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

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

BackgroundDementia 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.MethodsMultiple 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).ResultsOur 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.ConclusionsMachine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking

The association of APOE ε4 with cognitive function over the adult life course and incidence of dementia: 20 years follow-up of the Whitehall II study

International audienceAbstract Background Approximately 25% of the general population carries at least one ε4 allele of the Apolipoprotein E ( APOE ε4), the strongest genetic risk factor for late onset Alzheimer’s disease. Beyond its association with late-onset dementia, the association between APOE ε4 and change in cognition over the adult life course remains uncertain. This study aims to examine whether the association between Apolipoprotein E ( APOE ) ε4 zygosity and cognition function is modified between midlife and old age. Methods A cohort study of 5561 participants (mean age 55.5 (SD = 5.9) years, 27.1% women) with APOE genotyping and repeated cognitive tests for reasoning, memory, and semantic and phonemic fluency, during a mean (SD) follow-up of 20.2 (2.8) years (the Whitehall II study). We used joint models to examine the association of APOE genotype with cognitive function trajectories between 45 and 85 years taking drop-out, dementia, and death into account and Fine and Gray models to examine associations with dementia. Results Compared to non-carriers, heterozygote (prevalence 25%) and homozygote (prevalence 2%) APOE ε4 carriers had increased risk of dementia, sub-distribution hazard ratios 2.19 (95% CI 1.73, 2.77) and 5.97 (95% CI 3.85, 9.28) respectively. Using data spanning 45–85 years with non-ε4 carriers as the reference, ε4 homozygotes had poorer global cognitive score starting from 65 years; ε4 heterozygotes had better scores between 45 and 55 years, then no difference until poorer cognitive scores from 75 years onwards. In analysis of individual cognitive tests, better cognitive performance in the younger ε4 heterozygotes was primarily attributable to executive function. Conclusions Both heterozygous and homozygous ε4 carriers had poorer cognition and greater risk of dementia at older ages. Our findings show some support for a complex antagonist pleiotropic effect of APOE ε4 heterozygosity over the adult life course, characterized by cognitive advantage in midlife

Annonaceae Consumption Worsens Disease Severity and Cognitive Deficits in Degenerative Parkinsonism

International audienceBackground: High consumption of Annona muricata fruit has been previously identified as a risk factor for atypical parkinsonism in the French Caribbean islands.Objective: We tested whether consumption of Annonaceae products could worsen the clinical phenotype of patients with any form of degenerative parkinsonism.Methods: We analyzed neurological data from 180 Caribbean parkinsonian patients and specifically looked for dose effects of lifelong, cumulative Annonaceae consumption on cognitive performance. Using unsupervised clustering, we identified one cluster with mild/moderate symptoms (N = 102) and one with severe symptoms including cognitive impairment (N = 78).Results: We showed that even low cumulative consumption of fruits/juices (>0.2 fruit-years) or any consumption of herbal tea from Annonaceae worsen disease severity and cognitive deficits in degenerative parkinsonism including Parkinson's disease (OR fruits-juices: 3.76 [95% CI: 1.13-15.18]; OR herbal tea: 2.91 [95% CI: 1.34-6.56]).Conclusion: We suggest that more restrictive public health preventive recommendations should be made regarding the consumption of Annonaceae products. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society