Brain metabolic imaging with 18 F-PET-CT and machine-learning clustering analysis reveal divergent metabolic phenotypes in patients with amyotrophic lateral sclerosis.
Authors
Affiliations (18)
Affiliations (18)
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
- School of Intelligent Biomedical Engineering, Hunan University of Technology and Business, Changsha, Hunan, 410205, P.R. China.
- National Engineering Research Center for Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, Hunan, 410008, P.R. China.
- Department of Neurology, Xiangya Hospital, Central South University, National Regional Center for Neurological Diseases), Jiangxi, Nanchang, Jiangxi, 330038, P. R. China.
- Department of Neurology, Xiangya Hospital, Central South University, National Regional Center for Neurological Diseases), Jiangxi, Nanchang, Jiangxi, 330038, P. R. China. [email protected].
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P. R. China. [email protected].
- Key Laboratory of Hunan Province for Neurodegenerative Disorders, Central South University, Changsha, Hunan, 410008, P. R. China. [email protected].
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, P. R. China. [email protected].
- Engineering Research Center of Hunan Province for Cognitive Impairment Disorders, Central South University, Changsha, Hunan, 410008, P. R. China. [email protected].
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, Hunan, 410008, P. R. China. [email protected].
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy, TN, 38068, Italy.
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, P.R. China. [email protected].
- College of Health Solutions, Arizona State University, Lattie F. Coor Hall, Room 3407, 976 S Forest Mall, Tempe, AZ, USA. [email protected].
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, 404000, P.R. China. [email protected].
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, 404000, P.R. China. [email protected].
- School of Medicine, Chongqing University, Chongqing, 400030, P.R. China. [email protected].
- FuRong Laboratory, Xiangya Hospital, Changsha, Hunan, 410078, P.R. China. [email protected].
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, 91010, USA.
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by significant clinicopathologic heterogeneity. This study aimed to identify distinct ALS phenotypes by integrating brain 18 F-fluorodeoxyglucose positron emission tomography-computed tomography (18 F-FDG PET-CT) metabolic imaging with consensus clustering data. This study prospectively enrolled 127 patients with ALS and 128 healthy controls. All participants underwent a brain 18 F-FDG-PET-CT metabolic imaging, psychological questionnaires, and functional screening. K-means consensus clustering was applied to define neuroimaging-based phenotypes. Survival analyses were also performed. Whole exome sequencing (WES) was utilized to detect ALS-related genetic mutations, followed by GO/KEGG pathway enrichment and imaging-transcriptome analysis based on the brain metabolic activity on the 18 F-FDG-PET-CT imaging. Consensus clustering identified two metabolic phenotypes, i.e., the metabolic attenuation phenotype and the metabolic non-attenuation phenotype according to their glucose metabolic activity pattern. The metabolic attenuation phenotype was associated with worse survival (p = 0.022), poorer physical function (p = 0.005), more severe depression (p = 0.026) and greater anxiety level (p = 0.05). WES testing and neuroimaging-transcriptome analysis identified specific gene mutations and molecular pathways with each phenotype. We identified two distinct ALS phenotypes with varying clinicopathologic features, indicating that the unsupervised machine learning applied to PET imaging may effectively classify metabolic subtypes of ALS. These findings contributed novel insights into the heterogeneous pathophysiology of ALS, which should inform personalized therapeutic strategies for patients with ALS.