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Abstract Details
Predicting Non-Alcoholic Fatty Liver Disease for Adults Using Practical Clinical Measures: Evidence from the Multi-ethnic Study of Atherosclerosis
J Gen Intern Med. 2021 Sep;36(9):2648-2655. doi: 10.1007/s11606-020-06426-5.Epub 2021 Jan 26.
Luis A Rodriguez1, Stephen C Shiboski2, Patrick T Bradshaw3, Alicia Fernandez4, David Herrington5, Jingzhong Ding6, Ryan D Bradley7, Alka M Kanaya24
Author information
1Department of Epidemiology & Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Box 0560, San Francisco, CA, 94143, USA. Luis.Rodriguez@ucsf.edu.
2Department of Epidemiology & Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Box 0560, San Francisco, CA, 94143, USA.
3School of Public Health, Division of Epidemiology & Biostatistics, University of California, Berkeley, Berkeley, CA, USA.
4Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
5Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA.
6Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, NC, USA.
7School of Health Sciences, Department of Family Medicine and Public Health, University of California, San Diego, San Diego, USA.
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.