ArticleeLife2026
Heterogeneous associations of polygenic indices of 35 traits with mortality: a register-linked population-based follow-up study.
Article in eLife, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Polygenic indices (PGIs) of various traits abound, but knowledge remains limited on how they predict wide-ranging health indicators, including the risk of death. We investigated the associations between mortality and 35 different PGIs related to social, psychological, and behavioural traits, and typically non-fatal health conditions. Methods: Data consist of Finnish adults from population-representative genetically informed epidemiological surveys (FINRISK 1992-2012, Health 2000/2011, FinHealth 2017), linked to administrative registers (N: 40,097 individuals, 5948 deaths). Within-sibship analysis was complemented with dizygotic twins from Finnish twin study cohorts (N: 10,174 individuals, 2116 deaths). We estimated Cox proportional hazards models with mortality follow-up 1995-2019. Results: PGIs most strongly predictive of all-cause mortality were ever smoking (hazard ratio [HR]=1.12, 95% confidence interval [95% CI] 1.09; 1.14 per one standard deviation larger PGI), self-rated health (HR = 0.90, 95% CI 0.88; 0.93), body mass index (HR = 1.10, 95% CI 1.07; 1.12), educational attainment (HR = 0.91, 95% CI 0.89; 0.94), depressive symptoms (HR = 1.07, 95% CI 1.04; 1.10), and alcohol drinks per week (HR = 1.06, 95% CI 1.04; 1.09). Within-sibship estimates were approximately consistent with the population analysis. The investigated PGIs were typically more predictive for external than for natural causes of death. PGIs were more strongly associated with death occurring at younger ages, while among those who survived to age 80, the PGI-mortality associations were negligible. Conclusions: PGIs related to the best-established mortality risk phenotypes had the strongest associations with mortality. They offer moderate additional prediction even when mutually adjusting with their phenotype. Funding: HL was supported by the European Research Council [grant #101019329] as well as the Max Planck - University of Helsinki Center for Social Inequalities in Population Health. SL gratefully acknowledges funding from the Research Council of Finland (# 350399). PM was supported by the European Research Council under the European Union's Horizon 2020 research and innovation programme (#101019329), the Strategic Research Council (SRC) within the Research Council of Finland grants for ACElife (#352543-352572) and LIFECON (#345219), the Research Council of Finland profiling grant for SWAN (#136528219) and FooDrug (# 136528212), and grants to the Max Planck - University of Helsinki Centre for Social Inequalities in Population Health from the Jane and Aatos Erkko Foundation (#210046), the Max Planck Society (# 5714240218), University of Helsinki (#77204227), and Cities of Helsinki, Vantaa and Espoo (#4706914). The study does not necessarily reflect the Commission's views and in no way anticipates the Commission's future policy in this area. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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