A computational model of 1,5-AG dynamics during pregnancy.
Seyedeh M ZekavatSlava S ButkovichGrace J YoungDavid M NathanDanny PetrasekPublished in: Physiological reports (2018)
The importance of 1,5-anhydroglucitol (1,5-AG) as an intermediate biomarker for diabetic pregnancy is multi-fold: (1) it serves as a reliable indicator of moderate-level glycemic control, especially during early gestation; (2) it has been associated with increased risk of diabetes, independent of HbA1c and fasting glucose; and (3) it is an independent risk factor for the development of eclampsia during pregnancy. However, the clinical use of this biomarker during pregnancy has been underutilized due to physiological changes in glomerular filtration rate, plasma volume, and other hemodynamic parameters which have been hypothesized to bias gestational serum 1,5-AG concentrations. Here, we develop an in-silico model of gestational 1,5-AG by combining pre-existing physiological data in the literature with a two-compartment mathematical model, building off of a previous kinetic model described by Stickle and Turk (1997) Am. J. Physiol., 273, E821. Our model quantitatively characterizes how renal and hemodynamic factors impact measured 1,5-AG during normal pregnancy and during pregnancy with gestational diabetes and diabetes mellitus. During both normal and diabetic pregnancy, we find that a simple two-compartment model of 1,5-AG kinetics, with all parameters but reabsorption fraction adjusted for time in pregnancy, efficiently models 1,5-AG kinetics throughout the first two trimesters. Allowing reabsorption fraction to decrease after 25 weeks permits parameters closer to expected physiological values during the last trimester. Our quantitative model of 1,5-AG confirms the involvement of hypothesized renal and hemodynamic mechanisms during pregnancy, clarifying the expected trends in 1,5-AG to aid clinical interpretation. Further research and data may elucidate biological changes during the third trimester that account for the drop in 1,5-AG concentrations, and clarify physiological differences between diabetes subtypes during pregnancy.
Keyphrases
- quantum dots
- glycemic control
- type diabetes
- pregnancy outcomes
- highly efficient
- preterm birth
- visible light
- cardiovascular disease
- weight gain
- blood glucose
- systematic review
- metabolic syndrome
- electronic health record
- big data
- blood pressure
- insulin resistance
- preterm infants
- machine learning
- adipose tissue
- skeletal muscle
- high resolution
- mass spectrometry
- early onset
- high intensity
- wound healing