Brain–heart–eye axis revealed by multi-organ imaging genetics and proteomics
Bibliographic record
Abstract
Multi-organ research investigates interconnections among multiple human organ systems, enhancing our understanding of human aging and disease mechanisms. Here we use multi-organ imaging, individual- and summary-level genetics, and proteomics data consolidated via the MULTI Consortium to delineate a brain-heart-eye axis using brain patterns of structural covariance (PSCs), heart imaging-derived phenotypes (IDPs) and eye IDPs. We find that proteome-wide associations of the PSCs and IDPs show within-organ specificity and cross-organ interconnections. Pleiotropic effects of common single-nucleotide polymorphisms are observed across multiple organs, and key genetic parameters are estimated for single-nucleotide polymorphism-based heritability, polygenicity and selection signatures across the three organs. A gene-drug-disease network shows the potential of drug repurposing for cross-organ diseases. Co-localization and causal analyses reveal cross-organ causal relationships between PSC/IDP and chronic diseases, such as Alzheimer's disease, heart failure and glaucoma. Finally, integrating multi-organ/omics features improves prediction for systemic disease categories and cognition compared with single-organ/omics features, providing future avenues for modelling human aging and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".