Baseline Tyrosine Level Is Associated with Dynamic Changes in FAST Score in NAFLD Patients under Lifestyle Modification

Hwi Young Kim, Da Jung Kim, Hye Ah Lee, Joo Youn Cho, Won Kim

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Abstract

Noninvasive risk stratification is a challenging issue in the management of patients with nonalcoholic fatty liver disease (NAFLD). This study aimed to identify multiomics-based predictors of NAFLD progression, as assessed by changes in serial FibroScan-aspartate aminotransferase (FAST) scores during lifestyle modification. A total of 266 patients with available metabolomics and genotyping data were included. The follow-up sub-cohort included patients with paired laboratory and transient elastography results (n = 160). The baseline median FAST score was 0.37. The PNPLA3 rs738409 genotype was significantly associated with a FAST score > 0.35. Circulating metabolomics significantly associated with a FAST score > 0.35 included SM C24:0 (odds ratio [OR] = 0.642; 95% confidence interval [CI], 0.463–0.891), PC ae C40:6 (OR = 0.477; 95% CI, 0.340–0.669), lysoPC a C18:2 (OR = 0.570; 95% CI, 0.417–0.779), and tyrosine (OR = 2.743; 95% CI, 1.875–4.014). A combination of these metabolites and PNPLA3 genotype yielded a c-index = 0.948 for predicting a FAST score > 0.35. In the follow-up sub-cohort (median follow-up = 23.7 months), 47/76 patients (61.8%) with a baseline FAST score > 0.35 had a follow-up FAST score ≤ 0.35. An improved FAST score at follow-up was significantly associated with age, serum alanine aminotransferase, and tyrosine. In conclusion, baseline risk stratification in NAFLD patients may be assisted using a multiomics-based model. Particularly, patients with increased tyrosine may benefit from an earlier switch to pharmacologic approaches.

Original languageEnglish
Article number444
JournalMetabolites
Volume13
Issue number3
DOIs
StatePublished - Mar 2023

Bibliographical note

Funding Information:
This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (Grant No. 2018R1A5A2025286, 2021R1A2C2005820, and 2021M3A9E4021818), the Research Supporting Program of the Korean Association for the Study of the Liver (Grant No. KASL2018-01) and the Technology Innovation Program (or Industrial Strategic Technology Development Program-Bioindustry Strategic Technology Development Program) (Grant No. 20013712) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). The funding sources had no role in the design of this study and did not have any role during its execution, analyses, interpretation of the data, or decision to submit results.

Publisher Copyright:
© 2023 by the authors.

Keywords

  • genomics
  • metabolomics
  • multiomics
  • nonalcoholic steatohepatitis
  • outcome
  • prediction
  • risk stratification
  • steatohepatitis
  • steatosis
  • weight change

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