Extra Virgin Olive Oil Prevents the Age-Related Shifts of the Distribution of HDL Subclasses and Improves Their Functionality
Bibliographic record
Abstract
High-density lipoproteins (HDL) maintain cholesterol homeostasis through the role they play in regulating reverse cholesterol transport (RCT), a process by which excess cholesterol is transported back to the liver for elimination. However, RCT can be altered in the presence of cardiovascular risk factors, such as aging, which contributes to the increase in the incidence of cardiovascular diseases (CVD). The present study was aimed at investigating the effect of extra virgin olive oil (EVOO) intake on the cholesterol efflux capacity (CEC) of HDL, and to elucidate on the mechanisms by which EVOO intake improves the anti-atherogenic activity of HDL. A total of 84 healthy women and men were enrolled and were distributed, according to age, into two groups: 27 young (31.81 ± 6.79 years) and 57 elderly (70.72 ± 5.6 years) subjects. The subjects in both groups were given 25 mL/d of extra virgin olive oil (EVOO) for 12 weeks. CEC was measured using J774 macrophages radiolabeled with tritiated cholesterol ((3H) cholesterol). HDL subclass distributions were analyzed using the Quantimetrix Lipoprint® system. The HDL from the elderly subjects exhibited a lower level of CEC, at 11.12% (p < 0.0001), than the HDL from the young subjects. The CEC of the elderly subjects returned to normal levels following 12 weeks of EVOO intake. An analysis of the distribution of HDL subclasses showed that HDL from the elderly subjects were composed of lower levels of large HDL (L-HDL) (p < 0.03) and higher levels of small HDL (S-HDL) (p < 0.002) compared to HDL from the young subjects. A multiple linear regression analysis revealed a positive correlation between CEC and L-HDL levels (r = 0.35 and p < 0.001) as well as an inverse correlation between CEC and S-HDL levels (r = −0.27 and p < 0.01). This correlation remained significant even when several variables, including age, sex, and BMI as well as low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and glucose levels (β = 0.28, p < 0.002, and β = 0.24, p = 0.01) were accounted for. Consuming EVOO for 12 weeks modulated the age-related difference in the distribution of HDL subclasses by reducing the level of S-HDL and increasing the level of intermediate-HDL/large-HDL (I-HDL/L-HDL) in the elderly subjects. The age-related alteration of the CEC of HDL was due, in part, to an alteration in the distribution of HDL subclasses. A diet enriched in EVOO improved the functionality of HDL through an increase in I-HDL/L-HDL and a decrease in S-HDL.
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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 | 0.000 | 0.000 |
| 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".