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Association between serum urate levels and all-cause mortality, cardiovascular and renal outcomes among gout patients in Singapore

BMC Rheumatology volume 8, Article number: 71 (2024) Cite this article

Abstract

Objectives

We investigated the longitudinal association between Serum Urate (SU) level and Acute Myocardial Infarction (AMI), Stroke, End Stage Renal Failure (ESRF) and all-cause mortality.

Design

We conducted a retrospective hospital-based cohort study of individuals with gout managed in specialist outpatient clinics. Cox proportional hazards regression was used to estimate HR and 95% CI, with adjustments for potential confounders. Where the proportional hazard assumption was violated, stratified Cox regression was applied instead.

Setting

An acute care tertiary hospital in Singapore.

Participants

Individuals with a first gout diagnosis between 2007–2017, identified through (i) primary discharge diagnosis, (ii) diagnosis from the Rheumatology SOC (iii) patient history of a clinical encounter at the Rheumatology SOC plus use of urate-lowering therapy/colchicine.

Main outcome measures

All-cause mortality, AMI, Stroke and ESRF ascertained through data linkage with the National Registry of Diseases Office.

Results

The final cohort comprised 2,866 individuals. Post follow-up, there were 800 deaths and 362, 218 and 191 occurrences of AMI, ESRF and stroke respectively. Compared to the reference (second-lowest) SU quartile, being in the highest SU quartile was associated with a significantly increased hazard for mortality (HR:1.66, 95% CI:1.36–2.03), incident ESRF (HR:3.02, 95% CI:2.00-4.56), and increased hazard for incident AMI (HR:1.42, 95% CI:1.06–1.91). The p-trend for all 3 outcomes was significant. No significant association was found between SU quartile and hazard for incident stroke.

Conclusions

This study found that individuals with gout managed at SOC who had higher baseline SU levels had an increased hazard for all-cause mortality, ESRF, and AMI.

Clinical trial number

Not applicable.

Peer Review reports

Introduction

Gout is the most common form of inflammatory arthritis, with acute gout flares arising from persistently high Serum Urate (SU) levels leading to deposition of uric acid crystals in joints [1]. Globally, the disease burden of gout has been rising, with increasing prevalence and incidence rates over the years [2]. In Singapore, a 2012 study reported the prevalence of gout in ethnic Chinese aged between 45 and 75 years to be 4.1%[3], however, the overall population level prevalence is likely to be higher with the inclusion of the rest of Singapore’s ethnic minority groups [4]. Taken together the above suggests a growing need to explore means of mitigating the disease burden of gout, one key means of which is through tertiary prevention of serious adverse outcomes.

The acute burden of gout, with its impact on productivity loss, disability and quality of life is well established [5]. In addition, epidemiologic studies have suggested a link between gout, Acute Myocardial Infarction (AMI), stroke, and renal disease [3, 6, 7]. However, this association needs to be interpreted cautiously, given that gout is commonly comorbid with diabetes, hypertension and hyperlipidaemia, all of which are associated with the above conditions, and also share many lifestyle risk factors such as obesity and low physical activity with gout [2].

Further complicating the association between gout and the above adverse outcomes is the potential role of elevated SU in the pathogenesis of such conditions. Most genetic Mendelian Randomization (MR) studies have found that SU is not causally related to cardiovascular conditions, but rather that associations between SU and such conditions are likely due to genetic pleiotropy [8, 9]; while these studies confirm the causal role of SU in pathogenesis of gout, SU also plays a role in the development of hypertension, micro-vascular damage and inflammation [10], with at least part of these associations explained by the presence of shared genetic risk for both SU and each outcome. However, the possibility of a causal relationship between SU and specific outcomes such as heart failure have been found in at least one recent meta-analysis [11]. Furthermore, some epidemiologic studies such as the Framingham Heart Study [12] have demonstrated an independent association between clinical gout, but not SU levels, on adverse cardiovascular outcomes, while other studies have demonstrated independent effects of both clinical gout and elevated SU levels [13, 14]. One of these studies also demonstrated that the possibility of the joint impact of gout and hyperuricemia may be far greater than their individual contributions to cardiovascular risk [14]. Taken together these findings suggest that the association between SU and cardiovascular risk in gout patients warrants further investigation. Furthermore, much of the literature available to date focuses on Caucasian populations in Europe and North America, and sparse data is available from Asian countries. Therefore, in this study, we examine the association between SU levels and mortality, AMI, stroke, and ESRF among patients with a clinical diagnosis of gout in the multi-ethnic Asian city-state of Singapore.

Materials and methods

Study design, setting and population

We conducted a retrospective hospital-based cohort study utilising the electronic medical record (EMR) data of individuals with gout managed at specialist outpatient clinics between 2007 and 2017 in Tan Tock Seng Hospital (TTSH), an acute multi-disciplinary adult tertiary hospital that provides comprehensive medical services in Singapore. The overall recruitment flow for the study can be found in Fig. 1.

Fig. 1
figure 1

Flow diagram for selection of participants. #: Patients whose serum urate test done after 6 months from first diagnosis of gout were considered to have serum urate test done at baseline. ^: For inpatients, date of admission of the first gout episode. For outpatients, date of the first gout episode from diagnosis or medication

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Inclusion criteria

We included adult patients aged at least 21 years and who had a first encounter with the hospital for gout between 1 Jan 2007 and 31 Dec 2016. Gout diagnoses was confirmed based on fulfilling at least one of the following criteria: (1) a primary discharge diagnosis of gout on an inpatient admission, (2) a clinical diagnosis of gout made after a medical consultation encounter at the Rheumatology Specialist Outpatient Clinic (SOC)- clinics which serve as referral centres for patients from primary care and those discharged from inpatient admissions, (3) a combination of a history of any medical encounter at the Rheumatology SOC, and prescription of urate lowering therapy or colchicine. The recruitment date for patients was defined as the earliest date on which the eligibility criteria were satisfied. In cases where multiple dates met the criteria, the first occurrence was selected.

Exclusion criteria

We excluded individuals with a diagnosis of gout that was made prior to 1 January 2007, and individuals who had been started on allopurinol, benzbromarone, probenecid, febuxostat or colchicine prior to 1 January 2007. Furthermore, patients with no serum urate test done within 6 months of their first gout diagnosis were also excluded.

Exposure

We initially explored trajectories of SU levels over a 3-year period in our patient cohort, utilizing a group-based trajectory model for analysis, with each patient having at least one SU test in each year. However, we found that the best model for trajectories resulted in parallel, linear gradually declining trajectories (Fig. 2). This suggested that using trajectories did not add additional information compared to the use of baseline SU. We hence used baseline SU, defined as the value taken closest to the recruitment date, as the exposure of interest and analysed this both as a continuous variable and by quartiles. Where relevant, abnormal serum urate levels were defined as > 360µmol/L for females and > 420µmol/L for males [15].

Fig. 2
figure 2

Group-based trajectory modelling of eligible individuals with gout

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Outcomes

Occurrences of AMI, stroke and ESRF outcomes were obtained through data linkage with the National Registry of Diseases Office (NRDO). NRDO maintains these registries under an Act of Parliament (The National Registry of Diseases Act), and notification is mandatory, with additional data collection by registry nurses using patient medical records. NRDO identifies AMI and stroke outcomes using International Classification of Diseases (ICD) diagnosis codes made by clinicians and confirms them through case-note reviews done by registry coordinators. Patients with ESRF (Chronic Kidney Disease Stage 5) are notified by hospitals, dialysis centres and private nephrology clinics, and confirmed by registry co-ordinators through case-note reviews. Mortality data was obtained from the Registry of Births and Deaths Singapore. Mortality reporting is mandatory and complete in Singapore.

Individuals were excluded from the analyses for the respective outcomes for which they had prior occurrences.

Other covariates

Based on our review of existing academic literature as well as the availability of EMR data, we included the following covariates in our analyses: age, gender, ethnic group, comorbidities described by Charlson’s comorbidities index (CCI) [16], and the presence of hypertension, hyperlipidaemia and diabetes at recruitment.

Hypertension was defined using the following criteria: presence of a clinical diagnosis of hypertension in the electronic medical records, or use of antihypertensive medications, or mean systolic blood pressure ≥ 140 mmHg, or mean diastolic blood pressure ≥ 90 mmHg, calculated using all readings before recruitment. Hyperlipidaemia was defined using the following criteria: presence of a clinical diagnosis of hyperlipidaemia in the electronic medical records, or use of antilipemic medications, or mean low-density lipoprotein ≥ 4.9 mmol/L, or mean triglycerides ≥ 2.3 mmol/L [17]), calculated using all readings before recruitment. Finally, diabetes was defined using: presence of a clinical diagnosis of diabetes in the electronic medical records, or having ≥ 2 readings of fasting glucose/ fasting plasma glucose ≥ 7.0 mmol/L, ≥ 2 readings of 2 h Oral Glucose Tolerance Test ≥ 11.1 mmol/L, ≥ 2 readings of glycated haemoglobin (HbA1c) ≥ 6.5%[18], or use of antidiabetic medications, assessed using all readings before recruitment.

Statistical methods

Categorical variables were reported as frequencies and percentages whereas continuous variables were reported as either median with interquartile range (IQR) or mean with standard deviation (SD) for descriptive analyses. In bivariate analyses, Pearson’s χ2 test was used for categorical variables whereas the Kruskal-Wallis test was used for continuous variables. Any difference is deemed statistically significant if p < 0.05.

Cox proportional hazards models were first fitted to the data. Cox proportional hazard regression model was used to evaluate the effect of SU levels for each outcome of interest among those who are free from the respective outcome. Deaths were treated as competing events and censored in the regression models. Cause-specific hazard regression models were used as they are considered more appropriate to answering etiological questions as compared to sub-distribution hazard models such as the Fine-Gray model [19]. These models were then assessed for any violations to the proportional hazard assumption by testing for independence between the scaled Schoenfeld residuals and time in the adjusted effect of baseline SU level (i.e. exposure variable) on adverse outcomes. Apart from that, the global goodness-of-fit test was also used to determine if the proportional assumption holds for each of these models. In the event where the proportional hazard assumption was violated, stratified cox regression model [20] was constructed instead. We constructed an unadjusted model (Model 1) and a multivariable model (Model 2) adjusting for all covariates above (age, sex, race, CCI, presence of hypertension, hyperlipidemia, diabetes). Hazard ratios (HR) with 95% confidence intervals (CI) from regression analyses were presented. These analyses were performed using R 4.0.2 (Vienna, Austria) [21].

Results

Our retrospective cohort comprised 2866 patients with a first encounter with our hospital for gout between 1 Jan 2007 and 31 Dec 2016. The median (IQR) age was 61 (48, 74), and patients were predominantly male (79.3%) and of Chinese ethnicity (76.0%). SU levels at recruitment ranged from 108–1250µmol/L, with a median (IQR) of 493 (400, 580)µmol/L. Patients were divided into 4 SU quartiles, with ranges of 108–400, 400–493, 493–580 and 580–1250µmol/L respectively. In total, 2099 (73.2%) of study participants had baseline SU classified as within the abnormal range, with this proportion being similar to other registry-based cohort studies (22-31%[22, 23]). Participants in the higher SU quartiles tended to be younger and of Malay ethnicity. Additionally, a larger proportion of those in the highest quartile had comorbid diabetes, hypertension and hyperlipidaemia (Table 1). Prior to inclusion in the study, 138 participants (4.8%) had experienced an AMI while 150 (5.2%) had experienced a stroke and 60 (2.1%) had ongoing renal disease.

Table 1 Baseline characteristics of participants and study outcomes at baseline stratified by SU Quartiles
Full size table

During the study period, 800 (27.9%) patients died, and 362 (13.3%), 191 (7.0%), and 218 (7.8%) incidences of AMI, stroke and ESRF occurred respectively. These incidences excluded individuals who had already experienced the event before the study period. The highest SU quartile was observed to have the highest incidence of death and ESRF as compared to other quartiles among at risk population. However, this was not observed for incident AMI and stroke. (Table 2).

Table 2 Outcome occurrences and proportion of outcomes by quartile
Full size table

After adjustment, every 100- µmol/L increase in SU was associated with significantly increased hazard for mortality (HR:1.14, 95% CI:1.09–1.20), AMI (HR:1.13, 95% CI:1.05–1.22) and ESRF (HR:1.33, 95% CI:1.22–1.44), with no association with stroke (HR:0.94, 95% CI: 0.85–1.05) (Table 3).

Table 3 Association between SU levels and mortality, AMI incidence, stroke incidence and ESRF incidence
Full size table

Using the second UA quartile as the reference category, being in the highest UA quartile was associated with a significantly increased hazard for mortality (HR: 1.66, 95% CI:1.36–2.03),incident AMI (HR:1.42, 95% CI:1.06–1.91) and incident ESRF (HR: 3.02, 95% CI:2.00-4.56),. The test for linear trend over all 4 quartiles for these 3 outcomes was significant (p < 0.05). Increased serum urate levels were not associated with hazard of stroke (Table 3).

Discussion

Our study found evidence for a higher hazard for mortality, AMI and ESRF with higher serum urate levels, among patients with gout. These estimates were obtained after adjustment for diabetes, hypertension and hyperlipidaemia, adding to evidence that symptomatic hyperuricaemia has an independent effect on mortality and some cardiovascular outcomes among individuals with gout.

The association between higher SU and mortality risk in gout patients is consistent with the study by Krishnan et al. [24], where the authors found that individuals with both gout and hyperuricemia had higher risk of mortality compared to those with gout alone or hyperuricemia alone. Our study extends these findings to suggest that even in a cohort of people with gout, higher SU levels are associated with an increased hazard for all-cause mortality. The cause of death analysis from our dataset indicated that the largest proportion of deaths were due to cardiovascular conditions (37.9%) and infection (29.9%) (data not presented). This aligns with the current literature, where the link between high SU level and cardiovascular, all-cause mortality is consistently demonstrated [25, 26].

Several studies have found a positive relationship between SU levels and risk of renal disease in the context of patients with gout [27, 28]. The findings from our study provide further support for the presence of this relationship, specifically for ESRF, and within an Asian, multiethnic context. This association is well-established, and the underlying mechanism of action is related to chronic tubulointerstitial nephritis induced by the deposition of monosodium urate crystals in the collecting ducts, and the resultant inflammation [28].

Our study found that a 100µmol/L increase in SU significantly increased hazard for AMI by 14%, and there was a significant p for trend with increasing hazard of AMI in higher quartiles of SU. This is consistent with other studies that have found a significant association between SU and AMI in people with gout, such as the Essex et al. study [23], which found that individuals who were categorized as having SU of ≥ 8.01 mg/dL (equivalent to 708.2 µmol/L) had around a 1.5 times increased risk of AMI compared to those within the 0.01–6.00 mg/dL (equivalent to 0.88–532.5 µmol/L) SU range. Several mechanisms have been proposed to explain the association between SU and AMI. Xanthine oxidase activity during the production of uric acid has been suggested to bring out endothelial dysfunction, which is in turn associated with atherosclerosis [29, 30]. In addition, monosodium urate crystal formation due to high SU in gout also leads to the production of pro-inflammatory cytokines, which contributes to the vascular damage that predisposes towards AMI [31].

Our study did not find any association between SU levels and stroke in gout patients. While several meta-analyses support the presence of an effect of SU on stroke [32], findings from other studies regarding the effect of SU on the hazard of stroke have been inconsistent [33, 34]. This inconsistency can be due to a variety of factors. Some studies have reported effect modification by gender [35]. The subtype of stroke may also modify the relationship between SU and the hazard of stroke, however, findings have once again been inconsistent, with some studies reporting an effect of SU on ischaemic but not haemorrhagic stroke [36, 37], while others reported results in the opposite direction [35].

The findings from our study have clinical implications. Besides reducing SU level according to EULAR guidelines [38], the management of gout should include management of concomitant risk factors for cardiovascular and renal outcomes, such as poor diet, physical inactivity, and obesity [2], and include screening for and management of common chronic conditions. In particular, individuals with gout who have high SU levels should also be monitored for cardiovascular risk factors using locally validated instruments such as modified Framingham risk scores [39]. It may also be important to consider if more stringent efforts should be made to reduce SU levels among patients with gout.

The strengths of our study include: our data linkage to national stroke, death and acute myocardial infarction registries with comprehensive cover give us confidence that outcomes have been adequately captured; we have a relatively large sample of gout patients with SU measurements, and this is one of the few studies that have examined SU and cardiovascular and renal outcomes in Asian gout patients. Major limitations for our study include a lack of height and weight data for Body Mass Index (BMI) calculation - only 28% of our participants had data at baseline and we were unable to include BMI as a covariate, and confounding from obesity is possible, especially in view of the high prevalence of obesity in gout patients [2]. Additionally, our study was limited to individuals seen in the hospital specialist follow-up setting. These individuals either had severe enough gout episodes to warrant admission or had complicated gout which necessitated specialist follow-up care. Therefore, caution should be taken when comparing our results to studies of the general population. Thirdly, we could not account for the duration of gout; although we only included patients with a first encounter in the hospital for gout, we could not rule out pre-existing gout previously managed in the primary care setting or in other hospitals. Finally, the data from our study may over-estimate the proportion of individuals with incident gout but normal SU level: some patients in our study might have been managed for gout in other care settings prior to being followed-up at our centre, and have been treated for high SU levels already. Unfortunately, we do not have access to medical data of these patients recorded in other care settings.

Conclusion

This study demonstrated that high SU levels in gout patients are associated with increased hazard for mortality, ESRF and AMI, but not for stroke. While further research is needed to clarify the mechanism between stroke and SU in gout patients, our findings suggest a need to monitor individuals with gout for cardiovascular risk factors and renal disease progression.

Data availability

The data that support the findings of this study includes non-publicly available confidential information held by the Singapore National Registry of Diseases Office, and collected under the National Registry of Diseases Act, Singapore. (https://sso.agc.gov.sg/Act/NRDA2007?ProvIds=P14-#pr8-). As such restrictions apply to the availability of these data. Requests for data may be made to the authors upon reasonable request, however, release of data will be subject to permission of the Singapore National Registry of Diseases Office.

Abbreviations

SU:

Serum urate

AMI:

Acute myocardial infarction

ESRF:

End stage renal failure

MR:

Mendelian randomization

EMR:

Electronic medical records

TTSH:

Tan tock seng hospital

SOC:

Specialist outpatient clinics

NRDO:

National registry of diseases office

ICD:

International classification of diseases

CCI:

Charlson’s comorbidity index

IQR:

Inter-quartile range

SD:

Standard deviation

HR:

Hazard ratio

CI:

Confidence interval

BMI:

Body mass index

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Acknowledgements

The study team would like to thank the following individuals for their contributions to the study: Loh Wan Ning Janis from Tan Tock Seng Hospital for performing data extraction from the hospital medical records system. Tan Soo Hui Adriana and Chow Chengzi Tan Tock Seng Hospital for provision of trusted third party services and delivery of encrypted datasets to the National Registry of Diseases Office for analysis. Cai Mingshi from the National Registry of Diseases Office for helping to coordinate data transfer and analyses at the National Registry of Diseases Office.

Funding

No grant funding was obtained for this study.

Author information

Authors and Affiliations

  1. Department of Preventive and Population Medicine, Office of Clinical Epidemiology, Analytics, and Knowledge (OCEAN), Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, 308433, Singapore, Singapore

    Moses Yidong Lim, Weixiang Lian, Hwee Pin Phua & Wei-Yen Lim

  2. School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, 3004, Melbourne, Victoria, Australia

    Htet Lin Htun

  3. Department of Rheumatology, Allergy & Immunology, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore

    Kok Ooi Kong & Teo Min-Li Claire

  4. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Kok Ooi Kong & Wei-Yen Lim

  5. Yong Yoo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Kok Ooi Kong

  6. Research, Evaluation & Monitoring, Health Promotion Board, 3 Second Hospital Ave, Singapore, 168937, Singapore

    Ling Li Foo

Authors
  1. Moses Yidong Lim
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  2. Weixiang Lian
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  3. Hwee Pin Phua
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  4. Htet Lin Htun
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  5. Kok Ooi Kong
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  6. Ling Li Foo
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  7. Teo Min-Li Claire
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  8. Wei-Yen Lim
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Contributions

LWY and KOK were responsible for conceptualizing the study. MYL, WL, HLH and LWY designed the study methodology. MYL, WL and HLH contributed to curation of data. WL conducted the formal data analyses. MYL, HLH and WYL conducted the research investigation process. MYL, WL, HLH, LLF, PHP conducted validation of data and results. MYL and HLH assisted with project administration. MYL wrote the original draft of this manuscript. LWY provided overall supervision and guidance for the study. MYL, WL, HLH, LLF, TMC, PHP, LWY and KOK contributed to the writing, review and editing of the final draft of the manuscript.

Corresponding author

Correspondence to Wei-Yen Lim.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the hospital ethics review board (National Healthcare Group Domain Specific Review Board Ref 2019/00203). The requirement for informed consent was waived by the National Healthcare Group Domain Specific Review Board.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Prior publications

An abstract of this paper was previously presented at the Singapore Health and Biomedical Congress, held in Singapore in October 2023, and at the Society for Epidemiologic Research Conference held in Portland, Oregon, United States of America in June 2023.

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Lim, M.Y., Lian, W., Phua, H.P. et al. Association between serum urate levels and all-cause mortality, cardiovascular and renal outcomes among gout patients in Singapore. BMC Rheumatol 8, 71 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41927-024-00449-9

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41927-024-00449-9

Keywords

BMC Rheumatology

ISSN: 2520-1026

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