Lipid Ratios for Diagnosis and Prognosis of Pulmonary Hypertension
Bibliographic record
Abstract
Abstract Rationale Pulmonary hypertension (PH) poses a significant health threat. Current biomarkers for PH lack specificity and have poor prognostic capabilities. Objectives To develop better biomarkers for PH that are useful for patient identification and management. Methods An explorative analysis was conducted of a broad spectrum of metabolites in patients with PH, healthy control subjects, and diseased control subjects in training and validation cohorts, together with in vitro studies on human pulmonary arteries. Measurements and Main Results High-resolution mass spectrometry was performed in 233 subjects coupled with machine learning analysis. Histologic and gene expression analysis was conducted, with a focus on lipid metabolism in human pulmonary arteries of idiopathic pulmonary arterial hypertension lungs and assessment of the acute effects of extrinsic fatty acids (FAs). We enrolled a training cohort of 74 patients with PH, 30 diseased control subjects without PH, and 65 healthy control subjects, as well as an independent validation cohort of 64 subjects. Among other metabolites, FAs were significantly increased. Machine learning showed a high diagnostic potential for PH. In addition, we developed fully explainable lipid ratios with exceptional diagnostic accuracy for PH (areas under the curve of 0.89 in the training cohort and 0.90 in the external validation cohort), outperforming machine learning results. These ratios were also prognostic and complemented established clinical markers and scores, significantly increasing their hazard ratios for mortality risk. Idiopathic pulmonary arterial hypertension lungs showed lipid accumulation and altered expression of lipid homeostasis–related genes. In human pulmonary artery smooth muscle and endothelial cells, FAs caused excessive proliferation and barrier dysfunction, respectively. Conclusions Our metabolomics approach suggests that lipid alterations in PH provide diagnostic and prognostic information, complementing established markers. These alterations may reflect pathologic changes in the pulmonary arteries of patients with PH.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".