The Role of Consumption of Alpha-Linolenic, Eicosapentaenoic and Docosahexaenoic Acids in Human Metabolic Syndrome and Type 2 Diabetes- A Mini-Review
Bibliographic record
Abstract
The human metabolic syndrome and its frequent sequela, type 2 diabetes are epidemic around the world. Alpha-linolenic acid (ALA, 18:3 n-3), eicosapentaenoic acid (EPA, 20:5 n-3) and docosahexaenoic acid (DHA, 22:6 n-3) consumption ameliorates some of these epidemics' features thus leading one to question if consumption of EPA and DHA, and their metabolic precursor ALA reduce the conversion of metabolic syndrome to type 2 diabetes and reduce the major cause of death in the metabolic syndrome and type 2 diabetes-myocardial infarction. Contributing to myocardial infarction are metabolic syndrome's features of dyslipidemia (including elevated total cholesterol and LDL-c), oxidation, inflammation, hypertension, glucose intolerance, overweight and obesity. Inflammation, glucose and lipid levels are variously influenced by disturbances in various adipocytokines which are in turn positively impacted by n-3 polyunsaturated fatty acid consumption. Type 2 diabetes has all these features though elevated total cholesterol and LDL-c are rarer. It is concluded that EPA and DHA consumption significantly benefits metabolic syndrome and type 2 diabetes primarily in terms of dyslipidemia (particularly hypertriglyceridemia) and platelet aggregation with their impact on blood pressure, glucose control, inflammation and oxidation being less established. There is some evidence that EPA and/or DHA consumption, but no published evidence that ALA reduces conversion of metabolic syndrome to type 2 diabetes and reduces death rates due to metabolic syndrome and type 2 diabetes. ALA's only published significance appears to be platelet aggregation reduction in type 2 diabetes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".