Accelerated brain aging in amyotrophic lateral sclerosis and its prognostic associations: a cohort study.
Authors
Affiliations (11)
Affiliations (11)
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China.
- Department of Neurology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, 21287, USA.
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, 21287, USA.
- Department of PET/CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University Xi'an, Xi'an, 710061, China.
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University, Hefei, 230601, China.
- State Key Laboratory of Cognitive Science and Mental Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- State Key Laboratory of Cognitive Science and Mental Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China. [email protected].
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University Xi'an, Xi'an, 710061, China. [email protected].
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China. [email protected].
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, appearing to be associated with accelerated brain aging. Although increased brain age has been associated with mortality risk in older adults, it remains unclear how the brain aging process is accelerated in ALS and how this acceleration relates to patient prognosis. One hundred seventy sporadic ALS patients and 84 age- and sex-matched HCs were recruited and underwent neuropsychological tests and T1-weighted magnetic resonance imaging (MRI) scans. A brain-age prediction model was developed using a 3D-Conformer deep learning framework trained on 4310 healthy T1-weighted MRI data from public datasets. Brain age was predicted in HCs and ALS patients, and then the predicted-age difference (PAD = brain age-chronological age) was calculated. In the ALS cohort, the neuroanatomical association of PAD was examined, and cell-specific gene expression of PAD-related volume loss was analyzed using transcriptomic data from the Allen Human Brain Atlas. Associations between PAD, neuropsychological performance, clinical characteristics, and survival outcomes were further evaluated in ALS patients. The brain aging process was accelerated in ALS by an average PAD of 1.77 years, which was predominantly associated with a widespread loss of gray matter volume. Cell-specific gene expression analyses showed microglia, inhibitory neurons, endothelial cells, and pericytes as significant contributors to the accelerated brain aging in ALS, with endothelial cells and pericytes being essential for constructing the blood-brain barrier (BBB) and microglia being involved in neuroinflammation. The acceleration in brain aging was more prominent in older patients and was associated with cognitive impairments. Notably, older brain age at initial diagnosis was an independent risk factor for death (hazard ratio, 1.026; 95% CI [1.008, 1.045]) and was associated with shorter survival and more rapid disease progression. Accelerated brain aging is common in ALS; this could be hypothesized to be associated with the interplay between BBB breakdown and neuroinflammation, leading to gray matter degeneration. Furthermore, older brain age at initial diagnosis is associated with worse clinical outcomes. These findings suggest that therapeutic strategies targeting neuroinflammation and BBB integrity may help attenuate accelerated brain aging and improve prognosis.