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

medRxiv - Cardiovascular Medicine Pub Date : 2024-07-19 DOI:10.1101/2024.07.18.24310670

Ehsan Vaghefi, Songyang An, Shima Moghadam, Song Yang, Li Xie, Mary K Durbin, Huiyuan Hou, Robert N Weinreb, David Squirrell, Michael V McConnell

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Abstract

Background: There 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. Methods: Retinal 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. Results: In 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). Conclusion: A 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.

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视网膜生物年龄揭示了美国和英国人群的心血管-肾脏-代谢综合征指标

背景：越来越多的人认识到生理年龄与实际年龄之间的差异，以及心血管、肾脏和代谢（CKM）疾病（即 CKM 综合征）在缩短寿命和健康寿命方面的相互作用。检测出 CKM 综合征的指标可以提示生活方式和风险因素管理，以防止恶化为不良临床事件。在本研究中，我们测试了一种新型深度学习模型--视网膜 BioAge，以确定它是否能识别出与年龄相仿的同龄人相比，CKM 指标流行率更高的个体。研究方法对英国生物库人口健康研究和美国糖尿病患者 EyePACS 10K 数据集的视网膜图像和健康记录进行了分析。来自 44731 名独特参与者的 77887 张视网膜图像被用于训练视网膜生物年龄模型。为了进行验证，分别分析了来自英国生物库的 10,976 张图像（5,476 人）和来自 EyePACS 10K 的 19,856 张视网膜图像（9,786 人）。计算出每位参与者的视网膜年龄差距（视网膜生物年龄-实际年龄），并比较视网膜年龄差距四分位数最高和最低的参与者的血压、胆固醇、肾功能和血红蛋白 A1c 异常发生率。结果：在英国生物库中，视网膜 AgeGap 最高四分位数参与者的高血压患病率明显高于最低四分位数（36.3% 对 29.0%，p<0.001），而非高密度脂蛋白胆固醇升高（77.9% 对 78.4%，p=0.80）、肾功能受损（4.8% 对 4.2%，p=0.60）和糖尿病（3.1% 对 2.2%，p=0.24）的患病率相似。相比之下，EyePACS 10K 中视网膜 AgeGap 最高四分位数的人非高密度脂蛋白胆固醇升高（49.9% 对 43.0%，p<0.001）、肾功能受损（36.7% vs. 23.1%，p<0.001）、糖尿病控制不理想（76.5% vs. 60.0%，p<0.001）和糖尿病视网膜病变（52.9% vs. 8.0%，p<0.001）的发生率较高，但高血压（53.8% vs. 55.4%，p=0.33）的发生率不高。结论深度学习视网膜 BioAge 模型识别出了与同龄人相比，CKM 综合征潜在指标患病率较高的人，特别是在一个多样化的美国糖尿病患者数据集中。

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medRxiv - Cardiovascular Medicine

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