Abstract Body (Do not enter title and authors here): Introduction
Cardiovascular disease (CVD) is a leading global cause of mortality, often remaining undiagnosed until critical events occur. Retinal ischemic perivascular lesions (RIPLs), detectable via optical coherence tomography (OCT), have been proposed as non-invasive indicators of subclinical ischemia. Given the accessibility and low cost of OCT imaging, RIPLs may offer a practical tool for early CVD risk identification.
Research Question/Hypothesis:
We hypothesized that the presence of RIPLs is significantly associated with increased risk of all-cause cardiovascular mortality and that their detection may serve as an effective, low-cost method for identifying individuals at elevated risk of CVD.
Methods
A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. A comprehensive search of MEDLINE, Scopus, and Cochrane CENTRAL from 2015 to May 2025 identified observational studies evaluating the relationship between RIPLs and CVD. Studies involving adult populations and using OCT imaging for RIPL detection were included. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using a random-effects model. Heterogeneity was assessed using the heterogeneity statistic.
Results
Out of 100 studies screened, six met inclusion criteria, and four were eligible for meta-analysis, involving a total of 710 participants. The pooled OR for the association between RIPLs and CVD-related mortality was 2.8 (95% CI: 1.98–3.95), indicating that individuals with RIPLs had over twofold increased risk compared to those without. Heterogeneity was minimal (zero), supporting the robustness and consistency of findings across studies.
Conclusions
RIPLs identified on OCT are associated with elevated cardiovascular risk. Given the non-invasive, widely available, and cost-effective nature of OCT, RIPLs may serve as potential biomarker used as valuable screening tool for early CVD detection. Incorporation of RIPL assessment into routine care could facilitate timely intervention, reduce reliance on invasive diagnostics, minimize patient burden and help prevent expensive treatments. It will also be very useful in the future to consider incorporating this biomarker in artificial intelligence tools that detect risk factors for CVD.