Using Medical Big Data to Develop Personalized Medicine for Dry Eye Disease. (November 2020)

• Record Type: Journal Article
• Authors: Inomata, Takenori; Sung, Jaemyoung; Nakamura, Masahiro; Iwagami, Masao; Okumura, Yuichi; Iwata, Nanami; Midorikawa-Inomata, Akie; Fujimoto, Keiichi; Eguchi, Atsuko; Nagino, Ken; Fujio, Kenta; Miura, Maria; Shokirova, Hurramhon; Murakami, Akira
• Is Part Of: Cornea. Volume 39(2020)Supplement 1
• Publication Date: 2020-11
• Subjects: dry eye disease -- big data -- mobile health -- omics -- personalized medicine; Cornea -- Periodicals; Cornea -- Periodicals; Cornée -- Périodiques; 617.719
• DOI: 10.1097/ICO.0000000000002500
• Languages: English
• ISSNs: 0277-3740

Abstract

Abstract : Abstract: Dry eye disease (DED) is a chronic, multifactorial ocular surface disorder with multiple etiologies that results in tear film instability. Globally, the prevalence of DED is expected to increase with an aging society and daily use of digital devices. Unfortunately, the medical field is currently unprepared to meet the medical needs of patients with DED. Noninvasive, reliable, and readily reproducible biomarkers have not yet been identified, and the current mainstay treatment for DED relies on symptom alleviation using eye drops with no effective preventative therapies available. Medical big data analyses, mining information from multiomics studies and mobile health applications, may offer a solution for managing chronic conditions such as DED. Omics-based data on individual physiologic status may be leveraged to prevent high-risk diseases, accurately diagnose illness, and improve patient prognosis. Mobile health applications enable the portable collection of real-world medical data and biosignals through personal devices. Together, these data lay a robust foundation for personalized treatments for various ocular surface diseases and other pathologies that currently lack the components of precision medicine. To fully implement personalized and precision medicine, traditional aggregate medical data should not be applied directly to individuals without adjustments for personal etiology, phenotype, presentation, and symptoms.