Predialysis trajectories of estimated GFR and concurrent trends of Chronic Kidney Disease-relevant biomarkers

Hsiao-Mei Tsao; Tai-Shuan Lai; Yu-Hsiang Chou; Shuei-Liong Lin; Yung-Ming Chen

doi:10.1177/20406223231177291

Predialysis trajectories of estimated GFR and concurrent trends of Chronic Kidney Disease-relevant biomarkers

Ther Adv Chronic Dis. 2023 Jun 6:14:20406223231177291. doi: 10.1177/20406223231177291. eCollection 2023.

Authors

Hsiao-Mei Tsao^{1

2}, Tai-Shuan Lai^{3

2}, Yu-Hsiang Chou^{1

2}, Shuei-Liong Lin^{1

4}, Yung-Ming Chen^{2

5}

Affiliations

¹ Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
² Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
³ Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital, No.7, Chung-Shan S. Rd, Taipei 100225.
⁴ Graduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, Taiwan.
⁵ Department of Internal Medicine, National Taiwan University Hospital Bei-Hu Branch, Taipei, Taiwan.

Abstract

Background: The glomerular filtration rate (GFR) decline varies in patients with advanced chronic kidney disease (CKD), and the concurrent changes in CKD-related biomarkers are unclear.

Objectives: This study aimed to examine the changes in CKD-related biomarkers along with the kidney function decline in various GFR trajectory groups.

Design: This study was a longitudinal cohort study originated from the pre-end-stage renal disease (pre-ESRD) care program in a single tertiary center between 2006 and 2019.

Methods: We adopted a group-based trajectory model to categorize CKD patients into three trajectories according to estimated glomerular filtration rate (eGFR) changes. A repeated-measures linear mixed model was used to estimate the concurrent biomarker trends in a 2-year period before dialysis and to examine the differences among trajectory groups. A total of 15 biomarkers were analyzed, including urine protein, serum uric acid, albumin, lipid, electrolytes, and hematologic markers.

Results: Using longitudinal data from 2 years before dialysis initiation, 1758 CKD patients were included. We identified three distinct eGFR trajectories: persistently low eGFR levels, progressive loss of eGFR, and accelerated loss of eGFR. Eight of the 15 biomarkers showed distinct patterns among the trajectory groups. Compared with the group with persistently low eGFR values, the other two groups were associated with a more rapid increase in the blood urea nitrogen (BUN) level and urine protein-creatinine ratio (UPCR), especially in the year before dialysis initiation, and a more rapid decline in hemoglobin and platelet counts. A rapid eGFR decline was associated with lower levels of albumin and potassium, and higher levels of mean corpuscular hemoglobin concentration (MCHC) and white blood cell (WBC). The albumin level in the group with an accelerated loss of eGFR was below the normal range.

Conclusion: Using longitudinal data, we delineated the changes in CKD biomarkers with disease progression. The results provide information to clinicians and clues to elucidate the mechanism of CKD progression.

Keywords: biomarker; chronic kidney disease; longitudinal data; trajectory.