Association between early age at menarche and overactive bladder risk in women: insights from NHANES and Mendelian randomization analysis

Introduction

Overactive bladder (OAB) is a prevalent urological condition characterized by urinary urgency as the hallmark symptom, often accompanied by increased urinary frequency and nocturia, with or without urge urinary incontinence, as defined by the International Continence Society (1). The primary pathophysiological driver is detrusor muscle overactivity, though contributions from other urethrovesical dysfunctions, such as impaired bladder compliance or urethral instability, are also recognized (2). Epidemiologically, OAB poses a significant global health challenge, with U.S. prevalence among adults rising from 16.5% in 2000–2001 to 38.5% in 2021–2022, potentially reflecting an aging population and enhanced diagnostic criteria (3,4). In Europe, studies report a 16.6% prevalence among adults aged 18–40 years, with women exhibiting a markedly higher incidence than men (5). This rising prevalence translates into substantial clinical and economic burdens, including reduced quality of life, heightened risks of falls and fractures, and increased healthcare expenditures (6). Proposed mechanisms implicate estrogen fluctuations in modulating bladder sensory nerve excitability and detrusor contractility, yet the precise molecular pathways driving OAB remain elusive, necessitating further investigation into predisposing factors (7).

Early age at menarche (AAM) is increasingly implicated in long-term health outcomes, particularly through its association with metabolic dysregulation, elevated body mass index (BMI), and aberrant adiposity distribution (8-10). These conditions may compromise bladder function through intertwined hormonal and inflammatory pathways. Early AAM is thought to disrupt hypothalamic-pituitary-gonadal axis homeostasis due to prolonged estrogen exposure during puberty, which predisposes individuals to obesity and related disorders such as type 2 diabetes and cardiovascular disease (11-13). The female urinary tract, embryologically derived and highly sensitive to sex steroid hormones, may be particularly susceptible to these hormonal perturbations, potentially altering bladder innervation and contractility (14). Despite these associations, no studies have directly explored the relationship between AAM and OAB, and the potential mediating role of obesity in this context remains untested. Understanding these connections is critical, as early AAM may represent an early-life indicator that identifies women at risk for whom targeted interventions on modifiable mediators (e.g., obesity) may be implemented to reduce the risk of urological dysfunction.

Obesity, a well-documented correlation of early AAM, is a significant risk factor for OAB, with observational studies indicating that elevated BMI exacerbates detrusor overactivity and aberrant bladder afferent signaling, particularly in women (15,16). Mechanistically, obesity-induced chronic inflammation and oxidative stress may impair bladder function by promoting detrusor hyperactivity and altering sensory nerve thresholds (17). Despite these insights, the causal pathways linking AAM, obesity, and OAB remain speculative, prompting the hypothesis that early AAM indirectly increases OAB risk by promoting obesity, mediated through elevated BMI. This hypothesis is supported by three key premises: (I) early AAM induces metabolic dysfunction via prolonged estrogen exposure; (II) the female urinary tract exhibits heightened sensitivity to hormonal fluctuations; and (III) obesity-driven inflammatory cascades potentiate detrusor dysfunction (18-22). Testing this hypothesis requires robust analytical approaches to disentangle confounding variables and establish causality, addressing a critical gap in the urological literature.

To elucidate these relationships, this study leverages the National Health and Nutrition Examination Survey (NHANES) database to investigate the association between AAM and OAB and to quantify the mediating role of BMI in this pathway. Additionally, we employ Mendelian randomization (MR) analysis, utilizing genetic variants from the Integrative Epidemiology Unit (IEU) Open Genome-Wide Association Study (GWAS) database, to validate causal relationships while minimizing residual confounding inherent in observational studies. Our objectives are: (I) to evaluate the independent association between AAM and OAB; (II) to estimate the proportion of the AAM-OAB association mediated by BMI; and (III) to confirm causality through MR analysis. By integrating cross-sectional and genetic approaches, this study aims to provide novel insights into the etiological pathways linking AAM, obesity, and OAB, with potential implications for targeted prevention and therapeutic strategies in urology. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-415/rc).

Methods

Study design and participants

This study utilized publicly accessible de-identified summary-level data. The research design comprised two phases. The initial phase of the study involved the examination of the association between AAM and OAB, utilizing data from the NHANES database. The second phase adopted a two-sample MR approach to assess causal relationships between AAM and OAB. Three core MR assumptions were validated: (I) genetic variants must be strongly associated with exposures; (II) single-nucleotide polymorphisms (SNPs) must be independent of unmeasured confounders; and (III) genetic instruments influence outcomes exclusively through exposures.

This cross-sectional analysis included data from four NHANES cycles [2011–2018]. As illustrated in Figure 1, the exclusion criteria encompassed participants under the age of 20 years, those who identified as male, individuals in the state of pregnancy, and those with missing data on OAB/AAM. The final cohort comprised 9,647 participants. All original research received informed consent and ethical approval. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 Flow chart for the selection of included sample. NHANES, National Health and Nutrition Examination Survey; OAB, overactive bladder.

OAB definition

The OAB symptoms of patients was conducted using the urology questionnaires from the NHANES, and the severity of the condition was quantified using the Overactive Bladder Symptom Score (OABSS) (23). The OABSS integrates the frequency and severity of symptoms (e.g., urinary incontinence episodes, nocturia) into a composite score. To align NHANES data with the OABSS framework, we adopted a modified scoring approach based on Zhu et al. (24) establishing a threshold of ≥3 points to confirm an OAB diagnosis for epidemiological purposes. This cut-point balances sensitivity and specificity in population-based studies, though it is lower than thresholds used in clinical settings.

AAM definition

AAM was self-reported through the NHANES sexual health questionnaire item: the specific question posed was, “How old were you when you had your first menstrual period?”. Responses from females aged 12 years and older were analyzed (with reported AAM ranging from 7–22 years). In this study, AAM was analyzed both as a continuous variable and as a categorical variable, stratified into quartiles (Q1: <12 years; Q2: 12 years; Q3: 13–14 years; Q4: >14 years).

Covariates

In this study, we integrated a wide array of variables from various NHANES modules by leveraging databases and clinical expertise. These variables included laboratory examination results such as glycohemoglobin (as a marker of glycemic control, potentially relevant to OAB risk), lymphocyte count, white blood cell count (WBC), platelet count, monocyte count, and neutrophil count. Demographic information like age, the ratio of family income to poverty threshold (PIR), education, and race was also incorporated. Additionally, data derived from questionnaires, including smoke, alcohol consumption, and physical activity levels, as well as physical examination metrics such as systolic blood pressure (SBP), BMI, and diastolic blood pressure (DBP), were included. In addition, given prior evidence identifying number of pregnancies (NPS) as an independent risk factor for OAB (25), we also included NPS in the analyses.

MR

MR is a statistical method that uses genetic variants as tools to assess whether an exposure (e.g., AAM) causally affects an outcome (e.g., OAB). By leveraging genetic data, MR minimizes confounding and reverse causation, which are common in observational studies. Genetic association data for AAM and OAB were sourced from the IEU OpenGWAS project. Summary statistics for AAM (GWAS ID: ukb-b-3768) included 243,944 European participants and 9,851,867 SNPs. For OAB (26,27), the phenotype “Bladder: Calcified/Contracted/Overactive” (GWAS ID: ukb-b-373) was selected, comprising 463,010 samples and 9,851,867 SNPs. Instrumental variables (IVs) were identified using genome-wide significant SNPs (P<5.0×10⁻⁸). To minimize linkage disequilibrium (LD), clumping was performed with stringent parameters (r²<0.001, genetic distance =10,000 kb). SNPs significantly associated with exposures were extracted from outcome-specific GWAS datasets to construct IVs. We used a two-sample MR approach, which analyzes genetic data from two separate datasets—one for the exposure and one for the outcome—to estimate causal effects. Two-sample MR analyses were conducted using the inverse-variance weighted (IVW) method as the primary approach, which combines the effects of multiple genetic variants to estimate causality, complemented by MR-Egger, weighted median, weighted mode, and simple mode. Horizontal pleiotropy, where genetic variants affect the outcome through pathways other than the exposure, was evaluated using MR-Egger intercepts. Sensitivity analyses included MR-PRESSO outlier correction and leave-one-out validation to ensure the robustness of our findings.

Statistical analysis

Variables with >30% missing values were excluded; remaining variables underwent multiple imputation using the R ‘mice’ package, detailed information on this process can be found in Table S1 and Figure S1. The Shapiro-Wilk test was used to assess normality of continuous variables in baseline analyses. Normally distributed variables were expressed as mean ± standard deviation (SD), non-normally distributed variables as median with interquartile range (IQR), and categorical variables as frequencies with percentages. Multivariable logistic regression analyses were performed to evaluate associations between AAM and OAB occurrence, calculating adjusted odds ratios (ORs) with 95% confidence intervals (CIs), using AAM as both a continuous variable and a categorical variable divided into quartile intervals. To account for the complex survey design, analyses incorporated sampling weights using the R package “survey”. Specifically, as our study includes data from four NHANES cycles, we applied the weight variable “WTMEC2YR” adjusted by a factor of 1/4 (1/4 * WTMEC2YR) to account for the smallest sample size across the combined cycles, as recommended by NHANES guidelines. The analytical approach incorporated three progressively adjusted models: Model 1 remained unadjusted for covariates, Model 2 adjusted for demographic factors (age, gender, race), and Model 3 adjusted for all covariates including clinical and laboratory parameters. Weighted logistic regression was further employed to assess whether the results are nationally representative.

To investigate the association between AAM and OAB across population stratifications, subgroup analyses and interaction effect assessments were conducted. Restricted cubic spline (RCS) analyses with five knots were employed to assess potential non-linear dose-response relationships between AAM and both OAB and BMI. Previous studies have shown that obesity is related to both the dependent and independent variables in this study. Therefore, we conducted a linear regression analysis to examine the relationship between AAM and BMI. The “mediation” package in R was utilized to investigate the mediating effect of BMI. The statistical analysis was conducted using R 4.4.2. Statistical significance was defined as a two-tailed P value <0.05.

Results

Population characteristics of NHANES

The baseline characteristics of the study participants are presented in Table 1, with weighted baseline characteristics detailed in Table S2. The study population included a total of 9,647 individuals, of whom 2,971 (30.8%) were classified as positive for OAB. While the median AAM was identical between groups [13.0 years (IQR, 12.0–14.0 years)], a statistically significant difference was observed in AAM distribution by quantiles (P=0.009). The OAB group had a higher proportion of participants in the lowest AAM quantile (23.96% vs. 21.02%) and lower proportions in the third (22.75% vs. 24.40%) and fourth (27.47% vs. 28.64%) quantiles compared to the non-OAB group. Participants with OAB were older [median age 60.0 (IQR, 47.0–71.0) vs. 46.0 (32.0–61.0) years; P<0.001] and had lower median PIR [1.85 (1.02–3.48) vs. 2.06 (1.07–4.07); P<0.001]. The OAB group exhibited higher median BMI [30.8 (26.0–36.25) vs. 27.9 (23.6–33.3) kg/m²; P<0.001], and higher median SBP [126.0 (114.0–140.0) vs. 118.0 (108.0–131.0) mmHg; P<0.001]. Race distribution, education levels, marital status, smoking history, and diabetes status all showed statistically significant differences between groups (P<0.001). No significant differences were observed in WBC count, neutrophil count, alcohol consumption, or physical activity levels.

Association between AAM and OAB

The results of multivariable logistic regression analyses are shown in Table 2. In the initial unadjusted Model 1 analysis, a 1-year increase in AAM was linked to a 4% decrease in OAB risk, with an OR of 0.96 (95% CI: 0.93–0.98) and P<0.001. After adjusting for age, race, marital status, and education in Model 2, the risk reduction increased to 8% per 1-year increase in AAM (OR =0.92, 95% CI: 0.90–0.95, P<0.001). Each 1-year increase in AAM was linked to a 5% decrease in OAB risk in Model 3, which controlled for all other variables (OR =0.95, 95% CI: 0.93–0.98, P<0.001). Consistent findings were observed in the weighted analyses (Table S3): weighted Model 1 yielded OR =0.95 (95% CI: 0.92–0.99, P=0.007); weighted Model 2 yielded OR =0.92 (95% CI: 0.89–0.96, P<0.001); and weighted Model 3 yielded OR =0.96 (95% CI: 0.92–0.99, P=0.02), corroborating the inverse association between AAM and OAB risk. As demonstrated in Figure S2, to assess the discriminative ability of Model 3, a receiver operating characteristic curve analysis was performed. The area under this curve was 0.721, indicating good discriminative ability.

As demonstrated in Figure 2, stratified analyses were conducted by education level, marital status, BMI, race, smoking, physical activity, alcohol use, age, and diabetes to evaluate the AAM-OAB association. The results of the subgroup analysis indicated statistically significant associations in the overall sample (P=0.001). Interaction tests demonstrated no statistically significant effect modification across subgroups (all P for interaction >0.05), suggesting the presence of consistent associations.

Figure 2 Association between AAM and OAB in different subgroups, weighted. Analyses were adjusted for age, race, marital status, education, physical activity, BMI, smoking, alcohol consumption, and diabetes, except the subgroup variable. AAM, age at menarche; BMI, body mass index; OAB, overactive bladder.

The RCS analysis (Figure 3) revealed a significant association between AAM and OAB (P-overall <0.001). Given that the formal test for non-linearity was not statistically significant (P-non-linear =0.107), a predominantly linear trend was supported. Referenced to an AAM of 10.122 years (OR =1.0), the estimated odds ratio for OAB exceeded 1.0 for AAM values below this threshold, suggesting an increased risk with earlier menarche. Conversely, for AAM values above 10.122 years, the OR generally decreased, falling below 1.0. Notably, for AAM values greater than approximately 15 years, a statistically significant protective effect was observed, with the 95% CI lying entirely below 1.0, indicating a reduced risk of OAB with later menarche. The 95% CI were wider at the extreme ends of the AAM range, indicating less certainty in the estimated odds ratios for those ages that were very low or very high. In contrast, the CI were narrower closer to the reference point (AAM =10.122 years), suggesting more precise estimates in this central range.

Figure 3 RCS curve illustrating the dose-response relationship between AAM and the odds of OAB. The solid red line represents the estimated OR, and the shaded pink area denotes the 95% CI. The odds ratios are calculated with AAM at 10.122 years as the reference (OR =1.0), indicated by the vertical pink line. The horizontal dashed grey line represents an OR of 1.0. P-overall <0.001 indicates a significant overall association, while P-non-linear =0.107 suggests that the test for non-linearity was not statistically significant. AAM, age at menarche; CI, confidence interval; OAB, overactive bladder; OR, odds ratio; RCS, restricted cubic spline.

Association between AAM and BMI

The relationship between AAM and BMI was significant. As shown in Table S4, after adjusting for all covariates, AAM had a significant negative correlation with BMI, with each one-unit increase in AAM associated with an average decrease of approximately 0.57 units in BMI. The RCS analysis results are presented in Figure S3, indicating a significant non-linear relationship between AAM and BMI. Within the lower range of AAM, BMI significantly decreased as AAM increased. However, after AAM exceeded 14.29 years, this downward trend weakened and there might even be a slight upward trend.

Association between BMI and OAB

As shown in Table S5 and Figure S4, a significant association between BMI and OAB exists. After adjusting for all covariates and categorizing BMI, individuals with a BMI of 30 or higher exhibit a significantly increased risk of OAB compared to those with a BMI less than 18.5 (OR =1.62, 95% CI: 1.11 to 2.42, P=0.01). No significant differences in OAB risk were observed among individuals in the other BMI categories. According to the RCS research, OAB and BMI had a positive nonlinear association, with the risk of OAB progressively rising as BMI rose. Notably, when BMI exceeded 37.115, the OR value exceeded 1, indicating a further increase in OAB risk at higher BMI levels.

Mediation analysis

Previous studies have identified obesity as a critical factor in OAB development. Given the established correlation between early AAM and obesity, we conducted a mediation analysis to ascertain the mediating effect of BMI between AAM and OAB. The results of the mediation analysis are presented in Table 3. The total effect was observed between AAM and OAB (total effect =−0.015, 95% CI: −0.021 to −0.010, P<2×10⁻¹⁶). The mediating effect of BMI was found to be 30.89%, with an indirect effect of −0.004 (95% CI: −0.005 to −0.004, P<2×10⁻¹⁶). While these findings suggest partial mediation by BMI, further experimental studies are needed to elucidate the underlying mechanisms (Figure 4).

Figure 4 Mediation analysis of BMI on AAM and OAB. AAM, age at menarche; BMI, body mass index; CI, confidence interval; DE, direct effect; IE, indirect effects; OAB, overactive bladder.

MR of the causal effect of AAM on OAB

In the present study, 156 SNPs were selected for analysis. Subsequent to the selection of the aforementioned SNPs, horizontal pleiotropy tests were conducted. The MR-Egger intercept (−6.414848×10⁻⁶, P=0.87) and the MR-PRESSO global test (global test =155.06, P=0.61) demonstrated an absence of pleiotropy. IVW analysis confirmed a causal relationship (OR =0.998, 95% CI: 0.996–0.999, P=0.008). The results from five MR methods are displayed in Table S6 and Figure 5A, with individual SNP effects displayed in Figure 5B. Heterogeneity tests (IVW: Cochran’s Q =145.306, Q_df =153, P=0.66; MR-Egger: Q =145.277, Q_df =152, P=0.64). These results indicated the absence of heterogeneity (Figure 5C). The leave-one-out sensitivity analysis yielded consistent results (Figure 5D).

Figure 5 MR analysis of the association between AAM and OAB. (A) Scatter plot from genetically predicted AAM on OAB. (B) Forest plot of the MR effect analysis of AAM on OAB. (C) Funnel plots from genetically predicted AAM on OAB. (D) Leave-one-out sensitivity analysis of AAM on OAB using MR. AAM, age at menarche; IV, instrumental variable; MR, Mendelian randomization; OAB, overactive bladder; SE, standard error; SNP, single-nucleotide polymorphism.

Discussion

By employing a nationally representative sample of U.S. adult women [2011–2018], we identified a significant link between AAM and OAB. Multivariate logistic regression analyses demonstrated that earlier AAM was associated with increased OAB prevalence. When analyzed as a categorical variable stratified into quartiles (Q1: <12 years; Q2: 12 years; Q3: 13–14 years; Q4: >14 years), AAM in Q2 and Q4 demonstrated significantly reduced OAB risk compared to Q1. RCS analyses revealed a linear inverse association between AAM and OAB, consistent with logistic regression outcomes. Subgroup analyses confirmed the robustness of this association across all stratified populations. Mediation analysis revealed that BMI partially mediates this relationship, accounting for 30.89% of the association, while MR analysis, utilizing genetic variants from the IEU OpenGWAS database, corroborated a causal link, affirming early AAM as an independent risk factor for OAB.

Previous studies have established AAM as a determinant of obesity (8,28). A meta-analysis reported a mean BMI increase of 1.13 kg/m² in women with early AAM (<12 years) compared to those with AAM at ≥12 years (29). Asrullah et al. further demonstrated that each 1-year decrease in AAM corresponded to increases of 0.25 kg/m² in BMI and 0.6 cm in waist circumference—a pattern consistent with our findings of a nonlinear inverse relationship between AAM and BMI (0.57 kg/m² reduction per year of AAM delay) (30). Mechanistically, early AAM is driven by elevated estrogen levels, which are critical for initiating menarche (31). Estrogen promotes adipocyte differentiation and subcutaneous fat deposition, particularly in the hips and thighs, increasing total body fat percentage (32). Furthermore, premature menarche may disrupt leptin signaling, leading to appetite dysregulation and excessive energy intake, while childhood obesity can accelerate AAM through positive hormonal feedback loops (33,34). These bidirectional interactions underscore the complexity of the AAM-obesity relationship and its relevance to OAB pathogenesis.

Obesity mediates OAB pathogenesis through multiple pathways. Visceral fat accumulation, particularly abdominal adiposity, exerts mechanical pressure on the bladder and pelvic floor, compromising urinary function (35,36). Obesity induces chronic low-grade inflammation through proinflammatory cytokine secretion from visceral adipose tissue, which may compromise pelvic floor integrity and urethral sphincter function (37-39). Hormonal alterations, including insulin resistance and reduced estrogen levels, further exacerbate urinary dysfunction (40,41). Additionally, obesity-related psychological comorbidities, such as anxiety and depression, may amplify pelvic floor muscle tension and bladder hypersensitivity, potentially exacerbating OAB symptoms (42). Our mediation analysis, which identified BMI as a partial mediator (30.89% proportion mediated), suggests that obesity is a significant but not exclusive pathway linking early AAM to OAB. This finding highlights the need to explore additional mediators, such as hormonal or inflammatory biomarkers, to fully elucidate the AAM-OAB pathway.

The connection between AAM and OAB has not yet been the subject of any direct research. However, prior research indicates that the female reproductive tract and lower urinary tract share a common embryological origin in the urogenital sinus, rendering both systems sensitive to female sex hormones (43). Estrogen and progesterone receptors have been identified in the vagina, urethra, bladder, and pelvic floor muscles (44). Notably, women with premature AAM often exhibit anovulatory cycles and lower progesterone levels compared to those with normal AAM (45). An animal study by Fernández et al. demonstrated that progesterone induces relaxation of the porcine bladder neck through cGMP/NO pathways, involving activation of BKCa and KATP channels and inhibition of extracellular Ca²⁺ influx via L-type voltage-operated calcium channels, mediated by smooth muscle progesterone receptors (46). We propose that lower progesterone levels in women with early AAM may contribute to detrusor overactivity by impairing bladder relaxation, though this hypothesis is speculative and requires validation in human studies. Further research is required to elucidate the role of progesterone in human bladder function.

To our knowledge, this study is the first to directly link early AAM to increased OAB risk, offering novel insights into the developmental origins of urological dysfunction. The integration of NHANES data with MR analysis represents a significant advancement, as it combines the strengths of observational epidemiology with genetic causal inference. Our findings suggest that early AAM may serve as an early-life risk marker for OAB, with implications for preventive strategies, such as targeted weight management in women with early menarche. The partial mediation by BMI underscores the importance of addressing obesity as a modifiable risk factor, potentially through lifestyle interventions or pharmacological approaches to mitigate OAB risk.

There are several notable strengths in this study First, it is the first to establish early AAM as a novel risk factor for OAB, filling a critical gap in the urological literature. Second, the use of a large, nationally representative NHANES cohort (N=9,647) enhances the generalizability of our findings to U.S. women. Third, the integration of cross-sectional and MR analyses provides a robust framework for assessing both association and causality, addressing limitations inherent in observational designs. The MR approach, utilizing 156 SNPs from the IEU OpenGWAS database, minimized residual confounding and confirmed a causal relationship between AAM and OAB. Fourth, comprehensive adjustment for confounders, including demographic, clinical, and lifestyle factors, ensured rigorous statistical modeling. Fifth, the mediation analysis quantified BMI’s role, offering actionable insights for clinical practice. Finally, adherence to STROBE and STROBE-MR guidelines underscores the methodological transparency and reproducibility of our study.

Despite these strengths, several limitations must be acknowledged. First, the cross-sectional nature of the NHANES analysis precludes definitive causal inference, although MR partially mitigates this constraint. Second, self-reported AAM may introduce recall bias, particularly among older participants, potentially affecting the precision of exposure assessment. Third, while BMI was identified as a partial mediator, other potential mediators, such as hormonal profiles or inflammatory markers, were not explored due to data limitations. Fourth, the OAB definition relied on an OABSS threshold of ≥3 points, which may overestimate prevalence by capturing mild cases, potentially inflating the observed associations. Fifth, the study population was predominantly U.S.-based, and findings may not generalize to global populations, such as Asian or African cohorts, where AAM and OAB patterns may differ due to genetic or environmental factors. Finally, the MR analysis, while robust, assumes no horizontal pleiotropy beyond the tested SNPs, and unmeasured genetic effects could influence results. Future studies should address these limitations through longitudinal designs, objective AAM measures (e.g., medical records), and broader mediator assessments.

Conclusions

This study establishes early AAM as a novel risk factor for OAB, with BMI serving as a partial mediator. The integration of NHANES and MR analyses provides robust evidence of both association and causality, offering new avenues for understanding OAB pathogenesis. These findings advocate for early-life screening of AAM to identify at-risk populations and emphasize weight management as a potential strategy to reduce OAB burden. Future research should focus on longitudinal studies to confirm temporality, mechanistic investigations to uncover additional pathways, and validation in diverse populations to enhance generalizability.

Acknowledgments

We extend our sincere thanks to the editors and reviewers for their diligent efforts and thorough reviews, which significantly enhanced the quality of this manuscript.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-415/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-415/prf

Funding: This study was funded by the National Natural Science Foundation of China (No. 82060465) and the Natural Science Foundation of Jiangxi Province (No. 20212ACB206023).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-415/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Nambiar AK, Arlandis S, Bø K, et al. European Association of Urology Guidelines on the Diagnosis and Management of Female Non-neurogenic Lower Urinary Tract Symptoms. Part 1: Diagnostics, Overactive Bladder, Stress Urinary Incontinence, and Mixed Urinary Incontinence. Eur Urol 2022;82:49-59. [Crossref] [PubMed]
2. Chapple CR, Nazir J, Hakimi Z, et al. Persistence and Adherence with Mirabegron versus Antimuscarinic Agents in Patients with Overactive Bladder: A Retrospective Observational Study in UK Clinical Practice. Eur Urol 2017;72:389-99. [Crossref] [PubMed]
3. Sebesta EM, Gleicher S, Kaufman MR, et al. Associations Between Unmet Social Needs and Overactive Bladder. J Urol 2022;208:1106-15. [Crossref] [PubMed]
4. Stewart WF, Van Rooyen JB, Cundiff GW, et al. Prevalence and burden of overactive bladder in the United States. World J Urol 2003;20:327-36. [Crossref] [PubMed]
5. Corcos J, Przydacz M, Campeau L, et al. CUA guideline on adult overactive bladder. Can Urol Assoc J 2017;11:E142-73. [Crossref] [PubMed]
6. Irwin DE, Milsom I, Kopp Z, et al. Impact of overactive bladder symptoms on employment, social interactions and emotional well-being in six European countries. BJU Int 2006;97:96-100. [Crossref] [PubMed]
7. Pauwaert K, Goessaert AS, Ghijselings L, et al. Hormone therapy as a possible solution for postmenopausal women with nocturia: results of a pilot trial. Menopause 2021;28:502-10. [Crossref] [PubMed]
8. Rakic R, Pavlica T, Havrljenko J, et al. Association of Age at Menarche with General and Abdominal Obesity in Young Women. Medicina (Kaunas) 2024;60:1711. [Crossref] [PubMed]
9. Matsuo LH, Adami F, Pereira LJ, et al. Age at menarche and its association with overweight including obesity and socio-economic conditions of Brazilian schoolgirls: A time-trend analysis. Nutr Bull 2022;47:70-81. [Crossref] [PubMed]
10. Chen J, Yan M, Suolang D, et al. Mediation Effect of Obesity on the Association of Age at Menarche With Blood Pressure Among Women in Southwest China. J Am Heart Assoc 2023;12:e027544. [Crossref] [PubMed]
11. Luijken J, van der Schouw YT, Mensink D, et al. Association between age at menarche and cardiovascular disease: A systematic review on risk and potential mechanisms. Maturitas 2017;104:96-116. [Crossref] [PubMed]
12. Gaete X, Vivanco M, Lopez P, et al. Earlier puberty in boys with type 1 diabetes mellitus compared to a simultaneously recruited group of control adolescents. Pediatr Diabetes 2019;20:197-201. [Crossref] [PubMed]
13. Fu JF, Liang JF, Zhou XL, et al. Impact of BMI on gonadorelin-stimulated LH peak in premenarcheal girls with idiopathic central precocious puberty. Obesity (Silver Spring) 2015;23:637-43. [Crossref] [PubMed]
14. Wong D, Rowe E, Ruby R. Embryology and anatomy of the urogenital system. In: Equine Neonatal Medicine. John Wiley & Sons, Ltd; 2024:629-37.
15. Peyronnet B, Mironska E, Chapple C, et al. A Comprehensive Review of Overactive Bladder Pathophysiology: On the Way to Tailored Treatment. Eur Urol 2019;75:988-1000. [Crossref] [PubMed]
16. Al-Shaiji TF, Radomski SB. Relationship between Body Mass Index and Overactive Bladder in Women and Correlations with Urodynamic Evaluation. Int Neurourol J 2012;16:126-31. [Crossref] [PubMed]
17. Szasz T, Wenceslau CF, Burgess B, et al. Toll-Like Receptor 4 Activation Contributes to Diabetic Bladder Dysfunction in a Murine Model of Type 1 Diabetes. Diabetes 2016;65:3754-64. [Crossref] [PubMed]
18. Kim JH, Lim JS. Early menarche and its consequence in Korean female: reducing fructose intake could be one solution. Clin Exp Pediatr 2021;64:12-20. [Crossref] [PubMed]
19. Howard SR, Dunkel L. Delayed Puberty-Phenotypic Diversity, Molecular Genetic Mechanisms, and Recent Discoveries. Endocr Rev 2019;40:1285-317. [Crossref] [PubMed]
20. Yu Y, Zhang D, Xu J, et al. Adolescence is a sensitive period for acrylamide-induced sex hormone disruption: Evidence from NHANES populations and experimental mice. Ecotoxicol Environ Saf 2023;249:114413. [Crossref] [PubMed]
21. Naomi R, Teoh SH, Embong H, et al. The Role of Oxidative Stress and Inflammation in Obesity and Its Impact on Cognitive Impairments-A Narrative Review. Antioxidants (Basel) 2023;12:1071. [Crossref] [PubMed]
22. Lin HY, Lu JH, Lin RJ, et al. Effects of Nitric Oxide on Bladder Detrusor Overactivity through the NRF2 and HIF-1α Pathways: A Rat Model Induced by Metabolic Syndrome and Ovarian Hormone Deficiency. Int J Mol Sci 2024;25:11103. [Crossref] [PubMed]
23. Blaivas JG, Panagopoulos G, Weiss JP, et al. Validation of the overactive bladder symptom score. J Urol 2007;178:543-7; discussion 547. [Crossref] [PubMed]
24. Zhu S, Wang Z, Tao Z, et al. Relationship Between Marijuana Use and Overactive Bladder (OAB): A Cross-Sectional Research of NHANES 2005 to 2018. Am J Med 2023;136:72-8. [Crossref] [PubMed]
25. Finkelstein K, Glosner S, Sanchez RJ, et al. Prevalence of probable overactive bladder in a private obstetrics and gynecology group practice. Curr Med Res Opin 2008;24:1083-90. [Crossref] [PubMed]
26. Zhang X, Ma L, Li J, et al. Mental health and lower urinary tract symptoms: Results from the NHANES and Mendelian randomization study. J Psychosom Res 2024;178:111599. [Crossref] [PubMed]
27. Shen C, Fang M, Zhang X, et al. Causal effects of gut microbiota on risk of overactive bladder symptoms: a two-sample Mendelian randomization study. Front Microbiol 2024;15:1459634. [Crossref] [PubMed]
28. Xue P, Wang D, Chen Y, et al. Association between body fat distribution and age at menarche: a two sample Mendelian randomization study. Front Pediatr 2024;12:1349670. [Crossref] [PubMed]
29. Calthorpe L, Brage S, Ong KK. Systematic review and meta-analysis of the association between childhood physical activity and age at menarche. Acta Paediatr 2019;108:1008-15. [Crossref] [PubMed]
30. Asrullah M, L'Hoir M, Feskens EJM, et al. Trend in age at menarche and its association with body weight, body mass index and non-communicable disease prevalence in Indonesia: evidence from the Indonesian Family Life Survey (IFLS). BMC Public Health 2022;22:628. [Crossref] [PubMed]
31. Lacroix AE, Gondal H, Shumway KR, et al. Physiology, menarche. In: StatPearls. StatPearls Publishing; 2023 [cited 2025 Feb 19]. Available from: https://www.ncbi.nlm.nih.gov/sites/books/NBK470216/
32. Palmer BF, Clegg DJ. The sexual dimorphism of obesity. Mol Cell Endocrinol 2015;402:113-9. [Crossref] [PubMed]
33. Yeung AY, Tadi P. Physiology, obesity neurohormonal appetite and satiety control. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 Feb 19]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK555906/
34. Goldsammler M, Merhi Z, Buyuk E. Role of hormonal and inflammatory alterations in obesity-related reproductive dysfunction at the level of the hypothalamic-pituitary-ovarian axis. Reprod Biol Endocrinol 2018;16:45. [Crossref] [PubMed]
35. Otsubo A, Miyata Y, Matsuo T, et al. Excessive accumulation of visceral fat is associated with lower urinary symptoms including overactive bladder in female patients. Int J Urol 2021;28:397-403. [Crossref] [PubMed]
36. Gordon B, Shorter B, Isoldi KK, et al. Obesity with Comorbid Stress Urinary Incontinence in Women: A Narrative Review to Inform Dietetics Practice. J Acad Nutr Diet 2017;117:889-907. [Crossref] [PubMed]
37. Stolarczyk E. Adipose tissue inflammation in obesity: a metabolic or immune response? Curr Opin Pharmacol 2017;37:35-40.
38. Grover S, Srivastava A, Lee R, et al. Role of inflammation in bladder function and interstitial cystitis. Ther Adv Urol 2011;3:19-33. [Crossref] [PubMed]
39. Andersson KE. Oxidative stress and its possible relation to lower urinary tract functional pathology. BJU Int 2018;121:527-33. [Crossref] [PubMed]
40. Erdogan BR, Liu G, Arioglu-Inan E, et al. Established and emerging treatments for diabetes-associated lower urinary tract dysfunction. Naunyn Schmiedebergs Arch Pharmacol 2022;395:887-906. [Crossref] [PubMed]
41. Rabijewski M, Papierska L, Kuczerowski R, et al. Hormonal determinants of erectile dysfunction and lower urinary tract symptoms in middle-aged and elderly men with prediabetes. Aging Male 2015;18:256-64. [Crossref] [PubMed]
42. Zhang Y, Wu X, Liu G, et al. Association between overactive bladder and depression in American adults: A cross-sectional study from NHANES 2005-2018. J Affect Disord 2024;356:545-53. [Crossref] [PubMed]
43. Robinson D, Cardozo LD. The role of estrogens in female lower urinary tract dysfunction. Urology 2003;62:45-51. [Crossref] [PubMed]
44. Smith P, Heimer G, Norgren A, et al. Localization of steroid hormone receptors in the pelvic muscles. Eur J Obstet Gynecol Reprod Biol 1993;50:83-5. [Crossref] [PubMed]
45. Apter D, Vihko R. Early menarche, a risk factor for breast cancer, indicates early onset of ovulatory cycles. J Clin Endocrinol Metab 1983;57:82-6. [Crossref] [PubMed]
46. Fernandes VS, Ribeiro AS, Martínez-Sáenz A, et al. Underlying mechanisms involved in progesterone-induced relaxation to the pig bladder neck. Eur J Pharmacol 2014;723:246-52. [Crossref] [PubMed]