Salivary Flow Alteration in Patients Undergoing Treatment for Schizophrenia: Disease-Drug-Target Gene/Protein Association Study for Side-effects

Pharmacological interventions for antipsychotic-related sialorrhea: a systematic review and network meta-analysis of randomized trials

Antipsychotic-induced sialorrhea carries a significant burden, but evidence-based treatment guidance is incomplete, warranting network meta-analysis (NMA) of pharmacological interventions for antipsychotic-related sialorrhea. PubMed Central/PsycInfo/Cochrane Central database/Clinicaltrials.gov/WHO-ICTRP and the Chinese Electronic Journal Database (Qikan.cqvip.com) were searched for published/unpublished RCTs of antipsychotic-induced sialorrhea (any definition) in adults, up to 06/12/2023. We assessed global/local inconsistencies, publication bias, risk of bias (RoB2), and confidence in the evidence, conducting subgroup/sensitivity analyses. Co-primary efficacy outcomes were changes in saliva production (standardized mean difference/SMD) and study-defined response (risk ratios/RRs). The acceptability outcome was all-cause discontinuation (RR). Primary nodes were molecules; the mechanism of action (MoA) was secondary. Thirty-four RCTs entered a systematic review, 33 NMA (n = 1958). All interventions were for clozapine-induced sialorrhea in subjects with mental disorders. Regarding individual agents and response, metoclopramide (RR = 3.11, 95% C.I. = 1.39-6.98), cyproheptadine, (RR = 2.76, 95% C.I. = 2.00-3.82), sulpiride (RR = 2.49, 95% C.I. = 1.65-3.77), propantheline (RR = 2.39, 95% C.I. = 1.97-2.90), diphenhydramine (RR = 2.32, 95% C.I. = 1.88-2.86), benzhexol (RR = 2.32, 95% C.I. = 1.59-3.38), doxepin (RR = 2.30, 95% C.I. = 1.85-2.88), amisulpride (RR = 2.23, 95% C.I. = 1.30-3.81), chlorpheniramine (RR = 2.20, 95% C.I. = 1.67-2.89), amitriptyline (RR = 2.09, 95% C.I. = 1.34-3.26), atropine, (RR = 2.03, 95% C.I. = 1.22-3.38), and astemizole, (RR = 1.70, 95% C.I. = 1.28-2.26) outperformed placebo, but not glycopyrrolate or ipratropium. Across secondary nodes (k = 28, n = 1821), antimuscarinics (RR = 2.26, 95% C.I. = 1.91-2.68), benzamides (RR = 2.23, 95% C.I. = 1.75-3.10), TCAs (RR = 2.23, 95% C.I. = 1.83-2.72), and antihistamines (RR = 2.18, 95% C.I. = 1.83-2.59) outperformed placebo. In head-to-head comparisons, astemizole and ipratropium were outperformed by several interventions. All secondary nodes, except benzamides, outperformed the placebo on the continuous efficacy outcome. For nocturnal sialorrhea, neither benzamides nor atropine outperformed the placebo. Active interventions did not differ significantly from placebo regarding constipation or sleepiness/drowsiness. Low-confidence findings prompt caution in the interpretation of the results. Considering primary nodes' co-primary efficacy outcomes and head-to-head comparisons, efficacy for sialorrhea is most consistent for the following agents, decreasing from metoclopramide through cyproheptadine, sulpiride, propantheline, diphenhydramine, benzhexol, doxepin, amisulpride, chlorpheniramine, to amitriptyline, and atropine (the latter not for nocturnal sialorrhea). Shared decision-making with the patient should guide treatment decisions regarding clozapine-related sialorrhea.

Oral health, organic and inorganic saliva composition of men with Schizophrenia: Case-control study

BACKGROUND

Schizophrenia (SCZ) presents complex challenges related to diagnosis and clinical monitoring. The study of conditions associated with SCZ can be facilitated by using potential markers and patterns that provide information to support the diagnosis and oral health.

METHODS

The salivary composition of patients diagnosed with SCZ (n = 50) was evaluated and compared to the control (n = 50). Saliva samples from male patients were collected and clinical parameters were evaluated. The concentration of total proteins and amylase were determined and salivary macro- and microelements were quantified by ICP OES and ICP-MS. Exploratory data analysis based on artificial intelligence tools was used in the investigation.

RESULTS

There was a significant increase in the salivary concentrations of Al, Fe, Li, Mg, Na, and V, higher prevalence of caries (p < 0.001), periodontal disease (p < 0.001), and reduced salivary flow rate (p = 0.019) in SCZ patients. Also, samples were grouped into six clusters. As, Co, Cr, Cu, Mn, Mo, Ni, Se, and Sr were correlated with each other, while Fe, K, Li, Ti, and V showed the highest concentrations in the samples distributed in the clusters with the highest association between SZC patients and controls.

CONCLUSIONS

The results obtained indicate changes in salivary flow, organic composition, and levels of macro- and microelements in SCZ patients. Salivary concentrations of Fe, Mg, and Na may be related to oral conditions, higher prevalence of caries, and periodontal disease. The exploratory analysis showed different patterns in the salivary composition of SCZ patients impacted by associations between oral health conditions and the use of medications. Future studies are encouraged to confirm the results investigated in this study.

Identification of breast cancer risk modules via an integrated strategy

Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.