Year: 2020
Type: article
Abstract: Background Data‐driven modelling techniques, such as Subtype and Stage Inference (SuStaIn) (Young, et al., 2018), have been developed to disentangle the temporal and phenotypic heterogeneity of neurodegenerative diseases such as Alzheimer’s disease (AD) from widely available cross‐sectional datasets such as ADNI. This study builds on early work with SuStaIn by using multimodal data and missing‐data modelling to give a more detailed picture of the landscape of sporadic AD subtypes. Method SuStaIn was applied to cross‐sectional cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and positron emission tomography (PET) metrics, and cognitive scores from ADNI to identify multimodal disease subtypes with distinct progression patterns. The dataset consists of 789 subjects that passed quality control (182 cognitively normal (CN), 86 subjective memory concern (SMC), 241 early mild cognitive impairment (MCI), 163 late MCI and 117 AD). Amyloid‐negative (CSF Abeta < 192pg/ml) subjects were chosen as control subjects. A K‐Nearest Neighbour (KNN) algorithm was used to impute missing values (3% of the data), allowing for subtyping and staging of the full dataset. Result Figure 1 visualises the subtype progression patterns, with the precise model output being shown in Figure 2. Four different subtypes can be inferred from the data: 1) Typical AD with late tau: First changes are seen in CSF and PET measures of b‐amyloid, followed by cognitive scores impairment and finally, brain atrophy. 2) Typical AD: Changes in b‐amyloid closely followed by an increase in tau, both using CSF and PET based biomarkers. Cognitive scores and subcortical areas are affected next. 3) Cortical AD: Cortical atrophy is followed by subcortical atrophy and then increase in CSF and PET amyloid. 4) Cognitive decline with hippocampal atrophy: Mainly defined by a decrease in cognitive tests performance, followed by atrophy of the hippocampus and the amygdala. Conclusion This study provides a deeper insight into the heterogeneity of AD, revealing four subtypes with differing trajectories of biomarker abnormalities. Unravelling this heterogeneity will be a critical step in developing personalised medicine, allowing for improved power for clinical trials and better information for patients.
Source: Alzheimer s & Dementia
DOI: 10.1002/alz.045822
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W3112950200