Whole genome DNA methylation patterns in tissue and cfDNA associated with fibrosis reflect the complex signature of MASLD
Bibliographic record
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) can progress to steatohepatitis, being associated with inflammation, fibrosis, and immune cell interactions. Recent studies have reported an association between DNA methylation (DNAm) and MASLD. However, the relationship between DNAm and MASLD-related fibrosis is limited to liver tissue alone or focused on particular CpG sites. Moreover, despite the widely recognized sex differences in MASLD, studies that account for this variable remain limited. We performed whole-genome methylation sequencing (WGMS) on liver tissue and cell-free DNA (cfDNA) from patients with MASLD to investigate the association between fibrosis and DNAm. After initial filtering, the tissue and cfDNA data were further grouped by sex, and DNAm sites exceeding the minimum threshold for association with fibrosis were selected. Pathway analysis based on the genomic locations of CpG bins revealed both overlapping and distinct MASLD- and fibrosis-associated pathways across tissue and cfDNA, between males and females, and between intragenic and intergenic regions. Hepatic tissue deconvolution indicated the presence of immune cells and confirmed an increase in liver-derived cfDNA associated with fibrosis in cfDNA samples. Our study demonstrates the potential of WGMS as a platform for comprehensively observing the complex patterns of MASLD alterations.
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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 | 0.001 | 0.001 |
| 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".