Cumulative Meta‐Analysis of the Soy Effect Over Time
Bibliographic record
Abstract
Background Soy protein foods have attracted attention as useful plant protein foods with mild cholesterol-lowering effects that are suitable for inclusion in therapeutic diets. But on the basis of the lack of consistency in significant cholesterol reduction by soy in 46 randomized controlled trials, the US Food and Drug Administration (FDA) is reassessing whether the 1999 heart health claim for soy protein should be revoked. Methods and Results We have, therefore, performed a cumulative meta-analysis on the 46 soy trials identified by the FDA to determine if at any time, since the 1999 FDA final rule that established the soy heart health claim, the soy effect on serum cholesterol lost significance. The cumulative meta-analysis for both total cholesterol and low-density lipoprotein cholesterol demonstrated preservation of the small, but significant, reductions seen both before and during the subsequent 14 years since the health claim was originally approved. For low-density lipoprotein cholesterol, the mean reduction in 1999 was -6.3 mg/dL (95% CI, -8.7 to -3.9 mg/dL; P=0.00001) and remained in the range of -4.2 to -6.7 mg/dL ( P=0.0006 to P=0.0002, respectively) in the years after 1999. At no time point did the total cholesterol or low-density lipoprotein cholesterol reductions lose significance or were the differences at individual time points in the cumulative meta-analysis significantly different from those seen in 1999 when the health claim was approved. Conclusions A cumulative meta-analysis of the data selected by the FDA indicates continued significance of total cholesterol and low-density lipoprotein cholesterol reduction after soy consumption and supports the rationale behind the original soy FDA heart health claim.
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 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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.059 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".