AIMC Topic: Aging

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Aging at the Crossroads of Organ Interactions: Implications for the Heart.

Circulation research
Aging processes underlie common chronic cardiometabolic diseases such as heart failure and diabetes. Cross-organ/tissue interactions can accelerate aging through cellular senescence, tissue wasting, accelerated atherosclerosis, increased vascular sti...

AgeML: Age Modeling With Machine Learning.

IEEE journal of biomedical and health informatics
An approach to age modeling involves the supervised prediction of age using machine learning from subject features. The derived age metrics are used to study the relationship between healthy and pathological aging in multiple body systems, as well as...

Predicting and Evaluating Cognitive Status in Aging Populations Using Decision Tree Models.

American journal of Alzheimer's disease and other dementias
To improve the identification of cognitive impairment by distinguishing normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A recursive partitioning tree model was developed using ARMADA data and the NIH Toolbox, a...

Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data.

Brain research bulletin
The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insights into the pathological process of aging and diseases. Here we evaluated the performance of machin...

Decoding the crossroads of aging and cancer through single-cell analysis: Implications for precision oncology.

International journal of cancer
Single-cell analysis is a transformative approach to understanding cellular heterogeneity in aging and cancer, interconnected processes driven by mechanisms like senescence and immune modulation. This review explores how aging influences cancer initi...

Vascular-related biological stress, DNA methylation, allostatic load and domain-specific cognition: an integrated machine learning and causal inference approach.

BMC neurology
BACKGROUND: Vascular disease in aging populations spans a wide range of disorders including strokes, circulation disorders and hypertension. As individuals age, vascular disorders co-occur and hence exert combined effects. In the present study we int...

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction.

Scientific reports
Biological age (BA) and frailty represent two distinct health measures that offer valuable insights into the aging process. Comparing and analyzing blood-based biomarkers from the machine learning (ML) predictors of BA and frailty helps deepen our un...

Gui-Pi-Tang Defers Skeletal Muscle and Cardiac Muscle Aging by Promoting Mitochondrial Remodeling.

Drug design, development and therapy
PURPOSE: To determine whether Gui-Pi-Tang (GPT) has protective effects on skeletal muscle and cardiac muscle in aged mice.

Predicting depression and unravelling its heterogeneous influences in middle-aged and older people populations: a machine learning approach.

BMC psychology
BACKGROUND: Aging has become a global trend, and depression, as an accompanying issue, poses a significant threat to the health of middle-aged and older adults. Existing studies primarily rely on statistical methods such as logistic regression for sm...