Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD

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  • Lars Verschuren
  • Anne Linde Mak
  • Arianne van Koppen
  • Serdar Özsezen
  • Sonia Difrancesco
  • Martien P.M. Caspers
  • Jessica Snabel
  • David van der Meer
  • Anne Marieke van Dijk
  • Elias Badal Rashu
  • Puria Nabilou
  • Mikkel Parsberg Werge
  • Koen van Son
  • Robert Kleemann
  • Amanda J. Kiliaan
  • Eric J. Hazebroek
  • André Boonstra
  • Willem P. Brouwer
  • Michail Doukas
  • Saurabh Gupta
  • Cornelis Kluft
  • Max Nieuwdorp
  • Joanne Verheij
  • Adriaan G. Holleboom
  • Maarten E. Tushuizen
  • Roeland Hanemaaijer
Accurate non-invasive biomarkers to diagnose metabolic dysfunction-associated steatotic liver disease (MASLD)-related fibrosis are urgently needed. This study applies a translational approach to develop a blood-based biomarker panel for fibrosis detection in MASLD. A molecular gene expression signature identified from a diet-induced MASLD mouse model (LDLr−/−.Leiden) is translated into human blood-based biomarkers based on liver biopsy transcriptomic profiles and protein levels in MASLD patient serum samples. The resulting biomarker panel consists of IGFBP7, SSc5D and Sema4D. LightGBM modeling using this panel demonstrates high accuracy in predicting MASLD fibrosis stage (F0/F1: AUC = 0.82; F2: AUC = 0.89; F3/F4: AUC = 0.87), which is replicated in an independent validation cohort. The overall accuracy of the model outperforms predictions by the existing markers Fib-4, APRI and FibroScan. In conclusion, here we show a disease mechanism-related blood-based biomarker panel with three biomarkers which is able to identify MASLD patients with mild or advanced hepatic fibrosis with high accuracy.
OriginalsprogEngelsk
Artikelnummer4564
TidsskriftNature Communications
Vol/bind15
Udgave nummer1
Antal sider14
ISSN2041-1723
DOI
StatusUdgivet - 2024

Bibliografisk note

Funding Information:
The in vivo work was generated by a consortium that was supported by grant 114025001 from ZonMW and the TNO research program \u201CPredictive Health Technologies\u201D. The translation cohort work was supported by Health Holland, Top Sector Life Sciences & Health (LSH), grant number V201700972. In addition, clinical analyses were supported by a grant of the Rijnstate-Radboudumc Promotion Fund and the Shared Research Program GLoBAL, an initiative of Radboudumc, Rijnstate, and TNO supported by L.S.H. M.N. is supported by a personal ZONMW VICI grant 2020 [09150182010020]. A.G.H. is supported by the Amsterdam UMC Fellowship and TKI-PPP Health~Holland grants. A.B. was supported by: the European-Latin American ESCALON consortium, funded by the EU Horizon 2020 program, project number 825510, and the Foundation for Liver and Gastrointestinal Research (SLO). The authors express their gratitude to Miangela Lacle (Utrecht University Medical center, Utrecht, the Netherlands) for her insightful guidance in the selection of archived material. In addition, the authors thank the patients from the cohorts who contributed.

Publisher Copyright:
© The Author(s) 2024.

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