BMP10 reflects pre-capillary pulmonary hemodynamics: association of biomarkers and hemodynamic parameters in pulmonary hypertension
Bibliographic record
Abstract
BACKGROUND AND AIMS: The role of biomarkers in diagnosing pulmonary hypertension (PH) and distinguishing between pre- and post-capillary PH remains poorly understood. We aimed to identify biomarkers with a strong association with mean pulmonary arterial pressure, mPAP (PH diagnosis) and pulmonary vascular resistance, PVR (pre-capillary component), but not with pulmonary arterial wedge pressure, PAWP (post-capillary component). METHODS: Blood samples were collected in patients undergoing right heart catheterization within a prospective cross-sectional study. Biomarkers measured included BMP10, NT-proBNP, ANG2, ESM1/endocan, FGF23, GDF15, IGFBP7, IL6, MyBPC3, proC3, and proC6/endotrophin. Primary outcomes were mPAP, PVR, and PAWP, while secondary outcomes included PH diagnosis (mPAP > 20 mmHg) and elevated PVR (> 2 Wood units). Multivariable linear and logistic regression models were used to assess the relationship between biomarkers and outcomes. RESULTS: Of the 127 patients included (age 66 ± 13 years, 54% female), 73% were diagnosed with PH. BMP10, NT-proBNP, ANG2, MyBPC3, and FGF23 showed a strong association with mPAP (p < 0.001). BMP10 and NT-proBNP were strongly associated with PVR (p < 0.001), while NT-proBNP and ANG2 were strongly associated with PAWP (p < 0.001). NT-proBNP had the strongest association with the diagnosis of PH (area under the curve = 0.76). BMP10 was the only biomarker associated with elevated PVR (OR 1.60, 95%CI 1.01-2.54, p = 0.04) but not with PAWP (p = 0.86). CONCLUSIONS: Several biomarkers were strongly associated with mPAP, PAWP, and PVR. BMP10 was the only biomarker strongly associated with mPAP and PVR, but not with PAWP, thus reflecting the pre-capillary PH component. Measurement of BMP10 along with NT-proBNP may aid in diagnosing 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".