Microvascular Perfusion Heterogeneity Impairs Oxygenation and Contributes to Peripheral Vascular Disease in Metabolic Syndrome
Bibliographic record
Abstract
Development of the metabolic syndrome in obese Zucker rats (OZR) is associated with impaired fatigue‐resistance of in situ skeletal muscle paralleling a blunted functional hyperemia. However, recent studies suggest that reduced bulk oxygen delivery to skeletal muscle with elevated metabolic demand may not explain the compromised muscle performance. Using novel experimental data and recent insight into altered microvascular perfusion in OZR, we developed a simulation for tissue oxygenation with increasing metabolic demand in lean (LZR) and OZR skeletal muscle using physiologically‐realistic data and relationships. With elevated metabolic demand, blood flow to, and oxygen uptake by, in situ skeletal muscle increased in both strains, although the response was blunted in OZR. However, venous blood oxygen tension (P v O 2 ) draining the muscle of OZR was elevated versus LZR across metabolic demands; a paradoxical response given assumptions of increased microvascular residency time in OZR. Using a microvascular network model of multiple Krogh cylinders supplied by a network with homogeneous flow distribution at bifurcations (γ=0.5), we were unable to simulate tissue oxygenation and P v O 2 differences between LZR and OZR. However, with introduction of increasing perfusion asymmetry (γ>0.5), changes to microvascular hematocrit and increased plasma skimming throughout the network, our ability to simulate the experimental results was much improved. As a result, our data suggest that increased perfusion asymmetry (γ) within the muscle microcirculation is not only a defining characteristic of metabolic syndrome, it is required to effectively model and understand alterations in blood‐tissue oxygen exchange in this highly translationally relevant model of human disease risk. Support or Funding Information National Institutes of Health and American Heart Association
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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".