Overactive Bladder Predictors in Women Extend Beyond Age, BMI

Age, body mass index (BMI), household income, age at first menstruation, and number of vaginal deliveries emerged as the strongest predictors of overactive bladder (OAB) in US women, according to a machine learning analysis published in JMIR Medical Informatics.¹

Reproductive Factors May Improve OAB Risk Prediction

OAB, characterized by urinary urgency often accompanied by frequency and nocturia, is more common in women than in men. The condition remains underdiagnosed, however, relative to its impact on quality of life.² Most existing risk models rely on demographic and metabolic variables while largely overlooking female reproductive history, a gap the study authors said left prediction tools incomplete.¹

To address this, they used machine learning and nationally representative data from the National Health and Nutrition Examination Survey (NHANES) to examine reproductive factors, including age at first menstruation, number of pregnancies, and menopausal status, as predictors of OAB among women. By integrating these factors with other health characteristics, the researchers aimed to develop a risk prediction model to identify women at higher risk of OAB and inform personalized prevention and intervention strategies.

Machine Learning Model Highlights OAB Predictors in Women

Specifically, they analyzed 7884 women across 4 NHANES cycles, spanning 2011-2018, comprising a training set (n = 5519) and a test set (n = 2365). The researchers applied least absolute shrinkage and selection operator regression to narrow an initial variable pool down to 14 candidates, then trained and benchmarked 11 machine learning algorithms.

A random forest model performed best, posting an area under the receiver operating characteristic curve of 0.8536 (95% CI, 0.8435-0.8638) in the training set and 0.6999 (95% CI, 0.6768-0.7212) in the test set. The authors acknowledged that the model did not demonstrate a high level of predictive accuracy but described its performance as moderate and suitable for preliminary screening.

Shapley Additive Explanation analysis, which quantifies how much each variable pushes a prediction up or down, ranked age, BMI, and number of vaginal deliveries as the top 3 contributors to OAB risk. These were followed by systolic blood pressure, glycohemoglobin, income-to-poverty ratio (PIR), and age at first menstruation. Higher age, BMI, blood pressure, and delivery counts all pushed risk upward, whereas higher PIR pushed it down.

Restricted cubic spline modeling showed these relationships were not straight lines but had thresholds. OAB risk rose sharply once BMI passed 33.659 kg/m², within the class II obesity range; the authors linked this pattern to mechanical bladder pressure and inflammatory signaling from visceral fat once that threshold was crossed.

Age showed a similar acceleration beyond 58 years, somewhat later than the 40-to-50 window tied to menopause in prior studies, a difference the authors attributed to variation in study populations. PIR showed an inverse relationship once it dropped below 3.299, with risk climbing further as income declined. The researchers said this pattern aligns with economic strain delaying diagnosis and worsening symptoms.

Reproductive variables carried their own dose-response patterns. Earlier age at first menstruation, particularly before age 13, was tied to elevated OAB risk that eased as age at first menstruation increased. The authors linked this finding to prolonged lifetime estrogen exposure and the downstream association between younger age at first menstruation and adult obesity. Vaginal delivery count showed a stepwise climb in risk beyond zero deliveries, which they attributed to pelvic floor nerve and connective tissue trauma from childbirth.

The authors positioned the model as a potential low-cost screening tool for community health settings and for outpatient gynecology or urology clinics. In those settings, a brief questionnaire covering age, BMI, income, age at first menstruation, and delivery history could flag women for closer follow-up.

Findings Highlight Need for Future Research

The findings build on a growing body of NHANES-based OAB research pointing to modifiable, nonurological risk factors. A recent analysis found that better overall cardiovascular health, measured through the American Heart Association's Life's Essential 8 score, was tied to significantly lower OAB odds, with BMI and sleep health as the largest contributors.³

That pattern echoes a shift already underway in specialty guidelines. The 2024 American Urological Association/Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction update formally recognized obesity as a relevant nonurological contributor to OAB and dropped mandatory step therapy in favor of shared decision-making.⁴

At the same time, the authors acknowledged the study's limitations, including that its cross-sectional, retrospective design cannot establish causality.¹ In addition, self-reported measures such as age at first menstruation carry some risk of recall bias. They framed these gaps as a road map for refining the model.

“While these limitations exist, they also point to valuable directions for future improvement,” the authors wrote. “To enhance the model’s performance, future studies could first incorporate richer predictors, such as detailed pelvic floor electrophysiological data, bladder ultrasound parameters, genetic markers related to connective tissue metabolism, and specific quality of life or behavioral psychology data.”

References

1. Huang G, Lin S. A machine learning-based model to predict overactive bladder risk among US women: evidence from the National Health and Nutrition Examination Survey 2011-2018. JMIR Med Inform. 2026;14:e80133. doi:10.2196/80133
2. Schabert VF, Bavendam T, Goldberg EL, Trocio JN, Brubaker L. Challenges for managing overactive bladder and guidance for patient support. Am J Manag Care. 2009;15(4 Suppl):S118-S122.
3. Joszt L. Better cardiovascular health tied to lower overactive bladder risk. AJMC^®. September 1, 2026. Accessed September 23, 2026. https://www.ajmc.com/view/better-cardiovascular-health-tied-to-lower-overactive-bladder-risk
4. Cameron AP, Chung DE, Dielubanza EJ, et al. The AUA/SUFU guideline on the diagnosis and treatment of idiopathic overactive bladder. J Urol. 2024;212(1):11-20. doi:10.1097/JU.0000000000003985