Dysregulation of interleukin-8 is involved in the onset and relapse of schizophrenia: An independent validation and meta-analysis.

As a major mental health disorder, symptoms of schizophrenia (SCZ) include delusions, reduced motivation, hallucinations, reduced motivation and a variety of cognitive disabilities. Many of these symptoms are now known to be associated with abnormal regulation of the immune system. Low blood levels of cytokines and chemokines have been suggested to be one of the underlying causes of SCZ. However, their biological roles at different stages of SCZ remain unclear. Our objective was to investigate expression patterns of cytokines and chemokines at different stages of onset and relapse in SCZ patients and to conduct an analysis of their relationship to disease progression. We also aimed to identify immune features associated with different disease trajectories in patients with SCZ. Gene set enrichment analysis (GSEA) was used to interrogate the GSE27383 dataset and identify key genes associated with inflammation. These results led us to recruit 36 healthy controls, 40 patients with first-episode psychosis (FEP), and 39 patients with SCZ relapse. Meso Scale Discovery technology was used to independently validate serum levels of 35 cytokines and chemokines. This was followed by a meta-analysis to gain a more comprehensive understanding of the role of interleukin-8 (IL-8/CXCL8) in SCZ. Analysis of the GSE27383 database revealed 3596 genes with distinct expression patterns. A significant portion of these genes were identified as inflammation-related and showed remarkable enrichment in three key pathways: IL-17, cytokine-cytokine receptor, and AGE-RAGE signaling in diabetic complications. We observed co-expression of CXCL8 and IL-16 within these three pathways. In a subsequent analysis of independently validated samples, a notable discrepancy was detected in the inflammatory status between individuals experiencing FEP and those in relapse. In particular, expression of CXCL8 demonstrated superior predictive capability in FEP and relapsed patients. Notably, results of the meta-analysis confirmed that Chinese and European populations were consistent with the overall results (Z = 4.60, P < 0.001; Z = 3.70, P < 0.001). However, in the American subgroup, there was no significant difference in CXCL8 levels between patients with SCZ compared to healthy controls (Z = 1.09, P = 0.277). Our findings suggest that the inflammatory response in patients with SCZ differs across the different stages, with CXCL8 emerging as a potential predictive factor. Collectively, our data suggest that CXCL8 has the potential to serve as a significant immunological signature of SCZ subtypes. Trial registration: The clinical registration number for this trial is ChiCTR2100045240 (Registration Date: 2021/04/09).

Automatic Thoughts, Self-Stigma, and Resilience Among Schizophrenia Patients with Metabolic Syndrome: A Cross-Sectional Study.

Purpose: The study aims to clarify the negative psychological state and resilience impairments of schizophrenia (SCZ) with metabolic syndrome (MetS) while evaluating their potential as risk factors. Patients and Methods: We recruited 143 individuals and divided them into three groups. Participants were evaluated using the Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale (HAMD)-24, Hamilton Anxiety Rating Scale (HAMA)-14, Automatic Thoughts Questionnaire (ATQ), Stigma of Mental Illness scale and Connor-Davidson Resilience Scale (CD-RISC). Serum biochemical parameters were measured by automatic biochemistry analyzer. Results: The score of ATQ was highest in the MetS group (F = 14.5, p < 0.001), and the total score of CD-RISC, subscale tenacity score and subscale strength score of CD-RISC were lowest in the MetS group (F = 8.54, p < 0.001; F = 5.79, p = 0.004; F = 10.9, p < 0.001). A stepwise regression analysis demonstrated that a negative correlation was observed among the ATQ with employment status, high-density lipoprotein (HDL-C), and CD-RISC (ß=-0.190, t=-2.297, p = 0.023; ß=-0.278, t=-3.437, p = 0.001; ß=-0.238, t=-2.904, p = 0.004). A positive correlation was observed among the ATQ with waist, TG, WBC, and stigma (ß=0.271, t = 3.340, p = 0.001; ß=0.283, t = 3.509, p = 0.001; ß=0.231, t = 2.815, p = 0.006; ß=0.251, t=-2.504, p = 0.014). The area under the receiver-operating characteristic curve analysis showed that among all independent predictors of ATQ, the TG, waist, HDL-C, CD-RISC, and stigma presented excellent specificity at 0.918, 0.852, 0.759, 0.633, and 0.605, respectively. Conclusion: Results suggested that the non-MetS and MetS groups had grievous sense of stigma, particularly, high degree of ATQ and resilience impairment was shown by the MetS group. The TG, waist, HDL-C of metabolic parameters, CD-RISC, and stigma presented excellent specificity to predict ATQ, and the waist showed excellent specificity to predict low resilience level.

Detecting Manic State of Bipolar Disorder Based on Support Vector Machine and Gaussian Mixture Model Using Spontaneous Speech.

OBJECTIVE: This study was aimed to compare the accuracy of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) in the detection of manic state of bipolar disorders (BD) of single patients and multiple patients. METHODS: 21 hospitalized BD patients (14 females, average age 34.5±15.3) were recruited after admission. Spontaneous speech was collected through a preloaded smartphone. Firstly, speech features [pitch, formants, mel-frequency cepstrum coefficients (MFCC), linear prediction cepstral coefficient (LPCC), gamma-tone frequency cepstral coefficients (GFCC) etc.] were preprocessed and extracted. Then, speech features were selected using the features of between-class variance and within-class variance. The manic state of patients was then detected by SVM and GMM methods. RESULTS: LPCC demonstrated the best discrimination efficiency. The accuracy of manic state detection for single patients was much better using SVM method than GMM method. The detection accuracy for multiple patients was higher using GMM method than SVM method. CONCLUSION: SVM provided an appropriate tool for detecting manic state for single patients, whereas GMM worked better for multiple patients' manic state detection. Both of them could help doctors and patients for better diagnosis and mood state monitoring in different situations.

Analysis on speech signal features of manic patients.

Given the lack of effective biological markers for early diagnosis of bipolar mania, and the tendency for voice fluctuation during transition between mood states, this study aimed to investigate the speech features of manic patients to identify a potential set of biomarkers for diagnosis of bipolar mania. 30 manic patients and 30 healthy controls were recruited and their corresponding speech features were collected during natural dialogue using the Automatic Voice Collecting System. Bech-Rafaelsdn Mania Rating Scale (BRMS) and Clinical impression rating scale (CGI) were used to assess illness. The speech features were compared between two groups: mood group (mania vs remission) and bipolar group (manic patients vs healthy individuals). We found that the characteristic speech signals differed between mood groups and bipolar groups. The fourth formant (F4) and Linear Prediction Coefficient (LPC) (P < .05) were significantly differed when patients transmitted from manic to remission state. The first formant (F1), the second formant (F2), and LPC (P < .05) also played key roles in distinguishing between patients and healthy individuals. In addition, there was a significantly correlation between LPC and BRMS, indicating that LPC may play an important role in diagnosis of bipolar mania. In this study we traced speech features of bipolar mania during natural dialogue (conversation), which is an accessible approach in clinic practice. Such specific indicators may respectively serve as promising biomarkers for benefiting the diagnosis and clinical therapeutic evaluation of bipolar mania.

Clinical investigation of speech signal features among patients with schizophrenia.

BACKGROUND: A new area of interest in the search for biomarkers for schizophrenia is the study of the acoustic parameters of speech called 'speech signal features'. Several of these features have been shown to be related to emotional responsiveness, a characteristic that is notably restricted in patients with schizophrenia, particularly those with prominent negative symptoms. AIM: Assess the relationship of selected acoustic parameters of speech to the severity of clinical symptoms in patients with chronic schizophrenia and compare these characteristics between patients and matched healthy controls. METHODS: Ten speech signal features-six prosody features, formant bandwidth and amplitude, and two spectral features-were assessed using 15-minute speech samples obtained by smartphone from 26 inpatients with chronic schizophrenia (at enrollment and 1 week later) and from 30 healthy controls (at enrollment only). Clinical symptoms of the patients were also assessed at baseline and 1 week later using the Positive and Negative Syndrome Scale, the Scale for the Assessment of Negative Symptoms, and the Clinical Global Impression-Schizophrenia scale. RESULTS: In the patient group the symptoms were stable over the 1-week interval and the 1-week test-retest reliability of the 10 speech features was good (intraclass correlation coefficients [ICC] ranging from 0.55 to 0.88). Comparison of the speech features between patients and controls found no significant differences in the six prosody features or in the formant bandwidth and amplitude features, but the two spectral features were different: the Mel-frequency cepstral coefficient (MFCC) scores were significantly lower in the patient group than in the control group, and the linear prediction coding (LPC) scores were significantly higher in the patient group than in the control group. Within the patient group, 10 of the 170 associations between the 10 speech features considered and the 17 clinical parameters considered were statistically significant at the p<0.05 level. CONCLUSIONS: This study provides some support for the potential value of speech signal features as indicators (i.e., biomarkers) of the severity of negative symptoms in schizophrenia, but more detailed studies using larger samples of more diverse patients that are followed over time will be needed before the potential utility of such acoustic parameters of speech can be fully assessed.