Exploring peripheral biomarkers of response to simvastatin supplementation in schizophrenia.

Schizophrenia is one of the most debilitating mental disorders, and its diagnosis and treatment present significant challenges. Several clinical trials have previously evaluated the effectiveness of simvastatin, a lipid-lowering medication, as a novel add-on treatment for schizophrenia. However, treatment effects varied highly between patients and over time. In the present study, we aimed to identify biomarkers of response to simvastatin in recent-onset schizophrenia patients. To this end, we profiled relevant immune and metabolic markers in patient blood samples collected in a previous clinical trial (ClinicalTrials.gov: NCT01999309) before simvastatin add-on treatment was initiated. Analysed sample types included serum, plasma, resting-state peripheral blood mononuclear cells (PBMCs), as well as PBMC samples treated ex vivo with immune stimulants and simvastatin. Associations between the blood readouts and clinical endpoints were evaluated using multivariable linear regression. This revealed that changes in insulin receptor (IR) levels induced in B-cells by ex vivo simvastatin treatment inversely correlated with in vivo effects on cognition at the primary endpoint of 12 months, as measured using the Brief Assessment of Cognition in Schizophrenia scale total score (standardised ß ± SE = -0.75 ± 0.16, P = 2.2 × 10-4, Q = 0.029; n = 21 patients). This correlation was not observed in the placebo group (ß ± SE = 0.62 ± 0.39, P = 0.17, Q = 0.49; n = 14 patients). The candidate biomarker explained 53.4 % of the variation in cognitive outcomes after simvastatin supplementation. Despite the small sample size, these findings suggest a possible interaction between the insulin signalling pathway and cognitive effects during simvastatin therapy. They also point to opportunities for personalized schizophrenia treatment through patient stratification.

Perception of Apps for Mental Health Assessment With Recommendations for Future Design: United Kingdom Semistructured Interview Study.

BACKGROUND: Mental health care provision in the United Kingdom is overwhelmed by a high demand for services. There are high rates of under-, over-, and misdiagnosis of common mental health disorders in primary care and delays in accessing secondary care. This negatively affects patient functioning and outcomes. Digital tools may offer a time-efficient avenue for the remote assessment and triage of mental health disorders that can be integrated directly into existing care pathways to support clinicians. However, despite the potential of digital tools in the field of mental health, there remain gaps in our understanding of how the intended user base, people with lived experiences of mental health concerns, perceive these technologies. OBJECTIVE: This study explores the perspectives and attitudes of individuals with lived experiences of mental health concerns on mental health apps that are designed to support self-assessment and triage. METHODS: A semistructured interview approach was used to explore the perspectives of the interviewees using 5 open-ended questions. Interviews were transcribed verbatim from audio data recordings. The average interview lasted 46 minutes (rounded to the nearest min; SD 12.93 min). A thematic analysis was conducted. RESULTS: Overall, 16 individuals were interviewed in this study. The average age was 42.25 (SD 15.18) years, half of the interviewees identified as women (8/16, 50%), and all were White (16/16, 100%). The thematic analysis revealed six major themes: (1) availability and accessibility, (2) quality, (3) attitudes, (4) safety, (5) impact, and (6) functionality. CONCLUSIONS: Engaging in clear communication regarding data security and privacy policies, adopting a consent-driven approach to data sharing, and identifying gaps in the app marketplace to foster the inclusion of a range of mental health conditions and avoid oversaturation of apps for common mental health disorders (eg, depression and anxiety) were identified as priorities from interviewees' comments. Furthermore, reputation was identified as a driver of uptake and engagement, with endorsement from a respected source (ie, health care provider, academic institution) or direct recommendation from a trusted health care professional associated with increased interest and trust. Furthermore, there was an interest in the role that co-designed digital self-assessments could play in existing care pathways, particularly in terms of facilitating informed discussions with health care professionals during appointments and by signposting individuals to the most appropriate services. In addition, interviewees discussed the potential of mental health apps to provide waiting list support to individuals awaiting treatment by providing personalized psychoeducation, self-help tips, and sources of help. However, concerns regarding the quality of care being affected because of digital delivery have been reported; therefore, frequent monitoring of patient acceptability and care outcomes is warranted. In addition, communicating the rationale and benefits of digitizing services will likely be important for securing interest and uptake from health care service users.

Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach.

BACKGROUND: Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders. OBJECTIVE: In this study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking. METHODS: Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient-boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals. RESULTS: The analysis included data from 924 symptomatic individuals for predicting misdiagnosis and from a subset of 379 misdiagnosed participants who provided follow-up information when predicting help-seeking. Models achieved good predictive power, with area under the receiver operating characteristic curve of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behavior, the strongest predictors included shorter time elapsed since last speaking to a general practitioner about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages. CONCLUSIONS: This study provides a novel, machine learning-based approach to understand the interplay of factors that may contribute to the misdiagnosis and subsequent help-seeking in patients experiencing low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders.

Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression.

Importance: Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools. Objective: To identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information. Design, Setting, and Participants: This diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020. The primary objective was to identify BD in patients with a recent (within the past 5 years) diagnosis of MDD and current depressive symptoms (Patient Health Questionnaire-9 score of 5 or more). Participants were recruited online through voluntary response sampling. The analysis was carried out between February 2022 and July 2023. Main Outcomes and Measures: Patient data were collected using a purpose-built online questionnaire (n = 635 questions). DBS metabolites (n = 630) were analyzed using a targeted mass spectrometry-based platform. Mood disorder diagnoses were established using the Composite International Diagnostic Interview. Results: Of 241 patients in the discovery cohort, 170 (70.5%) were female; 67 (27.8%) were subsequently diagnosed with BD and 174 (72.2%) were confirmed as having MDD; and the mean (SD) age was 28.1 (7.1) years. Of 30 participants in the validation cohort, 16 (53%) were female; 9 (30%) were diagnosed with BD and 21 (70%) with MDD; and the mean (SD) age was 25.4 (6.3) years. DBS metabolite levels were assessed in 241 patients with depressive symptoms with a recent diagnosis of MDD, of whom 67 were subsequently diagnosed with BD by the Composite International Diagnostic Interview and 174 were confirmed as having MDD. The identified 17-biomarker panel provided a mean (SD) cross-validated area under the receiver operating characteristic curve (AUROC) of 0.71 (SD, 0.12; P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker. Combining biomarker data with patient-reported information significantly enhanced diagnostic performance of models based on extensive demographic data, PHQ-9 scores, and the outcomes from the Mood Disorder Questionnaire. The identified biomarkers were correlated primarily with lifetime manic symptoms and were validated in a separate group of patients who received a new clinical diagnosis of MDD (n = 21) or BD (n = 9) during the study's 1-year follow-up period, with a mean (SD) AUROC of 0.73 (0.06; P < .001). Conclusions and Relevance: This study provides a proof of concept for developing an accessible biomarker test to facilitate the differential diagnosis of BD and MDD and highlights the potential involvement of ceramides in the pathophysiological mechanisms of mood disorders.

Opportunities for the Implementation of a Digital Mental Health Assessment Tool in the United Kingdom: Exploratory Survey Study.

BACKGROUND: Every year, one-fourth of the people in the United Kingdom experience diagnosable mental health concerns, yet only a proportion receive a timely diagnosis and treatment. With novel developments in digital technologies, the potential to increase access to mental health assessments and triage is promising. OBJECTIVE: This study aimed to investigate the current state of mental health provision in the United Kingdom and understand the utility of, and interest in, digital mental health technologies. METHODS: A web-based survey was generated using Qualtrics XM. Participants were recruited via social media. Data were explored using descriptive statistics. RESULTS: The majority of the respondents (555/618, 89.8%) had discussed their mental health with a general practitioner. More than three-fourths (503/618, 81.4%) of the respondents had been diagnosed with a mental health disorder, with the most common diagnoses being depression and generalized anxiety disorder. Diagnostic waiting times from first contact with a health care professional varied by diagnosis. Neurodevelopmental disorders (30/56, 54%), bipolar disorder (25/52, 48%), and personality disorders (48/101, 47.5%) had the longest waiting times, with almost half (103/209, 49.3%) of these diagnoses taking >6 months. Participants stated that waiting times resulted in symptoms worsening (262/353, 74.2%), lower quality of life (166/353, 47%), and the necessity to seek emergency care (109/353, 30.9%). Of the 618 participants, 386 (62.5%) stated that they felt that their mental health symptoms were not always taken seriously by their health care provider and 297 (48.1%) were not given any psychoeducational information. The majority of the respondents (416/595, 77.5%) did not have the chance to discuss mental health support and treatment options. Critically, 16.1% (96/595) did not find any treatment or support provided at all helpful, with 63% (48/76) having discontinued treatment with no effective alternatives. Furthermore, 88.3% (545/617) of the respondents) had sought help on the web regarding mental health symptoms, and 44.4% (272/612) had used a web application or smartphone app for their mental health. Psychoeducation (364/596, 61.1%), referral to a health care professional (332/596, 55.7%), and symptom monitoring (314/596, 52.7%) were the most desired app features. Only 6.8% (40/590) of the participants said that they would not be interested in using a mental health assessment app. Respondents were the most interested to receive an overall severity score of their mental health symptoms (441/546, 80.8%) and an indication of whether they should seek mental health support (454/546, 83.2%). CONCLUSIONS: Key gaps in current UK mental health care provision are highlighted. Assessment and treatment waiting times together with a lack of information regarding symptoms and treatment options translated into poor care experiences. The participants' responses provide proof-of-concept support for the development of a digital mental health assessment app and valuable recommendations regarding desirable app features.

Inflammatory monocyte gene signature predicts beneficial within group effect of simvastatin in patients with schizophrenia spectrum disorders in a secondary analysis of a randomized controlled trial.

Immune dysregulation has been reported in schizophrenia spectrum disorders (SSD). In the past decade, several trials using anti-inflammatory agents for treatment of SSD have been completed, with so far limited success. One such anti-inflammatory agent used is simvastatin. A recent, large-scale, randomized controlled trial with simvastatin augmentation failed to show improvement in the predefined primary outcome. However, baseline inflammatory profiles were not taken into account. Here we employed a data-driven clustering approach to investigate whether patients with an inflammatory monocyte gene signature respond better to add-on simvastatin treatment than those without such a signature, over a treatment period of 2 years. In 61 patients (60 randomized, 1:1 placebo:simvastatin) and healthy controls, a previously validated monocyte gene expression signature was assessed using quantitative polymerase chain reaction. Resulting delta cycle threshold values were used to identify patient clusters. Two major patient clusters with either up- or downregulated pro-inflammatory factors were detected. Linear mixed models showed a significant three-way interaction between the inflammatory cluster, treatment, and time for psychotic symptoms. Only patients treated with simvastatin who were in the inflammatory group, showed a consistent improvement: symptom severity gradually decreased after 3 months and reached significance after 12 and 24 months compared to baseline (p.adj<0.05). The effects were small, and overall between-group effects were not significant. Here, we show that patient stratification based on inflammatory gene expression might be useful to select appropriate treatment augmentation for patients with SSD, highlighting the need for precision medicine approaches. Our findings corroborate the results of the primary analyses, showing that in the overall group, simvastatin was not effective; however, at the individual level the treatment might make a difference.

Learnings from user feedback of a novel digital mental health assessment.

Digital mental health interventions (DMHI) have the potential to address barriers to face-to-face mental healthcare. In particular, digital mental health assessments offer the opportunity to increase access, reduce strain on services, and improve identification. Despite the potential of DMHIs there remains a high drop-out rate. Therefore, investigating user feedback may elucidate how to best design and deliver an engaging digital mental health assessment. The current study aimed to understand 1304 user perspectives of (1) a newly developed digital mental health assessment to determine which features users consider to be positive or negative and (2) the Composite International Diagnostic Interview (CIDI) employed in a previous large-scale pilot study. A thematic analysis method was employed to identify themes in feedback to three question prompts related to: (1) the questions included in the digital assessment, (2) the homepage design and reminders, and (3) the assessment results report. The largest proportion of the positive and negative feedback received regarding the questions included in the assessment (n = 706), focused on the quality of the assessment (n = 183, 25.92% and n = 284, 40.23%, respectively). Feedback for the homepage and reminders (n = 671) was overwhelmingly positive, with the largest two themes identified being positive usability (i.e., ease of use; n = 500, 74.52%) and functionality (i.e., reminders; n = 278, 41.43%). The most frequently identified negative theme in results report feedback (n = 794) was related to the report content (n = 309, 38.92%), with users stating it was lacking in-depth information. Nevertheless, the most frequent positive theme regarding the results report feedback was related to wellbeing outcomes (n = 145, 18.26%), with users stating the results report, albeit brief, encouraged them to seek professional support. Interestingly, despite some negative feedback, most users reported that completing the digital mental health assessment has been worthwhile (n = 1,017, 77.99%). Based on these findings, we offer recommendations to address potential barriers to user engagement with a digital mental health assessment. In summary, we recommend undertaking extensive co-design activities during the development of digital assessment tools, flexibility in answering modalities within digital assessment, customizable additional features such as reminders, transparency of diagnostic decision making, and an actionable results report with personalized mental health resources.

Diagnostic model development for schizophrenia based on peripheral blood mononuclear cell subtype-specific expression of metabolic markers.

A significant proportion of the personal and economic burden of schizophrenia can be attributed to the late diagnosis or misdiagnosis of the disorder. A novel, objective diagnostic approaches could facilitate the early detection and treatment of schizophrenia and improve patient outcomes. In the present study, we aimed to identify robust schizophrenia-specific blood biomarkers, with the goal of developing an accurate diagnostic model. The levels of selected serum and peripheral blood mononuclear cell (PBMC) markers relevant to metabolic and immune function were measured in healthy controls (n = 26) and recent-onset schizophrenia patients (n = 36) using multiplexed immunoassays and flow cytometry. Analysis of covariance revealed significant upregulation of insulin receptor (IR) and fatty acid translocase (CD36) levels in T helper cells (F = 10.75, P = 0.002, Q = 0.024 and F = 21.58, P = 2.8 × 10-5, Q = 0.0004, respectively), as well as downregulation of glucose transporter 1 (GLUT1) expression in monocytes (F = 21.46, P = 2.9 × 10-5, Q = 0.0004). The most robust predictors, monocyte GLUT1 and T helper cell CD36, were used to develop a diagnostic model, which showed a leave-one-out cross-validated area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI: 0.66-0.92). The diagnostic model was validated in two independent datasets. The model was able to distinguish first-onset, drug-naïve schizophrenia patients (n = 34) from healthy controls (n = 39) with an AUC of 0.75 (95% CI: 0.64-0.86), and also differentiated schizophrenia patients (n = 22) from patients with other neuropsychiatric conditions, including bipolar disorder, major depressive disorder and autism spectrum disorder (n = 68), with an AUC of 0.83 (95% CI: 0.75-0.92). These findings indicate that PBMC-derived biomarkers have the potential to support an accurate and objective differential diagnosis of schizophrenia.

Using decision-analysis modelling to estimate the economic impact of the identification of unrecognised bipolar disorder in primary care: the untapped potential of screening.

BACKGROUND: Patients with bipolar disorder are often unrecognised and misdiagnosed with major depressive disorder leading to higher direct costs and pressure on the medical system. Novel screening tools may mitigate the problem. This study was aimed at investigating the direct costs of bipolar disorder misdiagnosis in the general population, evaluating the impact of a novel bipolar disorder screening algorithm, and comparing it to the established Mood Disorder Questionnaire. A decision analysis model was built to quantify the utility of one-time screening for bipolar disorder in primary care adults presenting with a depressive episode. A hypothetical population of interest comprised a healthcare system of one million users, corresponding to 15,000 help-seekers diagnosed with major depressive disorder annually, followed for five years. The model was used to calculate the impact of screening for bipolar disorder, compared to no screening, in terms of accuracy and total direct costs to a third-party payer at varying diagnostic cut-offs. Decision curve analysis was used to evaluate clinical utility. RESULTS: Compared to no screening, one-time screening for bipolar disorder using the algorithm reduced the number of misdiagnoses from 680 to 260, and overall direct costs from $50,936 to $49,513 per patient, accounting for $21.3 million savings over the five-year period. The algorithm outperformed the Mood Disorder Questionnaire, which yielded 367 misdiagnoses and $18.3 million savings over the same time. Decision curve analysis showed the screening model was beneficial. CONCLUSIONS: Utilisation of bipolar disorder screening strategies could lead to a substantial reduction in human suffering by reducing misdiagnosis, and also lessen the healthcare costs.

mHealth Solutions for Mental Health Screening and Diagnosis: A Review of App User Perspectives Using Sentiment and Thematic Analysis.

Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

Peripheral lymphocyte signaling pathway deficiencies predict treatment response in first-onset drug-naïve schizophrenia.

Despite being a major cause of disability worldwide, the pathophysiology of schizophrenia and molecular basis of treatment response heterogeneity continue to be unresolved. Recent evidence suggests that multiple aspects of pathophysiology, including genetic risk factors, converge on key cell signaling pathways and that exploration of peripheral blood cells might represent a practical window into cell signaling alterations in the disease state. We employed multiplexed phospho-specific flow cytometry to examine cell signaling epitope expression in peripheral blood mononuclear cell (PBMC) subtypes in drug-naïve schizophrenia patients (n = 49) relative to controls (n = 61) and relate these changes to serum immune response proteins, schizophrenia polygenic risk scores and clinical effects of treatment, including drug response and side effects, over the longitudinal course of antipsychotic treatment. This revealed both previously characterized (Akt1) and novel cell signaling epitopes (IRF-7 (pS477/pS479), CrkL (pY207), Stat3 (pS727), Stat3 (pY705) and Stat5 (pY694)) across PBMC subtypes which were associated with schizophrenia at disease onset, and correlated with type I interferon-related serum molecules CD40 and CXCL11. Alterations in Akt1 and IRF-7 (pS477/pS479) were additionally associated with polygenic risk of schizophrenia. Finally, changes in Akt1, IRF-7 (pS477/pS479) and Stat3 (pS727) predicted development of metabolic and cardiovascular side effects following antipsychotic treatment, while IRF-7 (pS477/pS479) and Stat3 (pS727) predicted early improvements in general psychopathology scores measured using the Brief Psychiatric Rating Scale (BPRS). These findings suggest that peripheral blood cells can provide an accessible surrogate model for intracellular signaling alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic and cardiovascular side effects following antipsychotic therapy.

The Current State and Validity of Digital Assessment Tools for Psychiatry: Systematic Review.

BACKGROUND: Given the role digital technologies are likely to play in the future of mental health care, there is a need for a comprehensive appraisal of the current state and validity (ie, screening or diagnostic accuracy) of digital mental health assessments. OBJECTIVE: The aim of this review is to explore the current state and validity of question-and-answer-based digital tools for diagnosing and screening psychiatric conditions in adults. METHODS: This systematic review was based on the Population, Intervention, Comparison, and Outcome framework and was carried out in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. MEDLINE, Embase, Cochrane Library, ASSIA, Web of Science Core Collection, CINAHL, and PsycINFO were systematically searched for articles published between 2005 and 2021. A descriptive evaluation of the study characteristics and digital solutions and a quantitative appraisal of the screening or diagnostic accuracy of the included tools were conducted. Risk of bias and applicability were assessed using the revised tool for the Quality Assessment of Diagnostic Accuracy Studies 2. RESULTS: A total of 28 studies met the inclusion criteria, with the most frequently evaluated conditions encompassing generalized anxiety disorder, major depressive disorder, and any depressive disorder. Most of the studies used digitized versions of existing pen-and-paper questionnaires, with findings revealing poor to excellent screening or diagnostic accuracy (sensitivity=0.32-1.00, specificity=0.37-1.00, area under the receiver operating characteristic curve=0.57-0.98) and a high risk of bias for most of the included studies. CONCLUSIONS: The field of digital mental health tools is in its early stages, and high-quality evidence is lacking. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/25382.

The druggable schizophrenia genome: from repurposing opportunities to unexplored drug targets.

There have been no new drugs for the treatment of schizophrenia in several decades and treatment resistance represents a major unmet clinical need. The drugs that exist are based on serendipitous clinical observations rather than an evidence-based understanding of disease pathophysiology. In the present review, we address these bottlenecks by integrating common, rare, and expression-related schizophrenia risk genes with knowledge of the druggability of the human genome as a whole. We highlight novel drug repurposing opportunities, clinical trial candidates which are supported by genetic evidence, and unexplored therapeutic opportunities in the lesser-known regions of the schizophrenia genome. By identifying translational gaps and opportunities across the schizophrenia disease space, we discuss a framework for translating increasingly well-powered genetic association studies into personalized treatments for schizophrenia and initiating the vital task of characterizing clinically relevant drug targets in underexplored regions of the human genome.

mHealth Solutions for Perinatal Mental Health: Scoping Review and Appraisal Following the mHealth Index and Navigation Database Framework.

BACKGROUND: The ever-increasing pressure on health care systems has resulted in the underrecognition of perinatal mental disorders. Digital mental health tools such as apps could provide an option for accessible perinatal mental health screening and assessment. However, there is a lack of information regarding the availability and features of perinatal app options. OBJECTIVE: This study aims to evaluate the current state of diagnostic and screening apps for perinatal mental health available on the Google Play Store (Android) and Apple App Store (iOS) and to review their features following the mHealth Index and Navigation Database framework. METHODS: Following a scoping review approach, the Apple App Store and Google Play Store were systematically searched to identify perinatal mental health assessment apps. A total of 14 apps that met the inclusion criteria were downloaded and reviewed in a standardized manner using the mHealth Index and Navigation Database framework. The framework comprised 107 questions, allowing for a comprehensive assessment of app origin, functionality, engagement features, security, and clinical use. RESULTS: Most apps were developed by for-profit companies (n=10), followed by private individuals (n=2) and trusted health care companies (n=2). Out of the 14 apps, 3 were available only on Android devices, 4 were available only on iOS devices, and 7 were available on both platforms. Approximately one-third of the apps (n=5) had been updated within the last 180 days. A total of 12 apps offered the Edinburgh Postnatal Depression Scale in its original version or in rephrased versions. Engagement, input, and output features included reminder notifications, connections to therapists, and free writing features. A total of 6 apps offered psychoeducational information and references. Privacy policies were available for 11 of the 14 apps, with a median Flesch-Kincaid reading grade level of 12.3. One app claimed to be compliant with the Health Insurance Portability and Accountability Act standards and 2 apps claimed to be compliant with General Data Protection Regulation. Of the apps that could be accessed in full (n=10), all appeared to fulfill the claims stated in their description. Only 1 app referenced a relevant peer-reviewed study. All the apps provided a warning for use, highlighting that the mental health assessment result should not be interpreted as a diagnosis or as a substitute for medical care. Only 3 apps allowed users to export or email their mental health test results. CONCLUSIONS: These results indicate that there are opportunities to improve perinatal mental health assessment apps. To this end, we recommend focusing on the development and validation of more comprehensive assessment tools, ensuring data protection and safety features are adequate for the intended app use, and improving data sharing features between users and health care professionals for timely support.

Novel open reading frames in human accelerated regions and transposable elements reveal new leads to understand schizophrenia and bipolar disorder.

Schizophrenia (SCZ) and bipolar disorder are debilitating neuropsychiatric disorders arising from a combination of environmental and genetic factors. Novel open reading frames (nORFs) are genomic loci that give rise to previously uncharacterized transcripts and protein products. In our previous work, we have shown that nORFs can be biologically regulated and that they may play a role in cancer and rare diseases. More importantly, we have shown that nORFs may emerge in accelerated regions of the genome giving rise to species-specific functions. We hypothesize that nORFs represent a potentially important group of biological factors that may contribute to SCZ and bipolar disorder pathophysiology. Human accelerated regions (HARs) are genomic features showing human-lineage-specific rapid evolution that may be involved in biological regulation and have additionally been found to associate with SCZ genes. Transposable elements (TEs) are another set of genomic features that have been shown to regulate gene expression. As with HARs, their relevance to SCZ has also been suggested. Here, nORFs are investigated in the context of HARs and TEs. This work shows that nORFs whose expression is disrupted in SCZ and bipolar disorder are in close proximity to HARs and TEs and that some of them are significantly associated with SCZ and bipolar disorder genomic hotspots. We also show that nORF encoded proteins can form structures and potentially constitute novel drug targets.

Toward an Extended Definition of Major Depressive Disorder Symptomatology: Digital Assessment and Cross-validation Study.

BACKGROUND: Diagnosing major depressive disorder (MDD) is challenging, with diagnostic manuals failing to capture the wide range of clinical symptoms that are endorsed by individuals with this condition. OBJECTIVE: This study aims to provide evidence for an extended definition of MDD symptomatology. METHODS: Symptom data were collected via a digital assessment developed for a delta study. Random forest classification with nested cross-validation was used to distinguish between individuals with MDD and those with subthreshold symptomatology of the disorder using disorder-specific symptoms and transdiagnostic symptoms. The diagnostic performance of the Patient Health Questionnaire-9 was also examined. RESULTS: A depression-specific model demonstrated good predictive performance when distinguishing between individuals with MDD (n=64) and those with subthreshold depression (n=140) (area under the receiver operating characteristic curve=0.89; sensitivity=82.4%; specificity=81.3%; accuracy=81.6%). The inclusion of transdiagnostic symptoms of psychopathology, including symptoms of depression, generalized anxiety disorder, insomnia, emotional instability, and panic disorder, significantly improved the model performance (area under the receiver operating characteristic curve=0.95; sensitivity=86.5%; specificity=90.8%; accuracy=89.5%). The Patient Health Questionnaire-9 was excellent at identifying MDD but overdiagnosed the condition (sensitivity=92.2%; specificity=54.3%; accuracy=66.2%). CONCLUSIONS: Our findings are in line with the notion that current diagnostic practices may present an overly narrow conception of mental health. Furthermore, our study provides proof-of-concept support for the clinical utility of a digital assessment to inform clinical decision-making in the evaluation of MDD.

Building the Digital Mental Health Ecosystem: Opportunities and Challenges for Mobile Health Innovators.

Digital mental health technologies such as mobile health (mHealth) tools can offer innovative ways to help develop and facilitate mental health care provision, with the COVID-19 pandemic acting as a pivot point for digital health implementation. This viewpoint offers an overview of the opportunities and challenges mHealth innovators must navigate to create an integrated digital ecosystem for mental health care moving forward. Opportunities exist for innovators to develop tools that can collect a vast range of active and passive patient and transdiagnostic symptom data. Moving away from a symptom-count approach to a transdiagnostic view of psychopathology has the potential to facilitate early and accurate diagnosis, and can further enable personalized treatment strategies. However, the uptake of these technologies critically depends on the perceived relevance and engagement of end users. To this end, behavior theories and codesigning approaches offer opportunities to identify behavioral drivers and address barriers to uptake, while ensuring that products meet users' needs and preferences. The agenda for innovators should also include building strong evidence-based cases for digital mental health, moving away from a one-size-fits-all well-being approach to embrace the development of comprehensive digital diagnostics and validated digital tools. In particular, innovators have the opportunity to make their clinical evaluations more insightful by assessing effectiveness and feasibility in the intended context of use. Finally, innovators should adhere to standardized evaluation frameworks introduced by regulators and health care providers, as this can facilitate transparency and guide health care professionals toward clinically safe and effective technologies. By laying these foundations, digital services can become integrated into clinical practice, thus facilitating deeper technology-enabled changes.

Personality, symptom, and demographic correlates of perceived efficacy of selective serotonin reuptake inhibitor monotherapy among current users with low mood: A data-driven approach.

BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) are often the first-line treatment option for depressive symptoms, however their efficacy varies across patients. Identifying predictors of response to SSRIs could facilitate personalised treatment of depression and improve treatment outcomes. The aim of this study was to develop a data-driven formulation of demographic, personality, and symptom-level factors associated with subjective response to SSRI treatment. METHODS: Participants were recruited online and data were collected retrospectively through an extensive digital mental health questionnaire. Extreme gradient boosting classification with nested cross-validation was used to identify factors distinguishing between individuals with low (n=37) and high (n=111) perceived benefit from SSRI treatment. RESULTS: The algorithm demonstrated a good predictive performance (test AUC=.88±.07). Positive affectivity was the strongest predictor of response to SSRIs and a major confounder of the remaining associations. After controlling for positive affectivity, as well as current wellbeing, severity of current depressive symptoms, and multicollinearity, only low positive affectivity, chronic pain, sleep problems, and unemployment remained significantly associated with diminished subjective response to SSRIs. LIMITATIONS: This was an exploratory analysis of data collected at a single time point, for a study which had a different primary aim. Therefore, the results may not reflect causal relationships, and require validation in future prospective studies. Furthermore, the data were self-reported by internet users, which could affect integrity of the dataset and limit generalisability of the results. CONCLUSIONS: Our findings suggest that demographic, personality, and symptom data may offer a potential cost-effective and efficient framework for SSRI treatment outcome prediction.

The Delta Study - Prevalence and characteristics of mood disorders in 924 individuals with low mood: Results of the of the World Health Organization Composite International Diagnostic Interview (CIDI).

OBJECTIVES: The Delta Study was undertaken to improve the diagnosis of mood disorders in individuals presenting with low mood. The current study aimed to estimate the prevalence and explore the characteristics of mood disorders in participants of the Delta Study, and discuss their implications for clinical practice. METHODS: Individuals with low mood (Patients Health Questionnaire-9 score ≥5) and either no previous mood disorder diagnosis (baseline low mood group, n = 429), a recent (≤5 years) clinical diagnosis of MDD (baseline MDD group, n = 441) or a previous clinical diagnosis of BD (established BD group, n = 54), were recruited online. Self-reported demographic and clinical data were collected through an extensive online mental health questionnaire and mood disorder diagnoses were determined with the World Health Organization Composite International Diagnostic Interview (CIDI). RESULTS: The prevalence of BD and MDD in the baseline low mood group was 24% and 36%, respectively. The prevalence of BD among individuals with a recent diagnosis of MDD was 31%. Participants with BD in both baseline low mood and baseline MDD groups were characterized by a younger age at onset of the first low mood episode, more severe depressive symptoms and lower wellbeing, relative to the MDD or low mood groups. Approximately half the individuals with BD diagnosed as MDD (49%) had experienced (hypo)manic symptoms prior to being diagnosed with MDD. CONCLUSIONS: The current results confirm high under- and misdiagnosis rates of mood disorders in individuals presenting with low mood, potentially leading to worsening of symptoms and decreased well-being, and indicate the need for improved mental health triage in primary care.

Proof-of-Concept Support for the Development and Implementation of a Digital Assessment for Perinatal Mental Health: Mixed Methods Study.

BACKGROUND: Perinatal mental health symptoms commonly remain underdiagnosed and undertreated in maternity care settings in the United Kingdom, with outbreaks of disease, like the COVID-19 pandemic, further disrupting access to adequate mental health support. Digital technologies may offer an innovative way to support the mental health needs of women and their families throughout the perinatal period, as well as assist midwives in the recognition of perinatal mental health concerns. However, little is known about the acceptability and perceived benefits and barriers to using such technologies. OBJECTIVE: The aim of this study was to conduct a mixed methods evaluation of the current state of perinatal mental health care provision in the United Kingdom, as well as users' (women and partners) and midwives' interest in using a digital mental health assessment throughout the perinatal period. METHODS: Women, partners, and midwives were recruited to participate in the study, which entailed completing an online survey. Quantitative data were explored using descriptive statistics. Open-ended response data were first investigated using thematic analysis. Resultant themes were then mapped onto the components of the Capability, Opportunity, and Motivation Behavior model and summarized using descriptive statistics. RESULTS: A total of 829 women, 103 partners, and 90 midwives participated in the study. The provision of adequate perinatal mental health care support was limited, with experiences varying significantly across respondents. There was a strong interest in using a digital mental health assessment to screen, diagnose, and triage perinatal mental health concerns, particularly among women and midwives. The majority of respondents (n=781, 76.42%) expressed that they would feel comfortable or very comfortable using or recommending a digital mental health assessment. The majority of women and partners showed a preference for in-person consultations (n=417, 44.74%), followed by a blended care approach (ie, both in-person and online consultations) (n=362, 38.84%), with fewer participants preferring online-only consultations (n=120, 12.88%). Identified benefits and barriers mainly related to physical opportunity (eg, accessibility), psychological capability (eg, cognitive skills), and automatic motivation (eg, emotions). CONCLUSIONS: This study provides proof-of-concept support for the development and implementation of a digital mental health assessment to inform clinical decision making in the assessment of perinatal mental health concerns in the United Kingdom.