Objective:To compare the differences in clinical characteristics among the patients at clinical high risk for bipolar disorder(CHR-BD),the patients with bipolar disorder(BD),and the healthy controls(HC)at low risk,and to provide the basis for the diognasis and treatment of CHR-BD.Methods:For the first time,the BD risk criteria and prospective structured assessment tools were jointly used in outpatients aged 16-30 years,and 43 CHR-BD patients were included to ensure the accuracy of the assessment.Meanwhile,33 BD patients and 32 HC subjects were also enrolled.The clinical symptoms,neurocognitive function,and global functional levels of the subjects in the three groups were evaluated using observer-rated and self-rated tools.The CHR-BD and BD groups were combined,and Logistic regression analysis was used to identify the independent influencing factors related to diagnostic status;Pearson or Spearman correlation analysis was used to analyze the correlations between the global functional levels and the symptoms or neurocognitive characteristics of the patients in CHR-BD and BD groups.Results:There were statistically significant differences in the scores of symptom and global functional level scales among HC,CHR-BD,and BD groups(P<0.05).Compared with HC group,the scores of mood symptoms(anxiety,depression,and mania/hypomania),psychotic symptoms,total affective temperament questionnaire scores,and some dimensions(cyclothymic,depressive,irritable,and anxious temperaments)in CHR-BD and BD groups were significantly increased(P<0.001),while the global functional levels were significantly decreased(P<0.001).Compared with BD group,the lowest global functional level score in the past year in CHR-BD group was significantly increased(P=0.022),while the current global functional level score was significantly decreased(P=0.005).No significant differences were observed in neurocognitive function scores among the three groups(P>0.05).The lowest global functional level score in the past year was an independent influencing factor for BD diagnosis[odds ratio(OR)=0.952,95%confidence interval(CI):0.917-0.988,P=0.010].In both CHR-BD and BD patients,the current global functional levels were negatively correlated with depressive(r=-0.417,P=0.005;r=-0.617,P<0.001)and anxiety symptoms(r=-0.360,P=0.018;r=-0.506,P=0.003).In BD patients,the current global functional level was negatively correlated with lifetime manic/hypomanic symptoms(r=-0.360,P=0.039),psychotic symptoms(r=-0.502,P=0.003),and affective temperament scores(r=-0.479,P=0.005),while the lowest global functional level in the past year was negatively correlated with lifetime manic/hypomanic symptoms(r=-0.391,P=0.024).Conclusion:CHR-BD patients share similar mood symptom characteristics with BD patients,and their global functional levels are negatively correlated with depressive and anxiety symptoms.BD patients exhibit worse lowest global functional levels in the past year,and their global functional levels are negatively correlated with manic/hypomanic symptoms.

BACKGROUND: The optimal antidepressant dosages remain controversial. This study aimed to analyze the efficacy of antidepressants and characterize their dose-response relationships in the treatments of major depressive disorders (MDD). METHODS: We searched multiple databases, including the Embase, Cochrane Central Register of Controlled Trials, PubMed, and Web of Science, for the studies that were conducted between January 8, 2016, and April 30, 2023. The studies are double-blinded, randomized controlled trials (RCTs) involving the adults (≥18 years) with MDD. The primary outcomes were efficacy of antidepressant and the dose-response relationships. A frequentist network meta-analysis was conducted, treating participants with various dosages of the same antidepressant as a single therapy. We also implemented the model-based meta-analysis (MBMA) using a Bayesian method to explore the dose-response relationships. RESULTS: The network meta-analysis comprised 135,180 participants from 602 studies. All the antidepressants were more effective than the placebo; toludesvenlafaxine had the highest odds ratio (OR) of 4.52 (95% confidence interval [CI]: 2.65-7.72), and reboxetine had the lowest OR of 1.34 (95%CI: 1.14-1.57). Moreover, amitriptyline, clomipramine, and reboxetine showed a linear increase in effect size from low to high doses. The effect size of toludesvenlafaxine increased significantly up to 80 mg/day and subsequently maintained the maximal dose up to 160 mg/day while the predictive curves of nefazodone were fairly flat in different dosages. CONCLUSIONS: Although most antidepressants were more efficacious than placebo in treating MDD, no consistent dose-response relationship between any antidepressants was observed. For most antidepressants, the maximum efficacy was achieved at lower or middle prescribed doses, rather than at the upper limit. REGISTRATION No. CRD42023427480; https://www.crd.york.ac.uk/prospero/display_record.php?
Humans ; Antidepressive Agents/therapeutic use* ; Depressive Disorder, Major/drug therapy* ; Dose-Response Relationship, Drug ; Randomized Controlled Trials as Topic

Objective:To examine the effect of antidepressant treatment on the longitudinal depressive burden in patients with bipolar depression.Methods:Subjects were recruited from a national multicenter, naturalistic observational project: Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder study (CAFE-BD). A total of 110 patients with bipolar depression (51 males, 59 females; aged 18-64 years, mean age 34.4±11.1 years) were consecutively enrolled between January 2012 and December 2013 from outpatients and inpatients of nine medical institutions, including six psychiatric hospitals and three general hospitals. Based on the use of antidepressants as defined in this study, patients were classified into a medicated group (Ads, n=74) and a non-medicated group (nAds, n=36). Diagnosis of bipolar depression was confirmed using the MINI (Chinese version), and baseline and follow-up assessments were conducted using the Assessment of Mood Disorders Evaluation (ADE) and the Clinical Monitoring Form (CMF). Depression burden indicators, including aggregate depression scores (SUM-D), number of depressive symptoms (NUM-D), and total depression burden, were compared between the Ads group and nAds group at mid-term (the 6 th month) and endpoint (the 12 nd month). Longitudinal changes in these indicators were also analyzed. Results:The proportion of bipolar depressive patients on antidepressants was 67% (74/110). Among them, 85% (63/74) were taking antidepressants at baseline; this dropped to 76% (56/74) at mid-term, and 64% (47/74) at the endpoint. SUM-D were higher in the Ads group than in the nAds group at baseline (9 (6.5, 11) vs 7.38 (5.5, 9.0); W=1 712.00, P=0.015), and there was no statistically significant difference in NUM-D and total depressive burden between two groups at any time points ( P>0.05). Compared to baseline, the Ads group had significantly lower SUM-D (0.5 (0, 1), 1.33 (0.5, 2.5) vs. 9 (6.5, 11); W=2 770.00, 2 743.00), NUM-D (0 (0, 0), 0 (0, 1) vs. 7 (5, 8); W=2 621.00, 2 601.50) and total depressive burden (c 2=64.36, 59.00) at both mid-term and endpoint (all P<0.001); While SUM-D (0.59 (0.4, 0.7), 1 (0.8, 2.5) vs. 7.38 (5.5, 9.0); W=664.50, W=666.00), NUM-D (0 (0, 0), 0 (0, 1) vs. 6 (4, 7); W=527.00, 528.00) and total depression burden ( χ 2=31.00, 31.00) in the nAds group were also significantly decreased at both mid-term and endpoint (all P<0.001). There were no statistically significant differences in the changes in depression burden indicators between the two groups from baseline to mid-follow-up or endpoint, nor from mid-follow-up to endpoint ( P>0.05). Conclusion:In a 12-month real-world naturalistic follow-up study, both medicated and non-medicated bipolar depression groups experienced significant and similar reductions in depression burden.

Objective:To examine the effect of antidepressant treatment on the longitudinal depressive burden in patients with bipolar depression.Methods:Subjects were recruited from a national multicenter, naturalistic observational project: Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder study (CAFE-BD). A total of 110 patients with bipolar depression (51 males, 59 females; aged 18-64 years, mean age 34.4±11.1 years) were consecutively enrolled between January 2012 and December 2013 from outpatients and inpatients of nine medical institutions, including six psychiatric hospitals and three general hospitals. Based on the use of antidepressants as defined in this study, patients were classified into a medicated group (Ads, n=74) and a non-medicated group (nAds, n=36). Diagnosis of bipolar depression was confirmed using the MINI (Chinese version), and baseline and follow-up assessments were conducted using the Assessment of Mood Disorders Evaluation (ADE) and the Clinical Monitoring Form (CMF). Depression burden indicators, including aggregate depression scores (SUM-D), number of depressive symptoms (NUM-D), and total depression burden, were compared between the Ads group and nAds group at mid-term (the 6 th month) and endpoint (the 12 nd month). Longitudinal changes in these indicators were also analyzed. Results:The proportion of bipolar depressive patients on antidepressants was 67% (74/110). Among them, 85% (63/74) were taking antidepressants at baseline; this dropped to 76% (56/74) at mid-term, and 64% (47/74) at the endpoint. SUM-D were higher in the Ads group than in the nAds group at baseline (9 (6.5, 11) vs 7.38 (5.5, 9.0); W=1 712.00, P=0.015), and there was no statistically significant difference in NUM-D and total depressive burden between two groups at any time points ( P>0.05). Compared to baseline, the Ads group had significantly lower SUM-D (0.5 (0, 1), 1.33 (0.5, 2.5) vs. 9 (6.5, 11); W=2 770.00, 2 743.00), NUM-D (0 (0, 0), 0 (0, 1) vs. 7 (5, 8); W=2 621.00, 2 601.50) and total depressive burden (c 2=64.36, 59.00) at both mid-term and endpoint (all P<0.001); While SUM-D (0.59 (0.4, 0.7), 1 (0.8, 2.5) vs. 7.38 (5.5, 9.0); W=664.50, W=666.00), NUM-D (0 (0, 0), 0 (0, 1) vs. 6 (4, 7); W=527.00, 528.00) and total depression burden ( χ 2=31.00, 31.00) in the nAds group were also significantly decreased at both mid-term and endpoint (all P<0.001). There were no statistically significant differences in the changes in depression burden indicators between the two groups from baseline to mid-follow-up or endpoint, nor from mid-follow-up to endpoint ( P>0.05). Conclusion:In a 12-month real-world naturalistic follow-up study, both medicated and non-medicated bipolar depression groups experienced significant and similar reductions in depression burden.

Objective:To utilize a deep learning approach based on speech to distinguish between depressive and manic mood states in patients with bipolar disorder (BD).Methods:Sixty-one BD patients who visited the outpatient department of psychiatry at Peking University Sixth Hospital were recruited to participate in the study from June 2018 to March 2022. Quick Inventory of Depressive Symptomatology, Mood Disorder Questionnaire and Young Mania Rating Scale were used to determine patients′ mood states. The voices of the patients were recorded, including 190 samples during the patient′s remission, depressive, and manic mood period respectively. A total of 136 features were extracted from the voice samples, including Mel-frequency cepstral coefficients and zero-crossing rates using the speech analysis library in Python. A LIGHT-SERNET-based network was then used to train a model for emotion classification. Accuracy is used to evaluate the performance of the model, using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic curve (ROC) to evaluate the predictive results of model for three mood states. Kruskal-Wallis H tests or χ 2 tests were conducted to compare the differences among the demographic information of three groups. Results:There were statistically significant differences among the three groups in age ( H=25.83, P<0.001), years of education ( H=25.25, P<0.001) and marital status (χ 2=23.81, P<0.001). There is no significant difference in gender (χ 2=4.63, P=0.099). The accuracy of the model in detecting the three emotional states was 0.84. The sensitivity and specificity in detecting remission were 0.88 and 0.93, respectively, and the positive predictive value and negative predictive value were 0.87 and 0.94, respectively. The sensitivity and specificity in detecting depressive episodes were 0.82 and 0.92, respectively, and the positive predictive value and negative predictive value were 0.84 and 0.92, respectively. The sensitivity and specificity in detecting manic episodes were 0.82 and 0.91, respectively, and the positive predictive value and negative predictive value were 0.83 and 0.91, respectively. The areas of the receiver operation characteristic curve for the three mood states were similar and all exceeded 0.90. Conclusion:The LIGHT-SERNET-based deep learning model shows good discrimination ability between depressive and manic mood states based on speech analysis.

Objective:To utilize a deep learning approach based on speech to distinguish between depressive and manic mood states in patients with bipolar disorder (BD).Methods:Sixty-one BD patients who visited the outpatient department of psychiatry at Peking University Sixth Hospital were recruited to participate in the study from June 2018 to March 2022. Quick Inventory of Depressive Symptomatology, Mood Disorder Questionnaire and Young Mania Rating Scale were used to determine patients′ mood states. The voices of the patients were recorded, including 190 samples during the patient′s remission, depressive, and manic mood period respectively. A total of 136 features were extracted from the voice samples, including Mel-frequency cepstral coefficients and zero-crossing rates using the speech analysis library in Python. A LIGHT-SERNET-based network was then used to train a model for emotion classification. Accuracy is used to evaluate the performance of the model, using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic curve (ROC) to evaluate the predictive results of model for three mood states. Kruskal-Wallis H tests or χ 2 tests were conducted to compare the differences among the demographic information of three groups. Results:There were statistically significant differences among the three groups in age ( H=25.83, P<0.001), years of education ( H=25.25, P<0.001) and marital status (χ 2=23.81, P<0.001). There is no significant difference in gender (χ 2=4.63, P=0.099). The accuracy of the model in detecting the three emotional states was 0.84. The sensitivity and specificity in detecting remission were 0.88 and 0.93, respectively, and the positive predictive value and negative predictive value were 0.87 and 0.94, respectively. The sensitivity and specificity in detecting depressive episodes were 0.82 and 0.92, respectively, and the positive predictive value and negative predictive value were 0.84 and 0.92, respectively. The sensitivity and specificity in detecting manic episodes were 0.82 and 0.91, respectively, and the positive predictive value and negative predictive value were 0.83 and 0.91, respectively. The areas of the receiver operation characteristic curve for the three mood states were similar and all exceeded 0.90. Conclusion:The LIGHT-SERNET-based deep learning model shows good discrimination ability between depressive and manic mood states based on speech analysis.