Background: Chronic inflammation, changes in body composition, and declining physical function are hallmarks of the ageing process. The aim of the present study was to provide a preliminary characterisation of the relationship among these age-related phenomena via multivariate modelling. Methods: Thirty-five old adults (OAs) and 17 young adults (YAs) were enrolled. The volume of skeletal muscle, subcutaneous adipose tissue (SAT), and intermuscular adipose tissue (IMAT) of the thigh was quantified by three-dimensional magnetic resonance imaging. Muscle strength was measured by knee extension strength testing. In OAs, physical performance was further assessed via the Short Physical Performance Battery (SPPB). Multi-block partial least squares-discriminant analysis (PLS-DA) was employed to explore the relationship among inflammatory profiles and functional and imaging parameters. Double cross-validation procedures were used to validate the predictive ability of the PLS-DA model. Results: The optimal complexity of the PLS-DA model was found to be two latent variables. The proportion of correct classification was 92.3% in calibration (94.1% in YAs and 91.4% in OAs), 84.6% in internal validation (95.3% in YAs and 78.5% in OAs), and 82.6% in external validation (94% in YAs and 76.9% in OAs). Relative to YAs, OAs were characterised by smaller muscle volume, greater IMAT volume, lower muscle strength, and higher levels of myeloperoxidase, P-selectin, soluble intercellular adhesion molecule 1, and vascular cell adhesion molecule 1. Compared with OAs with SPPB >8, those scoring ≤8 were characterised by smaller muscle volume, greater SAT volume, lower muscle strength, and higher levels of interleukin 1 beta, 6, 10, 12, 13, tumour necrosis factor alpha, and granulocyte-macrophage colony-stimulating factor. Conclusions: Multi-block PLS-DA identified distinct patterns of relationships among circulating cytokines and functional and imaging parameters in persons of different ages and varying levels of physical performance. The longitudinal implementation of such an innovative strategy could allow for the tracking of health status over time, the early detection of deviations in health trajectories, and the monitoring of response to treatments.