Relationship of Hair Cortisol Concentration With Perceived and Somatic Stress Indices: Cross-Sectional Pilot Study
Bibliographic record
Abstract
Background: Hair cortisol is an emerging biomarker of chronic stress. However, the psychological and physiological aspects of chronic stress that are reflected in hair cortisol concentration (HCC) have not been fully determined. Since physiological responses to stress do not always align with how stress is perceived, we conducted this study to evaluate whether HCC correlates with neuroendocrine stress indicators or stress perceptions. Objective: This study aimed to evaluate whether subjective (Perceived Stress Scale and Connor-Davidson Resilience Scale) and objective (plasma cortisol/dehydroepiandrosterone-sulfate [DHEA-S] and cortisol/high-sensitivity C-reactive protein) determinants of stress and resilience correlate with HCC. Methods: In this cross-sectional pilot validity study, scatter plots and Spearman correlation coefficients were used to measure the direction and magnitude of the relationship between stress and resilience measures among 51 predominantly male participants. In a subset (n=24), we performed a step-wise regression modeling approach to isolate the association of perceived and somatic stress on hair cortisol. Results: Bivariate correlations showed a weak inverse association of HCC with Perceived Stress Scale (Spearman correlation ρ=-0.14, P=.52) and a stronger positive association with somatic neuroendocrine stress indices cortisol/DHEA-S (ρ=0.24, P=.25) and cortisol/high-sensitivity C-reactive protein (ρ=0.21, P=.35). In linear regression models, HCC showed the strongest association with cortisol/DHEA-S (r2=0.10, P=.13, 1.01β 1.01, 95% CI 0.99-1.01). This relationship remained when age, gender, hair washing frequency, hair dye or bleach use, diabetes mellitus, obesity, cardiovascular disease, anxiety, medication use, and endocrine disorders were considered. Conclusions: Our results do not indicate a statistically significant association (at the P<.05 threshold) between HCC and stress perception or somatic measures of neuroendocrine response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".