Aims: The project aims to develop a prediction model to detect early menopause using a combination of genetic, transcriptomic, and clinical data. Early menopause can lead to serious health issues, and early prediction can significantly improve health outcomes through proactive management.
Scientific Rationale: Early menopause often remains undetected until symptoms occur. By integrating different types of biological and clinical data, we aim to build a model that predicts early menopause before symptoms arise. This can help identify women at risk and provide timely interventions.
Project Duration: The project is expected to span 3 years, including phases for data collection, model development, validation, and analysis.
Public Health Impact: The ability to predict early menopause could transform women’s healthcare by enabling early diagnosis and personalized treatment plans. This would help reduce long-term health risks, improve quality of life for women at risk, and assist with family planning decisions.