Protein biomarkers in pulmonary arterial hypertension: advances, clinical relevance, and translational challenges
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is a progressive and life-threatening disease characterized by pulmonary vasoconstriction and right ventricular dysfunction. Although classical circulating biomarkers such as brain natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP) are widely used in clinical settings, their low specificity and substantial variability limit their diagnostic and prognostic accuracy. In recent years, emerging protein biomarkers, such as Apelin, Osteopontin, and Endostatin, have provided deeper insight into disease mechanisms but require further validation. The advent of high-throughput proteomic platforms, including SOMAscan, Olink, and mass spectrometry-based assays, has revolutionized biomarker discovery by enabling the identification of novel candidates with greater sensitivity and specificity. Several proteomics-discovered biomarkers, including LTBP-2, IGFBP family members, NET4, TSP2, and FGF-23, have demonstrated superior prognostic value and may complement or surpass current standards in risk stratification. In this review, we comprehensively examine the landscape of circulating protein biomarkers in PAH, compare key proteomic technologies, and highlight translational challenges such as assay standardization and cohort heterogeneity. We propose an integrative approach combining proteomic, imaging, and genomic data to enhance precision diagnostics and personalized treatment strategies for patients with PAH.
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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) | 0.001 | 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 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".