Predicting NAFLD prevalence in the United States using National Health and Nutrition Examination Survey 2017-2018 transient elastography data and application of machine learning

Abstract

This cohort analysis investigated the prevalence of nonalcoholic fatty liver disease (NAFLD) and NAFLD with fibrosis at different stages, associated clinical characteristics, and comorbidities in the general United States population and a subpopulation with type 2 diabetes mellitus (T2DM), using the National Health and Nutrition Examination Survey (NHANES) database (2017-2018). Machine learning was explored to predict NAFLD identified by transient elastography (FibroScan^® ). Adults ≥20 years of age with valid transient elastography measurements were included; those with high alcohol consumption, viral hepatitis, or human immunodeficiency virus were excluded. Controlled attenuation parameter ≥302 dB/m using Youden's index defined NAFLD; vibration-controlled transient elastography liver stiffness cutoffs were ≤8.2, ≤9.7, ≤13.6, and >13.6 kPa for F0-F1, F2, F3, and F4, respectively. Predictive modeling, using six different machine-learning approaches with demographic and clinical data from NHANES, was applied. Age-adjusted prevalence of NAFLD and of NAFLD with F0-F1 and F2-F4 fibrosis was 25.3%, 18.9%, and 4.4%, respectively, in the overall population and 54.6%, 32.6%, and 18.3% in those with T2DM. The highest prevalence was among Mexican American participants. Test performance for all six machine-learning models was similar (area under the receiver operating characteristic curve, 0.79-0.84). Machine learning using logistic regression identified male sex, hemoglobin A1c, age, and body mass index among significant predictors of NAFLD (P ≤ 0.01). Conclusion: Data show a high prevalence of NAFLD with significant fibrosis (≥F2) in the general United States population, with greater prevalence in participants with T2DM. Using readily available, standard demographic and clinical data, machine-learning models could identify subjects with NAFLD across large data sets.

FIGURE 1

Participant disposition. ^aComplete transient elastography (FibroScan^®) examination was defined as a fasting time of ≥3 hours, ≥10 complete stiffness (E) measures, and a liver stiffness interquartile range/median E < 30%. ^bHeavy drinker was defined as drinking an average of ≥20 g/day and ≥30 g/day for women or men, respectively, based on the NHANES alcohol use survey. ^cNAFLD was defined as CAP ≥302 and VCTE ≥8.2, including cryptogenic cirrhosis (CAP <302, VCTE >13.6), borderline steatosis (CAP 274–302, VCTE 8.2–13.6), and control (CAP <274, VCTE 8.2–13.6). ^dNon‐NAFLD was defined as simple steatosis, CAP <302, and VCTE <8.2. Abbreviations: CAP, controlled attenuation parameter; E, ≥10 complete stiffness measures; HIV, human immunodeficiency virus; NAFLD, nonalcoholic fatty liver disease; NHANES, National Health and Nutrition Examination Survey; T2DM, type 2 diabetes mellitus; VCTE, vibration‐controlled transient elastography

FIGURE 2

Age‐adjusted prevalence of NAFLD and fibrosis. (A) NAFLD and fibrosis stages in the overall population and (B) participants with T2DM. (C) Fibrosis among participants with NAFLD in the overall population and (D) participants with T2DM. (E) NAFLD by ethnicity. Abbreviations: CI, confidence interval; NAFLD, nonalcoholic fatty liver disease; T2DM, type 2 diabetes mellitus

FIGURE 3

Test performance by AUROC for the six machine‐learning methods. Abbreviations: AUC, area under the curve; AUROC, area under the receiver operating characteristic curve; Ctree, classification tree; ElasticNet, elastic network; LogReg, logistic regression; NeuralNet, neural network; RF, random forest; SVM, support vector machine