Associations of Dietary Zinc Supplementation and Sleep Patterns with Chronic Kidney Disease Risk: A Prospective Cohort Study
Bibliographic record
Abstract
Background: Previous studies have indicated that both dietary zinc supplementation and sleep patterns may influence the development of chronic kidney disease (CKD). Additionally, it is established that dietary zinc can enhance sleep quality. Despite these insights, the interplay between zinc supplementation and sleep patterns, and their combined effect on CKD progression, is still not fully understood. Methods: This population-based cohort study used UK Biobank data (2006–2010) and employed cox regression models to assess the associations between dietary zinc supplementation, sleep patterns, and their combined effects on CKD. Results: Over a median follow-up of 14.8 years, 22,384 new CKD cases were identified. Zinc supplementation reduced CKD risk in individuals with poor (HR: 0.70, 95% CI: 0.50–0.98) and moderate (HR: 0.89, 95% CI: 0.81–0.98) sleep patterns but not in those with healthy sleep (HR: 1.00, 95% CI: 0.89–1.14). A significant interaction between zinc supplementation and sleep patterns was observed (p = 0.017), with sensitivity analyses confirming the results. Conclusions: These findings indicate a significant association between dietary zinc supplementation and reduced CKD risk, especially in individuals with poor sleep patterns. Further studies are needed to explore zinc supplementation as a targeted intervention for those at higher CKD risk due to poor sleep.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".