Ultra-processed food consumption and the risk of incident chronic kidney disease: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
BACKGROUND: Recent individual studies have indicated that ultra-processed food (UPF) consumption may be associated with the incidence of chronic kidney disease (CKD). We conducted a systematic review and meta-analysis based on those longitudinal studies evaluating the relationship between UPF consumption and the risk of incident CKD, and synthesizing the results. METHOD: PubMed, Embase, The Cochrane Library, Web of Science, and Scopus were searched from inception through 22 March 2023. Any longitudinal studies evaluating the relationship between UPF consumption and the risk of incident CKD were included. Two researchers independently conducted the literature screening and data extraction. RR and its 95% CI were regarded as the effect size. The Newcastle-Ottawa Scale (NOS) was applied to assess the quality of the studies included, and the effect of UPF consumption on the risk of incident CKD was analyzed with STATA version 15.1. This study's protocol was registered in PROSPERO (CRD42023411951). RESULTS: Four cohort studies with a total of 219,132 participants were included after screening. The results of the meta-analysis suggested that the highest UPF intake was associated with an increased risk of incident CKD (RR = 1.25; 95% CI: 1.18-1.33). CONCLUSIONS: High-dose UPF intake was associated with an increased risk of incident CKD. However, the underlying mechanisms remain unknown. Thus, more standardized clinical studies and further exploration of the mechanisms are needed in the future.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.010 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".