Growth differentiation factor-15 as a biomarker of atherosclerotic coronary plaque: Value in people living with and without HIV
Bibliographic record
Abstract
Background Increased rates of cardiovascular diseases (CVD) and larger subclinical high-risk coronary plaques in coronary CT angiography have been observed in people living with HIV (PLWH) treated with antiretroviral therapy (ART) compared to HIV-uninfected people. Growth differentiation factor-15 (GDF-15) is a cytokine emerging as an optimal marker for CVD in the general population. Methods We cross-sectionally analyzed plasma of 95 PLWH on ART and 52 controls. We measured GDF-15, fibroblast growth factor-21 (FGF-21), glucagon-like peptide-2 (GLP-2), soluble urokinase plasminogen activator receptor (suPAR), CRP, and anti-CMV and anti-EBV IgG levels. All participants had no clinical CVD and underwent coronary CT angiography with the 3D reconstruction of coronary artery atherosclerotic plaques. Total plaque volume (TPV) and low attenuation plaque volume (LAPV, defined as density <30 Hounsfield Units) were calculated (mm 3 ). Results In both PLWH and controls, GDF-15 levels were increased in participants with presence of coronary plaque vs. without ( p = 0.04 and p < 0.001, respectively) and correlated with TPV ( r = 0.27, p = 0.009 and r = 0.62, p < 0.001, respectively) and LAPV ( r = 0.28, p = 0.008, r = 0.60, p < 0.001, respectively). However, in a multivariate model, GDF-15 was independently associated with LAPV in controls only (adjusted OR 35.1, p = 0.04) and not in PLWH, mainly due to confounding by smoking. Other markers were not independently associated with plaque volume, except for anti-EBV IgGs in controls (adjusted OR 3.51, p = 0.02). Conclusion In PLWH, GDF-15 and smoking seemed to synergistically contribute to coronary plaque volume. Conversely, increased GDF-15 levels were associated with the presence of coronary artery plaques in people without HIV, independently of CV risk factors.
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 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 | 0.001 |
| 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".