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Genes linked to glucose and insulin gender differences

Speaking at the European Association for the Study of Diabetes (EASD) Annual Meeting in Stockholm last week, Diabetes UK-funded researchers reported significant differences between men and women in the genetic signals influencing glucose and insulin levels.

The researchers analysed information about the genetic basis of differences in fasting glucose and insulin levels from more than 75,000 men and women without diabetes.

Previously, the researchers identified 16 different genetic regions that influence fasting glucose levels, and two that influence fasting insulin levels. Now, led by Dr Inga Prokopenko and Professor Mark McCarthy of the University of Oxford, they report that some of these genetic variants have more impact in one sex than the other.

For example, signals near genes known as GCKR and G6PC2 have a greater effect on fasting glucose levels in men, while those near PCSK1 have a larger effect in women.

These findings are particularly interesting in light of the differences in rates of Type 2 diabetes and impaired glucose regulation in men and women. Specifically, impaired fasting glucose (IFG) is more common in men, and impaired glucose tolerance (IGT) more common in women, although the reasons for these differences are not yet clear.

Research could help understand risks

Dr Iain Frame, Director of Research at Diabetes UK, said:

“This research is the first of its kind to link specific genes to differences in glucose metabolism between men and women. Understanding the function of these genes and how they differ between sexes could help researchers to understand why men have a greater risk of developing insulin resistance and Type 2 diabetes than women – and, importantly, how we might improve current ways of preventing and treating the condition.”

The research was carried out at the University of Oxford and the Wellcome Trust Sanger Institute in collaboration with the VU University Amsterdam and other centres in Europe and North America, which are collectively part of MAGIC: the Meta Analysis of Glucose and Insulin-related traits Consortium.

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