Marilyn Albert’s research while affiliated with Johns Hopkins University and other places

Aging is associated with disruptions in circadian rhythms, lower brain white matter integrity, and cognitive changes. However, whether white matter integrity serves as a potential mechanism linking circadian dysfunction to age-related cognitive abilities in older adults is unclear. We investigated cross-sectional associations of actigraphic circadian rest/activity rhythms (RARs) with whole-brain white matter tract fractional anisotropy (FA) and executive function performance in 156 older adults without dementia from the BIOCARD study (mean age = 71.3 years, including 19 with mild cognitive impairment (MCI) and 137 cognitively unimpaired). We studied non-parametric metrics of RAR strength (relative amplitude [RA]), day-to-day stability (interdaily stability [IS]), and fragmentation (intradaily variability [IV]). After adjusting for age, sex, education, APOE-e4 genotype, vascular risk, and diagnostic group, we found that greater rhythm strength (higher RA) was associated with better executive function. Additionally, higher rhythm strength (RA) and stability (IS) was associated with greater whole-brain FA, reflecting better white matter integrity, whereas greater fragmentation (IV) was associated with lower FA. Greater white matter integrity was also associated with better executive function and statistically mediated the association of higher RA with better executive function performance. Findings underscore the relationships between RAR strength and cognitive health in older adults and suggest that white matter integrity may be a key mechanism underlying these associations.

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies to slow disease progression and onset. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that mitigates the model’s use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information mitigated, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two recent, popular, openly available T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI) (p-value = 0.023), but younger in participants already diagnosed with Alzheimer’s disease (AD) (p-value < 0.001). Classifiers using T1w MRI-based brain ages generally outperform those using dMRI-based brain age in classifying CN versus AD participants. Conversely, dMRI-based brain age may offer advantages over T1w MRI-based brain age in predicting the transition from CN to MCI.

BACKGROUND Alzheimer's disease (AD) is characterized by the abnormal accumulation of amyloid‐beta (Aβ) and tau that can be quantified in vivo through cerebrospinal fluid (CSF) sampling. Physical activity has emerged as a possible modifier of AD risk; however, its impact on CSF biomarkers and cognitive function is not yet fully understood. We examined whether higher levels of physical activity modifies associations between AD CSF biomarkers and cognitive function. METHODS One hundred and seventeen adults free of dementia from the BIOCARD study (mean age 72.2 ± 8.0 years, 70% women) wore a wrist accelerometer for 1 week, underwent lumbar puncture to collect CSF, and completed a comprehensive neuropsychological exam. Multivariable linear regression analyses were used to examine whether physical activity (total activity counts over the 10 most active hours of the day) moderates the association between AD CSF biomarkers [Aβ42/40, phosphorylated tau (p‐tau181), and total tau] and cognitive composite scores (episodic memory, executive function). RESULTS There were significant interactions between physical activity and p‐tau181 (p = 0.016) as well as between physical activity and total tau (p = 0.004) in relation to the executive function composite score. Among participants with higher levels of physical activity, the adverse relationship between CSF‐measured tau and executive function was diminished. In contrast, there were no significant interactions for episodic memory, and physical activity did not interact with Aβ42/40 (all interactions p > 0.05). CONCLUSION A physically active lifestyle may provide protection against AD‐related cognitive decline by reducing the impact of tau pathology. Highlights Older age was associated with lower levels of physical activity, worse CSF biomarker profiles, and poorer cognition. Physical activity moderates the impact of tau pathology on executive function but shows no significant effect on amyloid‐beta pathology. Physical activity may enhance cognitive reserve, thereby attenuating the influence of accumulating AD pathology on cognition.

INTRODUCTION White matter (WM) microstructure is essential for brain function but deteriorates with age and in neurodegenerative conditions such as Alzheimer's disease (AD). Diffusion MRI, enhanced by advanced bi‐tensor models accounting for free water (FW), enables in vivo quantification of WM microstructural differences. METHODS To evaluate how AD genetic risk factors affect limbic WM microstructure – crucial for memory and early impacted in disease – we conducted linear regression analyses in a cohort of 2,614 non‐Hispanic White aging adults (aged 50.12 to 100.85 years). The study evaluated 36 AD risk variants across 26 genes, the association between AD polygenic scores (PGSs) and WM metrics, and interactions with cognitive status. RESULTS AD PGSs, variants in TMEM106B, PTK2B, WNT3, and apolipoprotein E (APOE), and interactions involving MS4A6A were significantly linked to WM microstructure. DISCUSSION These findings implicate AD‐related genetic factors related to neurodevelopment (WNT3), lipid metabolism (APOE), and inflammation (TMEM106B, PTK2B, MS4A6A) that contribute to alternations in WM microstructure in older adults. Highlights AD risk variants in TMEM106B, PTK2B, WNT3, and APOE genes showed distinct associations with limbic FW‐corrected WM microstructure metrics. Interaction effects were observed between MS4A6A variants and cognitive status. PGS for AD was associated with higher FW content in the limbic system.

INTRODUCTION List-learning tasks are important for characterizing memory in ADRD research, but the Uniform Data Set neuropsychological battery (UDS-NB) lacks a list-learning paradigm; thus, sites administer a range of tests. We developed a harmonized memory composite that incorporates UDS memory tests and multiple list-learning tasks. METHODS: Item-banking confirmatory factor analysis was applied to develop a memory composite in a diagnostically heterogenous sample (n=5943) who completed the UDS-NB and one of five list-learning tasks. Construct validity was evaluated through associations with demographics, disease severity, cognitive tasks, brain volume, and plasma phosphorylated tau (p-tau181 and p-tau217). Test-retest reliability was assessed. Analyses were replicated in a racially/ethnically diverse cohort (n=1058). RESULTS: Fit indices, loadings, distributions, and test-retest reliability were adequate. Expected associations with demographics and clinical measures within development and validation cohorts supported validity. DISCUSSION: This composite enables researchers to incorporate multiple list-learning tasks with other UDS measures to create a single metric.

Background Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There is limited consensus regarding timescale choice in epidemiologic cognitive aging research. Methods Using a coordinated analytic approach in ten cohorts of older adults, we evaluated whether estimated effects of two exposures on memory change differed depending on timescale (time on study or current age). We modeled effects of APOE ε4 genotype (a time-invariant exposure) and diabetes (a potentially time-varying/acquired exposure evaluated at baseline) using mixed-effects models with linear and non-linear time specifications for both timescales. Results Among APOE ε4 carriers, model-estimated memory scores at baseline (time on study timescale) or at each cohort’s median baseline age (current age timescale) were lower and average rate of decline was faster than among APOE ε4 noncarriers. Model-estimated memory scores at baseline or at median baseline age were generally similar across baseline diabetes status, with variability across cohorts in the diabetes-memory change association. In some cohorts, trends in diabetes-memory change associations differed in direction across timescales. Conclusions Timescale was largely inconsequential for estimated effects of APOE genotype, but yielded differences in estimated diabetes effects, raising questions about the appropriate scale. Current age scale may be problematic because diabetes was measured heterogeneously in age across individuals, a common issue in aging cohorts. Our work demonstrates approaches to evaluate alternative timescales, including in multi-cohort analyses, and highlights potential implications for estimated exposure effects on cognitive change.

INTRODUCTION We examined long‐term plasma biomarker trajectories among participants who were cognitively unimpaired and primarily middle aged at baseline and whether trajectories differed by Alzheimer's disease (AD) genetic risk and among those who developed cognitive impairment. METHODS Plasma amyloid beta (Aβ)42/Aβ40, phosphorylated tau (p‐tau)181, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), soluble triggering receptor expressed on myeloid cells, and chitinase 3‐like protein 1 were measured longitudinally in 177 BIOCARD participants (M baseline age = 57.7 years; M follow‐up = 15.8 years), including 57 who developed cognitive impairment. Measures of AD genetic risk included apolipoprotein E (APOE) ε4 and an AD polygenic risk score (AD‐PRS). RESULTS Compared to non‐carriers, APOE ε4 carriers had lower Aβ42/Aβ40 and greater longitudinal increases in p‐tau181 and GFAP; in contrast, the AD‐PRS (excluding the APOE region) was associated with greater declines in Aβ42/Aβ40 among APOE ε4 non‐carriers. Rates of increase in p‐tau181, NfL, and GFAP were greater among those who later developed cognitive impairment. DISCUSSION Monitoring changes in plasma p‐tau181, NfL, and GFAP may be particularly informative during preclinical AD. Highlights We examined plasma biomarker changes in cognitively normal individuals over 15.8 years. Apolipoprotein E (APOE) ε4 was related to lower amyloid beta (Aβ)42/Aβ40 and greater increases in phosphorylated tau (p‐tau)181 and glial fibrillary acidic protein (GFAP). In APOE ε4 non‐carriers, higher Alzheimer's disease (AD) polygenic risk score was related to greater Aβ42/Aβ40 declines. P‐tau181, NfL, and GFAP increases were greater among those who progressed to mild cognitive impairment. Results highlight the predictive value of plasma biomarkers during preclinical AD.

Up to 30% of older adults meet pathological criteria for a diagnosis of Alzheimer’s disease at autopsy yet never show signs of cognitive impairment. Recent work has highlighted genetic drivers of this resilience, or better-than-expected cognitive performance given a level of neuropathology, that allow the aged brain to protect itself from the downstream consequences of amyloid and tau deposition. However, models of resilience have been constrained by reliance on measures of neuropathology, substantially limiting the number of participants available for analysis. We sought to determine if novel approaches using APOE allele status, age, and other demographic variables as a proxy for neuropathology could still effectively quantify resilience and uncover novel genetic drivers associated with better-than-expected cognitive performance while vastly expanding sample size and statistical power. Leveraging 20,513 participants from eight well-characterized cohort studies of aging, we determined the effects of genetic variants on resilience metrics using mixed-effects regressions. The outcome of interest was residual cognitive resilience, quantified from residuals in three cognitive domains (memory, executive function, and language) and built within two frameworks: “silver” models, which obviate the requirement for neuropathological data (n=17,241), and “gold” models, which include post-mortem neuropathological assessments (n=3,272). We then performed cross-ancestry genome wide association studies (European ancestry n=18,269, African ancestry n=2,244), gene and pathway-based tests, and genetic correlation analyses. All analyses were conducted across all participants and repeated when restricted to those with unimpaired cognition at baseline. Despite different modeling approaches, the silver and gold phenotypes were highly correlated (R=0.77-0.88) and displayed comparable performance in quantifying better-than or worse-than-expected cognition, enabling silver-gold meta-analyses. Genetic correlation analyses highlighted associations of resilience with multiple neuropsychiatric and cardiovascular traits (PFDR values < 5.0x10-2). In pathway-level tests, we observed three significant associations with resilience: metabolism of amino acids and derivatives (PFDR=4.1x10-2), negative regulation of transforming growth factor beta production (PFDR=1.9x10-2), and severe acute respiratory syndrome (PFDR=3.9x10-4). Finally, in single-variant analyses, we identified a locus on chromosome 17 approaching genome-wide significance among cognitively unimpaired participants (index single nucleotide polymorphism: rs757022, minor allele frequency = 0.18, β=0.08, P=1.1x10-7). The top variant at this locus (rs757022) was significantly associated with expression of numerous ATP-binding cassette genes in brain. Overall, through validating a novel modeling approach, we demonstrate the utility of silver models of resilience to increase statistical power and participant diversity.