22
views
0
recommends
+1 Recommend
1 collections
    1
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Multimorbidity 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 findings

          Data 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.

          Conclusions

          The 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.

          Abstract

          Archana Singh-Manoux and colleagues report on the contribution that midlife socioeconomic and behavioural factors make to multimorbidity and mortality in those with cardiometabolic disease.

          Author summary

          Why was this study done?
          • The prevalence of cardiometabolic multimorbidity increases with age, and any combination of diabetes, stroke, and coronary heart disease is associated with multiplicative mortality risk.

          • Previous studies have examined either risk factors for multimorbidity or the manner in which multimorbidity shapes adverse health outcomes. No previous study to our knowledge has examined how socioeconomic, behavioural, and clinical risk factors shape the development, progression, and outcome of cardiometabolic multimorbidity.

          What did the researchers do and find?
          • Data were collected on socioeconomic, behavioural, and clinical risk factors at age 50 years on 8,270 participants from the Whitehall II study, and the participants were followed over a mean 23.7 years for incident cardiometabolic disease (diabetes, coronary heart disease, or stroke), cardiometabolic multimorbidity (2 or more cardiometabolic diseases), and mortality.

          • Clinical risk factors (hypertension, overweight and obesity, high cholesterol, and family history of diabetes or cardiovascular disease) were important predictors of first cardiometabolic disease. However, socioeconomic and behavioural factors (physical activity, alcohol consumption, diet, and smoking) determined progression to multimorbidity, and only behavioural risk factors predicted mortality among participants with cardiometabolic disease or cardiometabolic multimorbidity.

          • When risk factors were considered individually, smoking was associated with accelerated transitions in the trajectory from the development of a first cardiometabolic disease to multimorbidity and death.

          What do these findings mean?
          • By considering risk factors in the progression from a disease-free state to death, we determined the changing influence of socioeconomic, behavioural, and clinical risk factors.

          • Our findings demonstrate that a simple focus on one point in the health trajectory of individuals misses the changing role of risk factors in the development, progression, and outcome of cardiometabolic multimorbidity, a major public health challenge worldwide.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: found
          • Article: not found

          Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.

          Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. In a cross-sectional study we extracted data on 40 morbidities from a database of 1,751,841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. 42·2% (95% CI 42·1-42·3) of all patients had one or more morbidities, and 23·2% (23·08-23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210,500 vs 194,996). Onset of multimorbidity occurred 10-15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9-11·2% in most deprived area vs 5·9%, 5·8%-6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59-6·90 for five or more disorders vs 1·95, 1·93-1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21-2·32 vs 1·08, 1·05-1·11). Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Scottish Government Chief Scientist Office. Copyright © 2012 Elsevier Ltd. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Aging with multimorbidity: a systematic review of the literature.

            A literature search was carried out to summarize the existing scientific evidence concerning occurrence, causes, and consequences of multimorbidity (the coexistence of multiple chronic diseases) in the elderly as well as models and quality of care of persons with multimorbidity. According to pre-established inclusion criteria, and using different search strategies, 41 articles were included (four of these were methodological papers only). Prevalence of multimorbidity in older persons ranges from 55 to 98%. In cross-sectional studies, older age, female gender, and low socioeconomic status are factors associated with multimorbidity, confirmed by longitudinal studies as well. Major consequences of multimorbidity are disability and functional decline, poor quality of life, and high health care costs. Controversial results were found on multimorbidity and mortality risk. Methodological issues in evaluating multimorbidity are discussed as well as future research needs, especially concerning etiological factors, combinations and clustering of chronic diseases, and care models for persons affected by multiple disorders. New insights in this field can lead to the identification of preventive strategies and better treatment of multimorbid patients. Copyright © 2011 Elsevier B.V. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The World report on ageing and health: a policy framework for healthy ageing.

              Although populations around the world are rapidly ageing, evidence that increasing longevity is being accompanied by an extended period of good health is scarce. A coherent and focused public health response that spans multiple sectors and stakeholders is urgently needed. To guide this global response, WHO has released the first World report on ageing and health, reviewing current knowledge and gaps and providing a public health framework for action. The report is built around a redefinition of healthy ageing that centres on the notion of functional ability: the combination of the intrinsic capacity of the individual, relevant environmental characteristics, and the interactions between the individual and these characteristics. This Health Policy highlights key findings and recommendations from the report.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                21 May 2018
                May 2018
                : 15
                : 5
                : e1002571
                Affiliations
                [1 ] INSERM, U1018, Centre for Research in Epidemiology and Population Health, Hôpital Paul Brousse, Villejuif, France
                [2 ] Department of Epidemiology and Public Health, University College London, London, United Kingdom
                University of Oxford, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-1244-5037
                http://orcid.org/0000-0003-3109-9720
                http://orcid.org/0000-0002-6234-3936
                http://orcid.org/0000-0002-4699-5627
                Article
                PMEDICINE-D-18-00379
                10.1371/journal.pmed.1002571
                5962054
                29782486
                93df4895-ebd4-4ade-a892-0f1bd47e75b6
                © 2018 Singh-Manoux et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 January 2018
                : 24 April 2018
                Page count
                Figures: 1, Tables: 4, Pages: 16
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: 643576
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: 633666
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R56AG056477
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R01AG034454
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: K013351
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: R024227
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002341, Academy of Finland;
                Award ID: 331492
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004785, NordForsk;
                Award Recipient :
                ASM received funding for the study from the Horizon 2020 Framework Programme (643576) and the National Institute on Aging (R56AG056477, R01AG034454). MK received funding from the Medical Research Council (K013351, R024227), Horizon 2020 Framework Programme (633666), the Academy of Finland (331492) and NordForsk. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Health Care
                Health Risk Analysis
                Medicine and Health Sciences
                Health Care
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Socioeconomic Aspects of Health
                Biology and Life Sciences
                Behavior
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Biology and Life Sciences
                Nutrition
                Diet
                Alcohol Consumption
                Medicine and Health Sciences
                Nutrition
                Diet
                Alcohol Consumption
                Medicine and Health Sciences
                Cardiovascular Medicine
                Cardiovascular Diseases
                Research and Analysis Methods
                Research Design
                Cohort Studies
                Medicine and Health Sciences
                Vascular Medicine
                Coronary Heart Disease
                Medicine and Health Sciences
                Cardiology
                Coronary Heart Disease
                Custom metadata
                Whitehall II data, protocols, and other metadata are available to the scientific community. Please refer to the Whitehall II data sharing policy at https://www.ucl.ac.uk/iehc/research/epidemiology-public-health/research/whitehallII/data-sharing.

                Medicine
                Medicine

                Comments

                Comment on this article