Measuring resilience to chronic pain in population surveys using hair cortisol

Publication type

Journal Article

Series Number

Authors

Publication date

August 15, 2025

Summary:

Background:

Chronic pain activates the HPA axis stress response resulting in the release of cortisol, although empirical associations are often contradictory. Quantile regression models of hair cortisol may help us measure HPA-axis dysregulation more accurately and establish more robust associations with chronic pain. We also examined whether people with chronic pain characterised by HPA-axis dysregulation are at risk of future mental ill-health.

Methods:

This study examined data from the English Longitudinal Study of Ageing (ELSA, n = 4,560) and the UK Household Longitudinal Survey-Innovation Panel (UKHLS-IP, n = 473) to assess whether quantile regression methods enable us to assess more robust associations between hair cortisol and chronic pain, and whether older adults with chronic pain characterised by HPA-axis dysregulation are at risk of future mental ill-health.

Results:

In ELSA, chronic pain was associated with a 15% (CI: 6%–23%) increase in cortisol at the 10th percentile of the hair cortisol distribution among older adults and a 19% (CI: 2%–37%) increase at the 80th percentile, but no association was found at the 30th or 40th percentiles. Having a low cortisol response to chronic pain protected against the recurrence of depression. These patterns of association were replicated in the UKHLS-IP sample.

Conclusions:

The associations demonstrated across two longitudinal population surveys from the UK indicate that quantile regression analysis of hair cortisol may be useful in identifying individuals resilient to chronic pain. Hair cortisol is a promising biomarker that can be measured in population studies to quantify the stress response and resilience to future mental ill-health.

Published in

Psychological Medicine

Volume

Volume: 55:e201

DOI

https://doi.org/10.1017/S0033291725101049

ISSN

00332917

Subjects

Notes

Open Access

© The Author(s), 2025. Published by Cambridge University Press.

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.

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