Mohamed Abdel-Mohsen

The reality of an aging population demands a deeper understanding of aging as a biological process, rather than as a chronological descriptor. Chronological age poorly captures interindividual heterogeneity in physiological and functional decline, disease susceptibility, and mortality risk. In contrast, biological age encompasses deterioration at the molecular, cellular, tissue, organ, functional, and organismal levels and provides insight into why two individuals with the same chronological age exhibit differences in physiological function, disease susceptibility, and mortality risk. While early models of biological age relied on functional markers or composite scores derived largely from longitudinal cohort studies, more recent models integrate molecular profiling with machine learning to ascertain biological aging trajectories. In parallel, new artificial intelligence tools have been applied to various imaging modalities and other forms of complex data to elucidate latent patterns and estimate biological age. In this state-of-the-art Review, we explore historical and modern approaches to estimating biological age and highlight key conceptual, technical, and translational challenges that remain unresolved. As geroscience-guided interventions are incorporated into clinical evaluations, robust and accurate interpretable measures of biological aging are crucial to ascertain treatment effects in clinical trials.

Analysis showing expression of human Siglec-10 on myeloid cells in B-hSIGLEC10 mice compared with C57BL/6 controls

Sequences of the monoclonal antibodies against Siglec-10

Flow cytometry analysis showing high expression of ITGA3 and ITGB1 on PDAC cells

Flow cytometry and functional assays showing that ITGA3 knockout reduces Siglec-10 binding and that ITGB1 expression enables PDAC cells to evade macrophage-mediated phagocytosis

Flow cytometry analysis showing that anti–Siglec-10 antibodies do not alter ITGA3, ITGB1, or Siglec-10 ligand levels on PDAC cells

Schematic and TCGA dataset analysis showing elevated expression of sialyltransferases that generate Siglec-10 ligands in pancreatic cancer compared to normal tissues

Correlation analysis in TCGA showing associations between ITGA3 or ITGB1 and Siglec-10 and its ligand-forming enzymes (ST3GAL1, B3GNT3)