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, Aristeidis Sotiras, Daniel S. Marcus, Pamela LaMontagne, Tammie L. S. Benzinger, Mark A. Espeland, Colin L. Masters, Paul Maruff, Lenore J. Launer, Jurgen Fripp, Sterling C. Johnson, John C. Morris, Marilyn S. Albert, R. Nick Bryan, Susan M. Resnick, Mohamad Habes, Haochang Shou, David A. Wolk, Ilya M. Nasrallah and Christos Davatzikos ()
Additional contact information
Sindhuja Tirumalai Govindarajan: University of Pennsylvania
Elizabeth Mamourian: University of Pennsylvania
Guray Erus: University of Pennsylvania
Ahmed Abdulkadir: ZHAW School of Engineering
Randa Melhem: University of Pennsylvania
Jimit Doshi: University of Pennsylvania
Raymond Pomponio: University of Pennsylvania
Duygu Tosun: University of California, San Francisco
Murat Bilgel: National Institutes of Health
Yang An: National Institutes of Health
Aristeidis Sotiras: Washington University School of Medicine
Daniel S. Marcus: Washington University School of Medicine
Pamela LaMontagne: Washington University School of Medicine
Tammie L. S. Benzinger: Washington University School of Medicine
Mark A. Espeland: Wake Forest School of Medicine
Colin L. Masters: The University of Melbourne
Paul Maruff: The University of Melbourne
Lenore J. Launer: National Institute on Aging
Jurgen Fripp: Australian e-Health Research Centre CSIRO
Sterling C. Johnson: University of Wisconsin School of Medicine and Public Health
John C. Morris: Washington University in St. Louis
Marilyn S. Albert: Johns Hopkins University School of Medicine
R. Nick Bryan: University of Pennsylvania
Susan M. Resnick: National Institutes of Health
Mohamad Habes: University of Texas San Antonio Health Science Center
Haochang Shou: University of Pennsylvania
David A. Wolk: University of Pennsylvania
Ilya M. Nasrallah: University of Pennsylvania
Christos Davatzikos: University of Pennsylvania

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-57867-7

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