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

Journal: Nature communications
PMID: 40108173

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.

Authors

  • Sindhuja Tirumalai Govindarajan
    Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA. sindhuja.tirumalaigovindarajan@pennmedicine.upenn.edu.
  • Elizabeth Mamourian
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Guray Erus
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Ahmed Abdulkadir
    Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
  • Randa Melhem
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Jimit Doshi
    Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
  • Raymond Pomponio
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Duygu Tosun
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
  • Murat Bilgel
    Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Yang An
    Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Aristeidis Sotiras
    Department of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA.
  • Daniel S Marcus
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Pamela LaMontagne
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Tammie L S Benzinger
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Mark A Espeland
    Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
  • Colin L Masters
    Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia.
  • Paul Maruff
    Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia.
  • Lenore J Launer
    Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA.
  • Jurgen Fripp
    CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia.
  • Sterling C Johnson
    Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
  • John C Morris
    Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.
  • Marilyn S Albert
    Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • R Nick Bryan
    Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., L.X., A.K., J.M.E., T.C., I.M.N., S.M., J.C.G.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (J.D.R., A.M.R.); Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pa (X.L., J.W.); University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (M.T.D.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); Department of Radiology, University of Texas, Austin, Tex (R.N.B.); and Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pa (I.M.N.).
  • Susan M Resnick
    Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Mohamad Habes
    Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, USA.
  • Haochang Shou
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • David A Wolk
    Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
  • Ilya M Nasrallah
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Christos Davatzikos
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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