NMR analysis of lipoprotein particle size does not increase sensitivity to the effect of soy protein on CVD risk when compared with the traditional lipid profile
Bibliographic record
Abstract
The traditional lipid profile compared with nuclear magnetic resonance (NMR) may underestimate the risk for cardiovascular disease and may explain some of the discrepancies in results between studies analyzing the salubrious effects of soy. Our purpose was to compare the traditional lipid profile with NMR quantification of the number of lipoprotein particles, subclasses, and diameters or sizes in 30 sedentary males, between 18 and 30 years of age, consuming 1 of the following 3 supplements daily for 28 days: milk protein (Milk), isoflavone-poor soy protein (Soy-), or isoflavone-rich soy protein (Soy+). The study used a double-blind, parallel-arm design with random assignment to 1 of the 3 protein supplement groups. Fasting EDTA blood samples were collected at baseline and after 28 days of supplementation and analyzed for the number and size of very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) particles, respectively. Fasting serum samples were analyzed for concentrations of total cholesterol (TC), LDL cholesterol (LDL-C), total HDL cholesterol (HDL-C), HDL(2)-C, HDL(3)-C, triglycerides (TGs), free fatty acids (FFAs), and glucose. Fasting heparin blood samples were collected at baseline and after supplementation and analyzed for apolipoproteins A-I, A-II, B, C-II, C-III, and E, as well as hepatic and lipoprotein lipase concentrations. HDL3-C increased by 47.2% after Soy+ supplementation and hepatic lipase decreased 19.2% after Soy- supplementation (p < 0.05). HDL-C and apolipoproteins A-I and A-II were found to increase in all 3 groups (p < 0.05). Results support that NMR analysis of lipoprotein particle number and size are not more sensitive to the effect of soy protein on CVD risk compared with the traditional lipid profile. Furthermore, the lack of isoflavones in soy protein seems to have a deleterious effect on hepatic lipase.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".