The Effects of Different Quantities and Qualities of Protein Intake in People with Diabetes Mellitus
Bibliographic record
Abstract
The recommended amount and quality of protein in diets of diabetic patients are highly controversial. In order to provide evidence-based information, the Diabetes Nutrition Study Group (DNSG) used a grading procedure used for quality of evidence and strength of recommendations (GRADE). A protein intake of 10% to 20% of energy intake (E%) or about 0.8 to 1.3 g/kg body weight in people below 65 years of age, and 15% to 20% of E% in people above 65 years of age appeared safe in weight-stable conditions. There were no intervention studies addressing metabolic effects, mortality, or cardiovascular events over prolonged periods. Body weight is closely linked to metabolic control and high protein diets are often recommended. Weight-loss diets that include 23% to 32% of E% as protein for up to one year reduced blood pressure and body weight slightly but significantly more than lower protein diets, whereas blood lipids, fasting blood glucose, and HbA1c improved similarly with higher or lower protein intakes in participants with a glomerular filtration rate (GFR) >60 mL/min/1.73 m2. Patients with a GFR <60 mL/min/1.73 m2 did not show a faster decline of GFR or kidney function with protein intakes around 0.8 g/kg body weight as compared with lower intakes, thereby arguing against a restriction. The effects of protein intake on diabetic eye or nerve disease have not been reported. There are a number of studies that have compared different types of animal proteins (milk, chicken, beef, pork, and fish) or compared animal with plant protein in diabetic patients and have reported a greater reduction of serum cholesterol with plant protein. In summary, the suggested range of protein intake appears to be safe and can be adapted according to personal dietary preferences.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".