Mood and Metabolism: A Bayesian Network Analysis of Depressive Symptoms in Major Depressive Disorder and Metabolic Syndrome

<b>Background/Objectives</b>: Major depressive disorder (MDD) and metabolic syndrome (MetS) are highly prevalent, bidirectionally linked conditions. Individuals with MetS are at increased risk of developing depression, while depression predisposes to metabolic dysfunction. Evidence suggests that comorbid MDD and MetS present a distinct psychopathological profile, with neurovegetative symptoms such as fatigue, sleep disturbances, and appetite dysregulation being more prominent. This study aimed to determine whether depressive symptom structures differ between MDD patients with and without MetS, applying Bayesian network methods to uncover probabilistic dependencies that may inform precision psychiatry. <b>Methods</b>: Data were drawn from 1779 adults with ICD-10-diagnosed MDD in the 2013-2020 National Health and Nutrition Examination Survey (NHANES). Using standard metabolic criteria, participants were categorized as MetS (<i>n</i> = 315) or non-MetS (<i>n</i> = 1464). Depressive symptoms were assessed with the Patient Health Questionnaire (PHQ-9). Directed Acyclic Graphs (DAGs) were estimated via a hill-climbing algorithm with 5000 bootstrap replications to ensure network stability. <b>Results</b>: MetS patients displayed a denser and more interconnected symptom network. Fatigue (PHQ4) emerged as a central hub linking sleep, appetite, cognition, and functional impairment. In contrast, non-MetS patients showed a more fragmented network dominated by affective symptoms (low mood, anhedonia) and negative self-cognitions. <b>Conclusions</b>: Depressive symptoms propagate differently depending on metabolic status. These results highlight the value of personalized medicine approaches, advocating for treatment strategies that address neurovegetative dysfunctions in MDD with MetS and affective-cognitive symptoms in non-MetS. Aligning interventions with individual symptom architectures and metabolic profiles may enhance therapeutic precision and improve clinical outcomes.