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Abstract Introduction Measurement of amyloid beta (Aβ40 and Aβ42) and tau (phosphorylated tau [p‐tau] and total tau [t‐tau]) in cerebrospinal fluid (CSF) can be utilized to differentiate clinical and preclinical Alzheimer's disease dementia (AD) from other neurodegenerative processes. Methods CSF biomarkers were measured in 150 participants from the Mayo Clinic Study of Aging and the Alzheimer's Disease Research Center. P‐tau/Aβ42 (Roche Elecsys, Fujirebio LUMIPULSE) and Aβ42/40 (Fujirebio LUMIPULSE) ratios were compared to one another and to amyloid positron emission tomography (PET) classification. Results Strong correlation was observed between LUMIPULSE p‐tau/Aβ42 and Aβ42/40, as well as Elecsys and LUMIPULSE p‐tau/Aβ42 and Aβ42/40 (Spearman's ρ = –0.827, –0.858, and 0.960, respectively). Concordance between LUMIPULSE p‐tau/Aβ42 and Aβ42/40 was 96% and between Elecsys p‐tau/Aβ42 and both LUMIPULSE ratios was 97%. All ratios had > 94% overall, positive, and negative percent agreement with amyloid PET classification. Discussion These data suggest that p‐tau/Aβ42 and Aβ42/40 ratios provide similar clinical information in the assessment of amyloid pathology.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

Background and Purpose: Cerebral microbleeds (CMBs) are represented by small areas of hemosiderin deposition, detected on brain magnetic resonance imaging (MRI), and found in ≈23% of the cognitively normal population over age of 60 years. CMBs predict risk of hemorrhagic and ischemic stroke. They correlate with increased cardiovascular mortality. In this article, we sought to determine in a population-based study whether antithrombotic medications correlate with CMBs and, if present, whether the association was direct or mediated by another variable. Methods: The study consisted of 1253 participants from the population-based Mayo Clinic Study of Aging who underwent T2* gradient-recalled echo magnetic resonance imaging. We tested the relationship between antithrombotic medications and CMB presence and location, using multivariable logistic-regression models. Ordinal logistic models tested the relationship between antithrombotics and CMB frequency. Using structural equation models, we assessed the effect of antithrombotic medications on presence/absence of CMBs and count of CMBs in the CMB-positive group, after considering the effects of age, sex, vascular risk factors, amyloid load by positron emission tomography, and apoE. Results: Two hundred ninety-five participants (26.3%) had CMBs. Among 678 participants taking only antiplatelet medications, 185 (27.3%) had CMBs. Among 95 participants taking only an anticoagulant, 43 (45.3%) had CMBs. Among 44 participants taking an anticoagulant and antiplatelet therapy, 21 (48.8%) had CMBs. Anticoagulants correlated with the presence and frequency of CMBs, whereas antiplatelet agents were not. Structural equation models showed that predictors for presence/absence of CMBs included older age at magnetic resonance imaging, male sex, and anticoagulant use. Predictors of CMB count in the CMB-positive group were male sex and amyloid load. Conclusions: Anticoagulant use correlated with presence of CMBs in the general population. Amyloid positron emission tomography correlated with the count of CMBs in the CMB-positive group.

Abstract While cerebrovascular disease can be observed in vivo using MRI, the multiplicity and heterogeneity in the mechanisms of cerebrovascular damage impede accounting for these measures in ageing and dementia studies. Our primary goal was to investigate the key sources of variability across MRI markers of cerebrovascular disease and evaluate their impact in comparison to amyloidosis on cognitive decline in a population-based sample. Our secondary goal was to evaluate the prognostic utility of a cerebrovascular summary measure from all markers. We included both visible lesions seen on MRI (white matter hyperintensities, cortical and subcortical infarctions, lobar and deep microbleeds) and early white matter damage due to systemic vascular health using diffusion changes in the genu of the corpus callosum. We identified 1089 individuals aged ≥60 years with concurrent amyloid-PET and MRI scans from the population-based Mayo Clinic Study of Aging. We divided these into discovery and validation datasets. Using the discovery dataset, we conducted principal component analyses and ascertained the main sources of variability in cerebrovascular disease markers. Using linear regression and mixed effect models, we evaluated the utility of these principal components and combinations of these components for the prediction of cognitive performance along with amyloidosis. Our main findings were (i) there were three primary sources of variability among the CVD measures—white matter changes are driven by white matter hyperintensities and diffusion changes; number of microbleeds (lobar and deep); and number of infarctions (cortical and subcortical); (ii) Components of white matter changes and microbleeds but not infarctions significantly predicted cognition trajectories in all domains with greater contributions from white matter; and (iii) The summary vascular score explained 3–5% of variability in baseline global cognition in comparison to 3–6% variability explained by amyloidosis. Across all cognitive domains, the vascular summary score had the least impact on memory performance (∼1%). Though there is mechanistic heterogeneity in the cerebrovascular disease markers measured on MRI, these changes can be grouped into three components and together explain variability in cognitive performance equivalent to the impact of amyloidosis on cognition. White matter changes represent dynamic ongoing damage, predicts future cognitive decline across all domains and diffusion measurements help capture white matter damage due to systemic vascular changes. Therefore, measuring and accounting for white matter changes using diffusion MRI and white matter hyperintensities along with microbleeds will allow us to capture vascular contributions to cognitive impairment and dementia.

BackgroundTo operationalize the National Institute on Aging – Alzheimer's Association (NIA‐AA) Research Framework for Alzheimer's Disease 6‐stage continuum of clinical progression for persons with abnormal amyloid.MethodsThe Mayo Clinic Study of Aging is a population‐based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We evaluated persons without dementia having 3 consecutive clinical visits. Measures for cross‐sectional categories included objective cognitive impairment (OBJ) and function (FXN). Measures for change included subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (ΔNBS). We calculated frequencies of the stages using different cutoff points and assessed stability of the stages over 15 months.ResultsAmong 243 abnormal amyloid participants, the frequencies of the stages varied with age: 66 to 90% were classified as stage 1 at age 50 but at age 80, 24 to 36% were stage 1, 32 to 47% were stage 2, 18 to 27% were stage 3, 1 to 3% were stage 4 to 6, and 3 to 9% were indeterminate. Most stage 2 participants were classified as stage 2 because of abnormal ΔOBJ only (44–59%), whereas 11 to 21% had SCD only, and 9 to 13% had ΔNBS only. Short‐term stability varied by stage and OBJ cutoff points but the most notable changes were seen in stage 2 with 38 to 63% remaining stable, 4 to 13% worsening, and 24 to 41% improving (moving to stage 1).InterpretationThe frequency of the stages varied by age and the precise membership fluctuated by the parameters used to define the stages. The staging framework may require revisions before it can be adopted for clinical trials. ANN NEUROL 2021;89:1145–1156

Abstract Objective Neuropsychiatric symptoms (NPS) are associated with the risk of incident mild cognitive impairment (MCI) and dementia. We examined associations between NPS and the outcomes of global and domain‐specific cognitive trajectories. Methods In this longitudinal study conducted in the setting of the population‐based Mayo Clinic Study of Aging, 5081 community‐dwelling, nondemented individuals aged ≥50 years (51% males) underwent NPS assessment using Neuropsychiatric Inventory Questionnaire (NPI‐Q), and Beck Depression and Anxiety Inventories (BDI‐II, BAI). Global and domain‐specific (memory, language, attention, and visuospatial skills) cognitive performance was assessed through neuropsychological testing every 15 months. Associations between baseline NPS and trajectories for individual yearly change in cognitive z‐scores were calculated using linear mixed‐effect models. Results Cognition declined regardless of NPS status over the median follow‐up of 4.5 years. Presence of NPS was associated with increased cognitive decline. Differences in annualized change in global cognition z‐scores for participants with NPS compared to without NPS ranged from −0.018 (95% CI −0.032, −0.004; p = 0.011) for irritability to −0.159 (−0.254, −0.065; p = 0.001) for hallucinations. Associations between NPS and annual decline in global cognition were significant for most NPI‐Q‐assessed NPS and clinical depression (BDI‐II≥13). Participants with NPI‐Q‐assessed depression, apathy, nighttime behavior, and clinical depression had greater decline in all domain‐specific z‐scores; presence of delusions and anxiety was associated with more pronounced decline in language, attention and visuospatial skills. Conclusion NPS were associated with a more accelerated cognitive decline. Clinical assessment and potential treatment of NPS is warranted even in a community setting as NPS may impact cognitive decline in nondemented individuals.

Background: The relationship between cerebral microbleeds (CMBs) on hemosiderin-sensitive MRI sequences and cerebral amyloid angiopathy (CAA) remains unclear in population-based participants or in individuals with dementia. Objective: To determine whether CMBs on antemortem MRI correlate with CAA. Methods: We reviewed 54 consecutive participants with antemortem T2*GRE-MRI sequences and subsequent autopsy. CMBs were quantified on MRIs closest to death. Autopsy CAA burden was quantified in each region including leptomeningeal/cortical and capillary CAA. By a clustering approach, we examined the relationship among CAA variables and performed principal component analysis (PCA) for dimension reduction to produce two scores from these 15 interrelated predictors. Hurdle models assessed relationships between principal components and lobar CMBs. Results: MRI-based CMBs appeared in 20/54 (37%). 10 participants had ≥2 lobar-only CMBs. The first two components of the PCA analysis of the CAA variables explained 74% variability. The first rotated component (RPC1) consisted of leptomeningeal and cortical CAA and the second rotated component of capillary CAA (RPC2). Both the leptomeningeal and cortical component and the capillary component correlated with lobar-only CMBs. The capillary CAA component outperformed the leptomeningeal and cortical CAA component in predicting lobar CMBs. Both capillary and the leptomeningeal/cortical components correlated with number of lobar CMBs. Conclusion: Capillary and leptomeningeal/cortical scores correlated with lobar CMBs on MRI but lobar CMBs were more closely associated with the capillary component. The capillary component correlated with APOE ɛ4, highlighting lobar CMBs as one aspect of CAA phenotypic diversity. More CMBs also increase the probability of underlying CAA.

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score > 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score > 0.5 was associated with infarcts ( p < 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

AbstractIntroductionWe determined whether cerebrospinal fluid (CSF) neurofilament light (NfL), neurogranin (Ng), and total‐tau (t‐tau) differentially mapped to magnetic resonance imaging (MRI) measures of cortical thickness, microstructural integrity (corpus callosum and cingulum fractional anisotropy [FA]), and white matter hyperintensities (WMH).MethodsAnalyses included 536 non‐demented Mayo Clinic Study of Aging participants with CSF NfL, Ng, t‐tau, amyloid beta (Aβ)42 and longitudinal MRI scans. Linear mixed models assessed longitudinal associations between CSF markers and MRI changes.ResultsHigher CSF NfL was associated with decreasing microstructural integrity and WMH. Higher t‐tau was associated with decreasing temporal lobe and Alzheimer's disease (AD) meta region of interest (ROI) cortical thickness. There was no association between Ng and any MRI measure. CSF Aβ42 interacted with Ng for declines in temporal lobe and AD meta ROI cortical thickness and cingulum FA.DiscussionCSF NfL predicts changes in white matter integrity, t‐tau reflects non‐specific changes in cortical thickness, and Ng reflects AD‐specific synaptic and neuronal degeneration.