Proteomics-based aging clocks in midlife and late-life and risk of dementia

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Background: Biological age can be quantified by composite proteomic scores, called aging clocks. We investigated whether biological acceleration (a discrepancy between chronological and age) in midlife late-life is associated with cognitive function risk of dementia. Methods: used two population-based cohort studies: Atherosclerosis Risk Communities (ARIC) Study Multi-Ethnic (MESA). Proteomics-based clocks (PACs) were created ARIC at (mean age: 58 years, n=11,758) 77 n=4,934) using elastic net regression models two-thirds dementia-free participants validated the remaining one-third participants. Age (AA) was calculated as residuals after regressing PACs on age. PAC MESA 62 n=5,829). multivariable linear Cox proportional hazards to assess association AA dementia incidence, respectively. Results: In ARIC, every five years lower global function: difference: -0.11, 95% confidence interval (CI): -0.16, -0.06) -0.17, CI: -0.23, -0.12 AA. Consistently, higher (hazard ratio [HR]: 1.20 [CI: 1.04, 1.36]) more prominently when (HR: 2.14 [CI:1.67, 2.73]). Similar findings observed study: (difference: -0.08 -0.14, -0.03]) (HR:1.23 1.46]). Conclusion: Accelerated – defined plasma proteome predicts a late-life.

Language: Английский

---

Epigenetic age across development in children and adolescents with ADHD

Psychiatry Research, Journal Year: 2025, Volume and Issue: 345, P. 116373 - 116373

Published: Jan. 20, 2025

Language: Английский

---

Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease

Frontiers in Aging, Journal Year: 2025, Volume and Issue: 5

Published: Jan. 23, 2025

Introduction DNA methylation (DNAm) age clocks are powerful tools for measuring biological age, providing insights into aging risks and outcomes beyond chronological age. While traditional models effective, their interpretability is limited by dependence on small potentially stochastic sets of CpG sites. Here, we propose that the reliability DNAm should stem from capacity to detect comprehensive targeted signatures. Methods We compiled publicly available whole-blood samples (n = 17,726) comprising entire human lifespan (0–112 years). used a pre-trained network-coherent autoencoder (NCAE) compress data embeddings, with which trained interpretable neural network epigenetic clocks. then retrieved age-specific signatures examined functional enrichments in age-associated processes. Results introduce NCAE-CombClock, novel highly precise (R 2 0.978, mean absolute error 1.96 years) deep clock integrating data-driven embeddings established markers. Additionally, developed suite NCAE-Age classifiers tailored adolescence young adulthood. These can accurately classify individuals at critical developmental ages youth (AUROC 0.953, 0.972, 0.927, 15, 18, 21 capture fine-grained, single-year enriched processes associated anatomic neuronal development, immunoregulation, metabolism. showcased practical applicability this approach identifying candidate mechanisms underlying altered pace observed pediatric Crohn’s disease. Discussion In study, present clock, named improves prediction accuracy large datasets, explainable robust classification across youth. Our offer broad applications personalized medicine research, valuable resource interpreting trajectories health

Language: Английский

---

The mechanisms, hallmarks, and therapies for brain aging and age-related dementia

Science Bulletin, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

---

A review of artificial intelligence-based brain age estimation and its applications for related diseases

Briefings in Functional Genomics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 22, 2024

Abstract The study of brain age has emerged over the past decade, aiming to estimate a person’s based on imaging scans. Ideally, predicted should match chronological in healthy individuals. However, structure and function change presence brain-related diseases. Consequently, also changes affected individuals, making gap (BAG)—the difference between age—a potential biomarker for health, early screening, identifying age-related cognitive decline disorders. With recent successes artificial intelligence healthcare, it is essential track latest advancements highlight promising directions. This review paper presents machine learning techniques used estimation (BAE) studies. Typically, BAE models involve developing regression model capture variations from scans individuals automatically predict new subjects. process involves estimating BAG as measure health. While we discuss clinical applications methods, studies biological that can be integrated into research. Finally, point out current limitations BAE’s

Language: Английский

---

Prenatal exposure to air pollution and maternal depression: Combined effects on brain aging and mental health in young adulthood

Progress in Neuro-Psychopharmacology and Biological Psychiatry, Journal Year: 2024, Volume and Issue: 134, P. 111062 - 111062

Published: June 19, 2024

Language: Английский

---

Epigenetic aging and fecundability: the Norwegian Mother, Father and Child Cohort Study

Human Reproduction, Journal Year: 2024, Volume and Issue: 39(12), P. 2806 - 2815

Published: Oct. 22, 2024

Is there an association between male or female epigenetic age acceleration (EAA) deceleration (EAD) and fecundability?

Language: Английский

---

The Utility of Biomarkers for Assessment and Intervention in Neurodevelopmental Disorders

Integrated science, Journal Year: 2024, Volume and Issue: unknown, P. 43 - 81

Published: Jan. 1, 2024

Language: Английский

---