Contribution of Alzheimer's disease pathology to biological and clinical progression
Poster No:
141
Submission Type:
Abstract Submission
Authors:
Wei Zhang1, Hui-Fu Wang1, Kevin Kuo1, JianFeng Feng1, Wei Cheng1
Institutions:
1Fudan University, Shanghai, China
First Author:
Co-Author(s):
Introduction:
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a triad of neuropathological hallmarks, including amyloid β (Aβ) plaques, tau neurofibrillary tangles, and neurodegeneration. The prevailing hypothesis has posited an instigating role of Aβ, in which the Aβ deposition could incite a cascade for tau propagation and neuritic alterations that subsequently lead to AD progression (Frisoni et al., 2022). National Institute on Aging-Alzheimer's Association (NIA-AA) has previously proposed the biomarker-based framework, which explicitly defines the mechanistic context of AD that reconciles the hypothesis (Jack et al., 2018). Since its release, the NIA-AA research framework has brought tremendous advancement to the standardized characterization of AD. However, this framework has made no a priori assumptions regarding the relative pathogenicity of different biomarkers, and thus the extent to which AD pathology accounts for disease progression and clinical symptoms has remained ambiguous. Unraveling the contributions of AD pathology, especially the precise pathological sequence and effects among each biomarker, is critical to improving the diagnostic framework and therapeutic approaches.
Methods:
Linear mixed models were used to estimate the change rates of Aβ, tau, and cortical thickness in each brain region over time. All models included baseline age, gender, race, years of education, apolipoprotein E (APOE) ε4 status, and time as fixed effects. Individual intercepts and slopes were modeled as random effects. Statistical significance was established as two-tailed P < 0.05 after FDR correction.
The relationships between AD imaging biomarkers, cognition and AD risk factors (age, gender, years of education, and APOE ε4 status) were assessed using partial least squares structural equation modeling (PLS-SEM) (Hair et al., 2019). The results were corrected using FDR (P < 0.05). Total explained variance of the PLS-SEM model was measured by the coefficient of determination. Shapley value regression was used to calculate the variance explained by each component separately (Strumbelj et al., 2014).
The relationships between AD imaging biomarkers, cognition and AD risk factors (age, gender, years of education, and APOE ε4 status) were assessed using partial least squares structural equation modeling (PLS-SEM) (Hair et al., 2019). The results were corrected using FDR (P < 0.05). Total explained variance of the PLS-SEM model was measured by the coefficient of determination. Shapley value regression was used to calculate the variance explained by each component separately (Strumbelj et al., 2014).
Results:
Aβ at baseline was positively correlated with the slope of tau (β = 0.436, P < 0.001), and the slope of tau were negatively correlated with the cortical thickness (β = -0.305, P = 0.016) at the final time point. The slope of Aβ was also positively correlated with tau (β = 0.418, P = 0.001) at the final time point. Tau at baseline was negatively correlated with the slope of cortical thickness (β = -0.306, P = 0.004), and the slope of cortical thickness was positively correlated with cognition (β = 0.288, P = 0.040) at the final time point. Cortical thickness at baseline was significantly positively correlated with the slope of cognition (β = 0.282, P = 0.040).
At the final time point, the PLS-SEM explained 37% of the variance in cognition, with the slope of Aβ, tau and cortical thickness each independently explaining 13%, 25%, and 35% of the variance and the AD risk factor of education and age explaining 11% and 10% of the variance. Meanwhile, APOE ε4 status explained 81% of the variance in Aβ. Aβ slope and age explained 61% and 20% of the variance in tau. Age, the slope of tau, and Aβ explained 47%, 37%, and 10% of the variance in cortical thickness, respectively.
At the final time point, the PLS-SEM explained 37% of the variance in cognition, with the slope of Aβ, tau and cortical thickness each independently explaining 13%, 25%, and 35% of the variance and the AD risk factor of education and age explaining 11% and 10% of the variance. Meanwhile, APOE ε4 status explained 81% of the variance in Aβ. Aβ slope and age explained 61% and 20% of the variance in tau. Age, the slope of tau, and Aβ explained 47%, 37%, and 10% of the variance in cortical thickness, respectively.
·Partial least squares structural equation model
·Quantifying the proportion of variance explained by imaging biomarkers and AD risk factors.
Conclusions:
In conclusion, our study has provided vital evidence concurring with the previously Aβ cascade hypothesis, in which Aβ burden significantly incites tau accumulation that subsequently leads to brain atrophy (Guo et al., 2021). The quantification of AD pathology's contribution to biological and clinical progression across the AD continuum has also substantiated the accurate effect of AD pathology on the disease progression and cognitive decline. Thus, treatments targeting Aβ and tau should be considered, as it can partially alleviate the process of neurodegeneration and prevent cognitive decline.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging 2
Keywords:
Aging
Data analysis
Positron Emission Tomography (PET)
STRUCTURAL MRI
Other - Alzheimer's disease
1|2Indicates the priority used for review
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Provide references using APA citation style.
Jack Jr, C. R. (2018). NIA‐AA research framework: toward a biological definition of Alzheimer's disease. Alzheimer's & dementia, 14(4), 535-562.
Hair, J. F. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24.
Strumbelj, E. (2014). Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems, 41, 647-665.
Guo, T. (2021). Longitudinal cognitive and biomarker measurements support a unidirectional pathway in Alzheimer’s disease pathophysiology. Biological psychiatry, 89(8), 786-794.