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Identifying and ranking non-traditional risk factors for cardiovascular disease prediction in people with type 2
Katarzyna Dziopa1,2,3, Nishi Chaturvedi4, Folkert W Asselbergs5,6,7
1Institute of Health Informatics, University College London, London, UK. katarzyna.dziopa.18@ucl.ac.uk.
Insights
New cardiovascular disease (CVD) predictors identified for type 2 diabetes (T2DM) patients. Non-traditional factors like cystatin C, self-reported health, and biochemistry significantly improve risk prediction in this population.
Area of Science:
- Cardiology
- Endocrinology
- Genetics
Background:
- Cardiovascular disease (CVD) prediction models show poor performance in individuals with type 2 diabetes (T2DM).
- Existing models often overlook crucial risk factors specific to this demographic.
- There is a need to identify novel predictors for various CVD facets in people with T2DM.
Purpose of the Study:
- To identify non-traditional predictors of cardiovascular disease (CVD) in people with type 2 diabetes (T2DM).
- To evaluate the performance of these predictors across six CVD outcomes.
- To enhance current CVD risk prediction models for T2DM patients.
Main Methods:
- Analysis of over 600 features from the UK Biobank, including participants with and without diabetes and CVD.
- Utilized a penalized generalized linear model to identify CVD-related features.
- Employed a 20% hold-out set for feature replication and importance ranking.
Main Results:
- Non-traditional risk factors are highly important for CVD prediction in people with T2DM, unlike those without diabetes where classical factors dominate.
- For T2DM patients without CVD, top predictors include cystatin C, self-reported health satisfaction, and biochemical markers of ill health.
- Unique diabetes-related risk factors identified include dietary patterns, mental health, and specific biochemistry measures (e.g., oestradiol, rheumatoid factor).
- Incorporating these features improved risk stratification, identifying additional cases of CVD and heart failure (HF) at higher risk.
Conclusions:
- Numerous non-traditional CVD risk factors have been identified and replicated in people with T2DM.
- These findings offer valuable insights for improving guideline-recommended risk prediction models.
- Current models need to incorporate these overlooked features to better serve the T2DM population.
Background:
Cardiovascular 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.
Methods:
We 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.
Results:
Here 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.
Conclusions:
This 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.
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