Impact of a one-year lifestyle modification program on cholesterol efflux capacities in men with abdominal obesity and dyslipidemia
Bibliographic record
Abstract
Cholesterol efflux capacities (CECs) are negatively associated with cardiovascular disease risk, irrespective of plasma high-density lipoprotein (HDL) cholesterol levels. Whether interventions targeting lifestyle improve HDL-CECs is unknown. Our objective was to determine whether improving dietary quality and increasing physical activity levels improves HDL-CECs in men with abdominal obesity and dyslipidemia. Our study sample included men (48 ± 8.5 yr) with an elevated waist circumference (≥90 cm) associated with dyslipidemia (triglycerides ≥1.69 and/or HDL cholesterol <1.03 mmol/l); 113 men completed a 1-yr intervention, consisting of a healthy eating and physical activity/exercise program, and 32 were included in a control group. An oral lipid tolerance test (OLTT) was performed in a subsample of 28 men who completed the intervention, and blood was collected every 2 h for 8 h. HDL-CECs were measured using [3H]cholesterol-labeled J774 macrophages and HepG2 hepatocytes. The lifestyle modification program led to an overall improvement in the cardiometabolic risk profile, increases in J774-HDL-CEC by 14.1% (+0.88 ± 1.09%, P < 0.0001), HepG2-HDL-CEC by 3.4% (+0.17 ± 0.75%, P = 0.01), and HDL cholesterol and apolipoprotein A-1 levels (13.5%, P < 0.0001 and 14.9%, P < 0.0001, respectively). J774-HDL-CECs and HepG2-HDL-CECs did not change in the control group. The best predictor for changes in HDL-CEC was apolipoprotein A-1 level. The lifestyle modification program also improved HDL-CEC response in postprandial lipemia during an OLTT. HDL-CEC did not change during the OLTT. Our results suggest that increasing physical activity levels and improving diet quality can have a positive impact on both HDL quantity and quality in men with abdominal obesity and dyslipidemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".