Introduction: The objective of this study was to examine the association between type 2 diabetes mellitus (T2DM) and genes identified in previous genome-wide association studies (GWASs) in rural Han Chinese adults. Methods: This prospective study included 1,832 adults aged ≥18 years in Deqing without diabetes at baseline. The subjects were followed up for 8.7 years on average. We selected 45 susceptible tag single-nucleotide polymorphisms (SNPs) for T2DM that have been identified in GWASs and genotyped. A Cox model was constructed to calculate the adjusted hazard ratios (aHRs) for the association between SNPs and incident T2DM. Results: The incidence rate of T2DM was 12.0 per 1,000 person-years. After adjustment for covariates and a Bonferroni correction, rs17584499 of protein tyrosine phosphatase, receptor-type D (PTPRD), rs11257655 and rs10906115 of cell division cycle 123 gene (CDC123), and rs12970134 of melanocortin-4 receptor (MC4R) were significantly associated with incident T2DM. The aHRs for incident T2DM were 1.75 (95% confidence interval [CI]: 1.28–2.40) and 1.61 (95% CI: 1.27–2.04) in association with an increasing number of T alleles in rs17584499 and rs11257655 under an additive genetic model, and the aHR was 1.72 (95% CI: 1.33–2.22) with an increasing number of A alleles in rs10906115. The aHRs under the dominant model were 1.82 (95% CI: 1.25–2.66) for TT + CT versus CC of rs17584499 and 2.04 (95% CI: 1.47–2.86) for AA + AG versus GG of rs10966115. The aHRs under the recessive model were 2.99 (95% CI: 1.30–6.89) for TT versus CT + CC of rs17584499, 1.92 (95% CI: 1.39–2.70) for TT versus CT + CC of rs11257655, and 2.54 (95% CI:1.22–5.29) for AA versus AG + GG of rs12970134. In addition, an increased incidence of T2DM was significantly associated with the TA haplotype of rs11257655 and rs10906115 (aHR = 1.81, 95% CI: 1.12–2.92), while a decreased incidence was associated with the CG haplotype (aHR = 0.49, 95% CI: 0.35–0.68) and the CT haplotype of rs1111875 and rs5015480 (aHR = 0.61, 95% CI: 0.37–0.98). Conclusion: Variants of the PTPRD, CDC123, and MC4R genes were associated with the T2DM incidence in a rural Han Chinese population.

Diabetes mellitus (DM), characterized as hyperglycemia that occurs when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin produced, is a well-known cause of premature death and disability worldwide. More than 90% of DM cases are type 2 diabetes mellitus (T2DM). The prevalence of T2DM has been rapidly increasing in China in recent years, and the number of diabetic patients is expected to increase from 110 million in 2015 to 151 million in 2040 [1].

The occurrence of T2DM is related to both environmental and genetic factors [2, 3]. With the rapid development of genotyping technology, genome-wide association studies (GWASs) have been accelerating. Since 2008, 1,791 susceptibility genes/loci of T2DM had been reported [4], such as the potassium voltage-gated channel, KQT-like subfamily, number 1 (KCNQ1); protein tyrosine phosphatase, receptor-type D (PTPRD); cell division cycle 123 gene (CDC123); serine racemase (SRR); and solute carrier family 30, member 8 (SLC30A8). Although large-scale GWASs have enriched our understanding of the genetic susceptibility to T2DM, most of them used a case-control design and had the limitations of poor representativeness of the general population and sometimes other biases such as population stratification. In addition, genes/loci affecting the onset of T2DM vary in different populations [5, 6] and genetic variants identified by GWASs need to be validated in different ethnic populations. We conducted a prospective cohort study among rural Chinese adults in Deqing, China, to validate 45 susceptibility genes/loci of T2DM from published GWASs conducted in Asian populations.

Source of Data and Population

This Rural Deqing Health Cohort Study was conducted in a rural community of Deqing County, Zhejiang Province, China, from 2006 to 2014, and the study participants signed an informed consent form and completed a survey questionnaire and a physical examination. We created a sub-cohort including 3,038 Han participants aged ≥18 years who had no diabetes at baseline and were living in the selected rural communities and had no plan to move out [7].

The questionnaire covered information on demographic characteristics (age, sex, education level, occupation, and household income), lifestyle factors (smoking status, alcohol use, physical activity, tea drinking, diet preference, vegetable consumption, and fruit consumption), health status, and family history of T2DM. Blood pressure, height, and weight were measured and 5-mL venous blood samples were collected after an overnight fast of at least 8 h to test for fasting plasma glucose (FPG).

Definitions of Co-Variables

Ever smoker (current or former smoker) was defined as having smoked at least 1 cigarette per day for >6 months during their lifetime [8]. Alcohol user was defined as drinking any alcohol at least once a week [8]. Physical activity was defined as exercising for >30 min and more than twice a week [8]. A tea drinker was defined as drinking tea at least 3 times per week [8]. According to self-reporting, participants were grouped into 3 types of diet preference (meat mainly, vegetable mainly, and equally), 2 categories of vegetable consumption (<300 or ≥300 g/day), and 2 categories of fruit consumption (<200 or ≥200 g/day). BMI was calculated from weight/height2 (kg/m2). According to the Chinese Ministry of Health standard, overweight was defined as a BMI in the range from 24.0 to 27.9 kg/m2 and obesity as BMI ≥28.0 kg/m2. A family history of T2DM was considered to be positive if one or more first-degree relatives had T2DM [7]. Hypertension was defined as having systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or a history of taking hypertension medication [7].

Outcome

This study followed up 1,832 (60.3%) participants until November 2015. When compared with those who were lost to follow-up, participants had a lower level of education, were older, were more likely to be a farmer, and were less likely to engage in physical activity, to drink tea, and to be overweight or hypertensive (online suppl. Table 1; see www.karger.com/doi/10.1159/000513891 for all online suppl. material). New cases of T2DM were ascertained through the Deqing Electronic Health Records system (EHRs), questionnaire interviews, and/or free physical examinations. A T2DM case was defined as having an FPG of 7.0 mmol/L or higher, self-reported physician-diagnosed diabetes or had a diagnosis of T2DM recorded in the EHRs, or the use of antidiabetic medications during the follow-up. Impaired fasting glucose (IFG) cases were subjects who had 6.1 mmol/L ≤ FPG <7.0 mmol/L [9].

Single-Nucleotide Polymorphism Selection and Genotyping

The tag single-nucleotide polymorphisms (SNPs) evaluated in the current study were previously found to be associated with T2DM based on the NHGRI-EBI GWAS catalog (available at http://www.ebi.ac.uk/gwas/, November 2016) with the following considerations: (1) the SNPs were found to be associated with T2DM in Asian populations (p < 5 × 10−8); (2) in the case of multiple positive loci in 1 gene, the linkage disequilibrium tag SNP Selection tool of the SNPinfo Web Server was used to screen the tag SNPs; (3) positive loci that were found to be associated with T2DM in populations of different races were given higher priority; and (4) according to the dbSNP database, the minimum allele frequency in Asian populations was >0.05. Subsequently, 45 susceptible tag SNPs from 43 genes were selected for this study, and the risk allele frequencies and the associations between SNPs with T2DM in previous studies are presented in online suppl. Table 2. Magnetic Genomic DNA kit (TIANGEN, Beijing, China) was used to extract DNA from the blood samples, and Sequenom MassARRAY technology was used for genotyping. All 45 tag SNPs were under Hardy-Weinberg equilibrium except for rs3802177, rs5945326, rs12010175, rs6769511, and rs6815464 (pHWE < 0.05), and all call rates were over 98% with over 99% concordance of the duplicate samples (n = 20) (online suppl. Table 3).

Statistical Analysis

Data were double-entered with Epidata3.1, and statistical analysis was performed using SAS software version 9.4 (SAS Institute, Cary, NC, USA) and R software version 4.0.1. Data were described as mean ± standard deviation for continuous variables and as frequencies (percentage) for categorical variables. Student’s t tests were used for continuous variables, and χ2 tests were used for categorical variables. Deviation from the Hardy-Weinberg equilibrium was assessed by using χ2 test. Haploview version 4.2 (Broad Institute, Cambridge, UK) and Phase software were used to calculate the coefficients (r2 and D′) and to infer any linkage disequilibrium. The person-years of follow-up were calculated from the date of recruitment until the date of T2DM diagnosis, the end of follow-up in November 2015, or the date of death, whichever came first. Cox proportional hazard models under additive, dominant, and recessive genetic models were employed to explore the associations between the SNPs and incident T2DM. In the multiple Cox proportional hazard models, adjusted hazard ratios (aHRs) and their 95% confidence intervals (CIs) were calculated after adjustment for age, sex, education level, household income, occupation, smoking status, alcohol use, physical activity, tea drinking, diet preference, vegetable consumption, fruit consumption, hypertension, overweight/obesity, IFG, and family history of T2DM at baseline. To estimate the population-level risk attributable to each risk genotype, we calculated population attributable fraction (PAF) and its 95% CI using the approach described by Eide and Gefeller [10] and the “AF” R package developed by Elisabeth and colleagues (R software version 4.0.1) [11]. The formula for individual PAF = Pe (RRe − 1)/[1 + Pe (RRe − 1)] ×100%, in which Pe is the prevalence of exposure (a risk genotype) and RRe is the relative risk for T2DM in association with the risk genotype. All tests were 2-tailed, and it was considered statistically significant if the p value was <0.05. The Bonferroni correction was performed for multiple comparisons of SNPs.

Baseline Characteristics of the Subjects

Of 1,832 subjects without diabetes at baseline, 190 developed T2DM during the average follow-up period of 8.7 (±1.3) years. The incidence rate of T2DM was 12.0 per 1,000 PYs. The participants were aged 48.7 (±9.7) years on average at baseline, 45.3% were men, and one-third received 9 or more years of education. Most of them were farmers and reported medium household income. Approximately one-third of them were ever smokers, more than one-quarter were alcohol users, and only a small proportion of them engaged in physical activity. Most participants had a balanced diet and ate vegetables and fruit daily. Approximately one-quarter of them had a BMI of 24.0 kg/m2 or greater and had hypertension. A small proportion of people had a family history of diabetes and IFG. Compared with non-T2DM subjects, T2DM patients were older, had a higher BMI, and were more likely to have a family history of diabetes, hypertension, or IFG (Table 1).

Table 1.

Characteristics of the cohort participants

Characteristics of the cohort participants
Characteristics of the cohort participants

Associations between Individual SNPs and Incident T2DM

Table 2 and online suppl. Table 4. A show the results of the associations between the SNPs and the incidence of T2DM from the Cox proportional hazard models. Of the 45 SNPs, we found that rs243021 (g.60357684G>A) of B-cell lymphoma/leukemia 11A (BCL11A), rs3923113 (g.164645339A>C) of growth factor receptor-bound protein 14 (GRB14), rs17584499 (g.8879118C>T) of PTPRD, rs7041847 (g.4287466A>G) of GLIS family zinc finger 3 (GLIS3), rs11257655 (g.12265895C>T) and rs10906115 (g.12272998A>G) of CDC123, and rs12970134 (g.60217517G>A) of melanocortin-4 receptor (MC4R) were significantly associated with the incidence of T2DM after adjustment for the covariates described in the Methods section. These associations remained significant only for rs17584499, rs11257655, rs10906115, and rs12970134 after applying a Bonferroni correction for multiple testing with the threshold for significance set at 0.001.

Table 2.

The association of SNPs with the risk of incident T2DM

The association of SNPs with the risk of incident T2DM
The association of SNPs with the risk of incident T2DM

Participants who carried the TT and CT genotypes of rs17584499 had a 2.85 (95% CI: 1.24–6.59) and 1.63 (95% CI: 1.15–2.31) times higher risk of developing T2DM than those with the CC genotype. In the additive genetic model, T of rs17584499 tended to increase the risk of T2DM (aHR = 1.75, 95% CI: 1.28–2.40). The aHR was 1.82 (TT + CT vs. CC, 95% CI: 1.25–2.66) in the dominant genetic model and 2.99 (TT vs. TC + CC, 95% CI: 1.30–6.89) in the recessive genetic model, respectively. The rs11257655 was associated with the risk of T2DM in both the additive (aHR = 1.61, 95% CI: 1.27–2.04) and the recessive (aHR = 1.92, 95% CI: 1.39–2.70) genetic models. The aHR was 1.92 (95% CI: 1.18–3.13) for the AA genotype of rs10906115 compared with the GG genotype. The T2DM incidence increased with the number of A alleles in the rs10906115 genotype with the aHR being 1.72 (95% CI: 1.33–2.22) in the additive model, and the aHR was 2.04 (95% CI: 1.47–2.86) for the genotype AA + AG versus GG in the dominant model. Besides, the recessive genetic model also revealed a detrimental effect of AA of rs12970134 (AA vs. GA + GG, aHR = 2.54, 95% CI: 1.22–5.29). In general, T of rs17584499, T of rs11257655, A of rs10906115, and A of rs12970134 were significantly associated with an increased risk of incident T2DM.

Table 2 shows the adjusted PAF for each risk genotype in relation to incident T2DM, too. The adjusted PAFs were 1.12% (0–2.22%) and 7.17% (95% CI: 1.52–12.81%) for the TT and CT genotypes of rs17584499 and 14.49% (95% CI: 3.29–25.70%) for the TT genotype of rs11257655. The PAF was 19.51% (95% CI: 5.35–33.66%) for the AA genotype of rs10906115 and was 2.33% (95% CI: 0.30–4.36%) for the AA genotype of rs12970134.

Association of Haplotypes with the Risk of Incident T2DM

We grouped the 45 tag SNPs into 2 modules. Module 1 included rs11257655 and rs10906115 of CDC123, and module 2 included rs1111875 and rs5015480 of the hematopoietically expressed homeobox protein gene (HHEX). Module 1 included TA, CG, CA, and TG (TA, CG, and CA were common haplotypes, and their proportions were 57.19, 37.33, and 5.33%, respectively). Module 2 included TT, CC, CT, and TC (TT, CC, and CT were common haplotypes, and their proportions were 72.63, 16.79, and 10.55%, respectively). After adjustment for co-variables, the Cox proportional hazard models showed that the TA haplotypes of rs11257655 and rs10906115 in CDC123 increased the risk of incident T2DM (aHR = 1.81, 95% CI: 1.12–2.92), while the CG haplotype decreased the risk (aHR = 0.49, 95% CI: 0.35–0.68). The CT haplotypes of rs111875 and rs5015480 in HHEX also decreased the risk of T2DM (aHR = 0.61, 95% CI: 0.37–0.98) (Table 3).

Table 3.

Association between haplotype and the risk of incident T2DM

Association between haplotype and the risk of incident T2DM
Association between haplotype and the risk of incident T2DM

Genetic variation plays an important role in the development of T2DM. A better understanding of the genetic factors in the development of T2DM is necessary to identify high-risk individuals for targeted screening and prevention. In this study, 45 tag SNPs were genotyped and the associations between those tag SNPs and T2DM were evaluated in the rural Han Chinese population. We demonstrated that T of rs17584499, T of rs11257655, A of rs10906115, and A of rs12970134 significantly increased the risk of incident T2DM. We also found that the TA haplotype of rs11257655 or rs10906115 in CDC123 increased the risk of incident T2DM while the CG haplotype of rs11257655/rs10906115 and the CT haplotype of rs111875/rs5015480 in HHEX decreased the risk.

In the original GWAS by Tsai et al., the risk variant in the PTPRD gene had the strongest association signal (joint p = 8.54 × 10–10) and the highest odds ratio (1.57) among all identified risk variants [12]. Two studies conducted in Mexican-American and Japanese populations [13, 14], however, showed that there was no relationship between PTPRD and T2DM. Our findings suggested that the risk allele T of rs17584499 in PTPRD was a risk factor for incident T2DM and the PAF for the TT genotype was 1.12% (0–2.22%), which is in agreement with the result from a study of 1,138 Han Chinese participants [15]. That cohort study found that subjects with the risk-conferring rs17584499 TT genotype were more likely to develop T2DM, with the HR being 8.82 (95% CI: 3.11–25.04) and AR% being 70.33% (95% CI: 31.03–87.21%), and the risk-conferring T allele of rs17584499 was believed to be associated with insulin resistance [15]. PTPRD belongs to the receptor type IIA (R2A) subfamily of protein tyrosine phosphatase δ (PTPδ), which has been implicated in neural development, cancer, and diabetes, and PTPRD genetic variation might modulate the blood glucose homeostasis and insulin sensitivity to cause T2DM [16-18].

This study found that the number of T alleles of rs11257655 in CDC123 were positively associated with incident T2DM (aHR: 1.61, 95% CI: 1.27–2.04), which was consistent with the results from a previous study [19]. Fogarty et al. reported that rs11257655 could affect transcriptional activity through altered binding of a protein complex that includes FOXA1 and FOXA2. Also, rs10906115 of the CDC123 gene was significantly associated with incident T2DM in this study, and the A allele was associated with an increased incidence of T2DM (aHR: 1.72, 95% CI: 1.33–2.22), and similar results were found in 2 previous studies of Chinese [20] and Japanese [21] populations. Currently, the mechanism for the association between rs10906115 and T2DM remains unclear. Some studies found no relationship between rs10906115 and insulin resistance or the pancreatic islet β cell function index [20, 21], while other studies claimed that genes in the vicinity of rs10906115 might encode a protein involved in cell cycle regulation [22] and a member of the Ca2+/calmodulin-dependent protein kinase 1 subfamily of serine threonine kinases [23]. In addition, we found that the haplotype TA of rs11257655 and rs10906115 was associated with an increased risk of incident T2DM, while the haplotype CG was a protective factor. Our data also showed that CDC123 was strongly associated with T2DM.

Previous studies reported that variants near the MC4R gene (rs17782313 and rs12970134) were associated with BMI/obesity [24, 25] but were inconsistently associated with the risk of T2DM among different ethnic populations [25, 26]. A meta-study confirmed that 2 SNPs were associated with T2DM after adjustment for BMI [27]. MC4R gene variants might alter insulin assistance [27], food intake, and energy consumption [28]. Our study provided evidence that individuals who carried the AA genotype of rs12970134 had a higher risk of incident T2DM (adjusted HR = 2.64; 95% CI: 1.36–5.14) although the AA genotype was rare (2.41%) in this population.

This study has some strengths and limitations. We used a prospective community-based cohort study design to confirm the susceptive tag SNPs for incident T2DM. Alleles and genotypes of those tag SNPs for incident T2DM were measured at the same time. In addition, all call rates were >98% with a >99% concordance of duplicate samples, which suggested satisfactory genotyping accuracy. There were still some limitations. First, the follow-up rate was only 60.3%, which might cause a selection bias in the context of some different demographic, socioeconomic, and lifestyle factors as well as health conditions between participants who were followed up and those who were lost to follow-up. Second, the incident cases of T2DM were identified by FPG or through the local EHRs or self-reporting, and there was a possibility of underdiagnosis. Third, the sample size was relatively small relative to the number of tag SNPs and was not large enough to explore the gene-gene or gene-environment interactions. After the Bonferroni correction for multiple comparisons, some SNPs, including rs243021, rs7041847, and rs3923113, in association with the incident T2DM were no longer statistically significant.

In conclusion, the T allele of rs17584499 in PTPRD, the T allele of rs11257655 and the A allele of rs10906115 in CDC123, and the A allele of rs12970134 in MC4R were significantly associated with an increased risk of incident T2DM in a rural Han Chinese population. We confirmed that the TA and CG haplotypes of rs11257655 and rs10906115 and the CT haplotypes of rs111875 and rs5015480 were associated with the development of T2DM.

We would like to thank all participants enrolled in this study and all the health workers of Deqing County Center for Disease Prevention and Control for their contribution to the study.

The Institutional Review Board of the Fudan University School of Public Health approved the study (IRB#0706-0099 and #2014-03-0480), and all participants gave written informed consent.

The authors declare no conflict of interest.

This work was supported by the National Nature Science Foundation of China (Grant No. 81473038), Shanghai Leading Academic Discipline Project of Public Health (Grant No. 15GWZK0801), National Key Research and Development Program (Grant No. 2016YFC1305503), and the Shanghai 3-Year Public Health Action Plan (Grant No. GWTD2015S04). The authors confirm independence from the sponsors; the content of this article has not been influenced by the sponsors.

Yun Chen contributed to the study conduct, statistical analysis, results interpretation, and draft and revision of the manuscript. X.Y.C., X.L.D., Y.Z.W., N.W., J.F.Z., and Q.W.J. contributed to the study concept and design, results interpretation, and revision of the manuscript. Yue Chen and C.W.F. provided overall guidance and contributed to the study concept and design, results interpretation, and substantial revisions to the manuscript. All authors approved the final version for publication. C.W.F. and Yue Chen are the guarantors of this work and, as such, had full access to all of the data in the study, and they take responsibility for the integrity of the data and the accuracy of the data analysis.

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