Circulating Transforming Growth Factor-β in Marfan Syndrome
Bibliographic record
Abstract
BACKGROUND: Marfan syndrome (MFS) is caused by mutations in the fibrillin-1 gene and dysregulation of transforming growth factor-beta (TGF-beta). Recent evidence suggests that losartan, an angiotensin II type 1 blocker that blunts TGF-beta activation, may be an effective treatment for MFS. We hypothesized that dysregulation of TGF-beta might be mirrored in circulating TGF-beta concentrations. METHODS AND RESULTS: Serum obtained from MFS mutant mice (Fbn1(C1039G/+)) treated with losartan was analyzed for circulating TGF-beta1 concentrations and compared with those from placebo-treated and wild-type mice. Aortic root size was measured by echocardiography. Data were validated in patients with MFS and healthy individuals. In mice, circulating total TGF-beta1 concentrations increased with age and were elevated in older untreated Fbn1(C1039G/+) mice compared with wild-type mice (P=0.01; n=16; mean+/-SEM, 115+/-8 ng/mL versus n=17; mean+/-SEM, 92+/-4 ng/mL). Losartan-treated Fbn1(C1039G/+) mice had lower total TGF-beta1 concentrations compared with age-matched Fbn1(C1039G/+) mice treated with placebo (P=0.01; n=18; 90+/-5 ng/mL), and circulating total TGF-beta1 levels were indistinguishable from those of age-matched wild-type mice (P=0.8). Correlation was observed between circulating TGF-beta1 levels and aortic root diameters in Fbn1(C1039G/+) and wild-type mice (P=0.002). In humans, circulating total TGF-beta1 concentrations were elevated in patients with MFS compared with control individuals (P<0.0001; n=53; 15+/-1.7 ng/mL versus n=74; 2.5+/-0.4 ng/mL). MFS patients treated with losartan (n=55) or beta-blocker (n=80) showed significantly lower total TGF-beta1 concentrations compared with untreated MFS patients (P< or =0.05). CONCLUSIONS: Circulating TGF-beta1 concentrations are elevated in MFS and decrease after administration of losartan, beta-blocker therapy, or both and therefore might serve as a prognostic and therapeutic marker in MFS.
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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 | 0.001 |
| 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".