Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis

Abstract Background Dementia is defined as a cognitive decline that affects functional status. Longitudinal ageing surveys often lack a clinical diagnosis of dementia though measure cognition and daily function over time. We used unsupervised machine learning and longitudinal data to identify transition to probable dementia. Methods Multiple Factor Analysis was applied to longitudinal function and cognitive data of 15,278 baseline participants (aged 50 years and more) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) (waves 1, 2 and 4–7, between 2004 and 2017). Hierarchical Clustering on Principal Components discriminated three clusters at each wave. We estimated probable or “Likely Dementia” prevalence by sex and age, and assessed whether dementia risk factors increased the risk of being assigned probable dementia status using multistate models. Next, we compared the “Likely Dementia” cluster with self-reported dementia status and replicated our findings in the English Longitudinal Study of Ageing (ELSA) cohort (waves 1–9, between 2002 and 2019, 7840 participants at baseline). Results Our algorithm identified a higher number of probable dementia cases compared with self-reported cases and showed good discriminative power across all waves (AUC ranged from 0.754 [0.722–0.787] to 0.830 [0.800–0.861]). “Likely Dementia” status was more prevalent in older people, displayed a 2:1 female/male ratio, and was associated with nine factors that increased risk of transition to dementia: low education, hearing loss, hypertension, drinking, smoking, depression, social isolation, physical inactivity, diabetes, and obesity. Results were replicated in ELSA cohort with good accuracy. Conclusions Machine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking.

Domaines

Neurosciences [q-bio.NC] Statistiques [stat]

Fichier principal

s13195-023-01357-9.pdf (1.09 Mo)

Origine	Fichiers éditeurs autorisés sur une archive ouverte

Laurent Jonchère : Connectez-vous pour contacter le contributeur

https://hal.science/hal-04346412

Soumis le : mardi 28 mai 2024-13:25:05

Dernière modification le : lundi 16 décembre 2024-10:37:32

Dates et versions

hal-04346412 , version 1 (28-05-2024)

Identifiants

HAL Id : hal-04346412 , version 1
DOI : 10.1186/s13195-023-01357-9

Citer

Amin Gharbi-Meliani, François Husson, Henri Vandendriessche, Eleonore Bayen, Kristine Yaffe, et al.. Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis. Alzheimer's Research and Therapy, 2023, 15 (1), ⟨10.1186/s13195-023-01357-9⟩. ⟨hal-04346412⟩

Exporter

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

INSERM UNIV-RENNES1 IRMAR UR2-HB CNRS INSA-RENNES INSMI IMRB UPEC IRMAR-STAT CHL UR1-MATH-STIC UNIV-RENNES2 UNIV-RENNES INSA-GROUPE UR1-MATH-NUM INSTITUT-AGRO-RENNES-ANGERS-UMR-IRMAR INSTITUT-AGRO-RENNES-ANGERS

55 Consultations

13 Téléchargements