Association between dietary protein intake and risk of chronic kidney disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective There is suggestive data indicating a correlation among dietary protein intake and the progression of chronic kidney disease (CKD). Nonetheless, the exact associations between dietary protein intake and the incidence of CKD have remained uncertain. We performed the first meta-analysis to explore the correlation among total protein, plant protein, animal protein intake and CKD risk. Methods The study conformed the PRISMA statement guidelines. We comprehensively searched PubMed, Web of Science, and Embase until to December 2023. The retrieved studies underwent rigorous evaluation for eligibility, and relevant data were meticulously extracted. The Newcastle-Ottawa Scale (NOS) tool was applied to evaluate the risk of bias. Subsequently, relevant data were extracted and pooled to evaluate the relations among dietary protein intake and CKD incidence. Results Totally, 6,191 articles were identified, six studies were eligible. A total of 148,051 participants with 8,746 CKD cases were included. All studies had a low overall risk of bias. Higher total, plant and animal protein intake were all correlated with decreased CKD incidence, pooled risk ratios (RRs) and 95% confidence intervals (CIs) were as follows: (RR = 0.82, 95% CI = 0.71–0.94, p = 0.005; I2 = 38%, p = 0.17); (RR = 0.77, 95% CI = 0.61–0.97, p = 0.03; I2 = 77%, p = 0.001); (RR = 0.86, 95% CI = 0.76–0.97, p = 0.02; I2 = 0%, p = 0.59), respectively. For fish and seafood within animal protein: RR = 0.84, 95% CI = 0.74–0.94. Subgroup analysis showed that geographical region, sample size, follow-up time, not assessing protein by food frequency questionnaire, using %energy as the measurement index, not adjusting for several covariates may be the sources of heterogeneity for plant protein. A significant non-linear relation among plant protein and incident CKD was observed by dose–response analysis. Conclusion The data showed a lower CKD risk significantly associated higher-level dietary total, plant or animal protein (especially for fish and seafood) intake. Further prospective studies demonstrating the correlations of precise sources, intake and duration of dietary protein and incident CKD are warranted.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.039 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".