Predicting clinical outcomes in Nonalcoholic Fatty Liver Disease (NAFLD)

16 Feb 2022

As NAFLD has progressively become one of the most common causes of liver disease [Ref1], the interest in assessing the risk of decompensation and other liver-related events in these patients is currently a priority in hepatology. Non-invasive fibrosis scores that have already been discussed in previous interventions were evaluated for this purpose [Ref2-3]. In particular, the interest in non-invasive scores is high because it potentially allows clinicians to classify their patients according to their risk of developing complications of liver disease without the need to resort to other tools that are certainly more invasive and expensive, therefore allowing for resource optimization.

A study from Pennisi et al. analyzed the role of the fibrosis-4 (FIB- 4) index in assessing the risk of liver related events in patients with NAFLD [Ref4]. They enrolled 229 patients with histologic diagnosis of NAFLD or clinical diagnosis of advanced fibrosis/cirrhosis due to NAFLD and followed them prospectively for at least six months (with an actual median follow up time of 66.1 months). Performing a multivariate Cox regression analysis, the following clinical variables were independently associated with occurrence of liver-related events (i.e. occurrence of ascites and/or bleeding varices and/or encephalopathy and/or jaundice and/or hepatocellular carcinoma): age between 55 and 65 years (HR, 13.96; 95% CI, 2.9- 67.23; P=0.001) or older than 65 years (HR, 17.96; 95% CI, 3.66- 88.12; P<0.001); Platelets between 110,000 and 150,000/mm3 (HR, 6.89; 95% CI, 2.74- 17.35; P<0.001) or <110,000/mm3 (HR, 13.54; 95% CI, 5.53-33.13; P<0.001); albumin <4 g/L (HR, 1.5; 95% CI, 1.00- 3.78; P=0.04); low HDL cholesterol (HR, 1.88; 95% CI, 1.02-3.44; P=0.04); and genetic variables such as TM6SF2 rs58542926 CT/TT (HR, 1.94; 95% CI, 1.00- 3.77; P=0.04) and HSD17B13 rs72613567 T/TA (HR, 1.83; 95% CI, 1.07-3.43; P=0.04), interactions between PNPLA3 rs738409 and male sex (HR, 0.32; 95% CI, 0.10-0.98; P=0.04) and between PNPLA3 rs738409 and diabetes (HR, 5.16; 95% CI, 1.30- 20.41; P=0.01). The generated Cox regression model showed good diagnostic accuracy for predicting liver related events, with area under the curve (AUC)=0.87 at 1, 3, and 5 years. They consequently created a model, called  Genetic and Metabolic Staging (GEMS), to predict liver-related events in patients with NAFLD. GEMS is based on the variables described above and ranges 0-10. GEMS categorizes the risk of liver-related events in five classes: the rate of liver related events in patients with a GEMS score 0-5 is 1.1% at 1 and 3 years and 3.5% at 5 years, in patients with a GEMS score 5-6 is 6% at 1 year, 10% at 3 years and 19.5% at 5 years, in patients with a GEMS score 6-7 is 8.4% at 1 year, 34% at 3 years and 40% at 5 years, in patients with a GEMS score 7-8 is 12% at 1 year, 45.5% at 3 years and 74.8% at 5 years, while in patients with a GEMS score 8-10 is 38.6% at 1 year, 79.5% at 3 years and 89.8% at 5 years. GEMS was then validated using a UK cohort of 675 patients with severe liver disease. Despite the promising data, GEMS score is currently not in use in clinical practice and further studies are needed to confirm its validity on a larger scale. In particular, the feasibility of using the genetic mutations included in this score to be used in clinical practice, which must be as simple as possible to obtain, rapid and inexpensive, must also be assessed. Whatever the accuracy of this score and its feasibility, however, its creation is the sign of the need in the clinical setting, and in particular in patients with NAFLD, to identify non-invasive strategies for the optimization of management of these patients. It is therefore desirable to quickly identify suitable tools for this purpose, possibly based on accessible tools that can be easily used in daily clinical practice.


  1. Younossi Z, Anstee QM, Marietti M, et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2018;15:11–20.
  2. Angulo P, Bugianesi E, Bjornsson ES, et al. Simple noninvasive systems predict long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology. 2013;145:782–789.e4.30.
  3. Castera L, Friedrich-Rust M, Loomba R. Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease. Gastroenterology. 2019;156:1264–1281.e4.
  4. Pennisi G, Pipitone RM, Enea M et al. A Genetic and Metabolic Staging System for Predicting the Outcome of Nonalcoholic Fatty Liver Disease. Hepatol Commun. 2022 Feb 11. Epub ahead of print.