Weight loss and proteinuria: systematic review of clinical trials and comparative cohorts
Bibliographic record
Abstract
BACKGROUND: Obesity is a risk factor for the progression of chronic kidney disease (CKD). The impact of weight loss on proteinuria and renal function is less clear. We aimed to determine the effect of intentional weight loss on proteinuria and kidney function. METHODS: Three bibliographic databases including Medline, Cochrane and SCUPOS as well as reference list of articles were searched. We included randomized and non-randomized controlled trials as well as single-arm trials published in English through May 2009 which examined urinary protein among obese or overweight adults before and after weight loss interventions including dietary restriction, exercise, anti-obesity medications and bariatric surgery. Study characteristics and methodological quality of trials were assessed. RESULTS: Five hundred twenty-two subjects from five controlled and eight uncontrolled trials were included. Weight loss interventions were associated with decreased proteinuria and microalbuminuria by 1.7 g [95% confidence interval (95% CI), 0.7 to 2.6 g] and 14 mg (95% CI, 11 to 17 mg), respectively (P < 0.05). Meta-regression showed that, independent of decline in mean arterial pressure, each 1 kg weight loss was associated with 110 mg (95% CI, 60 to 160 mg, P < 0.001) decrease in proteinuria and 1.1 mg (95% CI, 0.5 to 2.4 mg, P = 0.011) decrease in microalbuminuria, respectively. The decrease was observed across different designs and methods of weight loss. Only bariatric surgery resulted in a significant decrease in creatinine clearance. CONCLUSIONS: Weight loss is associated with decreased proteinuria and microalbuminuria. There were no data evaluating the durability of this decrease or the effect of weight loss on CKD progression.
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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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".