Differential methylation in visceral adipose tissue of obese men discordant for metabolic disturbances
Bibliographic record
Abstract
Obesity is associated with an increased risk of Type 2 diabetes and cardiovascular diseases (CVD). The severely obese population is heterogeneous regarding CVD risk profile. Our objective was to identify metabolic pathways potentially associated with development of metabolic syndrome (MetS) through an analysis of overrepresented pathways from differentially methylated genes between severely obese men with (MetS+) and without (MetS-) the MetS. Genome-wide quantitative DNA methylation analysis in VAT of severely obese men was carried out using the Infinium HumanMethylation450 BeadChip. Differences in methylation levels between MetS+ (n = 7) and MetS- (n = 7) groups were tested. Overrepresented pathways from the list of differentially methylated genes were identified and visualized with the Ingenuity Pathway Analysis system. Differential methylation analysis between MetS+ and MetS- groups identified 8,578 methylation probes (3,258 annotated genes) with significant differences in methylation levels (false discovery rate-corrected DiffScore ≥ |13| ∼ P ≤ 0.05). Pathway analysis from differentially methylated genes identified 41 overrepresented (P ≤ 0.05) pathways. The most overrepresented pathways were related to structural components of the cell membrane, inflammation and immunity and cell cycle regulation. This study provides potential targets associated with adipose tissue dysfunction and development of the MetS.
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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 | 0.000 |
| 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) | 0.001 | 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".