한빛사논문
- 조회560
Simon Nusinovici1,2,†, Tyler Hyungtaek Rim1,2,†, Marco Yu1, Geunyoung Lee3, Yih-Chung Tham1,2,4, Ning Cheung1,2, Crystal Chun Yuen Chong1, Zhi Da Soh1, Sahil Thakur1, Chan Joo Lee5, Charumathi Sabanayagam1,2, Byoung Kwon Lee6, Sungha Park7, Sung Soo Kim8, Hyeon Chang Kim9, Tien-Yin Wong1,2, Ching-Yu Cheng1,2,10
1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
2Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
3Medi Whale Inc., Seoul, South Korea
4Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
5Division of Cardiology, Severance Cardiovascular Hospital, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
6Division of Cardiology, Severance Cardiovascular Hospital, Gangnam Severance Hospital, Yonsei University Medical College of Medicine, Seoul, South Korea
7Division of Cardiology, Severance Cardiovascular Hospital and Integrated Research Center for Cerebrovascular and Cardiovascular Disease, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
8Department of Ophthalmology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
9Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea
10Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Address correspondence to: Tyler Hyungtaek Rim, Ching-Yu Cheng
†Contributed equally
Abstract
Background: ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA).
Objective: we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations.
Methods: we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs).
Results: in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42-1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69-3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31-1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14-1.69]) and 18% (HR = 1.18 [1.10-1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%).
Conclusions: the DL-derived RetiAGE provides a novel, alternative approach to measure ageing.
논문정보
- Research article
- 2022년 04월 (BRIC 등록일 2023-03-10)
- 국내+국외 연구진
- 바이오·의료융합 > 의료기기
댓글 작성 로그인
팝업 닫기관련 링크
연구자 키워드
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기
