Predictive performance of a genetic risk score using 11 susceptibility alleles for the incidence of Type 2 diabetes in a general Japanese population: a nested case-control study

Factors Associated With Type 2 Diabetes in Older Japanese With Similar Genetic Risk Scores: The Bunkyo Health Study

Context

Genome-wide association studies have identified numerous single-nucleotide variations (SNVs, formerly single-nucleotide polymorphisms) linked to type 2 diabetes (T2D), thus improving the accuracy of genetic risk scores (GRS) in predicting T2D.

Objective

This study aimed to investigate the association between the novel GRS and the prevalence of T2D and clarify the characteristics that differentiate individuals with and without T2D with similar genetic risk.

Methods

This cross-sectional study analyzed 1610 Japanese individuals aged 65 to 84 years. GRS were calculated using 110 SNVs associated with T2D in Japanese, and GRS classified individuals as having low, average, or high risk for T2D. The characteristics of participants with or without diabetes were compared by sex at each risk level.

Results

The prevalences of T2D were 7.8%, 14.7%, and 16.7% at low-, average-, and high-risk levels, respectively. The odds ratios at the high- and average-risk levels were significantly higher than those at the low-risk level, even after adjusting for confounding factors. The diabetes group had a higher visceral fat area (VFA) and Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) value, but a lower insulinogenic index, than the nondiabetes group across all risk levels. In the nondiabetes group, the II decreased significantly as GRS increased, but the HOMA-IR and Matsuda index values showed no association. In men with diabetes, VFA tended to decrease with higher GRS.

Conclusion

A higher GRS was significantly associated with increased T2D prevalence in older Japanese individuals. Our data demonstrated that the contribution of VFA to the development of diabetes varies with genetic risk.

Development and Validation of Prediction Models for the 5-year Risk of Type 2 Diabetes in a Japanese Population: Japan Public Health Center-based Prospective (JPHC) Diabetes Study

BACKGROUND

This study aimed to develop models to predict the 5-year incidence of type 2 diabetes mellitus (T2DM) in a Japanese population and validate them externally in an independent Japanese population.

METHODS

Data from 10,986 participants (aged 46-75 years) in the development cohort of the Japan Public Health Center-based Prospective Diabetes Study and 11,345 participants (aged 46-75 years) in the validation cohort of the Japan Epidemiology Collaboration on Occupational Health Study were used to develop and validate the risk scores in logistic regression models.

RESULTS

We considered non-invasive (sex, body mass index, family history of diabetes mellitus, and diastolic blood pressure) and invasive (glycated hemoglobin [HbA1c] and fasting plasma glucose [FPG]) predictors to predict the 5-year probability of incident diabetes. The area under the receiver operating characteristic curve was 0.643 for the non-invasive risk model, 0.786 for the invasive risk model with HbA1c but not FPG, and 0.845 for the invasive risk model with HbA1c and FPG. The optimism for the performance of all models was small by internal validation. In the internal-external cross-validation, these models tended to show similar discriminative ability across different areas. The discriminative ability of each model was confirmed using external validation datasets. The invasive risk model with only HbA1c was well-calibrated in the validation cohort.

CONCLUSION

Our invasive risk models are expected to discriminate between high- and low-risk individuals with T2DM in a Japanese population.

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).

A short review on the features of the non-obese diabetic Goto-Kakizaki rat intestine

The Goto-Kakizaki (GK) rat is a non-obese experimental model of type 2 diabetes mellitus (T2DM) that allows researchers to monitor diabetes-induced changes without jeopardizing the effects of obesity. This rat strain exhibits notable gastrointestinal features associated with T2DM, such as marked alterations in intestinal morphology, reduced intestinal motility, slow transit, and modified microbiota compared to Wistar rats. The primary treatments for diabetic patients include administration of hypoglycemic agents and insulin, and lifestyle changes. Emerging procedures, including alternative therapies, metabolic surgeries, and modulation of the intestinal microbiota composition, have been shown to improve the diabetic state of GK rats. This review describes the morpho-physiological diabetic-associated features of the gastrointestinal tract (GIT) of GK rats. We also describe promising strategies, e.g., metabolic surgery and modulation of gut microbiota composition, used to target the GIT of this animal model to improve the diabetic state.

Association of CDKN2A/B gene polymorphisms (rs10811661 and rs2383208) with type 2 diabetes mellitus in a sample of Iraqi population

Background

Type 2 diabetes mellitus (T2DM) is chronic metabolic disorder manifested by increased blood glucose (hyperglycemia) due to pancreatic β-cell dysfunction and/or decreased sensitivity of peripheral tissue to insulin. T2DM is a multifactorial disease that may results from interaction of environmental and genetic factors.

Methods

A case–control study consisting of 400 T2DM patients in addition to 400 as control. Phenotyping as well as anthropometric data included body mass index BMI, fasting plasma glucose (FPG), serum total cholesterol, serum triglyceride, VLDL, LDL, HDL insulin levels and Homeostatic Model Assessment for Insulin Resistance HOMA-IR were estimated for the two groups. PCR–RFLP was used to carry out genotyping of CDKN2A/B gene (rs10811661 T>C and rs2383208 A>G) SNPs.

Results

For rs10811661 SNP the genotype and allele frequencies of CDKN2A/B gene for T2DM and control subjects showed that the co-dominant model in patients with the homozygous (TT) was found to be significantly (OR 2.51, 95% CI 1.47–4.24, P 0.004) higher than those in control group. In contrast, the heterozygous genotype (TC) did not reveal this significance (OR 1.14, 95% CI 0.77–2.62, P = 0.13), ANOVA test for mean comparison of biochemical markers under the co-dominant model of rs10811661 SNP genotype in CDKN2A/B gene, revealed a significant difference for insulin (P < 0.0001) and HOMA-IR (P < 0.0001) in T2DM group as compared to control one; However (rs2383208) SNP did not show any significant association with T2DM and with the measured biochemical marker at any model.

Conclusions

CDKN2A/B gene rs10811661 SNP was implicated in T2DM pathogenesis in this sample of Iraqi population also it affects insulin level in those patients, whereas the rs2383208 SNP did not impact the disease.

PPARG, TMEM163, UBE2E2 and WFS1 Gene Polymorphisms Are Not Significant Risk Factors for Gestational Diabetes in the Polish Population

Gestational diabetes mellitus (GDM) is a common disorder that occurs in pregnant women, leading to many maternal and neonatal complications. The pathogenesis of GDM is complex and includes risk factors, such as: age, obesity, and family history of diabetes. Studies have shown that genetic factors also play a role in the pathogenesis of GDM. The present study investigated whether polymorphisms in the PPARG (rs1801282), TMEM163 (rs6723108 and rs998451), UBE2E2 (rs6780569), and WFS1 (rs4689388) genes are risk factors for the development of GDM and whether they affect selected clinical parameters in women with GDM. This study included 204 pregnant women with GDM and 207 pregnant women with normal glucose tolerance (NGT). The diagnosis of GDM was based on a 75 g oral glucose tolerance test (OGTT) at 24–28 weeks gestation, according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. There were no statistically significant differences in the distribution of polymorphisms studied between women with GDM and pregnant women with normal carbohydrate tolerance, which suggests that these polymorphisms are not risk factors for GDM. We also examined the associations between studied gene polymorphisms and clinical parameters: fasting glucose, daily insulin requirement, body mass before pregnancy, body mass at birth, body mass increase during pregnancy, BMI before pregnancy, BMI at birth, BMI increase during pregnancy, new-born body mass, and APGAR score in women with GDM. We observed lower BMI values before pregnancy and at birth in women with PPARG rs17036160 TT genotype. The results of this study suggest that the PPARG (rs1801282), TMEM163 (rs6723108 and rs998451), UBE2E2 (rs6780569), and WFS1 (rs4689388) gene polymorphisms are not significant risk factors for GDM development in the Polish population and do not affect the clinical parameters in women with GDM; only rs1801282 of the PPARG gene may influence BMI values in women with GDM.

Reviewing physical exercise in non-obese diabetic Goto-Kakizaki rats

There is a high incidence of non-obese type 2 diabetes mellitus (non-obese-T2DM) cases, particularly in Asian countries, for which the pathogenesis remains mainly unclear. Interestingly, Goto-Kakizaki (GK) rats spontaneously develop insulin resistance (IR) and non-obese-T2DM, making them a lean diabetes model. Physical exercise is a non-pharmacological therapeutic approach to reduce adipose tissue mass, improving peripheral IR, glycemic control, and quality of life in obese animals or humans with T2DM. In this narrative review, we selected and analyzed the published literature on the effects of physical exercise on the metabolic features associated with non-obese-T2DM. Only randomized controlled trials with regular physical exercise training, freely executed physical activity, or skeletal muscle stimulation protocols in GK rats published after 2008 were included. The results indicated that exercise reduces plasma insulin levels, increases skeletal muscle glycogen content, improves exercise tolerance, protects renal and myocardial function, and enhances blood oxygen flow in GK rats.

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.

Integrated analysis of probability of type 2 diabetes mellitus with polymorphisms and methylation of KCNQ1 gene: A nested case-control study

BACKGROUND

To estimate the associations between single-nucleotide polymorphisms (SNPs) and methylation of KCNQ1 gene and type 2 diabetes mellitus (T2DM) risk and the interactions among SNPs, methylation, and environmental factors on T2DM risk.

METHODS

We genotyped five SNPs and tested methylation at 39 CpG loci of KCNQ1 in 290 T2DM cases and 290 matched controls nested in the Rural Chinese Cohort Study. Conditional logistic regression model was used to estimate the associations between SNPs and KCNQ1 methylation and T2DM risk. Multifactor dimensionality reduction (MDR) analysis was used to estimate the effect of the interactions SNPs-SNPs, SNPs-methylation, methylation-methylation and SNPs, and methylation-environment on T2DM risk.

RESULTS

Probability of T2DM was decreased with rs2283228 of KCNQ1 (CA vs AA, odds ratio [OR] = 0.65, 95% confidence interval [CI] 0.42-0.99). T2DM probability was significantly increased with rs2237895 combined with hypertriglyceridemia (OR_eg  = 2.76, 95% CI 1.35-5.62), with hypertension (OR_eg  = 2.23, 95% CI 1.25-3.98), and with body mass index (BMI; OR_eg  = 1.93, 95% CI 1.12-3.34). T2DM probability was associated with methylation of CG11 and CG41 (OR = 1.89, 95% CI 1.23-2.89, P = .003). It was significantly associated with the interaction between BMI, hypertriglyceridemia, and CG5 methylation (P = .028 and .028), and the combined effects of CG11 with hypertriglyceridemia and hypertension. On MDR analysis, no significant interaction was observed.

CONCLUSION

T2DM probability was reduced 35% with rs2283228 polymorphism. It was associated with rs2237895 combined with hypertension, with BMI and with hypertriglyceridemia. The methylation at two CpG loci of KCNQ1 significantly increased T2DM risk by 89%.

Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study

Background: Previous studies have constructed prediction models for type 2 diabetes mellitus (T2DM), but machine learning was rarely used and few focused on genetic prediction. This study aimed to establish an effective T2DM prediction tool and to further explore the potential of genetic risk scores (GRS) via various classifiers among rural adults.

Methods: In this prospective study, the GRS for a total of 5,712 participants from the Henan Rural Cohort Study was calculated. Cox proportional hazards (CPH) regression was used to analyze the associations between GRS and T2DM. CPH, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) were used to establish prediction models, respectively. The area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were used to assess the discrimination ability of the models. The decision curve was plotted to determine the clinical-utility for prediction models.

Results: Compared with the individuals in the lowest quintile of the GRS, the HR (95% CI) was 2.06 (1.40 to 3.03) for those with the highest quintile of GRS (P_trend < 0.05). Based on conventional predictors, the AUCs of the prediction model were 0.815, 0.816, 0.843, and 0.851 via CPH, ANN, RF, and GBM, respectively. Changes with the integration of GRS for CPH, ANN, RF, and GBM were 0.001, 0.002, 0.018, and 0.033, respectively. The reclassifications were significantly improved for all classifiers when adding GRS (NRI: 41.2% for CPH; 41.0% for ANN; 46.4% for ANN; 45.1% for GBM). Decision curve analysis indicated the clinical benefits of model combined GRS.

Conclusion: The prediction model combined with GRS may provide incremental predictions of performance beyond conventional factors for T2DM, which demonstrated the potential clinical use of genetic markers to screen vulnerable populations.

Clinical Trial Registration: The Henan Rural Cohort Study is registered in the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.

[Association of polymorphisms of genes TCF7L2, FABP2, KCNQ1, ADIPOQ with the prognosis of the development of type 2 diabetes mellitus]

AIM

To study the possibility of using polymorphisms of genesTCF7L2,FABP2,KCNQ1,ADIPOQas markers for predicting the development of type 2 diabetes mellitus (T2D) in the population of Novosibirsk.

MATERIALS AND METHODS

On the basis of prospective observation of a representative population sample of residents of Novosibirsk (HAPIEE), 2 groups were formed according to the case-control principle (case people who had diabetes mellitus 2 over 10 years of observation, and control people who did not developed disorders of carbohydrate metabolism). T2D group (n=443, mean age 56.26.7 years, men 29.6%, women 70.4%), control group (n=532, mean age 56.17.1 years, men 32.7%, women 67.3%). DNA was isolated by phenol-chloroform extraction. Genotyping was performed by the method of polymerase chain reaction with subsequent analysis of restriction fragment length polymorphism, polymerase chain reaction in real time. Statistical processing was carried out using the SPSS 16.0 software package.

RESULTS AND DISCUSSION

No significant effect of rs1799883 of theFABP2gene, rs2237892 of theKCNQ1gene, and rs6773957 of theADIPOQgene on the risk of developing T2D was found. Genotypes TT and TC rs7903146 of theTCF7L2gene are genotypes for the risk of developing T2D (relative risk RR 3.90, 95% confidence interval CI 2.316.61,p0.001; RR 1.86, 95% CI 1.422.43,p0.001, respectively). The CC genotype rs7903146 of theTCF7L2gene is associated with a protective effect against T2D (RR 0.37, 95% CI 0.290.49,p0.001). When theTCF7L2gene is included in the model for assessing the risk of developing T2D rs7903146, it retains its significance in both men and women.

CONCLUSION

The rs7903146 polymorphism of theTCF7L2gene confirmed its association with the prognosis of the development of T2D, which indicates the possibility of considering it as a candidate for inclusion in a diabetes risk meter. Variants of risk meters have been developed to assess the prognosis of the development of diabetes mellitus 2 in men and women aged 4569 years during 10 years of follow-up. The association with the prognosis of the development of T2D polymorphisms rs1799883 of theFABP2gene, rs2237892 of theKCNQ1gene and rs6773957 of theADIPOQgene was not found.

Association Between Genetic Risk and Development of Type 2 Diabetes in a General Japanese Population: The Hisayama Study

CONTEXT

Although recent genetic studies have identified many susceptibility loci associated with type 2 diabetes (T2D), the usefulness of such loci for precision medicine remains uncertain.

OBJECTIVE

This study investigated the impact of genetic risk score (GRS) on the development of T2D in a general Japanese population.

PARTICIPANTS

The current study consists of 1465 subjects aged 40 to 79 years without diabetes who underwent a health examination in 2002.

DESIGN

The GRS was generated using the literature-based effect size for T2D of 84 susceptibility loci for the Japanese population, and the risk estimates of GRS on the incidence of T2D were computed by using a Cox proportional hazard model in a 10-year follow-up study. The influence of GRS on the predictive ability was estimated with Harrell C statistics, integrated discrimination improvement (IDI), and continuous net reclassification improvement (cNRI).

RESULTS

During the 10-year follow-up, 199 subjects experienced T2D. The risk of developing T2D increased significantly with elevating quintiles of GRS (multivariable-adjusted hazard ratio for the fifth vs first quintile, 2.85; 95% CI, 1.83 to 4.44). When incorporating GRS into the multivariable model comprising environmental risk factors, the Harrell C statistics (95% CI) increased from 0.681 (0.645 to 0.717) to 0.707 (0.672 to 0.742) and the predictive ability of T2D was significantly improved (IDI, 0.0376; 95% CI, 0.0284 to 0.0494; cNRI, 0.3565; 95% CI, 0.1278 to 0.5829). GRS was also associated with the risk of T2D independently of environmental risk factors.

CONCLUSIONS

These findings suggest the usefulness of GRS for identifying a high-risk population together with environmental risk factors in the Japanese population.

The tRNA-associated dysregulation in diabetes mellitus

Diabetes mellitus (DM) is a complex endocrine and metabolic disorder for human health and well-being. Deregulated glucose and lipid metabolism are the primary underlying manifestations associated with this disease. Transfer RNAs (tRNAs) are considered to mainly participate in protein translation and may contribute to complex human pathologies. Although the molecular mechanisms remain, for the most part, unknown, accumulating evidence indicates that tRNAs play a vital role in the pathogenesis of DM. This paper reviews different aspects of tRNA-associated dysregulation in DM, such as tRNA mutations, tRNA modifications, tRNA aminoacylation and tRNA derivatives, aiming at a better understanding of the pathogenesis of DM and providing new ideas for the personalized treatment of this metabolism-associated disease.