AIMC Topic: Aging
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Gene age gap estimate (GAGE) for major depressive disorder: A penalized biological age model using gene expression.
Neurobiology of aging
Apr 1, 2025
Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Residuals or "gaps" bet...
Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.
NeuroImage
Apr 1, 2025
Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in the detection of brain abnormalities. While deep learning is accurate in estimating brain age, the a...
Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study.
Frontiers in endocrinology
Apr 1, 2025
BACKGROUND: While the Cardiometabolic Index (CMI) serves as a novel marker for assessing adipose tissue distribution and metabolic function, its prognostic utility for cardiovascular disease (CVD) events remains incompletely understood. This investig...
Bioinformatics and machine learning approaches to explore key biomarkers in muscle aging linked to adipogenesis.
BMC musculoskeletal disorders
Mar 22, 2025
Adipogenesis is intricately linked to the onset and progression of muscle aging; however, the relevant biomarkers remain unclear. This study sought to identify key genes associated with adipogenesis in the context of muscle aging. Firstly, gene expre...
AI-based deformable hippocampal mesh reflects hippocampal morphological characteristics in relation to cognition in healthy older adults.
NeuroImage
Mar 21, 2025
Magnetic resonance imaging (MRI)-derived hippocampus measurements have been associated with different cognitive domains. The knowledge of hippocampal structural deformations as we age has contributed to our understanding of the overall aging process....
Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI.
BMC geriatrics
Mar 17, 2025
BACKGROUND: Depression has a detrimental effect on an individual's mental and musculoskeletal strength multiplying the risk of fall incidents. The current study aims to investigate the association between depression and falls in older adults using ma...
Biological age prediction using a DNN model based on pathways of steroidogenesis.
Science advances
Mar 14, 2025
Aging involves the progressive accumulation of cellular damage, leading to systemic decline and age-related diseases. Despite advances in medicine, accurately predicting biological age (BA) remains challenging due to the complexity of aging processes...
Biological age prediction and NAFLD risk assessment: a machine learning model based on a multicenter population in Nanchang, Jiangxi, China.
BMC gastroenterology
Mar 13, 2025
BACKGROUND: The objective was to develop a biological age prediction model (NC-BA) for the Chinese population to enrich the relevant studies in this population. And to investigate the association between accelerated age and NAFLD.
The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.
BMC psychiatry
Mar 3, 2025
BACKGROUND: Evidence indicates that patients with Major Depressive Disorder (MDD) exhibit a senescence phenotype or an increased susceptibility to premature senescence. However, the relationship between senescence-related genes (SRGs) and MDD remains...
Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning.
Artificial cells, nanomedicine, and biotechnology
Mar 1, 2025
Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets an...
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