Comorbidity‐driven multi‐modal subtype analysis in mild cognitive impairment of Alzheimer's disease

• Published in: Alzheimer's & dementia Vol. 19; no. 4; pp. 1428 - 1439
• Main Authors: Katabathula, Sreevani, Davis, Pamela B., Xu, Rong
• Format: Journal Article
• Language: English
• Published: United States 01.04.2023
• Subjects: Alzheimer Disease; Alzheimer's disease; Biomarkers; Cognitive Dysfunction; Comorbidity; Disease Progression; heterogeneity; Humans; mild cognitive impairment; mixed‐data clustering; subtypes; mild cognitive impairment; comorbidity; Alzheimer's disease; heterogeneity; mixed-data clustering; subtypes
• ISSN: 1552-5260; 1552-5279; 1552-5279
• DOI: 10.1002/alz.12792

Background Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi‐modality data‐driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well‐known risk factors for Alzheimer's disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD‐related comorbidities along with other AD‐relevant features and biomarkers. Methods A total of 325 MCI subjects with 32 AD‐relevant comorbidities and features were considered. Mixed‐data clustering is applied to discover and compare MCI subtypes with and without including AD‐related comorbidities. Finally, the relevance of each comorbidity‐driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates. Results We identified four (five) MCI subtypes: poor‐, average‐, good‐, and best‐AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity‐driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity‐driven MCI subtypes. Among the four comorbidity‐driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test‐related and plasma features, and by the proportion of comorbidities. Conclusions Our study indicates that AD comorbidities should be considered along with other diverse AD‐relevant characteristics to better understand MCI heterogeneity.