Validating the efficacy and value proposition of mental fitness vocal biomarkers in a psychiatric population: prospective cohort study.

Background: The utility of vocal biomarkers for mental health assessment has gained increasing attention. This study aims to further this line of research by introducing a novel vocal scoring system designed to provide mental fitness tracking insights to users in real-world settings. Methods: A prospective cohort study with 104 outpatient psychiatric participants was conducted to validate the "Mental Fitness Vocal Biomarker" (MFVB) score. The MFVB score was derived from eight vocal features, selected based on literature review. Participants' mental health symptom severity was assessed using the M3 Checklist, which serves as a transdiagnostic tool for measuring depression, anxiety, post-traumatic stress disorder, and bipolar symptoms. Results: The MFVB demonstrated an ability to stratify individuals by their risk of elevated mental health symptom severity. Continuous observation enhanced the MFVB's efficacy, with risk ratios improving from 1.53 (1.09-2.14, p=0.0138) for single 30-second voice samples to 2.00 (1.21-3.30, p=0.0068) for data aggregated over two weeks. A higher risk ratio of 8.50 (2.31-31.25, p=0.0013) was observed in participants who used the MFVB 5-6 times per week, underscoring the utility of frequent and continuous observation. Participant feedback confirmed the user-friendliness of the application and its perceived benefits. Conclusions: The MFVB is a promising tool for objective mental health tracking in real-world conditions, with potential to be a cost-effective, scalable, and privacy-preserving adjunct to traditional psychiatric assessments. User feedback suggests that vocal biomarkers can offer personalized insights and support clinical therapy and other beneficial activities that are associated with improved mental health risks and outcomes.

Early exposure to cannabis and bipolar disorder incidence: Findings from a 22-year birth cohort study in Brazil.

BACKGROUND AND OBJECTIVES: Bipolar disorder is a chronic condition affecting millions of people worldwide. Currently, there is some evidence to suggest that cannabis use during adolescence may be an environmental risk factor for its onset, however inconsistencies have been observed across the literature. Considering this, we aimed to assess whether early lifetime cannabis is associated with subsequent bipolar disorder in young adults between 18 and 22 years of age. METHODS: Using data from the 1993 Pelotas (Brazil) birth cohort (n = 5249), cannabis exposure was examined at age 18 by self-report, and bipolar disorder diagnosis was measured at age 22 using the Mini International Neuropsychiatric Interview (MINI). In order to control the analysis, we considered socioeconomic status index, sex, skin color, physical abuse by parents and lifetime cocaine use. RESULTS: A total of 3781 individuals were evaluated in 2015 aged 22 years, of whom 87 were diagnosed with the bipolar disorder onset after the age of 18. Lifetime cannabis use predicted bipolar disorder onset at 22 years old (OR 1.82, 95% CI [1.10, 2.93]), and the effect remained after adjusting for socioeconomic status, sex, skin color, and physical abuse by parents (OR 2.00, 95% CI [1.20, 3.25]). However, this association was attenuated to statistically non-significant after further adjustment for all available covariates, including lifetime cocaine use (OR 1.79, 95% CI [0.95, 3.19]). We also found similar results for early cocaine use, where the association with bipolar disorder onset did not maintain significance in the multivariate model (OR 1.35, 95% CI [0.62, 2.86]). Otherwise, when we considered cannabis or cocaine lifetime use as a unique feature, our findings showed that the adolescent exposure to cannabis or cocaine increased the odds by 1.95 times of developing bipolar disorder at 22 years age, even when controlling for all other study variables (OR 2.14, 95% CI [1.30, 3.47]). Finally, our models suggest that cocaine use may potentially exert a major influence on the effect of lifetime cannabis use on bipolar disorder onset, and that physical abuse by parents and sex may modify the effect of cannabis use for later bipolar disorder onset. CONCLUSION: Based on our findings, early cannabis exposure predicted bipolar disorder onset in young adults, but this association was confounded by cocaine use. Contrary to schizophrenia, cannabis as a sole exposure was not associated with bipolar disorder onset after adjusting for control variables.

Perinatal factors as risk factors of bipolar disorder onset in young adulthood: a 22-year birth cohort.

BACKGROUND: Bipolar disorder (BD) is a leading cause of disability-adjusted life years in young adults. Complications during prenatal periods have been associated with BD previously. The study aims to examine the association between perinatal factors and BD in order to prevent the risk of developing BD. METHODS: 3,794 subjects from the 1993 Pelotas population-based birth cohort study were included. We assessed 27 initial variables at birth and modelled BD onset at 18 and 22 years. We performed bivariate analysis, using binomial logistic regression models. The variables with p-value smaller than 0.05 were included into a multiple regression with confounding variables. RESULTS: Maternal smoking was associated with a 1.42-fold increased risk of BD at 18 or 22 years old (95% CI: 1.091-1.841), and maternal passive exposure to tobacco with a 1.43-fold increased risk (95% CI: 1.086-1.875). No association was found between other perinatal factors and BD after controlling for confounding factors. CONCLUSION: The results of this cohort corroborate with previous findings in the literature that already indicate the negative outcomes of maternal smoking during pregnancy. They may now be linked to other studies to target these factors for preventing the development of BD.

Premorbid intelligence quotient and school failure as risk markers for bipolar disorder and major depressive disorder.

Mood disorders significantly impact global health, with MDD ranking as the second leading cause of disability in the United States and BD ranking 18th. Despite their prevalence and impact, the relationship between premorbid intelligence and the subsequent development of BD and MDD remains inconclusive. This study investigates the potential of premorbid Intelligence Quotient (IQ) and school failure frequency as risk factors for Bipolar Disorder (BD) and Major Depressive Disorder (MDD) in a birth cohort setting. We analyze data from the Pelotas population-based birth cohort study, comprising 3580 participants aged 22, who had no prior mood disorder diagnoses. Utilizing regression models and accounting for potential confounders, we assess the impact of IQ and school failure, measured at age 18, on the emergence of BD and MDD diagnoses at age 22, using individuals without mood disorders as comparators. Results reveal that lower IQ (below 70) at 18 is associated with an increased risk of BD (Adjusted Odds Ratio [AOR] 1.75, 95%CI: 1.00-3.09, p < 0.05), while higher IQ (above 120) is linked to MDD (AOR 2.16, 95%CI: 1.24-3.75, p < 0.001). Moreover, an elevated number of school failures is associated with increased BD risk (AOR 1.23, 95%CI: 1.11-1.41, p < 0.001), particularly for BD type 1 (AOR 1.36, 95% CI: 1.17-1.58, p < 0.001). These findings offer insights into the distinct premorbid intellectual characteristics of BD and MDD and contribute to a deeper understanding of their developmental trajectories, potentially informing the development of risk assessment tools for mood disorders.

Predicting functional impairment in euthymic patients with mood disorder: A 5-year follow-up.

Major Depressive Disorder and Bipolar Disorder are psychiatric disorders associated with psychosocial impairment. Despite clinical improvement, functional complaints usually remain, mainly impairing occupational and cognitive performance. The aim of this study was to use machine learning techniques to predict functional impairment in patients with mood disorders. For that, analyzes were performed using a population-based cohort study. Participants diagnosed with a mood disorder at baseline and reassessed were considered (n = 282). Random forest (RF) with previous recursive feature selection and LASSO algorithms were applied to a training set with imputed data by bagged trees resulting in two main models. Following recursive feature selection, 25 variables were retained. The RF model had the best performance compared to LASSO. The most important variables in predicting functional impairment were sexual abuse, severity of depressive, anxiety, and somatic symptoms, physical neglect, emotional abuse, and physical abuse. The model demonstrated acceptable performance to predict functional impairment. However, our sample is composed of young participants and the model may not generalize to older individuals with mood disorders. More studies are needed in this direction. The presented calculator has clinical, sociodemographic, and environmental data, demonstrating that it is possible to use such information to predict functional performance.

The HARM models: Predicting longitudinal physical aggression in patients with schizophrenia at an individual level.

The prediction and prevention of aggression in individuals with schizophrenia remains a top priority within forensic psychiatric settings. While risk assessment methods are well rooted in forensic psychiatry, there are no available tools to predict longitudinal physical aggression in patients with schizophrenia within forensic settings at an individual level. In the present study, we used evidence-based risk and protective factors, as well as variables related to course of treatment assessed at baseline, to predict prospective incidents of physical aggression (4-month, 12-month, and 18-month follow-up) among 151 patients with schizophrenia within the forensic mental healthcare system. Across our HARM models, the balanced accuracy (sensitivity + specificity/2) of predicting physical aggressive incidents in patients with schizophrenia ranged from 59.73 to 87.33% at 4-month follow-up, 68.31-80.10% at 12-month follow-up, and 46.22-81.63% at 18-month follow-up, respectively. Additionally, we developed separate models, using clinician rated clinical judgement of short term and immediate violent risk, as a measure of comparison. Several modifiable evidence-based predictors of prospective physical aggression in schizophrenia were identified, including impulse control, substance abuse, impulsivity, treatment non-adherence, mood and psychotic symptoms, substance abuse, and poor family support. To the best of our knowledge, our HARM models are the first to predict longitudinal physical aggression at an individual level in patients with schizophrenia in forensic settings. However, it is important to caution that since these machine learning models were developed in the context of forensic settings, they may not be generalisable to individuals with schizophrenia more broadly. Moreover, a low base rate of physical aggression was observed in the testing set (6.0-11.6% across timepoints). As such, larger cohorts will be required to determine the replicability of these findings.

Prenatal and perinatal risk factors for bipolar disorder: A systematic review and meta-analysis.

BACKGROUND: Perinatal and prenatal risk factors may be implicated in the development of bipolar disorder, but literature lacks a comprehensive account of possible associations. METHODS: We performed a systematic review and meta-analyses of observational studies detailing the association between prenatal and perinatal risk factors and bipolar disorder in adulthood by searching PubMed, Embase, Web of Science and Psycinfo for articles published in any language between January 1st, 1960 and September 20th, 2021. Meta-analyses were performed when risk factors were available in at least two studies. FINDINGS: Twenty seven studies were included with 18 prenatal or perinatal factors reported across the literature. Peripartum asphyxia (k = 5, OR = 1.46 [1.02; 2.11]), maternal stress during pregnancy (k = 2, OR = 12.00 [3.30; 43.59]), obstetric complications (k = 6, OR = 1.41 [1.18; 1.69]), and birth weight less than 2500 g (k = 5, OR = 1.28 [1.04; 1.56]) were associated with an increased risk for bipolar disorder. INTERPRETATION: Perinatal and prenatal risk factors are implicated in the pathogenesis of bipolar disorder, supporting a role of prenatal care in preventing the condition.

Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy.

Although reducing criminal outcomes in individuals with mental illness have long been a priority for governments worldwide, there is still a lack of objective and highly accurate tools that can predict these events at an individual level. Predictive machine learning models may provide a unique opportunity to identify those at the highest risk of criminal activity and facilitate personalized rehabilitation strategies. Therefore, this systematic review and meta-analysis aims to describe the diagnostic accuracy of studies using machine learning techniques to predict criminal and violent outcomes in psychiatry. We performed meta-analyses using the mada, meta, and dmetatools packages in R to predict criminal and violent outcomes in psychiatric patients (n = 2428) (Registration Number: CRD42019127169) by searching PubMed, Scopus, and Web of Science for articles published in any language up to April 2022. Twenty studies were included in the systematic review. Overall, studies used single-nucleotide polymorphisms, text analysis, psychometric scales, hospital records, and resting-state regional cerebral blood flow to build predictive models. Of the studies described in the systematic review, nine were included in the present meta-analysis. The area under the curve (AUC) for predicting violent and criminal outcomes in psychiatry was 0.816 (95% Confidence Interval (CI): 70.57-88.15), with a partial AUC of 0.773, and average sensitivity of 73.33% (95% CI: 64.09-79.63), and average specificity of 72.90% (95% CI: 63.98-79.66), respectively. Furthermore, the pooled accuracy across models was 71.45% (95% CI: 60.88-83.86), with a tau squared (τ2) of 0.0424 (95% CI: 0.0184-0.1553). Based on available evidence, we suggest that prospective models include evidence-based risk factors identified in prior actuarial models. Moreover, there is a need for a greater emphasis on identifying biological features and incorporating novel variables which have not been explored in prior literature. Furthermore, available models remain preliminary, and prospective validation with independent datasets, and across cultures, will be required prior to clinical implementation. Nonetheless, predictive machine learning models hold promise in providing clinicians and researchers with actionable tools to improve how we prevent, detect, or intervene in relevant crime and violent-related outcomes in psychiatry.

Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis.

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.

Predicting offenses among individuals with psychiatric disorders - A machine learning approach.

BACKGROUND: Actuarial risk estimates are considered the gold-standard way to assess whether psychiatric patients are likely to commit prospective criminal offenses. However, these risk estimates cannot individually predict the type of criminal offense a patient will subsequently commit, and often simply assess the general likelihood of crime occurring in a group sample. In order to advance the predictive utility of risk assessments, better statistical strategies are required. AIM: To develop a machine learning model to predict the type of criminal offense committed in a large transdiagnostic sample of psychiatry patients, at an individual level. METHOD: Machine learning algorithms (Random Forest, Elastic Net, SVM), were applied to a representative and diverse sample of 1240 patients in the forensic mental health system. Clinical, historical, and sociodemographic variables were considered as potential predictors and assessed in a data-driven way. Separate models were created for each type of criminal offense, and feature selection methods were used to improve the interpretability and generalizability of our findings. RESULTS: Sexual offenses can be predicted from nonviolent and violent offenses at an individual level with a sensitivity of 82.44% and specificity of 60.00%, using only 36 variables. Furthermore, in a binary classification model, sexual and violent offenses can be predicted at an individual level with 83.26% sensitivity and 77.42% specificity using only 20 clinical variables. Likewise, non-violent and sexual offenses can be individually predicted with 74.60% sensitivity and 80.65% specificity using 30 clinical variables. CONCLUSION: The current results suggest that machine learning models can show greater accuracy than gold-standard risk assessment tools (AUCs 0.70-0.80). However, unlike existing risk tools, this approach allows for the prediction of cases at an individual level, which is more clinically useful. Despite this, it is important to note that a large subset of patients in the sample were involved in the criminal system in the past, prior to an official diagnosis. Therefore, many of the variables that predict offenses may be derived from the issues of prior offenses. Irrespective of this, the accuracy of prospective models is expected to only improve with further refinement.

Structural and Functional Brain Correlates of Neuroprogression in Bipolar Disorder.

Neuroprogression is associated with structural and functional brain changes that occur in parallel with cognitive and functioning impairments. There is substantial evidence showing early white matter changes, as well as trajectory-related gray matter alterations. Several structures, including prefrontal, parietal, temporal cortex, and limbic structures, seem to be altered over the course of bipolar disorder, especially associated with the number of episodes and length of the disease. An important limitation is that most of the studies used either a cross-sectional design or a short follow-up period, which may be insufficient to identify all neuroprogressive changes over time. In addition, the heterogeneity of patients with bipolar disorder is another challenge to determine which subjects will have a more pernicious trajectory. Larger studies and the use of new techniques, such as machine learning, may help to enable more discoveries and evidence on the role of neuroprogression in BD.

Bullying and psychotic symptoms in youth with bipolar disorder.

BACKGROUND: Childhood trauma is associated with psychosis in adults with bipolar disorder (BD). Although bullying represents a widespread form of childhood trauma, no studies thus far have investigated the association of bullying and psychosis in pediatric bipolar disorder (PBD). We aim to examine the association between psychosis in PBD with bullying victimization. METHODS: We included 64 children and adolescents (age± mean= 12±3.43) outpatients with BD spectrum disorders. Psychiatric diagnoses were assessed with the semi- structured interview Schedule for Affective Disorders and Schizophrenia for School Age Children-Present and Lifetime (KSADS-PL) version with additional depression and manic symptom items derived from the Washington University in St. Louis Kiddie Schedule for Affective Disorders (WASH-U-KSADS). Bullying, demographic, and clinical variables were assessed during the clinical interview. RESULTS: A lifetime history of psychotic symptoms was associated with bullying (p = 0.002), suicidal behavior (p = 0.006), low socioeconomic status (p = 0.04), and severity of PBD (p = 0.02). Only bullying (OR = 7.3; 95%CI = 2-32) and suicidal behavior (OR = 7.6; 95%CI = 1.5-47.8) remained significant after adjustment for confounders. In a supplementary analysis, we developed a model using supervised machine learning to identify the most relevant variables that differentiated participants with psychotic symptoms, which included bullying, Clinical Global Impression-Severity scale (CGI-S), and suicidal behavior (accuracy = 75%, [p = 0.03]; sensitivity = 77.91%; specificity = 69.05%; area under the curve [AUC] = 0.86). LIMITATIONS: Small sample, cross-sectional design, and generalizability of findings beyond the outpatient clinical sample. CONCLUSIONS: Findings underscore the importance of assessing bullying in PBD participants. Future longitudinal studies with larger samples are needed to replicate our findings and determine causality.

Clinical differences between patients with pediatric bipolar disorder with and without a parental history of bipolar disorder.

Pediatric Bipolar Disorder (PBD) is a highly heritable condition responsible for 18% of all pediatric mental health hospitalizations. Despite the heritability of this disorder, few studies have assessed potential differences in the clinical manifestation of PBD among patients with a clear parental history of BD. Additionally, while recent studies suggest that attentional deficits are a potential endophenotypic marker of PBD, it is unclear whether heritability is a relevant contributor to these symptoms. In order to address this gap, the present study assessed 61 youth with PBD (6-17 years old), corresponding to 27 offspring of BD patients, and 31 PBD patients without a parental history of the disorder. All standardized assessments, including the K-SADS-PL-W were performed by trained child and adolescent psychiatrists. We performed a logistic multivariate model using the variables of ADHD, rapid cycling, and lifetime psychosis. Rates of ADHD comorbidity were significantly higher among PBD patients who had a parent with BD. Furthermore, PBD patients who had a parent with BD showed a trend toward significance of earlier symptom onset. PBD offspring did not show increased rates of suicide attempts, rapid cycling, or psychosis. Given these findings, it appears that PBD patients who have a parent with BD may represent a distinct endophenotype of the disorder. Future longitudinal and larger studies are required to confirm our findings.

Agmatine as a potential therapeutic intervention in bipolar depression: the preclinical landscape.

INTRODUCTION: Present antidepressant treatments are only helpful in a quarter of patients with bipolar depression, and new strategies are warranted. Increasing evidence suggests that accelerated polyamine metabolism is associated with the pathophysiology of depression. Polyamines regulate stress responses, inflammation, and neuronal signaling in the central and enteric nervous system. Agmatine is a promising target of altered polyamine metabolism considering its unique ability to regulate intracellular polyamine content and neuroprotective effects. Areas covered: This review discusses the polyamine system and its relationship to the central and enteric nervous system, focusing on results from preclinical studies supporting the relationship between agmatine and the pathophysiology of depression. We also discussed the main mechanisms underlying the antidepressant and neuroprotective effects of agmatine. Expert opinion: Our review points out the possible relationship between polyamines and the pathophysiology of depression. It discusses the efficacy of agmatine in several models of depressive-like behaviour, and suggests that it may prove to be an efficacious adjunctive treatment in bipolar depression. Furthermore, it discusses a proposed pathway linking systemic inflammation, observed in a subset of bipolar disorder patients, to abnormal polyamine metabolism and associated changes in the epithelial gut barrier and blood-brain barrier.