ISSN 1662-4009 (online)

ESPE Yearbook of Paediatric Endocrinology (2023) 20 13.6 | DOI: 10.1530/ey.20.13.6

ESPEYB20 13. Editors' Choice Section (12 abstracts)

13.6. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

Ken Suzuki , Konstantinos Hatzikotoulas , Lorraine Southam , Henry J Taylor , Xianyong Yin & Kim M Lorenz



In Brief: The authors report the largest genetic study to date of Type 2 diabetes (T2D), pooling genome-wide association study (GWAS) data from 2 535 601 individuals (39.7% non-European ancestry), including 428 452 T2D cases. They identified 1289 independent association signals at genome-wide significance (P<5×10−8). These T2D GWAS signals could be separated into eight nonoverlapping clusters, characterised by distinct cardiometabolic disease associations.

Comment: The literature on genetics of T2D requires some effort to follow due to relatively frequent updates in differing populations. By contrast this paper is a major landmark, increasing three-fold the effective sample size compared to the previous largest studies. Notably as well as sample size, they highlight that nearly half of the T2D signals would not have been identified without the inclusion of under-represented ancestry groups, i.e. non-Europeans, who bring a richer, more diverse, extent of genetic variation.

The large number of independent T2D signals allowed a well-powered approach to identify distinct genetic clusters that contribute to this heterogeneous disease. These clusters are 1) beta-cell dysfunction with positive or 2) negative association with proinsulin, 3) insulin resistance mediated via obesity, 4) lipodystrophy, 5) liver/lipid metabolism, 6) residual glycaemic effects, 7) accumulations of body fat, and 8) the metabolic syndrome. These clusters showed differing strength of associations with other insulin resistance related disorders. For example, gestational diabetes was most strongly associated with the beta-cell dysfunction/positive proinsulin association cluster. Polycystic ovary syndrome was most strongly associated with the insulin resistance/obesity cluster. Coronary artery disease risk was positively associated with the lipodystrophy cluster and the insulin resistance/obesity cluster.

The authors conclude that one day we may be able to offer genetically-informed diabetes care and prevention. If this does indeed become a reality, it is important to ensure global access to such advances.

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