Knopman, David S.

AbstractBackgroundQuantifying white matter using diffusion MRI (dMRI) has been proposed for measuring early microstructural tissue changes due to cerebral small vessel disease and aid in quantifying vascular contributions to cognitive impairment and dementia (VCID). Our goal was to compare the usefulness of longitudinal white matter changes in the commonly available diffusion MRI measures for VCID prevention trials.MethodWe included 718 participants over 50 years of age (mean age: 71.1(9.6) years) from the Mayo Clinic Study of Aging, a population‐based sample, with at least two dMRI scans and structural imaging. We computed the commonly available single‐shell dMRI measures (fractional anisotropy, mean diffusivity, and two MarkVCID measures ‐ free water and peak‐width skeletonized mean diffusivity [PSMD]) at each time point. We tested for voxel‐wise associations between dMRI markers and vascular risk measured by cardiovascular metabolic condition (CMC) and observed a region‐specific dependance across all dMRI measures. Using both global and regional dMRI measures, we (i) examined the longitudinal association of dMRI measures with cognition and (ii) computed sample size estimates for a hypothetical clinical trial. We also included white matter hyperintensities (WMH) and our previously proposed composite vascular white matter score (combination of WMH and fractional anisotropy of the genu) as a comparison.ResultVascular risk was associated with all single shell dMRI measures in the genu of the corpus callosum, which we included as a regional dMRI marker for comparison (Figure 1). All dMRI markers correlated with cognitive performance longitudinally (Table 1) and had comparable sample size estimates required for hypothetical VCID clinical trials (Figure 2). Further, global free water and the composite vascular white matter score had the smallest sample size estimates.ConclusionAll commonly used dMRI markers had significant frontal lobe changes due to vascular risk. Both global and regional corpus callosum dMRI markers were sensitive to longitudinal cognitive decline, suggesting their utility in measuring the slowing down of VCID. The composite vascular white matter score, global free water, and WMH show promise as VCID biomarkers. Further work is needed to validate these markers on multiple populations.

AbstractBackgroundThere has been a recent proliferation of various quantities based on single shell Diffusion Tensor Imaging (DTI) for capturing overall cerebrovascular health, especially Small Vessel Disease (SVD) as a single number. The existing literature has gaps in the comparison of these measures, so we evaluated them as predictors of both current and future White Matter Hyperintensity (WMH) as a proxy of SVD.MethodUsing baseline 3T MRI examinations (T1, DTI, FLAIR scans) of 598 participants (>60 years, amyloid negative, to limit to those on the SVD pathway) from the Mayo Clinic Study of Aging, we calculated five summary DTI quantities with varying degree of complexity. We estimated their partial R2 from cross‐sectional linear regression models and computed semi‐partial R2 from mixed models predicting WMH volume as a percentage of Total Intracranial Volume (TIV). All models were adjusted for age, sex, and scanner model, and the WMH and based were log‐transformed before fitting. We also conducted subset analyses with high (WMH+) vs. low (WMH‐) baseline WMH load (cut at 1.62%) to compare the performance of the measures in early vs. widespread WMH.ResultAll MD‐based quantities computed on the whole brain, including FWF, performed similarly to each other, but Genu FA underperformed at explaining WMH variance (Table 1). PSMD (skeletonized or atlas based) was more sensitive in WMH+ but dramatically less so in WMH‐. In WMH‐ and overall cohort, FWF and JHU MD (which is a simple averaging of MD in the JHU atlas) performed well.ConclusionAll MD‐based measures performed similarly to each other in the whole dataset but dichotomizing by WMH load showed interesting differences. PSMD measurements are driven by the extreme end (95th percentile) of the MD distribution and therefore more sensitive in WMH+. Genu FA, a regional measure, was not useful for predicting whole brain WMH change. We noticed some variation in how the quantities are affected by scanner model (GE Signa HDxt and Discovery 750) which needs broader investigation. Simply averaging MD over WM avoids additional processing and is more sensitive to WM degeneration that is not (yet) severe.

Abstract US clinical practice guidelines for the diagnostic evaluation of cognitive impairment due to Alzheimer's Disease (AD) or AD and related dementias (ADRD) are decades old and aimed at specialists. This evidence‐based guideline was developed to empower all—including primary care—clinicians to implement a structured approach for evaluating a patient with symptoms that may represent clinical AD/ADRD. As part of the modified Delphi approach and guideline development process (7374 publications were reviewed; 133 met inclusion criteria) an expert workgroup developed recommendations as steps in a patient‐centered evaluation process. The workgroup provided a summary of validated instruments to measure symptoms in daily life (including cognition, mood and behavior, and daily function) and to test for signs of cognitive impairment in the office. This article distills this information to provide a resource to support clinicians in the implementation of this approach in clinical practice. The companion articles provide context for primary care and specialty clinicians with regard to how to fit these instruments into the workflow and actions to take when integration of performance on these instruments with clinical profile and clinician judgment support potential cognitive impairment.

Abstract Aims Excessive alcohol use is associated with an increased risk of cognitive impairment. Since increased amyloid plaque burden exacerbates cognitive decline, we sought to assess the potential impact of alcohol use disorder (AUD) on cognition, memory, and amyloid burden corresponding with age. Methods We conducted the retrospective analysis with 6036 subjects, including 269 AUD+ subjects. A four-item CAGE (C—Cutting Down, A—Annoyance by Criticism, G—Guilty Feeling, E—Eye-openers) alcohol questionnaire was given during the recruitment to determine AUD in each participant. We assessed cognitive function, focusing on memory using neuropsychological testing. For 1038 participants, including 57 AUD+ subjects, we measured amyloid burden using the 11C Pittsburgh Compound B tracer-based positron emission tomography imaging. Results AUD+ was significantly associated with lower scores of cognition and memory function relative to AUD− individuals. No significant association was found with AUD and elevated brain amyloid under the age of 65. However, further analysis showed that those aged ≥65 showed greater odds for abnormal amyloid in AUD+ compared to AUD− participants. Conclusions Our results underscore AUD as a risk factor for cognitive decline and diminished memory, particularly in aging populations. The role of AUD in brain amyloid accumulation requires further study.

Abstract There is increasing interest in Alzheimer’s disease related plasma biomarkers due to their accessibility and scalability. We hypothesized that integrating plasma biomarkers with other commonly used and available participant data (MRI, cardiovascular factors, lifestyle, genetics) using machine learning (ML) models can improve individual prediction of cognitive outcomes. Further, our goal was to evaluate the heterogeneity of these predictors across different age strata. This longitudinal study included 1185 participants from the Mayo Clinic Study of Aging who had complete plasma analyte work-up at baseline. We used the Quanterix Simoa immunoassay to measure neurofilament light, Aβ1–42 and Aβ1–40 (used as Aβ42/Aβ40 ratio), glial fibrillary acidic protein, and phosphorylated tau 181 (p-tau181). Participants’ brain health was evaluated through gray and white matter structural MRIs. The study also considered cardiovascular factors (hyperlipidemia, hypertension, stroke, diabetes, chronic kidney disease), lifestyle factors (area deprivation index, body mass index, cognitive and physical activities), and genetic factors (APOE, single nucleotide polymorphisms, and polygenic risk scores). An ML model was developed to predict cognitive outcomes at baseline and decline (slope). Three models were created: a base model with groups of risk factors as predictors, an enhanced model included socio-demographics, and a final enhanced model by incorporating plasma and socio-demographics into the base models. Models were explained for three age strata: younger than 65 years, 65–80 years, and older than 80 years, and further divided based on amyloid positivity status. Regardless of amyloid status the plasma biomarkers showed comparable performance (R² = 0.15) to MRI (R² = 0.18) and cardiovascular measures (R² = 0.10) when predicting cognitive decline. Inclusion of cardiovascular or MRI measures with plasma in the presence of socio-demographic improved cognitive decline prediction (R² = 0.26 and 0.27). For amyloid positive individuals Aβ42/Aβ40, glial fibrillary acidic protein and p-tau181 were the top predictors of cognitive decline while Aβ42/Aβ40 was prominent for amyloid negative participants across all age groups. Socio-demographics explained a large portion of the variance in the amyloid negative individuals while the plasma biomarkers predominantly explained the variance in amyloid positive individuals (21% to 37% from the younger to the older age group). Plasma biomarkers performed similarly to MRI and cardiovascular measures when predicting cognitive outcomes and combining them with either measure resulted in better performance. Top predictors were heterogeneous between cross-sectional and longitudinal cognition models, across age groups, and amyloid status. Multimodal approaches will enhance the usefulness of plasma biomarkers through careful considerations of a study population’s socio-demographics, brain and cardiovascular health.

BACKGROUND: Associations between magnetic resonance imaging markers of cerebral small vessel disease (CSVD) and dementia risk in older adults have been established, but it remains unclear how lifestyle factors, including psychosocial health, may modify this association. METHODS: Social support and social isolation were assessed among participants of the community-based ARIC (Atherosclerosis Risk in Communities) Study, via self-reported questionnaires (1990–1992). Following categorization of both factors, participants were classified as having strong or poor mid-life social relationships. At visit 5 (2011–2013), participants underwent 3T brain magnetic resonance imaging quantifying CSVD measures: white matter hyperintensity volume, microbleeds (subcortical), infarcts (lacunar), and white matter integrity (diffusion tensor imaging). Incident dementia cases were identified from the time of imaging through December 31, 2020 with ongoing surveillance. Associations between CSVD magnetic resonance imaging markers and incident dementia were evaluated using Cox proportional-hazard regressions adjusted for demographic and additional risk factors (from visit 2). Effect modification by mid-life social relationships was evaluated. RESULTS: Of the 1977 participants with magnetic resonance imaging, 1617 participants (60.7% women; 26.5% Black participants; mean age at visit 2, 55.4 years) were examined. In this sample, mid-life social relationships significantly modified the association between white matter hyperintensity volume and dementia risk ( P interaction=0.001). Greater white matter hyperintensity volume was significantly associated with risk of dementia in all participants, yet, more substantially in those with poor (hazard ratio, 1.84 [95% CI, 1.49–2.27]) versus strong (hazard ratio, 1.26 [95% CI, 1.08–1.47]) mid-life social relationships. Although not statistically significant, subcortical microbleeds in participants with poor mid-life social relationships were associated with a greater risk of dementia, relative to those with strong social relationships, in whom subcortical microbleeds were no longer associated with elevated dementia risk. CONCLUSIONS: The elevated risk of dementia associated with CSVD may be reduced in participants with strong mid-life social relationships. Future studies evaluating psychosocial health through the life course and the mechanisms by which they modify the relationship between CSVD and dementia are needed.

BACKGROUND: Studies of the neurovascular contribution to dementia have largely focused on cerebral small vessel disease (CSVD), but the role of intracranial atherosclerotic disease (ICAD) remains unknown in the general population. The objective of this study was to determine the risk of incident dementia from ICAD after adjusting for CSVD and cardiovascular risk factors in a US community-based cohort. METHODS: We acquired brain magnetic resonance imaging examinations from 2011 through 2013 in 1980 Black and White participants in the ARIC study (Atherosclerosis Risk in Communities), a prospective cohort conducted in 4 US communities. Magnetic resonance imaging examinations included high-resolution vessel wall magnetic resonance imaging and magnetic resonance angiography to identify ICAD. Of these participants, 1590 without dementia, without missing covariates, and with adequate magnetic resonance image quality were followed through 2019 for incident dementia. Associations between ICAD and incident dementia were assessed using Cox proportional hazard ratios adjusted for CSVD (characterized by white matter hyperintensities, lacunar infarctions, and microhemorrhages), APOE4 genotype (apolipoprotein E gene ε4), and cardiovascular risk factors. RESULTS: The mean age (SD) of study participants was 77.4 (5.2) years. ICAD was detected in 34.6% of participants. After a median follow-up of 5.6 years, 286 participants developed dementia. Compared with participants without ICAD, the fully adjusted hazard ratios (95% CIs) for incident dementia in participants with any ICAD, with ICAD only causing stenosis ≤50%, and with ICAD causing stenosis >50% in ≥1 vessel were 1.57 (1.17–2.11), 1.41 (1.02–1.95), and 1.94 (1.32–2.84), respectively. ICAD was associated with dementia even among participants with low white matter hyperintensities burden, a marker of CSVD. CONCLUSIONS: ICAD was associated with an increased risk of incident dementia, independent of CSVD, APOE4 genotype, and cardiovascular risk factors. The increased risk of dementia was evident even among participants with low CSVD burden, a group less likely to be affected by vascular dementia, and in participants with ICAD causing only low-grade stenosis. Our results suggest that ICAD may partially mediate the effect that cardiovascular risk factors have on the brain leading to dementia. Both ICAD and CSVD must be considered to understand the vascular contributions to cognitive decline.

[ 18 F]-Flortaucipir positron emission tomography (PET) is considered a good biomarker of Alzheimer’s disease. However, it is unknown how flortaucipir is associated with the distribution of tau across brain regions and how these associations are influenced by amyloid-β. It is also unclear whether flortaucipir can detect tau in definite primary age–related tauopathy (PART). We identified 248 individuals at Mayo Clinic who had undergone [ 18 F]-flortaucipir PET during life, had died, and had undergone an autopsy, 239 cases of which also had amyloid-β PET. We assessed nonlinear relationships between flortaucipir uptake in nine medial temporal and cortical regions, Braak tau stage, and Thal amyloid-β phase using generalized additive models. We found that flortaucipir uptake was greater with increasing tau stage in all regions. Increased uptake at low tau stages in medial temporal regions was only observed in cases with a high amyloid-β phase. Flortaucipir uptake linearly increased with the amyloid-β phase in medial temporal and cortical regions. The highest flortaucipir uptake occurred with high Alzheimer’s disease neuropathologic change (ADNC) scores, followed by low-intermediate ADNC scores, then PART, with the entorhinal cortex providing the best differentiation between groups. Flortaucipir PET had limited ability to detect PART, and imaging-defined PART did not correspond with pathologically defined PART. In summary, spatial patterns of flortaucipir mirrored the histopathological tau distribution, were influenced by the amyloid-β phase, and were useful for distinguishing different ADNC scores and PART.

Background: Conventional normative samples include individuals with undetected Alzheimer’s disease neuropathology, lowering test sensitivity for cognitive impairment. Objective: We developed Mayo Normative Studies (MNS) norms limited to individuals without elevated amyloid or neurodegeneration (A–N–) for Rey’s Auditory Verbal Learning Test (AVLT). We compared these MNS A–N– norms in female, male, and total samples to conventional MNS norms with varying levels of demographic adjustments. Methods: The A–N– sample included 1,059 Mayo Clinic Study of Aging cognitively unimpaired (CU) participants living in Olmsted County, MN, who are predominantly non-Hispanic White. Using a regression-based approach correcting for age, sex, and education, we derived fully-adjusted T-score formulas for AVLT variables. We validated these A–N– norms in two independent samples of CU ( n = 261) and mild cognitive impairment (MCI)/dementia participants ( n = 392) > 55 years of age. Results: Variability associated with age decreased by almost half in the A–N– norm sample relative to the conventional norm sample. Fully-adjusted MNS A–N– norms showed approximately 7– 9% higher sensitivity to MCI/dementia compared to fully-adjusted MNS conventional norms for trials 1– 5 total and sum of trials. Among women, sensitivity to MCI/dementia increased with each normative data refinement. In contrast, age-adjusted conventional MNS norms showed greatest sensitivity to MCI/dementia in men. Conclusions: A–N– norms show some benefits over conventional normative approaches to MCI/dementia sensitivity, especially for women. We recommend using these MNS A–N– norms alongside MNS conventional norms. Future work is needed to determine if normative samples that are not well characterized clinically show greater benefit from biomarker-refined approaches.