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Genetic contributions to the educational inequalities in coronary heart disease incidence: a population-based study of 32 000 middle-aged men and women
  1. Karri Silventoinen1,
  2. Hannu Lahtinen1,2,
  3. Kaarina Korhonen1,2,
  4. Tim T Morris3,
  5. Pekka Martikainen1,2,4
  1. 1Helsinki Institute for Demography and Population Health, University of Helsinki, Helsinki, Finland
  2. 2Max Planck – University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland
  3. 3University College London, London, UK
  4. 4Max-Planck-Institute for Demographic Research, Rostock, Germany
  1. Correspondence to Dr Karri Silventoinen; karri.silventoinen{at}helsinki.fi

Abstract

Background The background of educational disparities in coronary heart disease (CHD) risk is still not well understood. We used a polygenic score for education (PGSEDU), socioeconomic indicators and indicators of CHD risk to investigate whether these disparities result from causality or are influenced by shared factors.

Methods Population-based health surveys including baseline measures on cardiometabolic risk factors at 25–70 years of age (N=32 610) and PGSEDU were conducted in Finland between 1992 and 2011. Longitudinal information on education, social class, income and CHD incidence (1716 CHD cases up to 2019) was based on national registers. Linear regression, Poisson regression, Cox regression and linear structural equation models were used.

Results Education and PGSEDU were inversely associated with body mass index, systolic and diastolic blood pressure, total cholesterol and CHD incidence and positively associated with high-density lipoprotein cholesterol in men and women. Part of the associations of PGSEDU with CHD incidence (57% in men and 28% in women) and cardiometabolic factors (30%–55% and 31%–92%, respectively) were mediated by education, social class and income, but a substantial part of them was independent of socioeconomic factors. These associations were consistent across different levels of education.

Conclusions PGSEDU captures CHD risk that is not solely attributable to education and other socioeconomic indicators. This suggests that not only causality affects the educational disparities of CHD risk but also factors reflected by PGSEDU can contribute to them. Identifying these factors can help to understand and reduce socioeconomic health disparities.

  • CORONARY HEART DISEASE
  • EDUCATION
  • GENETICS
  • Health inequalities

Data availability statement

Data may be obtained from a third party and are not publicly available. The data underlying this article were provided by third party by permission and are not publicly available. Data will be shared on request to the corresponding author with permission of third party.

https://doi.org/10.1136/jech-2024-222618

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    Data availability statement

    Data may be obtained from a third party and are not publicly available. The data underlying this article were provided by third party by permission and are not publicly available. Data will be shared on request to the corresponding author with permission of third party.

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    Footnotes

    • Contributors All authors contributed to the study conception and design. KS performed the analyses and prepared the first draft of the manuscript. HL created the polygenic risk score and helped in the genetic part of the analyses. HL, KK, TTM and PM revised the manuscript critically for important intellectual content. All authors approved the final version of the manuscript are agree to be accountable for all aspects of the work. KS acts as the guarantor for the paper. englishedit.ai has been used to correct the grammar of the manuscript.

    • Funding PM and HL were supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 101019329), the Strategic Research Council (SRC) within the Academy of Finland grants for ACElife (#352543-352572) and LIFECON (# 345219), and grants to the Max Planck–University of Helsinki Center from the Jane and Aatos Erkko Foundation (#210046), the Max Planck Society (# 5714240218), University of Helsinki (#77204227) and Cities of Helsinki, Vantaa and Espoo. TTM is funded by the Economic and Social Research Council (ESRC) (ES/W013142/1).

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.