Sci Prog. 2026 Apr-Jun;109(2):368504261453276. doi: 10.1177/00368504261453276. Epub 2026 May 16.
ABSTRACT
Dyslipidemia comprises interacting disturbances in lipids and lipoproteins that track with metabolic status, vascular biology, and inflammation. Ocular imaging offers scalable, quantifiable phenotypes to interrogate lipid-related pathways and to develop oculomics. We conducted a scoping review to map evidence linking dyslipidemia and lipid-related biomarkers with ocular phenotypes across the ocular surface, lens, macula, retinal microvasculature, and vascular occlusive disease, and to consider implications for AI-based risk modeling. We searched PubMed, Embase, Scopus, and Web of Science, supplemented by reference screening, and charted lipid exposures such as LDL-C, non-HDL-C, apoB/apoA-I, and the triglyceride-glucose index. The biologically grounded patterns were observed in macular disease, where cholesterol- and apolipoprotein-related material within the RPE-Bruch's membrane complex and drusen-related phenotypes support lipid-handling and innate immune pathways in age-related macular degeneration. Retinal vascular phenotypes showed generally consistent signals compatible with endothelial stress and microvascular remodeling. Epidemiologic associations were apparent in metabolically co-traveling conditions such as meibomian gland dysfunction and diabetic retinopathy, in which triglyceride-rich dyslipidemia and insulin resistance markers were often more informative than LDL-C alone and associations were often non-linear or interaction-dependent. By contrast, findings for glaucoma and cataract were modest and inconsistent, while vascular occlusive phenotypes clustered with broader atherosclerotic risk. Statin associations varied by outcome and were vulnerable to confounding. Predicting individual lipid analytes from retinal images appears limited, whereas integrated ocular signatures may support cardiovascular risk stratification. Future studies should refine phenotype definitions, model non-linearity, account for lipid-lowering therapy, and prospectively validate multimodal oculomics and AI across devices and populations.
PMID:42142069 | DOI:10.1177/00368504261453276