Effect of smoking and obesity on the association between HMGCR rs12654264 and type 2 diabetes in a Korean population

Effect of smoking and obesity on the association between HMGCR rs12654264 and type 2 diabetes in a Korean population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect of smoking and obesity on the association between HMGCR rs12654264 and type 2 diabetes in a Korean population Jae Woong Sull, Jong Won Shin, Sun Ha Jee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8953617/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Methods A total of 4,239 participants were included. Fasting blood glucose (FBG) and lipid levels were measured after a 12-hour fast. Diabetes was defined as FBG ≥ 126 mg/dL, a prior diagnosis of diabetes, or use of antidiabetic medication. Associations were evaluated using linear regression under an additive genetic model and logistic regression under a recessive model. Interaction terms were included to assess effect modification by smoking and waist circumference. Results The rs12654264 SNP was associated with FBG and LDL-C levels. After adjustment for age, sex, and BMI, healthy men with the TA/AA genotype had an increased risk of diabetes compared with those with the TT genotype (OR 1.50; 95% CI 0.98–2.29). The association was stronger among male non-smokers and light smokers (OR 3.59; 95% CI 1.37–9.39; p = 0.0094) than among heavy or former smokers. The interaction between rs12654264 and smoking was significant (P for interaction = 0.0141). A stronger association was also observed in men with waist circumference < 86 cm (OR 9.12; 95% CI 1.21–68.6). Conclusions The HMGCR rs12654264 variant was associated with type 2 diabetes in Korean men. This association was stronger among lean individuals and non-smokers or light smokers, suggesting potential gene–environment interaction. HMGCR rs12654264 Type 2 diabetes LDL-C Smoking Genetic association Introduction Fasting blood glucose is a primary indicator used to diagnose diabetes [ 1 ]. A meta-analysis of randomized controlled trials reported that statin therapy, compared with usual care or placebo, increased the risk of type 2 diabetes mellitus (T2DM) [ 2 ]. HMGCR gene polymorphisms have been investigated as genetic proxies for statin-induced HMGCR inhibition [ 3 ]. Low-density lipoprotein cholesterol (LDL-C) has been associated with the rs12654264 SNP in the HMG-CoA reductase (HMGCR) gene [ 4 , 5 ]. A recent study reported a significant association between elevated fasting glucose and genetically proxied inhibition of HMGCR [ 6 ]. Another study showed that HMGCR polymorphisms were associated with diabetes risk in patients with premature triple-vessel disease [ 7 ]. In addition, the TT genotype of rs12654264 was associated with a lower risk of T2DM [ 8 ]. Genetic and environmental factors, as well as their interactions, contribute to the development of T2DM [ 8 – 10 ]. Smoking is an established risk factor for type 2 diabetes [ 11 ]. A recent study suggested that smoking may modify the effect of a glucagon-related genetic variant on type 2 diabetes risk [ 12 ]. However, previous studies have not examined whether smoking status modifies the association between rs12654264 and type 2 diabetes. Therefore, the aim of this study was to investigate whether smoking influences the association between the HMGCR rs12654264 polymorphism and type 2 diabetes in a Korean population. We also evaluated whether this association differed according to waist circumference. Methods Study population A total of 4,294 participants underwent general health examinations at university hospital health promotion centers [ 13 ]. As part of the Korean Cancer Prevention Study-II (KCPS-II) Biobank, biological samples for DNA extraction were prospectively collected between 2004 and 2013 [ 14 ]. Of the 4,294 participants, 55 were excluded due to missing data on rs12654264 genotype, body mass index (BMI), or fasting blood glucose. The final analytic sample included 4,239 participants, of whom 1,769 had cardiovascular disease (CVD). The remaining 2,470 participants were classified as healthy. Healthy participants were defined as individuals without cardiovascular disease. Missing data were observed for waist circumference (n = 862), LDL-C (n = 425), and smoking status (n = 301). All participants provided written informed consent prior to enrollment. The study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Eulji University (approval number: EUIRB2023-072). Data collection Trained interviewers administered a standardized questionnaire to collect information on demographics (age and sex), medical history, medication use, and smoking status (never, former, or current smoker). Participants were classified as current smokers if they were smoking at the time of examination, former smokers if they had smoked previously but had quit, and never smokers if they had never smoked. Height and weight were measured with participants wearing light clothing and no shoes. Peripheral venous blood samples were collected after a 12-hour overnight fast to measure fasting blood glucose (FBG), total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Samples were stored at − 70°C until analysis. Biochemical measurements were performed using a Hitachi 7600 analyzer (Hitachi Ltd., Tokyo, Japan). Detailed phenotype data have been described previously [ 13 , 14 ]. Genotyping assays The HMGCR rs12654264 SNP was genotyped using a TaqMan allelic discrimination assay [ 15 ]. SNPs with a concordance rate > 99% in duplicate samples and a genotype call rate > 98% were included in the analysis. Statistical analysis Continuous variables are presented as mean ± standard deviation. Most statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA). Linear regression under an additive genetic model was used to assess the association between rs12654264 and fasting blood glucose levels, adjusting for age and sex. Logistic regression under a recessive genetic model was used to evaluate the association between rs12654264 and diabetes. Waist circumference and BMI were dichotomized at their median values. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the association between rs12654264 and diabetes. Diabetes was defined as a fasting blood glucose level ≥ 126 mg/dL, a prior diagnosis of diabetes, or the use of antidiabetic medication. All statistical tests were two-sided, and p < 0.05 was considered statistically significant. Results The mean age was 51.9 years for men and 52.7 years for women. Overall, 10.7% of participants had diabetes. The mean age was 57.3 years among participants with diabetes and 51.6 years among those without diabetes. Compared with participants without diabetes, those with diabetes had higher BMI and waist circumference. They also had a higher proportion of men and a family history of diabetes (Table 1). After adjustment for age and sex, the associations between rs12654264 and fasting blood glucose (FBG) levels are shown in Table 2. The rs12654264 SNP in the HMGCR gene was associated with FBG and LDL-C levels (effect per allele: 0.999 mg/dL, p = 0.0354 for FBG; −2.864 mg/dL, p < 0.0001 for LDL-C). Among healthy participants, rs12654264 was associated with FBG and LDL-C levels (effect per allele: 1.260 mg/dL, p = 0.0211 for FBG; −3.603 mg/dL, p < 0.0001 for LDL-C). The association between rs12654264 and diabetes is presented in Table 3. After adjustment for age, sex, and BMI, healthy men with the TA/AA genotype had a 1.37-fold higher risk of diabetes compared with those with the TT genotype (OR 1.37; 95% CI 0.96–1.98). In men overall, the association was stronger (OR 1.50; 95% CI 0.98–2.29), whereas no significant association was observed in women (OR 1.06; 95% CI 0.53–2.14). Stratified analyses by smoking status in men are shown in Table 4. The association between rs12654264 and diabetes was stronger among male non-smokers and light smokers (OR 3.59; 95% CI 1.37–9.39; p = 0.0094) than among heavy smokers (p = 0.2928) or former smokers (p = 0.1099). Stratified analyses by BMI and waist circumference (WC) are also shown in Table 4. Among men with WC < 86 cm, the association between rs12654264 and diabetes was stronger (OR 9.12; 95% CI 1.21–68.6; p = 0.0317) than among those with WC ≥ 86 cm (p = 0.6197). Table 5 presents age-adjusted odds ratios for diabetes according to rs12654264 genotype across smoking categories among Korean men. Heavy smokers with the TT genotype had an OR of 3.25 (95% CI 1.44–7.32) compared with non-, former, or light smokers with the TA/AA genotype (P for interaction = 0.0141). Discussion The rs12654264 SNP in the HMGCR gene was associated with serum glucose levels in this cohort of 4,239 individuals. Previous studies have reported that T2DM is associated with genetic variants in the HMGCR gene [ 16 ]. A recent Mendelian randomization study showed that genetically proxied inhibition of HMGCR was associated with lower blood pressure and higher fasting glucose levels [ 6 ]. A meta-analysis of randomized controlled trials reported that statin therapy increased the risk of developing T2DM compared with placebo or usual care [ 2 ]. Another study examined rs17238484 and rs12916, two SNPs in the HMGCR gene, as genetic proxies for statin-induced HMGCR inhibition and found that both statin therapy and common HMGCR variants were associated with weight gain and an increased risk of T2DM [ 3 ]. In addition, HMGCR variants have been associated with T2DM and cardiovascular disease in multi-ethnic populations [ 17 ]. In the present study, the TT genotype of rs12654264 was associated with lower fasting glucose levels compared with the AA genotype. Type 2 diabetes is strongly associated with smoking [ 11 , 18 , 19 ]. In this study, the association between rs12654264 and diabetes was stronger among non-smokers and light smokers than among heavy smokers. Previous studies have suggested that smoking influences fasting glucose levels [ 12 , 20 ]. Smoking has also been reported to modify the effect of glucagon-related genetic variants on T2DM risk [ 12 ]. These findings support the possibility of gene–environment interaction between HMGCR variants and smoking. The rs12654264 SNP has been associated with LDL-C levels in genome-wide association studies conducted in East Asian populations [ 4 ]. A Chinese study also reported an association between rs12654264 and LDL-C levels [ 5 ]. Similarly, a Korean study found a significant association between rs12654264 and LDL-C levels [ 21 ]. However, no significant association between rs12654264 and plasma lipid levels was observed in a Czech population [ 22 ]. In the present study, rs12654264 was associated with LDL-C levels, consistent with findings from East Asian cohorts. Previous studies have reported associations between HMGCR variants and obesity-related traits, including waist circumference, body weight, and BMI [ 3 , 8 , 23 ]. Body weight is a major risk factor for T2DM and is closely related to insulin resistance [ 3 ]. This may partly explain the increased risk of T2DM observed with statin therapy. A recent study in children and adolescents with autism spectrum disorder also reported associations between HMGCR polymorphisms, lipid metabolism, and obesity-related phenotypes [ 24 ]. In our study, the association between rs12654264 and diabetes was stronger in men than in women among healthy participants. A Chinese study reported that rs12654264 was associated with LDL-C levels and that the association was stronger in women than in men [ 25 ]. Sex-specific effects of HMGCR variants on lipid traits have also been reported in other populations [ 26 ]. These findings suggest potential sex-specific genetic effects of HMGCR variants. HMG-CoA reductase (HMGCR), located on chromosome 5q13.3, is the rate-limiting enzyme in cholesterol biosynthesis and catalyzes the conversion of HMG-CoA to mevalonate [ 27 ]. Mevalonate is a precursor of cholesterol and several nonsterol isoprenoid compounds, including dolichol, ubiquinone, and isopentenyl tRNA [ 28 ]. In 5,414 participants from the Malmö Diet and Cancer Study, rs12654264 was associated with LDL-C levels [ 29 ]. The frequency of the rs12654264 A allele varies across populations: 61.7% in Europeans, 68.0% in Africans, and 46.1% in East Asians, according to HapMap data. This study has several limitations. Baseline characteristics, including the prevalence of diabetes, cardiovascular disease, and smoking, differed by sex. We were unable to distinguish between types of diabetes; however, type 1 diabetes accounts for approximately 1% of diabetes cases in Korea [ 30 ]. In addition, a large number of CVD patients were included because the case–control design of the KCPS-II dataset was originally established to collect CVD cases [ 14 ]. Therefore, we conducted subgroup analyses in participants without cardiovascular disease to examine the association between rs12654264 and diabetes. Conclusion Genetic backgrounds may differ across populations. In this Korean cohort, the HMGCR rs12654264 variant was associated with type 2 diabetes, particularly among lean men and male non-smokers or light smokers. These findings suggest a potential gene–environment interaction between HMGCR and smoking in the development of type 2 diabetes. Declarations Acknowledgements The authors have no acknowledgements to declare. Authors’ contributions JWS (Sull) conceived and designed the study, acquired the data, performed the analysis, interpreted the results, and drafted and revised the manuscript. JWS (Shin) critically revised the manuscript. SHJ conceived and designed the study, acquired the data, performed the analysis, interpreted the results, and critically revised the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (grant number: 2018R1D1A1B07050834). The funding body had no role in the design of the study; collection, analysis, and interpretation of data; or in writing the manuscript. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Ethical approval was granted by the Institutional Review Board of Eulji University (approval number: EUIRB2023-072). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Ogata E, Asahi K, Yamaguchi S, Iseki K, Sato H, Moriyama T, et al. Low fasting plasma glucose level as a predictor of new-onset diabetes mellitus in a large Japanese general population cohort. Sci Rep. 2018;8:13927. doi:10.1038/s41598-018-31744-4 Sattar N, Preiss D, Murray HM, Welsh P, Buckley BM, de Craen AJ, et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet. 2010;375:735–742. doi:10.1016/S0140-6736(09)61965-6 Swerdlow DI, Preiss D, Kuchenbaecker KB, Holmes MV, Engmann JE, Shah T, et al. 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Tables Table 1 General characteristics of the study population Subjects Diabetes* Non-diabetes P value N 455 3784 Mean ± SD Mean ± SD Age, year 57.3±9.5 51.6±10.1 <0.0001 Weight, kg 69.7±10.4 67.2±11.1 <0.0001 Waist circumference, cm 88.5±8.4 83.6±8.9 <0.0001 Body mass index, kg/m 2 25.3±3.0 24.3±2.9 <0.0001 Systolic blood pressure, mmHg 126.0±14.8 121.4±14.4 <0.0001 LDL-C, mg/dL 111.1±36.6 117.4±31.0 <0.0001 HDL-C, mg/dL 47.6±10.4 51.3±11.7 <0.0001 Triglyceride, mg/dL 180.3±136.6 139.2±89.5 <0.0001 % % Male 76.7 67.0 <0.0001 Smoking status Ex 33.6 27.9 0.0372 Current 26.3 27.0 Cardiovascular disease 59.8 39.6 <0.0001 Family history of diabetes 25.7 13.2 <0.0001 Diabetes was defined as fasting serum glucose ≥ 126 mg/dL, previous diagnosis of diabetes, or use of antidiabetic medication. Abbreviations: SD, standard deviation; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol. Table 2 Association between the rs12654264 in the HMGCR gene and fasting blood-sugar and lipids levels based on a linear regression model Genotypes Phenotypes TT TA AA Effect (mg/dL) P -value Mean±SD Mean±SD Mean±SD All subjects (N=1170) (N=2058) (N=1011) Fasting blood sugar, mg/dL 96.6±21.5 96.6±21.8 98.4±25.3 0.999 0.0354 Body mass index, kg/m 2 24.4±3.0 24.4±2.9 24.5±3.0 0.091 0.1378 LDL cholesterol, mg/dL 119.5±33.0 116.7±31.5 113.8±30.2 -2.864 <0.0001 HDL cholesterol, mg/dL 50.8±11.2 51.0±11.9 51.0±11.5 -0.011 0.9628 Triglyceride, mg/dL 146.5±91.4 141.1±92.3 145.4±109.4 -0.243 0.9049 % % % Cardiovascular disease 41.6 41.7 41.8 0.9973 Diabetes 11.4 10.7 10.2 0.6635 Healthy subjects (N=681) (N=1199) (N=590) Fasting blood sugar, mg/dL 93.9±18.8 94.1±18.4 96.3±23.1 1.260 0.0211 Body mass index, kg/m 2 24.1±2.8 24.1±3.0 24.0±2.9 -0.016 0.8370 LDL cholesterol, mg/dL 123.0±31.0 118.9±29.7 115.6±29.4 -3.603 <0.0001 HDL cholesterol, mg/dL 51.7±11.6 52.1±12.6 52.0±12.5 0.064 0.8435 Triglyceride, mg/dL 137.6±79.6 133.4±76.9 134.4±96.1 -1.273 0.5678 % % % Diabetes 8.3 7.7 6.2 0.3099 Effect sizes (β) and corresponding p values were estimated using multiple linear regression under an additive genetic model adjusted for age and sex. P values for cardiovascular disease and diabetes were calculated using the chi-square test. p < 0.05 was considered statistically significant. Table 3 Odds ratios (OR) of the polymorphic rs12654264 HMGCR genotypes for diabetesin the population Normal Diabetes a Model 1 Model 2 Subjects Genotype N (%) N (%) OR (95% CI) P -value OR (95% CI) P -value All TT 1051 (27.8) 119(26.1) 1.00 (reference) 1.00 (reference) (n = 4,239) TA /AA 2733 (72.2) 336(73.9) 1.12(0.89-1.40) 0.3423 1.11(0.88-1.39) 0.3845 Men TT 724 (28.6) 87(24.9) 1.00 (reference) 1.00 (reference) TA/AA 1811 (71.4) 262(75.1) 1.21(0.93-1.57) 0.1553 1.21(0.93-1.57) 0.1583 Women TT 327 (26.2) 32(30.2) 1.00 (reference) 1.00 (reference) TA/AA 922 (73.8) 74(69.8) 0.90(0.58-1.41) 0.6402 0.82(0.52-1.30) 0.3991 All Healthy TT 639 (27.9) 42(22.9) 1.00 (reference) 1.00 (reference) (n = 2,470) TA /AA 1648 (72.1) 141(77.1) 1.38(0.96-1.98) 0.0830 1.37(0.96-1.98) 0.0863 Men TT 418 (28.6) 30(21.7) 1.00 (reference) 1.00 (reference) TA/AA 1044 (71.4) 108(78.3) 1.49(0.97-2.27) 0.0669 1.50(0.98-2.29) 0.0637 Women TT 221 (26.8) 12(26.7) 1.00 (reference) 1.00 (reference) TA/AA 604 (73.2) 33(73.3) 1.14(0.57-2.29) 0.7096 1.06(0.53-2.14) 0.8731 Model 1 was adjusted for age and sex. Model 2 was adjusted for age, sex, and body mass index (BMI). a Diabetes was defined as fasting serum glucose ≥ 126 mg/dL, previous diagnosis of diabetes, or use of antidiabetic medication. Abbreviations: CI, confidence interval; BMI, body mass index. Table 4 Odds ratios (OR) of polymorphic rs12654264 HMGCR genotypes for diabetes a in Healthy Korean men (n = 1,600) Normal Diabetes* Subjects Genotype N (%) N (%) OR (95% CI) P -value Non smokers or Light smokers (1-19/day) TT 174(30.5) 5(11.9) 1.00 (reference) TA/AA 397(69.5) 37(88.1) 3.59(1.37-9.39) 0.0094 Ex smokers TT 148(27.9) 12(19.7) 1.00 (reference) TA/AA 382(72.1) 49(80.3) 1.72(0.88-3.36) 0.1099 Heavy smokers (≥20/day) TT 86(27.4) 11(34.4) 1.00 (reference) TA/AA 228(72.6) 21(65.6) 0.66(0.30-1.44) 0.2928 Waist circumference < 86 TT 125(29.9) 1(4.4) 1.00 (reference) TA/AA 293(70.1) 22(95.7) 9.12(1.21-68.6) 0.0317 Waist circumference ≥ 86 TT 123(28.7) 15(26.3) 1.00 (reference) TA/AA 305(71.3) 42(73.7) 1.17(0.63-2.20) 0.6197 BMI < 24.60 TT 209(28.1) 10(17.2) 1.00 (reference) TA/AA 534(71.9) 48(82.8) 1.89(0.93-3.81) 0.0773 BMI ≥ 24.60 TT 209(29.1) 20(25.0) 1.00 (reference) TA/AA 510(70.9) 60(75.0) 1.30(0.76-2.23) 0.3410 a Adjusted for age and body mass index * Diabetes were defined as fasting serum glucose ≥126 mg/dL or medication or previously diagnosed. Abbreviations: CI, confidence interval Table 5 Age-adjusted odds ratios (OR) for diabetes a according to HMGCR (rs12654264) genotypes in strata of smoking statusin Healthy Korean men (n = 1,600) No. of subjects by genotypes OR (95% CI) Subjects TT TA/AA P for interaction Smoking status 0.0141 Non/Ex/Light smokers 339/865 1.00 (reference) 2.23 (1.30-3.84) Heavy smokers 97/346 3.25 (1.44-7.32) 2.21 (1.13-4.35) a Adjusted for age Abbreviations: CI, confidence interval Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8953617","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611855621,"identity":"8b197ea0-4780-454e-96f0-334c8f6b6e73","order_by":0,"name":"Jae Woong Sull","email":"","orcid":"","institution":"Eulji University","correspondingAuthor":false,"prefix":"","firstName":"Jae","middleName":"Woong","lastName":"Sull","suffix":""},{"id":611855622,"identity":"60e5467b-5875-423c-a60e-45fd872665b9","order_by":1,"name":"Jong Won Shin","email":"","orcid":"","institution":"University of Ulsan College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jong","middleName":"Won","lastName":"Shin","suffix":""},{"id":611855623,"identity":"05af6b97-bdb6-4fd1-ad05-85bb9bd758e0","order_by":2,"name":"Sun Ha Jee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACxgYGBmYGBhsGBgkStaSRoAUEgFoOk6CFuf+M2eOCmvOJG263P2D4UUOMw2bkmBvPOHY7ccOdMwaMPceI0sJjJs3DBtRyI4eBgbeBGC1Ah0nz/DsH1JL+gPEvUVoacsykedsOALUkGDATZ8uMtDJp3r5k45k3cgwOyxDjF8P+w9ukeb7ZyfbdSH/48A0xIWYIdYojiD5AhAYGBnkobU+U6lEwCkbBKBiZAADZxTfTWP8n3gAAAABJRU5ErkJggg==","orcid":"","institution":"Yonsei University","correspondingAuthor":true,"prefix":"","firstName":"Sun","middleName":"Ha","lastName":"Jee","suffix":""}],"badges":[],"createdAt":"2026-02-24 06:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8953617/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8953617/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of smoking and obesity on the association between HMGCR rs12654264 and type 2 diabetes in a Korean population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFasting blood glucose is a primary indicator used to diagnose diabetes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A meta-analysis of randomized controlled trials reported that statin therapy, compared with usual care or placebo, increased the risk of type 2 diabetes mellitus (T2DM) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. HMGCR gene polymorphisms have been investigated as genetic proxies for statin-induced HMGCR inhibition [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Low-density lipoprotein cholesterol (LDL-C) has been associated with the rs12654264 SNP in the HMG-CoA reductase (HMGCR) gene [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A recent study reported a significant association between elevated fasting glucose and genetically proxied inhibition of HMGCR [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Another study showed that HMGCR polymorphisms were associated with diabetes risk in patients with premature triple-vessel disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, the TT genotype of rs12654264 was associated with a lower risk of T2DM [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenetic and environmental factors, as well as their interactions, contribute to the development of T2DM [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Smoking is an established risk factor for type 2 diabetes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A recent study suggested that smoking may modify the effect of a glucagon-related genetic variant on type 2 diabetes risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, previous studies have not examined whether smoking status modifies the association between rs12654264 and type 2 diabetes. Therefore, the aim of this study was to investigate whether smoking influences the association between the HMGCR rs12654264 polymorphism and type 2 diabetes in a Korean population. We also evaluated whether this association differed according to waist circumference.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA total of 4,294 participants underwent general health examinations at university hospital health promotion centers [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As part of the Korean Cancer Prevention Study-II (KCPS-II) Biobank, biological samples for DNA extraction were prospectively collected between 2004 and 2013 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOf the 4,294 participants, 55 were excluded due to missing data on rs12654264 genotype, body mass index (BMI), or fasting blood glucose. The final analytic sample included 4,239 participants, of whom 1,769 had cardiovascular disease (CVD). The remaining 2,470 participants were classified as healthy. Healthy participants were defined as individuals without cardiovascular disease. Missing data were observed for waist circumference (n\u0026thinsp;=\u0026thinsp;862), LDL-C (n\u0026thinsp;=\u0026thinsp;425), and smoking status (n\u0026thinsp;=\u0026thinsp;301).\u003c/p\u003e \u003cp\u003e All participants provided written informed consent prior to enrollment. The study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Eulji University (approval number: EUIRB2023-072).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eTrained interviewers administered a standardized questionnaire to collect information on demographics (age and sex), medical history, medication use, and smoking status (never, former, or current smoker). Participants were classified as current smokers if they were smoking at the time of examination, former smokers if they had smoked previously but had quit, and never smokers if they had never smoked.\u003c/p\u003e \u003cp\u003eHeight and weight were measured with participants wearing light clothing and no shoes.\u003c/p\u003e \u003cp\u003ePeripheral venous blood samples were collected after a 12-hour overnight fast to measure fasting blood glucose (FBG), total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Samples were stored at \u0026minus;\u0026thinsp;70\u0026deg;C until analysis. Biochemical measurements were performed using a Hitachi 7600 analyzer (Hitachi Ltd., Tokyo, Japan). Detailed phenotype data have been described previously [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGenotyping assays\u003c/h3\u003e\n\u003cp\u003eThe HMGCR rs12654264 SNP was genotyped using a TaqMan allelic discrimination assay [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. SNPs with a concordance rate\u0026thinsp;\u0026gt;\u0026thinsp;99% in duplicate samples and a genotype call rate\u0026thinsp;\u0026gt;\u0026thinsp;98% were included in the analysis.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Most statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA).\u003c/p\u003e \u003cp\u003eLinear regression under an additive genetic model was used to assess the association between rs12654264 and fasting blood glucose levels, adjusting for age and sex. Logistic regression under a recessive genetic model was used to evaluate the association between rs12654264 and diabetes. Waist circumference and BMI were dichotomized at their median values.\u003c/p\u003e \u003cp\u003eOdds ratios (ORs) and 95% confidence intervals (CIs) were calculated to estimate the association between rs12654264 and diabetes. Diabetes was defined as a fasting blood glucose level\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, a prior diagnosis of diabetes, or the use of antidiabetic medication. All statistical tests were two-sided, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age was 51.9 years for men and 52.7 years for women. Overall, 10.7% of participants had diabetes. The mean age was 57.3 years among participants with diabetes and 51.6 years among those without diabetes. Compared with participants without diabetes, those with diabetes had higher BMI and waist circumference. They also had a higher proportion of men and a family history of diabetes (Table 1).\u003c/p\u003e\n\u003cp\u003eAfter adjustment for age and sex, the associations between rs12654264 and fasting blood glucose (FBG) levels are shown in Table 2. The rs12654264 SNP in the HMGCR gene was associated with FBG and LDL-C levels (effect per allele: 0.999 mg/dL, p = 0.0354 for FBG; −2.864 mg/dL, p \u0026lt; 0.0001 for LDL-C). Among healthy participants, rs12654264 was associated with FBG and LDL-C levels (effect per allele: 1.260 mg/dL, p = 0.0211 for FBG; −3.603 mg/dL, p \u0026lt; 0.0001 for LDL-C).\u003c/p\u003e\n\u003cp\u003eThe association between rs12654264 and diabetes is presented in Table 3. After adjustment for age, sex, and BMI, healthy men with the TA/AA genotype had a 1.37-fold higher risk of diabetes compared with those with the TT genotype (OR 1.37; 95% CI 0.96–1.98). In men overall, the association was stronger (OR 1.50; 95% CI 0.98–2.29), whereas no significant association was observed in women (OR 1.06; 95% CI 0.53–2.14).\u003c/p\u003e\n\u003cp\u003eStratified analyses by smoking status in men are shown in Table 4. The association between rs12654264 and diabetes was stronger among male non-smokers and light smokers (OR 3.59; 95% CI 1.37–9.39; p = 0.0094) than among heavy smokers (p = 0.2928) or former smokers (p = 0.1099). Stratified analyses by BMI and waist circumference (WC) are also shown in Table 4. Among men with WC \u0026lt; 86 cm, the association between rs12654264 and diabetes was stronger (OR 9.12; 95% CI 1.21–68.6; p = 0.0317) than among those with WC ≥ 86 cm (p = 0.6197).\u003c/p\u003e\n\u003cp\u003eTable 5 presents age-adjusted odds ratios for diabetes according to rs12654264 genotype across smoking categories among Korean men. Heavy smokers with the TT genotype had an OR of 3.25 (95% CI 1.44–7.32) compared with non-, former, or light smokers with the TA/AA genotype (P for interaction = 0.0141).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe rs12654264 SNP in the HMGCR gene was associated with serum glucose levels in this cohort of 4,239 individuals. Previous studies have reported that T2DM is associated with genetic variants in the HMGCR gene [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A recent Mendelian randomization study showed that genetically proxied inhibition of HMGCR was associated with lower blood pressure and higher fasting glucose levels [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A meta-analysis of randomized controlled trials reported that statin therapy increased the risk of developing T2DM compared with placebo or usual care [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Another study examined rs17238484 and rs12916, two SNPs in the HMGCR gene, as genetic proxies for statin-induced HMGCR inhibition and found that both statin therapy and common HMGCR variants were associated with weight gain and an increased risk of T2DM [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, HMGCR variants have been associated with T2DM and cardiovascular disease in multi-ethnic populations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the present study, the TT genotype of rs12654264 was associated with lower fasting glucose levels compared with the AA genotype.\u003c/p\u003e \u003cp\u003eType 2 diabetes is strongly associated with smoking [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this study, the association between rs12654264 and diabetes was stronger among non-smokers and light smokers than among heavy smokers. Previous studies have suggested that smoking influences fasting glucose levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Smoking has also been reported to modify the effect of glucagon-related genetic variants on T2DM risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These findings support the possibility of gene\u0026ndash;environment interaction between HMGCR variants and smoking.\u003c/p\u003e \u003cp\u003eThe rs12654264 SNP has been associated with LDL-C levels in genome-wide association studies conducted in East Asian populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A Chinese study also reported an association between rs12654264 and LDL-C levels [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similarly, a Korean study found a significant association between rs12654264 and LDL-C levels [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, no significant association between rs12654264 and plasma lipid levels was observed in a Czech population [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the present study, rs12654264 was associated with LDL-C levels, consistent with findings from East Asian cohorts.\u003c/p\u003e \u003cp\u003ePrevious studies have reported associations between HMGCR variants and obesity-related traits, including waist circumference, body weight, and BMI [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Body weight is a major risk factor for T2DM and is closely related to insulin resistance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This may partly explain the increased risk of T2DM observed with statin therapy. A recent study in children and adolescents with autism spectrum disorder also reported associations between HMGCR polymorphisms, lipid metabolism, and obesity-related phenotypes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, the association between rs12654264 and diabetes was stronger in men than in women among healthy participants. A Chinese study reported that rs12654264 was associated with LDL-C levels and that the association was stronger in women than in men [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Sex-specific effects of HMGCR variants on lipid traits have also been reported in other populations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings suggest potential sex-specific genetic effects of HMGCR variants.\u003c/p\u003e \u003cp\u003eHMG-CoA reductase (HMGCR), located on chromosome 5q13.3, is the rate-limiting enzyme in cholesterol biosynthesis and catalyzes the conversion of HMG-CoA to mevalonate [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mevalonate is a precursor of cholesterol and several nonsterol isoprenoid compounds, including dolichol, ubiquinone, and isopentenyl tRNA [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In 5,414 participants from the Malm\u0026ouml; Diet and Cancer Study, rs12654264 was associated with LDL-C levels [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The frequency of the rs12654264 A allele varies across populations: 61.7% in Europeans, 68.0% in Africans, and 46.1% in East Asians, according to HapMap data.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Baseline characteristics, including the prevalence of diabetes, cardiovascular disease, and smoking, differed by sex. We were unable to distinguish between types of diabetes; however, type 1 diabetes accounts for approximately 1% of diabetes cases in Korea [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, a large number of CVD patients were included because the case\u0026ndash;control design of the KCPS-II dataset was originally established to collect CVD cases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, we conducted subgroup analyses in participants without cardiovascular disease to examine the association between rs12654264 and diabetes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGenetic backgrounds may differ across populations. In this Korean cohort, the HMGCR rs12654264 variant was associated with type 2 diabetes, particularly among lean men and male non-smokers or light smokers. These findings suggest a potential gene\u0026ndash;environment interaction between HMGCR and smoking in the development of type 2 diabetes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no acknowledgements to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJWS (Sull) conceived and designed the study, acquired the data, performed the analysis, interpreted the results, and drafted and revised the manuscript.\u003c/p\u003e\n\u003cp\u003eJWS (Shin) critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003eSHJ conceived and designed the study, acquired the data, performed the analysis, interpreted the results, and critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (grant number: 2018R1D1A1B07050834).\u003c/p\u003e\n\u003cp\u003eThe funding body had no role in the design of the study; collection, analysis, and interpretation of data; or in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Ethical approval was granted by the Institutional Review Board of Eulji University (approval number: EUIRB2023-072).\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 they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOgata E, Asahi K, Yamaguchi S, Iseki K, Sato H, Moriyama T, et al. Low fasting plasma glucose level as a predictor of new-onset diabetes mellitus in a large Japanese general population cohort. Sci Rep. 2018;8:13927. doi:10.1038/s41598-018-31744-4\u003c/li\u003e\n\u003cli\u003eSattar N, Preiss D, Murray HM, Welsh P, Buckley BM, de Craen AJ, et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet. 2010;375:735\u0026ndash;742. doi:10.1016/S0140-6736(09)61965-6\u003c/li\u003e\n\u003cli\u003eSwerdlow DI, Preiss D, Kuchenbaecker KB, Holmes MV, Engmann JE, Shah T, et al. HMG-coenzyme A reductase inhibition, type 2 diabetes, and body weight: evidence from genetic analysis and randomised trials. Lancet. 2015;385:351\u0026ndash;361. doi:10.1016/S0140-6736(14)61183-1\u003c/li\u003e\n\u003cli\u003eKim YJ, Go MJ, Hu C, Hong CB, Kim YK, Lee JY, et al. Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat Genet. 2011;43:990\u0026ndash;995. doi:10.1038/ng.939\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhou D, Zhang Z, Song Y, Zhang D, Zhao T, et al. Effects of genetic variants on lipid parameters and dyslipidemia in a Chinese population. 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N Engl J Med. 2009;359:2220\u0026ndash;2232.\u003c/li\u003e\n\u003cli\u003eHu FB. Globalization of diabetes: the role of diet, lifestyle, and genes. Diabetes Care. 2011;34:1249\u0026ndash;1257. doi:10.2337/dc11-0442\u003c/li\u003e\n\u003cli\u003eJee SH, Foong AW, Hur NW, Samet JM. Smoking and risk for diabetes incidence and mortality in Korean men and women. Diabetes Care. 2010;33:2567\u0026ndash;2572.\u003c/li\u003e\n\u003cli\u003eLi L, Gao K, Zhao J, Feng T, Yin L, Wang J, et al. Glucagon gene polymorphism modifies the effects of smoking and physical activity on risk of type 2 diabetes mellitus in Han Chinese. Gene. 2014;534:352\u0026ndash;355.\u003c/li\u003e\n\u003cli\u003eCho ER, Jee YH, Kim SW, Sull JW. Effect of obesity on the association between MYL2 (rs3782889) and high-density lipoprotein cholesterol among Korean men. J Hum Genet. 2016;61:405\u0026ndash;409.\u003c/li\u003e\n\u003cli\u003eJee YH, Emberson J, Jung KJ, Lee SJ, Lee S, Back JH, et al. Cohort profile: the Korean Cancer Prevention Study-II (KCPS-II) Biobank. Int J Epidemiol. 2018;47:385\u0026ndash;386f. doi:10.1093/ije/dyx226\u003c/li\u003e\n\u003cli\u003eHui L, DelMonte T, Ranade K. Genotyping using the TaqMan assay. Curr Protoc Hum Genet. 2008;Chapter 2:Unit 2.10. doi:10.1002/0471142905.hg0210s56\u003c/li\u003e\n\u003cli\u003eFerence BA, Robinson JG, Brook RD, Catapano AL, Chapman MJ, Neff DR, et al. Variation in PCSK9 and HMGCR and risk of cardiovascular disease and diabetes. N Engl J Med. 2016;375:2144\u0026ndash;2153. doi:10.1056/NEJMoa1604304\u003c/li\u003e\n\u003cli\u003eShu L, Chan KHK, Zhang G, Huan T, Kurt Z, Zhao Y, et al. Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States. PLoS Genet. 2017;13:e1007040. doi:10.1371/journal.pgen.1007040\u003c/li\u003e\n\u003cli\u003eKim SJ, Jee SH, Nam JM, Cho WH, Kim JH, Park EC. Do early onset and pack-years of smoking increase risk of type II diabetes? BMC Public Health. 2014;14:178. doi:10.1186/1471-2458-14-178\u003c/li\u003e\n\u003cli\u003eLyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359:2220\u0026ndash;2232.\u003c/li\u003e\n\u003cli\u003eMa X, Zhang J, Deng R, Ding S, Gu N, Guo X. Synergistic effect of smoking with genetic variants in the AMPK\u0026alpha;1 gene on the risk of coronary artery disease in type 2 diabetes. Diabetes Metab Res Rev. 2014;30:483\u0026ndash;488.\u003c/li\u003e\n\u003cli\u003ePark MH, Kim N, Lee JY, Park HY. Genetic loci associated with lipid concentrations and cardiovascular risk factors in the Korean population. J Med Genet. 2011;48:10\u0026ndash;15. doi:10.1136/jmg.2010.081000\u003c/li\u003e\n\u003cli\u003eHubacek JA, Adamkova V, Lanska V, Dlouha D. Polygenic hypercholesterolemia: examples of GWAS results and their replication in the Czech-Slavonic population. Physiol Res. 2017;66:S101\u0026ndash;S111. doi:10.33549/physiolres.933580\u003c/li\u003e\n\u003cli\u003eBesseling J, Kastelein JJ, Defesche JC, Hutten BA, Hovingh GK. Association between familial hypercholesterolemia and prevalence of type 2 diabetes mellitus. JAMA. 2015;313:1029\u0026ndash;1036. doi:10.1001/jama.2015.1206\u003c/li\u003e\n\u003cli\u003eKwon SJ, Hong KW, Choi S, Hong JS, Kim JW, Kim JW, et al. Association of 3-hydroxy-3-methylglutaryl-coenzyme A reductase gene polymorphism with obesity and lipid metabolism in children and adolescents with autism spectrum disorder. Metab Brain Dis. 2022;37:319\u0026ndash;328. doi:10.1007/s11011-021-00877-3\u003c/li\u003e\n\u003cli\u003eLuo H, Zhang X, Shuai P, Miao Y, Ye Z, Lin Y. Genetic variants influencing lipid levels and risk of dyslipidemia in a Chinese population. J Genet. 2017;96:985\u0026ndash;992. doi:10.1007/s12041-017-0864-x\u003c/li\u003e\n\u003cli\u003eTaylor KC, Carty CL, Dumitrescu L, Bůžkov\u0026aacute; P, Cole SA, Hindorff LA, et al. Investigation of gene-by-sex interactions for lipid traits in diverse populations from the population architecture using genomics and epidemiology study. BMC Genet. 2013;14:33. doi:10.1186/1471-2156-14-33\u003c/li\u003e\n\u003cli\u003eMohandas T, Heinzmann C, Sparkes RS, Wasmuth J, Edwards P, Lusis AJ. Assignment of human 3-hydroxy-3-methylglutaryl coenzyme A reductase gene to the q13\u0026ndash;q23 region of chromosome 5. Somat Cell Mol Genet. 1986;12:89\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eGoldstein JL, Brown MS. Regulation of the mevalonate pathway. Nature. 1990;343:425\u0026ndash;430.\u003c/li\u003e\n\u003cli\u003eKathiresan S, Melander O, Anevski D, Guiducci C, Burtt NP, Roos C, et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N Engl J Med. 2008;358:1240\u0026ndash;1249.\u003c/li\u003e\n\u003cli\u003eKwon S, Lee JS. Relationship between milk intake and prevalence of chronic diseases in Korean adults based on the 5th and 6th Korea National Health and Nutrition Examination Survey. J Nutr Health. 2017;50:158\u0026ndash;170.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e General characteristics of the study population\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSubjects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAge, year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.3\u0026plusmn;9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.6\u0026plusmn;10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eWeight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69.7\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.2\u0026plusmn;11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eWaist circumference, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.5\u0026plusmn;8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.6\u0026plusmn;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.3\u0026plusmn;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.3\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126.0\u0026plusmn;14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121.4\u0026plusmn;14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111.1\u0026plusmn;36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117.4\u0026plusmn;31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHDL-C, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.6\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.3\u0026plusmn;11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e180.3\u0026plusmn;136.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139.2\u0026plusmn;89.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eEx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eFamily history of diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eDiabetes was defined as fasting serum glucose \u0026ge; 126 mg/dL, previous diagnosis of diabetes, or use of antidiabetic medication.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e SD, standard deviation; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e Association between the rs12654264 in the HMGCR gene and fasting blood-sugar and lipids levels based on a linear regression model\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003cp\u003e(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAll subjects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(N=1170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e(N=2058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(N=1011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFasting blood sugar,\u0026nbsp;mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.6\u0026plusmn;21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e96.6\u0026plusmn;21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.4\u0026plusmn;25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.4\u0026plusmn;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e24.4\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.5\u0026plusmn;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119.5\u0026plusmn;33.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e116.7\u0026plusmn;31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113.8\u0026plusmn;30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDL cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.8\u0026plusmn;11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e51.0\u0026plusmn;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.0\u0026plusmn;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146.5\u0026plusmn;91.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e141.1\u0026plusmn;92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e145.4\u0026plusmn;109.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthy subjects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(N=681)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e(N=1199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(N=590)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFasting blood sugar,\u0026nbsp;mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.9\u0026plusmn;18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e94.1\u0026plusmn;18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.3\u0026plusmn;23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.1\u0026plusmn;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e24.1\u0026plusmn;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.0\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123.0\u0026plusmn;31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e118.9\u0026plusmn;29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115.6\u0026plusmn;29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDL cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.7\u0026plusmn;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e52.1\u0026plusmn;12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.0\u0026plusmn;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137.6\u0026plusmn;79.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e133.4\u0026plusmn;76.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134.4\u0026plusmn;96.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEffect sizes (\u0026beta;) and corresponding p values were estimated using multiple linear regression under an additive genetic model adjusted for age and sex. P values for cardiovascular disease and diabetes were calculated using the chi-square test. \u003cem\u003ep \u0026lt; 0.05 was considered statistically significant.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e Odds ratios (OR) of the polymorphic rs12654264 HMGCR genotypes for diabetesin the population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003eDiabetes \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubjects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1051 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e119(26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(n = 4,239)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA /AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2733 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e336(73.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12(0.89-1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11(0.88-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e724 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87(24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1811 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e262(75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21(0.93-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21(0.93-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e327 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32(30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e922 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74(69.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.90(0.58-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82(0.52-1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAll Healthy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e639 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42(22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(n = 2,470)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA /AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1648 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141(77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.38(0.96-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.37(0.96-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e418 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1044 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108(78.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.49(0.97-2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50(0.98-2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e221 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12(26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e604 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33(73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14(0.57-2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06(0.53-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1 was adjusted for age and sex. Model 2 was adjusted for age, sex, and body mass index (BMI).\u003cbr\u003e\u003csup\u003ea\u003c/sup\u003e Diabetes was defined as fasting serum glucose \u0026ge; 126 mg/dL, previous diagnosis of diabetes, or use of antidiabetic medication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CI, confidence interval; BMI, body mass index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e Odds ratios (OR) of polymorphic rs12654264 HMGCR genotypes for diabetes\u003csup\u003ea\u0026nbsp;\u003c/sup\u003ein Healthy Korean men (n = 1,600)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"730\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eDiabetes*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubjects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon smokers or Light smokers (1-19/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174(30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5(11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e397(69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37(88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.59(1.37-9.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEx smokers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e148(27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12(19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e382(72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49(80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.72(0.88-3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeavy smokers (\u0026ge;20/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86(27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e228(72.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21(65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.66(0.30-1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWaist circumference \u0026lt; 86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125(29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e293(70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22(95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.12(1.21-68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWaist circumference \u0026ge; 86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123(28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15(26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e305(71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42(73.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17(0.63-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI \u0026lt; 24.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e209(28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e534(71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48(82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.89(0.93-3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI \u0026ge; 24.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e209(29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e510(70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60(75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30(0.76-2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eAdjusted for age and body mass index\u0026nbsp;\u0026nbsp;* Diabetes were defined as fasting serum glucose \u0026ge;126 mg/dL or medication or previously diagnosed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CI, confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e Age-adjusted odds ratios (OR) for diabetes\u003csup\u003ea\u003c/sup\u003e according to HMGCR (rs12654264) genotypes in strata of smoking statusin Healthy Korean men (n = 1,600)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eNo. of subjects by genotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubjects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTA/AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Non/Ex/Light smokers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e339/865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.23 (1.30-3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Heavy smokers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97/346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.25 (1.44-7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.21 (1.13-4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eAdjusted for age\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CI, confidence interval\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HMGCR, rs12654264, Type 2 diabetes, LDL-C, Smoking, Genetic association","lastPublishedDoi":"10.21203/rs.3.rs-8953617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8953617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 4,239 participants were included. Fasting blood glucose (FBG) and lipid levels were measured after a 12-hour fast. Diabetes was defined as FBG\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, a prior diagnosis of diabetes, or use of antidiabetic medication. Associations were evaluated using linear regression under an additive genetic model and logistic regression under a recessive model. Interaction terms were included to assess effect modification by smoking and waist circumference.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe rs12654264 SNP was associated with FBG and LDL-C levels. After adjustment for age, sex, and BMI, healthy men with the TA/AA genotype had an increased risk of diabetes compared with those with the TT genotype (OR 1.50; 95% CI 0.98\u0026ndash;2.29). The association was stronger among male non-smokers and light smokers (OR 3.59; 95% CI 1.37\u0026ndash;9.39; p\u0026thinsp;=\u0026thinsp;0.0094) than among heavy or former smokers. The interaction between rs12654264 and smoking was significant (P for interaction\u0026thinsp;=\u0026thinsp;0.0141). A stronger association was also observed in men with waist circumference\u0026thinsp;\u0026lt;\u0026thinsp;86 cm (OR 9.12; 95% CI 1.21\u0026ndash;68.6).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe HMGCR rs12654264 variant was associated with type 2 diabetes in Korean men. This association was stronger among lean individuals and non-smokers or light smokers, suggesting potential gene\u0026ndash;environment interaction.\u003c/p\u003e","manuscriptTitle":"Effect of smoking and obesity on the association between HMGCR rs12654264 and type 2 diabetes in a Korean population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 18:39:55","doi":"10.21203/rs.3.rs-8953617/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-25T04:11:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-27T09:56:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T04:15:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T04:13:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2026-02-24T06:45:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a62effae-d9db-415a-b6d0-544c0205ccd7","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T18:39:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 18:39:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8953617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8953617","identity":"rs-8953617","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}