Plasma metabolomic profile in fibrosing pulmonary sarcoidosis.
Bibliographic record
Abstract
BACKGROUND: There is no known marker to screen patients with sarcoidosis to determine the risk of progression to pulmonary fibrosis. We aimed to identify potential noninvasive biomarkers for early detection of pulmonary fibrosing sarcoidosis. METHODS: A case-control study was performed on African Americans with confirmed sarcoidosis included 31 subjects with pulmonary fibrosis vs. 36 without pulmonary fibrosis. Plasma samples were analyzed by liquid chromatography-mass spectrum. Multivariate statistical analysis models were developed in a training set based on 50 age- and sex-matched samples to identify metabolites involved in the discrimination. Principal component analysis and orthogonal partial least squares-discriminant (OPLS) analysis coupled to the most influential variables were used to derive significant metabolic discriminations. RESULTS: Of the datasets from 171 feature peaks, 14 features including p-coumaroylagmatine and palmitoylcarnitine showed significant differences between fibrosing and non-fibrosing pulmonary sarcoidosis (p = 0.001). OPLS analysis presented clear separation between two groups with an acceptable goodness of fit (R(2) = 0.522) and predictive power (Q(2)=0.322). Discriminating metabolites involved collagen pathway metabolites especially those in the arginine-proline pathway. CONCLUSIONS: Metabolomics can provide a useful tool to detect pulmonary fibrosis in patients with sarcoidosis. Two discriminating metabolites, p-coumaroylagmatine and palmitoylcarnitine may be potential markers for fibrosing pulmonary sarcoidosis.
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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) | 0.000 | 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 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".