Serum Uric Acid Showed U-shaped Relationship with All-Cause Mortality and Cardiovascular Mortality in High Atherosclerosis Risk Patients: ASSURE Study

DOI: https://doi.org/10.21203/rs.3.rs-60123/v1

Abstract

Background: Previous studys have demonstrated association between hyperuricemia and cardiovascular disease (CVD), and Framingham study has confirmed patients with high atherosclerotic risk (HAR) had worse prognosis. But after controlling other traditional atherosclerotic risks, the association between serum uric acid (SUA) and all-cause mortality, cardiovascular mortality remains controversial, especially in HAR patients.

Objective: The aim of study was to reveal the relationship with SUA and all-cause mortality, cardiovascular mortality in HAR patients.

Methods: The multi-center cohort study comprised 3,640 participants (1927male, 1713 female),whose mean age was 60.2±10.4 years andmean follow-uptimewere 68.85±11.37 months. Factors related to cardiovascular mortality and all-cause mortality, major adverse cardiac events in-hospital during follow-up were tested by multivariate Cox regression analysis and log-rank test. Restricted cubic splines with knots were used to explore theshape of dose-response relationship with SUA levels and Hazard risk (HR) of all-cause and CVD mortality.

Results:The study showed SUA presented U-shaped relationship with all-cause and cardiovascular mortality. HR of all-cause mortality with hyperuricemia and hypouricemia was 2.11, 95% CI(1.61-3.07), and 2.05, 95% CI(1.35-2.90), respectively. HR of Cardiovascular mortality was 2.42, 95% CI (1.61-3.12), and 1.95, 95% CI(1.29-2.90), respectively.

Conclusion:Abnormal SUA levels maybe significant and independent risk factors for all-cause and cardiovascular mortality. Routine SUA evaluation and intensive management are pressing needed, especially in HAR patients.

Background

The association between hyperuricemia and arterial stiffness as well as endothelial dysfunction has been demonstrated in humans1 and serum uric acid (SUA) has been suggested to be an important modulator of the inflammatory process2. Several studies reported SUA severed as a marker of an underlying pathophysiological process3,4,and some evidence revealed elevated SUA concentrations were associated with higher risk of hypertension and CVD 6-8. But the relationship between SUA levels and CVD mortality remained controversial , due to it with established cardiovascular risk factors was complex, and latter could be considered as a confounding factor9. Moreover, although previous study simplied hyperuricemia as atherogenesis role in the development of CVD10-12, there was little research on the hyperuricemia and hypouricemia relationship with all-cause mortality and CVD mortality.

Meanwhile, Framingham Heart Study conferred sufferers with HAR had worse CVD prognosis21-23. And that, when abnormal SUA levels combinded with HAR, the synergistic effect on prognosis was still unclear. However, few multi-center cohort study focused on it, especially in China patients. Therefore, the aim of this research wasto expound the relationship with SUA levels and all-cause mortality, cardiovascular mortality in HAR patients.

Methods

Study population

The study (ClinicalTrials.gov Identifier: NCT03616769) was a multi-center prospective Cohort Study. The first cross-sectional survey was conducted in 2011. The eligible participants were followed up from November 2011 to June 2018 (mean follow-up month was 68.71±11.35). During the followup time, 147subjects had missing data and 126 had no compliance. Thus, the study sample actually comprised 3,640 valid participants (1927 male, 1713 female), whose age older than or equal to 35 years (mean age 60.2±10.4 years) were followed up. A total of hospitalized subjects were consecutively enrolled from the cardiology departments of Beijing university affiliated hospitals and Shanghai Tongji university affiliated hospitals. All subjects are under treatment because of cardiovascular diseases. The inclusion criteria were with HAR patients. The exclusion criteria were severe congestive heart failure and severe renal failure subjects. Severe congestive heart failure was defined that above or equal to cardiac functional classify 3 formulated by the New York Heart Association (NYHA). Severe renal failure was defined as an estimated glomerular filtration rate <30 ml/min/1.73m2 (Figure1). All participants gave written informed consent to this study, which was approved by the ethics committee of Tongji University.

Cardiovascular events definitions

Hospitalized myocardial infarction was classified as definite or probable based on chest pain symptoms, cardiac enzyme levels, and electrocardiographic findings, or angioplasty. Coronary heart disease was determined to be present if there was (1) electrocardiographic (ECG) evidence of a prior myocardial infarction, (2) prior coronary artery bypass surgery or angioplasty, (3) Coronary angiography show coronary heart disease, (4) have symptoms of angina and ECG revealed myocardial ischemia performance or laboratory tests showed cardiac enzymes increased and exclude other types of disease, (5) a self-reported history of a physician-diagnosed heart attack. CAD death was classified as “definite” based on chest pain symptoms, hospital records, and medical history.

Assessment of cardiovascular events and Identification of Death from All-Causes and CVD

Cardiovascular events are composed of cardiac including non-fatal myocardial infarction, unstable angina, and coronary revascularization procedures during follow-up time. Exclusion criteria were stale angina (>6 months), revascularization procedure for CAD (>6 months) and myocardial infarction(>6 months).

In this study, the cardiovascular death was only cardiac event death. Medical records and death certificates of all patients who had an event were obtained and validated by cardiologist. Death was confirmed from hospital records or by contact with participants and their families. All materials were reviewed independently by five senior physicians of the cohort study to confirm the cause of death.

Hyperuricemia and Hyporuricemia definitions

Hyperuricemia refers to undertake normal purpurine diet, twice results of determination in different days, SUA ≥ 420 μmol/L or 7mg/dl (male), ≥ 357 μmol/L or 6mg/dl (female) 15. Hyporuricemia refers to undertake normal purpurine diet, twice results of determination in different days, SUA ≤ 178μmol/L or  ≤ 3 mg/dl(male), SUA ≤ 149μmol/L or ≤ 2.5 mg/dl (female)26,27.

Framingham Risk Score and High Atherosclerosis Risk

The Framingham risk score (FRS) was calculated based on coronary risk factors, including age, gender, total cholesterol, LDL-C, hypertension and smoking status according to the National Cholesterol Education Program-Adult Treatment Panel III algorithm17. The calculated total scores were used to estimate the 10-year coronary heart disease risk in participants without previous CVD, and when FRS more than 20% or among 10% and 20% was considered HAR27.

Framingham Risk Factors

Diagnostic criteria of hypertension was receiving antihypertensive medication or systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg (1 mm Hg = 133 kPa) as well as both have. The criterion for hypertension refers to patients who have single hypertension disease.

Similarly, the criterion of dyslipidemia also refers to patients who have single dyslipidemia disease. The definition of dyslipidemias is that abnormalities in the serum levels of lipids, including overproduction or deficiency. Abnormal serum lipid profiles include high total cholesterol, high triglycerides, low HDL-C, and elevated LDL-C.

Measurement of Ankle and Arm Blood Pressures

Qualified ultrasonographers measured ankle and brachial systolic blood pressures. Doppler ultrasound (Nicolet Vascular, Elite 100R, USA) was used to measure systolic blood pressure (SBP) in the bilateral brachial, tibial and dorsal pedal arteries. ABI has been shown to be a powerful independent marker of cardiovascular risk, and predictive ability similar to the Framingham criteria29, 30.

A questionnaire was designed to collect information about general characteristics, diagnosis, medical history and relation factors, medical treatment and biochemical examination in all participants.

Baseline measurements

Variables were obtained in all subjects, which include daily habits, medical histories, and blood samples. These samples are measured of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Cr), serum uric acid (SUA), glucose and FPG. Blood samples were drawn from an antecubital vein with a 19-gauge needle in a vacutainer system and their serum concentrations were assessed with commercially available kits. Glomerular filtration rate (GFR) was calculated as GFR (ml/min/1.73m2)= 186×creatinine- 1.154× age- 0.203 and GFR (ml/min/1.73m2) =142× creatinine- 1.154 ×age-0.203 in males and females, respectively. Glucose was measured on a Hitachi 717 analyzer (Roche) using enzymatic reagents also from Roche. Presence of symptomatic peripheral arterial disease was evaluated by the Rose questionnaire31. A previous myocardial infarction or ischemic stroke was documented by hospital records. Physical examination data include Body-Mass index (BMI), blood pressure and ABI. Severe congestive heart failure was defined that above or equal to cardiac functional classify 3 formulated by New York Heart Association (NYHA). Severe renal failure was defined as an estimated glomerular filtration rate <30 ml/ min/1.73m2.

Follow-up methods

Follow-up participants were contacted by physicians of the cohort study at annual intervals. Outcomes were obtained during annual phone interviews, 6-yearly follow-up examinations, hospital records, and death records. The primary clinical event endpoints of this study were estimated with all-cause mortality and cardiovascularmortality. The secondary endpoints were examined coronary heart disease (CHD) and CHD risk equivale, including peripheral arterial disease (PAD), stroke, DM, cardiovascular disease incident. Follow-up time was the number of years from the baseline first visit to every participant. For subjects who had more than one event, all the clinical events were considered for the analysis.

Statistical Analysis

All analyses were performed using the R statistical package (version 3.6.2) (http://www.r-project.org32). Continuous variables are expressed as the mean ± SD, and categorical variables as percentage. Continuous and categorical variables differences comparison was made by Independent samples ANOVA (analysis of variance) and the Chi-square test, as appropriate. Kruskal-Wallis test for non-normally distributed continuous variables. A p-value < 0.05 was considered statistically significant. Due to skewed distribution, TC, TG, HDL-C, LDL-C, Cr, SUA were logarithm-transformed (log) in analyses. Crude deaths from all-cause and CVD were examined by SUA stratification. Cumulative event rates were estimated with Kaplan-Meier survival curves, and probability values were calculated with the log-rank test. Cox proportional hazard analyses were performed to test the association of the SUA and deaths from all-causes or CVD. Cox regression model was adjusted for potential confounders, including age, gender, duration of hypertension, smoking state, dyslipidemia history, chronic renal insufficiency history, diabetes mellitus (DM) history, percutaneouscoronary angioplasty ( PTCA) history, coronary artery bypass grafting ( CABG ) history, PAD history, myocardial infarction (MI) history, ischemic stroke history, hypertension, ABI, FRS, eGFR, use diuretics, center, year of screening examination. Potential confounding variables with P < 0.10 were adjusted for multivariate analysis.  Restricted cubic splines with knots were used to further explore theshape of the dose-response relationship between the SUA levels and the HR of all-cause mortality and CVD mortality. Knots were at the 5th, 95th, and quartile of SUA distribution. Missing values were handled by K-means clustering imputation. All data P values were 2-tailed, and less than 0.05 were considered significant.

Table 1

Comparison of Subjects’ Baseline Characteristics of Gender Categories According to Serum Uric Acid Levels.

 

Male

Female

characteristics

quartile of SUA levels, umol / L

quartile of SUA levels, umol / L

0-242.0

242.1-312.0

312.1-386.75

>386.75

P value

0-242.0

242.1-312.0

312.1-386.75

>386.75

P value

Age( years)

67.3±11.3

65.8±11.7

65.7±11.8

68.0±11.5

0.002

65.6±10.8

67.3±10.5

67.7±9.7

70.4±9.9

<0.001

Diabetes , N (%)

198(41.8%)

174(35.9%)

154(31.8%)

159(32.9%)

0.006

310(43.7%)

189(42.3%)

134(45.1%)

99(38.2%)

0.372

DM duration(years)

2.8±5.0

3.0±6.0

2.3±4.8

2.5±5.0

0.173

3.6±6.1

3.8±6.4

3.8±7.1

3.6±6.9

0.925

Hypertension, N (%)

309(65.2%)

324(66.8%)

343(70.7%)

380(78.7%)

<0.001

479(78.7%)

347(78.7%)

252(84.8%)

216(83.4)

<0.001

HT duration(years )

8.5±11.5

8.9±11.2

9.7±12.2

10.8±11.3

0.012

8.3±10.8

10.5±11.6

12.1±11.8

12.6±13.1

<0.001

Dyslipidemia, N (%)

134(34.6%)

152(38.0%)

185(46.7%)

171(40.8%)

0.005

271(46.5%)

161(42.5%)

117(50.0%)

98(44.1%)

0.297

DL duration( years )

1.3±0.31

1.8±0.45

1.8±0.41

2.1±0.46

0.069

1.9±0.39

1.9±0.43

2.0±0.42

1.6±0.34

0.532

Smoking, N (%)

310(65.4%)

307(63.3%)

325(67.0%)

320(66.3%)

0.648

57(8.0%)

48(10.7%)

27(9.1%)

29(11.2%)

0.318

Smoking duration( years )

20.7±1.86

20.0±1.81

21.4±1.85

21.3±1.91

0.552

2.7±1.02

3.4±1.08

2.7±0.97

4.3±1.32

0.218

MI History, N (%)

87(18.4%)

83(17.1%)

94(19.4%)

102(21.1%)

0.439

60(8.5%)

51(11.4%)

34(11.4%)

27(10.4%)

0.307

Diuretics, N (%)

105(22.2%)

100(20.7%)

132(27.2%)

195(40.5%)

<0.001

140(19.8%)

116(26.0%)

106(35.8%)

111(43.0%)

<0.001

PTCA History, N (%)

56(11.8%)

70(14.4%)

69(14.2%)

64(13.3%)

0.631

59(8.3%)

42(9.4%)

24(8.1%)

18(6.9%)

0.721

CABG History, N (%)

18(3.8%)

16(3.3%)

19(3.9%)

20(4.1%)

0.918

9(1.3%)

7(1.6%)

7(2.4%)

5(1.9%)

0.634

IS History, N (%)

182(38.4%)

163(33.6%)

161(33.2%)

167(34.6%)

0.315

212(29.9%)

139(31.1%)

83(27.9%)

83(32.0%)

0.719

CRI History, N (%)

31(6.8%)

29(6.3%)

38(8.1%)

83(17.6%)

<0.001

56(8.1%)

29(6.6%)

32(11.0%)

53(21.1%)

<0.001

TG, mmol/L

1.4±0.80

1.5±0.90

1.6±1.00

1.8±1.50

<0.001

1.6±1.10

1.8±1.3 0

2.0±1.40

2.0±1.20

<0.001

HDL-c, mmol/L

1.2±0.40

1.2±0.3 0

1.1±0.40

1.1±0.50

0.106

1.3±0.40

1.2±0.30

1.3±0.40

1.2±0.40

0.001

LDL-c, mmol/L

2.5±0.80

2.7±0.80

2.7±0.90

2.6±0.80

<0.001

2.8±0.90

2.9±0.80

3.0±0.90

2.8±1.00

0.052

CRE

93.6±7.39

103.2±8.06

109.2±8.55

138.2±13.17

<0.001

79.0±6.73

84.9±6.83

91.2±6.53

143.0±15.9.0

<0.001

Blood glucose

6.8±0.30

6.4±0.27

6.0±0.24

6.1±0.26

<0.001

6.7±0.32

6.6±0.28

6.6±0.28

6.6±0.32

0.816

BMI, kg/m2

23.2±3.4

24.7±3.3

24.6±3.4

24.9±3.7

<0.001

23.8±3.6

24.3±3.8

25.1±3.5

24.9±4.0

<0.001

ABI

1.01±0.20

1.02±0.20

0.99±0.20

0.99±0.20

0.066

0.99±0.20

0.97±0.20

0.95±0.20

0.91±0.30

<0.001

FRS

454(24.7%)

459(25.5%)

461(25.3%)

462(25.1%)

0.910

408(21.9%)

446(24.8%)

296(16.7%)

258(13.6%)

<0.001

FRS††

357(24.1%)

379(25.5%)

375(25.3%)

373(25.1%)

0.647

488(27.2%)

424(23.1%)

287(15.6%)

247(12.4%)

<0.001

 systolic blood pressure (SBP), diastolic blood pressure (DBP ), Hypertension (HT), BMI (body mass index.), Total cholesterol (TC), Triglycerides (TG), Fasting plasma glucose (FPG), high density lipoprotein (HDL-c), low density lipoprotein (LDL-c), serum creatinine (Cr),serum uric acid (SUA), diabetes mellitus (DM), myocardial infarction (MI), Ischemic Stroke (IS), chronic renal insufficiency (CRI), Dyslipidemia (DL), cardiovascular disease (CVD), Peripheral arterial disease (PAD), ankle-rachial index (ABI), percutaneouscoronary angioplasty (PTCA), Hazard risk (HR), coronary artery bypass grafting (CABG), Framingham Risk Score (FRS), FRSAnalysis participants with Framingham risk score 10%-20%,  FRS†† Analysis participants with Framingham risk score > 20% were identified as at high risks for 10-year coronary heart disease. 
 
Table 2

Adjusted Hazards Risks for All-cause Mortality and cardiovascular disease (CVD) Mortality By Cox Regression Models According to Serum Uric Acid Levels.

Variable

 

SUA

 

 

P for difference

Characteristic

SUA level, μmol/L

Quartile 1

Quartile 2

Quartile 3

Quartile 4

 

0-242.0

242.0SUA≤312.0

312.0SUA≤386.75

386.75SUA

 

All-causemortality

973

789

672

613

 

Number of deaths

238(24.5%)

151(19.1%)

154(22.9%)

220(35.9%)

 

Multivariableadjustment

 

 

 

 

 

Model1

2.13(1.45-3.09)

1

1.94(1.23-2.92)

2.20(1.73-3.17)

<0.001

Model 2

2.10(1.21-3.12)

1

1.90(1.19-2.98)

2.17(1.70-3.09)

<0.001

Model 3

2.06(1.35-2.90)

1

1.86(1.54-2.89)

2.12(1.63-3.17)

<0.001

Model 4

2.05(1.35-2.90)

1

1.85(1.54-2.76)

2.11(1.59-3.07)

<0.001

CV mortality

973

789

672

613

 

Number of deaths

119(12.2%)

85(10.8%)

90(13.4%)

112(18.3%)

 

Multivariableadjustment

 

 

 

 

 

Model1

2.21(1.32-3.04)

1

1.82(1.36-2.94)

2.57(1.77-3.32)

<0.001

Model 2

2.18(1.43-2.97)

1

1.81(1.32-2.93)

2.53(1.71-3.30)

<0.001

Model 3

1.98(1.32-2.94)

1

1.71(1.08-2.85)

2.45(1.67-3.22)

<0.001

Model 4

1.95(1.29-2.90)

1

1.70(1.05-2.81)

2.42(1.61-3.12)

<0.001

      

Model 1 was adjusted for age and gender, eGFR (estimated glomerular filtration rate), use diuretics

Model 2 was adjusted for model 1 covariates and smoking, alcohol drinking, use of diuretics, a history of heart failure,a history of diabetes, a history of renal insufficiency, a history of metabolic syndrome and a history of stroke

Model 3 was adjusted for model 2 covariates ABI (ankle - brachial index), FRS (Framingham risk score)

Model 4 was adjusted for model 3 covariates central effect, year of screening examination

 

Results

Baseline characteristics

A total of 3813 eligible participants with available baseline data were enrolled in this cohort. Among the participants, 126 individuals had missing follow-up data because of changing the telephone number or family address during follow-up times. 147 Subjects have poor complain (Fig1). Therefore, the study sample actually comprised 3640 valid participants Through careful calculation, the missing participants did not significantly affect the major results. Our research demonstrated that hyperuricemia and hypouricemia prevalence in these patients was 38.4% and 34.2%, respectively. Table1 presents the average SUA for the entire subjects was 322.65 ± 33.12 (standard deviation). Table 1 presents the baseline characteristics of our subjects. According to SUA levels, values were subdivided into 0-242.0, 242.0-312.0, 312.0–386.75 and more than 386.75 subgroups. Among all variables examined, our research revealed abnormal SUA subgroups had hypertension, higher proportion of male subjects, old enough, higher level of Cr, used diuretics and had chronic renal insufficiency. On account of subjects with HAR, there was no significant statistical difference on ischemic stroke, MI history, CABG history, PTCA history, DM, dyslipidemia and smoking state. Of note, femal participants with FRS risk stratification all had statistical differences. But on male participants, FRS risk stratification on subgroups all had not significant statistical difference. Meanwhile, in contrast with the reference group, participants were more likely to be treated with ACEI.

All-Cause and CVD Mortality Rates in the SUAQuartile Groups

As the Figure2 illustrated, amongthe SUA quartile categories, all-cause mortality rates in SUA ≤ 242.0, 242.0 < SUA ≤ 312.0, 312.0 < SUA ≤ 386.75, more than 386.75 four groups were 24.5%, 19.1%, 22.9% and 35.9%, respectively. Meanwhlie, CVD mortality rates were 8.7%, 5.4%, 6.4% and 13.2%, respectively. From the figure, there was a significant increasing tendency in all-cause mortality and cardiovascular mortality with higher and lower levels after 6 years of follow-up. (P < 0.001).

Survival Analysis of the SUA Quartile Groups

Figures 3A and B illustrated the relationship among the SUA quartile categories of all-cause mortality and CVD mortality, respectively. The Kaplan–Meier curves of survival showed that the survival rate decreasing with the two ends of quartile groups, not only all-cause mortality, but also CVD mortality. Fig4 revealed after multivariable adjusted,cubic spline models showed U-shaped association between SUA levels with all-cause mortality (A, B) and CVD mortality (C, D) among male and femal, respectively.

Mortality Risk According to SUA Quartile Groups

Figs5A, B, showed the adjusted proportional hazards risk (HR) of mortality according to SUA quartile groups. Compared with SUA (242.0 - 312.0) subgroup, after adjusting for gender, age, hypertension, stroke, MI history, smoking state, chronic renal disease, PAD history, PTCA history, and CABG history, SUA (0-242.0), SUA (312.0-386.75), SUA > 386.75 subgroups, all-cause mortality and cardiovascular mortality were 2.05, 95% CI (1.35–2.90), 1.85, 95% CI (1.54–2.76) and 2.11, 95% CI (1.59–3.07), respectively and 1.95, 95% CI (1.29–2.90), 1.70, 95% CI (1.05–2.81) and 2.42, 95% CI (1.61–3.12), respectively. Table 2 represented after multivariable adjusted, Cox regression models revealed that compared with SUA (242.0-312.0) subgroup, among SUA (0-242.0), SUA (312.0-386.75), and SUA > 386.75 three subgroups, all-cause mortality and cardiovascular mortality were 2.05, 95% CI (1.35–2.90), 1.85, 95% CI (1.54–2.76) and 2.11, 95% CI (1.59–3.07), respectively. And 1.95, 95% CI (1.29–2.90), 1.70, 95% CI (1.05–2.81) and 2.42, 95% CI (1.61–3.12), respectively. The results showed SUA>386.75 subgroup has mostly all-cause mortality and cardiovascular mortality, followed by SUA (0-242.0) subgroup and (312.0 - 386.75) subgroup. And yet, compared with these groups, SUA (242.0 - 312.0) subgroup has the least mortality.

Other Mortality Risk

From the Figures 6 A and B, Cox regression models also demonstrated that these mortality of risk factors has including gender, age, hypertension, stroke, metabolic syndrome, DM, MI history, PAD history, hyper lipidemia history, smoking state, PTCA history, CABG history, and lower eGFR. Of noted, among these risk factors, age, hypertension, stroke, DM, lower eGFR, abnormal ABI, MI history and CABG history occupied major position..

Discussion

Due to the close relationship between SUA levels and dietary structure, and obvious diet difference among northern and southern China, we selected in-hospital patients from Beijing and Shanghai, to represent the north and south of China. Currently, the association between SUA and all-cause mortality, cardiovascular mortality remains controversial, especially in HAR patients, owing to its complex inter -relationships with other established all-cause and cardiovascular risk factors, such as coronary heart disease, obesity, diabetes, metabolic syndrome, and chronic kidney disease. In this cohort study, according to quartile of SUA levels, after further adjusting potential confounders, the main finding suggest U-shaped, independent relationship with SUA levels and all-cause mortality, CVD mortality both male and female, instead of dose-response relationship.

The data from this research showed prevalence rate of hyperuricemia and hypouricemia was 26% and 17%, respectively. Previous study had indicated gender affected SUA metabolism and normal value range, therefore, gender stratification analysis was taken into account. Considering FRS was potential confounding factor, hence Cox regression model was adjusted for FRS between male and female. Results confirmed the second quartile of SUA level (242.0–312.0 µmol/L) had the least mortality compared with other quartiles. Furthermore, restricted cubic splines and Kaplan–Meier survival estimation implied high and low levels of SUA were undesirable. Due to this research adopted HAR subjects, thus, baseline characteristics indicated no significant statistical difference on stroke, DM, MI history, PTCA history, CABG history, hyperlipidemia and smoking state. Although in this study, we found use of diuretics and lower eGFR were substantially correlated with SUA, after controlling for eGFR and diuretics used, hazard estimates suggested changed only marginally and data represented some collinearity among the eGFR, diuretics and SUA. Consequently, the results revealed the association between SUA and all-cause mortality, CVD mortality was independent.

Similarly, several studies including longitudinal Taiwanese cohort study33, USA adults cohort study34 and Korea study35 also showed U-shaped associations between SUA levels and all-cause mortality as well as cardiovascular mortality. However, these studys were limited in the specific subjects or absence of important covariates such as BMI, diuretics used, a history of renal insufficiency and excluded severe renal dysfunction (eGFR < 60 mL/min per1.73 m2), therefore conclusion could not be extrapolated to other population. Although early studies including Framingham Heart Study14, Atherosclerosis Risk In Communities study36 and Vorarlberg Health Monitoring and Promotion Program37manifested SUA relationship with coronary heart disease and death, when multivariate adjustment was performed, data displayed this correlation affected by confounding factors .

Previous studies revealed hyperuricemia may determine the endothelium and functions of platelets38, induce oxidative stress, active local renin-angiotensin system in cultured vascular smooth muscle cells39and lead to attenuate nitric oxide bioavailability, promote proliferation of vascular smooth muscle38, 40, 41. Further studies had reported reduced SUA levels maybe contribute to treating hyperuricemia associated diseases28, 42.

It was note worthy, according to our results, hypouricemia was also found associated with all-cause mortality and cardiovascular mortality. Lately, several studies involve USA Adults Study reported similar results34. At present, there was no recognized standard for the diagnosis of hypouricemia. According to the definition of hypouricemia in most previous researchs26, 27, 43, 44, the reference limit of hypouricemia was defined as the level of SUA below 149 µmol/L or 2.5 mg/dl. A possible etiology of hypouricemia increasing all-cause mortality maybe caused by malignancy45, 46, DM and concomitant medication47, 48. But, these studies were limited by the absence of important covariates such as smoking status and BMI. Recently, growing evidence implied SUA played an important role in immune regulation and tumor inhibition48. Therefore, hypouricemia increasing mortality was partly due to cancer incidence 49. It was similar to our result, the research indicated cancer mortality account for 38% of all-cause mortality. Meanwhile, malignant tumor, as a kind of consumptive disease, later period appeared hypouricemia repeatedly50. These explicated hypouricemia populations tended to higher all-cause mortality, especially among aging patients. However, about how hypouricemia was associated with CVD remains to be clarified. The possible explanation was SUA acts as an antioxidant to protect endothelium within normal limits.

Besides hyperuricemia and hypouricemia, this study also demonstrated age, smoking state, hypertension, ischemic stroke, myocardial infarction, PAD, BMI and lower eGFR were independent mortality risk factors.

In conclusion, this finding revealed SUA levels showed U-shaped relationship with all-cause mortality and cardiovascular mortality in HAR patients whether male or female. The results implied hyperuricemia and hypouricemiaall increased mortality and SUA served as an ideal tool to predict mortality. Therefore, in clinical practice, more intensive management SUA are pressing needed, especially in HAR patients.

Study Limitations And Strengths

Firstly, with high atherosclerotic risk (HAR) patients had worse prognosis and in order to have a study population as homogenous as possible, this research adopted subjects with HAR. For this reason, results cannot be extended to the entire population. Secondly, as follow-up participant were contacted by annual phone interview, results maybe have information bias. In addition, some patients had inferior compliance,thus withdrawal bias maybe lied in this research. Finally, in comparison with western countries prospective cohort study, the follow-up times were not long. Hence, date from this research was not comprehensive, additional studies are also needed.

The strengths of our study included its prospective design and reliable assessment of mortality and cardiovascular events. Secondly, this research was multi-central prospective cohort registration study and had fine homogenous. In addition, compared with other China researches, this cohort study had the longer follow-up time and larger sample size.

Conclusion

After adjusting for gender and other covariates, the study revealed that the quartile of SUA levels showed U-shaped relationship with all-cause mortality and cardiovascular mortality. Abnormal SUA value was strongly, independently, and inversely correlated with all-cause and cardiovascular mortality. The HR of hyperuricemia and hypouricemia all-cause mortality was 2.11, 95% CI(1.59–3.07), and 2.05, 95% CI (1.35–2.90), respectively. HR of Cardiovascular mortality was 2.42, 95% CI (1.61–3.12) and 1.95, 95% CI (1.29–2.90), respectively. Abnormal SUA levels maybe ansignificant, independent risk factor for all-cause mortality and cardiovascular mortality with HAR patients.

Abbreviations

CVD: cardiovascular disease; SUA: serum uric acid; ABI: ankle–brachial index; HAR: high atherosclerotic risk; RCS: restricted cubic splines; BMI: body mass index; TC: total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C:LDL-cholesterol; DM: diabetes mellitus; HR:hazard ratios; FRS: Framingham risk score; PAD: Peripheral arterial disease; SBP: systolic blood pressure; DBP: diastolic blood pressure; IS: Ischemic Stroke; CRI: chronic renal insufficiency; DL: Dyslipidemia; GFR: Glomerular filtration rate; CHD: coronary heart disease ; PCI: percutaneous coronary stent implantation; CABG: coronary artery bypass grafting; MI: myocardial infarction; Cr: serum creatinine

Declarations

Ethics approval and consent to participate

Approved by the ethics committee of Tongji University ( NCT03616769 ).

Conflict of interest

The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper.

Consent to Publish  

The Author confirms:that the work described has not been published before; that it is not under consideration for publication elsewhere; that its publication has been approved by all co-authors. 

Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request.

Funding Sources

The study was sponsored by Shanghai Municipal Population and Family Planning Commission (Grant No: 15GWZK1002).

Acknowledgements

We thank all the people who contributed to this paper, especially all survey collaboration members.

Contributors

Yan Cang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Yan Cang. Acquisition of data: Yan Cang, ShaojieXu. Analysis and interpretation of data: Yan Cang. Drafting of the manuscript: Yan Cang. Critical revision of the manuscript for important intellectual content: Yan Cang. Statistical analysis: Yan Cang.Study supervision: Yan Cang, YaweiXu.

Author details

aDepartment of Cardiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China

bTongji University School of Medicine, Shanghai, China

cDepartment of Cardiology, Clinical Medical College of Shanghai Tenth People’s Hospital,Medical department of Soochow University, Suzhou, China

Conflict of interest

The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper.

Acknowledgements

We thank all the people who contributed to this paper, especially survey collaboration members and all participants.

References

  1. Khan F, George J, Wong K, McSwiggan S, Struthers AD, Belch JJF. The association between serum urate levels and arterial stiffness/endothelial function in stroke survivors. Atherosclerosis 2008;200:374-379.
  2. So A, Thorens B. Uric acid transport and disease. The Journal of clinical investigation 2010;120:1791-1799.
  3. Strazzullo P, Puig JG. Uric acid and oxidative stress: relative impact on cardiovascular risk? Nutrition, metabolism, and cardiovascular diseases : NMCD 2007;17:409-414.
  4. Doehner W, Landmesser U. Xanthine oxidase and uric acid in cardiovascular disease: clinical impact and therapeutic options. Seminars in nephrology 2011;31:433-440.
  5. Zhao G, Huang L, Song M, Song Y. Baseline serum uric acid level as a predictor of cardiovascular disease related mortality and all-cause mortality: a meta-analysis of prospective studies. Atherosclerosis 2013;231:61-68.
  6. Wang R, Song Y, Yan Y, Ding Z. Elevated serum uric acid and risk of cardiovascular or all-cause mortality in people with suspected or definite coronary artery disease: A meta-analysis. Atherosclerosis 2016;254:193-199.
  7. Zhang W, Iso H, Murakami Y, Miura K, Nagai M, Sugiyama D, Ueshima H, Okamura T, Epoch-Japan G. Serum Uric Acid and Mortality Form Cardiovascular Disease: EPOCH-JAPAN Study. Journal of atherosclerosis and thrombosis 2016;23:692-703.
  8. Magnoni M, Berteotti M, Ceriotti F, Mallia V, Vergani V, Peretto G, Angeloni G, Cristell N, Maseri A, Cianflone D. Serum uric acid on admission predicts in-hospital mortality in patients with acute coronary syndrome. International journal of cardiology 2017;240:25-29.
  9. Braga F, Pasqualetti S, Ferraro S, Panteghini M. Hyperuricemia as risk factor for coronary heart disease incidence and mortality in the general population: a systematic review and meta-analysis. Clinical chemistry and laboratory medicine 2016;54:7-15.
  10. Wang J, Qin T, Chen J, Li Y, Wang L, Huang H, Li J. Hyperuricemia and risk of incident hypertension: a systematic review and meta-analysis of observational studies. PloS one 2014;9:e114259-e114259.
  11. Huang H, Huang B, Li Y, Huang Y, Li J, Yao H, Jing X, Chen J, Wang J. Uric acid and risk of heart failure: a systematic review and meta-analysis. European journal of heart failure 2014;16:15-24.
  12. Li Q, Zhang Y, Ding D, Yang Y, Chen Q, Liu C, Li X, Hong C, Ling W. Association between Serum Uric Acid and Mortality among Chinese Patients with Coronary Artery Disease. Cardiology 2016;134:347-356.
  13. Bos MJ, Koudstaal PJ, Hofman A, Witteman JCM, Breteler MMB. Uric acid is a risk factor for myocardial infarction and stroke: the Rotterdam study. Stroke 2006;37:1503-1507.
  14. Culleton BF, Larson MG, Kannel WB, Levy D. Serum uric acid and risk for cardiovascular disease and death: the Framingham Heart Study. Annals of internal medicine 1999;131:7-13.
  15. Fang J, Alderman MH. Serum uric acid and cardiovascular mortality the NHANES I epidemiologic follow-up study, 1971-1992. National Health and Nutrition Examination Survey. JAMA 2000;283:2404-2410.
  16. Ioachimescu AG, Brennan DM, Hoar BM, Hazen SL, Hoogwerf BJ. Serum uric acid is an independent predictor of all-cause mortality in patients at high risk of cardiovascular disease: a preventive cardiology information system (PreCIS) database cohort study. Arthritis and rheumatism 2008;58:623-630.
  17. Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001;285:2486-2497.
  18. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 2007;297:611-619.
  19. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ (Clinical research ed.) 2007;335:136-136.
  20. Pencina MJ, Larson MG, D'Agostino RB. Choice of time scale and its effect on significance of predictors in longitudinal studies. Statistics in medicine 2007;26:1343-1359.
  21. Korn EL, Graubard BI, Midthune D. Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale. American journal of epidemiology 1997;145:72-80.
  22. Wilson PWF, Bozeman SR, Burton TM, Hoaglin DC, Ben-Joseph R, Pashos CL. Prediction of first events of coronary heart disease and stroke with consideration of adiposity. Circulation 2008;118:124-130.
  23. Greenland P, Knoll MD, Stamler J, Neaton JD, Dyer AR, Garside DB, Wilson PW. Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. JAMA 2003;290:891-897.
  24. Lu J, Mu Y, Su Q, Shi L, Liu C, Zhao J, Chen L, Li Q, Yang T, Yan L, Wan Q, Wu S, Liu Y, Wang G, Luo Z, Tang X, Chen G, Huo Y, Gao Z, Ye Z, Wang Y, Qin G, Deng H, Yu X, Shen F, Chen L, Zhao L, Sun J, Sun W, Wang T, Du R, Lin L, Dai M, Xu Y, Xu M, Bi Y, Lai S, Li D, Wang W, Ning G, Group RS. Reduced Kidney Function Is Associated With Cardiometabolic Risk Factors, Prevalent and Predicted Risk of Cardiovascular Disease in Chinese Adults: Results From the REACTION Study. Journal of the American Heart Association 2016;5:e003328.
  25. Goff DC, Jr., Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O'Donnell CJ, Robinson JG, Schwartz JS, Shero ST, Smith SC, Jr., Sorlie P, Stone NJ, Wilson PWF, Jordan HS, Nevo L, Wnek J, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke FW, Shen W-K, Tomaselli GF, American College of Cardiology/American Heart Association Task Force on Practice G. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;129:S49-S73.
  26. Bairaktari ET, Kakafika AI, Pritsivelis N, Hatzidimou KG, Tsianos EV, Seferiadis KI, Elisaf MS. Hypouricemia in individuals admitted to an inpatient hospital-based facility. American journal of kidney diseases : the official journal of the National Kidney Foundation 2003;41:1225-1232.
  27. Li Z, Ding H, Chen C, Chen Y, Wang DW, Lv Y. Novel URAT1 mutations caused acute renal failure after exercise in two Chinese families with renal hypouricemia. Gene 2013;512:97-101.
  28. Rizzo M, Obradovic M, Labudovic-Borovic M, Nikolic D, Montalto G, Rizvi AA, Mikhailidis DP, Isenovic ER. Uric acid metabolism in pre-hypertension and the metabolic syndrome. Current vascular pharmacology 2014;12:572-585.
  29. O'Hare AM, Katz R, Shlipak MG, Cushman M, Newman AB. Mortality and cardiovascular risk across the ankle-arm index spectrum: results from the Cardiovascular Health Study. Circulation 2006;113:388-393.
  30. Ankle Brachial Index C, Fowkes FGR, Murray GD, Butcher I, Heald CL, Lee RJ, Chambless LE, Folsom AR, Hirsch AT, Dramaix M, deBacker G, Wautrecht JC, Kornitzer M, Newman AB, Cushman M, Sutton-Tyrrell K, Lee AJ, Price JF, d'Agostino RB, Murabito JM, Norman PE, Jamrozik K, Curb JD, Masaki KH, Rodríguez BL, Dekker JM, Bouter LM, Heine RJ, Nijpels G, Stehouwer CDA, Ferrucci L, McDermott MM, Stoffers HE, Hooi JD, Knottnerus JA, Ogren M, Hedblad B, Witteman JC, Breteler MMB, Hunink MGM, Hofman A, Criqui MH, Langer RD, Fronek A, Hiatt WR, Hamman R, Resnick HE, Guralnik J. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA 2008;300:197-208.
  31. Rose G, McCartney P, Reid DD. Self-administration of a questionnaire on chest pain and intermittent claudication. British journal of preventive & social medicine 1977;31:42-48.
  32. R A. Language and Environment for Statistical Computing R Foundation for Statistical Computing,. Vienna, Austria.
  1. Tseng W-C, Chen Y-T, Ou S-M, Shih C-J, Tarng D-C, Taiwan Geriatric Kidney Disease Research G. U-Shaped Association Between Serum Uric Acid Levels With Cardiovascular and All-Cause Mortality in the Elderly: The Role of Malnourishment. Journal of the American Heart Association 2018;7:e007523.
  2. Hu L, Hu G, Xu BP, Zhu L, Zhou W, Wang T, Bao H, Cheng X. U-Shaped Association of Serum Uric Acid with All-cause and Cause-Specific Mortality in US Adults: A Cohort Study. The Journal of clinical endocrinology and metabolism 2020:dgz068.
  3. Cho SK, Chang Y, Kim I, Ryu S. U-Shaped Association Between Serum Uric Acid Level and Risk of Mortality: A Cohort Study. Arthritis & rheumatology (Hoboken, N.J.) 2018;70:1122-1132.
  4. Moriarity JT, Folsom AR, Iribarren C, Nieto FJ, Rosamond WD. Serum uric acid and risk of coronary heart disease: Atherosclerosis Risk in Communities (ARIC) Study. Annals of epidemiology 2000;10:136-143.
  5. Ulmer H, Kelleher C, Diem G, Concin H. Long-term tracking of cardiovascular risk factors among men and women in a large population-based health system: the Vorarlberg Health Monitoring & Promotion Programme. European heart journal 2003;24:1004-1013.
  6. Feig DI, Kang D-H, Johnson RJ. Uric acid and cardiovascular risk. The New England journal of medicine 2008;359:1811-1821.
  7. Corry DB, Eslami P, Yamamoto K, Nyby MD, Makino H, Tuck ML. Uric acid stimulates vascular smooth muscle cell proliferation and oxidative stress via the vascular renin-angiotensin system. Journal of hypertension 2008;26:269-275.
  8. Khosla UM, Zharikov S, Finch JL, Nakagawa T, Roncal C, Mu W, Krotova K, Block ER, Prabhakar S, Johnson RJ. Hyperuricemia induces endothelial dysfunction. Kidney international 2005;67:1739-1742.
  9. Kanbay M, Segal M, Afsar B, Kang D-H, Rodriguez-Iturbe B, Johnson RJ. The role of uric acid in the pathogenesis of human cardiovascular disease. Heart (British Cardiac Society) 2013;99:759-766.
  10. Xu L, Shi Y, Zhuang S, Liu N. Recent advances on uric acid transporters. Oncotarget 2017;8:100852-100862.
  11. Son C-N, Kim J-M, Kim S-H, Cho S-K, Choi C-B, Sung Y-K, Kim T-H, Bae S-C, Yoo D-H, Jun J-B. Prevalence and possible causes of hypouricemia at a tertiary care hospital. The Korean journal of internal medicine 2016;31:971-976.
  12. Kuwabara M, Niwa K, Ohtahara A, Hamada T, Miyazaki S, Mizuta E, Ogino K, Hisatome I. Prevalence and complications of hypouricemia in a general population: A large-scale cross-sectional study in Japan. PloS one 2017;12:e0176055-e0176055.
  13. Lesmes A, Díaz-Curiel M, Castrillo JM. Tumoural hypouricemia. Advances in experimental medicine and biology 1980;122A:145-148.
  14. Kelley WN. Hypouricemia. Arthritis and rheumatism 1975;18:731-737.
  15. Shichiri M, Iwamoto H, Shiigai T. Diabetic renal hypouricemia. Archives of internal medicine 1987;147:225-228.
  16. Bo S, Cavallo-Perin P, Gentile L, Repetti E, Pagano G. Hypouricemia and hyperuricemia in type 2 diabetes: two different phenotypes. European journal of clinical investigation 2001;31:318-321.
  17. Hisatome I, Ogino K, Kotake H, Ishiko R, Saito M, Hasegawa J, Mashiba H, Nakamoto S. Cause of persistent hypouricemia in outpatients. Nephron 1989;51:13-16.
  18. Sharaf El Din UAA, Salem MM, Abdulazim DO. Uric acid in the pathogenesis of metabolic, renal, and cardiovascular diseases: A review. Journal of advanced research 2017;8:537-548.