Abstract
Aims/hypothesisType 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk.MethodsWe developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study.ResultsThe model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10−21 and p = 9.6 × 10−31, respectively) and a 4.4-fold (p = 6.8 × 10−33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years.Conclusions/interpretationThis novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.Graphical abstract
Highlights
Diabetes increases the risk of serious and costly cardiovascular and renal complications [1, 2]
Creation of wPRS and multiPRS We identified 26 factors and outcomes that we grouped into ten groups of risk/outcomes: diabetes, obesity, BP, albuminuria, GFR, biomarkers, lipids, stroke, CVDs and low birthweight, with SNPs obtained from 47 publications cited in electronic supplementary material (ESM) Table 2 of large-scale Genome-wide association studies (GWAS)
In ADVANCE, we previously reported that increases in urinary albumin/creatinine ratio (UACR) or decreases in eGFR in individuals with type 2 diabetes were independent predictors of cardiovascular events and death [22]
Summary
Diabetes increases the risk of serious and costly cardiovascular and renal complications [1, 2]. Our aim was to develop a multi-polygenic risk score (multiPRS) prediction model that did not include any past risk/outcome data Because of their common risk factors, overlap of its pathogenic mechanisms and correlations among them, a multiPRS composed of ten weighted PRSs (10 wPRS), gathering genomic variants associated with cardiovascular and renal complications and their key risk factors, was combined with sex and first principal component (PC1) of ethnicity, age at onset and diabetes duration into one logistic regression model, to classify or to predict micro- and macrovascular endpoints of type 2 diabetes [19,20,21,22,23]. We assessed whether this approach could help in early identification of individuals who could benefit most from the intensive therapy such as administered in ADVANCE [28, 29]
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