The association between BDNF gene functional Val66Met polymorphism rs6265 and the schizophrenia is far from being consistent. In addition to the heterogeneous in schizophrenia per se leading to the inconsistent results, the interaction among multi-genes is probably playing the main role in the pathogenesis of schizophrenia, but not a single gene. Neurotrophic tyrosine kinase receptor 2 (NTRK2) is the high-affinity receptor of BDNF, and was reported to be associated with mood disorders, though no literature reported the association with schizophrenia. Thus, in the present study, total 402 patients with paranoid schizophrenia (the most common subtype of schizophrenia) and matched 406 healthy controls were recruited to investigate the role of rs6265 in BDNF, three polymorphisms in NTRK2 gene (rs1387923, rs2769605 and rs1565445) and their interaction in the susceptibility to paranoid schizophrenia in a Chinese Han population. We did not observe significant differences in allele and genotype frequencies between patients and healthy controls for all four polymorphisms separately. The haplotype analysis also showed no association between haplotype of NTRK2 genes (rs1387923, rs2769605, and rs1565445) and paranoid schizophrenia. However, we found the association between the interaction of BDNF and NTRK2 with paranoid schizophrenia by using the MDR method followed by conventional statistical analysis. The best gene-gene interaction model was a three-locus model (BDNF rs6265, NTRK2 rs1387923 and NTRK2 rs2769605), in which one low-risk and three high-risk four-locus genotype combinations were identified. Our findings implied that single polymorphism of rs6265 rs1387923, rs2769605, and rs1565445 in BDNF and NTRK2 were not associated with the development of paranoid schizophrenia in a Han population, however, the interaction of BDNF and NTRK2 genes polymorphisms (BDNF-rs6265, NTRK2-rs1387923 and NTRK2-rs2769605) may be involved in the susceptibility to paranoid schizophrenia.

Schizophrenia, a complex neuropsychiatric disorder, frequently experiences a high rate of misdiagnosis due to subjective symptom assessment. Consequently, there is an urgent need for innovative and objective diagnostic tools. In this study, we used cutting-edge extracellular vesicles’ (EVs) proteome profiling and XGBoost-based machine learning to develop new markers and personalized discrimination scores for schizophrenia diagnosis and prediction of treatment response. We analysed plasma and plasma-derived EVs from 343 participants, including 100 individuals with chronic schizophrenia, 34 first-episode and drug-naïve patients, 35 individuals with bipolar disorder, 25 individuals with major depressive disorder and 149 age- and sex-matched healthy controls. Our innovative approach uncovered EVs-based complement changes in patients, specific to their disease-type and status. The EV-based biomarkers outperformed their plasma counterparts, accurately distinguishing schizophrenia individuals from healthy controls with an area under curve (AUC) of 0.895, 83.5% accuracy, 85.3% sensitivity and 82.0% specificity. Moreover, they effectively differentiated schizophrenia from bipolar disorder and major depressive disorder, with AUCs of 0.966 and 0.893, respectively. The personalized discrimination scores provided a personalized diagnostic index for schizophrenia and exhibited a significant association with patients’ antipsychotic treatment response in the follow-up cohort. Overall, our study represents a significant advancement in the field of neuropsychiatric disorders, demonstrating the potential of EV-based biomarkers in guiding personalized diagnosis and treatment of schizophrenia.

Atypical antipsychotics are highly effective antischizophrenic medications but their clinical utility is limited by adverse metabolic sequelae. We investigated whether upregulation of macrophage migration inhibitory factor (MIF) underlies the insulin resistance that develops during treatment with the most commonly prescribed atypical antipsychotic, olanzapine. Olanzapine monotherapy increased BMI and circulating insulin, triglyceride, and MIF concentrations in drug-naive schizophrenic patients with normal MIF expression, but not in genotypic low MIF expressers. Olanzapine administration to mice increased their food intake and hypothalamic MIF expression, which led to activation of the appetite-related AMP-activated protein kinase and Agouti-related protein pathway. Olanzapine also upregulated MIF expression in adipose tissue, which reduced lipolysis and increased lipogenic pathways. Increased plasma lipid concentrations were associated with abnormal fat deposition in liver and skeletal muscle, which are important determinants of insulin resistance. Global MIF-gene deletion protected mice from olanzapine-induced insulin resistance, as did intracerebroventricular injection of neutralizing anti–MIF antibody, supporting the role of increased hypothalamic MIF expression in metabolic dysfunction. These findings uphold the potential pharmacogenomic value of MIF genotype determination and suggest that MIF may be a tractable target for reducing the metabolic side effects of atypical antipsychotic therapy.

Despite accumulating evidence suggesting improvement in one’s well-being as a result of meditation, little is known about if or how the brain and the periphery interact to produce these behavioral and mental changes. We hypothesize that meditation reflects changes in the neural representations of visceral activity, such as cardiac behavior, and investigated the integration of neural and visceral systems and the spontaneous whole brain spatiotemporal dynamics underlying traditional Tibetan Buddhist meditation. In a large cohort of long-term Tibetan Buddhist monk meditation practitioners, we found distinct transient modulations of the neural response to heartbeats in the default mode network (DMN), along with large-scale network reconfigurations in the gamma and theta bands of electroencephalography (EEG) activity induced by meditation. Additionally, temporal-frontal network connectivity in the EEG theta band was negatively correlated with the duration of meditation experience, and gamma oscillations were uniquely, directionally coupled to theta oscillations during meditation. Overall, these data suggest that the neural representation of cardiac activity in the DMN and large-scale spatiotemporal network integrations underlie the fundamental neural mechanism of meditation and further imply that meditation may utilize cortical plasticity, inducing both immediate and long-lasting changes in the intrinsic organization and activity of brain networks.

In recent years, research on meditation as an important alternative therapy has developed rapidly and been widely applied in clinical medicine. Mechanism studies of meditation have also developed progressively, showing that meditation has great impact on brain structure and function, and epigenetic and telomere regulation. In line with this, the application of meditation has gradually been expanded to mental illness, most often applied for major depressive disorders and substance‐related and addictive disorders. The focus of this paper is to illustrate the biological mechanisms of meditation and its application in mental disorders.