Dietary Patterns and Kidney Function in West Africans with CKD
Bibliographic record
Abstract
Background: There is little known about the impact of dietary patterns on chronic kidney disease (CKD) in West Africa. Our study describes associations of dietary patterns with estimated glomerular filtration rate (eGFR) and is the first study to do so in a wellphenotyped West African CKD cohort. Methods: We analyzed participants in the Diet, Apolipoprotein L1 and CKD (DCA) study from 7 centers in West Africa (Ghana and Nigeria). Data from 24-hour dietary recalls were categorized into 32 food groups and 3 dietary patterns were derived via principal component analysis (PCA). Using mixed effect linear regression models, we estimated the b coefficients and 95% confidence intervals for quartiles of the dietary patterns and eGFR (2009 CKD-EPI equation, not corrected for race). Results: Among 583 people with a mean age of 49±17 years and 51% males, the mean eGFR and median 24-hour urine protein were 68±39 mL/min/1.73m2 and 0.31(IQR=0.13-1.07) g respectively. We identified the Dried Fish and Oil dietary pattern, Poultry and Cereal dietary pattern and the Fruit and Cereal dietary pattern. Compared to Q1 (lowest consumption) of the Poultry and Cereal pattern, higher quartiles were associated with higher eGFR in the unadjusted model (p for trend<0.001, Table 1). Adjusting for covariates attenuated the association. Age was associated with the Poultry and Cereal dietary Pattern (-0.02, p < 0.001). No other dietary patterns were associated with eGFR.Table 1:: Associations on Dietary Patterns with eGFRConclusions: In our cross-sectional analyses, there was no association of dietary patterns with eGFR. Future studies on diet quality and nutrient contents and CKD progression in Africans are needed. Our next steps are to investigate other factors associated with dietary patterns in our population (aside from age) and examine longitudinal associations of dietary patterns with CKD progression. 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".