Ultra-processed foods and risk of declined renal function: a dose–response meta-analysis of 786,216 participants
Bibliographic record
Abstract
Earlier investigations have documented an association between elevated consumption of Ultra-Processed Foods (UPFs) and adverse renal outcomes. To explore this relationship further, we executed a comprehensive dose–response meta-analysis to examine the link between UPFs intake and the risk of declined renal function. Setting. A systematic search was completed utilizing the ISI Web of Science, Scopus, Embase as well as PubMed/MEDLINE databases (without any restrictions), up until September 5, 2024. Effect sizes of declined renal function were recalculated by applying a random effects model. The GRADE tool was adopted to assess the certainty of the evidence, while study quality and potential publication bias were examined via validated methods such as the Newcastle–Ottawa Scale, Egger’s regression asymmetry and Begg’s rank correlation test. Thirty-three studies (comprising 786,216 participants) were incorporated in the quantitative analysis. The results demonstrated that a greater UPFs intake was significantly associated with an enhanced risk of declined renal function (RR = 1.16; 95% CI: 1.09, 1.23; I 2 = 68.8%; p < 0.001; n = 37). Additionally, we observed that each 1-serving-per-day increase in UPFs consumption was associated to a 5% greater risk of reduced renal function (RR = 1.05; 95% CI: 1.02, 1.09; I 2 = 80.9%; p = 0.013; n = 9). A positive, linear association between UPF intake and the risk of declined renal function (P nonlinearity = 0.107, P dose–response < 0.001) was further displayed in the non-linear dose–response analysis. Greater exposure to UPFs is positively associated with the risk of declined renal function. The information emphasizes the importance of considering UPFs in the prevention and management of adverse renal outcomes.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.047 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".