How much variation in vaginal anatomy is explained by race or ethnicity versus age, parity, and BMI?
Executive summary
Race and ethnicity are associated with measurable differences in vaginal and pelvic-floor anatomy in multiple small-to-moderate studies, but they are neither the sole nor necessarily the dominant drivers of anatomical variation; age, parity and BMI consistently correlate with many vaginal and vulvar measures and explain a meaningful portion of interindividual differences, while large within-group variability and methodological limits prevent a single definitive partitioning of percent variance explained [1] [2] [3] [4].
1. Race and ethnicity: real differences, often modest and measure-specific
Numerous imaging and anthropometric studies report statistically significant differences between racial or ethnic groups in specific vaginal, pelvic‑floor and vulvar measures—for example, anterior/posterior vaginal dimensions and levator hiatus morphology differ between Black and White cohorts on MRI and ultrasound [1] [2], and a cross‑sectional comparison found ethnic Chinese nulliparous women had vaginal and labial dimensions 9–21% smaller than Western counterparts [3] [4]. These findings demonstrate that race/ethnicity can be associated with anatomically meaningful differences in particular metrics, but the magnitude and even the direction of those differences vary across studies and measurement methods [5] [6].
2. Age, parity and BMI: consistent, clinically relevant correlates
Age, the number of prior births (parity) and body mass index repeatedly show associations with pelvic and genital anatomy. Large pelvic‑health analyses list advancing age, parity and higher BMI as consistent risk factors for pelvic organ prolapse and other outcomes tied to anatomy [7], and several studies find positive correlations between BMI/weight and labial or introitus dimensions (r values ~0.5–0.66 reported in ethnic‑specific cohorts) as well as parity correlations with labial or levator hiatus measures [3] [8] [9]. In short, these demographic and reproductive factors explain observable, and often clinically relevant, variation across individuals [10] [9].
3. Head‑to‑head: what survives adjustment and what doesn’t
When studies control for age, BMI and parity, some racial differences persist while others attenuate—suggesting both independent and confounded effects. A recent MRI/ultrasound study reported significant racial differences in levator hiatus morphology even after adjusting for age, BMI and parity [2], whereas other investigations show that relationships between race and certain pelvic measures vary by delivery cohort or interact with parity [1] [11]. These mixed results indicate that race/ethnicity can have an independent association for some metrics, but the strength of that association depends on the anatomical measure, the population sampled and whether key confounders are modeled [1] [2].
4. The elephant in the data room: large within‑group variability and methodological limits
Across studies there is “substantial variation within each group,” meaning overlap between racial/ethnic groups is often large enough that individual predictions based on race are unreliable [3] [5]. Many studies are limited by small or convenience samples, differing measurement techniques (MRI, ultrasound, casting, external anthropometry), self‑reported race/ethnicity and cohort composition (nulliparous vs parous, symptomatic vs asymptomatic), all of which constrain how much of the total variance can be confidently attributed to race versus age, parity or BMI [4] [5]. Importantly, none of the provided sources consistently report a population‑level percentage of variance (R²) partitioned by these predictors, so a precise numeric split is not available in the reviewed literature [1] [2] [3].
5. Bottom line and research gaps that matter to clinicians and patients
The literature supports that race/ethnicity, age, parity and BMI each explain portions of variation in vaginal, vulvar and pelvic‑floor anatomy: race/ethnicity shows significant, measure‑specific effects (sometimes sizable in small studies such as 9–21% differences in dimensions), while age, parity and BMI are consistent, clinically meaningful correlates that often interact with or confound racial differences [3] [2] [7]. However, large within‑group heterogeneity, methodological differences and limited reporting of multivariate variance components mean current data cannot produce a single, robust percentage breakdown of variance explained by race versus the other factors; the field needs larger, diverse, standardized studies that report adjusted R² or similar metrics to answer that question quantitatively [4] [10].