Blood DNA methylomic signatures associated with CSF biomarkers of Alzheimer's disease in the EMIF‐AD study DOI Creative Commons
Rebecca G. Smith,

Ehsan Pishva,

Morteza Kouhsar

и другие.

Alzheimer s & Dementia, Год журнала: 2024, Номер unknown

Опубликована: Авг. 28, 2024

We investigated blood DNA methylation patterns associated with 15 well-established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration.

Язык: Английский

A brain DNA co‐methylation network analysis of psychosis in Alzheimer's disease DOI Creative Commons
Morteza Kouhsar, Luke Weymouth, Adam R. Smith

и другие.

Alzheimer s & Dementia, Год журнала: 2025, Номер 21(2)

Опубликована: Фев. 1, 2025

Abstract INTRODUCTION The presence of psychosis in Alzheimer's disease (AD) is suggested to be associated with distinct molecular and neuropathological profiles the brain. METHODS We assessed brain DNA methylation AD donors (AD+P) without (AD−P) using EPIC array. Weighted gene correlation network analysis identified modules co‐methylated genes a discovery cohort (PITT‐ADRC: N = 113 AD+P, 40 AD−P), validation an independent (BDR: 79 117 Gene Ontology cell‐type enrichment analysis. Genetic data were integrated identify quantitative trait loci (mQTLs), which co‐localized GWAS for related traits. RESULTS replicated one AD+P module, was enriched synaptic pathways excitatory inhibitory neurons. mQTLs this module variants schizophrenia educational attainment. DISCUSSION This represents largest epigenetic study date, identifying pleiotropic relationships between Highlights prefrontal cortex subjects AD−P. WGCNA six cohort. One mapping

Язык: Английский

Процитировано

1
A systematic exposure‐wide framework leveraging machine learning to identify multidomain exposure factors and their joint influence on cognitive function: Evidence from a neurological cohort DOI Creative Commons
Jingtao Wu, Bowen Yin, Rui Wen

и другие.

Alzheimer s & Dementia, Год журнала: 2025, Номер 21(2)

Опубликована: Фев. 1, 2025

Abstract INTRODUCTION Cognitive decline has become a growing public concern, yet large‐scale exposure data identifying the contributing factors remain limited. METHODS We conducted an exposure‐wide association study involving 1142 participants and 207 exposures, using machine learning to assess relative contribution joint effects of key factors. Cluster analysis intervention simulation trials helped identify high‐risk subpopulations potential benefits targeted interventions. RESULTS In adjusted mixed models, socioeconomic status domain emerged as strongest predictor longitudinal global cognitive score ( β = 2.91, p < 0.0001, q 0.0001), while dietary also played important role in memory function. The cluster found that “unfavorable lifestyle” dominated phenotype was associated with poorest outcomes. Simulation indicated scores could improve by shifting individuals from unfavorable favorable phenotypes. DISCUSSION health requires multidomain interventions, particularly fields, necessitates collaboration between government individuals. Highlights design, which assesses broad range is used novel variables understand their contributions findings indicate most significant contributor function, diet plays largest Increasing proportion phenotypes through interventions can significantly enhance health.

Язык: Английский

Процитировано

1
Blood‐based multivariate methylation risk score for cognitive impairment and dementia DOI Creative Commons
J.C. Koetsier, Rachel Cavill, Rick A. Reijnders

и другие.

Alzheimer s & Dementia, Год журнала: 2024, Номер unknown

Опубликована: Авг. 28, 2024

The established link between DNA methylation and pathophysiology of dementia, along with its potential role as a molecular mediator lifestyle environmental influences, positions blood-derived promising tool for early dementia risk detection.

Язык: Английский

Процитировано

6
Optimized Anti-Interference Dynamic Integral Neural Network Approach for Dementia Prediction in Health Care DOI

Pradeep Kumar B P,

William J. Ribbans,

Shiv Prasad Reddy B

и другие.

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113723 - 113723

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
The predictive power of profiling the DNA methylome in human health and disease DOI Creative Commons
Paraskevi Christofidou, Christopher G. Bell

Epigenomics, Год журнала: 2025, Номер unknown, С. 1 - 12

Опубликована: Май 10, 2025

Early and accurate diagnosis significantly improves the chances of disease survival. DNA methylation (5mC), major modification in human genome, is now recognized as a biomarker immense clinical potential. This due to its ability delineate precisely cell-type, quantitate both internal external exposures, well tracking chronological biological components aging process. Here, we survey current state predictor traits disease. includes Epigenome-wide association study (EWAS) findings that inform Methylation Risk Scores (MRS), EpiScore long-term estimators plasma protein levels, machine learning (ML) derived clocks. These all highlight significant benefits accessible peripheral blood surrogate measure. However, detailed biopsy analysis real-time also empowering pathological diagnosis. Furthermore, moving forward, this multi-omic biobank scale era, novel insights will be enabled by amplified power increasing sample sizes data integration.

Язык: Английский

Процитировано

0
Blood DNA methylomic signatures associated with CSF biomarkers of Alzheimer's disease in the EMIF‐AD study DOI Creative Commons
Rebecca G. Smith,

Ehsan Pishva,

Morteza Kouhsar

и другие.

Alzheimer s & Dementia, Год журнала: 2024, Номер unknown

Опубликована: Авг. 28, 2024

We investigated blood DNA methylation patterns associated with 15 well-established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration.

Язык: Английский

Процитировано

2