Use of Blood as a Surrogate Model for the Assessment of Visceral Adipose Tissue Methylation Profiles Associated with the Metabolic Syndrome in Men
Bibliographic record
Abstract
Epigenetic mechanisms are known to be involved in tissue-specific differentiation. DNA methylation patterns have \nbeen shown to be largely conserved across tissues but with variation for specific genes. However, it is unclear \nwhether the variability observed in the methylation profile of a metabolically active tissue is reflected in other sources \nsuch as hematopoietic tissue. This study aimed to test blood genome-wide CpG site methylation levels as a \nsurrogate model for visceral adipose tissue (VAT) methylation and to verify whether it appropriately reflects \ndifferences in methylation levels found in VAT between men discordant for the metabolic syndrome (MetS). Tissue \nspecimens (VAT and blood samples) were obtained from 16 severely obese individuals discordant for the MetS. \nCpG sites methylation levels were measured with the Infinium HumanMethylation450 BeadChip and correlations of \nmethylation levels between VAT and blood were computed. Differences in methylation levels between individuals \nwith and without MetS were tested in both tissues. Pathway analysis was conducted for differentially methylated \nCpG sites common to both tissues. High cross-tissue correlations were observed for VAT and blood (0.952±0.014) \nwhile some CpG sites had significantly different methylation levels in VAT versus blood. Differential methylation \nanalysis between individuals with and without MetS demonstrated a higher number of differentially methylated CpG \nsites in VAT than in blood (11,778 vs. 881, respectively) with nearly 4% of differentially methylated sites found in VAT \nbeing also represented in blood. Common differentially methylated sites were involved in inflammatory-, lipid- and \ndiabetes-related pathways. These results suggest that blood methylation levels of specific CpG sites may \nadequately reflect VAT methylation levels for some of the MetS-related genes, specifically for inflammatory, lipid and \nglucose metabolism genes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".