Dissecting the Etiology of Type 2 Diabetes in the Pima Indian Population

Prevalence and incidence of diabetes among Aboriginal people in remote communities of the Northern Territory, Australia: a retrospective, longitudinal data-linkage study

OBJECTIVES

To assess the prevalence and incidence of diabetes among Aboriginal peoples in remote communities of the Northern Territory (NT), Australia.

DESIGN

Retrospective cohort analysis of linked clinical and administrative data sets from 1 July 2012 to 30 June 2019.

SETTING

Remote health centres using the NT Government Primary Care Information System (51 out of a total of 84 remote health centres in the NT).

PARTICIPANTS

All Aboriginal clients residing in remote communities serviced by these health centres (N=21 267).

PRIMARY OUTCOME MEASURES

Diabetes diagnoses were established using hospital and primary care coding, biochemistry and prescription data.

RESULTS

Diabetes prevalence across all ages increased from 14.4% (95% CI: 13.9% to 14.9%) to 17.0% (95% CI: 16.5% to 17.5%) over 7 years. Among adults (≥20 years), the 2018/2019 diabetes prevalence was 28.6% (95% CI: 27.8% to 29.4%), being higher in Central Australia (39.5%, 95% CI: 37.8% to 41.1%) compared with the Top End region (24.2%, 95% CI: 23.3% to 25.1%, p<0.001). Between 2016/2017 and 2018/2019, diabetes incidence across all ages was 7.9 per 1000 person-years (95% CI: 7.3 to 8.7 per 1000 person-years). The adult incidence of diabetes was 12.6 per 1000 person-years (95% CI: 11.5 to 13.8 per 1000 person-years).

CONCLUSIONS

The burden of diabetes in the remote Aboriginal population of the NT is among the highest in the world. Strengthened systems of care and public health prevention strategies, developed in partnership with Aboriginal communities, are needed.

Systematic Meta-analysis Revealed an Association of PGC-1α rs8192678 Polymorphism in Type 2 Diabetes Mellitus

Background

Genome-wide association study (GWAS) provides an unprecedented opportunity to reveal substantial genetic contribution to type 2 diabetes mellitus (T2DM) and glycemic identification of allelic heterogeneity and population-specific genetic variants, yet it also faces difficulty due to the vast amount of potential confounding factors and limited availability of clinical data. To identify responsible susceptibility loci and genomic polymorphism for T2DM and glycemic traits, we have systematically investigated a genome-wide association study related to T2DM. Although GWAS has captured many common genetic variations, which are related to T2DM, each risk allele (RA) of single-nucleotide polymorphisms (SNPs) at these loci is not conclusive. Therefore, it is common to present a combination of several SNPs to infer T2DM risk, yet it is still insufficient to be deterministic. To streamline the identification of a deterministic genetic variation in T2DM, we developed this meta-analysis as a showcase to comprehensively identify the association between cumulative RAs and T2DM risk by combining different studies in reported literature and databases. After all, we identified that PGC-1α rs8192678 polymorphism can be considered as a potentially deterministic biomarker in T2DM risk. Previous studies have potentially linked PGC-1α rs8192678 polymorphism to type 2 diabetes mellitus (T2DM) risk, but the results remain inconsistent in different populations and are not conclusive. We developed a new meta-analysis approach to systematically identify the association between PGC-1α rs8192678 polymorphism and T2DM, and we have comprehensively assessed different ethnic groups to validate our findings.

Methods

We performed comprehensive information retrieval and knowledge discovery meta-analysis by searching extensively published literature and different electronic databases to acquire eligible studies for the above association study. We developed a method to use pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) in five genetic models (allelic, dominant, recessive, homozygous, and heterozygous genetic models) to identify the relationship among ethnicity subgroup analyses comprehensively.

Results

We identified 20 eligible studies consisting of 16,182 subjects (8,038 cases and 8,144 controls) in our meta-analysis. PGC-1α rs8192678 polymorphisms of all subjects showed a significant association with T2DM susceptibility under all genetic models: allelic (OR: 1.24, 95% CI: 1.13-1.35), dominant (OR: 1.27, 95% CI: 1.14-1.42), recessive (OR: 1.24, 95% CI: 1.14-1.36), homozygous (OR: 1.40, 95% CI: 1.20-1.64), and heterozygous (OR: 1.20, 95% CI: 1.06-1.35). In the subgroup analysis, we identified a significant association between PGC-1α rs8192678 polymorphism and T2DM in the Caucasian and Indian populations under all genetic models we investigated. This is the most comprehensive study of the subject to date.

Conclusion

Our development of meta-analysis revealed that the minor allele (A) carriers, especially AA genotype carriers, can lead to risk of T2DM in the Caucasian and Indian populations. This is the first report that such risk has been confirmed. Our finding shed new light into the genetic alteration in T2DM.

Genetics of Type 2 Diabetes: the Power of Isolated Populations

Type 2 diabetes (T2D) affects millions of people worldwide. Improving the understanding of the underlying mechanisms and ultimately improving the treatment strategies are, thus, of great interest. To achieve this, identification of genetic variation predisposing to T2D is important. A large number of variants have been identified in large outbred populations, mainly from Europe and Asia. However, to elucidate additional variation, isolated populations have a number of advantageous properties, including increased amounts of linkage disequilibrium, and increased probability for presence of high frequency disease-associated variants due to genetic drift. Collectively, this increases the statistical power to detect association signals in isolated populations compared to large outbred populations. In this review, we elaborate on why isolated populations are a powerful resource for the identification of complex disease variants and describe their contributions to the understanding of the genetics of T2D.