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Both timely diagnosis and progression monitoring are critical but challenging. Conventional characterization of glaucoma needs a combination of methods, calling for tedious procedures and experienced doctors. Herein, a platform through machine learning of tear metabolic fingerprinting (TMF) using nanoparticle enhanced laser desorption\u2013ionization mass spectrometry is built. Direct TMF is obtained noninvasively, with fast speed and high reproducibility, using trace tear samples (down to 10\u00a0nL). Consequently, glaucoma patients are screened against healthy controls with the area under the curve (AUC) of 0.866, through machine learning of TMF. Further, primary open\u2010angle glaucoma (POAG) is differentiated from primary angle\u2010closure glaucoma (PACG) and an early\u2010stage POAG is identified. Finally, a biomarker panel of six metabolites for glaucoma characterization (including screening, subtyping, and early diagnosis) with AUC of 0.827\u20130.891 is constructed, showing related metabolic pathways. The work will provide insights into eye diseases not limited to glaucoma.<\/jats:p>","DOI":"10.1002\/smtd.202200264","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T12:00:56Z","timestamp":1649332856000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Glaucoma Characterization by Machine Learning of Tear Metabolic Fingerprinting"],"prefix":"10.1002","volume":"6","author":[{"given":"Jiao","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory for Oncogenes and Related Genes School of Biomedical Engineering and Institute of Medical Robotics Shanghai Jiao Tong University  Shanghai 200030 P. R. 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