Related Experiment Video
Updated: Aug 27, 2025
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
Comorbidity-driven multi-modal subtype analysis in mild cognitive impairment of Alzheimer's disease
Sreevani Katabathula1, Pamela B Davis2, Rong Xu1
1Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
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.
Insights
Incorporating comorbidities into analyses reveals distinct subtypes of mild cognitive impairment (MCI), improving understanding of disease heterogeneity and prognosis for Alzheimer's disease (AD). These comorbidity-driven MCI subtypes show varied outcomes and clinical relevance.
Area of Science:
- Neurology
- Gerontology
- Biostatistics
Background:
- Mild cognitive impairment (MCI) is a complex condition with varied outcomes influenced by demographics, genetics, biomarkers, and comorbidities.
- Existing multi-modality approaches for discovering MCI subtypes have largely excluded the impact of comorbid diseases, which are known risk factors for Alzheimer's disease (AD).
Purpose of the Study:
- To investigate the heterogeneity of MCI by incorporating AD-related comorbidities alongside other AD-relevant features and biomarkers.
- To determine if including comorbidities in data-driven analyses leads to distinct MCI subtypes with different clinical relevance and prognostic value.
Main Methods:
- A cohort of 325 MCI subjects was analyzed, considering 32 AD-relevant comorbidities and features.
- Mixed-data clustering was employed to identify MCI subtypes both with and without the inclusion of AD-related comorbidities.
- The clinical relevance of identified subtypes was assessed by examining MCI to AD disease prognosis, conversion rates, and descriptive statistics.
Main Results:
- Four distinct MCI subtypes were identified when comorbidities were included, differing from the five subtypes found without comorbidity data.
- Comorbidity-driven MCI subtypes demonstrated significant differences in reversion to normal function, disease stability, and conversion rates to AD.
- These subtypes also showed significant variations in cognitive test performance, plasma biomarkers, and the prevalence of comorbidities.
Conclusions:
- AD comorbidities are crucial factors that should be integrated with other AD-relevant characteristics to accurately understand MCI heterogeneity.
- Considering comorbidities provides a more nuanced view of MCI subtypes, enhancing diagnostic and prognostic capabilities for Alzheimer's disease.
- This approach highlights the clinical significance of comorbidities in predicting the trajectory of mild cognitive impairment.
Related Concept Videos
01:26Alzheimer's Disease: Overview
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
01:22Alzheimer's Disease: Treatment
01:30Dementia
The progression of dementia is generally gradual....