27
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants

      research-article
      , MSc a , , PhD a , , PhD a , , PhD b , , PhD a , , Prof, MD a , *
      The Lancet. Public Health
      Elsevier, Ltd

      Read this article at

      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.

          Summary

          Background

          Frailty is associated with older age and multimorbidity (two or more long-term conditions); however, little is known about its prevalence or effects on mortality in younger populations. This paper aims to examine the association between frailty, multimorbidity, specific long-term conditions, and mortality in a middle-aged and older aged population.

          Methods

          Data were sourced from the UK Biobank. Frailty phenotype was based on five criteria (weight loss, exhaustion, grip strength, low physical activity, slow walking pace). Participants were deemed frail if they met at least three criteria, pre-frail if they fulfilled one or two criteria, and not frail if no criteria were met. Sociodemographic characteristics and long-term conditions were examined. The outcome was all-cause mortality, which was measured at a median of 7 years follow-up. Multinomial logistic regression compared sociodemographic characteristics and long-term conditions of frail or pre-frail participants with non-frail participants. Cox proportional hazards models examined associations between frailty or pre-frailty and mortality. Results were stratified by age group (37–45, 45–55, 55–65, 65–73 years) and sex, and were adjusted for multimorbidity count, socioeconomic status, body-mass index, smoking status, and alcohol use.

          Findings

          493 737 participants aged 37–73 years were included in the study, of whom 16 538 (3%) were considered frail, 185 360 (38%) pre-frail, and 291 839 (59%) not frail. Frailty was significantly associated with multimorbidity (prevalence 18% [4435/25 338] in those with four or more long-term conditions; odds ratio [OR] 27·1, 95% CI 25·3–29·1) socioeconomic deprivation, smoking, obesity, and infrequent alcohol consumption. The top five long-term conditions associated with frailty were multiple sclerosis (OR 15·3; 99·75% CI 12·8–18·2); chronic fatigue syndrome (12·9; 11·1–15·0); chronic obstructive pulmonary disease (5·6; 5·2–6·1); connective tissue disease (5·4; 5·0–5·8); and diabetes (5·0; 4·7–5·2). Pre-frailty and frailty were significantly associated with mortality for all age strata in men and women (except in women aged 37–45 years) after adjustment for confounders.

          Interpretation

          Efforts to identify, manage, and prevent frailty should include middle-aged individuals with multimorbidity, in whom frailty is significantly associated with mortality, even after adjustment for number of long-term conditions, sociodemographics, and lifestyle. Research, clinical guidelines, and health-care services must shift focus from single conditions to the requirements of increasingly complex patient populations.

          Funding

          CSO Catalyst Grant and National Health Service Research for Scotland Career Research Fellowship.

          Related collections

          Most cited references15

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

          A comparison of two approaches to measuring frailty in elderly people.

          Many definitions of frailty exist, but few have been directly compared. We compared the relationship between a definition of frailty based on a specific phenotype with one based on an index of deficit accumulation. The data come from all 2305 people 70 years old and older who composed the clinical examination cohort of the second wave of the Canadian Study of Health and Aging. We tested convergent validity by correlating the measures with each other and with other health status measures, and analyzed cumulative index distributions in relation to phenotype. To test criterion validity, we evaluated survival (institutionalization and all-cause mortality) by frailty index (FI) score, stratified by the phenotypic definitions as "robust," "pre-frail," and "frail." The measures correlated moderately well with each other (R=0.65) and with measures of function (phenotypic definition R=0.66; FI R=0.73) but less well with cognition (phenotypic definition R=-0.35; FI R=-0.58). The median FI scores increased from 0.12 for the robust to 0.30 for the pre-frail and 0.44 for the frail. Survival was also lower with increasing frailty, and institutionalization was more common, but within each phenotypic class, there were marked differences in outcomes based on the FI values-e.g., among robust people, the median 5-year survival for those with lower FI values was 85%, compared with 55% for those with higher FI values. The phenotypic definition of frailty, which offers ready clinical operationalization, discriminates broad levels of risk. The FI requires additional clinical translation, but allows the risk of adverse outcomes to be defined more precisely.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The ability of three different models of frailty to predict all-cause mortality: results from the European Male Aging Study (EMAS).

            Few studies have directly compared the ability of the most commonly used models of frailty to predict mortality among community-dwelling individuals. Here, we used a frailty index (FI), frailty phenotype (FP), and FRAIL scale (FS) to predict mortality in the EMAS. Participants were aged 40-79 years (n=2929) at baseline and 6.6% (n=193) died over a median 4.3 years of follow-up. The FI was generated from 39 deficits, including self-reported health, morbidities, functional performance and psychological assessments. The FP and FS consisted of five phenotypic criteria and both categorized individuals as robust when they had 0 criteria, prefrail as 1-2 criteria and frail as 3+ criteria. The mean FI increased linearly with age (r(2)=0.21) and in Cox regression models adjusted for age, center, smoking and partner status the hazard ratio (HR) for death for each unit increase of the FI was 1.49. Men who were prefrail or frail by either the FP or FS definitions, had a significantly increased risk of death compared to their robust counterparts. Compared to robust men, those who were FP frail at baseline had a HR for death of 3.84, while those who were FS frail had a HR of 3.87. All three frailty models significantly predicted future mortality among community-dwelling, middle-aged and older European men after adjusting for potential confounders. Our data suggest that the choice of frailty model may not be of paramount importance when predicting future risk of death, enabling flexibility in the approach used. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              General practice funding underpins the persistence of the inverse care law: cross-sectional study in Scotland.

              Universal access to health care, as provided in the NHS, does not ensure that patients' needs are met.
                Bookmark

                Author and article information

                Contributors
                Journal
                Lancet Public Health
                Lancet Public Health
                The Lancet. Public Health
                Elsevier, Ltd
                2468-2667
                14 June 2018
                July 2018
                14 June 2018
                : 3
                : 7
                : e323-e332
                Affiliations
                [a ]General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, 1 Horselethill Road, Glasgow, G12 9LX, Scotland, United Kingdom
                [b ]School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
                Author notes
                [* ]Correspondence to: Prof Frances S Mair, General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow G12 9LX, UK frances.mair@ 123456glasgow.ac.uk
                Article
                S2468-2667(18)30091-4
                10.1016/S2468-2667(18)30091-4
                6028743
                29908859
                ca3f8a95-7131-4714-9e2d-b5df36eaca00
                © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Categories
                Article

                Comments

                Comment on this article