Liquid chromatography mass spectrometry of segmented hair cortisol – A Bayesian retrospective window

This study aimed to develop a robust methodology for quantifying cortisol decay in segmented human hair and to establish accurate retrospective estimates of initial cortisol levels. Two independent probabilistic approaches were employed: i) a Bayesian multilevel analysis across 37 individuals with 3–6 hair segments each and ii) a Bayesian repeated sampling approach in a single individual with 10 segments collected over eight months. All hair segments were analyzed for cortisol concentration using liquid chromatography-mass spectrometry with on-line solid phase extraction. Both approaches demonstrated an exponential decay pattern of cortisol in hair, with estimated decay constants (k) of 0.16 (95 % Credible interval: 0.10–0.22) and 0.11 (95 % Credible interval: 0.06–0.15), respectively. A correction factor was introduced to significantly enhance the accuracy of initial cortisol level estimation, enabling more reliable comparisons with reference intervals obtained from proximal hair segments. This innovative method has the potential to significantly improve long-term cortisol monitoring and advance clinical research especially in psychiatry and endocrinology.