Effects of bariatric surgery on hepatic and intestinal lipoprotein particle metabolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: Insulin resistance and type 2 diabetes, driven largely by obesity, are characterized by an increase in triglyceride-rich lipoproteins (TRLs) due to both reduced TRL clearance from the circulation and increased production by the liver (apoB-100 containing VLDLs) and intestine (apoB-48 containing chylomicrons). Bariatric surgery is the only treatment currently that leads to marked, sustained weight loss. Here, we will review the effects of bariatric surgery on circulating triglyceride/TRL and TRL production and clearance. RECENT FINDINGS: Bariatric surgery leads to a marked reduction in fasting and postprandial plasma triglyceride. Only one study to date has assessed TRL kinetics after bariatric surgery and has reported a reduction in TRL apoB-100 concentration (i.e. the number of VLDL particles) due to reduced production and increased clearance and reduced TRL apoB-48 concentration (the number of chylomicron particles) due to reduced production. Some bariatric surgery studies have reported no/weak correlation between weight loss and improvements in triglyceride/TRL, suggesting that as yet unidentified factors beyond weight loss may contribute to the marked changes in TRL that occur postbariatric surgery. SUMMARY: Available data suggest that bariatric surgery reduces triglyceride and intestinal and hepatic TRL production with increased clearance of hepatic TRL particles. These effects of bariatric surgery on TRL kinetics need to be confirmed with additional studies. Further studies are also needed to compare the effects of various bariatric surgery procedures on TRL kinetics and to elucidate underlying mechanisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".