| Title: | Individualized statin associated type 2 diabetes risk estimation with a deep causal model |
| Journal: | Molecular Medicine |
| Published: | 3 Aug 2026 |
| DOI: | https://doi.org/10.1186/s10020-026-01589-z |
Publication 21232
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Abstract
BackgroundStatin therapy reduces cardiovascular risk, but recorded statin exposure is associated with subsequent type 2 diabetes (T2D) in some populations. Individualized risk estimation and separation of low-density lipoprotein cholesterol (LDL-C)-mediated from non-LDL components remain challenging in observational data.MethodsWe analyzed adults free of diagnosed diabetes and without substantial baseline hyperglycemia in UK Biobank, the All of Us Research Program, and the Abu Dhabi Public Health Center cohort. We used weighted survival and regression analyses, UK Biobank exposure-definition and propensity-score sensitivity analyses, two-stage residual inclusion Mendelian randomization mediation, and CausalT2DNet, a balanced deep counterfactual prediction model.ResultsRecorded statin exposure was associated with higher incident T2D risk over cohort-specific horizons. UK Biobank matched-method 10-year sensitivity analyses yielded adjusted risk ratios of 2.18-2.25, and a broader baseline-only IPTW sensitivity model yielded a weighted risk ratio of 1.95. In a separate CausalT2DNet 12-year absolute-risk scenario, predicted T2D risk increased from 2.24% under no statin exposure to 4.05% under statin exposure. Mediation and counterfactual analyses suggested that much of the excess risk was not explained by LDL-C lowering, under their respective assumptions.ConclusionsRecorded statin exposure was consistently associated with higher incident T2D risk across three longitudinal cohorts. The framework supports assumption-explicit research risk estimation and hypothesis generation and requires external recalibration and prospective validation before clinical use.</p>
13 Authors
- Hao Zhou
- Jorge Passamani Zubelli
- Haralampos Hatzikirou
- Andreas Henschel
- Laurent Alain Najman
- Daniel E. Platt
- Antonello Maruotti
- Siobhan O'Sullivan
- Lithe Basbous
- Cynthia Al Hageh
- Mariam Al Harbi
- Antoine Abchee
- Pierre Zalloua
1 Application
| Application ID | Title |
|---|---|
| 64823 | A Genomic Data Science Framework for the 1000 Arab genome project |
Enabling scientific discoveries that improve human health