Sportomics suggests that albuminuria is a sensitive biomarker of hydration in cross combat
Bibliographic record
Abstract
We have been using sportomics to understand hypermetabolic stress. Cross Combat (CCombat) has recently been initiated as a high-intensity functional training method inspired by CrossFit. We used a CCombat session to induce metabolic stress and evaluated its effects on hydration and kidney function. Blood samples were collected from 16 elite-level professional male athletes engaged in training sessions over a 96-h protocol. Blood myoglobin increased by ~ 3.5-fold (119 ± 21 to 369 ± 62 nmol/L; p = .001) in response to the protocol, returning to the pre-exercise level within 48 h. Furthermore, D-dimer levels increased from 6.5 ± 0.6 to 79.4 ± 21.3 μmol/L (p < .001) in response to exercise decreasing during recovery with high variability among the studied athletes. Albuminemia and creatininemia increased ~ 10% and cystatin C increased ~ 240% (1.7 ± 0.1 to 5.7 ± 0.5 mg/L; p < .001; effect size = 2.4) in response to the protocol. We measured albuminuria (HuA) to assess kidney permeability to albumin caused by exercise. HuA increased ~ 16-fold (0.16 ± 0.03 to 2.47 ± 0.41 μmol/L; p < .001; effect size = 1.4) in response to exercise, dropping and reaching basal levels during 48 h. Here, we suggest that microalbuminuria can be used as an early, sensitive, easy, and inexpensive biomarker to evaluate hydration status changes during intensive exercise, decreasing chronic impairment in renal function.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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 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".