Retinal age gap as a predictive biomarker for mortality risk

Zhu, Zhuoting; Shi, Danli; Peng, Guankai; Tan, Zachary; Shang, Xianwen; Hu, Wenyi; Liao, Huan; Xue-li, Zhang; Huang, Yu; Yu, Honghua; Meng, Wei; Wang, Wei; Ge, Zongyuan; Yang, Xiaohong; He, Mingguang

doi:10.1136/bjophthalmol-2021-319807

Cited by 83 publications

(78 citation statements)

References 37 publications

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“…Our results are in line with other reports which have demonstrated that DL algorithms can use retinal images to predict modifiable CVD risk factors, including diabetes, hypertension, and cholesterol [25,27,29,[46][47][48] and non-modifiable risk factors such as chronological age and gender [24]. However, like the Framingham equations, the algorithms published to date are unable to examine the relative contribution of each of the individual factors that comprise risk as they utilize a statistical method which imposes linearity between the individual parameters used during analysis.…”

Section: Discussionsupporting

confidence: 93%

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Vaghefi

Squirrell

Yang

et al. 2022

Preprint

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Purpose: To create and evaluate the accuracy of an artificial intelligence platform capable of using only retinal fundus images to predict both an individual overall 10 year Cardiovascular Disease (CVD) risk and the relative contribution of the component risk factors that comprise this risk (CVD-AI). Methods: The UK Biobank and the US-based AREDS 1 datasets were obtained and used for this study. The UK Biobank data was used for training, validation and testing, while the AREDS 1 dataset was used as an external testing dataset. Overall, we used 110,272 fundus images from 55,118 patient visits. A series of models were trained to predict the risk of CVD against available labels in the UK Biobank dataset. Results: In both the UK Biobank testing dataset and the external validation dataset (AREDS 1), the 10-year CV risk scores generated by CVD-AI were significantly higher for patients who had suffered an actual CVD event when compared to patients who did not experience a CVD event. In the UK Biobank dataset the median 10-year CVD risk for those individuals who experienced a CVD was higher than those who did not (4.9% [ICR 2.9-8%] v 2.3% [IQR 4.3- 1.3%] P<0.01.]. Similar results were observed in the AREDS 1 dataset The median 10-year CVD risk for those individuals who experienced a CVD event was higher than those who did not (6.2% [ICR 3.2%-12.9%] v 2.2% [IQR 3.9- 1.3%] P<0.01 Conclusion: Retinal photography is inexpensive and as fully automated, inexpensive camera systems are now widely available, minimal training is required to acquire them. As such, AI enabled retinal image-based CVD risk algorithms like CVD-AI will make cardiovascular health screening more affordable and accessible. CVD-AI unique ability to assess the relative contribution of the components that comprise an individual overall risk could inform personalized treatment decisions based on the specific needs of an individual, thereby increasing the likelihood of positive health outcomes.

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Section: Discussionsupporting

confidence: 93%

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Vaghefi

Squirrell

Yang

et al. 2022

Preprint

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“…To define a model of healthy cardiovascular ageing, as previously validated in other organ systems, 11,59–61 we first partitioned the 39,559 participants into a development set, which consisted of 5065 “healthy” individuals that were free of cardiac, metabolic or respiratory disease, with a body mass index below 30 (see Supplementary Material for details). We randomly split this group into separate training (80%, n = 4021) and test (20%, n = 1044) sets.…”

Section: Methodsmentioning

confidence: 99%

Environmental and genetic predictors of human cardiovascular ageing

Shah

Inácio

et al. 2022

Preprint

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Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we used machine learning approaches to quantify cardiovascular ageing from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing was found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identified prescribed medications that were potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.

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“…Incident CVD events were defined as the first occurrence of CVD events during the follow-up period, from the baseline examination to The development and validation of the DL model for age prediction were described in details elsewhere. 22 Briefly, all fundus images were preprocessed and fed into a DL model using a Xception architecture. Data augmentation was performed during training using random horizontal or vertical flips.…”

Section: Cvd Ascertainmentmentioning

confidence: 99%

Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease

Zhu

Chen

Wang

et al. 2022

Stroke

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BACKGROUND: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for aging and mortality. Therefore, we aim to investigate associations of retinal age gap with arterial stiffness index and incident cardiovascular disease (CVD). METHODS: A deep learning model was trained based on 19 200 fundus images of 11 052 participants without any medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35 917 participants. Regression models were used to assess the association between retinal age gap and arterial stiffness index. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. RESULTS: We found each 1-year increase in retinal age gap was associated with increased arterial stiffness index (β=0.002 [95% CI, 0.001–0.003]; P <0.001). After a median follow-up of 5.83 years (interquartile range: 5.73–5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each 1-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio=1.03 [95% CI, 1.01–1.06]; P =0.014). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (hazard ratio=1.05 [95% CI, 1.00–1.10]; P -overall <0.0001; P -nonlinear=0.0681). CONCLUSIONS: We found that retinal age gap was significantly associated with arterial stiffness index and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

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Retinal age gap as a predictive biomarker for mortality risk

Cited by 83 publications

References 37 publications

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Environmental and genetic predictors of human cardiovascular ageing

Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease

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