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Using two-step cluster analysis to classify inpatients with primary biliary cholangitis based on autoantibodies: A
Dan-Tong Zhao1, Hui-Ping Yan1,2, Hui-Yu Liao2
1Clinical Laboratory Center and Clinical Research Center for Autoimmune Liver Disease, Beijing You'An Hospital, Capital Medical University, Beijing, China.
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.
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