The ratio of uric acid to high-density lipoprotein cholesterol (UHR) was related to the risk of chronic kidney disease (CKD), we aimed to investigate the association of cumulative UHR (cumUHR) with incidence and progression of CKD.
Our study included a total of 49,913 participants (mean age 52.57 years, 77% males) from the Kailuan Study conducted between 2006 and 2018. Participants who completed three consecutive physical examinations were included. Cumulative UHR (cumUHR) was computed as the summed average UHR between two consecutive physical examinations, multiplied by the time between the two examinations. Participants were then categorized into four groups based on cumUHR quartiles. Subsequently, participants were further divided into a CKD group and a non-CKD group. The associations between cumUHR and CKD and it’s progression were assessed by Cox proportional hazards regression models. The cumulative incidence of endpoint events was compared between the cumUHR groups using the log-rank test. The C-index, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to assess the predictive performance of cumUHR.
After a mean follow-up of 8.0 ± 1.7 years, there were 4843 cases of new-onset CKD, 2504 of low eGFR, and 2617 of proteinuria in the non-CKD group. Within the CKD group, there were 1952 cases of decline in eGFR category, 1465 of >30% decline in eGFR, and 2100 of increased proteinuria. In the non-CKD group, the adjusted hazard ratios (HRs) and confidence intervals (CIs) in the fourth quartile were 1.484 (1.362–1.617), 1.643 (1.457–1.852), and 1.324 (1.179–1.486) for new-onset CKD, low eGFR, and proteinuria, respectively. In the CKD group, the adjusted HRs in the fourth quartile were 1.337 (1.164–1.534), 1.428 (1.216–1.677), and 1.446 (1.267–1.651) for decline in eGFR category, >30% decline in eGFR, and increase in proteinuria, respectively. In addition, we separately added a single UHR measurement and cumUHR to the CKD base prediction model and the CKD progression base prediction model, and found that the models added cumUHR had the highest predictive value.
High cumUHR exposure was an independent risk factor for the incidence and progression of CKD, and it was a better predictor than a single UHR measurement.