Hemken Philip M, Qin Xuzhen, Sokoll Lori J, Jackson Laurel, Feng Fan, Li Peng, Gawel Susan H, Tu Bailin, Lin Zhihong, Hartnett James, Hawksworth David, Tieman Bryan C, Yoshimura Toru, Kinukawa Hideki, Ning Shaohua, Liu Enfu, Meng Fanju, Chen Fei, Miao Juru, Mi Xuan, Tong Xin, Chan Daniel W, Davis Gerard J
Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
Department of Laboratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College Hospital, Beijing, China.
Clin Proteomics. 2023 Nov 28;20(1):53. doi: 10.1186/s12014-023-09444-7.
Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts.
To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model.
GP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls.
GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.
早期诊断肝脏疾病可改善预后并降低进展为恶性肿瘤的风险。肝活检是诊断肝脏疾病的金标准，但具有侵入性，且样本采集误差常见。肝功能和纤维化的血清生物标志物，结合患者因素，可能有助于肝脏疾病的非侵入性检测。在这项初步研究中，我们测试并验证了一种算法的性能，该算法将GP73和LG2m血清生物标志物与年龄和性别（GLAS）相结合，以区分两个独立队列中的肝病患者和健康个体。
为开发该算法，使用原型免疫测定法测量2003年至2016年间从病毒或非病毒病因的分期纤维化和肝硬化患者（n = 260）及健康受试者（n = 133）收集的残余血清样本中的GP73和LG2m。测试了使用来自开发队列的年龄、性别、GP73和/或LG2m组合的五种预测模型的性能。来自另一个肝病队列（纤维化、肝硬化或慢性肝病；n = 395）和健康受试者（n = 106）的残余样本用于验证表现最佳的模型。
在开发队列和验证队列中，肝病患者的GP73和LG2m浓度均高于健康对照，且肝硬化患者高于纤维化患者。表现最佳的模型包括GP73和LG2m加上年龄和性别（GLAS算法），其AUC为0.92（95%CI：0.90 - 0.95），敏感性为88.8%，特异性为75.9%。在验证队列中，GLAS算法的估计AUC为0.93（95%CI：0.90 - 0.95），敏感性为91.1%，特异性为80.2%。在两个队列中，GLAS算法在区分肝病患者和健康对照方面具有较高的预测概率。
GP73和LG2m血清生物标志物与年龄和性别相结合（GLAS算法），在两个独立队列中对肝病检测显示出高敏感性和特异性。GLAS算法需要在更大队列中进行验证和完善，并在纵向研究中进行测试，以区分随时间推移稳定与进展性肝病。
