Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.
Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia.
Age Ageing. 2022 Mar 1;51(3). doi: 10.1093/ageing/afac062.
retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age-chronological age) and incident Parkinson's disease (PD).
a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value.
a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01-1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13-6.22, P = 0.024; HR = 4.86, 95% CI: 1.59-14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821).
retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.
使用深度学习从眼底图像中得出的视网膜年龄已被验证为一种新的衰老生物标志物。我们旨在研究视网膜年龄差距（视网膜年龄-实际年龄）与帕金森病（PD）发病之间的关联。
使用在 11052 名无慢性疾病的参与者的 19200 张眼底图像上训练的深度学习（DL）模型来预测视网膜年龄。对于基线评估时无 PD 的其余 35834 名参与者，使用经过训练的 DL 模型生成视网膜年龄差距。利用 Cox 比例风险回归模型研究视网膜年龄差距与 PD 发病之间的关系。应用多变量逻辑模型预测 5 年 PD 风险，并使用接收者操作特征曲线下面积（AUC）来评估预测价值。
本研究共纳入 35834 名无 PD 的基线参与者（56.7±8.04 岁，55.7%为女性）。在调整混杂因素后，视网膜年龄差距增加 1 年，PD 发病风险增加 10%（风险比[HR]＝1.10，95%置信区间[CI]：1.01-1.20，P＝0.023）。与视网膜年龄差距最低四分位相比，第三和第四四分位的 PD 发病风险显著增加（HR＝2.66，95% CI：1.13-6.22，P＝0.024；HR＝4.86，95% CI：1.59-14.8，P＝0.005）。视网膜年龄和既定 PD 风险因素对 5 年 PD 风险的预测价值相当（AUC＝0.708 和 0.717，P＝0.821）。
视网膜年龄差距显示出识别未来 PD 高危个体的潜力。
