Association between mixed venous oxygen saturation and serum uric acid levels in patients with heart failure

doi:10.21203/rs.3.rs-3927803/v1

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Association between mixed venous oxygen saturation and serum uric acid levels in patients with heart failure

https://doi.org/10.21203/rs.3.rs-3927803/v1

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Hypoxia leads to increased purine metabolism in tissues, resulting in increased serum uric acid (UA) levels, and may also cause impaired UA excretion in the kidneys and intestinal tract. However, the relationship between hypoxia and serum UA levels in patients with heart failure remains largely unexplored. Because mixed venous oxygen saturation (SvO2) is an acute indicator of systemic oxygenation, in this study, we investigated the relationship between SvO2 and serum UA levels. This retrospective analysis included 386 patients with heart failure who underwent cardiac catheterization at our institution. The relationship between SvO2 and serum UA levels was examined by single regression analysis. Stratified regression analysis, structural equation modeling, and partial correlation analysis were used to examine the effects of eight factors known to influence SvO2 and serum UA levels. The single regression analysis showed a significant negative correlation between SvO2 and serum UA levels (P < 0.001). Significant negative correlations were also observed in many subgroups in the stratified analysis, in the path diagram based on structural equation modeling, and in the partial correlation analysis. These results suggest a strong and possibly direct relationship between SvO2 and serum UA levels that is not mediated by any known factor.

Health sciences/Biomarkers/Predictive markers

Health sciences/Biomarkers

Health sciences/Cardiology

Health sciences/Nephrology

Hyperuricemia has been associated with hypertension, metabolic syndrome, dementia, as well as coronary artery, cerebrovascular, and kidney disease.^1–7 Prior studies have also reported associated risk factors for hyperuricemia, including age, sex, body mass index (BMI), estimated glomerular filtration rate (eGFR), triglyceride (TG) levels, and glycated hemoglobin (HbA1c) levels, but some differences exist among reports.^8–13

Hypoxia adversely affects various tissues in the body. Although its effects on nucleic acid synthesis have not been fully investigated, it is possible that the production of uric acid (UA), a terminal metabolite of purines, is enhanced as energy metabolism is reduced.¹⁴ In addition, hypoxia in the kidneys and intestinal tract may also result in impaired UA excretion.^15–18 Hence, hypoxia is likely to cause hyperuricemia via accelerated synthesis and decreased excretion of UA. Nonetheless, few studies have examined the relationship between hypoxia and serum UA levels in clinical settings, particularly in patients with heart failure. Elevated UA levels have been reported in patients with sleep apnea syndrome.^19–21 Although these studies strongly suggest a relationship between hypoxia and hyperuricemia, they are limited to a specific patient population. In addition, a more sensitive index of tissue oxygenation may be needed to demonstrate the relationship between hypoxia and hyperuricemia more clearly.

Mixed venous oxygen saturation (SvO2) is measured in blood samples from the pulmonary artery, containing mixed blood from the superior and inferior vena cava and coronary sinus, and represents the amount of oxygen remaining in venous blood. Thus, SvO2 may be an appropriate indicator of the degree of systemic oxygenation.^22,23 Theoretically, SvO2 is related to arterial blood oxygen saturation (SaO2), oxygen consumption (VO2), hemoglobin (Hb) levels, cardiac output (CO), and other factors.^23,24 Clinically, SvO2 is a valuable indicator for guiding systemic management, particularly in critically ill patients. However, as SvO2 measurement requires cardiac catheterization, which is an invasive procedure, not many clinical studies have investigated SvO2.

Therefore, in the present study, we examined the relationship between SvO2 as an indicator of hypoxia and serum UA levels in patients with heart failure who underwent cardiac catheterization for cardiac function evaluation.

Characteristics of the study population

A total of 386 patients (male: n = 264, 68.4%) with a mean age of 70.2 ± 14.3 years were included in the analysis. The patients’ clinical characteristics are shown in Table 1. The mean SvO2 was 67.2 ± 7.62% and the mean serum UA level was 6.43 ± 2.24 mg/dL. The median age, BMI, cardiac index (CI), and TG, HbA1c, and Hb levels were 73 years, 22.4 kg/m², 2.52 L/min/m², 93 mg/dL, 5.9%, and 12.4 g/dL, respectively.

Table 1

Clinical characteristics of the study population
Characteristics (N = 386)	Number (%) or mean ± SD
Sex (male/female) Age (years) BMI (kg/m²) Active tobacco smoking Hb (g/dL) Cr (mg/dL) eGFR (mL/min/1.73 m²) UA (mg/dL) FBS (mg/dL) HbA1c (%) TG (mg/dL) HDL-C (mg/dL) LDL-C (mg/dL) LDL-C/HDL-C CRP (mg/dL) BNP (pg/mL) LVEF (%) CI (L/min/m²) SvO₂ (%)	264/122 (68.4/31.6) 70.2 ± 14.3 22.9 ± 4.43 57(14.8) 12.5 ± 2.33 2.10 ± 2.70 50.1 ± 28.8 6.43 ± 2.24 113.5 ± 38.1 6.05 ± 0.91 103.7 ± 45.6 52.8 ± 16.1 101.2 ± 33.3 2.06 ± 0.83 0.97 ± 2.25 468.9 ± 614.1 43.5 ± 15.2 2.6 ± 0.68 67.2 ± 7.62
Underlying disease
Atrial fibrillation Hypertension Diabetes mellitus Dyslipidemia Renal dysfunction* Hyperuricemia* Hemodialysis Ischemic heart disease Valvular heart disease Cardiomyopathy Constrictive pericarditis Myocarditis Congenital heart disease	48 (12.4) 286 (74.1) 126 (32.6) 197 (51.0) 241 (62.4) 224 (58.0) 44 (11.4) 149 (38.6) 120 (31.1) 103 (26.7) 6 (1.55) 4 (1.04) 3 (0.78)
Medications
ACE inhibitors ARBs Beta blockers Calcium channel blockers Diuretics Statins Non-statin for dyslipidemia Oral antidiabetic agents Insulin GLP-1 receptor agonist UA-lowering agents SGLT2 inhibitor	121 (31.3) 105 (27.2) 250 (64.8) 127 (32.9) 246 (63.7) 157 (40.7) 58 (15.0) 66 (17.1) 24 (6.22) 14 (3.63) 130 (33.7) 24 (6.22)
*Renal dysfunction = eGFR < 60 mL/min/1.73 m²
*Hyperuricemia = UA > 7.0 mg/dL or UA-lowering drug users
ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; BNP, brain natriuretic peptide; CI, cardiac index; Cr, creatinine; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; FBS, fasting blood sugar; GLP, glucagon-like peptide; HbA1c, glycated hemoglobin; HDL-C, high-density cholesterol; LDL-C, low-density cholesterol; LVEF, left ventricular ejection fraction; SD, standard deviation; SGLT, sodium–glucose cotransporter

Single regression analysis results

The results of the single regression analysis showed a significant negative correlation between SvO2 and serum UA levels (Table 2 and Fig. 1).

Table 2

Results of the single regression analysis of SvO2 and UA levels
Groups	Non-standardized coefficient		Standardized regression coefficient	Test statistic	P value	95% CI
Groups	Regression coefficient	Standard error	Standardized regression coefficient	Test statistic	P value	95% CI
All patients	-0.050	0.015	-0.170	-3.375	< 0.001	-0.079 to -0.021
CI, confidence interval; SvO2, mixed venous oxygen saturation; UA, uric acid

Stratified regression analysis results

In the regression analysis with stratification (Table 3), a significant negative correlation between SvO2 and serum UA levels was observed in all subgroups except in those with eGFR < 60 mL/min/1.73 m², HbA1c > 5.9%, CI > 2.52 L/min/m², CI < 2.52 L/min/m², and Hb < 12.4 g/dL.

Structural equation modeling results

The path diagram based on structural equation modeling is shown in Fig. 2. Detailed results of the structural equation modeling are presented in Table 4. We observed a significant negative correlation between SvO2 and serum UA levels, with a standardized estimate of -0.055 (P = 0.003).

Table 4

Structural equation modeling results
Clinical factor			Estimate	Standard error	Test statistic	P value	Standard estimate
Clinical factor			Estimate	Standard error	Test statistic	P value	Direct effect	Indirect effect	Total effect
SvO₂ (R² = 0.403)	←	Age	0.058	0.024	2.398	0.016	0.109	0	0.109
	←	Sex	-0.277	0.716	-0.387	0.699	-0.017	0	-0.017
	←	BMI	0.006	0.078	0.079	0.937	0.004	0	0.004
	←	eGFR	0.005	0.012	0.402	0.688	0.018	0	0.018
	←	TG	-0.007	0.007	-0.914	0.361	-0.039	0	-0.039
	←	HbA1c	-0.900	0.348	-2.583	0.010	-0.107	0	-0.107
	←	CI	6.918	0.481	14.382	< 0.001	0.623	0	0.623
	←	Hb	1.500	0.171	8.769	< 0.001	0.459	0	0.459
UA (R² = 0.096)	←	Age	0.001	0.009	0.077	0.938	0.004	-0.020	-0.016
	←	Sex	0.234	0.259	0.905	0.366	0.048	0.003	0.051
	←	BMI	-0.013	0.028	-0.466	0.641	-0.026	-0.001	-0.027
	←	eGFR	-0.009	0.004	-2.047	0.041	-0.112	-0.003	-0.115
	←	TG	0.002	0.003	0.857	0.391	0.045	0.007	0.052
	←	HbA1c	0.035	0.127	0.272	0.786	0.014	0.020	0.034
	←	CI	-0.128	0.217	-0.587	0.557	-0.039	-0.117	-0.156
	←	Hb	0.226	0.068	3.322	< 0.001	0.234	-0.086	0.148
	←	SvO₂	-0.055	0.019	-2.984	0.003	-0.188	0	-0.188
BMI, body mass index; CI, cardiac index; eGFR, estimated glomerular filtration rate; Hb, hemoglobin; HbA1c, glycated hemoglobin; SvO2, mixed venous oxygen saturation; TG, triglyceride; UA, uric acid

Partial correlation analysis results

Table 5 shows the results of the partial correlation analysis. Significant negative relationships between SvO2 and serum UA levels were observed with dialysis (P = 0.003), urate-lowering drugs (P < 0.001), diuretics (P = 0.008), SvO2 and all eight factors (P < 0.001), and all factors combined (P = 0.005) as control variables.

Table 5

Partial correlation analysis with all possible affecting factors as control factors
Groups	Control variable	Independent variable	Partial correlation coefficient	P value
All patients	HD	SvO₂<―>UA	-0.139	0.003
	Urate-lowering drugs		-0.173	< 0.001
	Diuretics		-0.122	0.008
	Age, sex, BMI, eGFR, TG, HbA1c, CI, Hb		-0.168	< 0.001
	Age, sex, BMI, eGFR, TG, HbA1c, CI, Hb, HD, urate-lowering drugs, diuretics		-0.134	0.005

BMI, body mass index; CI, cardiac index; eGFR, estimated glomerular filtration rate; HD, hemodialysis; Hb, hemoglobin; HbA1c, glycated hemoglobin; SvO2, mixed venous oxygen saturation; TG, triglyceride; UA, uric acid

In this study, we observed a significant negative relationship between SvO2 and serum UA levels, independent of all known factors (age, sex, BMI, eGFR, CI, TG, HbA1c, and Hb levels, dialysis, urate-lowering drugs, and diuretics) that may affect these two parameters. These results suggest that the relationship between SvO2 and serum UA levels is strong and probably direct, not mediated by any known factor.

The significant negative relationship between SvO2 and serum UA levels was observed in all analyses except in several subgroups in the stratified analysis. Although these results do not completely rule out the possibility of other factors influencing this relationship, it can be said that the relationship between SvO2 and serum UA levels is strong. One possible reason for the lack of significant correlation between these parameters in the stratified analysis in the high HbA1c, low eGFR, low Hb, and both CI subgroups is the small number of patients in these subgroups. However, other possible reasons should also be considered.

The levels of SvO2 and UA were not correlated in the high HbA1c subgroup. In insulin-resistant states, such as metabolic syndrome, hyperinsulinemia increases the protein expression of urate transporter 1 (URAT1) in the proximal tubules, leading to an enhanced uptake of UA. Conversely, in diabetes, the elevated glucose concentration in the proximal tubules affects glucose transporter 9 (GLUT9) isoform 2, promoting the exchange transport of glucose and uric acid. This may facilitate an increase in urinary excretion of UA. Additionally, there appears to be a mechanism in the collecting ducts that increases the excretion of UA. ²⁵ There is no apparent contradiction in considering these mechanisms as related, as supported by findings in other studies. ^11,26,27 The results of this study may be attributed to the likelihood that UA levels were relatively lower in the high HbA1c group, presumably due to the latter mechanism.

SvO2 and UA levels were also not associated in the low eGFR subgroup. Although the relationship between renal dysfunction and hyperuricemia is well known, various detailed mechanisms have been proposed. One mechanism of renal impairment induced by hyperuricemia is due to the effect of monosodium urate monohydrate crystals in the kidneys, as well as in joints and other organs, which activate the NLRP3 inflammasome cascade and lead to interleukin-1β activation.²⁸ Another proposed mechanism is that hyperuricemia induces intracellular oxidative stress, endothelial dysfunction, renal fibrosis, and glomerulosclerotic effects.^29,30 These mechanisms may result in impaired UA excretion, and other renal factors may also cause fluctuations in serum UA levels, making the relationship between SvO2 and serum UA levels less apparent.

SvO2 was not associated with UA levels in the low Hb subgroup. A prior study has reported a positive correlation between serum UA and iron and ferritin levels.³¹ Hence, the relationship between SvO2 and serum UA levels in this subgroup may be less prominent because iron deficiency may reduce xanthine oxidoreductase (XOR) activity and UA levels.^32,33

The lack of association between SvO2 and UA levels in both the low and high CI subgroups suggests that the low CI subgroup had a small number of patients, whereas the high CI subgroup had a generally higher SvO2 due to sufficient CO, which may have diminished the relationship with UA levels.

In other words, the present results for HbA1c, eGFR, Hb, and CI may mask the relationship between SvO2 and serum UA levels but do not negate this relationship, as each of these factors can be explained.

As described above, hypoxia may cause hyperuricemia via accelerated synthesis and decreased excretion of UA. The mechanisms underlying the relationship between hypoxia and increased UA synthesis are gradually being clarified. XOR is the enzyme required to produce UA in the purine metabolism.³⁴ As tissue XOR levels are relatively high in the liver, small intestine, vascular endothelial cells, and white adipose tissue,^34,35 endogenous UA synthesis is important in these tissues. Rauckhorst et al.³⁶ showed that hypoxia rapidly leads to enormous increases in purine degradation metabolites, such as hypoxanthine, xanthine, and UA, in the liver. In addition, Nagao et al.³⁷ showed that hypoxanthine is secreted from human adipose tissue and that the secretion is increased during hypoxia. This hypoxanthine might be converted to UA by XOR in other tissues, such as the liver and vascular endothelial cells. These purine metabolism changes in the liver and adipose tissue under hypoxic conditions might contribute to the increase in serum UA levels in patients with relatively low SvO2. Furthermore, as hypoxia itself leads to the activation of XOR in some cell lines,^38,39 the changes in purine metabolism induced by hypoxia may also be due to hypoxia-induced XOR activation.

The mechanisms underlying the relationship between hypoxia and decreased UA elimination have not been fully elucidated. Two-thirds of the UA in the human body is excreted from the kidneys, and one-third is excreted from the intestine. To the best of our knowledge, no studies have reported that hypoxia induces UA reabsorption in the kidney via URAT1 activation. As low SvO2 levels were correlated with low CI levels in the current study, it is possible that the reduced CI partially contributed to renal dysfunction and reduced urine synthesis,⁴⁰ which might have resulted in reduced UA excretion. Nonetheless, future studies should examine the effect of hypoxia on UA excretion in the urine. In the intestine, sirtuin-1 (SIRT1) accelerates UA excretion by activation of the ATP-binding cassette transporter G2 (ABCG2).^16,17 As SIRT1 activity is inhibited by hypoxia,¹⁵ low oxygen levels in the small intestine might lead to SIRT1 inactivation and impaired UA excretion by inactivation of the ABCG2.

This study has some limitations. First, the retrospective study design may have introduced selection bias and affects the generalizability of our findings. Second, the study was conducted at a single institution and our sample size was relatively small, which may have limited the statistical power to detect significant differences between groups in the structural equation modeling analysis. However, the fact that the relationship between SvO2 and serum UA levels was clearly demonstrated even with the limited number of patients is an indication of the strength of this relationship. Third, serum UA levels are affected not only by increased production and impaired excretion of UA but also by the amount of purine ingested in the diet. Although the patients in this study fasted prior to cardiac catheterization, the influence of daily diet cannot be completely ruled out. Fourth, SvO2 is theoretically related to SaO2, VO2, Hb levels, and CO. In the present study, data on SaO2 and VO2 were not available and were not included in the analysis. In particular, SaO2 may be related to UA levels. Although this is a subject for a future study, it is expected that SaO2 will show considerably higher values than SvO2, and the use of SvO2 seems more appropriate for the main purpose of this study. Finally, although the results of this study suggest that SvO2 and serum UA levels are closely related, it is not clear whether a truly direct relationship can be said to exist. We have merely demonstrated a strong correlation, and the possibility of other influencing factors cannot be ruled out. Continued exploration of factors affecting serum UA levels is needed.

The results of this study suggest a strong and direct negative correlation between SvO2 and serum UA levels in patients with heart failure, even after accounting for their respective confounding factors. This finding has important clinical implications in that it indicates that, in patients with heart failure, SvO2, which is measured by invasive procedures, may be estimated by serum UA levels, which are relatively easy to measure.

Study population

We retrospectively reviewed the records of patients with heart failure admitted to the Department of Cardiology of our hospital between June 2017 and May 2022. Patients who underwent cardiac catheterization for cardiac function evaluation and had SvO2 measurements performed were included. Patients who received oxygen therapy, those with pulmonary arterial hypertension, and those with left or right shunts were excluded. Pulmonary arterial hypertension was defined as a mean pulmonary arterial pressure of ≥ 20 mmHg and pulmonary arterial wedge pressure of ≤ 15 mmHg, according to the guidelines on pulmonary hypertension.

This study was approved by the Ethics Committee of The Jikei University School of Medicine for Biomedical Research (study protocol: 24–355(7121)). We complied with the routine ethical regulations of our institution. All clinical investigations were conducted in accordance with the principles set forth in the Declaration of Helsinki. We also posted a notice about the study design and contact information at the official website of our institution. As this was a retrospective study, the requirement for informed consent was waived by the Ethics Committee of The Jikei University School of Medicine for Biomedical Research and the Clinical Research Review Committee of The Jikei University School of Medicine for Biomedical Research. We also posted a notice about the study design and contact information at the official website of our institution (https://jikei.bvits.com/rinri/publish.aspx).

Parameter measurement

SvO2 measurement was performed using a Swan–Ganz thermodilution catheter (Bioptimal, Japan). The catheter was inserted through the femoral vein and its distal end was placed in the main pulmonary artery under fluoroscopy. Blood was drawn from the distal end; the first 10 mL of blood collected were discarded, after which blood was collected using a blood gas syringe, and any residual air was discarded immediately. The blood sample was immediately analyzed with a blood gas analyzer (ABL800 FLEX, Radiometer, Denmark) to determine the SvO2 value. CO was measured by thermodilution. Measurements were taken at least twice, and the average value was used as the measured value. Left ventricular catheterization was performed using a 4-F or 5-F Judkins right catheter (Terumo, Japan). Left ventricular ejection fraction was calculated by left ventriculography. In addition, various hematological and biochemical parameters were measured at the central laboratory of the hospital in peripheral venous blood samples obtained in the early morning on the day of catheterization after fasting.

Statistical analysis

Continuous data were summarized as mean ± standard deviation or median [upper and lower quartiles] depending on their distribution. Categorical data were summarized as frequencies with percentages.

First, we performed single correlation analysis between SvO2 and serum UA levels. Next, to eliminate as much as possible the effects of other factors on the relationship between SvO2 and serum UA levels, we conducted stratified regression analysis. Based on previous studies, age, sex, BMI, eGFR, TG level, and HbA1c level may influence serum UA levels, while Hb level and CO (in the current study we used the CI) may influence SvO2. Hence, in the stratified analysis, we examined whether the relationship between SvO2 and serum UA levels would be altered by these eight factors by dividing the study population into two groups for each factor. For age, sex, BMI, CI, and TG, HbA1c, and Hb levels, the cutoff values were the median values in our population, and for eGFR, the cutoff value was 60 mL/min/1.73 m². We performed single regression analysis in each subgroup. Next, we devised path diagrams based on structural equation modeling to examine the relationship between SvO2 and serum UA levels considering the possible effect of the above eight factors on SvO2 and serum UA levels, as well as the effect of SvO2 on serum UA levels. Finally, we conducted partial correlation analysis for the relationship between SvO2 and serum UA levels with dialysis, urate-lowering drugs, diuretics, the above eight factors, and all these combined as control variables.

Statistical analysis was performed using IBM SPSS Statistics version 28.0 (IBM Corp, Armonk, NY, USA). Structural equation modeling was performed using IBM SPSS Amos version 28 (Amos Development Corporation, Meadville, PA, USA). A P value of less than 0.05 was considered to indicate statistical significance.

Additional information

The authors declare no competing interests.

Author Contribution

YM, KO, and MY had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. RF, YT, TO, TT, TN, and MK contributed substantially to the study design, data analysis and interpretation, and writing of the manuscript.

Acknowledgements

We thank all participating physicians and nurses for their contributions to this study. We would also like to thank ELSEVIER Language Editing Services for English language editing.

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Table 3 is available in the Supplementary Files section.

No competing interests reported.

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Association between mixed venous oxygen saturation and serum uric acid levels in patients with heart failure

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Abstract

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Introduction

Results

Characteristics of the study population

Single regression analysis results

Stratified regression analysis results

Structural equation modeling results

Partial correlation analysis results

Discussion

Methods

Study population

Parameter measurement

Statistical analysis

Declarations

Additional information

Author Contribution

Acknowledgements

Data availability statement

References

Table 3

Additional Declarations

Supplementary Files

Status:

Version 1