Abstract
BackgroundGiven 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.MethodsLongitudinal 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.ResultsThe 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.ConclusionsOur 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.
Highlights
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
By employing a data-driven, statistical learning approach, we investigated whether distinct subgroups of AD were apparent in an integrated clinical trial dataset; and whether these subgroups were associated with specific clinical features or existing therapies that might have delayed AD progression
In this study we examined the use of statins, non-statin cholesterol-lowering drugs, AD medications, antidepressants, non-steroid anti-inflammatory drugs (NSAIDs), oestrogens, diabetes medications, vitamin E, omega-3 and derivatives, and medications for longterm asthma management
Summary
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. Given the complexity and progressive nature of AD, there are likely to be distinctive phenotypes and genotypes that respond to candidate therapies differently, and a precision approach to prevention and treatment is critical. Such an approach, where persons with the disease are considered based on an endotype, could identify therapeutics that could delay progression of disease to gain the 5-year window necessary to reduce incidence of the disease. We report that clinically meaningful subgroups can be identified and these might be used to stratify patient populations for better AD management and care
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
