Molecular Psychiatry最新文献

Negative symptoms (avolition, anhedonia, asociality, blunted affect, and alogia) are among the most disabling features of schizophrenia spectrum disorders. In the absence of treatment consensus guidelines, this PRISMA-compliant meta-analysis (PROSPERO: CRD42024613967) evaluated efficacy and clinical significance of interventions targeting this dimension. Web of Science/PsycInfo databases were searched from inception to December 2024. Five categories (antipsychotics, other pharmacological agents, brain stimulation, psychosocial, and lifestyle interventions) were analyzed across short/middle/long follow-up times. Categories were divided into 27 subcategories (e.g., 'other pharmacological agents' divided in 14 subcategories including antidepressants, antibiotics, immunomodulators) regardless of follow-up, assessing evidence with GRADE criteria. The primary outcome was the change in negative symptom severity, measured with validated scales (PANSS/SANS/BPRS/CAINS/BNSS) as standardized mean differences (SMD). A clinically meaningful SMD threshold was estimated from the regression between SMD and one-point reductions on the Clinical Global Impression-Severity (CGI-S) scale. This study meta-analyzed 451 trials (n = 42566). The clinically meaningful threshold, obtained from 122 trials reporting CGI-S, was SMD ≥ 0.457. In 214 high-quality studies (n = 19746), 2 category-by-follow-up combinations and 16 subcategories showed significant improvements. Clinically meaningful SMDs for subcategories were antibiotics (0.95; CI: 0.18-1.71; moderate-GRADE), integrated psychosocial interventions (0.93; CI: 0.53-1.33; very-low-GRADE), antidepressants (0.76; CI: 0.33-1.19; moderate-GRADE), physical activity (0.68; CI: 0.39-0.96; very-low-GRADE), transcranial current stimulation (0.52; CI: 0.17-0.86; low-GRADE), and immunomodulators (0.47; CI: 0.26-0.67; high-GRADE), typically as adjuncts to antipsychotics. Heterogeneity was the main limitation. While selected interventions may yield meaningful improvements, more rigorous designs are needed to identify reliable, personalized and scalable treatment options.

Background: Currently, studies on the lifetime prevalence of schizophrenia in the general population have not been updated or limited to a single country or region. The lifetime prevalence of schizophrenia may differ in other populations with certain relevant influencing factors or conditions. Globally, there is a lack of meta-analyses focusing on the lifetime prevalence of schizophrenia in specific populations. This study aims to determine the lifetime prevalence of schizophrenia in the general population, homeless populations, offenders, populations with comorbid mental disorders, populations with comorbid physical illnesses, populations at high genetic risk, populations exposed to stress, low-income populations, and indigenous populations, and to identify relevant factors.

Methods: Meta-analysis was used to estimate the combined lifetime prevalence of schizophrenia in the general population and eight other groups. To explore the sources of heterogeneity and identify factors associated with changes in prevalence, we conducted subgroup analyses of the general population, the homeless population, and the criminal population, examining geographic regions, sociodemographic factors, and methodological characteristics. Meta-regression in the general population examined the relationship between schizophrenia and mean age, publication year, and bias risk.

Results: 109 articles were included. Among them, 60 reported results from a sample of 20,910,871 individuals from the general population across 24 countries, 21 involved 6,605 individuals from the homeless population in 11 countries, there were 36 studies involving seven different population groups. The lifetime prevalence of schizophrenia is 0.62% (95% CI [0.51%-0.76%]) in the general population and 10.02% (95% CI [7.38%-13.47%]) in the homeless populations. Asia has the lowest lifetime prevalence of schizophrenia in both the general population and the homeless population, at 0.47% (95% CI [0.35%-0.64%]) and 4.68% (95% CI [2.11%-10.07%]).

Conclusions: Findings indicate that schizophrenia is more prevalent in special populations than in the general population. Understanding and addressing the risk factors contributing to elevated prevalence in vulnerable populations is essential for developing targeted prevention strategies and improving early intervention efforts.

Background: The early pathophysiology of depression is poorly understood. We elucidated the decadal temporal evolution of plasma proteomic alterations before depression diagnosis and evaluated their associations with comorbid conditions and neuroanatomical changes.

Methods: This study analyzed 31,114 depression-free participants and identified 1555 incident depression cases after a median follow-up of 7.8 years. Cox regression was used to identify depression-associated proteins, adjusting for sociodemographic, lifestyle, and genetic factors. Subsequent analyses of depression-related proteins included exome-wide association analysis (EWAS), temporal change modeling of pre-diagnostic protein dynamics via LOESS regression, association analyses with eight comorbid conditions and 58 regional brain volumes, and LightGBM-based predictive modeling.

Result: We found 64 depression-related proteins, with PIGR, HAVCR2, and IL4R validated in EWAS. For example, PIGR exhibited risk effects for depression (HR = 1.26, 95%CI: 1.13-1.40) and comorbid conditions, particularly diabetes (HR = 2.34, 95%CI: 2.12-2.58). Temporal profiling identified three protein clusters: one (cell-matrix adhesion) characterized by initial stability and subsequent decline, another (including PIGR and HAVCR2) characterized by MAPK cascade activation, and the third characterized by increased apoptosis and immune response. Neuroimaging correlations confirmed that elevated PIGR levels were associated with reduced volume in the bilateral ventral diencephalon (β = -0.062--0.061). A predictive model combined proteins and clinical features, achieving superior accuracy in depression prediction after 15 years (area under the curve = 0.74). Our findings reveal early peripheral pathophysiological changes in depression, suggesting a progression that may involve early apoptotic processes, an intermediate inflammatory phase, and later proteolytic dysregulation.

Conclusion: These insights hold significant potential for developing early biomarkers and precision therapies.

Antipsychotics-induced metabolic syndrome (APs-induced MetS) is a common side-effect of antipsychotics, significantly increasing the risk of cardiovascular diseases and mortality. However, the genetic risk factors underlying APs-induced MetS remain poorly understood. Thus, we conducted a sex-stratified genome-wide association study (GWAS) in 3067 patients from Schizophrenia In Non-Occidental participants (SINO) trial, and significant results were validated in an independent cohort (all samples = 200) and proteomic data. Post-GWAS analyses were used to further explore the genetic mechanisms involved in APs-induced MetS. Multi-omics prediction incorporating both polygenic risk and proteomic markers was conducted. After quality control, 1956 patients (965 males, 991 females) were included. We identified significant genetic variants (rs73762168; P = 1.77 × 10^-8) on chromosome 6q21, associated with three highly linked genes, NR2E1, SNX3 and AFG1L/LACE1, which were correlated with APs-induced MetS in male patients. Top SNP genotype was validated in independent cohort, showing associations with increased weight and waist circumference. Enrichment analyses across genetic and proteomic data consistently highlighted the PPAR signaling pathway involved in oxidative stress and fatty acid metabolism as a key contributor to APs-induced MetS development. Proteomic analyses confirmed baseline SNX3 protein levels associated with weight gain (P = 0.03) and increased waist circumference (P = 8.87 × 10^-3) following six-week antipsychotic treatment. The multi-omics prediction (R² = 0.18) yielded better prediction of APs-induced metabolic side effects than using either marker alone(R² = 0.13 or 0.07). This study provides novel genetic insights into the development of APs-induced MetS, particularly in males. The identified genetic variants and pathways offer potential targets for early risk prediction and personalized treatment strategies.