Original Article
Pancreas, Biliary Tract, and Liver
Development and Validation of a Scoring System, Based on Genetic and Clinical Factors, to Determine Risk of Steatohepatitis in Asian Patients with Nonalcoholic Fatty Liver Disease

https://doi.org/10.1016/j.cgh.2020.02.011Get rights and content

Background & Aims

There are no biomarkers of nonalcoholic steatohepatitis (NASH) that are ready for routine clinical use. We investigated whether an analysis of PNPLA3 and TM6SF2 genotypes (rs738409 and rs58542926) can be used to identify patients with nonalcoholic fatty liver disease (NAFLD), with and without diabetes, who also have NASH.

Methods

We collected data from the Boramae registry in Korea on 453 patients with biopsy-proven NAFLD with sufficient clinical data for calculating scores. Patients enrolled from February 2014 through March 2016 were assigned to cohort 1 (n = 302; discovery cohort) and patients enrolled thereafter were assigned to cohort 2 (n = 151; validation cohort). DNA samples were obtained from all participants and analyzed for the PNPLA3 rs738409 C>G, TM6SF2 rs58542926 C>T, SREBF2 rs133291 C>T, MBOAT7-TMC4 rs641738 C>T, and HSD17B13 rs72613567 adenine insertion (A-INS) polymorphisms. We used multivariable logistic regression analyses with stepwise backward selection to build a model to determine patients’ risk for NASH (NASH PT) using the genotype and clinical data from cohort 1 and tested its accuracy in cohort 2. We used the receiver operating characteristic (ROC) curve to compare the diagnostic performances of the NASH PT and the NASH scoring systems.

Results

We developed a NASH PT scoring system based on PNPLA3 and TM6SF2 genotypes, diabetes status, insulin resistance, and levels of aspartate aminotransferase and high-sensitivity C-reactive protein. NASH PT scores identified patients with NASH with an area under the ROC (AUROC) of 0.859 (95% CI, 0.817–0.901) in cohort 1. In cohort 2, NASH PT scores identified patients with NASH with an AUROC of 0.787 (95% CI, 0.715–0.860), which was significantly higher than the AUROC of the NASH score (AUROC, 0.729; 95% CI, 0.647–0.812; P = .007). The AUROC of the NASH PT score for detecting NASH in patients with NAFLD with diabetes was 0.835 (95% CI, 0.776–0.895) and in patients without diabetes was 0.809 (95% CI, 0.757–0.861). The negative predictive value of the NASH PT score <–0.785 for NASH in patients with NAFLD with diabetes reached 0.905.

Conclusions

We developed a scoring system, based on polymorphisms in PNPLA3 and TM6SF2 and clinical factors that identifies patients with NAFLD, with or without diabetes, who have NASH, with an AUROC value of 0.787. This system might help clinicians better identify NAFLD patients at risk for NASH.

Section snippets

Patients and Assessment of Liver Histology

We constructed a prospective cohort from the ongoing Boramae NAFLD registry.7 Among the eligible study participants, only biopsy-proven NAFLD patients with sufficient clinical data for calculating scores were finally included in this study (N = 453) (Supplementary Methods). They were divided into cohort 1 (N = 302) and cohort 2 (N = 151) according to the period of enrollment. Cohort 1 consisted of individuals enrolled from February 2014 to March 2016 to build a model to determine patients’ risk

Characteristics of the Discovery and Replication Cohorts

In cohort 1 (n = 302) and cohort 2 (n = 151), 145 (48.0%) and 67 (44.4%) patients, respectively, were classified as NASH (P = .464) (Table 1). Cohort 2 showed significantly lower high-density lipoprotein cholesterol, higher AST and alanine aminotransferase, lower albumin, and higher HOMA-IR levels than cohort 1 (P = .019, .016, .038, .001, and < .001, respectively) (Table 1).

The genotypic distributions of PNPLA3 rs738409, TM6SF2 rs58542926, SREBF2 rs133291, MBOAT7-TMC4 rs641738, and HSD17B13

Conclusions

In the current study, we successfully developed a NASH-identifying polygenic risk scoring model incorporating 2 genetic risk variants, such as PNPLA3 rs738409 and TM6SF2 rs58542926, from our biopsy-proven NAFLD cohort and validated it using an independent replication dataset. Its AUROC for differentiating NASH from NAFL was 0.859 and 0.787 in the discovery and replication cohorts, respectively. In the replication cohort, the AUROC of the newly developed model (NASH PT score) was significantly

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    CRediT Authorship Contributions Bo Kyung Koo (Data curation: Lead; Formal analysis: Lead; Investigation: Lead; Validation: Lead; Writing – original draft: Lead; Writing – review and editing: Lead) Sae Kyung Joo (Data curation: Lead; Formal analysis: Equal; Investigation: Lead; Resources: Lead) Donghee Kim (Conceptualization: Supporting; Supervision: Equal; Writing – original draft: Supporting; Writing – review and editing: Equal) Seonhwa Lee (Investigation: Supporting; Methodology: Supporting; Resources: Supporting) Jeong Mo Bae (Formal analysis: Supporting; Investigation: Lead; Resources: Lead) Jeong Hwan Park (Formal analysis: Supporting; Investigation: Lead; Resources: Equal) Jung Ho Kim (Formal analysis: Equal; Investigation: Lead; Resources: Equal) Mee Soo Chang (Formal analysis: Equal; Investigation: Equal; Resources: Supporting; Supervision: Equal) Won Kim, MD, PhD (Data curation: Lead; Formal analysis: Lead; Funding acquisition: Lead; Investigation: Lead; Methodology: Lead; Project administration: Lead; Resources: Lead; Supervision: Lead; Validation: Lead; Writing – original draft: Lead; Writing – review and editing: Lead)

    Conflicts of interest The authors disclose no conflicts.

    Funding This work was supported by a National Research Foundation of Korea grant funded by the Korea government (MEST) (2016R1D1A1B04934590), and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Ministry of Health & Welfare, Republic of Korea (H I17C0912). The funding did not affect the collection, analysis, or presentation of the data.

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