Resolving heterogeneity in Alzheimer's disease based on individualized structural covariance network

Abstract

The heterogeneity of Alzheimer's disease (AD) poses a challenge to precision medicine. We aimed to identify distinct subtypes of AD based on the individualized structural covariance network (IDSCN) analysis and to research the underlying neurobiology mechanisms. In this study, 187 patients with AD (age = 73.57 ± 6.00, 50% female) and 143 matched normal controls (age = 74.30 ± 7.80, 44% female) were recruited from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project database, and T1 images were acquired. We utilized the IDSCN analysis to generate individual-level altered structural covariance network and performed k-means clustering to subtype AD based on structural covariance network. Cognition, disease progression, morphological features, and gene expression profiles were further compared between subtypes, to characterize the heterogeneity in AD. Two distinct AD subtypes were identified in a reproducible manner, and we named the two subtypes as slow progression type (subtype 1, n = 104, age = 76.15 ± 6.44, 42% female) and rapid progression type (subtype 2, n = 83, age = 71.98 ± 8.72, 47% female), separately. Subtype 1 had better baseline visuospatial function than subtype 2 (p < 0.05), whereas subtype 2 had better baseline memory function than subtype 1 (p < 0.05). Subtype 2 showed worse progression in memory (p = 0.003), language (p = 0.003), visuospatial function (p = 0.020), and mental state (p = 0.038) than subtype 1. Subtype 1 often shared increased structural covariance network, mainly in the frontal lobe and temporal lobe regions, whereas subtype 2 often shared increased structural covariance network, mainly in occipital lobe regions and temporal lobe regions. Functional annotation further revealed that all differential structural covariance network between the two AD subtypes were mainly implicated in memory, learning, emotion, and cognition. Additionally, differences in gray matter volume (GMV) between AD subtypes were identified, and genes associated with GMV differences were found to be enriched in the terms potassium ion transport, synapse organization, and histone modification and the pathways viral infection, neurodegeneration-multiple diseases, and long-term depression. The two distinct AD subtypes were identified and characterized with neuroanatomy, cognitive trajectories, and gene expression profiles. These comprehensive results have implications for neurobiology mechanisms and precision medicine.

Keywords: Alzheimer's disease; Functional annotation; Gene expression profiles; Heterogeneity; Individualized structural covariance network; Subtypes.