Optimizing Nutrition in Renal Patients: Effects of a Low-Protein Diet Supplemented With Ketoacids
Bibliographic record
Abstract
Background Chronic kidney disease (CKD) is a non-communicable disease; it is a major cause of morbidity and mortality in Nigeria as the incidence has been increasing in Nigeria over the last few years. A low-protein diet supplemented with ketoacids has been duly documented to reduce the malnutrition associated with CKD as well as improve estimated glomeruli filtration rate while delaying the onset of dialysis in predialysis CKD patients. Objective The aim of this study was to determine the effects of a low-protein diet supplemented with ketoacids compared to a conventional low protein on nutritional indices in predialysis CKD patients. Methods and materials A randomized controlled trial with a total of 60 participants was conducted at Delta State University Teaching Hospital (DELSUTH), Oghara, Nigeria. Participants were patients older than 18 years with CKD stage 3-5 who were not on dialysis. They were recruited and randomized into the intervention group (low-protein diet supplemented with ketoacids) with 30 participants and the non-intervention group (low protein with placebo) with 30 participants. The mean outcome was changed in the nutritional indices from baseline till the end of the study. Results A total of 60 patients were randomly allocated to receive a low-protein diet supplemented with ketoacids (n=30) or control (n=30). All participants were included in the analysis of all outcomes. The mean change score in serum total protein, albumin, and triglycerides between the intervention and non-intervention groups were 1.1±1.1 g/dL vs 0.1±1.1 g/dL (p<0.001), 0.2±0.9 g/dL vs -0.3±0.8 g/dL (p<0.001), and 3.0±3.5 g/dL vs 1.8±3.7 g/dL, respectively. Conclusion and recommendation The use of low-protein diet supplemented with ketoacids improved the anthropometric and nutritional indices in patients with stage 3-5 CKD.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".