Ovarian aging is a natural aspect of biological aging, usually starting about a decade before other organs show functional decline. It is characterized by a gradual decrease in both the number and quality of oocytes, significantly impacting quality of life. Although some biomarkers like anti-Müllerian hormone (AMH) offer accurate predictions of imminent menopause, their effectiveness diminishes with time, highlighting AMH’s limitation to only assessing current ovarian reserve. To date, comprehensive markers for long-term ovarian aging prediction remain unidentified.
Studies have noted proteomic differences between menopausal and premenopausal women, but most lack longitudinal design and fail to explore causality or pre-existing protein abnormalities. Given the complexity of ovarian aging pathways-ranging from natural deterioration to medical interventions like oophorectomy-large-scale, prospective studies are essential. These should integrate proteomic and genomic data to enhance predictive models and understand underlying biological mechanisms.
This project aims to use an integrated clinical-proteomics-genetics approach to identify reliable biomarkers and genetic factors for ovarian aging prediction. Utilizing UK Biobank resources, we will explore associations between clinical data, plasma proteins, genetic factors, and ovarian aging etiologies. Our objectives include assessing predictive performance across various timelines, developing AI models for different aging subtypes, and identifying potential therapeutic targets to improve reproductive outcomes in older women. This holistic approach promises to advance early detection and intervention strategies, offering new avenues for extending reproductive longevity.