The association between liver fibrosis scores and chronic kidney disease

Front Med (Lausanne). 2023 Jan 30:10:1046825. doi: 10.3389/fmed.2023.1046825. eCollection 2023.

Abstract

Purpose: This study aimed to clarify the relationship between liver fibrosis scores (Fibrosis-4, BARD score, and BAAT score) and chronic kidney disease (CKD).

Methods: We collected a range of data from 11,503 subjects (5,326 men and 6,177 women) from the rural regions of Northeastern China. Three liver fibrosis scores (LFSs) including fibrosis-4 (FIB-4), BARD score, and BAAT score were adopted. A logistic regression analysis was used to calculate odds ratios and the 95% confidence interval. A subgroup analysis showed the association between LFSs and CKD under different stratifications. Restricted cubic spline could further explore whether there is a linear relationship between LFSs and CKD. Finally, we used C-statistics, Net Reclassification Index (NRI), and Integrated Discrimination Improvement (IDI) to assess the effect of each LFS on CKD.

Results: Through the baseline characteristics, we observed that LFSs were higher in the CKD population than in non-CKD. The proportion of participants with CKD also increased with LFSs. In a multivariate logistic regression analysis, the ORs of CKD were 6.71 (4.45-10.13) in FIB-4, 1.88 (1.29-2.75) in the BAAT score, and 1.72 (1.28-2.31) in the BARD score by comparing the high level with the low level in each LFSs. Moreover, after adding LFSs to the original risk prediction model, which consisted of age, sex, drinking, smoking, diabetes, low-density lipoprotein cholesterol, total cholesterol, triglycerides, and mean waist circumference, we found the new models have higher C-statistics. Furthermore, NRI and IDI both indicate LFSs had a positive effect on the model.

Conclusions: Our study showed that LFSs are associated with CKD among middle-aged populations in rural areas of northeastern China.

Keywords: BAAT score; BARD score; chronic kidney disease; fibrosis-4; liver fibrosis scores.

Grants and funding

This research was supported by the following funding: The National Key Research and Development Program from the Ministry of Science and Technology of China (Project Grant # 2018YFC1312400 and Sub-project Grant # 2018YFC1312403) and the Science and Technology Program of Liaoning Province, China (Grant #2020JH1/10300002).