Dietary Magnesium and Kidney Function Decline: The Healthy Aging in Neighborhoods of Diversity across the Life Span Study
Bibliographic record
Abstract
BACKGROUND: Prior studies suggest that certain aspects of the diet related to magnesium intake, such as dietary acid load, protein intake and dietary patterns rich in fruits and vegetables, may impact kidney disease risk. We hypothesized that lower dietary magnesium intake would be prospectively associated with more rapid kidney function decline. METHODS: Among participants in the Healthy Aging in Neighborhoods of Diversity across the Life Span study with estimated glomerular filtration rate (eGFR) ≥60 ml/min/1.73 m2 at baseline (2004-2009), dietary magnesium intake was calculated from two 24-hour dietary recalls. Rapid decline was defined as ≥3% eGFR decline per year. RESULTS: Median (25th-75th percentile) dietary magnesium intake was 116 (96-356) mg/1,000 kcal. Among 1,252 participants, those with lower dietary magnesium intake were younger, and were more likely to be African-American men. A total of 177 participants (14.1%) experienced rapid eGFR decline over a median follow-up of 5 years. Lower dietary magnesium intake was significantly associated with a greater odds of rapid eGFR decline (OR for tertile 1 vs. 3: 2.02, 95% CI 1.05-3.86, p value for trend across tertiles = 0.02) in analyses adjusted for sociodemographics (age, sex, race, education level, health insurance status, poverty status), kidney disease risk factors (smoking status, diabetes, hemoglobin A1c, hypertension, body mass index), baseline eGFR and dietary factors (total energy intake; diet quality; dietary intake of fiber, sodium, calcium, potassium and phosphorus). CONCLUSIONS: In this urban population, lower dietary magnesium intake was independently associated with greater odds of rapid kidney function decline.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".