Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning
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 invasiv...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202405580 |
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| author | Ruiyuan Weng Yudian Xu Xinjie Gao Linlin Cao Jiabin Su Heng Yang He Li Chenhuan Ding Jun Pu Meng Zhang Jiheng Hao Wei Xu Wei Ni Kun Qian Yuxiang Gu |
| author_facet | Ruiyuan Weng Yudian Xu Xinjie Gao Linlin Cao Jiabin Su Heng Yang He Li Chenhuan Ding Jun Pu Meng Zhang Jiheng Hao Wei Xu Wei Ni Kun Qian Yuxiang Gu |
| author_sort | Ruiyuan Weng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3d45c9e4fc6a4594ba36fee533cf5314 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-3d45c9e4fc6a4594ba36fee533cf53142025-08-20T02:30:35ZengWileyAdvanced Science2198-38442025-02-01128n/an/a10.1002/advs.202405580Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine LearningRuiyuan Weng0Yudian Xu1Xinjie Gao2Linlin Cao3Jiabin Su4Heng Yang5He Li6Chenhuan Ding7Jun Pu8Meng Zhang9Jiheng Hao10Wei Xu11Wei Ni12Kun Qian13Yuxiang Gu14Department of Neurosurgery Huashan Hospital of Fudan University Shanghai 200040 P. R. ChinaDepartment of Traditional Chinese Medicine RenJi Hospital School of Medicine Shanghai Jiao Tong University Shanghai 200127 P. R. ChinaDepartment of Neurosurgery Huashan Hospital of Fudan University Shanghai 200040 P. R. ChinaState 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. ChinaDepartment of Neurosurgery Huashan Hospital of Fudan University Shanghai 200040 P. R. ChinaDepartment of Neurosurgery Huashan Hospital of Fudan University Shanghai 200040 P. R. ChinaDepartment of Traditional Chinese Medicine RenJi Hospital School of Medicine Shanghai Jiao Tong University Shanghai 200127 P. R. ChinaDepartment of Traditional Chinese Medicine RenJi Hospital School of Medicine Shanghai Jiao Tong University Shanghai 200127 P. R. ChinaState 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. ChinaDepartment of Neurosurgery Liaocheng People's Hospital Shandong 252000 ChinaDepartment of Neurosurgery Liaocheng People's Hospital Shandong 252000 ChinaState 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. ChinaDepartment of Neurosurgery Huashan Hospital of Fudan University Shanghai 200040 P. R. ChinaSchool of Biomedical Engineering Institute of Medical Robotics and Med‐X Research Institute Shanghai Jiao Tong University Shanghai 200030 P. R. ChinaDepartment of Neurosurgery Huashan Hospital of Fudan University Shanghai 200040 P. R. ChinaAbstract 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.https://doi.org/10.1002/advs.202405580biomarkersfingerprintsmass spectrometrymoyamoya disease diagnosis |
| spellingShingle | Ruiyuan Weng Yudian Xu Xinjie Gao Linlin Cao Jiabin Su Heng Yang He Li Chenhuan Ding Jun Pu Meng Zhang Jiheng Hao Wei Xu Wei Ni Kun Qian Yuxiang Gu Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning Advanced Science biomarkers fingerprints mass spectrometry moyamoya disease diagnosis |
| title | Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning |
| title_full | Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning |
| title_fullStr | Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning |
| title_full_unstemmed | Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning |
| title_short | Non‐Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning |
| title_sort | non invasive diagnosis of moyamoya disease using serum metabolic fingerprints and machine learning |
| topic | biomarkers fingerprints mass spectrometry moyamoya disease diagnosis |
| url | https://doi.org/10.1002/advs.202405580 |
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