From Correlation to Causation: How Omics Technologies Illuminate the Role of INHBC in Cardiometabolic Disease
Bibliographic record
Abstract
The integration of omics technologies, such as genomics, proteomics, and metabolomics, has enabled researchers to uncover complex biological mechanisms underlying disease. In a recent study, Loh et al. used a multi-omics approach to investigate the liver-derived protein INHBC and its role in cardiometabolic health. Through bidirectional Mendelian Randomization and phenome-wide analysis, they identified INHBC as both a driver and consequence of metabolic dysfunction, including obesity, dyslipidemia, and inflammation. The study revealed that INHBC contributes to coronary artery disease risk by altering lipid levels and is associated with renal and liver traits. Functional assays demonstrated that INHBC, via activin C, signals through the ALK7 receptor to suppress fat breakdown in adipose tissue. These findings position INHBC as a potential biomarker and therapeutic target. Overall, this work illustrates how omics tools can move beyond correlation to reveal causality and provide mechanistic insights with translational relevance in complex disease pathways.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".