Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
PMID: 39737836

Abstract

Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new-onset stroke in patients with MMD. However, the current diagnostic methods are invasive and expensive, and non-invasive diagnosis using biomarkers of MMD is rarely reported. To address this issue, nanoparticle-enhanced laser desorption/ionization mass spectrometry (LDI MS) was employed to record serum metabolic fingerprints (SMFs) with the aim of establishing a non-invasive diagnosis method for MMD. Subsequently, a diagnostic model was developed based on deep learning algorithms, which exhibited high accuracy in differentiating the MMD group from the HC group (AUC = 0.958, 95% CI of 0.911 to 1.000). Additionally, hierarchical clustering analysis revealed a significant association between SMFs across different groups and vascular cognitive impairment in MMD. This approach holds promise as a novel and intuitive diagnostic method for MMD. Furthermore, the study may have broader implications for the diagnosis of other neurological disorders.

Authors

  • Ruiyuan Weng
    Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, 200040, P. R. China.
  • Yudian Xu
    Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
  • Xinjie Gao
    Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, 200040, P. R. China.
  • Linlin Cao
    State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China.
  • Jiabin Su
    Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, 200040, P. R. China.
  • Heng Yang
    State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
  • He Li
    National Soybean Processing Industry Technology Innovation Center, Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University Beijing 100048 China lihe@btbu.edu.cn liuxinqi@btbu.edu.cn.
  • Chenhuan Ding
    Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
  • Jun Pu
    Center for the Science of Therapeutics, Broad Institute of Harvard and MIT , 7 Cambridge Center, Cambridge, Massachusetts 02142, United States.
  • Meng Zhang
    College of Software, Beihang University, Beijing, China.
  • Jiheng Hao
    Department of Neurosurgery, Liaocheng People's Hospital, Shandong, 252000, China.
  • Wei Xu
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China.
  • Wei Ni
    Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Marsfield, NSW 2122, Australia.
  • Kun Qian
    Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, Beijing, China.
  • Yuxiang Gu
    Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, 200040, P. R. China.

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