LINE-1 methylation in visceral adipose tissue of severely obese individuals is associated with metabolic syndrome status and related phenotypes
Bibliographic record
Abstract
BACKGROUND: Epigenetic mechanisms may be involved in the regulation of genes found to be differentially expressed in the visceral adipose tissue (VAT) of severely obese subjects with (MetS+) versus without (MetS-) metabolic syndrome (MetS). Long interspersed nuclear element 1 (LINE-1) elements DNA methylation levels (%meth) in blood, a marker of global DNA methylation, have recently been associated with fasting glucose, blood lipids, heart diseases and stroke. AIM: To test whether LINE-1%meth levels in VAT are associated with MetS phenotypes and whether they can predict MetS risk in severely obese individuals. METHODS: DNA was extracted from VAT of 34 men (MetS-: n = 14, MetS+: n = 20) and 152 premenopausal women (MetS-: n = 84; MetS+: n = 68) undergoing biliopancreatic diversion for the treatment of obesity. LINE-1%meth levels were assessed by pyrosequencing of sodium bisulfite-treated DNA. RESULTS: The mean LINE-1%meth in VAT was of 75.8% (SD = 3.0%). Multiple linear regression analyses revealed that LINE-1%meth was negatively associated with fasting glucose levels (β = -0.04; P = 0.03), diastolic blood pressure (β = -0.65; P = 0.03) and MetS status (β = -0.04; P = 0.004) after adjustments for the effects of age, sex, waist circumference (except for MetS status) and smoking. While dividing subjects into quartiles based on their LINE-1%meth (Q1 to Q4: lower %meth to higher %meth levels), greater risk were observed in the first (Q1: odds ratio (OR) = 4.37, P = 0.004) and the second (Q2: OR = 4.76, P = 0.002) quartiles compared to Q4 (1.00) when adjusting for age, sex and smoking. CONCLUSIONS: These results suggest that lower global DNA methylation, assessed by LINE-1 repetitive elements methylation analysis, would be associated with a greater risk for MetS in the presence of obesity.
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) | 0.001 | 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".