Global LINE-1 DNA methylation is associated with blood glycaemic and lipid profiles
Bibliographic record
Abstract
BACKGROUND: Patterns of DNA methylation change with age and these changes are believed to be associated with the development of common complex diseases. The hypothesis that Long Interspersed Nucleotide Element 1 (LINE-1) DNA methylation (an index of global DNA methylation) is associated with biomarkers of metabolic health was investigated in this study. METHODS: Global LINE-1 DNA methylation was quantified by pyrosequencing in blood-derived DNA samples from 228 individuals, aged 49-51 years, from the Newcastle Thousand Families Study (NTFS). Associations between log-transformed LINE-1 DNA methylation levels and anthropometric and blood biochemical measurements, including triglycerides, total cholesterol, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, fasting glucose and insulin secretion and resistance were examined. RESULTS: Linear regression, after adjustment for sex, demonstrated positive associations between log-transformed LINE-1 DNA methylation and fasting glucose {coefficient 2.80 [95% confidence interval (CI) 0.39-5.22]}, total cholesterol [4.76 (95% CI 1.43-8.10)], triglycerides [3.83 (95% CI 1.30-6.37)] and LDL-cholesterol [5.38 (95% CI 2.12-8.64)] concentrations. A negative association was observed between log-transformed LINE-1 methylation and both HDL cholesterol concentration [-1.43 (95% CI -2.38 to -0.48)] and HDL:LDL ratio [-1.06 (95% CI -1.76 to -0.36)]. These coefficients reflect the millimoles per litre change in biochemical measurements per unit increase in log-transformed LINE-1 methylation. CONCLUSIONS: These novel associations between global LINE-1 DNA methylation and blood glycaemic and lipid profiles highlight a potential role for epigenetic biomarkers as predictors of metabolic disease and may be relevant to future diagnosis, prevention and treatment of this group of disorders. Further work is required to establish the role of confounding and reverse causation in the observed associations.
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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) | 0.002 | 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".