Although the dysregulation of bile acid (BA) composition has been associated with fibrosis progression, its precise roles in liver fibrosis is poorly understood. This study demonstrates that solute carrier family 27 member 5 (SLC27A5), an enzyme involved in BAs metabolism, is substantially downregulated in the liver tissues of patients with cirrhosis and fibrosis mouse models. The downregulation of SLC27A5 depends on RUNX family transcription factor 2 (RUNX2), which serves as a transcriptional repressor. The findings reveal that experimental SLC27A5 knockout (Slc27a5^−/−) mice display spontaneous liver fibrosis after 24 months. The loss of SLC27A5 aggravates liver fibrosis induced by carbon tetrachloride (CCI₄) and thioacetamide (TAA). Mechanistically, SLC27A5 deficiency results in the accumulation of unconjugated BA, particularly cholic acid (CA), in the liver. This accumulation leads to the activation of hepatic stellate cells (HSCs) by upregulated expression of early growth response protein 3 (EGR3). The re‐expression of hepatic SLC27A5 by an adeno‐associated virus or the reduction of CA levels in the liver using A4250, an apical sodium‐dependent bile acid transporter (ASBT) inhibitor, ameliorates liver fibrosis in Slc27a5^−/− mice. In conclusion, SLC27A5 deficiency in mice drives hepatic fibrosis through CA‐induced activation of HSCs, highlighting its significant implications for liver fibrosis treatment.

Objective: A meta-analysis is used to explore the relationship between polycystic ovary syndrome (PCOS) and the risk of Sleep disturbances.Method: Cochrane Library, PubMed, Embase, and Web of Science databases are searched by computer from their establishment to 1 May 2022. Review Manager 5.4 software is used for the meta-analysis.Results: A total of nine articles are included, with 1,107 subjects. The results show that PCOS is positively associated with the risk of Sleep disturbances. Comparing with the “PCOS group” (experimental group) with the “NON-PCOS group” (control group), the incidence of Sleep disturbances is higher (OR = 11.24, 95% CI: 2.00–63.10, Z = 2.75, p = 0.006); the Pittsburgh Sleep Quality Index (PSQI) scores of the PCOS group is higher than that of the NON-PCOS group (MD = 0.78, 95% CI: 0.32–1.25, Z = 3.30, p = 0.001); the Epworth Sleepiness Scale (ESS) scores of the PCOS group is higher than that of the NON-PCOS group (MD = 2.49, 95% CI: 0.80–4.18, Z = 2.88, p = 0.004); Apnea hypopnea index (AHIs) in the PCOS group are higher than those in the NON-PCOS group (MD = 2.68, 95% CI: 1.07–4.28, Z = 3.27, p = 0.001); the sleep efficiency of the PCOS group is lower than that of the NON-PCOS group (MD = -5.16, 95% CI: 9.39–-0.93, Z = 2.39, p = 0.02); the sleep onset latency of the PCOS group is higher than that of the NON-PCOS group (MD = 2.45, 95% CI: 1.40–3.50, Z = 4.57, p < 0.001); and the Rapid Eyes Movement (REM) sleep in the PCOS group is higher than that in the NON-PCOS group (MD = 17.19, 95% CI: 11.62–55.76, Z = 6.05, p < 0.001). The studies included in each analysis have publication biases of different sizes. After subgroup analysis and sensitivity analysis, the heterogeneity of each study in the meta-analysis is reduced, the bias is reduced accordingly, and the stability of the results can be maintained.Conclusion: PCOS is positively associated with the risk of Sleep disturbances. In order to reduce such risk, attention should be paid to the role of PCOS management, and PCOS prevention and treatment should be actively carried out.

Epidemiological studies have consistently demonstrated a robust association between long-term exposure to air pollutants and respiratory diseases. However, establishing causal relationships remains challenging due to residual confounding in observational studies. In this study, Mendelian randomization (MR) analysis was used to explore the causal and epigenetic relationships between various air pollutants and common respiratory diseases. We utilized a two-sample Mendelian randomization (TSMR) approach to explore the impact of PM_2.5, PM_2.5-10, PM₁₀, NO₂, and NO_X on the incidence of nine respiratory diseases using data from large-scale European GWAS datasets (N = 423,796-456,380 for exposures; N = 162,962-486,484 for outcomes). The primary analytical method was inverse variance weighting (IVW), which explored the exposure-outcome relationship using single nucleotide polymorphisms (SNPs) associated with air pollution. Sensitivity analyses, including MR-Egger regression and leave-one-out analyses, were employed to ensure result consistency. Multivariate MR (MVMR) was performed to adjust for potential smoking-related confounders, such as cigarettes per day, household smoking, exposure to tobacco smoke at home, ever smoked, second-hand smoke, smoking initiation, and age at smoking initiation, as well as the independent effects of each air pollutant. Additionally, methylation and enrichment analyses were conducted to further elucidate the potential effects of air pollution on respiratory diseases. TSMR analysis revealed that exposure to PM_2.5 increased the risk of early-onset chronic obstructive pulmonary disease (COPD), pneumonia, pulmonary embolism and lung cancer. PM_2.5-10 exposure was associated with an increased risk of lung cancer, while PM₁₀ exposure increased the risk of pneumonia and bronchiectasis. NO₂ exposure was associated with increased risks of lung cancer and adult asthma. Importantly, these associations remained robust even after controlling for potential tobacco-related confounders in the MVMR analyses. In the MVMR analysis adjusting for other pollutants, significant associations persisted between PM_2.5 and early-onset COPD, and between PM₁₀ and pneumonia. Genetic co-localization analyses confirmed that methylation of PM_2.5-associated CpG loci (cg11386376 near c1orf175, cg11846064 near rfx2, cg18612040 near rptor, and cg19765378 near c7orf50) was associated with an increased risk of early-onset COPD. Finally, SNPs significantly associated with exposure and outcome were selected for enrichment analysis. Our findings suggest that exposure to air pollutants may play a causal role in the development of respiratory diseases, with a potential role of epigenomic modifications emphasized. Strengthening comprehensive air pollution regulations by relevant authorities could potentially mitigate the risk of these diseases.

Background: Obstructive sleep apnea (OSA) is a serious disease with a high prevalence in the general population. The purpose of this study is to explore the effectiveness of the GOAL questionnaire in the clinical screening of OSA and compare it with other existing screening tools.Materials and methods: Outpatients and inpatients who underwent polysomnography (PSG) examination at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University from January 2013 to November 2016 were analyzed retrospectively. The basic data such as demographic, medical history, etc., and PSG data of the patients were collected, and the sensitivity, specificity, positive predictive value, negative predictive value and area under the curve (AUC) of GOAL and five other screening scales (the NoSAS score, Epworth Sleepiness Scale, the Berlin questionnaire, STOP, and STOP-Bang questionnaire) were calculated.Results: Data from 2,171 participants (1,644 male; 78%) were analyzed there were 1,507 OSA patients [Apnea Hypopnea Index (AHI) ≥ 5 events/h] among them, accounting for about 69.415%. No matter which cut-off point (AHI ≥ 5, 15 and 30 events/h), the AUC score reveals that GOAL questionnaire had comparable screening ability to the NoSAS and STOP-BANG, and performed better than the ESS, and the AUC scores of the STOP questionnaire and Epworth Sleepiness Scale (ESS) were both lower than 0.7. When the cut-off point of the AHI was 5 events/h, the AUC of GOAL was the highest at 0.799 (0.781–0.816), and its sensitivity was the highest at 89.1%. The sensitivity levels of the NoSAS score and STOP-Bang questionnaire were 67.4 and 78.8% respectively, while ESS and the Berlin questionnaire have higher specificity (70.2 and 72.3% respectively) but lower sensitivity (49.3 and 60.0% respectively).Conclusion: GOAL is a free, efficient and easy to manage tool with a screening ability comparable to NoSAS and STOP-Bang, and better than that of ESS.

Purpose: Obstructive sleep apnea (OSA) had been associated with asthma in observational studies, but the effect of OSA on the onset of asthma in childhood or adulthood remains unclear, and the causal inferences have not been confirmed. This study aims to investigate the potential causal association between OSA with asthma, including different age-of-onset subtypes, providing reliable basis for the clinical treatment of OSA and asthma. Patients and Methods: Causality between OSA and asthma was assessed using a two-sample bi-directional Mendelian randomization (MR) analysis. OSA data were obtained from the FinnGen consortium R9, while asthma and its subtypes (adult-onset asthma, child-onset asthma, and moderate-to-severe asthma) were sourced from the IEU OpenGWAS project. The inverse-variance weighted (IVW) method was chosen as the primary analysis and was complemented by various sensitivity analyses. The MR-PRESSO outlier test was employed to systematically identify and remove outlier variants, mitigating heterogeneity and potential effects of horizontal pleiotropy. Results: The MR analyses provided evidence of genetically predicted OSA having a promoting effect on child-onset asthma (OR,1.49; 95% CI, 1.05– 2.11; P=0.025) and moderate-to-severe asthma (OR,1.03; 95% CI, 1.00– 1.06; P=0.046). However, no causal association between OSA with asthma and adult-onset asthma was observed. Conclusion: Our study revealed a causal association between OSA and child asthma, but not in adults. Moderate-to-severe asthma may have a potential promoting effect on OSA. These findings underscore the importance of age-specific considerations in managing asthma and suggests the need for personalized approaches in clinical practice.