Multiorgan research investigates interconnections among multiple human organ systems, enhancing our understanding of human aging and disease mechanisms. Here, we used multiorgan imaging (N=105,433), individual and summary level genetics, and proteomics (N=53,940) from the UK Biobank, Baltimore Longitudinal Study of Aging, FinnGen, and Psychiatric Genomics Consortium to delineate a brain-heart-eye axis via 2003 brain patterns of structural covariance (PSC), 82 heart imaging-derived phenotypes (IDP) and 84 eye IDPs. Cross-organ phenotypic associations highlight the central autonomic network between the brain and heart and the central visual pathway between the brain and eye. Proteome wide associations of the PSCs and IDPs show both within organ specificity and cross-organ interconnections, verified by the RNA and protein expression profiles of the 2923 plasma proteins. Pleiotropic effects of common genetic variants are observed across multiple organs, and key genetic parameters, such as SNP-based heritability, polygenicity, and selection signatures, are comparatively evaluated among the three organs. A gene-drug-disease network shows the potential of drug repurposing for cross-organ diseases. Colocalization 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 to single-organ/omics features. This study depicts a detailed brain-heart-eye axis and highlights future avenues for modeling human aging and disease across multiple scales. All results are publicly available at https://labs-laboratory.com/medicine/.

The authors have declared no competing interest.

The MULTI consortium (J.W) aims to integrate multi-organ imaging with multi-omics data to advance our understanding of human aging and disease mechanisms. We want to express our sincere gratitude to the UK Biobank team for their invaluable contribution to advancing clinical research in our field (https://www.ukbiobank.ac.uk/). We also acknowledge the data sharing from the UKBB Eye and Vision Consortium (https://www.ukbiobank.ac.uk/enable-your-research/approved-research/genetic-contribution-to-vision-loss-and-disability-the-uk-biobank-eye-vision-consortium; Return ID: 1875) and UKB-PPP consortium (https://registry.opendata.aws/ukbppp/; Category code: 1838) to share the returned data with the community. We thank FinnGen (https://www.finngen.fi/en) and PGC (https://pgc.unc.edu/) for their generosity in sharing the GWAS summary statistics with the scientific community. We thank the BLSA participants and staff for their participation and continued dedication. The BLSA protocol was approved by the Institutional Review Board of the National Institute of Environmental Health Science, National Institutes of Health (03AG0325). This study used the UK Biobank resource under Application Number 35148 (D.C) under the NIH-funded iSTAGING consortium (D.C; grant number: RF1 AG054409). We thank Dr. Wenjia Bai for generously providing us access to the cardiac atlas utilized in his publication: https://wp.doc.ic.ac.uk/wbai/data/. We acknowledge the leadership of the Brain Imaging Genetics (BIG) workgroup, led by Dr. Tavia Evans, Dr. Natalia Vilor-Tejedor, and Dr. Junhao Wen, within the International Society to Advance Alzheimer's Research and Treatment (ISTAART) community, for advocating brain imaging genetics in Alzheimer's and aging research.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The original study committee had the consent of all participants. The ethics committee/IRB of the University of Pennsylvania gave ethical approval for this work. The ethics committee/IRB of Columbia University is in the process of approving the related IRB (the PI's lab moved to Columbia University on 1st January, j2025).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The GWAS summary statistics corresponding to this study are publicly available on the MEDICINE knowledge portal (https://labs-laboratory.com/medicine/) and the BRIDGEPORT knowledge portal (https://labs-laboratory.com/bridgeport). Our study used data generated by the TCGA Research Network (https://www.cancer.gov/tcga), the human protein atlas (HPA: https://www.proteinatlas.org), and the STRING data (https://string-db.org/). The two platforms curated and consolidated publicly available (single-cell) RNA-seq and protein data, including the GTEx project (https://gtexportal.org/home/). Genomic loci annotation used data from FUMA (https://fuma.ctglab.nl/). PheWAS used data from the GWAS Atlas platform (https://atlas.ctglab.nl/PheWAS). GWAS summary data for the DEs were downloaded from the official websites of FinnGen (R9: https://www.finngen.fi/en/access_results) and PGC (https://pgc.unc.edu/for-researchers/download-results/). Individual data from UKBB can be requested with proper registration at https://www.ukbiobank.ac.uk/. The gene-drug-disease network used data from the DrugBank database (v.5.1.9; https://go.drugbank.com/). The analysis for partitioned heritability estimates used data from ROADMAP (https://egg2.wustl.edu/roadmap/web_portal/) and ENTEx (https://www.encodeproject.org/). All unrestricted data supporting the findings are also available from the corresponding author upon request.

https://labs-laboratory.com/medicine/