Preeclampsia is a leading cause of maternal death during pregnancy, and the role of circadian rhythms in predicting preeclampsia is not well understood. We aimed to determine whether glucose circadian rhythm disruption is associated with preeclampsia and can be used to predict this disorder. We analyzed a dataset of 92 pregnant individuals recruited with Continuous Glucose Monitoring (CGM). To study rhythmicity, we performed a cosinor analysis using the packagesand, and we calculated the non-parametric circadian rhythm variables using thepackage in R. Furthermore, we performed multiple-component cosinor analysis to detect internal oscillations and identify glucose postprandial peaks using the packagein Python. Seventy-one participants (20 women with preeclampsia) had sufficient data for studying glucose circadian rhythmicity and performing all the chronobiological analyses. All the participants exhibited a significant circadian rhythm in their glucose oscillation. We developed a model including the time difference between the first postprandial peak and the last one, L5 start-time, and age that was predictive for preeclampsia. Patients diagnosed with preeclampsia from this model had a reduced amplitude and less robust glucose rhythmicity. We conclude that identifying abnormal glucose circadian rhythm during pregnancy may help to anticipate pregnancy-related disorders like preeclampsia. cosinor cosinor2nparACT CosinorPy
Circadian Biology