Acute cocoa flavanol supplementation improves muscle macro- and microvascular but not anabolic responses to amino acids in older men
Bibliographic record
Abstract
The anabolic effects of nutrition on skeletal muscle may depend on adequate skeletal muscle perfusion, which is impaired in older people. Cocoa flavanols have been shown to improve flow-mediated dilation, an established measure of endothelial function. However, their effect on muscle microvascular blood flow is currently unknown. Therefore, the objective of this study was to explore links between the consumption of cocoa flavanols, muscle microvascular blood flow, and muscle protein synthesis (MPS) in response to nutrition in older men. To achieve this objective, leg blood flow (LBF), muscle microvascular blood volume (MBV), and MPS were measured under postabsorptive and postprandial (intravenous Glamin (Fresenius Kabi, Germany), dextrose to sustain glucose ∼7.5 mmol·L(-1)) conditions in 20 older men. Ten of these men were studied with no cocoa flavanol intervention and a further 10 were studied with the addition of 350 mg of cocoa flavanols at the same time that nutrition began. Leg (femoral artery) blood flow was measured by Doppler ultrasound, muscle MBV by contrast-enhanced ultrasound using Definity (Lantheus Medical Imaging, Mass., USA) perflutren contrast agent and MPS using [1, 2-(13)C2]leucine tracer techniques. Our results show that although older individuals do not show an increase in LBF or MBV in response to feeding, these absent responses are apparent when cocoa flavanols are given acutely with nutrition. However, this restoration in vascular responsiveness is not associated with improved MPS responses to nutrition. We conclude that acute cocoa flavanol supplementation improves muscle macro- and microvascular responses to nutrition, independently of modifying muscle protein anabolism.
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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) | 0.001 | 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".