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Background and objectives: The pathologic substrates or neuroanatomic regions responsible for similarities in behavioral features seen in autism spectrum disorder and late-life dementia remain unknown. The present study examined the neuropathologic features of late-life dementia in research volunteers with and without antemortem behaviors characteristic of autism spectrum disorders. Methods: Antemortem cross-sectional assessment of autistic spectrum behaviors proximal to death in persons with diagnosis of mild cognitive impairment or dementia was completed using the Gilliam Autism Rating Scale, 2nd edition (GARS-2), followed by postmortem quantitative and semiquantitative neuropathologic assessment. All individuals who completed the GARS-2 prior to autopsy were included (n = 56) and we note that no participants had known diagnosis of autism spectrum disorder. The GARS-2 was used as an antemortem screening tool to stratify participants into two groups: "Autism Possible/Very Likely" or "Autism Unlikely." Data were analyzed using nonparametric statistics comparing location and scale to evaluate between-group differences in pathologic features. Results: Neurofibrillary tangles (NFT; p = 0.028) density and tau burden (p = 0.032) in the frontal region, the NFT density (p = 0.048) and neuritic plaque burden (p = 0.042), and the tau burden (p = 0.032) of the temporal region, were significantly different in scale between groups. For measures with significant group differences, the medians of the Autism Possible/Very Likely group were roughly equal to the 75th percentile of the Autism Unlikely group (i.e., the distributions were shifted to the right). Discussion: This study links behaviors characteristic of autism to increased pathologic tau burden in the frontal and temporal lobes in persons with late-life dementia. Additional studies are needed to determine causal factors and treatment options for behaviors characteristic of autism behaviors in late-life dementias. [ABSTRACT FROM AUTHOR]

Finite Markov chains are useful tools for studying transitions among health states; these chains can be complex consisting of a mix of transient and absorbing states. The transition probabilities, which are often affected by covariates, can be difficult to estimate due to the presence of many covariates and/or a subset of transitions that are rarely observed. The purpose of this article is to show how to estimate the effect of a subset of covariates of interest after adjusting for the presence of multiple other covariates by applying multidimensional dimension reduction to the latter. The case in which transitions within each row of the one-step transition probability matrix are estimated by multinomial logistic regression is discussed in detail. Dimension reduction for the adjustment covariates involves estimating the effect of the covariates by a product of matrices iteratively; at each iteration one matrix in the product is fixed while the second is estimated using either standard software or nonlinear estimation, depending on which of the matrices in the product is fixed. The algorithm is illustrated by an application where the effect of at least one Apolipoprotein-E (APOE) gene ϵ4 allele on transition probability is estimated in a Markov Chain that includes adjustment for eight covariates and focuses on transitions from normal cognition to several forms of mild cognitive impairment, with possible absorption into dementia. Data were drawn from annual cognitive assessments of 649 participants enrolled in the BRAiNS cohort at the University of Kentucky's Alzheimer's Disease Research Center. [ABSTRACT FROM AUTHOR]

Background: Memory assessment is a key factor for the diagnosis of cognitive impairment. However, memory performance over time may be quite heterogeneous within diagnostic groups. Method: To identify latent trajectories in memory performance and their associated risk factors, we analyzed data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants who were classified either as cognitively normal or as Mild Cognitive Impairment (MCI) at baseline and were administered the Rey Auditory Verbal Learning test (RAVLT) for up to 9 years. Group-based trajectory modeling on the 30-minute RAVLT delayed recall score was applied separately to the two baseline diagnostic groups. Results: There were 219 normal subjects with mean age 75.9 (range from 59.9 to 89.6) and 52.5% male participants, and 372 MCI subjects with mean age 74.8 (range from 55.1 to 89.3) and 63.7% male participants included in the analysis. For normal subjects, six trajectories were identified. Trajectories were classified into three types, determined by the shape, each of which may comprise more than one trajectory: stable (~30% of subjects), curvilinear decline (~ 28%), and linear decline (~ 42%). Notably, none of the normal subjects assigned to the stable stratum progressed to dementia during the study period. In contrast, all trajectories identified for the MCI group tended to decline, although some participants were later re-diagnosed with normal cognition. Age, sex, and education were significantly associated with trajectory membership for both diagnostic groups, while APOE ɛ4 was only significantly associated with trajectories among MCI participants. Conclusion: Memory trajectory is a strong indicator of dementia risk. If likely trajectory of memory performance can be identified early, such work may allow clinicians to monitor or predict progression of individual patient cognition. This work also shows the importance of longitudinal cognitive testing and monitoring. [ABSTRACT FROM AUTHOR]

Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient cognitive states and death as a competing risk. Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript, we apply a semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants. [ABSTRACT FROM AUTHOR]

Risk factors for mild cognitive impairment (MCI) and dementia are often investigated without accounting for the competing risk of mortality, which can bias results and lead to spurious conclusions, particularly regarding protective factors. Here, we apply a semi-Markov modeling approach to 531 participants in the University of Kentucky Biologically Resilient Adults in Neurological Studies (BRAiNS) longitudinal cohort, over one-third of whom died without transitioning to a cognitively impaired clinical state. A semi-Markov approach enables a statistical study of clinical state transitions while accounting for the competing risk of death and facilitates insights into both the odds that a risk factor will affect clinical transitions as well as the age at which the transition to MCI or dementia will occur. Risk factors assessed in the current study were identified by matching those reported in the literature with the data elements collected on participants. The presence of Type II diabetes at baseline shortens the time it takes cognitively intact individuals to transition to MCI by seven years on average while use of estrogen replacement therapy at enrollment (baseline) decreases the time required to convert from MCI to dementia by 1.5 years. Finally, smoking and being overweight do not promote transitions to impaired states but instead hasten death without a dementia. In contrast, conventional statistical analyses based on Cox proportional hazards models fail to recognize diabetes as a risk, show that being overweight increases the risk of clinical MCI, and that high blood pressure at baseline increases the risk of a dementia. [ABSTRACT FROM AUTHOR]

Background: We reported a significant 67% reduction in the hazard risk of incident Alzheimer’s disease (AD) with elective statin use in the AD Anti-inflammatory Prevention Trial (ADAPT), without a reduction in risk of incident mild cognitive impairment (MCI). Objective: To assess if cholesterol levels are associated with cognitive performance and determine if statin use alters cognitive performance after onset of MCI. Design: Fractionated cholesterol levels, neurological and cognitive status were evaluated annually. Comparisons of non-LLA (lipid-lowering agent) users or statin-LLA users were performed blind to the ADAPT medication randomization. Pearson’s correlations were validated using a time-dependent linear mixed model. Results: The MMSE performance significantly declined over time in non-LLA users, and, after adjusting for this, a significant positive correlation between MMSE and HDL was identified (p = 0.0002). A negative correlation between total and LDL cholesterol, and immediate and delayed recall of the Rivermead paragraph was significant (total cholesterol, p < 0.003; LDL, p < 0.02). Pilot data suggest a positive signal on delayed recall of both the Hopkins word list and Rivermead paragraph with deterioration in the non-LLA users and improvement in the statin users after conversion to MCI. Conclusion: Cholesterol levels may be associated with differential performance on the MMSE and measures of learning or memory. The trend for improved delayed recall in statin users with MCI compared to non-LLA users with MCI may have contributed to the reduced hazards risk of incident AD without reducing the risk of MCI. Copyright © 2010 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

The article focuses on Revisiting the Food and Drug Administration (FDA) Approval of Aducanumab. Topics discussed include the Food and Drug Administration (FDA) granted accelerated approval to aducanumab (Aduhelm) for treatment of Alzheimer's disease; and the FDA made a late-stage decision to grant approval on the basis of aducanumab's ability to lower levels of beta-amyloid observable on positron-emission tomographic brain imaging.

Abstract: Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)—15 normal controls (NC), 16 early MCI, and 17 early stage AD—are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD. [Copyright &y& Elsevier]

Abstract: The National Institute on Aging Preclinical Alzheimer''s disease Workgroup (PADW) has issued a preliminary report with recommendations for classifying preclinical Alzheimer''s disease (pAD) according to 3 early disease stages. Here we examine the PADW recommendations in relation to neuropathological features in a large, consecutive series of cognitively intact elderly persons, autopsied within a year after cognitive testing (n = 126 cognitively intact patients with mean age 83.7 years at death). Subjects were grouped based on a hypothetical construct correlating pathological features with PADW stages. Many cognitively intact individuals were classifiable as pAD (53/126 or 43%), as expected based on epidemiological and biomarker studies. Of these, most (48%) were in “stage 3”, which corresponds to amyloid pathology with early neurodegeneration. As with prior studies, our data indicate that the development of neocortical neurofibrillary tangles is the key pathological event that is not observed in pAD cases: Braak stages III or IV pathology are hence not truly a substrate for “intermediate likelihood” that cognitive impairment is due to Alzheimer''s disease (AD). We also stress the importance of comorbid non-Alzheimer''s disease brain pathologies (hippocampal sclerosis, neocortical alpha-synucleinopathy, cerebrovascular disease, and brains with hippocampal neurofibrillary tangles but no cortical amyloid plaques) that can contribute to the development of cognitive impairment, or which may serve as confounds in the application of the PADW recommendations. While the final recommendations from the PADW working group have not yet been released, this preliminary analysis provides a perspective on those recommendations from a neuropathological point of view. [Copyright &y& Elsevier]