Graff‐Radford, Jonathan

Background and Purpose— Cerebral microbleed (CMB) location (deep versus strictly lobar) may elucidate underlying pathology with deep CMBs being more associated with hypertensive vascular disease and lobar CMBs being more associated with cerebral amyloid angiopathy. The objective of this study was to determine whether neuroimaging signs of vascular disease and Alzheimer pathology are associated with different types of CMBs. Methods— Among 1677 nondemented ARIC (Atherosclerosis Risk in Communities) participants (mean age=76±5 years; 40% men; 26% black) with 3-Tesla MRI scans at the fifth examination (2011–2013), we fit multinomial logistic regression models to quantify relationships of brain volumes (Alzheimer disease signature regions, total gray matter, frontal gray matter, and white matter hyperintensity volumes), infarct frequencies (lacunar, nonlacunar, and total), and apolipoprotein E (number of ε4 alleles) with CMB location (none, deep/mixed, or strictly lobar CMBs). Models were weighted for the sample selection scheme and adjusted for age, sex, education, hypertension, ever smoking status, diabetes mellitus, race site membership, and estimated intracranial volume (brain volume models only). Results— Deep/mixed and strictly lobar CMBs had prevalences of 8% and 16%, respectively. Larger white matter hyperintensity burden, greater total infarct frequency, smaller frontal volumes (in women only), and smaller total gray matter volume were associated with greater risk of both deep and lobar CMBs relative to no CMBs. Greater white matter hyperintensity volume was also associated with greater risk of deep relative to lobar CMBs. Higher lacunar and nonlacunar infarct frequencies were associated with higher risk of deep CMBs, whereas smaller Alzheimer disease signature region volume and apolipoprotein E ε4 homozygosity were associated with greater risk of lobar CMBs. Conclusions— CMBs are a common vascular pathology in the elderly. Markers of hypertensive small-vessel disease may contribute to deep CMBs while cerebral amyloid angiopathy may drive development of lobar CMBs.

Objective To investigate the associations between age, vascular health, and Alzheimer disease (AD) imaging biomarkers in an elderly sample. Methods We identified 430 individuals along the cognitive continuum aged >60 years with amyloid positron emission tomography (PET), tau PET, and magnetic resonance imaging (MRI) scans from the population‐based Mayo Clinic Study of Aging. A subset of 329 individuals had fluorodeoxyglucose (FDG) PET. We ascertained presently existing cardiovascular and metabolic conditions (CMC) from health care records and used the summation of presence/absence of hypertension, hyperlipidemia, cardiac arrhythmias, coronary artery disease, congestive heart failure, diabetes mellitus, and stroke as a surrogate for vascular health. We used global amyloid from Pittsburgh compound B PET, entorhinal cortex tau uptake (ERC‐tau) from tau‐PET, and neurodegeneration in AD signature regions from MRI and FDG‐PET as surrogates for AD pathophysiology. We dichotomized participants into CMC = 0 (CMC − ) versus CMC > 0 (CMC + ) and tested for age‐adjusted group differences in AD biomarkers. Using structural equation models (SEMs), we assessed the impact of vascular health on AD biomarker cascade (amyloid leads to tau leads to neurodegeneration) after considering the direct and indirect age, sex, and apolipoprotein E effects. Results CMC + participants had significantly greater neurodegeneration than CMC − participants but did not differ by amyloid or ERC‐tau. The SEMs showed that (1) vascular health had a significant direct and indirect impact on neurodegeneration but not on amyloid; and (2) vascular health, specifically the presence of hyperlipidemia, had a significant direct impact on ERC‐tau. Interpretation Vascular health had quantifiably greater impact on neurodegeneration in AD regions than on amyloid deposition. Longitudinal studies are warranted to clarify the relationship between tau deposition and vascular health. Ann Neurol 2017;82:706–718