High-resolution daily profiles of tissue adrenal steroids by portable automated collection
Bibliographic record
Abstract
Rhythms are intrinsic to endocrine systems, and disruption of these hormone oscillations occurs at very early stages of the disease. Because adrenal hormones are secreted with both circadian and ultradian periods, conventional single-time point measurements provide limited information about rhythmicity and, crucially, do not provide information during sleep, when many hormones fluctuate from nadir to peak concentrations. If blood sampling is attempted overnight, then this necessitates admission to a clinical research unit, can be stressful, and disturbs sleep. To overcome this problem and to measure free hormones within their target tissues, we used microdialysis, an ambulatory fraction collector, and liquid chromatography-tandem mass spectrometry to obtain high-resolution profiles of tissue adrenal steroids over 24 hours in 214 healthy volunteers. For validation, we compared tissue against plasma measurements in a further seven healthy volunteers. Sample collection from subcutaneous tissue was safe, well tolerated, and allowed most normal activities to continue. In addition to cortisol, we identified daily and ultradian variation in free cortisone, corticosterone, 18-hydroxycortisol, aldosterone, tetrahydrocortisol and allo-tetrahydrocortisol, and the presence of dehydroepiandrosterone sulfate. We used mathematical and computational methods to quantify the interindividual variability of hormones at different times of the day and develop "dynamic markers" of normality in healthy individuals stratified by sex, age, and body mass index. Our results provide insight into the dynamics of adrenal steroids in tissue in real-world settings and may serve as a normative reference for biomarkers of endocrine disorders (ULTRADIAN, NCT02934399).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".