Positive Symptoms in Antipsychotic-naïve Schizophrenia are Associated with Increased Body Mass Index after Treatment

Association of Alexithymia With Positive Symptoms in Chinese Chronic Schizophrenia Patients With and Without Obesity

OBJECTIVE

A growing body of research suggests the presence of alexithymia (a form of social cognitive impairment) in patients with schizophrenia (SCZ), which may be related to their psychopathological symptoms. Patients with SCZ exhibit high rates of obesity. Interestingly, studies of the general population have found that alexithymia acts a pivotal role in the development and maintenance of obesity. However, little is known regarding the relationship between obesity, alexithymia, and clinical symptoms in SCZ patients. The study was aim to explore the relationship between obesity, alexithymia, and clinical symptoms in SCZ patients.

METHODS

Demographic and clinical data were collected from 507 patients with chronic SCZ. Their symptoms were assessed with the Positive and Negative Syndrome Scale (PANSS), and alexithymia was assessed with the Toronto Alexithymia Scale (TAS).

RESULTS

Compare with nonobese SCZ patients, obese SCZ patients scored higher on PANSS positive symptoms, TAS total score, difficulty identifying feelings, and difficulty describing feelings (all p<0.05). Correlation analysis revealed a significant association between difficulty identifying feelings and positive symptoms in SCZ patients. Further correlation analysis showed that this association was only present in obese SCZ patients (p<0.05).

CONCLUSION

Obesity may moderate the association between alexithymia and positive symptoms in chronic SCZ patients.

Effects of Dietary Habits on General and Abdominal Obesity in Community-dwelling Patients with Schizophrenia

Objective

To investigate the effects of dietary habits on general and abdominal obesity in community-dwelling patients with schizophrenia spectrum disorder according to sex.

Methods

A total of 270 patients with schizophrenia spectrum disorder registered at mental health welfare centers and rehabilitation facilities were recruited. General obesity was defined as a body mass index ≥ 30 kg/m², and abdominal obesity was defined as a waist circumstance ≥ 90 cm in men and ≥ 85 cm in women. Dietary habits were evaluated using dietary guidelines published by the Korean Ministry of Health and Welfare. Demographic and clinical characteristics along with dietary habits and information related to obesity were collected. Factors related to obesity were evaluated separately by sex.

Results

Dietary habits differed according to sex, in that scores for healthy eating habits were lower in men than in women. In men, the prevalences of general and abdominal obesity were 17.0% and 37.3%, respectively. In women, the prevalences of general and abdominal obesity were 23.1% and 38.5%, respectively. Regression analysis showed that the scores of regular eating habits were negatively associated with general and abdominal obesity in men, and the scores of healthy eating habits were negatively associated with general and abdominal obesity in women.

Conclusion

Among patients with schizophrenia, regular eating habits might reduce the risk of obesity in men, and healthy eating habits might reduce the risk of obesity in women. Nutrition education should be provided to community- dwelling patients with schizophrenia to prevent obesity in this population.

Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia

The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793−0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615−0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context.