Citations

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 Introduction As cerebrospinal fluid (CSF) neurofilament light protein (NfL) and the CSF/serum albumin ratio (Q Alb ) are used in the clinical routine, the impact of demographic factors on these biomarkers is important to understand. Methods Participants were derived from two Swedish samples: the population‐based H70 Study (n = 308, age 70) and a clinical routine cohort (CSF NfL, n = 8995, Q Alb , n = 39252, age 0 to 95). In the population‐based study, Q Alb and NfL were examined in relation to sex, cardiovascular risk factors, and cerebral white matter lesions (WMLs). In the clinical cohort, Q Alb and NfL sex differences were tested in relation to age. Results Men had higher Q Alb and NfL concentrations and had higher Q Alb and NfL concentrations from adolescence throughout life. NfL was not related to WML, but Q Alb correlated positively with WMLs. Discussion The CSF NfL sex difference could not be explained by vascular pathology. Future studies should consider using different reference limits for men and women.

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: Early-onset Parkinson’s disease (EOPD), occurring between ages 40 and 55, carries social, societal, and personal consequences and may progress, with fewer comorbidities than typical, later-onset disease. Objective: To examine the incidence and survival of EOPD and other Parkinsonism occurring before age 55 in the population-based cohort of residents in seven Minnesota counties. Methods: A movement-disorder specialist reviewed all the medical records in a 2010–2015 Parkinsonism-incident cohort to confirm diagnosis and subtypes. Results: We identified 27 patients diagnosed at ≤ 50 years with incident Parkinsonism 2010–15:11 (41%) cases of EOPD, 13 (48%) drug-induced Parkinsonism, and 3 (11%) other Parkinsonism; we also identified 69 incident cases of Parkinsonism ≤ 55 years, of which 28 (41%) were EOPD, 28 (41%) DIP, and 13 (19%) other Parkinsonism. Overall incidence for Parkinsonism ≤ 50 years was 1.98/100,000 person-years, and for EOPD was 0.81/100,000 person-years. In patients ≤ 55 years, Parkinsonism incidence was 5.05/100,000 person-years: in EOPD, 2.05/100,000 person-years. Levodopa-induced dyskinesia was present in 45%of EOPD (both ≤ 50 years and ≤ 55 years). Onset of cardinal motor symptoms was proximate to the diagnosis of EOPD, except for impaired postural reflexes, which occurred later in the course of EOPD. Among the 69 Parkinsonism cases ≤ 55 years, 9 (13%; all male) were deceased (only 1 case of EOPD). Men had a higher mortality risk compared to women (p = 0.049). Conclusion: The incidence of EOPD ≤ 50 years was 0.81/100,000 person-years (1.98 in Parkinsonism all type); prior to ≤ 55 years was 2.05/100,000 person-years (5.05 in Parkinsonism all type) with higher incidence in men than women. Men with Parkinsonism, all type, had higher mortality compared to women.

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.