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Alzheimers Res Ther. 2018 Jan 15;10(1):4. doi: 10.1186/s13195-017-0332-0.

Data-driven identification of endophenotypes of Alzheimer's disease progression: implications for clinical trials and therapeutic interventions.

Author information

1
Centre for Health Informatics, University of Manchester, Vaughan House, Portsmouth St, Manchester, M13 9GB, UK. nophar.geifman@manchester.ac.uk.
2
The Manchester Molecular Pathology Innovation Centre, University of Manchester, Manchester, UK. nophar.geifman@manchester.ac.uk.
3
School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
4
Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
5
Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
6
Microsoft Research, Cambridge, UK.
7
Department of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA.
8
Department of Neurology, College of Medicine, University of Arizona, Tucson, AZ, USA.
9
Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA.

Abstract

BACKGROUND:

Given the complex and progressive nature of Alzheimer's disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions.

METHODS:

Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer's Disease Assessment Scale-cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups' distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression.

RESULTS:

The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels.

CONCLUSIONS:

Our results, 'learned' from clinical data, indicate the existence of at least three subgroups of Alzheimer's patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.

KEYWORDS:

Alzheimer’s disease; Endophenotypes; Latent class mixed models; Machine learning; Precision medicine; Statistical learning

PMID:
29370871
DOI:
10.1186/s13195-017-0332-0
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