Research questions: Multimorbidity-defined as the co-occurrence of two or more chronic conditions-exerts substantial influence on chronic kidney disease (CKD) onset and progression. Nevertheless, critical evidence gaps persist in large-scale clinical research: the longitudinal impact of dynamic multimorbidity trajectories on renal decline remains unquantified; and whether metabolic and genetic factors differentially modulate CKD risk across varying multimorbidity patterns and burdens is poorly understood.
Objectives: To delineate the associations between distinct multimorbidity patterns/trajectories and the onset and progression of CKD; To assess whether and how multimorbidity clusters modulate the relationships of metabolic and genetic factors with CKD pathogenesis.
Scientific rationale: Multimorbidity presents a pervasive global health challenge with prevalence projected to rise substantially in coming decades. CKD frequently anchors these complex disease clusters, evidenced by 19.74% of CKD patients carrying !4 comorbid disorders – a pattern strongly associated with adverse outcomes. While data-driven quantification of disease co-occurrence can reveal pathophysiological heterogeneity underlying CKD development and identify prognostically significant clusters, critical knowledge gaps persist: (1) how temporal multimorbidity trajectories drive renal decline through shared mechanisms (e.g., metabolic-inflammatory axis dysregulation, mineral-bone disorders); and (2) whether interactions between comorbidity patterns and genetic determinants accelerate CKD pathogenesis. To address this, we intend to integrate longitudinal electronic health records with multi-omics data to: quantify comorbidity pattern effects on CKD incidence/progression; uncover underlying biological pathways; and thereby establish an evidence base for precision risk stratification, cross-disease therapeutic targeting, and patient-centered integrated care models for multimorbid CKD.
