Junk E, Tzivian L, Folkmane I, Folkmanis K, Jushinskis J, Strazda G, Folkmanis V, Kuzema V, Petersons A. Major adverse cardiovascular events and hyperuricemia as an effect-modifying factor in kidney transplant recipients. World J Transplant 2025; 15(3): 102287 [DOI: 10.5500/wjt.v15.i3.102287]
Corresponding Author of This Article
Inese Folkmane, MD, PhD, Professor, Centre of Nephrology, Pauls Stradiņš Clinical University Hospital, Pilsoņu 13, Riga LV-1002, Latvia. folkmane.inese@inbox.lv
Research Domain of This Article
Transplantation
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Elizabete Junk, Department of Internal Diseases, St. Bonifatius Hospital Lingen, Lingen 49808, Germany
Elizabete Junk, Lilian Tzivian, Inese Folkmane, Kristofs Folkmanis, Gunta Strazda, Valdis Folkmanis, Faculty of Medicine and Life Sciences, University of Latvia, Riga LV-1004, Latvia
Lilian Tzivian, Institute of Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University of Dusseldorf, Düsseldorf 40225, Germany
Inese Folkmane, Viktorija Kuzema, Centre of Nephrology, Pauls Stradiņš Clinical University Hospital, Riga LV-1002, Latvia
Kristofs Folkmanis, International Center for Robotic Urology, Kreisklinikum Siegen, Siegen 57076, North Rhine-Westphalia, Germany
Janis Jushinskis, Centre of Transplantation, Pauls Stradiņš Clinical University Hospital, Riga LV-1002, Latvia
Co-first authors: Elizabete Junk and Lilian Tzivian.
Author contributions: Junk E, Tzivian L, and Folkmane I were responsible for methodology, writing review and editing; Junk E and Tzivian L were responsible for formal analysis; Junk E, Tzivian L, and Folkmanis V were responsible for writing original draft preparation; Junk E and Folkmane I were responsible for conceptualization; Junk E, Folkmane I, Jushinskis J, Strazda G, and Kuzema V were responsible for investigation; Junk E and Folkmanis K were responsible for data curation; Tzivian L was responsible for software and validation; Folkmane I, Jushinskis J, Kuzema V, and Petersons A were responsible for resources; Folkmane I, Strazda G, and Petersons A were responsible for supervision; all of the authors read and approved the final version of the manuscript to be published.
Institutional review board statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Scientific Research Ethics Committee of the Institute of Cardiology and Regenerative Medicine of the University of Latvia (No. 5/2021).
Informed consent statement: Informed consent was obtained from all subjects involved in the study.
Conflict-of-interest statement: The authors declare no conflicts of interest.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at folkmane.inese@inbox.lv. Participants’ informed consent for data sharing was not obtained but the presented data are anonymized and risk of identification is low.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Inese Folkmane, MD, PhD, Professor, Centre of Nephrology, Pauls Stradiņš Clinical University Hospital, Pilsoņu 13, Riga LV-1002, Latvia. folkmane.inese@inbox.lv
Received: October 13, 2024 Revised: January 31, 2025 Accepted: February 20, 2025 Published online: September 18, 2025 Processing time: 186 Days and 8.1 Hours
Abstract
BACKGROUND
Major adverse cardiovascular (CV) events (MACEs) are the primary cause of morbidity and mortality in kidney transplantation (KT) recipients. The risk for MACEs is impacted by an array of traditional and transplant-related non-traditional CV risk factors.
AIM
To investigate the association between potential CV risk factors related to KT and MACEs, and their potential modification by hyperuricemia (HU).
METHODS
The relationship between CV risk factors related to KT and MACEs was examined in a cohort of 545 patients who underwent transplantation between 2008 and 2019. The mean age of patients at KT was 55.0 years ± 14.2 years (range 15.0–89.0 years). Univariate and multivariate logistic regression models were constructed to identify risk factors influencing MACEs. To explore the potential effect modification by uric acid (UA), patients were categorized into groups based on UA levels: (1) Low (< 356 μmol/L); (2) Normal (356–416 μmol/L); (3) High (416–475 μmol/L); and (4) Very high (> 475 μmol/L).
RESULTS
MACEs occurred in 145 of 545 (26.6%) KT recipients. The most prevalent comorbidities were hypertension (87%), dyslipidemia (78%), secondary hyperparathyroidism (68%), HU (63%) and anemia (33%). In the multivariate logistic regression model, the most significant factors associated with MACEs were previous CV events [odds ratio (OR) = 70.6, 95%CI: 24.9–200.1], left ventricular hypertrophy (LVH) (OR = 12.6, 95%CI: 2.7– 58.3), HU treatment (OR = 4.3, 95%CI: 2.4–7.6), and anemia (OR = 5.3, 95%CI: 2.9–9.8). Effect modification by the presence of HU revealed that independent factors associated with MACEs were age (OR = 1.03, 95%CI: 1.0–1.1), previous CV events (OR = 41.7, 95%CI: 13.6–127.6), LVH (OR = 15.3, 95%CI: 2.0–116.6), HU treatment (OR = 2.5, 95%CI: 1.3–4.6) and anemia (OR = 5.4, 95%CI: 2.8–10.5). Effect modification by UA levels dichotomized at 475 μmol/L (very high level of UA) revealed that HU treatment was not associated with MACEs in groups with or without very high UA levels.
CONCLUSION
A very high level of UA was observed to act as an effect-modifying factor for MACEs, especially when combined with other risk factors such as age, previous CV events, LVH, and anemia.
Core Tip: The relationship between hyperuricemia (HU) and major adverse cardiovascular (CV) events (MACEs) after kidney transplantation (KT) remains a topic of contention. We investigated the association between potential CV risk factors related to KT and MACEs, and their potential modification by HU. A very high level (> 475 μmol/L) of uric acid was found to act as an effect-modifying factor for MACEs, especially when combined with other risk factors such as previous CV events and anemia. This study makes an innovative contribution to the field by demonstrating the role of HU as a modulating factor on MACEs after KT.
Citation: Junk E, Tzivian L, Folkmane I, Folkmanis K, Jushinskis J, Strazda G, Folkmanis V, Kuzema V, Petersons A. Major adverse cardiovascular events and hyperuricemia as an effect-modifying factor in kidney transplant recipients. World J Transplant 2025; 15(3): 102287
Kidney transplantation (KT) is the preferred treatment of choice for patients with end-stage kidney disease due to improved long-term survival and quality of life[1]. Even though both graft and patient survival have seen gradual improvements in recent decades, there remains a significant discrepancy between short-term and long-term survival rates. Death with a functioning graft is one of the common causes of late kidney graft loss, with cardiovascular (CV) disease being the most frequent cause of death with a functioning graft[2,3]. The relatively high CV morbidity and mortality noticed post-transplantation can be credited to several pre-existing traditional and non-traditional CV risk factors. These factors are characteristic of dialysis patients and often persist even after KT, especially in recipients with weakened graft function. In addition to pre-existing risk factors, there exist unique transplant-associated non-traditional risk factors. These include the adverse metabolic effects of immunosuppression, acute graft rejection, graft dysfunction, proteinuria, left ventricular hypertrophy (LVH), chronic anemia, and proteinuria[4]. Recent studies have indicated that several other CV risk factors, such as hyperuricemia (HU), chronic inflammation, and bone mineral disorders, may be involved in KT[5]. These factors, in turn, can escalate the incidence of major adverse CV events (MACEs) among KT recipients. Recent cohort studies have shown that MACEs after KT are associated with an increased risk of long-term all-cause mortality[6,7].
The incidence of HU in KT recipients is remarkably high, with the upper limit reported to be over 80%, particularly in those on cyclosporine therapy[8]. In addition to traditional HU risk factors, such as decreased glomerular filtration rate (GFR), the use of diuretics, male gender, diabetes mellitus, cystic kidney disease, hypercalcemia, and higher body weight, these patients are also subjected to non-traditional risk factors like treatment with calcineurin inhibitors, notably cyclosporine[9]. Calcineurin inhibitors are still the fundamental immunosuppressive therapy for patients post-KT. However, their usage is also linked with a variety of well-established side effects, including nephrotoxicity, CV and metabolic toxicity. These drugs can double-fistedly impact the CV system. They can directly cause LVH, arrhythmias, myocardial fibrosis, hypertension, dyslipidemia, and coronary atherosclerosis, and they can indirectly affect it through metabolic abnormalities such as HU[10,11]. Cyclosporine-induced HU has been associated with the reduction in urinary clearance of uric acid (UA) due to increased proximal tubular reabsorption, decreased tubular secretion, and diminished GFR[9]. Tacrolimus, another calcineurin inhibitor, may also induce HU in KT patients, with no substantial difference between the two drugs concerning the incidence of HU and gout[12].
The role of UA in the pathogenesis of human CV and kidney disease has been extensively discussed. Over recent decades, experimental and epidemiological studies have shown that HU plays a pathogenic role in promoting CV and kidney diseases, mainly due to an intracellular mechanism that induces pro-inflammatory activity in vascular endothelial and smooth muscle cells, and intracellular oxidative stress. This leads to endothelial dysfunction and activation of the renin-angiotensin-aldosterone system[13-15]. Although endothelial function is impaired in patients with HU, it is yet to be firmly established whether HU itself is a causal risk factor for endothelial dysfunction. According to another pathogenic theory, UA functionally up-regulates xanthine oxidase, a key enzyme in purine metabolism. As a result, UA and reactive oxygen species are generated simultaneously, potentially impacting endothelial function negatively. Xanthine oxidase-derived reactive oxygen species may be involved in various unfavourable processes in the pathophysiology of CV complications, including cardiac hypertrophy, myocardial fibrosis, left ventricular remodelling, and contractility impairment[16,17].
The pathogenic role of HU in the development of CV events is further complicated by the fact that even a minor decrease in GFR can severely impair UA excretion. On the other hand, an increase in net UA reabsorption can also occur in situations of volume depletion. This might be one of the mechanisms through which diuretics induce HU in patients with heart failure[18].
The complicated causal link between HU and CV events, especially in regard to urate-lowering therapy, has resulted in non-uniform results from clinical trials on this matter. Although several clinical trials and meta-analyses demonstrate a correlation between HU and CV events and the benefits of lowering serum UA, particularly in asymptomatic HU populations[19-23], other publications have disputed these findings, especially in recent years[24-27]. Regrettably, most large clinical trials and meta-analyses examining the association of HU with CV events have centred on the general population or those with chronic kidney disease. Studies focusing on the KT population have been scarce and often yield contradictory results[28-30]. The relationship between HU and MACEs continues to be disputed. The optimal UA-lowering therapy initiation threshold for asymptomatic HU in the KT population remains undetermined. Therefore, our study’s objective was to explore the association between potential CV risk factors linked to KT and MACEs, along with the modifying effects of HU.
MATERIALS AND METHODS
Study design and population
This study encompassed 628 KT patients who received a kidney graft from January 2008 to December 2019 at Pauls Stradiņš Clinical University Hospital’s Center of Nephrology in Latvia. We conducted a retrospective cohort study using KT data obtained from the Electronic Patient Data Registry system and the clinical records at Pauls Stradiņš Clinical University Hospital, as well as the Registry of Kidney Transplant Patients of Latvia. Patients provided written consent for their data to be used for research purposes upon registry enrollment. Patients were monitored until graft failure resulting from patient death or return to renal replacement therapy (dialysis or retransplantation) or until the end of the follow-up period on December 31, 2022. Routine clinical follow-ups, which included assessments of patients’ health status, laboratory tests, radiological examinations, and treatment protocols, were recorded in the hospital’s electronic database and paper format in medical records and outpatient charts. Of the 628 patients who underwent KT, 83 were excluded from the study due to incomplete data or graft loss within twelve months of transplantation. Thus, the analysis considered data from 545 eligible patients. The mean age of patients at KT was 55.0 years ± 14.2 years (range 15.0–89.0 years). The flow chart of study participants, including the number of excluded participants, is shown in Figure 1.
Figure 1 Flow chart of kidney transplant recipients at the study centre, including the number of recipients lost to follow-up within 1 year.
Measurements
The demographic and baseline factors, including age, sex, cause of end-stage kidney disease, and medication use such as cardiac treatment and UA-lowering treatment, were obtained.
MACE was defined as the presence of myocardial infarction, invasive coronary artery therapy (such as coronary balloon angioplasty, stents, and bypass surgery), cerebral vascular events (like stroke and transient ischemic attacks), congestive heart failure, rhythm disturbances (including ventricular tachycardia, atrial fibrillation, and the need for a pacemaker), or cardiac death using International Classification of Diseases, 10th revision codes. The American Heart Association/American College of Cardiology Foundation criteria were used to diagnose and manage MACEs components[31].
To evaluate the relationships between MACEs and various independent variables, both traditional and transplant-specific (non-traditional) CV risk factors were analysed. Traditional risk factors included age, sex, diabetes mellitus, hypertension, dyslipidemia, smoking, and obesity.
Hypertension was defined as systolic blood pressure > 140 mmHg and diastolic blood pressure > 90 mmHg, or regular use of antihypertensive medical treatment.
Pre-transplant diabetes mellitus was defined as the presence of a diagnosis in patient records, or on fasting plasma glucose criteria (≥ 7.0 mmol/L), or on A1C criteria (≥ 6.5%), or use of antidiabetic medications. Dyslipidemia was defined as ongoing statin treatment or total serum cholesterol exceeding 6.2 mmol/L. World Health Organization classification was used to define obesity as a body mass index (BMI) of 30 kg/m2 or more.
Transplant-specific factors included time spent on dialysis, type of kidney donor (living or deceased), prior or repeat KTs, acute graft rejection, reduced GFR, post-transplant diabetes mellitus (PTDM), proteinuria, anemia, immunosuppressive treatment (such as steroids and calcineurin inhibitors), previous CV events, LVH, chronic inflammation, hyperparathyroidism (HPT), and HU.
Patients were further stratified based on their UA levels: (1) Low-UA level (< 356 μmol/L); (2) Normal-UA (356–416 μmol/L); (3) High-UA (416–475 μmol/L); and (4) Very high-UA level (> 475 μmol/L). HU was defined as a serum UA level exceeding 416 μmol/L (7.0 mg/dL) in men and 356 μmol/L (6.0 mg/dL) in women, or the administration of xanthine oxidase inhibitors for the treatment of HU.
The following laboratory parameters were obtained from the hospital’s Electronic Patient Data Registry system: (1) Serum creatinine; (2) Low-density lipoprotein cholesterol; (3) Triglycerides; (4) C-reactive protein; (5) Intact parathyroid hormone; (6) Haemoglobin; (7) UA; and (8) Spot urine dipstick or spot urine protein to creatinine ratio. Dyslipidemia, anemia, post-transplant HPT, proteinuria, and PTDM were defined according to the Kidney Disease: Improving Global Outcomes 2009 guidelines[32]. LVH was diagnosed by electrocardiograms, echocardiograms (moderately or severely abnormal myocardial wall thickness).
Kidney graft function was evaluated using the estimated GFR (eGFR), through the Chronic Kidney Disease Epidemiology Collaboration creatinine-based equation (2021) calculator[33]. Reduced graft function was defined as an eGFR of less than 60 mL/minute/1.73 m2, either with or without proteinuria. Acute graft rejection diagnoses were grounded on for-cause biopsies which were reviewed by a renal pathologist and categorized using the Banff criteria[34].
The standard protocol for immunosuppressive therapy includes induction with methylprednisolone, interleukin-2 receptor antibodies (basiliximab), or antithymocyte globulin. This is followed by maintenance treatment with calcineurin inhibitors (cyclosporine/tacrolimus), cell proliferation inhibitors (mycophenolate mofetil/mycophenolate sodium), and steroids. Doses and regimens of immunosuppressive medications are prescribed in accordance with the Kidney Disease: Improving Global Outcomes 2009 guidelines and individualized per the clinical situation[32].
Statistical analysis
Descriptive statistics were conducted for all study variables. For continuous variables with a normal distribution, means and SD were calculated and presented. Alternately, for continuous variables with a non-normal distribution, medians and interquartile range were presented. For group variables, the numbers and percentages in each group were also presented. Univariate analysis was performed to identify relationships between MACEs and independent variables. The Mann-Whitney test was used for continuous variables and the χ² test for group variables. At this stage, a two-sided significance level of 0.05 was considered. To construct a multiple logistic regression model, a direct acyclic graph (DAG) was created, considering previous CV events as a main exposure variable[35]. The minimally adjusted set, as assessed using the DAG, included factors such as age, gender, BMI, smoking, presence of dyslipidemia, hypertension, and HPT (Figure 2).
Figure 2 Direct acyclic graph of relationships between study variables.
BMI: Body mass index; CNI: Calcineurin inhibitor; CV: Cardiovascular; DM: Diabetes mellitus; GFR: Glomerular filtration rate; LVH: Left ventricular hypertrophy; MACE: Major adverse cardiovascular event.
In addition to the minimal adjustment set defined by DAG, we incorporated variables with clinical relevance into the model. However, at this point, we did not include the parameters of UA in the regression model, in anticipation of future effect modification analysis. We engaged in a backward reduction of non-significant variables to highlight the clear associations between independent variables and MACEs. Variables that exhibited statistical significance in the logistic regression model were represented by their odds ratio (OR) and 95%CI.
Effect modification by a level of UA
We further explored the potential for effect modification of the associations obtained by various elevated and high levels of UA. We independently examined the differences in the association among patients who did/did not have elevated levels of UA (416–475 μmol/L); those who did/did not exhibit excessively high levels of UA (above 475 μmol/L); and those who were/were not diagnosed with HU. OR and 95%CI were provided for the associations of independent factors with MACEs in both patient subgroups.
Sensitivity analysis
To confirm the accuracy of the main analysis’s model, we created a multiple logistic regression, incorporating the UA parameters. We reviewed the variables that exhibited statistical significance in this model and compared them along with the strength of their association with MACEs to the findings from our main model. The OR and 95%CI for the variables that showed statistical significance in the logistic regression model were presented.
RESULTS
Of the 545 patients enrolled in the study, approximately the same proportion were men and women (51.9% men and 48.1% women), with a mean age of 53.0 years (SD = 14.7). Out of these, 145 (26.6%) experienced MACEs. The majority of patients were not overweight (BMI > 30 kg/m2 was observed in 19.3% of participants; mean BMI 25.7, SD = 4.5), and did not smoke (80.7%). Most of the patients had dyslipidemia (78.2%), hypertension was exhibited in 87.0% of participants, LVH was present in 79.1%, and a history of CV events was absent in 85.5%. Acute graft rejection was diagnosed in 33.4% of patients, a very high level of UA was found in 35.6%, and 35.0% were undergoing treatment for HU. Pre-transplant diabetes mellitus was diagnosed in 39 patients (7.1%), while PTDM was diagnosed in 82 patients (15.0%). Despite the majority of patients not having specific health problems related to MACEs, 22.8% had congestive heart failure, 16.7% had coronary heart disease, and 6.4% died due to a CV event (Table 1).
Table 1 Sociodemographic and disease-related characteristics of study participants, n (%).
Variable
Sociodemographic data
Male gender
283 (51.9)
Age, median (25%-75%), min-max (years)
55.0 (41.0–64.0), 15.0–89.0
body mass index, median (25%-75%), min-max (kg/m2)
24.5 (22.8–28.4), 15.4–42.6
Current smokers
165 (30.3)
Problems and treatments related to cardiovascular function
Having major adverse cardiovascular events (cardiovascular of cerebrovascular events)
145 (26.6)
Having hypertension
474 (87.0)
Having left ventricular hypertrophy
431 (79.1)
Having dyslipidemia
426 (78.2)
Having previous cardiovascular events
79 (14.5)
Problems and treatments related to the urine system
UA, median (25%-75%), min-max (μmol/L)
436.0 (357.0–514.5), 201.0–761.0
Having elevated UA
116 (21.3)
Having a very high level of UA
194 (35.6)
Having hyperuricemia
347 (63.7)
Glomerular filtration rate, median (25%-75%), min-max (mL/minute)
Length of hemodialysis, median (25%-75%), min-max (months)
12.0 (7.0–23.0), 0.0–131.0
HPT, median (25%-75%), min-max (pg/mL)
120.0 (73.0–180.0), 23.0-1023.0
Having secondary HPT (> 90 pg/mL)
375 (68.8)
Inflammation, median (25%-75%), min-max (mg/L)
3.6 (1.3–5.5), 0.0-130.0
Having inflammation (> 5 mg/L)
145 (26.6)
Having anemia (hemoglobin level < 13 g/dL in male and < 12 g/dL in female)
180 (33.0)
Most of the independent variables investigated in this study had a univariate relationship with MACEs. Variables that did not demonstrate significant relationships at the 0.05 significance level included gender (P = 0.29), PTDM (P = 0.32), acute graft rejection (P = 0.84), and steroid use for more than one year (P = 0.50) (Table 2). While BMI was significantly related to MACEs, the presence of obesity did not exhibit a significant relationship (P = 0.09).
Table 2 Univariate relationships between having major adverse cardiovascular events and independent variables, n (%).
Variable
Without MACEs (n = 400)
With MACEs (n = 145)
P value
Male gender
202 (71.4)
81 (28.6)
0.29
Age, mean (SD) (years)
50.7 (14.5)
60.9 (12.3)
< 0.01
Body mass index, mean (SD) (kg/m2)
25.4 (4.5)
26.7 (4.5)
< 0.01
Current smokers
106 (64.2)
59 (35.8)
< 0.01
Having hypertension
332 (70.0)
142 (30.0)
< 0.01
Having left ventricular hypertrophy
288 (66.8)
143 (33.2)
< 0.01
Having dyslipidemia
302 (70.9)
124 (29.1)
0.01
Having previous cardiovascular events
5 (1.3)
74 (93.7)
< 0.01
Uric acid,mean (SD) (μmol/L)
411.26 (89.6)
520.0 (102.5)
< 0.01
Glomerular filtration rate, mean (SD) (mL/minute)
49.6 (25.1)
37.7 (21.4)
< 0.01
Having proteinuria
103 (59.9)
69 (40.1)
< 0.01
Using steroids for more than one year
305 (74.2)
106 (25.8)
0.50
Acute rejection
135 (74.2)
47 (25.8)
0.84
Receiving hyperuricemia treatment
105 (55.0)
86 (45.0)
< 0.01
Receiving cyclosporine therapy
71 (64.0)
40 (36.0)
0.02
Receiving tacrolimus therapy
312 (77.2)
92 (22.8)
< 0.01
New onset of diabetes mellitus
55 (67.1)
27 (32.9)
0.32
Length of hemodialysis, mean (SD) (months)
16.9 (17.4)
20.2 (18.4)
< 0.01
Hyperparathyroidism, mean (SD) (pg/mL)
150.9 (137.5)
173.2 (112.1)
< 0.01
Inflammation, mean (SD) (mg/L)
4.3 (8.5)
5.9 (6.1)
< 0.01
Having anemia
110 (61.1)
70 (38.9)
< 0.01
In the logistic regression model accounting for 62.1% of changes in MACEs, having a previous CV event was identified as the most significant factor correlated with MACEs, enhancing the likelihood of MACEs by almost 71 times. However, the 95%CI for this relation was remarkably broad (OR = 70.6, 95%CI: 24.9–200.1). Other factors that heightened the likelihood of MACEs encompassed: (1) The presence of LVH, undergoing treatment for HU; (2) The incidence of anaemia; and (3) An increment associated with each passing year of age (OR = 1.05, 95%CI: 1.03–1.08) (Table 3).
Table 3 Association between major adverse cardiovascular events and independent variables–main model.
Variable
Odds ratio
95%CI
P value
Wald statistics
Age (years)
1.05
1.03–1.08
< 0.01
21.13
Receiving hyperuricemia treatment
4.29
2.44–7.57
< 0.01
25.42
Having previous cardiovascular events
70.6
24.9–200.1
< 0.01
64.03
Having left ventricular hypertrophy
12.6
2.74–58.3
< 0.01
10.56
Having anemia
5.32
2.89–9.80
< 0.01
28.82
Effect modification by a level of UA
For patients with an elevated level of UA, only factors from the main model remained significant for patients without this condition. The factors that increased the probability of MACEs were age (OR = 1.06, 95%CI: 1.04–1.09), previous CV events (OR = 94.23, 95%CI: 25.34–350.47), LVH (OR = 12.26, 95%CI: 2.45–60.99), HU treatment (OR = 6.85, 95%CI: 3.52–13.33), and anaemia (OR = 5.52, 95%CI: 2.69–11.22). However, the only variables significantly associated with MACEs for patients with elevated UA were prior CV events (OR = 28.84, 95%CI: 4.24–196.25) and anaemia (OR = 6.07, 95%CI: 1.58–23.28). This data is not shown.
The presence of very high levels of UA indicated that undergoing HU treatment was not linked with MACEs, regardless of whether very high levels of UA were present or not. The condition of LVH was significantly related to MACEs only in those without high UA levels. All other factors linked to MACEs in the main model maintained their significance for both groups, and the degree of their association was comparable to that in the main model (Table 4).
Table 4 Effect modification by a very high level of uric acid (> 475 μmol/L) of the associations in the main model.
Variable
Not having elevated levels of UA
Having elevated levels of UA
OR
95%CI
P value
OR
95%CI
P value
Age (years)
1.05
1.01–1.08
0.01
1.08
1.04–1.12
< 0.01
Receiving hyperuricemia treatment
1.76
0.66–4.69
0.26
2.09
0.86–5.08
0.10
Having previous cardiovascular events
63.07
16.14–246.40
< 0.01
72.82
9.09–583.34
< 0.01
Having left ventricular hypertrophy
10.97
1.28–94.06
< 0.01
6.48
0.74–56.58
0.09
Having anemia
6.08
2.32–15.92
< 0.01
6.46
2.46–16.95
< 0.01
For those that had HU, the factors that were associated with MACEs remained stable only for the group with HU. The strength of the association remained stable in this case as well (Table 5). For the group without HU, the only association that remained significant was with having previous CV events. No other associations were observed for this group (data not shown).
Table 5 Effect modification by having hyperuricemia of the associations in the main model.
Variable
Having hyperuricemia
Odds ratio
95%CI
P value
Age (years)
1.06
1.03–1.09
< 0.01
Receiving hyperuricemia treatment
2.45
1.31–4.59
< 0.01
Having previous cardiovascular events
41.70
13.62–127.64
< 0.01
Having left ventricular hypertrophy
15.33
2.02–116.60
< 0.01
Having anemia
5.36
2.75–10.48
< 0.01
Sensitivity analysis
The sensitivity analysis did not alter the association between independent variables and MACEs, indicating the model’s stability. This model accounted for 65.3% of the variance in MACEs, mirroring the primary model. The incorporation of UA parameters into the model diminished the factor of receiving HU treatment from those significantly associated, but the OR for other variables did not change notably. The parameters for UA, which had an association with MACEs, barely increased its likelihood (OR = 1.01, 95%CI: 1.03–1.08). All other variables remain in the model with estimates similar to those in the primary model (Table 6).
Table 6 Association between major adverse cardiovascular events and independent variables when parameters of uric acid included in the model.
Variable
Odds ratio
95%CI
P value
Wald statistics
Age
1.06
1.03–1.08
< 0.01
22.73
Uric acid
1.01
1.00–1.01
< 0.01
40.39
Having previous cardiovascular events
69.01
22.15–215.05
< 0.01
53.32
Having left ventricular hypertrophy
9.22
2.05–41.54
< 0.01
8.36
Having anemia
4.97
2.65–9.30
< 0.01
25.12
DISCUSSION
Few studies have analysed long-term MACEs in KT recipients and their association with HU, a common, non-traditional, and potentially modifiable risk factor. Our study involved 545 KT recipients, followed for an average of 87.6 months ± 39.6 months. During this period, 145 (26.6%) recipients experienced MACEs episodes.
Epidemiological reports on the incidence of CV events vary considerably. Older studies reported a relatively high incidence of MACEs after KT. The 2007 United States Renal Data System annual data report showed that nearly 40% of patients experienced a CV event within 36 months of transplantation, primarily relating to congestive heart failure and coronary heart disease[36]. A recent study from South Korea found only 76 (1.8%) of KT recipients diagnosed with de novo MACEs, but their risk of all-cause mortality increased 7.45 times following MACEs, compared to KT recipients without MACEs over a 4.7-year follow-up[7].
The authors attributed this relatively low incidence of MACEs to the study’s exclusion of patients with pre-existing CV events and Asians’ typically lower prevalence of CV disease among KT recipients compared to Western cohorts. A study of CV Outcomes in Renal Transplantation from Ontario, Canada, revealed that 91 patients (7%) experienced MACEs. Of these, 52 (4%) had myocardial infarction events, and 24 (1.8%) were diagnosed with cardiac death[37].
In our study, we found that the majority of patients had dyslipidaemia, hypertension, and LVH. A high level of UA was diagnosed in 35.6% of KT recipients, with 35.0% receiving UA-lowering treatment. Although most patients did not experience specific MACEs-related complications, 22.8% had congestive heart failure, 16.7% had coronary heart disease, and 6.4% died from a CV event.
The incidence of MACEs in our study appears significantly higher than in recent reports. All KTs are centrally performed at Pauls Stradiņš Clinical University Hospital, Riga, with lifelong recipient follow-ups. Thus, our information may have greater accuracy compared to studies using large databases sourced from numerous centres, where authors often cite incomplete clinical information.
Evidence indicates that the incidence of CV events is highest in early post-transplant periods and continues to increase gradually in late periods[37,38]. Most independent variables investigated in our study had a univariate relationship with MACEs, similar to prior literature[4,5,39].
Our study’s variables, including gender, BMI, PTDM, acute rejection episodes, and steroid use beyond 1 year, did not demonstrate a statistically significant relationship. This aligns with the recently published 24-month post hoc analysis of the TRANSFORM study, which found that BMI, PTDM, and acute rejection episodes were not significantly associated with MACEs[40].
In our study, however, having a prior CV event represented the most significant factor associated with MACEs, increasing MACEs probability almost 71 times. Other contributing factors included LVH, associated anemia, HU treatment, and increased age. A study from the Netherlands also identified pre-transplant CV events as independent predictors for post-transplant MACEs[41], reaffirming the need for increased focus on CV disease history and more effective CV screening for kidney transplant candidates[42].
Although LVH often regresses before KT, it remains in about 50% of patients after transplantation, mainly as a remnant of dialysis before grafting[43]. A Canadian study involving 1063 adults undergoing pre-transplant transthoracic echocardiography demonstrated that both LVH and high relative wall thickness were associated with CV events in a multivariable survival regression analysis, independent of common pre-transplant MACEs risk factors[44]. Interestingly, LVH can correlate with kidney graft dysfunction and can be exacerbated by common complications of graft dysfunction, such as anemia, hyperhydration, and secondary HPT[39,43]. Unlike many studies, ours did not find graft dysfunction as a significant independent risk factor for MACEs, similar to the post hoc analysis of the TRANSFORM study, where only eGFR < 30 mL/minute/1.73 m2 represented a significant independent risk factor for MACEs[40].
We identified a significant association between HU treatment and MACEs. HU treatment elevated the probability of CV events by 4.29 times. However, it is critical to note that this study did not analyse the impact of UA-reducing therapy on HU reduction. Moreover, the HU treatment group contained patients with varying UA concentrations, which may have influenced the identified association. Therefore, it is plausible that this association may not necessarily reflect the therapeutic influence, but rather the effect of HU on MACEs. Existing evidence suggests increasing UA concentrations may function as an indicative marker or even a potential etiological cause in the pathogenesis of CV risk factors and CV disease. The link between HU and CV incidents has been deeply studied and proven in multiple studies and meta-analyses in general and chronic kidney disease populations[15,17,21,45,46]. However, in KT recipients, the association is frequently ambiguous and controversial, particularly regarding asymptomatic HU treatment[28,29,47,48]. Our examination found that a very high level of UA (> 475 μmol/L) was not linked with MACEs in those who received HU treatment, regardless of UA levels. This outcome can potentially be explained by the cardioprotective effect of urate-lowering therapy observed in other studies, including those involving KT recipients[29,47,49,50]. Still, this remains speculative in our study. All other independent factors linked with MACEs in the primary model remained significant for those with very high UA levels, except for LVH. In the analysis of effect modification by HU presence, factors associated with MACEs remained consistent, and the strength of their association mirrored that in the main model, including LVH. Our results align with Caliskan et al[51], who found an association between HU and LV posterior wall thickness plus LV mass index in KT recipients. Ultimately, when analysing the association between MACEs and independent factors, including UA in the model (Table 6), the odds of MACEs were barely affected.
Our study results suggest that HU and very high UA concentrations work as modulating factors for MACEs in KT recipients, but not UA alone. Despite the post hoc cohort analysis of the FAVORIT study not finding a connection between UA concentration and CV events in fully adjusted multivariable models[28]. A more recent meta-analysis study by Yang et al[29] demonstrated a statistically significant difference in CV event risk between the HU group and the normouricemic group in KT recipients. Moreover, the lack of a significant correlation between HU treatment and MACEs in the patient subgroup with considerably high UA concentrations underscores the prevailing idea that urate-lowering therapy should be examined for asymptomatic KT recipients with a CV event history. Even though KDIGO guidelines suggest treatment only for symptomatic HU patients[32], we recommend that HU should be managed and a UA target of < 297 μmol/L (5 mg/dL) should be considered for asymptomatic KT recipients at high CV risk, particularly those with past CV incidents. This aligns with the latest expert opinion[49].
The present study boasts several strengths. First, it was a large-scale, single-centre, nationwide registry-based study that incorporated multiple potential risk factors for KT patients. Second, it underscored the effect-modifying role of a non-traditional risk factor, HU, on the progression of MACEs. Third, the consolidation of kidney transplants at a single university clinic facilitated the acquisition of information with a high degree of accuracy, thereby decreasing the number of patients with missing or incomplete information. Fourth, our study aligns with existing research in the field.
The present study had several limitations. Primarily, the study employed a retrospective and register-based cross-sectional design, which precludes the establishment of causal relationships between HU and MACEs. Secondly, numerous additional risk factors may be associated with MACEs, with the potential for even more complex interactions. It is impossible to ascertain whether all confounding factors have been considered in a multivariate analysis. Thirdly, the effect of urate-lowering therapy on HU and MACEs was not analysed in detail. Rather, the analysis was limited to patients who did or did not receive this therapy. Consequently, it is only possible to hypothesise that urate-lowering therapy has a cardioprotective effect, particularly when applied to patients with high rates of HU.
CONCLUSION
Despite comprehensive CV screening of potential recipients before KT, the incidence of MACEs in our study was relatively high. The most significant factor associated with MACEs, which increased the probability by almost 71 times, was previous CV events. To reduce CV morbidity after KT, implementing more sensitive and accurate CV screening methods may be necessary. HU was identified as an effect-modifying factor for MACEs, especially when combined with other risk factors, including age, previous CV events, LVH, and anaemia. Further investigation into the relationship between MACEs and HU in KT recipients is warranted.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Corresponding Author's Membership in Professional Societies: American society of Nephrology, No. 582610.
Specialty type: Transplantation
Country of origin: Latvia
Peer-review report’s classification
Scientific Quality: Grade B, Grade C
Novelty: Grade B, Grade C
Creativity or Innovation: Grade B, Grade B
Scientific Significance: Grade A, Grade C
P-Reviewer: Gutiérrez-Cuevas J S-Editor: Luo ML L-Editor: A P-Editor: Zheng XM
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