Influence of Low Carbohydrate High Fat Ketogenic Diets on Renal and Liver Parameters
Bibliographic record
Abstract
In recent times the use of high fat ketogenic diet as a treatment strategy in some diseases and weight control has been on the increase. This study aims to elucidate the effect of high fat ketogenic diet on some renal and liver parameters. Forty albino rats were used and divided into four groups. Group A was control; B, C, and D were fed with diets including butter, coconut oil and olive oil respectively for eight weeks. Urine and serum samples were assayed spectrophotometrically. There was a significant difference in urinary albumin (0.13±0.01g/dl) of group D when compared with control (0.22 ± 0.03g/dl). Urinary creatinine concentrations of group D (4.32±0.70mg/dl) was higher than group C (1.75±0.46 mg/dl). Urea of group B (39.40±4.70 mg/dl), group C (29.90±1.46 mg/dl) and group D (40.20±2.62mg/dl) were lower than control group (64.20±3.41mg/dl). Serum creatinine concentrations of group B (1.05±0.09mg/dl), group C (0.85±0.07lmg/dl) and group D (1.03±0.07 mg/dl) were reduced significantly. Albumin: creatinine ratio of group A (120.6±32.04) was higher than that of group D (41.31±8.28). AST (260.1±17.80) was higher in group C compared with A (160.1± 9.510). ALT for D (91.20±18.70), group A (36.00±3.84), serum albumin concentrations of group D (3.590±0.1286), group C (3.590±0.1286) and group A (4.100±0.1814). Total protein concentration of group C (5.390±0.2105), D (5.280± 0.1104) and group A (6.190±0.2496g). Body weight of experimental groups reduced while the control groups increased. This study has confirmed that high fat ketogenic diet can be used for weight management however it could be harmful to the liver but did not show any harmful effects on the kidneys.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".