Evidence map›Paper›PMID 33437953›Full record

ArticleEClinicalMedicine2020

Education, biological ageing, all-cause and cause-specific mortality and morbidity: UK biobank cohort study.

Marc Chadeau-Hyam, Barbara Bodinier, Roel Vermeulen, Maryam Karimi, Verena Zuber, Raphaële Castagné, Joshua Elliott, David Muller, Dusan Petrovic, Matthew Whitaker and 7 more

Abstract read
In one paragraph

Article in EClinicalMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed.

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  7. Stress, Well-Being, and Reproduction in Wildlife.Advances in experimental medicine and biology · 2026
    Review
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  14. Impact ofProceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Marc Chadeau-HyamDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Barbara BodinierDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Roel VermeulenDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Maryam KarimiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Verena ZuberDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Raphaële CastagnéLEASP, UMR 1027, Inserm-Université Toulouse III Paul Sabatier, Toulouse, France.
Joshua ElliottDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
David MullerDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Dusan PetrovicDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Matthew WhitakerDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Silvia StringhiniUniversity Centre for General Medicine and Public Health (UNISANTE), Lausanne University, Lausanne, Switzerland.
Ioanna TzoulakiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Mika KivimäkiDepartment of Epidemiology and Public Health, University College London, London, United Kingdom.
Paolo VineisDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Paul ElliottDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.
Michelle Kelly-IrvingInstitute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, Netherlands.
Cyrille DelpierreInstitute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, Netherlands.

Funding

Education, socioeconomic status and Aging: transitions from multimorbidity to functional limitations and mortalityR01AG056477 · NIA · UNIVERSITY COLLEGE LONDON · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2018 to 2022
$2.8M
Cancer Research UK 22184Medical Research Council MC_PC_17228Medical Research Council MC_QA137853Medical Research Council MR/L01341X/1Medical Research Council MR/L01632X/1Medical Research Council MR/R024227/1Medical Research Council MR/S019669/1NIA NIH HHS R01 AG056477
6 · The paper itself

Abstract

backgroundSocioeconomic position as measured by education may be embodied and affect the functioning of key physiological systems. Links between social disadvantage, its biological imprint, and cause-specific mortality and morbidity have not been investigated in large populations, and yet may point towards areas for public health interventions beyond targeting individual behaviours.

methodsUsing data from 366,748 UK Biobank participants with 13 biomarker measurements, we calculated a Biological Health Score (BHS, ranging from 0 to 1) capturing the level of functioning of five physiological systems. Associations between BHS and incidence of cardiovascular disease (CVD) and cancer, and mortality from all, CVD, cancer, and external causes were examined. We explored the role of education in these associations. Mendelian randomisation using genetic evidence was used to triangulate these findings.

findingsAn increase in BHS of 0.1 was associated with all-cause (HR = 1.14 [1.12-1.16] and 1.09 [1.07-1.12] in men and women respectively), cancer (HR = 1.11 [1.09-1.14] and 1.07 [1.04-1.10]) and CVD (HR = 1.25 [1.20-1.31] and 1.21 [1.11-1.31]) mortality, CVD incidence (HR = 1.15 [1.13-1.16] and 1.17 [1.15-1.19]). These associations survived adjustment for education, lifestyle-behaviours, body mass index (BMI), co-morbidities and medical treatments. Mendelian randomisation further supported the link between the BHS and CVD incidence (HR = 1.31 [1.21-1.42]). The BHS contributed to CVD incidence prediction (age-adjusted C-statistic = 0.58), other than through education and health behaviours.

interpretationThe BHS captures features of the embodiment of education, health behaviours, and more proximal unknown factors which all complementarily contribute to all-cause, cancer and CVD morbidity and premature death.

Indexed as

Allostatic load mortalityBiological ageingBiomarkersIncidentpathologiesMendelian randomisationProspective cohortSocial embeddingUk biobank

Identifiers

PMID33437953
PMCPMC7788440

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.