Association Between Tfeb Gene Polymorphism, Gene–environment Interaction, and Fatty Liver Disease: a Case–control Study in China

Association Between Tfeb Gene Polymorphism, Gene–environment Interaction, and Fatty Liver Disease: a Case–control Study in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Association Between Tfeb Gene Polymorphism, Gene–environment Interaction, and Fatty Liver Disease: a Case–control Study in China Chunbao Mo, Tingyu Mai, Jiansheng Cai, Haoyu He, Huaxiang Lu, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-541251/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: Fatty liver disease (FLD) is a serious public health problem that is rapidly increasing. Evidences indicated that the transcription factor EB ( TFEB ) gene may be involved in the pathophysiology of FLD; however, whether TEFB polymorphism is association with FLD remains unclear. Objectives: To explore the association among TFEB polymorphism, gene–environment interaction, and FLD and provide epidemiological evidence for clarifying the genetic factors of FLD. Methods: This study is a case–control study. Sequenom MassARRAY was applied in genotyping. Logical regression was used to analyze the association between TFEB polymorphism and FLD, and the gene–environment interaction in FLD was evaluated by multiplication and additive interaction models. Results: (1) The alleles and genotypes of each single nucleotide polymorphism of TFEB in the case and control groups were evenly distributed; no statistically substantial difference was observed. (2) Logistic regression analysis indicated that TFEB polymorphism is not remarkably associated with FLD. (3) In the multiplicative interaction model, rs1015149, rs1062966, and rs11754668 had remarkable interaction with smoking amount. Rs1062966 and rs11754668 also had a considerable interaction with body mass index and alcohol intake, respectively. However, no remarkable additive interaction was observed. Conclusion: TFEB polymorphism is not directly associated with FLD susceptibility, but the risk can be changed through gene–environment interaction. Polymer Science TFEB gene polymorphism gene–environment interaction fatty liver disease Introduction Health problem has gradually aroused people's concern with the advancement of economy and society and the improvement of living standard. Fatty liver disease (FLD) is a chronic disease and a serious public health problem that is rapidly increasing.[1 , 2] FLD is the pathological process of excess adipose accumulation in liver cells, caused by many factors, such as disease and drug. Simple hepatic steatosis may transform into steatohepatitis or cirrhosis as FLD progresses. According to etiology, FLD is classified as nonalcoholic fatty liver disease (NAFLD) and alcoholic liver disease (ALD). Studies have indicated that the pathogenesis of FLD is affected by abnormal fat metabolism, immune response, environment, genetic, and other factors.[3] Currently, specific medicine for FLD is deficient; the effective prevention and control measures for FLD are early detection and intervention, including diet control and exercise; and alcohol abstinence is the chief measure for patients with ALD.[3] Similar with most diseases, FLD is influenced by environmental and genetic factors. Single nucleotide polymorphisms (SNPs) are the most common form of mutations in the human genome. Studies have found that SNPs are associated with FLD. Wen et al. suggested the association between rs780094 polymorphism and NAFLD in Uyghur population by case–control method.[4] Luigi et al. also reported that rs738409 polymorphism in pNPLA3 may be a genetic variant that is associated with NAFLD and ALD. [5] In recent years, transcription factor EB (TFEB) has attracted extensive attention in the study of autophagy mechanism. TFEB is the main gene involved in lysosome biosynthesis and encodes TFEB, which is an important regulatory factor for autophagy and lysosomal biosynthesis. TFEB is considered the main activator for autophagy–lysosomal gene transcription and refers to inflammation,[6] cell autophagy,[7] lipid metabolism[8] and other biological processes. Previous researches indicated that TFEB can regulate the expression of many genes related to lipid degradation, such as cluster of differentiation 36, fatty acid binding proteins, and carnitine acetyltransferase. Furthermore, TFEB can regulate lipid degradation factor, peroxisome proliferator-activated receptor alpha (PPARα), and an upstream factor of PPARα, proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α), through signal-mediated transfer to the nucleus; thus, PPARα is affected to participate in lipid metabolism.[9 , 10] Increasing TFEB levels in vivo may protect mice liver from alcohol-induced damage,[11] and promoting TFEB-mediated lysosomal biogenesis using formononetin can ameliorate the fatty disease process in mice liver.[12] Although increasing studies have provided etiological evidence to elucidate the mechanism between TFEB and fatty liver, few researches have focused on the relationship between TFEB and FLD. Therefore, in this study, a case–control approach was adopted to explore the association between TFEB polymorphism and FLD, and gene–environment interaction was evaluated to provide epidemiological evidence of the genetic factors of FLD. Materials And Methods 2.1. Study design and population This case–control study included 228 patients with FLD diagnosed by ultrasonography according to the diagnostic guidelines released by the Chinese Medical Association. Individuals with liver diseases, tumors, and autoimmune diseases caused by drugs and viruses were excluded. A total of 342 healthy individuals who were matched by sex and age (with variation of ±3 years) in a proportion of 1:1.5 were selected as the control group. All the participants were permanent residents in Gongcheng County, Guilin City, Guangxi Zhuang Autonomous Region, People’s Republic of China and signed the informed consent voluntarily after fully understanding the research content and importance of this project. Our research protocol was approved by the Ethics Committee of Guilin Medical University. 2.2 Data collection All the participants were required to answer a questionnaire from a trained researcher to collect information on demography, behavior, exercise, disease history, nutritional diet, and other data. Behavioral factors include smoking and alcohol consumption. The amount of smoking is expressed in pack year, that is, the number of packs (20 cigarettes per bag) per day multiplied by the number of years of smoking. In addition to daily alcohol intake, we also assessed the daily intake of 11 types of food, including cereals and their products, potatoes, vegetables, and fruits, via dietary survey. The participants were also examined by professional physicians to collect anthropometric indicators, such as height, weight, waist circumference, and blood pressure. Venous blood was collected for subsequent tests and experiments. 2.3 Biochemical testing Two venous blood samples were collected from each participant. One set of samples was tested for biochemical indicators, including triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), and uric acid (UA), at a local hospital. The other set of samples was used in the SNP typing experiment. 2.4 Selection and genotyping of SNPs Functional and validated SNP screening strategies were utilized to screen the target SNPs. This strategy focused on important functional loci and supplemented by susceptible loci. Finally, the rs1015149, rs1062966, rs14063, rs2273068, and rs11754668 of the TFEB gene were selected as the target SNPs for genotyping. Genomic DNA was isolated from venous blood using a commercial DNA extraction kit (Tiangen, Beijing, China). SNP genotyping was performed using the Sequenom MassARRAY matrix-assisted laser desorption ionization time-of-flight mass spectrometry platform (Sequenom, Inc., San Diego, CA, USA). The primers were designed and synthesized by Bio Miao Biological Technology Co., Ltd. (Table S1). 2.5 Statistical analysis Descriptive statistics for continuous and categorical variables were conducted using mean ± standard deviation (SD) and frequency (proportion), respectively. Student's t-test and chi-square test were applied to compare the differences among two groups and genotype subgroups. Pearson's chi-square test was utilized to evaluate the Hardy–Weinberg equilibrium (HWE) before analyzing SNP data. The samples were considered representative when p > 0.05. We performed logistic regression to estimate the effects of genotypes and gene–environment multiplicative interactions on FLD, and odds ratio (OR) and 95% confidence interval (95% CI) were calculated. The test level α = 0.05. However, logistic regression was limited to estimate additive interactions; hence, relative excess risk due to interaction (RERI), attributable proportion of interaction (AP), synergy index (SI), and their 95% CIs were calculated. Additive interactions were considered statistically significant when the 95% CI of RERI and AP did not include 0 and the 95% CI of SI did not contain 1.[13] SPSS 25.0 (IBM, Chicago, IL, USA) and PLINK 1.90 software were used to implement general statistical analysis and gene polymorphism analysis. In addition, R software 4.0.2 and "epiR" package were utilized to complete the calculation of RERI, AP, and SI. Results 3.1. Characteristics of the participants The demographic and behavioral characteristics of the participants are listed in Table 1. The subjects have a total number of 570 and a roughly equal gender proportion. The age range is 30–83 years with an average of 58.15 years. The majority of the subjects (78.42%) belong to Yao population. No remarkable differences in gender, age, ethnicity, marital status, hypertension, smoking, drinking, and other factors were observed between the control and case groups at baseline ( p > 0.05). However, the proportion of subjects with a history of hypertension and the average daily sitting time were significantly higher in the case group than in the control group ( p < 0.05). The dietary situation is shown in Table 2. Vegetables and cereals and their products were the main daily dietary intake of the participants. No significant difference was observed in the daily food intake of the two groups ( p > 0.05). The clinical indicators are exhibited in Table 3. The two groups showed no statistically significant difference in AST (t = −1.415, p = 0.158). However, HDL-C was significantly lower in the case group than in the control group, and the other indicators were significantly higher in the case group compared with the control group ( p < 0.05). Results indicate that the demographic, behavioral, and dietary variables in the two groups were matched preferably. 3.2 Basic information of SNPs The rs1015149, rs1062966, rs11754668, rs14063, and rs2273068 of TFEB are located in chromosome 6, and the minimum allele frequency of each locus was greater than 0.05. The success rate of genotyping was very high at nearly 100% by MassARRAY. All the SNPs’ loci were consistent with HWE ( p HWE > 0.05); therefore, the study subjects are representative (Table S2). 3.3 Genotypic frequency The alleles and genotypes of each SNP of TFEB in the two groups were evenly distributed. No statistically significant difference was observed ( p > 0.05, Table 4). 3.4 Associations between genotypes and FLD FLD was regarded as the dependent variable. Co-dominant, dominant, and recessive models were used for logistic regression analysis using gender and age as adjustment factors. The result indicated that no significant correlations exist between genotypes and FLD in the co-dominant, dominant, and recessive models ( p > 0.05, Table 5). 3.5 Interactions between environmental factors and SNP in FLD Multiplicative and additive models were used to evaluate the interactions of each SNP locus with environmental factors, including diabetes, smoking amount, alcohol intake, daily sitting time, waist circumference, and body mass index (BMI). Compared with single genes, some gene–environment interactions were remarkably associated with FLD susceptibility. In the multiplicative interaction model, rs1015149, rs1062966, and rs11754668 had substantial interaction with smoking amount. Among them, rs1062966 and rs11754668 also had remarkable interaction with BMI and alcohol intake, respectively. Notably, the 95% CIs of RERI and AP contained 0, and the 95% CI of SI contained 1; thus, all gene–environment additive interactions with FLD were not statistically significant (Table 6, Table S3). Discussion In this case–control study, we analyzed the association of TFEB polymorphisms and FLD and assessed gene–environment interactions to provide epidemiological evidence of the genetic factors related to the occurrence and development of FLD. Results showed that the alleles and genotypes of each SNP of TFEB in the case and control groups were evenly distributed; no statistically significant difference was observed. Logistic regression analysis indicated that TFEB polymorphism is not substantially associated with FLD. Previous studies have suggested that autophagy plays an important role in maintaining liver homeostasis.[14] TFEB knockout in mice may result in the hepatic accumulation of fatty acid-β and impaired oxidation in hepatocytes, which lead to elevated fatty acid and glycerol levels and lipid metabolism disorders in hepatocytes.[15] According to the results, TFEB may be involved in the pathophysiological basis of FLD. However, the relationship between TFEB polymorphism and FLD was not observed in this study; thus, these SNPs may not affect the normal expression of TFEB . Gene–environment interaction plays an important role in the occurrence and development of complex diseases, such as FLD. Zhu et al. demonstrated that the gene–gene interaction between AGTR1 and PPARγ is associated with the occurrence of NAFLD in Chinese population.[16] Zhang et al. found that people with 11391G/A(AA) and EC-SOD (CG+GG) genotypes suffer a higher risk of NAFLD, and these genotypes have an interaction with Helicobacter pylori infection.[17] Therefore, the analysis of gene–environment interaction might be conducive to understand etiological factors and guide the prevention and treatment of FLD. The result exhibited that some SNP loci, such as rs1015149, rs1062966, and rs11754668, had positive interactions with smoking, which is a risk factor for FLD susceptibility.[18] This finding is consistent with the results of Zhang et al. on the interactions between GPX-1 polymorphism and smoking in NAFLD and also agrees with the results of Oniki et al.[19] Interestingly, rs1015149 and rs2273068 had negative interaction with smoking in FLD; thus, they are considered “protective factors” (OR = 0.96 and 0.97, respectively). Smoking is a recognized risk factor that is remarkably associated with the occurrence of many diseases. However, the relationship between smoking and FLD is not clear yet.[20-23] In this study, we found that smoking might reduce the risk of FLD of individuals who carry the CT+CC genotype of rs1015149 or the TT+CT genotype of rs2273068. However, the results do not "advocate" smoking to these population for FLD prevention. A more rational explanation for the reduced FLD risk is that compared with individuals who carry other genotypes, people with the CT+CC genotype of rs1015149 or the TT+CT genotype of rs2273068 may be more able to offset the risk of FLD caused by smoking. The same explanation can also be utilized to explain the interaction between rs11754668 and alcohol intake in this study. No significant additive interaction was observed in this study. This result is in agreement with the result of Zhao et al.[24] In other words, additive interaction may not be remarkable even if the factors studied have substantial multiplicative interaction. In fact, the interaction between multiple factors is based on multiplication and synergism, whereas additive interaction is relatively rare. In the field of medicine, the analysis models for gene–environment, gene–gene, and gene–environment-gene interactions, such as cross-generation analysis,[25] multifactor dimensionality reduction (MDR),[26] and generalized MDR,[27] are based on multiplication. Although substantial multiplicative interaction results were not observed in the present study, this study still provided a meaningful attempt to explore the gene–environment interaction in FLD, which might be ignored. Dietary factors are important influencing factors of FLD. Numerous literatures have reported the association between different dietary patterns or food intake and the incidence of FLD. For example, the high intake of meat, high-fat dairy products, and refined grains may increase the risk of FLD, whereas a diet based on fruits, vegetables, whole grains, fish, and olive oil can reduce FLD risk.[28 , 29] A cross-sectional study based on Chinese adolescents illustrated that adolescents who have traditional Chinese diet have lower risks of FLD compared with those with Western diet.[30] Therefore, the potential impact of diet on the result needs to be fully considered and controlled to reduce analysis error. In addition, in this study, we ensured data quality and improved the reliability of the results. The advantages are as follows. (1) This study is the first epidemiological study to uncover the associations of TFEB polymorphism and gene–environment interaction with FLD. (2) All participants were from the same area with relatively similar genetic background, living environment, and habits; these similarities were helpful to control potential confounding factors. (3) In terms of grouping, gender and age (±3 years) were adopted in matching to reduce the influence of gender and age on the results to a certain extent. However, this study also has many deficiencies that need to be further improved. First, the sample size is relatively small, and sampling error is difficult to decrease. Second, the conclusions are based on the population from Gongcheng County. Therefore, the applicability of the conclusions to other populations is limited, and extrapolation is deficient. Third, the degree of FLD was not classified. Thus, the effect of research factors on the process of FLD might have been ignored. Finally, the causal demonstration power is not strong because of the case–control design. Conclusion The polymorphisms of the rs1015149, rs1062966, rs11754668, rs14063, and rs227306 of the TFEB gene are not directly associated with FLD susceptibility, but the risk can be changed through gene–environment interaction. Abbreviations FLD: fatty liver disease; TFEB: transcription factor EB; NAFLD: nonalcoholic fatty liver disease; SNPs: single nucleotide polymorphisms; PPARα: peroxisome proliferator-activated receptor alpha; PGC-1α: proliferator-activated receptor gamma coactivator 1 alpha; WC: waist circumference; BMI: body mass index; SBP/DBP: systolic/diastolic blood pressure; HbA1C: glycosylated hemoglobin; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; TG: triglyceride; TC: total cholesterol; GLU: fasting plasma glucose; ALB: albumin; ALT: alanine aminotransferase; AST aspartate transaminase; UA: uric acid; HWE: Hardy–Weinberg equilibrium; RERI: relative excess risk due to interaction; AP: attributable proportion of interaction; SI: synergy index; DM: Dominant model; RM: Recessive model ; MDR: multifactor dimensionality reduction Declarations Acknowledgments: Not applicable. Author contributions: Conception and design: Chunbao Mo, Tingyu Mai and Jiansheng Cai; Acquisition of data: Haoyu He, Huaxiang Lu, Xu Tang, Quanhui Chen, Xia Xu, Chuntao Nong. Shuzhen Liu, Tan Dechan, Qiumei Liu and Min Xu; Analysis and interpretation of data: Chunbao Mo, Tingyu Mai, Jiansheng Cai and Haoyu He; Writing, review, and/or revision of the manuscript: Chunbao Mo, Tingyu Mai, Jiansheng Cai, Li You and Jian Qin; Administrative, technical, or material support: You Li, Zhiyong Zhang and Jian Qin; Study supervision: Zhiyong Zhang and Jian Qin. All authors approved the final manuscript. Funding This work was funded by National Natural Science Foundation of China [No. 8196120388]; Guangxi Science and Technology Development Project [No. AD17129003]; Guangxi Graduate Education Innovation Project [NO. YCSW2020230] Availability of data and materials Not applicable. Ethics approval and consent to participate Our research protocol was approved by the Ethics Committee of Guilin Medical University. Consent for publication Not applicable. Competing interests The authors declare that there is no conflict of interests. Author details 1 Department of Pathophysiology, Faculty of Basic Medical Sciences, Guilin Medical University，Guilin 541004, Guangxi, China 2 Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, Guilin 541004, Guangxi, China 3 Department of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, Nanning 530021, Guangxi, China References Diehl AM, Farpour-Lambert NJ, Zhao L, et al. Why we need to curb the emerging worldwide epidemic of nonalcoholic fatty liver disease. Nat Metab. 2019;1(11):1027–9. Perumpail BJ, Khan MA, Yoo ER, et al. Clinical epidemiology and disease burden of nonalcoholic fatty liver disease. 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Tables Table 1 Demographic and behavioral characteristics of the study population Variables All Participants (n %) Control group (n %) Case group (n %) χ 2 /t p value Gender Male 321 (56.32) 192 (56.14) 129 (56.58) 0.011 0.918 Female 249 (43.68) 150 (43.86) 99 (43.42) Age (years) # 58.15±12.42 58.50±12.75 57.64±11.93 0.806 0.42 Nation Han 98 (17.19) 58 (16.96) 40 (17.54) 0.194 0.907 Yao 447 (78.42) 268 (78.36) 179 (78.51) Zhuang and others 25 (4.39) 16 (4.68) 9 (3.95) Marital status No partner 77 (13.51) 49 (14.33) 28 (12.28) 0.491 0.484 Have a partner 493 (86.49) 293 (85.67) 200 (87.72) Education Primary school and below 354 (62.11) 223 (65.20) 131 (57.46) 3.49 0.062 Junior high school and above 216 (37.89) 119 (34.80) 97 (42.54) Occupation Farmer 519 (91.05) 309 (90.35) 210 (92.11) 0.517 0.472 Others 51 (8.95) 33 (9.65) 18 (7.89) Household income (Yuan) ＜5000 154 (27.02) 88 (25.73) 66 (28.95) 0.718 0.397 ≥5000 416 (72.98) 254 (74.27) 162 (71.05) Diabetes No 546 (95.79) 333 (97.37) 213 (93.42) 5.285 0.022 * Yes 24 (4.21) 9 (2.63) 15 (6.58) Hypertension No 464 (81.40) 284 (83.04) 180 (78.95) 1.514 0.218 Yes 106 (18.60) 58 (16.96) 48 (21.05) Smoking No 465 (81.58) 273 (79.82) 192 (84.21) 1.751 0.186 Yes 105 (18.42) 69 (20.18) 36 (15.79) Drinking No 371 (65.09) 221 (64.62) 150 (65.79) 0.082 0.774 Yes 199 (34.91) 121 (35.38) 78 (34.21) Smoking amount (pack year) # 5.83±16.78 6.37±17.69 5.03±15.31 0.934 0.351 Alcohol intake (g/day) # 17.26±40.40 17.84±43.75 16.39±34.87 0.419 0.675 Strenuous physical activity (h/day) # 1.37±7.17 1.19±6.04 1.65±8.59 -0.743 0.458 Moderate physical activity (h/day) # 4.52±11.32 4.54±11.43 4.49±11.18 0.049 0.961 Daily walking time (h) # 3.27±2.21 3.38±2.24 3.09±2.14 1.526 0.128 Daily sitting time (h) # 3.64±1.79 3.50±1.76 3.84±1.83 -2.212 0.027 * # Mean ± SD; * p < 0.05 was considered statistically significant. Table 2 Daily food intake of the participants Food category All Participants Control group Case group t p value Cereals and their products 263.80±161.91 262.63±168.13 265.55±152.47 -0.211 0.833 Potatoes 38.56±86.34 39.72±91.08 36.82±78.86 0.392 0.695 Vegetables 313.68±315.28 325.79±336.87 295.53±279.52 1.123 0.262 Fruits 254.53±269.71 256.25±282.77 251.94±249.43 0.187 0.852 Beans and their products 36.04±50.25 36.04±51.40 36.04±48.59 -0.001 0.999 nuts 13.40±26.02 12.69±23.49 14.47±29.42 -0.803 0.422 Meat and poultry 83.37±88.86 87.23±94.64 77.58±79.25 1.271 0.204 Fish and aquatic products 17.88±29.63 17.28±29.14 18.77±30.39 -0.585 0.559 Milk and their products 35.78±36.31 34.85±35.59 37.18±37.40 -0.752 0.452 Eggs and their products 33.23±71.65 34.74±76.36 30.96±64.04 0.617 0.538 Cooking oil 35.00±26.66 35.71±27.58 33.94±25.23 0.776 0.438 Salt 9.60±7.51 9.93±8.24 9.11±6.24 1.276 0.202 Note: Data are expressed as mean ± SD. The unit for each food category is gram. Table 3 Clinical indicators of the study population Clinical indicators All Participants Control group Case group t p value WC 81.39±10.49 76.78±9.14 88.30±8.40 -15.23 <0.001 ** BMI 24.55±17.59 21.99±3.44 28.40±27.07 -3.558 <0.001 ** SBP 136.02±24.23 134.39±23.15 138.47±25.63 -1.972 0.049 * DBP 82.52±15.14 80.72±13.59 85.22±16.88 -3.512 <0.001 ** HbA1C 5.96±1.06 5.82±0.90 6.17±1.25 -3.595 <0.001 ** LDL-C 3.44±0.98 3.30±0.94 3.63±0.99 -4.026 <0.001 ** HDL-C 1.69±0.39 1.76±0.39 1.58±0.37 5.559 <0.001 ** TC 5.56±1.05 5.40±1.03 5.80±1.03 -4.533 <0.001 ** TG 1.60±1.61 1.21±1.28 2.18±1.86 -6.875 <0.001 ** GLU 5.07±1.53 4.87±1.24 5.37±1.85 -3.543 <0.001 ** ALB 44.06±2.34 43.77±2.34 44.50±2.29 -3.67 <0.001 ** ALT 21.90±12.99 19.29±12.29 25.81±13.06 -5.976 <0.001 ** AST 24.09±11.27 23.55±10.91 24.91±11.77 -1.415 0.158 UA 333.34±100.58 306.72±91.26 373.28±100.87 -8.175 <0.001 ** Note: Data are expressed as mean ± SD; * p < 0.05 and ** p < 0.001 were considered statistically significant; WC: waist circumference (cm), BMI: body mass index, SBP/DBP: systolic/diastolic blood pressure (mmHg), HbA1C: glycosylated hemoglobin (%), LDL-C: low-density lipoprotein cholesterol (mmol/L), HDL-C: high-density lipoprotein cholesterol (mmol/L), TG: triglyceride (mmol/L), TC: total cholesterol (mmol/L), GLU: fasting plasma glucose (mmol/L), ALB: albumin (g/L), ALT: alanine aminotransferase (U/L), AST aspartate transaminase (U/L), UA: uric acid (μmol/L). Table 4 Descriptive statistics of TEFB genotypes SNPs Alleles/Genotypes Control group (n %) Case group (n %) χ 2 p value rs1015149 C 390 (0.57) 267 (0.59) 0.210 0.647 T 292 (0.43) 189 (0.41) CC 114 (33.33) 78 (34.21) 1.018 0.797 CT 162 (47.37) 111 (48.68) TT 65 (19.01) 39 (17.11) rs1062966 C 550 (0.81) 369 (0.81) 0.004 0.947 T 128 (0.19) 85 (0.19) CC 221 (64.62) 147 (64.47) 0.727 0.867 CT 108 (31.58) 75 (32.89) TT 10 (2.92) 5 (2.19) rs11754668 C 624 (0.92) 406 (0.89) 2.404 0.121 G 56 (0.08) 50 (0.11) CC 286 (83.63) 181 (79.39) 3.828 0.281 GC 52 (15.20) 44 (19.30) GG 2 (0.58) 3 (1.32) rs14063 G 463 (0.68) 312 (0.69) 0.036 0.849 A 213 (0.32) 140 (0.31) AA 28 (8.19) 19 (8.33) 0.208 0.976 AG 157 (45.91) 102 (44.74) GG 153 (44.74) 105 (46.05) rs2273068 C 590 (0.87) 406 (0.89) 1.806 0.179 T 90 (0.13) 48 (0.11) CC 254 (74.27) 183 (80.26) 3.675 0.299 CT 82 (23.98) 40 (17.54) TT 4 (1.17) 4 (1.75) Table 5 Logistic regression analysis between TEFB polymorphism and FLD Genotype β S.E. Wald χ² p value OR 95% CI rs1015149 CC 0.376 0.829 1.000 CT -0.009 0.192 0.002 0.962 0.991 0.68~1.44 TT -0.142 0.251 0.321 0.571 0.868 0.53~1.42 Dominant model TT+CT vs. CC 0.046 0.181 0.063 0.802 1.047 0.73~1.49 Recessive model TT vs. CT+CC 0.137 0.224 0.373 0.541 1.146 0.74~1.78 rs1062966 CC 0.364 0.834 1.000 CT 0.049 0.185 0.071 0.789 1.051 0.73~1.51 TT -0.284 0.559 0.258 0.612 0.753 0.25~2.25 Dominant model TT+CT vs. CC -0.025 0.180 0.019 0.889 0.975 0.69~1.39 Recessive model TT vs. CT+CC 0.300 0.555 0.293 0.589 1.350 0.45~4.01 rs11754668 CC 2.507 0.286 GC 0.293 0.226 1.678 0.195 1.340 0.86~2.09 GG 0.888 0.919 0.933 0.334 2.430 0.40~14.73 Dominant model GG+GC vs. CC -0.322 0.221 2.120 0.145 0.725 0.47~1.12 Recessive model GG vs. GC+CC 0.322 0.221 2.120 0.145 1.380 0.89~2.13 rs14063 AA 0.150 0.928 AG 0.003 0.326 0.000 0.992 1.003 0.53~1.90 GG -0.066 0.180 0.136 0.712 0.936 0.66~1.33 Dominant model AA+AG vs. GG 0.056 0.173 0.104 0.747 1.057 0.75~1.48 Recessive model AA vs. AG+GG -0.037 0.314 0.014 0.906 0.964 0.52~1.78 rs2273068 CC 3.896 0.143 CT -0.413 0.218 3.605 0.058 0.662 0.43~1.01 TT 0.297 0.715 0.173 0.678 1.346 0.33~5.47 Dominant model TT+CT vs.CC 0.366 0.211 3.009 0.083 1.442 0.95~2.18 Recessive model TT vs. CT+CC -0.389 0.713 0.297 0.586 0.678 0.17~2.74 Table 6 Results of gene–environment multiplication and additive interactions (Only the parts with statistical significance are exhibited) Gene-environment interaction β p value OR (95% CI ) RERI (95% CI ) AP (95% CI ) SI (95% CI ) rs1015149 RM × Smoking amount -0.037 0.032 * 0.960 (0.930,1.000) 0.003 (-0.025~0.031) 0.002 (-0.017~0.021) 1.007 (0.947~1.072) rs1062966 DM × Smoking amount 0.027 0.035 * 1.030 (1.000,1.050) 0.000 (-0.015~0.015) 0.000 (-0.018~0.018) 1.000 (0.924~1.083) DM×BMI 0.270 <0.001 ** 1.310 (1.140,1.510) -0.256 (-0.374~-0.138) -121.1 (-509.0~266.7) 1.345 (1.145~1.579) rs11754668 DM × Smoking amount 0.032 0.039 * 0.970 (0.940,1.000) -0.007 (-0.021~0.007) -0.008 (-0.025~0.010) 1.067 (0.842~1.352) DM × Alcohol intake -0.017 0.037 * 0.980 (0.970,1.000) -0.006 (-0.020~0.007) -0.007 (-0.022~0.009) 1.098 (0.600~2.010) RM × Smoking amount 0.032 0.039 * 1.030 (1.000,1.060) -0.007 (-0.025~0.011) -0.006 (-0.021~0.008) 0.946 (0.771~1.162) RM × Alcohol intake 0.017 0.037 * 1.020 (1.000,100.030) -0.006 (-0.021~0.009) -0.006 (-0.019~0.008) 0.927 (0.572~1.501) rs2273068 DM × Smoking amount -0.028 0.024 0.970 (0.950,1.000) 0.000 (-0.029~0.029) 0.000 (-0.017~0.017) 1.000 (0.961~1.041) Note: DM: Dominant model; RM: Recessive model; * p < 0.05 and ** p <0.001 were considered as statistically significant Supplementary Files Appendices.docx Additional supporting information may be found in supplement tables: Table S1 Sequence information of primers Table S2 Basic information of SNPs Table S3 Results of gene–environment multiplication and additive interactions Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Zhang","suffix":""},{"id":28482617,"identity":"eff9b3cd-b7da-4d80-b573-26406048cb82","order_by":15,"name":"Jian Qin","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2021-05-20 05:18:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-541251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-541251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13694865,"identity":"cd47adfd-9886-49d3-b3db-18e342f9f53a","added_by":"auto","created_at":"2021-09-17 12:54:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":285599,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-541251/v1/ed96522a-53fc-47f7-b67a-5f90b0a658bc.pdf"},{"id":9561864,"identity":"30571ac7-75dc-4000-82ba-f5634be27fd7","added_by":"auto","created_at":"2021-05-25 16:03:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39220,"visible":true,"origin":"","legend":"Additional supporting information may be found in supplement tables:\nTable S1 Sequence information of primers\nTable S2 Basic information of SNPs\nTable S3 Results of gene–environment multiplication and additive interactions\n","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-541251/v1/b2922e72f8deee13395ab4ac.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAssociation Between Tfeb Gene Polymorphism, Gene–environment Interaction, and Fatty Liver Disease: a Case–control Study in China\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth problem has gradually aroused people's concern with the advancement of economy and society and the improvement of living standard. Fatty liver disease (FLD) is a chronic disease and a serious public health problem that is rapidly increasing.[1\u003csup\u003e, \u003c/sup\u003e2] FLD is the pathological process of excess adipose accumulation in liver cells, caused by many factors, such as disease and drug. Simple hepatic steatosis may transform into steatohepatitis or cirrhosis as FLD progresses. According to etiology, FLD is classified as nonalcoholic fatty liver disease (NAFLD) and alcoholic liver disease (ALD). Studies have indicated that the pathogenesis of FLD is affected by abnormal fat metabolism, immune response, environment, genetic, and other factors.[3] Currently, specific medicine for FLD is deficient; the effective prevention and control measures for FLD are early detection and intervention, including diet control and exercise; and alcohol abstinence is the chief measure for patients with ALD.[3]\u003c/p\u003e\n\u003cp\u003eSimilar with most diseases, FLD is influenced by environmental and genetic factors. Single nucleotide polymorphisms (SNPs) are the most common form of mutations in the human genome. Studies have found that SNPs are associated with FLD. Wen et al. suggested the association between rs780094 polymorphism and NAFLD in Uyghur population by case\u0026ndash;control method.[4] Luigi et al. also reported that rs738409 polymorphism in pNPLA3 may be a genetic variant that is associated with NAFLD and ALD. [5]\u003c/p\u003e\n\u003cp\u003eIn recent years, transcription factor EB (TFEB) has attracted extensive attention in the study of autophagy mechanism. \u003cem\u003eTFEB\u003c/em\u003e is the main gene involved in lysosome biosynthesis and encodes TFEB, which is an important regulatory factor for autophagy and lysosomal biosynthesis. TFEB is considered the main activator for autophagy\u0026ndash;lysosomal gene transcription and refers to inflammation,[6] cell autophagy,[7] lipid metabolism[8] and other biological processes. Previous researches indicated that TFEB can regulate the expression of many genes related to lipid degradation, such as cluster of differentiation 36, fatty acid binding proteins, and carnitine acetyltransferase. Furthermore, TFEB can regulate lipid degradation factor, peroxisome proliferator-activated receptor alpha (PPAR\u0026alpha;), and an upstream factor of PPAR\u0026alpha;, proliferator-activated receptor gamma coactivator 1 alpha (PGC-1\u0026alpha;), through signal-mediated transfer to the nucleus; thus, PPAR\u0026alpha; is affected to participate in lipid metabolism.[9\u003csup\u003e, \u003c/sup\u003e10]\u003c/p\u003e\n\u003cp\u003eIncreasing TFEB levels in vivo may protect mice liver from alcohol-induced damage,[11] and promoting TFEB-mediated lysosomal biogenesis using formononetin can ameliorate the fatty disease process in mice liver.[12] Although increasing studies have provided etiological evidence to elucidate the mechanism between TFEB and fatty liver, few researches have focused on the relationship between \u003cem\u003eTFEB\u003c/em\u003e and FLD. Therefore, in this study, a case\u0026ndash;control approach was adopted to explore the association between \u003cem\u003eTFEB\u003c/em\u003e polymorphism and FLD, and gene\u0026ndash;environment interaction was evaluated to provide epidemiological evidence of the genetic factors of FLD.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e2.1. Study design and population\u003c/p\u003e\n\u003cp\u003eThis case\u0026ndash;control study included 228 patients with FLD diagnosed by ultrasonography according to the diagnostic guidelines released by the Chinese Medical Association. Individuals with liver diseases, tumors, and autoimmune diseases caused by drugs and viruses were excluded. A total of 342 healthy individuals who were matched by sex and age (with variation of \u0026plusmn;3 years) in a proportion of 1:1.5 were selected as the control group. All the participants were permanent residents in Gongcheng County, Guilin City, Guangxi Zhuang Autonomous Region, People\u0026rsquo;s Republic of China and signed the informed consent voluntarily after fully understanding the research content and importance of this project. Our research protocol was approved by the Ethics Committee of Guilin Medical University.\u003c/p\u003e\n\u003cp\u003e2.2 Data collection\u003c/p\u003e\n\u003cp\u003eAll the participants were required to answer a questionnaire from a trained researcher to collect information on demography, behavior, exercise, disease history, nutritional diet, and other data. Behavioral factors include smoking and alcohol consumption. The amount of smoking is expressed in pack year, that is, the number of packs (20 cigarettes per bag) per day multiplied by the number of years of smoking. In addition to daily alcohol intake, we also assessed the daily intake of 11 types of food, including cereals and their products, potatoes, vegetables, and fruits, via dietary survey. The participants were also examined by professional physicians to collect anthropometric indicators, such as height, weight, waist circumference, and blood pressure. Venous blood was collected for subsequent tests and experiments.\u003c/p\u003e\n\u003cp\u003e2.3 Biochemical testing\u003c/p\u003e\n\u003cp\u003eTwo venous blood samples were collected from each participant. One set of samples was tested for biochemical indicators, including triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), and uric acid (UA), at a local hospital. The other set of samples was used in the SNP typing experiment.\u003c/p\u003e\n\u003cp\u003e2.4 Selection and genotyping of SNPs\u003c/p\u003e\n\u003cp\u003eFunctional and validated SNP screening strategies were utilized to screen the target SNPs. This strategy focused on important functional loci and supplemented by susceptible loci. Finally, the rs1015149, rs1062966, rs14063, rs2273068, and rs11754668 of the \u003cem\u003eTFEB\u003c/em\u003e gene were selected as the target SNPs for genotyping.\u003c/p\u003e\n\u003cp\u003eGenomic DNA was isolated from venous blood using a commercial DNA extraction kit (Tiangen, Beijing, China). SNP genotyping was performed using the Sequenom MassARRAY matrix-assisted laser desorption ionization time-of-flight mass spectrometry platform (Sequenom, Inc., San Diego, CA, USA). The primers were designed and synthesized by Bio Miao Biological Technology Co., Ltd. (Table S1).\u003c/p\u003e\n\u003cp\u003e2.5 Statistical analysis\u003c/p\u003e\n\u003cp\u003eDescriptive statistics for continuous and categorical variables were conducted using mean \u0026plusmn; standard deviation (SD) and frequency (proportion), respectively. Student's t-test and chi-square test were applied to compare the differences among two groups and genotype subgroups. Pearson's chi-square test was utilized to evaluate the Hardy\u0026ndash;Weinberg equilibrium (HWE) before analyzing SNP data. The samples were considered representative when \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05. We performed logistic regression to estimate the effects of genotypes and gene\u0026ndash;environment multiplicative interactions on FLD, and odds ratio (OR) and 95% confidence interval (95% CI) were calculated. The test level \u0026alpha; = 0.05. However, logistic regression was limited to estimate additive interactions; hence, relative excess risk due to interaction (RERI), attributable proportion of interaction (AP), synergy index (SI), and their 95% CIs were calculated. Additive interactions were considered statistically significant when the 95% CI of RERI and AP did not include 0 and the 95% CI of SI did not contain 1.[13] SPSS 25.0 (IBM, Chicago, IL, USA) and PLINK 1.90 software were used to implement general statistical analysis and gene polymorphism analysis. In addition, R software 4.0.2 and \"epiR\" package were utilized to complete the calculation of RERI, AP, and SI.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1. Characteristics of the participants\u003c/p\u003e\n\u003cp\u003eThe demographic and behavioral characteristics of the participants are listed in Table 1. The subjects have a total number of 570 and a roughly equal gender proportion. The age range is 30\u0026ndash;83 years with an average of 58.15 years. The majority of the subjects (78.42%) belong to Yao population. No remarkable differences in gender, age, ethnicity, marital status, hypertension, smoking, drinking, and other factors were observed between the control and case groups at baseline (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). However, the proportion of subjects with a history of hypertension and the average daily sitting time were significantly higher in the case group than in the control group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eThe dietary situation is shown in Table 2. Vegetables and cereals and their products were the main daily dietary intake of the participants. No significant difference was observed in the daily food intake of the two groups (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eThe clinical indicators are exhibited in Table 3. The two groups showed no statistically significant difference in AST (t = \u0026minus;1.415, \u003cem\u003ep\u003c/em\u003e = 0.158). However, HDL-C was significantly lower in the case group than in the control group, and the other indicators were significantly higher in the case group compared with the control group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eResults indicate that the demographic, behavioral, and dietary variables in the two groups were matched preferably.\u003c/p\u003e\n\u003cp\u003e3.2 Basic information of SNPs\u003c/p\u003e\n\u003cp\u003eThe rs1015149, rs1062966, rs11754668, rs14063, and rs2273068 of \u003cem\u003eTFEB \u003c/em\u003eare located in chromosome 6, and the minimum allele frequency of each locus was greater than 0.05. The success rate of genotyping was very high at nearly 100% by MassARRAY. All the SNPs\u0026rsquo; loci were consistent with HWE (\u003cem\u003ep\u003c/em\u003e\u003csub\u003eHWE\u003c/sub\u003e \u0026gt; 0.05); therefore, the study subjects are representative (Table S2).\u003c/p\u003e\n\u003cp\u003e3.3 Genotypic frequency\u003c/p\u003e\n\u003cp\u003eThe alleles and genotypes of each SNP of \u003cem\u003eTFEB\u003c/em\u003e in the two groups were evenly distributed. No statistically significant difference was observed (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05, Table 4).\u003c/p\u003e\n\u003cp\u003e3.4 Associations between genotypes and FLD\u003c/p\u003e\n\u003cp\u003eFLD was regarded as the dependent variable. Co-dominant, dominant, and recessive models were used for logistic regression analysis using gender and age as adjustment factors. The result indicated that no significant correlations exist between genotypes and FLD in the co-dominant, dominant, and recessive models (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05, Table 5).\u003c/p\u003e\n\u003cp\u003e3.5 Interactions between environmental factors and SNP in FLD\u003c/p\u003e\n\u003cp\u003eMultiplicative and additive models were used to evaluate the interactions of each SNP locus with environmental factors, including diabetes, smoking amount, alcohol intake, daily sitting time, waist circumference, and body mass index (BMI). Compared with single genes, some gene\u0026ndash;environment interactions were remarkably associated with FLD susceptibility. In the multiplicative interaction model, rs1015149, rs1062966, and rs11754668 had substantial interaction with smoking amount. Among them, rs1062966 and rs11754668 also had remarkable interaction with BMI and alcohol intake, respectively.\u003c/p\u003e\n\u003cp\u003eNotably, the 95% CIs of RERI and AP contained 0, and the 95% CI of SI contained 1; thus, all gene\u0026ndash;environment additive interactions with FLD were not statistically significant (Table 6, Table S3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this case\u0026ndash;control study, we analyzed the association of \u003cem\u003eTFEB\u003c/em\u003e polymorphisms and FLD and assessed gene\u0026ndash;environment interactions to provide epidemiological evidence of the genetic factors related to the occurrence and development of FLD. Results showed that the alleles and genotypes of each SNP of \u003cem\u003eTFEB\u003c/em\u003e in the case and control groups were evenly distributed; no statistically significant difference was observed. Logistic regression analysis indicated that \u003cem\u003eTFEB\u003c/em\u003e polymorphism is not substantially associated with FLD. Previous studies have suggested that autophagy plays an important role in maintaining liver homeostasis.[14] \u003cem\u003eTFEB\u003c/em\u003e knockout in mice may result in the hepatic accumulation of fatty acid-\u0026beta; and impaired oxidation in hepatocytes, which lead to elevated fatty acid and glycerol levels and lipid metabolism disorders in hepatocytes.[15] According to the results, \u003cem\u003eTFEB\u003c/em\u003e may be involved in the pathophysiological basis of FLD. However, the relationship between \u003cem\u003eTFEB\u003c/em\u003e polymorphism and FLD was not observed in this study; thus, these SNPs may not affect the normal expression of \u003cem\u003eTFEB\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eGene\u0026ndash;environment interaction plays an important role in the occurrence and development of complex diseases, such as FLD. Zhu et al. demonstrated that the gene\u0026ndash;gene interaction between \u003cem\u003eAGTR1\u003c/em\u003e and \u003cem\u003ePPAR\u0026gamma;\u003c/em\u003e is associated with the occurrence of NAFLD in Chinese population.[16] Zhang et al. found that people with 11391G/A(AA) and EC-SOD (CG+GG) genotypes suffer a higher risk of NAFLD, and these genotypes have an interaction with \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection.[17] Therefore, the analysis of gene\u0026ndash;environment interaction might be conducive to understand etiological factors and guide the prevention and treatment of FLD. The result exhibited that some SNP loci, such as rs1015149, rs1062966, and rs11754668, had positive interactions with smoking, which is a risk factor for FLD susceptibility.[18] This finding is consistent with the results of Zhang et al. on the interactions between \u003cem\u003eGPX-1\u003c/em\u003e polymorphism and smoking in NAFLD and also agrees with the results of Oniki et al.[19] Interestingly, rs1015149 and rs2273068 had negative interaction with smoking in FLD; thus, they are considered \u0026ldquo;protective factors\u0026rdquo; (OR = 0.96 and 0.97, respectively). Smoking is a recognized risk factor that is remarkably associated with the occurrence of many diseases. However, the relationship between smoking and FLD is not clear yet.[20-23] In this study, we found that smoking might reduce the risk of FLD of individuals who carry the CT+CC genotype of rs1015149 or the TT+CT genotype of rs2273068. However, the results do not \"advocate\" smoking to these population for FLD prevention. A more rational explanation for the reduced FLD risk is that compared with individuals who carry other genotypes, people with the CT+CC genotype of rs1015149 or the TT+CT genotype of rs2273068 may be more able to offset the risk of FLD caused by smoking. The same explanation can also be utilized to explain the interaction between rs11754668 and alcohol intake in this study.\u003c/p\u003e\n\u003cp\u003eNo significant additive interaction was observed in this study. This result is in agreement with the result of Zhao et al.[24] In other words, additive interaction may not be remarkable even if the factors studied have substantial multiplicative interaction. In fact, the interaction between multiple factors is based on multiplication and synergism, whereas additive interaction is relatively rare. In the field of medicine, the analysis models for gene\u0026ndash;environment, gene\u0026ndash;gene, and gene\u0026ndash;environment-gene interactions, such as cross-generation analysis,[25] multifactor dimensionality reduction (MDR),[26] and generalized MDR,[27] are based on multiplication. Although substantial multiplicative interaction results were not observed in the present study, this study still provided a meaningful attempt to explore the gene\u0026ndash;environment interaction in FLD, which might be ignored.\u003c/p\u003e\n\u003cp\u003eDietary factors are important influencing factors of FLD. Numerous literatures have reported the association between different dietary patterns or food intake and the incidence of FLD. For example, the high intake of meat, high-fat dairy products, and refined grains may increase the risk of FLD, whereas a diet based on fruits, vegetables, whole grains, fish, and olive oil can reduce FLD risk.[28\u003csup\u003e, \u003c/sup\u003e29] A cross-sectional study based on Chinese adolescents illustrated that adolescents who have traditional Chinese diet have lower risks of FLD compared with those with Western diet.[30] Therefore, the potential impact of diet on the result needs to be fully considered and controlled to reduce analysis error. In addition, in this study, we ensured data quality and improved the reliability of the results. The advantages are as follows. (1) This study is the first epidemiological study to uncover the associations of \u003cem\u003eTFEB\u003c/em\u003e polymorphism and gene\u0026ndash;environment interaction with FLD. (2) All participants were from the same area with relatively similar genetic background, living environment, and habits; these similarities were helpful to control potential confounding factors. (3) In terms of grouping, gender and age (\u0026plusmn;3 years) were adopted in matching to reduce the influence of gender and age on the results to a certain extent.\u003c/p\u003e\n\u003cp\u003eHowever, this study also has many deficiencies that need to be further improved. First, the sample size is relatively small, and sampling error is difficult to decrease. Second, the conclusions are based on the population from Gongcheng County. Therefore, the applicability of the conclusions to other populations is limited, and extrapolation is deficient. Third, the degree of FLD was not classified. Thus, the effect of research factors on the process of FLD might have been ignored. Finally, the causal demonstration power is not strong because of the case\u0026ndash;control design.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe polymorphisms of the rs1015149, rs1062966, rs11754668, rs14063, and rs227306 of the \u003cem\u003eTFEB\u003c/em\u003e gene are not directly associated with FLD susceptibility, but the risk can be changed through gene\u0026ndash;environment interaction.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFLD: fatty liver disease; TFEB: transcription factor EB; NAFLD: nonalcoholic fatty liver disease; SNPs: single nucleotide polymorphisms; PPAR\u0026alpha;: peroxisome proliferator-activated receptor alpha; PGC-1\u0026alpha;: proliferator-activated receptor gamma coactivator 1 alpha; WC: waist circumference; BMI: body mass index; SBP/DBP: systolic/diastolic blood pressure; HbA1C: glycosylated hemoglobin; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; TG: triglyceride; TC: total cholesterol; GLU: fasting plasma glucose; ALB: albumin; ALT: alanine aminotransferase; AST aspartate transaminase; UA: uric acid; HWE: Hardy\u0026ndash;Weinberg equilibrium; RERI: relative excess risk due to interaction; AP: attributable proportion of interaction; SI: synergy index; DM: Dominant model; RM: Recessive model ; MDR: multifactor dimensionality reduction\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Chunbao Mo, Tingyu Mai and Jiansheng Cai;\u003c/p\u003e\n\u003cp\u003eAcquisition of data: Haoyu He, Huaxiang Lu, Xu Tang, Quanhui Chen, Xia Xu, Chuntao Nong. Shuzhen Liu, Tan Dechan, Qiumei Liu and Min Xu;\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data: Chunbao Mo, Tingyu Mai, Jiansheng Cai and Haoyu He;\u003c/p\u003e\n\u003cp\u003eWriting, review, and/or revision of the manuscript: Chunbao Mo, Tingyu Mai, Jiansheng Cai, Li You and Jian Qin;\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: You Li, Zhiyong Zhang and Jian Qin;\u003c/p\u003e\n\u003cp\u003eStudy supervision: Zhiyong Zhang and Jian Qin. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by National Natural Science Foundation of China [No. 8196120388]; Guangxi Science and Technology Development Project [No. AD17129003]; Guangxi Graduate Education Innovation Project [NO. YCSW2020230]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research protocol was approved by the Ethics Committee of Guilin Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1 \u003c/sup\u003eDepartment of Pathophysiology, Faculty of Basic Medical Sciences, Guilin Medical University，Guilin 541004, Guangxi, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Department of Environmental Health and Occupational Medicine, School of Public Health, Guilin Medical University, Guilin 541004, Guangxi, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3 \u003c/sup\u003eDepartment of Environmental and Occupational Health, School of Public Health, Guangxi Medical University, Nanning 530021, Guangxi, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDiehl AM, Farpour-Lambert NJ, Zhao L, et al. 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Am J Hum Genet. 2007;80(6):1125\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassani ZS, Mansoori A, Hosseinzadeh M. Relationship between Dietary Patterns and Non-Alcoholic Fatty Liver Disease: a systematic review and meta-analysis. Journal of gastroenterology and hepatology. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalehi-Sahlabadi A, Sadat S, Beigrezaei S, et al. Dietary patterns and risk of non-alcoholic fatty liver disease. BMC Gastroenterol. 2021;21(1):41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Peng Y, Chen S, et al. An observational study on the association between major dietary patterns and non-alcoholic fatty liver disease in Chinese adolescents. Medicine. 2018;97(17):e576.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Demographic and behavioral characteristics of the study population\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"267\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003eAll Participants (n %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003eControl group\u003c/p\u003e\n\u003cp\u003e(n %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003eCase group\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(n %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026chi;\u003csup\u003e 2\u003c/sup\u003e/t\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\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 width=\"121\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e321 (56.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e192 (56.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e129 (56.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"73\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"70\"\u003e\n\u003cp\u003e0.918\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e249 (43.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e150 (43.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e99 (43.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eAge (years) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e58.15\u0026plusmn;12.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e58.50\u0026plusmn;12.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e57.64\u0026plusmn;11.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e0.806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eNation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eHan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e98 (17.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e58 (16.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e40 (17.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"73\"\u003e\n\u003cp\u003e0.194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"70\"\u003e\n\u003cp\u003e0.907\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eYao\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e447 (78.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e268 (78.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e179 (78.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eZhuang and others\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e25 (4.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e16 (4.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e9 (3.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eMarital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eNo partner\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e77 (13.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e49 (14.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e28 (12.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"73\"\u003e\n\u003cp\u003e0.491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"70\"\u003e\n\u003cp\u003e0.484\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eHave a partner\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e493 (86.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e293 (85.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e200 (87.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eEducation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003ePrimary school and below\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e354 (62.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e223 (65.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e131 (57.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"73\"\u003e\n\u003cp\u003e3.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"70\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eJunior high school and above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e216 (37.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e119 (34.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e97 (42.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eOccupation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eFarmer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e519 (91.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e309 (90.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e210 (92.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"73\"\u003e\n\u003cp\u003e0.517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"70\"\u003e\n\u003cp\u003e0.472\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e51 (8.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e33 (9.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e18 (7.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eHousehold income (Yuan)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e＜5000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e154 (27.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e88 (25.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e66 (28.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"73\"\u003e\n\u003cp\u003e0.718\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"70\"\u003e\n\u003cp\u003e0.397\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026ge;5000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e416 (72.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e254 (74.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e162 (71.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e546 (95.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e333 (97.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e213 (93.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"73\"\u003e\n\u003cp\u003e5.285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"70\"\u003e\n\u003cp\u003e0.022\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e24 (4.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e9 (2.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e15 (6.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e464 (81.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e284 (83.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e180 (78.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"73\"\u003e\n\u003cp\u003e1.514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"70\"\u003e\n\u003cp\u003e0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e106 (18.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e58 (16.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e48 (21.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eSmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e465 (81.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e273 (79.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e192 (84.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e1.751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.186\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e105 (18.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e69 (20.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e36 (15.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eDrinking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e371 (65.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e221 (64.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e150 (65.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.774\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e199 (34.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e121 (35.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e78 (34.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eSmoking amount\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(pack year)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5.83\u0026plusmn;16.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e6.37\u0026plusmn;17.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5.03\u0026plusmn;15.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e0.934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.351\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eAlcohol intake (g/day)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e17.26\u0026plusmn;40.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e17.84\u0026plusmn;43.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e16.39\u0026plusmn;34.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e0.419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.675\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eStrenuous physical activity (h/day)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e1.37\u0026plusmn;7.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e1.19\u0026plusmn;6.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e1.65\u0026plusmn;8.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e-0.743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.458\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eModerate physical activity (h/day)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e4.52\u0026plusmn;11.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e4.54\u0026plusmn;11.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e4.49\u0026plusmn;11.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.961\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eDaily walking time (h)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e3.27\u0026plusmn;2.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e3.38\u0026plusmn;2.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e3.09\u0026plusmn;2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e1.526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.128\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"121\"\u003e\n\u003cp\u003eDaily sitting time (h)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"145\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e3.64\u0026plusmn;1.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e3.50\u0026plusmn;1.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e3.84\u0026plusmn;1.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"73\"\u003e\n\u003cp\u003e-2.212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.027\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003csup\u003e#\u003c/sup\u003eMean \u0026plusmn; SD; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eTable 2 Daily food intake of the participants\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eFood category\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003eAll Participants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003eControl group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003eCase group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\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 width=\"112\"\u003e\n\u003cp\u003eCereals and their products\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e263.80\u0026plusmn;161.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e262.63\u0026plusmn;168.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e265.55\u0026plusmn;152.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e-0.211\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003ePotatoes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e38.56\u0026plusmn;86.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e39.72\u0026plusmn;91.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e36.82\u0026plusmn;78.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.695\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eVegetables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e313.68\u0026plusmn;315.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e325.79\u0026plusmn;336.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e295.53\u0026plusmn;279.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e1.123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.262\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eFruits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e254.53\u0026plusmn;269.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e256.25\u0026plusmn;282.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e251.94\u0026plusmn;249.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.187\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.852\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eBeans and their products\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e36.04\u0026plusmn;50.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e36.04\u0026plusmn;51.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e36.04\u0026plusmn;48.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e-0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003e\u0026nbsp;nuts\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e13.40\u0026plusmn;26.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e12.69\u0026plusmn;23.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e14.47\u0026plusmn;29.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e-0.803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.422\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eMeat and poultry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e83.37\u0026plusmn;88.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e87.23\u0026plusmn;94.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e77.58\u0026plusmn;79.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e1.271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.204\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eFish and aquatic products\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e17.88\u0026plusmn;29.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e17.28\u0026plusmn;29.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e18.77\u0026plusmn;30.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e-0.585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.559\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eMilk and their products\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e35.78\u0026plusmn;36.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e34.85\u0026plusmn;35.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e37.18\u0026plusmn;37.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e-0.752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.452\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eEggs and their products\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e33.23\u0026plusmn;71.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e34.74\u0026plusmn;76.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e30.96\u0026plusmn;64.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.617\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.538\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eCooking oil\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e35.00\u0026plusmn;26.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e35.71\u0026plusmn;27.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e33.94\u0026plusmn;25.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.438\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"112\"\u003e\n\u003cp\u003eSalt\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e9.60\u0026plusmn;7.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e9.93\u0026plusmn;8.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"134\"\u003e\n\u003cp\u003e9.11\u0026plusmn;6.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e1.276\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e0.202\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data are expressed as mean \u0026plusmn; SD. The unit for each food category is gram.\u003c/p\u003e\n\u003cp\u003eTable 3 Clinical indicators of the study population\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eClinical indicators\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eAll Participants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eControl group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eCase group\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\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 width=\"132\"\u003e\n\u003cp\u003eWC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e81.39\u0026plusmn;10.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e76.78\u0026plusmn;9.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e88.30\u0026plusmn;8.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-15.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e24.55\u0026plusmn;17.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e21.99\u0026plusmn;3.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e28.40\u0026plusmn;27.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-3.558\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eSBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e136.02\u0026plusmn;24.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e134.39\u0026plusmn;23.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e138.47\u0026plusmn;25.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-1.972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.049\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eDBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e82.52\u0026plusmn;15.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e80.72\u0026plusmn;13.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e85.22\u0026plusmn;16.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-3.512\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eHbA1C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e5.96\u0026plusmn;1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.82\u0026plusmn;0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e6.17\u0026plusmn;1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-3.595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eLDL-C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3.44\u0026plusmn;0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3.30\u0026plusmn;0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e3.63\u0026plusmn;0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-4.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eHDL-C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e1.69\u0026plusmn;0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.76\u0026plusmn;0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e1.58\u0026plusmn;0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.559\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e5.56\u0026plusmn;1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5.40\u0026plusmn;1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e5.80\u0026plusmn;1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-4.533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eTG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e1.60\u0026plusmn;1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.21\u0026plusmn;1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e2.18\u0026plusmn;1.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-6.875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eGLU\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e5.07\u0026plusmn;1.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.87\u0026plusmn;1.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e5.37\u0026plusmn;1.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-3.543\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eALB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e44.06\u0026plusmn;2.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e43.77\u0026plusmn;2.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e44.50\u0026plusmn;2.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-3.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eALT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e21.90\u0026plusmn;12.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e19.29\u0026plusmn;12.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e25.81\u0026plusmn;13.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-5.976\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eAST\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e24.09\u0026plusmn;11.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e23.55\u0026plusmn;10.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e24.91\u0026plusmn;11.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-1.415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.158\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eUA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e333.34\u0026plusmn;100.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e306.72\u0026plusmn;91.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e373.28\u0026plusmn;100.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e-8.175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data are expressed as mean \u0026plusmn; SD; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 were considered statistically significant; WC: waist circumference (cm), BMI: body\u0026nbsp;mass\u0026nbsp;index, SBP/DBP: systolic/diastolic blood pressure (mmHg), HbA1C: glycosylated hemoglobin (%), LDL-C: low-density lipoprotein cholesterol (mmol/L), HDL-C: high-density lipoprotein cholesterol (mmol/L), TG: triglyceride (mmol/L), TC: total cholesterol (mmol/L), GLU: fasting\u0026nbsp;plasma glucose (mmol/L), ALB: albumin (g/L), ALT: alanine aminotransferase (U/L), AST aspartate transaminase (U/L), UA: uric acid (\u0026mu;mol/L).\u003c/p\u003e\n\u003cp\u003eTable 4 Descriptive statistics of \u003cem\u003eTEFB\u003c/em\u003e genotypes\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eSNPs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eAlleles/Genotypes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003eControl group\u003c/p\u003e\n\u003cp\u003e(n %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003eCase group\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(n %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026chi;\u003csup\u003e 2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\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 width=\"88\"\u003e\n\u003cp\u003ers1015149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e390 (0.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e267 (0.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.210\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.647\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e292 (0.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e189 (0.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e114 (33.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e78 (34.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e1.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.797\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e162 (47.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e111 (48.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e65 (19.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e39 (17.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ers1062966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e550 (0.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e369 (0.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.947\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e128 (0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e85 (0.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e221 (64.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e147 (64.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.727\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.867\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e108 (31.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e75 (32.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e10 (2.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e5 (2.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ers11754668\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e624 (0.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e406 (0.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e2.404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.121\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e56 (0.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e50 (0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e286 (83.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e181 (79.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e3.828\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.281\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e52 (15.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e44 (19.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e2 (0.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e3 (1.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ers14063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e463 (0.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e312 (0.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.849\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e213 (0.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e140 (0.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e28 (8.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e19 (8.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.976\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eAG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e157 (45.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e102 (44.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e153 (44.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e105 (46.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ers2273068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e590 (0.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e406 (0.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e1.806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.179\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e90 (0.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e48 (0.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e254 (74.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e183 (80.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e3.675\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.299\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e82 (23.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e40 (17.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"143\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e4 (1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e4 (1.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr /\u003eTable 5 Logistic regression analysis between \u003cem\u003eTEFB\u003c/em\u003e polymorphism and FLD\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eGenotype\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta; \u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u003cem\u003eS.E.\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u003cem\u003eWald \u0026chi;\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e\u003cem\u003ep \u003c/em\u003evalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u0026nbsp; \u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003ers1015149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.962\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.68~1.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.251\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.571\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.868\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.53~1.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eDominant model TT+CT vs. CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.802\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.73~1.49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eRecessive model TT vs. CT+CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.224\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.541\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.74~1.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003ers1062966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.364\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.834\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.73~1.51\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.559\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.258\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.753\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.25~2.25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eDominant model TT+CT vs. CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.69~1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eRecessive model TT vs. CT+CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.45~4.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003ers11754668\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e2.507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.226\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e1.678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.340\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.86~2.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.888\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.919\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.933\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e2.430\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.40~14.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eDominant model GG+GC vs. CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.322\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e2.120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.47~1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eRecessive model GG vs. GC+CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.322\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e2.120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.89~2.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003ers14063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.928\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eAG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.326\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.53~1.90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.712\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.936\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.66~1.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eDominant model AA+AG vs. GG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.104\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.747\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.75~1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eRecessive model AA vs. AG+GG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.906\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.964\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.52~1.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003ers2273068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e3.896\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.413\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e3.605\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.43~1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.715\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.33~5.47\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eDominant model TT+CT vs.CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.211\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e3.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.083\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e1.442\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.95~2.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"227\"\u003e\n\u003cp\u003eRecessive model TT vs. CT+CC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e-0.389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.713\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"62\"\u003e\n\u003cp\u003e0.586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e0.678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.17~2.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr /\u003eTable 6 Results of gene\u0026ndash;environment multiplication and additive interactions (Only the parts with statistical significance are exhibited)\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"253\"\u003e\n\u003cp\u003eGene-environment interaction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026beta; \u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;p \u003c/em\u003evalue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eRERI (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003eAP (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eSI (95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003ers1015149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eRM \u0026times; Smoking amount\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e-0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.032\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e0.960 (0.930,1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e0.003 (-0.025~0.031)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.002 (-0.017~0.021)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e1.007 (0.947~1.072)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003ers1062966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eDM \u0026times; Smoking amount\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.035\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e1.030 (1.000,1.050)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e0.000 (-0.015~0.015)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.000 (-0.018~0.018)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e1.000 (0.924~1.083)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eDM\u0026times;BMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e0.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e1.310 (1.140,1.510)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e-0.256 (-0.374~-0.138)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e-121.1 (-509.0~266.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e1.345 (1.145~1.579)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003ers11754668\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eDM \u0026times; Smoking amount\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.039\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e0.970 (0.940,1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e-0.007 (-0.021~0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e-0.008 (-0.025~0.010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e1.067 (0.842~1.352)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eDM \u0026times; Alcohol intake\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e-0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.037\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e0.980 (0.970,1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e-0.006 (-0.020~0.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e-0.007 (-0.022~0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e1.098 (0.600~2.010)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eRM \u0026times; Smoking amount\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.039\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e1.030 (1.000,1.060)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e-0.007 (-0.025~0.011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e-0.006 (-0.021~0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e0.946 (0.771~1.162)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eRM \u0026times; Alcohol intake\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.037\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e1.020 (1.000,100.030)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e-0.006 (-0.021~0.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e-0.006 (-0.019~0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e0.927 (0.572~1.501)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003ers2273068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eDM \u0026times; Smoking amount\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e-0.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"70\"\u003e\n\u003cp\u003e0.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e0.970 (0.950,1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e0.000 (-0.029~0.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.000 (-0.017~0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003e1.000 (0.961~1.041)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: DM: Dominant model; RM: Recessive model; * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001 were considered as statistically significant\u003c/p\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":"TFEB, gene polymorphism, gene–environment interaction, fatty liver disease","lastPublishedDoi":"10.21203/rs.3.rs-541251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-541251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Fatty liver disease (FLD) is a serious public health problem that is rapidly increasing. Evidences indicated that the transcription factor EB (\u003cem\u003eTFEB\u003c/em\u003e) gene may be involved in the pathophysiology of FLD; however, whether \u003cem\u003eTEFB\u003c/em\u003e polymorphism is association with FLD remains unclear.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eObjectives: \u003c/strong\u003eTo explore the association among \u003cem\u003eTFEB \u003c/em\u003epolymorphism, gene–environment interaction, and FLD and provide epidemiological evidence for clarifying the genetic factors of FLD.\u003c/p\u003e\u003cp\u003eMethods: This study is a case–control study. Sequenom MassARRAY was applied in genotyping. Logical regression was used to analyze the association between \u003cem\u003eTFEB\u003c/em\u003e polymorphism and FLD, and the gene–environment interaction in FLD was evaluated by multiplication and additive interaction models.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e (1) The alleles and genotypes of each single nucleotide polymorphism of \u003cem\u003eTFEB\u003c/em\u003e in the case and control groups were evenly distributed; no statistically substantial difference was observed. (2) Logistic regression analysis indicated that \u003cem\u003eTFEB\u003c/em\u003e polymorphism is not remarkably associated with FLD. (3) In the multiplicative interaction model, rs1015149, rs1062966, and rs11754668 had remarkable interaction with smoking amount. Rs1062966 and rs11754668 also had a considerable interaction with body mass index and alcohol intake, respectively. However, no remarkable additive interaction was observed.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e \u003cem\u003eTFEB\u003c/em\u003e polymorphism is not directly associated with FLD susceptibility, but the risk can be changed through gene–environment interaction.\u003c/p\u003e","manuscriptTitle":"Association Between Tfeb Gene Polymorphism, Gene–environment Interaction, and Fatty Liver Disease: a Case–control Study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-25 15:59:58","doi":"10.21203/rs.3.rs-541251/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":"169a7371-2ef1-4422-b0f8-85685f3559f4","owner":[],"postedDate":"May 25th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4566073,"name":"Polymer Science"}],"tags":[],"updatedAt":"2021-06-07T16:43:17+00:00","versionOfRecord":[],"versionCreatedAt":"2021-05-25 15:59:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-541251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-541251","identity":"rs-541251","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}