Serum GDF15, a Promising Biomarker in Obese Patients Undergoing Heart Surgery
Why this work is in the frame
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Bibliographic record
Abstract
Background: Obesity is a risk factor that negatively impacts patient outcomes undergoing heart surgery by mechanisms that are not well defined nor predicated on BMI alone. This knowledge gap has fuelled a search for biomarkers associated with cardiovascular disease that could provide clinical insight to surgeons. One such biomarker is growth differentiation factor15(GDF15), associated with inflammation, metabolism and heart failure outcomes but not yet examined in the context of obesity and cardiac surgery outcomes. Methods: Patients undergoing open-heart surgery were consented and enrolled for blood and tissue (atria) sampling at the time of surgery. Biomarkers analysis was carried out using ELISA and western blot/qPCR respectively. Biomarkers screening was classified by inflammation(NLR, GDF15, Galectin3, ST2, TNFR2), heart failure(HF) /remodeling(NT-proBNP) and metabolism(glycemia, lipid profile). Patients were categorized based on BMI: obese group (BMI ≥30.0) and non-obese group(BMI 20.0- 29.9). Subsequent stratification of GDF15 high patients was conservatively set as being in the 75th percentile. Results: A total of 80 patients undergoing any open-heart surgical interventions were included in the study. Obese (mean BMI= 35.8, n=38) and non-obese (mean BMI= 25.7, n=42) groups had no significant differences in age, sex or co-morbidities. Compared to other biomarkers, plasma GDF15 (mean 1736 vs. 1207ng/l, p2years. However, patients with high GDF15 (>1580ng/l) had reduced survival (65%) compared to the remaining patients with lower GDF15 levels (95%) by Kaplan Meier Analysis (median >2 years; p=0.007). Conclusions: Circulating GDF15 is a salient biomarker likely sourced from heart tissue that appears to predict higher risk obese patients for adverse outcomes. More importantly, elevated GDF15 accounted for more sensitive outcome association that BMI at 2 years post-cardiac surgery, suggesting it heralds links to pathogenicity and should be actively studied prospectively and dynamically in a post-operative follow-up.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it