Electronic Health Record-Based Genome-Wide Meta-Analysis and Mendelian Randomization Identify Metabolic and Phenotypic Consequences of Non-Alcoholic Fatty Liver Disease
Bibliographic record
Abstract
Abstract Non-alcoholic fatty liver disease (NAFLD) has been associated with several blood biomarkers and chronic diseases. Whether these associations underlie causal effects remains to be determined. We aimed at identifying blood metabolites, blood proteins and human diseases that are causally impacted by the presence of NAFLD using Mendelian randomization. We created a NAFLD genetic instrument from NAFLD loci (MTARC1, GCKR, LPL, TRIB1, LMO3, FTO, TM6SF2, APOE and PNPLA3) identified in a new electronic health record based-GWAS meta-analysis (6715 cases and 682,748 controls). We found a potentially causal effect of NAFLD on tyrosine metabolism as well as on blood levels of eight proteins that could potentially represent new early biomarkers of NAFLD. Using results from the UK Biobank, FinnGen and the COVID-19 Host Genetics Initiative, we found that NAFLD was not causally associated with diseases outside the spectrum of liver diseases, suggesting that the resolution of NAFLD might not prevent other diseases.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.022 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".