| Title: | Identifying and ranking non-traditional risk factors for cardiovascular disease prediction in people with type 2 diabetes |
| Journal: | Communications Medicine |
| Published: | 14 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40082712/ |
| DOI: | https://doi.org/10.1038/s43856-025-00785-y |
| URL: | https://www.nature.com/articles/s43856-025-00785-y.pdf |
Publication 15591
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Abstract
BackgroundCardiovascular disease (CVD) prediction models perform poorly in people with type 2 diabetes (T2DM). We aimed to identify potentially non-traditional CVD predictors for six facets of CVD (including coronary heart disease, ischemic stroke, heart failure, and atrial fibrillation) in people with T2DM.MethodsWe analysed data on 600+ features from the UK Biobank, stratified by history of CVD and T2DM: 459,142 participants without diabetes or CVD, 14,610 with diabetes but without CVD, and 4432 with diabetes and CVD. A penalised generalized linear model with a binomial distribution was used to identify CVD-related features. Subsequently, a 20% hold-out set was used to replicate identified features and provide an importance based ranking.ResultsHere we show that non-traditional risk factors are of particular importance in people with diabetes. Classical CVD risk factors (e.g. family history, high blood pressure) rank highly in people without diabetes. For individuals with T2DM but no CVD, top predictors include cystatin C, self-reported health satisfaction, biochemical measures of ill health. In people with diabetes and CVD, key predictors are self-reported ill health and blood cell counts. Unique diabetes-related risk factors include dietary patterns, mental health and biochemistry measures (e.g. oestradiol, rheumatoid factor). Adding these features improves risk stratification; per 1000 people with diabetes, 133 CVD and 165 HF cases receive a higher risk.ConclusionsThis study identifies numerous replicated non-traditional CVD risk factors for people with T2DM, providing insight to improve guideline recommended risk prediction models which currently overlook these features.</p>
4 Authors
- Katarzyna Dziopa
- Nishi Chaturvedi
- Folkert W. Asselbergs
- Amand F. Schmidt
3 Applications
| Application ID | Title |
|---|---|
| 12113 | Harnessing associations of genetic and phenotypic traits for drug target validation, Ref: 12113. |
| 24711 | On the quest for tissue-specific contributions to disease |
| 44972 | A Data Mining-based Workbench: Advancing Precision Medicine by the Use of Machine Learning and Expert Knowledge |
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