Mendelian randomization analysis identifies blood tyrosine levels as a biomarker of non-alcoholic fatty liver disease
Bibliographic record
Abstract
Abstract Non-alcoholic fatty liver disease (NAFLD) is a complex cardiometabolic disease associated with premature mortality. The diagnosis of NAFLD is challenging and the identification of biomarkers causally influenced by NAFLD may be clinically useful. We aimed at identifying blood metabolites causally impacted by NAFLD using two-sample Mendelian randomization (MR) with validation in a population-based biobank and a cohort of patients undergoing bariatric surgery. Our instrument for genetically-predicted NAFLD (the study exposure) included all independent genetic variants (n=7 SNPs) from a recent genome-wide association study on NAFLD. The study outcomes included 123 blood lipids, lipoproteins and metabolites measured in 24,925 individuals from 10 European cohorts. After correction for multiple testing, we identified a positive effect of NAFLD on plasma tyrosine levels but not on other metabolites. The association between NAFLD and tyrosine levels was consistent across MR methods and robust to outliers and pleiotropy. In observational analyses performed in the Estonian Biobank (10,809 individuals including 359 patients with NAFLD), after multivariable adjustment, tyrosine levels were positively associated with the presence of NAFLD (odds ratio per 1-SD increment = 1.23 (95% confidence interval = 1.12-1.36, p = 2.19e-05). In a sample of 138 patients undergoing bariatric surgery, compared to patients without NAFLD, blood tyrosine levels were higher in those with NAFLD, but were comparable among patients with or without non-alcoholic steatohepatitis. This analysis revealed a potentially causal effect of NAFLD on blood tyrosine levels, suggesting that blood tyrosine levels may represent a new biomarker of NAFLD. Graphical abstract
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".