Citations

AbstractIntroductionMotoric cognitive risk (MCR), a clinical syndrome characterized by slow gait speed and subjective cognitive complaints, has been associated with dementia risk. The neuropathological features underlying MCR remain poorly understood.MethodsThe Atherosclerosis Risk in Communities (ARIC) community‐based cohort study classified participants using standardized criteria as MCR+/– and mild cognitive impairment (MCI)+/– at study baseline (2011–2013). We examined the 5‐year dementia risk and baseline brain structural/molecular abnormalities associated with MCR+ and MCI+ status.ResultsOf 5023 nondemented participants included, 204 were MCR+ and 1030 were MCI+. Both MCR+ and MCI+ participants demonstrated increased dementia risk. The pattern of structural brain abnormalities associated with MCR+ differed from that of MCI+. Whereas MCI+ was associated with comparatively smaller volumes in brain regions vulnerable to Alzheimer's disease pathology, MCR+ status was associated with smaller volumes in frontoparietal regions and greater white matter abnormalities.DiscussionMCR may represent a predementia syndrome characterized by prominent white matter abnormalities and frontoparietal atrophy.

AbstractWe aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge‐based cut‐offs for each component of ATN and compared it to two data‐driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data‐driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia‐like and normal groups. Both data‐driven classification methods performed better than the empirical cut‐offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data‐driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.

We present the case of a cognitively unimpaired 77-year-old man with elevated, asymmetric, and longitudinally increasing Flortaucipir tau PET despite normal (visually negative) amyloid PET. His atypical tau PET signal persisted and globally increased in a follow-up scan five years later. Across eight years of observations, temporoparietal atrophy was observed consistent with tau PET patterns, but he retained the cognitively unimpaired classification. Altogether, his atypical tau PET signal is not explained by any known risk factors or alternative pathologies, and other imaging findings were not remarkable. He remains enrolled for further observation.

Abstract Introduction Amyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression. Methods Within 248 Aβ‐positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ‐positron emission tomography [PET] uptake, regional tau‐PET uptake, and regional magnetic resonance imaging–based atrophy) and risk‐factors (age, sex, education, apolipoprotein E [ APOE ], and white‐matter lesions) on cross‐sectional cognitive impairment and longitudinal cognitive decline. Results Sixteen percent of variance in cross‐sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ–tau–atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively. Discussion These findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.

A rare APOE variant APOE3 -Jac reduces self-aggregation and promotes healthy brain aging.

AbstractIntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.MethodsWe identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.ResultsDisease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.DiscussionADNI has had a profound impact in improving clinical trials for AD.

AbstractIntroductionBlood‐based biomarkers of amyloid pathology and neurodegeneration are entering clinical use. It is critical to understand what factors affect the levels of these markers.MethodsPlasma markers (Aβ42, Aβ40, NfL, T‐tau, Aβ42/40 ratio) were measured on the Quanterix Simoa HD‐1 analyzer for 996 Mayo Clinic Study of Aging (MCSA) participants, aged 51 to 95 years. All other data were collected during in‐person MCSA visits or abstracted from the medical record.ResultsAmong cognitively unimpaired (CU) participants, all plasma markers correlated with age. Linear regression models revealed multiple relationships. For example, higher Charlson Comorbidity Index and chronic kidney disease were associated with higher levels of all biomarkers. Some relationships differed between mild cognitive impairment and dementia participants.DiscussionMultiple variables affect plasma biomarkers of amyloid pathology and neurodegeneration among CU in the general population. Incorporating this information is critical for accurate interpretation of the biomarker levels and for the development of reference ranges.

Background White matter abnormalities are a common feature of aging and Alzheimer disease, and tend to be more severe among Black individuals. However, the extent to which white matter abnormalities relate to amyloid deposition, a marker of Alzheimer pathology, remains unclear. This cross‐sectional study examined the association of white matter abnormalities with cortical amyloid in a community sample of older adults without dementia and examined the moderating effect of race. Methods and Results Participants from the ARIC‐PET (Atherosclerosis Risk in Communities‐Positron Emission Tomography) study underwent brain magnetic resonance imaging, which quantified white matter hyperintensity volume and microstructural integrity using diffusion tensor imaging. Participants received florbetapir positron emission tomography imaging to measure brain amyloid. Associations between measures of white matter structure and elevated amyloid status were examined using multivariable logistic regression. Among 322 participants (43% Black), each SD increase in white matter hyperintensity volume was associated with a greater odds of elevated amyloid (odds ratio [OR], 1.37; 95% CI, 1.03–1.83) after adjusting for demographic and cardiovascular risk factors. In race‐stratified analyses, a greater white matter hyperintensity volume was more strongly associated with elevated amyloid among Black participants (OR, 2.00; 95% CI, 1.15–3.50), compared with White participants (OR, 1.29; 95% CI, 0.89–1.89). However, the race interaction was not statistically significant ( P interaction=0.09). We found no association between white matter microstructure and elevated amyloid. Conclusions The results suggest a modest positive relationship between white matter hyperintensity and elevated amyloid in older adults without dementia. Although the results indicate that this association is nonsignificantly stronger among Black participants, these findings will need to be confirmed or refuted using larger multiracial cohorts.