Knopman, David S.

Abstract INTRODUCTION Limited data exist on the utility of plasma biomarkers to predict incident abnormal amyloid positron emission tomography (PET). In this study we evaluate the association of plasma Alzheimer's disease (AD) biomarkers with amyloid PET progression among initially amyloid PET negative (A−) individuals. METHODS We included 290 A−, cognitively unimpaired Mayo Clinic Study of Aging participants. We estimated the association of each baseline plasma biomarker with progression from A− to A+ and with rate of amyloid PET change. RESULTS Interquartile range differences in amyloid beta 42/40, percent phosphorylated tau 217 (%p‐tau217), and Amyloid Probability Score 2 were associated with 1.29 ( P = 0.09), 1.38 ( P < 0.001), and 1.20 ( P = 0.05) increases, respectively, in the hazard of progression from A− to A+ and 0.27 ( P = 0.16), 0.50 ( P = 0.007), and 0.28 ( P = 0.15) Centiloid/year increases, respectively, in annual rate of amyloid PET change. DISCUSSION Plasma %p‐tau217 may be a useful screening tool to enrich for participants with increased likelihood of progressing from normal to abnormal amyloid PET in a primary prevention trial. Highlights Plasma phosphorylated tau 217 was associated with amyloid positron emission tomography progression, negative to positive. The associations were weaker for amyloid beta 42/40 and Amyloid Probability Score 2. Age and apolipoprotein E ε4 carriership were also important predictors. These markers may be useful for enrichment of a primary prevention trial.

Background: Cerebral microbleeds (CMBs) are associated with incident dementia, but the impact of specific CMB patterns is unclear. CMBs in lobar regions suggest cerebral amyloid angiopathy (CAA), which is sometimes accompanied by superficial siderosis (SS), while subcortical CMBs indicate hypertensive origins. This study investigates the association between CMB patterns and dementia risk in the community-based longitudinal Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). Methods: All ARIC-NCS participants with a 3T research MRI at visit 5 (2011-13; aged 67-90) without intracerebral hemorrhage or dementia were included. CMB and SS presence and location were coded from T2* GRE sequences. Individuals were classified into one of four patterns: no CMBs, only subcortical, mixed (lobar and/or SS + subcortical), and only lobar and/or SS. Incident dementia diagnoses were defined by cognitive testing (in-person and telephone), informant interviews, and hospital discharge codes or death certificates. Cox proportional-hazards models assessed the association between the presence, patterns, and frequency of CMBs (0,1,2,3+), presence of SS, and incident dementia from visit 5 through 2020. Model covariates included demographics, vascular risk factors, and imaging markers of small vessel disease. Results: Among 1609 participants, 364 had CMBs (Table 1). Participants with CMBs tended to be older and female compared to those without CMBs. Compared to individuals with no CMBs, presence of any CMBs was associated with an increased risk of dementia (Table 2). Compared to individuals without CMBs, individuals with only lobar CMBs and/or SS had an increased risk of incident dementia, as did individuals with mixed CMBs, but individuals with subcortical-only did not. Participants with ≥3 CMBs of any variant had an increased risk of incident dementia vs no CMBs. Although SS was infrequent, its presence (vs no SS) was associated with an elevated risk of dementia. Conclusion: CMBs, particularly in a mixed or lobar and/or SS-only pattern, are linked to an increased risk of incident dementia. The number of recurrent CMBs and any SS also showed an increased dementia risk. These data support that dementia risk is high in individuals with a CAA-type CMB pattern, but also emphasize that a mixed pattern, not typical of CAA alone, is associated with a high risk of dementia. Further studies should evaluate mechanisms by which these different patterns contribute to dementia.

Background The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults. Objective To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults. Methods The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.e., age, sex, education), NPS (i.e., Neuropsychiatric Inventory Questionnaire; Beck Depression and Anxiety Inventories), at least one AD biomarker (i.e., plasma-, neuroimaging- and/or cerebrospinal fluid [CSF]-derived), and at least 2 repeated neuropsychological assessments. We trained and tested ML models using a stepwise feature addition approach to predict decline versus non-decline in global and domain-specific (i.e., memory, language, visuospatial, and attention/executive function) cognitive scores. Results ML models had better performance when NPS were included along with a) neuroimaging biomarkers for predicting decline in global cognition, as well as language and visuospatial skills; b) plasma-derived biomarkers for predicting decline in visuospatial skills; and c) CSF-derived biomarkers for predicting decline in attention/executive function, language, and memory. Conclusions NPS, added to ML models including demographic and AD biomarker data, improves prediction of downward trajectories in global and domain-specific cognitive scores among community-dwelling older adults, albeit effect sizes are small. These preliminary findings need to be confirmed by future cohort studies.