Journal of advanced research · 2025
Development and clinical validation of a novel protein biomarkers-based algorithm for risk prediction and diagnosis of advanced liver fibrosis: a multi-centre study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Cheung Chloe Yu-Yan, Wan Pei, Wan Heng, Xu Chenxin, Jia Xi, Fong Carol Ho-Yi, Lui David Tak-Wai, Song Erfei, Chen Xingying, Chow Wing-Sun, Woo Yu-Cho, Tan Kathryn Choon-Beng, Seto Wai-Kay, Wang Cunchuan, Shen Jie, Lam Karen Siu-Ling, Lee Chi-Ho, Xu Aimin
Аннотация
INTRODUCTION: Type 2 diabetes (T2D) and obesity contribute significantly to the elevated risk of liver fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD). However, there is a lack of reliable and cost-effective non-invasive test (NIT) for detecting liver fibrosis in T2D/obese individuals.
OBJECTIVES: This study aimed to develop a simple biomarker-based algorithm for detecting advanced liver fibrosis among T2D/obesity subjects with MASLD and to validate its diagnostic performance in both clinic- and community-based cohorts.
METHODS: Diagnostic performances of circulating thrombospondin-2 (TSP2), a novel fibrosis marker, and the three individual components of Enhanced Liver Fibrosis (ELF) test were evaluated in three independent cohorts. These included a clinic-based derivation cohort (N = 846) and a community-based validation cohort (N = 803), both comprising of T2D patients with vibration-controlled transient elastography (VCTE)-diagnosed MASLD. Additionally, a clinic-based validation cohort of morbidly-obese patients with biopsy-proven MASLD (N = 223) was included. An algorithm (TaP score) based on TSP2 and procollagen 3 N-terminal peptide (PIIINP), a component of ELF, was constructed from the multivariate logistic regression model and compared with existing NITs, including ELF, fibrosis-4 index (FIB-4) and NAFLD fibrosis score (NFS). The dual-cut-off approach was used to define the rule-in and rule-out cut-offs.
RESULTS: Circulating TSP2 (AUC[95 % CI]:0.844[0.810-0.878]) and PIIINP (AUC[95 % CI]:0.843(0.807-0.875]) showed excellent diagnostic performance and were used to construct the biomarker-based algorithm. The TaP score (AUC[95 % CI]:0.900[0.874-0.925]) significantly outperformed ELF (AUC[95 % CI]:0.809[0.773-0.843]), FIB-4 (AUC[95 % CI]:0.597[0.544-0.647]) and NFS (AUC[95 % CI]:0.585[0.528-0.639]) (all DeLong P < 0.001), showing high specificity (85.16 %), sensitivity (78.62 %), and negative predictive value (NPV) (95.08 %) at the optimal cut-off. This algorithm resulted in fewer patients with indeterminate results compared to ELF. Its diagnostic performance in the two external validation cohorts was comparable to that in the derivation cohort.
CONCLUSIONS: The TaP score demonstrated good diagnostic ability with generally better performance compared to ELF, and had the potential to be developed as a novel NIT.
OBJECTIVES: This study aimed to develop a simple biomarker-based algorithm for detecting advanced liver fibrosis among T2D/obesity subjects with MASLD and to validate its diagnostic performance in both clinic- and community-based cohorts.
METHODS: Diagnostic performances of circulating thrombospondin-2 (TSP2), a novel fibrosis marker, and the three individual components of Enhanced Liver Fibrosis (ELF) test were evaluated in three independent cohorts. These included a clinic-based derivation cohort (N = 846) and a community-based validation cohort (N = 803), both comprising of T2D patients with vibration-controlled transient elastography (VCTE)-diagnosed MASLD. Additionally, a clinic-based validation cohort of morbidly-obese patients with biopsy-proven MASLD (N = 223) was included. An algorithm (TaP score) based on TSP2 and procollagen 3 N-terminal peptide (PIIINP), a component of ELF, was constructed from the multivariate logistic regression model and compared with existing NITs, including ELF, fibrosis-4 index (FIB-4) and NAFLD fibrosis score (NFS). The dual-cut-off approach was used to define the rule-in and rule-out cut-offs.
RESULTS: Circulating TSP2 (AUC[95 % CI]:0.844[0.810-0.878]) and PIIINP (AUC[95 % CI]:0.843(0.807-0.875]) showed excellent diagnostic performance and were used to construct the biomarker-based algorithm. The TaP score (AUC[95 % CI]:0.900[0.874-0.925]) significantly outperformed ELF (AUC[95 % CI]:0.809[0.773-0.843]), FIB-4 (AUC[95 % CI]:0.597[0.544-0.647]) and NFS (AUC[95 % CI]:0.585[0.528-0.639]) (all DeLong P < 0.001), showing high specificity (85.16 %), sensitivity (78.62 %), and negative predictive value (NPV) (95.08 %) at the optimal cut-off. This algorithm resulted in fewer patients with indeterminate results compared to ELF. Its diagnostic performance in the two external validation cohorts was comparable to that in the derivation cohort.
CONCLUSIONS: The TaP score demonstrated good diagnostic ability with generally better performance compared to ELF, and had the potential to be developed as a novel NIT.