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Delineating Metabolic Dysfunction-Associated Steatotic Liver Disease and Comorbid Risk with Clinical, Blood and Metabolomic Parameters – A UK Biobank Analysis
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DOI：10.1016/j.jceh.2026.103537.png)
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Abstract
En 中文
Ascertaining risk of, and prognostication tools for, metabolic dysfunction-associated steatotic liver disease (MASLD) remain suboptimal. Using the UK Biobank, we investigated the role of multivariate supervised learning in predicting MASLD risk, and the presence of disease subgroups in stratifying the risk of comorbid outcomes.
Keywords:
NAFLD
MASLD
Learning models
Risk
Comorbidity
EHR
electronic health record
MASLD
metabolic dysfunction associated steatotic liver disease
MRI-PDFF
magnetic resonance imaging – proton density fat fraction
NAFLD
non-alcoholic fatty liver disease
NMR
nuclear magnetic resonance
SVM – LK
support vector machine – linear kernel
RF
random forest
PLS-DA
partial least square discriminant analysis
UKB
UK Biobank (UK standing for United Kingdom)
MVLM
multivariate learning model
FLI
Fatty Liver Index
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3.2
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152
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