Elevated A2F bisect N-glycans of serum IgA reflect progression of liver fibrosis in patients with MASLD
Bibliographic record
Abstract
BACKGROUND: Advanced liver fibrosis in cases of metabolic dysfunction-associated steatotic liver disease (MASLD) leads to cirrhosis and hepatocellular carcinoma. The current gold standard for liver fibrosis is invasive liver biopsy. Therefore, a less invasive biomarker that accurately reflects the stage of liver fibrosis is highly desirable. METHODS: This study enrolled 269 patients with liver biopsy-proven MASLD. Patients were divided into three groups (F0/1 (n = 41/85), F2 (n = 47), and F3/4 (n = 72/24)) according to fibrosis stage. We performed serum N-glycomics and identified glycan biomarker for fibrosis stage. Moreover, we explored the carrier proteins and developed a sandwich ELISA to measure N-glycosylation changes of carrier protein. RESULTS: Comprehensive N-glycomic analysis revealed significant changes in the expression of A2F bisect and its precursors as fibrosis progressed. The sum of neutral N-glycans carrying bisecting GlcNAc and core Fuc (neutral sum) had a better diagnostic performance to evaluate advanced liver fibrosis (AUC = 0.804) than conventional parameters (FIB4 index, aspartate aminotransferase-to-alanine aminotransferase ratio (AAR), and serum level of Mac-2-binding protein glycol isomer (M2BPGi). The combination of the neutral sum and FIB4 index enhanced diagnostic performance (AUC = 0.840). IgM, IgA, and complement C3 were identified as carrier proteins with A2F bisect N-glycan. A sandwich ELISA based on N-glycans carrying bisecting GlcNAc and IgA showed similar diagnostic performance than the neutral sum. CONCLUSIONS: A2F bisect N-glycan and its precursors are promising candidate biomarkers for advanced fibrosis in MASLD patients. Analysis of these glycan alterations on IgA may have the potential to serve as a novel ELISA diagnostic tool for MASLD in routine clinical practice. CLINICAL TRIAL NUMBER: UMIN000030720.
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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 | 0.000 |
| 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".