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Updated: May 7, 2026
07:36
Clinical Application of Intense Pulsed Light Therapy and Radio Frequency for Treatment of Ocular Surface Diseases
Published on: July 3, 2025
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Reimagining dry eye disease management - a multimodal approach targeting the key pathophysiological drivers
Mohd Radzi Hilmi1, James S Wolffsohn2
1Department of Optometry and Visual Science, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, 25200 Kuantan, Pahang, Malaysia; Integrated Omics Research Group, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, 25200 Kuantan, Pahang, Malaysia.
Summary
This study reveals varied prevalence of dry eye disease drivers, identifying four distinct patient clusters. This supports a precision medicine approach for personalized dry eye disease management.
Area of Science:
- Ophthalmology
- Dry Eye Disease Research
- Ocular Surface Disease
Background:
- Dry eye disease (DED) diagnosis traditionally uses limited metrics, failing to address its complex nature.
- The TFOS DEWS III framework emphasizes understanding DED heterogeneity.
- A multimodal approach is needed to accurately classify DED subtypes.
Purpose of the Study:
- Implement a novel, multimodal DED evaluation strategy.
- Determine the prevalence of pathophysiological DED drivers.
- Identify clinically relevant DED phenotypic clusters.
Main Methods:
- Prospective, cross-sectional study of 615 participants (aged 20-45) meeting TFOS DEWS II criteria.
- Comprehensive ocular surface tests assessed nine drivers across tear film, eyelid, and ocular surface domains.
- Cluster analysis identified DED phenotypes based on driver prevalence.
Main Results:
- Prevalence of DED drivers varied: mucin/glycocalyx deficiency (88%), aqueous deficiency (61%), bulbar hyperaemia (48%), meibomian gland dysfunction (43%), reduced corneal sensitivity (37%), incomplete lid closure (24%).
- Cluster analysis revealed a robust four-cluster model, partitioning patients into distinct subgroups.
- Clusters showed progressive driver involvement and severity, from early signs to severe multi-driver disease.
Conclusions:
- Confirms heterogeneous prevalence of DED drivers in a clinical population.
- Identifies four distinct DED phenotypic clusters, supporting a move towards precision medicine.
- Enables phenotyping and targeted therapy selection for personalized DED management.
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