Biology subjects

Ryan, N. S.

Publications and source records attributed to Ryan, N. S..

3 recordsLinked to original sources

bioRxiv2026

Biomarker Variability Limits Individualized Amyloid Time Estimation in Alzheimer Disease

ObjectiveDisease progression modeling (DPM) or "amyloid time" is increasingly used to stage Alzheimer disease (AD). DPM performance depends on within-individual heterogeneity in rates of pathological accumulation as well as test-retest reliability of the biomarker. The relative contributions of these variabilities have not been systematically assessed. This would be particularly relevant if extrapolations from DPM were to be used to make individual-level predictions for research, clinical trials, or potentially future clinical practice. MethodsWe conducted simulation studies incorporating empirically-derived noise properties from amyloid biomarkers to assess the contributions of inter- and intra-individual variability. Findings generalized in an autosomal dominant AD cohort with amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), and plasma biomarkers and in a sporadic AD cohort with both amyloid PET and plasma biomarkers. We assessed group level DPM performance via mean average error (MAE) and root mean squared error (RMSE). At the individual level, we evaluated distinctness of distributions of biomarker levels associated with specific disease timings. ResultsInter-individual variability was the dominant source of error in temporal estimates. Intra-individual variability reduced estimate stability. Optimal performance occurred in biomarkers with positive average accumulation rates where a subset of individuals had exceptionally high levels of accumulation. In research study data, amyloid PET outperformed CSF and plasma biomarkers. InterpretationDPM is fundamentally constrained by dynamic range, variability, and test-retest reliability of the biomarker of interest. Current DPM approaches are more robust at the group level, particularly when applied to biomarkers with more than 10-15% variability like fluid biomarkers. FundingNational Institute on Aging, Alzheimers Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Korean Ministry of Health & Welfare and Ministry of Science and ICT, Spanish Institute of Health.

bioinformatics↗

bioRxiv2024

Spectrum of gamma-Secretase dysfunction as a unifying predictor of ADAD age at onset across PSEN1, PSEN2 and APP causal genes

Autosomal Dominant Alzheimers Disease (ADAD), caused by mutations in Presenilins (PSEN1/2) and Amyloid Precursor Protein (APP) genes, typically manifests before age 65. Age at symptom onset (AAO) is relatively consistent among carriers of the same PSEN1 mutation, but more variable for PSEN2 and APP variants, with these mutations associated with later AAOs than PSEN1. Understanding this clinical variability is crucial for developing predictive models and tailored interventions in ADAD. Biochemical in vitro assessment of {gamma}-secretase function is valuable in evaluating PSEN1 variant pathogenicity, disease onset and progression. Here, we examined A{beta} profiles relationships to AAO across causal genes. Our analysis showed linear correlations between mutation-induced shifts in A{beta} profiles and AAO for PSEN2 and APP mutations. Integration with PSEN1 data revealed parallel but shifted correlations, indicating a common pathogenic mechanism with gene-specific onset timing shifts. Our data support a unified model of ADAD pathogenesis wherein {gamma}-secretase dysfunction and shifts in A{beta} profiles define disease onset. This biochemical analysis of ADAD causality and established quantitative relationships deepen our understanding of ADAD pathogenesis, offering potential for predictive AAO modelling with implications for clinical practice, genetic research and development of therapeutic strategies modulating {gamma}-secretase across ADAD forms and potentially more broadly in AD. SummaryWe examined the relationships between (full) A{beta} peptide profiles and age at symptom onset (AAO) across all Alzheimers disease causal genes. Our findings establish a quantitative framework for mutations pathogenicity assessment and AAO prediction; with implications for clinical practice, genetic counselling, fundamental and translational research.

neuroscience↗

bioRxiv2021

Plasma amyloid beta ratios in autosomal dominant Alzheimers disease: the influence of genotype

In-vitro studies of autosomal dominant Alzheimers disease (ADAD) implicate longer A{beta} peptides in pathogenesis, however less is known about the behaviour of ADAD mutations in-vivo. In this cross-sectional cohort study, we used liquid chromatography-tandem mass spectrometry to analyse 66 plasma samples from ADAD family members who were at-risk of inheriting a mutation or were already symptomatic. We tested for differences in plasma A{beta}42:38, 38:40 and 42:40 ratios between Presenilin1 (PSEN1) and Amyloid Precursor Protein (APP) carriers. We examined the relationship between plasma and in-vitro models of A{beta} processing and, among PSEN1 carriers, tested for associations with parental age at onset (AAO). 39 participants were mutation carriers (28 PSEN1 and 11 APP). Age- and sex-adjusted models showed marked differences in plasma A{beta} between APP and PSEN1: higher A{beta}42:38 in PSEN1 versus APP (p<0.001) and non-carriers (p<0.001); higher A{beta}38:40 in APP versus PSEN1 (p<0.001) and non-carriers (p<0.001), while A{beta}42:40 was higher in APP and PSEN1 compared to non-carriers (both p<0.001). A{beta} profiles were reasonably consistent in plasma and cell lines. Within PSEN1, sex-adjusted models demonstrated negative associations between (i)A{beta}42:40 (ii)A{beta}42:38 and parental AAO. In-vivo differences in A{beta} processing between APP and PSEN1 provide insights into ADAD pathophysiology which can inform therapy development.

neuroscience↗