A Precision Medicine Approach to Treating Alzheimer's Disease Using Rosiglitazone Therapy: A Biomarker Analysis of the REFLECT Trials

Sid E. O'Bryant, Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Robert A. Rissman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist. Objective: To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials. Methods: Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile. Results: A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNF, FABP-3, and PPY) correctly classified treatment response with 100%accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders. Conclusion: A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.

Original languageEnglish
Pages (from-to)557-568
Number of pages12
JournalJournal of Alzheimer's Disease
Volume81
Issue number2
DOIs
StatePublished - 2021

Keywords

  • Alzheimer's disease
  • clinical trial
  • predictive biomarker
  • rosiglitazone

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