Seasonal and Biological Variation of Urinary Epinephrine, Norepinephrine, and Cortisol in Healthy Women

Background: There is a significant circadian and seasonal periodicity in various endocrine functions. The present study describes the within-day and seasonal fluctuation for urinary catecholamines and cortisol and estimates the within- (CV(i)) and between-subject (CV(g)) coefficients of variation for healthy women undertaking their routine work. In addition, index of individuality (I(i)) and power calculations were derived.

Methods: Eleven healthy females undertaking their routine life-style at work participated in the study. Each subject collected six samples during 24 h 15 days over a year, giving a total number of 990 samples. Using a random effect analysis of variance, we estimated CV(g) and total within-subject variation (CV(ti)), i.e. combined within-subject and analytical variation, from logarithmically transformed data. Analytical variation was subtracted from CV(ti) to give CV(i). CV(i) was estimated from samples collected monthly during 1 year (CV(iy)), weekly during 1 month (CV(im)), and six to eight times/day (CV(id)).

Results: A seasonal variation was demonstrated for excretion of epinephrine, norepinephrine, and cortisol standardized with creatinine. Concentrations of urinary epinephrine were higher during June and July compared to the rest of the year, whereas concentrations of urinary cortisol were higher during December and January compared to the rest of the year. Excretion of norepinephrine was lower during working hours and higher during hours off work for June and July compared to the rest of the year. There was a high within- and between-subject variation, which could not be explained by menstrual cycle, behavioral, emotional, or cognitive stress reactions.

Conclusions: Despite high biological variation a reasonably low sample size, e.g. 10-50 individuals, is adequate for practical applicability, i.e. studying differences above 150%. The present study recommends to include the sampling time in the statistical evaluation of data and to be aware of the changes in diurnal variations over seasons. When single measurements are to be evaluated, reference intervals are recommended.