A Methodological Framework for Non-Invasive Body-Composition Phenotyping in Young Adults: Integrating Bioelectrical Impedance and Patient Similarity Networks

Body mass index (BMI) does not capture fat distribution or muscle–fat heterogeneity, so adverse patterns go undetected. We present a methodological framework—not a validated prediction tool—combining three laboratory-free constructs: an Office-Based Framingham cardiovascular risk score, a modified proxy-based FINDRISC, and a direct segmental multi-frequency bioelectrical impedance analysis (DSM-BIA)-derived Metabolically Unhealthy Obesity (MUO) index, with a weighted patient similarity network. We tested six predefined hypotheses in 1684 young adults (mean age 22.5 ± 8.3 years; 50.2% female). Framingham was applied off-label below 30 years, so its outputs give only relative within-cohort ordering; unavailable FINDRISC items were scored zero, so standard FINDRISC categories do not apply. BMI-defined obesity occurred in 6.9%, high visceral fat in 21.7%, high MUO in 21.8%, a BIA-defined TOFI (thin outside, fat inside)-like phenotype in 5.2%, and a sarcopenic-obesity-like phenotype in 9.9%. Visceral fat correlated with percent body fat (r = 0.855). The network resolved nine interpretable communities (modularity Q = 0.63; permutation p = 0.005), including a BIA-defined TOFI-like community (cross-validated AUC = 0.93); k-means, hierarchical, PCA and UMAP clustering recovered convergent axes. All hypotheses were supported, indicating internal construct consistency, not external validation. Laboratory, imaging and longitudinal validation is required before any diagnostic or prognostic claim.