Effect of Bariatric Surgery on Natriuretic Peptide Levels
Bibliographic record
Abstract
The effect of bariatric surgery on natriuretic peptide levels in patients with obesity is unclear. The purpose of this study was to conduct a systematic review and meta-analysis to determine the effect of bariatric surgery on B-type natriuretic peptide (BNP) and aminoterminal BNP (NT-proBNP) levels. MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials were searched to February 2020. Primary outcomes included change in NT-proBNP or BNP levels following bariatric surgery and change in weight and body mass index. Secondary outcomes included change in blood pressure, echocardiographic findings, and heart failure symptoms. MINORS tool was used to assess quality of evidence. Twelve studies with 622 patients were included. Most patients underwent Roux-en-Y gastric bypass (RYGB) (70.5%). Mean absolute reduction in body mass index was 23%. NT-proBNP levels increased significantly from baseline at 6 months (mean difference [MD] 53.67 pg/mL; 95% confidence interval [CI], 28.72-78.61; P ≤ 0.001, I2 = 99%; 8 studies) and 12 months (MD 51.16 pg/mL; 95% CI, 20.46-81.86; P = 0.001, I2 = 99%; 8 studies) postbariatric surgery. BNP levels also increased significantly at 6 months (MD 17.57 pg/mL; 95% CI, 7.62-27.51; P < 0.001, I2 = 95%; 4 studies). Systolic and diastolic blood pressure decreased significantly 12 months after surgery. Studies measuring echocardiographic findings saw improvement in left ventricle mass and the E/A ratio, but no significant change in ejection fraction. Bariatric surgery is associated with increased natriuretic peptide levels in the absence of deteriorating cardiac function, and may be associated with improved cardiac and metabolic function after the procedure.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".