Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population

Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank

AIMS

Type 2 diabetes (T2D) is affected by a combination of genetic and environmental factors. However, the comprehensive genomic risk scores (GRSs) for T2D prediction have not been evaluated.

METHODS

Using a meta-scoring approach, we developed a metaGRS for T2D; T2D-related traits consist of 1,692 genetic variants in the UK Biobank training set (n = 40,423 + 7,558 events) and evaluate this score in the validation set (n = 303,053).

RESULTS

The hazard ratio (HR) for T2D was 1.32 (95% confidence interval [CI]: 1.29-1.35) per standard deviation of metaGRS and was larger than previously published T2D-GRS. Individuals, in the top 25% of metaGRS, have an HR of 2.08 (95%CI: 1.93-2.23) compared with those in the bottom 25%. The addition of metaGRS to all conventional risk factors significantly increased the AUC (P < 0.001). Adding metaGRS to all conventional risk factors significantly improved the reclassification accuracy (continuous net reclassification improvement = 11.8%, 95%CI: 9.2%-14.2%). All analyses adjusted for age, sex, and 10PCs.

CONCLUSIONS

The metaGRS significantly improves T2D prediction ability.

A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors

Background

Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D.

Objective

To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans.

Methods

This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC).

Results

The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01).

Conclusion

The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.

Tissue inhibitor matrix metalloproteinase 1 and risk of type 2 diabetes in a Chinese population

INTRODUCTION

The non-invasive enhanced liver fibrosis (ELF) score-comprising tissue inhibitor of matrix metalloproteinases-1 (TIMP1), hyaluronic acid (HA) and amino-terminal propeptide of type III procollagen (PIIINP)-has been shown to accurately predict fibrosis stages among patients with non-alcoholic fatty liver disease (NAFLD). However, no study has examined whether the ELF score or its components would also be predictive of type 2 diabetes, which commonly coexists and shares the same pathogenic abnormalities with NAFLD. Therefore, we prospectively investigated their associations with type 2 diabetes risks for the first time.

RESEARCH DESIGN AND METHODS

The ELF score was measured among 254 type 2 diabetes cases and 254 age-matched and sex-matched controls nested within the prospective Singapore Chinese Health Study. Cases had hemoglobin A1c (HbA1c) levels <6.5% at blood collection (1999-2004) and reported to have diabetes during follow-up II (2006-2010). Controls had HbA1c levels <6.0% at blood-taking and remained free of diabetes at follow-up II. Multivariable conditional logistic regression models were used to assess the ELF-diabetes association.

RESULTS

Higher TIMP1 levels were associated with increased type 2 diabetes risk, and the OR comparing the highest versus lowest quartiles was 2.56 (95% CI 1.23 to 5.34; p trend=0.035). However, ELF score, PIIINP and HA were not significantly associated with type 2 diabetes risks.

CONCLUSIONS

Higher TIMP1 levels, but not ELF score, PIIIMP and HA, were associated with increased type 2 diabetes risk in Chinese adults. Our results suggested that elevated TIMP1 levels may contribute to the type 2 diabetes development through pathways other than liver fibrosis.

Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication

The incidence of type 2 diabetes (T2D) has rapidly increased over recent decades, and T2D has become a leading public health challenge in China. Compared with European descents, Chinese patients with T2D are diagnosed at a relatively young age and low BMI. A better understanding of the factors contributing to the diabetes epidemic is crucial for determining future prevention and intervention programs. In addition to environmental factors, genetic factors contribute substantially to the development of T2D. To date, more than 100 susceptibility loci for T2D have been identified. Individually, most T2D genetic variants have a small effect size (10-20% increased risk for T2D per risk allele); however, a genetic risk score that combines multiple T2D loci could be used to predict the risk of T2D and to identify individuals who are at a high risk. Furthermore, individualized antidiabetes treatment should be a top priority to prevent complications and mortality. In this article, we review the epidemiological trends and recent progress in the understanding of T2D genetic etiology and further discuss personalized medicine involved in the treatment of T2D.

Quantitative Relationship Between Cumulative Risk Alleles Based on Genome-Wide Association Studies and Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis

Many epidemiological studies have assessed the genetic risk of having undiagnosed or of developing type 2 diabetes mellitus (T2DM) using several single nucleotide polymorphisms (SNPs) based on findings of genome-wide association studies (GWAS). However, the quantitative association of cumulative risk alleles (RAs) of such SNPs with T2DM risk has been unclear. The aim of this meta-analysis is to review the strength of the association between cumulative RAs and T2DM risk. Systematic literature searches were conducted for cross-sectional or longitudinal studies that examined odds ratios (ORs) for T2DM in relation to genetic profiles. Logarithm of the estimated OR (log OR) of T2DM for 1 increment in RAs carried (1-ΔRA) in each study was pooled using a random-effects model. There were 46 eligible studies that included 74,880 cases among 249,365 participants. In 32 studies with a cross-sectional design, the pooled OR for T2DM morbidity for 1-ΔRA was 1.16 (95% confidence interval [CI], 1.13–1.19). In 15 studies that had a longitudinal design, the OR for incident T2DM was 1.10 (95% CI, 1.08–1.13). There was large heterogeneity in the magnitude of log OR (P < 0.001 for both cross-sectional studies and longitudinal studies). The top 10 commonly used genes significantly explained the variance in the log OR (P = 0.04 for cross-sectional studies; P = 0.006 for longitudinal studies). The current meta-analysis indicated that carrying 1-ΔRA in T2DM-associated SNPs was associated with a modest risk of prevalent or incident T2DM, although the heterogeneity in the used genes among studies requires us to interpret the results with caution.

Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction

Type 2 diabetes (T2D) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome-wide association studies (GWAS). Application of meta-analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D, and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%-20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D.

Development of a learning-oriented computer assisted instruction designed to improve skills in the clinical assessment of the nutritional status: a pilot evaluation

Computer assisted instruction (CAI) is an effective tool for evaluating and training students and professionals. In this article we will present a learning-oriented CAI, which has been developed for students and health professionals to acquire and retain new knowledge through the practice. A two-phase pilot evaluation was conducted, involving 8 nutrition experts and 30 postgraduate students, respectively. In each training session, the software developed guides users in the integral evaluation of a patient's nutritional status and helps them to implement actions. The program includes into the format clinical tools, which can be used to recognize possible patient's needs, to improve the clinical reasoning and to develop professional skills. Among them are assessment questionnaires and evaluation criteria, cardiovascular risk charts, clinical guidelines and photographs of various diseases. This CAI is a complete software package easy to use and versatile, aimed at clinical specialists, medical staff, scientists, educators and clinical students, which can be used as a learning tool. This application constitutes an advanced method for students and health professionals to accomplish nutritional assessments combining theoretical and empirical issues, which can be implemented in their academic curriculum.

Genetic Determinants of Type 2 Diabetes in Asians

Type 2 diabetes (T2D) has become a major health problem throughout the world and the epidemic is particularly severe in Asian countries. Compared with European populations, Asians tend to develop diabetes at a younger age and at much higher incidence rates given the same amount of weight gain. Genome-wide association studies (GWAS) have identified over 70 loci associated with T2D. Although the majority of GWAS results were conducted in populations of European ancestry, recent GWAS in Asians have made important contributions to the identification of T2D susceptibility loci. These studies not only confirmed T2D susceptibility loci initially identified in European populations, but also identified novel susceptibility loci that provide new insights into the pathophysiology of diseases. In this article, we review GWAS results of T2D conducted in East and South Asians and compare them to those of European populations. Currently identified T2D genetic variants do not appear to explain the phenomenon that Asians are more susceptible to T2D than European populations, suggesting further studies in Asian populations are needed.

Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies

Background: There is increasing interest in investigating genetic risk models in empirical studies, but such studies are premature when the expected predictive ability of the risk model is low. We assessed how accurately the predictive ability of genetic risk models can be estimated in simulated data that are created based on the odds ratios (ORs) and frequencies of single-nucleotide polymorphisms (SNPs) obtained from genome-wide association studies (GWASs).

Methods: We aimed to replicate published prediction studies that reported the area under the receiver operating characteristic curve (AUC) as a measure of predictive ability. We searched GWAS articles for all SNPs included in these models and extracted ORs and risk allele frequencies to construct genotypes and disease status for a hypothetical population. Using these hypothetical data, we reconstructed the published genetic risk models and compared their AUC values to those reported in the original articles.

Results: The accuracy of the AUC values varied with the method used for the construction of the risk models. When logistic regression analysis was used to construct the genetic risk model, AUC values estimated by the simulation method were similar to the published values with a median absolute difference of 0.02 [range: 0.00, 0.04]. This difference was 0.03 [range: 0.01, 0.06] and 0.05 [range: 0.01, 0.08] for unweighted and weighted risk scores.

Conclusions: The predictive ability of genetic risk models can be estimated using simulated data based on results from GWASs. Simulation methods can be useful to estimate the predictive ability in the absence of empirical data and to decide whether empirical investigation of genetic risk models is warranted.

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in high-risk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.

Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes

Background

Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population.

Methodology

We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI).

Results

We observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10⁻¹⁸<P<8.5×10⁻³), as well as nominal associations of NOTCH2, JAZF1, KCNJ11 and HNF1B (0.05<P<0.1) with T2D risk, which yielded odds ratios ranging from 1.07 to 2.09. The 8 significant SNPs exhibited joint effect on increasing T2D risk, fasting plasma glucose and use of insulin therapy as well as reducing HOMA-β, BMI, waist circumference and younger age of diagnosis of T2D. The addition of CGS marginally increased AUC (2%) but significantly improved the predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach (P<0.001).

Conclusion

In a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).

Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.

Added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review

AIMS

To provide a systematic overview of the added value of novel circulating and genetic biomarkers in predicting type 2 diabetes (T2DM).

METHODS

We searched MEDLINE and EMBASE (January 2000 to September 2012) for studies that reported a measure of improvement in the performance of T2DM risk prediction models subsequent to adding novel biomarkers to traditional risk factors. We extracted data on study methods and metrics of incremental predictive value of novel biomarkers.

RESULTS

We included 34 publications from 30 studies. All studies reported a change in the area under the receiver-operating characteristic curve, which was modest, ranging from -0.004 to 0.1, with claims of statistically significant improvements in eleven studies. The net reclassification index was evaluated in 11 studies, and ranged from -2.2% to 10.2% after inclusion of genetic markers in six studies (statistically significant in two cases), and from -0.5% to 27.5% after inclusion of non-genetic markers in five studies (non-significant in two studies). The integrated discrimination index (0-2.04) was reported in eight studies, being statistically significant in five of these.

CONCLUSIONS

Currently known novel circulating and genetic biomarkers do not substantially improve T2DM risk prediction above and beyond the ability of traditional risk factors.

Association of genetic variants with isolated fasting hyperglycaemia and isolated postprandial hyperglycaemia in a Han Chinese population

BACKGROUND

Though multiple single nucleotide polymorphisms (SNPs) associated with type 2 diabetes have been identified, the genetic bases of isolated fasting hyperglycaemia (IFH) and isolated postprandial hyperglycaemia (IPH) were still unclear. In present study, we aimed to investigate the association of genome-wide association study-validated genetic variants and IFH or IPH in Han Chinese.

METHODS/PRINCIPAL FINDINGS

We genotyped 27 validated SNPs in 6,663 unrelated individuals comprising 341 IFH, 865 IPH, 1,203 combined fasting hyperglycaemia and postprandial hyperglycaemia, and 4,254 normal glycaemic subjects of Han ancestry. The distributions of genotype frequencies of FTO, CDKAL1 and GCKR were significant different between individuals with IFH and those with IPH (SNP(ptrend ): rs8050136(0.0024), rs9939609(0.0049), rs7756992(0.0122), rs780094(0.0037)). Risk allele of FTO specifically increased the risk of IFH (rs8050136: OR 1.403 [95% CI 1.125-1.750], p = 0.0027; rs9939609: 1.398 [1.120-1.744], p = 0.0030). G allele of CDKAL1 specifically increased the risk of IPH (1.217 [1.092-1.355], p = 0.0004). G allele of GCKR increased the risk of IFH (1.167 [0.999-1.362], p = 0.0513), but decreased the risk of IPH (0.891 [0.801-0.991], p = 0.0331). In addition, TCF7L2 and KCNQ1 increased the risk of both IFH and IPH. When combined, each additional risk allele associated with IFH increased the risk for IFH by 1.246-fold (p<0.0001), while each additional risk allele associated with IPH increased the risk for IPH by 1.190-fold (p<0.0001).

CONCLUSION/SIGNIFICANCE

Our results indicate that genotype distributions of variants from FTO, GCKR, CDKAL1 were different between IPH and IFH in Han Chinese. Variants of genes modulating insulin sensitivity (FTO, GCKR) contributed to the risk of IFH, while variants of genes related to beta cell function (CDKAL1) increase the risk of IPH.

Association study of genetic variants of 17 diabetes-related genes/loci and cardiovascular risk and diabetic nephropathy in the Chinese She population

BACKGROUND

Genetic determinations are important in type 2 diabetes (T2DM) pathology. We investigated associations between genetic variants of 17 diabetes-related genes/loci, T2DM and diabetic complications in Chinese She subjects.

METHODS

A comprehensive gene-based association study was conducted using 17 single nucleotide polymorphisms in Chinese She subjects with normal glucose tolerance (n = 1119), impaired glucose regulation (n = 1767), and T2DM (n = 443). We applied major abnormal Minnesota Code findings to predict cardiovascular risk and estimated glomerular filtration rate to assess kidney function.

RESULTS

Nine variants in FTO rs8050136, WFS1 rs10010131, CDKN2A/B rs10811661, KCNJ11 rs5219, CDC123/CAMK1D rs12779790, JAZF1 rs864745, SLC30A8 rs13266634, CDKAL1 rs10946398, and HHEX/IDE rs5015480 were significantly associated with T2DM (P < 0.05). Single nucleotide polymorphisms in WFS1 rs10010131, CDKN2A/B rs10811661, CDC123/CAMK1D rs12779790, JAZF1 rs864745, FTO rs8050136, and HHEX/IDE rs5015480 were associated with T2DM and impaired glucose regulation. Risk alleles in WFS1 rs10010131, IGF2BP2 rs4402960, CDKAL1 rs10946398, FTO rs8050136, KCNQ1 rs2237897, and ADAMTS9 rs4607103 were significantly associated with decreased homeostatic model assessment (HOMA)-β (P < 0.05). After adjusting for age, gender and body mass index, genetic variants JAZF1 rs864745, FTO rs8050136, and HHEX/IDE rs5015480 were significantly related to reduced estimated glomerular filtration rate (P < 0.05). Genetic variants in WFS1 rs10010131, CDKN2A/B rs10811661, CDC123/CAMID rs12779790, JAZF1 rs864745, FTO rs80501360, CDKAL1 rs10946398, and HHEX/IDE rs5015480 correlated with abnormal major Minnesota Code findings (P < 0.05).

CONCLUSION

Variants in WFS1, CDKN2A/B, KCNJ11, CDC123/CAMK1D, JAZF1, SLC30A8, FTO, CDKAL1, and HHEX/IDE genes are significantly associated with T2DM in She Chinese subjects. JAZF1, FTO, CDKAL1, and HHEX/IDE are associated with diabetic nephropathy. WFS1, CDKN2A/B, CDC123/CAMK1D, JAZF1, FTO, CDKAL1, and HHEX/IDE are associated with cardiovascular risk.

Effect of Genetic Polymorphisms on Development of Gout

Objective.

To validate the association between genetic polymorphisms and gout in Japanese patients, and to investigate the cumulative effects of multiple genetic factors on the development of gout.

Methods.

Subjects were 153 Japanese male patients with gout and 532 male controls. The genotypes of 11 polymorphisms in the 10 genes that have been indicated to be associated with serum uric acid levels or gout were determined. The cumulative effects of the genetic polymorphisms were investigated using a weighted genotype risk score (wGRS) based on the number of risk alleles and the OR for gout. A model to discriminate between patients with gout and controls was constructed by incorporating the wGRS and clinical factors. C statistics method was applied to evaluate the capability of the model to discriminate gout patients from controls.

Results.

Seven polymorphisms were shown to be associated with gout. The mean wGRS was significantly higher in patients with gout (15.2 ± 2.01) compared to controls (13.4 ± 2.10; p < 0.0001). The C statistic for the model using genetic information alone was 0.72, while the C statistic was 0.81 for the full model that incorporated all genetic and clinical factors.

Conclusion.

Accumulation of multiple genetic factors is associated with the development of gout. A prediction model for gout that incorporates genetic and clinical factors may be useful for identifying individuals who are at risk of gout.

Diabetes genetic predisposition score and cardiovascular complications among patients with type 2 diabetes

OBJECTIVE

To examine the association between genetic predisposition to type 2 diabetes (T2D) and risk of cardiovascular disease (CVD) among patients with T2D.

RESEARCH DESIGN AND METHODS

The current study included 1,012 men and 1,310 women with T2D from the Health Professionals Follow-up Study and Nurses' Health Study, including 677 patients with CVD and 1,645 non-CVD control subjects. A genetic predisposition score (GPS) was calculated on the basis of 36 established independent diabetes-predisposing variants.

RESULTS

Each additional diabetes-risk allele in the GPS was associated with a 3% increased risk of CVD (odds ratio [OR] 1.03 [95% CI 1.00-1.06]). The OR was 1.47 (1.11-1.95) for CVD risk by comparing extreme quartiles of the GPS (P for trend = 0.01). We also found that the GPS was positively associated with hemoglobin A(1c) levels (P = 0.009).

CONCLUSIONS

Genetic predisposition to T2D is associated with an increased risk of cardiovascular complications in patients with T2D.

Joint effect of genetic and lifestyle risk factors on type 2 diabetes risk among Chinese men and women

More than 40 genetic susceptibility loci have been reported for type 2 diabetes (T2D). Recently, the combined effect of genetic variants has been investigated by calculating a genetic risk score. We evaluated 36 genome-wide association study (GWAS) identified SNPs in 2,679 T2D cases and 3322 controls in middle-age Han Chinese. Fourteen SNPs were significantly associated with T2D in analysis adjusted for age, sex and BMI. We calculated two genetic risk scores (GRS) (GRS1 with all the 36 SNPs and GRS2 with the 14 SNPs significantly associated with T2D). The odds ratio for T2D with each GRS point (per risk allele) was 1.08 (95% CI: 1.06–1.09) for GRS1 and 1.15 (95% CI: 1.13–1.18) for GRS2. The OR for quintiles were 1.00, 1.26, 1.69, 1.95 and 2.18 (P<0.0001) for GRS1 and 1.00, 1.33, 1.60, 2.03 and 2.80 (P<0.001) for GRS2. Participants in the higher tertile of GRS1 and the higher BMI category had a higher risk of T2D compared to those on the lower tertiles of the GRS1 and of BMI (OR = 11.08; 95% CI: 7.39–16.62). We found similar results when we investigated joint effects between GRS1 and WHR terciles and exercise participation. We finally investigated the joint effect between tertiles of GRSs and a composite high risk score (no exercise participation and high BMI and WHR) on T2D risk. We found that compared to participants with low GRS1 and no high risk factors for T2D, those with high GRS1 and three high risk factors had a higher risk of T2D (OR = 13.06; 95% CI: 8.65–19.72) but the interaction factor was of marginal significance. The association was accentuated when we repeated analysis with the GRS2. In conclusion we found an association between GRS and lifestyle factors, alone and in combination, contributed to the risk of and T2D among middle age Chinese.

Genetic advances of type 2 diabetes in Chinese populations

In recent decades, the prevalence of type 2 diabetes in China has increased significantly, underscoring the importance of investigating the etiological mechanisms, including genetic determinants, of the disease in Chinese populations. Numerous loci conferring susceptibility to type 2 diabetes (T2D) have been identified worldwide, with most having been identified in European populations. In terms of ethnic heterogeneity in pathogenesis as well as disease predisposition, it is imperative to explore the specific genetic architecture of T2D in Han Chinese. Replication studies of European-derived susceptibility loci have been performed, validating 11 of 32 loci in Chinese populations. Genetic investigations into heritable traits related to glucose metabolism are expected to provide new insights into the pathogenesis of T2D, and such studies have already inferred some new susceptibility loci. Other than replication studies of European-derived loci, efforts have been made to identify specific susceptibility loci in Chinese populations using methods such as genome-wide association studies. These efforts have identified additional new loci for the disease. Genetic studies can facilitate the prediction of risk for T2D and also promote individualized anti-diabetic treatment. Despite many advances in the field of risk prediction and pharmacogenetics, the pace of clinical application of these findings is rather slow. As a result, more studies into the practical utility of these findings remain necessary.

Genetics of type 2 diabetes in European populations

Type 2 diabetes (T2D) has become a leading health problem throughout the world. It is caused by environmental and genetic factors, as well as interactions between the two. However, until very recently, the T2D susceptibility genes have been poorly understood. During the past 5 years, with the advent of genome-wide association studies (GWAS), a total of 58 T2D susceptibility loci have been associated with T2D risk at a genome-wide significance level (P < 5 × 10(-8) ), with evidence showing that most of these genetic variants influence pancreatic β-cell function. Most novel T2D susceptibility loci were identified through GWAS in European populations and later confirmed in other ethnic groups. Although the recent discovery of novel T2D susceptibility loci has contributed substantially to our understanding of the pathophysiology of the disease, the clinical utility of these loci in disease prediction and prognosis is limited. More studies using multi-ethnic meta-analysis, gene-environment interaction analysis, sequencing analysis, epigenetic analysis, and functional experiments are needed to identify new susceptibility T2D loci and causal variants, and to establish biological mechanisms.

Analytical and simulation methods for estimating the potential predictive ability of genetic profiling: a comparison of methods and results

Various modeling methods have been proposed to estimate the potential predictive ability of polygenic risk variants that predispose to various common diseases. However, it is unknown whether differences between them affect their conclusions on predictive ability. We reviewed input parameters, assumptions and output of the five most common methods and compared their estimates of the area under the receiver operating characteristic (ROC) curve (AUC) using hypothetical data representing effect sizes and frequencies of genetic variants, population disease risk and number of variants. To assess the accuracy of the estimated AUCs, we aimed to reproduce the AUCs of published empirical studies. All methods assumed that the combined effect of genetic variants on disease risk followed a multiplicative risk model of independent genetic effects, but they either assumed per allele, per genotype or dominant/recessive effects for the genetic variants. Modeling strategy and input parameters differed. Methods used simulation analysis or analytical formulas with effect sizes quantified by odds ratios (ORs) or relative risks. Estimated AUC values were similar for lower ORs (<1.2). When AUCs were larger (>0.7) due to variants with strong effects, differences in estimated AUCs between methods increased. The simulation methods accurately reproduced the AUC values of empirical studies, but the analytical methods did not. We conclude that despite differences in input parameters, the modeling methods estimate similar AUC for realistic values of the ORs. When one or more variants have stronger effects and AUC values are higher, the simulation methods tend to be more accurate.

The combined effect of the T2DM susceptibility genes is an important risk factor for T2DM in non-obese Japanese: a population based case-control study

Background

Type 2 diabetes mellitus (T2DM) is a complex endocrine and metabolic disorder. Recently, several genome-wide association studies (GWAS) have identified many novel susceptibility loci for T2DM, and indicated that there are common genetic causes contributing to the susceptibility to T2DM in multiple populations worldwide. In addition, clinical and epidemiological studies have indicated that obesity is a major risk factor for T2DM. However, the prevalence of obesity varies among the various ethnic groups. We aimed to determine the combined effects of these susceptibility loci and obesity/overweight for development of T2DM in the Japanese.

Methods

Single nucleotide polymorphisms (SNPs) in or near 17 susceptibility loci for T2DM, identified through GWAS in Caucasian and Asian populations, were genotyped in 333 cases with T2DM and 417 control subjects.

Results

We confirmed that the cumulative number of risk alleles based on 17 susceptibility loci for T2DM was an important risk factor in the development of T2DM in Japanese population (P < 0.0001), although the effect of each risk allele was relatively small. In addition, the significant association between an increased number of risk alleles and an increased risk of T2DM was observed in the non-obese group (P < 0.0001 for trend), but not in the obese/overweight group (P = 0.88 for trend).

Conclusions

Our findings indicate that there is an etiological heterogeneity of T2DM between obese/overweight and non-obese subjects.

Determining genetic risk factors for pediatric type 2 diabetes

The prevalence of type 2 diabetes (T2D) is increasing significantly in the pediatric population. A strong family history of the disease suggests the involvement of genetic factors for diabetes development, but defining the molecular genetics of T2D in children is difficult due to a low number of subjects and the lack of robust diagnostic criteria. Thus, genetic studies of T2D have been carried out almost exclusively in adults. In this review, the genetics of T2D is summarized and options for discovering the missing heritability explored. The review concludes with a discussion of future research that will be required for determining genetic risk factors for pediatric T2D.

Analysis of 32 common susceptibility genetic variants and their combined effect in predicting risk of Type 2 diabetes and related traits in Indians

AIMS

Recent genome-wide association studies have identified several Type 2 diabetes-related loci. We investigated the effect of susceptibility genetic variants, individually, together and in combination with conventional risk factors, on Type 2 diabetes and diabetes-related traits in Indians.

METHODS

We genotyped 33 variants in 1808 Indian patients and 1549 control subjects and performed association analyses with Type 2 diabetes and related traits using an additive model for individual variant and for genetic risk score based on 32 polymorphisms. The discriminatory value of genetic risk over conventional risk factors was analysed using receiver-operating characteristics curve analysis.

RESULTS

The allelic odds ratio ranged from 1.01 (95% CI 0.85-1.19) to 1.66 (95% CI 1.32-2.01) for single-variant analyses. Although, only 16 variants had significant odds ratios, the direction of association for others was similar to earlier reports. The odds ratio for Type 2 diabetes at each genetic risk score point was 1.11 (95% CI 1.09-1.14; P = 5.6 × 10(-17)) and individuals with extremes of genetic risk score (≥ 29.0 and ≤ 17.0) had a 7.5-fold difference in risk of Type 2 diabetes. The discrimination rate between control subjects and patients improved marginally on addition of genetic risk score to conventional risk factors (area under curve = 0.959 and 0.963, respectively; P = 0.001). Of all the quantitative traits analysed, MC4R variants showed strong association with BMI (P = 4.1 × 10(-4)), fat mass per cent (P = 2.4 × 10(-4)) and other obesity-related traits, including waist circumference and hip circumference (P = 2.0 × 10(-3) for both), as well as insulin resistance (P =0.02).

CONCLUSIONS

We replicated the association of well-established common variants with Type 2 diabetes in Indians and observed a similar association as reported in Western populations. Combined analysis of 32 variants aids identification of subgroups at increased risk of Type 2 diabetes, but adds only a minor advantage over conventional risk factors.

The effect of KCNJ11 polymorphism on the risk of type 2 diabetes: a global meta-analysis based on 49 case-control studies

Potassium inwardly rectifying channel, subfamily-J, member 11 (KCNJ11) gene encodes Kir6.2 subunits of the adenosine triphosphate (ATP)-sensitive potassium channel involved in glucose-mediated metabolic signaling pathway and has attracted considerable attention as a candidate gene for type 2 diabetes (T2D) based on its function in glucose-stimulated insulin secretion. In the past decade, a number of case-control studies have been conducted to investigate the relationship between the KCNJ11 polymorphisms and T2D. However, these studies have yielded contradictory results. To investigate this inconsistency and derive a more precise estimation of the relationship, we conducted a comprehensive meta-analysis of 64,403 cases and 122,945 controls from 49 published studies. Using the random-effects model, we found a significant association between E23K (rs5219) polymorphism and T2D risk with per-allele odds ratio of 1.13 (95% confidence interval: 1.10-1.15; p<10(-5)). Significant results were found in East Asians and Caucasians when stratified by ethnicity; whereas no significant associations were found among South Asians and other ethnic populations. In subgroup analysis by sample size, mean age and body mass index (BMI) of cases, mean BMI of controls and diagnostic criterion, significantly increased risks were found in all genetic models. This meta-analysis suggests that the E23K polymorphism in KCNJ11 is associated with elevated T2D risk, but these associations vary in different ethnic populations.

Research advances at the Institute for Nutritional Sciences at Shanghai, China

Nutrition-related health issues have emerged as a major threat to public health since the rebirth of the economy in China starting in the 1980s. To meet this challenge, the Chinese Academy of Sciences established the Institute for Nutritional Sciences (INS) at Shanghai, China ≈ 8 y ago. The mission of the INS is to apply modern technologies and concepts in nutritional research to understand the molecular mechanism and provide means of intervention in the combat against nutrition-related diseases, including type 2 diabetes, metabolic syndrome, obesity, cardiovascular diseases, and many types of cancers. Through diligent and orchestrated efforts by INS scientists, graduate students, and research staff in the past few years, the INS has become the leading institution in China in the areas of basic nutritional research and metabolic regulation. Scientists at the INS have made important progress in many areas, including the characterization of genetic and nutritional properties of the Chinese population, metabolic control associated with nutrient sensing, molecular mechanisms underlying glucose and lipid metabolism, regulation of metabolism by adipokines and inflammatory pathways, disease intervention using functional foods or extracts of Chinese herbs, and many biological studies related to carcinogenesis. The INS will continue its efforts in understanding the optimal nutritional needs for Chinese people and the molecular causes associated with metabolic diseases, thus paving the way for effective and individualized intervention in the future. This review highlights the major research endeavors undertaken by INS scientists in recent years.

Joint analysis of multiple biomarkers for identifying type 2 diabetes in middle-aged and older Chinese: a cross-sectional study

OBJECTIVE

Identifying individuals with high risk of type 2 diabetes is important. To evaluate discriminatory ability of multiple biomarkers for type 2 diabetes in a Chinese population.

METHODS

Plasma adiponectin, plasminogen activator inhibitor-1, retinol-binding protein 4, resistin, C-reactive protein, interleukin 6 (IL-6), tumour necrosis factor α receptor 2 and ferritin were measured in a population-based sample of 3189 Chinese (1419 men and 1770 women) aged 50-70 years. A weighted biomarkers risk score (BRS) was developed based on the strength of associations of these biomarkers with type 2 diabetes. The discriminatory ability was tested by the area under receiver operating characteristics curve (AUC).

RESULTS

Adiponectin, plasminogen activator inhibitor-1, IL-6 and ferritin were independently associated with the prevalence of type 2 diabetes, and they were used to calculate the biomarkers risk score (BRS). After adjustment for the confounding factors, the ORs for type 2 diabetes and impaired fasting glucose with each point increment of BRS were 1.28 (95% CI 1.22 to 1.34) and 1.16 (1.12 to 1.20), respectively. Compared with those in the lowest quintile of the BRS, the participants in the highest quintile have an OR (95% CI) of 6.67 (4.21 to 10.55) for type 2 diabetes. The area under the curve for the BRS and conventional risk factors alone was 0.73 and 0.76, respectively, and substantially increased to 0.81 after combining both BRS and conventional risk factors (p<0.001).

CONCLUSIONS

These data suggest that combining multiple biomarkers and conventional risk factors might substantially enhance the ability to identify individuals with type 2 diabetes. More prospective data are warranted to confirm this observation.

Genetics of type 2 diabetes: pathophysiologic and clinical relevance

BACKGROUND

Recent genome-wide association studies enlarged our knowledge about the genetic background of type 2 diabetes.

AIMS

This review provides an overview of the role of these novel genetic findings for the pathophysiology, prediction and treatment of type 2 diabetes.

RESULTS

The genetic susceptibility to type 2 diabetes appears to be determined by many common variants in multiple gene loci with low effect sizes. Although at least 36 diabetes-associated genes were identified, only about 10% of the heritability of type 2 diabetes can be explained. Most of the discovered gene variants have been linked to beta-cell dysfunction rather than insulin resistance, which might challenge established thinking of type 2 diabetes as a predominant disorder of insulin action. Genetic data can lead to statistically significant, but not to clinically relevant contributions to risk prediction for type 2 diabetes. Nevertheless, preliminary evidence suggests interactions between genotypes and response to lifestyle changes or drug treatment.

CONCLUSIONS

Future studies need to target the issue of hidden heritability and to detect the causal gene variants within the identified gene loci. Improved understanding of the genetic contribution to type 2 diabetes may then help addressing the questions whether genotyping is useful to predict individual diabetes risk, identifies individual responsiveness to preventive and therapeutic interventions or at least allows for breaking down type 2 diabetes into smaller, clinically meaningful subtypes.