A longitudinal evaluation of the relationship between first morning urinary and salivary cortisol
Bibliographic record
Abstract
UNLABELLED: Cortisol is one of the most frequently used stress biomarkers in humans. Urine and saliva are the matrices of choice to longitudinally monitor cortisol levels. Salivary and urinary cortisol are often discussed as though they provide similar information. However, the relationship between "free" cortisol levels in urine (nonconjugated) and saliva (non-protein-bound) has yet to be properly evaluated using naturalistic designs. OBJECTIVES: To investigate the longitudinal relationship between salivary cortisol (SC) and first morning urinary cortisol (FMUC), and to compare the advantages and disadvantages of these matrices in assessing longitudinal changes in cortisol secretion using naturalistic designs. METHODS: Cortisol levels from 31 healthy, Kakchiquel Mayan women in Guatemala were compared in one first morning urine (FMU) and four saliva specimens collected daily across three alternate days. Linear mixed-effect regression models including fixed and random effects were used to analyze the repeated-measures data. RESULTS: FMUC levels (16.04-242.18 ng/ml) were higher than SC levels (0.21-5.16 ng/ml). A small but statistically significant relationship was found between FMUC and SC (each 1 ng/ml increase in FMUC predicted a 0.1% increase in SC; P < 0.05). CONCLUSIONS: Nonconjugated FMUC levels are related to non-protein-bound SC levels collected throughout the day. FMU presents several advantages over saliva for the longitudinal assessment of cortisol in naturalistic studies. Cortisol levels are about 53-fold higher in FMU than in saliva, which makes between- and within-individual variation easier to detect, and FMUC levels are less likely to be affected by confounders than diurnal SC levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".