Mechanism of insulin-stimulated clearance of plasma nonesterified fatty acids in humans
Bibliographic record
Abstract
Insulin increases plasma nonesterified fatty acid (NEFA) clearance in humans, but whether this is independent of change in plasma NEFA appearance is currently unknown. Nine nondiabetic men (age: 28+/-3 yr, body mass index: 27.2+/-1.7 kg/m2) underwent euglycemic clamps to maintain low (LINS) vs. high (HINS) physiological insulin levels for 6 h. An intravenous infusion of heparin+Intralipid (HI) was performed during 4 of the 6 h of the clamps (in the last 4 h at LINS and in the first 4 h at HINS), whereas saline infusion (SAL) was administered in the remaining 2 h to modulate plasma NEFA levels independently of plasma insulin levels. Four experimental conditions were obtained in each individual: LINS with saline (LINS/SAL) and with HI infusion (LINS/HI) and HINS with saline (HINS/SAL) and with HI infusion (HINS/HI). Plasma palmitate appearance during HINS/SAL was lower than during the three other experimental conditions (P<0.05). In contrast, plasma linoleate appearance, as expected, was increased by HI independently of insulin level (P<0.02). Plasma palmitate clearance during HINS/SAL was higher than LINS/SAL and LINS/HI (P<0.008), and this increase was blunted during HINS/HI. We observed a linear decrease in plasma palmitate clearance with increasing plasma NEFA appearance independent of insulin levels. Plasma NEFA levels increased exponentially with increase in plasma NEFA appearance. We conclude that insulin stimulates plasma NEFA clearance by reducing the endogenous appearance rate of NEFA. The relationship between plasma NEFA level and appearance rate is nonlinear.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".