Browsing by Author "Bourgeat, Pierrick"

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    Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume
    (Springer, 2022) Milicic, Lidija; Vacher, Michael; Porter, Tenielle; Doré, Vincent; Burnham, Samantha C.; Bourgeat, Pierrick; Shishegar, Rosita; Doecke, James; Armstrong, Nicola J.; Tankard, Rick; Maruff, Paul; Masters, Colin L.; Rowe, Christopher C.; Villemagne, Victor L.; Laws, Simon M.; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Australian Imaging Biomarkers and Lifestyle (AIBL) Study; Medical and Molecular Genetics, School of Medicine
    The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer's Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer's disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes.
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    Polygenic scores for Alzheimer’s disease risk and resilience predict age at onset of amyloid‐β
    (Wiley, 2025-01-03) O’Brien, Eleanor K.; Porter, Tenielle; Fernandez, Shane; Cox, Timothy; Dore, Vincent; Bourgeat, Pierrick; Goudey, Benjamin; Doecke, James D.; Masters, Colin L.; Rowe, Christopher C.; Villemagne, Victor L.; Cruchaga, Carlos; Saykin, Andrew J.; Laws, Simon M.; ADOPIC Consortium (AIBL, ADNI, OASIS); Radiology and Imaging Sciences, School of Medicine
    Background: Genome‐wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer’s disease (AD) risk, but genetic variation in the onset and progression of AD pathology is less understood. Accumulation of amyloid‐β (Aβ) in the brain is a key pathological hallmark of AD beginning 10 – 20 years prior to cognitive symptoms. We investigated the genetic basis of variation in age at onset (AAO) of brain Aβ by comparing the performance of polygenic scores (PGSs) based on AD risk and resilience with a Aβ‐AAO trait‐specific PGS. Method: 1122 participants from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) study underwent genome‐wide SNP genotyping and assessment of brain Aβ using positron emission tomography (PET) imaging at two or more timepoints. AAO was the age at which participants were estimated to have crossed the 20 centiloid (CL) threshold for high Aβ. We utilised AD risk and resilience GWAS summary statistics and conducted a GWAS for AAO using a cross‐validation approach (10 test‐validation folds). We used PRSice to identify optimal PGSs for Aβ‐AAO for risk (PGSRisk), resilience (PGSResilience) and Aβ‐AAO (PGSAAO). Result: PGSRisk and PGSResilience were both significantly associated with Aβ‐AAO, such that higher PGSRisk and lower PGSResilience were associated with an earlier Aβ‐AAO. PGSRisk showed the strongest association and explained more variance in Aβ‐AAO than did PGSAAO. When stratified by APOE ε4 carriage, the strongest genetic risk factor for AD, the association of PGSRisk with Aβ‐AAO was stronger among ε4 non‐carriers, whilst PGSResilience, was more strongly associated with Aβ‐AAO in ε4 carriers. Conclusion: PGS based on genetic risk and resilience for AD are both significant predictors of the age at which people are estimated to cross the threshold for high brain Aβ burden. Predicting the age at which a person will pass this threshold would enable treatment at an earlier stage, when it may more effectively delay or prevent symptom onset.