Effect of soy protein isolate on serum lipids in adults with type 2 diabetes
Bibliographic record
Abstract
Type 2 diabetes is highly prevalent in North America and is associated with increased risk of cardiovascular disease (CVD). Evidence from dietary interventions supports a role for soy protein in the reduction of serum lipids related to CVD risk, however few studies have focused on adults with type 2 diabetes. The purpose of this study was to determine the effect of consuming isoflavone‐rich soy protein isolate (SPI) on serum lipids in adults with type 2 diabetes. Using a double‐blind, randomized, crossover, placebo‐controlled intervention study design, adults with diet‐controlled type 2 diabetes (n=29) consumed SPI or milk protein isolate (MPI) for 57 days each separated by a 4‐week washout period. Fasted blood samples were collected on days 1 and 57 of each treatment period and analyzed for serum lipids and apolipoproteins (apos). Results showed that serum LDL‐cholesterol (chol), and the ratios of LDL‐chol/HDL‐chol and apoB/apoA‐I were significantly reduced following consumption of SPI when compared to MPI (p=0.04, p=0.02 and p=0.05, respectively). There were no significant effects of SPI on other serum lipids (total‐chol, HDL‐chol, triglycerides) or individual apos (apoB, apoA‐I). These data demonstrate that consumption of soy protein can modulate serum lipids in a direction beneficial for CVD risk in adults with type 2 diabetes. Supported by the Heart and Stroke Foundation of Ontario and the Solae Company.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".