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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Multiple Factor Analysis was applied to longitudinal function and cognitive data of 15,278 baseline participants (aged 50\u00a0years and more) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) (waves 1, 2 and 4\u20137, between 2004 and 2017). Hierarchical Clustering on Principal Components discriminated three clusters at each wave. We estimated probable or \u201cLikely Dementia\u201d 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 \u201cLikely Dementia\u201d cluster with self-reported dementia status and replicated our findings in the English Longitudinal Study of Ageing (ELSA) cohort (waves 1\u20139, between 2002 and 2019, 7840 participants at baseline).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u20130.787] to 0.830 [0.800\u20130.861]). \u201cLikely Dementia\u201d 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13195-023-01357-9","type":"journal-article","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T08:02:13Z","timestamp":1701244933000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis"],"prefix":"10.1186","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7788-3321","authenticated-orcid":false,"given":"Amin","family":"Gharbi-Meliani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7271-8877","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Husson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9319-0031","authenticated-orcid":false,"given":"Henri","family":"Vandendriessche","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0286-5521","authenticated-orcid":false,"given":"Eleonore","family":"Bayen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0919-3825","authenticated-orcid":false,"given":"Kristine","family":"Yaffe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3000-2210","authenticated-orcid":false,"given":"Anne-Catherine","family":"Bachoud-L\u00e9vi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6551-4641","authenticated-orcid":false,"given":"Laurent","family":"Cleret de Langavant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"1357_CR1","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association. 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SHARE was approved by committees at the University of Mannheim and the Max Planck society. ELSA was first approved by the London Multicenter Research Ethics Committee (MREC\/01\/2\/91). Information about the ethical committees of the following waves is available at\n                      \n                      .","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"E.B. is an advisory board member of SafelyYou. E.B. has vested stock options in SafelyYou stock. K.F. is a participant on a data safety monitoring board Eli Lilly and NIH-sponsored trials. K.F. is a board member of Alector. A.C.B.L. has received academic grants from the French National Research Agency (ANR-17-EURE-0017) and NeurATRIS (ANR-11-INBS-0011). A.C.B.L. has received fees for consultancy work from Roche. All other authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"209"}}