Exploring the Relationship Between Blood Glucose and the Menstrual Cycle

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Summary 

In this study, we examined the relationship between blood glucose levels and the menstrual cycle using continuous glucose monitoring and daily hormone testing.  The results demonstrated that glucose levels fluctuate throughout the menstrual cycle, with an increase in the luteal phase and a decrease in the late-follicular phase. The study suggests that blood glucose could be a significant factor in understanding menstrual health and warrants further investigation.

Context

Blood glucose levels can vary due to various factors, including body composition, diet, and exercise. Understanding these fluctuations is important for maintaining a healthy lifestyle as they can reveal or mask indicators of health problems like diabetes and vascular diseases. People who menstruate are particularly vulnerable to these issues due to hormonal imbalances, especially during life events such as pregnancy and menopause. Although the exact mechanisms are unclear, changes in estrogen levels throughout the menstrual cycle are believed to be involved. Therefore, studying glucose levels in non-diabetic menstruating individuals can further our understanding of glucose variation in relation to hormonal levels and management in pathological conditions.

Designing the Study

Previous studies had incongruent findings, potentially due to limited blood draws and reliance on self-reported menstrual cycles. To address these limitations, we conducted a study utilizing continuous glucose monitoring and daily hormone testing for more accurate and reliable data collection. The study involved 49 participants who wore a Dexcom G6 continuous glucose monitor and a Fitbit Sense smartwatch for an average of 79.3 ± 21.2 days. They also provided daily self-reports on their menstrual cycle characteristics and underwent daily hormone tests to determine their menstrual cycle phase.

Study Results

Our findings indicate that glucose levels peaked during the luteal phase, which follows ovulation, and reached a minimum during the late-follicular phase, which follows menstruation. These biphasic patterns remained consistent even after accounting for participant characteristics like age, BMI, and weight, and self-reported menstrual experiences like food cravings, bloating, and fatigue. We also discovered several other associations, such as lower glucose levels being linked to higher daily estrogen levels, increased step count, reduced fatigue, and vice versa. Moreover, higher levels of food cravings were positively associated with elevated median glucose levels.

The temporal distribution of days that were labeled with each menstrual cycle phase according to hormone data. The progression of the horizontal axis begins with the menstrual phase.
The temporal distribution of days that were labeled with each menstrual cycle phase according to hormone data. The progression of the horizontal axis begins with the menstrual phase. 
A plot of daily median glucose levels changes throughout the menstrual cycle starting from menstruation with a periodic restricted cyclic spline fit.
A plot of daily median glucose levels changes throughout the menstrual cycle starting from menstruation with a periodic restricted cyclic spline fit.

Implications for Menstrual Health

Our study holds significant implications for menstrual health in several ways. First, understanding the connection between blood glucose levels and the menstrual cycle enables individuals to interpret their glucose data more effectively. Moreover, the observed biphasic pattern suggests that blood glucose could potentially be used as an indicator for menstrual phase prediction. By integrating blood glucose monitoring and hormone tracking into wearable devices, we could enhance monitoring capabilities and facilitate trend identification.

Continuing the Research

To ensure a more representative sample, future research should focus on expanding the demographic variation of participants. Additionally, incorporating clinical tests to determine menstrual cycle phases and including objective measures like sleep trackers would strengthen and complement the findings. Furthermore, efforts towards developing user-friendly visualizations and applications that integrate data from multiple devices would enhance our understanding of various physiological trends during the menstrual cycle.

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Wearable Technology
Life Sciences > Health Sciences > Clinical Medicine > Biomedical Devices and Instrumentation > Wearable Technology
Reproductive Physiology
Life Sciences > Biological Sciences > Physiology > Reproductive Physiology
Diabetes
Life Sciences > Health Sciences > Clinical Medicine > Diseases > Diabetes
Health Care
Life Sciences > Health Sciences > Health Care
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