Smoking, but Not Malnutrition, Influences Promoter-Specific DNA Methylation of the Proopiomelanocortin Gene in Patients with and without Anorexia Nervosa
Bibliographic record
Abstract
OBJECTIVE: Our pilot study evaluates the impact of environmental factors, such as nutrition and smoking status, on epigenetic patterns in a disease-associated gene. METHOD: We measured the effects of malnutrition and cigarette smoking on proopiomelanocortin (POMC) promoter-specific DNA methylation in female patients with and without anorexia nervosa (AN). POMC and its derived peptides (alpha melanocyte stimulating hormone and adrenocorticotropic hormone) are implicated in stress and feeding response. Promoter-specific DNA methylation of the POMC gene was determined in peripheral blood mononuclear cells of 54 healthy female control subjects, 40 underweight patients with AN, and 21 weight-restored patients with AN using bisulfite sequencing. Malnutrition was characterized by plasma leptin. RESULTS: POMC promoter-specific DNA methylation was not affected by diagnosis or nutritional status but significantly negatively associated with cigarette smoking. CONCLUSIONS: Although malnutrition may be expected to reduce DNA methylation through its effects on one-carbon metabolism, our negative results are in line with several in vitro and clinical studies that did not show a direct relation between gene-specific DNA methylation and folate levels. In contrast, smoking has been repeatedly reported to alter DNA methylation of specific genes and should be controlled for in future epigenetic studies.
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) | 0.002 | 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".