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Updated: Jan 7, 2026

Evaluation of Biomaterials for Bladder Augmentation using Cystometric Analyses in Various Rodent Models
Published on: August 9, 2012
Low muscle, high leak? The aMFR wake-up call for women's bladders!
Jingyi Zhou1, Donghai Zhang1, Ruomeng Bi1
1Department of Clinical and Translational Research Center, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine Tongji University Shanghai China.
Objective:
This study aimed to determine the association between appendicular muscle-to-fat ratio (aMFR) and the risk of urinary incontinence (UI) in women.
Methods:
A total of 4393 participants recruited from the National Health and Nutrition Examination Database (NHANES) from 2011 to 2018 were included in this study. We screened variables using least absolute shrinkage and selection regression, multivariate logistic regression, dose-response curve and nomogram to estimate the relationship between aMFR and UI. The accuracy and discrimination of the nomogram were validated using calibration, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves.
Results:
Participants with UI had a lower aMFR than those without (I [0.57, interquartile range [IQR]: 0.49, 0.69] vs 0.63, IQR: 0.54, 0.77, P < 0.05). Dose-response curves and multivariate logistic regression showed a negative correlation between the aMFR and the risk of developing UI [adjusted odds ratio (aOR) = 0.35, 95% confidence interval (CI) = 0.226-0.537, P < 0.001]. Validation of the calibration curves, ROC curves and DCA curves revealed the good predictive ability of the UI nomogram, and the area under the ROC curve in the predictive model was 0.668 (95% CI = 0.641-0.695) in the training set and 0.660 (95% CI = 0.633-0.687) in the testing set, which demonstrated the good performance of the model.
Conclusion:
A low aMFR was significantly associated with an increased risk of UI in women in the US and could be included in risk prediction models for female UI.
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