DNA Methylation Near CPT1A and Changes in Triglyceride-rich Lipoproteins in Response to Weight-loss Diet Interventions
Bibliographic record
Abstract
CONTEXT: Carnitine palmitoyltransferase-1A, encoded by the CPT1A gene, plays a key role in the oxidation of long-chain fatty acids in the mitochondria and may be important in triglyceride metabolism. Previous work has shown that high fat intake was negatively associated with CPT1A methylation and positively associated with CPT1A expression. OBJECTIVE: We aim to investigate the association of DNA methylation (DNAm) at the CPT1A gene with reductions in triglycerides and triglyceride-rich lipoproteins (TRLs) in response to weight-loss diet interventions. METHODS: The current study included 538 White participants, who were randomly assigned to 1 of 4 diets varying in macronutrient components. We defined the regional DNAm at CPT1A as the average methylation level over CpGs within 500 bp of the 3 triglyceride-related DNAm sites. RESULTS: Dietary fat intake significantly modified the association between baseline DNAm at CPT1A and 2-year changes in total plasma triglycerides, independent of concurrent weight loss. Among participants assigned to a low-fat diet, a higher regional DNAm level at CPT1A was associated with a greater reduction in total plasma triglycerides at 2 years (P = .01), compared with those assigned to a high-fat diet (P = .64) (P interaction = .018). Further investigation on lipids and apolipoproteins in very low-density lipoprotein (VLDL) revealed similar interaction patterns for 2-year changes in VLDL-triglycerides, VLDL-cholesterol, and VLDL-apolipoprotein B (P interaction = .009, .002, and .016, respectively), but not for VLDL-apoC-III (P interaction = .36). CONCLUSION: Participants with a higher regional DNAm level at CPT1A benefit more in long-term improvement in triglycerides, particularly in the TRLs and related apolipoproteins when consuming a low-fat weight-loss diet.
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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 | 0.001 |
| 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".