Associations between overactive bladder and sleep patterns: a cross-sectional study based on 2007-2014 NHANES.

OBJECTIVES: To determine whether relationship exists between overactive bladder (OAB) and sleep patterns through the cross-sectional study. PATIENTS AND METHODS: Patients from the National Health and Nutrition Examination Survey (NHANES) 2007-2014 were included in this study. Data were extracted through questionnaires, including demographics, dietary and health-related behaviors, body measurements and disease information. Three sleep factors were included to aggregate overall sleep scores, ranging from 0 to 3. A sleep score of 0 to 1, 2 or 3 was expressed as a bad, intermediate or healthy sleep pattern, respectively. The Overactive Bladder Symptom Score (OABSS) scale was applied to quantify the severity of OAB for each participant. Weighted logistic regression models were used to investigate the associations between sleep and OAB. RESULTS: A total of 16,978 participants were enrolled in this study. The relationship between OAB and sleep patterns was statistically significant. After fully adjusting for confounding factors, the OAB risk of patients with intermediate and poor sleep patterns obviously increased by 26% and 38%, respectively, and mild (OR = 1.21, 95% CI [1.03,1.42]), moderate (OR = 1.45, 95% CI [1.27,1.66]) and severe (OR = 1.57, 95% CI [1.18,2.09]) OAB were significantly associated with sleep pattern grouping. The prevalence of OAB is significantly higher in patients with bad sleep patterns, and vice versa. CONCLUSION: This study indicated that there is a positive relationship between OAB and worse sleep-related issues.

Association between 23 drugs and Parkinson's disease: A two-sample Mendelian randomization study.

BACKGROUND: Parkinson's disease (PD) is a common degenerative nervous system disease. At present, there are certain limitations in various treatment options aimed at preventing or delaying the progression of PD. Therefore, the exploration of new drugs for PD is beneficial. Mendelian randomization (MR) analysis can be used to explore the association between drugs and diseases. In this study, MR analysis was adopted to investigate the causal relationship between 23 drugs and PD. These drugs have been approved for the treatment of different diseases, such as salicylic acid and derivatives (collectively called salicylates, e.g., aspirin, used for fever and pain relief), antithrombotic agents (e.g., warfarin, aspirin, used for preventing thrombotic events). METHODS: The GWAS data for the 23 drugs were obtained from the UK Biobank (UKB) project, while the GWAS data for PD were sourced from FinnGen. Single-Nucleotide Polymorphisms (SNPs) were selected as instrumental variables (IVs). We first performed a series of quality control steps (including MR-PRESSO) to select the appropriate SNPs. Two-sample MR analysis was performed using five different methods, including inverse variance weighting (IVW) with random-effects model, weighted median, MR-Egger, simple model, and weighted model. At the same time, sensitivity analysis was carried out using the MR-Egger and Cochran's Q test to ensure the authenticity and reliability of the results. RESULTS: In MR-PRESSO, salicylates and antithrombotic agents showed statistically significant associations with PD, respectively. In the main MR analysis (IVW), there was a negative causal relationship between salicylates and PD (OR = 0.73, 95% CI = 0.54-0.98, p = .039). Similarly, there was a negative causal relationship between antithrombotic agents and PD (OR = 0.70, 95%CI = 0.52-0.96, p = .027). No statistically significant association was found between the remaining 21 drugs and PD. CONCLUSION: This MR study demonstrated that salicylates and antithrombotic agents can reduce the risk of PD, thus providing a novel avenue for future drug exploration in PD.