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Molecular subtyping of dry eye disease: A machine learning approach using tear fluid proteins

Award Number: R01EY038397
ORGANIZATION: NATIONAL EYE INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/01/2026
PERIOD OF PERFORMANCE END DATE: 02/28/2029

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Molecular subtyping of dry eye disease: A machine learning approach using tear fluid proteins - PROJECT SUMMARY Dry eye disease (DED) is a prevalent ocular condition affecting up to 30 million Americans. It is characterized by insufficient ocular surface lubrication due to decreased tear production (aqueous-deficient dry eye, ADDE), excessive tear evaporation (evaporative dry eye, EDE), or a combination of both. Symptoms such as irritation, dryness, blurred vision, and a gritty sensation significantly impair patients’ quality of life. However, diagnosing and classifying DED remains challenging due to the subjective nature of clinical signs and symptoms, which vary based on factors such as time of day and assessment methods. Moreover, traditional diagnostic approaches fail to differentiate underlying pathophysiological mechanisms, leading to heterogeneous treatment responses. This project aims to address these limitations by defining molecular-based DED subtypes through advanced tear fluid biomarker profiling. High-resolution proteomic analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with machine learning algorithms will be used to identify distinct molecular signatures in tear fluid, classifying biologically relevant DED subtypes based on their molecular drivers. To enhance clinical applicability, a point-of-care (POC) diagnostic tool will be developed using multiplex immunoassays for reproducible, real-time classification in clinical settings. Additionally, this project will establish a publicly accessible tear proteomics database, integrating proteomic profiles with de-identified clinical data to facilitate collaborative DED research. Ultimately, this work has the potential to transform DED management by enabling precision diagnostics and personalized treatments targeting specific molecular pathways of the disease, thereby improving clinical outcomes for millions of affected individuals.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $495,229 )
2026	2026	AUGUSTA UNIVERSITY RESEARCH INSTITUTE, INC	AUGUSTA UNIVERSITY 1120 15TH STREET	AUGUSTA	GA	30912	RICHMOND	USA	Vision Research	000	1	4/3/2026	NEW	$495,229
														Subtotal = $495,229

Grand Total All Awards = $495,229

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Molecular subtyping of dry eye disease: A machine learning approach using tear fluid proteins

Award Number: R01EY038397

ORGANIZATION: NATIONAL EYE INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 04/01/2026

PERIOD OF PERFORMANCE END DATE: 02/28/2029

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