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 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 | 0.007 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".