features of HSD17B13 between lean and non-lean NAFLD, we leveraged the complete UK Biobank dataset to reveal the separate and combined effects of HSD17B13 rs72613567 and the other four NAFLD-related SNPs in lean versus non-lean individuals. Methods Data source and study population This study was conducted using data from the UK Biobank, a population-based prospective cohort comprising the epidemiological and genetic data of 502,505 individuals, aged 37–73 years, who were recruited across the UK between 2006 to 2010 [ 24 ]. We included the available information on NAFLD, cirrhosis, and HCC cases from 313, 309 people of European ancestry in the UK Biobank. Lean individuals were defined as persons with a BMI ≤ 25 kg/m 2 according to the World Health Organization norms for Europeans [ 25 ]. NAFLD, cirrhosis, and HCC cases were defined based on the diagnosis codes from hospitalization records (data-fields 41270 and 41271) in the UK Biobank. Individuals with ICD-10 code K76 and/or ICD-9 code 5718, but without hepatitis B or C infection, other types of viral hepatitis (as per the results of antigen testing and/or ICD-10 code listing as B15–B19 in hospital records), or other specific liver diseases were characterized as NAFLD cases [ 26 ]. Individuals with ICD-10 code K74 [ 26 ] were characterized as cirrhosis cases, and those with code C22 were characterized as HCC cases. Our analysis did not include patients whose standing height or weight data were lacking in the records. Moreover, the NAFLD study population excluded individuals considered to drink excessive amounts of alcohol, defined as > 30 g/day for males and > 20 g/day for females; the daily pure-alcohol intake in grams was calculated by multiplying the average number of alcoholic drinks consumed by the average grams of alcohol contained in each type of drink [ 27 ]. Participants enrolled in the UK Biobank signed the required consent forms. Anthropometric and biochemical data The definition of lean NAFLD is based on the BMI and involves only two anthropometric indicators—height and weight. In this study, we further focused on the following additional anthropometric indicators: waist circumference, hip circumference, waist-to-hip ratio (WHR), trunk fat percentage, trunk fat mass, body fat percentage, and whole-body fat mass. We also compared the differences in the following metabolism-associated serum indicators: albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), high-density lipoprotein (HDL), low-density lipoprotein (LDL), fasting blood glucose, triglycerides, hemoglobin A1c (HbA1c), and total cholesterol. Data extraction and genotyping Venous blood samples were collected and genomic DNA was subsequently extracted using the standard laboratory procedures in the UK Biobank [ 28 ]. We included four robustly validated risk loci ( TM6SF2 rs58542926, MBOAT7 rs641738, PNPLA3 rs738409, and GCKR rs1260326) and one protective locus ( HSD17B13 rs72613567) associated with NAFLD. Genotyping was performed using the Affymetrix UK BiLEVE Axiom array on 50,000 participants and the Affymetrix UK Biobank Axiom array on 450,000 participants in the UK Biobank [ 28 ]. Genotype call clustering in the UK Biobank was assessed using ScatterShot (McCarthy Group, Oxford, UK) (26). Statistical analyses In the study design phase, we used Logistic regression to test the differences in five NAFLD-associated SNPs between lean and non-lean patients ( TM6SF2 rs58542926, MBOAT7 rs641738, PNPLA3 rs738409, and GCKR rs1260326, HSD17B13 rs72613567). Further more, we characterized the study population according to the HSD17B13 genotypes (TT, TAT, and TATA) by using descriptive statistics. Continuous variables are presented here as the mean ± standard deviation, and categorical variables are reported as numbers and percentages. The WHR was calculated as the waist circumference divided by the hip circumference. The BMI was calculated as the body weight (in kilograms) divided by the body height (in meters) squared. Baseline characteristics among the genotypes of HSD17B13 rs72613567 were compared using the chi-square test, Student’s t -test, ANOVA, and Fisher’s exact test, as appropriate. We calculated the minor allele frequency (MAF) of HSD17B13 rs72613567 and analyzed the SNPs using codominant, dominant, and recessive models. Odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were used to estimate the effect. The multifactor dimensionality reduction (MDR) [ 29 , 30 ] algorithm was implemented to screen for the best synergistic model and detect and characterize the interactions between HSD17B13 rs72613567 and the other four SNPs in lean NAFLD and non-lean NAFLD. The 10-fold cross-validation consistency, the testing balanced accuracy, and the sign test results were calculated. Models featuring a cross-validation consistency of 10/10 and a testing balanced accuracy > 0.50 were regarded as the best interaction models. Interactions were considered to be absent if the P-value > 0.05. The population-attributable fraction (PAF) is used to estimate the proportion of a disorder attributable to a given risk factor [ 18 ]. The PAF was estimated for heterozygous and homozygous carriage by using the following formula [ 31 , 32 ]: $$\text{P}\text{A}\text{F}=\frac{\left(x-1\right)}{x}$$ , $$x={\left(1-p\right)}^{2}+2p\left(1-p\right){OR}_{1}+{p}^{2}{OR}_{2}$$ , where \(p\) is the allele frequency in lean NAFLD or non-lean NAFLD and OR 1 and OR 2 are the ORs associated with heterozygosity and homozygosity, respectively. Assuming no multiplicative interaction between the SNPs, we calculated the combined PAF for the five SNPs based on the individual PAFs for each associated SNP to estimate the combination effect [ 18 ]. The following formula was used for calculating the combined PAF: $$\text{P}\text{A}\text{F}=1-\left(1-{\text{P}\text{A}\text{F}}_{1}\right)\left(1-{\text{P}\text{A}\text{F}}_{2}\right)\left(1-{\text{P}\text{A}\text{F}}_{\text{n}}\right)$$ To determine whether the differential appearance of HSD17B13 rs72613567 between lean and non-lean patients occurred only in the case of NAFLD, we further estimated the effect of the allele in cirrhosis and HCC. The methods used here were the same as the aforementioned methods used for NAFLD. We additionally implemented multivariable logistic regression to assess the effect of HSD17B13 rs72613567 in NAFLD, cirrhosis, and HCC in lean versus non-lean individuals. The results are expressed as ORs with their corresponding 95% CIs. In multivariable logistic regression model, covariates were set in three ways, model 1 was adjusted age and sex, model 2 additional adjusted for BMI, WHR, trunk fat percentage, trunk fat mass, body fat percentage and body whole body fat mass, Model 3 was a fully adjusted model that additional adjusted for albumin, ALT, AST, fasting blood glucose, HDL, LDL, triglycerides, total cholesterol. Statistical analyses were performed using the “SNPassoc,” “survival,” and “ggplot2” packages in R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). The MDR approach was implemented using MDR 3.0.2 (build 2) software. Nominal two-sided asymptotic P-values are reported for all tests. P < 0.05 was considered statistically significant. Results Study population and baseline characteristics Overall, this study included 1464 NAFLD cases (109 lean and 1353 non-lean), 1559 cirrhosis cases (326 lean and 1213 non-lean), 526 HCC cases (131 lean and 394 non-lean), and 309,760 healthy controls. All the individuals were Caucasian. The main anthropometric and biochemical characteristics of NAFLD stratified according to the HSD17B13 rs72613567 genotype are summarized in Table 1 . Lean NAFLD accounted for 7.45% of the cases, and age distribution showed little difference between patients with lean and non-lean NAFLD (mean age 58.34 vs. 57.81 years). The proportion of males was lower among lean NAFLD than among non-lean NAFLD cases (38.53% vs. 43.75%). The BMI, waist circumference, and WHR values were significantly different across the HSD17B13 rs72613567 genotypes in lean NAFLD but not in non-lean NAFLD. Moreover, in the case of lean NAFLD, triglyceride levels were significantly higher in patients who were homozygous for the TA allele than in patients with the TT or TAT genotype for HSD17B13 rs72613567; however, this difference was not observed in non-lean NAFLD. In addition, fasting blood glucose levels were increased in the case of heterozygous TAT and homozygous TATA genotypes in patients with lean NAFLD (Table 1 ). Table 1 Characteristics of study participants according to HSD17B13 genotype between lean NAFLD and non-lean NAFLD Trait Lean NAFLD (n = 109) Non-lean NAFLD (n = 1353) Genotypes TT (53) TAT (41) TATA (15) Overall P value TT (744) TAT (510) TATA (99) Overall P value Sex (male/female) 18/35 15/26 9/6 42/67 0.182 331/413 219/291 42/57 593/762 0.940 Age (years, mean ± SD) 56.85 ± 8.75 59.44 ± 7.61 60.60 ± 8.32 58.34 ± 8.34 0.174 57.50 ± 7.81 58.14 ± 7.47 58.46 ± 7.19 57.81 ± 7.64 0.278 Weight (kg, mean ± SD) 62.21 ± 9.27 64.34 ± 7.66 67.63 ± 8.17 63.75 ± 8.67 0.086 90.78 ± 15.76 92.03 ± 17.64 91.08 ± 17.42 91.26 ± 16.61 0.476 Height (cm, mean ± SD) 166.37 ± 8.19 165.57 ± 8.29 168.85 ± 10.09 166.41 ± 8.49 0.444 167.11 ± 8.94 167.44 ± 9.34 167.31 ± 9.96 167.25 ± 9.16 0.941 BMI (kg/m 2 , mean ± SD) 22.39 ± 2.12 23.39 ± 1.15 23.65 ± 0.92 22.94 ± 1.75 0.004 32.49 ± 4.97 32.81 ± 5.80 32.45 ± 5.17 32.60 ± 5.31 0.543 Waist circumference (cm, mean ± SD) 78.82 ± 7.77 82.00 ± 8.72 84.53 ± 8.46 80.80 ± 8.42 0.033 102.64 ± 12.10 103.48 ± 13.23 102.88 ± 11.73 102.95 ± 12.51 0.318 Hip circumference (cm, mean ± SD) 93.67 ± 5.89 95.56 ± 4.66 94.60 ± 4.94 94.51 ± 5.35 0.237 110.92 ± 10.39 112.02 ± 12.48 110.79 ± 11.29 111.31 ± 11.29 0.151 WHR 0.84 ± 0.06 0.86 ± 0.08 0.90 ± 0.09 0.86 ± 0.08 0.046 0.93 ± 0.08 0.93 ± 0.09 0.93 ± 0.08 0.93 ± 0.08 0.937 Trunk fat percentage (%, mean ± SD) 25.51 ± 8.32 27.85 ± 7.12 26.65 ± 4.59 26.54 ± 7.48 0.336 37.14 ± 7.02 37.36 ± 7.23 37.29 ± 7.45 37.23 ± 7.12 0.956 Trunk fat mass (kg, mean ± SD) 9.00 ± 3.36 10.01 ± 2.87 10.23 ± 2.28 9.55 ± 3.07 0.196 18.47 ± 5.09 18.80 ± 5.49 18.44 ± 5.10 18.59 ± 5.24 0.654 Body fat percentage (%, mean ± SD) 27.02 ± 8.55 28.92 ± 7.57 26.21 ± 5.61 27.61 ± 7.85 0.393 37.37 ± 8.24 37.59 ± 8.59 37.67 ± 8.44 37.47 ± 8.38 0.952 Whole body fat mass (kg, mean ± SD) 17.13 ± 5.45 18.39 ± 4.85 17.47 ± 3.06 17.65 ± 4.95 0.486 34.04 ± 10.39 34.86 ± 11.90 34.13 ± 10.43 34.35 ± 10.98 0.539 Albumin (g/L, mean ± SD) 45.00 ± 3.28 45.00 ± 3.00 45.14 ± 3.21 45.02 ± 3.13 0.989 45.04 ± 2.84 45.03 ± 2.75 44.87 ± 2.58 45.02 ± 2.78 0.886 ALT (IU/L, mean ± SD) 29.47 ± 26.91 24.41 ± 16.21 27.31 ± 9.50 27.29 ± 21.56 0.557 37.34 ± 24.31 36.06 ± 20.79 33.25 ± 24.75 36.58 ± 23.08 0.301 AST (IU/L, mean ± SD) 32.16 ± 28.57 26.49 ± 8.34 28.96 ± 8.64 29.61 ± 20.93 0.454 32.75 ± 15.30 32.15 ± 15.12 32.27 ± 21.04 32.50 ± 15.69 0.851 Glucose (mmol/L, mean ± SD) 4.75 ± 0.53 5.49 ± 2.08 5.55 ± 1.26 5.15 ± 1.45 0.038 5.76 ± 2.21 5.74 ± 2.19 5.63 ± 2.13 5.74 ± 2.19 0.899 HDL (mmol/L, mean ± SD) 1.54 ± 0.40 1.44 ± 0.36 1.37 ± 0.49 1.48 ± 0.40 0.294 1.20 ± 0.30 1.20 ± 0.29 1.26 ± 0.35 1.21 ± 0.30 0.338 LDL (mmol/L, mean ± SD) 3.45 ± 0.83 3.52 ± 1.03 3.48 ± 0.93 3.48 ± 0.91 0.929 3.44 ± 0.95 3.43 ± 0.92 3.40 ± 0.88 3.43 ± 0.94 0.981 Triglycerides (mmol/L, mean ± SD) 1.96 ± 1.15 1.77 ± 0.86 2.83 ± 1.97 2.01 ± 1.23 0.019 2.41 ± 1.35 2.40 ± 1.22 2.34 ± 1.27 2.40 ± 1.29 0.833 Cholesterol (mmol/L, mean ± SD) 5.68 ± 1.02 5.66 ± 1.20 5.67 ± 1.25 5.67 ± 1.11 0.997 5.43 ± 1.27 5.43 ± 1.21 5.42 ± 1.19 5.43 ± 1.24 0.996 HbA1c (mmol/mol, mean ± SD) 35.42 ± 4.26 37.95 ± 9.10 36.66 ± 6.92 36.48 ± 6.72 0.231 40.75 ± 11.77 40.83 ± 10.97 39.84 ± 9.94 40.71 ± 11.34 0.782 BMI, Body Mass Index; WHR, Waist to Hip Ratio; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; HDL, High Density Lipoprotein cholesterol; LDL, Low Density Lipoprotein cholesterol; glucose, fasting blood glucose. HSD17B13 genotypes and NAFLD risk in lean versus non-lean individuals In the study design phase, we used Logistic regression to test the differences between lean and non-lean NAFLD for the five selected SNPs. HSD17B13 rs72613567: TA showed a risk effect in lean NAFLD, which was inconsistent with previous studies (Figure S1). Therefore, we comprehensively analyzed the effect of HSD17B13 rs72613567 in lean and non-lean NAFLD. The polymorphisms of HSD17B13 rs72613567 were significantly different between lean and non-lean NAFLD (Fig. 1 ), and the MAF of HSD17B13 was remarkably higher in lean NAFLD (32.57%) than in non-lean NAFLD (26.16%) (Table 2 ). Table 2 Associations between HSD17B13 status and nonalcoholic fatty liver disease among lean and non-lean individuals. Lean NAFLD Non-lean NAFLD All NAFLD cases Health N OR (95%CI) P value N OR (95%CI) P value N OR (95%CI) P value N NAFLD Codominant TT 53 Ref 0.089 744 Ref 0.064 797 Ref 0.111 160495 TAT 41 1.00 (0.67, 1.50) 510 0.88 (0.79, 0.99) 551 0.89 (0.80, 0.99) 124623 TATA 15 1.94 (1.09, 3.44) 99 0.87 (0.71, 1.08) 114 0.95 (0.78, 1.16) 24087 Dominant TT 53 Ref 0.466 744 Ref 0.019 797 Ref 0.046 TAT + TATA 56 1.15 (0.79, 1.67) 609 0.88 (0.79, 0.98) 665 0.90 (0.81, 1.00) Recessive TT + TAT 94 Ref 0.028 1254 Ref 0.433 1348 Ref 0.991 TATA 15 1.94 (1.12, 3.35) 99 0.92 (0.75, 1.13) 114 1.00 (0.83, 1.21) MAF 32.57 26.16 26.64 27.94 In lean NAFLD, homozygosity of the TA allele showed a significant risk effect in both the codominant (OR = 1.94; 95% CI: 1.09–3.44) and recessive models (OR = 1.94; 95% CI: 1.12–3.35). The addition of the TAT and TATA alleles showed a nonsignificant risk effect in the dominant model (OR = 1.15; 95% CI: 0.79–1.67). In non-lean NAFLD, the addition of the TAT and TATA alleles showed a significant protective effect in the dominant model (OR = 0.88; 95% CI: 0.79–0.98). Moreover, homozygosity of the TA allele showed a nonsignificant effect in both the codominant model (OR = 0.87; 95% CI: 0.71–1.08) and the recessive model (OR = 0.92; 95% CI: 0.75–1.13), whereas heterozygosity of the TA allele showed a significant protective effect in the codominant model (OR = 0.88; 95% CI: 0.79–0.98) (Table 2 ). The results of multivariable logistic regression showed that in the case of lean NAFLD, homozygosity of the TA allele exhibited a significant risk effect in the model adjusted for age and sex (OR = 1.94; 95% CI: 1.05–3.35), the model adjusted for BMI, WHR, trunk fat percentage, trunk fat mass, body fat percentage, and whole-body fat mass (OR = 2.04; 95% CI: 1.11–3.55), and the fully adjusted model (OR = 1.99; 95% CI: 1.03–3.59) (Fig. 2 ). Interaction between HSD17B13 and the other four genetic variants in NAFLD To further investigate the interaction between the distinct SNPs in NAFLD, we used MDR to construct an interaction model between HSD17B13 rs72613567 and the other four SNPs that were robustly validated to be associated with NAFLD. The results showed that all the interaction models presented a testing accuracy of > 0.53 in lean NAFLD and > 0.51 in non-lean NAFLD, and presented the best cross-validation consistency (10/10) for both lean and non-lean NAFLD. We did not detect any interaction between HSD17B13 rs72613567 and the other four SNPs in either lean or non-lean NAFLD in this study (P > 0.05) (Table 3 ). Table 3 Interaction analysis of HSD17B13 and GCKR , TM6SF4 , MBOAT7 , PNPLA3 for association with nonalcoholic fatty liver among lean and non-lean individuals. Gene*gene Training Testing Consistency P value OR (95%CI) Lean NAFLD HSD17B13 * TM6SF2 0.5533 0.5361 10/10 0.5306 1.5477 (0.3909,6.1285) HSD17B13 * BMOAT7 0.5713 0.5611 10/10 0.4155 1.6342 (0.4951,5.3935) HSD17B13 * PNPLA3 0.5943 0.5943 10/10 0.1413 2.3982 (0.7202,7.9855) HSD17B13 * GCKR 0.5763 0.5544 10/10 0.4531 1.6833 (0.4252,6.6629) Non-lean NAFLD HSD17B13 * TM6SF2 0.5221 0.5176 10/10 0.4126 1.1537 (0.8193,1.6248) HSD17B13 * BMOAT7 0.5253 0.5253 10/10 0.2388 1.2249 (0.8735,1.7178) HSD17B13 * PNPLA3 0.5378 0.5256 10/10 0.1919 1.2667 (0.8874,1.8082) HSD17B13 * GCKR 0.5261 0.5261 10/10 0.2173 1.2486 (0.8768,1.7780) TM6SF2, TM6SF2: rs58542926; HSD17B13, HSD17B13:rs72613567; MBOAT7, MBOAT7: rs641738; PNPLA3, PNPLA3: rs738409; GCKR, GCKR: rs1260326; PAF for HSD17B13 and the other four genetic variants in NAFLD The PAF calculated for each SNP in lean and non-lean NAFLD was the following (respectively): HSD17B13 rs72613567: TA, 9.07% and − 5.85%; TM6SF2 rs58542926: T, 10.94% and 5.57%; MBOAT7 rs641738: T, 21.58% and 8.24%; PNPLA3 rs738409: G, 14.47% and 14.79%; and GCKR rs1260326: T, 35.82% and 4.46%. Moreover, the combined PAF of HSD17B13 rs72613567: TA with the other SNPs in lean and non-lean NAFLD was the following (respectively): with TM6SF2 rs58542926: T, 19.02% and 0.04%; with MBOAT7 rs641738: T, 28.69% and 2.08%; with PNPLA3 rs738409: G, 22.23% and 9.81%; and with GCKR rs1260326: T, 41.64% and − 1.13%. The combined PAF for the five SNPs was 65.14% in lean NAFLD and 25.33% in non-lean NAFLD (Table 4 ). Table 4 The single and combined population attributable fraction for TM6SF2 , MBOAT7 , PNPLA3 , GCKR and HSD17B13 . Gene Genotypes MAF OR 1 (95% CI) OR 2 (95%CI) PAF Combined Lean NAFLD HSD17B13 TT: TAT: TATA = 53: 41:15 32.57 1.00 (0.66, 1.50) 1.94 (1.06, 3.35) 9.07 65.14 ## TM6SF2 CC: TC: TT = 87: 19: 3 11.47 1.35 (0.80, 2.16) 4.94 (1.21, 13.21) 10.94 19.02 MBOAT7 CC: TC: TT = 27: 60: 22 47.71 1.41 (0.90, 2.25) 1.31 (0.75, 2.31) 21.58 28.69 PNPLA3 CC: GC: GG = 61: 32: 16 29.36 0.93 (0.60, 1.41) 3.30 (1.84, 5.58) 14.47 22.23 GCKR CC: TC: TT = 27:58:24 48.62 1.62 (1.04, 2.60) 2.05 (1.17, 3.55) 35.82 41.64 Non-lean NAFLD HSD17B13 TT: TAT: TATA = 744: 510: 99 26.16 0.88 (0.79, 0.99) 0.87 (0.70, 1.07) -5.85 25.33 ## TM6SF2 CC: TC: TT = 1107:230:16 9.68 1.27 (1.10, 1.46) 2.25 (1,31, 3.57) 5.57 0.04 MBOAT7 CC: TC: TT = 388:667:289 46.32 1.09 (0.96, 1.23) 1.21 (1.04, 1.41) 8.24 2.87 PNPLA3 CC: GC: GG = 738:502:113 26.90 1.25 (1.12, 1.40) 2.04 (1.67, 2.48) 14.79 9.81 GCKR CC: TC: TT = 476:625:252 41.72 1.01 (0.90, 1.14) 1.24 (1.06, 1.44) 4.46 -1.13 TM6SF2 , TM6SF2 : rs58542926; HSD17B13 , HSD17B13 :rs72613567; MBOAT7 , MBOAT7 : rs641738; PNPLA3 , PNPLA3 : rs738409; GCKR , GCKR : rs1260326; ## means the PAF calculated combine five genes, Combined means the PAF combined with HSD17B13 rs72613567. HSD17B13 genotypes and risk of cirrhosis and HCC in lean versus non-lean individuals HSD17B13 rs72613567 polymorphism showed no significant difference in distribution between lean and non-lean individuals with respect to cirrhosis and HCC (Fig. 1 ). The MAF was slightly higher in lean cirrhosis (25.77%) than in non-lean cirrhosis (23.35%) and in lean HCC (27.10%) than in non-lean HCC (24.05%). HSD17B13 rs72613567: TA showed a protective effect against cirrhosis and HCC (OR < 1.0) in lean individuals, although the effect was not statistically significant. In non-lean individuals, the TA allele showed a significant protective effect against cirrhosis in the codominant model (heterozygosity: OR = 0.71; 95% CI: 0.63–0.80; homozygosity: OR = 0.74; 95% CI: 0.59–0.93) and the dominant model (OR = 0.71; 95% CI: 0.64–0.80). Moreover, TA also showed a significant protective effect against HCC in the codominant (heterozygosity: OR = 0.80; 95% CI: 0.64–0.98; homozygosity: OR = 0.70; 95% CI: 0.46–1.06) and dominant models (OR = 0.78; 95% CI: 0.64–0.95) (Table S1). Multivariable logistic regression analyses revealed that both heterozygosity and homozygosity of the TA allele had a significant protective effect against non-lean cirrhosis and non-lean HCC. Heterozygosity of the TA allele exhibited a significant protective effect (OR = 0.79; 95% CI: 0.64–0.98) against non-lean HCC (Table S2). In lean cirrhosis and lean HCC, all effects were protective, although some of the measured effects were not statistically significant. The point estimates were higher in lean individuals than in non-lean individuals. Discussion NAFLD is widely accepted to be closely linked to obesity [ 6 ], but a proportion of lean individuals also develop NAFLD; suggesting the presence of other severe metabolic conditions or a genetic predisposition for NAFLD in the lean patients [ 33 ]. PNPLA3 rs738409 polymorphism was previously shown to differ between lean and non-lean NAFLD [ 34 ], and the disease progression of lean NAFLD was found to be independent of the PNPLA3 gene signature [ 3 ]. The reported differences in TM6SF2 rs58542926 polymorphisms between lean and non-lean NAFLD have been inconsistent across studies [ 9 , 35 ]. However, most of the available genetic evidence for lean NAFLD has been derived from the description of baseline characteristics and is inconsistent across studies, and the genetic effects remain incompletely elucidated. Our study investigated the association of HSD17B13 rs72613567 with NAFLD, cirrhosis, and HCC in lean versus non-lean individuals to complement the existing knowledge. HSD17B13 rs72613567: TA has been regarded as a protective variant in various liver diseases. Our results showed that HSD17B13 rs72613567: TA increases the risk of lean NAFLD and thus revealed, for the first time, a risk effect of this variant in liver diseases. We found that the MAF of HSD17B13 rs72613567 in healthy people was 27.94%, which is comparable to that reported in a recent study that genotyped HSD17B13 in 6171 participants (27.6%) [ 18 ]. Moreover, a previous study found that the MAF of HSD17B13 in patients with alcohol-related cirrhosis and HCC was lower than that in healthy controls [ 18 ]. Our results complement these findings by showing that the MAF of HSD17B13 in NAFLD, cirrhosis, and HCC was also lower than that in healthy controls. However, intriguingly, our results showed that the MAF of HSD17B13 rs72613567 in lean NAFLD was remarkably higher than that in non-lean NAFLD, which indicates that unique genetic mechanisms might underlie lean NAFLD. We found that the TA allele in HSD17B13 rs72613567 was significantly associated with increased triglyceride levels, BMI, waist circumference, and WHR in lean NAFLD but not in non-lean NAFLD; this finding indicates that lipid metabolism is modified by HSD17B13 in lean NAFLD. Notably, triglyceride levels were higher in patients with lean NAFLD who were homozygous for the TA allele than in patients with non-lean NAFLD; this suggests that TA allele homozygosity in HSD17B13 plays a critical role in regulating the serum triglyceride levels in lean NAFLD, although further investigation of the underlying mechanism is warranted. Our results also showed that the fasting blood glucose level was lower in lean NAFLD patients than in non-lean NAFLD patients, which supporting the previously reported finding that diabetes prevalence is lower in patients with lean NAFLD than in patients with non-lean NAFLD [ 36 ]. The results of PAF analysis showed GCKR with the highest PAF in lean NAFLD (35.82%), while PNPLA3 with the highest PAF in non-lean NAFLD (14.79%). The risk of NAFLD appeared to be attenuated in non-lean NAFLD (PAF = 9.07%) and increased in lean NAFLD (PAF = − 5.85%) as a result of co-carriage of TA in HSD17B13 rs72613567, and under co-carriage of GCKR rs1260326: T and HSD17B13 rs72613567: TA, in particular, the combined PAF showed a marked difference between lean NAFLD (41.64%) and non-lean NAFLD (− 1.13%). Therefore, the differences in the five SNPs in single and combined PAFs indicate that lean and non-lean NAFLD feature distinct genetic profiles. Currently, weight loss is the recommended treatment strategy for NAFLD. Accordingly, weight reduction of 10% through dietary restriction and regular exercise was shown to be sufficient to reverse NASH in most patients [ 37 ]. However, this might not serve as an effective strategy in the case of patients with lean NAFLD. Therefore, it is desirable to investigate the underlying mechanisms of lean NAFLD and provide advice for its treatment and related drug development. As a protective allele for various liver diseases, HSD17B13 rs72613567 is considered to be an attractive target for therapeutic drug development [ 38 , 39 ]. Our results here showed that the effect of HSD17B13 rs72613567 in lean NAFLD differs from that in non-lean NAFLD, which implications for drug development and personalized treatment for lean NAFLD. NAFLD has commonly been underdiagnosed because of its mild clinical symptoms [ 40 ]. We speculate that lean NAFLD is less likely to be detected than non-lean NAFLD because obesity has been employed as a primary indicator for NAFLD diagnosis. Therefore, it is necessary to establish methods for diagnosing NAFLD in the lean population. Use of the genetic risk score (GRS) has been considered as a strategy to identify NAFLD patients at increased risk before developing advanced disease and to target these patients for preventive interventions [ 41 , 42 ], and HSD17B13 rs72613567: TA has been consistently employed as a protective variant of NAFLD in GRS construction [ 41 ]. Our findings indicate that lean patients should be distinguished when the GRS is used to construct NAFLD diagnostic tools and risk-prediction methods. The present study is based on a large-population prospective cohort. Moreover, a previous study suggested that the phenotypic definition of NAFLD in the UK Biobank based on the ICD-10 code in hospital records accurately reflects that of clinical diagnosed NAFLD [ 26 ]. These key points strengthen our study, but certain potential limitations remain. First, the NAFLD, cirrhosis, and HCC cases we included were obtained from hospitalization records; thus, the sample size was modest, particularly in terms of the relatively small number of lean individuals, and this might make the statistics inadequately powerful. Consequently, although the MDR analysis in this study did not detect the interaction between HSD17B13 rs72613567 and the other four SNPs, the results regarding the interaction should be interpreted with caution. Second, prognosis information is not available in the UK Biobank, and subsequent longitudinal assessment is required to understand the prognosis in patients carrying the TA allele of HSD17B13 rs72613567. Third, some of the OR values used for PAF calculation in this study did not reach statistical significance, and our PAF results should therefore be further validated in independent and relatively larger populations. In conclusion, the polymorphism of HSD17B13 rs72614567 was significantly different between lean NAFLD and non-lean NAFLD. Carriage of variants TA in HSD17B13 rs72614567 have protective effects on non-lean NAFLD, cirrhosis and HCC, but may have a risk effect on lean NAFLD. We have shown for the first time that HSD17B13 rs72613567: TA increases the risk of lean NAFLD. Our finding could facilitate the development of diagnostic and therapeutic strategies tailored for lean NAFLD Declarations Acknowledgments ： We sincerely appreciate the great work of the UK Biobank collaborators. This study was conducted under Application Number 58484 and 63726. Conflict of Interest: None. Funding: This study was supported by the Special Foundation for Science and Technology Basic Research Program (2019FY101103), the Natural Science Foundation of China (81772170) and by the National Key Research and Development Program of China (No. 2017YFC0211700). Data Availability: The UK Biobank data are available from the UK Biobank on request ( www.ukbiobank.ac.uk/ ). Ethical approval: The UK Biobank received ethical approval from the research ethics committee (REC reference for UK Biobank 11/NW/0382) and participants provided written informed consent. Animal Research (Ethics): Not applicable Clinical Trials Registration: Not applicable Author Contribution: HF and TZ conceived of the study design. HF, XZ and PZ performed the statistical analysis. HF, and ZL wrote the manuscript. All authors provided critical revisions of the draft and approved the submitted draft. All authors gave final approval of the version to be published and have contributed to the manuscript. TZ had the final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. 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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-1469582","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":93970771,"identity":"b13a12cc-4b2a-46b7-b069-1ae36336468b","order_by":0,"name":"Hong Fan","email":"","orcid":"https://orcid.org/0000-0003-2826-5564","institution":"Fudan University School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Fan","suffix":""},{"id":93970772,"identity":"3b79cf97-fed8-43fd-9ee7-e0e7acf12aea","order_by":1,"name":"Xin Zhang","email":"","orcid":"","institution":"Fudan 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00:26:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1469582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1469582/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19798066,"identity":"c44c3377-f482-4002-8c1f-324dda43ecc7","added_by":"auto","created_at":"2022-03-30 20:40:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1105474,"visible":true,"origin":"","legend":"\u003cp\u003eThe \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 genotype proportion among lean and non-lean individuals with NAFLD, cirrhosis and HCC. Figure1A, Nonalcoholic fatty liver disease; Figure1B, Cirrhosis; Figure1C, Hepatocellular carcinoma.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1469582/v1/9c41a2ce477bf5e94bf7a244.png"},{"id":19798067,"identity":"e23de14a-1244-4bab-97ed-b422754c3a6f","added_by":"auto","created_at":"2022-03-30 20:40:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1669117,"visible":true,"origin":"","legend":"\u003cp\u003eForrest plot of multivariable logistic regression showing genotype odds ratio for heterozygous and homozygous carriage of the \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA allele on nonalcoholic fatty liver diseases.\u003c/p\u003e\u003cp\u003eMultivariable regression model 1 adjusted for age and sex; Model 2 adjusted body measure indicator: body mass index, waist to hip ratio and whole body fat mass; Model 3 additional adjusted biochemical indicator: Albumin (g/L), ALT (U/L), AST (U/L), Fasting blood glucose (mmol/L), HDL (mmol/L), LDL (mmol/L), Triglycerides (mmol/L), Total cholesterol (mmol/L).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1469582/v1/a83db538d6df84f6596212d3.png"},{"id":20113825,"identity":"32185481-3f39-4ce0-839b-6e4f6641e739","added_by":"auto","created_at":"2022-04-08 13:19:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":697673,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1469582/v1/f51f60ca-cd32-477b-97bd-b15f879a9b27.pdf"},{"id":19798065,"identity":"756e28d1-0f00-4f5e-9c5d-469a4d544e08","added_by":"auto","created_at":"2022-03-30 20:40:02","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":381540,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1469582/v1/8741767e35f8d1128248aca6.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eGenetic variation of \u003cem\u003eHSD17B13 \u003c/em\u003eis associated with an increased risk of lean nonalcoholic fatty liver disease\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNonalcoholic fatty liver disease (NAFLD) has emerged as a leading cause of chronic liver disease worldwide in the past few decades. NAFLD is particularly common among patients with obesity, however, NAFLD also occurs in lean individuals [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; the prevalence of lean NAFLD varies across different regions and populations, with the overall prevalence being 19.2% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Compared with non-lean NAFLD, lean NAFLD presents better metabolic characteristics and a more benign clinical course [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], but higher cumulative mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and it also shows characteristics distinct from those of non-lean NAFLD in terms of parameters such as serological factors, intestinal flora, and metabolism [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, lean NAFLD has gradually been taken seriously as a distinct entity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenetic factors play a key role across the spectrum of NAFLD pathogenesis. Certain loci associated with increased risk of NAFLD have been robustly validated, with the most crucial of these being the loci for \u003cem\u003epatatin-like phospholipase domain-containing 3\u003c/em\u003e (\u003cem\u003ePNPLA3\u003c/em\u003e) rs738409, \u003cem\u003etransmembrane 6 superfamily 2\u003c/em\u003e (\u003cem\u003eTM6SF2\u003c/em\u003e) rs58542926, \u003cem\u003emembrane-bound O-acyltransferase domain-containing 7\u003c/em\u003e (\u003cem\u003eMBOAT7\u003c/em\u003e) rs641738, and \u003cem\u003eglucokinase regulatory protein\u003c/em\u003e (\u003cem\u003eGCKR\u003c/em\u003e) rs1260326. While in 2018, a genetic variant of \u003cem\u003e17-β-hydroxysteroid dehydrogenase 13\u003c/em\u003e (\u003cem\u003eHSD17B13\u003c/em\u003e) rs72613567 was identified as being protective against NAFLD in a genome-wide association study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eHSD17B13\u003c/em\u003e encodes the hepatic lipid-droplet enzyme HSD17B13, while NAFLD is histologically characterized by a massive accumulation of liver lipid droplets [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The TA allele in \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 is a loss-of-function variant harboring an indel (insertion of A) that leads to the production of a truncated protein exhibiting diminished enzymatic activity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In 2018, Abul-Husn et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] first reported that the \u003cem\u003eHSD17B13\u003c/em\u003e variant was associated with reduced levels of alanine transaminase (ALT) and showed a protective effect against chronic liver disease. Subsequent studies revealed that \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA was associated with the prevention of nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and superior prognosis for chronic liver disease [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In patients with NAFLD, carriage of TA in \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 was shown to be associated with a lower grade of hepatocyte ballooning [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous study showed the frequency of the TA allele of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 was markedly decreased in patients with chronic liver disease and HCC patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA recent study conducted in 111,612 individuals showed that the ALT-lowering effect of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA was amplified by increasing adiposity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and a genome-wide interaction study showed that \u003cem\u003eHSD17B13\u003c/em\u003e was modified by the body mass index (BMI) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In a study employing 356 biopsy-proven NAFLD cases, the protective effect of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA was found to disappear when BMI was included in the regression model [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and the protective effect of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA against steatohepatitis risk was recently shown to be only relevant among patients with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;35 kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These lines of evidence collectively indicate that adiposity might modify the effect of the \u003cem\u003eHSD17B13\u003c/em\u003e variant in NAFLD. However, the potential differences in the effect and frequency of the TA allele of \u003cem\u003eHSD17B13\u003c/em\u003e between lean and non-lean NAFLD remain obscure. An enhanced understanding of the unique characteristics of lean NAFLD could facilitate future prevention of NAFLD among distinct patient subgroups.\u003c/p\u003e \u003cp\u003eTo disentangle the features of \u003cem\u003eHSD17B13\u003c/em\u003e between lean and non-lean NAFLD, we leveraged the complete UK Biobank dataset to reveal the separate and combined effects of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 and the other four NAFLD-related SNPs in lean versus non-lean individuals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study population\u003c/h2\u003e \u003cp\u003eThis study was conducted using data from the UK Biobank, a population-based prospective cohort comprising the epidemiological and genetic data of 502,505 individuals, aged 37\u0026ndash;73 years, who were recruited across the UK between 2006 to 2010 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We included the available information on NAFLD, cirrhosis, and HCC cases from 313, 309 people of European ancestry in the UK Biobank. Lean individuals were defined as persons with a BMI\u0026thinsp;\u0026le;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e according to the World Health Organization norms for Europeans [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. NAFLD, cirrhosis, and HCC cases were defined based on the diagnosis codes from hospitalization records (data-fields 41270 and 41271) in the UK Biobank. Individuals with ICD-10 code K76 and/or ICD-9 code 5718, but without hepatitis B or C infection, other types of viral hepatitis (as per the results of antigen testing and/or ICD-10 code listing as B15\u0026ndash;B19 in hospital records), or other specific liver diseases were characterized as NAFLD cases [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Individuals with ICD-10 code K74 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] were characterized as cirrhosis cases, and those with code C22 were characterized as HCC cases. Our analysis did not include patients whose standing height or weight data were lacking in the records. Moreover, the NAFLD study population excluded individuals considered to drink excessive amounts of alcohol, defined as \u0026gt;\u0026thinsp;30 g/day for males and \u0026gt;\u0026thinsp;20 g/day for females; the daily pure-alcohol intake in grams was calculated by multiplying the average number of alcoholic drinks consumed by the average grams of alcohol contained in each type of drink [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Participants enrolled in the UK Biobank signed the required consent forms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric and biochemical data\u003c/h2\u003e \u003cp\u003eThe definition of lean NAFLD is based on the BMI and involves only two anthropometric indicators\u0026mdash;height and weight. In this study, we further focused on the following additional anthropometric indicators: waist circumference, hip circumference, waist-to-hip ratio (WHR), trunk fat percentage, trunk fat mass, body fat percentage, and whole-body fat mass. We also compared the differences in the following metabolism-associated serum indicators: albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), high-density lipoprotein (HDL), low-density lipoprotein (LDL), fasting blood glucose, triglycerides, hemoglobin A1c (HbA1c), and total cholesterol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData extraction and genotyping\u003c/h2\u003e \u003cp\u003eVenous blood samples were collected and genomic DNA was subsequently extracted using the standard laboratory procedures in the UK Biobank [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We included four robustly validated risk loci (\u003cem\u003eTM6SF2\u003c/em\u003e rs58542926, \u003cem\u003eMBOAT7\u003c/em\u003e rs641738, \u003cem\u003ePNPLA3\u003c/em\u003e rs738409, and \u003cem\u003eGCKR\u003c/em\u003e rs1260326) and one protective locus (\u003cem\u003eHSD17B13\u003c/em\u003e rs72613567) associated with NAFLD. Genotyping was performed using the Affymetrix UK BiLEVE Axiom array on 50,000 participants and the Affymetrix UK Biobank Axiom array on 450,000 participants in the UK Biobank [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Genotype call clustering in the UK Biobank was assessed using ScatterShot (McCarthy Group, Oxford, UK) (26).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eIn the study design phase, we used Logistic regression to test the differences in five NAFLD-associated SNPs between lean and non-lean patients (\u003cem\u003eTM6SF2\u003c/em\u003e rs58542926, \u003cem\u003eMBOAT7\u003c/em\u003e rs641738, \u003cem\u003ePNPLA3\u003c/em\u003e rs738409, and \u003cem\u003eGCKR\u003c/em\u003e rs1260326, \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567). Further more, we characterized the study population according to the \u003cem\u003eHSD17B13\u003c/em\u003e genotypes (TT, TAT, and TATA) by using descriptive statistics. Continuous variables are presented here as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables are reported as numbers and percentages. The WHR was calculated as the waist circumference divided by the hip circumference. The BMI was calculated as the body weight (in kilograms) divided by the body height (in meters) squared. Baseline characteristics among the genotypes of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 were compared using the chi-square test, Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, ANOVA, and Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e \u003cp\u003eWe calculated the minor allele frequency (MAF) of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 and analyzed the SNPs using codominant, dominant, and recessive models. Odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were used to estimate the effect.\u003c/p\u003e \u003cp\u003eThe multifactor dimensionality reduction (MDR) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] algorithm was implemented to screen for the best synergistic model and detect and characterize the interactions between \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 and the other four SNPs in lean NAFLD and non-lean NAFLD. The 10-fold cross-validation consistency, the testing balanced accuracy, and the sign test results were calculated. Models featuring a cross-validation consistency of 10/10 and a testing balanced accuracy\u0026thinsp;\u0026gt;\u0026thinsp;0.50 were regarded as the best interaction models. Interactions were considered to be absent if the P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThe population-attributable fraction (PAF) is used to estimate the proportion of a disorder attributable to a given risk factor [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The PAF was estimated for heterozygous and homozygous carriage by using the following formula [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{P}\\text{A}\\text{F}=\\frac{\\left(x-1\\right)}{x}$$\u003c/div\u003e\u003c/div\u003e,\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$x={\\left(1-p\\right)}^{2}+2p\\left(1-p\\right){OR}_{1}+{p}^{2}{OR}_{2}$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(p\\)\u003c/span\u003e\u003c/span\u003e is the allele frequency in lean NAFLD or non-lean NAFLD and OR\u003csub\u003e1\u003c/sub\u003e and OR\u003csub\u003e2\u003c/sub\u003e are the ORs associated with heterozygosity and homozygosity, respectively. Assuming no multiplicative interaction between the SNPs, we calculated the combined PAF for the five SNPs based on the individual PAFs for each associated SNP to estimate the combination effect [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The following formula was used for calculating the combined PAF:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{P}\\text{A}\\text{F}=1-\\left(1-{\\text{P}\\text{A}\\text{F}}_{1}\\right)\\left(1-{\\text{P}\\text{A}\\text{F}}_{2}\\right)\\left(1-{\\text{P}\\text{A}\\text{F}}_{\\text{n}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo determine whether the differential appearance of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 between lean and non-lean patients occurred only in the case of NAFLD, we further estimated the effect of the allele in cirrhosis and HCC. The methods used here were the same as the aforementioned methods used for NAFLD. We additionally implemented multivariable logistic regression to assess the effect of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 in NAFLD, cirrhosis, and HCC in lean versus non-lean individuals. The results are expressed as ORs with their corresponding 95% CIs. In multivariable logistic regression model, covariates were set in three ways, model 1 was adjusted age and sex, model 2 additional adjusted for BMI, WHR, trunk fat percentage, trunk fat mass, body fat percentage and body whole body fat mass, Model 3 was a fully adjusted model that additional adjusted for albumin, ALT, AST, fasting blood glucose, HDL, LDL, triglycerides, total cholesterol.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using the \u0026ldquo;SNPassoc,\u0026rdquo; \u0026ldquo;survival,\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; packages in R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). The MDR approach was implemented using MDR 3.0.2 (build 2) software. Nominal two-sided asymptotic P-values are reported for all tests. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy population and baseline characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOverall, this study included 1464 NAFLD cases (109 lean and 1353 non-lean), 1559 cirrhosis cases (326 lean and 1213 non-lean), 526 HCC cases (131 lean and 394 non-lean), and 309,760 healthy controls. All the individuals were Caucasian.\u003c/p\u003e\n \u003cp\u003eThe main anthropometric and biochemical characteristics of NAFLD stratified according to the \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 genotype are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Lean NAFLD accounted for 7.45% of the cases, and age distribution showed little difference between patients with lean and non-lean NAFLD (mean age 58.34 \u003cem\u003evs.\u003c/em\u003e 57.81 years). The proportion of males was lower among lean NAFLD than among non-lean NAFLD cases (38.53% \u003cem\u003evs.\u003c/em\u003e 43.75%). The BMI, waist circumference, and WHR values were significantly different across the \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 genotypes in lean NAFLD but not in non-lean NAFLD. Moreover, in the case of lean NAFLD, triglyceride levels were significantly higher in patients who were homozygous for the TA allele than in patients with the TT or TAT genotype for \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567; however, this difference was not observed in non-lean NAFLD. In addition, fasting blood glucose levels were increased in the case of heterozygous TAT and homozygous TATA genotypes in patients with lean NAFLD (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of study participants according to HSD17B13 genotype between lean NAFLD and non-lean NAFLD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eLean NAFLD (n\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eNon-lean NAFLD (n\u0026thinsp;=\u0026thinsp;1353)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTAT (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTATA (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT (744)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTAT (510)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTATA (99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18/35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42/67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e331/413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219/291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42/57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593/762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e56.85\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.14\u0026thinsp;\u0026plusmn;\u0026thinsp;7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight (kg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.34\u0026thinsp;\u0026plusmn;\u0026thinsp;7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.78\u0026thinsp;\u0026plusmn;\u0026thinsp;15.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.03\u0026thinsp;\u0026plusmn;\u0026thinsp;17.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.08\u0026thinsp;\u0026plusmn;\u0026thinsp;17.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.26\u0026thinsp;\u0026plusmn;\u0026thinsp;16.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e166.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.41\u0026thinsp;\u0026plusmn;\u0026thinsp;8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.11\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.44\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.60\u0026thinsp;\u0026plusmn;\u0026thinsp;5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaist circumference (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e78.82\u0026thinsp;\u0026plusmn;\u0026thinsp;7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.80\u0026thinsp;\u0026plusmn;\u0026thinsp;8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.64\u0026thinsp;\u0026plusmn;\u0026thinsp;12.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.48\u0026thinsp;\u0026plusmn;\u0026thinsp;13.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.95\u0026thinsp;\u0026plusmn;\u0026thinsp;12.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHip circumference (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e93.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.60\u0026thinsp;\u0026plusmn;\u0026thinsp;4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.51\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.02\u0026thinsp;\u0026plusmn;\u0026thinsp;12.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.79\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.31\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrunk fat percentage (%, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25.51\u0026thinsp;\u0026plusmn;\u0026thinsp;8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.54\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.14\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.23\u0026thinsp;\u0026plusmn;\u0026thinsp;7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrunk fat mass (kg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.59\u0026thinsp;\u0026plusmn;\u0026thinsp;5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody fat percentage (%, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.61\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.59\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhole body fat mass (kg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17.13\u0026thinsp;\u0026plusmn;\u0026thinsp;5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.39\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.65\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.86\u0026thinsp;\u0026plusmn;\u0026thinsp;11.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.13\u0026thinsp;\u0026plusmn;\u0026thinsp;10.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.35\u0026thinsp;\u0026plusmn;\u0026thinsp;10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin (g/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e45.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (IU/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e29.47\u0026thinsp;\u0026plusmn;\u0026thinsp;26.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.41\u0026thinsp;\u0026plusmn;\u0026thinsp;16.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.29\u0026thinsp;\u0026plusmn;\u0026thinsp;21.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.34\u0026thinsp;\u0026plusmn;\u0026thinsp;24.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.06\u0026thinsp;\u0026plusmn;\u0026thinsp;20.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.25\u0026thinsp;\u0026plusmn;\u0026thinsp;24.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.58\u0026thinsp;\u0026plusmn;\u0026thinsp;23.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST (IU/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e32.16\u0026thinsp;\u0026plusmn;\u0026thinsp;28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.49\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.96\u0026thinsp;\u0026plusmn;\u0026thinsp;8.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.61\u0026thinsp;\u0026plusmn;\u0026thinsp;20.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.75\u0026thinsp;\u0026plusmn;\u0026thinsp;15.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.15\u0026thinsp;\u0026plusmn;\u0026thinsp;15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.27\u0026thinsp;\u0026plusmn;\u0026thinsp;21.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.50\u0026thinsp;\u0026plusmn;\u0026thinsp;15.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.76\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglycerides (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCholesterol (mmol/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c (mmol/mol, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e35.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.95\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.66\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.48\u0026thinsp;\u0026plusmn;\u0026thinsp;6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.75\u0026thinsp;\u0026plusmn;\u0026thinsp;11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.84\u0026thinsp;\u0026plusmn;\u0026thinsp;9.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.71\u0026thinsp;\u0026plusmn;\u0026thinsp;11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eBMI, Body Mass Index; WHR, Waist to Hip Ratio; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; HDL, High Density Lipoprotein cholesterol; LDL, Low Density Lipoprotein cholesterol; glucose, fasting blood glucose.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eHSD17B13\u003c/span\u003e \u003cstrong\u003egenotypes and NAFLD risk in lean versus non-lean individuals\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the study design phase, we used Logistic regression to test the differences between lean and non-lean NAFLD for the five selected SNPs. \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA showed a risk effect in lean NAFLD, which was inconsistent with previous studies (Figure S1). Therefore, we comprehensively analyzed the effect of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 in lean and non-lean NAFLD.\u003c/p\u003e\n \u003cp\u003eThe polymorphisms of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 were significantly different between lean and non-lean NAFLD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), and the MAF of \u003cem\u003eHSD17B13\u003c/em\u003e was remarkably higher in lean NAFLD (32.57%) than in non-lean NAFLD (26.16%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations between \u003cem\u003eHSD17B13\u003c/em\u003e status and nonalcoholic fatty liver disease among lean and non-lean individuals.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eLean NAFLD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNon-lean NAFLD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAll NAFLD cases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNAFLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.67, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.79, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.80, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94 (1.09, 3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.71, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.78, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTAT\u0026thinsp;+\u0026thinsp;TATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (0.79, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.79, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 (0.81, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u0026thinsp;+\u0026thinsp;TAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94 (1.12, 3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.75, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.83, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eIn lean NAFLD, homozygosity of the TA allele showed a significant risk effect in both the codominant (OR\u0026thinsp;=\u0026thinsp;1.94; 95% CI: 1.09\u0026ndash;3.44) and recessive models (OR\u0026thinsp;=\u0026thinsp;1.94; 95% CI: 1.12\u0026ndash;3.35). The addition of the TAT and TATA alleles showed a nonsignificant risk effect in the dominant model (OR\u0026thinsp;=\u0026thinsp;1.15; 95% CI: 0.79\u0026ndash;1.67). In non-lean NAFLD, the addition of the TAT and TATA alleles showed a significant protective effect in the dominant model (OR\u0026thinsp;=\u0026thinsp;0.88; 95% CI: 0.79\u0026ndash;0.98). Moreover, homozygosity of the TA allele showed a nonsignificant effect in both the codominant model (OR\u0026thinsp;=\u0026thinsp;0.87; 95% CI: 0.71\u0026ndash;1.08) and the recessive model (OR\u0026thinsp;=\u0026thinsp;0.92; 95% CI: 0.75\u0026ndash;1.13), whereas heterozygosity of the TA allele showed a significant protective effect in the codominant model (OR\u0026thinsp;=\u0026thinsp;0.88; 95% CI: 0.79\u0026ndash;0.98) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe results of multivariable logistic regression showed that in the case of lean NAFLD, homozygosity of the TA allele exhibited a significant risk effect in the model adjusted for age and sex (OR\u0026thinsp;=\u0026thinsp;1.94; 95% CI: 1.05\u0026ndash;3.35), the model adjusted for BMI, WHR, trunk fat percentage, trunk fat mass, body fat percentage, and whole-body fat mass (OR\u0026thinsp;=\u0026thinsp;2.04; 95% CI: 1.11\u0026ndash;3.55), and the fully adjusted model (OR\u0026thinsp;=\u0026thinsp;1.99; 95% CI: 1.03\u0026ndash;3.59) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction between\u003c/strong\u003e \u003cspan class=\"BoldItalic\"\u003eHSD17B13\u003c/span\u003e \u003cstrong\u003eand the other four genetic variants in NAFLD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo further investigate the interaction between the distinct SNPs in NAFLD, we used MDR to construct an interaction model between \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 and the other four SNPs that were robustly validated to be associated with NAFLD. The results showed that all the interaction models presented a testing accuracy of \u0026gt;\u0026thinsp;0.53 in lean NAFLD and \u0026gt;\u0026thinsp;0.51 in non-lean NAFLD, and presented the best cross-validation consistency (10/10) for both lean and non-lean NAFLD. We did not detect any interaction between \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 and the other four SNPs in either lean or non-lean NAFLD in this study (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInteraction analysis of \u003cem\u003eHSD17B13\u003c/em\u003e and \u003cem\u003eGCKR\u003c/em\u003e, \u003cem\u003eTM6SF4\u003c/em\u003e, \u003cem\u003eMBOAT7\u003c/em\u003e, \u003cem\u003ePNPLA3\u003c/em\u003e for association with nonalcoholic fatty liver among lean and non-lean individuals.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene*gene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTesting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean NAFLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e*\u003cem\u003eTM6SF2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5477 (0.3909,6.1285)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e*\u003cem\u003eBMOAT7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6342 (0.4951,5.3935)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e*\u003cem\u003ePNPLA3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3982 (0.7202,7.9855)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e*\u003cem\u003eGCKR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6833 (0.4252,6.6629)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-lean NAFLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e*\u003cem\u003eTM6SF2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1537 (0.8193,1.6248)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e*\u003cem\u003eBMOAT7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2249 (0.8735,1.7178)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e*\u003cem\u003ePNPLA3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2667 (0.8874,1.8082)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e*\u003cem\u003eGCKR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2486 (0.8768,1.7780)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eTM6SF2, TM6SF2: rs58542926; HSD17B13, HSD17B13:rs72613567; MBOAT7, MBOAT7: rs641738; PNPLA3, PNPLA3: rs738409; GCKR, GCKR: rs1260326;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003ePAF for\u003c/strong\u003e \u003cspan class=\"BoldItalic\"\u003eHSD17B13\u003c/span\u003e \u003cstrong\u003eand the other four genetic variants in NAFLD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe PAF calculated for each SNP in lean and non-lean NAFLD was the following (respectively): \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA, 9.07% and \u0026minus;\u0026thinsp;5.85%; \u003cem\u003eTM6SF2\u003c/em\u003e rs58542926: T, 10.94% and 5.57%; \u003cem\u003eMBOAT7\u003c/em\u003e rs641738: T, 21.58% and 8.24%; \u003cem\u003ePNPLA3\u003c/em\u003e rs738409: G, 14.47% and 14.79%; and \u003cem\u003eGCKR\u003c/em\u003e rs1260326: T, 35.82% and 4.46%. Moreover, the combined PAF of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA with the other SNPs in lean and non-lean NAFLD was the following (respectively): with \u003cem\u003eTM6SF2\u003c/em\u003e rs58542926: T, 19.02% and 0.04%; with \u003cem\u003eMBOAT7\u003c/em\u003e rs641738: T, 28.69% and 2.08%; with \u003cem\u003ePNPLA3\u003c/em\u003e rs738409: G, 22.23% and 9.81%; and with \u003cem\u003eGCKR\u003c/em\u003e rs1260326: T, 41.64% and \u0026minus;\u0026thinsp;1.13%. The combined PAF for the five SNPs was 65.14% in lean NAFLD and 25.33% in non-lean NAFLD (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe single and combined population attributable fraction for \u003cem\u003eTM6SF2\u003c/em\u003e, \u003cem\u003eMBOAT7\u003c/em\u003e, \u003cem\u003ePNPLA3\u003c/em\u003e, \u003cem\u003eGCKR\u003c/em\u003e and \u003cem\u003eHSD17B13\u003c/em\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003csub\u003e1\u003c/sub\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003csub\u003e2\u003c/sub\u003e (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCombined\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLean NAFLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eHSD17B13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT: TAT: TATA\u0026thinsp;=\u0026thinsp;53: 41:15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.66, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94 (1.06, 3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.14 \u003csup\u003e##\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTM6SF2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC: TC: TT\u0026thinsp;=\u0026thinsp;87: 19: 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35 (0.80, 2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.94 (1.21, 13.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMBOAT7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC: TC: TT\u0026thinsp;=\u0026thinsp;27: 60: 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41 (0.90, 2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 (0.75, 2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePNPLA3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC: GC: GG\u0026thinsp;=\u0026thinsp;61: 32: 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.60, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30 (1.84, 5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGCKR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC: TC: TT\u0026thinsp;=\u0026thinsp;27:58:24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62 (1.04, 2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05 (1.17, 3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-lean NAFLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eHSD17B13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT: TAT: TATA\u0026thinsp;=\u0026thinsp;744: 510: 99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.79, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.70, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.33\u003csup\u003e##\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTM6SF2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC: TC: TT\u0026thinsp;=\u0026thinsp;1107:230:16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (1.10, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25 (1,31, 3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMBOAT7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC: TC: TT\u0026thinsp;=\u0026thinsp;388:667:289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (0.96, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (1.04, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePNPLA3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC: GC: GG\u0026thinsp;=\u0026thinsp;738:502:113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (1.12, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04 (1.67, 2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGCKR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC: TC: TT\u0026thinsp;=\u0026thinsp;476:625:252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.90, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (1.06, 1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eTM6SF2\u003c/em\u003e, \u003cem\u003eTM6SF2\u003c/em\u003e: rs58542926; \u003cem\u003eHSD17B13\u003c/em\u003e, \u003cem\u003eHSD17B13\u003c/em\u003e:rs72613567; \u003cem\u003eMBOAT7\u003c/em\u003e, \u003cem\u003eMBOAT7\u003c/em\u003e: rs641738; \u003cem\u003ePNPLA3\u003c/em\u003e, \u003cem\u003ePNPLA3\u003c/em\u003e: rs738409; \u003cem\u003eGCKR\u003c/em\u003e, \u003cem\u003eGCKR\u003c/em\u003e: rs1260326;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e## means the PAF calculated combine five genes, Combined means the PAF combined with \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eHSD17B13\u003c/span\u003e \u003cstrong\u003egenotypes and risk of cirrhosis and HCC in lean versus non-lean individuals\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 polymorphism showed no significant difference in distribution between lean and non-lean individuals with respect to cirrhosis and HCC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The MAF was slightly higher in lean cirrhosis (25.77%) than in non-lean cirrhosis (23.35%) and in lean HCC (27.10%) than in non-lean HCC (24.05%).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA showed a protective effect against cirrhosis and HCC (OR\u0026thinsp;\u0026lt;\u0026thinsp;1.0) in lean individuals, although the effect was not statistically significant. In non-lean individuals, the TA allele showed a significant protective effect against cirrhosis in the codominant model (heterozygosity: OR\u0026thinsp;=\u0026thinsp;0.71; 95% CI: 0.63\u0026ndash;0.80; homozygosity: OR\u0026thinsp;=\u0026thinsp;0.74; 95% CI: 0.59\u0026ndash;0.93) and the dominant model (OR\u0026thinsp;=\u0026thinsp;0.71; 95% CI: 0.64\u0026ndash;0.80). Moreover, TA also showed a significant protective effect against HCC in the codominant (heterozygosity: OR\u0026thinsp;=\u0026thinsp;0.80; 95% CI: 0.64\u0026ndash;0.98; homozygosity: OR\u0026thinsp;=\u0026thinsp;0.70; 95% CI: 0.46\u0026ndash;1.06) and dominant models (OR\u0026thinsp;=\u0026thinsp;0.78; 95% CI: 0.64\u0026ndash;0.95) (Table S1).\u003c/p\u003e\n \u003cp\u003eMultivariable logistic regression analyses revealed that both heterozygosity and homozygosity of the TA allele had a significant protective effect against non-lean cirrhosis and non-lean HCC. Heterozygosity of the TA allele exhibited a significant protective effect (OR\u0026thinsp;=\u0026thinsp;0.79; 95% CI: 0.64\u0026ndash;0.98) against non-lean HCC (Table S2). In lean cirrhosis and lean HCC, all effects were protective, although some of the measured effects were not statistically significant. The point estimates were higher in lean individuals than in non-lean individuals.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNAFLD is widely accepted to be closely linked to obesity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], but a proportion of lean individuals also develop NAFLD; suggesting the presence of other severe metabolic conditions or a genetic predisposition for NAFLD in the lean patients [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. \u003cem\u003ePNPLA3\u003c/em\u003e rs738409 polymorphism was previously shown to differ between lean and non-lean NAFLD [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and the disease progression of lean NAFLD was found to be independent of the \u003cem\u003ePNPLA3\u003c/em\u003e gene signature [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The reported differences in \u003cem\u003eTM6SF2\u003c/em\u003e rs58542926 polymorphisms between lean and non-lean NAFLD have been inconsistent across studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, most of the available genetic evidence for lean NAFLD has been derived from the description of baseline characteristics and is inconsistent across studies, and the genetic effects remain incompletely elucidated. Our study investigated the association of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 with NAFLD, cirrhosis, and HCC in lean versus non-lean individuals to complement the existing knowledge.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA has been regarded as a protective variant in various liver diseases. Our results showed that \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA increases the risk of lean NAFLD and thus revealed, for the first time, a risk effect of this variant in liver diseases. We found that the MAF of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 in healthy people was 27.94%, which is comparable to that reported in a recent study that genotyped \u003cem\u003eHSD17B13\u003c/em\u003e in 6171 participants (27.6%) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, a previous study found that the MAF of \u003cem\u003eHSD17B13\u003c/em\u003e in patients with alcohol-related cirrhosis and HCC was lower than that in healthy controls [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our results complement these findings by showing that the MAF of \u003cem\u003eHSD17B13\u003c/em\u003e in NAFLD, cirrhosis, and HCC was also lower than that in healthy controls. However, intriguingly, our results showed that the MAF of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 in lean NAFLD was remarkably higher than that in non-lean NAFLD, which indicates that unique genetic mechanisms might underlie lean NAFLD.\u003c/p\u003e \u003cp\u003eWe found that the TA allele in \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 was significantly associated with increased triglyceride levels, BMI, waist circumference, and WHR in lean NAFLD but not in non-lean NAFLD; this finding indicates that lipid metabolism is modified by \u003cem\u003eHSD17B13\u003c/em\u003e in lean NAFLD. Notably, triglyceride levels were higher in patients with lean NAFLD who were homozygous for the TA allele than in patients with non-lean NAFLD; this suggests that TA allele homozygosity in \u003cem\u003eHSD17B13\u003c/em\u003e plays a critical role in regulating the serum triglyceride levels in lean NAFLD, although further investigation of the underlying mechanism is warranted. Our results also showed that the fasting blood glucose level was lower in lean NAFLD patients than in non-lean NAFLD patients, which supporting the previously reported finding that diabetes prevalence is lower in patients with lean NAFLD than in patients with non-lean NAFLD [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of PAF analysis showed \u003cem\u003eGCKR\u003c/em\u003e with the highest PAF in lean NAFLD (35.82%), while \u003cem\u003ePNPLA3\u003c/em\u003e with the highest PAF in non-lean NAFLD (14.79%). The risk of NAFLD appeared to be attenuated in non-lean NAFLD (PAF\u0026thinsp;=\u0026thinsp;9.07%) and increased in lean NAFLD (PAF\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.85%) as a result of co-carriage of TA in \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567, and under co-carriage of \u003cem\u003eGCKR\u003c/em\u003e rs1260326: T and \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA, in particular, the combined PAF showed a marked difference between lean NAFLD (41.64%) and non-lean NAFLD (\u0026minus;\u0026thinsp;1.13%). Therefore, the differences in the five SNPs in single and combined PAFs indicate that lean and non-lean NAFLD feature distinct genetic profiles.\u003c/p\u003e \u003cp\u003eCurrently, weight loss is the recommended treatment strategy for NAFLD. Accordingly, weight reduction of 10% through dietary restriction and regular exercise was shown to be sufficient to reverse NASH in most patients [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, this might not serve as an effective strategy in the case of patients with lean NAFLD. Therefore, it is desirable to investigate the underlying mechanisms of lean NAFLD and provide advice for its treatment and related drug development. As a protective allele for various liver diseases, \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 is considered to be an attractive target for therapeutic drug development [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our results here showed that the effect of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 in lean NAFLD differs from that in non-lean NAFLD, which implications for drug development and personalized treatment for lean NAFLD.\u003c/p\u003e \u003cp\u003eNAFLD has commonly been underdiagnosed because of its mild clinical symptoms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. We speculate that lean NAFLD is less likely to be detected than non-lean NAFLD because obesity has been employed as a primary indicator for NAFLD diagnosis. Therefore, it is necessary to establish methods for diagnosing NAFLD in the lean population. Use of the genetic risk score (GRS) has been considered as a strategy to identify NAFLD patients at increased risk before developing advanced disease and to target these patients for preventive interventions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA has been consistently employed as a protective variant of NAFLD in GRS construction [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our findings indicate that lean patients should be distinguished when the GRS is used to construct NAFLD diagnostic tools and risk-prediction methods.\u003c/p\u003e \u003cp\u003eThe present study is based on a large-population prospective cohort. Moreover, a previous study suggested that the phenotypic definition of NAFLD in the UK Biobank based on the ICD-10 code in hospital records accurately reflects that of clinical diagnosed NAFLD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These key points strengthen our study, but certain potential limitations remain. First, the NAFLD, cirrhosis, and HCC cases we included were obtained from hospitalization records; thus, the sample size was modest, particularly in terms of the relatively small number of lean individuals, and this might make the statistics inadequately powerful. Consequently, although the MDR analysis in this study did not detect the interaction between \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 and the other four SNPs, the results regarding the interaction should be interpreted with caution. Second, prognosis information is not available in the UK Biobank, and subsequent longitudinal assessment is required to understand the prognosis in patients carrying the TA allele of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567. Third, some of the OR values used for PAF calculation in this study did not reach statistical significance, and our PAF results should therefore be further validated in independent and relatively larger populations.\u003c/p\u003e \u003cp\u003eIn conclusion, the polymorphism of \u003cem\u003eHSD17B13\u003c/em\u003e rs72614567 was significantly different between lean NAFLD and non-lean NAFLD. Carriage of variants TA in \u003cem\u003eHSD17B13\u003c/em\u003e rs72614567 have protective effects on non-lean NAFLD, cirrhosis and HCC, but may have a risk effect on lean NAFLD. We have shown for the first time that \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567: TA increases the risk of lean NAFLD. Our finding could facilitate the development of diagnostic and therapeutic strategies tailored for lean NAFLD\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003eWe sincerely appreciate the great work of the UK Biobank collaborators. This study was conducted under Application Number 58484 and 63726.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of Interest:\u003c/em\u003e\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/strong\u003e This study was supported by the Special Foundation for Science and Technology Basic Research Program (2019FY101103), the Natural Science Foundation of China (81772170) and by the National Key Research and Development Program of China (No. 2017YFC0211700).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Availability:\u003c/em\u003e\u003c/strong\u003e The UK Biobank data are available from the UK Biobank on request (\u003ca href=\"http://www.ukbiobank.ac.uk/\"\u003ewww.ukbiobank.ac.uk/\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical approval:\u003c/em\u003e\u003c/strong\u003e The UK Biobank received ethical approval from the research ethics committee (REC reference for UK Biobank 11/NW/0382) and participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnimal Research (Ethics):\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical Trials Registration:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contribution:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eHF and TZ conceived of the study design. HF, XZ and PZ performed the statistical analysis. HF, and ZL wrote the manuscript. All authors provided critical revisions of the draft and approved the submitted draft. All authors gave final approval of the version to be published and have contributed to the manuscript. TZ had the final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng RN, Du SS, Wang C, Li YC, Liu LY, Guo FC, et al. Lean-non-alcoholic fatty liver disease increases risk for metabolic disorders in a normal weight Chinese population. World J Gastroenterol. 2014;20:17932\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Q, Zou B, Yeo YH, Li J, Huang DQ, Wu Y, et al. 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Proc Natl Acad Sci U S A. 2014;111:11437\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen JC, Horton JD, Hobbs HH. Human fatty liver disease: old questions and new insights. Science. 2011;332:1519\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirola CJ, Garaycoechea M, Flichman D, Arrese M, San Martino J, Gazzi C, et al. Splice variant rs72613567 prevents worst histologic outcomes in patients with nonalcoholic fatty liver disease. J Lipid Res. 2019;60:176\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuukkonen PK, Tukiainen T, Juuti A, Sammalkorpi H, Haridas PAN, Niemel\u0026auml; O, et al. Hydroxysteroid 17-β dehydrogenase 13 variant increases phospholipids and protects against fibrosis in nonalcoholic fatty liver disease. JCI Insight 2020;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbout F, Abel L, Cobat A. HCV-Associated Liver Fibrosis and HSD17B13. N Engl J Med. 2018;379:1875\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang P, Wu CX, Li Y, Shen N. HSD17B13 rs72613567 protects against liver diseases and histological progression of nonalcoholic fatty liver disease: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. 2020;24:8997\u0026ndash;9007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStickel F, Lutz P, Buch S, Nischalke HD, Silva I, Rausch V, et al. Genetic Variation in HSD17B13 Reduces the Risk of Developing Cirrhosis and Hepatocellular Carcinoma in Alcohol Misusers. Hepatology. 2020;72:88\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTing YW, Kong AS, Zain SM, Chan WK, Tan HL, Mohamed Z, et al. Loss-of-function HSD17B13 variants, non-alcoholic steatohepatitis and adverse liver outcomes: results from a multi-ethnic Asian cohort. Clin Mol Hepatol 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Tr\u0026eacute;po E, Nahon P, Cao Q, Moreno C, Letouz\u0026eacute; E, et al. A 17-Beta-Hydroxysteroid Dehydrogenase 13 Variant Protects From Hepatocellular Carcinoma Development in Alcoholic Liver Disease. Hepatology. 2019;70:231\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGellert-Kristensen H, Nordestgaard BG, Tybjaerg-Hansen A, Stender S. High Risk of Fatty Liver Disease Amplifies the Alanine Transaminase-Lowering Effect of a HSD17B13 Variant. Hepatology. 2020;71:56\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao C, Marcketta A, Backman JD, O'Dushlaine C, Staples J, Ferreira MAR, et al. Genome-wide association analysis of serum alanine and aspartate aminotransferase, and the modifying effects of BMI in 388k European individuals. Genetic epidemiology 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVilar-Gomez E, Pirola CJ, Sookoian S, Wilson LA, Liang T, Chalasani N. The Protection Conferred by HSD17B13 rs72613567 Polymorphism on Risk of Steatohepatitis and Fibrosis May Be Limited to Selected Subgroups of Patients With NAFLD. Clin Transl Gastroenterol. 2021;12:e00400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85:660\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Zhang Y, Graham S, Wang X, Cai D, Huang M, et al. Causal relationships between NAFLD, T2D and obesity have implications for disease subphenotyping. J Hepatol. 2020;73:263\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Suo C, Zhao R, Yuan H, Jin L, Zhang T, et al. Genetic predisposition, lifestyle risk, and obesity associate with the progression of nonalcoholic fatty liver disease. Dig Liver Dis 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinf (Oxford England). 2003;19:376\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMei H, Cuccaro ML, Martin ER. Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables. Am J Hum Genet. 2007;81:1251\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuch S, Stickel F, Tr\u0026eacute;po E, Way M, Herrmann A, Nischalke HD, et al. A genome-wide association study confirms PNPLA3 and identifies TM6SF2 and MBOAT7 as risk loci for alcohol-related cirrhosis. Nat Genet. 2015;47:1443\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitte JS, Visscher PM, Wray NR. The contribution of genetic variants to disease depends on the ruler. Nat Rev Genet. 2014;15:765\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbhaisi S, Chowdhury A, Sanyal AJ. Non-alcoholic fatty liver disease in lean individuals. JHEP Rep. 2019;1:329\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei JL, Leung JC, Loong TC, Wong GL, Yeung DK, Chan RS, et al. Prevalence and Severity of Nonalcoholic Fatty Liver Disease in Non-Obese Patients: A Population Study Using Proton-Magnetic Resonance Spectroscopy. Am J Gastroenterol. 2015;110:1306\u0026ndash;14. quiz 1315.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung JC, Loong TC, Wei JL, Wong GL, Chan AW, Choi PC, et al. Histological severity and clinical outcomes of nonalcoholic fatty liver disease in nonobese patients. Hepatology. 2017;65:54\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSookoian S, Pirola CJ. Systematic review with meta-analysis: the significance of histological disease severity in lean patients with nonalcoholic fatty liver disease. Aliment Pharmacol Ther. 2018;47:16\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong VW, Chitturi S, Wong GL, Yu J, Chan HL, Farrell GC. Pathogenesis and novel treatment options for non-alcoholic steatohepatitis. lancet Gastroenterol Hepatol. 2016;1:56\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas H. An HSD17B13 variant reduces cirrhosis risk. Nat Rev Gastroenterol Hepatol. 2018;15:328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStender S, Romeo S. HSD17B13 as a promising therapeutic target against chronic liver disease. Liver Int. 2020;40:756\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomeo S, Kozlitina J, Xing C, Pertsemlidis A, Cox D, Pennacchio LA, et al. Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nat Genet. 2008;40:1461\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGellert-Kristensen H, Richardson TG, Davey Smith G, Nordestgaard BG, Tybjaerg-Hansen A, Stender S. Combined Effect of PNPLA3, TM6SF2, and HSD17B13 Variants on Risk of Cirrhosis and Hepatocellular Carcinoma in the General Population. Hepatology. 2020;72:845\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfeiffer RM, Rotman Y, O'Brien TR. Genetic Determinants of Cirrhosis and Hepatocellular Carcinoma Due to Fatty Liver Disease: What's the Score? Hepatology. 2020;72:794\u0026ndash;6.\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":"NAFLD, lean NAFLD, genetic variation, HSD17B13, PNPLA3, TM6SF2, cirrhosis, hepatocellular carcinoma, population-attributable fraction, multifactor dimensionality reduction, minor allele frequency","lastPublishedDoi":"10.21203/rs.3.rs-1469582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1469582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/purpose: \u003c/strong\u003eCarriage of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567:TA is associated with a reduced risk of nonalcoholic fatty liver disease (NAFLD); however, whether this protective effect exists in lean NAFLD remains unknown. Therefore, we compared the association of \u003cem\u003eHSD17B13 \u003c/em\u003ers72613567 and NAFLD between lean and non-lean individuals.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We tested the association of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 with NAFLD, cirrhosis, and hepatocellular carcinoma (HCC) in 313,309 individuals from the UK Biobank, including 1464 patients with NAFLD, 1559 with cirrhosis, and 526 with HCC. We calculated the minor allele frequency (MAF) of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 and analyzed this SNP using codominant, dominant, and recessive models. Furthermore, we calculated the population-attributable fraction (PAF) and the combined PAF for five SNPs (\u003cem\u003eHSD17B13\u003c/em\u003e rs72613567, \u003cem\u003eTM6SF2\u003c/em\u003e rs58542926, \u003cem\u003eMBOAT7\u003c/em\u003e rs641738,\u003cem\u003e PNPLA3\u003c/em\u003e rs738409, and \u003cem\u003eGCKR\u003c/em\u003e rs1260326) and used multifactor dimensionality reduction (MDR) to analyze the interactions between \u003cem\u003eHSD17B13\u003c/em\u003e and the other four SNPs. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe MAF of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567\u003cem\u003e \u003c/em\u003ewas considerably higher in lean NAFLD (32.57%) than in non-lean NAFLD (26.16%). Moreover, homozygosity of the TA allele showed a significant risk effect in the codominant (OR = 1.94; 95% CI: 1.09–3.44) and recessive models (OR = 1.94; 95% CI: 1.12–3.35). By contrast, in the case of cirrhosis, HCC, and non-lean NAFLD, both homozygosity and heterozygosity of the TA allele showed a protective effect. Finally, the MDR analysis did not detect any interaction between \u003cem\u003eHSD17B13\u003c/em\u003e and the other four SNPs. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The polymorphism of \u003cem\u003eHSD17B13\u003c/em\u003e rs72613567 is different between lean and non-lean NAFLD, and the TA allele showed a risk effect for lean NAFLD.\u003c/p\u003e","manuscriptTitle":"Genetic variation of HSD17B13 is associated with an increased risk of lean nonalcoholic fatty liver disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-30 20:40:00","doi":"10.21203/rs.3.rs-1469582/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":"5c638287-7802-4371-b837-c1a46f835e79","owner":[],"postedDate":"March 30th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-04-08T13:19:05+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-30 20:40:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1469582","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1469582","identity":"rs-1469582","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}