Physical, Psychiatric, and Social Comorbidities of Individuals with Schizophrenia Living in the Community in Japan

Schizophrenia and type 2 diabetes risk: a systematic review and meta-analysis

Objectives

The metabolic syndrome in patients with schizophrenia has consistently been a challenge for clinicians. Previous studies indicate that individuals with schizophrenia are highly prone to developing type 2 diabetes mellitus (T2DM). In recent years, a continuous stream of new observational studies has been reported, emphasizing the pressing need for clinicians to gain a more precise understanding of the association between schizophrenia and T2DM. The objective of this meta-analysis is to integrate new observational studies and further explore the potential link between schizophrenia and the risk of T2DM.

Methods

We conducted a comprehensive search of PubMed, Cochrane Library, Embase, and Web of Science using medical subject headings (MeSH) and relevant keywords. The risk of bias in cohort studies and case-control studies was assessed using the Newcastle-Ottawa Scale (NOS), while cross-sectional studies were evaluated using the Agency for Healthcare Research and Quality scale (AHRQ), scoring was based on the content of the original studies. A fixed-effects model was employed if P > 0.1 and I2 ≤ 50%, indicating low heterogeneity. Conversely, a random-effects model was utilized if I2 > 50%, indicating substantial heterogeneity. Publication bias was assessed using funnel plots and Egger's test. Statistical analyses were carried out using Stata statistical software version 14.0.

Results

This meta-analysis comprised 32 observational studies, involving a total of 2,007,168 patients with schizophrenia and 35,883,980 without schizophrenia, published from 2004 to 2023. The pooled analysis revealed a significant association between a history of schizophrenia and an increased risk of T2DM (Odds Ratio [OR] = 2.15; 95% Confidence Interval [CI]: 1.83-2.52; I2 = 98.9%, P < 0.001). Stratified by gender, females with schizophrenia (OR = 2.12; 95% CI: 1.70-2.64; I2 = 90.7%, P < 0.001) had a significantly higher risk of T2DM than males (OR = 1.68; 95% CI: 1.39-2.04; I2 = 91.3%, P < 0.001). Regarding WHO regions, EURO (OR = 2.73; 95% CI: 2.23-3.35; I2 = 97.5%, P < 0.001) exhibited a significantly higher risk of T2DM compared to WPRO (OR = 1.72; 95% CI: 1.32-2.23; I2 = 95.2%, P < 0.001) and AMRO (OR = 1.82; 95% CI: 1.40-2.37; I2 = 99.1%, P < 0.001). In terms of follow-up years, the >20 years subgroup (OR = 3.17; 95% CI: 1.24-8.11; I2 = 99.4%, P < 0.001) showed a significantly higher risk of T2DM than the 10-20 years group (OR = 2.26; 95% CI: 1.76-2.90; I2 = 98.6%, P < 0.001) and <10 years group (OR = 1.68; 95% CI: 1.30-2.19; I2 = 95.4%, P < 0.001).

Conclusions

This meta-analysis indicates a strong association between schizophrenia and an elevated risk of developing diabetes, suggesting that schizophrenia may function as an independent risk factor for T2DM.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023465826.

External Validation of a Machine Learning Model for Schizophrenia Classification

Background and Objective: Excellent generalizability is the precondition for the widespread practical implementation of machine learning models. In our previous study, we developed the schizophrenia classification model (SZ classifier) to identify potential schizophrenia patients in the Japanese population. The SZ classifier has exhibited impressive performance during internal validation. However, ensuring the robustness and generalizability of the SZ classifier requires external validation across independent sample sets. In this study, we aimed to present an external validation of the SZ classifier using outpatient data. Methods: The SZ classifier was trained by using online survey data, which incorporate demographic, health-related, and social comorbidity features. External validation was conducted using an outpatient sample set which is independent from the sample set during the model development phase. The model performance was assessed based on the sensitivity and misclassification rates for schizophrenia, bipolar disorder, and major depression patients. Results: The SZ classifier demonstrated a sensitivity of 0.75 when applied to schizophrenia patients. The misclassification rates were 59% and 55% for bipolar disorder and major depression patients, respectively. Conclusions: The SZ classifier currently encounters challenges in accurately determining the presence or absence of schizophrenia at the individual level. Prior to widespread practical implementation, enhancements are necessary to bolster the accuracy and diminish the misclassification rates. Despite the current limitations of the model, such as poor specificity for certain psychiatric disorders, there is potential for improvement if including multiple types of psychiatric disorders during model development.

Can feature structure improve model's precision? A novel prediction method using artificial image and image identification

Objectives

This study aimed to develop an approach to enhance the model precision by artificial images.

Materials and Methods

Given an epidemiological study designed to predict 1 response using f features with M samples, each feature was converted into a pixel with certain value. Permutated these pixels into F orders, resulting in F distinct artificial image sample sets. Based on the experience of image recognition techniques, appropriate training images results in higher precision model. In the preliminary experiment, a binary response was predicted by 76 features, the sample set included 223 patients and 1776 healthy controls.

Results

We randomly selected 10 000 artificial sample sets to train the model. Models' performance (area under the receiver operating characteristic curve values) depicted a bell-shaped distribution.

Conclusion

The model construction strategy developed in the research has potential to capture feature order related information and enhance model predictability.

The pathophysiological role of circulating adhesion molecules in schizophrenia: A systematic review and meta-analysis

BACKGROUND

Increasing evidence suggests an association between schizophrenia and atherosclerosis. We conducted a systematic review and meta-analysis of cell adhesion molecules, critically involved in early atherosclerosis, in schizophrenia.

METHODS

We searched electronic databases from inception to 11 November 2023 for case-control studies assessing vascular cell, VCAM-1, intercellular, ICAM-1, platelet endothelial cell, PECAM-1, neural cell, NCAM, and Down syndrome cell, DSCAM, adhesion molecules, selectins (E-, L-, and P-selectin), integrins, and cadherins in patients with schizophrenia and healthy controls. Risk of bias and certainty of evidence were assessed using the JBI checklist and GRADE, respectively.

RESULTS

In 19 eligible studies, there were non-significant between-group differences in the concentrations of cell adhesion molecules, barring higher P-selectin in patients with schizophrenia (standard mean difference, SMD = 2.05, 95 % CI 0.72 to 3.38, p = 0.003; I2 = 97.2 %, p<0.001; very low certainty of evidence). Limited or no information was available regarding PECAM-1, DSCAM, ESAM, integrins, and cadherins. In meta-regression and subgroup analysis, there were significant associations between the SMD of ICAM-1 and matrix used (plasma or serum) and pharmacological treatment of schizophrenia, and between the SMD of VCAM-1 and pharmacological treatment, but not with other study and patient characteristics.

CONCLUSIONS

The results of our systematic review and meta-analysis do not support a significant role of immunoglobulin-like adhesion molecules, selectins, integrins, or cadherins in mediating the associations between schizophrenia, atherosclerosis, and cardiovascular disease. Further studies are warranted to investigate these associations in patients with different cardiovascular risk and the effects of antipsychotic treatments on cell adhesion molecules and surrogate markers of atherosclerosis (PROSPERO registration number: CRD42023463916).

Classifying Schizophrenia Cases by Artificial Neural Network Using Japanese Web-Based Survey Data: Case-Control Study

BACKGROUND

In Japan, challenges were reported in accurately estimating the prevalence of schizophrenia among the general population. Retrieving previous studies, we investigated that patients with schizophrenia were more likely to experience poor subjective well-being and various physical, psychiatric, and social comorbidities. These factors might have great potential for precisely classifying schizophrenia cases in order to estimate the prevalence. Machine learning has shown a positive impact on many fields, including epidemiology, due to its high-precision modeling capability. It has been applied in research on mental disorders. However, few studies have applied machine learning technology to the precise classification of schizophrenia cases by variables of demographic and health-related backgrounds, especially using large-scale web-based surveys.

OBJECTIVE

The aim of the study is to construct an artificial neural network (ANN) model that can accurately classify schizophrenia cases from large-scale Japanese web-based survey data and to verify the generalizability of the model.

METHODS

Data were obtained from a large Japanese internet research pooled panel (Rakuten Insight, Inc) in 2021. A total of 223 individuals, aged 20-75 years, having schizophrenia, and 1776 healthy controls were included. Answers to the questions in a web-based survey were formatted as 1 response variable (self-report diagnosed with schizophrenia) and multiple feature variables (demographic, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities). An ANN was applied to construct a model for classifying schizophrenia cases. Logistic regression (LR) was used as a reference. The performances of the models and algorithms were then compared.

RESULTS

The model trained by the ANN performed better than LR in terms of area under the receiver operating characteristic curve (0.86 vs 0.78), accuracy (0.93 vs 0.91), and specificity (0.96 vs 0.94), while the model trained by LR showed better sensitivity (0.63 vs 0.56). Comparing the performances of the ANN and LR, the ANN was better in terms of area under the receiver operating characteristic curve (bootstrapping: 0.847 vs 0.773 and cross-validation: 0.81 vs 0.72), while LR performed better in terms of accuracy (0.894 vs 0.856). Sleep medication use, age, household income, and employment type were the top 4 variables in terms of importance.

CONCLUSIONS

This study constructed an ANN model to classify schizophrenia cases using web-based survey data. Our model showed a high internal validity. The findings are expected to provide evidence for estimating the prevalence of schizophrenia in the Japanese population and informing future epidemiological studies.