The goal of this observational study is to learn about the causal architecture of Polycystic Ovary Syndrome (PCOS) in women aged 18 to 45. The main questions it aims to answer are:

What does the hormonal, metabolic, and clinical architecture of PCOS look like across a diverse global population? Can the causal architecture of PCOS be reconstructed from existing lab results and clinical data? Are there distinct architectural patterns across different groups of women with PCOS? Participants who have been diagnosed with PCOS by a healthcare provider will complete an online questionnaire about their diagnosis, symptoms, clinical history, current treatment, and existing lab results. Participants will also be asked to submit copies of their most recent blood work and lab results. No intervention or treatment is involved. All data is de-identified.

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.

Inclusion Criteria:

• Female
• Aged 18 to 45 years
• Diagnosed with Polycystic Ovary Syndrome (PCOS) by a licensed healthcare provider
• Willing to voluntarily share existing lab results and clinical history
• Able to provide informed consent

Exclusion Criteria:

• Under 18 years of age
• Over 45 years of age
• No confirmed PCOS diagnosis from a licensed healthcare provider
• Unable or unwilling to provide informed consent

Women aged 18 to 45 who have been diagnosed with Polycystic Ovary Syndrome (PCOS) by a licensed healthcare provider. Participants are recruited globally with no geographic restriction. All participants must have a confirmed diagnosis and be willing to voluntarily submit existing lab results and clinical history.