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Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals

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Listed:
  • Ramsés Noguez Imm
  • Julio Muñoz-Benitez
  • Diego Medina
  • Everardo Barcenas
  • Guillermo Molero-Castillo
  • Pamela Reyes-Ortega
  • Jorge Armando Hughes-Cano
  • Leticia Medrano-Gracia
  • Manuel Miranda-Anaya
  • Gerardo Rojas-Piloni
  • Hugo Quiroz-Mercado
  • Luis Fernando Hernández-Zimbrón
  • Elisa Denisse Fajardo-Cruz
  • Ezequiel Ferreyra-Severo
  • Renata García-Franco
  • Juan Fernando Rubio Mijangos
  • Ellery López-Star
  • Marlon García-Roa
  • Van Charles Lansingh
  • Stéphanie C Thébault

Abstract

Given the ever-increasing prevalence of type 2 diabetes and obesity, the pressure on global healthcare is expected to be colossal, especially in terms of blindness. Electroretinogram (ERG) has long been perceived as a first-use technique for diagnosing eye diseases, and some studies suggested its use for preventable risk factors of type 2 diabetes and thereby diabetic retinopathy (DR). Here, we show that in a non-evoked mode, ERG signals contain spontaneous oscillations that predict disease cases in rodent models of obesity and in people with overweight, obesity, and metabolic syndrome but not yet diabetes, using one single random forest-based model. Classification performance was both internally and externally validated, and correlation analysis showed that the spontaneous oscillations of the non-evoked ERG are altered before oscillatory potentials, which are the current gold-standard for early DR. Principal component and discriminant analysis suggested that the slow frequency (0.4–0.7 Hz) components are the main discriminators for our predictive model. In addition, we established that the optimal conditions to record these informative signals, are 5-minute duration recordings under daylight conditions, using any ERG sensors, including ones working with portative, non-mydriatic devices. Our study provides an early warning system with promising applications for prevention, monitoring and even the development of new therapies against type 2 diabetes.

Suggested Citation

  • Ramsés Noguez Imm & Julio Muñoz-Benitez & Diego Medina & Everardo Barcenas & Guillermo Molero-Castillo & Pamela Reyes-Ortega & Jorge Armando Hughes-Cano & Leticia Medrano-Gracia & Manuel Miranda-Anaya, 2023. "Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-26, January.
    • Handle: RePEc:plo:pone00:0278388
    DOI: 10.1371/journal.pone.0278388
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    References listed on IDEAS

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    1. Gunjeet Kaur & P V M Lakshmi & Ashu Rastogi & Anil Bhansali & Sanjay Jain & Yot Teerawattananon & Henna Bano & Shankar Prinja, 2020. "Diagnostic accuracy of tests for type 2 diabetes and prediabetes: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-19, November.
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