Depression and Schizophrenia: Sleep, Medical Risk Factors, Biomarkers, and Treatment

Quantitative analysis of literature on diagnostic biomarkers of Schizophrenia: revealing research hotspots and future prospects

BACKGROUND

Schizophrenia (SCZ) is a complex mental disorder characterized by a wide range of symptoms and cognitive impairments. The search for reliable biomarkers for SCZ has gained increasing attention in recent years, as they hold the potential to improve early diagnosis and intervention strategies. To understand the research trends and collaborations in this field, a comprehensive Bibliometric analysis of SCZ and biomarkers research was conducted.

METHODS

A systematic search of the Web of Science Core Collection was performed to retrieve relevant articles published from January 2000 to July 2023. The search focused on SCZ and biomarkers. Bibliometric tools, including CiteSpace, VOSviewer, and R package Bibliometrix, were utilized to perform data extraction, quantitative analysis, and visualization.

RESULTS

The search focused on SCZ and biomarkers, and a total of 2935 articles were included in the analysis. The analysis revealed a gradual increase in the number of publications related to SCZ and biomarkers over the years, indicating a growing research focus in this area. Collaboration and research activity were found to be concentrated in the United States and Western European countries. Among the top ten most active journals, "Schizophrenia Research" emerged as the journal with the highest number of publications and citations related to SCZ and biomarkers. Recent studies published in this journal have highlighted the potential use of facial expressions as a diagnostic biomarker for SCZ, suggesting that facial expression analysis using big data may hold promise for future diagnosis and interventions. Furthermore, the analysis of key research keywords identified inflammatory factors, DNA methylation changes, and glutamate alterations as potential biomarkers for SCZ diagnosis.

CONCLUSION

This Bibliometric analysis provides valuable insights into the current state of research on SCZ and biomarkers. The identification of reliable biomarkers for SCZ could have significant implications for early diagnosis and interventions, potentially leading to improved outcomes for individuals affected by this challenging mental disorder. Further research and collaborations in this field are encouraged to advance our understanding of SCZ and enhance diagnostic and therapeutic approaches.

Correlation Between Monocyte Count, Monocyte-Lymphocyte Ratio, and Other Inflammatory Cells With Sleep and Psychiatric Symptoms in First-Episode Schizophrenia Patients

Background

More and more evidence shows that infection and immune abnormality are closely related to the increased severity of schizophrenia symptoms. This study aimed to explore the correlation between inflammatory cell counts, sleep quality, and psychiatric symptoms in first-episode schizophrenia patients.

Methods

A total of 103 first-episode schizophrenia patients (patient group) admitted to the Anhui Provincial Mental Health Center from November 2021 to August 2022 were included in the study, while 57 healthy individuals (control group) who met the criteria were recruited as the study subjects. The Positive and Negative Symptom Scale (PANSS) and Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the mental symptoms and sleep status of the patients. Blood analysis results were used to determine the peripheral blood white blood cells (WBC) and lymphocytes of the two groups. Count neutrophils, monocytes, and platelets (PLT) of the two groups. The neutrophil lymphocyte ratio (NLR), monocyte lymphocyte ratio (MLR), and platelet lymphocyte ratio (PLR) were calculated. Differential, correlation, and regression analysis were performed on survey data using SPSS 26.0.

Results

Results showed WBC, neutrophils, monocytes, NLR, MLR higher in case vs control group (p<0.05). Correlation analysis found monocytes negatively correlated with sleep time (rs=-0.205, p=0.037) and MLR with arousal factor (rs=-0.204, p=0.039). Linear regression showed that MLR positively affected arousal score (B=7.196, t=2.781, p=0.006) and monocytes negatively affected sleep time score (B=-0.851, t=-2.157, p=0.033). ROC analysis revealed high sensitivity and specificity of WBC, neutrophils, monocytes, NLR, MLR for SCZ symptom prediction.

Conclusion

The study concluded that elevated WBC, neutrophils, monocytes, NLR, and MLR levels in the case group were significantly associated with increased severity of schizophrenia symptoms, particularly affecting sleep and arousal factors, and demonstrated high predictive validity for SCZ symptoms.

Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia

This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group (p < 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach (p < 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.

Influence of White Sound on Sleep Quality, Anxiety, and Depression in Patients with Schizophrenia

BACKGROUND

Patients with schizophrenia frequently experience issues such as poor sleep quality, anxiety, and depression. White sound has been identified as a potential therapeutic strategy to enhance sleep quality and alleviate negative emotions. This study aimed to investigate the effectiveness of white sound in improving sleep quality, anxiety, and depression among patients with schizophrenia.

MATERIALS AND METHODS

This retrospective analysis included clinical data from 212 patients with schizophrenia divided into two groups based on their treatment approach. Group C (control, without white sound, n = 106) received standard pharmacological treatments, while group W (white sound, n = 106) was exposed to white sound (40-50 dB) for 2 hours nightly at 9:00 pm. All patients were assessed using the Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Positive and Negative Syndrome Scale (PANSS) before and after 12 weeks of intervention.

RESULTS

After 12 weeks, group W showed significant improvements in sleep latency, sleep efficiency, and overall PSQI scores compared to group C (P < 0.05). Furthermore, the HAMD and HAMA scores were significantly lower in group W (P < 0.05), indicating reduced levels of anxiety and depression. The negative symptoms score was significantly lower in group W (P < 0.05) after treatment.

CONCLUSION

White sound shows promise in improving sleep quality, and alleviating anxiety and depression in patients with schizophrenia.