Anglia Ruskin Research Online (ARRO)
Browse
Reid_et_al_2021.pdf (316.67 kB)

Validity and reliability of method used to analyse hair cortisol concentration

Download (316.67 kB)
journal contribution
posted on 2023-07-26, 15:23 authored by Jordan Reid, Katy Parker, Lydia Clemens, Matt Bristow
Hair cortisol analysis is a method of analysing the stress hormone cortisol that offers great potential for helping researchers understand the long-term impact of stress and distress on the body. Hair analysis not only provides an excellent method of studying the average production of cortisol over weeks and months, but also the potential to understand cortisol levels several months before the hair was collected. Whilst research with hair samples for cortisol analysis is a fast-developing field, there has been less analysis of the methods used to analyse hair cortisol. We report two studies where the novel hair analysis method developed at the Anglia Ruskin university (ARU) Biomarker Laboratory was tested for reliability and validity. In study 1, 32 participants provided hair samples for an examination of the reproducibility of the hair cortisol analysis method. In study 2, 53 participants provided a hair sample cut from the scalp, and the methanol that the cortisol was extracted into was split between two tubes and assayed at two different laboratories with different methods (ELISA, LC-MS/MS). Overall, the results demonstrate that the methods developed to analyse hair cortisol in the ARU Biomarker Laboratory were both reliable and valid. The discussion considers further avenues for research and optimisation of the methodology.

History

Refereed

  • No

Volume

10

Page range

349

Publication title

F1000Research

ISSN

2046-1402

Publisher

F1000 Research

File version

  • Published version

Language

  • eng

Legacy posted date

2021-05-06

Legacy creation date

2021-05-06

Legacy Faculty/School/Department

Faculty of Science & Engineering

Note

Paper accepted for publication with an online open review process, so at present this is a pre-print rather than a peer-reviewed article.

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC