Comorbidity patterns and immune-metabolic differences in patients with acute depressive episodes

Depression is frequently complicated by physical and psychiatric comorbidities. These conditions are closely linked to immune and metabolic alterations. Identifying comorbidity patterns and their distinct biological signatures is crucial for optimizing clinical stratification and treatment strategies.

This retrospective cross-sectional study analyzed electronic medical records of 7541 inpatients diagnosed with depressive episodes or recurrent depressive disorder at a tertiary psychiatric hospital in China. LCA was used to identify comorbidity patterns based on the 30 most common conditions. Generalized linear models examined differences in 12 peripheral blood immune and metabolic indicators across these patterns. P values were corrected using the FDR.

Comorbidity was observed in 71.81% of the study population. The study identified four distinct comorbidity classes which were compared to a non-comorbid depression-only reference group. The profiles revealed a clear biological bifurcation. The metabolic abnormalities cluster exhibited profound systemic inflammation with a significantly elevated SII (β = 92.697, P < 0.001). The mixed cardiovascular cluster displayed the most severe profile and was characterized by significantly reduced HDL-C (β = -0.071, P < 0.001) and elevated SII (β = 53.685, P = 0.017). Conversely, the anxiety and thyroid cluster was defined by endocrine dysfunction with elevated TSH (β = 1.142, P < 0.001) but a significantly suppressed SII (β = -56.312, P = 0.029). The anxiety-only cluster represented the largest subgroup and showed widespread downregulation of immune markers including white blood cell count (β = -0.266, P < 0.001).

Comorbidities in depression cluster into four patterns with divergent immune and metabolic signatures. Patients with metabolic and cardiovascular comorbidities showed a higher inflammatory profile, highlighting the necessity of cardiometabolic assessment. These findings suggest the potential utility of routine blood biomarkers for characterizing clinical heterogeneity among patients with depression.