Abstract 171: Impact of the Mediterranean Diet on Apolipoprotein AI Kinetic in Men with Metabolic Syndrome
Bibliographic record
Abstract
The impact of the Mediterranean diet (MedDiet) on HDL metabolism has not been extensively studied to date. The objective of the study was to investigate the effect of the MedDiet consumed under controlled isoenergetic feeding conditions on HDL as well as on apolipoprotein (apo) AI kinetic in men with metabolic syndrome (MetS). The diet of 26 men aged between 24 to 62 years with the MetS (NCEP-ATP III) was first standardized to a North American control diet, which was consumed for 5 weeks. MedDiet was then consumed over a subsequent period of 5 weeks. Both diets were consumed under isoenergetic feeding conditions with all foods, including red wine, provided to participants. During the last week of each diet, participants received a single bolus of [5,5,5- 2 H 3 ] L -leucine and fasting blood samples were collected at predetermined time points over a period of 96 hours. Kinetic parameters were derived using multicompartmental modeling of the enrichment data over time. Although no change in plasma HDL-C concentrations was observed, consumption of the MedDiet led to a significant reduction in plasma apoAI concentration and pool size (both P <0.05) compared with the control diet. The MedDiet was also associated with a trend towards a reduction in apoAI production rate (PR, P =0.07) but had no impact on apoAI fractional catabolic rate (FCR, P =0.64). However, only variations in apoAI FCR correlated with diet-induced variations in plasma HDL-C and in apoAI concentrations ( r = -0.50 and r = -0.49 respectively, P <0.02). Based on the individual HDL-C response to MedDiet, responders and non-responders were identified. Participants among whom HDL-C concentrations were increased with MedDiet (mean ΔHDL-C: +9.9 ± 3.2 %) showed significantly greater reductions in apoAI FCR and in apoB and VLDL-TG concentrations (all P <0.04) than those among whom HDL-C levels were reduced after the MedDiet (mean ΔHDL-C: -11.1 ± 4.5 %). Data from this controlled feeding study suggest that the heterogeneous response of HDL-C and apoAI to MedDiet is primarily determined by variations in apoAI FCR, which in turn may be due to concurrent changes in plasma TG concentrations.
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 | 0.000 | 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".