Evidence map›Paper›PMID 42749890›Full record

ArticleNature medicine2026

Sex-specific biological aging clocks across organs and omics.

MULTI Consortium, Zhiyuan Song, Derek Feng, Naowal Azraf Rahman, Michael R Duggan, Qu Tian, Jian Zeng, Xia Zhou, Chunrui Zou, Michael S Rafii and 10 more

Abstract read
PubMed Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

20 authors.

MULTI Consortium
Zhiyuan SongLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.ORCID http://orcid.org/0009-0001-4027-3041
Derek FengLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.
Naowal Azraf RahmanLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.ORCID http://orcid.org/0009-0008-4307-9659
Michael R DugganIntramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Qu TianIntramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-2706-1439
Jian ZengInstitute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
Xia ZhouDepartment of Computer Science, Columbia University, New York, NY, USA.
Chunrui ZouLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.
Michael S RafiiAlzheimer's Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, San Diego, CA, USA.ORCID http://orcid.org/0000-0003-2640-2094
Li ShenDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-5443-0503
Paul ThompsonImaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-4720-8867
Eleanor M SimonsickIntramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Keenan A WalkerIntramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-5989-9853
Andrew ZaleskyDepartment of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0003-2298-9908
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 http://orcid.org/0000-0002-1025-8561
Paul AisenAlzheimer's Therapeutic Research Institute, Keck School of Medicine of the University of Southern California, San Diego, CA, USA.ORCID http://orcid.org/0000-0002-2896-5838
Luigi FerrucciIntramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-6273-1613
Susan M ResnickIntramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Junhao WenLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA. junhao.wen89@gmail.com.ORCID http://orcid.org/0000-0003-2077-3070

Funding

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 RF1 AG092412
6 · The paper itself

Abstract

Sex differentially shapes aging, neurodevelopment and neurodegenerative diseases such as Alzheimer's disease (AD). However, most biological aging clocks (artificial intelligence-predicted age minus chronological age) were trained on sex-pooled samples and implicitly assume sex invariance.Here we developed 38 sex-specific biological aging clocks across 15 organ systems. We first demonstrate the importance of sex-stratified training for constructing sex-specific healthy normative references and then reveal marked divergence between female and male clocks. Key genetic parameters and Mendelian randomization results indicate that organ-specific aging liability and its relationships to cardiometabolic, endocrine and mental traits are configured differently in females and males. Proteomic analyses identify distinct, organ-resolved synaptic, immune, vascular and metabolic networks that differentially track female and male biological aging. In longitudinal survival analyses, sex-specific clocks predict whole-body systemic diseases and all-cause mortality in a sex-dependent and organ-dependent manner. Further analyses reveal sex-dependent associations between the brain aging clock and cognitive decline trajectory during a preclinical AD clinical trial. Sex-stratified clocks may offer distinct value by defining biological age against sex-appropriate normative references and revealing sex-dependent genetic, molecular and clinical signatures that pooled models may obscure. Meanwhile, sex-pooled and sex-interaction approaches remain valuable, as human aging and disease also share fundamental biological similarities between females and males. Together, these findings reveal sex-specific biological aging signatures in aging, AD and systemic health, highlighting the need for explicitly sex-stratified modeling approaches.

Identifiers

What OpenQuestion holds

Textmetadata
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.