Genetic polymorphisms of HTR2C, LEP and LEPR on metabolic syndromes in patients treated with atypical antipsychotic drugs

Unveiling immune tolerance pathways in preeclampsia placenta: implications for molecular targets and discovery of potential biomarkers

During pregnancy, there is a link between disruption of maternal immune tolerance and preeclampsia, but the molecular mechanisms that regulate maternal and fetal immune tolerance remain unclear. This study employs bioinformatics to identify new markers related to placental immune tolerance and explore their potential role in predicting preeclampsia. Analyzing preeclampsia-related gene expression profiles in the Gene Expression Omnibus (GEO) dataset reveals 211 differentially expressed genes (DEGs) in the placenta, mainly influencing immune cell differentiation and response pathways. Employing weighted gene co-expression network analysis (WGCNA) and lasso regression, four potential target genes (ANKRD37, CRH, LEP, SIGLEC6) are identified for potential prediction of preeclampsia. Validation using the GSE4707 dataset confirmed the diagnostic and predictive potential of these candidate genes. RT-qPCR verified up-regulation in the placenta, while ELISA showed their correlation with immune tolerance factors associated with placental immune tolerance. As a result of this study, identifies potential biomarkers associated with placental immunity and contributes to understanding the molecular mechanism of preeclampsia.

Multi-omics analysis identifies rare variation in leptin/PPAR gene sets and hypermethylation of ABCG1 contribute to antipsychotics-induced metabolic syndromes

Antipsychotic-induced metabolic syndrome (APs-induced Mets) is the most common adverse drug reaction, which affects more than 60% of the psychiatric patients. Although the etiology of APs-induced Mets has been extensively investigated, there is a lack of integrated analysis of the genetic and epigenetic factors. In this study, we performed genome-wide, whole-exome sequencing (WES) and epigenome-wide association studies in schizophrenia (SCZ) patients with or without APs-induced Mets to find the underlying mechanisms, followed by in vitro and in vivo functional validations. By population-based omics analysis, we revealed that rare functional variants across in the leptin and peroxisome proliferator-activated receptors (PPARs) gene sets were imbalanced with rare functional variants across the APs-induced Mets and Non-Mets cohort. Besides, we discovered that APs-induced Mets are hypermethylated in ABCG1 (chr21:43642166-43642366, adjusted P < 0.05) than Non-Mets, and hypermethylation of this area was associated with higher TC (total cholesterol) and TG (triglycerides) levels in HepG2 cells. Candidate genes from omics studies were furtherly screened in C. elegans and 17 gene have been verified to associated with olanzapine (OLA) induced fat deposit. Among them, several genes were expressed differentially in Mets cohort and APs-induced in vitro/in vivo models compared to controls, demonstrating the validity of omics study. Overexpression one of the most significant gene, PTPN11, exhibited compromised glucose responses and insulin resistance. Pharmacologic inhibition of PTPN11 protected HepG2 cell from APs-induced insulin resistance. These findings provide important insights into our understanding of the mechanism of the APs-induced Mets.

Pharmacogenomics research and its clinical implementation in Thailand: Lessons learned from the resource-limited settings

Several barriers present challenges to implementing pharmacogenomics into practice. This review will provide an overview of the current pharmacogenomics practices and research in Thailand, address the challenges and lessons learned from delivering clinical pharmacogenomic services in Thailand, emphasize the pharmacogenomics implementation issues that must be overcome, and identify current pharmacogenomic initiatives and plans to facilitate clinical implementation of pharmacogenomics in Thailand. Ever since the pharmacogenomics research began in 2004 in Thailand, a multitude of pharmacogenomics variants associated with drug responses have been identified in the Thai population, such as HLA-B∗15:02 for carbamazepine and oxcarbazepine, HLA-B∗58:01 for allopurinol, HLA-B∗13:01 for dapsone and cotrimoxazole, CYP2B6 variants for efavirenz, CYP2C9∗3 for phenytoin and warfarin, CYP3A5∗3 for tacrolimus, and UGT1A1∗6 and UGT1A1∗28 for irinotecan, etc. The future of pharmacogenomics guided therapy in clinical settings across Thailand appears promising because of the availability of evidence of clinical validity of the pharmacogenomics testing and support for reimbursement of pharmacogenomics testing.

Network pharmacology-based exploration of therapeutic mechanism of Liu-Yu-Tang in atypical antipsychotic drug-induced metabolic syndrome

BACKGROUND

Metabolic syndrome (MetS) is prevalent in patients receiving atypical antipsychotic drugs (AADs), but there are few effective interventions. The Traditional Chinese herbal decoction Liu-Yu-Tang (LYT) has achieved clinical improvement for AAD-induced MetS, but its pharmacological mechanism remains unclear.

METHOD

A network pharmacology-based method was utilized in this study. First, the TCMSP and SwissTargetPrediction database were used to acquire plasma-absorbed components and putative targets of LYT, respectively. Second, an interaction network between shared targets of LYT and MetS was constructed using STRING online tool. Topological analyses were performed to extract hub gene targets. Finally, we did a pathway analysis of gene targets using the Kyoto Encyclopedia of Genes and Genomes (KEGG) to find biological pathways of LYT.

RESULTS

We obtained 655 putative targets of LYT, 434 known targets of AADs, and 1577 MetS-related gene targets. There are 232 shared targets between LYT and MetS. Interaction network construction and topological analysis yielded 60 hub targets, of which 18 were major hub targets, among which IL-6, IL-8, TNF, PI3K, MAPK, and NF-κB (RELA) are the most important in LYT's treatment of AAD-induced MetS. Pathway enrichment analysis revealed a statistically high significance of the AGE-RAGE signaling pathway in diabetic complications, lipid and atherosclerosis and the insulin resistance pathway.

CONCLUSIONS

LYT may control activities of the pro-inflammatory cytokines IL-6, IL-8, TNF and the important signal transduction molecules PI3K, MAPKs, and NF-κB (RELA), regulating metabolic disturbance-related pathways like the AGE-RAGE signaling pathway in diabetic complications, lipid and atherosclerosis, and the insulin resistance pathway, generating therapeutic effects for AAD-induced MetS.

Clinical, Biochemical and Genetic Variables Associated With Metabolic Syndrome in Patients With Schizophrenia Spectrum Disorders Using Second-Generation Antipsychotics: A Systematic Review

Background: Individuals with severe mental illness experience increased morbidity and mortality compared to the general population. Adverse effects of antipsychotics, including weight gain, may contribute to the development of metabolic syndrome (MetS), which is associated with increased risks of all-cause and cardiovascular disease mortality. We aim to provide a comprehensive overview of clinical, biochemical and genetic factors associated with MetS among patients with schizophrenia spectrum disorders using second-generation antipsychotics (SGA). Methods: A literature search was performed in Pubmed and Embase to identify all cohort studies, cross-sectional studies and clinical trials investigating associations with MetS in patients with schizophrenia spectrum disorders using SGAs. We extracted and enumerated clinical, biochemical and genetic factors reported to be associated with MetS. We defined factors associated with MetS as factors being reported as associated with MetS in two or more studies. Results: 58 studies were included in this review (n = 12,123). In total, 62 factors were found to be associated with increased risk of MetS. Thirty one out of 58 studies investigated factors that were reported as associated with MetS in two or more studies. With regard to clinical factors, we found gender, higher age, concomitant use of mood stabilizers, higher baseline and current BMI, earlier SGA exposure, higher dose, longer duration of treatment, psychosis and tobacco smoking to be significantly associated with MetS. Furthermore, the biochemical factors hypo-adiponectinemia, elevated levels of C-reactive protein (CRP) and higher white blood cell (WBC) count were identified as factors associated with MetS. Among pharmacogenetic factors, the rs1414334 C-allele of the HTR2C-gene was associated with MetS in patients using SGA. Conclusion: In this systematic review investigating clinical, biochemical and genetic factors associated with MetS in patients using SGAs we found that higher age, higher baseline BMI, higher current BMI and male as well as female gender were positively associated with MetS across all antipsychotics. This study may set the stage for the application of clinical, biochemical and genetic factors to predict the risk of developing MetS in patients using SGAs. Future research is needed to determine which patients using SGAs are at risk to develop MetS in clinical practice.

Association between the -2548G/A polymorphism of the leptin gene and antipsychotic-induced weight gain: Analysis of the CATIE sample and meta-analysis

BACKGROUND

Antipsychotics, especially most of the second-generation antipsychotics, have a high risk for metabolic syndrome and antipsychotic-induced weight gain (AIWG). A promoter variant of the leptin (LEP) gene, -2548G/A (rs7799039), has been associated with AIWG in several studies. The aim of this study was to evaluate this association in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) sample, followed by meta-analysis.

METHODS

We investigated the association between rs7799039 and AIWG in a sub-sample of European (N = 164) individuals from the CATIE study. Body mass index (BMI) change and weight gain (presence or absence) was analyzed using ANCOVA and logistic regression, respectively. For the meta-analysis, a literature search was conducted using MEDLINE, Embase, and PsycINFO up to October 2019. The pooled odds ratio was calculated for presence or absence of weight gain (≥7% weight change) using a random effects model.

RESULTS

We did not detect an association between rs7799039 and BMI change or weight gain (presence or absence) in the CATIE sample. As for the meta-analysis, we included 12 studies. No significant associations between the LEP rs7799039 polymorphism and AIWG were observed under the allelic genetic model (allele A vs. allele G) (OR = 1.10 [0.71, 1.70], p = .68). In the subgroup analyses of first-episode schizophrenia patients, a significant association between the A-allele and weight gain was observed, respectively (OR = 2.32 [1.41, 3.82], p = .0009).

CONCLUSIONS

The present meta-analysis showed no significant effect of rs7799039 on AIWG. However, this variant may influence AIWG in first-episode schizophrenia patients. Further investigation of a larger and more homogenous sample is required to elucidate the role of the LEP gene in AIWG.

Association of the HTR2C-759C/T polymorphism and antipsychotic-induced weight gain: a meta-analysis

Background

Antipsychotic-induced weight gain (AIWG) is a crucial factor for the medication cessation of patients with schizophrenia. Multiple studies have shown that the functional polymorphism -759 C/T (rs3813929) in the HTR2C promoter region could possibly be correlated with AIWG.

Aim

To evaluate the genetic association of the HTR2C-759C/T polymorphism and AIWG in patients with schizophrenia with antipsychotic drugs (APDs) administration.

Methods

Eligible studies were identified by searching the following databases: PubMed, Embase, Web of Science, China Nation Knowledge Infrastructure (CNKI), VIP, Wanfang Data, Chinese Biomedical Literature Database (CBM) and the Airiti Library. The quality of studies was evaluated based on the Newcastle-Ottawa Scale. The pooled OR and 95% CI were calculated for the dominant (CT/TT/T vs CC/C) mode, and subgroup analyses were performed based on ethnicity, antipsychotic medication and gender; all statistical analyses were performed using the statistical software STATA V.12.0.

Result

A total of 17 studies with 3170 patients with schizophrenia were included in our meta-analysis. The result of the meta-analysis has shown that the association between the -759 C/T polymorphism and AIWG is statistically significant (OR 0.34, 95% CI: 0.20 to 0.57, z=4.11, p<0.001). The subgroup analyses revealed significant correlations between the -759 C/T polymorphism and AIWG in the Caucasian population (OR 0.33, 95% CI: 0.14 to 0.77, z=2.55, p=0.011), the Asian population (OR 0.31, 95% CI: 0.18 to 0.52, z=4.46, p<0.001), the patients with APDs administration (CT/TT/T vs CC/C: OR 0.63, 95% CI: 0.40 to 1.00, z=1.97, p=0.049) and the patients with atypical antipsychotic drug administration (CT/TT/T vs CC/C: OR 0.21, 95% CI: 0.09 to 0.47, z=3.83, p<0.001). The sensitivity analysis showed that the results were stable. Begg’s test (after correction z=1.07, p=0.287) and Egger’s test (t=−2.41, p=0.029) show that the included articles have no significant publication bias.

Conclusion

There is a significant genetic association between HTR2C-759C/T and AIWG, and patients with T allele are less likely to have AIWG.

Pharmacogenomic Characterization in Bipolar Spectrum Disorders

The holistic approach of personalized medicine, merging clinical and molecular characteristics to tailor the diagnostic and therapeutic path to each individual, is steadily spreading in clinical practice. Psychiatric disorders represent one of the most difficult diagnostic challenges, given their frequent mixed nature and intrinsic variability, as in bipolar disorders and depression. Patients misdiagnosed as depressed are often initially prescribed serotonergic antidepressants, a treatment that can exacerbate a previously unrecognized bipolar condition. Thanks to the use of the patient's genomic profile, it is possible to recognize such risk and at the same time characterize specific genetic assets specifically associated with bipolar spectrum disorder, as well as with the individual response to the various therapeutic options. This provides the basis for molecular diagnosis and the definition of pharmacogenomic profiles, thus guiding therapeutic choices and allowing a safer and more effective use of psychotropic drugs. Here, we report the pharmacogenomics state of the art in bipolar disorders and suggest an algorithm for therapeutic regimen choice.

Second-Generation Antipsychotics and Dysregulation of Glucose Metabolism: Beyond Weight Gain

Second-generation antipsychotics (SGAs) are the cornerstone of treatment for schizophrenia because of their high clinical efficacy. However, SGA treatment is associated with severe metabolic alterations and body weight gain, which can increase the risk of type 2 diabetes and cardiovascular disease, and greatly accelerate mortality. Several underlying mechanisms have been proposed for antipsychotic-induced weight gain (AIWG), but some studies suggest that metabolic changes in insulin-sensitive tissues can be triggered before the onset of AIWG. In this review, we give an outlook on current research about the metabolic disturbances provoked by SGAs, with a particular focus on whole-body glucose homeostasis disturbances induced independently of AIWG, lipid dysregulation or adipose tissue disturbances. Specifically, we discuss the mechanistic insights gleamed from cellular and preclinical animal studies that have reported on the impact of SGAs on insulin signaling, endogenous glucose production, glucose uptake and insulin secretion in the liver, skeletal muscle and the endocrine pancreas. Finally, we discuss some of the genetic and epigenetic changes that might explain the different susceptibilities of SGA-treated patients to the metabolic side-effects of antipsychotics.

The Effect of Long-Term Second-Generation Antipsychotics Use on the Metabolic Syndrome Parameters in Jordanian Population

Objectives: The aim of this study was to determine the incidence of metabolic syndrome in patients treated with second-generation antipsychotics (SGAs). Methods: In this retrospective study, we reviewed patients’ electronic medical records (EMRs) of all patients who received one SGA for at least six months, excluding patients who were taking other medications that are associated with significant effect on metabolic syndrome. Relevant clinical information was collected prior to starting the SGA and after six months of continuous use of the same SGA. Results: A total of 91 patients were included in the study. The majority of patients (72%) were diagnosed with schizophrenia. After six months of taking the SGA, 44% of patients experienced elevated systolic pressure, 54.9% had elevated triglyceride, and 31.9% had impaired glucose levels (p value < 0.05). Prior to initiating SGA therapy, 14.3% of patients had metabolic syndrome, while 37.4% had metabolic syndrome after six months of therapy, and it was more prominent in males compared to female patients (p value < 0.05). Conclusion: This study found a strong correlation between SGA use and the appearance of metabolic alterations, such as weight gain, glucose intolerance, and increased triglyceride levels. These findings highlight the importance of assessing metabolic deregulations to minimize SGA associated metabolic abnormalities.

The Effect of Long-Term Second-Generation Antipsychotics Use on the Metabolic Syndrome Parameters in Jordanian Population

Objectives: The aim of this study was to determine the incidence of metabolic syndrome in patients treated with second-generation antipsychotics (SGAs). Methods: In this retrospective study, we reviewed patients' electronic medical records (EMRs) of all patients who received one SGA for at least six months, excluding patients who were taking other medications that are associated with significant effect on metabolic syndrome. Relevant clinical information was collected prior to starting the SGA and after six months of continuous use of the same SGA. Results: A total of 91 patients were included in the study. The majority of patients (72%) were diagnosed with schizophrenia. After six months of taking the SGA, 44% of patients experienced elevated systolic pressure, 54.9% had elevated triglyceride, and 31.9% had impaired glucose levels (p value < 0.05). Prior to initiating SGA therapy, 14.3% of patients had metabolic syndrome, while 37.4% had metabolic syndrome after six months of therapy, and it was more prominent in males compared to female patients (p value < 0.05). Conclusion: This study found a strong correlation between SGA use and the appearance of metabolic alterations, such as weight gain, glucose intolerance, and increased triglyceride levels. These findings highlight the importance of assessing metabolic deregulations to minimize SGA associated metabolic abnormalities.

Effect of Metformin on Antipsychotic-Induced Metabolic Dysfunction: The Potential Role of Gut-Brain Axis

Antipsychotics are the first-line medications prescribed for patients with schizophrenia or other mental disorders. Cumulative evidence has revealed that metabolic dysfunctions frequently occur in patients receiving antipsychotics, especially second-generation antipsychotics, and these effects may decrease patient compliance and increase health costs. Metformin is an effective pharmaceutical adjuvant for ameliorating antipsychotic-induced metabolic dysfunction (AIMD) in clinical practice. However, the mechanism of the effects of metformin on AIMD remains unclear. The gut-brain axis is a bidirectional communication system between the gastrointestinal tract and the central nervous system and has been associated with many pathological and physiological conditions, such as those related to metabolism. Antipsychotics interact with and have affinity for dopamine receptors and other receptors in the brain, and treatment with these antipsychotics has been shown to influence gut microbiota metabolism and composition, as observed in both animal and human studies. Metformin exerts an antidiabetic effect that is correlated with activation of AMP-kinase in the hypothalamus, and metformin also influences gut flora. Therefore, the gut-brain axis may play a role in the effect of metformin on AIMD. Since no direct evidence is available, this perspective may provide a direction for further research.