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5. Towards a consensus definition of allostatic load: a multi-cohort, multi-system, multi-biomarker individual participant data (IPD) meta-analysis

Author

McCrory, C., McLoughlin, S., Layte, R., NiCheallaigh, C., O'Halloran, A.M., Barros, H., Berkman, L.F., Bochud, M., M Crimmins, E., T Farrell, M., Fraga, S., Grundy, E., Kelly-Irving, M., Petrovic, D., Seeman, T., Stringhini, S., Vollenveider, P., and Kenny, R.A.

Subjects
Humans, Glycated Hemoglobin, Allostasis/physiology, Consensus, Biomarkers, C-Reactive Protein/analysis, Cohort Studies, Allostatic load, Biomarker, Cohort study, Cumulative physiological dysregulation, Individual participant data meta-analysis
Abstract

Allostatic load (AL) is a multi-system composite index for quantifying physiological dysregulation caused by life course stressors. For over 30 years, an extensive body of research has drawn on the AL framework but has been hampered by the lack of a consistent definition. This study analyses data for 67,126 individuals aged 40-111 years participating in 13 different cohort studies and 40 biomarkers across 12 physiological systems: hypothalamic-pituitary-adrenal (HPA) axis, sympathetic-adrenal-medullary (SAM) axis, parasympathetic nervous system functioning, oxidative stress, immunological/inflammatory, cardiovascular, respiratory, lipidemia, anthropometric, glucose metabolism, kidney, and liver. We use individual-participant-data meta-analysis and exploit natural heterogeneity in the number and type of biomarkers that have been used across studies, but a common set of health outcomes (grip strength, walking speed, and self-rated health), to determine the optimal configuration of parameters to define the concept. There was at least one biomarker within 9/12 physiological systems that was reliably and consistently associated in the hypothesised direction with the three health outcomes in the meta-analysis of these cohorts: dehydroepiandrosterone sulfate (DHEAS), low frequency-heart rate variability (LF-HRV), C-reactive protein (CRP), resting heart rate (RHR), peak expiratory flow (PEF), high density lipoprotein cholesterol (HDL-C), waist-to-height ratio (WtHR), HbA1c, and cystatin C. An index based on five biomarkers (CRP, RHR, HDL-C, WtHR and HbA1c) available in every study was found to predict an independent outcome - mortality - as well or better than more elaborate sets of biomarkers. This study has identified a brief 5-item measure of AL that arguably represents a universal and efficient set of biomarkers for capturing physiological 'wear and tear' and a further biomarker (PEF) that could usefully be included in future data collection.

Published
2023

6. Changes in socioeconomic resources and mental health after the second COVID-19 wave (2020-2021): a longitudinal study in Switzerland

Author

Tancredi, S., Ulyte, A., Wagner, C., Keidel, D., Witzig, M., Imboden, M., Probst-Hensch, N., Amati, R., Albanese, E., Levati, S., Crivelli, L., Kohler, P., Cusini, A., Kahlert, C., Harju, E., Michel, G., Ludi, C., Ortega, N., Baggio, S., Chocano-Bedoya, P., Rodondi, N., Ballouz, T., Frei, A., Kaufmann, M., Von Wyl, V., Lorthe, E., Baysson, H., Stringhini, S., Schneider, V., Kaufmann, L., Wieber, F., Volken, T., Zysset, A., Dratva, J., Cullati, S., and Corona Immunitas Research Group

Abstract

BACKGROUND: During the 2020/2021 winter, the labour market was under the impact of the COVID-19 pandemic. Changes in socioeconomic resources during this period could have influenced individual mental health. This association may have been mitigated or exacerbated by subjective risk perceptions, such as perceived risk of getting infected with SARS-CoV-2 or perception of the national economic situation. Therefore, we aimed to determine if changes in financial resources and employment situation during and after the second COVID-19 wave were prospectively associated with depression, anxiety and stress, and whether perceptions of the national economic situation and of the risk of getting infected modified this association. METHODS: One thousand seven hundred fifty nine participants from a nation-wide population-based eCohort in Switzerland were followed between November 2020 and September 2021. Financial resources and employment status were assessed twice (Nov2020-Mar2021, May-Jul 2021). Mental health was assessed after the second measurement of financial resources and employment status, using the Depression, Anxiety and Stress Scale (DASS-21). We modelled DASS-21 scores with linear regression, adjusting for demographics, health status, social relationships and changes in workload, and tested interactions with subjective risk perceptions. RESULTS: We observed scores above thresholds for normal levels for 16% (95%CI = 15-18) of participants for depression, 8% (95%CI = 7-10) for anxiety, and 10% (95%CI = 9-12) for stress. Compared to continuously comfortable or sufficient financial resources, continuously precarious or insufficient resources were associated with worse scores for all outcomes. Increased financial resources were associated with higher anxiety. In the working-age group, shifting from full to part-time employment was associated with higher stress and anxiety. Perceiving the Swiss economic situation as worrisome was associated with higher anxiety in participants who lost financial resources or had continuously precarious or insufficient resources. CONCLUSION: This study confirms the association of economic stressors and mental health during the COVID-19 pandemic and highlights the exacerbating role of subjective risk perception on this association.

Published
2023

7. Association between education and quality of diabetes care in Switzerland

Author

Flatz A, Casillas A, Stringhini S, Zuercher E, Burn, B, and Peytremann-Bridevaux I

Subjects
Medicine (General), R5-920
Abstract

Aline Flatz, Alejandra Casillas, Silvia Stringhini, Emilie Zuercher, Bernard Burnand, Isabelle Peytremann-BridevauxInstitute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, SwitzerlandPurpose: Low socioeconomic status is associated with higher prevalence of diabetes, worse outcomes, and worse quality of care. We explored the relationship between education, as a measure of socioeconomic status, and quality of care in the Swiss context.Patients and methods: Data were drawn from a population-based survey of 519 adults with diabetes during fall 2011 and summer 2012 in a canton of Switzerland. We assessed patients and diabetes characteristics. Eleven indicators of quality of care were considered (six of process and five of outcomes of care). After bivariate analyses, regression analyses adjusted for age, sex, and diabetic complications were performed to assess the relationship between education and quality of care.Results: Of 11 quality-of-care indicators, three were significantly associated with education: funduscopy (patients with tertiary versus primary education were more likely to get the exam: odds ratio, 1.8; 95% confidence interval [CI], 1.004–3.3) and two indicators of health-related quality of life (patients with tertiary versus primary education reported better health-related quality of life: Audit of Diabetes-Dependent Quality of Life: β=0.6 [95% CI, 0.2–0.97]; SF-12 mean physical component summary score: β=3.6 [95% CI, 0.9–6.4]).Conclusion: Our results suggest the presence of educational inequalities in quality of diabetes care. These findings may help health professionals focus on individuals with increased needs to decrease health inequalities.Keywords: primary care, education, quality of care, diabetes

Published
2015

9. Association of neighbourhood disadvantage and individual socioeconomic position with all-cause mortality: a longitudinal multicohort analysis.

Author

Ribeiro, AI, Fraga, S, Severo, M, Kelly-Irving, M, Delpierre, C, Stringhini, S, Kivimaki, M, Joost, S, Guessous, I, Severi, G, Giles, G, Sacerdote, C, Vineis, P, Barros, H, LIFEPATH Consortium, Ribeiro, AI, Fraga, S, Severo, M, Kelly-Irving, M, Delpierre, C, Stringhini, S, Kivimaki, M, Joost, S, Guessous, I, Severi, G, Giles, G, Sacerdote, C, Vineis, P, Barros, H, and LIFEPATH Consortium

Abstract

BACKGROUND: Few studies have examined the interactions between individual socioeconomic position and neighbourhood deprivation and the findings so far are heterogeneous. Using a large sample of diverse cohorts, we investigated the interaction effect of neighbourhood socioeconomic deprivation and individual socioeconomic position, assessed using education, on mortality. METHODS: We did a longitudinal multicohort analysis that included six cohort studies participating in the European LIFEPATH consortium: the CoLaus (Lausanne, Switzerland), E3N (France), EPIC-Turin (Turin, Italy), EPIPorto (Porto, Portugal), Melbourne Collaborative Cohort Study (Melbourne, VIC, Australia), and Whitehall II (London, UK) cohorts. All participants with data on mortality, educational attainment, and neighbourhood deprivation were included in the present study. The data sources were the databases of each cohort study. Poisson regression was used to estimate the mortality rates and associations (relative risk, 95% CIs) with neighbourhood deprivation (Q1 being least deprived to Q5 being the most deprived). Baseline educational attainment was used as an indicator of individual socioeconomic position. Estimates were combined using pooled analysis and the relative excess risk due to the interaction was computed to identify additive interactions. FINDINGS: The cohorts comprised a total population of 168 801 individuals. The recruitment dates were 2003-06 for CoLaus, 1989-91 for E3N, 1992-98 for EPIC-Turin, 1999-2003 for EPIPorto, 1990-94 for MCCS, and 1991-94 for Whitehall II. We use baseline data only and mortality data obtained using record linkage. Age-adjusted mortality rates were higher among participants residing in more deprived neighbourhoods than those in the least deprived neighbourhoods (Q1 least deprived neighbourhoods, 369·7 per 100 000 person-years [95% CI 356·4-383·2] vs Q5-most deprived neighbourhoods 445·7 per 100 000 person-years [430·2-461·7]), but the magnitude of the associat

Published
2022

10. Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking

Author

Petrovic, D, Bodinier, B, Dagnino, S, Whitaker, M, Karimi, M, Campanella, G, Haugdahl Nost, T, Polidoro, S, Palli, D, Krogh, V, Tumino, R, Sacerdote, C, Panico, S, Lund, E, Dugue, P-A, Giles, GG, Severi, G, Southey, M, Vineis, P, Stringhini, S, Bochud, M, Sandanger, TM, Vermeulen, RCH, Guida, F, Chadeau-Hyam, M, Petrovic, D, Bodinier, B, Dagnino, S, Whitaker, M, Karimi, M, Campanella, G, Haugdahl Nost, T, Polidoro, S, Palli, D, Krogh, V, Tumino, R, Sacerdote, C, Panico, S, Lund, E, Dugue, P-A, Giles, GG, Severi, G, Southey, M, Vineis, P, Stringhini, S, Bochud, M, Sandanger, TM, Vermeulen, RCH, Guida, F, and Chadeau-Hyam, M

Abstract

Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.

Published
2022

11. Sars-CoV2- infection as a trigger of humoral response against apolipoprotein A-1

Author

Pagano, S, primary, Yerly, S, additional, Suh, N, additional, Le Terrier, C, additional, Farrera-Soler, L, additional, Piumatti, G, additional, Eberhardt, C S, additional, Siegrist, C A, additional, Eckerle, I, additional, Stringhini, S, additional, Guessous, I, additional, Kaiser, L, additional, Pugin, J, additional, Winssinger, N, additional, and Vuilleumier, N, additional

Published
2021
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12. SARS-COV2- infection as a trigger of humoral response against apolipoprotein A-1

Author

Pagano, S., primary, Yery, S., additional, Meyer, B., additional, Juillard, C., additional, Suh, N., additional, Le Terrier, C., additional, Daguer, J.-P., additional, Lluc, F.-S., additional, Barluenga, S., additional, Piumatti, G., additional, Hartley, O., additional, Lemaitre, B., additional, Eberhardt, C.S., additional, Siegrist, C.-A., additional, Eckerle, I., additional, Stringhini, S., additional, Guessous, I., additional, Kaiser, L., additional, Pugin, J., additional, Winssinger, N., additional, and Vuilleumier, N., additional

Published
2021
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13. Reducing socio-economic inequalities in all-cause mortality: A counterfactual mediation approach.

Author

Laine J.E., Baltar V.T., Stringhini S., Gandini M., Chadeau-Hyam M., Kivimaki M., Severi G., Perduca V., Hodge A.M., Dugue P.-A., Giles G.G., Milne R.L., Barros H., Sacerdote C., Krogh V., Panico S., Tumino R., Goldberg M., Zins M., Delpierre C., Laine J.E., Baltar V.T., Stringhini S., Gandini M., Chadeau-Hyam M., Kivimaki M., Severi G., Perduca V., Hodge A.M., Dugue P.-A., Giles G.G., Milne R.L., Barros H., Sacerdote C., Krogh V., Panico S., Tumino R., Goldberg M., Zins M., and Delpierre C.

Abstract

Background: Socio-economic inequalities in mortality are well established, yet the contribution of intermediate risk factors that may underlie these relationships remains unclear. We evaluated the role of multiple modifiable intermediate risk factors underlying socio- economic-associated mortality and quantified the potential impact of reducing early allcause mortality by hypothetically altering socio-economic risk factors. Method(s): Data were from seven cohort studies participating in the LIFEPATH Consortium (total n 179 090). Using both socio-economic position (SEP) (based on occupation) and education, we estimated the natural direct effect on all-cause mortality and the natural indirect effect via the joint mediating role of smoking, alcohol intake, dietary patterns, physical activity, body mass index, hypertension, diabetes and coronary artery disease. Hazard ratios (HRs) were estimated, using counterfactual natural effect models under different hypothetical actions of either lower or higher SEP or education. Result(s): Lower SEP and education were associated with an increase in all-cause mortality within an average follow-up time of 17.5 years. Mortality was reduced via modelled hypothetical actions of increasing SEP or education. Through higher education, the HR was 0.85 [95% confidence interval (CI) 0.84, 0.86] for women and 0.71 (95% CI 0.70, 0.74) for men, compared with lower education. In addition, 34% and 38% of the effect was jointly mediated for women and men, respectively. The benefits from altering SEP were slightly more modest. Conclusion(s): These observational findings support policies to reduce mortality both through improving socio-economic circumstances and increasing education, and by altering intermediaries, such as lifestyle behaviours and morbidities.Copyright © The Author(s) 2019.

Published
2021

14. Meta-analyses identify DNA methylation associated with kidney function and damage

Author

Schlosser, P. (Pascal), Tin, A. (Adrienne), Matias-Garcia, P. R. (Pamela R.), Thio, C. H. (Chris H. L.), Joehanes, R. (Roby), Liu, H. (Hongbo), Weihs, A. (Antoine), Yu, Z. (Zhi), Hoppmann, A. (Anselm), Grundner-Culemann, F. (Franziska), Min, J. L. (Josine L.), Adeyemo, A. A. (Adebowale A.), Agyemang, C. (Charles), Arnlov, J. (Johan), Aziz, N. A. (Nasir A.), Baccarelli, A. (Andrea), Bochud, M. (Murielle), Brenner, H. (Hermann), Breteler, M. M. (Monique M. B.), Carmeli, C. (Cristian), Chaker, L. (Layal), Chambers, J. C. (John C.), Cole, S. A. (Shelley A.), Coresh, J. (Josef), Corre, T. (Tanguy), Correa, A. (Adolfo), Cox, S. R. (Simon R.), de Klein, N. (Niek), Delgado, G. E. (Graciela E.), Domingo-Relloso, A. (Arce), Eckardt, K.-U. (Kai-Uwe), Ekici, A. B. (Arif B.), Endlich, K. (Karlhans), Evans, K. L. (Kathryn L.), Floyd, J. S. (James S.), Fornage, M. (Myriam), Franke, L. (Lude), Fraszczyk, E. (Eliza), Gao, X. (Xu), Gao, X. (Xin), Ghanbari, M. (Mohsen), Ghasemi, S. (Sahar), Gieger, C. (Christian), Greenland, P. (Philip), Grove, M. L. (Megan L.), Harris, S. E. (Sarah E.), Hemani, G. (Gibran), Henneman, P. (Peter), Herder, C. (Christian), Horvath, S. (Steve), Hou, L. (Lifang), Hurme, M. A. (Mikko A.), Hwang, S.-J. (Shih-Jen), Järvelin, M.-R. (Marjo-Riitta), Kardia, S. L. (Sharon L. R.), Kasela, S. (Silva), Kleber, M. E. (Marcus E.), Koenig, W. (Wolfgang), Kooner, J. S. (Jaspal S.), Kramer, H. (Holly), Kronenberg, F. (Florian), Kuhnel, B. (Brigitte), Lehtimaki, T. (Terho), Lind, L. (Lars), Liu, D. (Dan), Liu, Y. (Yongmei), Lloyd-Jones, D. M. (Donald M.), Lohman, K. (Kurt), Lorkowski, S. (Stefan), Lu, A. T. (Ake T.), Marioni, R. E. (Riccardo E.), Marz, W. (Winfried), McCartney, D. L. (Daniel L.), Meeks, K. A. (Karlijn A. C.), Milani, L. (Lili), Mishra, P. P. (Pashupati P.), Nauck, M. (Matthias), Navas-Acien, A. (Ana), Nowak, C. (Christoph), Peters, A. (Annette), Prokisch, H. (Holger), Psaty, B. M. (Bruce M.), Raitakari, O. T. (Olli T.), Ratliff, S. M. (Scott M.), Reiner, A. P. (Alex P.), Rosas, S. E. (Sylvia E.), Schottker, B. (Ben), Schwartz, J. (Joel), Sedaghat, S. (Sanaz), Smith, J. A. (Jennifer A.), Sotoodehnia, N. (Nona), Stocker, H. R. (Hannah R.), Stringhini, S. (Silvia), Sundstrom, J. (Johan), Swenson, B. R. (Brenton R.), Tellez-Plaza, M. (Maria), van Meurs, J. B. (Joyce B. J.), van Vliet-Ostaptchouk, J. V. (Jana V.), Venema, A. (Andrea), Verweij, N. (Niek), Walker, R. M. (Rosie M.), Wielscher, M. (Matthias), Winkelmann, J. (Juliane), Wolffenbuttel, B. H. (Bruce H. R.), Zhao, W. (Wei), Zheng, Y. (Yinan), Loh, M. (Marie), Snieder, H. (Harold), Levy, D. (Daniel), Waldenberger, M. (Melanie), Susztak, K. (Katalin), Kottgen, A. (Anna), Teumer, A. (Alexander), Schlosser, P. (Pascal), Tin, A. (Adrienne), Matias-Garcia, P. R. (Pamela R.), Thio, C. H. (Chris H. L.), Joehanes, R. (Roby), Liu, H. (Hongbo), Weihs, A. (Antoine), Yu, Z. (Zhi), Hoppmann, A. (Anselm), Grundner-Culemann, F. (Franziska), Min, J. L. (Josine L.), Adeyemo, A. A. (Adebowale A.), Agyemang, C. (Charles), Arnlov, J. (Johan), Aziz, N. A. (Nasir A.), Baccarelli, A. (Andrea), Bochud, M. (Murielle), Brenner, H. (Hermann), Breteler, M. M. (Monique M. B.), Carmeli, C. (Cristian), Chaker, L. (Layal), Chambers, J. C. (John C.), Cole, S. A. (Shelley A.), Coresh, J. (Josef), Corre, T. (Tanguy), Correa, A. (Adolfo), Cox, S. R. (Simon R.), de Klein, N. (Niek), Delgado, G. E. (Graciela E.), Domingo-Relloso, A. (Arce), Eckardt, K.-U. (Kai-Uwe), Ekici, A. B. (Arif B.), Endlich, K. (Karlhans), Evans, K. L. (Kathryn L.), Floyd, J. S. (James S.), Fornage, M. (Myriam), Franke, L. (Lude), Fraszczyk, E. (Eliza), Gao, X. (Xu), Gao, X. (Xin), Ghanbari, M. (Mohsen), Ghasemi, S. (Sahar), Gieger, C. (Christian), Greenland, P. (Philip), Grove, M. L. (Megan L.), Harris, S. E. (Sarah E.), Hemani, G. (Gibran), Henneman, P. (Peter), Herder, C. (Christian), Horvath, S. (Steve), Hou, L. (Lifang), Hurme, M. A. (Mikko A.), Hwang, S.-J. (Shih-Jen), Järvelin, M.-R. (Marjo-Riitta), Kardia, S. L. (Sharon L. R.), Kasela, S. (Silva), Kleber, M. E. (Marcus E.), Koenig, W. (Wolfgang), Kooner, J. S. (Jaspal S.), Kramer, H. (Holly), Kronenberg, F. (Florian), Kuhnel, B. (Brigitte), Lehtimaki, T. (Terho), Lind, L. (Lars), Liu, D. (Dan), Liu, Y. (Yongmei), Lloyd-Jones, D. M. (Donald M.), Lohman, K. (Kurt), Lorkowski, S. (Stefan), Lu, A. T. (Ake T.), Marioni, R. E. (Riccardo E.), Marz, W. (Winfried), McCartney, D. L. (Daniel L.), Meeks, K. A. (Karlijn A. C.), Milani, L. (Lili), Mishra, P. P. (Pashupati P.), Nauck, M. (Matthias), Navas-Acien, A. (Ana), Nowak, C. (Christoph), Peters, A. (Annette), Prokisch, H. (Holger), Psaty, B. M. (Bruce M.), Raitakari, O. T. (Olli T.), Ratliff, S. M. (Scott M.), Reiner, A. P. (Alex P.), Rosas, S. E. (Sylvia E.), Schottker, B. (Ben), Schwartz, J. (Joel), Sedaghat, S. (Sanaz), Smith, J. A. (Jennifer A.), Sotoodehnia, N. (Nona), Stocker, H. R. (Hannah R.), Stringhini, S. (Silvia), Sundstrom, J. (Johan), Swenson, B. R. (Brenton R.), Tellez-Plaza, M. (Maria), van Meurs, J. B. (Joyce B. J.), van Vliet-Ostaptchouk, J. V. (Jana V.), Venema, A. (Andrea), Verweij, N. (Niek), Walker, R. M. (Rosie M.), Wielscher, M. (Matthias), Winkelmann, J. (Juliane), Wolffenbuttel, B. H. (Bruce H. R.), Zhao, W. (Wei), Zheng, Y. (Yinan), Loh, M. (Marie), Snieder, H. (Harold), Levy, D. (Daniel), Waldenberger, M. (Melanie), Susztak, K. (Katalin), Kottgen, A. (Anna), and Teumer, A. (Alexander)

Abstract

Chronic kidney disease is a major public health burden. Elevated urinary albumin-to-creatinine ratio is a measure of kidney damage, and used to diagnose and stage chronic kidney disease. To extend the knowledge on regulatory mechanisms related to kidney function and disease, we conducted a blood-based epigenome-wide association study for estimated glomerular filtration rate (n = 33,605) and urinary albumin-to-creatinine ratio (n = 15,068) and detected 69 and seven CpG sites where DNA methylation was associated with the respective trait. The majority of these findings showed directionally consistent associations with the respective clinical outcomes chronic kidney disease and moderately increased albuminuria. Associations of DNA methylation with kidney function, such as CpGs at JAZF1, PELI1 and CHD2 were validated in kidney tissue. Methylation at PHRF1, LDB2, CSRNP1 and IRF5 indicated causal effects on kidney function. Enrichment analyses revealed pathways related to hemostasis and blood cell migration for estimated glomerular filtration rate, and immune cell activation and response for urinary albumin-to-creatinineratio-associated CpGs.

Published
2021

15. A multi-omics approach to investigate the inflammatory response to life course socioeconomic position

Author

Castagné, R., Kelly-Irving, M., Krogh, V., Palli, D., Panico, S., Sacerdote, C., Tumino, R., Hebels, D.G.A.J., Kleinjans, J.C.S., De Kok, T.M.C.M., Georgiadis, P., Kyrtopoulos, S.A., Vermeulen, R., Stringhini, S., Vineis, P., Chadeau-Hyam, M., Delpierre, C., IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, RS: MERLN - Instructive Biomaterials Engineering (IBE), CBITE, RS: MERLN - Cell Biology - Inspired Tissue Engineering (CBITE), Toxicogenomics, RS: GROW - R1 - Prevention, RS: FSE MaCSBio, RS: FPN MaCSBio, RS: FHML MaCSBio, Division Instructive Biomaterials Eng, Cancer Research UK, Commission of the European Communities, IRAS OH Epidemiology Chemical Agents, and dIRAS RA-2

Subjects
0301 basic medicine, Male, Cancer Research, STRESS, Social Determinants of Health, Life course epidemiology, CHILDHOOD, Bioinformatics, Epigenome, 0302 clinical medicine, Gene expression, 030212 general & internal medicine, GENE-EXPRESSION, Genetics & Heredity, DNA methylation, socioeconomic position, Methylation, Middle Aged, Socioeconomic position, CpG site, Italy, life course epidemiology, symbols, Life course approach, Female, HEALTH, medicine.symptom, Life Sciences & Biomedicine, Adult, Inflammation, Biology, 03 medical and health sciences, symbols.namesake, Genetics, medicine, Humans, ddc:613, 0604 Genetics, Science & Technology, CHRONIC LYMPHOCYTIC-LEUKEMIA, Protein, Cancer, 1103 Clinical Sciences, medicine.disease, SOCIAL DETERMINANTS, 030104 developmental biology, Bonferroni correction, Social Class, inflammation, CELLS, gene expression, RISK-FACTORS, BLOOD-SAMPLES, CpG Islands, protein
Abstract

Aim: Inflammation represents a potential pathway through which socioeconomic position (SEP) is biologically embedded. Materials & methods: We analyzed inflammatory biomarkers in response to life course SEP by integrating multi-omics DNA-methylation, gene expression and protein level in 178 European Prospective Investigation into Cancer and Nutrition-Italy participants. Results & conclusion: We identified 61 potential cis acting CpG loci whose methylation levels were associated with gene expression at a Bonferroni correction. We examined the relationships between life course SEP and these 61 cis-acting regulatory methylation sites individually and jointly using several scores. Less-advantaged SEP participants exhibit, later in life, a lower inflammatory methylome score, suggesting an overall increased expression of the corresponding inflammatory genes or proteins, supporting the hypothesis that SEP impacts adult physiology through inflammation.

Published
2020

16. Special Report: The Biology of Inequalities in Health: The Lifepath Consortium

Author

Vineis, P. (Paolo), Avendano-Pabon, M. (Mauricio), Barros, H. (Henrique), Bartley, M. (Mel), Carmeli, C. (Cristian), Carra, L. (Luca), Chadeau-Hyam, M. (Marc), Costa, G. (Giuseppe), Delpierre, C. (Cyrille), D'Errico, A. (Angelo), Fraga, S. (Silvia), Giles, G. (Graham), Goldberg, M. (Marcel), Kelly-Irving, M. (Michelle), Kivimaki, M. (Mika), Lepage, B. (Benoit), Lang, T. (Thierry), Layte, R. (Richard), MacGuire, F. (Frances), Mackenbach, J.P. (Johan), Marmot, M. (Michael), McCrory, C. (Cathal), Milne, R.L. (Roger), Muennig, P. (Peter), Nusselder, W.J. (Wilma), Petrovic, D. (Dusan), Polidoro, S. (Silvia), Ricceri, F. (Fulvio), Robinson, O. (Oliver), Stringhini, S. (Silvia), Zins, M. (Marie), Vineis, P. (Paolo), Avendano-Pabon, M. (Mauricio), Barros, H. (Henrique), Bartley, M. (Mel), Carmeli, C. (Cristian), Carra, L. (Luca), Chadeau-Hyam, M. (Marc), Costa, G. (Giuseppe), Delpierre, C. (Cyrille), D'Errico, A. (Angelo), Fraga, S. (Silvia), Giles, G. (Graham), Goldberg, M. (Marcel), Kelly-Irving, M. (Michelle), Kivimaki, M. (Mika), Lepage, B. (Benoit), Lang, T. (Thierry), Layte, R. (Richard), MacGuire, F. (Frances), Mackenbach, J.P. (Johan), Marmot, M. (Michael), McCrory, C. (Cathal), Milne, R.L. (Roger), Muennig, P. (Peter), Nusselder, W.J. (Wilma), Petrovic, D. (Dusan), Polidoro, S. (Silvia), Ricceri, F. (Fulvio), Robinson, O. (Oliver), Stringhini, S. (Silvia), and Zins, M. (Marie)

Abstract

Funded by the European Commission Horizon 2020 programme, the Lifepath research consortium aimed to investigate the effects of socioeconomic inequalities on the biology of healthy aging. The main research questions included the impact of inequalities on health, the role of behavioral and other risk factors, the underlying biological mechanisms, the efficacy of selected policies, and the general implications of our findings for theories and policies. The project adopted a life-course and comparative approach, considering lifetime effects from childhood and adulthood, and pooled data on up to 1.7 million participants of longitudinal cohort studies from Europe, USA, and Australia. These data showed that socioeconomic circumstances predicted mortality and functional decline as strongly as established risk factors currently targeted by global prevention programmes. Analyses also lo

Published
2020
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17. A multi-omics approach to investigate the inflammatory response to life course socioeconomic position

Author

IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, Castagné, R., Kelly-Irving, M., Krogh, V., Palli, D., Panico, S., Sacerdote, C., Tumino, R., Hebels, D.G.A.J., Kleinjans, J.C.S., De Kok, T.M.C.M., Georgiadis, P., Kyrtopoulos, S.A., Vermeulen, R., Stringhini, S., Vineis, P., Chadeau-Hyam, M., Delpierre, C., IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, Castagné, R., Kelly-Irving, M., Krogh, V., Palli, D., Panico, S., Sacerdote, C., Tumino, R., Hebels, D.G.A.J., Kleinjans, J.C.S., De Kok, T.M.C.M., Georgiadis, P., Kyrtopoulos, S.A., Vermeulen, R., Stringhini, S., Vineis, P., Chadeau-Hyam, M., and Delpierre, C.

Published
2020

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

Author

IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, Afd. Theologie, Chadeau-Hyam, M., Bodinier, B., Vermeulen, R., Karimi, M., Zuber, V., Castagné, R., Elliott, J., Muller, D., Petrovic, D., Whitaker, M., Stringhini, S., Tzoulaki, I., Kivimäki, M., Vineis, P., Elliott, P., Kelly-Irving, M., Delpierre, C., IRAS OH Epidemiology Chemical Agents, dIRAS RA-2, Afd. Theologie, Chadeau-Hyam, M., Bodinier, B., Vermeulen, R., Karimi, M., Zuber, V., Castagné, R., Elliott, J., Muller, D., Petrovic, D., Whitaker, M., Stringhini, S., Tzoulaki, I., Kivimäki, M., Vineis, P., Elliott, P., Kelly-Irving, M., and Delpierre, C.

Published
2020

19. Special Report: The Biology of Inequalities in Health: The Lifepath Consortium

Author

Vineis, P, Avendano-Pabon, M, Barros, H, Bartley, M, Carmeli, C, Carra, L, Chadeau-Hyam, M, Costa, G, Delpierre, C, D'Errico, A, Fraga, S, Giles, G, Goldberg, M, Kelly-Irving, M, Kivimaki, M, Lepage, B, Lang, T, Layte, R, MacGuire, F, Mackenbach, JP, Marmot, M, McCrory, C, Milne, RL, Muennig, P, Nusselder, W, Petrovic, D, Polidoro, S, Ricceri, F, Robinson, O, Stringhini, S, Zins, M, Vineis, P, Avendano-Pabon, M, Barros, H, Bartley, M, Carmeli, C, Carra, L, Chadeau-Hyam, M, Costa, G, Delpierre, C, D'Errico, A, Fraga, S, Giles, G, Goldberg, M, Kelly-Irving, M, Kivimaki, M, Lepage, B, Lang, T, Layte, R, MacGuire, F, Mackenbach, JP, Marmot, M, McCrory, C, Milne, RL, Muennig, P, Nusselder, W, Petrovic, D, Polidoro, S, Ricceri, F, Robinson, O, Stringhini, S, and Zins, M

Abstract

Funded by the European Commission Horizon 2020 programme, the Lifepath research consortium aimed to investigate the effects of socioeconomic inequalities on the biology of healthy aging. The main research questions included the impact of inequalities on health, the role of behavioral and other risk factors, the underlying biological mechanisms, the efficacy of selected policies, and the general implications of our findings for theories and policies. The project adopted a life-course and comparative approach, considering lifetime effects from childhood and adulthood, and pooled data on up to 1.7 million participants of longitudinal cohort studies from Europe, USA, and Australia. These data showed that socioeconomic circumstances predicted mortality and functional decline as strongly as established risk factors currently targeted by global prevention programmes. Analyses also looked at socioeconomically patterned biological markers, allostatic load, and DNA methylation using richly phenotyped cohorts, unraveling their association with aging processes across the life-course. Lifepath studies suggest that socioeconomic circumstances are embedded in our biology from the outset-i.e., disadvantage influences biological systems from molecules to organs. Our findings have important implications for policy, suggesting that (a) intervening on unfavorable socioeconomic conditions is complementary and as important as targeting well-known risk factors, such as tobacco and alcohol consumption, low fruit and vegetable intake, obesity and a sedentary lifestyle, and that (b) effects of preventive interventions in early life integrate interventions in adulthood. The report has an executive summary that refers to the different sections of the main paper.

Published
2020

20. Reducing socio-economic inequalities in all-cause mortality: a counterfactual mediation approach

Author

Laine, JE, Baltar, VT, Stringhini, S, Gandini, M, Chadeau-Hyam, M, Kivimaki, M, Severi, G, Perduca, V, Hodge, AM, Dugue, P-A, Giles, GG, Milne, RL, Barros, H, Sacerdote, C, Krogh, V, Panico, S, Tumino, R, Goldberg, M, Zins, M, Delpierre, C, Alenius, H, Vineis, P, Laine, JE, Baltar, VT, Stringhini, S, Gandini, M, Chadeau-Hyam, M, Kivimaki, M, Severi, G, Perduca, V, Hodge, AM, Dugue, P-A, Giles, GG, Milne, RL, Barros, H, Sacerdote, C, Krogh, V, Panico, S, Tumino, R, Goldberg, M, Zins, M, Delpierre, C, Alenius, H, and Vineis, P

Abstract

BACKGROUND: Socio-economic inequalities in mortality are well established, yet the contribution of intermediate risk factors that may underlie these relationships remains unclear. We evaluated the role of multiple modifiable intermediate risk factors underlying socio-economic-associated mortality and quantified the potential impact of reducing early all-cause mortality by hypothetically altering socio-economic risk factors. METHODS: Data were from seven cohort studies participating in the LIFEPATH Consortium (total n = 179 090). Using both socio-economic position (SEP) (based on occupation) and education, we estimated the natural direct effect on all-cause mortality and the natural indirect effect via the joint mediating role of smoking, alcohol intake, dietary patterns, physical activity, body mass index, hypertension, diabetes and coronary artery disease. Hazard ratios (HRs) were estimated, using counterfactual natural effect models under different hypothetical actions of either lower or higher SEP or education. RESULTS: Lower SEP and education were associated with an increase in all-cause mortality within an average follow-up time of 17.5 years. Mortality was reduced via modelled hypothetical actions of increasing SEP or education. Through higher education, the HR was 0.85 [95% confidence interval (CI) 0.84, 0.86] for women and 0.71 (95% CI 0.70, 0.74) for men, compared with lower education. In addition, 34% and 38% of the effect was jointly mediated for women and men, respectively. The benefits from altering SEP were slightly more modest. CONCLUSIONS: These observational findings support policies to reduce mortality both through improving socio-economic circumstances and increasing education, and by altering intermediaries, such as lifestyle behaviours and morbidities.

Published
2020

21. Corrigendum to: Reducing socio-economic inequalities in all-cause mortality: a counterfactual mediation approach.

Author

Laine, JE, Baltar, VT, Stringhini, S, Gandini, M, Chadeau-Hyam, M, Kivimaki, M, Severi, G, Perduca, V, Hodge, AM, Dugué, P-A, Giles, GG, Milne, RL, Barros, H, Sacerdote, C, Krogh, V, Panico, S, Tumino, R, Goldberg, M, Zins, M, Delpierre, C, LIFEPATH Consortium, Vineis, P, Laine, JE, Baltar, VT, Stringhini, S, Gandini, M, Chadeau-Hyam, M, Kivimaki, M, Severi, G, Perduca, V, Hodge, AM, Dugué, P-A, Giles, GG, Milne, RL, Barros, H, Sacerdote, C, Krogh, V, Panico, S, Tumino, R, Goldberg, M, Zins, M, Delpierre, C, LIFEPATH Consortium, and Vineis, P

Published
2020

31. Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis

Author

Fiorito, G., Mccrory, C., Robinson, O., Carmeli, C., Rosales, C. O., Zhang, Y., Colicino, E., Dugue, P. -A., Artaud, F., Mckay, G. J., Jeong, A., Mishra, P. P., Nost, T. H., Krogh, V., Panico, S., Sacerdote, C., Tumino, R., Palli, D., Matullo, G., Guarrera, S., Gandini, M., Bochud, M., Dermitzakis, E., Muka, T., Schwartz, J., Vokonas, P. S., Just, A., Hodge, A. M., Giles, G. G., Southey, M. C., Hurme, M. A., Young, I., Mcknight, A. J., Kunze, S., Waldenberger, M., Peters, A., Schwettmann, L., Lund, E., Baccarelli, A., Milne, R. L., Kenny, R. A., Elbaz, A., Brenner, H., Kee, F., Voortman, T., Probst-Hensch, N., Lehtimaki, T., Elliot, P., Stringhini, S., Vineis, P., Polidoro, S., Alberts, J., Alenius, H., Avendano, M., Baltar, V., Bartley, M., Barros, H., Bellone, M., Berger, E., Blane, D., Candiani, G., Carra, L., Castagne, R., Chadeau-Hyam, M., Cima, S., Clavel-Chapelon, F., Costa, G., Courtin, E., Delpierre, C., D'Errico, A., Dermitzakis, M., Elovainio, M., Elliott, P., Fagherazzi, G., Fraga, S., Gares, V., Gerbouin-Rerolle, P., Giles, G., Goldberg, M., Greco, D., Guessous, I., Haba-Rubio, J., Heinzer, R., Hodge, A., Joost, S., Karimi, M., Kelly-Irving, M., Kahonen, M., Karisola, P., Khenissi, L., Kivimaki, M., Laine, J., Lang, T., Laurent, A., Layte, R., Lepage, B., Lorsch, D., Macguire, F., Machell, G., Mackenbach, J., Marmot, M., de Mestral, C., Miller, C., Milne, R., Muennig, P., Nusselder, W., Petrovic, D., Pilapil, L., Preisig, M., Pulkki-Raback, L., Raitakari, O., Ribeiro, A. I., Ricceri, F., Recalcati, P., Reinhard, E., Valverde, J. R., Saba, S., Santegoets, F., Satolli, R., Simmons, T., Severi, G., Shipley, M. J., Tabak, A., Terhi, V., Tieulent, J., Vaccarella, S., Vigna-Taglianti, F., Vollenweider, P., Vuilleumier, N., Zins, M., Medical Research Council (MRC), Commission of the European Communities, BIOS Consortium, Lifepath consortium, Epidemiology, Dermitzakis, Emmanouil, and Stringhini, Silvia

Subjects
Male, Aging, Geriatrics & Gerontology, Disease, epigenetic clocks, Bioinformatics, 0601 Biochemistry and Cell Biology, DISEASE, Epigenesis, Genetic, Cohort Studies, 0302 clinical medicine, Risk Factors, DNA METHYLATION, media_common, 0303 health sciences, education, Lifepath consortium, VDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801, CARDIOVASCULAR RISK, Aged, Aging/genetics, Aging/psychology, DNA Methylation, Educational Status, Female, Humans, Life Style, Mutation, Social Class, biological aging, socioeconomic position, Longevity, ASSOCIATION, Biological aging, Education, Epigenetic clocks, Socioeconomic position, 3. Good health, WIDE METHYLATION, Aging/genetics/psychology, DNA methylation, Biomarker (medicine), HEALTH, BIOS Consortium, Life Sciences & Biomedicine, Research Paper, Cohort study, VDP::Medisinske Fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710::Medisinsk genetikk: 714, media_common.quotation_subject, CANCER-RISK, 610 Medicine & health, VDP::Medical disciplines: 700::Basic medical, dental and veterinary science disciplines: 710::Medical genetics: 714, Biology, PERIPHERAL-BLOOD, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Genetic, 360 Social problems & social services, 1112 Oncology and Carcinogenesis, Epigenetics, ddc:613, 030304 developmental biology, Science & Technology, Mechanism (biology), MUTATIONS, dNaM, Socioeconomic Position, Biological Aging, Epigenetic Clocks, Cell Biology, 0606 Physiology, DRIFT, VDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801, 030217 neurology & neurosurgery, Epigenesis
Abstract

Source at https://doi.org/10.18632/aging.101900. Differences in health status by socioeconomic position (SEP) tend to be more evident at older ages, suggesting the involvement of a biological mechanism responsive to the accumulation of deleterious exposures across the lifespan. DNA methylation (DNAm) has been proposed as a biomarker of biological aging that conserves memory of endogenous and exogenous stress during life. We examined the association of education level, as an indicator of SEP, and lifestyle-related variables with four biomarkers of age-dependent DNAm dysregulation: the total number of stochastic epigenetic mutations (SEMs) and three epigenetic clocks (Horvath, Hannum and Levine), in 18 cohorts spanning 12 countries. The four biological aging biomarkers were associated with education and different sets of risk factors independently, and the magnitude of the effects differed depending on the biomarker and the predictor. On average, the effect of low education on epigenetic aging was comparable with those of other lifestyle-related risk factors (obesity, alcohol intake), with the exception of smoking, which had a significantly stronger effect. Our study shows that low education is an independent predictor of accelerated biological (epigenetic) aging and that epigenetic clocks appear to be good candidates for disentangling the biological pathways underlying social inequalities in healthy aging and longevity.

Published
2019

32. Maternal educational inequalities in measured body mass index trajectories in three European countries

Author

Mccrory, C., Leahy, S., Ribeiro, A. I., Fraga, S., Barros, H., Avendano, M., Vineis, P., Layte, R., Alenius, H., Baglietto, L., Bartley, M., Bellone, M., Berger, E., Bochud, M., Candiani, G., Carmeli, C., Carra, L., Castagne, R., Chadeau-Hyam, M., Cima, S., Costa, G., Courtin, E., Delpierre, C., D'Errico, A., Donkin, A., Dugue, P. -A., Elliott, P., Fagherazzi, G., Fiorito, G., Gandini, Martina, Gares, V., Gerbouin-Rerrolle, P., Giles, G., Goldberg, M., Greco, D., Guida, F., Hodge, A., Karimi, M., Karisola, P., Kelly, M., Kivimaki, M., Laine, J., Lang, T., Laurent, A., Lepage, B., Lorsch, D., Machell, G., Mackenbach, J., Marmot, M., Milne, David Robert, Muennig, P., Nusselder, W., Petrovic, D., Polidoro, S., Preisig, M., Recalcati, P., Reinhard, E., Ricceri, F., Robinson, O., Jose, R., Severi, PAULA GABRIELA, Simmons, T., Stringhini, S., Terhi, V., Than, J., Vergnaud, A. -C., Vigna-Taglianti, F., Vollenweider, P., Zins, M., Epidemiology, Public Health, HRB, and ERC

Subjects
Male, Pediatric Obesity, obesity, Adolescent, Inequality, Epidemiology, body mass index, children, cohort study, growth curves, overweight, social inequalities, media_common.quotation_subject, Social gradient, Mothers, Prospective data, 03 medical and health sciences, 0302 clinical medicine, 030225 pediatrics, Prevalence, medicine, Humans, Social inequality, Prospective Studies, Child, media_common, 2. Zero hunger, 030219 obstetrics & reproductive medicine, Portugal, business.industry, 4. Education, Health Status Disparities, medicine.disease, Obesity, United Kingdom, Millennium Cohort Study (United States), Child, Preschool, Pediatrics, Perinatology and Child Health, Cohort, Educational Status, Female, business, Ireland, Body mass index, Demography
Abstract

BACKGROUND: Social inequalities in the prevalence of childhood overweight and obesity are well-established, but less is known about when the social gradient first emerges and how it evolves across childhood and adolescence.OBJECTIVE: This study examines maternal education differentials in children's body mass trajectories in infancy, childhood and adolescence using data from four contemporary European child cohorts.METHODS: Prospective data on children's body mass index (BMI) were obtained from four cohort studies-Generation XXI (G21-Portugal), Growing Up in Ireland (GUI) infant and child cohorts, and the Millennium Cohort Study (MCS-UK)-involving a total sample of 41,399 children and 120,140 observations. Children's BMI trajectories were modelled by maternal education level using mixed-effect models.RESULTS: Maternal educational inequalities in children's BMI were evident as early as three years of age. Children from lower maternal educational backgrounds were characterised by accelerated BMI growth, and the extent of the disparity was such that boys from primary-educated backgrounds measured 0.42 kg/m2 (95% CI 0.24, 0.60) heavier at 7 years of age in G21, 0.90 kg/m2 (95% CI 0.60, 1.19) heavier at 13 years of age in GUI and 0.75 kg/m2 (95% CI 0.52, 0.97) heavier in MCS at 14 years of age. The corresponding figures for girls were 0.71 kg/m2 (95% CI 0.50, 0.91), 1.31 kg/m2 (95% CI 1.00, 1.62) and 0.76 kg/m2 (95% CI 0.53, 1.00) in G21, GUI and MCS, respectively.CONCLUSIONS: Maternal education is a strong predictor of BMI across European nations. Socio-economic differentials emerge early and widen across childhood, highlighting the need for early intervention.

Published
2019

33. Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis.

Author

Kee F., Rosales C.O., Zhang Y., Colicino E., Dugue P.-A., Artaud F., McKay G.J., Jeong A., Mishra P.P., Elbaz A., Brenner H., Carmeli C., Voortman T., Probst-Hensch N., Lehtimaki T., Elliot P., Stringhini S., Vineis P., Polidoro S., Fiorito G., McCrory C., Robinson O., Nost T.H., Krogh V., Panico S., Sacerdote C., Tumino R., Palli D., Matullo G., Guarrera S., Gandini M., Bochud M., Dermitzakis E., Muka T., Schwartz J., Vokonas P.S., Just A., Hodge A.M., Giles G.G., Southey M.C., Hurme M.A., Young I., McKnight A.J., Kunze S., Waldenberger M., Peters A., Schwettmann L., Lund E., Baccarelli A., Milne R.L., Kenny R.A., Kee F., Rosales C.O., Zhang Y., Colicino E., Dugue P.-A., Artaud F., McKay G.J., Jeong A., Mishra P.P., Elbaz A., Brenner H., Carmeli C., Voortman T., Probst-Hensch N., Lehtimaki T., Elliot P., Stringhini S., Vineis P., Polidoro S., Fiorito G., McCrory C., Robinson O., Nost T.H., Krogh V., Panico S., Sacerdote C., Tumino R., Palli D., Matullo G., Guarrera S., Gandini M., Bochud M., Dermitzakis E., Muka T., Schwartz J., Vokonas P.S., Just A., Hodge A.M., Giles G.G., Southey M.C., Hurme M.A., Young I., McKnight A.J., Kunze S., Waldenberger M., Peters A., Schwettmann L., Lund E., Baccarelli A., Milne R.L., and Kenny R.A.

Abstract

Differences in health status by socioeconomic position (SEP) tend to be more evident at older ages, suggesting the involvement of a biological mechanism responsive to the accumulation of deleterious exposures across the lifespan. DNA methylation (DNAm) has been proposed as a biomarker of biological aging that conserves memory of endogenous and exogenous stress during life.We examined the association of education level, as an indicator of SEP, and lifestyle-related variables with four biomarkers of age-dependent DNAm dysregulation: the total number of stochastic epigenetic mutations (SEMs) and three epigenetic clocks (Horvath, Hannum and Levine), in 18 cohorts spanning 12 countries.The four biological aging biomarkers were associated with education and different sets of risk factors independently, and the magnitude of the effects differed depending on the biomarker and the predictor. On average, the effect of low education on epigenetic aging was comparable with those of other lifestyle-related risk factors (obesity, alcohol intake), with the exception of smoking, which had a significantly stronger effect.Our study shows that low education is an independent predictor of accelerated biological (epigenetic) aging and that epigenetic clocks appear to be good candidates for disentangling the biological pathways underlying social inequalities in healthy aging and longevity.

Published
2019

34. Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis

Author

Fiorito, G, McCrory, C, Robinson, O, Carmeli, C, Rosales, CO, Zhang, Y, Colicino, E, Dugue, P-A, Artaud, F, Mckay, GJ, Jeong, A, Mishra, PP, Nost, TH, Krogh, V, Panico, S, Sacerdote, C, Tumino, R, Palli, D, Matullo, G, Guarrera, S, Gandini, M, Bochud, M, Dermitzakis, E, Muka, T, Schwartz, J, Vokonas, PS, Just, A, Hodge, AM, Giles, GG, Southey, MC, Hurme, MA, Young, I, McKnight, AJ, Kunze, S, Waldenberger, M, Peters, A, Schwettmann, L, Lund, E, Baccarelli, A, Milne, RL, Kenny, RA, Elbaz, A, Brenner, H, Kee, F, Voortman, T, Probst-Hensch, N, Lehtimaki, T, Elliot, P, Stringhini, S, Vineis, P, Polidoro, S, Alberts, J, Alenius, H, Avendano, M, Baltar, V, Bartley, M, Barros, H, Bellone, M, Berger, E, Blane, D, Candiani, G, Carra, L, Castagne, R, Chadeau-Hyam, M, Cima, S, Clavel-Chapelon, F, Costa, G, Courtin, E, Delpierre, C, D'Errico, A, Manolis, Dermitzakis, Elovainio, M, Elliott, P, Fagherazzi, G, Fraga, S, Gares, V, Gerbouin-Rerolle, P, Giles, G, Goldberg, M, Greco, D, Guessous, I, Haba-Rubio, J, Heinzer, R, Hodge, A, Joost, S, Karimi, M, Kelly-Irving, M, Kahonen, M, Karisola, P, Khenissi, L, Kivimaki, M, Laine, J, Lang, T, Laurent, A, Layte, R, Lepage, B, Lorsch, D, MacGuire, F, Machell, G, Mackenbach, J, Marmot, M, de Mestral, C, Miller, C, Milne, R, Muennig, P, Nusselder, W, Petrovic, D, Lourdes, Pilapil, Preisig, M, Pulkki-Raback, L, Raitakari, O, Ribeiro, AI, Ricceri, F, Recalcati, P, Reinhard, E, Valverde, JR, Saba, S, Santegoets, F, Satolli, R, Simmons, T, Severi, G, Shipley, MJ, Tabak, A, Terhi, V, Tieulent, J, Vaccarella, S, Vigna-Taglianti, F, Vollenweider, P, Vuilleumier, N, Zins, M, Fiorito, G, McCrory, C, Robinson, O, Carmeli, C, Rosales, CO, Zhang, Y, Colicino, E, Dugue, P-A, Artaud, F, Mckay, GJ, Jeong, A, Mishra, PP, Nost, TH, Krogh, V, Panico, S, Sacerdote, C, Tumino, R, Palli, D, Matullo, G, Guarrera, S, Gandini, M, Bochud, M, Dermitzakis, E, Muka, T, Schwartz, J, Vokonas, PS, Just, A, Hodge, AM, Giles, GG, Southey, MC, Hurme, MA, Young, I, McKnight, AJ, Kunze, S, Waldenberger, M, Peters, A, Schwettmann, L, Lund, E, Baccarelli, A, Milne, RL, Kenny, RA, Elbaz, A, Brenner, H, Kee, F, Voortman, T, Probst-Hensch, N, Lehtimaki, T, Elliot, P, Stringhini, S, Vineis, P, Polidoro, S, Alberts, J, Alenius, H, Avendano, M, Baltar, V, Bartley, M, Barros, H, Bellone, M, Berger, E, Blane, D, Candiani, G, Carra, L, Castagne, R, Chadeau-Hyam, M, Cima, S, Clavel-Chapelon, F, Costa, G, Courtin, E, Delpierre, C, D'Errico, A, Manolis, Dermitzakis, Elovainio, M, Elliott, P, Fagherazzi, G, Fraga, S, Gares, V, Gerbouin-Rerolle, P, Giles, G, Goldberg, M, Greco, D, Guessous, I, Haba-Rubio, J, Heinzer, R, Hodge, A, Joost, S, Karimi, M, Kelly-Irving, M, Kahonen, M, Karisola, P, Khenissi, L, Kivimaki, M, Laine, J, Lang, T, Laurent, A, Layte, R, Lepage, B, Lorsch, D, MacGuire, F, Machell, G, Mackenbach, J, Marmot, M, de Mestral, C, Miller, C, Milne, R, Muennig, P, Nusselder, W, Petrovic, D, Lourdes, Pilapil, Preisig, M, Pulkki-Raback, L, Raitakari, O, Ribeiro, AI, Ricceri, F, Recalcati, P, Reinhard, E, Valverde, JR, Saba, S, Santegoets, F, Satolli, R, Simmons, T, Severi, G, Shipley, MJ, Tabak, A, Terhi, V, Tieulent, J, Vaccarella, S, Vigna-Taglianti, F, Vollenweider, P, Vuilleumier, N, and Zins, M

Abstract

Differences in health status by socioeconomic position (SEP) tend to be more evident at older ages, suggesting the involvement of a biological mechanism responsive to the accumulation of deleterious exposures across the lifespan. DNA methylation (DNAm) has been proposed as a biomarker of biological aging that conserves memory of endogenous and exogenous stress during life.We examined the association of education level, as an indicator of SEP, and lifestyle-related variables with four biomarkers of age-dependent DNAm dysregulation: the total number of stochastic epigenetic mutations (SEMs) and three epigenetic clocks (Horvath, Hannum and Levine), in 18 cohorts spanning 12 countries.The four biological aging biomarkers were associated with education and different sets of risk factors independently, and the magnitude of the effects differed depending on the biomarker and the predictor. On average, the effect of low education on epigenetic aging was comparable with those of other lifestyle-related risk factors (obesity, alcohol intake), with the exception of smoking, which had a significantly stronger effect.Our study shows that low education is an independent predictor of accelerated biological (epigenetic) aging and that epigenetic clocks appear to be good candidates for disentangling the biological pathways underlying social inequalities in healthy aging and longevity.

Published
2019

37. Socioeconomic status, non-communicable disease risk factors, and walking speed in older adults: Multi-cohort population based study

Author

Stringhini, S. (Silvia), Carmeli, C. (Cristian), Jokela, M. (Markus), Avendano, M. (Mauricio), McCrory, C. (Cathal), D'Errico, A. (Angelo), Bochud, M. (Murielle), Barros, A.I. (Ana), Costa, G. (Giuseppe), Chadeau-Hyam, M. (Marc), Delpierre, C. (Cyrille), Gandini, M. (Martina), Fraga, S. (Silvia), Goldberg, M. (Marcel), Giles, G.G. (Graham G.), Lassale, C. (Camille), Kenny, R.A. (Rose Anne), Kelly-Irving, M. (Michelle), Paccaud, F. (Fred), Layte, R. (Richard), Muennig, P. (Peter), Marmot, M. (Michael), Ribeiro, A.I. (Ana Isabel), Severi, G. (Gianluca), Steptoe, A. (Andrew), Shipley, M.J., Zins, M. (Marie), Mackenbach, J.P. (Johan), Vineis, P. (Paolo), Kivimaki, M. (Mika), Stringhini, S. (Silvia), Carmeli, C. (Cristian), Jokela, M. (Markus), Avendano, M. (Mauricio), McCrory, C. (Cathal), D'Errico, A. (Angelo), Bochud, M. (Murielle), Barros, A.I. (Ana), Costa, G. (Giuseppe), Chadeau-Hyam, M. (Marc), Delpierre, C. (Cyrille), Gandini, M. (Martina), Fraga, S. (Silvia), Goldberg, M. (Marcel), Giles, G.G. (Graham G.), Lassale, C. (Camille), Kenny, R.A. (Rose Anne), Kelly-Irving, M. (Michelle), Paccaud, F. (Fred), Layte, R. (Richard), Muennig, P. (Peter), Marmot, M. (Michael), Ribeiro, A.I. (Ana Isabel), Severi, G. (Gianluca), Steptoe, A. (Andrew), Shipley, M.J., Zins, M. (Marie), Mackenbach, J.P. (Johan), Vineis, P. (Paolo), and Kivimaki, M. (Mika)

Abstract

Objective To assess the association of low socioeconomic status and risk factors for non-communicable diseases (diabe

Published
2018
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38. Socioeconomic status, non-communicable disease risk factors, and walking speed in older adults: multi-cohort population based study

Author

Stringhini, S, Carmeli, C, Jokela, M, Avendano, M, McCrory, C, D'Errico, A, Bochud, M, Barros, H, Costa, G (Giuseppe), Chadeau-Hyam, M, Delpierre, C, Gandini, M, Fraga, S, Goldberg, M, Giles, GG, Lassale, C, Kenny, RA, Kelly-Irving, M, Paccaud, F, Layte, R, Muennig, P, Marmot, MG, Ribeiro, AI, Severi, G, Steptoe, A, Shipley, MJ, Zins, M, Mackenbach, Johan, Vineis, P, Kivimaki, M, Stringhini, S, Carmeli, C, Jokela, M, Avendano, M, McCrory, C, D'Errico, A, Bochud, M, Barros, H, Costa, G (Giuseppe), Chadeau-Hyam, M, Delpierre, C, Gandini, M, Fraga, S, Goldberg, M, Giles, GG, Lassale, C, Kenny, RA, Kelly-Irving, M, Paccaud, F, Layte, R, Muennig, P, Marmot, MG, Ribeiro, AI, Severi, G, Steptoe, A, Shipley, MJ, Zins, M, Mackenbach, Johan, Vineis, P, and Kivimaki, M

Published
2018

39. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women

Author

Stringhini, S, Carmeli, C, Jokela, M, Avendaño, M, Muennig, P, Guida, F, Ricceri, F, d'Errico, A, Barros, H, Bochud, M, Chadeau-Hyam, M, Chavel-Chapelon, F, Costa, G, Delpierre, C, Fraga, S, Goldberg, M, Giles, GG, Krogh, V, Kelly-Irving, M, Layte, R, Lasserre, AM, Marmot, MG, Preisig, M, Shipley, MJ, Vollenweider, P, Zins, M, Kawachi, I, Steptoe, A, Mackenbach, JP, Vineist, P, Kivimäkit, M, and Instituto de Saúde Pública

Subjects
Premature mortality, Socioeconomic status
Abstract

Background: In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors. Methods: We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors. Findings: During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98–1·11) for obesity in men and 2 ·17 (2·06–2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38–1·45 for men; 1·34, 1·28–1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21–1·32, men and women combined). The population attributable fraction was highest for smoking, followed by physical inactivity then socioeconomic status. Low socioeconomic status was associated with a 2·1-year reduction in life expectancy between ages 40 and 85 years, the corresponding years-of-life-lost were 0·5 years for high alcohol intake, 0·7 years for obesity, 3·9 years for diabetes, 1·6 years for hypertension, 2·4 years for physical inactivity, and 4·8 years for current smoking. Interpretation: Socioeconomic circumstances, in addition to the 25 × 25 factors, should be targeted by local and global health strategies and health risk surveillance to reduce mortality. European Commission, Swiss State Secretariat for Education, Swiss National Science Foundation, the Medical Research Council, NordForsk, Portuguese Foundation for Science and Technology.

Published
2017

40. Socioeconomic status and the 25 x 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women

Author

Stringhini, S, Carmeli, C, Jokela, M, Avendano, M, Muennig, P, Guida, F, Ricceri, F, D'Errico, A, Barros, H, Bochud, M, Chadeau-Hyam, M, Clavel-Chapelon, F, Costa, G, Delpierre, C, Fraga, S, Goldberg, M, Giles, GG, Krogh, V, Kelly-Irving, M, Layte, R, Lasserre, AM, Marmot, MG, Preisig, M, Shipley, MJ, Vollenweider, P, Zins, M, Kawachi, I, Steptoe, A, Mackenbach, JP, Vineis, P, Kivimaki, M, Public Health, Commission of the European Communities, and Medical Research Council (MRC)

Subjects
COUNTRIES, Adult, Male, Alcohol Drinking, UNITED-STATES, Cohort Studies, Medicine, General & Internal, SDG 3 - Good Health and Well-being, Risk Factors, General & Internal Medicine, Humans, LIFE EXPECTANCY, Obesity, Exercise, 11 Medical and Health Sciences, INCOME, Science & Technology, Mortality, Premature, Smoking, ASSOCIATION, Middle Aged, LIFEPATH consortium, Social Class, NONCOMMUNICABLE DISEASES, INEQUALITIES, Female, HEALTH, BURDEN, Life Sciences & Biomedicine
Abstract

Background: In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors. Methods: We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors. Findings: During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98–1·11) for obesity in men and 2 ·17 (2·06–2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38–1·45 for men; 1·34, 1·28–1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21–1·32, men and women combined). The population attributable fraction was highest for smoking, followed by physical inactivity then socioeconomic status. Low socioeconomic status was associated with a 2·1-year reduction in life expectancy between ages 40 and 85 years, the corresponding years-of-life-lost were 0·5 years for high alcohol intake, 0·7 years for obesity, 3·9 years for diabetes, 1·6 years for hypertension, 2·4 years for physical inactivity, and 4·8 years for current smoking. Interpretation: Socioeconomic circumstances, in addition to the 25 × 25 factors, should be targeted by local and global health strategies and health risk surveillance to reduce mortality.

Published
2017

41. Thirteen‐year trends in the prevalence of diabetes in an urban region of Switzerland: a population‐based study.

Author

Mestral, C., Stringhini, S., Guessous, I., and Jornayvaz, F. R.

Subjects
*DIAGNOSIS of diabetes, *BLOOD sugar, *CONFIDENCE intervals, *DIABETES, *DIAGNOSIS, *MEDICAL screening, *METROPOLITAN areas, *SEX distribution, *POPULATION health, *DISEASE prevalence, *CROSS-sectional method
Abstract

Aim: To assess trends in prevalence of total and diagnosed diabetes, and in the probability of detecting undiagnosed diabetes in the Swiss population. Methods: The Bus Santé study is an annual cross‐sectional study of adults residing in Geneva state, Switzerland. We included 8532 participants (51% women) from the years 2005 to 2017, when fasting plasma glucose data became available. Total diabetes was defined as the sum of diagnosed and undiagnosed diabetes, while diagnosed diabetes was defined as having a previous diagnosis, and undiagnosed diabetes as having fasting plasma glucose level of ≥7 mmol/l and no previous diagnosis. We calculated the probability of finding undiagnosed diabetes among participants without a diagnosis. We examined for linear and quadratic trends, grouping survey years into five survey periods. Results: In total, 711 diabetes cases were identified over 13 years. The age‐ and gender‐standardized prevalence of total diabetes decreased between the periods 2005–2009 and 2012–2013 from 9.6% (95% CI 8.3, 10.9) to 7.1% (95% CI 5.8, 8.4), but increased to 8.6% (95% CI 7.3%, 9.9%) by 2016–2017 (P‐quadratic <0.01). For diagnosed diabetes, the prevalence decreased between 2005–2009 and 2014–2015 from 8.3% (95% CI 7.0%, 9.5%) to 6.1% (95% CI 5.0%, 7.2%), but increased slightly again to 7.0% (95% CI 5.8%, 8.2%) by 2016–2017 (P‐quadratic = 0.01). Men generally had a higher prevalence of total and diagnosed diabetes than women, except in 2016–2017, when the prevalence of total diabetes was 9.5% (95% CI 7.6, 11.5) among men and 7.7% (95% CI 6.0, 9.5) among women (P >0.05). The probability of finding undetected diabetes among participants without a diabetes diagnosis decreased slightly between 2005–2009 and 2012–2013 from 1.5% (95% CI 0.9, 2.0) to 1.0% (95% CI 0.5, 1.5), but increased afterwards to 1.7% (95% CI 1.0, 2.3) by 2016–2017 (P‐quadratic = 0.06); in 2016–2017, it was 2.6% (95% CI 1.5, 3.7) among men and 0.7% (95% CI 0.1, 1.3) among women (P <0.01). Conclusion: The prevalence of diabetes has remained relatively constant over time. However, the probability of finding undetected cases of diabetes in the population without diabetes may be increasing among men. What's new?: Although diabetes prevalence continues to increase worldwide, some evidence suggests that a few wealthy countries have seen no increase in prevalence for years.Total diabetes prevalence has remained relatively constant over a 13‐year period among adults in the state of Geneva, an urban region of Switzerland.The probability of finding undetected cases of diabetes in the population without diabetes appears to have increased over time.Greater efforts are needed in clinical practice to screen for diabetes, particularly among men. [ABSTRACT FROM AUTHOR]

Published
2020
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43. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: A multicohort study and meta-analysis of 1·7 million men and women

Author

Stringhini, S. (Silvia), Carmeli, C. (Cristian), Jokela, M. (Markus), Avendano, M. (Mauricio), Muennig, P. (Peter), Guida, F. (Florence), Ricceri, F. (Fulvio), d'Errico, A. (Angelo), Barros, H. (Henrique), Bochud, M. (Murielle), Chadeau-Hyam, M. (Marc), Clavel-Chapelon, F. (Françoise), Costa, G. (Giuseppe), Delpierre, C. (Cyrille), Fraga, S. (Silvia), Goldberg, M. (Marcel), Giles, G.G. (Graham G), Krogh, V. (Vittorio), Kelly-Irving, M. (Michelle), Layte, R. (Richard), Lasserre, A.M. (Aurélie M), Marmot, M.G. (Michael G), Preisig, M. (Martin), Shipley, M.J. (Martin J), Vollenweider, P. (Peter), Zins, M. (Marie), Kawachi, I. (Ichiro), Steptoe, A. (Andrew), Mackenbach, J.P. (Johan P), Vineis, P. (Paolo), Kivimaki, M. (Mika), Stringhini, S. (Silvia), Carmeli, C. (Cristian), Jokela, M. (Markus), Avendano, M. (Mauricio), Muennig, P. (Peter), Guida, F. (Florence), Ricceri, F. (Fulvio), d'Errico, A. (Angelo), Barros, H. (Henrique), Bochud, M. (Murielle), Chadeau-Hyam, M. (Marc), Clavel-Chapelon, F. (Françoise), Costa, G. (Giuseppe), Delpierre, C. (Cyrille), Fraga, S. (Silvia), Goldberg, M. (Marcel), Giles, G.G. (Graham G), Krogh, V. (Vittorio), Kelly-Irving, M. (Michelle), Layte, R. (Richard), Lasserre, A.M. (Aurélie M), Marmot, M.G. (Michael G), Preisig, M. (Martin), Shipley, M.J. (Martin J), Vollenweider, P. (Peter), Zins, M. (Marie), Kawachi, I. (Ichiro), Steptoe, A. (Andrew), Mackenbach, J.P. (Johan P), Vineis, P. (Paolo), and Kivimaki, M. (Mika)

Abstract

Background: In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors. Methods: We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a tot

Published
2017
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44. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1·7 million men and women.

Author

LIFEPATH consortium, Alenius, H., Avendano, M., Barros, H., Bochud, M., Carmeli, C., Carra, L., Castagné, R., Chadeau-Hyam, M., Clavel-Chapelon, F., Costa, G., Courtin, E., Delpierre, C., D'Errico, A., Dugué, P.A., Elliott, P., Fraga, S., Gares, V., Giles, G., Goldberg, M., Greco, D., Hodge, A., Irving, M.K., Karisola, P., Kivimäki, M., Krogh, V., Lang, T., Layte, R., Lepage, B., Mackenbach, J., Marmot, M., McCrory, C., Milne, R., Muennig, P., Nusselder, W., Panico, S., Petrovic, D., Polidoro, S., Preisig, M., Raitakari, O., Ribeiro, A.I., Ricceri, F., Robinson, O., Valverde, J.R., Sacerdote, C., Satolli, R., Severi, G., Shipley, M.J., Stringhini, S., Tumino, R., Vineis, P., Vollenweider, P., Zins, M., Jokela, M., Avendaño, M., Guida, F., d'Errico, A., Giles, G.G., Kelly-Irving, M., Lasserre, A.M., Marmot, M.G., Kawachi, I., Steptoe, A., Mackenbach, J.P., LIFEPATH consortium, Alenius, H., Avendano, M., Barros, H., Bochud, M., Carmeli, C., Carra, L., Castagné, R., Chadeau-Hyam, M., Clavel-Chapelon, F., Costa, G., Courtin, E., Delpierre, C., D'Errico, A., Dugué, P.A., Elliott, P., Fraga, S., Gares, V., Giles, G., Goldberg, M., Greco, D., Hodge, A., Irving, M.K., Karisola, P., Kivimäki, M., Krogh, V., Lang, T., Layte, R., Lepage, B., Mackenbach, J., Marmot, M., McCrory, C., Milne, R., Muennig, P., Nusselder, W., Panico, S., Petrovic, D., Polidoro, S., Preisig, M., Raitakari, O., Ribeiro, A.I., Ricceri, F., Robinson, O., Valverde, J.R., Sacerdote, C., Satolli, R., Severi, G., Shipley, M.J., Stringhini, S., Tumino, R., Vineis, P., Vollenweider, P., Zins, M., Jokela, M., Avendaño, M., Guida, F., d'Errico, A., Giles, G.G., Kelly-Irving, M., Lasserre, A.M., Marmot, M.G., Kawachi, I., Steptoe, A., and Mackenbach, J.P.

Abstract

In 2011, WHO member states signed up to the 25 × 25 initiative, a plan to cut mortality due to non-communicable diseases by 25% by 2025. However, socioeconomic factors influencing non-communicable diseases have not been included in the plan. In this study, we aimed to compare the contribution of socioeconomic status to mortality and years-of-life-lost with that of the 25 × 25 conventional risk factors. We did a multicohort study and meta-analysis with individual-level data from 48 independent prospective cohort studies with information about socioeconomic status, indexed by occupational position, 25 × 25 risk factors (high alcohol intake, physical inactivity, current smoking, hypertension, diabetes, and obesity), and mortality, for a total population of 1 751 479 (54% women) from seven high-income WHO member countries. We estimated the association of socioeconomic status and the 25 × 25 risk factors with all-cause mortality and cause-specific mortality by calculating minimally adjusted and mutually adjusted hazard ratios [HR] and 95% CIs. We also estimated the population attributable fraction and the years of life lost due to suboptimal risk factors. During 26·6 million person-years at risk (mean follow-up 13·3 years [SD 6·4 years]), 310 277 participants died. HR for the 25 × 25 risk factors and mortality varied between 1·04 (95% CI 0·98-1·11) for obesity in men and 2 ·17 (2·06-2·29) for current smoking in men. Participants with low socioeconomic status had greater mortality compared with those with high socioeconomic status (HR 1·42, 95% CI 1·38-1·45 for men; 1·34, 1·28-1·39 for women); this association remained significant in mutually adjusted models that included the 25 × 25 factors (HR 1·26, 1·21-1·32, men and women combined). The population attributable fraction was highest for smoking, followed by physical inactivity then socioeconomic status. Low socioeconomic status was associated with a 2·1-year reduction in life expectancy between ages 40 and 85 years, the

Published
2017

45. Socioeconomic indicators in epidemiologic research: A practical example from the LIFEPATH study

Author

Ciccozzi, M, d'Errico, A, Ricceri, F, Stringhini, S, Carmeli, C, Kivimaki, M, Bartley, M, McCrory, C, Bochud, M, Vollenweider, P, Tumino, R, Goldberg, M, Zins, M, Barros, H, Giles, G, Severi, G, Costa, G, Vineis, P, Ciccozzi, M, d'Errico, A, Ricceri, F, Stringhini, S, Carmeli, C, Kivimaki, M, Bartley, M, McCrory, C, Bochud, M, Vollenweider, P, Tumino, R, Goldberg, M, Zins, M, Barros, H, Giles, G, Severi, G, Costa, G, and Vineis, P

Abstract

BACKGROUND: Several social indicators have been used in epidemiological research to describe socioeconomic position (SEP) of people in societies. Among SEP indicators, those more frequently used are education, occupational class and income. Differences in the incidence of several health outcomes have been reported consistently, independently from the indicator employed. Main objectives of the study were to present the socioeconomic classifications of the social indicators which will be employed throughout the LIFEPATH project and to compare social gradients in all-cause mortality observed in the participating adult cohorts using the different SEP indicators. METHODS: Information on the available social indicators (education, own and father's occupational class, income) from eleven adult cohorts participating in LIFEPATH was collected and harmonized. Mortality by SEP for each indicator was estimated by Poisson regression on each cohort and then evaluated using a meta-analytical approach. RESULTS: In the meta-analysis, among men mortality was significantly inversely associated with both occupational class and education, but not with father's occupational class; among women, the increase in mortality in lower social strata was smaller than among men and, except for a slight increase in the lowest education category, no significant differences were found. CONCLUSIONS: Among men, the proposed three-level classifications of occupational class and education were found to predict differences in mortality which is consistent with previous research. Results on women suggest that classifying them through their sole SEP, without considering that of their partners, may imply a misclassification of their social position leading to attenuation of mortality differences.

Published
2017

46. The biology of inequalities in health: the LIFEPATH project

Author

Vineis, P, Avendano-Pabon, M, Barros, H, Chadeau-Hyam, M, Costa, G (Giuseppe), Dijmarescu, M, Delpierre, C, D'Errico, A, Fraga, S, Giles, G, Goldberg, M, Zins, M, Kelly-Irving, M, Kivimaki, M, Lang, T, Layte, R, Mackenbach, Johan, Marmot, M, McCrory, C, Carmeli, C, Milne, RL, Muennig, P, Nusselder, Wilma, Polidoro, S, Ricceri, F, Robinson, O, Stringhini, S, Vineis, P, Avendano-Pabon, M, Barros, H, Chadeau-Hyam, M, Costa, G (Giuseppe), Dijmarescu, M, Delpierre, C, D'Errico, A, Fraga, S, Giles, G, Goldberg, M, Zins, M, Kelly-Irving, M, Kivimaki, M, Lang, T, Layte, R, Mackenbach, Johan, Marmot, M, McCrory, C, Carmeli, C, Milne, RL, Muennig, P, Nusselder, Wilma, Polidoro, S, Ricceri, F, Robinson, O, and Stringhini, S

Published
2017

47. Lifecourse socioeconomic status and type 2 diabetes: the role of chronic inflammation in the English Longitudinal Study of Ageing

Author

Stringhini, S., Zaninotto, P., Kumari, M., Kivimäki, M., and Batty, G.D.

Subjects
Aged, 80 and over, Inflammation, Male, Incidence, Aged, Diabetes Mellitus, Type 2/epidemiology, England/epidemiology, Female, Humans, Inflammation/complications, Life Style, Longitudinal Studies, Middle Aged, Risk Assessment, Social Class, Article, Diabetes Mellitus, Type 2, England, 80 and over, Diabetes Mellitus, Type 2/epidemiology
Abstract

We examined the association between lifecourse socioeconomic status (SES) and the risk of type 2 diabetes at older ages, ascertaining the extent to which adult lifestyle factors and systemic inflammation explain this relationship. Data were drawn from the English Longitudinal Study of Ageing (ELSA) which, established in 2002, is a representative cohort study of ≥50-year olds individuals living in England. SES indicators were paternal social class, participants' education, participants' wealth, and a lifecourse socioeconomic index. Inflammatory markers (C-reactive protein and fibrinogen) and lifestyle factors were measured repeatedly; diabetes incidence (new cases) was monitored over 7.5 years of follow-up. Of the 6218 individuals free from diabetes at baseline (44% women, mean aged 66 years), 423 developed diabetes during follow-up. Relative to the most advantaged people, those in the lowest lifecourse SES group experienced more than double the risk of diabetes (hazard ratio 2.59; 95% Confidence Interval (CI) = 1.81-3.71). Lifestyle factors explained 52% (95%CI:30-85) and inflammatory markers 22% (95%CI:13-37) of this gradient. Similar results were apparent with the separate SES indicators. In a general population sample, socioeconomic inequalities in the risk of type 2 diabetes extend to older ages and appear to partially originate from socioeconomic variations in modifiable factors which include lifestyle and inflammation.

Published
2016
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