Schizophrenia

Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning

Importance

The diagnosis of schizophrenia and bipolar disorder is often delayed several years despite illness typically emerging in late adolescence or early adulthood, which impedes initiation of targeted treatment.

Objective

To investigate whether machine learning models trained on routine clinical data from electronic health records (EHRs) can predict diagnostic progression to schizophrenia or bipolar disorder among patients undergoing treatment in psychiatric services for other mental illness.

Design, Setting, and Participants

This cohort study was based on data from EHRs from the Psychiatric Services of the Central Denmark Region. All patients aged 15 to 60 years with at least 2 contacts (at least 3 months apart) with the Psychiatric Services of the Central Denmark Region between January 1, 2013, and November 21, 2016, were included. Analysis occurred from December 2022 to November 2024.

Exposures

Predictors based on EHR data, including medications, diagnoses, and clinical notes.

Main Outcomes and Measures

Diagnostic transition to schizophrenia or bipolar disorder within 5 years, predicted 1 day before outpatient contacts by means of elastic net regularized logistic regression and extreme gradient boosting (XGBoost) models. The area under the receiver operating characteristic curve (AUROC) was used to determine the best performing model.

Results

The study included 24 449 patients (median [Q1-Q3] age at time of prediction, 32.2 [24.2-42.5] years; 13 843 female [56.6%]) and 398 922 outpatient contacts. Transition to the first occurrence of either schizophrenia or bipolar disorder was predicted by the XGBoost model, with an AUROC of 0.70 (95% CI, 0.70-0.70) on the training set and 0.64 (95% CI, 0.63-0.65) on the test set, which consisted of 2 held-out hospital sites. At a predicted positive rate of 4%, the XGBoost model had a sensitivity of 9.3%, a specificity of 96.3%, and a positive predictive value (PPV) of 13.0%. Predicting schizophrenia separately yielded better performance (AUROC, 0.80; 95% CI, 0.79-0.81; sensitivity, 19.4%; specificity, 96.3%; PPV, 10.8%) than was the case for bipolar disorder (AUROC, 0.62, 95% CI, 0.61-0.63; sensitivity, 9.9%; specificity, 96.2%; PPV, 8.4%). Clinical notes proved particularly informative for prediction.

Conclusions and Relevance

These findings suggest that it is possible to predict diagnostic transition to schizophrenia and bipolar disorder from routine clinical data extracted from EHRs, with schizophrenia being notably easier to predict than bipolar disorder.

Evaluation of the content and quality of schizophrenia on TikTok: a cross-sectional study

Schizophrenia is a common and serious heterogeneous mental disorder that has a significant health and economic impact on families and society. As the prevalence of schizophrenia increases each year, public awareness of the disorder is growing. However, it remains unclear whether the quality of information presented in these videos is satisfactory. Therefore, a comprehensive assessment of the quality and content of schizophrenia-related videos on video-sharing platforms is necessary. We screened 123 schizophrenia-related videos from the video-sharing platform TikTok that met the criteria, and evaluated and analyzed them. First, the basic information provided in the videos was recorded and analyzed. The source and content type of each video was then determined. The educational content and quality of all videos were then evaluated using three rating instruments, Modified DISCERN, Global Quality Scale, and Journal of the American Medical Association (JAMA). Videos from different sources were also compared to see if they were statistically different in terms of educational content and quality. We identified 4 categories of content for the videos. The science introduction category had the largest number of 50, and the least number of videos was etiology and causation at 15. Six categories of sources of videos were also identified: the for-profit organizations category has the lowest number of 10, the health professionals category has the highest number of 45. There was a significant correlation between video source and duration (P = 0.014). The JAMA score was significantly positively correlated with the number of video likes (r = 0.721, P < 0.001). This study evaluated the content and information quality of 123 videos related to schizophrenia on the video-sharing platform TikTok. The findings indicate that while these videos offer some valuable insights into schizophrenia, the overall quality falls short of satisfactory levels, with inadequate reliability and accuracy.

Review of Intranasal Active Pharmaceutical Ingredient Delivery Systems

In recent decades, there has been an increased interest in the development of intranasal delivery systems for active pharmaceutical ingredients (APIs) not only for treating local nasal diseases but also for treating systemic diseases, central nervous system (CNS) disorders, and vaccine delivery. The nasal cavity possesses a unique set of anatomical characteristics for delivering active pharmaceutical ingredients, but there are several limitations that recent research in the field of the intranasal administration of APIs aims to overcome. For the effective delivery of nasal preparations, active pharmaceutical ingredients are incorporated into various micro- and nanosystems. Some of the most commonly encountered API delivery systems in the scientific literature include liposomal systems, polymer particles with mucoadhesive properties, in situ gels, nano- and microemulsions, and solid lipid particles. This article provides a review of research on the development of nasal preparations for treating local nasal cavity diseases (in particular, for antibiotic delivery), systemic diseases (analgesics, drugs for cardiovascular diseases, antiviral and antiemetic drugs), CNS disorders (Alzheimer's disease, Parkinson's disease, epilepsy, schizophrenia, depression), and vaccine delivery. The literature data show that active research is underway to reformulate drugs of various pharmacotherapeutic groups into a nasal form.

Exploration on the potential efficacy and mechanism of methyl salicylate glycosides in the treatment of schizophrenia based on bioinformatics, molecular docking and dynamics simulation

The etiological and therapeutic complexities of schizophrenia (SCZ) persist, prompting exploration of anti-inflammatory therapy as a potential treatment approach. Methyl salicylate glycosides (MSGs), possessing a structural parent nucleus akin to aspirin, are being investigated for their therapeutic potential in schizophrenia. Utilizing bioinformation mining, network pharmacology, molecular docking and dynamics simulation, the potential value and mechanism of MSGs (including MSTG-A, MSTG-B, and Gaultherin) in the treatment of SCZ, as well as the underlying pathogenesis of the disorder, were examined. 581 differentially expressed genes related to SCZ were identified in patients and healthy individuals, with 349 up-regulated genes and 232 down-regulated genes. 29 core targets were characterized by protein-protein interaction (PPI) network, with the top 10 core targets being BDNF, VEGFA, PVALB, KCNA1, GRIN2A, ATP2B2, KCNA2, APOE, PPARGC1A and SCN1A. The pathogenesis of SCZ primarily involves cAMP signaling, neurodegenerative diseases and other pathways, as well as regulation of ion transmembrane transport. Molecular docking analysis revealed that the three candidates exhibited binding activity with certain targets with binding affinities ranging from -4.7 to -109.2 kcal/mol. MSTG-A, MSTG-B and Gaultherin show promise for use in the treatment of SCZ, potentially through their ability to modulate the expression of multiple genes involved in synaptic structure and function, ion transport, energy metabolism. Molecular dynamics simulation revealed good binding abilities between MSTG-A, MSTG-B, Gaultherin and ATP2B2. It suggests new avenues for further investigation in this area.

Clinical decision-making before discharge in hospitalized persons with schizophrenia: a Spanish Delphi expert consensus

Introduction

The care of people with schizophrenia (PWS) is usually provided in an outpatient setting by community mental health teams. However, PWS frequently require inpatient treatment because of a wide array of clinical, personal and/or social situations. Unfortunately, to our knowledge, there are no guidelines available to help psychiatrists in the decision-making process on hospital discharge for PWS. The aim of this project was to develop an expert consensus on discharge criteria for PWS after their stay in an acute inpatient psychiatric unit.

Methods

Using a modified Delphi method a group of 42 psychiatrists throughout Spain evaluated four areas of interest regarding this issue: clinical symptomatology, treatment-related factors, follow-up health care units after discharge, and physical health and monitoring.

Results

After two rounds, among the 64 statements, a consensus was reached for 59 (92.2%) statements. In three (17.7%) of the 17 statements on 'clinical symptomatology' and 2 (13.3%) of the 15 statements on 'follow-up health care units after discharge', a consensus was not reached; in contrast, a consensus was reached for all statements concerning 'treatment-related factors' and those concerning 'physical health and monitoring'. The consensus results highlight the importance for discharge of the control of symptoms rather than their suppression during admission and of tolerability in the selection of anantipsychotic.

Discussion

Although there is a lack of relevant data for guiding the discharge of PWS after hospitalization in an acute inpatient psychiatric unit, we expect that this consensus based on expert opinion may help clinicians to take appropriate decisions.

Serum Levels of HCY, MIF, and hs-CRP Correlate with Glycolipid Metabolism in Adults with Never-Medicated First-Episode Schizophrenia

Objective

It has been reported that the prevalence of metabolic syndrome (MS) in multiepisode patients with schizophrenia is 35.3%, which is 2- to 4-fold higher than in the general population. The study is designed to compare the glycolipid metabolism in patients with first-episode schizophrenia (FES) with sex- and age-matched healthy controls to investigate changes in serum levels of homocysteine (Hcy), macrophage migration inhibitory factor (MIF), and high-sensitive C-reactive protein (hs-CRP) and their relationships with the glycolipid metabolism in patients with FES.

Methods

His case-control study included 88 patients diagnosed with FES and 88 sex- and age-matched healthy controls. Patient psychopathology was assessed using the Positive and Negative Syndrome Scale (PANSS), Young Mania Rating Scale (YMRS), and 17-item Hamilton Rating Scale for Depression (HAMD-17). Patients with FES were classified into MS and non-MS groups.

Results

There were significant differences in the education level, body mass index (BMI), and waist circumference between the patients with FES and healthy controls (all p > 0.05). The patients with FES had higher levels of FPG and blood glucose at the oral glucose tolerance test (OGTT) (2 h glucose) concomitant with higher proportion of impaired glucose tolerance (IGT) and homeostasis model assessment of insulin resistance (HOMA2-IR) than healthy controls (all p < 0.001). It was revealed that the patients with FES showed higher serum levels of Hcy, MIF, and hs-CRP than healthy controls (all p < 0.001). The serum level of Hcy shared positive correlations with the score of PANSS totals (r = 0.551) and the negative syndrome of the PANSS scale (r = 0.494). The serum levels of MIF and hs-CRP was only positively correlated with the negative syndrome of the PANSS scale (r = 0.320 and r = 0.446). The level of Hcy shared positive correlations with the levels of FPG, 2 h glucose, and HOMA2-IR; the level of MIF was only positively correlated with the level of HOMA2-IR; the level of hs-CRP had a positive correlation with both levels of FPG and 2 h glucose (all p < 0.001). The levels of Hcy, MIF, and hs-CRP all shared positive correlations with the TG level and negative correlations with the HDL-C level (all p < 0.001). There were remarkable differences between the MS and non-MS groups with regard to BMI, waist circumference, negative subscale of the PANSS scale, FPG, TG, and HDL-C (all p < 0.05). Elevated levels of Hcy, MIF, and hs-CRP were detected in the MS group compared to the non-MS group (all p < 0.05).

Conclusion

These findings suggest that increased concentrations of HCY, MIF, and hs-CRP may contribute to the abnormal glycolipid metabolism in the context of schizophrenia.

Nanotechnology Approaches for Enhanced CNS Drug Delivery in the Management of Schizophrenia

Schizophrenia is a neuropsychiatric disorder mainly affecting the central nervous system, presented with auditory and visual hallucinations, delusion and withdrawal from society. Abnormal dopamine levels mainly characterise the disease; various theories of neurotransmitters explain the pathophysiology of the disease. The current therapeutic approach deals with the systemic administration of drugs other than the enteral route, altering the neurotransmitter levels within the brain and providing symptomatic relief. Fluid biomarkers help in the early detection of the disease, which would improve the therapeutic efficacy. However, the major challenge faced in CNS drug delivery is the blood-brain barrier. Nanotherapeutic approaches may overcome these limitations, which will improve safety, efficacy, and targeted drug delivery. This review article addresses the main challenges faced in CNS drug delivery and the significance of current therapeutic strategies and nanotherapeutic approaches for a better understanding and enhanced drug delivery to the brain, which improve the quality of life of schizophrenia patients.

Expression and function of circular RNAs in the mammalian brain

Mammalian brain presents extraordinary complexity reflected in the structure, function, and dynamic changes in the biological and physiological processes of development, maturity, and aging. Recent transcriptomic profiles from the brain tissues of distinct species have described a novel class of transcripts with a covalently closed-loop structure, called circular RNAs (circRNAs), which are produced by alternative back-splicing and derived from genes associated with synaptogenesis and neural activities. Brain is a tightly regulated and largely unexplored organ where circRNAs are highly enriched and expressed in the cell type-, spatiotemporal-specific, sex-biased, and age-related manner. Although the biological functions of most of the circRNAs in the brain remain elusive, increased evidence suggests that dynamic changes in circRNA expression are critical for brain function and the maintenance of physiological homeostasis in the brain. Here, we review the latest immense progresses in the understanding of circRNA expression and function in the mammalian brain. We also discuss possibly biological functions of circRNAs in the brain, which may provide new sights of understanding brain development and aging, as well as the pathogenesis of mental diseases.