Decrease in Coronary Blood Flow Reserve During Hyperlipidemia Is Secondary to an Increase in Blood Viscosity
Bibliographic record
Abstract
BACKGROUND: During maximal hyperemia, capillaries provide the greatest resistance to flow. A major determinant of capillary resistance is viscosity. We, therefore, hypothesized that abnormal coronary blood flow (CBF) reserve observed during hyperlipidemia is secondary to increased blood viscosity and not abnormal coronary vasomotion. METHODS AND RESULTS: Maximal hyperemia was induced in 9 dogs using adenosine. Serum triglyceride levels were increased by incremental doses of Intralipid. A good correlation was noted between serum triglyceride levels and blood viscosity (r=0.82). Neither total coronary blood volume nor myocardial blood volume changed with increasing serum triglyceride levels, indicating lack of vasomotion. Myocardial vascular resistance (MVR) increased with increasing triglyceride levels (r=0.84), while hyperemic myocardial blood flow (MBF) decreased (r=-0.64). The decrease in hyperemic MBF was associated with a decrease in blood velocity (r=-0.56). These findings were confirmed with direct intravital microscopic observations in the mice cremaster muscle. CONCLUSIONS: Increasing lipid levels in a fully dilated normal coronary bed causes no change in large or small vessel dimensions. Instead, the increase in blood viscosity causes capillary resistance to rise, which attenuates hyperemic CBF. Therefore, the abnormal CBF reserve associated with hyperlipidemia is due to increase blood viscosity and not abnormal vascular function.
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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".