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To compare cognitive phenotypes of participants with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), estimate progression to MCI/dementia by phenotype and assess classification error with machine learning.
Dataset consisted of 163 participants with SCD and 282 participants with aMCI from the Czech Brain Aging Study. Cognitive assessment included the Uniform Data Set battery and additional tests to ascertain executive function, language, immediate and delayed memory, visuospatial skills, and processing speed. Latent profile analyses were used to develop cognitive profiles, and Cox proportional hazards models were used to estimate risk of progression. Random forest machine learning algorithms reported cognitive phenotype classification error.
Latent profile analysis identified three phenotypes for SCD, with one phenotype performing worse across all domains but not progressing more quickly to MCI/dementia after controlling for age, sex, and education. Three aMCI phenotypes were characterized by mild deficits, memory and language impairment (dysnomic aMCI), and severe multi-domain aMCI (i.e., deficits across all domains). A dose–response relationship between baseline level of impairment and subsequent risk of progression to dementia was evident for aMCI profiles after controlling for age, sex, and education. Machine learning more easily classified participants with aMCI in comparison to SCD (8% vs. 21% misclassified).
Cognitive performance follows distinct patterns, especially within aMCI. The patterns map onto risk of progression to dementia.
In response to the rising financial pressure on old-age pension systems in industrialised economies, many European countries plan to increase the eligibility age for retirement pensions. We used data from Sweden to examine whether (and if so, how) retirement after age 65 – the eligibility age for basic pension – compared to retiring earlier affects older adults’ (between ages 70 and 85) cognitive functioning. Using a propensity score matching (PSM) approach, we addressed the selection bias potentially introduced by non-random selection into either early or late retirement. We also examined average and heterogeneous treatment effects (HTEs). HTEs were evaluated for different levels of cognitive stimulation from occupational activities before retirement and from leisure activities after retirement. We drew from a rich longitudinal data-set linking two nationally representative Swedish surveys with a register data-set and found that, on average, individuals who retire after age 65 do not have a higher level of cognitive functioning than those who retire earlier. Similarly, we did not observe HTEs from occupational activities. With respect to leisure activities, we found no systematic effects on cognitive functioning among those working beyond age 65. We conclude that, in general, retirement age does not seem to affect cognitive functioning in old age. Yet, the rising retirement age may put substantial pressure on individuals who suffer from poor health at the end of their occupational career, potentially exacerbating social- and health-related inequalities among older people.
To expand on prior literature by examining how various education parameters (performance-based reading literacy, years of education, and self-rated quality of education) relate to a cognitive screening measure's total and subscale scores of specific cognitive abilities.
Black adults (age range: 55–86) were administered self-rated items years of education and quality of education, and a measure of performance-based reading literacy. The Mini-Mental State Examination (MMSE) was used to screen for overall cognitive functioning as well as performance on specific cognitive abilities.
Sixty-nine percent of the sample had reading grade levels that were less than their reported years of education. Lower years of education and worse reading literacy are associated with poorer MMSE performance, particularly on the attention and calculation subscales.
Years of education, a commonly used measure for education, may not be reflective of Black adults’ educational experiences/qualities. Thus, it is important to account for the unique educational experiences of adults that could influence their MMSE performance. Incorporating quality and quantity of education will provide a more comprehensive understanding of the individual's performance on cognitive measures, specifically as it relates to sociocultural differences.
Older Puerto Rican adults have particularly high risk of diabetes compared to the general US population. Diabetes is associated with both higher depressive symptoms and cognitive decline, but less is known about the longitudinal relationship between cognitive decline and incident depressive symptoms in those with diabetes. This study investigated the association between cognitive decline and incident depressive symptoms in older Puerto Rican adults with diabetes over a four-year period.
Households across Puerto Rico were visited to identify a population-based sample of adults aged 60 years and over for the Puerto Rican Elderly: Health Conditions study (PREHCO); 680 participants with diabetes at baseline and no baseline cognitive impairment were included in analyses. Cognitive decline and depressive symptoms were measured using the Mini-Mental Cabán (MMC) and Geriatric Depression Scale (GDS), respectively. We examined predictors of incident depressive symptoms (GDS ≥ 5 at follow-up but not baseline) and cognitive decline using regression modeling.
In a covariate-adjusted logistic regression model, cognitive decline, female gender, and greater diabetes-related complications were each significantly associated with increased odds of incident depressive symptoms (p < 0.05). In a multiple regression model adjusted for covariates, incident depressive symptoms and older age were associated with greater cognitive decline, and higher education was related to less cognitive decline (p < 0.05).
Incident depressive symptoms were more common for older Puerto Ricans with diabetes who also experienced cognitive decline. Efforts are needed to optimize diabetes management and monitor for depression and cognitive decline in this population.
Research shows that lipid levels may be associated with cognitive function, particularly among women. We aimed to examine total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein (HDL), and HDL/LDL ratio in relation to cognitive performance, measured with six well-established cognitive domains and a composite cognitive score (CCS).
In this cross-sectional study, biomarkers and neuropsychological assessment were available for 141 adults with MMSE scores ≥ 24 (mean age = 69 years, 47% female, mean education = 14.4 years) attending a neuropsychological evaluation. Ordinary least squares regressions were adjusted for age, gender, education, and depressive symptoms in Model 1 and also for apolipoprotein E4 (APOE4) status in Model 2.
High-density lipoprotein cholesterol (HDL-C) was associated with better CCS (β = 0.24; p = 0.014). This association was significant among women (β = 0.30; p = 0.026) and not among men (β = 0.20; p = 0.124). HDL-C was also related to attention/working memory (β = 0.24; p = 0.021), again only among women (β = 0.37; p = 0.012) and not men (β = 0.15; p = 0.271). Adjusting for APOE4 yielded significance for high HDL-C and CCS (β = 0.24; p = 0.022).
HDL-C was the main lipoprotein affecting cognitive function, with results somewhat more pronounced among women. Research should investigate the possibility of finding ways to boost HDL-C levels to potentially promote cognitive function.
We explored the effect of education and occupational complexity on the
rate of cognitive decline (as measured by the Mini-Mental State
Examination) in 171 patients with a confirmed Alzheimer's disease
(AD) diagnosis. Complexity was measured as substantive complexity of work
and complexity of work with data, people, and things. Average lifetime
occupational complexity was calculated based on years at each occupation.
Participants were followed for an average of 2.5 years and 3.7 visits. In
multivariate mixed-effects models, high education, high substantive
complexity, and high complexity of work with data and people predicted
faster rates of cognitive decline, controlling for age, gender, native
language, dementia severity, and entry into the analyses at initial
versus follow-up testing. These results provide support for the
concept of cognitive reserve according to which greater reserve may
postpone clinical onset of AD but also accelerate cognitive decline after
the onset. (JINS, 2006, 12, 147–152.)
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