Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.

Autor: Hemken PM; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA. philip.hemken@abbott.com., Qin X; Department of Laboratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College Hospital, Beijing, China., Sokoll LJ; Division of Clinical Chemistry, Department of Pathology, The Johns Hopkins University, Baltimore, MD, USA., Jackson L; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA., Feng F; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA., Li P; Department of Laboratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College Hospital, Beijing, China., Gawel SH; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA., Tu B; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA., Lin Z; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA., Hartnett J; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA., Hawksworth D; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA., Tieman BC; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA., Yoshimura T; Research & Development, Abbott Japan, Matsudo, Japan., Kinukawa H; Research & Development, Abbott Japan, Matsudo, Japan., Ning S; Diagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China., Liu E; Diagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China., Meng F; Diagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China., Chen F; Diagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China., Miao J; Diagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China., Mi X; Diagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China., Tong X; Diagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China., Chan DW; Division of Clinical Chemistry, Department of Pathology, The Johns Hopkins University, Baltimore, MD, USA., Davis GJ; Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA. gerard.davis@abbott.com.
Jazyk: angličtina
Zdroj: Clinical proteomics [Clin Proteomics] 2023 Nov 28; Vol. 20 (1), pp. 53. Date of Electronic Publication: 2023 Nov 28.
DOI: 10.1186/s12014-023-09444-7
Abstrakt: Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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