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Cluster Analysis of Cortical Amyloid Burden for Identifying Imaging-driven Subtypes in Mild Cognitive Impairment
Ruiming Wu1, Bing He2, Bojian Hou1
1University of Pennsylvania, Philadelphia, PA.
Abstract:
Over the past decade, Alzheimer's disease (AD) has become increasingly severe and gained greater attention. Mild Cognitive Impairment (MCI) serves as an important prodromal stage of AD, highlighting the urgency of early diagnosis for timely treatment and control of the condition. Identifying the subtypes of MCI patients exhibits importance for dissecting the heterogeneity of this complex disorder and facilitating more effective target discovery and therapeutic development. Conventional method uses clinical measurements such as cognitive score and neurophysical assessment to stratify MCI patients into two groups with early MCI (EMCI) and late MCI (LMCI), which shows their progressive stages. However, such clinical method is not designed to de-convolute the heterogeneity of the disorder. This study uses a data-driven approach to divide MCI patients into a novel grouping of two subtypes based on an amyloid dataset of 68 cortical features from positron emission tomography (PET), where each subtype has a homogeneous cortical amyloid burden pattern. Experimental evaluation including visual two-dimensional cluster distribution, Kaplan-Meier plot, genetic association studies, and biomarker distribution analysis demonstrates that the identified subtypes performs better across all metrics than the conventional EMCI and LMCI grouping.
Insights
This study introduces a novel data-driven approach to subtype patients with Mild Cognitive Impairment (MCI), improving upon traditional methods for better Alzheimer's disease (AD) research and treatment strategies.
Area of Science:
- Neuroscience
- Medical Imaging
- Biostatistics
Background:
- Alzheimer's disease (AD) is a progressive neurological disorder with increasing prevalence.
- Mild Cognitive Impairment (MCI) is a critical prodromal stage of AD, necessitating early and accurate diagnosis.
- Current clinical stratification of MCI into early (EMCI) and late (LMCI) does not fully capture disease heterogeneity.
Purpose of the Study:
- To develop a data-driven method for identifying novel subtypes of MCI patients.
- To improve the understanding of MCI heterogeneity for targeted therapeutic development.
- To compare the efficacy of novel MCI subtypes against conventional EMCI/LMCI groupings.
Main Methods:
- Utilized a dataset of 68 cortical features from positron emission tomography (PET) amyloid imaging.
- Employed a data-driven approach to cluster MCI patients into homogeneous subtypes based on amyloid burden patterns.
- Conducted experimental evaluations including cluster distribution, survival analysis, genetic association, and biomarker analysis.
Main Results:
- Identified two novel MCI subtypes characterized by distinct and homogeneous cortical amyloid burden patterns.
- Demonstrated superior performance of the novel subtypes across multiple evaluation metrics compared to EMCI/LMCI.
- The novel clustering revealed a more nuanced understanding of MCI heterogeneity.
Conclusions:
- The data-driven, amyloid-based subtyping of MCI offers a more refined classification than conventional methods.
- This novel approach enhances the potential for personalized treatment strategies in AD.
- Further research into these subtypes can facilitate the discovery of targeted therapies for AD progression.
