Measurement of cortisol in human Hair as a biomarker of systemic exposure
Bibliographic record
Abstract
PURPOSE: Current methods for measuring long-term endogenous production of cortisol can be challenging due to the need to take multiple urine, saliva or serum samples. Hair grows approximately 1 centimeter per month, and hair analysis accurately reflects exposure to drug abuse and environmental toxins. Here we describe a new assay for measurement of cortisol in hair, and determined a reference range for non-obese subjects. METHODS: For measurement of cortisol in hair we modified an immunoassay originally developed for measuring cortisol in saliva. We compared hair samples obtained from various parts of the head, and assessed the effect of hair dying. We analyzed hair samples from non-obese subjects, in whom we also obtained urine, saliva and blood samples for cortisol measurements. RESULTS: The mean extraction recovery for hair cortisol standards of 100 ng/ml, 50 ng/ml and 2 ng/ml (n=6) was 87.9%, 88.9% and 87.4%, respectively. Hair cortisol levels were not affected by hair color or by dying hair samples after they were obtained. Cortisol levels were decreased in hair that was artificially colored before taking the sample. The coefficient of variation was high for cortisol levels in hair from different sections of the head (30.5 %), but was smaller when comparing between hair samples obtained from the vertex posterior (15.6%). The reference range for cortisol in hair was 17.7-153.2 pg/mg of hair (median 46.1 pg/mg). Hair cortisol levels correlated significantly with cortisol in 24-hour urine (r=0.33; P=0.041). CONCLUSION: The correlation of hair cortisol with 24-hour urine cortisol supports its relevance as biomarker for long-term exposure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".