Using two-step cluster analysis to classify inpatients with primary biliary cholangitis based on autoantibodies: A real-world retrospective study of 537 patients in China

Abstract

Background: A variety of autoantibodies have been detected in primary biliary cholangitis (PBC), while the presence of autoantibody clusters and their clinical significance have not been fully understood. We aimed at defining autoantibody clusters and to better understand the clinical features and prognosis of PBC patients based on autoantibody clusters under real-world conditions.

Methods: We retrospectively analyzed 788 inpatients with PBC evaluated between October 2008 and July 2019, and included 537 patients. Nineteen autoantibodies which were measured routinely were investigated for cluster analysis. Two-step clustering, Kaplan-Meier survival, and Cox regression analyses were used.

Results: Five clusters were defined. A cluster of antinuclear antibodies (ANA) and anti-gp210 positive patients were identified with a high rate of cirrhosis at baseline and low survival rate; a cluster of ANA, anti-centromere antibodies (ACA) and/or anti-CENP-B female dominant patients with older disease onset, low level of platelet count at baseline, high rate of hepatic decompensation, and low survival rate was also characterized; and another cluster of anti-mitochondrial antibodies (AMA) and/or AMA-M2, anti-Ro52 and a high rate of anti-gp210 positive patients were identified with a high proportion of male patients and low survival rate. A subgroup of patients with anti-SSA and/or anti-SSB coexists with SjS was also identified; patients with only AMA and/or AMA-M2-positive with a benign clinical outcome and relatively high complication of non-alcoholic fatty liver disease (NAFLD) were also identified. Only anti-gp210 was considered as a significant predictor for poor outcomes especially in patients with cirrhosis.

Conclusion: Clustering methods allow the identification of distinct autoantibody profiles of PBC that form clinical subsets and can be useful for personalized approaches to diagnosis, clinical management, and the prediction of clinical outcomes. Anti-gp210 was the strongest predictive factor for poor outcomes especially in PBC patients with cirrhosis under real-world conditions.

Keywords: autoantibody; primary biliary cholangitis; real-world study; retrospective study; two-step cluster analysis.

Figure 1

(A) Flowchart for selection of patients with PBC and follow-up of the study cohort stratified by autoantibody clusters. (B) Main characteristics of the five clusters (clusters 1-5) of patients with PBC.

Figure 2

(A) Autoantibody clusters of patients with PBC in the study. Heatmap shows the Z Scores of the frequencies for each autoantibody by cluster. The scale on the right denotes Z Scores of antibody frequencies from grey (-2.0, low) to red (+2.0, high). Heatmap rows corresponding to clusters 1, 2, 3, 4 and 5 are indicated. (B) Radar plots shows the Z Scores of the frequencies for each autoantibody by cluster. For a given circle each radius represents Z Scores of each autoantibody frequency. Points at the center represent -2.0, whereas points at the perimeter represent +2.0.

Figure 3

(A) Kaplan-Meier survival curves for the comparisons of transplant-free survival rates among the five clusters of patients with PBC. (B) Forest plot showing hazard ratios of liver-related death or liver transplantation rates and 95% confidence intervals for the five clusters of patients with PBC. Black line perpendicular to the horizontal axis shows the hazard ratio for the reference group of cluster 1. The horizontal broken line indicates the diving line for hazard ratios of liver-related death or liver transplantation rates before and after adjusted for age and sex. Colors represent the different clusters as indicated in (A, C) Kaplan-Meier survival curves for the comparisons of adverse outcome-free survival rates among the five clusters of patients with PBC. (D) Forest plot showing hazard ratios of adverse outcome rates and 95% confidence intervals for the five clusters of patients with PBC. Black line perpendicular to the horizontal axis shows the hazard ratio for the reference group of cluster 1. The horizontal broken line indicates the diving line for hazard ratios of adverse outcome rates before and after adjusted for age and sex. Colors represent the different clusters as indicated in (A).

Figure 4

(A) Kaplan-Meier survival curves for the comparison of transplant-free survival rates between anti-gp210 positive PBC patients and anti-gp210 negative PBC patients. (B) Kaplan-Meier survival curves for the comparison of adverse outcome-free survival rates between anti-gp210 positive PBC patients and anti-gp210 negative PBC patients. (C) Kaplan-Meier survival curves for the comparison of transplant-free survival rates between anti-gp210 positive PBC patients with cirrhosis and anti-gp210 negative PBC patients with cirrhosis at baseline. (D) Kaplan-Meier survival curves for the comparison of adverse outcome-free survival rates between anti-gp210 positive PBC patients with cirrhosis and anti-gp210 negative PBC patients with cirrhosis at baseline. (E) Kaplan-Meier survival curves for the comparison of transplant-free survival rates between anti-gp210 positive PBC patients without cirrhosis and anti-gp210 negative PBC patients without cirrhosis at baseline. (F) Kaplan-Meier survival curves for the comparison of adverse outcome-free survival rates between anti-gp210 positive PBC patients without cirrhosis and anti-gp210 negative PBC patients without cirrhosis at baseline.