7 research outputs found
Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume
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 Alzheimerās 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-Ī² PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimerās 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-Ī² positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimerās disease-related phenotypes, including measures of cognition or brain Amyloid-Ī² 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
Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18
7 10 124 ) or temporal stage (p = 3.96
7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine
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Cross-sectional and longitudinal evaluation of plasma glial fibrillary acidic protein to detect and predict clinical syndromes of Alzheimer's disease
Introduction: This study examined plasma glial fibrillary acidic protein (GFAP) as a biomarker of cognitive impairment due to Alzheimer's disease (AD) with and against plasma neurofilament light chain (NfL), and phosphorylated tau (p-tau)181+231. Methods: Plasma samples were analyzed using Simoa platform for 567 participants spanning the AD continuum. Cognitive diagnosis, neuropsychological testing, and dementia severity were examined for cross-sectional and longitudinal outcomes. Results: Plasma GFAP discriminated AD dementia from normal cognition (adjusted mean differenceĀ =Ā 0.90 standard deviation [SD]) and mild cognitive impairment (adjusted mean differenceĀ =Ā 0.72 SD), and demonstrated superior discrimination compared to alternative plasma biomarkers. Higher GFAP was associated with worse dementia severity and worse performance on 11 of 12 neuropsychological tests. Longitudinally, GFAP predicted decline in memory, but did not predict conversion to mild cognitive impairment or dementia. Discussion: Plasma GFAP was associated with clinical outcomes related to suspected AD and could be of assistance in a plasma biomarker panel to detect in vivo AD. Ā© 2023 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]