Evidence map›Paper›PMID 29782486›Full record

ArticlePLoS medicine2018

Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study.

Archana Singh-Manoux, Aurore Fayosse, Séverine Sabia, Adam Tabak, Martin Shipley, Aline Dugravot, Mika Kivimäki

Abstract read
In one paragraph

Article in PLoS medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
139citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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

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

2 · The registry

The trial behind it

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

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

3 · Its place in the literature

Who cites it

139 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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79 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

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

5 · Who and what money

Authors and funding

7 authors.

Archana Singh-ManouxINSERM, U1018, Centre for Research in Epidemiology and Population Health, Hôpital Paul Brousse, Villejuif, France.ORCID 0000-0002-1244-5037
Aurore FayosseINSERM, U1018, Centre for Research in Epidemiology and Population Health, Hôpital Paul Brousse, Villejuif, France.
Séverine SabiaINSERM, U1018, Centre for Research in Epidemiology and Population Health, Hôpital Paul Brousse, Villejuif, France.ORCID 0000-0003-3109-9720
Adam TabakDepartment of Epidemiology and Public Health, University College London, London, United Kingdom.ORCID 0000-0002-6234-3936
Martin ShipleyDepartment of Epidemiology and Public Health, University College London, London, United Kingdom.
Aline DugravotINSERM, U1018, Centre for Research in Epidemiology and Population Health, Hôpital Paul Brousse, Villejuif, France.
Mika KivimäkiDepartment of Epidemiology and Public Health, University College London, London, United Kingdom.ORCID 0000-0002-4699-5627

Funding

Education, socioeconomic status and Aging: transitions from multimorbidity to functional limitations and mortalityR01AG056477 · NIA · UNIVERSITY COLLEGE LONDON · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2018 to 2022
$2.8M
Health behaviors over the adult lifecourse and cognitive agingR01AG034454 · NIA · UNIVERSITY COLLEGE LONDON · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2009 to 2013
$1.5M
Education and Aging: transitions from multi-morbidity to functional limitationsR56AG056477 · NIA · UNIVERSITY COLLEGE LONDON · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2017 to 2017
$365k
British Heart Foundation RG/16/11/32334Medical Research Council K013351Medical Research Council MR/R024227/1Medical Research Council R024227NIA NIH HHS R01 AG034454NIA NIH HHS R01 AG056477NIA NIH HHS R56 AG056477
6 · The paper itself

Abstract

backgroundMultimorbidity is increasingly common and is associated with adverse health outcomes, highlighting the need to broaden the single-disease framework that dominates medical research. We examined the role of midlife clinical characteristics, socioeconomic position, and behavioural factors in the development of cardiometabolic multimorbidity (at least 2 of diabetes, coronary heart disease, and stroke), along with how these factors modify risk of mortality. METHODS AND

findingsData on 8,270 men and women were drawn from the Whitehall II cohort study, with mean follow-up of 23.7 years (1985 to 2017). Three sets of risk factors were assessed at age 50 years, each on a 5-point scale: clinical profile (hypertension, hypercholesterolemia, overweight/obesity, family history of cardiometabolic disease), occupational position, and behavioural factors (smoking, alcohol consumption, diet, physical activity). The outcomes examined were cardiometabolic disease (diabetes, coronary heart disease, stroke), cardiometabolic multimorbidity, and mortality. We used multi-state models to examine the role of risk factors in 5 components of the cardiometabolic disease trajectory: from healthy state to first cardiometabolic disease, from first cardiometabolic disease to cardiometabolic multimorbidity, from healthy state to death, from first cardiometabolic disease to death, and from cardiometabolic multimorbidity to death. A total of 2,501 participants developed 1 of the 3 cardiometabolic diseases, 511 developed cardiometabolic multimorbidity, and 1,406 died. When behavioural and clinical risk factors were considered individually, only smoking was associated with all five transitions. In a model containing all 3 risk factor scales, midlife clinical profile was the strongest predictor of first cardiometabolic disease (hazard ratio for the least versus most favourable profile: 3.74; 95% CI: 3.14-4.45) among disease-free participants. Among participants with 1 cardiometabolic disease, adverse midlife socioeconomic (1.54; 95% CI: 1.10-2.15) and behavioural factors (2.00; 95% CI: 1.40-2.85), but not clinical characteristics, were associated with progression to cardiometabolic multimorbidity. Only midlife behavioural factors predicted mortality among participants with cardiometabolic disease (2.12; 95% CI: 1.41-3.18) or cardiometabolic multimorbidity (3.47; 95% CI: 1.81-6.66). A limitation is that the study was not large enough to estimate transitions between each disease and subsequent outcomes and between all possible pairs of diseases.

conclusionsThe importance of specific midlife factors in disease progression, from disease-free state to single disease, multimorbidity, and death, varies depending on the disease stage. While clinical risk factors at age 50 determine the risk of incident cardiometabolic disease in a disease-free population, midlife socioeconomic and behavioural factors are stronger predictors of progression to multimorbidity and mortality in people with cardiometabolic disease.

Indexed as

AdultAgedCardiovascular DiseasesCohort StudiesFemaleHumansMaleMarkov ChainsMiddle AgedMultimorbidityProportional Hazards ModelsPsychologyRisk FactorsSocioeconomic FactorsUnited Kingdom

Identifiers

PMID29782486
PMCPMC5962054

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