C. Cunha, M. Garcia-Urena, R. Sanz Martlnez, M. J. Romero-Lado, M. V. Fernandez, O. A. Andreassen, R. Sims, M. Tsolaki, K. Sleegers, M. Hiltunen, G. Nicolas, P. Sanchez-Juan, M. Ingelsson, V. Giedraitis, R. Ghidoni, H. Holstege, C. van Duijn, S. van der Lee, A. Ramirez, C. Bellenguez, J.-C. Lambert, R. Frikke-Schmidt, EADB, T. O. Kilpeläinen, R. J. F. Loos
Body mass index (BMI), type 2 diabetes (T2D) and related cardiometabolic features associate with Alzheimer's disease (AD) risk, yet shared mechanisms remain poorly understood. By combining multi-trait, machine learning and single-cell transcriptomics with genotyping data for BMI and T2D, we investigate how these traits converge on shared genetic pathways to AD risk. Variant-level analyses reveal AD risk associates with genetically-driven hypotension and hypoglycaemia. We identify sixteen high-confidence effector genes in seven independent loci colocalizing between BMI/T2D and AD, mapping primarily to peripheral immune-metabolic tissues and cell-types. Eight high-confidence risk genes are currently targeted by ongoing therapeutic programmes, substantiating translatability relevance. Exploratory approaches using sex- and age-stratified genotyping data for BMI and T2D pinpoints potential sex-specific cardiometabolic liability linked to higher BMI-associated risk in women and T2D-driven risk in men. These findings elucidate shared cardiometabolic correlates with AD, prioritizes biological pathways, and reveal computationally-nominated drug candidates for preclinical testing.