Patients with schizophrenia are susceptible to low bone mineral density (BMD). Many risk factors have been suggested. However, it remains uncertain whether the risk factors differ between men and women. In addition, the study of bone density in men is neglected more often than that in women. This study aims to examine specific risk factors of low BMD in different sexes. Men (n=80) and women (n=115) with schizophrenia, similar in demographic and clinical characteristics, were enrolled in three centers. Clinical and laboratory variables (including blood levels of prolactin, sex and thyroid hormones, cortisol, calcium, and alkaline phosphatase) were collected. BMD was measured using a dual-energy X-ray absorptiometer. Men had lower BMD than women. Predictors for BMD in men included hyperprolactinemia (B=-0.821, P=0.009), body weight (B=0.024, P=0.046), and Global Assessment of Functioning score (B=0.027, P=0.043); in women, BMD was associated with menopause (B=-1.070, P<0.001), body weight (B=0.027, P=0.003), and positive symptoms (B=0.094, P<0.001). In terms of the effect of psychotic symptoms, positive symptoms were related positively to BMD in women, but not in men. The findings suggest that sex-specific risk factors should be considered for an individualized intervention of bone loss in patients with schizophrenia. Physicians should pay particular attention to bone density in men with hyperprolactinemia and postmenopausal women. Further prospective studies in other populations are warranted to confirm these findings.
Abstract Background N-methyl-D-aspartate receptors (NMDARs) are crucial components of brain function involved in memory and neurotransmission. Sodium benzoate is a promising NMDAR enhancer and has been proven to be a novel, safe, and efficient therapy for patients with Alzheimer disease (AD). However, in addition to the role of sodium benzoate as an NMDA enhancer, other mechanisms of sodium benzoate in treating AD are still unclear. To elucidate the potential mechanisms of sodium benzoate in Alzheimer disease, this study employed label-free quantitative proteomics to analyze serum samples from AD cohorts with and without sodium benzoate treatment. Methods The serum proteins from each patient were separated into 24 fractions using an immobilized pH gradient, digested with trypsin, and then subjected to nanoLC‒MS/MS to analyze the proteome of all patients. The nanoLC‒MS/MS data were obtained with a label-free quantitative proteomic approach. Proteins with fold changes were analyzed with STRING and Cytoscape to find key protein networks/processes and hub proteins. Results Our analysis identified 861 and 927 protein groups in the benzoate treatment cohort and the placebo cohort, respectively. The results demonstrated that sodium benzoate had the most significant effect on the complement and coagulation cascade pathways, amyloidosis disease, immune responses, and lipid metabolic processes. Moreover, Transthyretin, Fibrinogen alpha chain, Haptoglobin, Apolipoprotein B-100, Fibrinogen beta chain, Apolipoprotein E, and Alpha-1-acid glycoprotein 1 were identified as hub proteins in the protein‒protein interaction networks. Conclusions These findings suggest that sodium benzoate may exert its influence on important pathways associated with AD, thus contributing to the improvement in the pathogenesis of the disease.
The D-amino acid oxidase activator (DAOA, also known as G72) gene is a strong schizophrenia susceptibility gene. Higher G72 protein levels have been implicated in patients with schizophrenia. The current study aimed to differentiate patients with schizophrenia from healthy individuals using G72 single nucleotide polymorphisms (SNPs) and G72 protein levels by leveraging computational artificial intelligence and machine learning tools. A total of 149 subjects with 89 patients with schizophrenia and 60 healthy controls were recruited. Two G72 genotypes (including rs1421292 and rs2391191) and G72 protein levels were measured with the peripheral blood. We utilized three machine learning algorithms (including logistic regression, naive Bayes, and C4.5 decision tree) to build the optimal predictive model for distinguishing schizophrenia patients from healthy controls. The naive Bayes model using two factors, including G72 rs1421292 and G72 protein, appeared to be the best model for disease susceptibility (sensitivity = 0.7969, specificity = 0.9372, area under the receiver operating characteristic curve (AUC) = 0.9356). However, a model integrating G72 rs1421292 only slightly increased the discriminative power than a model with G72 protein alone (sensitivity = 0.7941, specificity = 0.9503, AUC = 0.9324). Among the three models with G72 protein alone, the naive Bayes with G72 protein alone had the best specificity (0.9503), while logistic regression with G72 protein alone was the most sensitive (0.8765). The findings remained similar after adjusting for age and gender. This study suggests that G72 protein alone, without incorporating the two G72 SNPs, may have been suitable enough to identify schizophrenia patients. We also recommend applying both naive Bayes and logistic regression models for the best specificity and sensitivity, respectively. Larger-scale studies are warranted to confirm the findings.
Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia' functional outcomes such as Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we leveraged a bagging ensemble machine learning method with a feature selection algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble machine learning algorithm with other state-of-the-art models such as linear regression, support vector machine, multilayer feedforward neural networks, and random forests. The analysis reported that the bagging ensemble algorithm with feature selection outperformed other predictive algorithms to forecast the QLS functional outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. Furthermore, the bagging ensemble algorithm with feature selection surpassed other predictive algorithms to forecast the GAF functional outcome of schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the bagging ensemble machine learning algorithm with feature selection might present an applicable approach to provide software tools for forecasting the functional outcomes of schizophrenia using molecular biomarkers.
Abstract Aims More than one-half of betel-quid (BQ) chewers have betel-quid use disorder (BUD). However, no medication has been approved. We performed a randomised clinical trial to test the efficacy of taking escitalopram and moclobemide antidepressants on betel-quid chewing cessation (BQ-CC) treatment. Methods We enrolled 111 eligible male BUD patients. They were double-blinded, placebo-controlled and randomised into three treatment groups: escitalopram 10 mg/tab daily, moclobemide 150 mg/tab daily and placebo. Patients were followed-up every 2 weeks and the length of the trial was 8 weeks. The primary outcome was BQ-CC, defined as BUD patients who continuously stopped BQ use for ⩾6 weeks. The secondary outcomes were the frequency and amount of BQ intake, and two psychological rating scales. Several clinical adverse effects were measured during the 8-week treatment. Results Intention-to-treat analysis shows that after 8 weeks, two (5.4%), 13 (34.2%) and 12 (33.3%) of BUD patients continuously quit BQ chewing for ⩾6 weeks among placebo, escitalopram, moclobemide groups, respectively. The adjusted proportion ratio of BQ-CC was 6.3 (95% CI 1.5–26.1) and 6.8 (95% CI 1.6–28.0) for BUD patients who used escitalopram and moclobemide, respectively, as compared with those who used placebo. BUD patients with escitalopram and moclobemide treatments both exhibited a significantly lower frequency and amount of BQ intake at the 8th week than those with placebo. Conclusions Prescribing a fixed dose of moclobemide and escitalopram to BUD patients over 8 weeks demonstrated treatment benefits to BQ-CC. Given a relatively small sample, this study provides preliminary evidence and requires replication in larger trials.
Objectives Hypofunction of NMDA receptor is implicated in the pathophysiology, particularly cognitive impairment, of schizophrenia. Sarcosine, a glycine transporter I (GlyT-1) inhibitor, and sodium benzoate, a d-amino acid oxidase (DAAO) inhibitor, can both enhance NMDA receptor-mediated neurotransmission. We proposed simultaneously inhibiting DAAO and GlyT-1 may be more effective than inhibition of either in improving the cognitive and global functioning of schizophrenia patients. Methods This study compared add-on sarcosine (2 g/day) plus benzoate (1 g/day) vs. sarcosine (2 g/day) for the clinical symptoms, as well as the cognitive and global functioning, of chronic schizophrenia patients in a 12-week, double-blind, randomised, placebo-controlled trial. Participants were measured with the Positive and Negative Syndrome Scale and the Global Assessment of Functioning Scale every 3 weeks. Seven cognitive domains, recommended by the Measurement and Treatment Research to Improve Cognition in Schizophrenia Committee, were measured at weeks 0 and 12. Results Adjunctive sarcosine plus benzoate, but not sarcosine alone, improved the cognitive and global functioning of patients with schizophrenia, even when their clinical symptoms had not improved. Conclusions This finding suggests N-methyl-d-aspartate receptor-enhancement therapy can improve the cognitive function of patients with schizophrenia, further indicating this pro-cognitive effect can be primary without improvement in clinical symptoms.
Abstract Objectives Cases of patients with bipolar disorder (BD) having neuropsychological impairment have been reported, although inconsistently. The possibility of comorbidity with anxiety disorder (AD) has been suggested. The association between mood episodes and AD comorbidity on neuropsychological performance is unclear and thus was investigated in the current study. Methods All participants were informed about and agreed to participate in this study. Patients with BD were recruited from outpatient and inpatient settings, and healthy controls (HCs) were recruited as a comparison group. Six hundred and twenty‐eight participants (175 HCs and 453 BD—56 BDI and 397 BDII) were studied based on their current mood episode, namely, depressive (BD d ), manic/hypomanic (BDm), mixed (BDmix), and euthymic (BDeu), compared with/without AD comorbidity (164 with AD). Results Compared to HCs, all BD groups had significantly more impaired neuropsychological profiles, but the BDeu group was found to have less impairment in memory and executive function than the episodic BD groups. The percentage of AD comorbidity in BDd, BDm, BDmix, and BDeu was 33.9%, 40.3%, 33.0%, and 35.6%, respectively ( χ 2 = 1.61, p > .05). The results show that AD plays a different role in neuropsychological impairment across various mood episodes in BD. Conclusion Memory impairment and executive dysfunction may be state‐like cognitive phenotypes and are affected by AD comorbidity during mixed and depressive episodes in BD, while sustained attention deficiencies are more like trait markers, regardless of mood episodes, and persist beyond the course of the illness. The AD comorbidity effect on attentional deficit is greater when suffering from a manic episode.
The bacterial pathogen Helicobacter pylori (Hp) is the leading risk factor for the development of gastric cancer. Hp virulence factor, cytotoxin-associated gene A (CagA) interacted with cholesterol-enriched microdomains and leads to induction of inflammation in gastric epithelial cells (AGS). In this study, we identified a triterpenoid methylantcinate B (MAB) from the medicinal mushroom Antrodia camphorata which inhibited the translocation and phosphorylation of CagA and caused a reduction in hummingbird phenotype in HP-infected AGS cells. Additionally, MAB suppressed the Hp-induced inflammatory response by attenuation of NF-κB activation, translocation of p65 NF-κB, and phosphorylation of IκB-α, indicating that MAB modulates CagA-mediated signaling pathway. Additionally, MAB also suppressed the IL-8 luciferase activity and its secretion in HP-infected AGS cells. On the other hand, molecular structure simulations revealed that MAB interacts with CagA similarly to that of cholesterol. Moreover, binding of cholesterol to the immobilized CagA was inhibited by increased levels of MAB. Our results demonstrate that MAB is the first natural triterpenoid which competes with cholesterol bound to CagA leading to attenuation of Hp-induced pathogenesis of epithelial cells. Thus, this study indicates that MAB may have a scope to develop as a therapeutic candidate against Hp CagA-induced inflammation.
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