Metabolomic Profiling Identifies Early Biomarkers of Frailty, Balance Impairment, and Fall Risks in Older Adults

Introduction: The global aging population poses significant challenges to healthcare, with frailty, balance impairment, and fall risks being prominent issues. However, the conventional clinical assessments often fail to detect early signs of these conditions. This study aimed to explore the potential of Metabolomics in early identification of biomarkers related to frailty, poor balance, and fall risks in older adults. Methods: We analyzed plasma samples from 110 participants aged 25 to 98 years using untargeted metabolomic analysis. Clinical assessments, including Instrumental Activities of Daily Living (IADL), Morse Fall Risk Scale, Timed Up and Go (TUG), Fried Frailty Criteria, etc., were performed. We examined the correlation between metabolomic results, aging-related blood tests, and clinical assessments. Statistical analysis and pathway analysis were used to identify key metabolic alterations. Results: The metabolomics analysis identified 914 metabolites matching in the human metabolome database, with 293 metabolites significantly correlated with age. Metabolomic profiles showed distinct alterations in older adults, with significant metabolic changes observed in the Old-Old group, particularly in pathways related to Lipid Metabolism, Sphingolipid Signaling, and Fatty Acid Metabolism. A new age classification based on metabolic profiles revealed significant differences in frailty risks across groups, with metabolic signatures linked to poor balance and fall risks. Conclusion: Metabolomics offers a promising approach to identify early biomarkers of frailty, balance impairment, and fall risks in older adults. The integration of metabolic profiles with clinical assessments could lead to more precise and personalized healthcare interventions, improving fall prevention strategies and frailty management. Future studies with larger cohorts are needed to validate these findings and explore the clinical utility of Metabolomics in aging-related healthcare.