Prediction of biochemical nonresolution in patients with chronic drug‐induced liver injury: A large multicenter study

Abstract

Background and Aims To clarify high‐risk factors and develop a nomogram model to predict biochemical resolution or biochemical nonresolution (BNR) in patients with chronic DILI. Approach and Results Retrospectively, 3655 of 5326 patients with chronic DILI were enrolled from nine participating hospitals, of whom 2866 underwent liver biopsy. All of these patients were followed up for over 1 year and their clinical characteristics were retrieved from electronic medical records. The endpoint was BNR, defined as alanine aminotransferase or aspartate aminotransferase >1.5× upper limit of normal or alkaline phosphatase >1.1× ULN, at 12 months from chronic DILI diagnosis. The noninvasive high‐risk factors for BNR identified by multivariable logistic regression were used to establish a nomogram, which was validated in an independent external cohort. Finally, 19.3% (707 of 3655) patients presented with BNR. Histologically, with the increase in liver inflammation grades and fibrosis stages, the proportion of BNR significantly increased. The risk of BNR was increased by 21.3‐fold in patients with significant inflammation compared to none or mild inflammation (p < 0.001). Biochemically, aspartate aminotransferase and total bilirubin, platelets, prothrombin time, sex, and age were associated with BNR and incorporated to construct a nomogram model (BNR‐6) with a concordance index of 0.824 (95% CI, 0.798–0.849), which was highly consistent with liver histology. These results were successfully validated both in the internal cohort and external cohort. Conclusions Significant liver inflammation is a robust predictor associated with biochemical nonresolution. The established BNR‐6 model provides an easy‐to‐use approach to assess the outcome of chronic DILI.

Mulberrin confers protection against hepatic fibrosis by Trim31/Nrf2 signaling

“…Inflammatory response in hepatocytes participates in HSCs activation and liver fibrosis [ 3 , 17 , 39 ], and thus was explored by the use of L02 cells and the isolated primary mouse hepatocytes ( Supplementary Fig. 4A ).…”

“…Inflammasome activation plays a key role in the progression of liver disease. NLRP3 is the most well-studied inflammasome and is a multiprotein cytoplasmic complex that consists of ASC and the effector molecule pro-Caspase-1 [ [15] , [16] , [17] ]. Under stimulation by pathogen-associated molecular patterns and damage-related molecular patterns, NLRP3 inflammasome activation mediates the cleavage of Capase-1, leading to the maturation and extracellular releases of IL-1β and IL-18 [ 43 , 44 ].…”

“…Therefore, blockade of inflammation in hepatocytes emerges as a novel therapeutic target to reduce liver inflammation and fibrosis in different types of hepatic diseases such as NASH [ 56 ]. NLRP3 inflammasome and inflammatory response in hepatocytes led to the HSC activation, which is then responsible for collagen deposition and fibrosis [ 16 , 17 , 46 , 47 ]. The profibrogenic effects of ROS are compounded by the fact that NOX4 up-regulation in hepatocytes results in the cell damage, further inducing the cascade of cellular events that lead to HSCs activation and cirrhosis [ 52 , 57 ].…”

“…Under stimuli conditions, NLRP3 assembles into a large cytoplasmic complex through recruiting apoptosis associated speck-like protein (ASC) and Caspase-1, thereafter, causing the cleavage of pro-IL-1β, which enables its maturation and releases from cells depending on NF-κB activation [ 15 ]. Increasing studies have demonstrated that excessive NLRP3 inflammasome activation plays an essential role in the regulation of liver inflammation and fibrosis [ 16 , 17 ]. Therefore, we hypothesized that TRIM31/NLRP3 signaling pathway might be involved in liver fibrosis.…”

Letter to the editor: Selection of appropriate statistical methods for prediction model

“…Wang et al elucidated the histologic and biochemical associations in chronic DILI from a large, retrospective, biopsy-based multicenter cohort study, and further developed a new noninvasive nomogram model. [1] Personalized predictive model and the following precision medicine efforts have improved health care effectiveness in recent years. Modern artificial intelligence practices have paved the path for modern precision medicine.…”