DNA Methylation of Birthweight–Blood Pressure Genes and Changes of Blood Pressure in Response to Weight-Loss Diets in the POUNDS Lost Trial
Bibliographic record
Abstract
Background: DNA methylation (DNAm) may play a critical role in bridging prenatal adverse events and cardiometabolic disorders including hypertension in later life. Methods: We included 672 adult participants with overweight or obesity, who participated in a 2-year randomized weight-loss dietary intervention study. We defined the regional DNAm levels as the average methylation level of 5’-cytosine-phosphate-guanine-3’ within 500 bp of LINC00319 (cg01820192), ATP2B1 (cg00508575), and LMNA (cg12593793), respectively. Generalized linear regression models were used to assess the association between the regional DNAm and 2-year blood pressure changes. Trajectory analysis was used to identify subgroups that shared a similar underlying trajectory of 2-year blood pressure changes. Results: The regional DNAm at LINC00319 , showed significantly different associations with 2-year changes in systolic blood pressure and diastolic blood pressure among participants assigned to low- or high-fat diets ( P for interaction<0.05 for all). In response to the low-fat diet, per SD higher regional DNAm at LINC00319 was associated with greater reductions in both 2-year changes in systolic blood pressure (β, −1.481; P =0.020) and diastolic blood pressure (β, −1.096; P =0.009). Three trajectories of changes in systolic blood pressure or diastolic blood pressure were identified, and participants with higher regional DNAm at LINC00319 were more likely to experience and maintain decreased systolic blood pressure and diastolic blood pressure (odds ratio of being in decrease-stable versus stable [95% CI], 1.542 [1.146–2.076] and 1.463 [1.125–1.902]). Conclusions: Our findings suggest that DNAm could be a metabolic memory bridging early and later life, and an indicator of more benefits from eating 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".