Novel metabolic phenotypes for extrahepatic complication of nonalcoholic fatty liver disease

Abstract

Background and aims: Phenotypic heterogeneity among patients with NAFLD is poorly understood. We aim to identify clinically important phenotypes within NAFLD patients and assess the long-term outcomes among different phenotypes.

Methods: We analyzed the clinical data of 2311 participants from the Third National Health and Nutrition Examination Survey (NHANES III) and their linked mortality data through December 2019. NAFLD was diagnosed by ultrasonographic evidence of hepatic steatosis without other liver diseases and excess alcohol use. A 2-stage cluster analysis was applied to identify clinical phenotypes. We used Cox proportional hazard models to explore all-cause and cause-specific mortality between clusters.

Results: We identified 3 NAFLD phenotypes. Cluster 1 was characterized by young female patients with better metabolic profiles and lower prevalence of comorbidities; Cluster 2 by obese females with significant insulin resistance, diabetes, inflammation, and advanced fibrosis and Cluster 3 by male patients with hypertension, atherogenic dyslipidemia, and liver and kidney damage. In a median follow-up of 26 years, 989 (42.8%) all-cause mortality occurred. Cluster 1 patients presented the best prognosis, whereas Cluster 2 and 3 had higher risks of all-cause (Cluster 2-adjusted HR: 1.48, 95% CI: 1.16-1.90; Cluster 3-adjusted HR: 1.29, 95% CI: 1.01-1.64) and cardiovascular (Cluster 2-adjusted HR: 2.01, 95% CI: 1.18-3.44; Cluster 3-adjusted HR: 1.75, 95% CI: 1.03-2.97) mortality.

Conclusions: Three phenotypically distinct and clinically meaningful NAFLD subgroups have been identified with different characteristics of metabolic profiles. This study reveals the substantial disease heterogeneity that exists among NAFLD patients and underscores the need for granular assessments to define phenotypes and improve clinical practice.

FIGURE 1

The bar plot determines the optimum number of clusters (A) and dendrogram of the final hierarchical clustering model (B). Wald’s minimum-variance hierarchical clustering method and the bottom-up approach were used. All subjects were clustered into a single final group. At each generation of clusters, samples were merged into larger clusters to minimize the within-cluster sum of squares or maximize the between-cluster sum of squares. With successive clustering, 3 groups became obvious. Abbreviations: NHANES indicates National Health and Nutrition Examination Surveys.

FIGURE 2

Bar plot of the cluster profiles using clustering variables. The Z score of the variables has been used to standardize each variable. Values above 0.25 indicate those variables able to phenotype the 3 clusters. Abbreviations: ALT indicates alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CRP, C-reactive protein; DBP, diastolic blood pressure; HbA1c, glycohemoglobin; HDL-C high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment-insulin resistance; SBP, systolic blood pressure.

FIGURE 3

Kaplan-Meier survival curves for all-cause mortality of the 3 clusters of NAFLD patients. Pairwise Log-Rank test: Cluster 1 versus Cluster 2, p<0.001; Cluster 1 versus Cluster 3, p<0.001; Cluster 2 versus Cluster 3, p=0.26.