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N-terminal propeptide of type 3 collagen-based sequential algorithm can identify high-risk steatohepatitis and fibrosis in MAFLD

N-terminal propeptide of type 3 collagen-based sequential algorithm can identify high-risk steatohepatitis and fibrosis in MAFLD
N-terminal propeptide of type 3 collagen-based sequential algorithm can identify high-risk steatohepatitis and fibrosis in MAFLD
Background and aims: With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence sharply increasing globally, there is an urgent need for non-invasive diagnostic tests to accurately screen high-risk MAFLD patients for liver inflammation and fibrosis. We aimed to develop a novel sequential algorithm based on N-terminal propeptide of type 3 collagen (PRO-C3) for disease risk stratification in patients with MAFLD.

Methods: A derivation and independent validation cohort of 327 and 142 patients with biopsy-confirmed MAFLD were studied. We compared the diagnostic performances of various non-invasive scores in different disease states, and a novel sequential algorithm was constructed by combining the best performing non-invasive scores.

Results: For patients with high-risk progressive steatohepatitis (i.e., steatohepatitis + NAFLD activity score ≥ 4 + F ≥ 2), the AUROC of FAST score was 0.801 (95% confidence interval (CI): 0.739-0.863), and the negative predictive value (NPV) was 0.951. For advanced fibrosis (≥ F3) and cirrhosis (F4), the AUROCs of ADAPT and Agile 4 were 0.879 (95%CI 0.825-0.933) and 0.943 (95%CI 0.892-0.994), and the NPV were 0.972 and 0.992. Sequential algorithm of ADAPT + Agile 4 combination was better than other combinations for risk stratification of patients with severe fibrosis (AUROC = 0.88), with similar results in the validation cohort. Meanwhile, in all subgroup analyses (stratifying by sex, age, diabetes, NAS, BMI and ALT), ADAPT + Agile 4 had a good diagnostic performance.

Conclusions: The new sequential algorithm reliably identifies liver inflammation and fibrosis in MAFLD, making it easier to exclude low-risk patients and recommending high-risk MAFLD patients for clinical trials and emerging pharmacotherapies.
Fibrosis staging, Metabolic dysfunction-associated fatty liver disease, Sequential algorithm, Steatohepatitis
1936-0533
190-201
Tang, Liang-Jie
81b1ee7d-5a3f-487b-89f9-514dcbc143d8
Li, Gang
192b2d26-a01b-455b-8f1a-ee0741e4a232
Eslam, Mohammed
2ee4af3b-79eb-40cb-bc45-9e0a5bd1a836
Zhu, Pei-Wu
3575381e-eb0b-4198-918f-0fcbc3737c2a
Chen, Sui-Dan
3bc79c7a-9d51-4aac-97fe-8e0829a59353
Leung, Howard Ho-Wai
38ac6f74-57eb-4a2a-9b15-820caebc968a
Huang, Ou-Yang
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Wong, Grace Lai-Hung
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Zhou, Yu-Jie
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Karsdal, Morten
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Leeming, Diana Julie
e45f968a-72c8-4169-bbfe-30d711fdcfa3
Jiang, Pei
b2fd919e-5863-4c04-a35a-6f8ca5bc3881
Wang, Cong
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Yuan, Hai-Yang
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Byrne, Christopher
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Targher, Giovanni
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George, Jacob
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Wong, Vincent Wai-Sun
7cddf4bb-4720-45f1-a052-7b87fb64c6d6
Zheng, Ming-Hua
916f7560-c0d7-46d9-8540-55d80d1f9752
Tang, Liang-Jie
81b1ee7d-5a3f-487b-89f9-514dcbc143d8
Li, Gang
192b2d26-a01b-455b-8f1a-ee0741e4a232
Eslam, Mohammed
2ee4af3b-79eb-40cb-bc45-9e0a5bd1a836
Zhu, Pei-Wu
3575381e-eb0b-4198-918f-0fcbc3737c2a
Chen, Sui-Dan
3bc79c7a-9d51-4aac-97fe-8e0829a59353
Leung, Howard Ho-Wai
38ac6f74-57eb-4a2a-9b15-820caebc968a
Huang, Ou-Yang
0d829086-7868-417c-8dde-57c58cdcbab8
Wong, Grace Lai-Hung
60674a37-fad6-43d5-9ec0-976b11d0ff86
Zhou, Yu-Jie
b4facbba-b685-4358-94ac-5e0037d85051
Karsdal, Morten
06be6470-ee88-4f85-aebf-c2967c518b62
Leeming, Diana Julie
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Jiang, Pei
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Wang, Cong
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Yuan, Hai-Yang
4e38785f-9596-49da-a6b9-fd3b2ea08dc9
Byrne, Christopher
1370b997-cead-4229-83a7-53301ed2a43c
Targher, Giovanni
1e2b8514-ade0-4a4a-91b8-75d4a1831916
George, Jacob
7af8b0fb-3c2c-498b-9771-1bfc7d43d2a8
Wong, Vincent Wai-Sun
7cddf4bb-4720-45f1-a052-7b87fb64c6d6
Zheng, Ming-Hua
916f7560-c0d7-46d9-8540-55d80d1f9752

Tang, Liang-Jie, Li, Gang, Eslam, Mohammed, Zhu, Pei-Wu, Chen, Sui-Dan, Leung, Howard Ho-Wai, Huang, Ou-Yang, Wong, Grace Lai-Hung, Zhou, Yu-Jie, Karsdal, Morten, Leeming, Diana Julie, Jiang, Pei, Wang, Cong, Yuan, Hai-Yang, Byrne, Christopher, Targher, Giovanni, George, Jacob, Wong, Vincent Wai-Sun and Zheng, Ming-Hua (2023) N-terminal propeptide of type 3 collagen-based sequential algorithm can identify high-risk steatohepatitis and fibrosis in MAFLD. Hepatology International, 17 (1), 190-201. (doi:10.1007/s12072-022-10420-w).

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Abstract

Background and aims: With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence sharply increasing globally, there is an urgent need for non-invasive diagnostic tests to accurately screen high-risk MAFLD patients for liver inflammation and fibrosis. We aimed to develop a novel sequential algorithm based on N-terminal propeptide of type 3 collagen (PRO-C3) for disease risk stratification in patients with MAFLD.

Methods: A derivation and independent validation cohort of 327 and 142 patients with biopsy-confirmed MAFLD were studied. We compared the diagnostic performances of various non-invasive scores in different disease states, and a novel sequential algorithm was constructed by combining the best performing non-invasive scores.

Results: For patients with high-risk progressive steatohepatitis (i.e., steatohepatitis + NAFLD activity score ≥ 4 + F ≥ 2), the AUROC of FAST score was 0.801 (95% confidence interval (CI): 0.739-0.863), and the negative predictive value (NPV) was 0.951. For advanced fibrosis (≥ F3) and cirrhosis (F4), the AUROCs of ADAPT and Agile 4 were 0.879 (95%CI 0.825-0.933) and 0.943 (95%CI 0.892-0.994), and the NPV were 0.972 and 0.992. Sequential algorithm of ADAPT + Agile 4 combination was better than other combinations for risk stratification of patients with severe fibrosis (AUROC = 0.88), with similar results in the validation cohort. Meanwhile, in all subgroup analyses (stratifying by sex, age, diabetes, NAS, BMI and ALT), ADAPT + Agile 4 had a good diagnostic performance.

Conclusions: The new sequential algorithm reliably identifies liver inflammation and fibrosis in MAFLD, making it easier to exclude low-risk patients and recommending high-risk MAFLD patients for clinical trials and emerging pharmacotherapies.

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Accepted/In Press date: 31 August 2022
e-pub ahead of print date: 24 September 2022
Published date: 1 February 2023
Additional Information: Funding Information: This paper was funded by grants from the National Natural Science Foundation of China (82070588), High Level Creative Talents from Department of Public Health in Zhejiang Province (S2032102600032) and Project of New Century 551 Talent Nurturing in Wenzhou. GT is supported in part by grants from the School of Medicine, University of Verona, Verona, Italy. CDB is supported in part by the Southampton NIHR Biomedical Research Centre (IS-BRC-20004), UK. Vincent Wong is supported in part by a Direct Grant from The Chinese University of Hong Kong (2020.045). ME and JG are supported by the Robert W. Storr Bequest to the Sydney Medical Foundation, University of Sydney; a National Health and Medical Research Council of Australia (NHMRC) Program Grant (APP1053206), an Investigator Grant (APP1196492) and Project and ideas grants (APP2001692, APP1107178 and APP1108422). Publisher Copyright: © 2022, Asian Pacific Association for the Study of the Liver.
Keywords: Fibrosis staging, Metabolic dysfunction-associated fatty liver disease, Sequential algorithm, Steatohepatitis

Identifiers

Local EPrints ID: 469931
URI: http://eprints.soton.ac.uk/id/eprint/469931
ISSN: 1936-0533
PURE UUID: 83f2266f-bf9e-4e65-abae-0b455401f056
ORCID for Christopher Byrne: ORCID iD orcid.org/0000-0001-6322-7753

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Date deposited: 28 Sep 2022 17:11
Last modified: 17 Mar 2024 07:29

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Contributors

Author: Liang-Jie Tang
Author: Gang Li
Author: Mohammed Eslam
Author: Pei-Wu Zhu
Author: Sui-Dan Chen
Author: Howard Ho-Wai Leung
Author: Ou-Yang Huang
Author: Grace Lai-Hung Wong
Author: Yu-Jie Zhou
Author: Morten Karsdal
Author: Diana Julie Leeming
Author: Pei Jiang
Author: Cong Wang
Author: Hai-Yang Yuan
Author: Giovanni Targher
Author: Jacob George
Author: Vincent Wai-Sun Wong
Author: Ming-Hua Zheng

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