Physiological Expression Mapping in Cardiometabolic Disease: A State-Space Framework with Clinical and Hemodynamic Evidence

Contemporary clinical medicine relies on a risk-factor paradigm in which individual diseases are managed using population-derived thresholds. This reductionist approach is increasingly limited in cardiometabolic multimorbidity, where abnormalities arise from interacting dysregulations across cardiovascular, metabolic, renal, and endocrine systems, and interpretation of patient condition often requires an organism-level representation of physiological state. We introduce the digiphysiomic framework, which proposes a transition from isolated risk-factor management to a multidimensional state-space representation centered on physiological expression mapping. In this framework, patient condition is represented as a point in a coordinate system defined by measurable and therapeutically relevant physiological processes. Clinical measurements are transformed into a normalized phenotype vector relative to reference models derived from population and clinical datasets. A knowledge-informed weighting structure—refinable using observational data, randomized trials, and guideline-based evidence—defines the geometry of the space and enables construction of the physiological expression map, a structured visualization of regulatory imbalance designed to support mechanism-oriented clinical decision making. Cardiometabolic disease provides a representative application because it inherently involves multiple interacting regulatory systems. The digiphysiomic space can be constructed using routinely collected clinical variables together with measurements previously used in physiological and hemodynamic phenotyping studies, while maintaining a stable coordinate structure that allows heterogeneous datasets to be integrated without redefining physiological dimensions. Within this space, longitudinal change and therapeutic effects can be represented as transitions, enabling alternative treatment strategies to be compared within a unified physiological framework. By establishing an explicit physiological coordinate system centered on the physiological expression map, the digiphysiomic framework may provide an intermediate layer between raw clinical data and predictive algorithms. Prior work on multidimensional physiological phenotyping and mechanism-guided therapy supports the feasibility of this representation, and with further validation, this approach may support future AI-assisted decision-support systems for individualized management of cardiometabolic multimorbidity while remaining consistent with clinical reasoning and guideline-based care.