SummaryBackground Metabolic dysfunction is a major risk factor for neurodegeneration, yet the genetic architecture linking systemic metabolism to Alzheimer's disease (AD) and Parkinson's disease (PD) remains unclear. Methods We integrated genome-wide association data for 249 circulating metabolites and proglucagon with summary statistics for AD, PD, and cardiometabolic traits. Genetic correlations, polygenic overlap, causal relationships, and shared genetic loci were quantified using linkage disequilibrium score regression, high-definition likelihood, bivariate mixture modelling, Mendelian randomisation, and conjunctional false discovery rate analyses, followed by functional and tissue-specific enrichment analyses. Findings AD displayed a metabolic-genetic profile aligned with body mass index, type 2 diabetes, coronary artery disease, and stroke, whereas PD exhibited largely opposing patterns (Spearman's rs = −0.26). Mendelian randomization analyses supported causal effects of lipoprotein subclasses, glutamine, and proglucagon on AD risk, with opposite or null effects in PD. Shared loci between metabolites and AD were enriched for lipid metabolism and cholesterol transport, whereas PD-associated loci were enriched for mitochondrial function, vesicle trafficking, and stress-response signalling. Interpretation AD and PD are shaped by fundamentally distinct metabolic-genetic architectures. Metabolically targeted interventions, particularly those modulating lipid, amino acid, and proglucagon pathways, may require disease-specific and genetically informed strategies for prevention and treatment of neurodegenerative diseases. Funding Novo Nordisk Foundation (NNF23OC0099658), Marie Skłodowska-Curie Actions (801133), the Research Council of Norway (334920, 351751, 296030, 324252, 324499, 326813), the National Institutes of Health (U24DA041123, R01AG076838, U24DA055330, OT2HL161847, 5R01MH124839-02), NordForsk (164218), South-Eastern Norway Regional Health Authority (2020060), and the European Union's Horizon 2020 (847776, 964874, 101057454).
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Stinson et al. (Fri,) studied this question.
synapsesocial.com/papers/69db37ca4fe01fead37c5cce — DOI: https://doi.org/10.1016/j.ebiom.2026.106254
Sara Stinson
Alexey A. Shadrin
Zillur Rahman
EBioMedicine
University of California, San Diego
University of Oslo
University of Bergen
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