Association of retinal microvascular density and complexity with incident coronary heart disease

Yuechuan Fu; Mayinuer Yusufu; Yueye Wang; Mingguang He; Danli Shi; Ruobing Wang

doi:10.1016/j.atherosclerosis.2023.117196

Association of retinal microvascular density and complexity with incident coronary heart disease

Atherosclerosis. 2023 Sep:380:117196. doi: 10.1016/j.atherosclerosis.2023.117196. Epub 2023 Jul 27.

Authors

Yuechuan Fu¹, Mayinuer Yusufu², Yueye Wang³, Mingguang He⁴, Danli Shi⁵, Ruobing Wang⁶

Affiliations

¹ Department of Ophthalmology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
² Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, Australia.
³ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
⁴ The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China. Electronic address: mingguang.he@polyu.edu.hk.
⁵ The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China. Electronic address: sdlmed@163.com.
⁶ Department of Ophthalmology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China. Electronic address: adawrb@126.com.

PMID: 37562159
DOI: 10.1016/j.atherosclerosis.2023.117196

Abstract

Background and aims: The high mortality rate and huge disease burden of coronary heart disease (CHD) highlight the importance of its early detection and timely intervention. Given the non-invasive nature of fundus photography and recent development in the quantification of retinal microvascular parameters with deep learning techniques, our study aims to investigate the association between incident CHD and retinal microvascular parameters.

Methods: UK Biobanks participants with gradable fundus images and without a history of diagnosed CHD at recruitment were included for analysis. A fully automated artificial intelligence system was used to extract quantitative measurements that represent the density and complexity of the retinal microvasculature, including fractal dimension (Df), number of vascular segments (NS), vascular skeleton density (VSD) and vascular area density (VAD).

Results: A total of 57,947 participants (mean age 55.6 ± 8.1 years; 56% female) without a history of diagnosed CHD were included. During a median follow-up of 11.0 (interquartile range, 10.88 to 11.19) years, 3211 incident CHD events occurred. In multivariable Cox proportional hazards models, we found decreasing Df (adjusted HR = 0.80, 95% CI, 0.65-0.98, p = 0.033), lower NS of arteries (adjusted HR = 0.69, 95% CI, 0.54-0.88, p = 0.002) and venules (adjusted HR = 0.77, 95% CI, 0.61-0.97, p = 0.024), and reduced arterial VSD (adjusted HR = 0.72, 95% CI, 0.57-0.91, p = 0.007) and venous VSD (adjusted HR = 0.78, 95% CI, 0.62-0.98, p = 0.034) were related to an increased risk of incident CHD.

Conclusions: Our study revealed a significant association between retinal microvascular parameters and incident CHD. As the lower complexity and density of the retinal vascular network may indicate an increased risk of incident CHD, this may empower its prediction with the quantitative measurements of retinal structure.

Keywords: Coronary heart disease; Deep learning; Retinal microvascular complexity; Retinal microvascular density.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Coronary Disease* / diagnosis
Coronary Disease* / epidemiology
Female
Humans
Incidence
Male
Microvascular Density
Microvessels
Middle Aged
Risk Factors