Association of Macronutrients with CKD Progression in West Africans
Bibliographic record
Abstract
Background: Dietary modifications slow chronic kidney disease (CKD) progression. However, the association of macronutrients with CKD progression is not well understood. Methods: We obtained 24h dietary recall data from participants in the Diet, Apolipoprotein L1 and CKD (DCA) study. We determined macronutrient consumption (g/day) for protein, carbohydrates, polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs) and saturated fats; and expressed consumption in quartiles. Our outcome was the total eGFR slope over 3 years. We used a mixed effect linear regression model with the clinical center as a random effect to evaluate the association of macronutrient consumption with total eGFR slope. The results were expressed as beta coefficients with 95% confidence intervals. Results: We analyzed data from 510 participants: mean age 49 (SD=18) years, 53% male, and mean eGFR was 71ml/min/1.73m2 (SD=39). In the adjusted models compared to the lowest quartiles of consumption (Q1), Q4 of carbohydrate consumption and PUFAs consumption were associated with a positive eGFR slope change; and saturated fats (Q4 vsQ1) was associated with eGFR slope decline. The p for trend was significant moving from lower to higher polyunsaturated fatty acid consumption (Table 1). Conclusion: To our knowledge, this is the first study showing the association of macronutrients with CKD progression in a well-phenotyped West African CKD population. Our observations of a positive association between saturated fat consumption and CKD progression and a protective effect of PUFAs are consistent with previous publications. Carbohydrate consumption results were likely due to the higher quality carbohydrates consumed in West Africa. Funding: NIDDK Support
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".