Observational Study on the Causal Architecture of Polycystic Ovary Syndrome (PCOS)

Polycystic Ovary Syndrome (PCOS) affects approximately 1 in 10 women of reproductive age worldwide. Despite its prevalence, the causal architecture of PCOS remains poorly characterized. Current treatment approaches focus on symptom management rather than addressing underlying biological structure.

This study takes a different approach. Using a computational methodology called Biology First Intelligence, developed by AnnieGuard, the study aims to reconstruct the causal architecture of PCOS from participant-submitted clinical data. Rather than predicting outcomes from statistical correlations, the methodology maps how the condition is structurally built at the biological level, with the goal of identifying architectural patterns that may inform future treatment strategies.

The study collects data across six clinical domains: androgen profile (total testosterone, free testosterone, DHEA-S, androstenedione, SHBG), reproductive hormones (LH, FSH, AMH, estradiol, progesterone, prolactin), metabolic markers (fasting insulin, fasting glucose, HbA1c, HOMA-IR), thyroid function (TSH, free T4, free T3, TPO antibodies), lipid profile (total cholesterol, HDL, LDL, triglycerides), and inflammatory and nutritional markers (vitamin D, ferritin, B12, CRP, cortisol, 17-hydroxyprogesterone).

Participants complete a structured online questionnaire covering demographics, diagnosis history, Rotterdam criteria, current symptoms, comorbidities, family history, current medications, dietary approach, exercise habits, and self-reported lab values. Participants are also asked to submit copies of their lab results directly to the research team for verification.

The study recruits globally with no geographic restriction. Eligibility requires a confirmed PCOS diagnosis from a licensed healthcare provider, age 18 to 45, and willingness to share clinical data voluntarily.

The primary outcome is the computational reconstruction of the causal architecture of PCOS across the study cohort. Secondary outcomes include identification of cohort-level architectural patterns, characterization of PCOS subtypes based on biological architecture, and assessment of the relationship between current treatment approaches and underlying causal structure.