Metabolomics identifies and validates serum androstenedione as novel biomarker for diagnosing primary angle closure glaucoma and predicting the visual field progression

Shengjie Li; Jun Ren; Zhendong Jiang; Yichao Qiu; Mingxi Shao; Yingzhu Li; Jianing Wu; Yunxiao Song; Xinghuai Sun; Shunxiang Gao; Wenjun Cao

doi:10.7554/eLife.91407

eLife (Feb 2024)

Metabolomics identifies and validates serum androstenedione as novel biomarker for diagnosing primary angle closure glaucoma and predicting the visual field progression

Shengjie Li,
Jun Ren,
Zhendong Jiang,
Yichao Qiu,
Mingxi Shao,
Yingzhu Li,
Jianing Wu,
Yunxiao Song,
Xinghuai Sun,
Shunxiang Gao,
Wenjun Cao

Affiliations

Shengjie Li: ORCiD; Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China; Department of Ophthalmology & Visual Science, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China; Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China; NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
Jun Ren: Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
Zhendong Jiang: Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
Yichao Qiu: Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
Mingxi Shao: Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
Yingzhu Li: Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
Jianing Wu: Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
Yunxiao Song: Department of Clinical Laboratory, Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
Xinghuai Sun: Department of Ophthalmology & Visual Science, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China; Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China; NHC Key Laboratory of Myopia, Fudan University, Shanghai, China
Shunxiang Gao: ORCiD; Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
Wenjun Cao: Department of Clinical Laboratory, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China; Department of Ophthalmology & Visual Science, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China; Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China; NHC Key Laboratory of Myopia, Fudan University, Shanghai, China

DOI: https://doi.org/10.7554/eLife.91407
Journal volume & issue: Vol. 12

Abstract

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Background: Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness in Asia, and no reliable, effective diagnostic, and predictive biomarkers are used in clinical routines. A growing body of evidence shows metabolic alterations in patients with glaucoma. We aimed to develop and validate potential metabolite biomarkers to diagnose and predict the visual field progression of PACG. Methods: Here, we used a five-phase (discovery phase, validation phase 1, validation phase 2, supplementary phase, and cohort phase) multicenter (EENT hospital, Shanghai Xuhui Central Hospital), cross-sectional, prospective cohort study designed to perform widely targeted metabolomics and chemiluminescence immunoassay to determine candidate biomarkers. Five machine learning (random forest, support vector machine, lasso, K-nearest neighbor, and GaussianNaive Bayes [NB]) approaches were used to identify an optimal algorithm. The discrimination ability was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed by Hosmer-Lemeshow tests and calibration plots. Results: Studied serum samples were collected from 616 participants, and 1464 metabolites were identified. Machine learning algorithm determines that androstenedione exhibited excellent discrimination and acceptable calibration in discriminating PACG across the discovery phase (discovery set 1, AUCs=1.0 [95% CI, 1.00–1.00]; discovery set 2, AUCs = 0.85 [95% CI, 0.80–0.90]) and validation phases (internal validation, AUCs = 0.86 [95% CI, 0.81–0.91]; external validation, AUCs = 0.87 [95% CI, 0.80–0.95]). Androstenedione also exhibited a higher AUC (0.92–0.98) to discriminate the severity of PACG. In the supplemental phase, serum androstenedione levels were consistent with those in aqueous humor (r=0.82, p=0.038) and significantly (p=0.021) decreased after treatment. Further, cohort phase demonstrates that higher baseline androstenedione levels (hazard ratio = 2.71 [95% CI: 1.199–6.104], p=0.017) were associated with faster visual field progression. Conclusions: Our study identifies serum androstenedione as a potential biomarker for diagnosing PACG and indicating visual field progression. Funding: This work was supported by Youth Medical Talents – Clinical Laboratory Practitioner Program (2022-65), the National Natural Science Foundation of China (82302582), Shanghai Municipal Health Commission Project (20224Y0317), and Higher Education Industry-Academic-Research Innovation Fund of China (2023JQ006).

Published in eLife

ISSN: 2050-084X (Online)
Publisher: eLife Sciences Publications Ltd
Country of publisher: United Kingdom
LCC subjects: Medicine; Science: Biology (General)
Website: https://elifesciences.org

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