Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs

An, Songyang; Vaghefi, Ehsan; Yang, Song; Xie, Li; Squirrell, David

doi:10.21203/rs.3.rs-2609919/v1

Cited by 1 publication

(2 citation statements)

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“…33,40 Prior retinal AI studies have focused primarily on predicting chronic kidney disease based on eGFR only. [17][18][19] Here we show that retinal BioAge is predictive of higher prevalence of chronic kidney disease using the KDIGO criteria (i.e., eGFR and UACR). In contrast, retinal BioAge was not predictive in UK Biobank, where the overall prevalence of chronic kidney disease was very low (~5%).…”

Section: Discussionmentioning

confidence: 87%

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Retinal BioAge Reveals Indicators of Cardiovascular-Kidney-Metabolic Syndrome in US and UK Populations

Vaghefi,

An,

Moghadam

et al. 2024

Preprint

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BackgroundThere is a growing recognition of the divergence between biological and chronological age, as well as the interaction among cardiovascular, kidney, and metabolic (CKM) diseases, known as CKM syndrome, in shortening both lifespan and healthspan. Detecting indicators of CKM syndrome can prompt lifestyle and risk-factor management to prevent progression to adverse clinical events. In this study, we tested a novel deep-learning model, retinal BioAge, to determine whether it could identify individuals with a higher prevalence of CKM indicators compared to their peers of similar chronological age.MethodsRetinal images and health records were analyzed from both the UK Biobank population health study and the US-based EyePACS 10K dataset of persons living with diabetes. 77,887 retinal images from 44,731 unique participants were used to train the retinal BioAge model. For validation, separate test sets of 10,976 images (5,476 individuals) from UK Biobank and 19,856 retinal images (9,786 individuals) from EyePACS 10K were analyzed. Retinal AgeGap (retinal BioAge – chronological age) was calculated for each participant, and those in the top and bottom retinal AgeGap quartiles were compared for prevalence of abnormal blood pressure, cholesterol, kidney function, and hemoglobin A1c.ResultsIn UK Biobank, participants in the top retinal AgeGap quartile had significantly higher prevalence of hypertension compared to the bottom quartile (36.3% vs. 29.0%, p<0.001), while the prevalence was similar for elevated non-HDL cholesterol (77.9% vs. 78.4%, p=0.80), impaired kidney function (4.8% vs. 4.2%, p=0.60), and diabetes (3.1% vs. 2.2%, p=0.24). In contrast, EyePACS 10K individuals in the top retinal AgeGap quartile had higher prevalence of elevated non-HDL cholesterol (49.9% vs. 43.0%, p<0.001), impaired kidney function (36.7% vs. 23.1%, p<0.001), suboptimally controlled diabetes (76.5% vs. 60.0%, p<0.001), and diabetic retinopathy (52.9% vs. 8.0%, p<0.001), but not hypertension (53.8% vs. 55.4%, p=0.33).ConclusionA deep-learning retinal BioAge model identified individuals who had a higher prevalence of underlying indicators of CKM syndrome compared to their peers, particularly in a diverse US dataset of persons living with diabetes.Clinical PerspectiveWhat Is New?Accelerated biological aging predicted by a novel deep-learning analysis of standard retinal images was able to detect multiple indicators of the new cardiovascular-kidney-metabolic syndrome in US and UK populations.What Are the Clinical Implications?Rapid, point-of-care analysis of images from routine eye exams can broaden access to the detection and awareness of adverse cardiovascular, kidney, and metabolic health.With the broad range of prevention interventions to reduce progression of cardiovascular-kidney-metabolic syndrome, earlier and broader detection is important to improve public health outcomes.

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Section: Discussionmentioning

confidence: 87%

“…10,11 We and others have shown that these DL models can detect cardiovascular risk, chronic kidney disease, and diabetic retinopathy. [12][13][14][15][16][17][18][19][20][21] DL models analyzing retinal images have also been developed to estimate "biological age. "…”

Section: Introductionmentioning

confidence: 99%