Metabolomics of Human Cerebrospinal Fluid Identifies Signatures of Malignant Glioma
Bibliographic record
Abstract
Cerebrospinal fluid is routinely collected for the diagnosis and monitoring of patients with neurological malignancies. However, little is known as to how its constituents may change in a patient when presented with a malignant glioma. Here, we used a targeted mass-spectrometry based metabolomics platform using selected reaction monitoring with positive/negative switching and profiled the relative levels of over 124 polar metabolites present in patient cerebrospinal fluid. We analyzed the metabolic profiles from 10 patients presenting malignant gliomas and seven control patients that did not present malignancy to test whether a small sample size could provide statistically significant signatures. We carried out multiple unbiased forms of classification using a series of unsupervised techniques and identified metabolic signatures that distinguish malignant glioma patients from the control patients. One subtype identified contained metabolites enriched in citric acid cycle components. Newly diagnosed patients segregated into a different subtype and exhibited low levels of metabolites involved in tryptophan metabolism, which may indicate the absence of an inflammatory signature. Together our results provide the first global assessment of the polar metabolic composition in cerebrospinal fluid that accompanies malignancy, and demonstrate that data obtained from high throughput mass spectrometry technology may have suitable predictive capabilities for the identification of biomarkers and classification of neurological diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".