Contribution of Triglyceride-rich Lipoproteins to Plasma Free Fatty Acids
Bibliographic record
Abstract
Free fatty acids are the major lipid fuel of the body. Dysregulation of adipose tissue lipolysis results in increased plasma free fatty acid concentrations, and via that mechanism contributes to insulin resistance in obesity and type 2 diabetes mellitus. Adipose tissue hormone sensitive lipase is thought to be responsible for the production of the majority of free fatty acids. However, a separate contribution comes from the action of endothelial lipases, especially lipoprotein lipase, on triglyceride-rich lipoproteins via a process known as spillover. The primary substrate for spillover appears to be chylomicrons derived from dietary fat. The spillover of fatty acids into the free fatty acid pool varies from one tissue to another. For example, spillover is low ( approximately 14%) in the forearm of healthy volunteers, suggesting that triglyceride fatty acid storage is relatively efficient in skeletal muscle. In contrast, spillover appears to be higher in adipose tissue and may also be higher in the splanchnic bed, based on preliminary data. If systemic spillover is increased in insulin resistant states such as diabetes, this could represent a mechanism contributing to the abnormal increases in plasma concentrations of free fatty acids in that condition.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.004 |
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".