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Oculomics: A Crusade Against the Four Horsemen of Chronic Disease
Emily J Patterson1, Alistair D Bounds2, Siegfried K Wagner3,4
1Occuity, The Blade, Abbey Square, Reading, Berkshire, RG1 3BE, UK. Emily.Patterson@Occuity.com.
Insights
Oculomics, using eye biomarkers for disease detection, offers a non-invasive approach to identify systemic conditions. Research shows promising links between eye structures and cardiovascular, neurodegenerative, and metabolic diseases, with potential for early cancer detection.
Area of Science:
- Ophthalmology
- Biomarkers
- Systemic Disease Detection
Background:
- Chronic diseases are a major health burden, necessitating accessible and cost-effective early detection methods.
- Ocular biomarkers (oculomics) offer a non-invasive, rapid, and low-cost avenue for systemic disease screening.
- Early diagnosis through oculomics can significantly improve patient outcomes in prevention, monitoring, and treatment.
Purpose of the Study:
- To review the associations between ocular biomarkers and the four leading causes of global morbidity and mortality: cardiovascular, cancer, neurodegenerative, and metabolic diseases.
- To evaluate the potential of oculomics for early detection and diagnosis of these major non-communicable diseases.
- To highlight the role of the retina and artificial intelligence in advancing oculomics.
Main Methods:
- Literature review of studies investigating ocular biomarkers for systemic diseases.
- Analysis of structural biomarkers in various eye components (e.g., retina, choroid, lens, tear fluid).
- Examination of the diagnostic utility of oculomics for cardiovascular, cancer, neurodegenerative, and metabolic diseases.
Main Results:
- Neurodegenerative diseases show strong potential with biomarkers in multiple ocular structures.
- Cardiovascular disease biomarkers are found in the choroid, retinal vasculature, and retinal nerve fiber layer.
- Metabolic disease biomarkers are identified in the eyelid, tear fluid, lens, and retinal vasculature; tear fluid is a promising target for cancer detection.
Conclusions:
- Oculomics presents a promising field for early detection of major systemic diseases, particularly neurodegenerative conditions.
- Combining data from multiple ocular structures and leveraging AI can enhance diagnostic specificity.
- Future development of targeted instruments is crucial for advancing differential diagnoses using oculomics.
Abstract:
Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.
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