Assessment of canonical diurnal variations in plasma glucose using quantile regression modelling and Chronomaps

Diurnal variation of plasma glucose levels may contribute to diagnostic uncertainty. The permissible time interval, pT(t), 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.

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, pU, were also estimated by a population-specific approach to render Chronomaps that depict pT(t) for any timestamp of interest.

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

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.