Lactate and glucose interactions during rest and exercise in men: effect of exogenous lactate infusion
Bibliographic record
Abstract
To test the hypothesis that lactate plays a central role in the distribution of carbohydrate (CHO) potential energy for oxidation and glucose production (GP), we performed a lactate clamp (LC) procedure during rest and moderate intensity exercise. Blood [lactate] was clamped at approximately 4 mM by exogenous lactate infusion. Subjects performed 90 min exercise trials at 65 % of the peak rate of oxygen consumption (V(O(2))(,peak); 65 %), 55 % V(O(2))(,peak) (55 %) and 55 % V(O(2))(,peak) with lactate clamped to the blood [lactate] that was measured at 65 % V(O(2))(,peak) (55 %-LC). Lactate and glucose rates of appearance (R(a)), disappearance (R(d)) and oxidation (R(ox)) were measured with a combination of [3-(13)C]lactate, H(13)CO(3)(-), and [6,6-(2)H(2)]glucose tracers. During rest and exercise, lactate R(a) and R(d) were increased at 55 %-LC compared to 55 %. Glucose R(a) and R(d) were decreased during 55 %-LC compared to 55 %. Lactate R(ox) was increased by LC during exercise (55 %: 6.52 +/- 0.65 and 55 %-LC: 10.01 +/- 0.68 mg kg(-1) min(-1)) which was concurrent with a decrease in glucose oxidation (55 %: 7.64 +/- 0.4 and 55 %-LC: 4.35 +/- 0.31 mg kg(-1) min(-1)). With LC, incorporation of (13)C from tracer lactate into blood glucose (L GNG) increased while both GP and calculated hepatic glycogenolysis (GLY) decreased. Therefore, increased blood [lactate] during moderate intensity exercise increased lactate oxidation, spared blood glucose and decreased glucose production. Further, exogenous lactate infusion did not affect rating of perceived exertion (RPE) during exercise. These results demonstrate that lactate is a useful carbohydrate in times of increased energy demand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".