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Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy

Eye (2024)Cite this article

Subjects

Abstract

Objectives

To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers.

Methods

A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry.

Results

Among 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively.

Conclusions

Tear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR.

Clinical trial registration

Chinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177. Date of registration: 2021/12/12.

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Fig. 1: Study flow chart and differentially expressed proteins.
Fig. 2: Gene ontology enrichment analysis.
Fig. 3: Kyoto Encyclopedia of Genes and Genomes enrichment analysis.
Fig. 4: Results of biomarker selection and validation for discriminating diabetic retinopathy from diabetes mellitus.

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Data availability

Deidentified individual participant data will be made available.

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Acknowledgements

The authors thank the support of colleagues from Shenmei Eye Hospital, the Seventh Affiliated Hospital of Sun Yat-Sen University, and Sustech Core Research Facilities of Southern University of Science and Technology. This study was supported by National Natural Science Foundation of China (No. 82271103), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515012326), Shenzhen Key Medical Discipline Construction Fund (No. SZXK038), and Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties (No. SZGSP014).

Funding

The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author information

Author notes

  1. These authors contributed equally: Zixin Fan, Yarou Hu.

Authors and Affiliations

  1. Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, Guangdong, 518040, China

    Zixin Fan, Yarou Hu, Laijiao Chen, Xiaofeng Lu, Lei Zheng, Dahui Ma & Guoming Zhang

  2. International Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518040, China

    Zixin Fan

  3. Shenmei Eye Hospital, Meizhou, Guangdong, 514000, China

    Zhiqiang Li & Jingwen Zhong

  4. Southern University of Science and Technology, Shenzhen, Guangdong, 518040, China

    Lin Lin

  5. New York University, New York, NY 10003, USA

    Sifan Zhang

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Contributions

Conception and design, Administrative support, and Provision of study materials or patients: Guoming Zhang. Conception and design, Collection and assembly of data, Data analysis and Interpretation, and Manuscript writing: Zixin Fan. Administrative support and Provision of study materials or patients: Dahui Ma. Administrative support and Provision of study materials or patients: Zhiqiang Li. Collection and assembly of data, and Data analysis and interpretation: Yarou Hu. Collection and assembly of data: Laijiao Chen. Collection and assembly of data: Xiaofeng Lu. Collection and assembly of data: Lei Zheng. Collection and assembly of data: Jingwen Zhong. Collection and assembly of data and Data analysis and interpretation: Lin Lin. Collection and assembly of data and Data analysis and interpretation: Sifan Zhang. Final approval of manuscript: all authors.

Corresponding author

Correspondence to Guoming Zhang.

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Fan, Z., Hu, Y., Chen, L. et al. Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy. Eye (2024). https://doi.org/10.1038/s41433-024-02938-0

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