Biostatistics

Discovering potential blood-based cytokine biomarkers for Alzheimer’s disease using Firth Logistic Regression


Abstract


Background:

Alzheimer’s disease 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 panel of blood biomarkers with high sensitivity and specificity for early diagnosis. 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 used penalized Maximum Likelihood in parameter estimation.

Methods:

This paper reports a Firth logistic regression application in finding potential blood-based cytokine’s biomarker for Alzheimer’s disease match case control study. We used principle component analysis to discriminate the correlated complete separation 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 have significant relationship and elevated risk for the AD group as compared to HC. Principal component analysis with varimax rotation for the nine biomarkers revealed four factors (total variance explained=85.5%). The main principal component biomarkers are IL-1β, IL-6, IL-13 and MCP-1 (total variance explained=62.3%). Firth’s logistic regression model with the first principal component has 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 separation of data.




DOI: https://doi.org/10.2427/13173

References



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EBPH Epidemiology, Biostatistics and Public Health | ISSN 2282-0930

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.