Purpose: To evaluate the association between retinal microvasculature (vascular density) on optical coherence tomography-angiography (OCT-A) and the cardiovascular profile of patients hospitalized for acute coronary syndrome (ACS). Methods: EYE-Myocardial Infarction (EYE-MI) study is a prospective cross-sectional study in the Cardiology Intensive Care Unit of Dijon University Hospital. Retinal OCT-A was performed for each patient within 2 days after admission. Superficial retinal capillary plexus (SCP) vascular density was measured. The population was divided into tertiles according to OCT-A data. Results: Overall, 237 cases were retained for analysis. Patients in the tertile with the lowest retinal vascular density (RVD) were older, and more frequently had systemic hypertension and diabetes. Moreover, American Heart Association (AHA) risk and Global Registry of Acute Coronary Events (GRACE) scores were higher and left ventricular ejection fraction (LVEF) was lower in these patients. In multivariate analysis, the AHA risk score (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.04–1.09; P < 0.001) and LVEF (OR, 0.95; 95% CI, 0.93–0.98; P = 0.001) were significantly associated with the lowest tertile of RVD. The association between RVD and a high-risk cardiovascular profile was confirmed by a moderate correlation with the GRACE scores (Spearman r = −0.33, P < 0.001). Conclusions: SCP density measured on OCT-A was associated with the cardiovascular risk profile and with impaired LVEF in patients with a high-risk cardiovascular status. In the future, quantitative retinal microvascular data could be considered a good surrogate of the cardiovascular risk profile and could improve cardiovascular risk assessments.

The healthcare burden of cardiovascular diseases remains a major issue worldwide. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. Thus, the structure of retinal vessels could be considered a witness of the systemic vascular status. A new approach called “oculomics” using retinal image datasets and artificial intelligence algorithms recently increased the interest in retinal microvascular biomarkers. Despite the large volume of associated research, the role of retinal biomarkers in the screening, monitoring, or prediction of systemic vascular disease remains uncertain. A PubMed search was conducted until August 2022 and yielded relevant peer-reviewed articles based on a set of inclusion criteria. This literature review is intended to summarize the state of the art in oculomics and cardiovascular disease research.

To identify patterns summarizing the retinal vascular network in the elderly and to investigate the relationship of these vascular patterns with cardiovascular history. We conducted a population-based study, the Montrachet study (Maculopathy Optic Nerve nuTRition neurovAsCular and HEarT diseases), in participants older than 75 years. The history of cardiovascular disease and a score-based estimation of their 10-year risk of cardiovascular mortality (Heart SCORE) were collected. Retinal vascular network analysis was performed by means of Singapore “I” Vessel Assessment (SIVA) software. Principal component analysis was used to condense the information contained in the high number of variables provided and to identify independent retinal vascular patterns. Overall, 1069 photographs (1069 participants) were reviewed with SIVA software. The mean age was 80.0 ± 3.8 years. We extracted three vascular patterns summarizing 41.3% of the vascular information. The most clinically relevant pattern, Sparse vascular network, accounted for 17.4% of the total variance. It corresponded to a lower density in the vascular network and higher variability in vessel width. Diabetic participants with hypoglycemic treatment had a sparser vascular network pattern than subjects without such treatment (odds ratio, [OR], 1.68; 95% CI, 1.04–2.72; P = 0.04). Participants with no history of cardiovascular disease who had a sparser vascular network were associated with a higher Heart SCORE (OR, 1.76; 95% CI, 1.08–2.25; P = 0.02). Three vascular patterns were identified. The Sparse vascular network pattern was associated with having a higher risk profile for cardiovascular mortality risk at 10 years.

To investigate the association of commonly used systemic medications with glaucoma and intraocular pressure (IOP) in the European population. Meta-analysis of eleven population-based cohort studies of the European Eye Epidemiology (E3) consortium. A total of 143240 participants were included in the glaucoma analyses and 47177 participants in the IOP analyses. We examined associations of four categories of systemic medications (antihypertensive medications: beta-blockers, diuretics, calcium channel blockers [CCBs], alpha-agonists, angiotensin-converting-enzyme inhibitors, angiotensin II receptor blockers; lipid-lowering medications; antidepressants; antidiabetic medications) with glaucoma prevalence and IOP. Glaucoma ascertainment and IOP measurement method were according to individual study protocols. Multivariable regression analyses were carried out in each study and results were pooled using random effects meta-analyses. Associations with antidiabetic medications were examined in diabetic participants only. Glaucoma prevalence and IOP. In the meta-analyses of our maximally-adjusted multivariable models, use of CCBs was associated with a higher prevalence of glaucoma (odds ratio [OR] with corresponding 95% confidence interval [95% CI]: 1.23 [1.08 to 1.39]). This association was stronger for monotherapy of CCBs with direct cardiac effects (OR [95% CI]: 1.96 [1.23 to 3.12]). The use of other antihypertensive medications, lipid-lowering medications, antidepressants or antidiabetic medications were not clearly associated with glaucoma. Use of systemic beta-blockers was associated with a lower IOP (Beta [95% CI]: -0.33 [-0.57 to -0.08] mmHg). Monotherapy of both selective (Beta [95% CI]: -0.45 [-0.74 to -0.16] mmHg) and non-selective (Beta [95% CI]: -0.54 [-0.94 to -0.15] mmHg) systemic beta-blockers was associated with lower IOP. There was a suggestive association between use of high-ceiling diuretics and lower IOP (Beta [95% CI]: -0.30 [-0.47; -0.14] mmHg), but not when used as monotherapy. Use of other antihypertensive medications, lipid-lowering medications, antidepressants, or antidiabetic medications were not associated with IOP. We identified a potentially harmful association between use of CCBs and glaucoma prevalence. Additionally, we observed and quantified the association of lower IOP with systemic beta-blocker use. Both findings are potentially important given that glaucoma patients frequently use systemic antihypertensive medications. Determining whether the CCB association is causal should be a research priority.

Purpose: Assessment of cardiovascular risk is the keystone of prevention in cardiovascular disease. The objective of this pilot study was to estimate the cardiovascular risk score (American Hospital Association [AHA] risk score, Syntax risk, and SCORE risk score) with machine learning (ML) model based on retinal vascular quantitative parameters. Methods: We proposed supervised ML algorithm to predict cardiovascular parameters in patients with cardiovascular diseases treated in Dijon University Hospital using quantitative retinal vascular characteristics measured with fundus photography and optical coherence tomography – angiography (OCT-A) scans (alone and combined). To describe retinal microvascular network, we used the Singapore “I” Vessel Assessment (SIVA), which extracts vessel parameters from fundus photography and quantitative OCT-A retinal metrics of superficial retinal capillary plexus. Results: The retinal and cardiovascular data of 144 patients were included. This paper presented a high prediction rate of the cardiovascular risk score. By means of the Naïve Bayes algorithm and SIVA + OCT-A data, the AHA risk score was predicted with 81.25% accuracy, the SCORE risk with 75.64% accuracy, and the Syntax score with 96.53% of accuracy. Conclusions: Performance of these algorithms demonstrated in this preliminary study that ML algorithms applied to quantitative retinal vascular parameters with SIVA software and OCT-A were able to predict cardiovascular scores with a robust rate. Quantitative retinal vascular biomarkers with the ML strategy might provide valuable data to implement predictive model for cardiovascular parameters. Translational Relevance: Small data set of quantitative retinal vascular parameters with fundus and with OCT-A can be used with ML learning to predict cardiovascular parameters.