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Research Article| Volume 127, P1-11, September 2019

Identification of five frailty profiles in community-dwelling individuals aged 50–75: A latent class analysis of the SUCCEED survey data

  • Lauriane Segaux
    Correspondence
    Corresponding author at: Service de Santé Publique, Hôpital Henri-Mondor, F-94010 Créteil Cedex, France.
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    Assistance Publique Hôpitaux de Paris (AP-HP), Hôpital Henri-Mondor, Clinical Research Unit (URC Mondor), Créteil, France
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  • Nadia Oubaya
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    AP-HP, Hôpital Henri-Mondor, Department of Public Health, Créteil, France
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  • Amaury Broussier
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    AP-HP, Hôpital Henri-Mondor/Emile Roux, Department of Geriatrics, Créteil, France
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  • Marjolaine Baude
    Affiliations
    AP-HP, Hôpital Henri-Mondor, Department of Public Health, Créteil, France
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  • Florence Canouï-Poitrine
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    AP-HP, Hôpital Henri-Mondor, Department of Public Health, Créteil, France
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  • Henri Naga
    Affiliations
    AP-HP, Hôpital Henri-Mondor/Emile Roux, Department of Geriatrics, Créteil, France
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  • Marie Laurent
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    AP-HP, Hôpital Henri-Mondor/Albert Chenevier, Department of Geriatrics, Créteil, France
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  • Claire Leissing-Desprez
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    AP-HP, Hôpital Henri-Mondor/Emile Roux, Department of Geriatrics, Créteil, France
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  • Etienne Audureau
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    AP-HP, Hôpital Henri-Mondor, Department of Public Health, Créteil, France
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  • Emilie Ferrat
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    UPEC, Faculté de médecine de Créteil, Primary Care Department, Créteil, France
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  • Christophe Chailloleau
    Affiliations
    AP-HP, Hôpital Henri-Mondor, Department of Medical Informatics, Créteil, France
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  • Isabelle Fromentin
    Affiliations
    AP-HP, Hôpital Henri-Mondor/Emile Roux, Department of Geriatrics, Créteil, France
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  • Author Footnotes
    1 These two authors contributed equally to the study.
    Jean-Philippe David
    Footnotes
    1 These two authors contributed equally to the study.
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    AP-HP, Hôpital Henri-Mondor/Emile Roux, Department of Geriatrics, Créteil, France
    Search for articles by this author
  • Author Footnotes
    1 These two authors contributed equally to the study.
    Sylvie Bastuji-Garin
    Footnotes
    1 These two authors contributed equally to the study.
    Affiliations
    Université Paris Est (UPEC), IMRB, A-TVB DHU, CEpiA EA 7376 (Clinical Epidemiology and Ageing Unit), Créteil, France

    Assistance Publique Hôpitaux de Paris (AP-HP), Hôpital Henri-Mondor, Clinical Research Unit (URC Mondor), Créteil, France

    AP-HP, Hôpital Henri-Mondor, Department of Public Health, Créteil, France
    Search for articles by this author
  • Author Footnotes
    1 These two authors contributed equally to the study.

      Highlights

      • Frailty is a commonly used approach to the health status of older people.
      • Frailty increases the likelihood of adverse health outcomes.
      • Frailty profiles have been identified among relatively young seniors.
      • Frailty could be detected in young seniors to prevent adverse health outcomes.

      Abstract

      Objectives

      We sought to identify frailty profiles in individuals aged 50–75 by considering frailty as an unobservable latent variable in a latent class analysis (LCA).

      Study design

      589 prospectively enrolled community-dwelling individuals aged 50–75 (median: 61.7 years) had undergone a standardized, multidomain assessment in 2010–2015. Adverse health outcomes (non-accidental falls, fractures, unplanned hospitalizations, and death) that had occurred since the assessment were recorded in 2016–2017.

      Main outcome measures

      The LCA used nine indicators (unintentional weight loss, relative slowness, weakness, impaired balance, osteoporosis, impaired cognitive functions, executive dysfunction, depression, and hearing impairment) and three covariates (age, gender, and consultation for health complaints). The resulting profiles were characterized by the Fried phenotype and adverse health outcomes.

      Results

      We identified five profiles: “fit” (LC1, 29.7% of the participants; median age: 59 years); “weight loss, relative slowness, and osteoporosis” (LC2, 33.2%; 63 years); “weakness and osteopenia” (LC3, 21.9%; 60 years); “impaired physical and executive functions” (LC4, 11%; 67 years); and “impaired balance, cognitive functions, and depression” (LC5, 4.3%; 70 years). Almost all members of LC3 and LC4 were female, and were more likely than members of other profiles to have a frail or pre-frail Fried phenotype. Non-accidental falls were significantly more frequent in LC4. LC5 (almost all males) had the highest number of comorbidities and cardiovascular risk factors but none was frail.

      Conclusions

      Our data-driven approach covered most geriatric assessment domains and identified five frailty profiles. With a view to tailoring interventions and prevention, frailty needs to be detected among young seniors.

      Keywords

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