Mass Spectrometry-Based Metabolomic Aging Clocks: Analytical Workflows, Metabolite Signatures, and Clinical Perspectives

Metabolomic aging clocks—quantitative models that estimate biological age based on small-molecule profiles in biological fluids—have become dynamic tools for assessing individual aging trajectories and predicting the risk of age-related diseases. Although NMR-based approaches have been previously reviewed, the analytical landscape of mass spectrometry (MS)-based metabolomic clocks has not been systematically assessed. This review examines the key components of the MS-based analytical workflow underlying the development of metabolomic aging clocks, including biological matrix selection, chromatographic separation strategies, MS instrumentation, data preprocessing, metabolite annotation, and machine learning-based modeling approaches. Twelve published studies on MS-based metabolomic clocks were identified and systematically compared. These clocks provide predictions of chronological age with mean absolute errors of 3.5–10 years and demonstrate robust associations between metabolomic age acceleration and cardiometabolic risk, frailty, and mortality. Recurrent age-related metabolite classes include tryptophan–kynurenine pathway metabolites, acylcarnitines, sphingolipids, modified nucleosides, and tricarboxylic acid (TCA) cycle intermediates. Currently, liquid chromatography with electrospray ionization (LC-ESI) coupled to high-resolution QTOF or Orbitrap instruments dominates current workflows, with elastic net regression being the most commonly applied modeling strategy. Significant heterogeneity in analytical conditions, incomplete methodological descriptions, and limited cross-study validation remain key obstacles to clinical application. Standardization of pre-analytical protocols, the use of certified reference materials, and harmonized validation frameworks are identified as critical priorities for advancing mass spectrometry-based metabolomic clocks toward clinical translation.