Identification of Predictors of Physical Frailty and Sarcopenia through a New Multi-Marker Approach

Background and Objectives. Physical frailty & sarcopenia (PF&S) is a prototypical geriatric condition characterized by reduced physical function and low muscle mass. Biomarker discovery for PF&S requires a comprehensive approach to cope with the multifaceted pathophysiology of the condition. The aim of the present study was to provide an initial selection of biomarkers for PF&S using a novel multivariate analytic strategy. Methods. Two-hundred older community-dwellers, 100 with PF&S and 100 non-sarcopenic, non-frail (nonPF&S) controls aged 70 and older were enrolled as part of the BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons (BIOSPHERE) study. A panel of 74 circulating analytes involved in inflammation, muscle growth and remodeling, neuromuscular junction damage, and amino acid metabolism was assayed. Biomarker selection was accomplished through sequential and orthogonalized covariance selection (SO-CovSel) analysis. Results. Several SO-CovSel models were constructed. The model with the best prediction ability obtained with the smallest number of variables was built using seven biomolecules. This model allowed correct classification of 80.6 ± 5.3% PF&S participants and 79.9 ± 5.1% nonPF&S controls. The PF&S biomarker profile was characterized by higher serum levels of asparagine, aspartic acid, and citrulline. Higher serum concentrations of platelet-derived growth factor-BB, heat shock protein 72, myeloperoxidase, and alpha-aminobutyric acid defined the serum profile of nonPF&S participants. Conclusion. SO-CovSel analysis allowed identifying a set of potential biomarkers for PF&S. The adoption of such an innovative multivariate approach could help address the complex pathophysiology of PF&S, translate biomarker discovery from bench to bedside, and unveil novel targets for interventions.