Predictive Potential of Retina-Based Biological Age in Assessing Chronic Obstructive Pulmonary Disease Risk

IF 4.9 2区医学 Q1 OPHTHALMOLOGY

Clinical and Experimental Ophthalmology Pub Date : 2025-02-06 DOI:10.1111/ceo.14501

Qingsheng Peng, Tyler Hyungtaek Rim, Zhi Da Soh, Miao Li Chee, Yih-Chung Tham, Zhuoting Zhu, Simon Nusinovici, Charumathi Sabanayagam, Ah. Young Leem, Chan Joo Lee, Byoung Kwon Lee, Sungha Park, Sung Soo Kim, Hyeon Chang Kim, Marco Chak Yan Yu, Tien Yin Wong, Ching-Yu Cheng

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引用次数: 0

Abstract

Background

Previously, based on retinal photographs, we developed a deep-learning algorithm to predict biological age (termed, RetiAGE) that was associated with future risks of morbidity and mortality. This study specifically aimed to evaluate the performance of RetiAGE in predicting future risks of chronic obstructive pulmonary disease (COPD).

Methods

RetiAGE scores were generated from retinal images in the UK Biobank and stratified into tertiles. We used Cox proportional hazards models to evaluate the longitudinal association between RetiAGE and incident COPD, adjusting for calendar age, gender, smoking, asthma history, and socio-economic status. In addition, we performed a cross-sectional analysis using generalised linear models to examine the association between RetiAGE and baseline respiratory function, specifically the forced expiratory volume in 1 s to forced vital capacity ratio (FEV1/FVC) and peak expiratory flow (PEF), adjusting for the same confounders.

Results

Among 45 438 UK Biobank participants without a history of COPD at baseline, 448 (0.9%) developed COPD over a mean follow-up period of 9.8 ± 0.7 years. Participants in the moderate-risk and high-risk tertiles of RetiAGE had significantly lower baseline respiratory function (all p < 0.05) and a higher risk of incident COPD (HR = 1.60; 95% CI, 1.18–2.19) compared to the low-risk tertile, after adjusting for confounders. Adding RetiAGE to the multivariable risk model improved predictive performance, as demonstrated by significant enhancements in C-statistics (p < 0.001) and likelihood ratio tests (p = 0.002).

Conclusion

Our deep-learning-based retinal aging biomarker, RetiAGE, can potentially stratify the risk of developing COPD.

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基于视网膜的生物年龄在评估慢性阻塞性肺疾病风险中的预测潜力

背景：之前，基于视网膜照片，我们开发了一种深度学习算法来预测与未来发病率和死亡率风险相关的生物年龄（称为RetiAGE）。本研究旨在评估RetiAGE在预测慢性阻塞性肺疾病（COPD）未来风险方面的表现。方法：从英国生物银行（UK Biobank）的视网膜图像中生成RetiAGE评分，并将其分层。我们使用Cox比例风险模型评估了年龄、性别、吸烟、哮喘史和社会经济地位等因素后，RetiAGE与COPD发病率之间的纵向关联。此外，我们使用广义线性模型进行了横断面分析，以检查RetiAGE与基线呼吸功能之间的关系，特别是1秒内用力呼气量与用力肺活量比（FEV1/FVC）和呼气峰流量（PEF），并对相同的混杂因素进行了调整。结果：在45438名基线时无COPD病史的英国生物银行参与者中，448名（0.9%）在平均9.8±0.7年的随访期间患上了COPD。结论：我们基于深度学习的视网膜衰老生物标志物RetiAGE可以潜在地对发生COPD的风险进行分层。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical and Experimental Ophthalmology 医学-眼科学

CiteScore

7.60

自引率

12.50%

发文量

150

审稿时长

4-8 weeks

期刊介绍： Clinical & Experimental Ophthalmology is the official journal of The Royal Australian and New Zealand College of Ophthalmologists. The journal publishes peer-reviewed original research and reviews dealing with all aspects of clinical practice and research which are international in scope and application. CEO recognises the importance of collaborative research and welcomes papers that have a direct influence on ophthalmic practice but are not unique to ophthalmology.