Lauriane Segaux1, Nadia Oubaya2, Amaury Broussier3, Marjolaine Baude4, Florence Canouï-Poitrine2, Henri Naga5, Marie Laurent6, Claire Leissing-Desprez3, Etienne Audureau2, Emilie Ferrat7, Christophe Chailloleau8, Isabelle Fromentin5, Jean-Philippe David3, Sylvie Bastuji-Garin9. 1. 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. Electronic address: lauriane.segaux@aphp.fr. 2. 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. 3. 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. 4. AP-HP, Hôpital Henri-Mondor, Department of Public Health, Créteil, France. 5. AP-HP, Hôpital Henri-Mondor/Emile Roux, Department of Geriatrics, Créteil, France. 6. 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. 7. 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. 8. AP-HP, Hôpital Henri-Mondor, Department of Medical Informatics, Créteil, France. 9. 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.
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.
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.