INTRODUCTION
Obesity is highly prevalent in schizophrenia (SCZ) and contributes to unfavorable clinical and cognitive outcomes, yet its underlying metabolic mechanisms remain unclear. We applied non-targeted metabolomics to characterize body mass index (BMI) related metabolic signatures in chronic SCZ.
METHODS
A total of 347 chronic SCZ patients were stratified into high BMI (n = 187) and low BMI (n = 160) groups. The Positive and Negative Symptom Scale (PANSS) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) scales were used to evaluate psychopathology and cognition. Fasting serum samples were collected and analyzed using non-targeted metabolomics via ultra-high-performance liquid chromatography-high-resolution mass spectrometry. Multivariate and univariate statistical approaches, pathway enrichment analyses, receiver operating characteristic (ROC) analyses, and linear regression models were applied.
RESULTS
Fifty-eight metabolites differed significantly between high and low BMI groups (primarily involving 12 amino acids and 8 fatty acids). Pathway analyses revealed significant perturbations in Arginine biosynthesis (p < 0.001), Nicotinate and nicotinamide metabolism (p = 0.008) and Phenylalanine metabolism (p = 0.037). Panels combining differential amino acids and fatty acids demonstrated moderate discriminative performance for high BMI status (AUC > 0.7). Moreover, C-Mannosyl Valine and C-Mannosyl Phenylalanine were associated with cognitive impairment in the low BMI group (all p < 0.05), whereas 2-Aminoheptanoic acid were associated with negative symptoms severity in the high BMI group (p < 0.001).
CONCLUSION
Distinct metabolic profiles characterize BMI heterogeneity in SCZ and are differentially associated with cognitive impairment and negative symptoms.
BACKGROUND
Major depressive disorder (MDD) is highly prevalent and often complicated by anxiety symptoms, yet the neurobiological features associated with anxious depression remain incompletely characterized. The amygdala-prefrontal cortex (PFC) circuit, central to affect regulation, has been implicated in anxious depressive symptoms.
METHODS
We recruited 40 MDD patients and 69 healthy controls (HCs) from Tianjin Anding Hospital. All participants underwent resting-state functional magnetic resonance imaging(fMRI) and clinical assessments. Seed-based functional connectivity (FC) analyses were performed using the bilateral amygdala based on the Automated Anatomical Labeling (AAL) atlas. Group comparisons, partial correlations, and regression analyses tested associations between amygdala-based FC and HDRS-17 symptom dimensions, with subgroup analyses distinguishing MDD patients with and without anxiety.
RESULTS
Compared with HCs, MDD patients exhibited reduced FC between the right amygdala and prefrontal cortex, including the right middle frontal gyrus (MFG) and the left inferior frontal gyrus, triangular part (IFGtri) (GRF: voxel-level p < 0.001, cluster-level p < 0.05). Within the MDD group, higher right amygdala-MFG FC values were associated with HDRS-17 symptoms, including total scores and depressive and somatic symptom dimensions (r = 0.393, p = 0.020; r = 0.360, p = 0.033; r = 0.351, p = 0.039, uncorrected p). In exploratory subgroup analyses, right amygdala-MFG FC showed a positive association with HDRS-17 scores in MDD patients with anxiety (R2 = 0.280, F = 8.957, p = 0.006, Beta = 0.529, t = 2.993, p = 0.006); however, the FC × anxiety subgroup interaction did not reach statistical significance.
CONCLUSION
Our findings identify disrupted right amygdala-MFG connectivity as a potential neural correlate of symptom heterogeneity in MDD, particularly in patients with anxiety symptoms.
Chuhao Zhang, Li-Jun Wang, Wen-Jie Sun et al.· Neuroscience Letters· 0 citations
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