Evidence map›Paper›PMID 40108173›Full record

ArticleNature communications2025

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Sindhuja Tirumalai Govindarajan, Elizabeth Mamourian, Guray Erus, Ahmed Abdulkadir, Randa Melhem, Jimit Doshi, Raymond Pomponio, Duygu Tosun, Murat Bilgel, Yang An and 19 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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

29 authors.

Sindhuja Tirumalai GovindarajanCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA. sindhuja.tirumalaigovindarajan@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0003-1741-0906
Elizabeth MamourianCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0001-8581-4887
Guray ErusCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0001-6633-4861
Ahmed AbdulkadirCentre for Artificial Intelligence, ZHAW School of Engineering, Winterthur, Switzerland.
Randa MelhemCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Jimit DoshiCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Raymond PomponioCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Duygu TosunDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0001-8644-7724
Murat BilgelLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.ORCID http://orcid.org/0000-0001-5042-7422
Yang AnLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Aristeidis SotirasDepartment of Radiology, Washington University School of Medicine, St. Louis, MO, USA.ORCID http://orcid.org/0000-0003-0795-8820
Daniel S MarcusDepartment of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Pamela LaMontagneDepartment of Radiology, Washington University School of Medicine, St. Louis, MO, USA.ORCID http://orcid.org/0000-0002-6752-8518
Tammie L S BenzingerDepartment of Radiology, Washington University School of Medicine, St. Louis, MO, USA.ORCID http://orcid.org/0000-0002-8114-0552
Mark A EspelandSticht Center for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA.
Colin L MastersFlorey Institute, The University of Melbourne, Parkville, VIC, Australia.
Paul MaruffFlorey Institute, The University of Melbourne, Parkville, VIC, Australia.
Lenore J LaunerNeuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA.ORCID http://orcid.org/0000-0002-3238-7612
Jurgen FrippCSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia.
Sterling C JohnsonWisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.ORCID http://orcid.org/0000-0002-8501-545X
John C MorrisKnight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.
Marilyn S AlbertDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
R Nick BryanDepartment of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-1115-7145
Mohamad HabesBiggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA.ORCID http://orcid.org/0000-0001-9447-5805
Haochang ShouCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-3043-047X
David A WolkDepartment of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
Ilya M NasrallahCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0003-2346-7562
Christos DavatzikosCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA. Christos.davatzikos@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0002-1025-8561

Funding

Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
WASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 1985 to 2019
$52.1M
Project 2 MeasurementU19AG033655 · NIA · JOHNS HOPKINS UNIVERSITY · PI JUAN TRONCOSO · 2014 to 2026
$51.5M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Wisconsin Registry for Alzheimer's Prevention: Sex Differences in DNA MethylationR01AG027161 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Nathaniel Ark Chin, Sterling C Johnson · 2007 to 2026
$35.6M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
Research Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI Susan Lynn Stark · 2020 to 2026
$28.7M
Research Education ComponentP30AG072979 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVID A WOLK · 2021 to 2026
$24.8M
Wake Forest University School of Medicine Alzheimer's Disease Research CenterP30AG072947 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI SUZANNE CRAFT · 2021 to 2026
$24.3M
MORPHOMETRY BIOMEDICAL INFORMATICS RESEARCH NETWORKU24RR021382 · NCRR · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2004 to 2008
$24.1M
STRUCTURE, FUNCTION AND COGNITION IN SCHIZOPHRENIAP50MH071616 · NIMH · WASHINGTON UNIVERSITY · PI BARCH, DEANNA · 2004 to 2008
$11.2M
NCATS NIH HHS UL1 TR000448NCRR NIH HHS U24 RR021382NHLBI NIH HHS HHSN268200900041CNHLBI NIH HHS HHSN268201300025CNHLBI NIH HHS HHSN268201300026CNHLBI NIH HHS HHSN268201300027CNHLBI NIH HHS HHSN268201300028CNHLBI NIH HHS HHSN268201300029CNIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072979NIA NIH HHS P50 AG005681NIA NIH HHS R01 AG021910NIA NIH HHS R01 AG027161NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG054047NIA NIH HHS R01 AG054409NIA NIH HHS R01 AG062819NIA NIH HHS R01 AG067103NIA NIH HHS R01 AG080821NIA NIH HHS R01 AG083865NIA NIH HHS R01 AG085571NIA NIH HHS RF1 AG054409NIA NIH HHS U19 AG033655NIBIB NIH HHS R01 EB009352NIDA NIH HHS HHSN271201600059CNIMH NIH HHS P50 MH071616NINDS NIH HHS U24 NS130411
6 · The paper itself

Abstract

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45-85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45-64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies.

Indexed as

BrainCardiovascular DiseasesMachine LearningMetabolic DiseasesAgedAged, 80 and overAtrophyCognitionCohort StudiesDiabetes Mellitus, Type 2FemaleHumansMagnetic Resonance ImagingMaleMiddle AgedRisk Factors

Identifiers

PMID40108173
PMCPMC11923046

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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.