Effects of exercise accumulation on plasma lipids and lipoproteins
Bibliographic record
Abstract
Debate exists as to whether improvements in some cardiometabolic risk factors following exercise training result more from the last session of, or from an accumulation of, exercise sessions. This study was designed to compare the effect of a single exercise session with 3 consecutive days of exercise on triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), and low-density lipoprotein-cholesterol (LDL-C). Twelve young adult (aged 22.5±2.5 years), overweight (body mass index=29.7±4 kg·m(-2)), sedentary, black (n=5) and white (n=7) men (n=6) and women (n=6) completed, in random order, a single treadmill exercise session at 60% maximal oxygen uptake for 90 min (1EX), accumulated exercise sessions (same as for 1EX) for 3 consecutive days (3EX), and a control protocol (no exercise for 6 days). Plasma samples were collected from baseline through 24, 48, and 72 h postexercise. Significant treatment-by-time interactions (p<0.05) existed in HDL-C and LDL-C. Postexercise responses of HDL-C differed at 48 h (1EX: -3.6, 3EX: +3.7 mg·dL(-1)) and 72 h (1EX: -1.7, 3EX: +3.2 mg·dL(-1)). LDL-C responses differed at 48 h (1EX: -16, 3EX: +6 mg·dL(-1)). Although not statistically significant, TG concentrations decreased by 29% at 24 h after 3EX, compared with -7% after 1EX. An inverse relationship between baseline and postexercise reduction in TG was present with 3EX (r=-0.655; p<0.05). In conclusion, 3EX increased HDL-C and decreased TG more than 1EX, while the decrease in LDL-C after 1EX was suppressed. Blood lipid panel changes may be due to more accumulated effects over time rather than just a result of the most recent exercise session.
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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) | 0.001 | 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".