DNA Methylation at <i>ABCG1</i> and Long-term Changes in Adiposity and Fat Distribution in Response to Dietary Interventions: The POUNDS Lost Trial
Bibliographic record
Abstract
OBJECTIVE: To examine whether participants with different levels of diabetes-related DNA methylation at ABCG1 might respond differently to dietary weight loss interventions with long-term changes in adiposity and body fat distribution. RESEARCH DESIGN AND METHODS: The current study included overweight/obese participants from the POUNDS Lost trial. Blood levels of regional DNA methylation at ABCG1 were profiled by high-resolution methylC-capture sequencing at baseline among 673 participants, of whom 598 were followed up at 6 months and 543 at 2 years. Two-year changes in adiposity and computed tomography-measured body fat distribution were calculated. RESULTS: Regional DNA methylation at ABCG1 showed significantly different associations with long-term changes in body weight and waist circumference at 6 months and 2 years in dietary interventions varying in protein intake (interaction P < 0.05 for all). Among participants assigned to an average-protein (15%) diet, lower baseline regional DNA methylation at ABCG1 was associated with greater reductions in body weight and waist circumference at 6 months and 2 years, whereas opposite associations were found among those assigned to a high-protein (25%) diet. Similar interaction patterns were also observed for body fat distribution, including visceral adipose tissue, subcutaneous adipose tissue, deep subcutaneous adipose tissue, and total adipose tissue at 6 months and 2 years (interaction P < 0.05 for all). CONCLUSIONS: Baseline DNA methylation at ABCG1 interacted with dietary protein intake on long-term decreases in adiposity and body fat distribution. Participants with lower methylation at ABCG1 benefitted more in long-term reductions in body weight, waist circumference, and body fat distribution when consuming an average-protein 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".