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

被引:3
作者
Li, Shengjie [1 ,2 ,3 ,4 ,5 ]
Ren, Jun [1 ]
Jiang, Zhendong [1 ]
Qiu, Yichao [1 ]
Shao, Mingxi [1 ]
Li, Yingzhu [1 ]
Wu, Jianing [1 ]
Song, Yunxiao [6 ]
Sun, Xinghuai [2 ,3 ,4 ,5 ]
Gao, Shunxiang [7 ,8 ]
Cao, Wenjun [1 ,2 ,3 ,4 ,5 ]
机构
[1] Fudan Univ, Eye & ENT Hosp, Shanghai Med Coll, Dept Clin Lab, Shanghai, Peoples R China
[2] Fudan Univ, Eye & ENT Hosp, Shanghai Med Coll, Dept Ophthalmol & Visual Sci, Shanghai, Peoples R China
[3] Fudan Univ, Inst Brain Sci, State Key Lab Med Neurobiol, Shanghai, Peoples R China
[4] Chinese Acad Med Sci, Key Lab Myopia, Shanghai, Peoples R China
[5] Fudan Univ, NHC Key Lab Myopia, Shanghai, Peoples R China
[6] Fudan Univ, Shanghai Xuhui Cent Hosp, Dept Clin Lab, Shanghai, Peoples R China
[7] Shanghai Jiao Tong Univ, Shanghai Gen Hosp, Sch Med, Dept Ophthalmol, Shanghai, Peoples R China
[8] Natl Clin Res Ctr Eye Dis, Shanghai Engn Ctr Visual Sci & Photomed, Shanghai Key Lab Ocular Fundus Dis, Shanghai, Peoples R China
来源
ELIFE | 2024年 / 12卷
基金
中国国家自然科学基金;
关键词
primary angle closure glaucoma; metabolomics; androstenedione; diagnose; predict; visual field progression; Human; PREVALENCE; METABOLITES; ACID;
D O I
10.7554/eLife.91407
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:26
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