In this prospective multicenter cohort study, hemodialysis patients were recruited at the dialysis outpatient units of 15 medical institutions in Tokyo, Japan [16, 17]. Baseline visits for patient enrollment were conducted between May 1, 2011 and March 31, 2012, and enrolled patients were followed up to June 1, 2016. Patients were older than 20 years, had spent at least 3 months on dialysis therapy, and regularly received thrice-weekly hemodialysis (3–5 h/session). Patients with acute gastrointestinal bleeding, acute coronary syndrome, and liver dysfunction at baseline were excluded. The study protocol was reviewed and approved by the ethics committee of the Jikei Institutional Review Board at Jikei University School of Medicine (22-182 6359). This study was also approved by each participating institution’s review board. All study procedures were in accordance with the Declaration of Helsinki and its revisions. Written informed consent was obtained from all patients prior to inclusion in the study.
Age, body mass index (BMI), sex, dialysis vintage, primary illness leading to kidney dysfunction, and past medical history were extracted from medical records. Medication information (use of anti-hyperuricemic drugs including allopurinol and febuxostat, antiplatelet drugs, vitamin K antagonists, phosphate binders, vitamin D receptor agonists, cinacalcet, antihypertensive medications, and statins) was obtained from prescription records. Comorbidities and medications were determined by chart review and standardized interviews at baseline.
Blood samples were collected at study entry, before the hemodialysis session conducted after the longest inter-dialysis period. Routine biochemical measurements included serum creatinine, uric acid, sodium, potassium, phosphorus, calcium, serum albumin, blood urea nitrogen (BUN), alkaline phosphatase, hematocrit, intact parathyroid hormone (PTH), and C-reactive protein (CRP) levels. The delivered dialysis dose was measured by single pool Kt/V.
Clinical outcomes were prospectively recorded and coded, and blinded from clinical and biochemical data. This information was collected by study investigators. After review of the available information, the cause of death was classified as CVD, infectious disease, malignancy, or other. The primary outcome was all-cause mortality. In all analyses, follow-up was censored at loss to follow-up, renal transplantation, or the end of the study.
Non-normally distributed data are presented as medians (25th and 75th percentiles), and normally distributed data are summarized as means ± standard deviation (SD). Binary data are summarized as percentages. Differences between more than two groups were analyzed by analysis of variance or the Kruskal–Wallis test, as appropriate. Nominal variables were analyzed by the χ2 test. Patient characteristics were described by uric acid levels (quartiles). Serum uric acid levels were categorized by ABCG2 functions and use of anti-hyperuricemic drugs. To clarify the serum uric acid levels by ABCG2 functions, multiple regression analysis was used. Age, dialysis vintage, BMI, systolic blood pressure, diabetes mellitus, hemoglobin, albumin, BUN, creatinine, potassium, calcium, phosphorus, CRP, Kt/V, angiotensin covering enzyme inhibitors, angiotensin II receptor blockers, statins, antiplatelet drugs, and anti-hyperuricemic drugs were adopted as covariates.
Cox proportional hazard models were used to investigate the association between all-cause mortality and uric acid levels. Age, dialysis vintage, sex, diabetes mellitus, BMI, systolic blood pressure, hemoglobin, albumin, creatinine, potassium, calcium, phosphate, CRP, Kt/V, past history of CVD events, use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers, use of statins, use of vitamin D receptor agonists, use of antiplatelet drugs, and use of anti-hyperuricemic drugs were adopted as covariates. An adjusted restricted cubic spline curve with three knots was generated to show the non-linear association with all-cause mortality, and serum uric acid as a continuous variable was also examined using a fully adjusted model.
To investigate the association between ABCG2 function and all-cause mortality, Kaplan–Meier survival curves and log-rank tests were used, as well as a Cox proportional hazard model. Univariate and multivariate analyses are presented as [hazard ratio (HR); 95% confidence interval (CI)]. The following covariates were used for Cox proportional hazard models. Model I included age, dialysis vintage, sex, diabetes mellitus, BMI, systolic blood pressure, hemoglobin, albumin, creatinine, potassium, calcium, phosphate, CRP, Kt/V, past history of CVD, use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers, use of statins, use of vitamin D receptor agonists, and use of antiplatelet drugs. Model II included all covariates of Model I, uric acid, and use of anti-hyperuricemic drugs. Significance was set at P < 0.05. All statistical analyses were performed using STATA 13.0 (STATA Corp., College Station, TX, USA).
Genetic analysis and assessment of ABCG2 function
Genomic DNA was extracted from whole peripheral blood cells. Genotyping of ABCG2 dysfunctional variants (Q126X and Q141K) was performed using the TaqMan method (Life Technologies Corporation, Carlsbad, CA, USA) with a Light Cycler 480 (Roche Diagnostics, Mannheim, Germany), as previously described [11, 18]. Custom TaqMan assay probes were designed as follows: for Q126X, VIC-CCACTAATACTTACTTGTACCAC and FAM-CCACTAATACTTACTTATACCAC; and for Q141K, VIC-CTGCTGAGAACTGTAAGTT and FAM-CTGCTGAGAACTTTAAGTT.
To confirm their genotypes, DNA sequencing analysis was performed with the following primers: for Q126X, forward 5′-TGTACAATGAAAAGAGAAAGGTGAG-3′ and reverse 5′-CTGCCTTTTCACATAAGTGTC-3′; and for Q141K, forward 5′-ATGGAGTTAACTGTCATTTGC-3′ and reverse 5′-CACGTTCATATTATGTAACAAGCC-3′. PCR products were sequenced with the ABI PRISM 3700 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). We previously reported that Q126X is a nonfunctional variant, Q141K is a half-functional variant for urate excretion compared to the wild-type, and there was no simultaneous presence of the minor alleles of Q126X and Q141K in one haplotype [3, 5]. Thus, three haplotypes were defined as *1 (126Q and 141Q), *2 (126Q and 141 K), and *3 (126X and 141Q), and all patients could be divided into the following ABCG2 functional groups: full function (*1/*1), 3/4 function (mild dysfunction, *1/*2), 1/2 function (moderate dysfunction, *1/*3 or *2/*2), and ≤ 1/4 function (severe dysfunction, *2/*3 or *3/*3) .