A Multimodal Neurodemographic Signature for Immunometabolic Depression

The underlying neurobiology of a recently described subtype of major depressive disorder (MDD), immunometabolic depression (IMD), characterized by low-grade inflammation and metabolic dysregulation, remains unclear.

We integrated multimodal neuroimaging (structural and functional magnetic resonance imaging [MRI]) and demographic data from 145 patients with MDD and 68 healthy control (HC) participants. After defining a composite IMD score derived from C-reactive protein, body mass index, triglycerides, and high-density lipoprotein cholesterol levels by principal component analysis, we implemented a binary classification task using machine learning to distinguish high IMD score (IMD group, n = 37) from low IMD score (non-IMD group, n = 37) subgroups. Structural MRI (cortical thickness and gray matter volume), resting-state functional MRI (regional homogeneity [ReHo]/fractional amplitude of low-frequency fluctuations [fALFF]), and demographic covariates were integrated as predictors.

The multimodal model showed promise in distinguishing the IMD group from the non-IMD group (mean ± SD cross-validated area under the receiver operating characteristic curve [AUC] = 0.826 ± 0.098). Furthermore, its performance appeared somewhat more pronounced for within-MDD subtyping compared with differentiating MDD from HC participants (mean cross-validated AUCs of 0.647 ± 0.151 for non-IMD group vs. HC group and 0.741 ± 0.111 for IMD group vs. HC group), indicating subtype specificity. Key predictors included right amygdala volume and functional activity (ReHo/fALFF) in the hippocampus and midcingulate cortex. Clinically, the IMD group exhibited significantly higher anhedonia (p = .04), but lower somatic symptom scores (p < .05), compared with the non-IMD group.

Our analysis shows that IMD is characterized by a distinct, multimodal neurodemographic signature involving corticolimbic circuitry. This signature demonstrates high specificity for unraveling MDD heterogeneity and is clinically linked to anhedonia, supporting the potential for biologically informed patient stratification.