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

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 d...

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Veröffentlicht in:Nature communications Jg. 16; H. 1; S. 2724 - 12
Hauptverfasser: Govindarajan, Sindhuja Tirumalai, Mamourian, Elizabeth, Erus, Guray, Abdulkadir, Ahmed, Melhem, Randa, Doshi, Jimit, Pomponio, Raymond, Tosun, Duygu, Bilgel, Murat, An, Yang, Sotiras, Aristeidis, Marcus, Daniel S., LaMontagne, Pamela, Benzinger, Tammie L. S., Espeland, Mark A., Masters, Colin L., Maruff, Paul, Launer, Lenore J., Fripp, Jurgen, Johnson, Sterling C., Morris, John C., Albert, Marilyn S., Bryan, R. Nick, Resnick, Susan M., Habes, Mohamad, Shou, Haochang, Wolk, David A., Nasrallah, Ilya M., Davatzikos, Christos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 19.03.2025
Nature Publishing Group
Nature Portfolio
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Aged
Aged, 80 and over
Alzheimer's disease
Anatomy
Atrophy
Brain
Brain - diagnostic imaging
Brain - pathology
Brain architecture
Cardiovascular diseases
Cardiovascular Diseases - diagnostic imaging
Cardiovascular Diseases - epidemiology
Cardiovascular Diseases - pathology
Cognition - physiology
Cognitive ability
Cohort Studies
Dementia
Dementia disorders
Diabetes mellitus
Diabetes Mellitus, Type 2
Female
Humanities and Social Sciences
Humans
Hyperlipidemia
Hypertension
Learning algorithms
Machine Learning
Magnetic Resonance Imaging
Male
Metabolic Diseases - diagnostic imaging
Metabolic Diseases - pathology
Metabolic disorders
Middle Aged
multidisciplinary
Neuroimaging
Risk Factors
Science
Science (multidisciplinary)
Signatures
Substantia alba
White Matter - diagnostic imaging
White Matter - pathology
β-Amyloid
ISSN:2041-1723, 2041-1723
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Zusammenfassung: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. Cardiovascular and metabolic risk factors (CVM) impact brain structure and increase dementia risk. Here, the authors developed and validated machine learning models to measure the neuroanatomical changes in people with cardiovascular and metabolic diseases that are cognitively unimpaired.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-025-57867-7