Przybelski, Scott A.

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.

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.

AbstractBackgroundDementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, after Alzheimer’s disease (AD). While alpha‐synuclein pathology is the hallmark of DLB, AD and cerebrovascular pathologies are common in DLB patients. However, the contribution of these pathologies towards neurodegeneration in DLB is largely unknown. We investigated associations of amyloid‐beta, tau, and cerebrovascular biomarkers with regional gray matter (GM) volume in DLB patients and cognitively unimpaired (CU) controls.MethodWe included 30 DLB patients (69.3±10.2 years old, 87% men) and 100 CU individuals balanced on age and sex (Table 1). We used 11C‐Pittsburgh Compound‐B (PiB) and 18F‐Flortaucipir positron emission tomography (PET) to assess amyloid‐beta and tau in‐vivo. We used structural MRI to assess white matter hyperintensity (WMH) volume, a marker of cerebrovascular lesion load, and regional GM volume (a marker of neurodegeneration). We used correlations and ANCOVA in the whole dataset and structural equation modelling in the DLB patients to investigate associations of WMH volume and regional amyloid‐beta and tau PET standardized uptake value ratios (SUVRs) with regional GM volume.ResultDLB patients showed lower GM volume across all cortical and subcortical regions except for cuneus, putamen, and pallidum (Fig.1). Greater WMH volume was associated with lower volume in the medial and orbital frontal cortices, insula, fusiform cortex, and thalamus in DLB patients (Fig.2). Greater PiB SUVR was associated with lower volume in the inferior temporal cortex, while Flortaucipir SUVR did not correlate with GM volume (Fig.2). Structural equation modelling showed that higher age and positivity for the APOE e4 genotype were significant predictors of greater WMH, and WMH in turn was a significant predictor of GM volume in medial and orbital frontal cortices, insula, and inferior temporal cortex (Fig.3). In contrast, we observed two distinct paths for the fusiform cortex, with age having an effect through PiB and Flortaucipir SUVRs on the one path, and through WMH on the other path (Fig.3).ConclusionDLB patients have widespread cortical atrophy, most of which likely is influenced by alpha‐synuclein pathology. Although amyloid‐beta, tau, and cerebrovascular pathologies often coexist in DLB, their contributions to neurodegeneration seem to be region specific.

AbstractBackgroundTwo pathways that help explain cognitive health are 1) brain maintenance – referring to the relative absence of brain changes over time; 2) cognitive reserve – a property of the brain that allows for sustained cognitive ability in the presence of brain pathology. Both pathways influence cognitive aging and are best understood using longitudinal data. Our goal was to assess the effect of modifiable vascular/metabolic and lifestyle enrichment factors on these pathways using longitudinal brain changes measured on structural magnetic resonance imaging (MRI) and longitudinal cognitive decline. Lastly, we combined these pathways and their relationships in the same model.MethodWe identified 1250 participants from a well‐characterized sample of older adults from the Mayo Clinic Study of Aging with both longitudinal MRI and cognitive data. Linear mixed models were performed to assess the impact of modifiable factors on brain maintenance and cognitive reserve. Latent growth curve models (SEM) were used to assess the impact of brain structure and cognitive reserve on longitudinal cognition.ResultThe mean age of our sample was 78.8 years, with 45.4% female, and 27.1% APOE4 carriers. Mean follow‐up time was 4.0 years with a mean of 3.2 MRI scans per participant. Linear mixed models showed that poorer vascular/metabolic health (presence of vascular/metabolic conditions) was associated with lower cross‐sectional temporal region cortical thickness (‐0.014 (0.003), p<0.001) and lower global cognition (‐0.042 (0.018), p = 0.024). Higher estimated cognitive reserve at baseline as assessed by education/occupation composite (0.103 (0.01), p<0.001) was associated with higher global cognition. Unexpectedly, late‐life physical activities were related to lower global cognition (‐0.05 (0.007), p = 0.004). Our final latent growth curve SEM fit the data well showing temporal associations between unmodifiable characteristics, cognitive reserve, and brain structural pathways, as well as the influence of modifiable vascular/metabolic factors.ConclusionWe found that brain maintenance and cognitive reserve pathways were related to better cognition over time. We further showed that vascular/metabolic health impacts long‐term cognitive outcomes (i.e. cognitive aging) primarily through the brain maintenance pathway. These data provide a modeling scheme for future studies and can aid in assessing the pathways by which individual factors may influence cognitive aging.