Predictors of total mortality and their differential association on premature or late mortality in the SUN cohort.

Alcohol Late mortality Mediterranean diet Physical activity Predictors Premature mortality

Journal

Experimental gerontology
ISSN: 1873-6815
Titre abrégé: Exp Gerontol
Pays: England
ID NLM: 0047061

Informations de publication

Date de publication:
02 2023
Historique:
received: 21 10 2022
revised: 02 12 2022
accepted: 06 12 2022
pubmed: 16 12 2022
medline: 25 1 2023
entrez: 15 12 2022
Statut: ppublish

Résumé

Several studies have tried to analyse the association between all-cause mortality and different risk factors, (especially those which are modifiable, such as smoking, diet or exercise), to develop public health preventive strategies. However, a specific analysis of predictors of premature and late mortality is needed to give more precise recommendations. Considering that there are risk factors which exert an influence on some diseases and not on others, we expect that, similarly, they may have a different impact depending on the timing of mortality, separating premature (≤65 years) from late mortality (>65 years). Thus, we prospectively followed-up during a median of 12 years a cohort of 20,272 university graduates comprising an ample range of ages at inception. Time-dependent, covariate-adjusted Cox models were used to estimate adjusted hazard ratios (HR) and their 95 % confidence intervals (CI) for each predictor. The strongest independent predictor of mortality at any age was physical activity which was associated with reduced risk of total, premature and late mortality (range of HRs when comparing the highest vs. the lowest level: 0.24 to 0.48). Specific strong predictors for premature mortality were smoking, HR: 4.22 (95 % CI: 2.42-7.38), and the concurrence of ≥2 metabolic conditions at baseline, HR: 1.97 (1.10-3.51). The habit of sleeping a long nap (≥30 min/d), with HR: 2.53 (1.30-4.91), and poor adherence to the Mediterranean Diet (≤3 points in a 0 to 8 score vs. ≥6 points), with HR: 2.27 (1.08-4.76), were the strongest specific predictors for late mortality. Smoking, diet quality or lifestyles, probably should be differentially assessed as specific predictors for early and late mortality. In the era of precision medicine, this approach will allow tailored recommendations appropriate to each person's age and baseline condition.

Identifiants

pubmed: 36521566
pii: S0531-5565(22)00357-6
doi: 10.1016/j.exger.2022.112048
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

112048

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest Authors declare no conflict of interest.

Auteurs

María Barbería-Latasa (M)

University of Navarra, Department of Preventive Medicine and Public Health, Irunlarrea, 1, 31008 Pamplona, Spain; University of Navarra, Department of Preventive Medicine and Public Health, IdisNA, Irunlarrea, 1, 31008 Pamplona, Spain. Electronic address: mbarberia.3@unav.es.

Miguel A Martínez-González (MA)

University of Navarra, Department of Preventive Medicine and Public Health, Irunlarrea, 1, 31008 Pamplona, Spain; University of Navarra, Department of Preventive Medicine and Public Health, IdisNA, Irunlarrea, 1, 31008 Pamplona, Spain; CIBERobn, Instituto de Salud Carlos III, Madrid, Spain. Electronic address: mamartinez@unav.es.

Carmen de la Fuente-Arrillaga (C)

University of Navarra, Department of Preventive Medicine and Public Health, Irunlarrea, 1, 31008 Pamplona, Spain; University of Navarra, Department of Preventive Medicine and Public Health, IdisNA, Irunlarrea, 1, 31008 Pamplona, Spain; CIBERobn, Instituto de Salud Carlos III, Madrid, Spain. Electronic address: cfuente@unav.es.

Maira Bes-Rastrollo (M)

University of Navarra, Department of Preventive Medicine and Public Health, Irunlarrea, 1, 31008 Pamplona, Spain; University of Navarra, Department of Preventive Medicine and Public Health, IdisNA, Irunlarrea, 1, 31008 Pamplona, Spain; CIBERobn, Instituto de Salud Carlos III, Madrid, Spain. Electronic address: mbes@unav.es.

Silvia Carlos (S)

University of Navarra, Department of Preventive Medicine and Public Health, Irunlarrea, 1, 31008 Pamplona, Spain; University of Navarra, Department of Preventive Medicine and Public Health, IdisNA, Irunlarrea, 1, 31008 Pamplona, Spain; CIBERobn, Instituto de Salud Carlos III, Madrid, Spain. Electronic address: scarlos@unav.es.

Alfredo Gea (A)

University of Navarra, Department of Preventive Medicine and Public Health, Irunlarrea, 1, 31008 Pamplona, Spain; University of Navarra, Department of Preventive Medicine and Public Health, IdisNA, Irunlarrea, 1, 31008 Pamplona, Spain; CIBERobn, Instituto de Salud Carlos III, Madrid, Spain. Electronic address: ageas@unav.es.

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