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Page 6 of 10 Kim et al. Hepatoma Res 2020;6:85 I http://dx.doi.org/10.20517/2394-5079.2020.96

Figure 1. Toward personalized medicine based on genetic information. Personalized medicine makes it possible to tailor a treatment as
individualized as the disease. This approach can be applied to solve tackling diseases that have far eluded effective treatments

NAFLD development and progression. Several NAFLD risk scoring models that incorporate both genetic
[64]
[65]
and clinical information have also been proposed . For example, Hyysalo et al. developed a model for
predicting NASH, which combines clinical variables and genetic information, based on European cohorts
with biopsy-proven NAFLD. In another study, NAFLD-HCC was identified based on genotype information,
age, sex, obesity, T2DM, and severe fibrosis, showing an AUROC of 0.96 ± 0.04 (89% specificity and 96%
sensitivity) .
[33]
Additional research is needed before PRSs, GRSs, or prediction models using both clinical variables and
genetic information can be effectively applied for NAFLD investigation in clinical practice. This knowledge
of genetic loci is potentially useful for risk stratification in patients with NAFLD. Moreover, considering
[66]
that there are presently no approved drugs for NAFLD treatment , there is an urgent need for more
research and development of therapeutic targeting of the products of these genes in NAFLD patients with
specific genetic variants that could provide insight into personalized treatments for NAFLD [Figure 1].

TRANSLATIONAL IMPLICATIONS AND CHALLENGES
As discussed above, several attempts have been made to predict NAFLD and/or NASH using genetic
information alone or in combination with clinical information. The results of these efforts can be applied
to the development of a new scoring model with better diagnostic performance compared to the previous
models. Notably, a prediction model developed by combining serum metabolites, serum biochemical
parameters, and genotype information was reported to discriminate NASH from NAFL with a good
diagnostic performance . Such a model would be appealing for clinical translation, considering that the
[67]
current gold standard for NASH diagnosis is liver biopsy, which is an invasive method. However, there are
several challenges hindering the clinical translation of genetic information in NAFLD.

Firstly, most studies performed to evaluate the diagnostic accuracy of predictive models for NAFLD and/
or NASH risk have been based on a cross-sectional design. Although these results can be useful, it is not an
optimal study design for investigating models based on genetic information. Indeed, unlike classical factors
such as biochemical results (AST, ALT), genetic variants have the strength of being stable over time. Thus,