Psychiatric Comorbidity and Metabolic Heterogeneity in a Multimorbid Cardiometabolic Cohort: An Exploratory Real-World Analysis

Background: Cardiometabolic disorders and psychiatric conditions frequently coexist and may interact bidirectionally through shared metabolic, inflammatory, and neuroendocrine pathways. However, real-world clinical datasets often reveal substantial heterogeneity in multimorbidity patterns, and the extent to which psychiatric comorbidity clusters with metabolic dysfunction remains insufficiently characterized. This study aimed to evaluate the relationships between psychiatric diagnoses, metabolic biomarkers, hepatic and renal indicators, and polypharmacy within a clinically diverse cohort. Methods: We conducted a cross-sectional analysis of 47 patients from a cohort in a real-world clinical database. Psychiatric comorbidity was identified using diagnostic text-mining. Cardiometabolic markers included TyG index, FIB-4 score, serum creatinine, UACR, and total medication count. Group comparisons used Shapiro–Wilk testing for normality and either unpaired t-tests or Mann–Whitney tests as appropriate. Spearman correlations and a heatmap visualization were used to explore interactions among biomarkers. Results: Psychiatric comorbidity was present in 48.9% of patients and was associated with higher medication burden (6.0 ± 2.5 vs. 3.3 ± 2.1) and elevated TyG index (9.15 ± 0.80 vs. 6.19 ± 4.80), although differences did not reach statistical significance. Hepatic (FIB-4) and renal (creatinine) biomarkers exhibited wide variability, particularly among individuals without psychiatric diagnoses. Correlation analyses revealed weak-to-moderate associations among biomarkers, underscoring the heterogeneous nature of organ involvement in this cohort. Conclusions: Psychiatric comorbidity clustered with increased metabolic stress and polypharmacy, suggesting an integrated cardiometabolic–psychiatric vulnerability. The marked heterogeneity of hepatic and renal markers indicates that multimorbidity follows non-linear patterns not captured by single biomarkers. Integrated, multidisciplinary management strategies and larger longitudinal studies are needed to clarify causal pathways and optimize care for patients with combined cardiometabolic and psychiatric risk.