Utilizing Genetic Predisposition Score in Predicting Risk of Type 2 Diabetes Mellitus Incidence: A Community-based Cohort Study on Middle-aged Koreans

The utility of a type 2 diabetes polygenic score in addition to clinical variables for prediction of type 2 diabetes incidence in birth, youth and adult cohorts in an Indigenous study population

AIMS/HYPOTHESIS

There is limited information on how polygenic scores (PSs), based on variants from genome-wide association studies (GWASs) of type 2 diabetes, add to clinical variables in predicting type 2 diabetes incidence, particularly in non-European-ancestry populations.

METHODS

For participants in a longitudinal study in an Indigenous population from the Southwestern USA with high type 2 diabetes prevalence, we analysed ten constructions of PS using publicly available GWAS summary statistics. Type 2 diabetes incidence was examined in three cohorts of individuals without diabetes at baseline. The adult cohort, 2333 participants followed from age ≥20 years, had 640 type 2 diabetes cases. The youth cohort included 2229 participants followed from age 5-19 years (228 cases). The birth cohort included 2894 participants followed from birth (438 cases). We assessed contributions of PSs and clinical variables in predicting type 2 diabetes incidence.

RESULTS

Of the ten PS constructions, a PS using 293 genome-wide significant variants from a large type 2 diabetes GWAS meta-analysis in European-ancestry populations performed best. In the adult cohort, the AUC of the receiver operating characteristic curve for clinical variables for prediction of incident type 2 diabetes was 0.728; with the PS, 0.735. The PS's HR was 1.27 per SD (p=1.6 × 10-8; 95% CI 1.17, 1.38). In youth, corresponding AUCs were 0.805 and 0.812, with HR 1.49 (p=4.3 × 10-8; 95% CI 1.29, 1.72). In the birth cohort, AUCs were 0.614 and 0.685, with HR 1.48 (p=2.8 × 10-16; 95% CI 1.35, 1.63). To further assess the potential impact of including PS for assessing individual risk, net reclassification improvement (NRI) was calculated: NRI for the PS was 0.270, 0.268 and 0.362 for adult, youth and birth cohorts, respectively. For comparison, NRI for HbA1c was 0.267 and 0.173 for adult and youth cohorts, respectively. In decision curve analyses across all cohorts, the net benefit of including the PS in addition to clinical variables was most pronounced at moderately stringent threshold probability values for instituting a preventive intervention.

CONCLUSIONS/INTERPRETATION

This study demonstrates that a European-derived PS contributes significantly to prediction of type 2 diabetes incidence in addition to information provided by clinical variables in this Indigenous study population. Discriminatory power of the PS was similar to that of other commonly measured clinical variables (e.g. HbA1c). Including type 2 diabetes PS in addition to clinical variables may be clinically beneficial for identifying individuals at higher risk for the disease, especially at younger ages.

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).

Assessment of genetic risk of type 2 diabetes among Pakistanis based on GWAS-implicated loci

Genome-wide association studies (GWAS) have identified multiple type 2 diabetes (T2D) loci, mostly among populations of European descent. There is a high prevalence of T2D among Pakistanis. Both genetic and environmental factors may be responsible for this high prevalence. In order to understand the shared genetic basis of T2D among Pakistanis and Europeans, we examined 77 genome-wide significant variants previously implicated among European populations. We genotyped 77 single-nucleotide polymorphisms (SNPs) by iPLEX® Gold or TaqMan® assays in a case-control sample of 1,683 individuals. Association analysis was performed using logistic regression. A total of 16 SNPs (TCF7L2/rs7903146, GLIS3/rs7041847, CHCHD9/rs13292136, PLEKHA1/rs2292626, FTO/rs9936385, CDKAL1/rs7756992, KCNJ11/rs5215, LOC105372155/rs12970134, KCNQ1/rs163182, CTRB1/rs7202877, ST6GAL1/rs16861329, ADAMTS9-AS2/rs6795735, LOC105370275/rs1359790, C5orf67/rs459193, ZBED3-AS1/rs6878122 and UBE2E2/rs7612463) showed statistically significant associations after controlling for the false discovery rate. While KCNQ1/rs163182 and ZBED3-AS1/rs6878122 showed opposite allelic effects, the remaining significant SNPs had the same allelic effects as reported previously. Our data indicate that a selected number of T2D loci previously identified among populations of European descent also affect the risk of T2D in the Pakistani population.

Rapamycin inhibits activation of AMPK-mTOR signaling pathway-induced Alzheimer's disease lesion in hippocampus of rats with type 2 diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is strongly correlated with Alzheimer's disease (AD). Rapamycin has important uses in oncology, cardiology and transplantation medicine. This study aims to investigate effects of rapamycin on AD in hippocampus of T2DM rat by AMPK/mTOR signaling pathway.

METHODS

Morris water maze test was applied to evaluate the learning and memory abilities. The fasting plasma glucose (FBG), glycosylated haemoglobin, total cholesterol, triglyceride and serum insulin level were measured. RT-qPCR and Western blot analysis were performed to test expression of AMPK and mTOR. Immunohistochemistry was used to detect the Aβ deposition and immunoblotting to test the total tau, p-tau and Aβ precursor APP expressions.

RESULTS

After treated with rapamycin, T2DM rats and rats with T2DM and AD showed increased learning-memory ability, and decreased levels of FBG, glycosylated hemoglobin, total cholesterol, triglyceride and serum insulin, decreased expression of APP and p-tau, increased AMPK mRNA expression and p-AMPK and decreased Aβ deposition, mTOR mRNA expression and p-mTOR.

CONCLUSION

The study demonstrated that rapamycin reduces the risk of AD in T2DM rats and inhibits activation of AMPK-mTOR signaling pathway, thereby improving AD lesion in hippocampus of T2DM rats.

The utility of diabetes risk score items as predictors of incident type 2 diabetes in Asian populations: An evidence-based review

BACKGROUND

The prevalence of type 2 diabetes mellitus is rising, with many Asian countries featured in the top 10 countries with the highest numbers of persons with diabetes. Reliable diabetes risk scores enable the identification of individuals at risk of developing diabetes for early intervention.

OBJECTIVES

This article aims to identify common risk factors in the risk scores with the highest discrimination; factors with the most influence on the risk score in Asian populations, and to propose a set of factors translatable to the multi-ethnic Singapore population.

METHODS

A systematic search of PubMed and EMBASE databases was conducted to identify studies published before August 2016 that developed risk prediction models for incident diabetes.

RESULTS

12 studies were identified. Risk scores that included laboratory measurements had better discrimination. Coefficient analysis showed fasting glucose and HbA1c having the greatest impact on the risk score.

CONCLUSION

A proposed Asian risk score would include: family history of diabetes, age, gender, smoking status, body mass index, waist circumference, hypertension, fasting plasma glucose, HbA1c, HDL-cholesterol and triglycerides. Future research is required on the influence of ethnicity in Singapore. The risk score may potentially be used to stratify individuals for enrolment into diabetes prevention programmes.