Hypertensive eye disease

Cheung, Carol Y.; Biousse, Valérie; Keane, Pearse A.; Schiffrin, Ernesto L.; Wong, Tien‐Yin

doi:10.1038/s41572-022-00342-0

Cited by 39 publications

(29 citation statements)

References 284 publications

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“…Recent studies have found that optic nerve damage from hypertension is prevalent in the general adult population. 19 The availability of noninvasive OCT allows the examination of these alterations and their value in hypertension management and dementia risk stratification. This study analyzed the effects of BP and arterial stiffness on RNFLT and GCIPLT, including macular and optic disc area parameters, in two large cohorts.…”

Section: Discussionmentioning

confidence: 99%

Effects of blood pressure and arterial stiffness on retinal neurodegeneration: Cross-sectional and longitudinal evidence from UK Biobank and Chinese cohorts

Huang

Yuan

Wang

et al. 2022

Preprint

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Background: Retinal neurodegeneration, an easily accessible biomarker of dementia risk, is exacerbated by age and hypertension; however, the relative roles of systolic and diastolic blood pressure (SBP and DPB) remain unclear. This study aimed to determine the cross-sectional and longitudinal associations between BP and atherosclerosis levels along with the different retinal neurodegeneration parameters. Methods: This study used cross-sectional data from the United Kingdom (UK) BioBank (UKB) and longitudinal data from the Chinese Ocular Imaging Project (COIP). The macular ganglion cell-inner plexiform layer thickness (mGCIPLT) and macular retinal nerve fiber layer thickness (mRNFLT) were measured using spectral domain optical coherence tomography imaging. Swept-source optical coherence tomography was performed at each follow-up visit to obtain the longitudinal trajectory of the mGCIPLT and peripapillary RNFLT (pRNFLT) in the COIP cohort. Multivariable linear models were used to analyze the cross-sectional and longitudinal associations between BP metrics and retinal measurements. Results: In a cross-sectional analysis of 22,801 participants from the UKB, thinner mGCIPLT was related to older age (per 10 years, β = −0.274, 95% confidence interval (CI): −0.358 to −0.189, p < 0.001), female sex (female vs male, β = −0.897, 95% CI: −1.031 to −0.763, p = 0.000), higher SBP (per 10 mmHg increase, β = −0.085, 95% CI: −0.125 to −0.045, p = 0.000), and higher DBP (per 10 mmHg increase, β = −0.105, 95% CI: −0.174 to −0.036, p = 0.003), and was significantly associated with higher mean arterial pressure (MAP per 10 mmHg increase, β = −0.116, 95% CI: −0.176, −0.056, p = 0.000) and higher mean pulse pressure (MPP per 10 mmHg increase, β = −0.099, 95% CI: −0.155, −0.043, p = 0.001). In a longitudinal analysis of 2,012 eligible COIP participants, higher levels of baseline SBP, DBP, MAP, and MPP were associated with faster thinning in mGCIPLT and pRNFLT (all p < 0.001). The strongest association was with MAP, which produced an effect on mGCIPLT (β = −0.118, 95% CI: −0.175 to −0.061, p < 0.001) per 10 mmHg increase, comparable to a 5-year increase in age (β = −0.210, 95% CI: −0.282 to −0.138, p < 0.001). The results of the analysis of mRNFL and pRNFL were consistent with those of mGCIPLT. Conclusion: BP levels were independently and consistently associated with various retinal neurodegenerative exacerbations, both cross-sectionally and longitudinally, regardless of the race and disease status. BP plays a key role in neurodegeneration, and long-term prevention in the population requires the control of BP levels.

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Section: Discussionmentioning

confidence: 99%

Effects of blood pressure and arterial stiffness on retinal neurodegeneration: Cross-sectional and longitudinal evidence from UK Biobank and Chinese cohorts

Huang

Yuan

Wang

et al. 2022

Preprint

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“…Signs of hypertensive retinopathy, such as focal arteriolar narrowing, arteriovenous nicking, haemorrhages, microaneurysms and exudates, are well known to be related to CVD [16 ▪ ]; however, there are no existing deep learning models to detect hypertensive retinopathy signs from retinal photographs automatically.…”

Section: Applications Of Artificial Intelligence In Retinal Photograp...mentioning

confidence: 99%

“…Studies demonstrated parallel pathological changes morphologically between the retinal vasculature and the coronary and cerebral vasculatures [8 ▪ ,13–15]. Traditionally, retinal vascular changes were assessed via qualitative and quantitative parameters from ophthalmic examination and retinal photographs with semi-automated software [16 ▪ ]. Qualitative measurements include focal arteriolar narrowing, arteriovenous nicking, and retinopathy signs such as microaneurysms, haemorrhage and exudates, while quantitative measurements include retinal vessel calibre, tortuosity, bifurcation angle, tortuosity and fractal dimension [11,17,18].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions

Wong

Lam

Ran

et al. 2022

Current Opinion in Ophthalmology

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Purpose of reviewRetinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting. Recent findingsArtificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice.

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“…Hypertensive patients experience continuous contraction and tension of systemic arterioles. Long-term hypertension can cause arterial lumen stenosis, subsequently leading to hypertensive arteriosclerosis, resulting in retinal vascular stenosis thinning [18,19]. In the early stages, the fundus of patients with hypertension may not show any pathological changes.…”

Section: Application Of Ai Image Analysis In Hypertensivementioning

confidence: 99%

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

Chen

Liu

et al. 2022

Disease Markers

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The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in the eyes. Typical diseases include diabetic retinopathy, hypertensive retinopathy, thyroid associated ophthalmopathy, optic neuromyelitis, and Behcet’s disease. Systemic disease-related ophthalmopathy is usually a chronic disease, and the analysis of imaging markers is helpful for a comprehensive diagnosis of these diseases. Recently, artificial intelligence (AI) technology based on deep learning has rapidly developed, leading to numerous achievements and arousing widespread concern. Presently, AI technology has made significant progress in research on imaging markers of systemic disease-related ophthalmopathy; however, there are also many limitations and challenges. This article reviews the research achievements, limitations, and future prospects of AI image analysis technology in systemic disease-related ophthalmopathy.

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Hypertensive eye disease

Cited by 39 publications

References 284 publications

Effects of blood pressure and arterial stiffness on retinal neurodegeneration: Cross-sectional and longitudinal evidence from UK Biobank and Chinese cohorts

Effects of blood pressure and arterial stiffness on retinal neurodegeneration: Cross-sectional and longitudinal evidence from UK Biobank and Chinese cohorts

Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

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