Triangulated causal inference with deep counterfactual learningfor individualized statin-associated type 2 diabetes risk

Determining the causal architecture of drug induced metabolic side effects is a fundamental challenge in digital medicine. While statins are the cornerstone of cardiovascular prevention, their association with type 2 diabetes incidence, particularly in normoglycemic individuals, remains a critical clinical concern. We addressed this problem using a triangulated causal inference framework that integrated two stage residual inclusion Mendelian randomization with CausalT2DNet, a deep counterfactual neural network that uses Maximum Mean Discrepancy and Standardized Mean Difference penalties to improve covariate balance. Analyses of 12 year longitudinal data from the UK Biobank, including more than 100,000 normoglycemic individuals, validation in the diverse All of Us cohort, and confirmatory analysis in a newly assembled Middle Eastern health system cohort in the United Arab Emirates showed that statin initiation was associated with a marked elevation in type 2 diabetes risk. Adjusted 10 year risk ratios were 2.30 in the UK cohort and 2.54 in the US cohort, and the adjusted 5 year risk ratio was 2.01 in the UAE cohort. All associations were highly statistically significant. Causal decomposition on the log odds scale indicated that this excess risk is predominantly driven by an LDL independent pathway, with a direct effect log odds of 0.4198 and a P value of 4.88 × 10⁻⁷⁷, whereas the LDL mediated component was modest and not statistically significant, with a Sobel P value of 0.1088. CausalT2DNet estimated that statin exposure increases 12 year type 2 diabetes risk from 2.24 percent to 4.05 percent, with 1.29 percentage points of this excess risk attributable to non lipid mechanisms on the risk scale. The resulting counterprediction framework yields individualized type 2 diabetes risk projections under alternative statin treatment scenarios, providing a scalable digital health tool to support shared clinical decision making.