Bariatric Surgery Rapidly Decreases Cardiac Dietary Fatty Acid Partitioning and Hepatic Insulin Resistance Through Increased Intra-abdominal Adipose Tissue Storage and Reduced Spillover in Type 2 Diabetes
Bibliographic record
Abstract
Reduced storage of dietary fatty acids (DFAs) in abdominal adipose tissues with enhanced cardiac partitioning has been shown in subjects with type 2 diabetes (T2D) and prediabetes. We measured DFA metabolism and organ partitioning using positron emission tomography with oral and intravenous long-chain fatty acid and glucose tracers during a standard liquid meal in 12 obese subjects with T2D before and 8–12 days after bariatric surgery (sleeve gastrectomy or sleeve gastrectomy and biliopancreatic diversion with duodenal switch). Bariatric surgery reduced cardiac DFA uptake from a median (standard uptake value [SUV]) 1.75 (interquartile range 1.39–2.57) before to 1.09 (1.04–1.53) after surgery (P = 0.01) and systemic DFA spillover from 56.7 mmol before to 24.7 mmol over 6 h after meal intake after surgery (P = 0.01), with a significant increase in intra-abdominal adipose tissue DFA uptake from 0.15 (0.04–0.31] before to 0.49 (0.20–0.59) SUV after surgery (P = 0.008). Hepatic insulin resistance was significantly reduced in close association with increased DFA storage in intra-abdominal adipose tissues (r = −0.79, P = 0.05) and reduced DFA spillover (r = 0.76, P = 0.01). We conclude that bariatric surgery in subjects with T2D rapidly reduces cardiac DFA partitioning and hepatic insulin resistance at least in part through increased intra-abdominal DFA storage and reduced spillover.
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 | 0.000 | 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".