Approved Research

Premature Ovarian Insufficiency (POI) prediction model establishment and precision diagnosis and treatment transformation

Lay summary

Primary Ovarian Insufficiency (POI) is a common issue for women of childbearing age, affecting their reproductive health. It's diagnosed in women under 40 who experience missed periods or very infrequent periods, have high levels of a hormone called FSH (over 25 IU/L), low estrogen, and other hormonal imbalances. The most severe form of POI is Premature Ovarian Failure (POF), which can cause symptoms like reproductive organ shrinkage, hot flashes, excessive sweating, and mood swings. In China alone, around 1% to 2% of women in this age group, totaling about 2 million, suffer from POI, and the numbers are growing each year. This condition not only reduces or even eliminates the ability to have children, but it also leads to other health issues like osteoporosis and heart disease due to low estrogen levels.

The causes of POI vary greatly and can include genetic issues, autoimmune disorders, or even medical treatments, but in over half the cases, the cause remains unknown. This is referred to as idiopathic POI. Unfortunately, current methods for diagnosing POI are limited, often only detecting the condition at its late stage (POF stage). Hence, there's a pressing need for early diagnosis and prediction methods.

Our aim is to develop a mathematical model for predicting POI using a large amount of data from the UK biobank, including genetic studies, surveys, and detailed clinical data. By applying advanced machine learning techniques, we plan to analyze how lifestyle, clinical signs, and individual genetic factors contribute to POI. This model could help in accurately diagnosing and treating POI on an individual level.