Association of serum vitamin B12 levels with stage of liver fibrosis and treatment outcome in patients with chronic hepatitis C virus genotype 1 infection: a retrospective study
Bibliographic record
Abstract
BACKGROUND: Chronic hepatitis C (CHC) is a global health challenge. New therapeutic agents with excellent sustained virological response (SVR) rates are available mainly in developed countries, while the majority of CHC patients live in countries with low health budget. Predictors of therapeutic response are therefore necessary. Vitamin B12 appears to be involved in hepatitis C virus replication. METHODS: We therefore studied retrospectively the relationship between baseline serum vitamin B12 levels and clinical features in 116 CHC genotype 1 infected patients. Logistic regression models with univariate and multivariate analysis were used in the statistical analysis. RESULTS: Baseline serum vitamin B12 levels were found to be positively associated with serum transaminase activities (AST, p = 0.002, ALT, p = 0.04), baseline viral load (p < 0.0001), stage of fibrosis (p = 0.0001) and favorable interferon-λ3/4 (IFNL3/IFNL4) rs12979860 genotypes (p = 0.04), and inversely with SVR (p < 0.001) as well as with rapid virological response (p = 0.001). Patients with baseline serum vitamin B12 levels below a cut-off value of 570 ng/L achieved a SVR rate of 59% with an odds ratio (OR) of 13.4 [confidence interval (CI) 4.3-41.9, p < 0.0001] compared to patients above the cut-off value. By combining serum vitamin B12 levels and IFNL3/IFNL4 rs12979860 genotypes, patients with baseline serum vitamin B12 levels below the cut-off value of 570 ng/L and IFNL3/IFNL4 rs12979860 CC genotype achieved a SVR rate of even 80% with an OR of 54 (CI 9.9-293, p < 0.0001) compared to patients above the cut-off value and non-CC-genotypes. CONCLUSION: Our data suggest baseline serum vitamin B12 levels as useful noninvasive marker for characterizing CHC patients. They might further help to identify responders to a standard treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".