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Heart failure and cardiomyopathies
Original research
Uric acid in advanced heart failure: relation to central haemodynamics and outcome
  1. Tania Deis1,
  2. Kasper Rossing1,
  3. Mads Kristian Ersbøll1,
  4. Emil Wolsk2 and
  5. Finn Gustafsson1
  1. 1Department of Cardiology, Rigshospitalet, Copenhagen, Denmark
  2. 2Department of Cardiology, Herlev-Gentofte, Copenhagen, Denmark
  1. Correspondence to Dr Tania Deis; tania.deis{at}regionh.dk

Abstract

Objective The role of hyperuricaemia as a prognostic maker has been established in chronic heart failure (HF) but limited information on the association between plasma uric acid (UA) levels and central haemodynamic measurements is available.

Methods A retrospective study on patients with advanced HF referred for right heart catherisation. Regression analyses were constructed to investigate the association between UA and haemodynamic variables. Cox models were created to investigate if UA was a significant predictor of adverse outcome where log1.1(UA) was used to estimate the effect on outcome associated with a 10% increase in UA levels.

Results A total of 228 patients were included (77% males, age 49±12 years, mean left ventricular ejection fraction (LVEF) of 17%±8%). Median UA was 0.48 (0.39–0.61) mmol/L. UA level was associated to pulmonary capillary wedge pressure (PCWP) and cardiac index (CI) in univariable (both p<0.001) and multivariable regression analysis (p<0.004 and p=0.025 for PCWP and CI). When constructing multivariable Cox models including PCWP, CI, central venous pressure, age, estimated glomerular filtration rate (eGFR), use of loop diuretics and LVEF, log1.1(UA) independently predicted the combined endpoint (left ventricular assist device, total artificial heart implantation, heart transplantation or all-cause mortality) (hazard ratio (HR): 1.10 (1.03–1.17), p=0.004) as well as all-cause mortality (HR: 1.15 (1.06–1.25), p=0.001).

Conclusions Elevated UA is associated with greater haemodynamic impairment in advanced HF. In adjusted Cox models (age, eGFR, LVEF and haemodynamics), UA predicts the combined endpoint and all-cause mortality in long-term follow-up.

  • heart failure
  • biomarkers
  • heart failure, systolic

Data availability statement

Data are available on reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article 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, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/openhrt-2022-002092

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    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • Hyperuricaemia is a strong predictor of mortality in chronic heart failure (HF) but the exact pathophysiological link between hyperuricaemia and HF has yet to be fully clarified and the role of deranged haemodynamics is largely unknown.

    WHAT THIS STUDY ADDS

    • We now know that an association between central haemodynamics and plasma uric acid levels exist but mechanisms beyond haemodynamic impairment explain the increased adverse outcome related to hyperuricaemia.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • This study brings pathophysiological insights into the world of hyperuricaemia in HF and may guide further research exploring the link between hyperuricaemia and HF.

    Introduction

    Heart failure (HF) is a progressive condition associated with poor quality of life1 and high mortality.2 It is characterised by a complex pathophysiology, including elevated ventricular filling pressure and impaired organ perfusion, affecting homeostasis and causing metabolic derangement. Hyperuricaemia refers to elevated uric acid (UA) level and is a metabolic complication frequently observed in HF. UA is the end-product of the metabolic breakdown of purines catalysed by the enzyme xanthine oxidase (XO) known to generate free oxygen radicals, and UA may be viewed as a marker of oxidative stress.3 In HF, UA is predictive of symptom burden according to New York Heart Association (NYHA) class4 and has been recognised as a strong independent predictor of morbidity and mortality in HF.5–7 The exact pathophysiological link between hyperuricaemia and HF has yet to be established and it is unclear if hyperuricaemia plays a causative role in disease progression or is merely a marker associated with a poorer prognosis.

    Sparse literature has been published on the association between haemodynamics and hyperuricaemia.8 9 This paper explores the relation between UA and haemodynamic measurements in patients with advanced HF to bring insights into the pathophysiological mechanisms underlining the disease.

    Methods

    Study design and patient population

    Details of the study design have previously been published.10

    This is a retrospective study on HF patients referred for non-urgent right heart catherisation (RHC) for evaluation for advanced therapy (ie, implantation of left ventricular assist device, total artificial heart implantation or heart transplantation) or as a part of assessment of advanced HF at the Department of Cardiology at Copenhagen University Hospital, Rigshospitalet from 1 January 2002 to 31 October 2020. Patients were identified through the hospital’s cardiac catheterisation database, where data were extracted and linked to the hospital’s echocardiography database and patients’ medical records. In case of repeated RHC during the time period, only data from the first catheterisation was used. The inclusion criteria were a left ventricular ejection fraction (LVEF)<45% and a measurement of plasma UA within 14 days of the RHC. Exclusion criteria comprised congenital heart defects, young age (under 16 years), prior heart transplantation or mechanical circulatory support at the time of the haemodynamic evaluation, unstable patients requiring intensive care, dialysis or assisted ventilation. We identified 228 patients who fulfilled the criteria, and they formed the studied population.

    Since patients were not required to have symptoms of advanced HF at the time of the RHC, some patients were classified as NYHA II. These patients were included, as they had recently experienced advanced HF symptoms and/or their HF condition was considered so severe that it justified referral for evaluation for advanced therapies. All patients were considered to have an element of chronic HF, but some patients could be in the state of worsening HF with decompensation during the time of their RHC.

    Patient and public involvement

    Patients or members of the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research (not appropriate or possible).

    Alcohol consumption

    Information was retrieved from the patients’ medical records. High alcohol consumption was defined as more or equal to 14 units/week for women and 21 units/week for men according to the Danish Health Authority’s guidelines 2010 (the limits of appropriate alcohol intake in the national guidelines are not universally accepted).

    Haemodynamic evaluation

    The invasive protocol followed the department’s routine diagnostic procedure. Four different experienced physicians performed all the haemodynamic evaluations in the cardiac catheterisation laboratory using a Swan-Ganz catheter. Appropriate zeroing and calibration of the pressure transducer were performed before measurements. The catheter was inserted in the femoral vein or the internal jugular vein where correct placement was confirmed by fluoroscopy and by visualisation of pressure curves on a monitor.

    Patients underwent RHC with determination of central venous pressure (CVP), pulmonary capillary wedge pressure (PCWP), pulmonary artery systolic pressure, pulmonary artery diastolic pressure, mean pulmonary artery pressure, right ventricular systolic pressure, right ventricular diastolic pressure, cardiac output (CO) using thermodilution technique, heart rate (HR), systolic blood pressure and diastolic blood pressure. Arterial blood pressure was measured noninvasively using a semiautomatic inflatable blood pressure cuff.

    Derived variables were calculated as follows: Cardiac Index (CI) was determined as CO divided by the body surface area (BSA). BSA was determined using the DuBois method. MAP was estimated using the formula Embedded Image .

    Blood samples

    Laboratory tests were collected within 2 days of the RHC except plasma UA where measurements within 14 days of the procedure were included. Estimated glomerular filtration rate (eGFR) was estimated using the chronic kidney disease epidemiology collabaration (CKD-EPI) formula.11 The following values for UA were defined by our laboratory as; upper normal limit for women: 0.40 mmol/L (~6.7 mg/dL) (age 50–125 years) and upper normal limit for men: 0.48 mmol/L (~8.1 mg/dL) (age 18–125 years).

    Statistical analysis

    All continuous variables were reported as mean±SD except N-terminal pro-B-type natriuretic peptide (NT-proBNP) and UA, which were reported as median with IQR as they were non-normally distributed. Categorical variables were reported as numbers and/or percentages.

    Regression models were constructed to test the association between UA and central haemodynamics and other relevant variables known to impact UA levels. A multivariable model was constructed using statistically significant variables from univariable models and analysed using forward analysis. UA was log transformed for a better model fit. Data are presented backtransformed in table 2.

    View this table:
    Table 2

    Linear regression models—association between UA and haemodynamic and clinical variables

    End of follow-up was set to 31 October 2020. Events were defined as following: death from all causes, left ventricular assist device implantation, total artificial heart implantation or heart transplantation. Kaplan-Meier survival curves were plotted, dividing patients into normal and high UA levels (according to gender-specific cut-off values) and presented with a rank p value. Cox proportional hazards models was used to estimate the ability of UA to predict (1) all-cause mortality and (2) the combined endpoint of all-cause mortality, left ventricular assist device, total artificial heart or heart transplantation and presented as estimated HRs with 95% CI. Since plasma levels of UA has a relatively narrow range, we used log1.1(UA) (Embedded Image) to estimate the effect on outcome associated with a 10% increase in UA levels. The interaction effect of gender on log1.1(UA)’s ability to predict outcome was tested.

    Model assumptions for all models were assessed including proportional hazard for Cox regression analyses and lack of collinearity by tolerance and variance inflation factor and assumptions were met. Two-sided p values were used, and a p<0.05 was considered statistically significant. Statistical analyses were performed using SPSS (V.25, IBM) and R.

    Results

    Clinical characteristics

    A total of 228 patients formed the study population. Their characteristics are presented in table 1.

    View this table:
    Table 1

    Baseline characteristics

    The cohort was of relatively young age (49±12 years) and was dominated by men (77%). They had a severely reduced LVEF (17±8) and most patients were in NYHA class III or IV (67%). Almost one-third (29%) had ischaemic HF aetiology and two-thirds non-ischaemic aetiology (71%). Median UA was 0.48 (0.39–0.61) mmol/L. The average time between UA measurements and RHC was 2.6 days.

    The cohort was divided into two groups according to high (women>0.40 mmol/L, men>0.48 mmol/L) or normal UA levels, where 132 patients (58%) had high UA levels and 96 patients (42%) had normal UA levels. Compared with patients with normal UA levels patients with high UA had lower eGFR (70±24 mL/min/1.73 m2 vs 82±26, p=0.001) and were more often prescribed loop diuretics (93% vs 84%, p=0.034). Sixty patients (74%) with normal UA levels were on high doses of loop diuretics (defined as more or equal to 80 mg furosemide) and 100 patients (82%) with high UA levels were on high doses of loop diuretics (p=0.178). There were no differences between groups in standard of care HF medication or inotropic support (dopamine, dobutamine, milrinone or norepinephrine) at the time of the haemodynamic evaluation nor in use of metolazone, allopurinol or colchicine. Further, there were no statistically significant differences in age, sex, LVEF, body mass index (BMI), medical history of diabetes mellitus or alcohol consumption between the two groups.

    Patients with high UA had higher PCWP (22±8 vs 19±8, p=0.010), higher CVP (19±8 vs 10±5, p<0.001) as well as lower CI (2.3±0.6 vs 2.5±0.7, p=0.012) compared with patients with normal UA. There were no differences in MAP (p=0.518) or HR (p=0.774).

    Association between UA and different haemodynamic and clinical variables

    A significant association was found between UA and the haemodynamic parameters CVP (p<0.001), PCWP (p<0.001) and CI (p<0.001) (figure 1) as well as eGFR (p<0.001), LVEF (p=0.009) and sex (p<0.001) in univariable analysis. MAP (p=0.846), allopurinol (p=0.195), haemoglobin (p=0.291), age (p=0.474), high alcohol consumption (p=0.761), loop diuretics (p=0.052) and BMI (p=0.055) were not statistically significantly associated with UA.

    Figure 1
    Figure 1

    (A, B) The association between the natural logarithm of uric acid (UA) and pulmonary capillary wedge pressure (PCWP) and CI.

    In a multivariable analysis including all significant variables from the univariable analysis, PCWP (p=0.004), CI (p=0.025), eGFR (p<0.001) and sex (p<0.001) was found to be significantly associated with UA. CVP and LVEF did not reach statistical significance in the multivariable analysis (table 2). There were no signs of multicollinearity in the multivariable analysis.

    UA and outcome

    Mean follow-up time for the cohort was 9 years. One patient was lost to follow-up. At the end of the study, 87 patients (38%) had died, 44 (19%) had an LVAD implanted and 76 (33%) had received a heart transplantation. LVAD implantation was used as destination therapy or bridge-to-transplantation, and 22 out of 44 patients who had an LVAD implantation were later transplanted. Of the patients alive at follow-up, 70 (50%) were alive with an LVAD or a heart transplant, whereas 70 (50%) were alive without.

    The results of the univariable and multivariable Cox regression models are presented in table 3. In univariable analysis, log1.1(UA) predicted the combined endpoint of all-cause mortality, left ventricular assist device implantation, total artificial heart implantation or heart transplantation (HR 1.17, 95% CI 0.10 to 1.24, p<0.001) and all-cause mortality (HR 1.14, 95% CI 1.07 to 1.21, p<0.001).

    View this table:
    Table 3

    Cox proportional hazard models estimating the association between log1.1(uric acid) and the risk of the combined endpoint and all-cause mortality in patients with chronic heart failure

    In multivariable Cox models adjusted for the haemodynamic parameters CI, CVP and PCWP, log 1.1(UA) remained independently associated with the combined endpoint (HR 1.12, 95% CI 1.05 to 1.19, p<0.001) and all-cause mortality (HR 1.12, 95% CI 1.05 to 1.20; p<0.001). When further adjusting for age, eGFR, use of loop diuretics and LVEF in the multivariable models, log1.1(UA) remained an independent predictor of the combined endpoint (HR 1.10, 95% CI 1.03 to 1.17, p=0.004) and all-cause mortality (HR 1.15, 95% CI 1.06 to 1.25, p=0.001). Further adjusting for use of allopurinol (sensitivity analysis, online supplemental file) did not impact the ability of log1.1(UA) to predict the combined endpoint (HR 1.10, 95% CI 1.03 to 1.17, p=0.003) or all-cause mortality (HR 1.16, 95% CI 1.06 to 1.26, p=0.001).

    There was no interaction effect of gender on log1.1(UA)’s ability to predict freedom from the combined endpoint (p=0.192) and all-cause mortality (p=0.962) in univariable Cox models.

    A Kaplan-Meier curve demonstrated significant differences between patients with high UA levels and normal UA levels for the combined endpoint (log rank p<0.001) and all-cause mortality (log rank p=0.034) (figure 2).

    Figure 2
    Figure 2

    Kaplan-Meier curves. Normal uric acid (UA) level (black): Uric acid less or equal to 40 mmol/L (female) or 0.48 mmol/L (male). High UA (yellow) level: Uric acid more than 40 mmol/L (female) or 0.48 mmol/L (male). UA, uric acid.

    Discussion

    In the current study investigating the relation between central haemodynamics and UA levels, an association between UA and left sided filling pressure (PCWP) was demonstrated, as was the case for CI in multivariable regression models. We further found that UA was a strong independent predictor of all-cause mortality and the combined endpoint of all-cause mortality, left ventricular assist device, total artificial heart or heart transplantation in multivariable Cox hazard models.

    Hyperuricaemia is commonly observed in HF and has been established as an independent predictor of increased risk of HF hospitalisations and death by several studies.5–7 Not only is hyperuricaemia a predictor of poor prognosis in HF populations, studies have showed that UA predicts incident HF with both preserved12 and reduced ejection fraction.13–16 There also seems to be a concentration–effect relationship between UA levels and risk of incident HF, and Huang et al showed in a meta-analysis, that for every 1 mg/dL increase in UA, the odds of developing HF increase by 19%.7

    In agreement, we found UA to be a strong predictor of adverse outcome in our adjusted Cox models, where a 10% increase in plasma UA was associated with a 10% increased risk of the combined endpoint and a 15% increased risk of all-cause mortality.

    Although UA’s prognostic value has been established in HF, the exact mechanisms behind the relation of UA levels and outcome have yet to be fully clarified. UA is mainly excreted by the kidneys and renal impairment (frequently observed in HF) as well as diuretic-induced impaired UA excretion can lead to plasma UA accumulation. However, several studies have shown that hyperuricaemia remains a strong predictor of worse outcome even after adjusting for renal function and loop diuretics. Our study is in line with these findings, as we found that the risk of worse outcome with higher UA persisted after adjustment for renal function and diuretic use. Hyperuricaemia may also be a result of increased UA production due to upregulated XO activity.17 XO can produce free oxygen radicals (ROS) that can lead to oxidative stress-related cardiac and vascular tissue injury and contribute to the pathophysiology of cardiac remodelling and HF.18 However, use of UA-lowering agents by XO inhibition has been investigated and neither the OPT-CHF study (oxypurinol compared with placebo for class III-IV NYHA congestive heart failure)19 (oxypurinol) nor the EXACT study20(effects of xanthine oxidase inhibition in hyperuricemic heart failure patients: the xanthine oxidase inhibition for hyperuricemic heart failure patients) (allopurinol) showed significant benefit of pharmacological lowering of UA levels. In a small group of CHF patient, UA was lowered by Benzbromarone, a uricosuric treatment without XO inhibition, but it had no effect on either LVEF, BNP, echocardiographic measurements or NYHA class,21 indicating that lowering UA in itself may not have a beneficial role in CHF pathophysiology. Whether there is potential effect of lowering UA still needs to be fully elucidated and more work is needed to test the clinical efficacy of pharmacological therapies lowering UA before any final conclusions can be made. In the current study, allopurinol was used in 8% of the patients, with no significant difference in the proportion of patients presenting with normal (treated) versus abnormal levels of UA. The low number of patients treated with allopurinol precludes robust analyses of the importance of uricosuric treatment in this study but adding allopurinol treatment to the Cox regression analyses did not change the association between UA and survival significantly.

    Deranged haemodynamics characterise advanced HF, but sparse attention has been given to haemodynamics in research on hyperuricaemia in HF populations. Only a few, smaller studies have been published showing an association (not causality) between deranged haemodynamics and hyperuricaemia. More than a decade ago Hoeper et al8 presented a short report on the relation between haemodynamics and UA levels on 36 HF patients referred for heart transplantation. They showed that UA was significantly associated to PCWP but not CVP or CI in multivariate regression analysis. Amin et al9 also explored the relationship between UA and haemodynamics investigating 50 patients with a Swan-Ganz catheter and they found similar results. In our cohorts of 228 patients, we also found a strong association between UA and left sided filling pressure. However, in contrast to the two above-mentioned studies, we also found a significant association between UA and CI. The discrepancies could possibly be explained by a greater power in the current study, since we investigated a larger number of patients than in prior studies.

    Whether the association is related to high filling pressure and/or inadequate perfusion leading to end-organ damage and increased cell turnover or potentially UA’s proinflammatory mechanisms worsening circulatory decompensation is speculative and interpretation should be made with caution since causality still needs to be established. More work is needed investigating the link between haemodynamic derangements and hyperuricaemia to establish if there is a cause–effect relationship or if it is a question of unmeasured confounding. Future studies should test whether changes in haemodynamics is followed by changes in UA levels including data from repeated haemodynamic evaluations and UA measurements.

    UA and haemodynamics are associated (in regression analysis) and while UA and haemodynamics are known to correlate with outcome independent of other factors (age, gender, etc), we cannot conclude that the two are independent of each other.

    Sodium-glucose cotransporter 2 (SGLT-2) inhibitors, used to treat diabetic patients with poor glycaemic control, have proven beneficial on HF hospitalisations and cardiovascular mortality regardless of the presence of diabetes in HF patients22 but the exact mechanism behind its cardioprotective effects is unknown. A recent study has demonstrated that dapagliflozin significantly reduce UA levels in patients with HF23 and similar findings have been reported for empagliflozin.24 Not only do SGLT-2 inhibitors reduce UA levels, but interestingly, it has recently been demonstrated that SGLT-2 inhibitors also have a significant effect on central haemodynamics, decreasing left sided filling pressure compared with placebo-treated controls.25 Whether part of the treatment effect of SGLT-2 inhibitors is related to lowering left-sided filling pressure is unknown and needs to be explored further.

    Study limitations

    The study is limited by its retrospective design, where we cannot conclude on any cause–effect relationship. The generalisability of our results is subject to certain limitations. For instance, the study included a selected HF patient population that had an indication for RHC and was referred for evaluation at a single specialised centre, exposing the study to the possibility of selection bias.

    Further, a much younger HF population than the average HF patients was investigated, thereby making these findings less generalisable to the entire HF population. Only two patients were prescribed SGLT-2 inhibitors making it impossible to evaluate the effect of SGLT-2 inhibitors in this study. No data on clinical diagnoses of gout were available so implications of repeated episodes of inflammation associated with hyperuricaemia cannot be assessed from our data.

    Conclusion

    An association between central haemodynamics and UA exist, but it is unknown if this represents a causal relationship or whether is remains a question of unmeasured confounding. Adjusting for central haemodynamics in Cox models made no significant differences and mechanisms beyond haemodynamic impairment are more likely to explain the increased adverse outcome related to hyperuricaemia.

    Data availability statement

    Data are available on reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The study complies with the Declaration of Helsinki and the research protocol was approved by the local research ethics committee (3-3013-1365/1) and the Data Protection Agency (P-2020-1087). Individual patient consent was not required due to the retrospective study design.

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    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    Footnotes

    • Presented at This study was presented as an abstract at Heart Failure 2022 Congress (https://doi.org/10.1002/ejhf.2569)

    • Contributors Conception and design: FG and TD. Data extraction and data analysis: TD. All authors discussed the results and contributed to the final manuscript. All authors reviewed and approved of the final manuscript before submission. Guarantors and responsible for the overll content: FG and TD

    • Funding KR has received fees from Abbott, AstraZeneca, Pfizer, Novartis, Boehringer Ingelheim and Orion Pharma. FG has received research grants from the Novo Nordisk Foundation (grant no NNF20OC0060561) and reports fees from Abbott, AstraZeneca, Pfizer, Novartis, Boehringer Ingelheim, Pharmacosmos, Orion Pharma and Alnylam.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.