ArticleDiabetologia2021
Data-driven assessment, contextualisation and implementation of 134 variables in the risk for type 2 diabetes: an analysis of Lifelines, a prospective cohort study in the Netherlands.
Article in Diabetologia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Association between stress-related exposures and risk of pre-diabetes and type 2 diabetes: results from the population-based Lifelines cohort study.BMJ open diabetes research & care · 2026Article
- Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.PLoS biology · 2021Article
- Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes.Diabetes care · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
aims/hypothesisWe aimed to assess and contextualise 134 potential risk variables for the development of type 2 diabetes and to determine their applicability in risk prediction.
methodsA total of 96,534 people without baseline diabetes (372,007 person-years) from the Dutch Lifelines cohort were included. We used a risk variable-wide association study (RV-WAS) design to independently screen and replicate risk variables for 5-year incidence of type 2 diabetes. For identified variables, we contextualised HRs, calculated correlations and assessed their robustness and unique contribution in different clinical contexts using bootstrapped and cross-validated lasso regression models. We evaluated the change in risk, or 'HR trajectory', when sequentially assigning variables to a model.
resultsWe identified 63 risk variables, with novel associations for quality-of-life indicators and non-cardiovascular medications (i.e., proton-pump inhibitors, anti-asthmatics). For continuous variables, the increase of 1 SD of HbA
conclusionsMany variables show weak or inconsistent associations with the development of type 2 diabetes, and only a handful can reliably explain disease risk. Newly discovered risk variables will yield little over established factors, and existing prediction models can be simplified. A systematic, data-driven approach to identify risk variables for the prediction of type 2 diabetes is necessary for the practice of precision medicine.
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