How does penis size vary by age and BMI, and what adjustment factors do studies use?

1. Age: modest, often negligible changes across adult life

Several studies report only minor age-related differences in measured penile length: some datasets show a very small negative correlation between age and flaccid or stretched length but no clinically meaningful decline across typical adult age ranges; meta-analyses and large cross-sectional samples emphasize geographic and methodological heterogeneity more than age effects ^{[5] [6]}. Studies focused on developmental cohorts (children/early puberty) show substantive growth tied to pubertal timing and bone‑fusion milestones, but in adults the age signal is weak compared with other sources of measurement variation ^{[7] [6]}.

2. Adult BMI: mixed findings, method-dependent associations

The relationship between adult BMI and penile size is inconsistent: some large studies find no correlation between BMI and stretched or flaccid length when using multivariate models ^{[1] [8]}, while others report that increasing BMI or weight is associated with shorter visible/erect length—often attributed to a larger suprapubic fat pad that hides penile shaft and reduces measured external length ^{[2] [3]}. Where associations are found, effect sizes are generally small (a few millimeters to centimeters) and can differ between flaccid, stretched and erect measures ^{[2] [3]}.

3. Lifespan timing: prepubertal obesity vs adult adiposity

Emerging research that reconstructs childhood body composition suggests prepubertal obesity may have a longer-term association with shorter adult penile dimensions: a recent retrospective study using 3D-modeled childhood BMI reported prepubertal obesity linked to shorter stretched and pubic‑to‑tip flaccid lengths in adulthood, while the participants’ current adult BMI did not show significance in multivariate regressions ^[4]. This distinction points to potential developmental windows—influence during puberty and growth—rather than a purely reversible adult‑adiposity effect ^{[4] [7]}.

4. Adjustment factors and statistical approaches used in studies

Studies use a mix of univariate correlations and multivariate linear or mixed models to adjust for confounders such as height, age, weight, waist/hip ratio, testicular volume, smoking and geographic region; important adjustments include depressing pubic fat to measure pubic‑to‑tip length and including height to account for overall body size ^{[2] [8] [5]}. Multivariable regressions often reduce or eliminate BMI associations seen in univariate analyses, indicating confounding by other somatometric variables; some paediatric growth studies employ linear mixed models to identify predictors across time, finding BMI can predict growth trajectories when measured longitudinally ^{[8] [7] [4]}.

5. Measurement and methodological caveats that drive inconsistent results

Variation in measurement technique (flaccid vs stretched vs erect, whether pubic fat is depressed), observer differences, sample selection (clinic-based vs population), and demographic heterogeneity (region/ethnicity) explain much discordance: self-reported lengths tend to overestimate compared with clinical measures, and small measurement errors can alter inferred associations with BMI or age ^{[9] [6]}. Many models explain only a modest fraction of variance in stretched penile length (e.g., R2 ≈ 0.10 in one multivariate model), signaling that measured somatic predictors leave most individual variation unexplained ^[8].

6. Bottom line: modest, context‑dependent effects and remaining uncertainties

The preponderance of evidence indicates that adult BMI has at best a modest, method‑dependent relationship with external penile measures—sometimes detectable after simple analyses but often attenuated in multivariable models—whereas prepubertal obesity and developmental timing may exert longer‑term influence on adult size; age within typical adult ranges shows little consistent impact ^{[1] [4] [2]}. Limitations across the literature—heterogeneous methods, cross‑sectional designs, and under‑reported confounders—mean firm causal claims are premature and individualized predictions remain unreliable ^{[9] [8]}.