Petersen, Ronald C.

AbstractObjectiveTo investigate the association between standard pure tone and speech audiometry with neuroimaging characteristics reflective of aging and dementia in older adults.Study DesignProspective population‐based study.SettingSingle tertiary care referral center.MethodsParticipants from the Mayo Clinic Study of aging 60 years old or older with normal cognition or mild cognitive impairment, baseline neuroimaging, and a behavioral audiogram associated with neuroimaging were eligible for study. Imaging modalities included structural MRI (sMRI) and fluid‐attenuated inversion recovery MRI (FLAIR‐MRI; N = 605), diffusion tensor imaging MRI (DTI‐MRI; N = 444), and fluorodeoxyglucose‐positron emission tomography (FDG‐PET; N = 413). Multivariable logistic and linear regression models were used to evaluate associations with neuroimaging outcomes.ResultsMean (SD) pure tone average (PTA) was 33 (15) dB HL and mean (SD) word recognition score (WRS) was 91% (14). There were no significant associations between audiometric performance and cortical thinning assessed by sMRI. Each 10‐dB increase in PTA was associated with increased likelihood of abnormal white‐matter hyperintensity (WMH) from FLAIR‐MRI (odds ratio 1.26, P = .02). From DTI‐MRI, participants with <100% WRSs had significantly lower fractional anisotropy in the genu of the corpus callosum (parameter estimate [PE] −0.012, P = .008) compared to those with perfect WRSs. From FDG‐PET, each 10% decrease in WRSs was associated with decreased uptake in the anterior cingulate cortex (PE −0.013, P = .001).ConclusionPoorer audiometric performance was not significantly associated with cortical thinning but was associated with white matter damage relevant to cerebrovascular disease (increased abnormal WMH, decreased corpus callosum diffusion). These neuroimaging results suggest a pathophysiologic link between hearing loss and cerebrovascular disease.

Abstract Alzheimer's disease (AD) staging criteria lack standardized, empirical description. Well‐defined AD staging criteria are an important consideration in protocol design, influencing a more standardized inclusion/exclusion criteria and defining what constitutes meaningful differentiation among the stages. However, many trials are being designed on the basis of biomarker features and the two need to be coordinated. The Alzheimer's Association Research Roundtable (AARR) Spring 2021 meeting discussed the implementation of preclinical AD staging criteria, and provided recommendations for how they may best be incorporated into clinical trials research. Discussion also included what currently available tools for global clinical trials may best define populations in preclinical AD trials, and if are we able to differentiate preclinical from clinical stages of the disease. Well‐defined AD staging criteria are key to improving early detection, diagnostics, clinical trial enrollment, and identifying statistically significant clinical changes, and researchers discussed how emerging blood biomarkers may help with more efficient screening in preclinical stages.

Abstract INTRODUCTION The timing of plasma biomarker changes is not well understood. The goal of this study was to evaluate the temporal co‐evolution of plasma and positron emission tomography (PET) Alzheimer's disease (AD) biomarkers. METHODS We included 1408 Mayo Clinic Study of Aging and Alzheimer's Disease Research Center participants. An accelerated failure time (AFT) model was fit with amyloid beta (Aβ) PET, tau PET, plasma p‐tau217, p‐tau181, and glial fibrillary acidic protein (GFAP) as endpoints. RESULTS Individual timing of plasma p‐tau progression was strongly associated with Aβ PET and GFAP progression. In the population, GFAP became abnormal first, then Aβ PET, plasma p‐tau, and tau PET temporal meta‐regions of interest when applying cut points based on young, cognitively unimpaired participants. DISCUSSION Plasma p‐tau is a stronger indicator of a temporally linked response to elevated brain Aβ than of tau pathology. While Aβ deposition and a rise in GFAP are upstream events associated with tau phosphorylation, the temporal link between p‐tau and Aβ PET was the strongest. Highlights Plasma p‐tau progression was more strongly associated with Aβ than tau PET. Progression on plasma p‐tau was associated with Aβ PET and GFAP progression. P‐tau181 and p‐tau217 become abnormal after Aβ PET and before tau PET. GFAP became abnormal first, before plasma p‐tau and Aβ PET.

AbstractINTRODUCTIONCost‐effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non‐imaging indicators of VCID using magnetic resonance imaging (MRI)‐measured white matter (WM) damage and hypothesized that these indicators differ based on age.METHODSIn 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid‐attenuated inversion recovery (FLAIR)‐MRI, we examined associations between baseline non‐imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles.RESULTSVRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata.DISCUSSIONNon‐imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID.Highlights Non‐imaging indicators of VCID can aid in prediction of MRI‐measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non‐imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non‐imaging VCID indicators proposed in the future.

The study included 1,738 Mayo Clinic Study of Aging participants (≥50 years old; 1,460 cognitively unimpaired and 278 with mild cognitive impairment (MCI)) and examined the cross-sectional association between cerebrovascular (CVD) imaging biomarkers (e.g., white matter hyperintensities (WMH), infarctions) and Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) scores, as well as their association with MCI. High (abnormal) WMH burden was significantly associated with having BDI-II>13 and BAI > 7 scores, and both (CVD imaging biomarkers and depression/anxiety) were significantly associated with MCI when included simultaneously in the model, suggesting that both were independently associated with the odds of MCI.

Abstract Alzheimer's disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential.