Journal article
Predicting Non-Alcoholic Fatty Liver Disease for Adults Using Practical Clinical Measures: Evidence from the Multi-ethnic Study of Atherosclerosis
Journal of general internal medicine : JGIM, Vol.36(9), pp.2648-2655
09/2021
PMID: 33501527
Metrics
5 Record Views
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Abstract
Background
Many adults have risk factors for non-alcoholic fatty liver disease (NAFLD). Screening all adults with risk factors for NAFLD using imaging is not feasible.
Objective
To develop a practical scoring tool for predicting NAFLD using participant demographics, medical history, anthropometrics, and lab values.
Design
Cross-sectional.
Participants
Data came from 6194 white, African American, Hispanic, and Chinese American participants from the Multi-Ethnic Study of Atherosclerosis cohort, ages 45–85 years.
Main Measures
NAFLD was identified by liver computed tomography (≤ 40 Hounsfield units indicating > 30% hepatic steatosis) and data on 14 predictors was assessed for predicting NAFLD. Random forest variable importance was used to identify the minimum subset of variables required to achieve the highest predictive power. This subset was used to derive (n = 4132) and validate (n = 2063) a logistic regression–based score (NAFLD-MESA Index). A second NAFLD-Clinical Index excluding laboratory predictors was also developed.
Key Results
NAFLD prevalence was 6.2%. The model included eight predictors: age, sex, race/ethnicity, type 2 diabetes, smoking history, body mass index, gamma-glutamyltransferase (GGT), and triglycerides (TG). The NAFLD-Clinical Index model excluded GGT and TG. In the NAFLD-MESA model, the derivation set achieved an AUCNAFLD-MESA = 0.83 (95% CI, 0.81 to 0.86), and the validation set an AUCNAFLD-MESA = 0.80 (0.77 to 0.84). The NAFLD-Clinical Index model was AUCClinical = 0.78 [0.75 to 0.81] in the derivation set and AUCClinical = 0.76 [0.72 to 0.80] in the validation set (pBonferroni-adjusted < 0.01).
Conclusions
The two models are simple but highly predictive tools that can aid clinicians to identify individuals at high NAFLD risk who could benefit from imaging.
Details
- Title
- Predicting Non-Alcoholic Fatty Liver Disease for Adults Using Practical Clinical Measures: Evidence from the Multi-ethnic Study of Atherosclerosis
- Creators
- Luis A. Rodriguez - University of California, San FranciscoStephen C. Shiboski - University of California, San FranciscoPatrick T. Bradshaw - University of California, BerkeleyAlicia Fernandez - University of California, San FranciscoDavid Herrington - Wake Forest UniversityJingzhong Ding - Wake Forest UniversityRyan D. Bradley - University of California, San DiegoAlka M. Kanaya - University of California, San Francisco
- Publication Details
- Journal of general internal medicine : JGIM, Vol.36(9), pp.2648-2655
- Publisher
- Springer Nature
- Number of pages
- 8
- Grant note
- MESA was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC- 95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01- HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). L.A.R. was supported by the National Institute of Diabetes And Digestive And Kidney Diseases of the National Institutes of Health (NIH) under Award Number F31DK115029, and by a University of California Dissertation-Year Fellowship Award. A.F. was supported by NIH grant K24DK102057.
- Identifiers
- 991013036328902368
- Copyright
- © 2022 Springer Nature Switzerland AG. Part of Springer Nature.
- Academic Unit
- National Centre for Naturopathic Medicine
- Language
- English
- Resource Type
- Journal article