Fibroblast growth factor 23 levels decline following sleeve gastrectomy
Bibliographic record
Abstract
OBJECTIVE: Levels of fibroblast growth factor 23 (FGF23) have been positively associated with measures of adiposity, cardiovascular disease and mortality. It is unclear whether the relationship of FGF23 with cardiovascular disease and mortality is confounded by obesity. We aimed to determine whether FGF23 concentrations decline following a reduction in adiposity after sleeve gastrectomy (SG). DESIGN: The effect of SG on FGF23 was evaluated in 22 obese adults (59% male) with type 2 diabetes. Fat mass, weight, BMI, plasma intact FGF23, parathyroid hormone (PTH) and leptin were determined at baseline and at 12 months following SG. RESULTS: , 95% CI: -12.9 to -7.2, P < 0.0001), fat mass (-20.0 kg, 95% CI: -26.7 to -12.4, P < 0.0001) and weight (-34.7 kg, 95% CI -40.0 to -23.1, P < 0.0001) were observed after SG. FGF23 (-12.4 pg/mL, 95% CI: -19.5 to 2.0, P = 0.005), PTH (-1.1 pmol/L, 95% CI: -1.7 to 0.2, P = 0.009) and leptin (-1687 pg/mL, 95% CI -4524 to -563, P = 0.01) declined following SG. Change in FGF23 was not significantly associated with change in measures of adiposity, PTH or leptin. CONCLUSIONS: FGF23 concentrations decline in the setting of significant weight loss following SG, implying that increased FGF23 concentrations are a downstream consequence of obesity, which may confound its association with cardiometabolic dysfunction. Mediators of the relationship between adiposity and FGF23 require further elucidation.
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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".