Associations between Polygenic Risk Scores of Alzheimer’s Disease, NODDI, and Cognitive Function
Poster No:
80
Submission Type:
Abstract Submission
Authors:
Rui Zou1,2, Koji Kamagata1, Kaito Takabayashi1, Christina Andica1,2,3, Wataru Uchida3, Sen Guo1, Ryo Tameda1, Rinako Iseki1, Takafumi Kitagawa1,2, Takuya Ozawa1, Koyo Mizuta1,2, Akifumi Hagiwara1, Keigo Shimoji1,2,3, Shigeki Aoki1,2,3
Institutions:
1Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan, 2Department of Data Science, Juntendo University Graduate School of Medicine, Tokyo, Japan, 3Faculty of Health Data Science, Juntendo University, Chiba, Japan
First Author:
Rui Zou
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Co-Author(s):
Kaito Takabayashi
Department of Radiology, Juntendo University Graduate School of Medicine
Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine
Tokyo, Japan
Christina Andica
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Takafumi Kitagawa
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Koyo Mizuta
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine
Tokyo, Japan|Tokyo, Japan
Akifumi Hagiwara
Department of Radiology, Juntendo University Graduate School of Medicine
Tokyo, Japan
Department of Radiology, Juntendo University Graduate School of Medicine
Tokyo, Japan
Keigo Shimoji
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Shigeki Aoki
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Department of Radiology, Juntendo University Graduate School of Medicine|Department of Data Science, Juntendo University Graduate School of Medicine|Faculty of Health Data Science, Juntendo University
Tokyo, Japan|Tokyo, Japan|Chiba, Japan
Introduction:
Alzheimer's disease (AD) is a progressive neurodegenerative disorder influenced by a complex interplay of genetic factors, characterized by brain atrophy and alterations in white matter (WM) microstructure. Polygenic risk scores (PRS) quantify the aggregate impact of numerous genetic variants, providing a comprehensive measure of genetic predisposition for AD (Leonenko et al., 2021). Recent studies have shown that higher AD PRS (PRSAD) is associated with cortical thinning, reduced brain volumes, and WM alterations, including decreased fractional anisotropy (FA) and increased mean diffusivity (MD) (He et al., 2023). Advanced diffusion magnetic resonance imaging (dMRI) techniques, such as Neurite Orientation Dispersion and Density Imaging (NODDI), enable detailed assessment of WM microstructural changes, helping to distinguish features like neurite density and extracellular fluid (Zhang et al., 2012). This study aims to examine the associations between PRSAD, NODDI metrics, and cognitive function, thereby contributing to the understanding of AD pathogenesis and informing future therapeutic strategies.
Methods:
Participant data were obtained from the UK Biobank cohort, including individuals with available T1-weighted MRI, dMRI, and PRSAD data. Participants diagnosed with AD or other forms of dementias were excluded (N~42,096). Diffusion tensor imaging (DTI) and NODDI metrics were extracted from 48 brain regions defined by JHU WM Atlas (Sudlow et al., 2015). To assess the associations between PRSAD, DTI/NODDI metrics, and cognitive function, general linear model (GLM) was employed, adjusting for covariates including age, age squared, sex, intracranial volume, and the first four genetic principal components (PC1-PC4). Furthermore, participants were stratified into high-PRS and low-PRS groups, corresponding to the top and bottom quartiles of PRSAD, respectively, to facilitate group comparisons of MRI metrics. Effect sizes for significant group differences were quantified using Cohen's d.
Results:
PRSAD was significantly negatively correlated with FA and intracellular volume fraction (ICVF) in several regions, including the cingulum, hippocampus, fornix, and posterior thalamic radiation. Conversely, PRSAD was positively correlated with MD and isotropic volume fraction (ISOVF) in these same regions as shown in Figure 1 A–B. Higher PRS was significantly associated with poorer performance on cognitive assessments, such as symbol digit substitution test, indicating a negative impact of genetic risk on cognitive performance (Figure 1C). Additionally, MRI metrics of fornix regions were significantly associated with performance on symbol digit substitution test (coefficient = 0.065; p < 0.0001) and trail making test (coefficient = -0.053; p < 0.0001). Group comparisons between high-PRS and low-PRS participants revealed significantly reduced ICVF in the high-PRS group across multiple brain regions, with larger effect sizes indicated by Cohen's d values as observed in Figure 2.
Conclusions:
The findings of this study demonstrate that PRSAD is associated with both WM microstructure changes and cognitive impairments, establishing a link between genetic risk for AD and WM degeneration. Specific brain regions, including the posterior thalamic radiation, fornix, and hippocampal cingulum, were found to be more susceptible to genetic risk, underscoring their potential role in early AD pathogenesis and progression.
The associations between PRSAD, FA and MD were consistent with previous studies, and ICVF also showed associations in the same regions. Additionally, group comparisons revealed significant differences in more brain regions for ICVF, with larger Cohen's d values observed in these regions, suggesting that ICVF may be a more sensitive marker for genetic susceptibility in AD. These findings provide critical insights into the underlying mechanisms of AD and have potential implications for early diagnosis and the development of targeted therapeutic strategies.
The associations between PRSAD, FA and MD were consistent with previous studies, and ICVF also showed associations in the same regions. Additionally, group comparisons revealed significant differences in more brain regions for ICVF, with larger Cohen's d values observed in these regions, suggesting that ICVF may be a more sensitive marker for genetic susceptibility in AD. These findings provide critical insights into the underlying mechanisms of AD and have potential implications for early diagnosis and the development of targeted therapeutic strategies.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Genetics:
Genetic Association Studies 2
Keywords:
Data analysis
DISORDERS
MRI
White Matter
1|2Indicates the priority used for review
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2. Leonenko, G., Baker, E., Stevenson-Hoare, J., Sierksma, A., Fiers, M., Williams, J., de Strooper, B., & Escott-Price, V. (2021). Identifying individuals with high risk of Alzheimer's disease using polygenic risk scores. Nat Commun, 12(1), 4506.
3. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med, 12(3), e1001779.
4. Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 61(4), 1000-1016.