A Systematic Review and Meta‐analysis of the Effects of Soy Products on Blood Cholesterol Levels
Bibliographic record
Abstract
A systematic review was undertaken to assess the evidence for effects of soy products on blood cholesterol. Peer‐reviewed studies were included if they described intervention or observational (cohort and nested case‐control) studies; included a non‐soy protein control; quantified soy protein intake; included generally healthy or mildly hypercholesterolemic adults (蠅18 y) not taking lipid level‐altering medications, not dieting; lasted 蠅 3 weeks; and reported changes in serum triglycerides, total, LDL‐, and/or HDL‐cholesterol. Significant reductions in total cholesterol (TC) and LDL‐cholesterol (LDL‐C) levels were observed with soy products vs. non‐soy control. Weighted mean differences for TC were ‐5.8 (95% confidence interval ‐8.1 to ‐3.1) mg/dL and for LDL‐C ‐5.8 (‐7.3 to ‐4.3) mg/dL, representing reductions of 2.6% and 4% for TC and LDL‐C, respectively. For TC, reduction was significantly greater in hypercholesterolemic (p<0.00001) than normocholesterolemic subjects (p=0.08). For LDL‐C, subgroup analyses showed that baseline cholesterol levels, source of soy protein, study design, gender, type of diet, pattern of consumption, quality of studies and balancing of caloric and macronutrient profiles between control and treatments groups had little influence on weighted mean differences and statistical significance. Scientific evidence supports a cholesterol‐lowering effect for foods containing soy protein with associated isoflavones. Epidemiological and intervention data suggest that for every 1% reduction in LDL‐C there is a corresponding 1‐2% reduction in cardiovascular events, making reduction of elevated LDL‐C a significant public health goal.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".