Abstract 17150: LDL-C Response to Portfolio Foods Containing High Levels of Phytosterols, Whole Food Fiber, and Alpha-Linoleic Acid in Statin Reluctant Patients: Impact of CYP7A1-rs3808607 and APOE Isoforms
Bibliographic record
Abstract
Introduction: Up to 20% of outpatients receiving HMG-CoA reductase inhibitors (statins) experience treatment reluctance on the basis of side effects, leaving a large population at increased risk of CVD. We hypothesized that a practical food-based approach can be utilized to lower LDL-C in statin reluctant patients and that the lipid response can be predicted based upon CYP7A1-rs3808607 (CYP7A1) and APOE genetic isoforms. Methods: This was a multicenter, randomized, double-blind, free-living cross-over study composed of 2 phases of 4 wk each, separated by a 4 wk washout. Participants (n=54) received an assortment of individually packaged, shelf stable snacks along with printed instructions to ingest 2 servings of the foods per day as a substitute for similar items they were eating already. No other dietary or behavior adjustments were requested. Treatment products supplied at least 1800 mg alpha-linoleic acid (ALA), 5 g of fiber and 1g of phytosterols per serving. Control products were calorie-matched like items drawn from the general grocery marketplace. Lipid parameters were measured and averaged over 2 d at baseline and at 4 wk of each phase. Ingestion of study foods was confirmed by C18:3n3 serum level assessment. Single nucleotide polymorphisms and APOE isoform were assessed by Taqman genotyping assay. Results: As compared to control, LDL-C decreased 8.8% in the treatment arm (p<0.0001, range -37.6% to +20.5%) and total cholesterol fell 5.1% (p<0.004). No significant change was seen in HDL-C, TG or fasting glucose levels. LDL-C was reduced by diet in CYP7A1 T/T homozygotes (-0.3924+0.1271mmol/l, p = 0.0033) and APOE4 carriers ( -0.3808+0.098 mmol/l, p = 0.0003). Conclusions: Significant LDL-C reductions can be affected through a simple food intervention supplying high levels of phytosterols, fiber and ALA. CYP7A1 and APOE isoforms influence the variability in LDL-C response and can help identify individual patients especially appropriate for a food-based LDL-C lowering approach. Given the large number of patients unable/unwilling to take statins, our findings have significant impact for management of this challenging population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".