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INTRODUCTION: Both educational attainment and genetic propensity to education (PGS Edu) have been associated with geographic mobility. Socioeconomic conditions are, in turn, associated with individuals' health. Geographic mobility could therefore lead to better health for some since it could provide better opportunities, like education. Our aim was to study how attained education and genetic predisposition for higher education are related to geographic mobility, and how they affect the association between geographic mobility and mortality. METHODS: We used data from the Swedish Twin Registry (twins born 1926-1955; n = 14,211) in logistic regression models to test if attained education and PGS Edu predicted geographic mobility. Cox regression models were then performed to test if geographic mobility, attained education, and PGS Edu were associated with mortality. RESULTS: The results show that both attained education and PGS Edu predicted geographic mobility, in both independent and joint effect models, with higher education associated with higher mobility. Geographic mobility was associated with lower mortality in the independent effect model, but joint effect models showed that this association was completely explained by attained education. CONCLUSIONS: To conclude, both attained education and PGS Edu were associated with geographic mobility. Moreover, attained education explained the relationship between geographic mobility and mortality.

Background: Evidence indicates that the adverse health effects of obesity differ between genetically and environmentally influenced obesity. We examined differences in the association between obesity and cardiovascular disease (CVD) between individuals with a genetically predicted low, medium, or high body mass index (BMI). Methods: We used cohort data from Swedish twins born before 1959 who had BMI measured between the ages of 40–64 years (midlife) or at the age of 65 years or later (late-life), or both, and prospective CVD information from nationwide register linkage through 2016. A polygenic score for BMI (PGSBMI) was used to define genetically predicted BMI. Individuals missing BMI or covariate data, or diagnosed with CVD at first BMI measure, were excluded, leaving an analysis sample of 17,988 individuals. We applied Cox proportional hazard models to examine the association between BMI category and incident CVD, stratified by the PGSBMI. Co-twin control models were applied to adjust for genetic influences not captured by the PGSBMI. Findings: Between 1984 and 2010, the 17,988 participants were enrolled in sub-studies of the Swedish Twin Registry. Midlife obesity was associated with a higher risk of CVD across all PGSBMI categories, but the association was stronger with genetically predicted lower BMI (hazard ratio from 1.55 to 2.08 for those with high and low PGSBMI, respectively). Within monozygotic twin pairs, the association did not differ by genetically predicted BMI, indicating genetic confounding not captured by the PGSBMI. Results were similar when obesity was measured in late-life, but suffered from low power. Interpretation: Obesity was associated with CVD regardless of PGSBMI category, but obesity influenced by genetic predisposition (genetically predicted high BMI) was less harmful than obesity influenced by environmental factors (obesity despite genetically predicted low BMI). However, additional genetic factors, not captured by the PGSBMI, still influence the associations. Funding: The Strategic Research Program in Epidemiology at Karolinska Institutet; Loo and Hans Osterman's Foundation; Foundation for Geriatric Diseases at Karolinska Institutet; the Swedish Research Council for Health, Working Life and Welfare; the Swedish Research Council; and the National Institutes of Health. CC BY 4.0© 2023 The Author(s)Corresponding author: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-1177 Stockholm, Sweden.E-mail address: ida.karlsson@ki.se (I.K. Karlsson).This work was supported by the Strategic Research Program in Epidemiology at Karolinska Institutet; Loo and Hans Osterman’s Foundation (2022-01222); Foundation for Geriatric Diseases at Karolinska Institutet (2022-01296); the Swedish Research Council for Health, Working Life and Welfare (2018-01201 and 2022-00672); the Swedish Research Council (2016-03081); and the National Institutes of Health (R01 AG060470). We acknowledge the Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant no. 2017-00641. The STR sub-studies were supported by the National Institutes of Health (grants R01 AG10175, R01 AG08724, R01 AG08861, R01 AG028555, and U01 DK066134), the MacArthur Foundation Research Network on Successful Aging, the Axel and Margaret Ax:son Johnsons Foundation, the Swedish Research Council, the Swedish Foundation for Health Care Sciences and Allergy Research, and the Swedish Council for Working Lifeand Social Research (2013-2292).