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Correlation between Blood Uric Acid Fluctuation and Prognosis in Patients with Chronic Kidney Disease Combined with
Shuang Cai1, Jie Yuan2, Zhipeng Gao2
1Department of General Practice, The Second Affiliated Hospital of Dalian Medical University of China, Dalian, China.
Introduction:
In clinical practice, we observed significant fluctuations in serum uric acid (SUA) levels among patients with hyperuricemia (HUA). However, few studies have explored whether SUA variability in HUA patients with chronic kidney disease (CKD) is associated with CKD progression, all-cause mortality, or cardiovascular mortality. To address this gap, we conducted a multicenter real-world study to investigate these potential associations.
Methods:
Using the China Kidney Disease Big Data Collaboration Network, we included 51,297 HUA and CKD patients from 32 medical centers between October 1, 2012, and October 1, 2023, and calculated the variability of SUA over three consecutive months. The coefficient of variation was used as the exposure variable, and the population was divided into four groups based on quartiles. Kidney disease outcomes (eGFR decline ≥50% or overall eGFR decline <15 mL/min/1.73 m2), all-cause mortality, and cardiovascular mortality risk, specifically major adverse cardiovascular events (MACEs) (acute myocardial infarction, ischemic stroke, or cardiovascular death), were used as outcome variables, with missing values addressed through multiple imputation. Cox proportional hazards models were employed to calculate the hazard ratios (HRs) and 95% confidence intervals for the association between SUA variability and CKD progression, all-cause mortality risk, and MACEs over different follow-up periods.
Results:
The group with the highest variability tended to be older and more often male, had lower BMI, SBP, DBP, RBC, Hb, and serum sodium levels, and were more likely to be on medication, with higher WBC, hsCRP, drugs, and cardiovascular death. During follow-ups of 90 days and 6 months, the population with the highest SUA variability was associated with CKD progression, with HRs of 1.924 and 1.584, respectively, compared to the lowest variability group. After 10 years of follow-up, the population with the highest SUA variability was associated with all-cause mortality risk, with an HR of 1.783 compared to the lowest variability group. There was no significant association between the highest SUA variability and MACEs after 5 and 10 years of follow-up, but higher blood uric acid variability was associated with cardiovascular death. In subgroup analyses, SUA fluctuations in the northeastern population and patients treated with sodium bicarbonate were linked to a higher risk of all-cause mortality. During the 24-month follow-up period, the risk of MACE results due to SUA fluctuations was not significantly associated with the population.
Conclusion:
(1) With follow-up over different time periods, bigger SUA fluctuations are linked to higher risks of CKD progression, all-cause mortality, and cardiovascular death; as follow-up time increases, the correlation between the highest SUA variability and CKD progression risk gradually decreases, which may relate to the increasing confounding factors as CKD progresses over time. (2) In the northeastern population of China and among patients treated with sodium bicarbonate, greater SUA fluctuations are associated with higher all-cause mortality risk. During the 24-month follow-up period, the risk of MACE results due to SUA fluctuations was not significantly associated with the population; SUA fluctuations in populations with tumors, diabetes, hyperkalemia, metabolic acidosis, gender, and those treated with febuxostat do not show a significant link to all-cause mortality risk.
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