Evidence map›Paper›PMID 41102562›Full record

ArticleNature medicine2026

MRI-based multi-organ clocks for healthy aging and disease assessment.

MULTI Consortium, Huizi Cao, Zhiyuan Song, Michael R Duggan, Guray Erus, Dhivya Srinivasan, Ye Ella Tian, Wenjia Bai, Michael S Rafii, Paul Aisen and 6 more

Abstract read
In one paragraph

Article in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

MULTI Consortium
Huizi CaoLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.
Zhiyuan SongLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.
Michael R DugganLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Guray ErusArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-6633-4861
Dhivya SrinivasanArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Ye Ella TianSystems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0003-3107-5550
Wenjia BaiDepartment of Brain Sciences and Department of Computing, Imperial College London, London, UK.ORCID 0000-0003-2943-7698
Michael S RafiiAlzheimer's Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, San Diego, CA, USA.ORCID 0000-0003-2640-2094
Paul AisenAlzheimer's Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, San Diego, CA, USA.ORCID 0000-0002-2896-5838
Daniel W BelskyRobert N Butler Columbia Aging Center and Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA.ORCID 0000-0001-5463-2212
Keenan A WalkerLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-5989-9853
Andrew ZaleskySystems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0003-2298-9908
Luigi FerrucciNational Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Christos DavatzikosArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-1025-8561
Junhao WenLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA. junhao.wen89@gmail.com.ORCID 0000-0003-2077-3070

Funding

Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortiumRF1AG054409 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2023
$6.3M
Multi-Organ Chart of Personalized Susceptibility to Alzheimer's Disease and AgingRF1AG092412 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WEN, JUNHAO · 2025 to 2025
$3.5M
NIA NIH HHS P30 AG066507NIA NIH HHS RF1 AG054409NIA NIH HHS RF1 AG092412U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) RF1AG092412
6 · The paper itself

Abstract

Biological aging clocks across organ systems and tissues have advanced understanding of human aging and disease. In this study, we expand this framework to develop seven magnetic resonance imaging-based multi-organ biological age gaps (MRIBAGs), including the brain, heart, liver, adipose tissue, spleen, kidney and pancreas. Using data from 313,645 individuals curated by the MULTI Consortium, we link the seven MRIBAGs to 2,923 plasma proteins, 327 metabolites and 6,477,810 common genetic variants. Genome-wide associations identify 53 MRIBAG-locus pairs (P < 5 × 10

Indexed as

AgingHealthy AgingMagnetic Resonance ImagingAdipose TissueAgedBrainFemaleGenome-Wide Association StudyHumansKidneyLiverMaleMendelian Randomization AnalysisMiddle AgedPancreasSpleen

Identifiers

PMID41102562
PMCPMC12823390

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.