Multimodal Factor Analysis Reveals Five Robust Phenotypes of Healthy Aging in a Russian Population Cohort

Background/Objectives: Population aging necessitates a shift from disease-focused paradigms to a holistic characterization of biological aging processes. While chronological age remains the primary metric, it poorly captures inter-individual variability in physiological resilience and health trajectories. This study aimed to identify robust, multidimensional aging phenotypes independent of chronological age and sex using integrative factor analysis of heterogeneous biomedical data from a Russian cohort—a population underrepresented in aging research. Methods: We analyzed data from 1201 conditionally healthy adults (aged 18–99 years) enrolled in the RUSS AGE study. A comprehensive dataset comprising 118 variables across 11 modalities—including biochemical markers, anthropometry, physical function, cognitive-emotional assessments, lifestyle factors, and psychosocial indicators—was integrated using Multi-Omics Factor Analysis v2 (MOFA2). Following the extraction of 16 latent factors and residualization for demographic confounders, consensus clustering was performed to identify distinct aging phenotypes. Phenotype stability was internally recapitulated using gradient-boosting classifiers (XGBoost, CatBoost) in a stratified five-fold cross-validation and on a held-out test set. Results: MOFA2 identified 16 stable latent factors, explaining 21.3% of the total variance and capturing coordinated variation across metabolic, inflammatory, cardiovascular, cognitive, and behavioral domains. Consensus clustering revealed five reproducible phenotypes—Anemic (n = 82), Metabolically Subcompensated (n = 99), Metabolically Decompensated (n = 304), Overloaded (n = 302), and Balanced (n = 414)—characterized by distinct multisystem profiles independent of age (p > 0.05 after FDR correction) and sex. Supervised classification achieved high discriminative performance (macro F1-score = 0.75, OvR ROC-AUC = 0.93 on the held-out test set), quantifying the internal reconstructability of the phenotype labels from the original feature space rather than external generalization to an independent cohort. Conclusions: This study demonstrates the feasibility of data-driven, biologically coherent phenotyping of healthy aging using integrative factor analysis. The identified phenotypes represent stable configurations of physiological, functional, and psychosocial characteristics that transcend chronological age, providing a foundation for the future development of risk-stratification tools, preventive interventions, and biological-age calculators, subject to subsequent validation in longitudinal and independent external cohorts.