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151. Sex differences in the relationship between socioeconomic status ans hypertension in France: results from a cross-sectional analysis of the Constances cohort

Author

Neufcourt, Lola, Deguen, Séverine, Zins, M., Grimaud, Olivier, École des Hautes Études en Santé Publique [EHESP] (EHESP), Recherche en Pharmaco-épidémiologie et Recours aux Soins (REPERES), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École des Hautes Études en Santé Publique [EHESP] (EHESP), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP), Cohortes épidémiologiques en population (CONSTANCES), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université Paris Cité (UPCité), Université Paris Descartes - Faculté de Médecine (UPD5 Médecine), and Université Paris Descartes - Paris 5 (UPD5)

Subjects
[SDV]Life Sciences [q-bio], Hypertension, Socioeconomic status, Constances cohort, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, France
Abstract

International audience; Background There is ample evidence that hypertension prevalence increases when socioeconomic status (SES) decreases. However, sex differences in this relationship has been less studied. Investigating potential sex differences could help understand the mechanisms of social health disparities. The aim of this work was to explore the pattern of associations between several indicators of SES and hypertension across sexes in a large sample of French adults.Methods In this cross-sectional analysis, participants are adults aged between 18 and 69 years old recruited to the CONSTANCES cohort over the period 2012–2015 in 16 recruitment centers. SES was estimated using education (individual level), income (household level) and an indicator of residential socioeconomic deprivation, FDep (municipal level). Log-binomial and Poisson regressions with robust variance estimations were used to estimate the Risk Ratios (RR) comparing the extreme levels of SES and to test for interaction of sex in the associations between SES and hypertension prevalence.Results A total of 62,247 individuals (53% women, mean age 48±13 years) were included. Age-standardized prevalence of hypertension was 30.1% [95%CI=29.7–30.6], higher in men (37.3% [95%CI=36.6–38.0]) than in women (23.2% [95%CI=22.7–23.8]).Globally, we found steep socioeconomic gradients of hypertension in both genders. Education showed the strongest association with hypertension prevalence, especially among women (p for interaction between sex and education

Published
2019

152. 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

153. Long-Term effects of compulsory schooling on physical, mental and cognitive ageing: A natural experiment

Author

Courtin, E. (Emilie), Nafilyan, V. (Vahe), Glymour, M.M. (Maria), Goldberg, M. (Marcel), Berr, C. (Claudine), Berkman, L.F. (Lisa), Zins, M. (Marie), Avendano, M. (Mauricio), Courtin, E. (Emilie), Nafilyan, V. (Vahe), Glymour, M.M. (Maria), Goldberg, M. (Marcel), Berr, C. (Claudine), Berkman, L.F. (Lisa), Zins, M. (Marie), and Avendano, M. (Mauricio)

Abstract

Background Longer schooling is associated with better physical, mental and cognitive functioning, but there is controversy as to whether these associations

Published
2019
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154. Maternal educational inequalities in measured body mass index trajectories in three European countries

Author

McCrory, C, Leahy, S, Ribeiro, AI, 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, M, 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, R, Muennig, P, Nusselder, W, Petrovic, D, Polidoro, S, Preisig, M, Recalcati, P, Reinhard, E, Ricceri, F, Robinson, O, Valverde, JR, Severi, G, Simmons, T, Stringhini, S, Terhi, V, Than, J, Vergnaud, A-C, Vigna-Taglianti, F, Vollenweider, P, Zins, M, McCrory, C, Leahy, S, Ribeiro, AI, 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, M, 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, R, Muennig, P, Nusselder, W, Petrovic, D, Polidoro, S, Preisig, M, Recalcati, P, Reinhard, E, Ricceri, F, Robinson, O, Valverde, JR, Severi, G, Simmons, T, Stringhini, S, Terhi, V, Than, J, Vergnaud, A-C, Vigna-Taglianti, F, Vollenweider, P, and Zins, M

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

155. 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

157. Job strain and loss of healthy life years between ages 50 and 75 by sex and occupational position:analyses of 64 934 individuals from four prospective cohort studies

Author

Mgnusson Hanson, LL, Westerlund, H, Chungkham, HS, Vahtera, J, Rod, NH, Alexanderson, K, Goldberg, M, Kivimäki, M, Stenholm, S, Platts, LG, Zins, M, Head, J, Clinicum, Department of Public Health, University of Helsinki, Yhteiskuntatieteiden tiedekunta - Faculty of Social Sciences, and University of Tampere

Subjects
WORK, organisation of work, GAZEL COHORT, FINNISH PUBLIC-SECTOR, WHITEHALL-II, PARTICIPANT DATA, 3142 Public health care science, environmental and occupational health, workload, stress, SELF-REPORTED HEALTH, RATED HEALTH, RISK-FACTOR, Terveystiede - Health care science, longitudinal studies, epidemiology, CORONARY-HEART-DISEASE, SOCIOECONOMIC POSITION
Abstract

OBJECTIVES: Poor psychosocial working conditions increase the likelihood of various types of morbidity and may substantially limit quality of life and possibilities to remain in paid work. To date, however, no studies to our knowledge have quantified the extent to which poor psychosocial working conditions reduce healthy or chronic disease-free life expectancy, which was the focus of this study.METHODS: Data were derived from four cohorts with repeat data: the Finnish Public Sector Study (Finland), GAZEL (France), the Swedish Longitudinal Occupational Survey of Health (Sweden) and Whitehall II (UK). Healthy (in good self-rated health) life expectancy (HLE) and chronic disease-free (free from cardiovascular disease, cancer, respiratory disease and diabetes) life expectancy (CDFLE) was calculated from age 50 to 75 based on 64 394 individuals with data on job strain (high demands in combination with low control) at baseline and health at baseline and follow-up.RESULTS: Multistate life table models showed that job strain was consistently related to shorter HLE (overall 1.7 years difference). The difference in HLE was more pronounced among men (2.0 years compared with 1.5 years for women) and participants in lower occupational positions (2.5 years among low-grade men compared with 1.7 years among high-grade men). Similar differences in HLE, although smaller, were observed among those in intermediate or high occupational positions. Job strain was additionally associated with shorter CDFLE, although this association was weaker and somewhat inconsistent.CONCLUSIONS: These findings suggest that individuals with job strain have a shorter health expectancy compared with those without job strain.

Published
2018

158. HIV is associated with airway obstruction : a matched controlled study

Author

Makinson, A., Hayot, M., Eymard-Duvernay, Sabrina, Ribet, C., Raffi, F., Pialoux, G., Zucman, D., Poizot-Martin, I., Bonnet, F., Abgrall, S., Tattevin, P., Cheret, A., Ferry, T., Mauboussin, J. M., Marchand, L., Rouzaud, C., Reynes, J., Zins, M., Le Moing, V., and ANRS EP48 HIV CHEST study Team

Subjects
comorbidity, HIV, tobacco use, airway obstruction, smoking, chronic obstructive pulmonary disease
Abstract

Objective:To explore whether airway obstruction is associated with HIV in a cohort of HIV-infected and uninfected smokers.Methods:People living with HIV (PLWHIV) participated in the ANRS EP48 HIV CHEST study, an early lung cancer diagnosis study with low-dose chest tomography. HIV-uninfected study participants were from the CONSTANCES cohort. Inclusion criteria were an age greater than 40 years, a smoking history of at least 20 pack-years, and for PLWHIV, a CD4(+) T-lymphocyte nadir less than 350/l and last CD4(+) cell count more than 100 cells/l. Two randomly selected HIV-uninfected study participants were matched by age and sex with one PLWHIV. Prebronchodilatator forced expiratory volume in 1s (FEV1) to forced vital capacity (FVC) ratio was the primary outcome, and association of FEV1/FVC ratio less than 0.70 and FEV1 less than 80% of the theoretical value, as a proxy of chronic obstructive pulmonary disease, the secondary outcome.Results:In total, 351 PLWHIV and 702 HIV-uninfected study participants were included. Median age was 50 years, and 17% of study participants were women. Plasma HIV RNA was less than 50 copies/ml in 89% of PLWHIV, with a median CD4(+) cell count of 573 cells/l. HIV ( -2.19), age (per 10 years increase; -2.81), tobacco use (per 5 pack-years increase; -0.34), and hepatitis C virus serology (-2.50) were negatively associated with FEV1/FVC. HIV [odds ratio (OR: 1.72)], age (per 10 years increase; OR 1.77), and tobacco use (per 5 pack-years increase; OR 1.11) were significantly associated with the secondary outcome.Conclusion:Our study found a significant association of airway obstruction with HIV status in smokers aged more than 40 years with previous immunodeficiency.

Published
2018

173. 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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174. Work stress and risk of death in men and women with and without cardiometabolic disease:a multicohort study

Author

Kivimäki, M. (Mika), Pentti, J. (Jaana), Ferrie, J. E. (Jane E.), Batty, G. D. (G. David), Nyberg, S. T. (Solja T.), Jokela, M. (Markus), Virtanen, M. (Marianna), Alfredsson, L. (Lars), Dragano, N. (Nico), Fransson, E. I. (Eleonor I.), Goldberg, M. (Marcel), Knutsson, A. (Anders), Koskenvuo, M. (Markku), Koskinen, A. (Aki), Kouvonen, A. (Anne), Luukkonen, R. (Ritva), Oksanen, T. (Tuula), Rugulies, R. (Reiner), Siegrist, J. (Johannes), Singh-Manoux, A. (Archana), Suominen, S. (Sakari), Theorell, T. (Tores), Vaananen, A. (Ari), Vahtera, J. (Jussi), Westerholm, P. J. (Peter J. M.), Westerlund, H. (Hugo), Zins, M. (Marie), Strandberg, T. (Timo), Steptoe, A. (Andrew), Deanfield, J. (John), Kivimäki, M. (Mika), Pentti, J. (Jaana), Ferrie, J. E. (Jane E.), Batty, G. D. (G. David), Nyberg, S. T. (Solja T.), Jokela, M. (Markus), Virtanen, M. (Marianna), Alfredsson, L. (Lars), Dragano, N. (Nico), Fransson, E. I. (Eleonor I.), Goldberg, M. (Marcel), Knutsson, A. (Anders), Koskenvuo, M. (Markku), Koskinen, A. (Aki), Kouvonen, A. (Anne), Luukkonen, R. (Ritva), Oksanen, T. (Tuula), Rugulies, R. (Reiner), Siegrist, J. (Johannes), Singh-Manoux, A. (Archana), Suominen, S. (Sakari), Theorell, T. (Tores), Vaananen, A. (Ari), Vahtera, J. (Jussi), Westerholm, P. J. (Peter J. M.), Westerlund, H. (Hugo), Zins, M. (Marie), Strandberg, T. (Timo), Steptoe, A. (Andrew), and Deanfield, J. (John)

Abstract

Background: Although some cardiovascular disease prevention guidelines suggest a need to manage work stress in patients with established cardiometabolic disease, the evidence base for this recommendation is weak. We sought to clarify the status of stress as a risk factor in cardiometabolic disease by investigating the associations between work stress and mortality in men and women with and without pre-existing cardiometabolic disease. Methods: In this multicohort study, we used data from seven cohort studies in the IPD-Work consortium, initiated between 1985 and 2002 in Finland, France, Sweden, and the UK, to examine the association between work stress and mortality. Work stress was denoted as job strain or effort–reward imbalance at work. We extracted individual-level data on prevalent cardiometabolic diseases (coronary heart disease, stroke, or diabetes [without differentiation by diabetes type]) at baseline. Work stressors, socioeconomic status, and conventional and lifestyle risk factors (systolic and diastolic blood pressure, total cholesterol, smoking status, BMI, physical activity, and alcohol consumption) were also assessed at baseline. Mortality data, including date and cause of death, were obtained from national death registries. We used Cox proportional hazards regression to study the associations of work stressors with mortality in men and women with and without cardiometabolic disease. Results: We identified 102 633 individuals with 1 423 753 person-years at risk (mean follow-up 13·9 years [SD 3·9]), of whom 3441 had prevalent cardiometabolic disease at baseline and 3841 died during follow-up. In men with cardiometabolic disease, age-standardised mortality rates were substantially higher in people with job strain (149·8 per 10 000 person-years) than in those without (97·7 per 10 000 person-years; mortality difference 52·1 per 10 000 person-years; multivariable-adjusted hazard ratio [HR] 1·68, 95% CI 1·19–2·35). This mortality difference for job

Published
2018

175. 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

176. 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

177. 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

185. 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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186. 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

187. 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

188. 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

189. An integrated approach for the identification of predictive markers of type 2 diabetes

Author

Pujos-Guillot, Estelle, Brandolini-Bunlon, Marion, Grissa, Dhouha, Liu, Yunfei, Pééra, Mélanie, Joly, Charlotte, Lyan, Bernard, Czernichow, Sébastien, Zins, M, Goldberg M, M, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Metabolomics Society., and Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA)

Subjects
Alimentation et Nutrition, Food and Nutrition, biomarkers, prediction, cohort, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

190. Metabolomics as part of an integrated approach for the identification of predictive markers of type 2 diabetes

Author

Pujos-Guillot , Estelle, Brandolini-Bunlon , Marion, Grissa , Dhouha, Liu , Yunfei, Petera , Mélanie, Joly , Charlotte, Lyan , Bernard, Czernichow , Sébastien, Zins , M., Goldberg , M., Comte , Blandine, Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Cohortes épidémiologiques en population (CONSTANCES), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université Paris Cité (UPCité), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université Paris Descartes - Paris 5 (UPD5), INRA DID’IT Metaprogramme, ProdInra, Archive Ouverte, Unité de Nutrition Humaine - Clermont Auvergne (UNH), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), UMS011 Cohortes épidémiologiques en population (CONSTANCES), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Nutrition, Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université de Paris (UP), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Unité de Nutrition Humaine - Clermont Auvergne ( UNH ), Université Clermont Auvergne ( UCA ) -Institut national de la recherche agronomique [Auvergne/Rhône-Alpes] ( INRA Auvergne/Rhône-Alpes ), Laboratoire Lorrain de Recherche en Informatique et ses Applications ( LORIA ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université Henri Poincaré - Nancy 1 ( UHP ) -Université Nancy 2-Institut National Polytechnique de Lorraine ( INPL ) -Centre National de la Recherche Scientifique ( CNRS ), UMS11 Cohortes en population, Institut National de la Santé et de la Recherche Médicale ( INSERM ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Assistance publique - Hôpitaux de Paris (AP-HP)-CHU Pitié-Salpêtrière [APHP], and Université Paris Descartes - Paris 5 ( UPD5 )

Subjects
metabolomics, biomarkers, prediction, type 2 diabetes, cohort, diabète de type 2, [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, type ii diabetes, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, cohorte, Alimentation et Nutrition, Food and Nutrition, biomarker, biomarqueur, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, métabolomique
Abstract

The trajectory and underlying mechanisms of human health are determined by a complex interplay between intrinsic and extrinsic factors. Its evolution is a continuum of transitions, involving multifaceted processes at multiple levels and there is an urgent need for integrative biomarkers that can characterize and predict health status evolution. The objective of the present study was to identify accurate and robust multidimensional markers, predictive of type 2 diabetes (T2D). A case-control approach was used within the French population-based cohort GAZEL (n~20,000) [1]. Male overweight subjects (n=112, 25≤BMI

Published
2016

196. Reply

Author

Hodel, J., primary, Blanc, R., additional, Kalsoum, E., additional, Zins, M., additional, and Leclerc, X., additional

Published
2017
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198. Integrated and predictive approach for identifying markers of health status evolution toward metabolic syndrome: the role of nutrition

Author

Pétéra, Mélanie, Joly, Charlotte, Rambeau, Mathieu, Brandolini-Bunlon, Marion, Herquelot, E., Plessz, Marie, Czernichow, S., Zins, M., Goldberg, Mikäel, Pujos-Guillot, Estelle, Unité de Nutrition Humaine (UNH), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Alimentation et sciences sociales (ALISS), Institut National de la Recherche Agronomique (INRA), Réseau Francophone de Métabolomique et Fluxomique (RFMF). FRA., and Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université

Subjects
ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract

National audience

Published
2015

199. Loss of healthy life years between ages 50-75 years attributed to job strain : analyses of 64,533 individuals from four prospective European cohort studies

Author

Magnusson Hanson, Linda L., Westerlund, Hugo, Chungkham, Holendro Singh, Vahtera, J., Rod, N. H., Alexandersson, K., Goldberg, M., Stenholm, S., Platts, Loretta G., Zins, M., Head, J., Magnusson Hanson, Linda L., Westerlund, Hugo, Chungkham, Holendro Singh, Vahtera, J., Rod, N. H., Alexandersson, K., Goldberg, M., Stenholm, S., Platts, Loretta G., Zins, M., and Head, J.

Abstract

Introduction: Poor working conditions potentially limit quality of life and the possibilities for individuals to remain in paid employment because of poor health. However, no studies so far have investigated how psychosocial working conditions might impact on how long older workers can expect to stay healthy. This study examines whether job strain in older workers is associated with healthy life expectancy (HLE). Methods: We used repeated measures data for 64,533 individuals from four cohort studies: Whitehall II (UK), Finnish Public Sector Study (Finland), GAZEL (France), and Swedish Longitudinal Occupational Survey of Health (Sweden). Job strain at baseline and two different measures of HLE were computed based on self-rated health and chronic health conditions. Multistate life table models were used to estimate partial life expectancy (LE) and HLE from ages 50 to 75 by job strain, cohort, occupational position and sex. Results: Job strain was consistently related to shorter HLE, but not total LE. Particularly men in lower occupational positions with job strain had shorter HLE. The HLE in good self-rated health was 2–3 years shorter in this group. The corresponding HLE based on chronic disease was almost 2 years shorter although the relation was less pronounced for GAZEL. Women with job strain in lower occupational positions also lived 1–2 fewer years in good health. Conclusions: The results indicate that job strain affects how long people remain healthy, and that interventions to prevent high job strain in older workers might enable people to work for longer in good health.

Published
2016
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200. LOSS OF HEALTHY LIFE YEARS BETWEEN AGES 50-75 YEARS ATTRIBUTED TO JOB STRAIN:ANALYSES OF 64,533 INDIVIDUALS FROM FOUR PROSPECTIVE EUROPEAN COHORT STUDIES

Author

Hanson, L. L. Maiusson, Westerlund, H., Chungkham, H. S., Vahtera, J., Rod, N. H., Alexanderson, K., Goldberg, M., Stenholm, S., Platts, L. G., Zins, M., Head, J., Hanson, L. L. Maiusson, Westerlund, H., Chungkham, H. S., Vahtera, J., Rod, N. H., Alexanderson, K., Goldberg, M., Stenholm, S., Platts, L. G., Zins, M., and Head, J.

Published
2016

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