Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Abstract

Background: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.

Methods and findings: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one.

Conclusions: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.

Trial registration: ClinicalTrials.gov NCT03814915.

Fig 1. Pearson pairwise correlation matrix of clinical variables (data are inverse normal transformed) in the cohort combining participants with and without diabetes in IMI DIRECT (n = 1,049).

The magnitude and direction of the correlation are reflected by the size (larger is stronger) and color (red is positive and blue is negative) of the circles, respectively. ActGLP1min0, concentration of fasting active GLP-1 in plasma; ALT, alanine transaminase; AST, aspartate transaminase; AST_ALT, AST to ALT ratio; BasalISR, insulin secretion at the beginning of the oral glucose tolerance test/mixed-meal tolerance test; BMI, body mass index; CHOI, total daily intake of dietary carbohydrates; Chol, total cholesterol; Clins, mean insulin clearance during the oral glucose tolerance test/mixed-meal tolerance test, calculated as (mean insulin secretion)/(mean insulin concentration); Clinsb, insulin clearance calculated from basal values as (insulin secretion)/(insulin concentration); DBP, mean diastolic blood pressure; FatI, total daily intake of dietary fats; FLI, fatty liver index; FibreI, total daily intake of dietary Association of Official Analytical Chemists (AOAC) fiber; GGTP, gamma-glutamyl transpeptidase; Glucagonmin0, fasting glucagon concentration; Glucose, fasting glucose from venous plasma samples; GlucoseSens, glucose sensitivity, slope of the dose–response relating insulin secretion to glucose concentration; HbA1c, hemoglobin A1C; HDL, fasting high-density lipoprotein cholesterol; IncGLP1min60, 1-hour GLP-1 increment; IncGlucagonmin60, 1-hour glucagon increment; Insulin, fasting insulin from venous plasma samples; LDL, fasting low-density lipoprotein cholesterol; Matsuda, insulin sensitivity index according to the method of Matsuda et al. [23]; MeanGlucose, mean glucose during the oral glucose tolerance test/mixed-meal tolerance test; MeanInsulin, mean insulin during the oral glucose tolerance test/mixed-meal tolerance test; MUFatI, daily intake of dietary monounsaturated fats; OGIS, oral glucose insulin sensitivity index according to the method of Mari et al. [24]; PA_intensity_0_48f, number of values in high-pass-filtered vector magnitude physical activity at ≥0 and ≤48; PA_intensity_154_389f, number of values in high-pass-filtered vector magnitude physical activity at ≥154 and ≤389; PA_intensity_389_9999f, number of values in high-pass-filtered vector magnitude physical activity at ≥389 and ≤9,999; PA_intensity_48_154f, number of values in high-pass-filtered vector magnitude physical activity at ≥48 and ≤154; PA_intensity_mean, mean high-pass-filtered vector magnitude physical activity intensity; PFR, potentiation factor ratio; ProteinI, total daily intake of dietary proteins; PUFatI, daily intake of dietary polyunsaturated fats; RateSens, rate sensitivity (parameter characterizing early insulin secretion); SatFatI, daily intake of dietary saturated fats; SBP, mean systolic blood pressure; Stumvoll, insulin sensitivity index according to the method of Stumvoll et al. [25]; SugarI, total daily intake of dietary; TEI, total daily energy intake based on validated multi-pass food habit questionnaire; TG, fasting triglycerides; TotalISR, integral of insulin secretion during the whole oral glucose tolerance test/mixed-meal tolerance test; TotGLP1min0, concentration of fasting total GLP-1 in plasma; TwoGlucose, 2-hour glucose after oral glucose tolerance test/mixed-meal tolerance test; TwoInsulin, 2-hour insulin; Waist_Hip, waist to hip ratio.

Fig 2. Overview of the different stages involved in data processing and model training.

Data sources: clinical (C), genetic (G), transcriptomic (T), exploratory proteomic (E-P), targeted proteomic (T-P), targeted metabolomic (T-M), and untargeted metabolomic (U-M). The green and blue dashed boxes illustrate the feature selection step, the details of which can be found in S5 Fig. ROCAUC, receiver operating characteristic area under the curve.

Fig 3. Receiver operating characteristic area under the curve (ROCAUC) with 95% confidence interval (error bars) for clinical models 1–3, fatty liver index (FLI), hepatic steatosis index (HSI), and non-alcoholic fatty liver disease liver fat score (NAFLD-LFS) in the IMI DIRECT cohorts.

Model 1 includes 6 non-serological input variables: waist circumference, body mass index(BMI), mean systolic blood pressure, mean diastolic blood pressure, alcohol consumption, and diabetes status. Model 2 includes 8 input variables: waist circumference, BMI, fasting triglycerides (TG), alanine transaminase (ALT), aspartate transaminase (AST), fasting glucose (or hemoglobin A1C if fasting glucose is not available), alcohol consumption, and diabetes status. Model 3 includes 9 variables: waist circumference, BMI, TG, ALT, AST, fasting glucose, fasting insulin, alcohol consumption, and diabetes status. The FLI uses TG, waist circumference, BMI, and gamma-glutamyl transpeptidase. NAFLD-FLS was calculated using fasting insulin, AST, ALT, type 2 diabetes (T2D), and metabolic syndrome defined according to the International Diabetes Federation. The HSI uses BMI, sex, T2D diagnosis (yes/no), and the ratio of ALT to AST.

Fig 4. Measurements of sensitivity, specificity, F1 (a score considering sensitivity and precision combined), and balanced accuracy at different cutoffs for model 3 in the diabetes, non-diabetes, and combined cohorts of IMI-DIRECT.

The measurements are calculated by defining the predicted probabilities of fatty liver equal to or above these cutoffs as fatty liver, and below as non-fatty liver. Model 3 includes 9 variables: waist circumference, body mass index, fasting triglycerides, alanine transaminase, aspartate transaminase, fasting glucose, fasting insulin, alcohol consumption, and diabetes status.

Fig 5. Receiver operating characteristic area under the curve (ROCAUC) with 95% confidence interval for the clinical model and the omics separately or in combination with the clinical model in the IMI DIRECT combined cohort.

Clinical (C), model 4, with the 22 selected clinical variables. Genetic (G), model 5, with 23 SNPs. C+G, model 6, with clinical plus genetic variables. Transcriptomic (T), model 7, with 93 protein-coding genes. T+C, model 8, with transcriptomic plus clinical variables. Proteomic (P), model 9, with 22 proteins from exploratory proteomics. P+C, model 10, with proteomic plus clinical variables. Metabolomic (M), model 11, with 25 metabolites from targeted metabolomics. M+C, model 12, with metabolomic plus clinical variables. G+T+M+P, model 13, with all omics together. C+G+T+M+P, model 14, with all the omics combined with the clinical model.

Fig 6. Variable importance for the advanced model 14 with 185 omics and clinical input variables (clinical = 22, genetic = 23, transcriptomic = 93, exploratory proteomic = 22, and targeted metabolomic = 25).

The y-axis shows the top 20 predictors in the model. The x-axis shows the variable importance calculated, via a permutation accuracy importance measure using random forest analysis, as the difference in prediction accuracy before and after the permutation for each variable scaled by the standard error. ALT, alanine transaminase; AST, aspartate transaminase; BasalISR, insulin secretion at the beginning of the oral glucose tolerance test/mixed-meal tolerance test; BMI, body mass index; Clins, mean insulin clearance during the oral glucose tolerance test/mixed-meal tolerance test calculated as (mean insulin secretion)/(mean insulin concentration); FLT3, fetal liver tyrosine kinase-3; Insulin, fasting insulin from venous plasma samples; MYLIP, myosin regulatory light chain interacting protein; OGIS, oral glucose insulin sensitivity index according to the method of Mari et al. [24]; TG, fasting triglycerides; TotGLP1min0, concentration of fasting total GLP-1 in plasma; TwoInsulin, 2-hour insulin after oral glucose tolerance test/mixed meal tolerance test.