Epidemiology of Hepatic Steatosis at a Tertiary Care Center: An MRI-based Analysis

Tyler J Fraum; Daniel R Ludwig; Scott Kilian; William A Curtis; Thomas K Pilgram; Claude B Sirlin; Kathryn J Fowler

doi:10.1016/j.acra.2017.10.002

Epidemiology of Hepatic Steatosis at a Tertiary Care Center: An MRI-based Analysis

Acad Radiol. 2018 Mar;25(3):317-327. doi: 10.1016/j.acra.2017.10.002. Epub 2017 Dec 2.

Authors

Tyler J Fraum¹, Daniel R Ludwig², Scott Kilian³, William A Curtis², Thomas K Pilgram², Claude B Sirlin⁴, Kathryn J Fowler²

Affiliations

¹ Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, Campus Box 8131, Saint Louis, MO 63110. Electronic address: tylerjfraum@wustl.edu.
² Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, Campus Box 8131, Saint Louis, MO 63110.
³ Southern Illinois University School of Medicine, Springfield, Illinois.
⁴ Liver Imaging Group, Department of Radiology, San Diego School of Medicine, University of California, La Jolla, California.

PMID: 29199057
DOI: 10.1016/j.acra.2017.10.002

Abstract

Rationale and objectives: Little is known about the frequency and risk factors of hepatic steatosis in the tertiary care setting. Such knowledge is essential to clinicians making decisions about testing for this condition. Thus, our aim was to describe the epidemiology of hepatic steatosis, as captured by magnetic resonance imaging (MRI), at a tertiary care center.

Materials and methods: A near-consecutive cohort of 1006 adult patients underwent standard-of-care liver MRIs. Images were retrospectively processed to derive proton density fat fraction (PDFF) maps. Data from three spatially distinct regions of interest (ROIs) were aggregated to derive overall hepatic PDFF values. Demographic, anthropometric, clinical, and laboratory variables were included in a multivariate analysis to determine predictors of hepatic steatosis grades (based on established PDFF cutoffs). Hepatic steatosis grades derived from single vs aggregated ROIs were compared.

Results: Hepatic steatosis was observed in 25% of patients (19% grade 1; 3% grade 2; 3% grade 3). Controlling for all other variables, the odds of hepatic steatosis increased by 7%-9% (P <.001) for each whole point increase in body mass index (BMI), whereas elevated serum bilirubin was associated with lower odds of hepatic steatosis (P = .002). Race, diabetes mellitus, dyslipidemia, and metabolic syndrome were not independently predictive of hepatic steatosis when controlling for other variables (eg, BMI). Employing single ROIs (rather than three aggregated ROIs) resulted in incorrect steatosis grading in up to 8.0% of patients.

Conclusion: Many adult patients undergoing liver MRI at a tertiary care center have hepatic steatosis, with larger BMIs as the only independent predictor of higher grades. This information can be used by clinicians at such centers to make evidence-based decisions about when to test for hepatic steatosis in their patients.

Keywords: Hepatic steatosis; fat quantification; hepatic siderosis; iron quantification; proton density fat fraction (PDFF).

MeSH terms

Body Mass Index
Fatty Liver / diagnostic imaging*
Fatty Liver / epidemiology*
Female
Humans
Liver Function Tests
Magnetic Resonance Imaging
Male
Middle Aged
Multivariate Analysis
Retrospective Studies
Risk Factors
Tertiary Care Centers