Machine learning prediction of hepatic steatosis using body composition parameters: A UK Biobank Study

Delbert Almerick T Boncan; Yan Yu; Miaoru Zhang; Jie Lian; Varut Vardhanabhuti

doi:10.1038/s41514-023-00127-z

Machine learning prediction of hepatic steatosis using body composition parameters: A UK Biobank Study

NPJ Aging. 2024 Jan 9;10(1):4. doi: 10.1038/s41514-023-00127-z.

Authors

Delbert Almerick T Boncan^{1

2}, Yan Yu¹, Miaoru Zhang³, Jie Lian³, Varut Vardhanabhuti^{4

5}

Affiliations

¹ Snowhill Science Ltd, Units 801-803, Level 8, Core C, Hong Kong SAR, China.
² School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.
³ Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
⁴ Snowhill Science Ltd, Units 801-803, Level 8, Core C, Hong Kong SAR, China. varv@hku.hk.
⁵ Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. varv@hku.hk.

Abstract

Non-alcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver disease worldwide, yet detection has remained largely based on surrogate serum biomarkers, elastography or biopsy. In this study, we used a total of 2959 participants from the UK biobank cohort and established the association of dual-energy X-ray absorptiometry (DXA)-derived body composition parameters and leveraged machine learning models to predict NAFLD. Hepatic steatosis reference was based on MRI-PDFF which has been extensively validated previously. We found several significant associations with traditional measurements such as abdominal obesity, as defined by waist-to-hip ratio (OR = 2.50 (male), 3.35 (female)), android-gynoid ratio (OR = 3.35 (male), 6.39 (female)) and waist circumference (OR = 1.79 (male), 3.80 (female)) with hepatic steatosis. Similarly, A Body Shape Index (Quantile 4 OR = 1.89 (male), 5.81 (female)), and for fat mass index, both overweight (OR = 6.93 (male), 2.83 (female)) and obese (OR = 14.12 (male), 5.32 (female)) categories were likewise significantly associated with hepatic steatosis. DXA parameters were shown to be highly associated such as visceral adipose tissue mass (OR = 8.37 (male), 19.03 (female)), trunk fat mass (OR = 8.64 (male), 25.69 (female)) and android fat mass (OR = 7.93 (male), 21.77 (female)) with NAFLD. We trained machine learning classifiers with logistic regression and two histogram-based gradient boosting ensembles for the prediction of hepatic steatosis using traditional body composition indices and DXA parameters which achieved reasonable performance (AUC = 0.83-0.87). Based on SHapley Additive exPlanations (SHAP) analysis, DXA parameters that had the largest contribution to the classifiers were the features predicted with significant association with NAFLD. Overall, this study underscores the potential utility of DXA as a practical and potentially opportunistic method for the screening of hepatic steatosis.