Elsevier

Neurobiology of Aging

Volume 91, July 2020, Pages 5-14
Neurobiology of Aging

Regular article
CSF amyloid is a consistent predictor of white matter hyperintensities across the disease course from aging to Alzheimer's disease

https://doi.org/10.1016/j.neurobiolaging.2020.03.008Get rights and content

Highlights

  • WMH burden is inversely associated with CSF Aβ1-42 across diagnostic groups.

  • The relationship between WMH and CSF Aβ1-42 is independent of CSF tau, age, and APOE ε4 status.

  • There is no association between WMH and CSF t-tau or CSF p-tau.

  • Age is the strongest predictor of WMH.

Abstract

This study investigated the relationship between white matter hyperintensities (WMH) and cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers. Subjects included 180 controls, 107 individuals with a significant memory concern, 320 individuals with early mild cognitive impairment, 171 individuals with late mild cognitive impairment, and 151 individuals with AD, with 3T MRI and CSF Aβ1-42, total tau (t-tau), and phosphorylated tau (p-tau) data. Multiple linear regression models assessed the relationship between WMH and CSF Aβ1-42, t-tau, and p-tau. Directionally, a higher WMH burden was associated with lower CSF Aβ1-42 within each diagnostic group, with no evidence for a difference in the slope of the association across diagnostic groups (p = 0.4). Pooling all participants, this association was statistically significant after adjustment for t-tau, p-tau, age, diagnostic group, and APOE-ε4 status (p < 0.001). Age was the strongest predictor of WMH (partial R2~16%) compared with CSF Aβ1-42 (partial R2~5%). There was no evidence for an association with WMH and either t-tau or p-tau. These data are supportive of a link between amyloid burden and presumed vascular pathology.

Introduction

The co-existence of cerebrovascular disease (CVD) and Alzheimer's disease (AD) is increasingly well recognized; patients with AD often display both AD pathology and underlying vascular pathologies at autopsy such as small vessel disease (SVD), microvascular injury, and cerebrovascular lesions (Brayne et al., 2009, Esiri et al., 2014, Jellinger and Attems, 2006, Kapasi et al., 2017, Schneider et al., 2007, Schneider et al., 2009). Cerebrovascular pathology is well established as an important contributor to cognitive decline, acting as the primary cause of at least 20% of dementias (Gorelick et al., 2011). However, the relationship between CVD and the hallmark Alzheimer's pathologies including tau and amyloid beta (Aβ) is yet to be fully characterized.

Aβ deposition in the brain can be globally measured in vivo by analyzing cerebrospinal fluid (CSF) levels of Aβ1-42, whereby an increase in parenchymal Aβ is signified by a decrease in CSF Aβ1-42 (Blennow et al., 2010). Cerebral tau levels can also be assessed using CSF analysis, with the levels of total tau (t-tau) and phosphorylated tau (p-tau) thought to indicate neuronal damage and pathological tau deposition, respectively (Jack et al., 2018). Markers of CVD are visible and quantifiable on magnetic resonance imaging (MRI), with perhaps the best characterized being white matter hyperintensities (WMH) that appear hyperintense on T2-weighted or FLAIR imaging and hypointense on T1-weighted imaging (Wardlaw et al., 2013). WMH have numerous histopathological correlates, including ependymal loss, cerebral ischemia, and demyelination (Gouw et al., 2011). However, it may be that WMH are not only a marker of CVD, but also occur as a result of Alzheimer's pathologies (Mcaleese et al., 2015, McAleese et al., 2017).

Research studies are now moving toward a more biomarker led approach to AD (Jack et al., 2018), making it important to understand how possible biomarkers are interrelated at different stages of disease. Although no cerebrovascular biomarkers have been included in the research framework proposed by Jack et al. (2018), WMH are a good candidate marker to enhance our understanding of the associations between AD and vascular pathology.

WMH have shown associations with amyloid accumulation, as measured by positron emission tomography (Kandel et al., 2016, Marnane et al., 2016) and CSF biomarkers (Marnane et al., 2016, Pietroboni et al., 2018). Tau aggregation has also been linked to WMH burden (Mcaleese et al., 2015, Tosto et al., 2015), although results are somewhat less consistent (Kester et al., 2014, Osborn et al., 2018). The aim of this study was to add evidence to further characterize these complex relationships between AD and vascular biomarkers, and to extend the current literature by examining these relationships across the full spectrum from normal aging to AD. We used continuous as opposed to binary measures of AD biomarkers, to more sensitively examine relationships with WMH. To the best of our knowledge, this study is the first to look at relationships between traditional biomarkers of AD (CSF Aβ1-42, t-tau, and p-tau) and WMH in controls, individuals with a significant memory concern (SMC), individuals with early MCI (EMCI), individuals with late MCI (LMCI), and individuals with AD. We hypothesize that WMH will show associations with CSF Aβ1-42, t-tau, and p-tau biomarkers, reflecting the potential relationships between vascular dysfunction, amyloid deposition, neurodegeneration, and tau deposition. Because WMH are likely to represent different admixtures of pathology at different disease stages, with recent evidence suggesting in later disease stages WMH associations with tau may be stronger (McAleese et al., 2017) and Aβ1-42 may be weaker (Zhou et al., 2009), we also hypothesize that the relationships between Aβ and tau biomarkers and WMH may change throughout the disease course with stronger associations between tau markers and WMH at later disease stages, and weaker associations between Aβ1-42 and WMH at the more advanced stages.

Section snippets

Cohort

The demographic, imaging, and biological data used for this study were downloaded from the ADNI database (http://adni.loni.usc.edu/). Both ADNIGO and ADNI2 data were used. ADNI is a multicenter, longitudinal public-private funded partnership, with the primary goal of using demographic, biomarker, neuropsychological, and MRI data to monitor progression of AD. Since 2003, Principle Investigator Michael W. Weiner, MD, has overseen recruitment of healthy controls, MCI and AD subjects from over 60

Group demographics

There were 929 subjects with useable WMH values and available demographic data. Table 1 shows demographic, imaging, and CSF biomarker summary statistics for each diagnostic group. Between-group differences were seen in age, with the AD group being the oldest and the EMCI group being the youngest. Participants differed as expected in terms of Mini-Mental State Examination, APOE-ε4 status, WMH volume, CSF Aβ1-42, CSF t-tau, and CSF p-tau with poorer scores, greater ε4 carriage, and CSF biomarkers

Discussion

In this study, we found strong evidence for a consistent association between CSF Aβ1-42 and WMH in all diagnostic groups, whereby a decrease in CSF Aβ1-42 was associated with an increase in WMH, that was independent of CSF t-tau and independent of CSF p-tau. Furthermore, this relationship remained significant in the pooled cohort when adjusting for age, diagnostic group, TIV, and APOE-ε4 status. There was very little evidence for a relationship between t-tau pathology and WMH, with a borderline

Conclusion

In summary, our study suggests that CSF Aβ1-42 is a consistent predictor of WMH across all diagnostic groups, in a manner that is not dependent on CSF tau. As WMH are thought to be largely vascular in origin, this study adds to the literature exploring the complex relationship between amyloid and presumed vascular pathologies. Understanding the different relationships between disease markers are important to build more realistic models of biomarkers changes across the full spectrum of normal

Disclosure statement

The authors have no actual or potential conflicts of interest.

CRediT authorship contribution statement

Phoebe Walsh: Conceptualization, Data curation, Methodology, Writing - original draft, Writing - review & editing. Carole H. Sudre: Methodology, Software. Cassidy M. Fiford: Methodology, Data curation. Natalie S. Ryan: Supervision. Tammaryn Lashley: Supervision. Chris Frost: Methodology, Formal analysis, Writing - review & editing. Josephine Barnes: Conceptualization, Writing - review & editing, Supervision.

Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica,

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