Brain Age Gap as a Predictive Biomarker: Linking Aging, Lifestyle, and Neuropsychiatric Health DOI
Zhengxing Huang, Ruixia Zhang,

Yi Fan

et al.

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

Published: April 16, 2025

Abstract Background The brain age gap (BAG), a neuroimaging-derived biomarker of accelerated aging, faces translational challenges due to model inaccuracies and unclear disease-mechanism linkages. We systematically evaluated BAG's clinical relevance across neuropsychiatric disorders, cognitive trajectories, mortality, lifestyle interventions. Methods Using multi-cohort data (UK Biobank [n = 38,967], Alzheimer’s Disease Neuroimaging Initiative [ADNI; n 1,402], Parkinson’s Progression Markers [PPMI; 1,182]), we developed 3D Vision Transformer (3D-ViT) for whole-brain estimation. Survival analyses, restricted cubic splines, stratified regressions assessed BAG’s associations with cognition, 16 mortality. Lifestyle modulation effects were quantified through longitudinal BAG progression. Results demonstrated robust predictive accuracy, achieving mean absolute error (MAE) 2.68 years in the UK cohort 2.99–3.20 external validation cohorts (ADNI/PPMI). Per 1-year increment was linearly associated elevated risks Alzheimer's disease (HR 1.165, 95% CI 1.086–1.249; +16.5% risk/year), mild impairment 1.040, 1.030–1.050; +4.0%), all-cause mortality 1.12, 1.09–1.15; +12%; all p < 0.001). Individuals highest quartile (Q4) faced substantially amplified risks: 2.8-fold 2.801), 6.4-fold multiple sclerosis 6.417), 1.5-fold major depressive disorder 1.466). Notably, prodromal Parkinson's exhibited paradoxical rejuvenation (mean Δ=−1.441 years, 0.001), contrasting nonsignificant incident cases 1.830, 0.154). Cognitive decline followed nonlinear critical thresholds domain-specific emerging at Q4 (BAG > 2.48 years). interventions synergistically attenuated progression advanced neurodegeneration (Q3–Q4; 0.05), particularly smoking cessation, moderated alcohol consumption, physical activity. Interpretation: robustly predicts multimorbidity, Its stage-dependent modifiability underscore utility risk stratification personalized prevention strategies.

Language: Английский

Decoding brain aging trajectory: predictive discrepancies, genetic susceptibilities, and emerging therapeutic strategies DOI Creative Commons
Yulia K. Komleva,

Kristina Shpiliukova,

Nikolai Bondar

et al.

Frontiers in Aging Neuroscience, Journal Year: 2025, Volume and Issue: 17

Published: March 19, 2025

The global extension of human lifespan has intensified the focus on aging, yet its underlying mechanisms remain inadequately understood. article highlights aspects genetic susceptibility to impaired brain bioenergetics, trends in age-related gene expression related neuroinflammation and senescence, impact stem cell exhaustion quiescence accelerated aging. We also review accumulation senescent cells, mitochondrial dysfunction, metabolic disturbances as central pathological processes emphasizing how these factors contribute inflammation disrupt cellular competition defining aging trajectory. Furthermore, we discuss emerging therapeutic strategies future potential integrating advanced technologies refine assessments. combination several methods including analysis, neuroimaging techniques, cognitive tests digital twins, offer a novel approach by simulating monitoring individual health trajectories, thereby providing more accurate personalized insights. Conclusively, estimation trajectories is crucial for understanding managing processes, potentially transforming preventive improve outcomes populations.

Language: Английский

Citations

0
Brain Age Prediction in Type II GM1 Gangliosidosis DOI Creative Commons
Connor Lewis,

Selby I. Chipman,

Precilla D’Souza

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

GM1 gangliosidosis is an inherited, progressive, and fatal neurodegenerative lysosomal storage disorder with no approved treatment. We calculated a predicted brain ages Brain Structures Age Gap Estimation (BSAGE) for 81 MRI scans from 41 Type II patients 897 556 neurotypical controls (NC) utilizing BrainStructuresAges , machine learning analysis pipeline. NC showed whole aging at rate of 0.83 per chronological year compared 1.57 in juvenile 12.25 late-infantile patients, accurately reflecting the clinical trajectories two disease subtypes. Accelerated distinct was also observed throughout midbrain structures including thalamus caudate nucleus, hindbrain cerebellum brainstem, ventricles to NC. Predicted age BSAGE both correlated cross-sectional longitudinal assessments, indicating their importance as surrogate neuroimaging outcome measures trials gangliosidosis.

Language: Английский

Citations

0
Brain Age Gap as a Predictive Biomarker: Linking Aging, Lifestyle, and Neuropsychiatric Health DOI
Zhengxing Huang, Ruixia Zhang,

Yi Fan

et al.

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

Published: April 16, 2025

Abstract Background The brain age gap (BAG), a neuroimaging-derived biomarker of accelerated aging, faces translational challenges due to model inaccuracies and unclear disease-mechanism linkages. We systematically evaluated BAG's clinical relevance across neuropsychiatric disorders, cognitive trajectories, mortality, lifestyle interventions. Methods Using multi-cohort data (UK Biobank [n = 38,967], Alzheimer’s Disease Neuroimaging Initiative [ADNI; n 1,402], Parkinson’s Progression Markers [PPMI; 1,182]), we developed 3D Vision Transformer (3D-ViT) for whole-brain estimation. Survival analyses, restricted cubic splines, stratified regressions assessed BAG’s associations with cognition, 16 mortality. Lifestyle modulation effects were quantified through longitudinal BAG progression. Results demonstrated robust predictive accuracy, achieving mean absolute error (MAE) 2.68 years in the UK cohort 2.99–3.20 external validation cohorts (ADNI/PPMI). Per 1-year increment was linearly associated elevated risks Alzheimer's disease (HR 1.165, 95% CI 1.086–1.249; +16.5% risk/year), mild impairment 1.040, 1.030–1.050; +4.0%), all-cause mortality 1.12, 1.09–1.15; +12%; all p < 0.001). Individuals highest quartile (Q4) faced substantially amplified risks: 2.8-fold 2.801), 6.4-fold multiple sclerosis 6.417), 1.5-fold major depressive disorder 1.466). Notably, prodromal Parkinson's exhibited paradoxical rejuvenation (mean Δ=−1.441 years, 0.001), contrasting nonsignificant incident cases 1.830, 0.154). Cognitive decline followed nonlinear critical thresholds domain-specific emerging at Q4 (BAG > 2.48 years). interventions synergistically attenuated progression advanced neurodegeneration (Q3–Q4; 0.05), particularly smoking cessation, moderated alcohol consumption, physical activity. Interpretation: robustly predicts multimorbidity, Its stage-dependent modifiability underscore utility risk stratification personalized prevention strategies.

Language: Английский

Citations

0