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Peer-reviewedCC BY-NC-NDMolecular Medicine

Individualized statin associated type 2 diabetes risk estimation with a deep causal model

  • Hao Zhou1,2
  • Jorge Passamani Zubelli3
  • Haralampos Hatzikirou3
  • Andreas Henschel4,5
  • Laurent Alain Najman3,6
  • Daniel E Platt7
  • Antonello Maruotti8,9,10
  • Siobhan O'Sullivan11
  • Lithe Basbous9
  • Cynthia Al Hageh9
  • Mariam Al Harbi12
  • Antoine Abchee13
1 more authors
  • Pierre Zalloua14,15,16
16 affiliations
  1. 1College of Medicine and Health Sciences, Dept of Biological Sciences, Khalifa University, Abu Dhabi, UAE. [email protected].
  2. 2Center for Biotechnology (BTC), Khalifa University, Abu Dhabi, UAE. [email protected].
  3. 3College of Computing and Mathematical Sciences, Dept of Mathematics, Khalifa University, Abu Dhabi, UAE.
  4. 4Department of Computer Science, Khalifa University, Abu Dhabi, UAE.
  5. 5Center for Cyberphysical Systems (C2PS), Khalifa University, Abu Dhabi, UAE.
  6. 6Univ. Gustave Eiffel, Marne-la-Vallée, 77454, France.
  7. 7IBM, New York, NY, USA.
  8. 8Center for Biotechnology (BTC), Khalifa University, Abu Dhabi, UAE.
  9. 9Dept. of Public Health and Epidemiology, Khalifa University, Abu Dhabi, UAE.
  10. 10Dept. GEPLI, LUMSA University, Rome, Italy.
  11. 11College of Medicine and Health Sciences, Dept of Biological Sciences, Khalifa University, Abu Dhabi, UAE.
  12. 12Abu Dhabi Public Health Center, Abu Dhabi, UAE.
  13. 13Faculty of Medicine, University of Balamand, Balamand, Lebanon.
  14. 14Dept. of Public Health and Epidemiology, Khalifa University, Abu Dhabi, UAE. [email protected].
  15. 15Faculty of Medicine, University of Balamand, Balamand, Lebanon. [email protected].
  16. 16Harvard T.H. Chan School of Public Health, Boston, MA, USA. [email protected].
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Abstract

Statin 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. We 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. Recorded 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. Recorded 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.

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Cite

BibTeXRIS
BibTeX
@article{zhou2026individualized,
  title = {{Individualized statin associated type 2 diabetes risk estimation with a deep causal model}},
  author = {Hao Zhou and Jorge Passamani Zubelli and Haralampos Hatzikirou and Andreas Henschel and Laurent Alain Najman and Daniel E Platt and Antonello Maruotti and Siobhan O'Sullivan and Lithe Basbous and Cynthia Al Hageh and Mariam Al Harbi and Antoine Abchee and Pierre Zalloua},
  journal = {Molecular Medicine},
  year = {2026},
  month = aug,
  doi = {10.1186/s10020-026-01589-z},
  url = {https://hal.science/hal-05758630v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Individualized statin associated type 2 diabetes risk estimation with a deep causal model
AU  - Hao Zhou
AU  - Jorge Passamani Zubelli
AU  - Haralampos Hatzikirou
AU  - Andreas Henschel
AU  - Laurent Alain Najman
AU  - Daniel E Platt
AU  - Antonello Maruotti
AU  - Siobhan O'Sullivan
AU  - Lithe Basbous
AU  - Cynthia Al Hageh
AU  - Mariam Al Harbi
AU  - Antoine Abchee
AU  - Pierre Zalloua
PY  - 2026
DA  - 2026/08/03
JO  - Molecular Medicine
DO  - 10.1186/s10020-026-01589-z
UR  - https://hal.science/hal-05758630v1
AB  - Statin 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. We 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. Recorded 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. Recorded 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.
SN  - 1076-1551
SN  - 1528-3658
ER  -