Targeted metabolomics in colorectal cancer: a strategic approach using standardized laboratory tests of the blood and urine
Bibliographic record
Abstract
Background: Glycolytic markers have been detected in colorectal cancer (CRC) using advanced analytical methods. Methods: Using commercially available assays, by-products of anaerobic metabolism were prospectively measured in the blood and urine of 20 patients with metastatic colorectal cancer (mCRC) and 20 patients with local disease. Twenty-four-hour urine citrate, plasma lactate, ketones, venous blood gas, anion gap, and osmolar gap were investigated. Results of patients with metastatic and local CRC were compared using two-sample t -tests or equivalent nonparametric tests. In addition, plasma total CO 2 concentrations in our local hospital (5,931 inpatients and 1,783 outpatients) were compared retrospectively with those in our dedicated cancer center (1,825 outpatients) over 1 year. Results: The average venous pCO 2 was higher in patients with mCRC (50.2 mmHg; standard deviation [SD]=9.36) compared with those with local disease (42.8 mmHg; SD=8.98), p =0.045. Calculated serum osmolarity was higher in mCRC and attributed to concomitant sodium and urea elevations. In our retrospective analysis, plasma total CO 2 concentrations (median=27 mmol/L) were higher in cancer patients compared to both hospital inpatients (median=23 mmol/L) and outpatients (median=24 mmol/L), p <0.0001. Conclusion: Patients with mCRC had higher venous pCO 2 levels than those with local disease. Although causation cannot be established, we hypothesize that pCO 2 elevation may stem from a perturbed metabolism in mCRC. Keywords: metabolism, metabolomics, biomarker, colorectal cancer, hypoxia, venous CO 2
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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) | 0.001 | 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 | 0.001 |
| 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 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".