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Gianna C Teague, Namrata Nandakumar, Jie Ma, Kyle V Marra, Jorge G Arroyo, Megan E Baldwin, Walter Johnson, Kameran Lashkari; Development of Biomarker Equations based on Multi-Dimensional Modeling of Cytokine Shifts in Retinal Diseases. Invest. Ophthalmol. Vis. Sci. 2014;55(13):1919. doi: https://doi.org/.
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We have hypothesized that levels of individual cytokines reflect underlying disease processes. Cumulative (multi-dimensional) shifts among cytokine families may be used to define specific disease processes and lead to novel disease-specific biomarkers.
Human vitreous samples collected from a variety of retinal diseases were subjected to multiplex analysis of cytokine markers. The five disease groups examined were proliferative diabetic retinopathy (PDR), proliferative vitreoretinopathy (PVR), retinal vascular occlusions (RVO) and neovascular glaucoma (NVG). The control samples included epiretinal membranes, floaters and macular holes. Mean, chi-squares and t-scores of log-transformed data were calculated and used to model multi-dimensional equations.
From the 35 cytokines examined, 29 were selected for which a Gaussian distribution of log-transformed data was observed in the control group. Single cytokine analysis for individuals in particular disease groups showed that certain diseases were associated with large t-score shifts from the controls for specific cytokines. The three cytokines showing the greatest shifts for the diseases studied are as follows: For PDR, PLGF, VEGF-A, and TNF-α; for PVR, TIE-2, Prolactin, and FGF-basic; for RVO: PLGF, VEGF-A, and EGF; for NVG: PLGF, VEGF-A, and EGF.
Elevated t-scores correlate with the impact of a particular cytokine on a specific disease process. Biomarker equations could be derived from multi-dimensional t-scores to adequately predict ocular disease processes and distinguish one disease from another.
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