Endocrine Abstracts

In recent years, scalp hair analysis had been successfully introduced as a novel biomarker for long-term cortisol levels. Traditionally used methods to measure cortisol (blood, saliva, urine) are complicated by the circadian rhythm and pulsatile way of cortisol secretion, as well as the influence of acute stress and day-to-day variation. In contrast to these short-term measurements, hair cortisol analysis allows to quantify the average cortisol production for a prolonged period of time ranging from 1 month to several months or even years, depending on the length of the hairs.

Hair analysis provides exciting new opportunities to study the influence of long-term cortisol exposure on a wide range of health outcomes and diseases. In this context, increased long-term cortisol levels have been associated to obesity, metabolic syndrome, cardiovascular diseases, psychiatric diseases and a wide variety of stress-related conditions. Also for endocrine diseases hair cortisol analysis has shown its value. Hair cortisol measurement has been shown to be a convenient, non-invasive tool to diagnose Cushing’s syndrome with high sensitivity and specificity. Evidence is accumulating that this method can also be used for monitoring of hydrocortisone replacement therapy to improve its refinement. As hair grows on average one centimeter per month, scalp hair offers the unique opportunity to create historical timelines of long-term levels of cortisol. This facilitates the measurement of cortisol levels before and after an intervention or event using one single hair sample. In addition, in cases of cyclical Cushing’s syndrome, retrospective hair analysis seems of great value to assist in diagnosing this rare disorder. In conclusion, hair seems a promising matrix to reliably measure cumulative cortisol levels over prolonged periods of time, yielding multiple clinical applications.

Disclosure: This work was supported by the Netherlands Organisation for Scientific Research (NWO) (grant number 916.96.069), Thrasher Research Fund (grant number TRF11643), the Netherlands Brain Foundation (grant number F2011(1)-12), and an Erasmus MC Fe.