Beverage Patterns and Risk of CKD Progression in the Diet, CKD, and Apolipoprotein L1 (DCA) Study
Bibliographic record
Abstract
Background: A few studies have established a relationship between beverage patterns and CKD progression, with sugar-sweetened beverages increasing the risk of CKD. We aim to identify beverage consumption patterns of well-phenotyped West Africans with CKD and examine their association with CKD progression. Methods: We conducted a prospective analysis from the DCA cohort (n=748) to investigate diet-gene interactions in APOL1. We included data from a subset of participants with complete information on beverage intake from the 24-hour dietary recalls. CKD progression was defined as ≥40% decline from baseline eGFR at enrollment to DCA (2021-2023) and initiation of dialysis. Beverage consumption patterns were identified through principal component (PC) analysis, and their associations with CKD progression were analyzed using multivariate Cox proportional hazard models. Results: There were 52 cases of CKD progression over a median follow-up period of 31 (IQR: 33.41,25.17) months. We identified four beverage patterns (Table), of which Low Sugar-Sweetened Beverages Plus Alcohol Beverage Pattern demonstrated a significantly lower risk of CKD progression [tertile 2 vs 1 HR: 0.42, 95% CI: 0.19,0.93]. Conclusion: The Low Sugar-Sweetened Beverages Plus Alcohol Beverage Pattern was associated with lower risk of CKD progression, indicating that moderate adherence to this pattern is linked to a reduced risk of CKD progression. The study is the first to establish the beverage patterns of well-phenotyped West African CKD patients and their association with CKD progression. Future investigations should conduct long-term prospective studies to validate and further explore the observed association. Funding: NIDDK Support - NIDDK Support, NIDDK Support
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".