Vemuri, Prashanthi

Abstract Background White matter hyperintensities (WMH) are considered hallmark features of cerebral small vessel disease and have recently been linked to Alzheimer’s disease pathology. Their distinct spatial distributions, namely periventricular versus deep WMH, may differ by underlying age-related and pathobiological processes contributing to cognitive decline. We aimed to identify the spatial patterns of WMH using the 4-scale Fazekas visual assessment and explore their differential association with age, vascular health, Alzheimer’s imaging markers, namely amyloid and tau burden, and cognition. Because our study consisted of scans from GE and Siemens scanners with different resolutions, we also investigated inter-scanner reproducibility and combinability of WMH measurements on imaging. Methods We identified 1144 participants from the Mayo Clinic Study of Aging consisting of older adults from Olmsted County, Minnesota with available structural magnetic resonance imaging (MRI), amyloid, and tau positron emission tomography (PET). WMH distribution patterns were assessed on FLAIR-MRI, both 2D axial and 3D, using Fazekas ratings of periventricular and deep WMH severity. We compared the association of periventricular and deep WMH scales with vascular risk factors, amyloid-PET and tau-PET standardized uptake value ratio, WMH volume, and cognition using Pearson partial correlation after adjusting for age. We also evaluated vendor compatibility and reproducibility of the Fazekas scales using intraclass correlations (ICC). Results Periventricular and deep WMH measurements showed similar correlations with age, cardiometabolic conditions score (vascular risk), and cognition, (p < 0.001). Both periventricular WMH and deep WMH showed weak associations with amyloidosis (R = 0.07, p = < 0.001), and none with tau burden. We found substantial agreement between data from the two scanners for Fazekas measurements (ICC = 0.78). The automated WMH volume had high discriminating power for identifying participants with Fazekas ≥ 2 (area under curve = 0.97). Conclusion Our study investigates risk factors underlying WMH spatial patterns and their impact on global cognition, with no discernible differences between periventricular and deep WMH. We observed minimal impact of amyloidosis on WMH severity. These findings, coupled with enhanced inter-scanner reproducibility of WMH data, suggest the combinability of inter-scanner data assessed by harmonized protocols in the context of vascular contributions to cognitive impairment and dementia biomarker research.

Abstract Vascular brain injury results in loss of structural and functional connectivity and leads to cognitive impairment. Its various manifestations, including microinfarcts, microhaemorrhages and white matter hyperintensities, result in microstructural tissue integrity loss and secondary neurodegeneration. Among these, tissue microstructural alteration is a relatively early event compared with atrophy along the aging and neurodegeneration continuum. Understanding its association with cognition may provide the opportunity to further elucidate the relationship between vascular health and clinical outcomes. Magnetic resonance elastography offers a non-invasive approach to evaluate tissue mechanical properties, providing a window into the microstructural integrity of the brain. This retrospective study evaluated brain stiffness as a potential biomarker for vascular brain injury and its role in mediating the impact of vascular dysfunction on cognitive impairment. Seventy-five participants from the Mayo Clinic Study of Aging underwent brain imaging using a 3T MR imager with a spin-echo echo-planar imaging sequence for magnetic resonance elastography and T1- and T2-weighted pulse sequences. This study evaluated the effects of vascular biomarkers (white matter hyperintensities and cardiometabolic condition score) on brain stiffness using voxelwise analysis. Partial correlation analysis explored associations between brain stiffness, white matter hyperintensities, cardiometabolic condition and global cognition. Mediation analysis determined the role of stiffness in mediating the relationship between vascular biomarkers and cognitive performance. Statistical significance was set at P-values &lt; 0.05. Diagnostic accuracy of magnetic resonance elastography stiffness for white matter hyperintensities and cardiometabolic condition was evaluated using receiver operator characteristic curves. Voxelwise linear regression analysis indicated white matter hyperintensities negatively correlate with brain stiffness, specifically in periventricular regions with high white matter hyperintensity levels. A negative association between cardiovascular risk factors and stiffness was also observed across the brain. No significant patterns of stiffness changes were associated with amyloid load. Global stiffness (µ) negatively correlated with both white matter hyperintensities and cardiometabolic condition when all other covariables including amyloid load were controlled. The positive correlation between white matter hyperintensities and cardiometabolic condition weakened and became statistically insignificant when controlling for other covariables. Brain stiffness and global cognition were positively correlated, maintaining statistical significance after adjusting for all covariables. These findings suggest mechanical alterations are associated with cognitive dysfunction and vascular brain injury. Brain stiffness significantly mediated the indirect effects of white matter hyperintensities and cardiometabolic condition on global cognition. Local cerebrovascular diseases (assessed by white matter hyperintensities) and systemic vascular risk factors (assessed by cardiometabolic condition) impact brain stiffness with spatially and statistically distinct effects. Global brain stiffness is a significant mediator between vascular disease measures and cognitive function, highlighting the value of magnetic resonance elastography-based mechanical assessments in understanding this relationship.

AbstractINTRODUCTIONThis study evaluated the performance of the Lumipulse plasma beta‐amyloid (Aβ) 42/40 and pTau181 compared to other assays to detect an abnormal amyloid‐positron emission tomography (PET).METHODSPlasma samples from cognitively unimpaired (N = 179) and MCI/AD dementia (N = 36) individuals were retrospectively evaluated. Plasma Aβ42/40 and pTau181 were measured using the Lumipulse and Simoa immunoassays. An immunoprecipitation mass spectrometry (IP‐MS) assay for plasma Aβ42/40 was also evaluated. Amyloid‐PET status was the outcome measure.RESULTSLumipulse and IP‐MS Aβ42/40 exhibited the highest diagnostic accuracy for detecting an abnormal amyloid‐PET (areas under the curve [AUCs] of 0.81 and 0.84, respectively). The Lumipulse and Simoa pTau181 assays exhibited lower performance (AUCs of 0.74 and 0.72, respectively). The Simoa Aβ42/40 assay demonstrated the lowest diagnostic accuracy (AUC 0.57). Combining Aβ42/40 and pTau181 did not significantly improve performance over Aβ42/40 alone for Lumipulse (AUC 0.83) or over pTau181 alone for Simoa (AUC 0.71)DISCUSSIONThe Lumipulse Aβ42/40 assay showed similar performance to the IP‐MS Aβ42/40 assay for detection of an abnormal amyloid‐PET; and both assays performed better than the two p‐tau181 immunoassays. The Simoa Aβ42/Aβ40 assay was the least accurate at predicting an abnormal amyloid‐PET status.Highlights Lumipulse plasma Aβ42/Aβ40 AUC for abnormal amyloid‐PET detection was 0.81. This performance was comparable to previously reported IP‐MS and higher than Simoa. Performance of Alzheimer's disease blood biomarkers varies between assays.

Abstract BACKGROUND We compared the ability of several plasma biomarkers versus amyloid positron emission tomography (PET) to predict rates of memory decline among cognitively unimpaired individuals. METHODS We studied 645 Mayo Clinic Study of Aging participants. Predictor variables were age, sex, education, apolipoprotein E ( APOE ) ε 4 genotype, amyloid PET, and plasma amyloid beta (Aβ)42/40, phosphorylated tau (p‐tau)181, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and p‐tau217. The outcome was a change in a memory composite measure. RESULTS All plasma biomarkers, except NfL, were associated with mean memory decline in models with individual biomarkers. However, amyloid PET and plasma p‐tau217, along with age, were key variables independently associated with mean memory decline in models combining all predictors. Confidence intervals were narrow for estimates of population mean prediction, but person‐level prediction intervals were wide. DISCUSSION Plasma p‐tau217 and amyloid PET provide useful information about predicting rates of future cognitive decline in cognitively unimpaired individuals at the population mean level, but not at the individual person level.