Zinc status following different bariatric procedures: systematic review and meta-analysis
Bibliographic record
Abstract
Introduction This study evaluated perioperative changes in serum zinc levels following different bariatric procedures and provided evidence-based recommendations for postoperative monitoring and supplementation.Methods PubMed, Embase, the Cochrane Library, Web of Science and CNKI were systematically searched from inception to July 2025. Eligible studies compared pre- and postoperative serum zinc levels in individuals with obesity undergoing bariatric surgery. Study quality was assessed using the Newcastle–Ottawa Scale (NOS), and the certainty of evidence was graded using the GRADE approach. Pooled analyses were conducted with StataSE 17.0.Results Twelve studies including 2,529 participants were analysed, with overall quality rated as high. Compared with baseline, pooled standardized mean differences (SMDs) in serum zinc at 3 months, 6 months, 1 year, and 2 years postoperatively were −0.12 (95% CI: −0.27 to 0.04, I2 = 57.9%, τ2 = 0.0265, p = 0.149), −0.36 (95% CI: −0.58 to −0.14, I2 = 82.2%, τ2 = 0.1043, p = 0.001), −0.35 (95% CI: −0.53 to −0.16, I2 = 81.9%, τ2 = 0.0769, p = 0.001), and −0.36 (95% CI: −0.95 to 0.24, I2 = 97.2%, τ2 = 0.3515, p = 0.240), respectively. Subgroup analysis showed no significant changes at 3 months across procedures. However, zinc levels significantly decreased at 6 and 12 months after Roux-en-Y gastric bypass (RYGB) and mini-gastric bypass (MGB), but not after sleeve gastrectomy (SG). At 2 years, no significant reduction was observed in any group. The certainty of evidence for zinc changes was rated as moderate.Conclusion Serum zinc levels decline significantly during the first postoperative year, particularly after RYGB and MGB, while SG shows minimal impact. Routine zinc monitoring and individualized supplementation are recommended within the first year after surgery to prevent deficiency-related complications.Registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251138846
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".