α-Lipoic acid as a triglyceride-lowering nutraceutical
Bibliographic record
Abstract
Considering the current obesity epidemic in the United States (>100 million adults are overweight or obese), the prevalence of hypertriglyceridemia is likely to grow beyond present statistics of ∼30% of the population. Conventional therapies for managing hypertriglyceridemia include lifestyle modifications such as diet and exercise, pharmacological approaches, and nutritional supplements. It is critically important to identify new strategies that would be safe and effective in lowering hypertriglyceridemia. α-Lipoic acid (LA) is a naturally occurring enzyme cofactor found in the human body in small quantities. A growing body of evidence indicates a role of LA in ameliorating metabolic dysfunction and lipid anomalies primarily in animals. Limited human studies suggest LA is most efficacious in situations where blood triglycerides are markedly elevated. LA is commercially available as dietary supplements and is clinically shown to be safe and effective against diabetic polyneuropathies. LA is described as a potent biological antioxidant, a detoxification agent, and a diabetes medicine. Given its strong safety record, LA may be a useful nutraceutical, either alone or in combination with other lipid-lowering strategies, when treating severe hypertriglyceridemia and diabetic dyslipidemia. This review examines the current evidence regarding the use of LA as a means of normalizing blood triglycerides. Also presented are the leading mechanisms of action of LA on triglyceride metabolism.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".