Multiracial Cytokine Profiling Reveals Immune Dysregulation not Chronic Inflammation in Polycystic Ovary Syndrome.

CONTEXT: Polycystic ovary syndrome (PCOS) is a common endocrine disorder often associated with chronic inflammation, but comprehensive characterization of immune markers across diverse patient populations is lacking.

OBJECTIVE

To determine whether PCOS exhibits a profile of chronic inflammation or immune dysregulation when analyzed across a diverse patient population.

DESIGN

Retrospective cross-sectional study of samples collected between 1987 and 2010 as part of the Androgen Excess Biorepository.

SETTING

Reproductive endocrinology clinics at the University of Alabama at Birmingham in Birmingham, Alabama, and Cedars-Sinai Medical Center in Los Angeles, California, where women and adolescents presented for evaluation of androgen excess.

PATIENTS: Forty premenopausal women (20 with PCOS, 20 controls) aged 18 to 45 years, categorized by race (Black and White) and PCOS diagnosis. Participants were race, age, and body mass index matched.

MAIN OUTCOME MEASURES: Fasted follicular phase plasma concentrations of C-reactive protein and 96 circulating immune markers, including interleukin (IL)-6, tumor necrosis factor-α (TNF-α), and IL-18, measured using a Milliplex Luminex xMAP assay.

RESULTS

PCOS patients showed significantly lower levels of circulating immune markers (P < .05). Growth factors (vascular endothelial growth factor A, platelet-derived growth factor), proinflammatory cytokines (IL-8, TNF-α, interferon-γ), and several chemokines were reduced in PCOS patients, independent of race. Only IL-18 and C-X-C motif chemokine ligand 16 showed statistically significant differences between Black and White women.

CONCLUSION

This study challenges the prevailing notion of PCOS as a disorder of chronic low-grade inflammation, suggesting instead that immune suppression and impaired angiogenic signaling may be factors in PCOS pathophysiology, especially in nonobese patients. Further research with larger sample sizes and inclusion of metabolic metrics is needed to confirm these findings before they can be applied in clinical practice.