Peripheral Blood Signature of Vasodilator-Responsive Pulmonary Arterial Hypertension
Bibliographic record
Abstract
BACKGROUND: Heterogeneity in response to treatment of pulmonary arterial hypertension (PAH) is a major challenge to improving outcome in this disease. Although vasodilator-responsive PAH (VR-PAH) accounts for a minority of cases, VR-PAH has a pronounced response to calcium channel blockers and better survival than vasodilator-nonresponsive PAH (VN-PAH). We hypothesized that VR-PAH has a different molecular cause from VN-PAH that can be detected in the peripheral blood. METHODS AND RESULTS: Microarrays of cultured lymphocytes from VR-PAH and VN-PAH patients followed at Vanderbilt University were performed with quantitative polymerase chain reaction performed on peripheral blood for the 25 most different genes. We developed a decision tree to identify VR-PAH patients on the basis of the results with validation in a second VR-PAH cohort from the University of Chicago. We found broad differences in gene expression patterns on microarray analysis including cell-cell adhesion factors and cytoskeletal and rho-GTPase genes. Thirteen of 25 genes tested in whole blood were significantly different: EPDR1, DSG2, SCD5, P2RY5, MGAT5, RHOQ, UCHL1, ZNF652, RALGPS2, TPD52, MKNL1, RAPGEF2, and PIAS1. Seven decision trees were built with the use of expression levels of 2 genes as the primary genes: DSG2, a desmosomal cadherin involved in Wnt/β-catenin signaling, and RHOQ, which encodes a cytoskeletal protein involved in insulin-mediated signaling. These trees correctly identified 5 of 5 VR-PAH patients in the validation cohort. CONCLUSIONS: VR-PAH and VN-PAH can be differentiated with the use of RNA expression patterns in peripheral blood. These differences may reflect different molecular causes of the 2 PAH phenotypes. This biomarker methodology may identify PAH patients who have a favorable treatment response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".