HAI Book 2025 - Flipbook - Page 233
Garcia Condado, Jorge
52
Relationship between Polygenetic Risk Score of BrainAge and plasma
biomarkers in the A4/Elearn Study
Jorge Garcia Condado1,2,3, Colin Birkenbihl3, Jesus M Cortes1,4, Rachel F Buckley3,5, Ibai
Diez1,4,6,7
1
Computational Neuroimaging Lab, Biobizkaia Health Research Institute, Barakaldo, ES
Universidad del Pais Vasco (UPV/EHU), Leioa, ES
3
Massachusetts General Hospital, Department of Neurology, Boston, MA, US
4
IKERBASQUE Basque Foundation for Science, Bilbao, ES
5
Melbourne School of Pyschological Sciences, University of Melbourne, Melbourne, AU
6
Gordon Center for Medical Imaging, Department of Radiology, Boston, MA, US
7
Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, US
2
Increasing research is studying the interplay between genetic factors, brain aging, and neurodegeneration,
particularly in the context of Alzheimer's disease. Tools such as Brain Age Gap, which measures the discrepancy
between chronological and predicted brain age based on neuroimaging, and Polygenic Risk Scores (PRS), which
quantify genetic predisposition to specific traits like accelerated brain aging, are proving valuable in elucidating
the genetic underpinnings of neurodegeneration.
We analyzed genetic information and plasma biomarkers from the A4/ELearn study on 3014 cognitively normal
subjects (71.4 ± 4.6 age; 40.3% male). PRS of BrainAge models were calculated for each subject using the
summary GWAS statistics of Wen et. al, Nature Communications 2024 for three types of BrainAge models: Grey
Matter (GM), White Matter (WM) and Functional Connectivity (FC). We focused on plasma biomarkers from the
study: pTau217 (N=736), FP42/FP40 (N=2429), plasma APOE4 prtoein levels (N=588), GFAP (N=1641), Neuro
Filament Light Chain (N=1643) and pTauC2 (N=1163). We used a general linear model to study the association
between the biomarkers with PRS and with the interaction between age and PRS for each BrainAge model.
We found that the interaction between age and GM PRS, was positively associated with pTAU217 (p-value=0.0073)
and pTauC2 (p-value=0.040). The GM PRS alone was also associated with pTauC2 after adjusting for age and the
interaction (p-value=0.042). Moreover, the interaction between age and WM PRS was associated positively with
FP42/40 (p-value=0.020) and pTauC2 (p-value=0.043). We did not find significant differences in PRS values
splitting by amyloid positivity, by APOE4 genotype or using FC PRS.
This study highlights the significance of genetic factors contributing to accelerated brain aging and its
association with neurodegenerative biomarkers. The effects of these genetic predispositions become apparent
when combined with aging, suggesting that the interaction between genetics and age may better reflect the role
of genetic factors in neurodegeneration.
Keywords: Brain Age, Polygenetic Risk Score, Aging, Alzheimer’s disease, Plasma Biomarkers
HAI2025 - 233
