Exploring the latent cognitive structure in schizophrenia: implications for antipsychotic treatment responses.

BACKGROUND: Individuals diagnosed with schizophrenia present diverse degrees and types of cognitive impairment, leading to variations in responses to antipsychotic treatments. Understanding the underlying cognitive structures is crucial for assessing this heterogeneity. Utilizing latent profile analysis (LPA) enables the delineation of latent categories of cognitive function. Integrating this approach with a dimensional perspective allows for the exploration of the relationship between cognitive function and treatment response. METHODS: This study examined 647 patients from two distinct cohorts. Utilizing LPA within the discovery cohort (n = 333) and the replication cohort (n = 314), latent subtypes were identified categorically. The stability of cognitive structures was evaluated employing Latent Transition Analysis (LTA). The relationship between cognitive function and treatment response were investigated by comparing Positive and Negative Syndrome Scale (PANSS) reduction rates across diverse cognitive subtypes. Furthermore, dimensional insights were gained through correlation analyses between cognitive tests and PANSS reduction rates. RESULTS: In terms of categorical, individuals diagnosed with schizophrenia can be categorized into three distinct subtypes: those 'without cognitive deficit', those 'with mild-moderate cognitive 'eficit', and those 'with moderate-severe cognitive deficit'. There are significant differences in PANSS reduction rates among patients belonging to these subtypes following antipsychotic treatment (p < 0.05). Furthermore, from a dimensional perspective, processing speed at baseline is positively correlated with PANSS score reduction rates at week 8/week 10 (p < 0.01). CONCLUSIONS: Our findings have unveiled the latent subtypes of cognitive function in schizophrenia, illuminating the association between cognitive function and responses to antipsychotic treatment from both categorical and dimensional perspectives.

Early non-response as a predictor of later non-response to antipsychotics in schizophrenia: a randomized trial.

BACKGROUND: It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. METHODS: This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a < 20% reduction in the total scores of PANSS from baseline to endpoint. Severity ratings of mild, moderate, and severe illness corresponded to baseline PANSS total scores of 58, 75, and 95, respectively. RESULTS: At week 2, a reduction of < 5% in the PANSS total score showed the highest total accuracy in the severe and mild schizophrenia patients (total accuracy, 75.0% and 80.8%, respectively), and patients who were treated with the risperidone and amisulpride groups (total accuracy, 82.4%, and 78.2%, respectively). A 10% decrease exhibited the best overall accuracy in the moderate schizophrenia patients (total accuracy, 84.0%), olanzapine (total accuracy, 79.2%), and aripiprazole group (total accuracy, 77.4%). At week 4, the best predictive cut-off value was < 20%, regardless of the antipsychotic or severity of illness (total accuracy ranging from 89.8 to 92.1%). CONCLUSIONS: Symptom reduction at week 2 has acceptable discrimination in predicting later non-response to antipsychotics in schizophrenia, and a more accurate predictive cut-off value should be determined according to the medication regimen and baseline illness severity. The response to treatment during the next 2 weeks after week 2 could be further assessed to determine whether there is a need to change antipsychotic medication during the first four weeks. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov (NCT03451734).

Association between depression and diabetes mellitus and the impact of their comorbidity on mortality: Evidence from a nationally representative study.

BACKGROUND: Depression and diabetes mellitus (DM) are major chronic noncommunicable diseases that impair one's mental and physical well-being and impose substantial burdens on the health system. Depressed individuals have an increased risk of impaired blood glucose, weight gain and dyslipidemia which could induce poorer long-term survival. METHODS: 37,040 individuals from the National Health and Nutrition Examination Survey (NHANES) were included. Depressive symptoms were assessed by the Patient Health Questionnaire (PHQ-9) and classified by the total scores as no (0-4), mild (5-9), moderate (10-14), and severe (15-27). DM was determined based on self-reported medical history, clinical test results, and medication use. Logistic and Cox regression were the main statistical models. All analyses were based on weighted data from complex sampling. RESULTS: The prevalence of DM was higher in depressed than non-depressed individuals (21.26 % vs. 13.75 %). The adjusted odds ratio (OR) (95 % CI) of comorbid DM increased with depression severity, from 1.00 (reference) for no depression, to 1.22 (1.09,1.36) for mild, 1.62 (1.37,1.92) for moderate, and 1.52(1.28,1.82) for severe depression. Comorbidity of DM and depression significantly associated with a higher risk of all-cause mortality, with a hazard ratio (HR) (95 % CI) = 2.09 (1.64,2.66). LIMITATIONS: Dynamic demographic and metabolic data were not available. CONCLUSION: Depression is associated with a higher risk of DM, which may be related to biological, socioeconomic, and medication-related factors. Comorbidity of the two worsens long-term survival. Therefore, blood glucose management and prevention of DM should be emphasized in depressed patients.

Relapse in patients with schizophrenia and amisulpride-induced hyperprolactinemia or olanzapine-induced metabolic disturbance after switching to other antipsychotics.

Hyperprolactinemia and metabolic disturbance are common side effects of antipsychotics that cause intolerance. Despite its potential influence on relapse, there are no established guidelines for antipsychotic switching. This naturalistic study explored the association between antipsychotic switching, baseline clinical status, metabolic changes, and relapse in patients with schizophrenia. In total, 177 patients with amisulpride-induced hyperprolactinemia and 274 with olanzapine-induced metabolic disturbance were enrolled. Relapse was determined by assessing changes in Positive and Negative Syndrome Scale (PANSS) total scores from baseline to 6 months (increased over 20% or 10% reaching 70). Metabolic indices were measured at baseline and 3 months. Patients with baseline PANSS >60 were more likely to relapse. Further, patients switching to aripiprazole had a higher risk of relapse regardless of their original medication. Participants who originally used amisulpride had reduced prolactin levels following medication change, while switching to olanzapine caused increased weight and blood glucose levels. In patients originally using olanzapine, only switching to aripiprazole reduced insulin resistance. Adverse effects on weight and lipid metabolism were observed in patients who switched to risperidone, while amisulpride improved lipid profiles. Changing schizophrenia treatment requires careful consideration of multiple variables, particularly the choice of substituted drug and the patient's baseline symptoms.

The Microbiome-Gut-Brain Axis, a Potential Therapeutic Target for Substance-Related Disorders.

Substance addiction is a complex worldwide public health problem. It endangers both personal life and social stability, causing great loss on economy. Substance-related disorder is considered to be a complicated chronic brain disorder. It resulted from interactions among pharmacological properties of addictive substances, individual susceptibility, and social-environmental factors. Unfortunately, there is still no ideal treatment for this disorder. Recent lines of evidence suggest that gut microbiome may play an important role in the pathogenesis of neuropsychiatric disorders, including substance-related disorders. This review summarizes the research on the relationship between gut microbiome and substance-related disorders, including different types of substance, different individual susceptibility, and the occurrence and development of substance-induced mental disorders. We also discuss the potentiation of gut microbiome in the treatment of substance-related disorders, especially in the treatment of substance-induced mental disorders and manipulation on individuals' responsiveness to addictive substances.