Biological Variation Data in Triathletes for Metabolism and Growth-Related Biomarkers Included in the Athlete Biological Passport
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Bibliographic record
Abstract
BACKGROUND: When using biological variation (BV) data, BV estimates need to be robust and representative. High-endurance athletes represent a population under special physiological conditions, which could influence BV estimates. Our study aimed to estimate BV in athletes for metabolism and growth-related biomarkers involved in the Athlete Biological Passport (ABP), by 2 different statistical models. METHODS: Thirty triathletes were sampled monthly for 11 months. The samples were analyzed for human growth hormone (hGH), insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), insulin, and N-terminal propeptide of type III procollagen (P-III-NP) by immunoassay. Bayesian and ANOVA methods were applied to estimate within-subject (CVI) and between-subject BV. RESULTS: CVI estimates ranged from 7.8% for IGFBP-3 to 27.0% for insulin, when derived by the Bayesian method. The 2 models gave similar results, except for P-III-NP. Data were heterogeneously distributed for P-III-NP for the overall population and in females for IGF-1 and IGFBP-3. BV components were not estimated for hGH due to lack of steady state. The index of individuality was below 0.6 for all measurands, except for insulin. CONCLUSIONS: In an athlete population, to apply a common CVI for insulin would be appropriate, but for IGF-1 and IGFBP-3 gender-specific estimates should be applied. P-III-NP data were heterogeneously distributed and using a mean CVI may not be representative for the population. The high degree of individuality for IGF-1, IGFBP-3, and P-III-NP makes them good candidates to be interpreted through reference change values and the ABP.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it