Physicians' Academy for Cardiovascular Education

Combining BMI and polygenic score to predict lifetime risk of diabetes

News - Aug. 31, 2020

Integrating the Effect of BMI and Polygenic Scores to estimate Lifetime Risk and Identify Optimal Treatment Targets to Prevent or Reverse Diabetes

Presented at the ESC congress 2020 by: Prof. Brian Ference Cambridge, UK

Introduction and methods

Diabetes is a risk factor for CVD, observed in observational studies and RCTs. Diabetes doubles the risk of CV events. Prevention of diabetes can be a strategy for the reduction of CVD. Polygenic scores (PGS) have emerged as a risk factor for diabetes. As genetic variants are present from birth, calculating a PGS early in life may identify those with a high lifetime risk of diabetes.

This study was performed to evaluate the risk of diabetes at all levels of a PGS for diabetes depending on differences in BMI. Furthermore, the effect of lifelong exposure to increased BMI with short-term exposure to increased BMI was evaluated on risk of diabetes at all levels of polygenic predisposition.

This study used data of 445,765 participants in the UK Biobank. PGS for diabetes was calculated using 6.9 million genetic variants. In addition, a BMI genetic score was calculated including 255 variants to assess lifelong exposure to BMI on risk of diabetes. The score was used as an instrumental randomization to naturally randomize participants to a higher or lower lifelong exposure to BMI. The primary outcomes was T2DM. In observational studies middle-life BMI was measured.

Main results


BMI and polygenetic predisposition for diabetes have independent and additive effect on risk of developing diabetes. In addition, BMI appears to have a greater impact on risk of diabetes than PGS. Lifelong exposure to increased BMI in Mendelian randomization appears to have same effect on risk of diabetes as short-term exposure to BMI in observational studies. The authors suggest that PGS for diabetes should not be used alone to estimate risk, but should be integrated with known modifiable causes of diseases to more accurately predict risk and more accurately identify those who may benefit from interventions to reduce risk.

- Our reporting is based on the information provided at the ESC congress -

Share this page with your colleagues and friends: