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Background: REM-sleep behavior disorder (RBD) and other non-motor symptoms such as hyposmia were proposed by the Movement Disorder Society as research criteria for prodromal Parkinson's disease (P-PD). Global cognitive deficit was later added. Objective: To compare non-motor symptoms, focusing on cognition, between a P-PD group and a matched control group. Methods: In this cross-sectional, case-control study, in a first set of analyses, we performed extensive cognitive testing on people with (n = 76) and a control group without (n = 195) probable RBD and hyposmia. Furthermore, we assessed motor and non-motor symptoms related to Parkinson's Disease (PD). After propensity score matching, we compared 62 P-PD with 62 age- and sex-matched controls. In addition, we performed regression analyses on the total sample (n = 271). In a second set of analyses, we used, a.o., the CUPRO to evaluate retrograde procedural memory and visuo-constructive functions. Results: People with P-PD showed significantly poorer performances in global cognition, visuo-constructive and executive functions, mainly in mental flexibility (p < 0.001; p = 0.004; p = 0.003), despite similar educational levels (p = 0.415). We observed significantly more motor and non-motor symptoms (p < 0.001; p = 0.004), higher scores for depression (p = 0.004) and apathy (p < 0.001) as well as lower quality of life (p < 0.001) in P-PD. CONCLUSIONS: Our findings confirm that global cognitive, executive, and visuo-constructive deficits define the P-PD group. In addition, depression, apathy, and lower quality of life were more prevalent in P-PD. If replicated in other samples, executive and visuo-constructive deficits should be considered in non-motor P-PD. Determining specific patterns will support early recognition of PD, secondary prevention of complications and the development of neuroprotective treatments. [ABSTRACT FROM AUTHOR]

PurposeWe sought to examine how different activities performed during employment gaps are associated with later cognitive function and change.MethodsFive cognitive measures were used to indicate cognitive impairment of 18,259 respondents to the Survey of Health, Ageing, and Retirement in Europe (ages 50-73) in 2004/5 or 2006/7. Using complete employment histories, employment gaps of ≥6 months between ages 25 and 65 were identified.ResultsControlling for early life socioeconomic status, school performance, and education, higher risk of cognitive impairment was associated with employment gaps described as unemployment (odds ratio [OR], 1.18; 95% confidence interval [CI], 1.04-1.35) and sickness (OR, 1.78; 95% CI, 1.52-2.09). In contrast, lower risk of cognitive impairment was associated with employment gaps described as training (OR, 0.73; 95% CI, 0.52-1.01) or maternity leave (OR, 0.65; 95% CI, 0.57-0.79). In longitudinal mixed effects models, training and maternity leave were associated with lower 2-year aging-related cognitive decline.DiscussionPeriods away from work described as unemployment or sickness are associated with lower cognitive function, whereas maternity and training leaves are associated with better late-life cognitive function. Both causation and selection mechanisms may explain these findings.

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS: Risk‐profiling tools may help identify high‐risk populations for clinical trials; however, their performance needs improvement. New risk‐profiling and trial‐recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug‐repurposing efforts and prioritization of disease‐modifying therapeutics. DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. Highlights: Artificial intelligence (AI) is not widely used in the dementia prevention field.Risk‐profiling tools are not used in clinical practice.Causal insights are needed to understand risk factors over the lifespan.AI will help personalize risk‐management tools for dementia prevention.AI could target specific patient groups that will benefit most for clinical trials. [ABSTRACT FROM AUTHOR]

Background: Individuals with depression have an increased dementia risk, which might be due to modifiable risk factors for dementia. This study investigated the extent to which the increased risk for dementia in depression is explained by modifiable dementia risk factors. Methods: We used data from the English Longitudinal Study of Ageing (2008–2009 to 2018–2019), a prospective cohort study. A total of 7460 individuals were included [mean(standard deviation) age, 65.7 ± 9.4 years; 3915(54.7%) were women]. Depressive symptoms were assessed with the Center for Epidemiologic Studies Depression Scale-8 (score ≥3) or self-reported doctor's diagnosis. Ten modifiable risk factors for dementia were combined in the 'LIfestyle for BRAin health' (LIBRA) score. Dementia was determined by physician diagnosis, self-reported Alzheimer's disease or the shortened version of the Informant Questionnaire on Cognitive Decline in the Elderly (average score ≥3.38). Structural equation modelling was used to test mediation of LIBRA score. Results: During 61 311 person-years, 306 individuals (4.1%) developed dementia. Participants aged 50–70 years with depressive symptoms had higher LIBRA scores [difference(s.e.) = 1.15(0.10)] and a 3.59 times increased dementia risk [HR(95% CI) = 3.59(2.20–5.84)], adjusted for age, sex, education, wealth and clustering at the household level. In total, 10.4% of the dementia risk was mediated by differences in LIBRA score [indirect effect: HR = 1.14(1.03–1.26)], while 89.6% was attributed to a direct effect of depressive symptoms on dementia risk [direct effect: HR = 3.14(2.20–5.84)]. Conclusions: Modifiable dementia risk factors can be important targets for the prevention of dementia in individuals with depressive symptoms during midlife. Yet, effect sizes are small and other aetiological pathways likely exist. [ABSTRACT FROM AUTHOR]