Jack, Clifford R

BACKGROUND: White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease but reflect late-stage injury. Diffusion magnetic resonance imaging (MRI) biomarkers have been proposed to capture earlier small vessel disease-related microstructural damage but their temporal progression relative to WMH and risk factors associated with progression remain unexplored. METHODS: We identified 2077 participants from the population-based cohort study of the Mayo Clinic Study of Aging in Olmsted County, Minnesota (aged 50–101 years) collected between 05/2005 and 09/2024 with longitudinal neuroimaging. Using multioutput nonlinear mixed-effects models in those with at least 2 fluid-attenuated inversion recovery-MRI and diffusion MRI scans, we characterized the temporal progression of WMH and 4 diffusion MRI biomarkers: fractional anisotropy of the genu of the corpus callosum, peak width of skeletonized mean diffusivity, free water, and Arteriolosclerosis-score, which were automatically estimated. Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, cardiometabolic conditions). RESULTS: The study population had a mean age of 78 years, 47% were women, 28% were APOE ε4 allele carriers, and 80% were cognitively unimpaired, with an average follow-up of 5.2 years (SD, 4.3 years) for fluid-attenuated inversion recovery-MRI and 4.3 years (SD, 3.9 years) for diffusion MRI. Arteriolosclerosis-score, fractional anisotropy of the genu of the corpus callosum, free water, and peak width of skeletonized mean diffusivity became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH become abnormal (half-width of CI <1 year), respectively. Global markers (Arteriolosclerosis-score, free water, peak width of skeletonized mean diffusivity, and WMH) were correlated, indicating shared substrates of widespread white matter injury. Fractional anisotropy of the genu of the corpus callosum, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Female participants showed earlier WMH, fractional anisotropy of the genu of the corpus callosum, and Arteriolosclerosis-score abnormalities, whereas male participants exhibited earlier peak width of skeletonized mean diffusivity and free water abnormalities. CONCLUSIONS: Diffusion MRI biomarkers were abnormal at least a decade before WMH become abnormal in the population, revealing a prolonged phase of early small vessel disease and highlighting their potential for small vessel disease prevention.

Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimer's disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimer's Disease Neuroimaging Initiative (ADNI) includes over 10 dMRI protocols, necessitating robust methods to model protocol-related variability when pooling data. Here, we compared three harmonization approaches: (1) mixed-effects models, (2) ComBat-GAM, and (3) eHarmonize, a reference-based lifespan method. We assessed their ability to reduce protocol-related variability in diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) while preserving associations with cognitive impairment (CI), and amyloid-beta (Aβ) and tau PET burden in 1,086 ADNI3/4 participants. All approaches yielded more closely aligned FA/MD distributions across protocols. Associations with clinical indicators of CI were highly consistent across approaches, whereas PET associations were less widespread and more variable. Overall, multiple strategies effectively modeled protocol-related variability while preserving AD-related associations.

Abstract The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [18F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85 amyloid-PET-negative cognitively normal age-matched participants and fitted into Subtype and Stage Inference (SuStaIn)—an unsupervised clustering algorithm that simultaneously models subtypes and progression from cross-sectional data. The derived subtypes were subsequently characterized by baseline and longitudinal clinical, cognitive, MRI, tau and amyloid PET features. We identified three tau-PET-based subtypes: on average, Subtype 1/Typical (n = 144, 40%) showed a predominant bilateral temporoparietal pattern typical of Alzheimer’s disease. Subtype 2/Left temporal (n = 111, 31%) showed predominant left temporal binding. Subtype 3/Posterior (n = 104, 29%) showed early and permeating occipitoparietal involvement. Subtypes did not differ in demographics or global amyloid burden, but were relatively more enriched for specific clinical presentations: S1/Typical for amnestic presentations, S2/Left Temporal for primary progressive aphasia, and S3/Posterior for posterior cortical atrophy. Baseline tau PET subtypes aligned with cortical atrophy patterns and domain-specific cognitive impairment. When follow-up tau PET scans were fitted to SuStaIn trained on baseline data, 85.6% (n = 172/201) of participants retained the same subtype classification, indicating subtype temporal stability, and progressed within subtypes by 0.56 ± 0.70 SuStaIn stage/year. Longitudinal voxel-wise linear mixed-effects modelling revealed tau accumulation patterns for each subtype in regions relatively spared at baseline: occipital lobe accumulation predominated in S1/Typical, bilateral frontal and right temporal in S2/Left Temporal, and bilateral frontotemporal lobes in S3/Posterior. All subtypes showed longitudinal increases in Clinical Dementia Rating-Sum of Boxes, but with slower worsening in S3/Posterior compared with the other subtypes. Our findings reveal robust subtypes in sporadic early-onset Alzheimer’s disease characterized by distinct spatiotemporal tau patterns that parallel differences in clinical presentations and trajectories of neurodegeneration. These subtypes extend beyond traditional clinical syndromes and support a more nuanced framework for individualized prognosis and care. Incorporating tau PET subtyping into clinical trial design could enable more targeted therapeutic approaches for this younger population.

Abstract Alzheimer’s disease (AD) emerges from multi-scale interactions between molecular pathology and disruptions in large-scale brain network dynamics. Understanding how these processes co-evolve and relate to disease stages is essential for advancing complex systems models of aging and AD, and for developing system-informed interventions. However, progress has been limited by a lack of large-scale longitudinal data. To address this, we examined the longitudinal relationship between subsystems of the default mode network (DMN) (posterior DMN, ventral DMN, anterior dorsal DMN) using task-free functional MRI (fMRI) and amyloid positron emission tomography (PET) imaging in a large longitudinal cohort spanning the clinico-biological spectrum of AD (n = 1,451; 2,763 time points) using mixed-effect models. We also assessed whether patterns of DMN connectivity predicted conversion to amyloid positivity, mild cognitive impairment (MCI), and dementia using Cox proportional hazards models. Our findings reveal a dynamic interplay between amyloid accumulation and connectivity within and between DMN subsystems, with both hyper- and hypoconnectivity emerging across DMN subsystems in association with increasing amyloid burden. Importantly, survival models showed that DMN connectivity patterns predicted conversion to critical stages of the disease, including not only conversion to MCI and dementia, but also conversion to amyloid positivity in otherwise clinically unimpaired individuals who were amyloid negative at baseline. These associations were independent of age, APOE4 status, sex, education, and in-scanner motion. These results support a model in which breakdowns in tightly regulated feedback loops governing DMN physiology represent a core systems-level pathophysiology of AD. Notably, this functional dyshomeostasis precedes detectable amyloidosis on imaging. Future studies should focus on the development of robust biomarkers of brain function that can be applied at the individual level, which could in turn help support the development of therapeutic approaches targeting system-level pathophysiology.

BACKGROUND: Cerebral microbleeds (CMBs) are associated with dementia, but the impact of specific microbleed patterns (with distinct pathophysiologies) is unclear. Lobar CMBs commonly result from cerebral amyloid angiopathy (sometimes with cortical superficial siderosis [cSS]), while subcortical microbleeds indicate a hypertensive cause. This study investigates the association of CMB presence, patterns, and frequency with dementia risk. METHODS: Participants from the ARIC-NCS (Atherosclerosis Risk in Communities-Neurocognitive Study), a community-based longitudinal cohort, with a 3T research magnetic resonance imaging scan at visit 5 (2011–2013) and without prior intracerebral hemorrhage or dementia were included. CMB and cSS presence and location were evaluated using a T2* gradient-recalled echo sequence. Each individual’s pattern of CMBs was classified as no CMBs, subcortical only, mixed (subcortical and lobar±cSS), lobar±cSS only, or cSS. Dementia adjudication was determined by blinded review of cognitive testing, informant interviews, and hospital discharge codes or death certificates (from follow-up visits and ongoing surveillance). Cox proportional hazards models assessed associations between CMBs and incident dementia through 2020, adjusted for demographics, vascular risk factors, and white matter hyperintensities. RESULTS: Among 1583 participants (mean age, 76 years; 24.7% cumulative dementia incidence over a median of 23.6 follow-up years), 361 had CMBs. Compared with individuals without CMBs, those with mixed (hazard ratio, 1.99 [95% CI, 1.40–2.83]) or lobar±cSS (hazard ratio, 1.96 [95% CI, 1.30–2.97]) CMB patterns had increased dementia risk, whereas individuals with a subcortical-only pattern did not (hazard ratio, 1.04 [95% CI, 0.78–1.39]). cSS presence (n=21; versus absence) showed an increased risk of dementia (hazard ratio, 2.57 [95% CI, 1.36–4.89]). Greater numbers of microbleeds across all CMB subtypes exhibited an increased risk of dementia. CONCLUSIONS: Lobar and mixed CMB patterns and increasing numbers of CMBs are associated with incident dementia. Certain CMB patterns have differential associations with dementia, but severe enough disease burden, regardless of CMB distribution, is associated with increased dementia risk.

Abstract Associations of Alzheimer’s disease biomarker progression with cognitive decline are important to inform patient prognosis. Of particular interest is how newly available plasma biomarkers evolve relative to cognitive decline. The goals of this work are to measure how much earlier versus later an individual’s progression on plasma and PET Alzheimer’s disease biomarkers is associated with earlier versus later cognitive progression and to estimate the average timeline of progression of these processes in the population. In this cohort study of 2369 Mayo Clinic Study of Aging (MCSA) and 1591 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants, we fit non-linear mixed-effects models to estimate how much earlier versus later each individual progresses on plasma phosphorylated tau (p-tau)217, amyloid PET, tau PET and auditory verbal learning test (AVLT) sum of trials relative to the population mean (individual adjustment), the associations of these individual adjustments among biomarker pairs and how covariates affect the timing of biomarker progression. The association of individual adjustments implies mechanistic associations and the amount of variability in cognitive decline accounted for by each biomarker. By applying cut-off points, we also estimated the relative timing that these biomarkers become abnormal in the population. Associations of individual adjustments were moderate between all biomarkers and AVLT (R = 0.38–0.47) in the MCSA and stronger (R = 0.74–0.81) in ADNI; plasma p-tau217 accounted for 16% of the variability in timing of AVLT decline in the MCSA and 64% in ADNI. APOE ɛ4 carriership was associated with earlier biomarker progression. AVLT became abnormal after the biomarkers up to age 90, after which AVLT was estimated to become abnormal prior to tau biomarkers. The association of the timing of plasma and PET Alzheimer’s disease biomarker progression with cognitive decline was modest in the MCSA population-based sample and stronger in the Alzheimer’s disease-enriched ADNI cohort. The timing of plasma p-tau217 progression explained a similar degree of variability in AVLT progression as amyloid PET, supporting its utility as a marker of disease progression. The estimated temporal ordering of biomarkers and cognitive abnormality was as anticipated (amyloid, tau, cognition) up to the age of 90, beyond which AVLT was estimated to become abnormal prior to tau biomarkers, likely related to the effects of non-Alzheimer’s disease co-pathologies.

Abstract Blood-based biomarkers show promise as a noninvasive method for measuring Alzheimer’s disease pathology and neurodegeneration throughout the lifecourse. However, the relationship between longitudinal changes in these biomarkers and late-life brain morphology in community-dwelling populations requires further investigation. Between 2011 and 2013, 1,977 participants from the Atherosclerosis Risk in Communities Study received underwent 3 Tesla magnetic resonance imaging scans. Whole-brain cortical thickness was quantified by Freesurfer. Stored plasma samples collected in midlife (1993-95, mean age 59.0 years) and late-life (2011-13, mean age 76.8 years) from a subsample of 1,515 participants (60.7% female, 25.5% Black adults) were assayed in 2022 using Quanterix SiMoA. The assay quantified amyloid-β (Aβ)42/40, phosphorylated tau at threonine 181 (p-Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Linear regression models estimated the association of blood-based biomarkers from midlife, late-life, and the change from midlife to late-life with cortical thickness in late-life. In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, higher midlife measures of p-Tau181 and increases from midlife to late-life in NfL and GFAP were associated with lower cortical thickness in late-life. Concurrent measures of p-Tau181, NfL, and GFAP were associated with lower cortical thickness in late-life and exhibited a stronger association among participants ≥75 years old compared to participants &lt;75 years old. Additional longitudinal research is needed to determine whether blood-based biomarkers predict cortical thinning over time and may serve as an early indicator of accelerated brain atrophy.

Abstract Background Amyloid related imaging abnormality edema (ARIA‐E) occurs in about 19% of individuals with autosomal dominant Alzheimer disease (ADAD) treated with an anti‐amyloid‐b monoclonal antibody (Salloway et al., 2021). In a previously reported case, ARIA‐E appeared to colocalize with decreases in PiB‐PET uptake (Joseph‐Mathurin, Llibre‐Guerra, et al, 2022), suggesting an association between amyloid‐b (Aβ) removal and ARIA‐E. Here, we compare longitudinal neuroimaging and corresponding neuropathology in an ADAD individual treated with gantenerumab, who experienced multiple ARIA‐E episodes as sulcal effusions that resolved by the end of the four‐year trial. Methods PiB‐PET and MRI were collected pre‐ and post‐randomization (2 and 4 years). The participant consented to brain donation, which was received a year after trial completion. Four tissue samples were taken from the location of the ARIA‐E in the parieto‐occipital left hemibrain as identified by MRI (one block contained the sulcus affected by ARIA‐E; the rest of the coronal section was captured in the remaining three blocks). Area fractions (AFs) of Aβ (10D5 immunohistochemistry (IHC)), tauopathy (PHF1), microglia (Iba1), and astrocytes (GFAP) in one sulcal region of interest (ROI) from each block and PiB‐PET SUVRs of corresponding ROIs were extracted ( n = 4, Figure 1). Additionally, we included PiB‐PET SUVRs corresponding to ARIA‐E findings observed in the right hemisphere at baseline, two‐year, and four‐year visits ( n = 6). Results Overall PiB‐PET uptake increased during the first two years (before ARIA‐E) and decreased during the last two years (including ARIA‐E episodes) (0.03±0.05 vs. ‐0.04±0.04 SUVR/year, p ‐value=0.01, Figure 2). The decrease seemed more pronounced in ARIA‐E ROIs versus normal‐appearing sulcus ROIs (‐0.05±0.04 vs. ‐0.02±0.03 SUVR/year). Lower PiB uptake at last visit appeared associated with lower Aβ AF (estimates=0.03±0.01, p ‐value=0.07). The ARIA‐E ROI had an Aβ AF of 0.009 while normal‐appearing sulcus ROIs had a mean of 0.02+0.003 (Figure 3). AFs for tauopathy, microglia, and astrocytes were within the range of those of normal‐appearing sulcus ROIs, suggesting an Aβ‐specific effect, although unexamined markers may play a role. Conclusions Our preliminary findings indicated that ARIA‐E is associated with longitudinal PiB‐PET decrease and Aβ AF, supporting the link between ARIA‐E, changes in PiB‐PET, and local Aβ removal observed at autopsy. Funding : K01AG080123; U01AG042791; R01AG046179; R01AG053267

Abstract INTRODUCTION Brief digital cognitive measures may be a valuable and scalable alternative to traditional tests for improving early detection and disease monitoring in patients with early‐onset Alzheimer's disease (EOAD). METHODS We examined the convergent, diagnostic, and neuroanatomical validity of Tablet‐based Cognitive Assessment Tool (TabCAT) tests in the multisite Longitudinal Early‐Onset Alzheimer's Disease Study. Four hundred seventy‐five participants from 16 sites (83 cognitively unimpaired participants, 146 individuals diagnosed with mild cognitive impairment [MCI], and 246 with mild dementia) underwent TabCAT testing, including two executive/attentional tasks (Match and Flanker) and two visuospatial tasks (Line Length and Line Orientation). RESULTS TabCAT tests showed good convergent validity with traditional pen‐and‐paper tests. Match was the most accurate TabCAT test in differentiating among clinical groups (controls vs. MCI vs. dementia) and predicting amyloid positron emission tomography (PET) positivity in clinically impaired participants. TabCAT tests were associated with regional tau PET patterns. DISCUSSION TabCAT digital measures have promise for frontline identification of patients with EOAD. Highlights Four hundred seventy‐five participants from 16 US sites underwent tablet‐based cognitive assessment. Digital tests assessed executive function and visuospatial abilities. Digital tests correlated with traditional pen‐and‐paper cognitive tests. Match (test of executive function) differentiated clinical groups. Digital tests were associated with amyloid positron emission tomography (PET) status and regional tau PET patterns.

ABSTRACT Background Previous studies on the relationship between prediabetes, HbA1c and cognitive trajectories show mixed results. Therefore, we investigated the association of prediabetes, diabetes and HbA1c with change in global and domain‐specific cognitive scores in cognitively unimpaired (CU) adults, as well as the potential effect modification by sex or age. Methods This longitudinal study included 4236 CU persons aged ≥ 50 years from the population‐based Mayo Clinic Study of Aging. We ran linear mixed‐effect models with baseline prediabetes, diabetes and HbA1c predicting longitudinal global and domain‐specific (i.e., memory, language, attention/executive function, and visuospatial skills) cognitive z‐scores and raw scores. Models were adjusted for age, sex, education, medical comorbidity, repeated cognitive testing, and ApoEɛ4. We additionally ran models with sex and age interactions. Results Compared to normoglycemic individuals, individuals with prediabetes and diabetes showed poorer performance on cognitive tests, i.e., TMT‐B, category fluency, and block design subtest over time. Additionally, the presence of prediabetes, diabetes, longer diabetes duration and higher HbA1c were associated with faster global and domain‐specific cognitive decline over a median follow‐up of 6.4 years (range 1–19). Three‐way interactions showed that the effect of diabetes on decline in global cognition, attention and visuospatial domains was more pronounced in women than in men. Age did not modify the effect of diabetes or higher HbA1c on cognition. Conclusions The results suggest that prediabetes and diabetes significantly shorten the timeframe before a potentially clinically noticeable change in global and domain‐specific cognition is reached. Thus, lifestyle modification to reverse prediabetes and prevent diabetes could potentially reduce the rate of cognitive decline in aging populations. The findings differed by sex (i.e., the effect of diabetes on decline in global cognition, attention and visuospatial domains was more pronounced in women than men).