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

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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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Abbreviations

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

AAR:

Aspartate aminotransferase to alanine aminotransferase ratio

APRI:

Aspartate aminotransferase-platelet ratio index

AUROC:

Area under the receiver operating characteristic curve

BMI:

Body mass index

CI:

Confidence interval

DCA:

Decision curve analysis

FIB-4:

Fibrosis-4

FLIP:

Fatty liver inhibition of progression

MAFLD:

Metabolic dysfunction-associated fatty liver disease

NFS:

NAFLD fibrosis score

NPV:

Negative predictive value

PPV:

Positive predictive value

PRO-C3:

N-terminal propeptide of type 3 collagen

VCTE:

Vibration-controlled transient elastography

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Funding

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).

Author information

Authors and Affiliations

  1. MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, No. 2 Fuxue Lane, Wenzhou, 325000, China

    Liang-Jie Tang, Gang Li, Ou-Yang Huang, Hai-Yang Yuan & Ming-Hua Zheng

  2. Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital, Westmead, and University of Sydney, Sydney, Australia

    Mohammed Eslam & Jacob George

  3. Department of Laboratory Medicine, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Pei-Wu Zhu

  4. Department of Pathology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Sui-Dan Chen

  5. Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Hong Kong, China

    Howard Ho-Wai Leung

  6. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, 9/F Prince of Wales Hospital, 30-32 Ngan Shing Street, Sha Tin, Hong Kong, China

    Grace Lai-Hung Wong & Vincent Wai-Sun Wong

  7. State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China

    Grace Lai-Hung Wong & Vincent Wai-Sun Wong

  8. Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China

    Yu-Jie Zhou

  9. Nordic Bioscience Biomarkers and Research A/S, Herlev, Denmark

    Morten Karsdal & Diana Julie Leeming

  10. Fosun Diagnostics (Shanghai) Co., Ltd, Shanghai, China

    Pei Jiang & Cong Wang

  11. Southampton National Institute for Health Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, Southampton, UK

    Christopher D. Byrne

  12. Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy

    Giovanni Targher

  13. Institute of Hepatology, Wenzhou Medical University, Wenzhou, China

    Ming-Hua Zheng

  14. Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China

    Ming-Hua Zheng

  15. Cancer Center, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China

    Liang-Jie Tang

Authors
  1. Liang-Jie Tang
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  2. Gang Li
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  3. Mohammed Eslam
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  4. Pei-Wu Zhu
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  7. Ou-Yang Huang
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  8. Grace Lai-Hung Wong
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  13. Cong Wang
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  17. Jacob George
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  18. Vincent Wai-Sun Wong
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  19. Ming-Hua Zheng
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Contributions

L-JT: conceptualization, investigation, data curation, and writing—original draft. GL: conceptualization and writing—original draft. ME: investigation, writing—review and editing. P-WZ: data curation, writing—review and editing. S-DC: data curation, writing—review and editing. HH-WL: data curation and writing—review and editing. O-YH: data curation and writing—review and editing. GL-HW: investigation and writing—review and editing. Y-JZ: investigation and writing—review and editing. MK: investigation and writing—review and editing. DJL: investigation and writing—review and editing. PJ: investigation and writing—review and editing. CW: investigation and writing—review and editing. H-YY: investigation, writing—review and editing. CDB: investigation, writing—review and editing. GT: investigation and writing—review and editing. JG: conceptualization, methodology, and writing—review and editing. VW-SW: conceptualization, methodology, investigation, writing—review and editing, and project administration. M-HZ: conceptualization, methodology, investigation, data curation, writing—review and editing, and project administration. All authors contributed to the manuscript for important intellectual content and approved the final submission of the manuscript.

Corresponding authors

Correspondence to Vincent Wai-Sun Wong or Ming-Hua Zheng.

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Conflict of interest

Although the PRO-C3 ELISA test was carried out at Nordic Bioscience under a research collaboration, we confirm that cohort generation, research conceptualization, analysis, and manuscript drafting were carried out independently of the Nordic Bioscience team. ADAPT has not been developed as a proprietary test. The PRO-C3 ELISA test is not currently commercially available, but can be obtained as a Nordic Bioscience research test for research use only. Diana Julie Leeming and Morten Karsdal are employed by, and own stock at Nordic Bioscience. Grace Lai-Hung Wong and Vincent Wai-Sun Wong have served as speakers and/or consultants for Echosens. Liang-Jie Tang, Gang Li, Mohammed Eslam, Pei-Wu Zhu, Sui-Dan Chen, Howard Ho-Wai Leung, Ou-Yang Huang, Yu-Jie Zhou, Pei Jiang, Cong Wang, Hai-Yang Yuan, Christopher D. Byrne, Giovanni Targher, Jacob George and Ming-Hua Zheng have no conflicts of interest.

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The study was approved by the institutional review boards of both centres and all participants gave their written informed consent.

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Supplementary Information

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Supplementary Figure 1.

The AUROC of FAST, ADAPT and Agile 4 scores in the derivation cohort. (TIF 622 KB)

Supplementary Figure 2.

The dual cut-off approach of FAST, ADAPT and Agile 4 scores in the derivation cohort. (TIF 469 KB)

Supplementary file3 (DOCX 22 KB)

Supplementary file4 (DOCX 26 KB)

Supplementary file5 (DOCX 23 KB)

Supplementary file6 (DOCX 19 KB)

Supplementary file7 (DOCX 19 KB)

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Tang, LJ., Li, G., Eslam, M. et al. N-terminal propeptide of type 3 collagen-based sequential algorithm can identify high-risk steatohepatitis and fibrosis in MAFLD. Hepatol Int 17, 190–201 (2023). https://doi.org/10.1007/s12072-022-10420-w

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