Effects of Energy and Carbohydrate Intake on Serum High-Density Lipoprotein-Cholesterol Levels
Bibliographic record
Abstract
High-density lipoprotein (HDL) is a lipoprotein which has anti-atherogenic property by reverse cholesterol transport from the peripheral tissues to liver. Low HDL-cholesterol (HDL-C) level is associated with the development of coronary artery diseases. We previously studied effects of energy and carbohydrate intake on serum HDL-C to make “Dietary Reference Intakes for Japanese 2015”, and reported the results by reviewing papers by 2012. Here we review meta-analyses about the effects of energy and carbohydrate intake on serum HDL-C levels which were published from 2012 to 2018, to make “Dietary Reference Intake for Japanese 2020”, by using PubMed, Embase and Google Scholar. Effects of energy restriction on HDL-C may depend on backgrounds of subjects studied, the ratio of carbohydrate, protein and fat. Low carbohydrate diet may increase HDL-C, which may be due to reduction of body weight and/or amelioration of insulin resistance. Regarding intake of free sugar, further studies including effects of free sugar intake on other risk factors in addition to HDL-C should be performed. Fructose intake may exert no effect on HDL-C; however, the fructose intake equal to or less than 100 g/day may be recommended considering unfavorable effects on triglyceride (TG) and low-density lipoprotein-cholesterol (LDL-C). J Endocrinol Metab. 2018;8(2-3):27-31 doi: https://doi.org/10.14740/jem504w
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".