June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Linear Discriminate Analysis for Design of High-Value Biomarkers in Proliferative Diabetic Retinopathy
Author Affiliations & Notes
  • Alexander Hua
    Schepens Eye Research Institute, Boston, Massachusetts, United States
  • Walter Johnson
    Physics, Suffolk University, Boston, Massachusetts, United States
  • Namrata Nandakumar
    Schepens Eye Research Institute, Boston, Massachusetts, United States
  • Gianna C Teague
    Schepens Eye Research Institute, Boston, Massachusetts, United States
  • Megan E Baldwin
    Opthea Pty Ltd, South Yarra, Victoria, Australia
  • Kameran Lashkari
    Schepens Eye Research Institute, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Alexander Hua, None; Walter Johnson, None; Namrata Nandakumar, None; Gianna Teague, None; Megan Baldwin, Opthea Pty Ltd (S); Kameran Lashkari, None
  • Footnotes
    Support  Opthea Pty Ltd
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 2511. doi:
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      Alexander Hua, Walter Johnson, Namrata Nandakumar, Gianna C Teague, Megan E Baldwin, Kameran Lashkari; Linear Discriminate Analysis for Design of High-Value Biomarkers in Proliferative Diabetic Retinopathy. Invest. Ophthalmol. Vis. Sci. 2017;58(8):2511.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Efforts have been made to identify biomarkers to quantify characteristics of various disease processes and to establish therapeutic endpoints. In this study, we aimed to develop and validate a method of combining multiple factors into a composite biomarker using linear discriminant functions. The purpose of a composite biomarker is to provide a useful tool to accurately classify disease-state proliferative diabetic retinopathy (PDR) from disease-free subjects without complicated data interpretation.

Methods : Vitreous samples collected from control subjects (n=28) and those with PDR (n=35) were subjected to ELISA or multiplexing to detect levels of 4 distinct factors that have been classically associated with PDR: VEGF-A, PLGF, PAI-1 and IL-8. All four factors were analyzed individually and in pairs in a linear discriminant function to produce a final composite biomarker. Comparison of success rates for correct diagnosis of disease condition, PDR vs. control was performed with training and test sets using the randomized k-fold stratified validation method.

Results : In a discriminant function analysis using one proangiogenic factor (VEGF-A or PLGF) or one pro-inflammatory factor (PAI-1 or IL-8), the results for successful classification of PDR was approximately 82.52% in a 7-fold stratified test data set. In a two-factor linear discriminant function analysis, the pair combination of one proangiogenic and one pro-inflammatory factor into a multi-cytokine biomarker demonstrated approximately 90% success in distinguishing between PDR and control. The derived linear discrimination function of the combination of VEGF-A and IL-8 was tested against independent published data demonstrated a 90% success classification rate in identifying PDR from control.

Conclusions : Our study provides a useful method for identification of disease process in unknown subjects by applying linear discriminant analysis of 4 distinct biomarkers that have been widely accepted to be associated with PDR. The application of multiple factors to construct a composite biomarker provides stronger evidence in identifying disease processes than a single biomarker alone. This methodology may be extended to plasma/serum biomarkers, which ultimately could allow a simple blood test to be used for diagnosis of a specific eye disease.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Correct Classification Success Rate of PDR using Pro-angiogenic and Pro-Inflammatory Factors

Correct Classification Success Rate of PDR using Pro-angiogenic and Pro-Inflammatory Factors

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