Ovarian aging, a natural facet of biological aging, precedes the functional decline of other organ systems by approximately a decade. It is marked by a progressive decrease in the quantity and quality of oocytes, leading to a significant decline in ovarian function with profound implications for quality of life and well-being. Lifestyle factors, including diet, smoking, exercise, and stress levels, have been shown to influence the rate of ovarian aging, with diets rich in antioxidants potentially slowing this process. However, the complex interplay and cumulative effects of these factors on ovarian aging remain to be fully understood, as do the impacts of other lifestyle and environmental factors such as alcohol consumption, sleep patterns, social interactions, and environmental exposures.
Previous research has identified numerous genetic determinants of ovarian aging, including 290 genetic markers linked to the age at natural menopause in women of European ancestry and 195 pathogenic variants associated with primary ovarian insufficiency. Despite these advances, the connection between lifestyle factors and ovarian aging through gene expression, protein activity, or metabolic changes is underexplored. Bridging human genetics with large-scale proteomics and health outcome phenotypes could shed light on the influence of lifestyle and environmental factors on ovarian aging.
This proposal seeks to explore novel factors associated with ovarian aging by leveraging data from the UK Biobank to examine the relationship between lifestyle, environmental exposures, and ovarian aging over three years. It will include medical conditions related to ovarian aging, blood biomarkers, and physical measurements for comparison, integrating genetic and plasma proteomics data to construct a network of lifestyle factors, gene expression, and protein activities. By employing machine learning models, the project aims to predict how various lifestyle factors affect ovarian aging and identify key regulatory mechanisms.
This research has the potential to reveal new lifestyle predictors of ovarian aging, offering accessible insights for the public to potentially prolong reproductive health and inform public health strategies for reproductive health maintenance.