Dietary cholesterol and other nutritional considerations in people with diabetes
Bibliographic record
Abstract
BACKGROUND: Nutrition therapy is an integral component of lifestyle intervention and self-management of people with diabetes. The goals of nutrition therapy are to optimise or maintain quality of life, physiological and mental health, and to prevent and treat acute and long-term complications of diabetes, the associated comorbid conditions and concomitant disorders. Monitoring dietary cholesterol consumption and salt intake are important nutritional aspects to lower the risk for and treatment of cardiovascular disease and hypertension. AIMS: To evaluate the role of nutritional therapy and notably the effect of egg consumption on cardiovascular disease (CVD) risk in people with diabetes. METHODOLOGY: Literature review of nutritional therapy and clinical studies on egg consumption and CVD risk for people with diabetes were conducted and appraised. RESULTS: The Harvard Egg Study on two large prospective US cohorts found that eating one or more eggs a day had no adverse effects on lipid profile or cardiovascular disease risk in men or women. Similar findings were observed in the NHANES-I and Physicians' Health Study. The only exception was people with diabetes, where CVD was increased with eating more than one egg per day. CONCLUSIONS: Consumption of one or more eggs per day is associated with an elevated risk of coronary heart disease in people with diabetes. The mechanism for this association remains unknown but should be explored in randomised clinical trials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".