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BACKGROUND/OBJECTIVE: Poor air quality is implicated as a risk factor for cognitive impairment and dementia. Few studies have examined these associations longitudinally in well-characterized population-based cohorts with standardized annual assessment of both mild cognitive impairment (MCI) and dementia. We investigated the association between estimated ambient fine particulate matter (PM(2.5)) and risk of incident MCI and dementia in a post-industrial region known for historically poor air quality. SETTING/PARTICIPANTS: Adults aged 65+ years in a population-based cohort (n=1572). MEASUREMENTS: Census tract level PM(2.5) from Environmental Protection Agency (EPA) air quality monitors; Clinical Dementia Rating(CDR)®. DESIGN: We estimated ambient PM(2.5) exposure (μg/m(3), single-year and five-year averages) by geocoding participants’ residential addresses to census tracts with daily EPA PM(2.5) measurements from 2002–2014. Using Bayesian spatial regression modeling adjusted for age, sex, education, smoking history, and household income, we examined the association between estimated PM(2.5) exposure and risk of incident mild cognitive impairment (CDR=0.5) and incident dementia (CDR≥1.0). RESULTS: Modeling estimated single-year exposure, each 1 μg/m(3) higher ambient PM(2.5) was associated with 67% higher adjusted risk of incident dementia (Hazard Ratio[HR]=1.669, 95% Credible Interval[CI]: 1.298, 2.136) and 75% higher adjusted risk of incident MCI (HR=1.746, 95% CI: 1.518, 2.032). Estimates were higher when modeling five-year ambient PM(2.5) exposure for incident dementia (HR=2.082, 95% CI:1.528, 3.015) and incident MCI (HR=3.419, 95% CI:2.806, 4.164). CONCLUSIONS: Higher estimated ambient PM(2.5) was associated with higher risk of incident MCI and dementia, particularly when considering longer term exposure, and independent of demographic characteristics and smoking history. Targeting poor air quality may be a reasonable population-wide intervention to reduce risk of cognitive impairment in older adults, particularly in regions exceeding current recommendations for safe exposure to PM(2.5).

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein e4 (APOE e4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

Introduction: Although some members of families with autosomal dominant Alzheimer's disease mutations learn their mutation status, most do not. How knowledge of mutation status affects clinical disease progression is unknown. This study quantifies the influence of mutation awareness on clinical symptoms, cognition, and biomarkers. / Methods: Mutation carriers and non‐carriers from the Dominantly Inherited Alzheimer Network (DIAN) were stratified based on knowledge of mutation status. Rates of change on standard clinical, cognitive, and neuroimaging outcomes were examined. / Results: Mutation knowledge had no associations with cognitive decline, clinical progression, amyloid deposition, hippocampal volume, or depression in either carriers or non‐carriers. Carriers who learned their status mid‐study had slightly higher levels of depression and lower cognitive scores. / Discussion: Knowledge of mutation status does not affect rates of change on any measured outcome. Learning of status mid‐study may confer short‐term changes in cognitive functioning, or changes in cognition may influence the determination of mutation status.

Introduction Quantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B–based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design. Methods Pittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally. Results Global amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers. Discussion Although the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.

Author(s): Leverenz, James B; Bekris, Lynn M; Berman, Sarah; Fleisher, Jori E; Galasko, Doug R; Galvin, James E; Goldman, Jennifer; Hall, Deborah A; Irwin, David J; Kaufer, Daniel; Lippa, Carol; Litvan, Irene; Lopez, Oscar L; Sabbagh, Marwan N; Tsuang, Debby W; Zabetian, Cyrus P

Background/Objective In population studies, most individuals with mild cognitive impairment (MCI) do not progress to dementia in the near term, but rather remain stable MCI or revert to normal cognition. Here, we characterized MCI subgroups with different outcomes over 5 years. Setting/Participants A population‐based cohort (N=1603). Measurements Clinical Dementia Rating (CDR); self‐reported medical conditions, subjective cognitive concerns, self‐rated health, depressive symptoms, blood pressure, medications, blood pressure, APOE genotype, cognitive domain composite scores. Design We compared 3 MCI subgroups who progressed to dementia (n=86), stabilized at MCI (n=384), or reverted to normal (n=252), to those who remained consistently normal (n=881), defining MCI as CDR = 0.5 and dementia as CDR≥1. Using multinomial logistic regression models adjusted for demographics, we examined the associations of each group with selected baseline characteristics. Results With the normal group for reference, worse subjective cognitive concerns, functional impairments, self‐rated health, and depressive symptoms were associated with being in any MCI group. Taking more prescription medications was associated with being in the stable MCI and reverter groups; diabetes and low diastolic blood pressure were associated with stable MCI. The APOE4 genotype was associated with stable and progressive MCI; stroke was associated with progressive MCI. All MCI subgroups were likely to have lower mean composite scores in all cognitive domains and more operationally defined impairments in attention, language, and executive function; reverters were more likely to lack memory and visuospatial impairments. Conclusions MCI subgroups with different 5‐year outcomes had some distinct characteristics suggesting different underlying causes. The progressors, unlike the reverters, had a profile broadly typical of Alzheimer's disease; the stable MCIs had other, including vascular, morbidity. These data shed light on the heterogeneity of MCI in the population. J Am Geriatr Soc 67:232–238, 2019.

Dopamine can form reactive oxygen species and other reactive metabolites that can modify proteins and other cellular constituents. In this study, we tested the effect of dopamine oxidation products, other generators of reactive oxygen species, and a sulfhydryl modifier on the function of glutamate transporter proteins. We also compared any effects with those on the dopamine transporter, a protein whose function we had previously shown to be inhibited by dopamine oxidation. Preincubation with the generators of reactive oxygen species, ascorbate (0.85 mM) or xanthine (500 microM) plus xanthine oxidase (25 mU/ml), inhibited the uptake of [3H]glutamate (10 microM) into rat striatal synaptosomes (-54 and -74%, respectively). The sulfhydryl-modifying agent N-ethylmaleimide (50-500 microM) also led to a dose-dependent inhibition of [3H]glutamate uptake. Preincubation with dopamine (100 microM) under oxidizing conditions inhibited [3H]glutamate uptake by 25%. Exposure of synaptosomes to increasing amounts of dopamine quinone by enzymatically oxidizing dopamine with tyrosinase (2-50 U/ml) further inhibited [3H]glutamate uptake, an effect prevented by the addition of glutathione. The effects of free radical generators and dopamine oxidation on [3H]glutamate uptake were similar to the effects on [3H]dopamine uptake (250 nM). Our findings suggest that reactive oxygen species and dopamine oxidation products can modify glutamate transport function, which may have implications for neurodegenerative processes such as ischemia, methamphetamine-induced toxicity, and Parkinson's disease.