Genetic-risk assessment of GWAS-derived susceptibility loci for type 2 diabetes in a 10 year follow-up of a population-based cohort study

A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

The clinical relevance of a polygenic risk score for type 2 diabetes mellitus in the Korean population

The clinical utility of a type 2 diabetes mellitus (T2DM) polygenic risk score (PRS) in the East Asian population remains underexplored. We aimed to examine the potential prognostic value of a T2DM PRS and assess its viability as a clinical instrument. We first established a T2DM PRS for 5490 Korean individuals using East Asian Biobank data (269,487 samples). Subsequently, we assessed the predictive capability of this T2DM PRS in a prospective longitudinal study with baseline data and data from seven additional follow-ups. Our analysis showed that the T2DM PRS could predict the transition of glucose tolerance stages from normal glucose tolerance to prediabetes and from prediabetes to T2DM. Moreover, T2DM patients in the top-decile PRS group were more likely to be treated with insulin (hazard ratio = 1.69, p value = 2.31E-02) than were those in the remaining PRS groups. T2DM PRS values were significantly high in the severe diabetes subgroup, characterized by insulin resistance and β -cell dysfunction (p value = 0.0012). The prediction models with the T2DM PRS had significantly greater Harrel's C-indices than did corresponding models without it. By utilizing prospective longitudinal study data and extensive clinical risk factor information, our analysis provides valuable insights into the multifaceted clinical utility of the T2DM PRS.

Interactions of genetic variations in FAS, GJB2 and PTPRN2 are associated with noise-induced hearing loss: a case-control study in China

BACKGROUND

This study aimed to screen and validate noise-induced hearing loss (NIHL) associated single nucleotide polymorphisms (SNPs), construct genetic risk prediction models, and evaluate higher-order gene-gene, gene-environment interactions for NIHL in Chinese population.

METHODS

First, 83 cases and 83 controls were recruited and 60 candidate SNPs were genotyped. Then SNPs with promising results were validated in another case-control study (153 cases and 252 controls). NIHL-associated SNPs were identified by logistic regression analysis, and a genetic risk model was constructed based on the genetic risk score (GRS), and classification and regression tree (CART) analysis was used to evaluate interactions among gene-gene and gene-environment.

RESULTS

Six SNPs in five genes were significantly associated with NIHL risk (p < 0.05). A positive dose-response relationship was found between GRS values and NIHL risk. CART analysis indicated that strongest interaction was among subjects with age ≥ 45 years and cumulative noise exposure ≥ 95 [dB(A)·years], without personal protective equipment, and carried GJB2 rs3751385 (AA/AB) and FAS rs1468063 (AA/AB) (OR = 10.038, 95% CI = 2.770, 47.792), compared with the referent group. CDH23, FAS, GJB2, PTPRN2 and SIK3 may be NIHL susceptibility genes.

CONCLUSION

GRS values may be utilized in the evaluation of the cumulative effect of genetic risk for NIHL based on NIHL-associated SNPs. Gene-gene, gene-environment interaction patterns play an important role in the incidence of NIHL.

Genetics of diabetes

Diabetes mellitus is a complicated disease characterized by a complex interplay of genetic, epigenetic, and environmental variables. It is one of the world's fastest-growing diseases, with 783 million adults expected to be affected by 2045. Devastating macrovascular consequences (cerebrovascular disease, cardiovascular disease, and peripheral vascular disease) and microvascular complications (like retinopathy, nephropathy, and neuropathy) increase mortality, blindness, kidney failure, and overall quality of life in individuals with diabetes. Clinical risk factors and glycemic management alone cannot predict the development of vascular problems; multiple genetic investigations have revealed a clear hereditary component to both diabetes and its related complications. In the twenty-first century, technological advancements (genome-wide association studies, next-generation sequencing, and exome-sequencing) have led to the identification of genetic variants associated with diabetes, however, these variants can only explain a small proportion of the total heritability of the condition. In this review, we address some of the likely explanations for this "missing heritability", for diabetes such as the significance of uncommon variants, gene-environment interactions, and epigenetics. Current discoveries clinical value, management of diabetes, and future research directions are also discussed.

Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study

BACKGROUND

Previous work on predicting type 2 diabetes by integrating clinical and genetic factors has mostly focused on the Western population. In this study, we use genome-wide polygenic risk score (gPRS) and serum metabolite data for type 2 diabetes risk prediction in the Asian population.

METHODS

Data of 1425 participants from the Korean Genome and Epidemiology Study (KoGES) Ansan-Ansung cohort were used in this study. For gPRS analysis, genotypic and clinical information from KoGES health examinee (n = 58,701) and KoGES cardiovascular disease association (n = 8105) sub-cohorts were included. Linkage disequilibrium analysis identified 239,062 genetic variants that were used to determine the gPRS, while the metabolites were selected using the Boruta algorithm. We used bootstrapped cross-validation to evaluate logistic regression and random forest (RF)-based machine learning models. Finally, associations of gPRS and selected metabolites with the values of homeostatic model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) were further estimated.

FINDINGS

During the follow-up period (8.3 ± 2.8 years), 331 participants (23.2%) were diagnosed with type 2 diabetes. The areas under the curves of the RF-based models were 0.844, 0.876, and 0.883 for the model using only demographic and clinical factors, model including the gPRS, and model with both gPRS and metabolites, respectively. Incorporation of additional parameters in the latter two models improved the classification by 11.7% and 4.2% respectively. While gPRS was significantly associated with HOMA-B value, most metabolites had a significant association with HOMA-IR value.

INTERPRETATION

Incorporating both gPRS and metabolite data led to enhanced type 2 diabetes risk prediction by capturing distinct etiologies of type 2 diabetes development. An RF-based model using clinical factors, gPRS, and metabolites predicted type 2 diabetes risk more accurately than the logistic regression-based model.

FUNDING

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2019M3E5D1A02070863 and 2022R1C1C1005458). This work was also supported by the 2020 Research Fund (1.200098.01) of UNIST (Ulsan National Institute of Science & Technology).

Genetic risk score is associated with T2DM and diabetes complications risks

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a prototypical complex disease with polygenic architecture playing an important role in determining susceptibility to develop the disease (and its complications) in subjects exposed to modifiable lifestyle factors. A current challenge is to quantify the degree of the individual's genetic risk using genetic risk scores (GRS) capturing the results of genome-wide association studies while incorporating possible ethnicity- or population-specific differences.

METHODS

This study included three groups of T2DM (T2DM-I, N=1,032; T2DM-II, N=353; and T2DM-III, N=399) patients and 2,481 diabetes-free subjects. The status of the microvascular and macrovascular diabetes complications were known for the T2DM-I patients. Overall, 21 single nucleotide polymorphisms (SNPs) were analyzed, and selected subsets were used to determine the GRS (both weighted - wGRS and unweighted - uGRS) for T2DM risk predictions (6 SNPs) and for predicting the risks of complications (7 SNPs).

RESULTS

The strongest T2DM markers (P<0.0001) were within the genes for TCF7L2 (transcription factor 7-like 2), FTO (fat mass and obesity associated protein) and ARAP1 (ankyrin repeat and PH domain 1). The T2DM-I subjects with uGRS values greater (Odds Ratio, 95% Confidence Interval) than six had at least twice (2.00, 1.72-2.32) the risk of T2DM development (P<0.0001), and these results were confirmed in the independent groups (T2DM-II 1.82, 1.45-2.27; T2DM-III 2.63, 2.11-3.27). The wGRS (>0.6) further improved (P<0.000001) the risk estimations for all three T2DM groups. The uGRS was also a significant predictor of neuropathy (P<0.0001), nephropathy (P<0.005) and leg ischemia (P<0.0005).

CONCLUSIONS

If carefully selected and specified, GRS, both weighted and unweighted, could be significant predictors of T2DM development, as well as the diabetes complications development.

Insulin resistance in children

PURPOSE OF REVIEW

Insulin resistance (IR) is a clinical condition due to the decline in the efficiency of insulin promoting glucose uptake and utilization. The aim of this review is to provide an overview of the current knowledge on IR in children, focusing on its physiopathology, the most appropriate methods of measurement of IR, the assessment of risk factors, the effects of IR in children, and finally giving indications on screening and treatment.

RECENT FINDINGS

IR has evolved more and more to be a global public health problem associated with several chronic metabolic diseases.

SUMMARY

Detecting a correct measurement method and specific risk predictors, in order to reduce the incidence of IR, represents a challenging goal.

Knowledge discovery in genetics of diabetes in Iran, a roadmap for future researches

Purpose

The pathogenesis of diabetes is considered polygenic as a result of complex interactions between genetic/epigenetic and environmental factors. This review intended to evaluate the scientometric and knowledge gap of diabetes genetics researches conducted in Iran as a case of developing countries, and drawn up a roadmap for future studies.

Methods

We searched Scopus and PubMed databases from January 2015 until December 2019 using the keywords: (diabetes OR diabetic) AND (Iran). All publications were reviewed by two experts and after choosing relevant articles, they were categorized based on the subject, level of evidence, study design, publication year, and type of genetic studies.

Results

Of 10,540 records, 428 articles were met the inclusion criteria. Generally, the number of researches about diabetes genetics rose since 2015. Case-control/cross-sectional and animal studies were the common types of study design and based on the subject, the most frequent researches were about genetic factors involved in diabetes development (38%). Briefly, the top seven genes that were evaluated for T2DM were TCF7L2, APOAII, FTO, PON1, ADIPOQ, MTHFR, and PPARG respectively, and also, CTL4 for T1DM. miR-21, miR-155, and miR-375 respectively were the most micro-RNAs that were evaluated. Furthermore, there were six studies about lncRNAs.

Discussion and Conclusion

Investigation about the genetic of diabetes is progressed although there are some limitations like non-homogenous data from Iran, heterogeneity of ethnicity, and rationale of studies. Compared to the previous analysis in Iran, still, GWAS and large-scale studies are required to achieve better policies for manage and control of diabetes disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-021-00838-8.

Relationship between glucose homeostasis and obesity in early life-A study of Italian children and adolescents

OBJECTIVES

Epidemic obesity is the most important risk factor for prediabetes and type 2 diabetes (T2D) in youth as it is in adults. Obesity shares pathophysiological mechanisms with T2D and is likely to share part of the genetic background. We aimed to test if weighted genetic risk scores (GRSs) for T2D, fasting glucose (FG) and fasting insulin (FI) predict glycaemic traits and if there is a causal relationship between obesity and impaired glucose metabolism in children and adolescents.

DESIGN AND PATIENTS

Genotyping of 42 SNPs established by genome-wide association studies for T2D, FG and FI was performed in 1660 Italian youths aged between 2 and 19 years. We defined GRS for T2D, FG and FI and tested their effects on glycaemic traits, including FG, FI, indices of insulin resistance/beta cell function, and body mass index (BMI). We evaluated causal relationships between obesity and FG/FI using one-sample Mendelian Randomization analyses in both directions.

RESULTS

GRS-FG associated with FG (beta = 0.075 mmol/l, SE = 0.011, P = 1.58 × 10-11) and beta cell function (beta = -0.041, SE = 0.0090 P = 5.13 × 10-6). GRS-T2D also demonstrated an association with beta cell function (beta = -0.020, SE = 0.021 P = 0.030). We detected a causal effect of increased BMI on levels of FI in Italian youths (beta = 0.31 ln (pmol/l), 95%CI [0.078, 0.54], P = 0.0085), while there was no effect of FG/FI levels on BMI.

CONCLUSION

Our results demonstrate that the glycaemic and T2D risk genetic variants contribute to higher FG and FI levels and decreased beta cell function in children and adolescents. The causal effects of adiposity on increased insulin resistance are detectable from childhood age.

Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer's disease

Alzheimer's disease (AD) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology meta-analytical approach across three human AD cohorts, encompassing six cortical brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies and quantitative trait loci to further characterize the genetic architecture of AD. We perform co-expression network analysis across more than 1200 human brain samples, identifying robust AD-associated dysregulation of the transcriptome, unaltered in normal human aging. We assess the cell-type specificity of AD gene co-expression changes and estimate cell-type proportion changes in human AD by integrating co-expression modules with single-cell transcriptome data generated from 27 321 nuclei from human postmortem prefrontal cortical tissue. We also show that genetic variants of AD are enriched in a microglial AD-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human AD gene expression datasets, which can be easily accessed using our online resource (https://swaruplab.bio.uci.edu/consensusAD).

Biomarkers for type 2 diabetes

Background

The prevalence and incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic loci in interplay with lifestyle and environmental factors. Recently over 400 distinct association signals were published; these explain 18% of the risk of T2D.

Scope of review

In this review there is a major focus on risk factors and genetic and non-genetic biomarkers for the risk of T2D identified especially in large prospective population-based studies, and studies testing causality of the biomarkers for T2D in Mendelian randomization studies. Another focus is on understanding genome-phenome interplay in the classification of individuals with T2D into subgroups.

Major conclusions

Several recent large population-based studies and their meta-analyses have identified multiple potential genetic and non-genetic biomarkers for the risk of T2D. Combination of genetic variants and physiologically characterized pathways improves the classification of individuals with T2D into subgroups, and is also paving the way to a precision medicine approach, in T2D.

Prediction model for the efficacy of folic acid therapy on hyperhomocysteinaemia based on genetic risk score methods

No risk assessment tools for the efficacy of folic acid treatment for hyperhomocysteinaemia (HHcy) have been developed. We aimed to use two common genetic risk score (GRS) methods to construct prediction models for the efficacy of folic acid therapy on HHcy, and the best gene-environment prediction model was screened out. A prospective cohort study enrolling 638 HHcy patients was performed. We used a logistic regression model to estimate the associations of two GRS methods with the efficacy. Performances were compared using area under the receiver operating characteristic curve (AUC). The simple count genetic risk score (SC-GRS) and weighted genetic risk score (wGRS) were found to be independently associated with the efficacy of folic acid treatment for HHcy. Using the SC-GRS, per risk allele increased with a 1·46-fold increased failure risk (P < 0·001) after adjustment for traditional risk factors, including age, sex, BMI, smoking, alcohol consumption, history of diabetes, history of hypertension, history of hyperlipidaemia, history of stroke and history of CHD. When used the wGRS, the association was strengthened (OR = 2·08, P < 0·001). Addition of the SC-GRS and wGRS to the traditional risk model significantly improved the predictive ability by AUC (0·859). A precise gene-environment predictive model with good performance was developed for predicting the treatment failure rate of folic acid therapy for HHcy.

Screening of noise-induced hearing loss (NIHL)-associated SNPs and the assessment of its genetic susceptibility

BACKGROUND

The aim of this study was to screen for noise-induced hearing loss (NIHL)-associated single nucleotide polymorphisms (SNPs) and to construct genetic risk prediction models for NIHL in a Chinese population.

METHODS

Four hundred seventy-six subjects with NIHL and 476 matched controls were recruited from a cross-sectional survey on NIHL in China. A total of 83 candidate SNPs were genotyped using nanofluidic dynamic arrays on a Fluidigm platform. NIHL-associated SNPs were screened with a multiple logistic model, and a genetic risk model was constructed based on the genetic risk score (GRS). The results were validated using a prospective cohort population.

RESULTS

Seven SNPs in the CDH23, PCDH15, EYA4, MYO1A, KCNMA1, and OTOG genes were significantly (P < 0.05) associated with the risk of NIHL, whereas seven other SNPs were marginally (P > 0.05 and P < 0.1) associated with the risk of NIHL. A positive correlation was observed between GRS values and odds ratio (OR) for NIHL. Two SNPs, namely, rs212769 and rs7910544, were validated in the cohort study. Subjects with higher GRS (≧9) showed a higher risk of NIHL incidence with an OR of 2.00 (95% CI = 1.04, 3.86).

CONCLUSIONS

Genetic susceptibility plays an important role in the incidence of NIHL. GRS values, which are based on NIHL-associated SNPs. GRS may be utilized in the evaluation of genetic risk for NIHL and in the determination of NIHL susceptibility.

Genetic variants associated with patent ductus arteriosus in extremely preterm infants

OBJECTIVE

Patent ductus arteriosus (PDA) is a commonly observed condition in preterm infants. Prior studies have suggested a role for genetics in determining spontaneous ductal closure. Using samples from a large neonatal cohort we tested the hypothesis that common genetic variations are associated with PDA in extremely preterm infants.

STUDY DESIGN

Preterm infants (n = 1013) enrolled at NICHD Neonatal Research Network sites were phenotyped for PDA. DNA was genotyped for 1634 single nucleotide polymorphisms (SNPs) from candidate genes. Analyses were adjusted for ancestral eigenvalues and significant epidemiologic variables.

RESULTS

SNPs in several genes were associated with the clinical diagnosis of PDA and with surgical ligation in extremely preterm neonates diagnosed with PDA (p < 0.01). None of the associations were significant after correction for multiple comparisons.

CONCLUSION

We identified several common genetic variants associated with PDA. These findings may inform further studies on genetic risk factors for PDA in preterm infants.

Predicting risk of type 2 diabetes mellitus in Korean adults aged 40-69 by integrating clinical and genetic factors

AIMS

The purpose of our investigation was to identify the genetic and clinical risk factors of type 2 diabetes mellitus (T2DM) and to predict the incidence of T2DM in Korean adults aged 40-69 at follow-up intervals of 5, 7, and 10years.

METHODS

Korean Genome and Epidemiology Study (KoGES) cohort data (n=10,030) were used to develop T2DM prediction models. Both clinical-only and integrated (clinical factors+genetic factors) models were derived using the Cox proportional hazards model. Internal validation was performed to evaluate the prediction capabilities of the clinical and integrated models.

RESULTS

The clinical model included 10 selected clinical risk factors. The selected SNPs for the integrated model were rs9311835 in PTPRG, rs10975266 in RIC1, rs11057302 in TMED2, rs17154562 in ADAM12, and rs8038172 in CGNL1. For the clinical model, validated c-indices with time points of 5, 7, and 10 years were 0.744, 0.732, and 0.732, respectively. Slightly higher validated c-indices were observed for the integrated model at 0.747, 0.736, and 0.738, respectively. The p-values of the survival net reclassification improvement (NRI) for the SNP point-based score were statistically significant.

CONCLUSIONS

Clinical and integrated models can be effectively used to predict the incidence of T2DM in Koreans.

Genetic risk score of common genetic variants for impaired fasting glucose and newly diagnosed type 2 diabetes influences oxidative stress

We tested the hypothesis that the cumulative effects of common genetic variants related to elevated fasting glucose are collectively associated with oxidative stress. Using 25 single nucleotide polymorphisms (SNPs), a weighted genetic risk score (wGRS) was constructed by summing nine risk alleles based on nominal significance and a consistent effect direction in 1,395 controls and 718 patients with impaired fasting glucose (IFG) or newly diagnosed type 2 diabetes. All the participants were divided into the following three groups: low-wGRS, middle-wGRS, and high-wGRS groups. Among the nine SNPs, five SNPs were significantly associated with IFG and type 2 diabetes in this Korean population. wGRS was significantly associated with increased IFG and newly diagnosed type 2 diabetes (p = 6.83 × 10⁻¹⁴, odds ratio = 1.839) after adjusting for confounding factors. Among the IFG and type 2 diabetes patients, the fasting serum glucose and HbA_1c levels were significantly higher in the high-wGRS group than in the other groups. The urinary 8-epi-PGF_2α and malondialdehyde concentrations were significantly higher in the high-wGRS group than in the other groups. Moreover, general population-level instrumental variable estimation (using wGRS as an instrument) strengthened the causal effect regarding the largely adverse influence of high levels of fasting serum glucose on markers of oxidative stress in the Korean population. Thus, the combination of common genetic variants with small effects on IFG and newly diagnosed type 2 diabetes are significantly associated with oxidative stress.

Prospective association of a genetic risk score with major adverse cardiovascular events in patients with coronary artery disease

Many susceptibility loci associated with coronary artery disease (CAD) have been identified using genome-wide association studies (GWAS). This study aimed to examine whether a composite of single nucleotide polymorphisms (SNPs) derived from GWAS could identify the risk of major adverse cardiovascular events (MACEs) in patients with established CAD. There were 1059 patients with CAD were included in the analysis. Of the participants, 686 were on statin treatment at the start of follow-up. A weighted genetic risk score (wGRS) was calculated as the sum of risk alleles multiplied by the hazard ratio for a particular SNP. In single variant analyses, rs579459, rs4420638, and rs2107595 were associated with an increased risk of MACE. A wGRS was further constructed to evaluate the cumulative effect of the 3 SNPs on the prognosis of CAD. The risk of MACE among patients with high and intermediate wGRS was 1.968- and 1.838-fold, respectively, higher than those with low wGRS. This effect was more evident in patients using lipid-lowering medication and with hypertension. Furthermore, the interaction analysis revealed that lipid-lowering medication and hypertension interacted with the genetic effect off wGRS on the risk of MACE in patients using lipid-lowering medication or with hypertension (Pinteraction < .001). We further analyzed the follow-up change in low-density lipoprotein cholesterol (LDL-C) level at 6 months after CAD disclosure and evaluated whether that was due to wGRS or statin use. The lowest reduction in LDL-C was observed in patients with high GRS who received statin treatment. Furthermore, LDL-C reduction of patients with intermediate wGRS was less than those with low wGRS in patients treated with statin. Taken together, a wGRS comprised of SNPs significantly predicts MACE in CAD patients receiving statin treatment and hypertension.

Genetic risk scores in the prediction of plasma glucose, impaired insulin secretion, insulin resistance and incident type 2 diabetes in the METSIM study

AIMS/HYPOTHESIS

Many SNPs have been associated with glycaemic traits and type 2 diabetes, but their joint effects on glycaemic traits and the underlying mechanisms leading to hyperglycaemia over time are largely unknown. We aimed to investigate the association of six genetic risk scores (GRSs) with changes in plasma glucose, insulin sensitivity, insulin secretion and incident type 2 diabetes in the prospective METabolic Syndrome In Men (METSIM) study.

METHODS

We generated weighted GRSs for fasting plasma glucose ([FPG] GRS_FPG, 35 SNPs), 2 h plasma glucose ([2hPG] GRS_2hPG, 9 SNPs), insulin secretion (GRS_IS, 17 SNPs), insulin resistance (GRS_IR, 9 SNPs) and BMI (GRS_BMI, 95 SNPs) and a non-weighted GRS for type 2 diabetes (GRS_T2D, 76 SNPs) in up to 8749 non-diabetic Finnish men. Linear regression was used to test associations of the GRSs with changes in glycaemic traits over time.

RESULTS

GRS_T2D, GRS_FPG and GRS_IS were associated with an increase in FPG, GRS_T2D with an increase in glucose AUC and a decrease in insulin secretion, and GRS_2hPG with an increase in 2hPG during the follow-up (p < 0.0017 for all models). GRS_T2D, GRS_FPG and GRS_IS were associated with incident type 2 diabetes (p < 0.008 for all models). GRS_BMI and GRS_IR were not significantly associated with any changes in glycaemic traits.

CONCLUSIONS/INTERPRETATION

In the METSIM follow-up study, GRS_T2D, GRS_FPG and GRS_IS were associated with the worsening of FPG and an increase in incident type 2 diabetes. GRS_T2D was additionally associated with a decrease in insulin secretion, and GRS_2hPG with an increase in 2hPG.