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http://dx.doi.org/10.31478/202503a | DOI Listing |
Nat Aging
September 2025
Aging Biomarker Consortium (ABC), Beijing, China.
The global surge in the population of people 60 years and older, including that in China, challenges healthcare systems with rising age-related diseases. To address this demographic change, the Aging Biomarker Consortium (ABC) has launched the X-Age Project to develop a comprehensive aging evaluation system tailored to the Chinese population. Our goal is to identify robust biomarkers and construct composite aging clocks that capture biological age, defined as an individual's physiological and molecular state, across diverse Chinese cohorts.
View Article and Find Full Text PDFNat Aging
September 2025
Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, Lørenskog, Norway.
Beyond their classical functions as redox cofactors, recent fundamental and clinical research has expanded our understanding of the diverse roles of nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP) in signaling pathways, epigenetic regulation and energy homeostasis. Moreover, NAD and NADP influence numerous diseases as well as the processes of aging, and are emerging as targets for clinical intervention. Here, we summarize safety, bioavailability and efficacy data from NAD-related clinical trials, focusing on aging and neurodegenerative diseases.
View Article and Find Full Text PDFGeroscience
September 2025
Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
Cognitive decline is common in multiple sclerosis (MS), although neural mechanisms are not fully understood. The objective was to investigate the impact of mild cognitive impairment (MCI) on the relationship between resting state functional connectivity (RSFC) and cognitive function in older adults with multiple sclerosis (OAMS) and age matched healthy controls. Participants underwent magnetic resonance imaging (MRI) scans and cognitive assessments.
View Article and Find Full Text PDFNat Biomed Eng
September 2025
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Phenotype-driven approaches identify disease-counteracting compounds by analysing the phenotypic signatures that distinguish diseased from healthy states. Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem and predicts the perturbagens needed to achieve a desired response by embedding disease cell states into networks, learning a latent representation of these states, and identifying optimal combinatorial perturbations.
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