Determination of cortisone and cortisol in human scalp hair using an improved LC-MS/MS-based method

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Abstract

Objectives: 

Human scalp hair is an easily available but complex matrix for determination of cortisone and cortisol, and has been shown to reflect long-term glucocorticoid exposure. Hair glucocorticoid analysis has been used to detect hypo- and hypercortisolism. In this study, we describe the development and validation of a LC-MS/MS method for quantification of cortisone and cortisol in human scalp hair, and provide a novel approach for analysis and interpretation of the results.

Methods:

Improved sample preparation using pulverization and solid phase extraction allowed for low sample volumes (10 mg). Baseline chromatographic separation without matrix interference was achieved by reversed phase chromatography and MRM measurement in negative ion mode. Run-to-run time was 8 min. Mixed model analyses were performed to create individual patterns of cortisone and cortisol concentrations.

Results:

Matrix matched calibration curves showed excellent linearity up to 100 pg (analyte)/mg (hair) for both cortisone and cortisol (R2>0.995). LLOQ was 1.5 and 1.0 pg/mg for cortisone and cortisol, respectively. Matrix effect was negligible for hair color (recoveries 95-105 %). Cortisone and cortisol concentrations decreased from proximal to distal hair segments, following a predictable, but subject-specific pattern, with less individual variation for cortisone than for cortisol. 

Conclusions:

This improved LC-MS/MS method is able to accurately quantify cortisone and cortisol in human hair with minimum matrix interference. This new way of data analysis and interpretation including individual patterns of cortisone and cortisol will be of help with detection of pathological concentrations in both the high - and the low ranges of glucocorticoids.

Original languageEnglish
Pages (from-to)118-127
Number of pages10
JournalClinical Chemistry and Laboratory Medicine
Volume62
Issue number1
Early online date25 Jul 2023
DOIs
Publication statusPublished - 1 Jan 2024

Bibliographical note

Publisher Copyright:
© 2023 the author(s).

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