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Glucagon gene polymorphism modifies the effects of smoking and physical activity on risk of type 2 diabetes mellitus in Han Chinese
Gene 534 (2014) 352–355
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
Glucagon gene polymorphism modifies the effects of smoking and physical activity on risk of type 2 diabetes mellitus in Han Chinese Linlin Li a,1, Kaiping Gao b,1, Jingzhi Zhao c, Tianping Feng c, Lei Yin c, Jinjin Wang d, Chongjian Wang a, Chunyang Li a, Yan Wang a, Qian Wang a, Yujia Zhai a, Haifei You a, Yongcheng Ren a, Bingyuan Wang a, Dongsheng Hu b,⁎ a
Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China Shenzhen University School of Medicine, Shenzhen, 518060, People's Republic of China c Military Hospital of Henan Province, Zhengzhou, 450003, People's Republic of China d Discipline of Public Health and Preventive Medicine, Center of Preventive Medicine Research and Assessment, Henan University of Traditional Chinese Medicine, Zhengzhou, 450008, People's Republic of China b
a r t i c l e
i n f o
Article history: Accepted 30 September 2013 Available online 1 November 2013 Keywords: Genetics Type 2 diabetes Smoking Alcohol consumption Physical activity
a b s t r a c t Few genome-wide association studies have considered interactions between multiple genetic variants and environmental factors associated with disease. The interaction was examined between a glucagon gene (GCG) polymorphism and smoking, alcohol consumption and physical activity and the association with risk of type 2 diabetes mellitus (T2DM) in a case–control study of Chinese Han subjects. The rs12104705 polymorphism of GCG and interactions with environmental variables were analyzed for 9619 participants by binary multiple logistic regression. Smoking with the C-C haplotype of rs12104705 was associated with increased risk of T2DM (OR = 1.174, 95% CI = 1.013–1.361). Moderate and high physical activity with the C-C genotype was associated with decreased risk of T2DM as compared with low physical activity with the genotype (OR = 0.251, 95% CI = 0.206–0.306 and OR = 0.190, 95% CI = 0.164–0.220). However, the interaction of drinking and genotype was not associated with risk of T2DM. Genetic polymorphism in rs12104705 of GCG may interact with smoking and physical activity to modify the risk of T2DM. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Type 2 diabetes (T2DM) has become one of the leading and fastest growing health problems in the world. Global estimates by the International Diabetes Federation (IDF) in 2011 were approximately 90 million Chinese and over 366 million people worldwide. By 2030, these estimates are expected to increase to more than 130 million in China and 552 million people worldwide (Whiting et al., 2011). Although the exact etiology is unknown, T2DM is a multifactorial disease, resulting from numerous interactions between multiple genetic variants and environmental factors related to diet, physical activity, smoking, drinking alcohol and medical treatment (Mensink, 2005). In the last few years, T2DM has been the subject of genome-wide association studies (GWAS) (Hindorff et al., 2009) and candidate gene studies (Doria et al., 2008; Grant et al., 2009), which have identified variants in genes that may play a role in the etiology of T2DM. However, because of practical and statistical challenges, none of the GWAS have considered
Abbreviations: GWAS, Genome-wide association studies; MAF, Minor allele frequency; SNP, Single nucleotide polymorphism; OR, Odds ratio; CI, Confidence interval. ⁎ Corresponding author. Tel.: +86 755 86671909; fax: +86 755 86671906. E-mail address: [email protected] (D. Hu). 1 Linlin Li and Kaiping Gao contributed equally to this manuscript. 0378-1119/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gene.2013.09.121
interactions among the variants and the environment, and only relatively few candidate gene studies have pursued such studies (Black et al., 2008; Cauchi et al., 2008; Miyake et al., 2009; Qi et al., 2007; Sparso et al., 2009). Glucagon gene (GCG), located on chromosome 2q24.2, comprises 6 exons and encodes glucagon, glucagon-like peptide (GLP)-1 and GLP-2 and oxyntomodulin proteins (Holst, 1997). No large-scale sequencing findings for GCG have been published. On the basis of the crucial role of GCG in energy metabolism, we hypothesized that the frequency variants of GCG were associated with diabetes. Here, a case–control study was performed to investigate the interaction between GCG polymorphism and the environment associated with risk of T2DM in Chinese Han subjects. 2. Methods 2.1. Samples Subjects (N = 9619, 1842 cases and 7777 controls) were recruited from the northern Chinese Han population in northeast China and obtained blood samples from all subjects. All subjects resided in Henan Province, North China. A total of 1842 T2DM patients were consecutively recruited from the outpatient clinics of three hospitals (1032 cases) or
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from communities (810 cases) in Henan Province. Diabetes mellitus was diagnosed by fasting blood glucose (FPG) ≥ 7.00 mmol/L or 2-h plasma glucose ≥ 11.0 mmol/L (American Diabetes Association, 2005) during an oral glucose tolerance test (OGTT) with diabetes clinical symptoms or previously diagnosed diabetes was defined if the participant (1) was receiving insulin treatment, (2) was receiving an oral hypoglycemic agent or (3) presented with a history of diabetes during the interview; all other types of diabetes were excluded on the basis of clinical data. Patients who were pregnant or lactating were excluded. All patients were unrelated and had been diagnosed with T2DM after age 20 years. Unrelated non-diabetic controls were recruited from communities in Henan Province. The participants were excluded with body mass index (BMI) b 18.50 or who were pregnant, mentally disturbed, handicapped or obese (caused by disease) or taking certain drugs or had cancer. Control subjects were included if they were Han Chinese N20 years old and had no family history of diabetes, FPG b6.1 mmol/L and normal OGTT results. Written, informed consent was obtained from each participant after a full explanation of the study, which was approved by the Ethics Committee of Zhengzhou University.
Logistic regression models (SAS version 9.1.3) were used to assess the effects of the SNP on T2DM after adjusting for sex, age, anthropometric measurements (BMI, waist circumference and blood pressure) and environment (smoking, drinking and physical activity) with calculation of odds ratios (ORs), 95% confidence intervals (95% CI) and corresponding p values for risk of T2DM. The interaction between the SNP and the environment was modeled by use of logistic regression multiplicative interaction models. P b 0.05 was considered statistically significant.
2.2. Primer design
3.2. Genotype frequencies and allelic estimates for GCG SNP
Primers and probes were synthesized by Shanghai Generay Biotech Co.; the forward primer was 5′-ACCTTTTGGTGTCTCTACTTC-3′, and the reverse primer was 5′-TGTGTCATGTTGATTGGCTGT-3′.
The SNP rs12104705 was in the Hardy–Weinberg equilibrium independently among cases and controls (P N 0.1). Genotyping results did not differ between cases and controls and had a minor allele (T allele) frequency of 7.13 % (Table 2). The rs12104705 genotyping was not associated with disease susceptibility (P N 0.05) even after adjusting for sex, age, anthropometric measurements (BMI, waist circumference and blood pressure) and environment (smoking, drinking and physical activity) (Table 3).
2.3. Single nucleotide polymorphism (SNP) selection and genotyping We selected one tagging SNP (rs12104705) from the Hap Map Phase 3 Chinese population using Haploview version 4.1 (Barrett et al., 2005). The criterion for screening was minor allele frequency N 0.1 in Han Chinese and r2 ≥ 0.8. Genomic DNA was isolated from whole blood by use of a blood genome DNA extraction kit (Yaneng BIO). Polymorphic regions were amplified by PCR, then rs12104705 genotyping involved ligase detection reactions (LDRs) performed in a 200-μl PCR tube, with a final volume of 10 μl containing 2 μl PCR products (100 ng/μl), 1 μl 10* Taq DNA ligase buffer, 1 μl LDR probes (12.5 pmol/μl), 6 μl sterile H2O and 0.125 μl Taq DNA ligase (40 U/μl). The reactions involved heating for 2 min at 95 °C, then 30 thermal cycles of 95 °C for 30 s (denaturation) and 56 °C for 2 min (annealing and ligation). The LDR products were analyzed by electrophoresis with use of the ABI PRISM 3100 DNA sequenator.
3. Results 3.1. Subject characteristics BMI and waist circumference were significantly greater in cases than controls (P b 0.001), cases featured more males than did controls (P b 0.001), and the median age was higher for cases than controls (P b 0.001) (Table 1). Moderate and high physical activities were lower among cases than controls, with no difference between groups in alcohol drinking.
3.3. Association of gene–environment interaction and T2DM risk With non-smoking and the C-C haplotype of rs12104705 as the reference, smoking with the C-C haplotype was associated with increased risk of T2DM (OR = 1.174, 95% CI = 1.013–1.361). As compared with low physical activity with the C-C genotype, moderate and high physical activity with the genotype was associated with decreased risk of T2DM (OR = 0.251, 95% CI = 0.206–0.306 and OR = 0.190, 95% CI = 0.164–0.220, respectively), high physical activity with the T-T haplotype was associated with increased risk of T2DM (OR = 7.748, 95% CI = 1.408–42.635). The interaction of alcohol drinking and rs12104705 was not associated with risk of T2DM (Table 4).
2.4. Environmental factors 4. Discussion Smoking status included current smoker and not current smoker. Participants who currently smoked and/or had smoked at least 100 cigarettes during their lifetime were classified as current smokers if they answered affirmatively to the questions, “Do you smoke cigarettes now?” and “Have you smoked at least 100 cigarettes during your lifetime?” Self-reported alcohol consumption data were also collected from the questionnaire using the following question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you drink?” Alcohol drinkers consuming 100 ml liquor in 30 days were included in the study. Physical activity level for each individual was classified as low, moderate or high based on the International Physical activity Questionnaire (IPAQ) [www.ipaq.ki.se]. 2.5. Data analysis Categorical variables are shown as number (percentage) and were analyzed by chi-square test. Continuous variables are shown as median (range) for data with skewed distribution. The Mann–Whitney–Wilcoxon test and the Kruskal–Wallis rank test were used to assess differences between cases and controls. Haploview version 4.2 was used to test for Hardy Weinberg equilibrium independently among cases and controls.
In the present study, a case–control study of 9619 Chinese Han subjects was examined the association of the interaction of one SNP, rs12104705, in GCG with environmental factors and risk of T2DM. The allelic frequencies of rs12104705 were identical between cases and controls. We found the interaction between smoking and physical activity and rs12104705 associated with risk of T2DM. The GCG gene, encoding glucagon, GLP-1 and GLP-2 and oxyntomodulin proteins, for crucial for energy metabolism, is involved in multiple steps of metabolism. Blood glucose and serum insulin levels were lower and insulin sensitivity higher in GCG-knockout than wildtype mice (Hayashi et al., 2009). The GCG polymorphism rs12104705 is a newly discovered, single genetic variant possibly associated with risk of T2DM, but only 1 study has investigated the association of a GCG variant and risk of T2DM, in a Danish population (Torekov et al., 2011): the Ile158Val variant was associated with risk of T2DM (OR = 1.83 [1.05–3.9]), but rs4664447 and rs7581952 were not. The inclusion of more than 9000 subjects conferred sufficient power to separate the 3 genotypes, but the results were not significant. The binary logistic modeling in this study revealed no association of rs12104705 of GCG and risk of T2DM even when adjusting for sex, age,
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L. Li et al. / Gene 534 (2014) 352–355
Table 1 Characteristics of study participants. Characteristics Sex Male Female Age (years) Body mass index (kg/m2) Waist circumference (cm) Smoking Drinking Physical activity
Non-smoking Smoking Non-drinking Drinking Low Moderate High
Cases (n = 1842)
Controls (n = 7777)
P
925 (50.2) 917 (49.8) 54 (20–85) 27.4 (18.5–50.5) 90.5 (60.6–200.0) 1394 (75.7) 448 (24.3) 1524 (86.4) 241 (13.7) 1108 (62.6) 203 (11.5) 459 (25.9)
3214 (41.3) 4563 (58.7) 49 (25–75) 23.8 (18.5–43.5) 81.4 (58.0–155.0) 6035 (77.6) 1742 (22.4) 6787 (87.3) 990 (12.7) 1933 (24.9) 1511 (19.4) 4333 (55.7)
b0.001 b0.001 b0.001 b0.001 0.077 0.296 b0.001
Data are presented as number (%) or median (range).
anthropometric measurements (BMI, waist circumference and blood pressure) and environment (smoking, drinking and physical activity). This case–control study highlights that gene–environment interactions may modulate the risk of T2DM in Han Chinese. As compared with non-smoking and the C-C haplotype of rs12104705, smoking with the C-C genotype was associated with increased risk of T2DM, so the gene–environment interaction was the risk factor for T2DM. Many studies have indicated that smoking is an independent risk factor for the development of insulin resistance and T2DM (Bergman et al., 2012; Chen et al., 2012; Hayashino et al., 2008; Houston et al., 2006; Kowall et al., 2010). In a cohort study (Kowall et al., 2010), never-smokers exposed to environmental tobacco smoke showed an increased risk of diabetes in the total sample (OR = 2.5, 95% CI = 1.1–5.6) and in a subgroup with pre-diabetes at baseline (OR = 4.4, 95% CI = 1.5–13.4) after adjusting for age, sex, parental diabetes, socioeconomic status and lifestyle factors. Active smoking was also associated with diabetes incidence in the total sample (OR = 2.8, 95% CI = 1.3–6.1); this 7-year prospective study showed that both passive and active smoking was associated with increased risk of T2DM. Our results were in agreement with this research and showed an association of the GCG–smoking interaction and T2DM risk. Moderate alcohol consumption is associated with reduced cardiovascular disease in no diabetic populations. However, the effect of alcohol consumption in patients with diabetes is not well defined. Some research showed reduced FPG in patients with T2DM who had previously abstained from alcohol, then initiated moderate daily alcohol consumption (150 ml wine/13 g alcohol) (Bantle et al., 2008; Shai et al., 2007). We did not find an association of the GCG–alcohol consumption interaction and T2DM, perhaps because the frequency of alcohol drinkers was low (13.7%). Regular physical activity is increasingly considered an effective therapeutic strategy for managing T2DM (Lindström et al., 2006). Indeed, aerobic and regular physical activity improves metabolic status and insulin sensitivity, thus reducing the risk of cardiovascular disease (Bassuk and Manson, 2005). In this study, as compared with low physical activity with the C-C haplotype, moderate and high physical activity
Table 2 Genotype and allele distribution of the single nucleotide polymorphism (SNP) rs12104705 of glucagon gene (GCG) among Han Chinese in China. Genotype/allele
Cases (n = 1842)
Controls (n = 7777)
P
TT CT CC T C
9 (0.49) 239 (13.26) 1555 (86.25) 257 (7.13) 3349 (92.87)
46 (0.60) 1021 (13.32) 6597 (86.08) 1113 (7.26) 14215 (92.74)
0.875
0.078
with the haplotype were both significantly associated with T2DM. Hence, the interaction of physical activity and GCG genotype was a protective factor for risk of T2DM, which confirms other findings. High physical activity with the T-T haplotype was associated with increased risk of T2DM; however, the frequency of high physical activity with T-T genotype was small, the values of confidence interval in our results was a wide range and the result was unreliable, so a large sample size study need to test and verity the results. The current data were not enough to manifest T-T haplotype was associated with increased risk of T2DM. In conclusion, this study explored the interaction of the SNP rs12104705 of GCG with smoking, alcohol drinking and physical activity and the association with risk of T2DM. Although we found no substantial association of the SNP and T2DM, genetic polymorphisms in rs12104705 may interact with smoking and physical activity to modify the risk of T2DM. However, more independent replication studies with a large sample size or meta-analysis are warranted to verify the results.
Conflict of interest The authors declare that there are no conflicts of interest.
Acknowledgements This study was supported by the National Natural Science Foundation of China (grant no. 81072359), the Natural Science Foundation of Guangdong Province (grant no. S2013010016791), the Science and Technology Development Foundation of Shenzhen (grant nos. JCYJ20120613112221107 and JCYJ20130326110246234) and the Natural Science Foundation of Shenzhen University (grant no. 80100035911).
Table 3 Association of genotypes of SNP rs12104705 of GCG and risk of type 2 diabetes mellitus in Han Chinese in China. Genotypes
Unadjusted OR (95% CI)
P
Adjusted OR (95% CI)⁎
P⁎
CC CT TT TT vs. CT + CC TT + CT vs. CC
Reference 0.993 (0.854–1.155) 0.830 (0.405–1.699) 0.831 (0.406–1.700) 0.986 (0.850–1.144)
— 0.928 0.610 0.612 0.853
Reference 0.985 (0.821–1.183) 1.034(0.464–2.303) 1.036 (0.465–2.307) 0.987 (0.825–1.181)
— 0.873 0.936 0.932 0.889
OR, odds ratio; 95% CI, 95% confidence interval. ⁎ Adjusted for sex, age, anthropometric measurements (BMI, waist circumference, blood pressure) and environment (smoking, drinking and physical activity).
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Table 4 Interaction of SNP rs12104705 of GCG and environmental variables associated with risk of type 2 diabetes mellitus. Genotype
Status
Cases/controls
P⁎
Adjusted OR⁎
CC
Non-smoking Smoking Non-smoking Smoking Non-smoking Smoking Non-drinking Drinking Non-drinking Drinking Non-drinking Drinking Low physical activity Moderate physical activity High physical activity Low physical activity Moderate physical activity High physical activity Low physical activity Moderate physical activity High physical activity
1199/5124 356/1473 170/796 69/225 7/31 2/15 1302/5758 199/839 195/894 37/127 8/37 1/9 943/1646 170/1270 393/3681 147/243 30/207 55/571 3/15 1/10 5/21
— 0.033 0.281 0.095 0.841 0.674 — 0.075 0.513 0.525 0.873 0.671 — b0.001 b0.001 0.846 0.490 0.387 0.229 0.900 0.019
Reference 1.174 0.897 1.385 1.097 0.685 Reference 0.836 0.940 1.171 1.069 0.607 Reference 0.251 0.190 1.026 1.199 0.834 0.446 1.173 7.748
CT TT CC CT TT CC
CT
TT
95% CI 1.013–1.361 0.736–1.093 0.945–2.030 0.444–2.709 0.117–4.002 0.686–1.018 0.782–1.131 0.719–1.907 0.471–2.429 0.061–6.083 0.206–0.306 0.164–0.220 0.793–1.327 0.716–2.007 0.553–1.259 0.120–1.661 0.096–14.297 1.408–42.635
⁎ Adjusted for sex, age and anthropometric measurements (BMI, waist circumference and blood pressure).
References American Diabetes Association, 2005. Standards of medical care in diabetes. Diabetes Care 28 (Suppl. 1), S4–S36. Bantle, A.E., Thomas, W., Bantle, J.P., 2008. Metabolic effects of alcohol in the form of wine in persons with type 2 diabetes mellitus. Metabolism 57 (2), 241–245. Barrett, J.C., Fry, B., Maller, J., Daly, M.J., 2005. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265. Bassuk, S.S., Manson, J.E., 2005. Epidemiological evidence for the role of physical activity in reducing risk of type 2 diabetes and cardiovascular disease. J. Appl. Physiol. 99 (3), 1193–1204. Bergman, B.C., et al., 2012. Novel and reversible mechanisms of smoking-induced insulin resistance in humans. Diabetes 61 (12), 3156–3166. Black, M.H., et al., 2008. Evidence of interaction between PPARG2 and HNF4A contributing to variation in insulin sensitivity in Mexican Americans. Diabetes 57, 1048–1056. Cauchi, S., et al., 2008. Post genome wide association studies of novel genes associated with type 2 diabetes show gene–gene interaction and high predictive value. PLoS One 3, e2031. Chen, H., Saad, S., Sandow, S.L., Bertrand, P.P., 2012. Cigarette smoking and brain regulation of energy homeostasis. Front. Pharmacol. 3, 147. Doria, A., Patti, M.E., Kahn, C.R., 2008. The emerging genetic architecture of type 2 diabetes. Cell Metab. 8, 186–200. Grant, R.W., Moore, A.F., Florez, J.C., 2009. Genetic architecture of type 2 diabetes: recent progress and clinical implications. Diabetes Care 32, 1107–1114. Hayashi, Y., et al., 2009. Mice deficient for glucagon gene-derived peptides display normoglycemia and hyperplasia of islet alpha-cells but not of intestinal L-cells. Mol. Endocrinol. 23, 1990–1999. Hayashino, Y., et al., 2008. A prospective study of passive smoking and risk of diabetes in a cohort of workers: the high-risk and population strategy for occupational health promotion (HIPOP-OHP) study. Diabetes Care 31 (4), 732–734. Hindorff, L.A., et al., 2009. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. U. S. A. 106, 9362–9367.
Holst, J.J., 1997. Enteroglucagon. Annu. Rev. Physiol. 59, 257–271. Houston, T.K., Person, S.D., Pletcher, M.J., Liu, K., Iribarren, C., Kiefe, C.I., 2006. Active and passive smoking and development of glucose intolerance among young adults in a prospective cohort: CARDIA study. BMJ 332 (7549), 1064–1069. International Physical activity and Questionnaire, 2012. Short Last 7 Days Self-Administered Format 2005. Available at: http://www.ipaq.ki.se (accessed 18.04.12). Kowall, B., et al., 2010. Association of passive and active smoking with incident type 2 diabetes mellitus in the elderly population: the KORA S4/F4 cohort study. Eur. J. Epidemiol. 25 (6), 393–402. Lindström, P., et al., 2006. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet 368 (9548), 1673–1679. Mensink, M., 2005. Lifestyle intervention, glucose tolerance, and risk of developing type 2 diabetes mellitus. Metab. Syndr. Relat. Disord. 3, 26–34. Miyake, K., et al., 2009. Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association. J. Hum. Genet. 54, 236–241. Qi, L., van Dam, R.M., Asselbergs, F.W., Hu, F.B., 2007. Gene–gene interactions between HNF4A and KCNJ11 in predicting Type 2 diabetes in women. Diabet. Med. 24, 1187–1191. Shai, I., et al., 2007. Glycemic effects of moderate alcohol intake among patients with type 2 diabetes: a multicenter, randomized, clinical intervention trial. Diabetes Care 30 (12), 3011–3016. Sparso, T., et al., 2009. Combined analysis of 19 common validated type 2 diabetes susceptibility gene variants shows moderate discriminative value and no evidence of gene– gene interaction. Diabetologia 52, 1308–1314. Torekov, S.S., et al., 2011. Homozygous carriers of the G allele of rs4664447 of the glucagon gene (GCG) are characterised by decreased fasting and stimulated levels of insulin, glucagon and glucagon-like peptide (GLP)-1. Diabetologia 54, 2820–2831. Whiting, D.R., Guariguata, L., Weil, C., Shaw, J., 2011. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res. Clin. Pract. 94 (3), 311–321.
Contents lists available at ScienceDirect
Gene journal homepage: www.elsevier.com/locate/gene
Glucagon gene polymorphism modifies the effects of smoking and physical activity on risk of type 2 diabetes mellitus in Han Chinese Linlin Li a,1, Kaiping Gao b,1, Jingzhi Zhao c, Tianping Feng c, Lei Yin c, Jinjin Wang d, Chongjian Wang a, Chunyang Li a, Yan Wang a, Qian Wang a, Yujia Zhai a, Haifei You a, Yongcheng Ren a, Bingyuan Wang a, Dongsheng Hu b,⁎ a
Department of Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, People's Republic of China Shenzhen University School of Medicine, Shenzhen, 518060, People's Republic of China c Military Hospital of Henan Province, Zhengzhou, 450003, People's Republic of China d Discipline of Public Health and Preventive Medicine, Center of Preventive Medicine Research and Assessment, Henan University of Traditional Chinese Medicine, Zhengzhou, 450008, People's Republic of China b
a r t i c l e
i n f o
Article history: Accepted 30 September 2013 Available online 1 November 2013 Keywords: Genetics Type 2 diabetes Smoking Alcohol consumption Physical activity
a b s t r a c t Few genome-wide association studies have considered interactions between multiple genetic variants and environmental factors associated with disease. The interaction was examined between a glucagon gene (GCG) polymorphism and smoking, alcohol consumption and physical activity and the association with risk of type 2 diabetes mellitus (T2DM) in a case–control study of Chinese Han subjects. The rs12104705 polymorphism of GCG and interactions with environmental variables were analyzed for 9619 participants by binary multiple logistic regression. Smoking with the C-C haplotype of rs12104705 was associated with increased risk of T2DM (OR = 1.174, 95% CI = 1.013–1.361). Moderate and high physical activity with the C-C genotype was associated with decreased risk of T2DM as compared with low physical activity with the genotype (OR = 0.251, 95% CI = 0.206–0.306 and OR = 0.190, 95% CI = 0.164–0.220). However, the interaction of drinking and genotype was not associated with risk of T2DM. Genetic polymorphism in rs12104705 of GCG may interact with smoking and physical activity to modify the risk of T2DM. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Type 2 diabetes (T2DM) has become one of the leading and fastest growing health problems in the world. Global estimates by the International Diabetes Federation (IDF) in 2011 were approximately 90 million Chinese and over 366 million people worldwide. By 2030, these estimates are expected to increase to more than 130 million in China and 552 million people worldwide (Whiting et al., 2011). Although the exact etiology is unknown, T2DM is a multifactorial disease, resulting from numerous interactions between multiple genetic variants and environmental factors related to diet, physical activity, smoking, drinking alcohol and medical treatment (Mensink, 2005). In the last few years, T2DM has been the subject of genome-wide association studies (GWAS) (Hindorff et al., 2009) and candidate gene studies (Doria et al., 2008; Grant et al., 2009), which have identified variants in genes that may play a role in the etiology of T2DM. However, because of practical and statistical challenges, none of the GWAS have considered
Abbreviations: GWAS, Genome-wide association studies; MAF, Minor allele frequency; SNP, Single nucleotide polymorphism; OR, Odds ratio; CI, Confidence interval. ⁎ Corresponding author. Tel.: +86 755 86671909; fax: +86 755 86671906. E-mail address: [email protected] (D. Hu). 1 Linlin Li and Kaiping Gao contributed equally to this manuscript. 0378-1119/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.gene.2013.09.121
interactions among the variants and the environment, and only relatively few candidate gene studies have pursued such studies (Black et al., 2008; Cauchi et al., 2008; Miyake et al., 2009; Qi et al., 2007; Sparso et al., 2009). Glucagon gene (GCG), located on chromosome 2q24.2, comprises 6 exons and encodes glucagon, glucagon-like peptide (GLP)-1 and GLP-2 and oxyntomodulin proteins (Holst, 1997). No large-scale sequencing findings for GCG have been published. On the basis of the crucial role of GCG in energy metabolism, we hypothesized that the frequency variants of GCG were associated with diabetes. Here, a case–control study was performed to investigate the interaction between GCG polymorphism and the environment associated with risk of T2DM in Chinese Han subjects. 2. Methods 2.1. Samples Subjects (N = 9619, 1842 cases and 7777 controls) were recruited from the northern Chinese Han population in northeast China and obtained blood samples from all subjects. All subjects resided in Henan Province, North China. A total of 1842 T2DM patients were consecutively recruited from the outpatient clinics of three hospitals (1032 cases) or
L. Li et al. / Gene 534 (2014) 352–355
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from communities (810 cases) in Henan Province. Diabetes mellitus was diagnosed by fasting blood glucose (FPG) ≥ 7.00 mmol/L or 2-h plasma glucose ≥ 11.0 mmol/L (American Diabetes Association, 2005) during an oral glucose tolerance test (OGTT) with diabetes clinical symptoms or previously diagnosed diabetes was defined if the participant (1) was receiving insulin treatment, (2) was receiving an oral hypoglycemic agent or (3) presented with a history of diabetes during the interview; all other types of diabetes were excluded on the basis of clinical data. Patients who were pregnant or lactating were excluded. All patients were unrelated and had been diagnosed with T2DM after age 20 years. Unrelated non-diabetic controls were recruited from communities in Henan Province. The participants were excluded with body mass index (BMI) b 18.50 or who were pregnant, mentally disturbed, handicapped or obese (caused by disease) or taking certain drugs or had cancer. Control subjects were included if they were Han Chinese N20 years old and had no family history of diabetes, FPG b6.1 mmol/L and normal OGTT results. Written, informed consent was obtained from each participant after a full explanation of the study, which was approved by the Ethics Committee of Zhengzhou University.
Logistic regression models (SAS version 9.1.3) were used to assess the effects of the SNP on T2DM after adjusting for sex, age, anthropometric measurements (BMI, waist circumference and blood pressure) and environment (smoking, drinking and physical activity) with calculation of odds ratios (ORs), 95% confidence intervals (95% CI) and corresponding p values for risk of T2DM. The interaction between the SNP and the environment was modeled by use of logistic regression multiplicative interaction models. P b 0.05 was considered statistically significant.
2.2. Primer design
3.2. Genotype frequencies and allelic estimates for GCG SNP
Primers and probes were synthesized by Shanghai Generay Biotech Co.; the forward primer was 5′-ACCTTTTGGTGTCTCTACTTC-3′, and the reverse primer was 5′-TGTGTCATGTTGATTGGCTGT-3′.
The SNP rs12104705 was in the Hardy–Weinberg equilibrium independently among cases and controls (P N 0.1). Genotyping results did not differ between cases and controls and had a minor allele (T allele) frequency of 7.13 % (Table 2). The rs12104705 genotyping was not associated with disease susceptibility (P N 0.05) even after adjusting for sex, age, anthropometric measurements (BMI, waist circumference and blood pressure) and environment (smoking, drinking and physical activity) (Table 3).
2.3. Single nucleotide polymorphism (SNP) selection and genotyping We selected one tagging SNP (rs12104705) from the Hap Map Phase 3 Chinese population using Haploview version 4.1 (Barrett et al., 2005). The criterion for screening was minor allele frequency N 0.1 in Han Chinese and r2 ≥ 0.8. Genomic DNA was isolated from whole blood by use of a blood genome DNA extraction kit (Yaneng BIO). Polymorphic regions were amplified by PCR, then rs12104705 genotyping involved ligase detection reactions (LDRs) performed in a 200-μl PCR tube, with a final volume of 10 μl containing 2 μl PCR products (100 ng/μl), 1 μl 10* Taq DNA ligase buffer, 1 μl LDR probes (12.5 pmol/μl), 6 μl sterile H2O and 0.125 μl Taq DNA ligase (40 U/μl). The reactions involved heating for 2 min at 95 °C, then 30 thermal cycles of 95 °C for 30 s (denaturation) and 56 °C for 2 min (annealing and ligation). The LDR products were analyzed by electrophoresis with use of the ABI PRISM 3100 DNA sequenator.
3. Results 3.1. Subject characteristics BMI and waist circumference were significantly greater in cases than controls (P b 0.001), cases featured more males than did controls (P b 0.001), and the median age was higher for cases than controls (P b 0.001) (Table 1). Moderate and high physical activities were lower among cases than controls, with no difference between groups in alcohol drinking.
3.3. Association of gene–environment interaction and T2DM risk With non-smoking and the C-C haplotype of rs12104705 as the reference, smoking with the C-C haplotype was associated with increased risk of T2DM (OR = 1.174, 95% CI = 1.013–1.361). As compared with low physical activity with the C-C genotype, moderate and high physical activity with the genotype was associated with decreased risk of T2DM (OR = 0.251, 95% CI = 0.206–0.306 and OR = 0.190, 95% CI = 0.164–0.220, respectively), high physical activity with the T-T haplotype was associated with increased risk of T2DM (OR = 7.748, 95% CI = 1.408–42.635). The interaction of alcohol drinking and rs12104705 was not associated with risk of T2DM (Table 4).
2.4. Environmental factors 4. Discussion Smoking status included current smoker and not current smoker. Participants who currently smoked and/or had smoked at least 100 cigarettes during their lifetime were classified as current smokers if they answered affirmatively to the questions, “Do you smoke cigarettes now?” and “Have you smoked at least 100 cigarettes during your lifetime?” Self-reported alcohol consumption data were also collected from the questionnaire using the following question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you drink?” Alcohol drinkers consuming 100 ml liquor in 30 days were included in the study. Physical activity level for each individual was classified as low, moderate or high based on the International Physical activity Questionnaire (IPAQ) [www.ipaq.ki.se]. 2.5. Data analysis Categorical variables are shown as number (percentage) and were analyzed by chi-square test. Continuous variables are shown as median (range) for data with skewed distribution. The Mann–Whitney–Wilcoxon test and the Kruskal–Wallis rank test were used to assess differences between cases and controls. Haploview version 4.2 was used to test for Hardy Weinberg equilibrium independently among cases and controls.
In the present study, a case–control study of 9619 Chinese Han subjects was examined the association of the interaction of one SNP, rs12104705, in GCG with environmental factors and risk of T2DM. The allelic frequencies of rs12104705 were identical between cases and controls. We found the interaction between smoking and physical activity and rs12104705 associated with risk of T2DM. The GCG gene, encoding glucagon, GLP-1 and GLP-2 and oxyntomodulin proteins, for crucial for energy metabolism, is involved in multiple steps of metabolism. Blood glucose and serum insulin levels were lower and insulin sensitivity higher in GCG-knockout than wildtype mice (Hayashi et al., 2009). The GCG polymorphism rs12104705 is a newly discovered, single genetic variant possibly associated with risk of T2DM, but only 1 study has investigated the association of a GCG variant and risk of T2DM, in a Danish population (Torekov et al., 2011): the Ile158Val variant was associated with risk of T2DM (OR = 1.83 [1.05–3.9]), but rs4664447 and rs7581952 were not. The inclusion of more than 9000 subjects conferred sufficient power to separate the 3 genotypes, but the results were not significant. The binary logistic modeling in this study revealed no association of rs12104705 of GCG and risk of T2DM even when adjusting for sex, age,
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Table 1 Characteristics of study participants. Characteristics Sex Male Female Age (years) Body mass index (kg/m2) Waist circumference (cm) Smoking Drinking Physical activity
Non-smoking Smoking Non-drinking Drinking Low Moderate High
Cases (n = 1842)
Controls (n = 7777)
P
925 (50.2) 917 (49.8) 54 (20–85) 27.4 (18.5–50.5) 90.5 (60.6–200.0) 1394 (75.7) 448 (24.3) 1524 (86.4) 241 (13.7) 1108 (62.6) 203 (11.5) 459 (25.9)
3214 (41.3) 4563 (58.7) 49 (25–75) 23.8 (18.5–43.5) 81.4 (58.0–155.0) 6035 (77.6) 1742 (22.4) 6787 (87.3) 990 (12.7) 1933 (24.9) 1511 (19.4) 4333 (55.7)
b0.001 b0.001 b0.001 b0.001 0.077 0.296 b0.001
Data are presented as number (%) or median (range).
anthropometric measurements (BMI, waist circumference and blood pressure) and environment (smoking, drinking and physical activity). This case–control study highlights that gene–environment interactions may modulate the risk of T2DM in Han Chinese. As compared with non-smoking and the C-C haplotype of rs12104705, smoking with the C-C genotype was associated with increased risk of T2DM, so the gene–environment interaction was the risk factor for T2DM. Many studies have indicated that smoking is an independent risk factor for the development of insulin resistance and T2DM (Bergman et al., 2012; Chen et al., 2012; Hayashino et al., 2008; Houston et al., 2006; Kowall et al., 2010). In a cohort study (Kowall et al., 2010), never-smokers exposed to environmental tobacco smoke showed an increased risk of diabetes in the total sample (OR = 2.5, 95% CI = 1.1–5.6) and in a subgroup with pre-diabetes at baseline (OR = 4.4, 95% CI = 1.5–13.4) after adjusting for age, sex, parental diabetes, socioeconomic status and lifestyle factors. Active smoking was also associated with diabetes incidence in the total sample (OR = 2.8, 95% CI = 1.3–6.1); this 7-year prospective study showed that both passive and active smoking was associated with increased risk of T2DM. Our results were in agreement with this research and showed an association of the GCG–smoking interaction and T2DM risk. Moderate alcohol consumption is associated with reduced cardiovascular disease in no diabetic populations. However, the effect of alcohol consumption in patients with diabetes is not well defined. Some research showed reduced FPG in patients with T2DM who had previously abstained from alcohol, then initiated moderate daily alcohol consumption (150 ml wine/13 g alcohol) (Bantle et al., 2008; Shai et al., 2007). We did not find an association of the GCG–alcohol consumption interaction and T2DM, perhaps because the frequency of alcohol drinkers was low (13.7%). Regular physical activity is increasingly considered an effective therapeutic strategy for managing T2DM (Lindström et al., 2006). Indeed, aerobic and regular physical activity improves metabolic status and insulin sensitivity, thus reducing the risk of cardiovascular disease (Bassuk and Manson, 2005). In this study, as compared with low physical activity with the C-C haplotype, moderate and high physical activity
Table 2 Genotype and allele distribution of the single nucleotide polymorphism (SNP) rs12104705 of glucagon gene (GCG) among Han Chinese in China. Genotype/allele
Cases (n = 1842)
Controls (n = 7777)
P
TT CT CC T C
9 (0.49) 239 (13.26) 1555 (86.25) 257 (7.13) 3349 (92.87)
46 (0.60) 1021 (13.32) 6597 (86.08) 1113 (7.26) 14215 (92.74)
0.875
0.078
with the haplotype were both significantly associated with T2DM. Hence, the interaction of physical activity and GCG genotype was a protective factor for risk of T2DM, which confirms other findings. High physical activity with the T-T haplotype was associated with increased risk of T2DM; however, the frequency of high physical activity with T-T genotype was small, the values of confidence interval in our results was a wide range and the result was unreliable, so a large sample size study need to test and verity the results. The current data were not enough to manifest T-T haplotype was associated with increased risk of T2DM. In conclusion, this study explored the interaction of the SNP rs12104705 of GCG with smoking, alcohol drinking and physical activity and the association with risk of T2DM. Although we found no substantial association of the SNP and T2DM, genetic polymorphisms in rs12104705 may interact with smoking and physical activity to modify the risk of T2DM. However, more independent replication studies with a large sample size or meta-analysis are warranted to verify the results.
Conflict of interest The authors declare that there are no conflicts of interest.
Acknowledgements This study was supported by the National Natural Science Foundation of China (grant no. 81072359), the Natural Science Foundation of Guangdong Province (grant no. S2013010016791), the Science and Technology Development Foundation of Shenzhen (grant nos. JCYJ20120613112221107 and JCYJ20130326110246234) and the Natural Science Foundation of Shenzhen University (grant no. 80100035911).
Table 3 Association of genotypes of SNP rs12104705 of GCG and risk of type 2 diabetes mellitus in Han Chinese in China. Genotypes
Unadjusted OR (95% CI)
P
Adjusted OR (95% CI)⁎
P⁎
CC CT TT TT vs. CT + CC TT + CT vs. CC
Reference 0.993 (0.854–1.155) 0.830 (0.405–1.699) 0.831 (0.406–1.700) 0.986 (0.850–1.144)
— 0.928 0.610 0.612 0.853
Reference 0.985 (0.821–1.183) 1.034(0.464–2.303) 1.036 (0.465–2.307) 0.987 (0.825–1.181)
— 0.873 0.936 0.932 0.889
OR, odds ratio; 95% CI, 95% confidence interval. ⁎ Adjusted for sex, age, anthropometric measurements (BMI, waist circumference, blood pressure) and environment (smoking, drinking and physical activity).
L. Li et al. / Gene 534 (2014) 352–355
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Table 4 Interaction of SNP rs12104705 of GCG and environmental variables associated with risk of type 2 diabetes mellitus. Genotype
Status
Cases/controls
P⁎
Adjusted OR⁎
CC
Non-smoking Smoking Non-smoking Smoking Non-smoking Smoking Non-drinking Drinking Non-drinking Drinking Non-drinking Drinking Low physical activity Moderate physical activity High physical activity Low physical activity Moderate physical activity High physical activity Low physical activity Moderate physical activity High physical activity
1199/5124 356/1473 170/796 69/225 7/31 2/15 1302/5758 199/839 195/894 37/127 8/37 1/9 943/1646 170/1270 393/3681 147/243 30/207 55/571 3/15 1/10 5/21
— 0.033 0.281 0.095 0.841 0.674 — 0.075 0.513 0.525 0.873 0.671 — b0.001 b0.001 0.846 0.490 0.387 0.229 0.900 0.019
Reference 1.174 0.897 1.385 1.097 0.685 Reference 0.836 0.940 1.171 1.069 0.607 Reference 0.251 0.190 1.026 1.199 0.834 0.446 1.173 7.748
CT TT CC CT TT CC
CT
TT
95% CI 1.013–1.361 0.736–1.093 0.945–2.030 0.444–2.709 0.117–4.002 0.686–1.018 0.782–1.131 0.719–1.907 0.471–2.429 0.061–6.083 0.206–0.306 0.164–0.220 0.793–1.327 0.716–2.007 0.553–1.259 0.120–1.661 0.096–14.297 1.408–42.635
⁎ Adjusted for sex, age and anthropometric measurements (BMI, waist circumference and blood pressure).
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