Net protein anabolic response is not altered during hyperaminoacidemic (hyperAA) clamp in men with Type 2 Diabetes (T2D)
Bibliographic record
Abstract
Reduced 24h net protein balance in hyperglycemic T2D was explained by less protein accretion during hyperinsulinemic euglycemic, isoaminoacidemic (isoAA) clamps. We tested whether protein anabolism would worsen with glycemia at 8.0 mM and AA at iso (fasting) or hyper (postprandial). Eight T2D men (57±2 y; BMI: 34±2; FPG: 8.4±0.6mM) underwent hyperinsulinemic (600 pM), hyperglycemic clamps with glucose and leucine kinetics measured at 2 steady‐states: isoAA and hyperAA. Results are compared to 10 T2D and 10 lean nondiabetic men studied euglycemic, isoAA and 9 studied hyperglycemic, hyperAA. In T2D 1) Leucine flux was higher (4.3±0.1 vs. 3.1±0.2 micromol/FFM.min) but net protein balance not different (0±.03 micromol/FFM.min) with isoAA when glycemia was at 8.0 vs. 5.5; net balance was less than in lean (0.25±0.02); however response to hyperAA was as in lean (0.9±.09) 2) endogenous glucose production was less suppressed (1.7±0.4 vs. 0.7±0.2mg/FFM.min) and glucose uptake higher (4.8±.6 vs. 3.6±0.3 mg/FFM.min) with glycemia at 8 vs. 5.5 and with hyperAA. 3) hyperAA did not lower glucose metabolic clearance rate (3.3±0.3 vs. 3.6±0.4 ml/FFM.min), as in lean (9.3±0.7 vs. 5.7±0.4). Thus, protein anabolic response to postprandial AA in T2D is not altered despite indication of insulin resistance of protein. (CIHR)
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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".