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Fulltext The architecture of risk for type 2 diabetes: understanding Asia in the context of global findings

Abdullah N, Attia J, Oldmeadow C, Scott RJ, Holliday EG

Int J Endocrinol, 2014;2014:593982.
PMID: 24744783 DOI: 10.1155/2014/593982

The prevalence of Type 2 diabetes is rising rapidly in both developed and developing countries. Asia is developing as the epicentre of the escalating pandemic, reflecting rapid transitions in demography, migration, diet, and lifestyle patterns. The effective management of Type 2 diabetes in Asia may be complicated by differences in prevalence, risk factor profiles, genetic risk allele frequencies, and gene-environment interactions between different Asian countries, and between Asian and other continental populations. To reduce the worldwide burden of T2D, it will be important to understand the architecture of T2D susceptibility both within and between populations. This review will provide an overview of known genetic and nongenetic risk factors for T2D, placing the results from Asian studies in the context of broader global research. Given recent evidence from large-scale genetic studies of T2D, we place special emphasis on emerging knowledge about the genetic architecture of T2D and the potential contribution of genetic effects to population differences in risk.
Characterizing the genetic risk for type 2 diabetes in a Malaysian multi-ethnic cohort

Abdullah N, Abdul Murad NA, Attia J, Oldmeadow C, Mohd Haniff EA, Syafruddin SE, et al.

Diabet Med, 2015 Oct;32(10):1377-84.
PMID: 25711284 DOI: 10.1111/dme.12735

AIMS: To characterize the association with Type 2 diabetes of known Type 2 diabetes risk variants in people in Malaysia of Malay, Chinese and Indian ancestry who participated in the Malaysian Cohort project.
METHODS: We genotyped 1604 people of Malay ancestry (722 cases, 882 controls), 1654 of Chinese ancestry (819 cases, 835 controls) and 1728 of Indian ancestry (851 cases, 877 controls). First, 62 candidate single-nucleotide polymorphisms previously associated with Type 2 diabetes were assessed for association via logistic regression within ancestral groups and then across ancestral groups using a meta-analysis. Second, estimated odds ratios were assessed for excess directional concordance with previously studied populations. Third, a genetic risk score aggregating allele dosage across the candidate single-nucleotide polymorphisms was tested for association within and across ancestral groups.
RESULTS: After Bonferroni correction, seven individual single-nucleotide polymorphisms were associated with Type 2 diabetes in the combined Malaysian sample. We observed a highly significant excess in concordance of effect directions between Malaysian and previously studied populations. The genetic risk score was strongly associated with Type 2 diabetes in all Malaysian groups, explaining from 1.0 to 1.7% of total Type 2 diabetes risk variance.
CONCLUSION: This study suggests there is substantial overlap of the genetic risk alleles underlying Type 2 diabetes in Malaysian and other populations.
Study name: The Malaysian Cohort (TMC) project
Fulltext Differing Contributions of Classical Risk Factors to Type 2 Diabetes in Multi-Ethnic Malaysian Populations

Abdullah N, Murad NAA, Attia J, Oldmeadow C, Kamaruddin MA, Jalal NA, et al.

Int J Environ Res Public Health, 2018 Dec 10;15(12).
PMID: 30544761 DOI: 10.3390/ijerph15122813

The prevalence of type 2 diabetes is escalating rapidly in Asian countries, with the rapid increase likely attributable to a combination of genetic and lifestyle factors. Recent research suggests that common genetic risk variants contribute minimally to the rapidly rising prevalence. Rather, recent changes in dietary patterns and physical activity may be more important. This nested case-control study assessed the association and predictive utility of type 2 diabetes lifestyle risk factors in participants from Malaysia, an understudied Asian population with comparatively high disease prevalence. The study sample comprised 4077 participants from The Malaysian Cohort project and included sub-samples from the three major ancestral groups: Malay (n = 1323), Chinese (n = 1344) and Indian (n = 1410). Association of lifestyle factors with type 2 diabetes was assessed within and across ancestral groups using logistic regression. Predictive utility was quantified and compared between groups using the Area Under the Receiver-Operating Characteristic Curve (AUC). In predictive models including age, gender, waist-to-hip ratio, physical activity, location, family history of diabetes and average sleep duration, the AUC ranged from 0.76 to 0.85 across groups and was significantly higher in Chinese than Malays or Indians, likely reflecting anthropometric differences. This study suggests that obesity, advancing age, a family history of diabetes and living in a rural area are important drivers of the escalating prevalence of type 2 diabetes in Malaysia.
Predicting type 2 diabetes using genetic and environmental risk factors in a multi-ethnic Malaysian cohort

Abdullah N, Abdul Murad NA, Mohd Haniff EA, Syafruddin SE, Attia J, Oldmeadow C, et al.

Public Health, 2017 Aug;149:31-38.
PMID: 28528225 DOI: 10.1016/j.puhe.2017.04.003

OBJECTIVE: Malaysia has a high and rising prevalence of type 2 diabetes (T2D). While environmental (non-genetic) risk factors for the disease are well established, the role of genetic variations and gene-environment interactions remain understudied in this population. This study aimed to estimate the relative contributions of environmental and genetic risk factors to T2D in Malaysia and also to assess evidence for gene-environment interactions that may explain additional risk variation.
STUDY DESIGN: This was a case-control study including 1604 Malays, 1654 Chinese and 1728 Indians from the Malaysian Cohort Project.
METHODS: The proportion of T2D risk variance explained by known genetic and environmental factors was assessed by fitting multivariable logistic regression models and evaluating McFadden's pseudo R(2) and the area under the receiver-operating characteristic curve (AUC). Models with and without the genetic risk score (GRS) were compared using the log likelihood ratio Chi-squared test and AUCs. Multiplicative interaction between genetic and environmental risk factors was assessed via logistic regression within and across ancestral groups. Interactions were assessed for the GRS and its 62 constituent variants.
RESULTS: The models including environmental risk factors only had pseudo R(2) values of 16.5-28.3% and AUC of 0.75-0.83. Incorporating a genetic score aggregating 62 T2D-associated risk variants significantly increased the model fit (likelihood ratio P-value of 2.50 × 10(-4)-4.83 × 10(-12)) and increased the pseudo R(2) by about 1-2% and AUC by 1-3%. None of the gene-environment interactions reached significance after multiple testing adjustment, either for the GRS or individual variants. For individual variants, 33 out of 310 tested associations showed nominal statistical significance with 0.001
Cross-Cancer Genome-Wide Association Study of Endometrial Cancer and Epithelial Ovarian Cancer Identifies Genetic Risk Regions Associated with Risk of Both Cancers

Glubb DM, Thompson DJ, Aben KKH, Alsulimani A, Amant F, Annibali D, et al.

Cancer Epidemiol Biomarkers Prev, 2021 Jan;30(1):217-228.
PMID: 33144283 DOI: 10.1158/1055-9965.EPI-20-0739

BACKGROUND: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers.
METHODS: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data.
RESULTS: Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10-5). We found seven loci associated with risk for both cancers (P Bonferroni < 2.4 × 10-9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10-7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation.
CONCLUSIONS: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis.
IMPACT: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.