Citations

Background and Purpose— White matter hyperintensity (WMH) burden is associated with stroke and cognitive decline. Risk factors associated with the longitudinal progression of WMH in the general population have not been systematically investigated. To investigate the primary midlife and current cardiometabolic risk factors associated with changes in WMH over time in a population cohort. Methods— This cohort study included participants enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study in Olmsted County, Minnesota with at least 2 consecutive WMH assessments on fluid-attenuated inversion recovery-magnetic resonance images (n=554, ≥60 years with midlife assessments) with relevant baseline laboratory measures of interest. Linear mixed model regression was used to determine the important components of cardiometabolic risk profile at baseline that were associated with future progression of WMH. These analyses were controlled for age and sex. Sensitivity analyses were conducted using stratification by sex. The main outcome measure was percent change in WMH normalized to total intracranial volume. Three sets of models were constructed to evaluate individual (1) midlife risk factors, (2) current risk factors including the presence of metabolic syndrome and its constituents, and (3) baseline measurements of continuous laboratory measures of cardiometabolic risk. Results— Age was the strongest predictor of progression in WMH ( P <0.001). Baseline hypertension ( P <0.001), midlife hypertension ( P =0.003), and baseline fasting glucose in males ( P =0.01) were predictive of WMH change. The presence of metabolic syndrome was not associated with progressive WMH. In sensitivity analyses, associations between hypertension and WMH progression were stronger in females. Baseline serum glucose was associated with increase in WMH but was not significant in females in the stratified analysis. Other continuous laboratory measures of vascular risk were not associated with progressive WMH. Conclusions— Midlife and current hypertension in all participants and fasting glucose in males were associated with quantitative changes in white matter. Prospective clinical studies should determine optimal blood pressure to reduce stroke and cognitive impairment during aging.

ObjectiveTo investigate the multifactorial processes underlying cognitive aging based on the hypothesis that multiple causal pathways and mechanisms (amyloid, vascular, and resilience) influence longitudinal cognitive decline in each individual through worsening brain health.MethodsWe identified 1,230 elderly subjects (aged ≥50 years) with an average of 4.9 years of clinical follow‐up and with amyloid positron emission tomography, diffusion tensor imaging, and structural magnetic resonance imaging scans from the population‐based Mayo Clinic Study of Aging. We examined imaging markers of amyloid and brain health (white matter microstructural integrity and cortical thinning), systemic vascular health preceding the imaging markers, and early to midlife intellectual enrichment to predict longitudinal cognitive trajectories. We used latent growth curve models for modeling longitudinal cognitive decline.ResultsAll the pathways (amyloid, vascular, resilience) converged through their effects on cortical thinning and worsening cognition and together explained patterns in cognitive decline. Resilience and vascular pathways (aging process, sex differences, education/occupation, and systemic vascular health) had significant impact on white matter microstructural integrity. Education/occupation levels contributed to white matter integrity through systemic vascular health. Worsening white matter integrity contributed to significant cortical thinning and subsequently longitudinal cognitive decline. Baseline amyloidosis contributed to a significant proportion of cognitive decline that accelerated with longer follow‐up times, and its primary impact was through cortical thinning.InterpretationWe developed an integrated framework to help explain the dynamic and complex process of cognitive aging by considering key causal pathways. Such an approach is important for both better comprehension of cognitive aging processes and will aid in the development of successful intervention strategies. ANN NEUROL 2019;86:866–877

Abstract Introduction Neuroimaging biomarkers are important for early diagnosis of Alzheimer's disease, and comparing multimodality neuroimaging to autopsy data is essential. Methods We compared the pathologic findings from a prospective autopsy cohort (n = 100) to Pittsburgh compound B PET (PiB‐PET), 18 F‐fluorodeoxyglucose PET (FDG‐PET), and MRI. Correlations between neuroimaging biomarkers and neuropathologic schemes were assessed. Results PiB‐PET showed strong correlations with Thal amyloid phase and Consortium to Establish a Registry for Alzheimer's Disease score and categorized 44% of Thal phase 1 participants as positive. FDG‐PET and MRI correlated modestly with Braak tangle stage in Alzheimer's type pathology. A subset of participants with “none” or “sparse” neuritic plaque scores had elevated PiB‐PET signal due to diffuse amyloid plaque. Participants with findings characterized as “suspected non‐Alzheimer's pathophysiology” represented 15% of the group. Discussion PiB‐PET is associated with Alzheimer's disease, neuritic plaques, and diffuse plaques. FDG‐PET and MRI have modest correlation with neuropathologic schemes. Participants with findings characterized as suspected non‐Alzheimer's pathophysiology most commonly had primary age‐related tauopathy.

Abstract Although white matter hyperintensities have traditionally been viewed as a marker of vascular disease, recent pathology studies have found an association between white matter hyperintensities and Alzheimer’s disease pathologies. The objectives of this study were to investigate the topographic patterns of white matter hyperintensities associated with Alzheimer’s disease biomarkers measured using PET. From the population-based Mayo Clinic Study of Aging, 434 participants without dementia (55% male) with FLAIR and gradient recall echo MRI, tau-PET (AV-1451) and amyloid-PET scans were identified. A subset had cerebral microbleeds detected on T2* gradient recall echo scans. White matter hyperintensities were semi-automatically segmented using FLAIR MRI in participant space and normalized to a custom template. We used statistical parametric mapping 12-based, voxel-wise, multiple-regression analyses to detect white matter hyperintense regions associated with Alzheimer’s biomarkers (global amyloid from amyloid-PET and meta-regions of interest tau uptake from tau-PET) after adjusting for age, sex and hypertension. For amyloid associations, we additionally adjusted for tau and vice versa. Topographic patterns of amyloid-associated white matter hyperintensities included periventricular white matter hyperintensities (frontal and parietal lobes). White matter hyperintense volumes in the detected topographic pattern correlated strongly with lobar cerebral microbleeds (P < 0.001, age and sex adjusted Cohen’s d = 0.703). In contrast, there were no white matter hyperintense regions significantly associated with increased tau burden using voxel-based analysis or region-specific analysis. Among non-demented elderly, amyloid load correlated with a topographic pattern of white matter hyperintensities. Further, the amyloid-associated, white matter hyperintense regions strongly correlated with lobar cerebral microbleeds suggesting that cerebral amyloid angiopathy contributes to the relationship between amyloid and white matter hyperintensities. The study did not support an association between increased tau burden and white matter hyperintense burden.