Assessment of Canonical Diurnal Variations in Plasma Glucose Using Quantile Regression Modelling and Chronomaps

Objectives: Diurnal variation of plasma glucose levels may contribute to diagnostic uncertainty. The permissible time interval, (), was proposed as a time-dependent characteristic to specify the time within which glucose levels from two consecutive samples are not biased by the time of blood collection. A major obstacle is the lack of population-specific data that reflect the diurnal course of a measurand. To overcome this issue, an approach was developed to detect and assess diurnal courses from big data.

Methods: A quantile regression model, QRM, was developed comprising two-component cosinor analyses and time, age, and sex as predictors. Population-specific canonical diurnal courses were generated employing more than two million plasma glucose values from four different hospital laboratory sites. Permissible measurement uncertainties, , were also estimated by a population-specific approach to render Chronomaps that depict () for any timestamp of interest.

Results: The QRM revealed significant diurnal rhythmometrics with good agreement between the four sites. A minimum () of 3 h exists for median glucose levels that is independent from sampling times. However, amplitudes increase in a concentration-dependent manner and shorten () down to 72 min. Assessment of () in 793,048 paired follow-up samples from 99,453 patients revealed a portion of 24.2 % sample pairs that violated the indicated ().

Conclusions: QRM is suitable to render Chronomaps from population specific time courses and suggest that more stringent sampling schedules are required, especially in patients with elevated glucose levels.