MRI and blood marker factors distinguish Alzheimer’s risk, fluid transport and vascular health
Poster No:
1963
Submission Type:
Abstract Submission
Authors:
Ella Rowsthorn1, Ming Ann Sim1, William O'Brien1, Stuart McDonald1, Lucy Vivash1, Terence O'Brien1, Trevor Chong1, Xingfeng Shao2, Danny Wang2, Meng Law1, Ian Harding3, Matthew Pase1
Institutions:
1Monash University, Melbourne, Victoria, 2University of Southern California, Los Angeles, CA, 3QIMR Berghofer Medical Research Institute, Brisbane, Queensland
First Author:
Co-Author(s):
Introduction:
The neurovascular and fluid transport systems of the brain are essential for health and are dysfunctional early in Alzheimer's disease (AD). While individual MRI-derived measures each offer a snapshot of elements within these complex systems, their combination may enable a more holistic characterization, improving understanding of these systems as an integrated whole. For instance, our prior work suggests that inter-dependencies among MRI measures of neurovascular health provide a novel insight into multi-compartmental fluid transport (Rowsthorn et al. 2024). Identifying shared biological constructs underlying measures of neurovascular health, fluid transport and neurodegenerative pathology may offer an avenue to investigate these otherwise enigmatic systems. Here, combined brain MRI metrics, fluid biomarkers and risk factor data with the aim to identify latent constructs of brain health and explored their relationships with age and cognition.
Methods:
We analyzed data from 127 dementia-free older adults (mean age: 67 years, SD: 5.3; 68% female) from the Brain and Cognitive Health (BACH) cohort study. Participants underwent 3T MRI, BMI and blood pressure measurement, blood collection (including HDL and LDL cholesterol measurement), and a neuropsychology battery. The MRI scan included: T1 MPRAGE (for enlarged perivascular space volume fraction, ePVS), T2 FLAIR (white matter hyperintensity volume, WMH), diffusion (white matter free water volume fraction, FW), pCASL (gray matter cerebral blood flow, CBF), and diffusion-prepared pCASL (BBB water exchange rate, BBB kw; Shao et al. 2019). Plasma biomarkers were analyzed with ALZpath and Quanterix assays, including amyloid-beta (Aβ)42, Aβ40, phosphorylated tau (pTau)181, pTau217, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). A global cognitive composite was comprised of tests spanning domains of memory, processing speed, executive function, and visual processing.
We conducted exploratory factor analysis on MRI, cardiovascular and plasma biomarker data using maximum likelihood estimation with rotation to allow factor correlations. Linear regression models, adjusting for age, sex, intracranial volume and education, were used to examine relationships between construct composites, age and cognitive performance.
We conducted exploratory factor analysis on MRI, cardiovascular and plasma biomarker data using maximum likelihood estimation with rotation to allow factor correlations. Linear regression models, adjusting for age, sex, intracranial volume and education, were used to examine relationships between construct composites, age and cognitive performance.
Results:
A four-factor model best characterized the data (Tucker-Lewis Index=0.937; RMSR=0.04). BBB kw, WMH, BMI and LDL cholesterol did not load onto any factors. The identified constructs were: (1) "AD Biomarkers" comprising Aβ42/40 ratio, pTau181, pTau217 and GFAP; (2) "Neuronal Injury" comprising GFAP and NfL; (3) "Fluid Transport" comprising white matter ePVS and FW; and (4) "Vascular Health" comprising basal ganglia ePVS, CBF, HDL cholesterol and systolic blood pressure (Figure 1).
The constructs were not associated with each other (all p>.05). The AD Biomarkers (β=0.21, SE=0.09, p=.019), Neuronal Injury (β=0.40, SE=0.08, p<.001) and Vascular Health constructs were associated with age (β=-0.50, SE=0.07, p<.001), whereas the Fluid Transport construct had a nominal association (β=-0.15, SE=0.08, p=.068; Figure 2). None of the constructs were associated with global cognitive performance (all p>.05).
The constructs were not associated with each other (all p>.05). The AD Biomarkers (β=0.21, SE=0.09, p=.019), Neuronal Injury (β=0.40, SE=0.08, p<.001) and Vascular Health constructs were associated with age (β=-0.50, SE=0.07, p<.001), whereas the Fluid Transport construct had a nominal association (β=-0.15, SE=0.08, p=.068; Figure 2). None of the constructs were associated with global cognitive performance (all p>.05).
·Figure 1: Combined MRI, cardiovascular health and plasma biomarker factor loadings.
·Figure 2: Construct associations with age.
Conclusions:
Our multi-modal factor analysis of MRI and biofluid measures identified distinct constructs representing brain health, neurovascular function, and pathology in aging. The separation between constructs comprising imaging and plasma biomarkers highlights their complementary roles, with MRI measure sets offering insights into vascular and fluid regulation mechanisms that the selected plasma biomarkers may not. These constructs provide a framework for probing neurobiological systems in aging and early AD. Future research should validate these findings in diverse populations, explore longitudinal trajectories, and continue to investigate their relevance to cognition and dementia risk.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Lifespan Development:
Aging
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Neuroanatomy Other
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 1
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Keywords:
ADULTS
Aging
Blood
Cerebral Blood Flow
Cerebrovascular Disease
Cognition
Degenerative Disease
MRI
Other - Neurovascular
1|2Indicates the priority used for review
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Shao, X., Ma, S. J., Casey, M., D’Orazio, L., Ringman, J. M., & Wang, D. J. J. (2019). Mapping water exchange across the blood-brain barrier using 3D diffusion-prepared arterial spin labeled perfusion MRI. Magnetic Resonance in Medicine, 81(5), 3065–3079. https://doi.org/10.1002/mrm.27632