COL6A3-derived endotrophin mediates the effect of obesity on coronary artery disease: an integrative proteogenomics analysis
Bibliographic record
Abstract
Abstract Obesity strongly increases the risk of cardiometabolic diseases, yet the underlying mediators of this relationship are not fully understood. Given that obesity has broad effects on circulating protein levels, we investigated circulating proteins that mediate the effects of obesity on coronary artery disease (CAD), stroke, and type 2 diabetes—since doing so may prioritize targets for therapeutic intervention. By integrating proteome-wide Mendelian randomization (MR) screening 4,907 plasma proteins, colocalization, and mediation analyses, we identified seven plasma proteins, including collagen type VI α3 (COL6A3). COL6A3 was strongly increased by body mass index (BMI) ( β = 0.32, 95% CI: 0.26–0.38, P = 3.7 × 10 -8 per s.d. increase in BMI) and increased the risk of CAD (OR = 1.47, 95% CI:1.26–1.70, P = 4.5 × 10 -7 per s.d. increase in COL6A3). Notably, COL6A3 is cleaved at its C-terminus to produce endotrophin, which was found to mediate this effect on CAD. In single-cell RNA sequencing of adipose tissues and coronary arteries, COL6A3 was highly expressed in cell types involved in metabolic dysfunction and fibrosis. Finally, we found that body fat reduction can reduce plasma levels of COL6A3-derived endotrophin, thereby highlighting a tractable way to modify endotrophin levels. In summary, we provide actionable insights into how circulating proteins mediate the effect of obesity on cardiometabolic diseases and prioritize endotrophin as a potential therapeutic target.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".