Healthy Dietary Patterns and Incidence of CKD
Bibliographic record
Abstract
Background and objectives Whether a healthy dietary pattern may prevent the incidence of developing CKD is unknown. This study evaluated the associations between dietary patterns and the incidence of CKD in adults and children. Design, setting, participants, & measurements This systematic review and meta-analysis identified potential studies through a systematic search of MEDLINE, Embase and references from eligible studies from database inception to February 2019. Eligible studies were prospective and retrospective cohort studies including adults and children without CKD, where the primary exposure was dietary patterns. To be eligible, studies had to report on the primary outcome, incidence of CKD (eGFR<60 ml/min per 1.73 m 2 ). Two authors independently extracted data, assessed risk of bias and evidence certainty using the Newcastle–Ottawa scale and GRADE. Results Eighteen prospective cohort studies involving 630,108 adults (no children) with a mean follow-up of 10.4±7.4 years were eligible for analysis. Included studies had an overall low risk of bias. The evidence certainty was moderate for CKD incidence and low for eGFR decline (percentage drop from baseline or reduced by at least 3 ml/min per 1.73 m 2 per year) and incident albuminuria. Healthy dietary patterns typically encouraged higher intakes of vegetables, fruit, legumes, nuts, whole grains, fish and low-fat dairy, and lower intakes of red and processed meats, sodium, and sugar-sweetened beverages. A healthy dietary pattern was associated with a lower incidence of CKD (odds ratio [OR] 0.70 (95% confidence interval [95% CI], 0.60 to 0.82); I 2 =51%; eight studies), and incidence of albuminuria (OR 0.77, [95% CI, 0.59 to 0.99]; I 2 =37%); four studies). There appeared to be no significant association between healthy dietary patterns and eGFR decline (OR 0.70 [95% CI, 0.49 to 1.01], I 2 =49%; four studies). Conclusions A healthy dietary pattern may prevent CKD and albuminuria.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".