Improvement of kidney function in patients with chronic kidney disease and severe obesity after bariatric surgery: A systematic review and meta‐analysis
Bibliographic record
Abstract
ABSTRACT The general management for chronic kidney disease (CKD) includes treating reversible causes, including obesity, which may be both a driver and comorbidity for CKD. Bariatric surgery has been shown to reduce the likelihood of CKD progression and improve kidney function in observational studies. We performed a systematic review and meta‐analysis of patients with at least stage 3 CKD and obesity receiving bariatric surgery. We searched Embase, MEDLINE, CENTRAL and identified eligible studies reporting on kidney function outcomes in included patients before and after bariatric surgery with comparison to a medical intervention control if available. Risk of bias was assessed with the Newcastle‐Ottawa Risk of Bias score. Nineteen studies were included for synthesis. Bariatric surgery showed improved eGFR with a mean difference (MD) of 11.64 (95%CI: 5.84 to 17.45, I2 = 66%) ml/min/1.73m2 and reduced SCr with MD of −0.24 (95%CI −0.21 to −0.39, I2 = 0%) mg/dl after bariatric surgery. There was no significant difference in the relative risk (RR) of having CKD stage 3 after bariatric surgery, with a RR of −1.13 (95%CI: −0.83 to −2.07, I2 = 13%), but there was reduced likelihood of having uACR >30 mg/g or above with a RR of −3.03 (95%CI: −1.44 to −6.40, I2 = 91%). Bariatric surgery may be associated with improved kidney function with the reduction of BMI and may be a safe treatment option for patients with CKD. Future studies with more robust reporting are required to determine the feasibility of bariatric surgery for the treatment of CKD.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".