This study aims to explore the mechanism by which gut microbiota influences the antidepressant effects of serotonin reuptake inhibitors (SSRIs) through metabolic pathways. A total of 126 patients were analyzed for their gut microbiota and metabolomics. Patients received SSRI treatment and were categorized into responder and non-responder groups based on changes in their Hamilton Depression Rating Scale (HAMD-17) scores before and after treatment. The association between gut microbiota composition and the efficacy of SSRIs was investigated through 16S rRNA gene sequencing and metabolomic analysis, and a predictive model was developed. As a result, the study found significant differences in gut microbiota composition between the responder and resistant groups. Specific taxa, such as Ruminococcus, Bifidobacterium, and Faecalibacterium, were more abundant in the responder group. Functional analysis revealed upregulation of acetate degradation and neurotransmitter synthesis pathways in the responder group. The machine learning model indicated that gut microbiota and metabolites are potential biomarkers for predicting SSRIs efficacy. In conclusion, gut microbiota influences the antidepressant effects of SSRIs through metabolic pathways. The diversity and function of gut microbiota can serve as biomarkers for predicting the treatment response, providing new insights for personalized treatment.

Purpose: Nicotine withdrawal is a multifaceted physiological and psychological process that can induce a spectrum of mood disturbances. Gaining a more nuanced understanding of how pure nicotine withdrawal influences cognitive control functions may provide valuable insights for the enhancement of smoking cessation programs. This study investigated changes in inhibitory control function in smokers after 2-hour nicotine withdrawal using the event-related potential (ERP) technique. Participants and Methods: 28 nicotine dependence (ND) patients and 28 health controls (HCs) completed a smoking-cued Go/No-go task containing two different types of picture stimuli, smoking-cued and neutral picture stimuli. We analyzed the behavioral and ERP data using a mixed model Repeated Measure Analysis of Variance (ANOVA). Results: No-go trials accuracy rate (ACC) at baseline (time 1) was lower in the ND group compared to HCs with smoking-cued stimuli, and No-go trials ACC after 2-hour nicotine withdrawal (time 2) was not lower in the ND group compared to HCs. When confronted with smoking-cued stimuli, the No-go trials ACC was higher in time 2 than in time 1 in the ND group. For the ERP component, the No-go N2 amplitudes in the ND group with smoking-cued stimuli were lower than that of HCs, whereas after 2-hour nicotine withdrawal, the ND group’s No-go N2 amplitudes higher than that at time 1, and did not differ from that of HCs. No-go P3 amplitudes were not significantly different between the two groups. Conclusion: Evidenced from ERP data, ND patients have an inhibitory control dysfunction in the face of smoking cues, which is mainly manifested in the early stage of response inhibition rather than in the late stage. Two-hour nicotine withdrawal improves inhibitory control dysfunction in ND patients. The No-go N2 component is an important and sensitive neuroelectrophysiological indicator of inhibitory control function in ND patients.

Background: The intricate pathophysiological mechanisms of major depressive disorder (MDD) necessitate the development of comprehensive early indicators that reflect the complex interplay of emotional, physical, and cognitive factors. Despite its potential to fulfill these criteria, interoception remains underexplored in MDD. This study aimed to evaluate the potential of interoception in transforming MDD's clinical practices by examining interoception deficits across various MDD stages and analyzing their complex associations with the spectrum of depressive symptoms.Methods: This study included 431 healthy individuals, 206 subclinical depression individuals, and 483 MDD patients. Depressive symptoms and interoception function were assessed using the PHQ‐9 and MAIA‐2, respectively.Results: Interoception dysfunction occurred in the preclinical phase of MDD and further impaired in the clinical stage. Antidepressant therapies showed limited efficacy in improving interoception and might damage some dimensions. Interoceptive dimensions might predict depressive symptoms, primarily enhancing negative thinking patterns. The predictive model based on interoception was built with random split verification and demonstrated good discrimination and predictive performance in identifying MDD.Conclusions: Early alterations in the preclinical stage, multivariate associations with depressive symptoms, and good discrimination and predictive performance highlight the importance of interoception in MDD management, pointing to a paradigm shift in diagnostic and therapeutic approaches.