Marc Chadeau-Hyam1,2, Barbara Bodinier1,2, Roel Vermeulen1,3, Maryam Karimi1,2, Verena Zuber1,2, Raphaële Castagné4, Joshua Elliott1,2, David Muller1,2, Dusan Petrovic1,2, Matthew Whitaker1,2, Silvia Stringhini5,6, Ioanna Tzoulaki1,2,7, Mika Kivimäki8,9, Paolo Vineis1,2,10, Paul Elliott1,2,11,12, Michelle Kelly-Irving3, Cyrille Delpierre3. 1. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. 2. MRC Centre for Environment and Health, Imperial College, London, United Kingdom. 3. Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, Netherlands. 4. LEASP, UMR 1027, Inserm-Université Toulouse III Paul Sabatier, Toulouse, France. 5. University Centre for General Medicine and Public Health (UNISANTE), Lausanne University, Lausanne, Switzerland. 6. Unit of Population Epidemiology, Department of Primary Care, Geneva University Hospitals, Geneva, Switzerland. 7. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece. 8. Department of Epidemiology and Public Health, University College London, London, United Kingdom. 9. Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 10. Italian Institute for Genomic Medicine IIGM, Torino, Italy. 11. National Institute for Health Research, Biomedical Research Centre, Imperial College London, London, United Kingdom. 12. Health Data Research UK London at Imperial College London, London, London, United Kingdom.
Abstract
BACKGROUND: Socioeconomic 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. METHODS: Using 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. FINDINGS: An 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. INTERPRETATION: The 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.
BACKGROUND: Socioeconomic 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. METHODS: Using 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. FINDINGS: An 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. INTERPRETATION: The 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.
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