Association of longitudinal white matter degeneration and cerebrospinal fluid biomarkers of neurodegeneration, inflammation and Alzheimer’s disease in late-middle-aged adults

Abstract

Alzheimer’s disease (AD) is characterized by substantial neurodegeneration, including both cortical atrophy and loss of underlying white matter fiber tracts. Understanding longitudinal alterations to white matter may provide new insights into trajectories of brain change in both healthy aging and AD, and fluid biomarkers may be particularly useful in this effort. To examine this, 151 late-middle-aged participants enriched with risk for AD with at least one lumbar puncture and two diffusion tensor imaging (DTI) scans were selected for analysis from two large observational and longitudinally followed cohorts. Cerebrospinal fluid (CSF) was assayed for biomarkers of AD-specific pathology (phosphorylated-tau/Aβ42 ratio), axonal degeneration (neurofilament light chain protein, NFL), dendritic degeneration (neurogranin), and inflammation (chitinase-3-like protein 1, YKL-40). Linear mixed effects models were performed to test the hypothesis that biomarkers for AD, neurodegeneration, and inflammation, or two-year change in those biomarkers, would be associated with worse white matter health overall and/or progressively worsening white matter health over time. At baseline in the cingulum, phosphorylated-tau/Aβ42 was associated with higher mean diffusivity (MD) overall (intercept) and YKL-40 was associated with increases in MD over time. Two-year change in neurogranin was associated with higher mean diffusivity and lower fractional anisotropy overall (intercepts) across white matter in the entire brain and in the cingulum. These findings suggest that biomarkers for AD, neurodegeneration, and inflammation are potentially important indicators of declining white matter health in a cognitively healthy, late-middle-aged cohort.

Gut microbiome alterations in Alzheimer’s disease

“…YKL-40 was quantified using sandwich ELISAs (R&D Systems, Minneapolis, Minn., USA). CSF assays were performed in two batches and corrected for batch differences as previously described 69 , 70 . For correlational statistical analysis, we used the “cor.test” function in R to calculate Spearman’s rank correlation coefficients between CSF biomarker levels and normalized relative abundances for the 13 differentially abundant genera.…”

Longitudinal white matter microstructural change in Parkinson's disease

“…Diffusion weighted imaging data were preprocessed using a robust pipeline also used in a number of previous studies (Adluru et al, 2014; Ly et al, 2016; Merluzzi et al, 2016; Racine et al, 2017). DWI data from baseline and follow-up visits were first converted from Digital Imaging and Communication in Medicine (DICOM) format to Neuroimaging Informatics Technology Initiative (NIFTI) format and visually inspected for artifacts.…”

White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

“…Age-related trajectories of white matter macro-and microstructure typically reflect increases in anisotropy and decreases in diffusivity during childhood, adolescence and early adulthood (Krogsrud et al, 2016;Tamnes et al, 2018;Westlye et al, 2010), and subsequent anisotropy decreases and diffusivity increase in adulthood and senescence (Cox et al, 2016;Davis et al, 2009). While the field has primarily been dominated by cross-sectional studies, which by design lack information on individual trajectories (Schaie, 2005), longitudinal studies in the last decade have shown corresponding white matter changes in both development and ageing (Barrick et al, 2010;Bender et al, 2016;Bender & Raz, 2015;de Groot et al, 2016;Likitjaroen et al, 2012;Racine et al, 2019;Sexton et al, 2014;Storsve et al, 2016;Teipel et al, 2010). However, studies that have evaluated individual differences in change across a wide age range are rare (Bender et al, 2016).…”