Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study.

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

The effect of inflammation markers on cortical thinning in major depressive disorder: A possible mediator of depression and cortical changes.

BACKGROUND: Major depressive disorder (MDD) is a prevalent mental health condition with significant societal impact. Owing to the intricate biological diversity of MDD, treatment efficacy remains limited. Immune biomarkers have emerged as potential predictors of treatment response, underscoring the interaction between the immune system and the brain. This study investigated the relationship between cytokine levels and cortical thickness in patients with MDD, focusing on the corticolimbic circuit, to elucidate the influence of neuroinflammation on structural brain changes and contribute to a deeper understanding of the pathophysiology of MDD. METHOD: A total of 114 patients with MDD and 101 healthy controls (HC) matched for age, sex, and body mass index (BMI) were recruited. All participants were assessed for depression severity using the Hamilton Depression Rating Scale (HDRS), and 3.0 T T1 weighted brain MRI data were acquired. Additionally, cytokine levels were measured using a highly sensitive bead-based multiplex immunosorbent assay. RESULTS: Patients diagnosed with MDD exhibited notably elevated levels of interleukin-6 (p = 0.005) and interleukin-8 (p = 0.005), alongside significant cortical thinning in the left anterior cingulate gyrus and left superior frontal gyrus, with these findings maintaining significance even after applying Bonferroni correction. Furthermore, increased interleukin-6 and interleukin-8 levels in patients with MDD are associated with alterations in the left frontomarginal gyrus and right anterior cingulate cortex (ACC). CONCLUSIONS: This suggests a potential influence of neuroinflammation on right ACC function in MDD patients, warranting longitudinal research to explore interleukin-6 and interleukin-8 mediated neurotoxicity in MDD vulnerability and brain morphology changes.

Network analysis of plasma proteomes in affective disorders.

The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality.

BACKGROUND: Assessing a patient's suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. OBJECTIVE: This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. METHODS: We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. RESULTS: A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. CONCLUSIONS: Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

Latent class analysis of psychotic-affective disorders with data-driven plasma proteomics.

Data-driven approaches to subtype transdiagnostic samples are important for understanding heterogeneity within disorders and overlap between disorders. Thus, this study was conducted to determine whether plasma proteomics-based clustering could subtype patients with transdiagnostic psychotic-affective disorder diagnoses. The study population included 504 patients with schizophrenia, bipolar disorder, and major depressive disorder and 160 healthy controls, aged 19 to 65 years. Multiple reaction monitoring was performed using plasma samples from each individual. Pathologic peptides were determined by linear regression between patients and healthy controls. Latent class analysis was conducted in patients after peptide values were stratified by sex and divided into tertile values. Significant demographic and clinical characteristics were determined for the latent clusters. The latent class analysis was repeated when healthy controls were included. Twelve peptides were significantly different between the patients and healthy controls after controlling for significant covariates. Latent class analysis based on these peptides after stratification by sex revealed two distinct classes of patients. The negative symptom factor of the Brief Psychiatric Rating Scale was significantly different between the classes (t = -2.070, p = 0.039). When healthy controls were included, two latent classes were identified, and the negative symptom factor of the Brief Psychiatric Rating Scale was still significant (t = -2.372, p = 0.018). In conclusion, negative symptoms should be considered a significant biological aspect for understanding the heterogeneity and overlap of psychotic-affective disorders.

Association between the number of hours of sleep during weekdays and suicidality among Korean adolescents: Mediating role of depressive and anxiety symptoms.

BACKGROUND: Adolescent suicide is a serious concern worldwide. Sleep problems are a risk factor for suicide. Therefore, the aim of this study was to evaluate associations between sleep duration and suicidal ideation/suicide attempts and determine the extent to which depressive and anxiety symptoms mediate these associations. METHODS: Data from 54,948 middle and high school students in South Korea were collected by the stratified cluster method through the Korea Youth Risk Behavior Web-based Survey. RESULTS: The weighted prevalences of short and long sleep durations were 19.5 % (95 % confidence interval [CI] = 18.9-20.2) and 4.6 % (95 % CI = 4.3-4.8), respectively. Short sleep duration (<5 h/day) increased the odds of suicidal ideation and suicide attempts by 1.43 (95 % CI = 1.29-1.58) and 1.78 (95 % CI = 1.41-2.25), respectively. Long sleep duration (>9 h/day) increased the odds of suicide attempts by 1.5 (95 % CI = 1.02-2.21). Depressive and anxiety symptoms significantly mediated the relationship between sleep duration and suicidal intensity with a satisfactory goodness of fit. LIMITATIONS: Causal relationships could not be examined due to the cross-sectional study design. Information on other psychopathologies, besides depression and anxiety, was unavailable. CONCLUSIONS: Short sleep duration was associated with suicidal ideation and suicide attempts among Korean adolescents. Long sleep duration was associated with suicide attempts only. Both depressive and anxiety symptoms mediated the association between sleep duration and suicidal intensity; therefore, both sleep hour restoration and treatment of depressive/anxiety symptoms should be the goals of suicide prevention strategies.

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker.

Background: Depression and suicide are critical social problems worldwide, but tools to objectively diagnose them are lacking. Therefore, this study aimed to diagnose depression through machine learning and determine whether it is possible to identify groups at high risk of suicide through words spoken by the participants in a semi-structured interview. Methods: A total of 83 healthy and 83 depressed patients were recruited. All participants were recorded during the Mini-International Neuropsychiatric Interview. Through the suicide risk assessment from the interview items, participants with depression were classified into high-suicide-risk (31 participants) and low-suicide-risk (52 participants) groups. The recording was transcribed into text after only the words uttered by the participant were extracted. In addition, all participants were evaluated for depression, anxiety, suicidal ideation, and impulsivity. The chi-square test and student's T-test were used to compare clinical variables, and the Naive Bayes classifier was used for the machine learning text model. Results: A total of 21,376 words were extracted from all participants and the model for diagnosing patients with depression based on this text confirmed an area under the curve (AUC) of 0.905, a sensitivity of 0.699, and a specificity of 0.964. In the model that distinguished the two groups using statistically significant demographic variables, the AUC was only 0.761. The DeLong test result (p-value 0.001) confirmed that the text-based classification was superior to the demographic model. When predicting the high-suicide-risk group, the demographics-based AUC was 0.499, while the text-based one was 0.632. However, the AUC of the ensemble model incorporating demographic variables was 0.800. Conclusion: The possibility of diagnosing depression using interview text was confirmed; regarding suicide risk, the diagnosis accuracy increased when demographic variables were incorporated. Therefore, participants' words during an interview show significant potential as an objective and diagnostic marker through machine learning.

Effect of Attitude Toward Suicide on Suicidal Behavior: Based on the Korea National Suicide Survey.

OBJECTIVE: Suicide is a complex phenomenon; therefore, it should be approached in light of sociocultural perspectives and the general attitude toward suicide. This study aimed to extract factors from the Attitude Toward Suicide Scale (ATTS) and investigate the relationship between attitudes toward suicide and suicidal behavior (i.e., suicidal idea, plan, and attempt) by using a representative sample of Korean adults. METHODS: Three thousand Koreans aged 19 to 75 years were surveyed cross-sectionally in 2013 and 2018. The data collected were subjected to exploratory factor analysis. Extracted attitude factors were compared using a suicidal behavior continuum. Univariate and multivariate logistic models were constructed to compare the association between attitude factors and suicidal behaviors. RESULTS: Among the participants, 477 (15.9%) experienced suicidal idea only, 85 (2.8%) had a suicidal plan without attempt, and 58 (1.9%) attempted suicide. Four meaningful factors were extracted from the factor analysis: "permissiveness," "unjustified behavior," "preventability/readiness to help," and "loneliness." "Permissiveness," "unjustified behavior," and "loneliness" factors showed significant trends across the suicidal behavior continuum. Permissive attitude toward suicide increased the odds of suicidal idea, suicidal plan, and suicide attempt (adjusted odds ratio [aOR]=1.49, 95% confidence interval [CI]=1.25-1.79; aOR=2.79, 95% CI=1.84-4.25; aOR=2.67, 95% CI=1.65-4.33), while attitude toward suicide as unjustified behavior decreased the odds of suicidal ideation and attempt (aOR=0.79, 95% CI=0.67-0.94; aOR=0.64, 95% CI=0.42-0.99). CONCLUSION: A significant association was found between attitude toward suicide and suicidal behaviors. Attitude toward suicide is a modifiable factor that can be used to develop prevention policies.

Proteomic profiling of postmortem prefrontal cortex tissue of suicide completers.

Suicide is a leading cause of death worldwide, presenting a serious public health problem. We aimed to investigate the biological basis of suicide completion using proteomics on postmortem brain tissue. Thirty-six postmortem brain samples (23 suicide completers and 13 controls) were collected. We evaluated the proteomic profile in the prefrontal cortex (Broadmann area 9, 10) using tandem mass tag-based quantification with liquid chromatography-tandem mass spectrometry. Bioinformatics tools were used to elucidate the biological mechanisms related to suicide. Subgroup analysis was conducted to identify common differentially expressed proteins among clinically different groups. Of 9801 proteins identified, 295 were differentially expressed between groups. Suicide completion samples were mostly enriched in the endocannabinoid and apoptotic pathways (CAPNS1, CSNK2B, PTP4A2). Among the differentially expressed proteins, GSTT1 was identified as a potential biomarker among suicide completers with psychiatric disorders. Our findings suggest that the previously under-recognized endocannabinoid system and apoptotic processes are highly involved in suicide.

Effect of suicidal loss on bereaved individuals' suicidal ideation: Structural equation model using attitudes towards suicide scale and moderation effect of interest in news media.

INTRODUCTION: Exposure to suicidal death may cause trauma and change the bereaved family/friends' attitudes towards suicide and increase their suicide-related behavior. We aimed to examine the life-time prevalence of loss experience among the general population of South Korea, the relationship between attitudes towards suicide and suicidal intensity, and the moderation effect of interest in news media. METHODS: After analyzing 2973 structured interviews, we hypothesized structural equation model and conducted a moderation analysis. RESULTS: A total of 10.1% (n = 301) respondents had experienced the suicide of acquaintances. Acceptive attitudes such as "suicide as right" and "suicide as normal-common" were higher in the "experienced" group. All fit indices of the hypothesized model were satisfied, and experience of suicidal loss was positively associated with both acceptive attitudes and suicidal intensity. "Suicide as normal-common" positively affected suicidal intensity, but "suicide as right" was not significant. "Interest in news media" significantly moderated the relationship between loss experience and suicidal intensity. LIMITATIONS: Since our study was cross-sectional design, further longitudinal studies are needed to draw casual inferences between factors. We used the at home interview method, which might have resulted underestimated experience of suicidal loss. CONCLUSION: Our findings showed that experiencing suicide death of any acquaintances could increase individual's acceptance of suicide and also increase the risk of suicide. Frequent exposure to suicide-related news amplified their risk of suicide. To reduce the suicide risk behavior, targeted intervention with those bereaved by suicide and restriction of media reports on suicide news will be needed as prevention strategies.

Detection of Minor and Major Depression through Voice as a Biomarker Using Machine Learning.

Both minor and major depression have high prevalence and are important causes of social burden worldwide; however, there is still no objective indicator to detect minor depression. This study aimed to examine if voice could be used as a biomarker to detect minor and major depression. Ninety-three subjects were classified into three groups: the not depressed group (n = 33), the minor depressive episode group (n = 26), and the major depressive episode group (n = 34), based on current depressive status as a dimension. Twenty-one voice features were extracted from semi-structured interview recordings. A three-group comparison was performed through analysis of variance. Seven voice indicators showed differences between the three groups, even after adjusting for age, BMI, and drugs taken for non-psychiatric disorders. Among the machine learning methods, the best performance was obtained using the multi-layer processing method, and an AUC of 65.9%, sensitivity of 65.6%, and specificity of 66.2% were shown. This study further revealed voice differences in depressive episodes and confirmed that not depressed groups and participants with minor and major depression could be accurately distinguished through machine learning. Although this study is limited by a small sample size, it is the first study on voice change in minor depression and suggests the possibility of detecting minor depression through voice.

Self-Transcendence Mediates the Relationship between Early Trauma and Fatal Methods of Suicide Attempts.

BACKGROUND: Early trauma is known to be a risk factor of suicide-related behavior. On the other hand, people who attempt suicide using a fatal method are reported to be more likely to complete suicide. In this study, we assumed that early trauma affects an individual's temperament and character and thereby increases the risk of a fatal method of suicide attempts. METHODS: We analyzed 92 people with a history of previous suicide attempts. We compared the Temperament and Character Inventory-Revised scores between the groups with and without early trauma, and between the groups with and without a history of suicide attempt using fatal methods through an analysis of covariance with age, sex, and presence of a psychiatric history as covariates. A mediation analysis was conducted of the relationship between early trauma and fatal methods of suicide attempt with self-transcendence as a mediator. RESULTS: Higher self-transcendence was reported in the fatal group (27.71 ± 13.78 vs. 20.97 ± 12.27, P = 0.010) and the early trauma group (28.05 ± 14.30 vs. 19.43 ± 10.73, P = 0.001), respectively. The mediation model showed that self-transcendence mediates the relationship between early trauma and fatal methods of suicide attempt. The 95% confidence intervals for the direct and indirect effect were (-0.559, 1.390) and (0.026, 0.947), respectively. CONCLUSION: Self-transcendence may mediate the relationship between early trauma and fatal methods of suicide attempt. Self-transcendence may be associated with unhealthy defenses and suicidal behavior for self-punishment and may constitute a marker of higher suicide risk.

Comparison of Suicide Risk by Mental Illness: a Retrospective Review of 14-Year Electronic Medical Records.

BACKGROUND: Korea is one of the countries with the highest rate of suicide, while suicidality is known to be closely related to mental illnesses. The study aimed to evaluate the suicide rates in psychiatric patients, to compare it to that of the general population, and to investigate the differences among psychiatric diagnoses and comorbidities. METHODS: Medical records and mortality statistics of psychiatric patients at Seoul National University Hospital from 2003 to 2017 were reviewed. The standardized mortality ratio (SMR) for suicide was calculated to compare the psychiatric patients with the general population. The diagnosis-specific standardized mortality rate and hazard ratio (HR) were adjusted by age, sex, and psychiatric comorbidity (i.e., personality disorder and/or pain disorder). RESULTS: A total of 40,692 survivors or non-suicidal deaths and 597 suicidal death were included. The suicide rate among psychiatric patients was 5.13-fold higher than that of the general population. Psychotic disorder had the highest SMR (13.03; 95% confidence interval [CI], 11.23-15.03), followed by bipolar disorder (10.26; 95% CI, 7.97-13.00) and substance-related disorder (6.78; 95% CI, 4.14-10.47). In survival analysis, psychotic disorder had the highest HR (4.16; 95% CI, 2.86-6.05), which was further increased with younger age, male sex, and comorbidity of personality disorder. CONCLUSION: All psychiatric patients are at a higher risk of suicide compared to the general population, and the risk is highest for those diagnosed with psychotic disorder.

Cost analysis of depression using the national insurance system in South Korea: a comparison of depression and treatment-resistant depression.

BACKGROUND: The incidence and burden of depressive disorders are increasing in South Korea. There are many differences between pharmaceutically treated depression (PTD) and treatment-resistant depression (TRD), including the economic consequences; however, to our knowledge, the economic burden of depression is understudied in South Korea. Therefore, the objective of the present study was to calculate the different economic costs of PTD and TRD in South Korea, specifically by comparing several aspects of medical care. METHODS: This study comprised patients aged 18 and over who were newly prescribed antidepressants for more than 28 days with a depression code included from January 1, 2012, to December 31, 2012, by the Health Insurance Review and Assessment Service (HIRA). TRD was classified as more than two antidepressant regimen failures in PTD patients. The cost was calculated based on the cost reflected on the receipt registered with HIRA. RESULTS: Of the 834,694 patients with PTD, 34,812 patients (4.17%) were converted to TRD. The cost of medical care for TRD (6,610,487 KRW, 5881 USD) was approximately 5 times higher than the cost of non-TRD (1,273,045 KRW, 1133 USD) and was significantly higher for patients with or without depression and suicide codes. Medical expenses incurred by non-psychiatrists were roughly 1.7 times higher than those incurred by psychiatrists. CONCLUSIONS: TRD patients had significantly higher healthcare costs than PTD patients. Identifying these financial aspects of care for depression can help to establish a more effective policy to reduce the burden on mentally ill patients.

The epidemiology of antidepressant use in South Korea: Does short-term antidepressant use affect the relapse and recurrence of depressive episodes?

BACKGROUND: The duration of antidepressant use affects the treatment of depression. Using the National Health Insurance database, which covers almost the entire national population, we verified the factors associated with the inadequate short-term use of initially prescribed antidepressants and their effects on the relapse and recurrence of depressive episodes. METHODS: There were 752,190 patients included who had been newly prescribed antidepressants in 2012 with the diagnosis of depressive disorder. They were followed-up until December 31, 2015. They were classified as short-term and long-term antidepressant users depending on whether they used a specific initial antidepressant for at least four weeks. Sociodemographic, clinical, and medical utilization factors affecting the duration of antidepressant use were investigated. We also identified whether the duration of antidepressant use affected the risk of relapse and recurrence, which was defined by the restarting of antidepressants. RESULTS: Initial antidepressants were taken for less than 28 days by 458,057 (60.84%) patients. Tricyclic antidepressants were used as the initial antidepressant more frequently than selective serotonin reuptake inhibitors (64.5% versus 19.3%). The type of initial antidepressant, polypharmacy, psychiatric and medical comorbidities, type of insurance coverage, and type of medical institution visited were associated with short-term use. Short-term use marginally increased the risk of relapse and recurrence of depressive episodes (Hazard ratio: 1.06, 95% confidence intervals 1.048-1.075). CONCLUSIONS: Short-term antidepressant use is widespread in Korea, and assessment in various aspects are necessary to set proper treatment plans.

The Mediating Effects of Affect on Associations between Impulsivity or Resilience and Internet Gaming Disorder.

Internet gaming disorder (IGD) is a new disease proposed by the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5), and has been studied extensively in relation to depression and impulsivity. The relationship between resilience and disease has been found in a variety of addictive disorders, but studies on IGD are lacking. In this study, 71 IGD patients and 78 healthy controls (HCs) were recruited. Impulsivity, resilience, affects, and the degree of internet game addiction were measured using formal tools. The measured values were analyzed by mediation analysis to evaluate the mediating role of affects on resilience and impulsivity related to IGD symptoms. The IGD group showed higher impulsivity, lower resilience, lower positive affect, and higher negative affect than the HC group. The mediation analysis showed that a positive affect was a mediator between impulsivity and the severity of addiction in both groups. Negative affect mediated impulsivity/resilience and the severity of addiction only in the IGD group. Although the results of this study are based on a narrow category of subjects, who are young male adults around 25 years of age, the results suggest that positive affect can be strengthened to prevent the IGD illness, and that illness symptoms may be alleviated by reducing negative affect.