Association between Water Intake, Chronic Kidney Disease, and Cardiovascular Disease: A Cross-Sectional Analysis of NHANES Data
Bibliographic record
Abstract
BACKGROUND: Evidence from animal and human studies suggests a protective effect of higher water intake on kidney function and cardiovascular disease (CVD). Here the associations between water intake, chronic kidney disease (CKD) and CVD were examined in the general population. METHODS: We conducted a cross-sectional analysis of the 2005-2006 National Health and Nutrition Examination Survey. Non-pregnant adults with an estimated glomerular filtration rate (eGFR) ≥30 ml/min/1.73 m(2) who were not taking diuretics were included. Total water intake from foods and beverages was categorized as low (<2.0 l/day), moderate (2.0-4.3 l/day) and high (>4.3 l/day). We examined associations between low total water intake and CKD (eGFR 30-60 ml/min/1.73 m(2)) and self-reported CVD. RESULTS: Of 3,427 adults (mean age 46 (range 20-84); mean eGFR 95 ml/min/1.73 m(2) (range 30-161)), 13% had CKD and 18% had CVD. CKD was higher among those with the lowest (<2.0 l/day) vs. highest total water intake (>4.3 l/day) (adjusted odds ratio (OR) 2.52; 95% confidence interval (CI) 0.91-6.96). When stratified by intake of (1) plain water and (2) other beverages, CKD was associated with low intake of plain water: adjusted OR 2.36 (95% CI 1.10-5.06), but not other beverages: adjusted OR 0.87 (95% CI 0.30-2.50). There was no association between low water intake and CVD (adjusted OR 0.76; 95% CI 0.37-1.59). CONCLUSIONS: Our results provide additional evidence suggesting a potentially protective effect of higher total water intake, particularly plain water, on the kidney.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".