Abstract
The population of adults with Alzheimer’s disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinically-homogeneous groups of AD patients and link clinical characteristics with biological markers. We used an innovative big data analysis strategy, the 3C strategy, that incorporates medical knowledge into the data analysis process. A large set of preprocessed AD Neuroimaging Initiative (ADNI) data was analyzed with 3C. The data analysis yielded 6 new disease subtypes, which differ from the assigned diagnosis types and present different patterns of clinical measures and potential biomarkers. Two of the subtypes, “Anosognosia dementia” and “Insightful dementia”, differentiate between severe participants based on clinical characteristics and biomarkers. The “Uncompensated mild cognitive impairment (MCI)” subtype, demonstrates clinical, demographic and imaging differences from the “Affective MCI” subtype. Differences were also observed between the “Worried Well” and “Healthy” clusters. The use of data-driven analysis yielded sub-phenotypic clinical clusters that go beyond current diagnoses and are associated with biomarkers. Such homogenous sub-groups can potentially form the basis for enhancement of brain medicine research.
Highlights
Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia[1] according to the 2018 Alzheimer’s association report[2] an estimated 5.7 million Americans of all ages are living with AD in 2018
In the Categorize step, the 144 clinical characteristics were screened for association with the assigned diagnosis, and the leading 12 as indicated by VSURF16 were selected for unsupervised clustering
The original Alzheimer’s Disease Neuroimaging Initiative (ADNI) data consisted of 5 diagnosis groups: AD (Alzheimer’s disease, N = 110); LMCI (Late Mild Cognitive Impairment, N = 133); EMCI (Early Mild Cognitive Impairment, N = 148); SMC (Significant Memory Concern, N = 94); and CN (Cognitively Normal, N = 173)
Summary
Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia[1] according to the 2018 Alzheimer’s association report[2] an estimated 5.7 million Americans of all ages are living with AD in 2018. Current diagnostic subgroupings are informative, they are quite crude as they are based on rough criteria[7,8] This may lead astray supervised data mining tools that rely solely on these definitions while trying to predict or associate disease manifestation with clinical and biological markers. Associations between biological markers (i.e. imaging, pathology, and genetics) and disease manifestations may be hard to discover Such associations are especially difficult to find for neurological and psychiatric conditions, as compensatory mechanisms are very common. Still, once discovered, they shed light on interesting pathophysiological processes and may offer directions for developing precise treatments. If the data has few subjects per feature, or even less than one (horizontal, or wide data) which is common in medical big data, the challenge is greater
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
