Abstract 15604: Dietary Patterns, Blood Pressure and Proteinuria in a West African Chronic Kidney Disease Population
Bibliographic record
Abstract
Introduction: There is little known about the impact of dietary patterns on blood pressure (BP) in West Africa. Our study describes associations of dietary patterns with BP and albuminuria and is the first study to do so in a well-phenotyped West African CKD cohort. Research question: What are the associations of dietary patterns with BP and proteinuria in West African CKD patients? Methods: We performed a cross-sectional analysis of participants from the Diet, Apolipoprotein L1, and CKD study; an ancillary study under the H3 Africa Network. We analyzed 24-hour dietary recalls in 583 participants. Dietary patterns were derived via principal component analysis, and the first 3 principal components with eigenvalues > 1 were selected. We measured proteinuria using 24-hour urine samples. Mixed effect linear regression models were used to estimate the β coefficient and 95% confidence interval for the quartiles of the dietary patterns with BP and proteinuria after adjusting for covariates. Results: The mean age was 49±17 years with 51% males and the mean eGFR (2009 CKD-Epidemiology collaboration formula) was 68±39 mL/min/1.73m 2 . The median 24-hour urine protein was 0.31g (IQR=0.13-1.07). We identified the Dried fish, Oils and vegetables; Poultry and Cereal; and the Fruit and Cereal dietary patterns . Compared to Q1 (lowest consumption), higher quartiles of intake of the Poultry and Cereal dietary pattern were associated with lower systolic BP (SBP) and diastolic (DBP). Higher consumption of the Poultry and Cereal dietary pattern was associated with lower 24hr urine protein, with a significant p for trend (p=0.05). Compared to Q1, Q4 of this pattern was associated with reduced odds of proteinuria ( Table 1 ). Conclusions: Consuming a Poultry and Cereal dietary pattern may be beneficial for BP and proteinuria in West African CKD patients.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".