Abstract
<jats:p>The female reproductive system is one of the first major organ systems to show signs of age-related decline, and menopause is associated with increased risk of several diseases, including osteoporosis and cardiovascular disease. Menstrual fluid contains a mixture of blood and endometrial tissue and is a noninvasive biological sample type that has immense potential for diagnostics related to female reproductive aging. However, existing epigenetic aging clocks show limited performance in hormone-dependent tissues such as the endometrium. At Xella Health, we collected menstrual fluid (MF) samples, from a diverse patient cohort (n=66) and quantified genome-wide 5mC methylation levels. We then developed a novel, deep learning-based epigenetic aging clock that is optimized for performance in menstrual fluid and endometrial tissue. Our model, the Xella Clock, outperforms other widely used epigenetic aging clocks at predicting chronological age from MF data and on endometrial tissue. The model is a useful tool for advancing the study of female reproductive aging and can be used to examine associations between endometrial age acceleration and clinical factors.</jats:p>