Fatty Acid Metabolic Remodeling During Type 2 Diabetes Remission After Bariatric Surgery
Bibliographic record
Abstract
Hypertrophic remodeling of white adipose tissues is associated with overexposure of lean organs to circulating triglycerides (TGs) and nonesterified fatty acids (NEFAs), ultimately leading to insulin resistance. Bariatric surgery promotes type 2 diabetes (T2D) remission through a succession of weight loss-dependent and -independent mechanisms. However, the longitudinal contribution of adipocyte size reduction and fatty acid metabolic handling remain unknown. Here we show that severely obese participants with T2D display hypertriglyceridemia and excessive systemic lipolysis during intravenous lipid overload. Three days after biliopancreatic diversion with duodenal switch (DS), whole-body glycerol turnover was normalized and associated with lower HOMA-insulin resistance index. A mean excess weight loss of 84% was achieved 12 months after DS. The smaller subcutaneous adipocyte size predicted better glycemic control in T2D. TG disposal and acylcarnitine production during lipid overload, along with muscle insulin sensitivity, improved with weight loss. Nevertheless, systemic NEFA fluxes and NEFA spillover remained similar, suggesting that increased NEFA storage capacity per volume of adipose tissue exactly compensated for the decrease in fat mass during weight loss. In conclusion, T2D remission after DS is mainly associated with greater circulating TG disposal, lower systemic lipolysis, and better fatty acid handling by lean tissues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".