Circulating Galectin-3 Levels Are Not Associated With Nonalcoholic Fatty Liver Disease: A Mendelian Randomization Study
Bibliographic record
Abstract
CONTEXT: The impact of galectin-3 inhibitors on nonalcoholic fatty liver diseases (NAFLD)-related outcomes is currently under investigation in randomized clinical trials. Whether there is a causal association between plasma galectin-3 levels and NAFLD is unknown. OBJECTIVE: To evaluate the causal effect of circulating galectin-3 levels on NAFLD as well as >800 other human diseases. DESIGN: Inverse variance-weighted (IVW) Mendelian randomization (MR) and phenome-wide MR. SETTING: Summary statistics of genome-wide association studies. PATIENTS: Participants of the UK Biobank, Electronic Medical Records and Genomics (eMERGE), FinnGen, Prevention of Renal and Vascular End-Stage Disease (PREVEND), and IMPROVE cohorts. INTERVENTION: Identification of independent single-nucleotide polymorphisms (SNPs) associated with galectin-3 levels (P < 5 × 10-8) in the PREVEND (14 SNPs) and IMPROVE (3 SNPs) cohorts. MAIN OUTCOME MEASURES: Presence of NAFLD in a meta-analysis of genome-wide association study of the eMERGE, UK Biobank, and FinnGen cohorts (3042 NAFLD cases and 504 853 controls), as well as >800 other human diseases in the UK Biobank and FinnGen. RESULTS: Using IVW-MR, we found no causal association between galectin-3 levels and NAFLD in the meta-analysis of the 3 cohorts or in each individual cohort. After correction for multiple testing, we found no causal association between galectin-3 levels and >800 human disease-related traits. CONCLUSIONS: This MR study revealed no causal associations between circulating galectin-3 levels and NAFLD or any other disease traits, suggesting that plasma galectin-3 levels may not be directly implicated in the pathogenesis of NAFLD or other human diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".