Effect of nuts on coronary heart disease and cancer risk in type 2 diabetes (825.8)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Diabetes increases the risk of coronary heart disease (CHD) by 2‐4 fold, and the risk of the majority of cancers by 1.2‐2‐fold, where CHD and cancer are two of the leading causes of morbidity and mortality worldwide. Dietary factors have long been implicated in both cancer and CHD, where diet and lifestyle changes are said to reduce risk by 10‐80%. Nuts are one dietary factor that have been associated with reduced CHD and cancer risk in epidemiological studies. Objective: To determine the effect of nut consumption on cancer cell proliferation and its association with oxidative damage. Methods: A 12 week randomized controlled trial was completed by 100 participants with type 2 diabetes consuming one of 3 isocaloric supplemental diets; 1) full‐dose nut diet (~75 g mixed nuts per day), 2) half‐dose nut diet, or 3) control diet, incorporated into a background NCEP Step 2 diet. In vitro prostate cancer cell (LNCaP) proliferation, used as a marker of cancer risk, and markers of oxidative damage for CHD risk were analyzed using available fasting blood obtained at weeks 0 and 12. Results: No treatment differences were observed. However, across treatments, when data were pooled, nut consumption was associated with increased protein thiols (P=0.001) and decreased LNCaP proliferation (P=0.01). Conjugated dienes were positively correlated with LNCaP proliferation (R=0.22, P=0.03). Conclusion: Nut consumption has been associated with reduced CHD risk, though more studies may be required to elucidate the mechanism for CHD reduction, as well as the effect on cancer risk. Grant Funding Source : Supported by the Canadian Foundation for Dietetic Research (CFDR)
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.
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 | 0.001 | 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) | 0.000 | 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 it