Abstract 15687: Triglyceride Levels and Non-Alcoholic Fatty Liver Disease: A Multivariable Mendelian Randomization Study
Bibliographic record
Abstract
Introduction: Non-alcoholic fatty liver disease (NAFLD) affects approximately 25% of the adult population worldwide. Individuals with NAFLD are twice as likely to develop atherosclerotic cardiovascular diseases compared to individuals without NAFLD. Whether plasma triglyceride levels increase NAFLD risk independently of other lipoprotein subfractions, and hence represent a potential therapeutic target for NAFLD, is unknown. Our objective was to investigate the causal effect of genetically predicted plasma lipids levels on NAFLD using multivariable Mendelian randomization. Methods: We performed univariable and multivariable two-sample MR to investigate the respective causal contribution of triglycerides, apolipoprotein B (apoB), and HDL-C on NAFLD. As genetic instruments for HDL-C, triglycerides and apo B levels, we selected all robustly (p<5e-8) and independently (linkage disequilibrium R2 < 0.001) genetic variants from genome-wide association studies in the UK Biobank (n = 441,016). As outcome, we included the largest NAFLD GWAS (8434 cases and 770,614 controls). Results: In univariable MR analysis, triglyceride levels were robustly associated with NAFLD risk (OR = 1.32, 95% CI=1.20-1.48, p=2.8e-7). In multivariable MR analysis, triglycerides remained robustly associated with NAFLD risk upon adjustment for other correlated lipoprotein subfraction (adjusted OR = 1.30, 95% CI = 1.12-1.51, p = 5.1e-4) (Figure). These results were consistent across a range of sensitivity analyses (MVMR-lasso, MVMR-Egger and MVMR-Median). Conclusions: Results of this two-sample MR study revealed that genetically predicted triglyceride levels may increase the risk of NAFLD independently of other lipoprotein subfraction. These results suggest that triglyceride lowering in the prevention of NAFLD might warrant further investigation.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".