Background: The clinical heterogeneity of Polycystic Ovary Syndrome (PCOS) complicates risk stratification, particularly when phenotypic presentations mismatch endocrine severity. This study investigated clinical, anthropometric, and biochemical variations in Iranian PCOS patients to evaluate if physical traits reliably reflect underlying gonadotropin and androgenic disruptions.
Materials and Methods: In this cross-sectional study, 211 women diagnosed with PCOS (Rotterdam criteria) at a regional referral center were stratified based on ultrasonographic evidence of polycystic ovarian morphology (PCOM) into PCOM-Negative (n = 147) and PCOM-Positive (n = 64) groups. Anthropometric parameters, clinical signs (hirsutism, acne), and serum biochemical profiles were compared.
Results: The mean age was 22.92 ± 2.63 years. Clinical hirsutism and acne affected 73.5% and 47.9% of the total population, respectively. Paradoxically, peripheral clinical features and anthropometric distributions showed structural dissociation; clinical hirsutism (p = 0.316), acne (p = 0.231), and BMI categories (p = 0.230) were uniformly distributed regardless of ovarian morphology. Conversely, the biochemical profile revealed profound divergence; PCOM-Positive patients exhibited significantly higher LH levels (6.79 ± 0.95 vs. 4.93 ± 0.49 mIU/mL, p < 0.001) and marked hyperandrogenemia than their PCOM-Negative counterparts.
Conclusion: Peripheral phenotypic traits are poor predictors of internal endocrine severity in PCOS. Normal ovarian morphology can mask severe gonadotropin derangements. Objective biochemical stratification must be prioritized over subjective clinical scoring.
Implications for Patient Care: Clinicians must implement multidimensional screening protocols granting equal weight to biochemical profiling (testosterone spikes and LH/FSH inversions) alongside imaging. This eliminates diagnostic blind spots for high-risk patients with normal morphology, optimizing resource allocation and facilitating earlier targeted metabolic therapies in resource-limited regional settings.