ArticleFrontiers in endocrinology2025
Association between triglyceride glucose-waist height ratio index and overactive bladder: based on NHANES 2005-2018.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The triglyceride glucose-waist height ratio (TyG-WHtR) index is a useful marker for predicting the risk of cardiovascular and metabolic diseases. Metabolic diseases are known to be high-risk factors for overactive bladder (OAB). However, no studies have explored the association between the TyG-WHtR index and the risk of developing OAB. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) was utilized, and a weighted multivariate logistic regression analysis was conducted to investigate the relationship between TyG-WHtR and OAB. Subgroup analyses and interaction tests were also performed. Additionally, sensitivity analyses were conducted to validate the robustness of the findings. A smooth curve fitting and threshold effect analysis explored the nonlinear relationship between TyG-WHtR and the risk of developing OAB. The predictive value of the TyG-WHtR index for OAB was assessed using Receiver Operating Characteristic (ROC) curves, and the area under the ROC curve (AUC) was calculated. Results: A total of 14,652 adults aged 20 and above were included in this study. After weighting, the population size was estimated to be 197,598,146.7, among which 37,872,284.55 individuals were diagnosed with OAB. The median TyG-WHtR for the entire population was 4.98, while it was 5.44 for those with OAB. Weighted logistic regression analysis revealed a significant positive association between TyG-WHtR and the occurrence of OAB (OR=1.646; 95% CI: 1.562, 1.735; Conclusions: The TyG-WHtR index is significantly positively associated with the occurrence of OAB and could potentially serve as a novel risk predictor for OAB. Future research is needed to validate findings, explore causality, and improve early detection through multifactorial models across diverse populations.
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