Effects of obesity on time-frequency components of electroretinogram signal using continuous wavelet transform

Erkaymaz, Okan; Yapici, İrem Şenyer; Arslan, Rukiye Uzun

doi:10.1016/j.bspc.2020.102398

Cited by 17 publications

(7 citation statements)

References 21 publications

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“…Particularly, our high-fat diet is more enriched in lipids than carbohydrates 50 . While the ERG B-wave is affected in patients with obesity 54,55 , there is no previous report of the effect of obesity on OPs in humans. In our group of patients with diabetes but no DR, the amplitude and implicit time of the OPs tend to decrease and increase, respectively, as previously reported 56 .…”

Section: Discussionmentioning

confidence: 96%

Spontaneous electroretinogram signals to screen people with early risk factors for diabetic retinopathy

Imm

Muñoz-Benitez

Medina

et al. 2022

Preprint

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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, but existing methods are insufficient to screen early risk factors of diabetic retinopathy (DR). Here, we introduce non-evoked ERG as a simple, fast modality to record spontaneous activity, from which we developed a single random forest-based model that predicts disease cases in rodent models of obesity and in people with overweight, obesity, and metabolic syndrome. Classification performance was validated using a dataset from an independent eye center. Our algorithm can be coupled with different ERG sensors, including ones working with portative, non-mydriatic devices. Principal component and discriminant analysis suggest slow spontaneous ERG frequencies as main discriminators for our predictive model. Our study will facilitate the implementation of interventions for the prevention of overweight and obesity by providing a robust, quantitative, and non-invasive identification and follow-up approach, which should ultimately reduce DR incidence.

show abstract

Section: Discussionmentioning

confidence: 96%

Spontaneous electroretinogram signals to screen people with early risk factors for diabetic retinopathy

Imm

Muñoz-Benitez

Medina

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Particularly, our high-fat diet is more enriched in lipids than carbohydrates [50]. While the ERG B-wave is affected in patients with obesity [54,55], there is no previous report of the effect of obesity on OPs in humans. In patients with diabetes but no DR, the amplitude and implicit time of the OPs tend to decrease and increase, respectively, as previously reported [56].…”

Section: Strengths and Interpretation Of Spontaneous Erg Signals' Pre...mentioning

confidence: 90%

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

Imm

Muñoz-Benitez

Medina

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…Particularly, our high-fat diet is more enriched in lipids than carbohydrates [ 68 ]. While the ERG B-wave is affected in patients with obesity [ 71 , 72 ], there is no previous report of the effect of obesity on OPs in humans. In patients with diabetes but no DR, the amplitude and implicit time of the OPs tend to decrease and increase, respectively, as previously reported [ 73 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Effects of obesity on time-frequency components of electroretinogram signal using continuous wavelet transform

Cited by 17 publications

References 21 publications

Spontaneous electroretinogram signals to screen people with early risk factors for diabetic retinopathy

Spontaneous electroretinogram signals to screen people with early risk factors for diabetic retinopathy

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

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

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