Effects of Intake of Soy and Non-Soy Legume on Serum HDL-Cholesterol Levels
Bibliographic record
Abstract
High-density lipoprotein (HDL) has been shown to have a variety of functions that contribute to anti-atherogenesis. Here we review meta-analyses on the effects of intake of soy protein and isoflavones on serum HDL-cholesterol (HDL-C) levels, and we would also review meta-analysis on the effects of intake of non-soy legume on serum HDL-C, to make “Dietary Reference Intake for Japanese 2020”. We searched meta-analyses of randomized, placebo-controlled trials. A search was conducted by using PubMed, Embase and Google Scholar, with the following keywords: soy and HDL and meta-analysis. The search period was comprised from 2007 up to July 2018. We found three meta-analyses about effects of intake of soy protein and isoflavones on HDL-C after 2007. All meta-analyses reported that intake of soy protein and isoflavones was associated with a significant increase of HDL-C. We found the meta-analysis which evaluated effects of intake of non-soy legume on HDL-C, in which a significant association of intake of non-soy legume with HDL-C was not obtained due to a significant heterogeneity of collected data. In conclusion, intake of soy was significantly associated with elevation of HDL-C; however, non-soy consumption was not associated with a significant increase of HDL. J Endocrinol Metab. 2018;8(5):83-86 doi: https://doi.org/10.14740/jem524w
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".