■原发性闭角型青光眼(PACG)是亚洲不可逆失明的主要原因,没有可靠的,有效的诊断,和预测性生物标志物用于临床常规。越来越多的证据表明青光眼患者的代谢改变。我们旨在开发和验证潜在的代谢物生物标志物,以诊断和预测PACG的视野进展。
■这里,我们使用了五个阶段(发现阶段,验证阶段1,验证阶段2,补充阶段,和队列阶段)多中心(EENT医院,上海徐汇中心医院),横截面,前瞻性队列研究旨在进行广泛靶向的代谢组学和化学发光免疫测定以确定候选生物标志物。五个机器学习(随机森林,支持向量机,套索,K-最近邻,和GaussianNaiveBayes[NB])方法用于识别最优算法。使用受试者工作特征曲线下面积(AUC)评价辨别能力。通过Hosmer-Lemeshow测试和校准图评估校准。
■研究了616名参与者的血清样本,并鉴定了1464种代谢物。机器学习算法确定雄烯二酮在发现阶段(发现集1,AUC=1.0[95%CI,1.00-1.00];发现集2,AUC=0.85[95%CI,0.80-0.90])和验证阶段(内部验证,AUC=0.86[95%CI,0.81-0.91];外部验证,AUC=0.87[95%CI,0.80-0.95])。雄烯二酮还表现出更高的AUC(0.92-0.98)以区分PACG的严重性。在补充阶段,血清雄烯二酮水平与房水一致(r=0.82,p=0.038),治疗后显著下降(p=0.021)。Further,队列阶段显示较高的基线雄烯二酮水平(风险比=2.71[95%CI:1.199-6.104],p=0.017)与更快的视野进展相关。
■我们的研究将血清雄烯二酮确定为诊断PACG和指示视野进展的潜在生物标志物。
■这项工作得到了青年医学人才-临床实验室从业者计划(2022-65)的支持,国家自然科学基金(82302582),上海市卫生健康委员会项目(20224Y0317),中国高等教育产学研创新基金(2023JQ006)。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Our study identifies serum androstenedione as a potential biomarker for diagnosing PACG and indicating visual field progression.
UNASSIGNED: 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).