ObjectiveTo construct a network of depressive and sleep symptoms in patients with bipolar disorder and to explore core symptoms and potential causal relationships. MethodsA total of 212 inpatients with bipolar disorder hospitalized at the Fifth People’s Hospital of Nanning from January 2022 to December 2024 were enrolled. Depressive symptoms were assessed using the depression dimension of the Symptom Checklist-90 (SCL-90), and sleep status was evaluated using the Pittsburgh Sleep Quality Index (PSQI). A Gaussian graphical model was used to analyze correlations between depressive symptoms and sleep symptoms, and a directed acyclic graph (DAG) was constructed to analyze potential causal relationships. ResultsAmong the 212 patients with bipolar disorder, the median (IQR) score for the SCL-90 depression factor was 21.50 (14.00, 38.75), and the median (IQR) PSQI score was 9.00 (5.00, 14.00). Partial correlation network analysis showed a positive correlation between depressive symptoms and sleep symptoms. Centrality analysis identified three core symptoms: feeling depressed (S30), excessive worry (S31), and feeling worthless (S79). The directed acyclic graph (DAG) indicated a potential influence of depressive symptoms on sleep symptoms, with feeling worthless (S79) serving as a key mediating node that could exert direct or indirect effects on other symptoms. ConclusionsFeeling worthless (S79) occupies a central position in the symptom network and may readily trigger other symptoms. Clinical treatments and interventions should prioritize this symptom to reduce its negative impact on patients’ overall health status.

Mental disorders have become a global public health challenge, with the patient population continuously expanding. However, previous research has primarily focused on the general population, with insufficient attention given to this group. Therefore, the aim of this study is to explore the complex network relationships and path mechanisms between the activities of daily living limitations (ADLl), mania, and anxiety in patients with mental disorders. A survey was conducted with 1,050 patients from a mental health hospital in a southern province of China using the Self-Rating Anxiety Scale, the Mania Scale, and the Activities of Daily Living Disability Scale. The network analysis results indicate a complex interaction between ADLl, mania, and anxiety in mental disorder patients. ADLl plays both a strong predictive and a “mediating” role in the network structure. Further path analysis shows that ADLl can affect anxiety through the mediation of mania, and mania can influence anxiety through the mediation of ADLl, with mediation effects accounting for 33.2

Abstract Schizophrenia (SCZ) is a serious psychiatric condition. While PM2.5 exposure has been linked to SCZ, the specific effects of its components remain poorly understood. This study aimed to explore the relationships between PM2.5 constituents (including BC: black carbon, OM: organic matter, SO42−: sulfate, NH4+: ammonium, and NO3−: nitrate) and SCZ. It incorporated the hospitalization records of 16,082 SCZ patients from Nanning Fifth People's Hospital, spanning from 1 January 2014, to 31 December 2023. The daily concentration data of PM2.5 and its five chemical components were sourced from Tracking Air Pollution in China (TAP). A distributed lag nonlinear model (DLNM) was employed to measure the dynamic correlation between PM2.5 components and the risk of hospitalization for SCZ. Further analysis was conducted by stratifying based on gender, age, and cold/warm seasons to identify susceptible populations. Our study revealed that OM and BC demonstrated lagged effects on SCZ hospitalization, with significant associations observed at lag 3 day(lag3)and lag4. The strongest effect was identified at lag4, with OM showing an relative risks (RR) of 1.010 (95%CI: 1.001, 1.019) and BC exhibiting a higher RR of 1.010 (1.001, 1.019). And the lag effect of the OM relative percentage was identified at lag 3 (RR = 1.013, 95% CI: 1.005–1.022). PM2.5, SO42−, and NH4+ showed lagged response trends but no statistically significant effects (p > 0.05). Subgroup analysis indicated that males, 45 years and younger, and those exposed during the warm season had higher risks associated with SO42−, OM, BC, and PM2.5. Short‐term exposure to OM is significantly related to SCZ hospitalization.

Current diagnostics for ischemic stroke (IS) lack timeliness and accessibility, highlighting the need for novel molecular diagnostic models. Three gene expression datasets (GSE16561, GSE22255 and GSE58294), encompassing both IS patients and healthy control subjects, were retrieved from a public database. The mitochondrial dysfunction genes retrieve from the intersection of the GeneCards and MitoCarta3.0 databases. The limma and WGCNA package were used to obtain the genes related to IS. Feature genes were screened using LASSO, RF, SVM, and diagnostic models were constructed using NeighborMethod, NeuralNet, and BayesMethod. 3548 differentially expressed genes (DEGs) (1538 upregulated, 2010 downregulated) were identified in IS patients when compared to controls. WGCNA analysis yielded 10 IS-related modules containing 1643 genes. The intersection of DEGs, module genes, and mitochondrial dysfunction genes yielded 100 mitochondrial dysfunction genes associated with IS. These genes collectively regulate biological processes like mitochondrial ATP synthesis coupled electron transport and respiratory electron transport chain, and participate in IS-associated signaling pathways such as reactive oxygen species and oxidative phosphorylation. Further machine learning methods identified 4 feature genes, including MCL1, MRPL46, MTX3 and RNASEH1. These four genes exhibited robust diagnostic potential in the merged dataset (all AUC > 0.7). The machine learning models achieved AUC values of 0.814 (NeighborMethod), 0.852 (NeuralNet), and 0.842 (BayesMethod). External validation using an independent cohort confirmed that all models maintained high diagnostic accuracy (AUC range: 0.730–0.783). This study established a multi-gene diagnostic model for IS, identifying novel molecular biomarkers to improve the timeliness and accessibility of IS diagnosis.

Background S100B (serum S100 calcium‐binding protein B) has been linked to blood–brain barrier disruption and neuroinflammation, but its prognostic value for ischemic stroke remains controversial. We aimed to examine baseline serum S100B and its interplay with high‐sensitivity C‐reactive protein and platelet count in association with clinical outcomes after ischemic stroke. Methods Data were derived from a prospective multicenter cohort involving 3249 patients with acute ischemic stroke in 26 hospitals across China. Serum S100B concentrations were measured at baseline, and patients were followed up for 3 months after ischemic stroke. Study outcomes included death, major disability, and vascular events. Multivariable‐adjusted Cox proportional hazards regression models and logistic regression models were applied to estimate hazard ratios (HRs) or odds ratios (ORs) and their 95%CIs. Results During the 3‐month follow‐up, 92 (2.8%) patients died. Higher baseline serum S100B levels were associated with an increased risk of death (quartile 4 versus quartile 1: adjusted HR, 2.09 [95% CI, 1.09–4.02]; Ptrend=0.028). Each SD increase in log‐transformed serum S100B was associated with a 38% greater risk of death. A spline regression model showed a linear relationship between serum S100B and death risk (Plinearity=0.006). Significant joint effects were observed between S100B and high‐sensitivity C‐reactive protein, and between S100B and platelet count, in association with death after ischemic stroke. The highest risk was observed among patients with elevations in all 3 biomarkers. Conclusions Elevated serum S100B levels in the acute phase of ischemic stroke were independently associated with increased mortality risk. A single combined measure of S100B, high‐sensitivity C‐reactive protein, and platelet count may further improve risk stratification. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT01840072.

Per- and polyfluoroalkyl substances (PFAS) are a class of neurotoxic persistent organic pollutants that may increase the risk of cognitive impairment and dementia, yet their association with schizophrenia (SCZ) remains unclear. This study aimed to investigate the relationship between plasma PFAS levels and the risk of schizophrenia. The concentrations of PFAS in plasma were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS). To evaluate the single and combined effects of PFAS exposure on the risk of schizophrenia, we employed multivariable logistic regression, restricted cubic splines (RCS), Bayesian kernel machine regression (BKMR), and generalized weighted quantile sum (gWQS) models. Logistic regression analysis revealed that, compared with the lowest quartile (Q1), the odds ratios (ORs) for PFOS and PFHxS in the highest quartile (Q4) were 0.17 (95% CI:0.09∼0.36) and 0.39 (95% CI:0.21∼0.71), respectively. RCS analysis suggested nonlinear relationships between PFUnDA, PFOS, PFOA, and PFHxS and schizophrenia. In the assessment of mixture exposure effects, the BKMR model indicated an overall negative association between PFAS mixtures and schizophrenia risk, and gWQS analysis further identified PFHxS as the component with the highest weight contribution. Additionally, correlations were observed between the levels of PFUnDA, PFOS, and PFHxS and inflammatory indices in patients with schizophrenia. Future large-scale prospective studies combined with toxicological mechanism research are needed to further validate these findings.

Objective To investigate the interrelationships between individual symptoms of anxiety and obsessive-compulsive symptoms in patients with anxiety disorders, and to analyze the interactions between these symptom clusters. Methods A retrospective analysis was performed on inpatients with anxiety disorders at a tertiary psychiatric hospital. Anxiety levels were assessed using the self-rating anxiety scale(SAS), while obsessive-compulsive levels were evaluated via the obsessive-compulsive subscale of the Symptom Checklist-90(SCL-90). Network analysis was employed to characterize the interconnections between anxiety and obsessive-compulsive symptoms. Results A total of 464 patients with anxiety disorders were included, with a mean age of(46.79±16.57) years. Among them, 284 cases(61.21%) were female and 180 cases(38.79%) were male. Network analysis identified three key bridge symptoms linking anxiety and obsessive-compulsive symptom clusters: "feeling that thoughts are swirling in the mind and cannot be shaken off(L3, bridge strength=2.69)", "feeling more easily nervous or anxious than usual(S1, bridge strength=2.23)", and "feeling that the mind has gone blank(L51, bridge strength=1.79)". The bridge strength stability coefficient was 0.36. Conclusion As a core and bridge symptom, "feeling that the mind has gone blank(L51)" may play a critical linking role between the obsessive-compulsive and anxious symptom clusters in patients with anxiety disorders. Designating it as a priority intervention target can provide new insights for alleviating both anxiety and obsessive-compulsive symptoms in these patients.

BACKGROUND:Previous studies have explored the effects of metal exposure on sleep indifferent age groups, but few have examined the effects in postmenopausal women. The study was aimed to investigate the single and mixed effects of exposure to 22 metals on sleep quality in postmenopausal women. METHODS:The baseline data of 1914 postmenopausal women were extracted from the Prospective Cohort of Chronic Diseases in Guangxi Ethnic Minority Natural Population in China. Concentrations of 22 metals in urine were measured by inductively coupled plasma mass spectrometry (ICP-MS). Pittsburgh Sleep Quality Index (PSQI) was used to evaluate sleep quality in postmenopausal women. The binary logistic regression model was used to analyze the effect of single metals exposure on the risk of poor sleep quality and quantile g-computation regression model was applied to assess the mixed effects of multiple metals exposure. RESULTS:Among the 1914 participants,736(38.5%) had poor sleep quality. In single-metal analyses, only manganese showed a positive association with poor sleep quality before multiple comparison correction (continuous variable, adjusted OR=1.21,95%CI:1.03-1.41), but this did not remain significant after false discovery rate (FDR) correction. Similarly, quartile-based analyses showed nominally positive associations for Mn (Q2:OR=1.51,95%CI:1.16-1.98,Q4:OR=1.41, 95%CI:1.07-1.85), Zn (Q2:OR=1.40, 95%CI:1.07-1.83), Ca (Q3:OR=1.36,95%CI:1.04-1.77), and Mo (Q3:OR=1.30, 95%CI:1.00-1.70), but none remained significant after FDR correction. However, the qgcomp mixture analysis revealed that exposure to the essential metal mixture was significantly associated with poor sleep quality (OR=1.23, 95%CI:1.05-1.44), with Mn, Zn, Ca, and Mo as the main positive contributors. CONCLUSION:No single metal remained significantly associated with poor sleep quality after FDR correction, whereas the essential metal mixture showed a significant positive association, with Mn identified as the primary contributor. Associations for Ca, Zn, and Mo were observed only in specific quartiles with non-monotonic patterns and should therefore be considered exploratory. These findings highlight the importance of mixture-based approaches in environmental health research. Further longitudinal studies, animal experiments, and cell-based investigations are warranted to validate these findings.

Gout typically develops from hyperuricemia (HUA), but the metabolic alterations driving this transition remain poorly understood, limiting our understanding of disease pathogenesis. To identify stage-specific putative biomarker candidates and to characterize dysregulated metabolic pathways distinguishing gout from HUA. We conducted a targeted metabolomics assay on the baseline plasma samples from a Zhuang minority cohort using LC-MS/MS. The analyzed sample set comprised 38 HUA patients, 47 gout patients, and 52 healthy controls. Sex-stratified differential metabolite analysis was performed across all participants, as well as in female and male subgroups. Pathway enrichment analysis was carried out using the KEGG database. Machine learning approaches, including the Boruta algorithm and support vector machine (SVM), were employed for putative biomarker discovery and model evaluation in male participants. Among all participants, 24 metabolites reached nominal significance (P < 0.05), but only uric acid remained significant after FDR correction. In sex-stratified analyses, no metabolite survived FDR correction in females, whereas in males, seven metabolites (flavone, glutamine, L-2-aminoadipic acid, L-pipecolic acid, N1-methyl-2-pyridone-5-carboxamide, phenyllactic acid, and uric acid) showed significant differences among healthy controls, HUA patients, and gout patients (FDR < 0.1). These metabolites were primarily involved in nitrogen metabolism, arginine biosynthesis, D-amino acid metabolism, nicotinate and nicotinamide metabolism, and purine metabolism. Machine learning identified four metabolites (N1-methyl-2-pyridone-5-carboxamide, flavone, glutamine, and phenyllactic acid) that distinguished gout from healthy controls, with AUCs of 0.902 and 0.800 in the training and validation sets, respectively. A second model (L-pipecolic acid, glutamine, phenyllactic acid, and flavone) discriminated gout from HUA, achieving AUCs of 0.850 and 1.000. Sensitivity analyses excluding obese or hypertriglyceridemic participants confirmed the robust performance of both models. This study suggests sex-specific metabolic alterations in gout and provides robust machine learning-based models for male participants. The identified metabolite signatures appear to extend purine metabolism to involve amino acid and energy metabolic pathways. These findings provide a basis for mechanism-targeted strategies in HUA management. External validation remains essential.

Objective To analyze the association between mixed metal exposure and sleep quality within the Guangxi natural population cohort, providing scientific evidence for developing prevention and control strategies for sleep disorders. Methods A cross-sectional study design was implemented using baseline data from the "Prospective Cohort of Chronic Diseases in Guangxi Ethnic Minority Natural Population", enrolling 5, 486 participants. Sleep quality was evaluated with the Pittsburgh Sleep Quality Index (PSQI), while basic information was collected via standardized questionnaires and physical examinations. Concentrations of 22 metal elements in firstmorning urine samples were quantified via inductively coupled plasma mass spectrometry. The least absolute shrinkage and selection operator (LASSO) regression was employed for metal feature selection, followed by logistic regression to assess associations between the selected metals and sleep disorder risk. Furthermore, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) models were integrated to systematically evaluate the joint effects of mixed metal exposure and identify the primary contributing metals. Results The detection rate of sleep disturbance among the study participants was 29.4% (1, 613/5, 486). LASSO regression selected 10 metals associated with sleep disorder risk: titanium, manganese, zinc, strontium, molybdenum, cadmium, tin, antimony, barium, and thallium. Logistic regression results showed that concentrations of manganese (OR=1.1, 95% CI: 1.0-1.2), zinc (OR=1.2, 95% CI: 1.0-1.6), and barium (OR=1.1, 95% CI: 1.0-1.2) were significantly associated with an increased risk of sleep disorders, while antimony (OR=0.8, 95% CI: 0.7-0.9) concentration was associated with a decreased risk. Joint effect analysis revealed that the positive WQS index for mixed exposure to the 10 metals was significantly associated with an increased risk of sleep disorders (OR=1.15, 95% CI: 1.01-1.31). The primary contributing metals identified by the WQS regression, qgcomp, and BKMR models were consistent with the logistic regression findings. Stratified and sensitivity analyses further confirmed that the associations between manganese, zinc, barium, antimony, and sleep disorder risk were consistent with the logistic regression findings. Conclusion Urinary levels of manganese, zinc, barium, and antimony are significantly associated with the risk of sleep disturbance in the general population of Guangxi. Furthermore, combined exposure to these metals may be associated with an elevated risk of sleep disturbance.

The rapid accumulation of lactate following cerebral ischemia has been well documented, however, the pathophysiological mechanisms underlying hyperlactate-induced lactylation in modulating neurological injury and prognosis of acute ischemic stroke (AIS) remain to be fully elucidated. We constructed transcriptomic profiles of peripheral blood mononuclear cells (PBMCs) from 60 AIS patients and 60 healthy controls using the Illumina sequencing platform. Unsupervised consensus clustering based on 56 lactylation-related genes (LRGs) was applied to stratify AIS molecular subtypes. Single-cell sequencing data analysis was conducted to evaluate the regulatory effects of LRGs enrichment on inter-neuronal communication. 265 differentially expressed genes (DEGs), including 162 upregulated and 103 downregulated, were identified between AIS patients and healthy controls. And DEGs were found to be enriched in natural killer cell-mediated cytotoxicity, cytokine-cytokine receptor interaction, and TGF-β signaling pathways. Unsupervised consensus clustering stratified AIS into two subtypes, with AIS2 exhibiting more severe neuronal injury and inflammatory responses. Single-cell sequencing analysis revealed that LRGs were predominantly enriched in NK T cells following cerebral ischemia. Cell communication analysis further suggested that LRGs might regulate interactions between NK T cells and neural cells via the Nampt-Insr and Tnf-Tnfsf1b ligand-receptor pair. LASSO regression selected four prognosis-related genes (SUMO2, SUB1, LSP1, EEF1G) with robust diagnostic efficacy for prognosis prediction (AUC value = 0.805, sensitivity = 0.875, specificity = 0.706). Our findings reveal significant correlations between LRGs in AIS patients and both neurological deficit severity and inflammatory status, highlighting LRGs as potential prognostic biomarkers for AIS.

Emerging evidence links dysregulation of circular RNAs (circRNAs) to neuropsychiatric disorders, but their pathophysiological roles in schizophrenia (SCZ) remain unclear. We investigated the clinical significance, biological function, and underlying mechanisms of circSUCO22-20 in SCZ. Previous whole-transcriptome sequencing identified circSUCO22-20 as differentially expressed in SCZ. Sanger sequencing and RNase R digestion confirmed its covalently closed-loop structure. RT-qPCR validation was performed in 158 patients with SCZ and 158 healthy controls. Associations with Positive and Negative Syndrome Scale (PANSS) scores and systemic inflammatory indicators were assessed using Pearson correlation and restricted cubic spline (RCS) analyses. Cell Counting Kit-8 (CCK-8) and apoptosis assays were used to detect the viability and apoptosis of MK-801 (dizocilpine)-treated SH-SY5Y cells. A dual-luciferase assay was used to confirm the binding relationships among circSUCO22-20, hsa-miR-3913-3p, and SLC7A11. The GSE25673 dataset (iPSC-derived neurons) was analyzed for downstream validation. circSUCO22-20 was significantly upregulated in both patients with SCZ and MK-801-treated SH-SY5Y cells. RCS analysis revealed significant non-linear dose–response correlations between circSUCO22-20 and white blood cell (WBC) count (Poverall = 0.035, Pnon-linear = 0.011), neutrophil count (NEUT) (Poverall = 0.018, Pnon-linear = 0.005), and lymphocyte percentage (LYMPH

The mechanisms underlying the contribution of toxic heavy metals to the risk of schizophrenia (SCZ) are not fully understood. This study aims to uncover molecular signatures of toxic heavy metal exposure via transcriptomics and metabolomics, and further reveal potential pathways linking exposure to toxic heavy metals to schizophrenia through mediation analysis. First, logistic regression, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and quantile g-computation (qgcomp) were used to assess the association between single and mixed toxic heavy metal exposure and SCZ risk. Subsequently, Orthogonal Two-Level Partial Least Squares (O2PLS) and Data Integration Analysis for Biomarker discovery using Latent Components (DIABLO) were used for integrated multi-omics to identify toxic heavy metal-related differentially expressed genes (DEGs) and metabolites. Finally, chain mediation models assessed the chain mediating roles of genes and metabolites in the relationship between mixed heavy metal exposure and SCZ. Logistic regression analysis preliminary revealed Cr and Pb exposure were associated with an increased schizophrenia risk (Pb: OR 1.81, 95 % CI 1.13-2.88; Cr: OR 2.28, 95 % CI 1.07-4.83), and As exposure with a decreased risk (OR 0.46, 95 % CI 0.30-0.98). Mixture models (BKMR, WQS, qgcomp) yielded preliminary evidence of revealed a positive correlation between exposure to toxic heavy metal mixtures and SCZ risk, with Pb being the primary contributor. Pathway enrichment analysis revealed that transcriptomic and metabolomic data related to toxic heavy metals were enriched in pathways involving tryptophan, glucose, and folate-mediated one-carbon metabolism. The combined O2PLS and DIABLO models identified five DEGs and metabolites associated with toxic heavy metal exposure. Additionally, our preliminary chain mediation analysis generated hypothesis-driven evidence that five DEGs and three metabolites may potentially mediate the impact of toxic heavy metal exposure on SCZ risk, highlighting potential molecular pathways significant for prevention and treatment strategies in high-risk populations.

The increased prevalence of loneliness and depression among adolescent and young adults is attributed to social media use, academic pressures, and future worries. This study aimed to model the relationship between loneliness and depressive symptoms in adolescents and investigate their dynamic interplay using cross-lagged panel analysis. A total of 649 secondary school students participated in our study. This study evaluated loneliness and depressive symptoms using an online questionnaire. This study constructed a network model to identify the core and bridge symptoms of loneliness using network analysis and employed a cross-lagged panel network model to investigate the dynamic relationship between loneliness and depressive symptoms. At both survey time points, the bridge symptom was U11 (T1: Strength = 1.68; T2: Strength = 1.46). The symptom with the highest influence on others was U20 (OEI = 1.04), while the symptom most influenced by others was U14 (IEI = 0.73). Timely interventions for adolescent loneliness can prevent depression and mitigate its severity in affected individuals.

Schizophrenia may be exacerbated by ambient air pollution. In this study, we aim to explore the association of air pollution with hospital admission for schizophrenia in Liuzhou, China. The daily concentration of air pollutants was gathered from an average of seven fixed monitoring sites in Liuzhou, while the daily admission data for schizophrenia was received from The Guangxi Zhuang Autonomous Region Brain Hospital. A Poisson generalized linear regression model in conjunction with a distributed lag nonlinear model was utilized to quantify the exposure-lag-response connection between ambient air pollution and schizophrenia hospitalization. The stratification analysis was then carried out by age, gender, and season. PM2.5, PM10, and SO2 was significantly associated with elevated number of schizophrenia hospitalization. We observed the largest single-day effects of PM2.5 at lag 17 day, PM10 at lag 17 day, and SO2 at lag 28 day, with the corresponding RRs being 1.01611 (95

Dysregulation of long non-coding RNAs (lncRNAs) is implicated in the pathophysiology of ischemic stroke (IS). However, the molecular mechanism of the lncRNA SERPINB9P1 in IS remains unclear. Our study aimed to explore the role and molecular mechanism of the lncRNA SERPINB9P1 in IS. This study revealed downregulation of the lncRNA SERPINB9P1 in the peripheral blood of IS patients, which was corroborated by the GSE140275 dataset. Furthermore, high lncRNA SERPINB9P1 expression was associated with lower National Institutes of Health Stroke Scale (NIHSS) scores and favorable outcome. Clinically, lncRNA SERPINB9P1 expression was correlated with inflammation and coagulation parameters in IS patients. Furthermore, lncRNA SERPINB9P1 silencing inhibited cell viability, induced apoptosis and inflammatory response under oxygen–glucose deprivation/reperfusion ; however, these effects were reversed upon its overexpression. Additionally, Chromatin Isolation by RNA Purification and mass spectrometry (CHIRP-MS) and western blot confirmed that the lncRNA SERPINB9P1 was involved in the pathological process of IS through binding to heat shock protein 2 (HSPA2). HSPA2 was upregulated in IS patients, and its protein interaction network was significantly enriched in IS-related pathways. In conclusion, the lncRNA SERPINB9P1 may ameliorate neurological injury in IS patients by interacting with the HSPA2 protein and engaging in IS-related pathways, providing new insights into treatment strategies for IS.

Exposure to metals has been associated with metabolic syndrome (MetS), yet the combined effects of multiple metals and their underlying molecular mechanisms remain unclear. This study aimed to investigate the associations between urinary metal levels and the risk of MetS and its components, and further explored potential mechanistic pathways. Data were obtained from 11,115 participants in the Guangxi Ethnic Minority Cohort Study of Chronic Diseases (2018-2019). Logistic regression, restricted cubic splines (RCS), Bayesian Kernel Machine Regression (BKMR), and generalized Weighted Quantile Sum (gWQS) models were applied to assess both individual and combined effects of 22 urinary metals on MetS and its components. Mechanistic insights were explored using the Comparative Toxicogenomics Database (CTD) and functional enrichment analyses, while mediation analysis was performed to identify MetS components that may act as intermediaries in the metal-MetS pathway. Our findings indicated that Mn, Cu, Zn, Pb, and Sb exposure was positively associated with MetS risk, whereas Mg, Ti, Rb, and Tl levels were inversely associated(P < 0.05). Both BKMR and gWQS models consistently revealed that combined exposure to metal mixtures increased the risk of MetS and its clinical components, with Zn and Mn identified as major contributors. Mechanistically, these metal mixtures may promote MetS development through pathways involving oxidative stress, metabolic dysregulation, vascular injury and adipose accumulation, with EGF, SOD1, HMGCR, VEGFA, CCL2, and ABCB1 serving as pivotal regulatory genes. At the clinical phenotype level, mediation analysis suggested that central obesity and reduced HDL-C were key mediators in the metal-MetS association. Collectively, this study provides novel epidemiological and mechanistic evidence linking mixed metal exposure to MetS, underscoring the need for enhanced environmental monitoring and public health interventions to mitigate the growing MetS burden.

Dysregulation of circular RNA (circRNA) is associated with neuropsychiatric disorders, yet its role in schizophrenia (SCZ) has rarely been reported. This study aimed to elucidate the clinical relevance and functional role of novel_circ_037817 in schizophrenia patients. qRT-PCR was used to measure the expression levels of novel_circ_037817, TNF‑α, and IL‑1β in PBMCs from 97 patients and 101 healthy controls. Spearman correlation and restricted cubic spline analyses were used to assess the associations among circRNA expression, PANSS scores, and immune and endocrine indices. In an SH‑SY5Y injury model induced by MK‑801, novel_circ_037817 expression was knocked down using siRNA, and its effects on cell viability and apoptosis were evaluated using CCK‑8 assays, flow cytometry, and western blotting. This study revealed that novel_circ_037817, TNF-α, and IL-1β expressions were significantly upregulated in PBMCs from SCZ patients compared with those from the control group. novel_circ_037817 was weakly correlated with both TNF-α (Rs = 0.418; P < 0.001) and IL-1β (Rs = 0.259; P < 0.001). novel_circ_037817 expression was positively correlated with PANSS positive total scores, delusions, conceptual disorganization, monocyte proportion, and thyroid-related indicators in SCZ patients but negatively correlated with unusual thought content. RCS analysis revealed a nonlinear dose–response relationship with PANSS negative total scores, lack of response, blunted affect, emotional withdrawal, and difficulty in abstract thinking. Under MK-801 treatment conditions, knockdown of novel_circ_037817 expression increased cell viability, inhibited apoptosis, increased Bcl-2 expression, and reduced Bax expression. In summary, this study provides the first evidence that novel_circ_037817 may be involved in SCZ and offers a novel target for SCZ intervention.

Background and aims: The prevalence of hyperuricemia (HUA) and metabolic syndrome (MetS) in the Zhuang minority had not been examined. We aimed to determine the prevalence of HUA and MetS, and explore the interrelationship among the serum uric acid to creatinine (SUA/Cr) ratio, MetS, and its components. Methods and results: A cross-sectional study was conducted with structured questionnaire and physical examination based on the Zhuang minority cohort. A Structural Equation Model was performed to examine the hypothesis link between the SUA/Cr ratio, MetS, and its components. 10,902 aged 35-74 years Zhuang minority adults were included. The total prevalence of HUA and MetS was 17.5% and 23.7%, respectively. The SUA/Cr ratio had a positive effect on MetS (the standardized coefficient Or was 0.311 in males and 0.401 in females). The SUA/Cr ratio was positively associated with obesity (Or = 0.215), dyslipidemia (Or = 0.177), and high blood pressure (Or = 0.034) in males and was positively associated with obesity (Or = 0.303), dyslipidemia (Or = 0.162), and hyperglycemia (Or = 0.036) in females. Conclusions: The prevalence of HUA in the aged 35-74 years Zhuang minority adults was high while the prevalence of MetS was relatively low. As HUA is an earlier -onset metabolic disorder and the SUA/Cr ratio had a positive effect on MetS and its components, the prevention measures of MetS should be strengthened. And the SUA/Cr ratio can be used as an early warning sign to implement the intervention measures of MetS. (c) 2023 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

BACKGROUND:The potential link between environmental pollutants, including metals, and schizophrenia development remains debated. This study aimed to explore the association between plasma levels of three non-essential metals-barium (Ba), tungsten (W), and uranium (U)-and schizophrenia risk among Chinese individuals.METHOD:We recruited a total of 221 patients and 219 healthy controls. Plasma levels of three non-essential metals were measured using inductively coupled plasma mass spectrometry. We employed unconditional logistic regression and Bayesian kernel machine regression (BKMR) to explore the relationship between exposure to multiple metals and the risk of schizophrenia.RESULTS:Logistic regression analysis revealed that the highest quartile (Q4) of W had an odds ratio (OR) of 1.87 (95% CI: 1.08-3.21) compared to the lowest quartile (Q1), with a significant P-trend of 0.017. For U, the ORs (95% CI) for Q2, Q3, and Q4 were 2.06 (1.19-3.56), 1.99 (1.15-3.44), and 1.74 (1.00-3.00), respectively. BKMR analyses revealed a progressive increase in the risk of schizophrenia with increasing cumulative levels of the three metals at concentrations below 35%, with U playing a major role in this association. U showed a non-linear positive correlation with schizophrenia, particularly at the 75th percentile level. Moreover, potential interactions were observed between W and Ba, as well as between W and U.CONCLUSION:Higher plasma W and U concentrations were positively associated with the risk of schizophrenia, which was potentially related to the severity of symptoms in schizophrenic patients.