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The age of onset (AOO) is a key factor for heterogeneity in major depressive disorder (MDD). Looking at the effect of AOO on symptomatology may improve clinical outcomes. This study aims to examine whether and how AOO affects symptomatology using a machine learning approach and latent profile analysis (LPA).The study enrolled 915 participants diagnosed with MDD from eight hospitals across China. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale. The relationship between symptom profiles and AOO was explored using Random Forest. The effect of AOO on symptom clusters and subtypes was investigated using multiple linear regression and LPA. A continuous AOO indicator was used to conduct the analyses.Based on the Random Forest, symptom profiles were closely associated with AOO. The regression model showed that the severity of neurovegetative symptoms was positively associated with AOO (β = 0.18, p 0.001), and the severity of cognitive-behavioral symptoms was negatively associated with AOO (β = -0.12, p 0.001). LPA demonstrated that the subgroups characterized by suicide and guilt had earlier onset of depression. The subgroup with the lowest global severity of depression had the latest onset.AOO was recalled retrospectively. The relative scarcity of participants with childhood and adolescence onset depression.AOO has an important impact on symptomatology. The findings may enhance clinical evaluations for MDD and assist clinicians in promoting earlier detection and individualized care in vulnerable individuals.

Anhedonia and cognitive impairment are core features of major depressive disorder (MDD), and are essential to the treatment and prognosis. Here, we aimed to investigate anhedonia and its cognitive correlates between first episode of depression (FED) and recurrent depression (RD), which was part of the National Survey on Symptomatology of Depression.In this study, 1400 drug naïve FED patients and 487 on medicine RD patients were included. Differences of anhedonia, cognitive symptoms and other clinical characteristics between groups were compared via Student's t-test, or the chi-square test as appropriate. Partial correlation analysis was used to analyze the correlations between anhedonia and cognitive symptoms after adjusting for potential confounders. A stepwise logistic regression analysis was performed to identify relapse risk factors among symptomatic variables, demographic factors, clinical characteristics and medication use.Compared to FED, RD patients displayed more comprehensive depressive, impaired cognitive and anhedonia symptoms. Cognitive symptoms were significantly related with the anhedonia symptoms with varying aspects. Patients taking emotional stabilizers displayed more abnormal cognitive symptoms, followed by benzodiazepines, and finally SSRIs, SNRIs and TCAs. The effect of drug use on anhedonia is not as extensive as that of cognitive symptoms.Collectively, the results of this investigation advance the knowledge on changes in anhedonia and cognitive symptoms in MDD.As this is a cross sectional study, it is difficult to draw any causal conclusions between cognitive impairment and anhedonia in MDD, and to ascertain the worse cognitive performances identified here were induced by current drug use.

Background. Sleep disturbances and benzodiazepine (BZD)/Z-drug use are common in patients with bipolar disorder (BD). Objective. To investigate the short- and long-term effects of BZD/Z-drug use during acute affective episode. Methods. Participants diagnosed with BD as well as sleep disturbance chose BZDs/Z-drugs or not at will. Manic and depressive symptoms were assessed by Mental Disorders Questionnaire (MDQ) and Quick Inventory of Depressive Symptoms (QIDS) as self-reporting surveys. The participants were assessed by trained evaluators at baseline and months 1, 3, 6, and 9. Results. 61 patients with BD combined sleep disturbances were studied. At baseline, patients who used BZDs/Z-drugs had more amount of mood stabilizers ( p = 0.038 ), other psychotropic medications ( p = 0.040 ), and more risk of suicide attempt ( p = 0.019 ). The BZD/Z-drug group had a significantly higher QIDS reductive ratio as compared with the no BZD/Z-drug group at month 1; no significant differences in the variability of MDQ, QIDS reductive ratio, or recurrence rate were found between these two groups at baseline, month 1, month 3, month 6, or month 9. Conclusions. During acute affective episode, patients with BD combined sleep disturbance who took BZDs/Z-drugs tended to use more amount of mood stabilizers. Polytherapy of BZDs/Z-drugs or other psychiatric drugs could increase suicide attempt during an acute affective episode. BZD/Z-drug use, however, had a significant effect on helping depressive symptoms alleviate during affective period.

Background: Most patients with the major depressive disorder (MDD) have varying degrees of impaired social functioning, and functional improvement often lags behind symptomatic improvement. However, it is still unclear if certain neurobiological factors underlie the deficits of social function in MDD. The aim of this study was to investigate the biomarkers of social function in MDD using structural magnetic resonance imaging (MRI). Methods: 3T anatomical MRI was obtained from 272 subjects including 46 high-functioning (high-SF, Sheehan Disability Scale (SDS) rating < 18) and 63 low-functioning (low-SF, SDS score ≥ 18) patients with MDD and 163 healthy controls (HC). Voxel-based morphometry (VBM) was employed to locate brain regions with grey matter (GM) volume differences in relation to social function in MDD. Regions showing GM differences in relation to social function at baseline were followed up longitudinally in a subset of 38 patients scanned after 12-week treatment. Results: Volume of right parahippocampal gyrus (rPHG) was significantly reduced in low-SF patients with MDD when compared to high-SF ones (FDR-corrected p < 0.05). Over 12 weeks of follow-up, though SF improved overall, the high and low-SF subgroups continued to differ in their SF, but had no progressive changes in PHG volume. Limitations: Limited functional assessment, high drop-out rate and median-based grouping method. Conclusions: Greater GM volume (GMV) of the rPHG may mark better social function in patients with MDD.

Background This study aimed to explore subtypes of treatment-resistant depression (TRD). Methods Latent class analysis (LCA) was performed on clinical and demographic data collected from 375 patients with TRD. Clinical variables were compared across subtypes. Treatment outcomes across subtypes of TRD were compared separately for those within each subtype with anxiety (those with a HRSD-17 anxiety/somatization factor score ≥ 7) and those without anxiety. LCA subtypes were compared using Cochran's and Mantel–Haenszel χ2 test, respectively. Unordered multinomial logistic regression was used to assess clinical correlates of TRD subtypes. Results Three categories were detected: severe depression (66%), moderate depression with anxiety (9%) and mild depression with anxiety/somatization (25%). Gender, age, age at first onset, family monthly income, number of hospitalizations, HRSD-17 and clinical global impression-severity (CGI) scores were significantly different across the three groups. Remission rates were significantly different among anxious cases with severe (43.75%), moderate (22.73%) and mild (26.25%) depression subtypes. Compared to cases in the mild depression group, those in the severe depression group had a greater likelihood of being male, having a later age of first onset, higher numbers of hospitalization, higher HRSD-17 and CGI total scores, and lower family income. Those in the moderate depression group were more likely to be male and have lower family income than those in the mild depression group. Limitations Representative bias, relatively small sample size, unbalanced group size and incomplete indicator variables might have a negative effect on the validity and generalization of the findings. Conclusions Depression severity could be a basis for subtype classification of patients with TRD. The classification of latent class of TRD observed in our study was similar to the structure found in MDD. Longitudinal research into the stability of the latent structure of TRD across illness course is merited as is research into treatment outcomes for TRD subtypes.

Somatic symptoms are prevalent in patients with major depressive disorder (MDD) and often associated with a high risk of suicide. However, which somatic symptoms display as significant risk factors for suicidal ideation (SI) is still poorly understood in MDD.Two thousand and seventeen Chinese patients with first-episode MDD from the National Survey on Symptomatology of Depression were included in this study. A doctor-rating assessment questionnaire was constructed to evaluate depression related somatic symptoms, and stepwise logistic regression analysis was performed to explore the relationship between somatic symptoms and SI.Our results showed a high prevalence of current SI in first-episode MDD (50.87%), while no significant gender differences (53.32% vs. 49.26%, P = 0.076) were observed. In addition, patients who have more somatic symptoms would be at the higher risk to elicit SI, and stepwise logistic regression analysis indicated that age (β = -0.020, P 0.001), Pre-verbal physical complaints (β = 0.356, P = 0.001), Sensory system complaints (β = 0.707, P = 0.000), Other pain conditions (β = 0.434, P 0.001), Late insomnia (β = 0.267, P = 0.008), Hypersomnia (β = 0.936, P 0.001), Weight loss (β = 0.272, P = 0.006), Hyposexuality (β = 0.513, P = P 0.001) were strongly associated with current SI in first-episode Chinese major depression.Somatic symptoms are strongly associated with SI in first-episode MDD. It is suggestive for clinicians to show concerns for patients' somatic symptoms in practice.

Background: Sleep disturbance and benzodiazepines (BZDs)/Z-drugs use are known to be common during affective episodes. Hence, we identified the probable outcomes of bipolar disorder that correlate with BZDs/Z-drugs use, aside from mood symptoms. We conducted an open-label, prospective study to describe the current use of BZDs and Z-drugs by patients with bipolar disorder during affective episodes. We evaluated the difference of characteristics between bipolar patients with sleep disturbance who chose BZDs/Z-drugs, and those who did not chose the drugs during and after affective disorder. The influences of BZDs/Z-drugs use on suicide attempt and psychotic symptoms during affective disorder were also investigated. Results: Seventy patients with current affective episodes were studied. Among them, 61 had sleep disturbances. The amount of mood stabilizers use in the BZDs/Z-drugs group was significantly greater than that in the no BZDs/Z-drugs group (p=0.038) during affective episodes. After affective episode, sleep disturbances, especially midnight wakes, became more improved in BZDs/Z-drugs group compared to the no BZDs/Z-drugs group. By contrast, attention and decisiveness became more improved in the no BZDs/Z-drugs group than in the BZDs/Z-drugs group. Furthermore, we observed that BZDs/Z-drugs had an OR of 4.338 (95% CI 1.068-17.623, p=0.040), and other psychiatric drugs had an OR of 1.835 (95% CI 1.105-3.047, p=0.019) in relation to suicide attempt. After nine months, we found that BZDs/Z-drugs use was of no significant effect to depressive or manic severity, or to recurrence rate.Conclusion: BZDs/Z-drugs use have no significant influence on variations in depressive or manic severity during the course of an affective episode. Nevertheless, BZDs/Z-drugs users took a greater amount of mood stabilizers than no BZDs/Z-drugs users. Finally, BZDs/Z-drugs or other psychiatric drugs polytherapy was regarded as a risk factor of suicide attempt during an affective episode.

The advances in the Internet and related technologies may lead to changes in professional roles of psychiatrists and psychotherapists. The application of artificial intelligence (AI) and electronic measurement-based care (eMBC) in the treatment of depressive disorder has addressed more interest. AI could play a role in population health management and patient administration as well as assist physicians to make a decision in the real-world clinical practice. The eMBC strengthens MBC through web/mobile devices and telephone consulting services, to monitor disease progression, and customizes the MBC interface in electronic medical record systems (EMRs).

Background Symptomatology differences of major depressive disorder (MDD) in psychiatric and general hospitals in China leads to possible misdiagnosis. Looking at the symptomatology of first-visit patients with MDD in different mental health services, and identifying predictors of health-seeking behavior using machine learning may help to improve diagnostic accuracy. Methods 1500 patients first diagnosed with MDD were recruited from 16 psychiatric hospitals and 16 general hospitals across China. Socio-demographic characteristics, causal attribution, symptoms of depression within and outside Diagnostic and Statistical Manual of Mental Disorders (DSM) framework were collected using a self-made questionnaire. A predictive model of 62 variables was established using Random forest, symptom frequencies of patients in general hospitals and psychiatric hospitals were compared. Results The machine learning approach revealed that symptoms were strong predictors of health-seeking behavior among patients with MDD. General hospitals patients had higher frequencies of suicidal ideation (χ2=15.230, p Limitations Stigma and preference bias were not measured. Severity of current depressive episodes was not assessed. Data of previous episode(s) was not presented. Conclusions Symptom evaluation targeting specific patient population in different hospitals is crucial for diagnostic accuracy. Suicide prevention reliant on collaboration between general hospitals and psychiatric hospitals is required in the future construction of Chinese mental health system.

The advances in the Internet and related technologies may lead to changes in professional roles of psychiatrists and psychotherapists. The application of artificial intelligence (AI) and electronic measurement-based care (eMBC) in the treatment of depressive disorder has addressed more interest. AI could play a role in population health management and patient administration as well as assist physicians to make a decision in the real-world clinical practice. The eMBC strengthens MBC through web/mobile devices and telephone consulting services, to monitor disease progression, and customizes the MBC interface in electronic medical record systems (EMRs).

BackgroundInflammation that is mediated by microglia activation plays an important role in the pathogenesis of depression. Microglia activation can lead to an increase in the levels of proinflammatory cytokines, including TNF-α, which leads to neuronal apoptosis in the specific neural circuits of some brain regions, abnormal cognition and treatment-resistant depression (TRD). Protein kinase C (PKC) is a key regulator of the microglia activation process. We assume that the abnormality in PKC might result in abnormal microglia activation, neuronal apoptosis, significant changes in emotional and cognitive neural circuits, and TRD. In the current study, we plan to target at the PKC signal pathway to improve the TRD treatment outcome.Methods and analysisThis is a 12-week, ongoing, randomised, placebo-controlled trial. Patients with TRD (N=180) were recruited from Shanghai Mental Health Center, Shanghai Jiao Tong University. Healthy control volunteers (N=60) were recruited by advertisement. Patients with TRD were randomly assigned to ‘escitalopram+golimumab (TNF-α inhibitor)’, ‘escitalopram+calcium tablet+vitamin D (PKC activator)’ or ‘escitalopram+placebo’ groups. We define the primary outcome as changes in the 17-item Hamilton Depression Rating Scale (HAMD-17). The secondary outcome is defined as changes in anti-inflammatory effects, cognitive function and quality of life.DiscussionThis study might be the first randomised, placebo-controlled trial to target at the PKC signal pathway in patients with TRD. Our study might help to propose individualised treatment strategies for depression.Trial registration numberThe trial protocol is registered with ClinicalTrials.gov under protocol ID 81930033 and ClinicalTrials.gov ID NCT04156425.

It has been suggested that inflammation is involved in the pathophysiology of depression. As tissue-specific macrophages in the central nervous system (CNS), microglia play an important role in neuroinflammation. Resident microglia become activated towards the pro-inflammatory (M1) phenotype or the anti-inflammatory (M2) phenotype during neuroinflammation. In the CNS, neurons report to microglia regarding their statuses and can regulate microglial activation, while microglia also modulate neuronal activities, including neuroplasticity. The molecular mechanisms underlying the communication between microglia and neurons, which include intracellular and extracellular signalling pathways, might be complex and of great importance for new research on the pathogenesis of depression. The present review aims to discuss the common cellular and molecular mechanisms for microglial activation and aberrant neuroplasticity in depression and the role of these processes in the pathogenesis of depression.

Background Suicide risk is greatly increased in depression. Detection of those at risk is clinically important. Hence, this study aimed to evaluate the prevalence and identify independent risk factors associated with suicidal ideation (SI) in a widespread symptomatology within and outside DSM framework. Methods This study was part of the National Survey on Symptomatology of Depression (NSSD) which was designed to investigate the magnitude of symptoms of current major depressive episode in China. Stepwise multivariable logistic regression was performed to examine the independent risk factors for SI, including variables that are statistically significant in univariate analysis. Receiver operating characteristic (ROC) was used to evaluate the performance of the regression model. Results A total of 3275 patients (1293 males and 1982 females) were included in our analysis. Of these, 1750 patients (53.4%) had SI. Independent risk predictors included crying (P = 0.000; odds ratio = 1.827), helplessness (P = 0.000; odds ratio = 1.514), worthlessness (P = 0.001; odds ratio = 1.359), hopelessness (P = 0.000; odds ratio = 1.805), unusually restless (P = 0.005; odds ratio = 1.276), self-harm (P = 0.000; odds ratio = 3.385), mood-incongruent psychosis (P = 0.000; odds ratio = 2.782), feeling losing control of oneself (P = 0.009; odds ratio = 1.352), hypersomnia (P = 0.000; odds ratio = 1.805), sensory system complaints (P = 0.000; odds ratio = 1.546), derealization (P = 0.006; odds ratio = 1.580), guilt (P = 0.002; odds ratio = 1.332), suicidal attempts (P = 0.000; odds ratio = 2.841), male gender (P = 0.001; odds ratio = 0.756), the total course of depression (P = 0.010; odds ratio = 1.003) in the regression model. In addition, the areas under the curve of the ROC and the accuracy for the regression model were 0.80 and 0.76, respectively. Conclusions This study provided an effective risk model for SI in MDD and indicated that all these factors in our model allow better the employment of preventative measures.