Effect of Obesity on High‐density Lipoprotein Metabolism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reduced levels of high-density lipoproteins (HDL) in non-obese and obese states are associated with increased risk for the development of coronary artery disease. Therefore, it is imperative to determine the mechanisms responsible for reduced HDL in obese states and, conversely, to examine therapies aimed at increasing HDL levels in these individuals. This paper examines the multiple causes for reduced HDL in obese states and the effect of exercise and diet--two non-pharmacologic therapies--on HDL metabolism in humans. In general, the concentration of HDL-cholesterol is adversely altered in obesity, with HDL-cholesterol levels associated with both the degree and distribution of obesity. More specifically, intra-abdominal visceral fat deposition is an important negative correlate of HDL-cholesterol. The specific subfractions of HDL that are altered in obese states include the HDL2, apolipoprotein A-I, and pre-beta1 subfractions. Decreased HDL levels in obesity have been attributed to both an enhancement in the uptake of HDL2 by adipocytes and an increase in the catabolism of apolipoprotein A-I on HDL particles. In addition, there is a decrease in the conversion of the pre-beta1 subfraction, the initial acceptor of cholesterol from peripheral cells, to pre-beta2 particles. Conversely, as a means of reversing the decrease in HDL levels in obesity, sustained weight loss is an effective method. More specifically, weight loss achieved through exercise is more effective at raising HDL levels than dieting. Exercise mediates positive effects on HDL levels at least partly through changes in enzymes of HDL metabolism. Increased lipid transfer to HDL by lipoprotein lipase and reduced HDL clearance by hepatic triglyceride lipase as a result of endurance training are two important mechanisms for increases in HDL observed from exercise.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it