RENAL14: Colorimetric Detection App as a Component of a Blood-Free Test Kit for Home Detection of Inflammation Markers

Abstract

Background: Inflammation is a central driver of chronic kidney and heart disease. Presently, other than conventional blood-based lab tests, no rapid testing method exists to readily diagnose systemic inflammation. Several inflammation mediators/biomarkers (i.e., MMP9, Cystatin C, IL-6, C-reactive Protein) are present in gingival crevicular fluid (GCF) excreted from the periodontal pocket. As GCF may be sampled without the need for blood draw, we created an at-home test kit consisting of a paper microfluidic device for immunodetection, as well as an app adaptable for cell phone use for colorimetric detection and readout of the microfluidic device. Here we test the ability of the companion app to accurately detect color and color intensity change utilizing a standardized color scale. Methods: A kit was designed including: 1) a paper microfluidic device and 2) a companion app for cell phone use. The app was designed with a Python script for colorimetric analysis of biomarker quantitation. The intensity of color change produced within the microfluidic device, as a result of biomarker binding to tagged antibodies, is measured by the application, and a readout of biomarker presence and relative quantitation is produced. The color intensity detection app was validated with three defined shades of red utilizing the Pantone color chart (i.e., Pantone 185, 186, 187) as well as one shade of green (i.e., Pantone 355) to ensure color detection efficacy. App generated color readout, as well as its relative intensity in the form of high, medium, and low, were compared with expected values. Results: The developed app accurately differentiated between colors representative of expected biomarker range. The program readily detected colors on the ROYGBIV spectrum through a readout of “Red” for Pantone 185, 186, 187 and a readout of “Green” for Pantone 355, indicating a 100% color detection accuracy. Detection of color intensity was also seen to be 100% accurate based on readouts of “medium” saturation for Pantone 185 and 186 and “high” for Pantone 187. Presently, some inconsistency is noted related to HSL (hue, saturation, lightness) values compared to an external HSL color generator. Further experimentation is underway to strengthen the consistency and accuracy of this component of the app. Conclusion: A kit system has been designed allowing simple, blood-free detection of inflammation biomarkers associated with heart and kidney disease via GCF sampling and measurement. Detection is based on colorimetric assessment. The color detection and assessment app designed demonstrates high accuracy in terms of color detection and color intensity detection allowing for biomarker quantitation. Further work is underway to enhance app accuracy related to readout hue, saturation and lightness to allow enhanced biomarker quantitation. Full development of this kit system will provide a useful tool for early detection and modification of risk associated with heart and kidney disease.