Influence of a low-carbohydrate diet on endothelial microvesicles in overweight women
Bibliographic record
Abstract
Low-carbohydrate diets (LCD) are increasing in popularity, but their effect on vascular health has been questioned. Endothelial microvesicles (EMV) are membrane-derived vesicles with the potential to act as a sensitive prognostic biomarker of vascular health and endothelial function. The aim of this study was to examine the influence of a LCD on EMV and other endothelial biomarkers of protein origin. Twenty-four overweight women (age, 48.4 ± 0.6 years; height, 1.60 ± 0.07 m; body mass, 76.5 ± 9.1 kg; body mass index, 28.1 ± 2.7 kg·m(-2); waist circumference, 84.1 ± 7.4 cm; mean ± standard deviation) were randomised to either 24 weeks on their normal diet (ND) or a LCD, after which they crossed over to 24 weeks on the alternative diet. Participants were assisted in reducing carbohydrate intake, but not below 40 g·day(-1). Body composition and endothelial biomarkers were assessed at the crossover point and at the end of the study. Daily carbohydrate intake (87 ± 7 versus 179 ± 11 g) and the percentage of energy derived from carbohydrate (29% versus 44%) were lower (p < 0.05) on the LCD compared to the ND, but absolute fat and saturated fat intake were unchanged. Body mass and waist circumference were 3.7 ± 0.8 kg and 3.5 ± 1.0 cm lower (p < 0.05), respectively, after the LCD compared with the ND phases. CD31(+)CD41(-)EMV, soluble (s) thrombomodulin, sE-selectin, sP-selectin, serum amyloid A and C-reactive protein were lower (p < 0.05) after the LCD compared to the ND, but serum lipids and apolipoproteins were not different. EMV along with a range of endothelial and inflammatory biomarkers are reduced by a LCD that involves modest weight loss.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".