Plasma sgp130 is an independent predictor of non-alcoholic fatty liver disease severity
Bibliographic record
Abstract
ABSTRACT Background Interleukin-6 (IL-6) plays important and dynamic roles in inflammation associated with fatty liver disease over all stages, from simple steatosis to steatohepatitis, cirrhosis and cancer. IL-6 signals locally, but also circulates with multiple co-factors that control paracrine and endocrine signaling. As inflammation is a main driver of liver fibrosis, we investigated relationships between circulating components of the interleukin-6 signaling pathway (IL-6, sIL-6R and sgp130) and liver pathology in subjects with metabolically associated fatty liver disease (MAFLD) or steatohepatitis (MASH). Methods Predictive performances of plasma IL-6, sIL-6R and sgp130 were investigated in two independent cohorts: 1) patients with biopsy-confirmed MASH ( n =49), where magnetic resonance spectroscopy (MRS), imaging (MRI) and elastography (MRE) assessed liver fat, volume and stiffness; and 2) patients with morbid obesity ( n =245) undergoing bariatric surgery where histological staging of steatosis, activity, and fibrosis determined MASH severity. Correlations were evaluated between IL-6, sIL-6R and sgp130 and anthropomorphic characteristics, plasma markers of metabolic disease or liver pathology. Results In patients with MASH, plasma IL-6 and sgp130 strongly correlated with liver stiffness, which for sgp130 was independent of age, sex, BMI, diabetes, hyperlipidemia, hypertension or history of HCC. Plasma sgp130 was the strongest predictor of liver stiffness compared to common predictors and risk scores. Plasma sIL-6R correlated with liver volume independent of age, sex, and BMI. In patients with morbid obesity, circulating sgp130 correlated with advanced liver fibrosis. Conclusion Levels of circulating sgp130 can predict progressing MASH and may be used alone or in combination with other predictors as a non-invasive measure of liver disease severity. GRAPHICAL ABSTRACT
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) | 0.002 | 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".