Abstract P213: Regular Exercise Improves the Lipoprotein Subclass Profile: Meta-Analysis of 10 Exercise Training Intervention Groups
Bibliographic record
Abstract
OBJECTIVE: The goal was to examine lipoprotein subclass responses to regular exercise as measured in 10 exercise interventions derived from six cohorts. We hypothesized that regular exercise has beneficial effects on the overall lipoprotein subclass profile in previously sedentary adults. METHODS: NMR spectroscopy (LipoScience Inc., Raleigh, NC) was used to quantify average particle size, total and subclass concentrations of very low-density lipoprotein, low-density lipoprotein, and high-density lipoprotein particles (VLDL-P, LDL-P, and HDL-P respectively) before and after an exercise intervention in 1,430 adults from six studies, encompassing 10 exercise training groups: APOE (N=106), DREW (N=298), GERS (N=79), HERITAGE (N=715), STRRIDE I (N=130) and II (N=102). Random-effects meta-analysis was performed to quantify the overall mean change across the unadjusted and adjusted mean change estimates from each exercise group of each study. A Bonferroni-adjusted p-value ≤ 0.003 was considered statistically significant. RESULTS: Meta-analysis of unadjusted data found that regular exercise induced significant decreases in the concentration of large VLDL-P (P=1.2x10 -6 ) and mean VLDL-P size (P=9.0x10 -5 ), with significant increases in the concentration of large LDL-P (P=4.9x10 -13 ). The changes in large VLDL-P and large LDL-P concentration and VLDL-P size remained significant after adjustment for age, sex, race, baseline body mass index, and baseline trait value ( Figure 1 ); while the increase in LDL-P size (P=0.003) became significant after adjustment. Nominally significant decreases in the concentration of small LDL-P (P=0.004) and medium HDL-P (P=0.007) and increases in large HDL-P (P=0.008) were observed in the adjusted meta-analysis. CONCLUSIONS: Despite differences in exercise programs and study populations, regular exercise led to significant improvements in the lipoprotein subclass profile across 10 exercise interventions, as highlighted by changes in VLDL and LDL subfractions.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.039 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".