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Background: Air pollution has been consistently linked with dementia and cognitive decline. However, it is unclear whether risk is accumulated through long-term exposure or whether there are sensitive/critical periods. A key barrier to clarifying this relationship is the dearth of historical air pollution data.Objective: To demonstrate the feasibility of modelling historical air pollution data and using them in epidemiologicalmodels.Methods: Using the EMEP4UK atmospheric chemistry transport model, we modelled historical fine particulate matter (PM2.5) concentrations for the years 1935, 1950, 1970, 1980, and 1990 and combined these with contemporary modelled data from 2001 to estimate life course exposure in 572 participants in the Lothian Birth Cohort 1936 with lifetime residential history recorded. Linear regression and latent growth models were constructed using cognitive ability (IQ) measured by the Moray House Test at the ages of 11, 70, 76, and 79 years to explore the effects of historical air pollution exposure. Covariates included sex, IQ at age 11 years, social class, and smoking.Results: Higher air pollution modelled for 1935 (when participants would have been in utero) was associated with worse change in IQ from age 11-70 years (β = -0.006, SE = 0.002, p = 0.03) but not cognitive trajectories from age 70-79 years (p > 0.05). There was no support for other critical/sensitive periods of exposure or an accumulation of risk (all p > 0.05).Conclusion: The life course paradigm is essential in understanding cognitive decline and this is the first study to examine life course air pollution exposure in relation to cognitive health. [ABSTRACT FROM AUTHOR]

Background: Cognitive dispersion, variation in performance across cognitive domains, is posited as a non-invasive and cost-effective marker of early neurodegeneration. Little work has explored associations between cognitive dispersion and Alzheimer's disease (AD) biomarkers in healthy older adults. Even less is known about the influence or interaction of biomarkers reflecting brain pathophysiology or other risk factors on cognitive dispersion scores.Objective: The main aim of this study was to examine whether higher cognitive dispersion was associated with cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42), total tau (t-tau), phosphorylated tau (p-tau), and amyloid positivity in a cohort of older adults at various severities of AD. A secondary aim was to explore which AD risk factors were associated with cognitive dispersion scores.Methods: Linear and logistic regression analyses explored the associations between dispersion and CSF levels of Aβ42, t-tau, and p-tau and amyloid positivity (Aβ42 < 1000 pg/ml). Relationships between sociodemographics, APOEɛ4 status, family history of dementia, and levels of depression and dispersion were also assessed.Results: Dispersion did not emerge as associated with any of the analytes nor amyloid positivity. Older (β= -0.007, SE = 0.002, p = 0.001) and less educated (β= -0.009, SE = 0.003, p = 0.009) individuals showed greater dispersion.Conclusion: Dispersion was not associated with AD pathology, but was associated with age and years of education, highlighting individual differences in cognitive aging. The use of this metric as a screening tool for existing AD pathology is not supported by our analyses. Follow-up work will determine if dispersion scores can predict changes in biomarker levels and/or positivity status longitudinally. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity (the co-occurrence of multiple chronic conditions) is increasingly common, especially among people with dementia. Few neuroimaging studies have explored amyloid biomarkers in people with multimorbidity.Objective: We aimed to conduct the first study of the association between multimorbidity and cerebrospinal fluid amyloid-β42 (CSF Aβ).Method: The European Prevention of Alzheimer's Dementia (EPAD) Longitudinal Cohort Study V500.0 dataset includes volunteers aged ≥50 years from 12 sites. Participants undergo detailed phenotyping, including CSF measures and a self-reported medical history. Using logistic and linear regression analyses, we explored the association between multimorbidity and continuous chronic condition count with CSF Aβ positivity (Aβ42 <1000pg/ml) and continuous CSF Aβ concentration. All models were adjusted for age, sex, APOE status, education, and family history of dementia.Results: Among 447 eligible participants without dementia, the mean (SD) age was 66.6 (6.6) years, 234 (52.3%) were women, and 157 (35.1%) were amyloid positive. With chronic conditions regarded as pseudo-continuous, each additional condition carried a decreased likelihood of amyloid positivity (OR = 0.82, 95% CI: 0.68-0.97; p = 0.026). With CSF Aβ as a continuous variable, each additional condition was associated with an increase of 54.2 pg/ml (95% CI: 9.9-98.5, p = 0.017). Having ≥2 conditions was inversely associated with amyloid positivity (OR 0.59, 95% CI: 0.37-0.95, p = 0.030) compared to one or none.Conclusion: Our findings suggest that the established association between multimorbidity and dementia may be due to a pathway other than amyloid. However, this cross-sectional study does not allow us to make causal inferences. Longitudinal work is required to confirm the inverse association found. [ABSTRACT FROM AUTHOR]

Background: In a previous report, we used latent class analysis (LCA) to identify natural subgroups of older adults in the Einstein Aging Study (EAS) based on neuropsychological performance. These subgroups differed in demographics, genetic profile, and prognosis. Herein, we assess the generalizability of these findings to an independent sample, the Rush Memory and Aging Project (MAP), which used an overlapping, but distinct neuropsychological battery.Objective: Our aim was to identify the association of natural subgroups based on neuropsychological performance in the MAP cohort with incident dementia and compare them with the associations identified in the EAS.Methods: MAP is a community-dwelling cohort of older adults living in the northeastern Illinois, Chicago. Latent class models were applied to baseline scores of 10 neuropsychological measures across 1,662 dementia-free MAP participants. Results were compared to prior findings from the EAS.Results: LCA resulted in a 5-class model: Mixed-Domain Impairment (n = 71, 4.3%), Memory-specific-Impairment (n = 274, 16.5%), Average (n = 767, 46.1%), Frontal Impairment (n = 222, 13.4%), and a class of Superior Cognition (n = 328, 19.7%). Similar to the EAS, the Mixed-Domain Impairment, the Memory-Specific Impairment, and the Frontal Impairment classes had higher risk of incident Alzheimer's disease when compared to the Average class. By contrast, the Superior Cognition had a lower risk of Alzheimer's disease when compared to the Average class.Conclusions: Natural cognitive subgroups in MAP are similar to those identified in EAS. These similarities, despite study differences in geography, sampling strategy, and cognitive tests, suggest that LCA is capable of identifying classes that are not limited to a single sample or a set of cognitive tests. [ABSTRACT FROM AUTHOR]