ORIGINAL RESEARCH article
Front. Psychol.
Sec. Health Psychology
Recognition of Adaptive Stress Response Patterns in Adolescents Based on Multiple Hair Biomarkers
Provisionally accepted
- School of Nursing, Xuzhou Medical University, Xuzhou, China
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Abstract Background: The Adaptive Calibration Model (ACM) explains how individuals' stress response systems (SRS) develop different calibration patterns under environmental stress, subsequently shaping physiological responses and behavioral strategies during growth. However, research on adaptive stress response patterns has yielded inconsistent results across studies, warranting further investigation. Methods: This study used hair cortisol, cortisol/DHEA ratio (CD), and cortisol/cortisone ratio (CC) as biomarkers reflecting multidimensional functional indicators of Hypothalamic–Pituitary–Adrenal (HPA) axis adaptation. Latent profile analysis identified heterogeneous stress adaptation patterns in adolescents (N=124,age range :12-18). We compared differences in these patterns across sex and disadvantaged/non-disadvantaged groups, and examined their relationship with depressive symptoms. Results: A 4-profile solution was selected based on theoretical interpretability. The buffered profile was most prevalent (35.5%). Children from disadvantaged backgrounds, compared to non-disadvantaged children, were characterized by a significantly higher proportion of the vigilant profile. However, no significant differences in depression scores were found among the different stress adaptation patterns. Conclusion: This study provides the first validation of ACM-proposed stress adaptation patterns from multidimensional functional indicators of HPA axis functional perspective. Early disadvantaged environments appear to promote vigilant stress adaptation patterns. These findings provide valuable reference for future mechanism analysis and intervention development for stress-related conditions in adolescents.
Keywords: Adaptive calibration model, Adolescents', hair cortisol, HPA axis, stress
Received: 07 Oct 2025; Accepted: 21 Jan 2026.
Copyright: © 2026 Wang, Shen, Su, Liu and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Weiliang Wang
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