GDF10 is a negative regulator of vascular calcification
Bibliographic record
Abstract
Cardiovascular mortality is particularly high and increasing in patients with chronic kidney disease, with vascular calcification (VC) as a major pathophysiologic feature. VC is a highly regulated biological process similar to bone formation involving osteogenic transdifferentiation of vascular smooth muscle cells (VSMCs). We have previously demonstrated that loss of T-cell death-associated gene 51 (TDAG51) expression leads to an attenuation of medial VC. We now show a significant induction of circulating levels of growth differentiation factor 10 (GDF10) in TDAG51 −/− mice, which was of interest due to its established role as an inhibitor of osteoblast differentiation. The objective of this study was to examine the role of GDF10 in the osteogenic transdifferentiation of VSMCs. Using primary mouse and human VSMCs, as well as ex vivo aortic ring cultures, we demonstrated that treatment with recombinant human (rh) GDF10 mitigated phosphate-mediated hydroxyapatite (HA) mineral deposition. Furthermore, ex vivo aortic rings from GDF10 −/− mice exhibited increased HA deposition compared to C57BL/6J controls. To explain our observations, we identified that rhGDF10 treatment reduced protein expression of runt-related transcription factor 2, a key driver of osteogenic transdifferentiation of VSMCs and VC. In support of these findings, in vivo treatment with rhGDF10 attenuated VD 3 -induced VC. Furthermore, we demonstrated an increase in circulating GDF10 in patients with chronic kidney disease with clinically defined severe VC, as assessed by coronary artery calcium score. Thus, our studies identify GDF10 as a novel inhibitor of mineral deposition and as such, may represent a potential novel biomarker and therapeutic target for the detection and management of VC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".