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.

Comparative studies of hair shaft components between healthy and diseased donors.

Globally, the rapid aging of the population is predicted to become even more severe in the second half of the 21st century. Thus, it is expected to establish a growing expectation for innovative, non-invasive health indicators and diagnostic methods to support disease prevention, care, and health promotion efforts. In this study, we aimed to establish a new health index and disease diagnosis method by analyzing the minerals and free amino acid components contained in hair shaft. We first evaluated the range of these components in healthy humans and then conducted a comparative analysis of these components in subjects with diabetes, hypertension, androgenetic alopecia, major depressive disorder, Alzheimer's disease, and stroke. In the statistical analysis, we first used a student's t test to compare the hair components of healthy people and those of patients with various diseases. However, many minerals and free amino acids showed significant differences in all diseases, because the sample size of the healthy group was very large compared to the sample size of the disease group. Therefore, we attempted a comparative analysis based on effect size, which is not affected by differences in sample size. As a result, we were able to narrow down the minerals and free amino acids for all diseases compared to t test analysis. For diabetes, the t test narrowed down the minerals to 15, whereas the effect size measurement narrowed it down to 3 (Cr, Mn, and Hg). For free amino acids, the t test narrowed it down to 15 minerals. By measuring the effect size, we were able to narrow it down to 7 (Gly, His, Lys, Pro, Ser, Thr, and Val). It is also possible to narrow down the minerals and free amino acids in other diseases, and to identify potential health indicators and disease-related components by using effect size.

Adaption and validation of the Rwandese version of the Mood Disorder Questionnaire for the screening of bipolar disorder.

BACKGROUND: Bipolar disorder is challenging to diagnose. In Rwanda, a sub-Saharan country with a limited number of psychiatrists, the number of people with an undetected diagnosis of bipolar disorder could be high. Still, no screening tool for the disorder is available in the country. This study aimed to adapt and validate the Mood Disorder Questionnaire in the Rwandan population. METHODS: The Mood Disorder Questionnaire was translated into Kinyarwanda. The process involved back-translation, cross-cultural adaptation, field testing of the pre-final version, and final adjustments. A total of 331 patients with either bipolar disorder or unipolar major depression from two psychiatric outpatient hospitals were included. The statistical analysis included reliability and validity analyses and receiver operating characteristic curve (ROC) analysis. The optimal cut-off was chosen by maximizing Younden's index. RESULTS: The Rwandese version of The Mood Disorder Questionnaire had adequate internal consistency (Cronbach's alpha =0.91). The optimal threshold value was at least six positive items, which yielded excellent sensitivity (94.7%), and specificity (97.3%). The ROC area under the curve (AUC) was 0.99. CONCLUSION: The adapted tool showed good psychometric properties in terms of reliability and validity for the screening of bipolar disorder, with a recommended cutoff value of six items on the symptom checklist for a positive score and an exclusion of items 14 and 15. The tool has the potential to be a crucial instrument to identify otherwise undetected cases of bipolar disorder in Rwanda, improving access to mental health treatment, thus enhancing the living conditions of people with bipolar disorder.

Major depressive disorder recognition by quantifying EEG signal complexity using proposed APLZC and AWPLZC.

BACKGROUND: Seeking objective quantitative indicators is important for accurately recognizing major depressive disorder (MDD). Lempel-Ziv complexity (LZC), employed to characterize neurological disorders, faces limitations in tracking dynamic changes in EEG signals due to defects in the coarse-graining process, hindering its precision for MDD objective quantitative indicators. METHODS: This work proposed Adaptive Permutation Lempel-Ziv Complexity (APLZC) and Adaptive Weighted Permutation Lempel-Ziv Complexity (AWPLZC) algorithms by refining the coarse-graining process and introducing weight factors to effectively improve the precision of LZC in characterizing EEGs and further distinguish MDD patients better. APLZC incorporated the ordinal pattern, while False Nearest Neighbor and Mutual Information algorithms were introduced to determine and adjust key parameters adaptively. Furthermore, we proposed AWPLZC by assigning different weights to each pattern based on APLZC. Thirty MDD patients and 30 healthy controls (HCs) were recruited and their 64-channel resting EEG signals were collected. The complexities of gamma oscillations were then separately computed using LZC, APLZC, and AWPLZC algorithms. Subsequently, a multi-channel adaptive K-nearest neighbor model was constructed for identifying MDD patients and HCs. RESULTS: LZC, APLZC, and AWPLZC algorithms achieved accuracy rates of 78.29 %, 90.32 %, and 95.13 %, respectively. Sensitivities reached 67.96 %, 85.04 %, and 98.86 %, while specificities were 88.62 %, 95.35 %, and 89.92 %, respectively. Notably, AWPLZC achieved the best performance in accuracy and sensitivity, with a specificity limitation. LIMITATION: The sample size is relatively small. CONCLUSION: APLZC and AWPLZC algorithms, particularly AWPLZC, demonstrate superior effectiveness in differentiating MDD patients from HCs compared with LZC. These findings hold significant clinical implications for MDD diagnosis.

AI algorithm combined with RNA editing-based blood biomarkers to discriminate bipolar from major depressive disorders in an external validation multicentric cohort.

Bipolar disorder (BD) is a leading cause of disability worldwide, as it can lead to cognitive and functional impairment and premature mortality. The first episode of BD is usually a depressive episode and is often misdiagnosed as major depressive disorder (MDD). Growing evidence indicates that peripheral immune activation and inflammation are involved in the pathophysiology of BD and MDD. Recently, by developing a panel of RNA editing-based blood biomarkers able to discriminate MDD from depressive BD, we have provided clinicians a new tool to reduce the misdiagnosis delay observed in patients suffering from BD. The present study aimed at validating the diagnostic value of this panel in an external independent multicentric Switzerland-based cohort of 143 patients suffering from moderate to major depression. The RNA-editing based blood biomarker (BMK) algorithm developped allowed to accurately discriminate MDD from depressive BD in an external cohort, with high accuracy, sensitivity and specificity values (82.5 %, 86.4 % and 80.8 %, respectively). These findings further confirm the important role of RNA editing in the physiopathology of mental disorders and emphasize the possible clinical usefulness of the biomarker panel for optimization treatment delay in patients suffering from BD.

Identifying data-driven subtypes of major depressive disorder with electronic health records.

BACKGROUND: Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration. METHODS: We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups. RESULTS: Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LIMITATIONS: Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions. CONCLUSION: These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

Developing a prediction model to identify people with severe mental illness without regular contact to their GP - a study based on data from the Danish national registers.

INTRODUCTION: People with severe mental illness (SMI) face a higher risk of premature mortality due to physical morbidity compared to the general population. Establishing regular contact with a general practitioner (GP) can mitigate this risk, yet barriers to healthcare access persist. Population initiatives to overcome these barriers require efficient identification of those persons in need. OBJECTIVE: To develop a predictive model to identify persons with SMI not attending a GP regularly. METHOD: For individuals with psychotic disorder, bipolar disorder, or severe depression between 2011 and 2016 (n = 48,804), GP contacts from 2016 to 2018 were retrieved. Two logistic regression models using demographic and clinical data from Danish national registers predicted severe mental illness without GP contact. Model 1 retained significant main effect variables, while Model 2 included significant bivariate interactions. Goodness-of-fit and discriminating ability were evaluated using Hosmer-Lemeshow (HL) test and area under the receiver operating characteristic curve (AUC), respectively, via cross-validation. RESULTS: The simple model retained 11 main effects, while the expanded model included 13 main effects and 10 bivariate interactions after backward elimination. HL tests were non-significant for both models (p = 0.50 for the simple model and p = 0.68 for the extended model). Their respective AUC values were 0.789 and 0.790. CONCLUSION: Leveraging Danish national register data, we developed two predictive models to identify SMI individuals without GP contact. The extended model had slightly better model performance than the simple model. Our study may help to identify persons with SMI not engaging with primary care which could enhance health and treatment outcomes in this group.

Study on exosomes for identifying bipolar disorder in early stage: A cross-sectional and validation study protocol.

BACKGROUND: The difficulty is remained to accurately distinguish bipolar disorder (BD) from major depressive disorder (MDD) in early stage, with a delayed diagnosis for 5-10 years. BD patients are often treated with antidepressants systematically due to being diagnosed with MDD, affecting the disease course and clinical outcomes. The current study aims to explore the role of plasma exosomes as biomarker to distinguish BD from MDD in early stage. METHODS: Two stages are included. The first stage is a cross-sectional study, comparing the concentrations of plasma exosome microRNA and related proteins among BD group, MDD group, and healthy controls (HC) group (n = 40 respectively), to identify target biomarkers preliminarily. The "Latent Class Analysis" and "Receiver Operating Characteristic" analysis will be performed to determine the optimal concentration range for each biomarker. The second stage is to validate target markers in subjects, coming from an ongoing study focusing on patients with a first depressive episode. All target biomarkers will be test in plasma samples reserved at the initial stage to detect whether the diagnosis indicated by biomarker level is consistent with the diagnosis by DSM-5. Furthermore, the correlation between specific biomarkers and the manic episode, suicidal ideation, and adverse reactions will also be observed. DISCUSSION: Exosome-derived microRNA and related proteins have potential in serving as a good medium for exploring mental disorders because it can pass through the blood-brain barrier bidirectionally and convey a large amount of information stably. Improving the early diagnosis of BD would help implement appropriate intervention strategy as early as possible and significantly reduce the burden of disease.

Managing major depressive disorder in adolescents in primary care.

ABSTRACT: Major depressive disorder (MDD) remains a significant risk to adolescent health and well-being, recently amplified by the COVID-19 pandemic. Access to adolescent mental health care services remains challenging in many areas, resulting in many adolescents diagnosed with MDD remaining untreated. Primary care providers are becoming increasingly crucial in promptly diagnosing and treating this concern. Various clinical guidelines can support clinicians in developing strategies for screening, diagnosing, and managing a vulnerable population with MDD. Standardized screenings, algorithms, and treatment guidelines can help improve the quality of life and functional impairment of those with MDD.

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

The relationship between serum resolvin D1, NLRP3, cytokine levels, and adolescents with first-episode medication-naïve major depressive disorder.

BACKGROUND: Inflammation has become a critical pathological mechanism of Major Depressive Disorder (MDD). NLRP3 is a critical inflammatory pathway to maintain the immune balance. Recently, preclinical evidence showed that Resolvin D1 might potentially offer a new option for antidepressant treatment due to its protective effects through the inhibition of neuroinflammation. However, whether they have clinical value in the diagnosis and treatment evaluation of adolescent depression was unclear. METHODS: Forty-eight untreated first-episode adolescent patients with moderate to severe major depressive disorder, as well as 30 healthy adolescents (HCs, age and gender-matched), were enrolled for this study. Their ages ranged from 13 to 18 (15.75 ± 1.36) years. The patients were treated with fluoxetine for 6-8 weeks. HDRS-17 was used to evaluate the severity of depressive symptoms. Venous blood samples were collected at baseline for the two groups and at the time-point of post-antidepressant treatment for the patients. Serum concentrations of RvD1, NLRP3, IL-1ß, IL-18, and IL-4 were measured by enzyme-linked immunosorbent assays (ELISA) pre- and post-fluoxetine treatment. RESULTS: Serum levels of RvD1 and anti-inflammatory cytokine IL-4 were significantly elevated in adolescents with MDD compared to healthy adolescents, but no significant difference in NLRP3, IL-1ß, and IL-18 between the two groups. Meanwhile, RvD1 (positively) and IL-4 (negatively) were correlated with the severity of symptoms (HDRS-17 scores) after adjusting age, gender, and BMI. Interestingly, fluoxetine treatment significantly reduced the serum levels of RvD1, NLRP3, IL-1ß, and IL-18 in MDD adolescents but increased the levels of IL-4 relative to baseline. Furthermore, we observed that serum levels of RvD1 might be an excellent distinguishing indicator for depression and healthy adolescents. CONCLUSIONS: Our study is the first to compare RvD1 and NLRP3 between adolescent MDD and HCs. Our findings of reactive increase of RvD1 in adolescent MDD comprised a novel and critical contribution. Our results showed the presence of inflammation resolution unbalanced in adolescents with MDD and indicated that RvD1 might be an ideal biomarker for diagnosing and treating adolescent MDD.

Cluster analysis dissecting cognitive deficits in older adults with major depressive disorder and the association with neurofilament light chain.

BACKGROUND: Cognitive impairment is a growing problem with increasing burden in global aging. Older adults with major depressive disorder (MDD) have higher risk of dementia. Neurofilament light chain (NfL) has been proven as a potential biomarker in neurodegenerative disease, including dementia. We aimed to investigate the association between cognitive deficits and NfL levels in older adults with MDD. METHODS: In this cross-sectional study, we enrolled 39 MDD patients and 15 individuals with mild neurocognitive disorder or major neurocognitive disorder, Alzheimer's type, as controls, from a tertiary psychiatric hospital. Both groups were over age 65 and with matched Mini-Mental State Examination (MMSE) score. Demographic data, clinical variables, and plasma NfL levels were obtained. We used cluster analysis according to their cognitive profile and estimated the correlation between plasma NfL levels and each cognitive domain. RESULTS: In the MDD group, participants had higher rate of family psychiatry history and current alcohol use habit compared with controls. Control group of neurocognitive disorders showed significantly lower score in total MMSE and higher plasma NfL levels. Part of the MDD patients presented cognitive deficits clustered with that of neurocognitive disorders (cluster A). In cluster A, the total MMSE score (r=-0.58277, p=0.0287) and the comprehension domain (r=-0.71717, p=0.0039) were negatively correlated to NfL levels after adjusting for age, while the associations had not been observed in the other cluster. CONCLUSIONS: We noted the negative correlation between NfL levels and cognition in MDD patients clustered with neurodegenerative disorder, Alzheimer's type. NfL could be a promising candidate as a biomarker to predict subtype of patients in MDD to develop cognitive decline. Further longitudinal studies and within MDD cluster analysis are required to validate our findings for clinical implications.

Prevalence of major depressive disorder and its associated factors among adult patients with neurolathyrism in Dawunt District, Ethiopia; 2022: community-based cross-sectional study.

INTRODUCTION: Major Depressive Disorder (MDD) is one of the commonest mental disorders affecting more than 250 million people globally. Patients with chronic illnesses had higher risks for developing MDD than the general population. Neurolathyrism is a chronic illness characterized by lifelong incurable spastic paralysis of lower extremities; causing permanent disability. It is highly prevalent in Dawunt district, Ethiopia; with a point prevalence of 2.4%. Despite this, there were no previous studies assessing the prevalence of MDD among patients with neurolathyrism in Ethiopia. OBJECTIVE: To assess the prevalence of MDD and to identify its associated factors among patients with neurolathyrism in Dawunt district, Ethiopia. METHODS: A community based cross-sectional study was conducted on 260 samples in Dawunt district from February 01 to March 30/ 2021. Multistage sampling technique was used to select study participants. The patient Health Questionnaire-9 (PHQ-9) depression screening tool was used to diagnose MDD. PHQ-9 is a standardized depression screening tool and a PHQ-9 score of ≥ 10 has a sensitivity and specificity of 88.0% [95% CI (83.0-92.0%)] and 85.0% [95% CI (82.0-88.0%)] for screening MDD. Data were collected by interview; entered to EpiData version 4.2.0; exported to SPSS version 25.0 for analysis; descriptive statistics and binary logistic regression model were used; AOR with 95% CI was used to interpret the associations; and finally results were presented by texts, charts, graphs, and tables. RESULTS: A total of 256 adult patients with neurolathyrism were participated; and the prevalence of MDD was found to be 38.7%. Being female [AOR = 3.00; 95% CI (1.15, 7.84)], living alone [AOR = 2.77; 95% CI (1.02-7.53)], being on neurolathyrism stage-3 [AOR = 3.22; 95% CI (1.09, 9.54)] or stage-4 [AOR = 4.00; 95% CI (1.28, 12.48)], stigma [AOR = 2.69; 95% CI (1.34, 5.39)], and lack of social/ family support [AOR = 3.61; 95% CI (1.80, 7.24)] were found to have statistically significant association with an increased odds of MDD; while regular exercise and ever formal counselling were found to have statistically significant association with a decreased odds of MDD. CONCLUSION: The prevalence of MDD among neurolathyrism patients in Dawunt district was high. Lack of social support, stigma, not getting formal counselling, and not involving in regular exercise were modifiable risk factors. Therefore, social support, reducing stigma, formal counselling, and encouraging regular exercise might help to reduce the burden of MDD among neurolathyrism patients.

Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients-a randomized rater-blinded trial.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) is essential for antidepressant treatment of major depressive disorder (MDD). Our repeated studies suggest that DNA methylation of a specific CpG site in the promoter region of exon IV of the BDNF gene (CpG -87) might be predictive of the efficacy of monoaminergic antidepressants such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and others. This trial aims to evaluate whether knowing the biomarker is non-inferior to treatment-as-usual (TAU) regarding remission rates while exhibiting significantly fewer adverse events (AE). METHODS: The BDNF trial is a prospective, randomized, rater-blinded diagnostic study conducted at five university hospitals in Germany. The study's main hypothesis is that {1} knowing the methylation status of CpG -87 is non-inferior to not knowing it with respect to the remission rate while it significantly reduces the AE rate in patients experiencing at least one AE. The baseline assessment will occur upon hospitalization and a follow-up assessment on day 49 (± 3). A telephone follow-up will be conducted on day 70 (± 3). A total of 256 patients will be recruited, and methylation will be evaluated in all participants. They will be randomly assigned to either the marker or the TAU group. In the marker group, the methylation results will be shared with both the patient and their treating physician. In the TAU group, neither the patients nor their treating physicians will receive the marker status. The primary endpoints include the rate of patients achieving remission on day 49 (± 3), defined as a score of ≤ 10 on the Hamilton Depression Rating Scale (HDRS-24), and the occurrence of AE. ETHICS AND DISSEMINATION: The trial protocol has received approval from the Institutional Review Boards at the five participating universities. This trial holds significance in generating valuable data on a predictive biomarker for antidepressant treatment in patients with MDD. The findings will be shared with study participants, disseminated through professional society meetings, and published in peer-reviewed journals. TRIAL REGISTRATION: German Clinical Trial Register DRKS00032503. Registered on 17 August 2023.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB).

BACKGROUND: The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear. METHODS: In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB). RESULTS: We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone. CONCLUSIONS: Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

Gender Differences in the Prevalence and Clinical Correlates of Metabolic Syndrome in First-Episode and Drug-Naïve Patients With Major Depressive Disorder.

OBJECTIVE: Major depressive disorder (MDD) is a severe psychiatric symptom worldwide, and the coexistence of MDD with metabolic syndrome (MetS) is common in clinical practice. However, gender differences in comorbid MetS in first-episode and drug-naïve (FEDN) MDD patients have not been reported. Here, we explored potential gender differences in the prevalence and clinical correlates of comorbid MetS in FEDN MDD patients. METHODS: A cross-sectional study of 1718 FEDN MDD patients was conducted. Demographic and clinical data were collected. The Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale, and Positive and Negative Syndrome Scale positive subscale were used to evaluate depression, anxiety, and psychotic symptoms, respectively. RESULTS: The prevalence of MetS was 1.645-fold higher in female MDD patients (38.50%) than in male patients (26.53%). Patients with MetS had higher HAMD score, Hamilton Anxiety Scale score, and Positive and Negative Syndrome Scale positive subscale score than patients without MetS (p values < .001). Furthermore, suicide attempts (male: odds ratio [OR] = 1.706, p = .034; female: OR = 1.639, p = .004) and HAMD score (male: OR = 1.251, p < .001; female: OR = 1.148, p < .001) were independently associated with MetS in male and female patients, whereas age of onset was independently associated with MetS only in female patients (OR = 1.744, p = .047). CONCLUSIONS: Our findings suggest significant gender differences in the prevalence and clinical correlates of comorbid MetS in FEDN MDD patients. Clinical variables (suicide attempts and HAMD scores) may be independently associated with MetS in MDD patients.

Psychiatric disorders in pregnant women admitted to the high-risk obstetric service in a Colombian university clinic between 2013 and 2017.

OBJECTIVE: To determine the psychiatric diagnoses and treatments of patients admitted to the high-risk obstetric service who underwent a consultation with a liaison psychiatrist. METHODS: A descriptive observational study that included pregnant women from the high-risk obstetric service of a highly specialised clinic in Medellín, who had a liaison psychiatry consultation between 2013 and 2017. The main variables of interest were psychiatric and obstetric diagnoses and treatments, in addition to biopsychosocial risk factors. RESULTS: A total of 361 medical records were screened, with 248 patients meeting the inclusion criteria. The main prevailing psychiatric diagnosis was major depressive disorder (29%), followed by adaptive disorder (21.8%) and anxiety disorders (12.5%). The pharmacologic treatments most used by the psychiatry service were SSRI antidepressants (24.2%), trazodone (6.8%) and benzodiazepines (5.2%). The most common primary obstetric diagnosis was spontaneous delivery (46.4%), and the predominant secondary obstetric diagnoses were hypertensive disorder associated with pregnancy (10.4%), gestational diabetes (9.2%) and recurrent abortions (6.4%). Overall, 71.8% of the patients had a high biopsychosocial risk. CONCLUSIONS: The studied population's primary psychiatric disorders were major depressive disorder, adjustment disorder and anxiety disorders, which implies the importance of timely recognition of the symptoms of these perinatal mental pathologies, together with obstetric and social risks, in the prenatal consultation. Psychiatric intervention should be encouraged considering the negative implications of high biopsychosocial risk in both mothers and children.

A Chinese adaptation of the Patient Health Questionnaire for Adolescents (PHQ-A): factor structure and psychometric properties.

BACKGROUND: To examine the factor structure and psychometric properties of the Patient Health Questionnaire for Adolescents (PHQ-A) in Chinese children and adolescents with major depressive disorder (MDD). METHODS: A total of 248 MDD patients aged between 12 and 18 years were recruited and evaluated by the Patient Health Questionnaire for Adolescents (PHQ-A), the Center for Epidemiological Survey Depression Scale (CES-D), the Mood and Feelings Questionnaire (MFQ), and the improved Clinical Global Impression Scale, Severity item (iCGI-S). Thirty-one patients were selected randomly to complete the PHQ-A again one week later. Confirmatory factor analysis (CFA) was used to test the construct validity of the scale. Reliability was evaluated by Macdonald Omega coefficient. Pearson correlation coefficient was used to assess the item-total correlation and the correlation of PHQ-A with CES-D and MFQ respectively. Spearman correlation coefficient was used to assess test-retest reliability. The optimal cut-off value, sensitivity, and specificity of the PHQ-A were achieved by estimating the Receiver Operating Characteristics (ROC) curve. RESULTS: CFA reported adequate loadings for all items, except for item 3. Macdonald Omega coefficient of the PHQ-A was 0.87. The Spearman correlation coefficient of the test-retest reliability was 0.70. The Pearson correlation coefficients of the PHQ-A with CES-D and MFQ were 0.87 and 0.85, respectively (p < 0.01). By taking the iCGI-S as the remission criteria for MDD, the optimal cut-off value, sensitivity and specificity of the PHQ-A were 7, 98.7%, 94.7% respectively. CONCLUSION: The PHQ-A presented as a unidimensional construct and demonstrated satisfactory reliability and validity among the Chinese children and adolescents with MDD. A cut-off value of 7 was suggested for remission.

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder.

BACKGROUND: International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. AIMS: To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. METHOD: Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. RESULTS: Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. CONCLUSIONS: The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

Psychometric properties and factor structure of the suicidal narrative inventory in major depression: A multicentric evaluation.

BACKGROUND: The Suicidal Narrative Inventory (SNI) is a 38-item self-report measure developed to assess elements of the suicidal narrative, a subacute, predominantly cognitive, presuicidal construct. Our objectives were to assess the factor structure, validity, and reliability of the SNI-38 among adults with major depressive disorder (MDD). METHODS: Using a cross-sectional design, we administered the Hindi version of the SNI along with other self-report measures to adults with MDD, recruited from 24 tertiary care hospitals across India. Confirmatory factor analysis (CFA) was performed to assess the factor structure of SNI-38. Reliability (internal consistency) was assessed using Cronbach's alpha (α). Convergent, discriminant, and criterion validity of the SNI-38 were tested by comparing it against other appropriate measures. RESULTS: We collected usable responses from 654 Hindi-speaking participants (Mean age = 36.9 ± 11.9 years, 50.2% female). The eight-factor solution of the SNI showed good model fit indices (χ2[637] = 3345.58, p <.001, CFI =.98, and RMSEA =.08). Internal consistencies for the SNI subscale scores were good to excellent, α ranging from .73 to.92. While most subscales significantly converged with other measures, associations were comparatively weaker and inconsistent for the 'thwarted belongingness' and 'goal reengagement' subscales. CONCLUSION: Consistent with prior data, our study confirmed an eight-factor solution and demonstrated adequate psychometric properties for the Hindi version of the SNI-38 in our sample. These findings provide empirical support for the use of SNI to assess the suicidal narrative among Indian adults with MDD.