The long non-coding RNA <i>TRIB1AL</i> links metabolic dysfunction-associated steatotic liver disease, cardiometabolic risk and human lifespan
Bibliographic record
Abstract
Abstract Genome-wide association studies (GWAS) have identified dozens of genetic loci linked with metabolic dysfunction-associated steatotic liver disease (MASLD). To identify liver-expressed genes that may represent therapeutic candidates for MASLD, we conducted a new GWAS meta-analysis including 16,532 cases and 1,240,188 controls, as well as RNA sequencing of liver samples and genome-wide genotyping of 504 individuals of the Quebec Obesity Biobank. Using Mendelian randomization (MR) and genetic colocalization, we confirm the implication of genes previously linked with MASLD and identified novel ones including AKNA (AT-hook transcription factor), EPHA2 (EPH receptor A2), CHEK2 (encoding Checkpoint kinase 2) and PCCB (Propionyl-CoA carboxylase subunit beta). More specifically, we found a strong and positive effect of the long non-coding RNA TRIB1AL on MASLD. The lead genetic variant was not linked with expression levels of the nearby protein-coding gene TRIB1 (Tribbles Pseudokinase 1). In participants of the UK Biobank with whole exome sequencing data available, rare loss-of-function variants in TRIB1 were not associated with liver fat accumulation or plasma triglyceride levels, suggesting that the long non-coding RNA TRIB1AL may carry cardiometabolic effects independently of TRIB1 . Targeted- and phenome-wide MR also identified lower liver-expressed TRIB1AL as being associated with reduced liver fat accumulation, lower plasma lipoprotein-lipid levels, decreased atherosclerotic cardiovascular disease risk, and increased human lifespan. These results open the door to liver-targeted therapeutics silencing of the non-coding genome for the prevention and treatment of MASLD and cardiometabolic diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".