Wenyi Hu1,2,3, Wei Wang4, Yueye Wang4, Yifan Chen5, Xianwen Shang2, Huan Liao6, Yu Huang1, Gabriella Bulloch2, Shiran Zhang4, Katerina Kiburg2, Xueli Zhang1, Shulin Tang1, Honghua Yu1, Xiaohong Yang1, Mingguang He1,2,3,4, Zhuoting Zhu1,2. 1. Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China. 2. Ophthalmology, Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia. 3. Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia. 4. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China. 5. John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. 6. Neural Regeneration Group, Institute of Reconstructive Neurobiology, University of Bonn, Bonn, Germany.
Abstract
INTRODUCTION: 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). METHODS: 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. RESULTS: 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). CONCLUSION: retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.
INTRODUCTION: 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). METHODS: 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. RESULTS: 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). CONCLUSION: retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.
Authors: Johnny Wang; Maria J Knol; Aleksei Tiulpin; Florian Dubost; Marleen de Bruijne; Meike W Vernooij; Hieab H H Adams; M Arfan Ikram; Wiro J Niessen; Gennady V Roshchupkin Journal: Proc Natl Acad Sci U S A Date: 2019-10-01 Impact factor: 11.205
Authors: Nimesh B Patel; Mimi Lim; Avni Gajjar; Kelsey B Evans; Ronald S Harwerth Journal: Invest Ophthalmol Vis Sci Date: 2014-07-22 Impact factor: 4.799
Authors: Taryn O Hall; Jia Y Wan; Ignacio F Mata; Kathleen F Kerr; Katherine W Snapinn; Ali Samii; John W Roberts; Pinky Agarwal; Cyrus P Zabetian; Karen L Edwards Journal: Genet Med Date: 2012-12-06 Impact factor: 8.822