Subtyping mild cognitive impairment based on imaging and CSF biomarker levels. (December 2021)

• Record Type: Journal Article
• Authors: Dadar, Mahsa; Shafiee, Neda; Collins, Louis; Camicioli, Richard; Duchesne, Simon
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.054129
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Individuals with mild cognitive impairment (MCI) have variable clinical outcomes, with a proportion converting to dementia in a short follow‐up period. Subtyping MCI subjects based on biomarker levels can provide additional insights on why some individuals progress faster, aiding clinical treatment strategy and facilitating cohort enrichment for clinical trials. Method: Data included 562 individuals with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project with baseline MRI, CSF amyloid beta (Aβ), t‐tau, and p‐tau data and a minimum of one‐year follow‐up information on clinical diagnosis. White matter hyperintensities (WMHs) were segmented using a previously validated random forests classifier (Dadar et al. 2017). SNIPE (Scoring by Nonlocal Image Patch Estimator) was used to measure Alzheimer's‐disease‐like atrophy patterns in the hippocampi (HC) and entorhinal cortex (EC) (Coupé et al. 2019). CSF Aβ, p‐tau and t‐tau levels were obtained from project files. Agglomerative hierarchical clustering as used to cluster the data based on baseline age, education, APOE4 status, CSF Aβ, p‐tau and t‐tau levels, WMH load, and HC and EC grading scores. We then investigated differences in baseline ADAS13 cognitive scores as well as rate of conversion to dementia between different subtypes. Result: Figure 1 shows the clustering dendrogram, splitting the data into three subtypes of 152, 200, and 210 participants, respectively. Figure 2 compares biomarker values across subtypes. Baseline ADAS13 scores and conversion rates were significantly different across all subtypes (p<0.0001). Subtype 1 had the oldest subjects with largest WMH loads (p<0.0001), with a conversion rate of 30.2%. Subtype 2 had the highest conversion rate (70.0%), with significantly lower CSF Aβ and EC grading scores, and higher CSF p‐tau and t‐tau values (p<0.0001). Subtype 3 had the lowest conversion rate (15.2%), with significantly higher HC and EC grading scores and lower age and WMH loads than the other two subtypes (p<0.0001). Conclusion: Biomarker‐based subtyping of MCI subjects led to three distinct groups with significantly different clinical outcomes: 1) older individuals with cerebrovascular pathology and moderate conversion rates, 2) individuals with abnormal CSF biomarker levels, hippocampal and entorhinal atrophy, and high conversion rates, and 3) younger individuals without abnormal biomarker levels and low conversion rates.