Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression.

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

Clinical distinctions in symptomatology and psychiatric comorbidities between misdiagnosed bipolar I and bipolar II disorder versus major depressive disorder.

BACKGROUND: To explore the demographic and clinical features of current depressive episode that discriminate patients diagnosed with major depressive disorder (MDD) from those with bipolar I (BP-I) and bipolar II (BP-II) disorder who were misdiagnosed as having MDD . METHODS: The Mini-International Neuropsychiatric Interview (MINI) assessment was performed to establish DSM-IV diagnoses of MDD, and BP-I and BP-II, previously being misdiagnosed as MDD. Demographics, depressive symptoms and psychiatric comorbidities were compared between 1463 patients with BP-I, BP-II and MDD from 8 psychiatric settings in mainland China. A multinomial logistic regression model was performed to assess clinical correlates of diagnoses. RESULTS: A total of 14.5% of the enrolled patients initially diagnosed with MDD were eventually diagnosed with BP. Broad illness characteristics including younger age, higher prevalence of recurrence, concurrent dysthymia, suicidal attempts, agitation, psychotic features and psychiatric comorbidities, as well as lower prevalence of insomnia, weight loss and somatic symptoms were featured by patients with BP-I and/or BP-I, compared to those with MDD. Comparisons between BP-I and BP-II versus MDD indicated distinct symptom profiles and comorbidity patterns with more differences being observed between BP-II and MDD, than between BP-I and MDD . CONCLUSION: The results provide evidence of clinically distinguishing characteristics between misdiagnosed BP-I and BP- II versus MDD. The findings have implications for guiding more accurate diagnoses of bipolar disorders.

Altered N-linked glycosylation in depression: A pre-clinical study.

BACKGROUND: Neuroimmune plays an important role in major depressive disorders (MDD). N-linked protein glycosylation (NLG) might contribute to depression by regulating the neuroinflammatory response. As microglia is the main executor of neuroimmune function in the central neural system (CNS), targeting the process of N-linked protein glycosylation of microglia in the mice used for studying depression might potentially offer new avenues for the strategy for MDD. METHODS: The chronic unpredictable mild stress (CUMS) mouse model was established for the whole brain microglia isolating. Then, RNA samples of microglia were extracted for transcriptome sequencing and mRNA analysis. Immunofluorescence (IF) was used to identify the expression level of NLG-related enzyme, B4galt1, in microglia. RESULTS: The data showed that NLG was positively related to depression. Moreover, the NLG-related gene, B4galt1 increased expression in the microglia of CUMS mice. Then, the inhibition of NLG reversed the depressive behavior in CUMS mice. The expression level of B4galt1 in CUMS mice was upregulating following the NLG-inhibitor treatment. Similar results haven't been observed in neurons. Information obtained from these experiments showed increasing expression of B4galt1 in microglia following depressive-like behaviors. CONCLUSIONS: These findings indicate that NLG in microglia is associated with MDD, and suggest that therapeutically targeting NLG might be an effective strategy for depression. LIMITATIONS: How to modulate the B4galt1 or NLG pathways in microglia efficiently and economically request new technologies.

Randomizing Human Brain Function Representation for Brain Disease Diagnosis.

Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including (1) subjective selection bias in choosing from various brain atlases, (2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; (3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

Relationship between biological rhythm dysregulation and suicidal ideation in patients with major depressive disorder.

BACKGROUND: Although the disturbance of circadian rhythms represents a significant clinical feature of major depressive disorder (MDD), the relationship between biological rhythm disturbances and the severity of suicidal ideation in individuals with MDD remains unclear. We aimed to explore the characteristics of different biological rhythm dimensions in MDD and their association with the severity of depressive symptoms and suicidal ideation. METHODS: A total of 50 MDD patients and 50 healthy controls were recruited and their general information was collected. The severity of depressive symptoms was assessed with the 17-item Hamilton Depression Rating Scale (HDRS17). The intensity of suicidal ideation was evaluated with the Beck Scale for Suicide Ideation (BSS). The Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN) scale was utilized to assess the participants' biological rhythm dysregulation. Multiple logistic regression analysis was conducted to explore the relationship between biological rhythm and the risk of MDD. Multiple linear regression analysis was performed in the MDD group to investigate the relationship between different biological rhythm dimensions and suicide ideation. RESULTS: Significant differences were observed between the MDD group and the control group in total BRIAN score (Z=-5.41, P < 0.001) as well as scores for each dimension. After adjusting for confounding factors, multiple logistic regression analysis revealed a significant association between total BRIAN score and the presence of MDD (OR = 1.20, 95% CI = 1.10-1.29, P < 0.001), as well as between scores in different BRIAN dimensions and the presence of MDD (activity: OR = 1.47, 95% CI = 1.24-1.74, P < 0.001; sleep: OR = 1.52, 95% CI = 1.28-1.79, P < 0.001; social: OR = 1.80, 95% CI = 1.32-2.46, P < 0.001; eating pattern: OR = 1.34, 95% CI = 1.12-1.60, P = 0.001). In patients with MDD, linear regression analysis demonstrated a positive relationship between BSS scores and BRIAN eating pattern scores (ß = 0.34, P = 0.022), even after adjusting for demographic factors and the severity of depression. CONCLUSIONS: Patients with MDD exhibited significantly higher levels of dysregulation in all four biological rhythm dimensions compared to healthy controls and the degree of dysregulation was associated with the severity of depression. More importantly, dysregulation of eating pattern may increase the intensity of suicidal ideation in MDD, thus elevating the risk of suicide.

Program of algorithm for pharmacological treatment of major depressive disorder in China: Benefits or not?

Background: This research was designed to investigate Algorithm Guided Treatment (AGT) and clinical traits for the prediction of antidepressant treatment outcomes in Chinese patients with major depressive disorder (MDD). Methods: This study included 581 patients who had reached treatment response and 406 patients remained non-responded observed after three months of treatment. Sociodemographic factors, clinical traits, and psychiatric rating scales for evaluating therapeutic responses between the two groups were compared. Logistic regression analysis was adopted to determine the risk factors of unresponsive to antidepressant (URA) in MDD. Kaplan-Meier survival analysis was utilized to compare the therapeutic response between AGT and treatment as usual (TAU). Results: Compared to the MDD responsive to antidepressant (RA) group, the URA group had significantly lower rates of the following clinical traits: married status, anxious distress, moderate to severe depressive symptoms, and higher rates of comorbidity (p-value < 0.05). Logistic Regression Analysis showed that eight clinical traits from psychiatric rating scales, such as anxious characteristics, were correlated positively with URA, while the other eight symptoms, such as autonomic symptoms, were negatively correlated. Time to symptomatic remission was longer in TAU without statistically significant (p-value = 0.11) by log-rank testing. Conclusions: The factors may affect the therapeutic responses and compliance of patients, increasing the non-response risk for antidepressants. Therapeutic responses might be improved by increasing the clarification and elucidation of different symptom clusters of patients. Benefits on treatment response to AGT were not found in our study, indicating a one-size-fits-all approach may not work.Trial Registration: We registered as a clinical trial at the International Clinical Trials Registry Platform (No. NCT01764867) and obtained ethical approval 2012-42 from SMHC.

Does decreased autophagy and dysregulation of LC3A in astrocytes play a role in major depressive disorder?

Astrocytic dysfunction contributes to the molecular pathogenesis of major depressive disorder (MDD). However, the astrocytic subtype that mainly contributes to MDD etiology and whether dysregulated autophagy in astrocytes is associated with MDD remain unknown. Using a single-nucleus RNA sequencing (snRNA-seq) atlas, three astrocyte subtypes were identified in MDD, while C2 State-1Q astrocytes showed aberrant changes in both cell proportion and most differentially expressed genes compared with other subtypes. Moreover, autophagy pathways were commonly inhibited in astrocytes in the prefrontal cortices (PFCs) of patients with MDD, especially in C2 State-1Q astrocytes. Furthermore, by integrating snRNA-seq and bulk transcriptomic data, we found significant reductions in LC3A expression levels in the PFC region of CUMS-induced depressed mice, as well as in postmortem PFC tissues and peripheral blood samples from patients with MDD. These results were further validated by qPCR using whole-blood samples from patients with MDD and healthy controls. Finally, LC3A expression in the whole blood of patients with MDD was negatively associated with the severity of depressive symptoms. Overall, our results underscore autophagy inhibition in PFC astrocytes as a common molecular characteristic in MDD and might reveal a novel potential diagnostic marker LC3A.

Potential mechanisms of non-suicidal self-injury (NSSI) in major depressive disorder: a systematic review.

Background: Non-suicidal self-injury (NSSI) is a frequent and prominent phenomenon in major depressive disorder (MDD). Even though its prevalence and risk factors are relatively well understood, the potential mechanisms of NSSI in MDD remain elusive. Aims: To review present evidence related to the potential mechanisms of NSSI in MDD. Methods: According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, articles for this systematic review were searched on Medline (through PubMed), Embase (through Elsevier), PsycINFO (through OVID) and Web of Science databases for English articles, as well as China National Knowledge Infrastructure (CNKI), SinoMed, Wanfang Data, and the Chongqing VIP Chinese Science and Technology Periodical (VIP) Databases for Chinese articles published from the date of inception to 2 August 2022. Two researchers (BW, HZ) independently screened studies based on inclusion and exclusion criteria and assessed their quality. Results: A total of 25 157 studies were searched. Only 25 of them were ultimately included, containing 3336 subjects (1535 patients with MDD and NSSI, 1403 patients with MDD without NSSI and 398 HCs). Included studies were divided into 6 categories: psychosocial factors (11 studies), neuroimaging (8 studies), stress and hypothalamic-pituitary-adrenal (HPA) axis (2 studies), pain perception (1 study), electroencephalogram (EEG) (2 studies) and epigenetics (1 study). Conclusions: This systematic review indicates that patients with MDD and NSSI might have specific psychosocial factors, aberrant brain functions and neurochemical metabolisms, HPA axis dysfunctions, abnormal pain perceptions and epigenetic alterations.

Associations between sleep disturbance, inflammatory markers and depressive symptoms: Mediation analyses in a large NHANES community sample.

Both depression and sleep disturbance have been linked to inflammation. However, the role that inflammation plays in the relationship between sleep disturbance and depression remains unclear. We examined pairwise associations between inflammatory markers (neutrophil-to-lymphocyte ratio [NLR] and C-reactive protein level [CRP]), sleep disturbance, and depressive symptoms in a robust, ethnically diverse sample (n = 32,749) from the National Health and Nutrition Examination Survey (NHANES). We found higher levels of inflammatory markers in participants with depression and/or sleep disturbance compared to those without depression or sleep disturbance. Sleep disturbance was positively associated with inflammatory markers and depressive symptoms even after considering a wide range of potential confounders (e.g., age, sex, body mass index). Inflammatory marker levels were nonlinearly associated with depressive symptoms and were positively associated with depressive symptoms after reaching the inflection point (NLR, 1.67; CRP, 0.22 mg/dL). Inflammatory markers mediated a marginal portion (NLR, 0.0362%, p = 0.026; CRP, 0.0678%; p = 0.018) of the potential effects of sleep disturbance on depressive symptoms. Our research showed that inflammatory markers, sleep disturbance, and depression are pairwise correlated. Increased inflammatory markers levels slightly mediate the association between sleep disturbance and depression.

A Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD): study protocol for a randomized clinical trial and prognosis study.

BACKGROUND: Major depressive disorder (MDD) with atypical features, namely depression with atypical features (AFD), is one of the most common clinical specifiers of MDD, closely associated with bipolar disorder (BD). However, there is still a lack of clinical guidelines for the diagnosis, treatment, and prognosis of AFD. Our study mainly focuses on three issues about how to identify AFD, what is the appropriate individualized treatment for AFD, and what are the predictive biomarkers of conversion to BD. METHODS: The Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD) is a multicenter, prospective, open-label study consisting of a 12-week randomized controlled trial (RCT) and a continued follow-up until 4 years or reaching the study endpoint. It is enrolling 480 patients with AFD (120 per treatment arm), 100 patients with BD, and 100 healthy controls (HC). Multivariate dimension information is collected including clinical features, cognitive function, kynurenine pathway metabolomics, and multimodal magnetic resonance imaging (MRI) data. Firstly, multivariate informatics analyses are performed to recognize patients with AFD from participants including the first-episode and recurrent atypical depression, patients with BD, and patients with HC. Secondly, patients with atypical depression are randomly allocated to one of the four treatment groups including "single application of selective serotonin reuptake inhibitor (SSRI) or serotonin-noradrenaline reuptake inhibitor (SNRI)", "SSRI/SNRI combined with mood stabilizer," "SSRI/SNRI combined with quetiapine (≥ 150 mg/day)," or "treatment as usual (TAU)" and then followed up 12 weeks to find out the optimized treatment strategies. Thirdly, patients with atypical depression are followed up until 4 years or switching to BD, to explore the risk factors of conversion from atypical depression to BD and eventually build the risk warning model of conversion to BD. DISCUSSION: The first enrolment was in August 2019. The iDoT-AFD study explores the clinical and biological markers for the diagnosis, treatment, and prognosis of AFD and further provides evidence for clinical guidelines of AFD. TRIAL REGISTRATION: ClinicalTrials.gov NCT04209166. Registered on December 19, 2019.

Characteristics and symptomatology of major depressive disorder with atypical features from symptom to syndromal level.

OBJECTIVE: To explore clinical characteristics and symptomatology of major depressive disorder (MDD) with atypical features based on DSM criteria or only reversed vegetative symptoms. METHOD: A total of 3187 patients who met DSM-IV TR criteria for MDD were enrolled. Demographics and symptomatology covering multiple symptom domains were assessed and compared between three groups of cases: those who met DSM criteria for atypical specifier (the DAD group), those who had at least one reversed vegetative symptoms (hypersomnia or hyperphagia) (the SAD group) without meeting DSM atypical specifier criteria, and those without any reversed vegetative symptoms (the NAD group). RESULTS: The DAD and SAD group accounted for 4.4% and 14.4% of the participants, respectively. The DAD cases were characterized by a highest proportion of hospitalizations, longest duration of current episode and worst quality of life. The DAD and SAD cases were more likely to adopt unhealthy behaviors (smoking and alcohol drinking). Most depressive symptoms related to higher illness severity and treatment resistance were more frequent in the DAD cases, followed by the SAD cases, and least frequent in the NAD cases. LIMITATIONS: A cross-sectional design and a non-validated questionnaire were used. CONCLUSIONS: The findings support the role of DSM defined atypical depression as a valid MDD subtype and provide evidence for clinical utility of the simplified approach of defining atypical features based on only reversed vegetative symptoms. This has implications for illness screening, public health, suicide prevention and better treatment planning for depressed individuals with atypical features even below syndromal level.

Clinical Guideline (CANMAT 2016) Discordance of Medications for Patients with Major Depressive Disorder in China.

Objective: This survey aims to explore the current medical treatment of major depressive disorder (MDD) in China and match its degree with Canadian Network for Mood and Anxiety Treatments (CANMAT). Methods: A total of 3275 patients were recruited from 16 mental health centers and 16 general hospitals in China. Descriptive statistics presented the total number and percentage of drugs, as well as all kinds of treatments. Results: Selective serotonin reuptake inhibitors (SSRIs) accounted for the largest proportion (57.2%), followed by serotonin-noradrenaline reuptake inhibitors (SNRIs) (22.8%) and mirtazapine (7.0%) in the first therapy, while that of SNRIs (53.9%) followed by SSRIs (39.2%) and mirtazapine (9.8%) in the follow-up therapy. An average of 1.85 medications was administered to each MDD patient. Conclusion: SSRIs were the first choice in the first therapy, while the proportion of those drugs decreased during the follow-up therapy and were replaced by SNRIs. Plenty of combined pharmacotherapies were directly selected as the first trial of patients, which was inconsistent with guideline recommendations.

Differences in cognitive functions of atypical and non-atypical depression based on propensity score matching.

BACKGROUND: Clinical and etiological heterogeneity have hindered our understanding of depression, thus driving the studies of major depressive disorder (MDD) subtypes. Atypical depression (AD) is a subtype of MDD with atypical features. Cognitive impairment is one of the factors that contribute to the suffering of patients with MDD. Therefore, this study investigated the characteristics and differences in cognitive functioning of AD and non-atypical depression (non-AD) using the MATRICS Consensus Cognitive Battery (MCCB). METHODS: A total of 101 patients with AD and 252 patients with non-AD were assessed with the MCCB and clinical scales. Propensity score matching (PSM) was used to balance confounders between groups. After PSM, between-group differences were compared for cognitive and clinical variables. In addition, multiple linear regression analyses were performed to explore the effects of cognitive and clinical variables on the quality of life. RESULTS: The AD group scored significantly lower in attention/vigilance and social cognition in all cognitive domains than the non-AD group. Attention/vigilance and social cognition were significant positive predictors of quality of life, whereas atypical symptoms and depressive severity were significant negative predictors. CONCLUSIONS: This study suggests significant differences in cognitive functions between the AD and non-AD subtypes. Atypical symptoms and impaired cognition have a negative impact on patients' quality of life. Attention/vigilance and social cognition are worse in AD than non-AD, which the atypical features of patients with AD may explain. The pathological mechanisms and treatment strategies of AD should be further explored in the future to promote individualized treatment strategies.

Changes of anhedonia and cognitive symptoms in first episode of depression and recurrent depression, an analysis of data from NSSD.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Collectively, the results of this investigation advance the knowledge on changes in anhedonia and cognitive symptoms in MDD. LIMITATIONS: 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.

Categorical and Dimensional Deficits in Hippocampal Subfields Among Schizophrenia, Obsessive-Compulsive Disorder, Bipolar Disorder, and Major Depressive Disorder.

BACKGROUND: The hippocampus is a core region of interest for all major mental disorders, and its subfields implement distinctive functions. It is unclear whether the mental disorders exhibit common patterns of hippocampal impairments, and we lack knowledge on whether and how hippocampal subfields represent deficit spectra across mental disorders. METHODS: Using brain images of 1123 individuals scanned on a single magnetic resonance imaging scanner, we examined the commonality, specificity, and symptom associations of the volume of hippocampal subfields across patients with schizophrenia, patients with obsessive-compulsive disorder, patients with bipolar disorder, patients with major depressive disorder, and healthy control subjects. We further performed a transdiagnostic analysis of the individual variability of the volume of hippocampal subfields to reflect cross-disease gradients in the hippocampus. RESULTS: We found common and disease-specific abnormalities in a few hippocampal fields and identified 2 reliable transdiagnostic factors in the hippocampal subfields, each reflecting a spectrum of mental disorders. The plane spanned by the 2 most reliable factors provided a clearer view of hippocampal volume abnormality spectra among the major mental disorders. In addition, functional and genetic enrichment analyses supported the different roles of the 2 hippocampal factors in mental disorders. CONCLUSIONS: The volume of hippocampal subfields reflected some commonality and specificity among the 3 major mental disorders. We propose a new pathophysiological dimensional view of the hippocampus, reflecting at least 2 spectra of mental disorders, suggesting multivariate links among the diseases. This work highlights the value of the complementary categorical and dimensional views of the hippocampal deficits in mental disorders.

Age of onset for major depressive disorder and its association with symptomatology.

BACKGROUND: 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). METHODS: 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. RESULTS: 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 (ß = 0.18, p < 0.001), and the severity of cognitive-behavioral symptoms was negatively associated with AOO (ß = -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. LIMITATIONS: AOO was recalled retrospectively. The relative scarcity of participants with childhood and adolescence onset depression. CONCLUSIONS: 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.

Case report: Treatment of psychiatric symptoms for an acromegalic patient with pituitary adenoma.

Acromegalic patients always demonstrate a wide range of clinic manifestations, including typical physical changes such as acral and facial features, as well as untypical neuropsychiatric and psychological disturbances. However, there is still a lack of clinical guidance on the treatment for acromegalic patients with psychiatric comorbidities. We therefore share this case to provide a reference for clinicians to manage the acromegalic patients with psychiatric symptoms. This case report describes a 41-year-old male with an 8-year history of acromegaly due to growth hormone-secreting pituitary adenoma, the maximum cross-sectional area of which was 42 mm × 37 mm demonstrated by pituitary magnetic resonance imaging (MRI). The patient received conservative medicine treatment by regularly injecting with Sandostatin LAR 10 mg per month. Two days before admission, he suddenly presented with an acute psychotic episode. In addition to the typical acromegaly-associated changes, his main clinical presentations were olfactory/auditory hallucinations, reference/persecutory delusions, instable emotion and impulsive behavior. Considering the schizophrenic-like psychoses and course features, he was diagnosed with Brief Psychotic Disorder according to Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) after a multidisciplinary consultation and evaluation. He was prescribed Aripiprazole, which had less extrapyramidal symptoms and minimal influence on prolactin elevation, with the dose of 5 mg per day to control the psychiatric symptoms and he responded quite well. At the time of discharge and the follow-up 2 month later, the patient was stable without recurrence of any psychotic symptoms. The levels of growth hormone (GH) and insulin-like growth factor 1 (IGF-1) 1 week after discharge were 2.22 ng/mL [normal range (0-2.47 ng/mL)] and 381 µg/L [normal range (94-284 µg/L)], respectively, which were similar to those before the psychotic episode. Results from this report further supported that small dose of Aripiprazole had little influence on hormonal levels and the development of pituitary macroadenoma. This particular case emphasizes the importance for the clinician to master and carefully identify the possible symptoms of mental disorders associated with acromegaly, and also highlights the need for further investigation in more efficient treatment strategies for acromegalic cases with psychiatric comorbidities.

Reliability and validity of the Chinese version of the biological rhythms interview of assessment in neuropsychiatry in patients with major depressive disorder.

BACKGROUND: Although disturbances in biological rhythms are closely related to the onset of major depressive disorder (MDD), they are not commonly assessed in Chinese clinical practice. The Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN) has been used to evaluate disturbances in biological rhythms in MDD. We aimed to assess and confirm the reliability and validity of the Chinese version of the BRIAN (C-BRIAN) in patients with MDD. METHODS: A total of 120 patients with MDD and 40 age- and sex-matched controls were recruited consecutively. Reliability was estimated using Cronbach's alpha, the split-half coefficient, and the test-retest coefficient; test-retest reliability was assessed using Spearman's correlation coefficient. A confirmatory factor analysis was used to determine the construct validity of the scale. The Pittsburgh Sleep Quality Index (PSQI) and the Morningness-Eveningness Questionnaire (MEQ) were used to check concurrent validity by evaluating the correlation between the C-BRIAN, PSQI, and MEQ. RESULTS: The overall Cronbach's α value was 0.898, indicating good internal consistency. The Guttman split-half coefficient was 0.792, indicating good split-half reliability. Moreover, the test-retest reliability for both the total and individual item score was excellent. Confirmatory factor analysis revealed that construct validity was acceptable (χ2/df = 2.117, GFI = 0.80, AGFI = 0.87, CFI = 0.848, and RMSEA = 0.097). Furthermore, total BRIAN scores were found to be negatively correlated with MEQ (r = - 0.517, P < 0.001) and positively correlated with PSQI (r = 0.586, P < 0.001). In addition, patients with MDD had higher BRIAN scores than those in controls. CONCLUSIONS: This study revealed that the C-BRIAN scale has great validity and reliability in evaluating the disturbance of biological rhythms in patients with MDD.

TNF-α, IL-6 and hsCRP in patients with melancholic, atypical and anxious depression: an antibody array analysis related to somatic symptoms.

Background: The association between inflammation and major depressive disorder (MDD) remains poorly understood, given the heterogeneity of patients with MDD. Aims: We investigated inflammatory markers, such as interleukin (IL)-6, high-sensitivity C reactive protein (hsCRP) and tumour necrosis factor-α (TNF-α) in melancholic, atypical and anxious depression and explored whether baseline inflammatory protein levels could indicate prognosis. Methods: The sample consisted of participants (aged 18-55 years) from a previously reported multicentre randomised controlled trial with a parallel-group design registered with ClinicalTrials.gov, including melancholic (n=44), atypical (n=37) and anxious (n=44) patients with depression and healthy controls (HCs) (n=33). Subtypes of MDD were classified according to the 30-item Inventory of Depressive Symptomatology, Self-Rated Version and the 17-item Hamilton Depression Rating Scale. Blood levels of TNF-α, IL-6 and hsCRP were assessed using antibody array analysis. Results: Patients with MDD, classified according to melancholic, atypical and anxious depression subtypes, and HCs did not differ significantly in baseline TNF-α, IL-6 and hsCRP levels after adjustment. In patients with anxious depression, hsCRP levels increased significantly if they experienced no pain (adjusted (adj.) p=0.010) or mild to moderate pain (adj. p=0.038) compared with those with severe pain. However, the patients with anxious depression and severe pain showed a lower trend in hsCRP levels than patients with atypical depression who experienced severe pain (p=0.022; adj. p=0.155). Baseline TNF-α (adj. p=0.038) and IL-6 (adj. p=0.006) levels in patients in remission were significantly lower than those in patients with no remission among the participants with the atypical depression subtype at the eighth-week follow-up. Conclusions: This study provides evidence of differences in inflammatory proteins in patients with varied symptoms among melancholic, atypical and anxious depression subtypes. Further studies on the immunoinflammatory mechanism underlying different subtypes of depression are expected for improved individualised therapy. Trial registration number: NCT03219008.