Lipoprotein lipase as a target for obesity/diabetes related cardiovascular disease
Bibliographic record
Abstract
Worldwide, the prevalence of obesity and diabetes have increased, with heart disease being their leading cause of death. Traditionally, the management of obesity and diabetes has focused mainly on weight reduction and controlling high blood glucose. Unfortunately, despite these efforts, poor medication management predisposes these patients to heart failure. One instigator for the development of heart failure is how cardiac tissue utilizes different sources of fuel for energy. In this regard, the heart switches from using various substrates, to predominantly using fatty acids (FA). This transformation to using FA as an exclusive source of energy is helpful in the initial stages of the disease. However, over the progression of diabetes this has grave end results. This is because toxic by-products are produced by overuse of FA, which weaken heart function (heart disease). Lipoprotein lipase (LPL) is responsible for regulating FA delivery to the heart, and its function during diabetes has not been completely revealed. In this review, the mechanisms by which LPL regulates fuel utilization by the heart in control conditions and following diabetes will be discussed in an attempt to identify new targets for therapeutic intervention. Currently, as treatment options to directly target diabetic heart disease are scarce, research on LPL may assist in drug development that exclusively targets fuel utilization by the heart and lipid accumulation in macrophages to help delay, prevent, or treat cardiac failure, and provide long-term management of this condition during diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 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".