Known T2D Susceptible Loci
The etiology of T2D is known to have a considerable genetic component. According to estimates, the heritability of T2D ranges from 30 to 70% (15). According to the Framingham Offspring Study, the diabetes risk OR for an individual with one affected parent was 3.4–3.5, and the OR increased to 6.1 when both parents were affected (16). Twin studies have also revealed that the concordance rate was higher in monozygotic twins (0.29–1.00) than in dizygotic twins (0.10–0.43), indicating a significant genetic basis for T2D (17). Furthermore, there is a significant difference in the prevalence of T2D among different ethnic groups. European and Asian populations are mildly and moderately susceptible to T2D, respectively. However, Pima Indians have a very high prevalence of T2D, and approximately 50% of adults above 35 years of age having T2D.
Over the past two decades, linkage analyses, candidate gene approaches, and large-scale GWAS have successfully identified more than 100 genes that confer susceptibility to T2D among the world’s major ethnic populations (see Fig. 4 for a summary of common T2D loci), most of which were discovered in European populations. However, less than 50% of these European-derived loci have been successfully confirmed in East Asian populations. The studies in Chinese (18,19) investigating these variants discovered in the European populations in the Han Chinese population found that only the variants near PPARG, KCNJ11, CDKAL1, CDKN2A/B, IDE-KIF11-HHEX, IGF2BP2, SLC30A8, HNF1B, DUSP9, ZFAND3, FTO, and TCF7L2 were associated with T2D. The failure of the replication study may be due to discrepancies in the allelic frequencies and effect sizes in different ethnic groups. Therefore, there is a need to identify specific genes that are associated with T2D in other ethnic populations.
In 2008, the first GWAS of T2D in East Asian populations were concurrently conducted by two independent Japanese groups and confirmed that KCNQ1, which was previously implicated in insulin secretion, was a novel T2D susceptibility locus with the OR ranging from 1.26 to 1.41 (20,21). KCNQ1 encodes the pore-forming α-subunit of the voltage-gated K+ channel, which is mainly expressed in the cardiac muscles and pancreas. The association between KCNQ1 and T2D was further confirmed in Korean (22), Chinese (23), and Singaporean (24) populations. Therefore, KCNQ1 is considered the strongest locus for T2D in populations of East Asian ethic origin. Subsequently, additional GWAS have been performed in East Asian populations. In 2010, five new loci, namely, PTPRD, SRR, SPRY2, UBE2E2, and C2CD4A/B, were demonstrated to confer T2D risk in East Asian populations (25–27). In 2012, Cho et al. (28) performed a meta-analysis of three-stage GWAS in East Asian populations and identified eight novel T2D-associated loci. These loci were mapped in or near GLIS3, PEPD, FITM2-R3HDML-HNF4A, KCNK16, MAEA, GCC1-PAX4, PSMD6, and ZFAND3 and increased the T2D risk by 8–13%. Subsequently, three new T2D loci were discovered in Han Chinese populations as susceptible loci for T2D in 2013, namely, PAX4 (29), GRK5 (30) and RASGRP1 (30). Notably, the rs10229583 variant, which is located at 7q32 near PAX4 and plays a critical role in the development of pancreatic β-cells, could increase the T2D risk by 18%.
Although many genetic loci have been shown to confer susceptibility to T2D, the mechanism by which these loci participate in the pathogenesis of T2D remains unknown. Most T2D loci are located near genes that are related to β-cell function, including TCF7L2, WFS1, KCNJ11, HNF1B, IGF2BP2, CDKN2A/B, SLC30A8, HHEX/IDE, CDKAL1, KCNQ1, THADA, TSPAN8/LGR5, CDC123/CAMK1D, JAZF1, MTNR1B, GCK, PROX1, DGKB/TMEM195, ADCY5, CENTD2, SRR, ST6GAL1, KCNK16, HNF4A, FITM2-R3HDML-HNF4A, GLIS3, ANK1, BCAR1, GRB14, RASGRP1, and TMEM163, whereas PPARG, ADAMTS9, GCKR, IRS1, PTPRD, DUSP9, RBMS1/ITGB6, HMGA2, KLF14, GRB14, ANKRD55, and GRK5 have an impact on the action of insulin, emphasizing the important role of β-cell dysfunction in the pathogenesis of T2D.
In addition, most single nucleotide polymorphisms (SNPs) contributing to the T2D risk are located in introns, but whether these SNPs directly modify gene expression or are involved in linkage disequilibrium with unknown causal variants remains to be investigated. Furthermore, the loci discovered thus far collectively account for less than 15% of the overall estimated genetic heritability. More studies, including trans-ethnic mapping, and the use of new technologies, such as deep sequencing and whole-exome sequencing, are needed to further identify the underlying genetic factors of T2D in Chinese and other populations.
Clinical Utility of Genetic Information: Prediction of T2D
The early identification of individuals at a high risk of T2D can prevent or delay the onset of T2D through effective lifestyle and pharmacological interventions. Models based solely on conventional risk factors have achieved only a moderate predictive performance (31). Therefore, T2D risk models incorporating genetic information are needed to increase the predictive performance.
Numerous studies have constructed a genetic risk score (GRS) to evaluate the predictive ability of current genetic information. The GRS combines information from multiple variants to represent an individual’s genetic predisposition to T2D. Between 2006 and 2016, 38 studies were performed to construct a GRS model and evaluate the performance of the GRS model in predicting the prevalence or incidence of T2D. Of these studies, 17 were case-control or cross-sectional studies, 18 were prospective cohort studies, 2 were nested case-control studies, and 1 was a mixed cohort and case-control study.
Case-Control and Cross-Sectional Studies.
Studies exploring the performance of T2D genetic risk models were first conducted in European populations. These studies incorporated 3–38 SNPs and reported that each additional risk allele was associated with a 7–28% increased risk of T2D. In 2009, Hu et al. (19) first constructed a GRS model using 11 SNPs and assessed the predictive effect of this model in a Chinese population (1,523 control vs. 1,359 case subjects). The results showed that the OR for T2D per risk allele was 1.265 (95% CI 1.214–1.318). Similarly, Imamura et al. (32) investigated a GRS model combining 49 SNPs in a Japanese population (1,786 control vs. 2,613 case subjects) and found that individuals with a GRS ≥60 were approximately 10-fold more likely to develop T2D than those with a GRS <46. Thus, a GRS model combining multiple coexisting genetic variants may be useful for identifying individuals at high genetic risk for developing T2D.
Genetic risk models have also been useful in predicting incident T2D in prospective studies. In 2013, Andersson et al. (33) genotyped 46 variants and subsequently constructed a GRS model in a Danish population (n = 5,850 at baseline). The risk of incident T2D was increased with a hazard ratio (HR) of 1.06 (95% CI 1.03–1.08) per risk allele during a median follow-up of 11 years. In 2014, another study used 65 and 89 SNPs to construct two GRS models (GRS-1, 65 European-derived loci associated with T2D; GRS-2, GRS-1 combined with 24 fasting plasma glucose–raising SNPs) and explored the contribution of the GRS to the incidence of T2D in 4,075 individuals in the Data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) study over a 9-year follow-up period (34). The two GRS models were both significantly associated with an increased incidence of T2D (per allele: GRS-1, HR 1.07, 95% CI 1.03–1.10; GRS-2, HR 1.05, 95% CI 1.02–1.08).
However, few studies focusing on the capacity of the GRS to predict the incidence of T2D in East Asian populations have been conducted. In 2013, Kwak et al. (35) constructed a weighted GRS (wGRS) model based on 48 T2D genetic variants in a prospective cohort study involving 395 Korean women with gestational diabetes mellitus. After a median follow-up period of 3.8 years, the women with gestational diabetes mellitus who developed diabetes had a significantly higher wGRS than those who did not develop diabetes (9.36 ± 0.92 vs. 8.78 ± 1.07, P < 1.56 × 10−7). Recently, one study (36) genotyped 89 SNPs to determine T2D susceptibility in a Chinese cross-sectional population (n = 6,822) and then selected 40 SNPs that were significantly associated with the diabetes risk (P < 0.05) to construct a wGRS. These authors found that the wGRS could predict the incidence of T2D and impaired glucose regulation in the Cox model (HR 1.129, 95% CI 1.026–1.242) in a Chinese 9-year prospective cohort study (n = 2,495). Moreover, the wGRS predicted blood glucose deterioration due to the changes in function of β-cells (β −0.0480, P = 9.66 × 10−5 and β −0.0303, P = 3.32 × 10−5 for Stumvoll first- and second-phase insulin secretion, respectively).
Predictive Performance of Genetic Variants Compared With Conventional Clinical Risk Models
The areas under the receiver operating characteristic curves (AUCs) are usually used to assess the discriminative accuracy of an approach. The AUC values range from 0.5 to 1.0, where an AUC of 0.5 represents a lack of discrimination and an AUC of 1 represents perfect discrimination. An AUC ≥0.75 is considered clinically useful. The dominant conventional risk factors, including age, sex, BMI, waist circumference, blood pressure, family history of diabetes, physical activity level, smoking status, and alcohol consumption, can be combined to construct conventional risk factor–based models (CRM). Several studies have compared the predictive capacities of models with and without genetic information. The addition of genetic markers to a CRM could slightly improve the predictive performance. For example, one European study showed that the addition of an 11-SNP GRS to a CRM marginally improved the risk prediction (AUC was 0.74 without and 0.75 with the genetic markers, P < 0.001) in a prospective cohort of 16,000 individuals (37). A meta-analysis (38) consisting of 23 studies investigating the predictive performance of T2D risk models also reported that the AUCs only slightly increased with the addition of genetic information to the CRM (median AUC was increased from 0.78 to 0.79).
The potential explanation for this phenomenon is that genetic variants may have exerted their effect on the onset of T2D through certain conventional risk factors (39). For example, a family history of T2D, which is a major risk factor included in most CRMs, could capture the genetic information provided by the GRS (39). Moreover, gene–gene and gene–environment interactions also contribute to the diabetes epidemic and should be considered to assess the enhanced predictive capacity of genetic variants.
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