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Background and Hypothesis Antipsychotics (APs), the cornerstone of schizophrenia treatment, confer a relatively high risk of constipation. However, the mechanisms underpinning AP-induced constipation are poorly understood. Thus, we hypothesized that (1) schizophrenia patients with AP-induced constipation have distinct metabolic patterns; (2) there is more than one mechanism at play in producing this adverse drug effect; and (3) AP-associated changes in the gut microbiome are related to the altered metabolic profiles. Study Design Eighty-eight schizophrenia patients, including 44 with constipation (C) and 44 matched patients without constipation (NC), were enrolled in this study. Constipation was diagnosed by Rome IV criteria for constipation and colonic transit time using radiopaque markers (ROMs) while severity was evaluated with the Bristol Stool Form Scale (BSS) and Constipation Assessment Scale (CAS). Fasting blood samples were drawn from all participants and were subjected to non-targeted liquid chromatography-mass spectrometry (LC-MS) metabolomic analysis. Study Results Eleven metabolites were significantly altered in AP-induced constipation which primarily disturbed sphingolipid metabolism, choline metabolism, and sphingolipid signaling pathway (P value <.05, FDR < 0.05). In the C group, changes in the gut bacteria showed a certain degree of correlation with 2 of the significantly altered serum metabolites and were associated with alterations in choline metabolism. Conclusions Our findings indicated that there were disturbances in distinct metabolic pathways that were associated with AP-induced constipation. In addition, this study presents evidence of a link between alterations in the gut microbiome and host metabolism which provides additional mechanistic insights on AP-induced constipation. [ABSTRACT FROM AUTHOR]

Background: It is clinically challenging to distinguish bipolar disorder (BD) from major depressive disorder (MDD) in the early stages. While the hypomania checklist-32 (HCL-32) is a proper auxiliary tool that is useful to differentiate between BD and MDD, there is currently no standard cut-off value. The variations in HCL-32 cut-off values could potentially be influenced by personality traits. Therefore, the aim of this study is to explore the effect of personality traits on the screening performance of HCL-32. Methods: In this retrospective cross-sectional study, 168 patients with BD or MDD were evaluated with the Eysenck Personality Questionnaire (EPQ) and HCL-32. The associations between demographic data, diagnosis and clinical rating scales were analyzed. Results: Diagnosis was not associated with extraversion but was related to neuroticism. HCL-32 scores in typical extraverted patients were higher in contrast to atypical extraverted patients. The best cut-off value for BD recognition of typical and atypical extraversion groups were 15 and 12.5, respectively. In patients with MDD, HCL-32 score of typical neuroticism was higher than the atypical type, but there was no difference in patients with BD. In typical neuroticism, there was no difference in HCL-32 scores between patients with MDD and BD. But among atypical neurotic patients, HCL-32 scores of BD were higher compared to MDD, with a cut-off value of 14.5. Limitations: This study had a small sample size. Conclusion: HCL-32 scores were affected by personality traits, with higher scores for typical extraversion and neuroticism. Clinicians should also consider the patients' personality traits when referring to HCL-32 scores, so as to increase the recognition rate of BD and eliminate false positives. [ABSTRACT FROM AUTHOR]

Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first‐line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first‐episode, drug‐naïve patients and 109 healthy controls from three different multicenters underwent resting‐state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft‐threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future. [ABSTRACT FROM AUTHOR]