Combining stacked polygenic scores with clinical risk factors improves cardiovascular risk prediction in people with type 2 diabetes
Authors: Dziopa K , Chaturvedi N , Vugt M. , Gratton J , Maclean R , Hingorani A , Asselbergs F W , Finan C , Schmidt A F 
Published: September 2022

Background

Recommended CVD prediction models do not perform well in people with diabetes. We aimed to determine whether models combining polygenic scores (PGS) with clinical risk factors could more accurately predict 10-year risk of six facets of CVD, including: coronary heart disease (CHD), heart failure (HF), and atrial fibrillation (AF).

Methods

Three groups were selected from the UK Biobank: 143,459 control participants without diabetes or a history of CVD, 5,229 with diabetes but without CVD, and 1,621 with diabetes and a history of CVD. Data from 29 phenotype-specific polygenic scores (PGS) were stacked and combined with clinical risk-factors. Performance was evaluated using a 20% independent hold-out sample, with results stratified on duration of diabetes.

Results

In people without diabetes combining the stacked PGS with clinical risk factor modestly outperformed models that exclusively used clinical risk factors, with the largest improvement observed for AF (c-statistic difference: 0.03). In people with diabetes, models that combined the stacked PGS with clinical risk factors showed marked improved performance compared to the risk factor only models. This difference was largest in people with newly diagnosed diabetes (without a history of CVD), with a PGS + clinical risk factor model c-statistic: 0.83 (95%CI 0.83; 0.84) for CHD and 0.84 (95%CI 0.82; 0.85) for HF, compared to a clinical risk factor model c-statistic: 0.68 (95%CI 0.68; 0.69) and 0.60 (95%CI 0.58; 0.62) for CHD and HF respectively.

Conclusions

Combining PGS with clinical risk factors improves CVD risk prediction in people with diabetes.

Research in context

What is already known about this subject?

Cardiovascular disease (CVD) remains a prominent cause of morbidity and mortality for people with type 2 diabetes. The currently available CVD prediction models do not provide sufficiently accurate prediction in people with diabetes, prohibiting much-needed personalization of management strategies.

In the general population, phenotype-specific polygenic scores (PGS) have shown to modestly improve CVD risk prediction. However, models for CVD prediction in the general population are often already highly accurate, limiting the scope for PGS to further improve performance.

Given the multifactorial etiology of CVD, combining information (stacking) from multiple trait-specific PGS (e.g., on CHD, LDL-C and blood pressure) is expected to improve performance.

What is the key question?

What is the added benefit of incorporating PGS with conventional clinical risk factors in CVD prediction for people with type 2 diabetes?

What are the new findings?

In people with diabetes, models that combined the stacked PGS with clinical risk factors showed marked improved performance compared to the risk factor-only models.

While age was the predominant risk factor in people without diabetes, in people with diabetes the contribution of age was outranked by our stacked PGS.

Model performance depended on the duration of diabetes, with models performing better in people with a recent diagnosis, for example in this group the c-statistic for CHD was 0.83 (95%CI 0.83; 0.84), and for HF 0.84 (95%CI 0.82; 0.85).

How might this impact on clinical practice in the foreseeable future?

Combining PGS with clinical risk factors improves CVD risk prediction in people with diabetes. Incorporating PGS in risk prediction models may offer unique possibilities to reliably identify people with a meaningful risk of developing CVD.

ACRONYMS