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Editors Selection IGR 23-4

Prognostic factors: Systemic factors associated with Glaucoma Progression

Joo Young Shin
Sally Baxter

Comment by Joo Young Shin & Sally Baxter on:

107485 Systemic factors associated with 10-year glaucoma progression in South Korean population: a single center study based on electronic medical records, Yoon JS; Kim YE; Lee EJ et al., Scientific reports, 2023; 13: 530

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In this study, Yoon et al. evaluated factors associated with faster retinal nerve fiber layer (RNFL) thinning on optical coherence tomography in primary open-angle glaucoma (POAG) patients using electronic medical record data. They employed decision tree models, random forest models, and models based on permutation methods, interpreted by the Shapley additive explanation (SHAP) method. In addition to detecting previously known ophthalmic risk factors, they also identified several systemic risk factors.

In the decision tree model, a higher lymphocyte ratio (> 34.65%) was the most important systemic variable discriminating faster or slower RNFL thinning, and higher mean corpuscular hemoglobin (> 32.05 pg) and alkaline phosphatase (> 88.0 IU/L) concentrations were distinguishing factors in the eyes with lymphocyte ratios > 34.65% and ??34.65%, respectively. In the random forest model, higher lymphocyte ratio and higher platelet count were the strongest systemic factors associated with faster RNFL thinning. Previous studies have identified altered immunity as POAG risk factors. Song et al. recently reported a genetic predisposition of higher lymphocyte count to be associated with glaucoma.1 However, previous studies investigating neutrophil to lymphocyte ratio in POAG have reported controversial results,2-5 and no significant difference was found in a recently reported meta-analysis, which also suggested different results among patients of different ethnicities. 6 Further large-scale prospective longitudinal studies with diverse patients will be needed to confirm these findings.

The strength of machine learning models, over conventional linear regression models, is their consideration of potential non-linear relationships and interactions among features. However, one challenge is the limited interpretability of machine learning models. Recently, there have been numerous efforts made for a more explainable artificial intelligence,7 such as the SHAP method used in this study.8,9 Overall, this study demonstrated the use of machine learning approaches using large-scale data in detecting risk factors associated with multifactorial diseases such as POAG.


  1. Song DJ, Fan B, Li GY. Blood cell traits and risk of glaucoma: A two-sample mendelian randomization study. Front Genet. 2023;14:1142773.
  2. Atalay K, Erdogan Kaldirim H, Kirgiz A, Asik Nacaroglu S. Neutrophil to Lymphocyte and Platelet to Lymphocyte Ratios in Normal Tension Glaucoma. Med Hypothesis Discov Innov Ophthalmol. 2019;8(4):278-282.
  3. Karahan M, Kilic D, Guven S. Systemic inflammation in both open-angle and angle-closure glaucoma: role of platelet-to-lymphocyte ratio. Bratisl Lek Listy. 2021;122(1):45-48.
  4. Ozgonul C, Sertoglu E, Mumcuoglu T, Kucukevcilioglu M. Neutrophil-to- Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio as Novel Biomarkers of Primary Open-Angle Glaucoma. J Glaucoma. 2016;25(10):e815-e20.
  5. Tang B, Li S, Han J, Cao W, Sun X. Associations between Blood Cell Profiles and Primary Open-Angle Glaucoma: A Retrospective Case-Control Study. Ophthalmic research. 2020;63(4):413-422.
  6. Shirvani M, Soufi F, Nouralishahi A, et al. The Diagnostic Value of Neutrophil to Lymphocyte Ratio as an Effective Biomarker for Eye Disorders: A Meta-Analysis. Biomed Res Int. 2022;2022:5744008.
  7. Gu B, Sidhu S, Weinreb RN, et al. Review of Visualization Approaches in Deep Learning Models of Glaucoma. Asia Pac J Ophthalmol (Phila). 2023;12(4):392-401.
  8. Lundberg SM, Lee S-I. A Unified Approach to Interpreting Model Predictions. ArXiv. 2017;abs/1705.07874.
  9. Rodriguez-Perez R, Bajorath J. Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values. J Med Chem. 2020;63(16):8761-8777.

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