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

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

PloS one, 2023-01, Vol.18 (1), p.e0278388-e0278388 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals
Autor: Noguez Imm, Ramsés ; Muñoz-Benitez, Julio ; Medina, Diego ; Barcenas, Everardo ; Molero-Castillo, Guillermo ; Reyes-Ortega, Pamela ; Hughes-Cano, Jorge Armando ; Medrano-Gracia, Leticia ; Miranda-Anaya, Manuel ; Rojas-Piloni, Gerardo ; Quiroz-Mercado, Hugo ; Hernández-Zimbrón, Luis Fernando ; Fajardo-Cruz, Elisa Denisse ; Ferreyra-Severo, Ezequiel ; García-Franco, Renata ; Rubio Mijangos, Juan Fernando ; López-Star, Ellery ; García-Roa, Marlon ; Lansingh, Van Charles ; Thébault, Stéphanie C
Barnes, Steven
Assuntos: Age ; Animal models ; Animals ; Biology and Life Sciences ; Biomarkers ; Body weight ; Complications and side effects ; Correlation analysis ; Daylight ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - diagnosis ; Diabetic retinopathy ; Diabetic Retinopathy - diagnosis ; Diabetic Retinopathy - prevention & control ; Diagnosis ; Diet ; Discriminant analysis ; Early warning systems ; Electrodes ; Electroretinograms ; Electroretinography - methods ; Ethics ; Eye diseases ; Global health ; Glucose ; Health care ; Health risks ; Humans ; Insulin ; Medicine and Health Sciences ; Metabolic disorders ; Metabolic syndrome ; Neurobiology ; Neurosciences ; Obesity ; Oscillations ; Overweight ; Physical Sciences ; Prediction models ; Research and Analysis Methods ; Retinopathy ; Risk analysis ; Risk Factors ; Type 2 diabetes
É parte de: PloS one, 2023-01, Vol.18 (1), p.e0278388-e0278388
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Competing Interests: The authors have declared that no competing interests exist.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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

United States: Public Library of Science

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