Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease

Abstract

The diagnosis of Vogt-Koyanagi-Harada (VKH) disease is mainly based on a complex clinical manifestation while it lacks objective laboratory biomarkers. To explore the potential molecular biomarkers for diagnosis and disease activity in VKH, we performed an untargeted urine metabolomics analysis by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Through univariate and multivariate statistical analysis, we found 9 differential metabolites when comparing VKH patients with healthy controls, and 26 differential metabolites were identified when comparing active VKH patients with inactive VKH patients. Pathway enrichment analysis showed that glycine, serine and threonine metabolism, and arginine and proline metabolism were significantly altered in VKH versus healthy controls. Lysine degradation and biotin metabolism pathways were significantly altered in active VKH versus inactive VKH. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that the combination of acetylglycine and gamma-glutamylalanine could differentiate VKH from healthy controls with an area under the curve (AUC) of 0.808. A combination of ureidopropionic acid and 5′-phosphoribosyl-5-amino-4-imidazolecarboxamide (AICAR) had an excellent AUC of 0.958 for distinguishing active VKH from inactive VKH. In summary, this study identified abnormal metabolites in urine of patients with VKH disease. Further studies are needed to confirm whether these metabolites are specific for this disease.

Classification of Vogt-Koyanagi-Harada disease using feature selection and classification based on wide-field swept-source optical coherence tomography angiography

“… Chen et al (2020) used plasma metabolomics to identify significant differences in plasma metabolic phenotypes of VKH patients and identified diagnostic biomarkers for VKH disease. Chang et al (2021) used urine metabolomics to identify predictive urine biomarkers for VKH disease. However, the VKH classification based on metabolomic analysis remains in laboratory research, and it is an expensive and time-consuming examination.…”

“…Feature selection is one of the effective ways to reduce dimensionality, which helps to reduce the risk of overfitting, improve the generalization ability of the model and save computational effort because only a fewer features are calculated ( Shilaskar and Ghatol, 2013 ). On the other hand, machine learning methods, such as support vector machines (SVM), logistic regression (LR), random forests (RF), K-nearest neighbors (KNN), and decision trees (DT), are widely used for classification and prediction of ophthalmic diseases, such as myopia and keratitis ( Tang et al, 2020 ; Herber et al, 2021 ), glaucoma, uveitis, cataract, and age-related macular degeneration ( Lin et al, 2020 ; Standardization of Uveitis Nomenclature SUN Working Group, 2021b ; Ting et al, 2021 ), and recently also for VKH classification ( Standardization of Uveitis Nomenclature SUN Working Group, 2021a ; Chang et al, 2021 ), because of their good classification performance in small datasets. These classifiers are often trained with hyperparameters, which need to be optimized to obtain the best classification performance.…”

Vogt-Koyanagi-Harada disease

“…No single diagnostic test for VKH exists. Although recent metabolomic analyses have revealed urine metabolites that are useful in differentiating VKH from healthy controls, as well as active from inactive VKH, further studies are needed to determine whether these metabolites are specific to VKH [51]. Fortunately, the well delineated nature of the disease, along with characteristic systemic and ocular findings, provide useful clues to the diagnosis.…”

Serum Metabolic Profiles of Chinese Women With Perimenopausal Obesity Explored by the Untargeted Metabolomics Approach

“…Serum metabolic profiling analysis was performed using a 1290 UHPLC system (Agilent Technologies, Santa Clara, CA, USA) with a Waters UPLC BEH Amide column (1.7 mm; 2.1 × 100 mm) coupled to Triple TOF 6600 (AB Sciex, Framingham, MA, USA) & QTOF 6550 (Agilent) according to a previous study (18). The aim of using the two instruments here was to improve the quality and reliability of the experiments.…”