Metabolomic Biomarkers for the Early Detection of Infectious Diseases: A Systematic Review and Meta-Analysis of Diagnostic Performance and Clinical Utility

Infectious diseases continue to pose a substantial global health burden, and their timely diagnosis remains challenging because traditional diagnostic methods are frequently time-consuming and have limited sensitivity for early disease detection. A new high-throughput analytical technique, metabolomics, can be used to identify small-molecule biomarkers of real-time physiological response to infection. The objective of the present study was to critically analyze the diagnostic performance and clinical utility of the metabolomics biomarkers in the early detection of infectious diseases. The systematic review and meta-analysis were conducted according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines. Extensive literature searches were conducted in PubMed, Scopus, Web of Science, and Cochrane Library of studies published between January 2014 and December 2025. Studies that evaluated metabolomics biomarkers to diagnose and report infectious diseases were included in the analyses to measure quantitative results, including sensitivity, specificity, and area under the curve (AUC). Quality was assessed with Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Diagnostic estimates were pooled via a random-effects model, and the Cochran’s Q and I² tests were used to evaluate heterogeneity. Subgroup, sensitivity, and the publication bias analyses were also carried out. A total of 22 studies with 3,842 participants were included. The pooled analysis demonstrated high diagnostic performance of metabolomics biomarkers, with a sensitivity of 0.84 (95% CI: 0.80-0.88), specificity of 0.81 (95% CI: 0.77-0.85), and an overall AUC of 0.88 (95% CI: 0.85-0.91). Subgroup analysis showed that the diagnostic accuracy of bacterial infections (AUC = 0.90) was the best, then viral and parasitic infections. Liquid chromatography-mass spectrometry (LC-MS) demonstrated a high level of performance (AUC = 0.91) in comparison to gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR). There was moderate heterogeneity (I² > 50%), and no publication bias was found. The findings were robust, as confirmed by sensitivity analysis. Metabolomics biomarkers demonstrated high diagnostic accuracy and significant potential for the early detection of infectious diseases, although definitions of early-stage infection varied across the included studies. These findings support the integration of metabolomics into diagnostic pathways to facilitate timely diagnosis and improve patient outcomes. Further methodological standardization and large-scale validation studies are required to support clinical implementation.