Discovering potential blood-based cytokine biomarkers for Alzheimer’s disease using Firth Logistic Regression - Mohammad Nasir Abdullah; Yap Bee Wah; Yuslina Zakaria; Abu Bakar Abdul Majeed; Ong Seng Huat (Epidemiology, Biostatistics and Public Health)
Background: Alzheimer’s disease (AD) is a neurodegenerative disorder where patients suffer from memory loss, cognitive impairment and progressive disability. Individual blood biomarkers have not been successful in defining the disease pathology, progression and diagnosis of AD. There is a need to identify multiplex panels of blood biomarkers for early diagnosis of AD with high sensitivity and specificity. This study focused on identification of cytokine biomarkers. The maximum likelihood estimates of the ordinary logistic regression model cannot be obtained when there is complete separation and the alternative is Firth logistic regression which uses a penalised Maximum Likelihood in parameter estimation. Methods: This paper reports a Firth logistic regression application in finding potential blood-based cytokine biomarkers for Alzheimer’s disease in a matched case control study. We used a principle component analysis to discriminate the correlated, completely separated covariates. Results: The Firth logistic regression results showed that nine individual biomarkers IL-1β, IL-6, IL-12, IFN-γ, IL-10, IL-13, IP-10, MCP-1 and MIP-1α had a significant relationshipwith elevated risk for AD as compared to the healthy control (HC). Principal component analysis with varimax rotation for the nine biomarkers revealed four factors (total variance explained=85.5%). The main principal component biomarkers were IL-1β, IL-6, IL-13 and MCP-1 (total variance explained=62.3%). Firth’s logistic regression model with the first principal component had accuracy of 78.2% with sensitivity and specificity of 71.8% and 75% respectively. Conclusion: Firth’s logistic regression is a useful technique in identification of significant biomarkers when there is an issue of data separation.