Fatty liver disease (FLD), either alcoholic or nonalcoholic, is the most common liver disorder in developed countries. In the United States, alcohol-associated liver disease (ALD) has an estimated prevalence of 2%(1) and is the leading indication for liver transplant listing. Nonalcoholic fatty liver disease (NAFLD), which is associated with obesity and insulin resistance, affects 25% to 30% of adults worldwide.(2) ALD and NAFLD are leading causes of cirrhosis and hepatocellular carcinoma (HCC), and despite having different etiologies, these diseases share histological features and genetic risk factors, suggesting common underlying biological pathways.
Current guidelines recommend specialist referral only for patients with advanced ALD(3) or NAFLD.(4) Surveillance for HCC is only advocated for FLD patients with established cirrhosis.(4) Importantly, risk stratification currently identifies patients after they have advanced fibrosis. A better strategy might identify FLD patients at increased risk prior to developing advanced disease and target them for preventive interventions. The genetic risk score (GRS) proposed by Gellert-Kristensen et al.(5) could aid that approach.
In this fine study, the authors assessed the association of GRS with risk of cirrhosis and HCC in general population cohorts from Denmark and the United Kingdom with more than 400,000 combined participants. The GRS, ranging from 0 to 6, equaled the number of risk alleles from three variants with known effects on FLD (patatin-like phospholipase domain-containing protein 3 [PNPLA3] I148M; transmembrane 6 superfamily member 2 [TM6SF2] E167K; hydroxysteroid 17-beta dehydrogenase 13 [HSD17B13] rs72613567). Comparing participants with GRS of 5 or 6 to those with GRS of 0, the investigators found large odds ratios (ORs) for cirrhosis (OR = 12) and HCC (OR = 29). However, only approximately 0.5% of individuals had a GRS in that range. A GRS of 4, which still conveyed large risks (cirrhosis, OR = 5.2; HCC, OR = 3.3), was found in approximately 5% of this population. Despite some exciting findings, the authors are appropriately cautious regarding widespread application of the GRS.
We wish to add our comments on why this GRS should not be applied to a general population and discuss the potential of the GRS for risk stratification among FLD patients. Consider a “general population” in which few individuals have GRS greater than or equal to 4 (approximately 6% for a population of European ancestry). A key statistic for assessing the usefulness of surveillance this “high-risk” group for cirrhosis or HCC is the positive predictive value (PPV), which is the probability that someone with GRS greater than or equal to 4 will develop cirrhosis or HCC. The PPV is a risk probability that captures not only the strengths of association of a risk factor with disease but also the outcome’s prevalence (or incidence). The PPV can be calculated as the sensitivity of the test multiplied by the probability of the outcome divided by the probability that the test is positive in the whole population.(6) Based on the data from Gellert-Kristensen et al., the PPV of the GRS-based test among the Danes is 0.008 for cirrhosis and 0.003 for HCC. In other words, among 1,000 persons with GRS greater than or equal to 4, only 8 will develop cirrhosis and 3 will develop HCC. In the U.K. population, the PPV for cirrhosis was 0.003, whereas the PPV for HCC was only 0.0008. Thus, despite strong associations between higher GRS and cirrhosis or HCC, these PPVs are too low to make the GRS useful in a “general population.” These low PPVs are partly explained by the low sensitivities of our “test,” which ranged from 13.8% to 20.4% for cirrhosis or HCC in these cohorts. Other key factors are imperfect specificity and the low incidence of cirrhosis and HCC in a general population. For both outcomes, specificity is approximately 94%, which may seem good, but even a test with “good” specificity yields many false-positives when applied to a rare disease. Thus, GRS should not be used for surveillance in the general population because few individuals with a high score would develop the disease.
PPV calculations highlight that clinical decisions should be based on the absolute risk of an outcome (i.e., probabilities) rather than relative risk measures (e.g., ORs). The clinically most relevant measure of absolute risk is the cumulative incidence—namely, the probability that a person with defined risk factors who is free of disease at the age of risk assessment will develop disease over a given future age interval. Absolute risk models that incorporate the GRS with other risk factors might be developed for target populations of FLD patients.
One could consider applying the GRS to FLD patients to help identify those who should be referred for hepatology specialist care. Current approaches advocate a sequential combination of serum tests and imaging, which improves specificity (>90%) for detecting advanced disease but has limited sensitivity (approximately 30% for FIB-4). Suppose the sensitivity and specificity of GRS are the same in FLD patients as in the general Danish population. Although the lifetime risk of cirrhosis for NAFLD or ALD is unknown, we assume for illustration purposes that it is 20% in a patient with ALD. Then, among those with GRS greater than or equal to 4, the PPV is 46%. Using a lifetime risk of cirrhosis in a patient with NAFLD of 4%, among those with GRS greater than or equal to 4, the PPV is 12%. This suggests that the GRS by itself is insufficient to identify FLD patients at high risk of progression. Perhaps better stratification among patients with FLD could be achieved by combining GRS with other predictors of progression, such as behavioral, clinical, or laboratory factors.
The GRS could also be relevant for HCC surveillance, which is currently advocated for FLD patients with established cirrhosis. Although HCC occurs in NAFLD patients without cirrhosis, its incidence is much too low (0.008% per year)(7) to justify routine screening, and applying the GRS to that population is unlikely to identify a group of patients among whom surveillance is warranted. However, Gellert-Kristensen et al. present potentially useful information on the relationship between GRS and cumulative incidence of developing HCC or dying following onset of cirrhosis after considering competing causes of mortality (figure 4). Individuals with cirrhosis and GRS greater than or equal to 5 had a high risk of HCC and death that could well affect clinical management; however, despite the very large study size, there were few such patients, and that result was not statistically precise. In contrast, Gellert-Kristensen et al. found HCC to be very rare among patients with cirrhosis with GRS less than or equal to 2. If these findings were confirmed, GRS, perhaps in conjunction with other markers of HCC risk, might help determine the appropriate intensity of HCC surveillance for individuals with cirrhosis due to FLD.
Before any model is implemented, its performance must be assessed (chapter 6).(6) An absolute risk model can be evaluated in terms of its ability to accurately predict the number of events arising in a population (calibration). Good calibration is essential for most risk model applications, including counseling of patients regarding their prognosis, designing preventive intervention trials, estimating absolute risk reduction in the population from preventive interventions, and allocating preventive resources.(8) Another popular measure of model performance is discriminatory accuracy, which measures how well separated the distribution of risk is in those who develop the outcome from the distribution in those who do not. Discrimination is often measured by the area under the receiver operator characteristic curve. Discriminatory accuracy is important for deciding which patients should be under surveillance for a rare outcome.(8)
Gellert-Kristensen et al. have shown that their GRS strongly predicts cirrhosis and HCC in a general population as well as HCC risk among individuals with cirrhosis due to FLD. The GRS highlights the importance of genetic variation in FLD progression; however, it would have limited utility as a sole predictor of risk. Risk models that combine GRS with other predictors of progression should be evaluated to determine the potential of GRS in clinical management and public health.
Acknowledgments
Financial Support: This study was supported by the Intramural Research Program of the National Institutes of Health (Division of Cancer Epidemiology and Genetics, National Cancer Institute; and National Institute of Diabetes and Digestive and Kidney Diseases). The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. government.
Abbreviations:
- ALD
 alcohol-associated liver disease
- FLD
 fatty liver disease
- GRS
 genetic risk score
- HCC
 hepatocellular carcinoma
- NAFLD
 nonalcoholic fatty liver disease
- OR
 odds ratio
- PPV
 positive predictive value
Footnotes
Potential conflict of interest: Nothing to report.
REFERENCES
- 1).Younossi ZM, Stepanova M, Afendy M, Fang Y, Younossi Y, Mir H, et al. Changes in the prevalence of the most common causes of chronic liver diseases in the United States from 1988 to 2008. Clin Gastroenterol Hepatol 2011;9:524–530.e1. [DOI] [PubMed] [Google Scholar]
 - 2).Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease: meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64:73–84. [DOI] [PubMed] [Google Scholar]
 - 3).Crabb DW, Im GY, Szabo G, Mellinger JL, Lucey MR. Diagnosis and treatment of alcohol-associated liver diseases: 2019 practice guidance from the American Association for the Study of Liver Diseases. Hepatology 2020;71:306–333. [DOI] [PubMed] [Google Scholar]
 - 4).Chalasani N, Younossi Z, Lavine JE, Charlto M, Cusi K, Rinella M, et al. The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. Hepatology 2018;67:328–357. [DOI] [PubMed] [Google Scholar]
 - 5).Gellert-Kristensen H, Richardson TG, Davey Smith G, Nordestgaard BG, Tybjærg-Hansen A, Stender S. Combined effect of PNPLA3, TM6SF2, and HSD17B13 variants on risk of cirrhosis and hepatocellular carcinoma in the general population. Hepatology. 2020. Sep;72(3):845–856. doi: 10.1002/hep.31238. [DOI] [PubMed] [Google Scholar]
 - 6).Pfeiffer RM, Gail MH. Assessment of risk model performance. In: Absolute Risk: Methods and Applications in Clinical Management and Public Health. Baton Rouge, LA: Chapman and Hall/CRC Taylor and Francis Group; 2017:73–95. [Google Scholar]
 - 7).Kanwal F, Kramer JR, Mapakshi S, Natarajan Y, Chayanupatkul M, Richardson PA, et al. Risk of hepatocellular cancer in patients with non-alcoholic fatty liver disease. Gastroenterology 2018;155:1828–1837.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
 - 8).Gail MH, Pfeiffer RM. Breast cancer risk model requirements for counseling, prevention, and screening. J Natl Cancer Inst 2018;110:994–1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
 
