Fast nanoDSF Tear Fluid Profiling: Toward Diagnosis of Age-Related Macular Degeneration

Background: Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in older adults. An important challenge is the recognition of its early asymptomatic stages and the monitoring of its progression, which requires reliable biomarkers. Growing evidence indicates that AMD-related biochemical changes are reflected in the proteome of tear fluid (TF). Although TF is a non-invasive and easily collectable diagnostic material, its proteomic analysis is complex and costly and therefore has limited clinical value. Methods: In this pilot single-center retrospective cross-sectional study, we developed a new method for dry AMD screening based on analysis of nano-differential scanning fluorimetry (nanoDSF) tear protein denaturation profiles (TDPs) within 15 min. The TDPs were recorded in representative groups of dry AMD patients (37% early, 48% intermediate, 15% geographic atrophy), and in control groups, including patients with refractive abnormalities (basic control), other retinal degenerative diseases (diabetic retinopathy, peripheral retinal dystrophy), or TF-affecting conditions (dry eye syndrome). High-dimensional TDP data were processed using unsupervised machine learning followed by k-means cluster analysis. Results: The presented pipeline distinguished AMD from the basic control with 74% accuracy and a sensitivity of 0.81 without relying on prior labels. The specificity of AMD detection was confirmed by its effective differentiation from diabetic retinopathy (72%; 0.74), peripheral retinal dystrophy (79%; 0.76) and dry eye disease (76%; 0.81). Classifying the AMD group from the entire population of other patients yielded an accuracy of 71% and a sensitivity of 85%, with a false-negative rate of only 15%. Conclusions: This study is a proof of concept for the nanoDSF-based approach, which can be considered a fast, cost-effective, and convenient tool for population screening for dry AMD, suitable for use in preventive medicine and public health.

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Journal
Life
Published
2026-06-24
DOI
https://doi.org/10.3390/life16071048
Primary Topic
Retinal Diseases and Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

Fast nanoDSF Tear Fluid Profiling: Toward Diagnosis of Age-Related Macular Degeneration

Е. Н. Иомдина, Sergey Yu. Petrov, Vladimir A. Mitkevich, Ivan I. Senin et al.
Life
Retinal Diseases and Treatments
article

Fast nanoDSF Tear Fluid Profiling: Toward Diagnosis of Age-Related Macular Degeneration

Е. Н. Иомдина, Sergey Yu. Petrov, Vladimir A. Mitkevich, Ivan I. Senin, O. I. Markelova, E. A. Suleiman, Nina Yu. Kushnarevich, Evgeni Yu. Zernii, O. M. Filippova, Dmitry V. Chistyakov, Natalia G. Shebardina, Anton Bougaev, Philipp O. Tsvetkov, Violetta N. Papyan, Mikhail L. Shishkin, Dmitriy V. Lipatov, Veronika V. Tiulina, Timofey A. Chistyakov
article en

Abstract

Background: Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in older adults. An important challenge is the recognition of its early asymptomatic stages and the monitoring of its progression, which requires reliable biomarkers. Growing evidence indicates that AMD-related biochemical changes are reflected in the proteome of tear fluid (TF). Although TF is a non-invasive and easily collectable diagnostic material, its proteomic analysis is complex and costly and therefore has limited clinical value. Methods: In this pilot single-center retrospective cross-sectional study, we developed a new method for dry AMD screening based on analysis of nano-differential scanning fluorimetry (nanoDSF) tear protein denaturation profiles (TDPs) within 15 min. The TDPs were recorded in representative groups of dry AMD patients (37% early, 48% intermediate, 15% geographic atrophy), and in control groups, including patients with refractive abnormalities (basic control), other retinal degenerative diseases (diabetic retinopathy, peripheral retinal dystrophy), or TF-affecting conditions (dry eye syndrome). High-dimensional TDP data were processed using unsupervised machine learning followed by k-means cluster analysis. Results: The presented pipeline distinguished AMD from the basic control with 74% accuracy and a sensitivity of 0.81 without relying on prior labels. The specificity of AMD detection was confirmed by its effective differentiation from diabetic retinopathy (72%; 0.74), peripheral retinal dystrophy (79%; 0.76) and dry eye disease (76%; 0.81). Classifying the AMD group from the entire population of other patients yielded an accuracy of 71% and a sensitivity of 85%, with a false-negative rate of only 15%. Conclusions: This study is a proof of concept for the nanoDSF-based approach, which can be considered a fast, cost-effective, and convenient tool for population screening for dry AMD, suitable for use in preventive medicine and public health.

LifeVol. 16(7)
Engelhardt Institute of Molecular Biology (RU), Centre National de la Recherche Scientifique (FR), Lomonosov Moscow State University (RU), Purdue University West Lafayette (US), Endocrinology Research Center (RU), Institut de Neurophysiopathologie (FR), Helmholtz Moscow Research Institute of Eye Diseases (RU)
Russian Science Foundation
Good health and well-being
Openalex Percentile: Top 6%
Retinal Diseases and Treatments
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