Key result
The FLI+ model outperformed the original FLI in predicting liver fat content, delivering a 47% lower mean absolute error and a 49% increased adjusted R2 statistic.
Why the study?
Does FLI+ improve the accuracy of predicting liver fat content compared to the original FLI?
Observational (n=28,796)
Yes
Does FLI+ improve the accuracy of predicting liver fat content compared to the original FLI?
Effect estimate: 47% lower MAE
Absolute Event Rate: 7.75% vs 14.6%
FLI+ provides a more accurate representation of liver fat content than the original FLI, potentially improving the diagnosis and stratification of hepatic steatosis.
May refine noninvasive liver fat assessment in cohorts; extends FLI validation but leaves open clinical outcome impact.
BACKGROUND: The fatty liver index (FLI) is frequently used as a non-invasive clinical marker for research, prognostic and diagnostic purposes. It is also used to stratify individuals with hepatic steatosis such as non-alcoholic fatty liver disease (NAFLD), and to detect the presence of type 2 diabetes or cardiovascular disease. The FLI is calculated using a combination of anthropometric and blood biochemical variables; however, it reportedly excludes 8.5-16.7% of individuals with NAFLD. Moreover, the FLI cannot quantitatively predict liver fat, which might otherwise render an improved diagnosis and assessment of fatty liver, particularly in longitudinal studies. We propose FLI+ using predictive regression modelling, an improved index reflecting liver fat content that integrates 12 routinely-measured variables, including the original FLI. METHODS AND FINDINGS: We evaluated FLI+ on a dataset from the UK Biobank containing 28,796 individual estimates of proton density fat fraction derived from magnetic resonance imaging across normal to severe levels and interpolated to align with the original FLI range. The results obtained for FLI+ outperform the original FLI by delivering a lower mean absolute error by approximately 47%, a lower standard deviation by approximately 20%, and an increased adjusted R2 statistic by approximately 49%, reflecting a more accurate representation of liver fat content. CONCLUSIONS: Our proposed model predicting FLI+ has the potential to improve diagnosis and provide a more accurate stratification than FLI between absent, mild, moderate and severe levels of hepatic steatosis.
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Asaturyan et al. (2022) conducted an observational in Hepatic steatosis (n=28,796). FLI+ (predictive regression model) vs. Original Fatty Liver Index (FLI) was evaluated on Mean absolute error (MAE) in predicting mapped proton density fat fraction (47% lower MAE). The FLI+ model outperformed the original FLI in predicting liver fat content, delivering a 47% lower mean absolute error and a 49% increased adjusted R2 statistic.
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