Normal Postprandial Nonesterified Fatty Acid Uptake in Muscles Despite Increased Circulating Fatty Acids in Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Postprandial plasma nonesterified fatty acid (NEFA) appearance is increased in type 2 diabetes. Our objective was to determine whether skeletal muscle uptake of plasma NEFA is abnormal during the postprandial state in type 2 diabetes. RESEARCH DESIGN AND METHODS: Thigh muscle blood flow and oxidative metabolism indexes and NEFA uptake were determined using positron emission tomography coupled with computed tomography (PET/CT) with [(11)C]acetate and 14(R,S)-[(18)F]fluoro-6-thia-heptadecanoic acid ((18)FTHA) in seven healthy control subjects (CON) and seven subjects with type 2 diabetes during continuous oral intake of a liquid meal to achieve steady postprandial NEFA levels with insulin infusion to maintain similar plasma glucose levels in both groups. RESULTS: In the postprandial state, plasma NEFA level was higher in type 2 diabetic subjects versus CON (P < 0.01), whereas plasma glucose was at the same level in both groups. Muscle NEFA fractional extraction and blood flow index levels were 56% (P < 0.05) and 24% (P = 0.27) lower in type 2 diabetes, respectively. However, muscle NEFA uptake was similar to that of CON (quadriceps femoris [QF] 1.47 ± 0.23 vs. 1.37 ± 0.24 nmol·g(-1)·min(-1), P = 0.77; biceps femoris [BF] 1.54 ± 0.26 vs. 1.46 ± 0.28 nmol·g(-1)·min(-1), P = 0.85). Muscle oxidative metabolism was similar in both groups. Muscle NEFA fractional extraction and blood flow index were strongly and positively correlated (r = 0.79, P < 0.005). CONCLUSIONS: Postprandial muscle NEFA uptake is normal despite elevated systemic NEFA levels and acute normalization of plasma glucose in type 2 diabetes. Lower postprandial muscle blood flow with resulting reduction in muscle NEFA fractional extraction may explain this phenomenon.
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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".