Parkinson’s Disease Phenotype Stratification using Multiple Correspondence Analysis

Author

• Kelly Astudillo, The Graduate Center, City University of New YorkFollow

Date of Degree

6-2026

Document Type

Master's Thesis

Degree Name

Master of Science

Program

Cognitive Neuroscience

Advisor

Anjali Krishnan

Subject Categories

Categorical Data Analysis | Medical Genetics | Multivariate Analysis | Nervous System Diseases | Neurology

Keywords

Multiple Correspondence Analysis, Parkinson's disease, Phenotype Stratification

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, with over 12 million people projected to be affected by 2040 (Dorsey et al., 2018). Deep phenotyping and stratification can provide useful information regarding PD pathogenesis and can aid in the  development of disease modifying therapies that aim to delay the progression or prevent the onset of neurodegeneration (Blandini et al., 2019; Smith & Schapira, 2022). Utilizing multivariate methods such as multiple correspondence analysis (MCA) permits for the simultaneous analysis of distinct data modalities. To the best of our knowledge, MCA has not been previously used to explore phenotype patterns in the context of PD. In this study, we used MCA to extract the dimensions that explained the variability across phenotypes and identified the clinical features that significantly contributed to the differences in the observed phenotypes. We also used MCA to stratify and describe phenotypes patterns within our sample. Lastly, we explored genotype group differences. Overall, the results of MCA were consistent with previously reported findings relating to phenotype profiles in PD. MCA is a powerful method that can be used to analyze large and complex datasets such as those generated by the PPMI study.

Recommended Citation

Astudillo, Kelly, "Parkinson’s Disease Phenotype Stratification using Multiple Correspondence Analysis" (2026). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/6721