A blood-based biomarker panel (NIS4) for non-invasive diagnosis of non-alcoholic steatohepatitis and liver fibrosis: a prospective derivation and global validation study.


Journal

The lancet. Gastroenterology & hepatology
ISSN: 2468-1253
Titre abrégé: Lancet Gastroenterol Hepatol
Pays: Netherlands
ID NLM: 101690683

Informations de publication

Date de publication:
11 2020
Historique:
received: 25 02 2020
revised: 27 05 2020
accepted: 29 05 2020
pubmed: 9 8 2020
medline: 6 11 2020
entrez: 9 8 2020
Statut: ppublish

Résumé

Non-invasive tests that can identify patients with non-alcoholic steatohepatitis (NASH) at higher risk of disease progression are lacking. We report the development and validation of a blood-based diagnostic test to non-invasively rule in and rule out at-risk NASH (defined as non-alcoholic fatty liver disease [NAFLD] activity score [NAS] ≥4 and fibrosis stage ≥2). In this prospective derivation and global validation study, blood samples, clinical data, and liver biopsy results from three independent cohorts with suspected NAFLD were used to develop and validate a non-invasive blood-based diagnostic test, called NIS4. Derivation was done in the discovery cohort, which comprised 239 prospectively recruited patients with biopsy-confirmed NASH (NAFLD NAS ≥3; fibrosis stage 0-3) from the international GOLDEN-505 phase 2b clinical trial. A complete matrix based on 23 variables selected for univariate association with the presence of at-risk NASH and avoiding high multi-collinearity was used to derive the model in a bootstrap-based process that minimised the Akaike information criterion. The overall diagnostic performance of NIS4 was externally validated in two independent cohorts: RESOLVE-IT diag and Angers. The RESOLVE-IT diag cohort comprised the first 475 patients screened for potential inclusion into the RESOLVE-IT phase 3 clinical trial. Angers was a retrospective cohort of 227 prospectively recruited patients with suspected NAFLD and clinical risk factors for NASH or fibrosis stage 2 or more according to abnormal elastography results or abnormal liver biochemistry. Both external validation cohorts were independently analysed and were combined into a pooled validation cohort (n=702) to assess clinical performance of NIS4 and other non-invasive tests. The derived NIS4 algorithm comprised four independent NASH-associated biomarkers (miR-34a-5p, alpha-2 macroglobulin, YKL-40, and glycated haemoglobin; area under the receiver operating characteristics curve [AUROC] 0·80, 95% CI 0·73-0·85), and did not require adjustment for age, sex, body-mass index (BMI), or aminotransferase concentrations. Clinical cutoffs were established within the discovery cohort to optimise both rule out and rule in clinical performance while minimising indeterminate results. NIS4 was validated in the RESOLVE-IT diag cohort (AUROC 0·83, 95% CI 0·79-0·86) and the Angers cohort (0·76, 0·69-0·82). In the pooled validation cohort, patients with a NIS4 value less than 0·36 were classified as not having at-risk NASH (ruled out) with 81·5% (95% CI 76·9-85·3) sensitivity, 63·0% (57·8-68·0) specificity, and a negative predictive value of 77·9% (72·5-82·4), whereas those with a NIS4 value of more than 0·63 were classified as having at-risk NASH (ruled in) with 87·1% (83·1-90·3) specificity, 50·7% (45·3-56·1) sensitivity, and a positive predictive value of 79·2% (73·1-84·2). The diagnostic performance of NIS4 within the external validation cohorts was not influenced by age, sex, BMI, or aminotransferase concentrations. NIS4 is a novel blood-based diagnostic that provides an effective way to non-invasively rule in or rule out at-risk NASH in patients with metabolic risk factors and suspected disease. Use of NIS4 in clinical trials or in the clinic has the potential to greatly reduce unnecessary liver biopsies in patients with lower risk of disease progression. Genfit.

Sections du résumé

BACKGROUND
Non-invasive tests that can identify patients with non-alcoholic steatohepatitis (NASH) at higher risk of disease progression are lacking. We report the development and validation of a blood-based diagnostic test to non-invasively rule in and rule out at-risk NASH (defined as non-alcoholic fatty liver disease [NAFLD] activity score [NAS] ≥4 and fibrosis stage ≥2).
METHODS
In this prospective derivation and global validation study, blood samples, clinical data, and liver biopsy results from three independent cohorts with suspected NAFLD were used to develop and validate a non-invasive blood-based diagnostic test, called NIS4. Derivation was done in the discovery cohort, which comprised 239 prospectively recruited patients with biopsy-confirmed NASH (NAFLD NAS ≥3; fibrosis stage 0-3) from the international GOLDEN-505 phase 2b clinical trial. A complete matrix based on 23 variables selected for univariate association with the presence of at-risk NASH and avoiding high multi-collinearity was used to derive the model in a bootstrap-based process that minimised the Akaike information criterion. The overall diagnostic performance of NIS4 was externally validated in two independent cohorts: RESOLVE-IT diag and Angers. The RESOLVE-IT diag cohort comprised the first 475 patients screened for potential inclusion into the RESOLVE-IT phase 3 clinical trial. Angers was a retrospective cohort of 227 prospectively recruited patients with suspected NAFLD and clinical risk factors for NASH or fibrosis stage 2 or more according to abnormal elastography results or abnormal liver biochemistry. Both external validation cohorts were independently analysed and were combined into a pooled validation cohort (n=702) to assess clinical performance of NIS4 and other non-invasive tests.
FINDINGS
The derived NIS4 algorithm comprised four independent NASH-associated biomarkers (miR-34a-5p, alpha-2 macroglobulin, YKL-40, and glycated haemoglobin; area under the receiver operating characteristics curve [AUROC] 0·80, 95% CI 0·73-0·85), and did not require adjustment for age, sex, body-mass index (BMI), or aminotransferase concentrations. Clinical cutoffs were established within the discovery cohort to optimise both rule out and rule in clinical performance while minimising indeterminate results. NIS4 was validated in the RESOLVE-IT diag cohort (AUROC 0·83, 95% CI 0·79-0·86) and the Angers cohort (0·76, 0·69-0·82). In the pooled validation cohort, patients with a NIS4 value less than 0·36 were classified as not having at-risk NASH (ruled out) with 81·5% (95% CI 76·9-85·3) sensitivity, 63·0% (57·8-68·0) specificity, and a negative predictive value of 77·9% (72·5-82·4), whereas those with a NIS4 value of more than 0·63 were classified as having at-risk NASH (ruled in) with 87·1% (83·1-90·3) specificity, 50·7% (45·3-56·1) sensitivity, and a positive predictive value of 79·2% (73·1-84·2). The diagnostic performance of NIS4 within the external validation cohorts was not influenced by age, sex, BMI, or aminotransferase concentrations.
INTERPRETATION
NIS4 is a novel blood-based diagnostic that provides an effective way to non-invasively rule in or rule out at-risk NASH in patients with metabolic risk factors and suspected disease. Use of NIS4 in clinical trials or in the clinic has the potential to greatly reduce unnecessary liver biopsies in patients with lower risk of disease progression.
FUNDING
Genfit.

Identifiants

pubmed: 32763196
pii: S2468-1253(20)30252-1
doi: 10.1016/S2468-1253(20)30252-1
pii:
doi:

Substances chimiques

Biomarkers 0
CHI3L1 protein, human 0
Chitinase-3-Like Protein 1 0
Glycated Hemoglobin A 0
MIRN34 microRNA, human 0
MicroRNAs 0
alpha-Macroglobulins 0

Types de publication

Journal Article Randomized Controlled Trial Research Support, Non-U.S. Gov't Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

970-985

Commentaires et corrections

Type : CommentIn
Type : CommentIn

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Auteurs

Stephen A Harrison (SA)

Summit Clinical Research, San Antonio, TX, USA; Radcliffe Department of Medicine, University of Oxford, Oxford, UK.

Vlad Ratziu (V)

Sorbonne Université, Institute for Cardiometabolism and Nutrition, Hôpital Pitié-Salpêtrière, Paris, France.

Jérôme Boursier (J)

Service d'Hépato-Gastroentérologie, Centre Hospitalier Universitaire d'Angers, Angers, France; Laboratoire HIFIH, UPRES EA3859, SFR 4208, Université d'Angers, Angers, France.

Sven Francque (S)

Department of Gastroenterology and Hepatology, Antwerp University Hospital, Antwerp, Belgium; Translational Sciences in Inflammation and Immunology & InflaMed Consortium of Excellence, University of Antwerp, Antwerp, Belgium.

Pierre Bedossa (P)

Liverpat, Paris, France; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.

Zouher Majd (Z)

Genfit, Loos, France.

Geneviève Cordonnier (G)

Genfit, Loos, France.

Fouad Ben Sudrik (FB)

Genfit, Loos, France.

Raphael Darteil (R)

Genfit, Loos, France; ENYO Pharma, Lyon, France.

Roman Liebe (R)

Genfit, Loos, France; Klinik für Gastroenterologie, Hepatologie und Infektiologie, Universitätsklinikum Düsseldorf, Düsseldorf, Germany.

Jérémy Magnanensi (J)

Genfit, Loos, France.

Yacine Hajji (Y)

Genfit, Loos, France.

John Brozek (J)

Genfit, Loos, France.

Alice Roudot (A)

Genfit, Loos, France.

Bart Staels (B)

Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1011 - EGID, F-59000 Lille, France.

Dean W Hum (DW)

Genfit, Loos, France.

Sophie Jeannin Megnien (SJ)

Summit Clinical Research, San Antonio, TX, USA; Genfit, Loos, France.

Suneil Hosmane (S)

Genfit, Loos, France.

Noémie Dam (N)

Genfit, Loos, France.

Pierre Chaumat (P)

Genfit, Loos, France.

Rémy Hanf (R)

Genfit, Loos, France. Electronic address: remy.hanf@genfit.com.

Quentin M Anstee (QM)

Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Newcastle NIHR Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, Newcastle upon Tyne, UK.

Arun J Sanyal (AJ)

Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, VA, USA.

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Classifications MeSH