Diabetes Mellitus (DM) is a metabolic disease where the body struggles to regulate blood glucose levels. DM is categorized as Type 1 and Type 2, both involving issues with insulin, but differ in their onset and progression.
The two main hormone classes that contribute to sex differences in humans are estrogens and androgens. Estrogen levels typically peak midway through a menstrual cycle right before ovulation, and once estrogen levels start to fall after ovulation, glucose levels go up. This is important because research shows that estrogen can provide some protection against insulin resistance in cyclic women.
While men are more likely to be diagnosed with diabetes at a younger age, women face higher risks of diabetes as they reach 65 and up. This is primarily because after menopause, women lose the potency of estradiol, the primary form of estrogen, making them more vulnerable to insulin resistance and diabetes. Given these hormonal differences, there is a need to better understand how sex differences affect diabetes.
Most datasets do not have labels to indicate if a women has a menstrual cycle or if she doesn’t have one. Despite this, previous research has established that other physiological metrics, particularly basal body temperature, have a strong correlation with menstrual cyclicity. The figure below shows that temperature follows a sinusoidal pattern that aligns with different phases of the menstrual cycle. Our group’s idea was to apply this concept of identifying cyclicity to label-free glucose data. We expected for there to be some influence of menstrual cycles on glucose levels.
References
Lin, G., Siddiqui, R., Lin, Z., Blodgett, J.M., Patel, S.N., Truong, K.N., & Mariakakis, A. (2023). Blood glucose variance measured by continuous glucose monitors across the menstrual cycle. npj Digital Medicine, 6, 140. https://doi.org/10.1038/s41746-023-00884-x
Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., Colagiuri, S., Guariguata, L., Motala, A. A., Ogurtsova, K., Shaw, J. E., Bright, D., Williams, R., & IDF Diabetes Atlas Committee (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Research and Clinical Practice, 157, 107843. https://doi.org/10.1016/j.diabres.2019.107843
Kryder, C. (2023). How Oura data can help you understand your menstrual cycle. Oura. https://ouraring.com/blog/menstrual-cycle-variation/