首页|Fudan University Reports Findings in Age-Related Macular Degeneration(Serum met abolite biomarkers for the early diagnosis andmonitoring of age-related macular degeneration)
Fudan University Reports Findings in Age-Related Macular Degeneration(Serum met abolite biomarkers for the early diagnosis andmonitoring of age-related macular degeneration)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Eye Diseases and Condi tions - Age-Related Macular Degenerationis the subject of a report. According t o news reporting out of Shanghai, People’s Republic of China,by NewsRx editors, research stated, “Age-related macular degeneration (AMD) is a leading cause of irreversibleblindness worldwide, with significant challenges for early diagnosi s and treatment. To identifynew biomarkers that are important for the early dia gnosis and monitoring of the severity/progression ofAMD.”Our news journalists obtained a quote from the research from Fudan University, “ We investigatedthe diagnostic and monitoring potential of blood metabolites in a cohort of 547 individuals (167 healthycontrols, 240 individuals with other ey e diseases as eye disease controls, and 140 individuals with AMD)from 2 centers over three phases: discovery phase 1, discovery phase 2, and an external valida tion phase.The samples were analyzed via a mass spectrometry-based, widely targ eted metabolomic workflow. Indiscovery phases 1 and 2, we built a machine learn ing algorithm to predict the probability of AMD. In theexternal validation phas e, we further confirmed the performance of the biomarker panel identified by thealgorithm. We subsequently evaluated the performance of the identified biomarke r panel in monitoring theprogression and severity of AMD. We developed a clinic ally specific three-metabolite panel (hypoxanthine,2-furoylglycine, and 1-hexad ecyl-2-azelaoyl-sn-glycero-3-phosphocholine) via five machine learning models.T he random forest model effectively discriminated patients with AMD from patents in the other two groupsand showed acceptable calibration (area under the curve (AUC) = 1.0; accuracy = 1.0) in both discoveryphases 1 and 2. An independent va lidation phase confirmed the diagnostic model’s efficacy (AUC = 0.962;accuracy = 0.88). The three-biomarker panel model demonstrated an AUC of 1.0 in different iating theseverity of AMD via RF machine learning, which was consistent across both the discovery and externalvalidation phases. Additionally, the biomarker c oncentrations remained stable under repeated freeze-thawcycles (P > 0.05).”
ShanghaiPeople’s Republic of ChinaAs iaAge-Related MacularDegenerationBiomarkersCyborgsDiagnostics and Scree ningEmerging TechnologiesEye Diseasesand ConditionsHealth and MedicineM achine LearningMacular DegenerationRetinal DegenerationRetinal Diseases an d Conditions
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