| Title: | Characterisation of liver fat in the UK Biobank cohort |
| Journal: | PLOS ONE |
| Published: | 27 Feb 2017 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/28241076/ |
| DOI: | https://doi.org/10.1371/journal.pone.0172921 |
| URL: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0172921&type=printable |
| Citations: | 119 (37 in last 2 years) as of 8 Aug 2024 |
Publication 4469
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Non-alcoholic fatty liver disease and the risk of progression to steatohepatitis, cirrhosis and hepatocellular carcinoma have been identified as major public health concerns. We have demonstrated the feasibility and potential value of measuring liver fat content by magnetic resonance imaging (MRI) in a large population in this study of 4,949 participants (aged 45-73 years) in the UK Biobank imaging enhancement. Despite requirements for only a single (≤3min) scan of each subject, liver fat was able to be measured as the MRI proton density fat fraction (PDFF) with an overall success rate of 96.4%. The overall hepatic fat distribution was centred between 1-2%, and was highly skewed towards higher fat content. The mean PDFF was 3.91%, and median 2.11%. Analysis of PDFF in conjunction with other data fields available from the UK Biobank Resource showed associations of increased liver fat with greater age, BMI, weight gain, high blood pressure and Type 2 diabetes. Subjects with BMI less than 25 kg/m2 had a low risk (5%) of high liver fat (PDFF > 5.5%), whereas in the higher BMI population (>30 kg/m2) the prevalence of high liver fat was approximately 1 in 3. These data suggest that population screening to identify people with high PDFF is possible and could be cost effective. MRI based PDFF is an effective method for this. Finally, although cross sectional, this study suggests the utility of the PDFF measurement within UK Biobank, particularly for applications to elucidating risk factors through associations with prospectively acquired data on clinical outcomes of liver diseases, including non-alcoholic fatty liver disease.</p>
11 Authors
- Henry R. Wilman
- Matt Kelly
- Steve Garratt
- Paul M. Matthews
- Matteo Milanesi
- Amy Herlihy
- Micheal Gyngell
- Stefan Neubauer
- Jimmy D. Bell
- Rajarshi Banerjee
- E. Louise Thomas
Enabling scientific discoveries that improve human health