The Correlation Between Small Dense LDL and Reactive Oxygen Metabolites in a Physical Activity Intervention in Hyperlipidemic Subjects
Bibliographic record
Abstract
BACKGROUND: Small dense low-density lipoprotein (sdLDL), which has a small LDL particle size with a greater susceptibility to oxidation, is considered a risk marker for cardiovascular disease (CVD). The diacron reactive oxygen metabolites (d-ROMs) have recently been introduced as a clinically useful oxidative stress-related marker. Physical activity can reduce the CVD risk. The present study investigated the correlation between the changes of the mean LDL particle size and the oxidative stress status, as assessed by the d-ROMs, in a physical activity intervention in hyperlipidemic subjects. METHODS: We performed a 6-month intervention study of 30 hyperlipidemic subjects (12 male/18 female, mean age 64 years), focusing on a moderate physical activity increase. The clinical data, including the atherosclerotic risk factors besides the mean LDL particle size measured with the gel electrophoresis and the d-ROMs, were evaluated pre- and post-intervention. RESULTS: The mean LDL particle size was significantly larger in the post-intervention than in the pre-intervention evaluation (26.9 ± 0.3 (SD) vs. 27.1 ± 0.4 nm, P < 0.01), while the d-ROMs levels were significantly reduced in the post-intervention period compared to those at pre-intervention (319 ± 77 vs. 290 ± 73 U. Carr., P < 0.05). A stepwise multiple regression analysis revealed that there was an independent, significant and inverse correlation between the pre- and post-intervention changes of the d-ROMs and the mean LDL particle size (β = -0.55, P < 0.01). CONCLUSIONS: The intervention study suggests that sdLDL and oxidative stress can concomitantly affect the risk of developing CVD and that both factors can improve by even a moderate increase in physical activity among hyperlipidemic subjects.
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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".