Objective Endogenous hormones regulate numerous physiological processes in humans. Some of them are routinely measured in blood, saliva and/or urine for the diagnosis of disorders. The analysis of fluids may,… Click to show full abstract
Objective Endogenous hormones regulate numerous physiological processes in humans. Some of them are routinely measured in blood, saliva and/or urine for the diagnosis of disorders. The analysis of fluids may, however, require multiple samples collected at different time points to avoid the high variability in the concentration of some hormones. In contrast, hair analysis has been proposed as an interesting alternative to reveal average hormone levels over a longer period. In this work, we developed and validated an analytical method for analyzing 36 endogenous steroid and thyroid hormones and one pineal hormone in human hair using ultra-performance liquid chromatography (UPLC)-tandem mass spectrometry (MS/MS). Methods Sample preparation involved hair decontamination, pulverization, methanol extraction, and purification with C18-solid phase extraction. Extracts were then divided into two portions, respectively injected into an UPLC-MS/MS system, and analyzed using two different instrumental methods. The method was applied to a healthy female population aged 25–45 years. Results The method was validated on supplemented hair samples for the 37 targeted hormones, and its application to the population under study allowed to detect 32 compounds in 2–100% of the samples. Complete reference intervals (2.5–97.5th percentiles) were established for estrone, 17β-estradiol, androstenedione, dehydroepiandrosterone, progesterone, 17α-hydroxyprogesterone, cortisone, cortisol and 3,3’,5-triiodo-L-thyronine. Hair cortisone, cortisol, tetrahydrocortisone and tetrahydrocortisol concentrations were highly correlated with each other, with Kendall’s τ correlation coefficients ranging from 0.52 to 0.68. Conclusion Allowing the detection of 32 hormones from different chemical classes, the present method will allow to broaden hormonal profiling for better identifying endocrine disorders.
               
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