The effect of acute endurance exercise on lipoproteins: a comparison of the nuclear magnetic resonance technique with the conventional lipid profile in healthy men
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) of lipoprotein particle size and number may provide greater sensitivity to detecting cardiovascular disease risk compared with the conventional lipid profile in some individuals. The salubrious effect of cardiovascular exercise on blood lipids using the conventional profile is well documented; however, NMR analysis is lacking. The purpose of this study was to examine the effect of a 60-min bout of dynamic exercise on lipoprotein particle size and number as measured by NMR and compare it with the conventional blood lipid profile. Eight active, healthy men (26 ± 5.17 years) ran for 60 min at 70% maximal oxygen uptake on a motor-driven treadmill. Fasting blood samples were drawn at pre-exercise and 5-10 min and 24 h postexercise. The conventional lipid profile showed a significant change in triglycerides (p = 0.019) immediately after exercise with an increase of 22% and a nonsignificant decrease of 13% from baseline after 24 h. The NMR profile showed a significant change in the large high-density lipoprotein particle concentration (p = 0.046) with an increase of 5.8% immediately after exercise, and a decrease of 6.7% at 24 h after exercise. None of the NMR profile changes were significantly different from the baseline value. These data suggest that sensitivity differences between techniques depend on the variable considered; however, they do not warrant concomitant analysis in future studies using this population. Finally, no appreciable favorable or adverse effect was observed in the overall cardiovascular disease risk profile in active, normolipidemic males.
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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) | 0.000 | 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".