Bibliographic record
Abstract
Plasma apoB is a more accurate marker of the risk of CVD and type 2 diabetes (T2D) than LDL-cholesterol; however, nutritional reviews targeting apoB are scarce. Here we reviewed eighty-seven nutritional studies and present conclusions in order of strength of evidence. Plasma apoB was reduced in all studies that induced weight loss of 6-12 % using hypoenergetic diets (seven studies; 5440-7110 kJ/d; 1300-1700 kcal/d; 34-50 % carbohydrates; 27-39 % fat; 18-24 % protein). When macronutrients were compared in isoenergetic diets (eleven studies including eight randomised controlled trials (RCT); n 1189), the diets that reduced plasma apoB were composed of 26-51 % carbohydrates, 26-46 % fat, 11-32 % protein, 10-27 % MUFA, 5-14 % PUFA and 7-13 % SFA. Replacement of carbohydrate by MUFA, not SFA, decreased plasma apoB. Moreover, dietary enriching with n-3 fatty acids (FA) (from fish: 1·1-1·7 g/d or supplementation: 3·2-3·4 g/d EPA/DHA or 4 g/d EPA), psyllium (about 8-20 g/d), phytosterols (about 2-4 g/d) or nuts (30-75 g/d) also decreased plasma apoB, mostly in hyperlipidaemic subjects. While high intake of trans-FA (4·3-9·1 %) increased plasma apoB, it is unlikely that these amounts represent usual consumption. Inconsistent data existed on the effect of soya proteins (25-30 g/d), while the positive association of alcohol consumption with low plasma apoB was reported in cross-sectional studies only. Five isoenergetic studies using Mediterranean diets (including two RCT; 823 subjects) reported a decrease of plasma apoB, while weaker evidence existed for Dietary Approaches to Stop Hypertension (DASH), vegetarian, Nordic and Palaeolithic diets. We recommend using a Mediterranean dietary pattern, which also encompasses the dietary components reported to reduce plasma apoB, to target hyperapoB and reduce the risks of CVD and T2D.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".