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Dose-response relationship between serum uric acid levels and risk of incident coronary heart disease in the Dongfeng-Tongji Cohort
International Journal of Cardiology 224 (2016) 299–304
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Dose-response relationship between serum uric acid levels and risk of incident coronary heart disease in the Dongfeng-Tongji Cohort Xuefeng Lai a, Liangle Yang a, Sébastien Légaré a, Francesca Angileri a, Xuguang Chen a, Qin Fang a, Handong Yang b, Ce Zhang b, Xiulou Li b, Xinwen Min b, Chengwei Xu c, Jing Yuan a, Mei-an He a, Tangchun Wu a, Xiaomin Zhang a,⁎ a Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China b Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, China c Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
a r t i c l e
i n f o
Article history: Received 2 June 2016 Received in revised form 31 August 2016 Accepted 15 September 2016 Available online 19 September 2016 Keywords: Serum uric acid Hyperuricemia Incident coronary heart disease Dose-response association
a b s t r a c t Background: In prospective studies, relationship of serum uric acid (SUA) with risk of coronary heart disease (CHD) incidence is inconsistent. We evaluated the association of SUA with incident CHD and the potential modifying effect of major CHD risk factors related to SUA among a middle aged and elderly Chinese population. Methods: We included 16, 063 participants who were free of CHD, stroke, cancer and renal diseases at baseline from Sep. 2008 to June 2010, and were followed until Oct. 2013. Cox proportional hazard model was used to estimate the hazard ratios (HR) and 95% confidence interval (95% CI) of CHD incidence in relation to SUA. Results: The adjusted HR for incident CHD increased gradually with the increasing SUA levels (P for linear trend = 0.005), and the HR across sex-specific SUA quartile was 1.26 (95% CI: 1.09, 1.47), 1.13 (95% CI: 0.97, 1.31), 1.23 (95% CI: 1.06, 1.43) and 1.00 (reference; P for trend = 0.014), respectively. In particular, the association was more evident in individuals with normal-weight and those without hypertension or metabolic syndrome (all P for interactions b 0. 05). Conclusions: These findings suggested that higher SUA levels were independently associated with a doseresponse increased risk of CHD incidence. © 2016 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Serum uric acid (SUA), a final enzymatic product of purine metabolism, has been widely associated with a variety of cardiovascular conditions, including hypertension, diabetes, obesity, stroke, metabolic syndrome and more in numerous epidemiologic studies [1–5]. However, the role of SUA as an independent risk factor in coronary heart disease (CHD) incidence is still controversial [6]. Some prospective studies and two recent meta-analysis based on nine studies, showed that high SUA level or hyperuricemia may increase the risk of CHD incidence independently of traditional risk factors [7–12], whereas others studies, including an earlier meta-analysis of eight studies, indicated null associations with adjustment for possible confounders [13–19]. In
⁎ Corresponding author at: Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China. E-mail address: [email protected] (X. Zhang).
http://dx.doi.org/10.1016/j.ijcard.2016.09.035 0167-5273/© 2016 Elsevier Ireland Ltd. All rights reserved.
addition, whether any dose-response relationship exists between SUA level and risk of CHD occurrence remains unknown. Very recently, a new meta-analysis including 29 prospective cohort studies found that hyperuricemia might increase the risk of CHD morbidity and mortality; Dose-response analysis indicated that SUA was only associated with risk of CHD mortality in females, but no significant trend was found for CHD morbidity [20]. In addition, it was observed in previous studies that the associations between high SUA or hyperuricemia and cardiovascular disease (CVD) events appear to be stronger in women [10], normotensive individuals [8], and those without metabolic risk factors [9]. SUA is also known to be associated with other important CHD risk factors. Thus, it remains unclear whether such risk factors modify the dose-response relationship between SUA level and CHD incidence. Since hyperuricemia was common in Chinese population, and the prevalence of hyperuricemia was higher in economically developed regions than in other regions in China [21–23], the public health importance of high SUA levels or hyperuricemia as a possible CVD risk factor should not be ignored. We therefore tested the hypothesis whether SUA was associated with incident CHD in a dose-response manner and
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whether the association was modified by major CHD risk factors in a middle aged and elderly Chinese population.
heart disease, percutaneous transluminal coronary angioplasty or coronary artery bypass graft, and cardiac arrest (I46) or death with CHD (I20–I25) as the underlying cause [34].
2. Materials and method
2.5. Statistical analyses
2.1. Study population
Baseline characteristics of the participants were reported as mean ± SD for continuous variables and numbers (percentages) for categorical variables. Trends were evaluated with linear or logistic regression using the median value of SUA for each quartile as an ordinal variable adjusted for age and sex. We applied Cox proportional models to evaluate the relationship of SUA quartiles, hyperuricemia, or continuous serum uric acid (per 100 μmol/L increasement) with risk of incident CHD after adjusting for potential confounders. We fitted two multivariate proportional hazard models. Model 1 adjusted for age, gender, BMI, smoking status (current, former, never), drinking status (current, former, never), physical activity, education levels, hypertension, diabetes, hyperlipidemia, and family history of CHD. Model 2 further adjusted for use of diuretics, eGFR, and diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits). Test for linear trend was performed using the median SUA concentration for each quartile as a continuous variable in the multivariate model. Additionally, nonlinear trends of the relationship between SUA levels and risk for incident CHD was tested by restricted cubic spline Cox regression using 4 knots placed at the 5th, 35th, 65th, and 95th percentiles of SUA levels respectively, with 200 μmol/L (approximate the first sexspecific quartile) as the reference group. Stratified analyses were also performed by major baseline characteristics. Moreover, we tested potential interactions by adding the products of these covariates with SUA levels in total population, respectively. All statistical analyses were carried out using SAS version 9.3 (SAS institute Inc., Cary, NC). A 2-sided P value b 0.05 was considered to be statistically significant.
Data were derived from Dongfeng-Tongji Cohort Study and the design, methods of the cohort was described in detail previously [24,25]. Briefly, 31,000 retirees at Dongfeng Motor Corporation (DMC) were invited to participate in the Dongfeng-Tongji cohort study. Except for those who did not respond to the invitation, a total of 27,009 (approximately 87% response rate) retired employees agreed and completed baseline questionnaire, medical examinations and provided fasting blood samples between September 2008 and June 2010. Five years later, the participants were invited to the follow-up survey via telephone. In total, 25,978 individuals (96.2% of those at baseline) completed the first follow-up until October 2013. At the follow-up investigation, participants repeated the questionnaire interview, physical examinations, and blood collection as those during the baseline survey. After exclusion of participants who had cancers, CHD, stroke and renal diseases at baseline (n = 7531), missing data of SUA (n = 1669) and other covariates (n = 715), a total of 16,063 eligible individuals were included in the analyses. The study was approved by the Ethics and Human Subject committee of the School of Public Health, Tongji Medicine College, and Dongfeng General Hospital, DMC. Written informed consents were received from all participants. 2.2. Assessment of SUA SUA and other biochemical indexes (such as creatinine, fasting plasma glucose, and blood lipids) were determined at the DMC-owned hospital's laboratory with ARCHITECT Ci8200 automatic analyzer (ABBOTT Laboratories. Abbott Park, Illinois, USA). SUA level was categorized into four groups according to the quartiles of gender-specific distribution: b271, 271–316, 316–370 and ≥370 μmol/L for men; b214, 214–253, 253–298 and ≥298 μmol/L for women. Hyperuricemia was defined as a SUA level ≥ 420 μmol/L (7.0 mg/dL) for men and ≥360 μmol/L (6.0 mg/dL) for women. 2.3. Assessment of covariates Trained interviewers performed face-to-face semi-structured questionnaire interviews and collected information on socio-demographic characteristics (age, gender, education and marital status), diet, lifestyle such as smoking status (current, former, never), drinking status (current, former, never) and physical activity, occupational history, environmental exposures, family history, and medical history (diagnosed medical conditions, use of health services and use of medicines for the most recent 2 weeks). Participants who were smoking at least one cigarette per day for more than half a year were defined as current smokers; those who were drinking at least one time per week for more than half a year were considered as current drinkers. Physical activity was defined as those who regularly exercised N20 min per day over the last six months. For diet, we transformed the diet frequency into times per week uniformly according to four respondent frequency categories (daily, weekly, monthly, times per year) for four kinds of main regular food including meat and poultry, fishery product, soy products, vegetables and fruits, all of which might modify SUA levels [26,27]. We finally divided the consumption frequency per week into 3 categories: never, 1–3 times per week, and ≥4 times per week [28]. Body mass index (BMI) was calculated as mass (kg) divided by the square of height (m2). Hypertension was defined as individuals with self-reported physician diagnosis of hypertension, or blood pressure ≥ 140/90 mmHg, or current use of antihypertensive medication [29]. Diabetes was defined as self-reported physician diagnosis of diabetes, fasting glucose level ≥ 7.0 mmol/L, or taking oral hypoglycemic medication or insulin [30]. Hyperlipidemia was defined as total cholesterol N 5.72 mmol/L or triglycerides N1.70 mmol/L at medical examination, or a previous self-reported physician diagnosis of hyperlipidemia, or taking lipid-lowering medication [31]. Metabolic syndrome, according to the new International Diabetes Federation (IDF) definition [32], was defined when the participants had central obesity (waist circumference ≥ 90 cm for Chinese men and ≥80 cm for Chinese women) plus any two of the following four factors: (1) high blood pressure: systolic ≥130 mmHg, diastolic ≥85 mmHg, or known treatment for hypertension; (2) hypertriglyceridemia: fasting serum triglycerides ≥1.7 mmol/L; (3) low HDL cholesterol: fasting HDL cholesterol b1.03 mmol/L in men and b1.29 mmol/L in women; and (4) hyperglycemia: fasting glucose level of ≥5.6 mmol/L (≥100 mg/dL) or known treatment for diabetes. The estimated glomerular filtration rate (eGFR) was calculated by using the Modification of Diet in Renal Disease (MDRD) equation applied for Chinese patients with chronic kidney disease (CKD) [33]. 2.4. Ascertainment of incident CHD All retired employees were covered by DMC's health-care service system and each participant had a unique medical insurance card number and ID, making it easy to track disease incidence and mortality. The incidence of CHD was identified through this medical insurance system and medical record reviews in the DMC-owned hospitals. The diagnosis of CHD was made based on well-accepted international standards by cardiologists in the DMC-owned hospitals. We defined incident CHD as the first hospital admission with an occurrence of an angina pectoris (ICD-10 code I20), acute myocardial infarction (AMI, I21), subsequent myocardial infarction (I22), other forms of acute (I24) or chronic (I25)
3. Results 3.1. Characteristics of study population Baseline characteristics of participants by sex-specific quartiles of SUA levels are presented in Table 1. Compared with those in the lowest quartile, participants in the highest quartile were older and more likely to be males, overweight and presented a greater proportion of hypertension, diabetes, hyperlipidemia and metabolic syndrome. In addition, higher SUA levels were associated with a lower eGFR.
3.2. Relationship between SUA and CHD incidence As shown in Table 2, we found that increasing SUA quartiles were independently associated with elevated risk of CHD incidence after adjustment for age, gender, BMI, education levels, smoking, drinking, physical activity, hypertension, diabetes, hyperlipidemia and family history of CHD. Compared with the first quartile of SUA levels, the adjusted HRs for CHD incidence from the second to the highest SUA quartile were 1.25 (95% CI: 1.08, 1.46), 1.16 (95% CI: 1.00, 1.35) and 1.33 (95% CI: 1.15, 1.54; P for trend = 0.001). Additional adjustment for use of diuretics, eGFR, and diet frequency categories did not substantially change the association. The adjusted HR for each 100 μmol/L increasement in SUA levels was 1.14 (95% CI: 1.01, 1.29) for CHD incidence. Hyperuricemia was also associated with a 14% increased risk of incident CHD (HR = 1.14; 95% CI: 1.01, 1.29). The restricted cubic splines showed that the risk of CHD incidence increased gradually with continuous SUA levels (P for linear trend = 0.005, Fig. 1). The significant linear trend test implied that there was dosage effects and no obvious evidence of a threshold effect on the risk of CHD incidence.
3.3. Stratified analyses for association of SUA with incident CHD We subsequently conducted stratification analyses by major characteristics of the study population. The significant relationship between SUA levels and CHD incidence was more evident in individuals with normal-weight, normal renal function (N 90 mL/min/1.73 m2), and those without hypertension or metabolic syndrome (Table 3). In addition, the significant interactions were found between SUA levels and overweight, hypertension and metabolic syndrome (all P for interactions b 0.05).
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Table 1 Baseline characteristics of participants across sex-specific quartiles of serum uric acid levels. Characteristics
Q1
Q2
Q3
Q4
P for trend
N (participants) Uric acid, μmol/L Age, year Male, n (%) Body mass index, kg/m2 Education level, n (%) Primary school or below Middle school High school or above Current smoking, n (%) Current drinking, n (%) Physical activity, n (%) Regular food consumption, ≥4 times per week, n (%) Meat and poultry Fishery products Soy products Vegetables and fruits Family history of CHD, n (%) Hypertension, n (%) Diabetes, n (%) Hyperlipidemia, n (%) Metabolic syndrome, n (%) Usage of diuretics, n (%) eGFR, mL/min/1.73m2
4033 204.6 ± 38.5 61.8 ± 7.5 1797 (44.6) 23.4 ± 3.2
4019 260.2 ± 31.8 62.3 ± 7.5 1791 (44.6) 24.0 ± 3.2
3994 304.2 ± 36.4 62.9 ± 7.7 1802 (45.1) 24.6 ± 3.3
4017 387.1 ± 65.9 64.5 ± 7.8 1800 (44.8) 25.5 ± 3.4
– – b0.0001 b0.0001 b0.0001 b0.0001
1193 (29.6) 1557 (38.6) 1283 (31.8) 812 (20.1) 891 (22.1) 3581 (88.8)
1204 (30.0) 1470 (36.6) 1345 (33.5) 765 (19.0) 920 (22.9) 3567 (88.8)
1131 (28.3) 1452 (36.4) 1411 (35.3) 753 (18.9) 906 (22.7) 3566 (89.3)
1225 (30.5) 1422 (35.4) 1370 (34.1) 708 (17.6) 913 (22.7) 3572 (88.9)
0.379 0.059 0.465
1421 (35.2) 404 (10.0) 1265 (31.4) 3918 (97.2) 226 (5.6) 1631 (40.4) 642 (15.9) 1689 (41.9) 730 (18.1) 48 (1.2) 95.8 (22.7)
1323 (32.9) 435 (10.8) 1252 (31.2) 3912 (97.3) 221 (5.5) 1883 (46.9) 627 (15.6) 1888 (47.0) 953 (23.7) 45 (1.1) 92.0 (21.4)
1344 (33.7) 429 (10.7) 1287 (32.2) 3985 (97.5) 216 (5.4) 2025 (50.7) 624 (15.6) 2133 (53.4) 1175 (29.4) 44 (1.1) 88.5 (21.1)
1317 (32.8) 416 (10.4) 1285 (32.0) 3911 (97.4) 158 (3.9) 2502 (62.3) 835 (20.8) 2642 (65.8) 1682 (41.9) 75 (1.9) 81.1 (21.7)
0.289 0.010 0.658 0.592 0.098 b0.0001 b0.0001 b0.0001 b0.0001 0.031 b0.0001
Abbreviation: eGFR: estimated glomerular filtration rate. Data was presented as means ± SD for continuous variable and numbers (percentage) for category variables. P for trend tested with linear regression for continuous variables or logistic regression using the median uric acid value for each quartile after adjusting for age and gender, except for itself. The quartiles of serum uric acid concentration were calculated by gender respectively. In male, the cutoff of serum uric acid levels was b271, 271–316, 316–370 and ≥370 μmol/L; in female, the cutoff of serum uric acid levels was b214, 214–253, 253–298 and ≥298 μmol/L.
4. Discussion In this prospective study, we found a positive and significant doseresponse relationship between SUA levels and CHD incidence. Hyperuricemia was confirmed as an independent risk factor for CHD incidence in middle-aged and elderly Chinese population after controlling for a variety of potential confounders. Our findings are in line with and extend previous studies [7–10]. Chuang et al. showed that hyperuricemia was independently associated with ischemic heart disease incidence among 128,569 Chinese adults ≥ 20 years old with low CVD risk in Taiwan [9], which was confirmed in our study. However, the prevalence of hyperuricemia in their lowrisk population was higher than 3 to 4-fold in our participants. A recent meta-analysis reported that the pooled prevalence of hyperuricemia was 13.3% in Mainland of China [21]. The prevalence was similar to that of most developing countries including Thailand (10.6%) [35] and Turkey (12.1%) [36], but lower than that of several developed countries such as the United States (21.4%) [22] and Japan (25.8%) [23]. The
prevalence of hyperuricemia in our population was 11.1% in men and 8.2% in women, which was close to that of the latest meta-analysis in Mainland of China and other developing countries. Whereas the prevalence was 42.6% in men and 23.4% in women in Chuang's study conducted in Taiwan, an economically-developed region in China. This means that our population is more representative of the Chinese population living in the mainland. The different prevalence of hyperuricemia between ours and Chuang's study might be partly due to inherent lifestyle such as higher rate of current drinking (70.6%) in that study compared with that in ours (22.6%), or intake of purine rich diet. Additionally, although that study had a large sample size, it did not further explore any underlying dose-response association between SUA levels and CHD incidence. Furthermore, the degree of adjustment for possible confounding variables has also differed substantially between that study and ours. In that study, the major known risk factors of CHD, including hypertension, diabetes, hyperlipidemia, smoking habits, alcohol consumption and the usage of diuretics were controlled, but other important covariates such as diet and eGFR were not accounted for. In
Table 2 Hazard ratios (95% CI) for CHD incidence according to continuous or quartiles of serum uric acid and hyperuricemia. Hazard ratios (95% CI) Variable types of serum uric acid Trend across serum uric acid quartiles Q1 Q2 Q3 Q4 P for trend Presence of hyperuricemia No Yes Serum uric acid per 100 μmol/L
Incidents/participants
Model 1
Model 2
307/4033 403/4019 409/3994 541/4017 –
1.00 (ref.) 1.25 (1.08, 1.46) 1.16 (1.00, 1.35) 1.33 (1.15, 1.54) 0.001
1.00 (ref.) 1.23 (1.06, 1.43) 1.13 (0.97, 1.31) 1.26 (1.09, 1.47) 0.014
1258/13,238 402/2825 –
1.00 (ref.) 1.19 (1.06, 1.34) 1.13 (1.06, 1.34)
1.00 (ref.) 1.14 (1.01, 1.29) 1.14 (1.01, 1.29)
Model 1 adjusted covariates for age, gender, body mass index, smoking, drinking, physical activity, education levels, hypertension, diabetes, hyperlipidemia, family history of CHD. Model 2 adjusted covariates for model 1 plus eGFR, usage of diuretics and diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits). The quartiles of serum uric acid concentration were calculated by gender respectively. In male, the cutoff of serum uric acid concentration was b271, 271–316, 316–370 and ≥370 μmol/L; in female, the cutoff of serum uric acid concentration was b214, 214–253, 253–298 and ≥298 μmol/L.
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Fig. 1. Adjusted hazard ratios (solid lines) and 95% confidence interval (dashed lines) for CHD incidence from restricted cubic splines in a multivariate-adjusted Cox proportional hazard model. The model was adjusted for age, gender, body mass index, smoking, drinking, physical activity, education levels, family history of CHD, hypertension, hyperlipidemia, diabetes, eGFR (estimated glomerular filtration, use of diuretics, diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits).
contrast, after controlling for a wide range of traditional risk factors and further adjusting for eGFR and diet frequency, our results showed that the risk of CHD incidence gradually increased with the increasing SUA levels even in high-normal extent, possibly suggesting no threshold effect of SUA on CHD incidence. Thus, that clinically dichotomous definition of hyperuricemia might be inadequate for the prevention for CHD incidence. Another larger Swedish study found that the risk of incident
AMI seemed to increase gradually from lower to higher SUA levels in middle-aged subjects without prior CVD [10], whereas the endpoint was not extended to CHD. The major limitation of the study was that it did not adjust for other important CHD factors such as smoking, drinking, physical activity, diet, eGFR, and especially uptake of diuretics and antihypertensive medications, which could potentially attenuate the findings relating SUA to incident AMI. In addition, the prevalence of hypertension (0.7%) and diabetes (3.2%) was clearly underestimated as the definition of hypertension was based on hospital discharge diagnosis and diabetes was according to fasting glucose measurement at baseline or discharge diagnosis. Compared with above studies, our study had access to a wide range of covariates and minimized the residual confounding, and still demonstrated a clear dose-response relationship. On the contrary, a few other prospective studies reported that the positive association disappeared after adjusting for established risk factors such as hypertension, diabetes and diuretics [13–18]. Several reasons might account for the disparity between our findings and those from previous studies. CHD incidence in the Framingham [16] and atherosclerosis risk in communities (ARIC) [17] studies were 5.26 and 4.70 per 1000 person-years, respectively. In comparison, the incidence of CHD in the present study was up to 23.25 per 1000 person-years, as the mean participants' age was about 10 years older than that of the above studies. Therefore, the low CHD incidence due to younger participants, low number of CHD events [15,18], and insufficient statistical power might be responsible for failing to detect the significant effect of SUA on CHD incidence in previous studies. Meanwhile, the inconsistent results might be due to different definition of endpoints. In other two studies conducted in Germany [13] and Norway [14], only fatal and non-fatal myocardial infarction were defined as endpoints, which may obviously underestimate the hazard risk of CHD incidence, while our study included all types of CHD (ICD-10) and cardiac death as outcome. In addition, because the large number of risk factors are related to both CHD and SUA, differences in measurement and adjustment for these potential confounders in prior studies could have contributed to
Table 3 Associations of CHD incidence with serum uric acid stratified by baseline characteristics. Stratification covariates Overweight a No (BMI b 25 kg/m2) Incidents/participants Hazard ratios (95% CI) Yes (BMI ≥ 25 kg/m2) Incidents/participants Hazard ratios (95% CI) Hypertension a No Incidents/participants Hazard ratios ((95% CI) Yes Incidents/participants Hazard ratios (95% CI) Metabolic syndrome b No Incidents/participants Hazard ratios (95% CI) Yes Incidents/participants Hazard ratios (95% CI) eGFR a iN90 mL/min/1.73m2 Incidents/participants Hazard ratios (95% CI) ≤90 mL/min/1.73m2 Incidents/participants Hazard ratios (95% CI)
Q1
Q2
Q3
Q4
P for trend
P for interaction 0.035
0.005 189/2903 reference
233/2565 1.34 (1.11, 1.63)
219/2273 1.30 (1.06, 1.58)
222/1851 1.40 (1.14, 1.71)
118/1130 reference
170/1454 1.07 (0.84, 1.35)
190/1721 0.91 (0.72, 1.15)
319/2166 1.08 (0.86, 1.35)
0.485
0.043 0.022 126/2402 reference
162/2136 1.24 (0.98, 1.57)
150/1969 1.22 (0.95, 1.55)
146/1515 1.39 (1.07, 1.79)
181/1631 reference
241/1883 1.19 (0.98, 1.44)
259/2025 1.06 (0.87, 1.28)
395/2502 1.19 (0.99, 1.43)
0.160
0.034 0.0002 226/3303 reference
278/3066 1.28 (1.07, 1.52)
271/2819 1.29 (1.07, 1.54)
282/2335 1.45 (1.21, 1.75)
81/730 reference
125/953 1.08 (0.82, 1.44)
138/1175 0.87 (0.66, 1.15)
259/1682 1.05 (0.81, 1.36)
0.720
0.314 0.007 152/2329 reference
169/1966 1.32 (1.05, 1.64)
139/1623 1.16 (0.91, 1.47)
138/1111 1.49 (1.16, 1.89)
155/1704 reference
234/2053 1.19 (0.97, 1.45)
270/2371 1.12 (0.91, 1.36)
403/2906 1.22 (1.01, 1.48)
0.093
a Adjusted covariates for age, gender, BMI, smoking, drinking, physical activity, education levels, hypertension, diabetes, hyperlipidemia, family history of CHD, eGFR and usage of diuretics, diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits) except for the stratified covariate itself. b Adjusted covariates for age, gender, BMI, smoking, drinking, physical activity, education levels, family history of CHD, eGFR, and usage of diuretics, diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits).
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the discrepancies. Furthermore, inherent differences in population characteristics, shorter observation time, and sample size might also contribute to the disparate results. Notably, the dose-response association between SUA levels and CHD incidence was only seen among persons with normal weight, normal renal function (eGFR N 90 mL/min/1.73 m2) and those without comorbidities such as hypertension, or metabolic syndrome. These findings were similar to previous studies, which found that the association was more pronounced for individuals without hypertension [8] and metabolic syndrome [9]. Although the underlying pathophysiological mechanisms are unclear, it is possible that the presence of CHD risk factors would cover up the effect of SUA on risk of CHD among high-risk persons and leave the pernicious effects prominent in relatively healthy adults. Several potential mechanisms might explain the association between uric acid and CHD incidence. First, uric acid has been associated with oxidative stress and insulin resistance as it may enhance lipid peroxidation and platelet adhesiveness [37–39]. Second, uric acid could induce vascular inflammation, as a significant association between SUA and several inflammation markers was found in epidemiological studies [40,41]. Third, high uric acid levels could lead to endothelial dysfunction [42,43], stimulating vascular smooth cell proliferation and reducing nitric oxide production [44,45]. In addition, it has been demonstrated that uric acid levels were closely related to arterial intima-media thickness, a precursor to atherosclerosis [46]. There were several limitations in the present this study. Firstly, like almost all of the previous studies, serum uric acid was measured only once at baseline, so we were unable to account for within-individual variability in the present study. Secondly, participants included in our study were middle-aged and older Chinese free of cardiovascular disease, stroke, cancer and renal disease, thus the generalizability of our findings to the general populations of younger age, or other ethnicities and different health conditions needs to be further verified. In addition, although a variety of potential confounding factors were accounted for in multivariate analysis, the possibility of residual and unmeasured confounders could not be ruled out in observational studies.
5. Conclusion In summary, our findings confirm hyperuricemia as an independent risk factor and showed a positive dose-response relationship between SUA levels and CHD incidence. This association might be modified by comorbidities such as hypertension, overweight, and metabolic syndrome. Author contributions Authors X. Lai, X. Zhang take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. Concept and design: H. Yang, T. Wu, X. Zhang. Acquisition, analysis or interpretation of data: all authors. Drafting of the manuscript: X. Lai, L. Yang, and X. Zhang. Critical revision of the manuscript for important intellectual content: X. Lai, L. Yang, S. Légaré, F. Angileri, X. Zhang. Statistical analysis: X. Lai, X. Zhang. Administrative, technical, or material support: X. Li, X. Min, C. Zhang, C. Xu, M. He, J. Yuan.
Source(s) of funding This work was supported by the Natural National Scientific Foundation of China (81373093, 81230069, and 81390542), 111 Project (No. B12004); and the Program for Changjiang Scholars; Innovative Research Team in University of Ministry of Education of China (No. IRT1246); China Medical Board (No. 12-113).
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Conflict of interest None.
Acknowledgements The authors would like to thank all study subjects for participating in the present Dongfeng-Tongji Cohort study as well as volunteers for assisting in collecting the sample, questionnaire data and clinic data.
References [1] J. Wang, T. Qin, J. Chen, Y. Li, L. Wang, H. Huang, et al., Hyperuricemia and risk of incident hypertension: a systematic review and meta-analysis of observational studies, PLoS One 9 (2014), e114259. [2] T. Wang, Y. Bi, M. Xu, Y. Huang, Y. Xu, X. Li, et al., Serum uric acid associates with the incidence of type 2 diabetes in a prospective cohort of middle-aged and elderly Chinese, Endocrine 40 (2011) 109–116. [3] T.Y. Yu, J.H. Jee, J.C. Bae, S.M. Jin, J.H. Baek, M.K. Lee, et al., Serum uric acid: A strong and independent predictor of metabolic syndrome after adjusting for body composition, Metab. Clin. Exp. 65 (2016) 432–440. [4] N.F. Chu, D.J. Wang, S.H. Liou, S.M. Shieh, Relationship between hyperuricemia and other cardiovascular disease risk factors among adult males in Taiwan, Eur. J. Epidemiol. 16 (2000) 13–17. [5] M. Li, W. Hou, X. Zhang, L. Hu, Z. Tang, Hyperuricemia and risk of stroke: a systematic review and meta-analysis of prospective studies, Atherosclerosis 232 (2014) 265–270. [6] A.H. Wu, J.D. Gladden, M. Ahmed, A. Ahmed, G. Filippatos, Relation of serum uric acid to cardiovascular disease, Int. J. Cardiol. (2015). [7] E. Krishnan, J.F. Baker, D.E. Furst, H.R. Schumacher, Gout and the risk of acute myocardial infarction, Arthritis Rheum. 54 (2006) 2688–2696. [8] M.J. Bos, P.J. Koudstaal, A. Hofman, J.C. Witteman, M.M. Breteler, Uric acid is a risk factor for myocardial infarction and stroke: the Rotterdam study, Stroke 37 (2006) 1503–1507. [9] S.Y. Chuang, J.H. Chen, W.T. Yeh, C.C. Wu, W.H. Pan, Hyperuricemia and increased risk of ischemic heart disease in a large Chinese cohort, Int. J. Cardiol. 154 (2012) 316–321. [10] I. Holme, A.H. Aastveit, N. Hammar, I. Jungner, G. Walldius, Uric acid and risk of myocardial infarction, stroke and congestive heart failure in 417,734 men and women in the Apolipoprotein MOrtality RISk study (AMORIS), J. Intern. Med. 266 (2009) 558–570. [11] S.Y. Kim, J.P. Guevara, K.M. Kim, H.K. Choi, D.F. Heitjan, D.A. Albert, Hyperuricemia and coronary heart disease: a systematic review and meta-analysis, Arthritis Care Res. 62 (2010) 170–180. [12] F. Braga, S. Pasqualetti, S. Ferraro, M. Panteghini, Hyperuricemia as risk factor for coronary heart disease incidence and mortality in the general population: a systematic review and meta-analysis, Clin. Chem. Lab. Med. 54 (2016) 7–15. [13] C. Meisinger, W. Koenig, J. Baumert, A. Doring, Uric acid levels are associated with all-cause and cardiovascular disease mortality independent of systemic inflammation in men from the general population: the MONICA/KORA cohort study, Arterioscler. Thromb. Vasc. Biol. 28 (2008) 1186–1192. [14] H.M. Storhaug, J.V. Norvik, I. Toft, B.O. Eriksen, M.L. Lochen, S. Zykova, et al., Uric acid is a risk factor for ischemic stroke and all-cause mortality in the general population: a gender specific analysis from The Tromso Study, BMC Cardiovasc. Disord. 13 (2013) 115. [15] A.D. Liese, H.W. Hense, H. Lowel, A. Doring, M. Tietze, U. Keil, Association of serum uric acid with all-cause and cardiovascular disease mortality and incident myocardial infarction in the MONICA Augsburg cohort. World Health Organization Monitoring Trends and Determinants in Cardiovascular Diseases, Epidemiology 10 (1999) 391–397. [16] B.F. Culleton, M.G. Larson, W.B. Kannel, D. Levy, Serum uric acid and risk for cardiovascular disease and death: the Framingham Heart Study, Ann. Intern. Med. 131 (1999) 7–13. [17] J.T. Moriarity, A.R. Folsom, C. Iribarren, F.J. Nieto, W.D. Rosamond, Serum uric acid and risk of coronary heart disease: Atherosclerosis Risk in Communities (ARIC) Study, Ann. Epidemiol. 10 (2000) 136–143. [18] P.E. Puddu, M. Lanti, A. Menotti, M. Mancini, A. Zanchetti, M. Cirillo, et al., Serum uric acid for short-term prediction of cardiovascular disease incidence in the Gubbio population Study, Acta Cardiol. 56 (2001) 243–251. [19] J.G. Wheeler, K.D. Juzwishin, G. Eiriksdottir, V. Gudnason, J. Danesh, Serum uric acid and coronary heart disease in 9,458 incident cases and 155,084 controls: prospective study and meta-analysis, PLoS Med. 2 (2005) e76. [20] M. Li, X. Hu, Y. Fan, K. Li, X. Zhang, W. Hou, et al., Hyperuricemia and the risk for coronary heart disease morbidity and mortality a systematic review and dose-response meta-analysis, Sci. Rep. 6 (2016) 19520. [21] R. Liu, C. Han, D. Wu, X. Xia, J. Gu, H. Guan, et al., Prevalence of hyperuricemia and gout in mainland China from 2000 to 2014: a systematic review and metaanalysis, Biomed. Res. Int. 2015 (2015) 762820. [22] Y. Zhu, B.J. Pandya, H.K. Choi, Prevalence of gout and hyperuricemia in the US general population: the National Health and Nutrition Examination Survey 2007-2008, Arthritis Rheum. 63 (2011) 3136–3141.
304
X. Lai et al. / International Journal of Cardiology 224 (2016) 299–304
[23] K. Nagahama, K. Iseki, T. Inoue, T. Touma, Y. Ikemiya, S. Takishita, Hyperuricemia and cardiovascular risk factor clustering in a screened cohort in Okinawa, Japan, Hypertens. Res. 27 (2004) 227–233. [24] F. Wang, J. Zhu, P. Yao, X. Li, M. He, Y. Liu, et al., Cohort profile: the Dongfeng-Tongji cohort study of retired workers, Int. J. Epidemiol. 42 (2013) 731–740. [25] L. Yang, H. Yang, M. He, A. Pan, X. Li, X. Min, et al., Longer sleep duration and midday napping are associated with a higher risk of CHD incidence in middle-aged and older Chinese: the Dongfeng-Tongji Cohort Study, Sleep 39 (2016) 645–652. [26] N. Schlesinger, Dietary factors and hyperuricaemia, Curr. Pharm. Des. 11 (2005) 4133–4138. [27] R. Villegas, Y.B. Xiang, T. Elasy, W.H. Xu, H. Cai, Q. Cai, et al., Purine-rich foods, protein intake, and the prevalence of hyperuricemia: the Shanghai Men's Health Study, Nutr. Metab. Cardiovasc. Dis. 22 (2012) 409–416. [28] Q. Su, B. Yu, H. He, Q. Zhang, G. Meng, H. Wu, et al., Nut consumption is associated with depressive symptoms among Chinese adults, Depress. Anxiety (2016). [29] L. Meng, D. Chen, Y. Yang, Y. Zheng, R. Hui, Depression increases the risk of hypertension incidence: a meta-analysis of prospective cohort studies, J. Hypertens. 30 (2012) 842–851. [30] K.B. Lam, C.Q. Jiang, G.N. Thomas, T. Arora, W.S. Zhang, S. Taheri, et al., Napping is associated with increased risk of type 2 diabetes: the Guangzhou Biobank Cohort Study, Sleep 33 (2010) 402–407. [31] W. Feng, X. He, M. Chen, S. Deng, G. Qiu, X. Li, et al., Urinary metals and heart rate variability: a cross-sectional study of urban adults in Wuhan, China, Environ. Health Perspect. 123 (2015) 217–222. [32] K.G. Alberti, P. Zimmet, J. Shaw, Metabolic syndrome—a new world-wide definition. A consensus statement from the International Diabetes Federation, Diabet. Med. 23 (2006) 469–480. [33] Y.C. Ma, L. Zuo, J.H. Chen, Q. Luo, X.Q. Yu, Y. Li, et al., Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease, J. Am. Soc. Nephrol. 17 (2006) 2937–2944. [34] World Health Organisation, International statistical classification of diseases and related health problems, 10th revision, http://apps.who.int/classifications/icd10/ browse/2013/en2013 (Accessed Oct 10,2013).
[35] V. Lohsoonthorn, B. Dhanamun, M.A. Williams, Prevalence of hyperuricemia and its relationship with metabolic syndrome in Thai adults receiving annual health exams, Arch. Med. Res. 37 (2006) 883–889. [36] I. Sari, S. Akar, B. Pakoz, A.R. Sisman, O. Gurler, M. Birlik, et al., Hyperuricemia and its related factors in an urban population, Izmir, Turkey, Rheumatol. Int. 29 (2009) 869–874. [37] Y.Y. Sautin, T. Nakagawa, S. Zharikov, R.J. Johnson, Adverse effects of the classic antioxidant uric acid in adipocytes: NADPH oxidase-mediated oxidative/nitrosative stress, Am. J. Physiol. Cell Physiol. 293 (2007) C584–C596. [38] M.A. Yu, L.G. Sanchez-Lozada, R.J. Johnson, D.H. Kang, Oxidative stress with an activation of the renin-angiotensin system in human vascular endothelial cells as a novel mechanism of uric acid-induced endothelial dysfunction, J. Hypertens. 28 (2010) 1234–1242. [39] J.X. Zhang, Y.P. Zhang, Q.N. Wu, B. Chen, Uric acid induces oxidative stress via an activation of the renin-angiotensin system in 3T3-L1 adipocytes, Endocrine 48 (2015) 135–142. [40] C. Ruggiero, A. Cherubini, A. Ble, A.J. Bos, M. Maggio, V.D. Dixit, et al., Uric acid and inflammatory markers, Eur. Heart J. 27 (2006) 1174–1181. [41] A.Y. Tiong, D. Brieger, Inflammation and coronary artery disease, Am. Heart J. 150 (2005) 11–18. [42] J. Kanellis, D.H. Kang, Uric acid as a mediator of endothelial dysfunction, inflammation, and vascular disease, Semin. Nephrol. 25 (2005) 39–42. [43] P. Li, L. Zhang, M. Zhang, C. Zhou, N. Lin, Uric acid enhances PKC-dependent eNOS phosphorylation and mediates cellular ER stress: A mechanism for uric acid-induced endothelial dysfunction, Int. J. Mol. Med. 37 (2016) 989–997. [44] W.S. Waring, S.R. Maxwell, D.J. Webb, Uric acid concentrations and the mechanisms of cardiovascular disease, Eur. Heart J. 23 (2002) 1888–1889. [45] J. Dawson, T. Quinn, M. Walters, Uric acid reduction: a new paradigm in the management of cardiovascular risk? Curr. Med. Chem. 14 (2007) 1879–1886. [46] T. Montalcini, G. Gorgone, C. Gazzaruso, G. Sesti, F. Perticone, A. Pujia, Relation between serum uric acid and carotid intima-media thickness in healthy postmenopausal women, Intern. Emerg. Med. 2 (2007) 19–23.
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Dose-response relationship between serum uric acid levels and risk of incident coronary heart disease in the Dongfeng-Tongji Cohort Xuefeng Lai a, Liangle Yang a, Sébastien Légaré a, Francesca Angileri a, Xuguang Chen a, Qin Fang a, Handong Yang b, Ce Zhang b, Xiulou Li b, Xinwen Min b, Chengwei Xu c, Jing Yuan a, Mei-an He a, Tangchun Wu a, Xiaomin Zhang a,⁎ a Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China b Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, China c Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
a r t i c l e
i n f o
Article history: Received 2 June 2016 Received in revised form 31 August 2016 Accepted 15 September 2016 Available online 19 September 2016 Keywords: Serum uric acid Hyperuricemia Incident coronary heart disease Dose-response association
a b s t r a c t Background: In prospective studies, relationship of serum uric acid (SUA) with risk of coronary heart disease (CHD) incidence is inconsistent. We evaluated the association of SUA with incident CHD and the potential modifying effect of major CHD risk factors related to SUA among a middle aged and elderly Chinese population. Methods: We included 16, 063 participants who were free of CHD, stroke, cancer and renal diseases at baseline from Sep. 2008 to June 2010, and were followed until Oct. 2013. Cox proportional hazard model was used to estimate the hazard ratios (HR) and 95% confidence interval (95% CI) of CHD incidence in relation to SUA. Results: The adjusted HR for incident CHD increased gradually with the increasing SUA levels (P for linear trend = 0.005), and the HR across sex-specific SUA quartile was 1.26 (95% CI: 1.09, 1.47), 1.13 (95% CI: 0.97, 1.31), 1.23 (95% CI: 1.06, 1.43) and 1.00 (reference; P for trend = 0.014), respectively. In particular, the association was more evident in individuals with normal-weight and those without hypertension or metabolic syndrome (all P for interactions b 0. 05). Conclusions: These findings suggested that higher SUA levels were independently associated with a doseresponse increased risk of CHD incidence. © 2016 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Serum uric acid (SUA), a final enzymatic product of purine metabolism, has been widely associated with a variety of cardiovascular conditions, including hypertension, diabetes, obesity, stroke, metabolic syndrome and more in numerous epidemiologic studies [1–5]. However, the role of SUA as an independent risk factor in coronary heart disease (CHD) incidence is still controversial [6]. Some prospective studies and two recent meta-analysis based on nine studies, showed that high SUA level or hyperuricemia may increase the risk of CHD incidence independently of traditional risk factors [7–12], whereas others studies, including an earlier meta-analysis of eight studies, indicated null associations with adjustment for possible confounders [13–19]. In
⁎ Corresponding author at: Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China. E-mail address: [email protected] (X. Zhang).
http://dx.doi.org/10.1016/j.ijcard.2016.09.035 0167-5273/© 2016 Elsevier Ireland Ltd. All rights reserved.
addition, whether any dose-response relationship exists between SUA level and risk of CHD occurrence remains unknown. Very recently, a new meta-analysis including 29 prospective cohort studies found that hyperuricemia might increase the risk of CHD morbidity and mortality; Dose-response analysis indicated that SUA was only associated with risk of CHD mortality in females, but no significant trend was found for CHD morbidity [20]. In addition, it was observed in previous studies that the associations between high SUA or hyperuricemia and cardiovascular disease (CVD) events appear to be stronger in women [10], normotensive individuals [8], and those without metabolic risk factors [9]. SUA is also known to be associated with other important CHD risk factors. Thus, it remains unclear whether such risk factors modify the dose-response relationship between SUA level and CHD incidence. Since hyperuricemia was common in Chinese population, and the prevalence of hyperuricemia was higher in economically developed regions than in other regions in China [21–23], the public health importance of high SUA levels or hyperuricemia as a possible CVD risk factor should not be ignored. We therefore tested the hypothesis whether SUA was associated with incident CHD in a dose-response manner and
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whether the association was modified by major CHD risk factors in a middle aged and elderly Chinese population.
heart disease, percutaneous transluminal coronary angioplasty or coronary artery bypass graft, and cardiac arrest (I46) or death with CHD (I20–I25) as the underlying cause [34].
2. Materials and method
2.5. Statistical analyses
2.1. Study population
Baseline characteristics of the participants were reported as mean ± SD for continuous variables and numbers (percentages) for categorical variables. Trends were evaluated with linear or logistic regression using the median value of SUA for each quartile as an ordinal variable adjusted for age and sex. We applied Cox proportional models to evaluate the relationship of SUA quartiles, hyperuricemia, or continuous serum uric acid (per 100 μmol/L increasement) with risk of incident CHD after adjusting for potential confounders. We fitted two multivariate proportional hazard models. Model 1 adjusted for age, gender, BMI, smoking status (current, former, never), drinking status (current, former, never), physical activity, education levels, hypertension, diabetes, hyperlipidemia, and family history of CHD. Model 2 further adjusted for use of diuretics, eGFR, and diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits). Test for linear trend was performed using the median SUA concentration for each quartile as a continuous variable in the multivariate model. Additionally, nonlinear trends of the relationship between SUA levels and risk for incident CHD was tested by restricted cubic spline Cox regression using 4 knots placed at the 5th, 35th, 65th, and 95th percentiles of SUA levels respectively, with 200 μmol/L (approximate the first sexspecific quartile) as the reference group. Stratified analyses were also performed by major baseline characteristics. Moreover, we tested potential interactions by adding the products of these covariates with SUA levels in total population, respectively. All statistical analyses were carried out using SAS version 9.3 (SAS institute Inc., Cary, NC). A 2-sided P value b 0.05 was considered to be statistically significant.
Data were derived from Dongfeng-Tongji Cohort Study and the design, methods of the cohort was described in detail previously [24,25]. Briefly, 31,000 retirees at Dongfeng Motor Corporation (DMC) were invited to participate in the Dongfeng-Tongji cohort study. Except for those who did not respond to the invitation, a total of 27,009 (approximately 87% response rate) retired employees agreed and completed baseline questionnaire, medical examinations and provided fasting blood samples between September 2008 and June 2010. Five years later, the participants were invited to the follow-up survey via telephone. In total, 25,978 individuals (96.2% of those at baseline) completed the first follow-up until October 2013. At the follow-up investigation, participants repeated the questionnaire interview, physical examinations, and blood collection as those during the baseline survey. After exclusion of participants who had cancers, CHD, stroke and renal diseases at baseline (n = 7531), missing data of SUA (n = 1669) and other covariates (n = 715), a total of 16,063 eligible individuals were included in the analyses. The study was approved by the Ethics and Human Subject committee of the School of Public Health, Tongji Medicine College, and Dongfeng General Hospital, DMC. Written informed consents were received from all participants. 2.2. Assessment of SUA SUA and other biochemical indexes (such as creatinine, fasting plasma glucose, and blood lipids) were determined at the DMC-owned hospital's laboratory with ARCHITECT Ci8200 automatic analyzer (ABBOTT Laboratories. Abbott Park, Illinois, USA). SUA level was categorized into four groups according to the quartiles of gender-specific distribution: b271, 271–316, 316–370 and ≥370 μmol/L for men; b214, 214–253, 253–298 and ≥298 μmol/L for women. Hyperuricemia was defined as a SUA level ≥ 420 μmol/L (7.0 mg/dL) for men and ≥360 μmol/L (6.0 mg/dL) for women. 2.3. Assessment of covariates Trained interviewers performed face-to-face semi-structured questionnaire interviews and collected information on socio-demographic characteristics (age, gender, education and marital status), diet, lifestyle such as smoking status (current, former, never), drinking status (current, former, never) and physical activity, occupational history, environmental exposures, family history, and medical history (diagnosed medical conditions, use of health services and use of medicines for the most recent 2 weeks). Participants who were smoking at least one cigarette per day for more than half a year were defined as current smokers; those who were drinking at least one time per week for more than half a year were considered as current drinkers. Physical activity was defined as those who regularly exercised N20 min per day over the last six months. For diet, we transformed the diet frequency into times per week uniformly according to four respondent frequency categories (daily, weekly, monthly, times per year) for four kinds of main regular food including meat and poultry, fishery product, soy products, vegetables and fruits, all of which might modify SUA levels [26,27]. We finally divided the consumption frequency per week into 3 categories: never, 1–3 times per week, and ≥4 times per week [28]. Body mass index (BMI) was calculated as mass (kg) divided by the square of height (m2). Hypertension was defined as individuals with self-reported physician diagnosis of hypertension, or blood pressure ≥ 140/90 mmHg, or current use of antihypertensive medication [29]. Diabetes was defined as self-reported physician diagnosis of diabetes, fasting glucose level ≥ 7.0 mmol/L, or taking oral hypoglycemic medication or insulin [30]. Hyperlipidemia was defined as total cholesterol N 5.72 mmol/L or triglycerides N1.70 mmol/L at medical examination, or a previous self-reported physician diagnosis of hyperlipidemia, or taking lipid-lowering medication [31]. Metabolic syndrome, according to the new International Diabetes Federation (IDF) definition [32], was defined when the participants had central obesity (waist circumference ≥ 90 cm for Chinese men and ≥80 cm for Chinese women) plus any two of the following four factors: (1) high blood pressure: systolic ≥130 mmHg, diastolic ≥85 mmHg, or known treatment for hypertension; (2) hypertriglyceridemia: fasting serum triglycerides ≥1.7 mmol/L; (3) low HDL cholesterol: fasting HDL cholesterol b1.03 mmol/L in men and b1.29 mmol/L in women; and (4) hyperglycemia: fasting glucose level of ≥5.6 mmol/L (≥100 mg/dL) or known treatment for diabetes. The estimated glomerular filtration rate (eGFR) was calculated by using the Modification of Diet in Renal Disease (MDRD) equation applied for Chinese patients with chronic kidney disease (CKD) [33]. 2.4. Ascertainment of incident CHD All retired employees were covered by DMC's health-care service system and each participant had a unique medical insurance card number and ID, making it easy to track disease incidence and mortality. The incidence of CHD was identified through this medical insurance system and medical record reviews in the DMC-owned hospitals. The diagnosis of CHD was made based on well-accepted international standards by cardiologists in the DMC-owned hospitals. We defined incident CHD as the first hospital admission with an occurrence of an angina pectoris (ICD-10 code I20), acute myocardial infarction (AMI, I21), subsequent myocardial infarction (I22), other forms of acute (I24) or chronic (I25)
3. Results 3.1. Characteristics of study population Baseline characteristics of participants by sex-specific quartiles of SUA levels are presented in Table 1. Compared with those in the lowest quartile, participants in the highest quartile were older and more likely to be males, overweight and presented a greater proportion of hypertension, diabetes, hyperlipidemia and metabolic syndrome. In addition, higher SUA levels were associated with a lower eGFR.
3.2. Relationship between SUA and CHD incidence As shown in Table 2, we found that increasing SUA quartiles were independently associated with elevated risk of CHD incidence after adjustment for age, gender, BMI, education levels, smoking, drinking, physical activity, hypertension, diabetes, hyperlipidemia and family history of CHD. Compared with the first quartile of SUA levels, the adjusted HRs for CHD incidence from the second to the highest SUA quartile were 1.25 (95% CI: 1.08, 1.46), 1.16 (95% CI: 1.00, 1.35) and 1.33 (95% CI: 1.15, 1.54; P for trend = 0.001). Additional adjustment for use of diuretics, eGFR, and diet frequency categories did not substantially change the association. The adjusted HR for each 100 μmol/L increasement in SUA levels was 1.14 (95% CI: 1.01, 1.29) for CHD incidence. Hyperuricemia was also associated with a 14% increased risk of incident CHD (HR = 1.14; 95% CI: 1.01, 1.29). The restricted cubic splines showed that the risk of CHD incidence increased gradually with continuous SUA levels (P for linear trend = 0.005, Fig. 1). The significant linear trend test implied that there was dosage effects and no obvious evidence of a threshold effect on the risk of CHD incidence.
3.3. Stratified analyses for association of SUA with incident CHD We subsequently conducted stratification analyses by major characteristics of the study population. The significant relationship between SUA levels and CHD incidence was more evident in individuals with normal-weight, normal renal function (N 90 mL/min/1.73 m2), and those without hypertension or metabolic syndrome (Table 3). In addition, the significant interactions were found between SUA levels and overweight, hypertension and metabolic syndrome (all P for interactions b 0.05).
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Table 1 Baseline characteristics of participants across sex-specific quartiles of serum uric acid levels. Characteristics
Q1
Q2
Q3
Q4
P for trend
N (participants) Uric acid, μmol/L Age, year Male, n (%) Body mass index, kg/m2 Education level, n (%) Primary school or below Middle school High school or above Current smoking, n (%) Current drinking, n (%) Physical activity, n (%) Regular food consumption, ≥4 times per week, n (%) Meat and poultry Fishery products Soy products Vegetables and fruits Family history of CHD, n (%) Hypertension, n (%) Diabetes, n (%) Hyperlipidemia, n (%) Metabolic syndrome, n (%) Usage of diuretics, n (%) eGFR, mL/min/1.73m2
4033 204.6 ± 38.5 61.8 ± 7.5 1797 (44.6) 23.4 ± 3.2
4019 260.2 ± 31.8 62.3 ± 7.5 1791 (44.6) 24.0 ± 3.2
3994 304.2 ± 36.4 62.9 ± 7.7 1802 (45.1) 24.6 ± 3.3
4017 387.1 ± 65.9 64.5 ± 7.8 1800 (44.8) 25.5 ± 3.4
– – b0.0001 b0.0001 b0.0001 b0.0001
1193 (29.6) 1557 (38.6) 1283 (31.8) 812 (20.1) 891 (22.1) 3581 (88.8)
1204 (30.0) 1470 (36.6) 1345 (33.5) 765 (19.0) 920 (22.9) 3567 (88.8)
1131 (28.3) 1452 (36.4) 1411 (35.3) 753 (18.9) 906 (22.7) 3566 (89.3)
1225 (30.5) 1422 (35.4) 1370 (34.1) 708 (17.6) 913 (22.7) 3572 (88.9)
0.379 0.059 0.465
1421 (35.2) 404 (10.0) 1265 (31.4) 3918 (97.2) 226 (5.6) 1631 (40.4) 642 (15.9) 1689 (41.9) 730 (18.1) 48 (1.2) 95.8 (22.7)
1323 (32.9) 435 (10.8) 1252 (31.2) 3912 (97.3) 221 (5.5) 1883 (46.9) 627 (15.6) 1888 (47.0) 953 (23.7) 45 (1.1) 92.0 (21.4)
1344 (33.7) 429 (10.7) 1287 (32.2) 3985 (97.5) 216 (5.4) 2025 (50.7) 624 (15.6) 2133 (53.4) 1175 (29.4) 44 (1.1) 88.5 (21.1)
1317 (32.8) 416 (10.4) 1285 (32.0) 3911 (97.4) 158 (3.9) 2502 (62.3) 835 (20.8) 2642 (65.8) 1682 (41.9) 75 (1.9) 81.1 (21.7)
0.289 0.010 0.658 0.592 0.098 b0.0001 b0.0001 b0.0001 b0.0001 0.031 b0.0001
Abbreviation: eGFR: estimated glomerular filtration rate. Data was presented as means ± SD for continuous variable and numbers (percentage) for category variables. P for trend tested with linear regression for continuous variables or logistic regression using the median uric acid value for each quartile after adjusting for age and gender, except for itself. The quartiles of serum uric acid concentration were calculated by gender respectively. In male, the cutoff of serum uric acid levels was b271, 271–316, 316–370 and ≥370 μmol/L; in female, the cutoff of serum uric acid levels was b214, 214–253, 253–298 and ≥298 μmol/L.
4. Discussion In this prospective study, we found a positive and significant doseresponse relationship between SUA levels and CHD incidence. Hyperuricemia was confirmed as an independent risk factor for CHD incidence in middle-aged and elderly Chinese population after controlling for a variety of potential confounders. Our findings are in line with and extend previous studies [7–10]. Chuang et al. showed that hyperuricemia was independently associated with ischemic heart disease incidence among 128,569 Chinese adults ≥ 20 years old with low CVD risk in Taiwan [9], which was confirmed in our study. However, the prevalence of hyperuricemia in their lowrisk population was higher than 3 to 4-fold in our participants. A recent meta-analysis reported that the pooled prevalence of hyperuricemia was 13.3% in Mainland of China [21]. The prevalence was similar to that of most developing countries including Thailand (10.6%) [35] and Turkey (12.1%) [36], but lower than that of several developed countries such as the United States (21.4%) [22] and Japan (25.8%) [23]. The
prevalence of hyperuricemia in our population was 11.1% in men and 8.2% in women, which was close to that of the latest meta-analysis in Mainland of China and other developing countries. Whereas the prevalence was 42.6% in men and 23.4% in women in Chuang's study conducted in Taiwan, an economically-developed region in China. This means that our population is more representative of the Chinese population living in the mainland. The different prevalence of hyperuricemia between ours and Chuang's study might be partly due to inherent lifestyle such as higher rate of current drinking (70.6%) in that study compared with that in ours (22.6%), or intake of purine rich diet. Additionally, although that study had a large sample size, it did not further explore any underlying dose-response association between SUA levels and CHD incidence. Furthermore, the degree of adjustment for possible confounding variables has also differed substantially between that study and ours. In that study, the major known risk factors of CHD, including hypertension, diabetes, hyperlipidemia, smoking habits, alcohol consumption and the usage of diuretics were controlled, but other important covariates such as diet and eGFR were not accounted for. In
Table 2 Hazard ratios (95% CI) for CHD incidence according to continuous or quartiles of serum uric acid and hyperuricemia. Hazard ratios (95% CI) Variable types of serum uric acid Trend across serum uric acid quartiles Q1 Q2 Q3 Q4 P for trend Presence of hyperuricemia No Yes Serum uric acid per 100 μmol/L
Incidents/participants
Model 1
Model 2
307/4033 403/4019 409/3994 541/4017 –
1.00 (ref.) 1.25 (1.08, 1.46) 1.16 (1.00, 1.35) 1.33 (1.15, 1.54) 0.001
1.00 (ref.) 1.23 (1.06, 1.43) 1.13 (0.97, 1.31) 1.26 (1.09, 1.47) 0.014
1258/13,238 402/2825 –
1.00 (ref.) 1.19 (1.06, 1.34) 1.13 (1.06, 1.34)
1.00 (ref.) 1.14 (1.01, 1.29) 1.14 (1.01, 1.29)
Model 1 adjusted covariates for age, gender, body mass index, smoking, drinking, physical activity, education levels, hypertension, diabetes, hyperlipidemia, family history of CHD. Model 2 adjusted covariates for model 1 plus eGFR, usage of diuretics and diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits). The quartiles of serum uric acid concentration were calculated by gender respectively. In male, the cutoff of serum uric acid concentration was b271, 271–316, 316–370 and ≥370 μmol/L; in female, the cutoff of serum uric acid concentration was b214, 214–253, 253–298 and ≥298 μmol/L.
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Fig. 1. Adjusted hazard ratios (solid lines) and 95% confidence interval (dashed lines) for CHD incidence from restricted cubic splines in a multivariate-adjusted Cox proportional hazard model. The model was adjusted for age, gender, body mass index, smoking, drinking, physical activity, education levels, family history of CHD, hypertension, hyperlipidemia, diabetes, eGFR (estimated glomerular filtration, use of diuretics, diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits).
contrast, after controlling for a wide range of traditional risk factors and further adjusting for eGFR and diet frequency, our results showed that the risk of CHD incidence gradually increased with the increasing SUA levels even in high-normal extent, possibly suggesting no threshold effect of SUA on CHD incidence. Thus, that clinically dichotomous definition of hyperuricemia might be inadequate for the prevention for CHD incidence. Another larger Swedish study found that the risk of incident
AMI seemed to increase gradually from lower to higher SUA levels in middle-aged subjects without prior CVD [10], whereas the endpoint was not extended to CHD. The major limitation of the study was that it did not adjust for other important CHD factors such as smoking, drinking, physical activity, diet, eGFR, and especially uptake of diuretics and antihypertensive medications, which could potentially attenuate the findings relating SUA to incident AMI. In addition, the prevalence of hypertension (0.7%) and diabetes (3.2%) was clearly underestimated as the definition of hypertension was based on hospital discharge diagnosis and diabetes was according to fasting glucose measurement at baseline or discharge diagnosis. Compared with above studies, our study had access to a wide range of covariates and minimized the residual confounding, and still demonstrated a clear dose-response relationship. On the contrary, a few other prospective studies reported that the positive association disappeared after adjusting for established risk factors such as hypertension, diabetes and diuretics [13–18]. Several reasons might account for the disparity between our findings and those from previous studies. CHD incidence in the Framingham [16] and atherosclerosis risk in communities (ARIC) [17] studies were 5.26 and 4.70 per 1000 person-years, respectively. In comparison, the incidence of CHD in the present study was up to 23.25 per 1000 person-years, as the mean participants' age was about 10 years older than that of the above studies. Therefore, the low CHD incidence due to younger participants, low number of CHD events [15,18], and insufficient statistical power might be responsible for failing to detect the significant effect of SUA on CHD incidence in previous studies. Meanwhile, the inconsistent results might be due to different definition of endpoints. In other two studies conducted in Germany [13] and Norway [14], only fatal and non-fatal myocardial infarction were defined as endpoints, which may obviously underestimate the hazard risk of CHD incidence, while our study included all types of CHD (ICD-10) and cardiac death as outcome. In addition, because the large number of risk factors are related to both CHD and SUA, differences in measurement and adjustment for these potential confounders in prior studies could have contributed to
Table 3 Associations of CHD incidence with serum uric acid stratified by baseline characteristics. Stratification covariates Overweight a No (BMI b 25 kg/m2) Incidents/participants Hazard ratios (95% CI) Yes (BMI ≥ 25 kg/m2) Incidents/participants Hazard ratios (95% CI) Hypertension a No Incidents/participants Hazard ratios ((95% CI) Yes Incidents/participants Hazard ratios (95% CI) Metabolic syndrome b No Incidents/participants Hazard ratios (95% CI) Yes Incidents/participants Hazard ratios (95% CI) eGFR a iN90 mL/min/1.73m2 Incidents/participants Hazard ratios (95% CI) ≤90 mL/min/1.73m2 Incidents/participants Hazard ratios (95% CI)
Q1
Q2
Q3
Q4
P for trend
P for interaction 0.035
0.005 189/2903 reference
233/2565 1.34 (1.11, 1.63)
219/2273 1.30 (1.06, 1.58)
222/1851 1.40 (1.14, 1.71)
118/1130 reference
170/1454 1.07 (0.84, 1.35)
190/1721 0.91 (0.72, 1.15)
319/2166 1.08 (0.86, 1.35)
0.485
0.043 0.022 126/2402 reference
162/2136 1.24 (0.98, 1.57)
150/1969 1.22 (0.95, 1.55)
146/1515 1.39 (1.07, 1.79)
181/1631 reference
241/1883 1.19 (0.98, 1.44)
259/2025 1.06 (0.87, 1.28)
395/2502 1.19 (0.99, 1.43)
0.160
0.034 0.0002 226/3303 reference
278/3066 1.28 (1.07, 1.52)
271/2819 1.29 (1.07, 1.54)
282/2335 1.45 (1.21, 1.75)
81/730 reference
125/953 1.08 (0.82, 1.44)
138/1175 0.87 (0.66, 1.15)
259/1682 1.05 (0.81, 1.36)
0.720
0.314 0.007 152/2329 reference
169/1966 1.32 (1.05, 1.64)
139/1623 1.16 (0.91, 1.47)
138/1111 1.49 (1.16, 1.89)
155/1704 reference
234/2053 1.19 (0.97, 1.45)
270/2371 1.12 (0.91, 1.36)
403/2906 1.22 (1.01, 1.48)
0.093
a Adjusted covariates for age, gender, BMI, smoking, drinking, physical activity, education levels, hypertension, diabetes, hyperlipidemia, family history of CHD, eGFR and usage of diuretics, diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits) except for the stratified covariate itself. b Adjusted covariates for age, gender, BMI, smoking, drinking, physical activity, education levels, family history of CHD, eGFR, and usage of diuretics, diet frequency categories (including meat and poultry, fishery products, soy products, vegetables and fruits).
X. Lai et al. / International Journal of Cardiology 224 (2016) 299–304
the discrepancies. Furthermore, inherent differences in population characteristics, shorter observation time, and sample size might also contribute to the disparate results. Notably, the dose-response association between SUA levels and CHD incidence was only seen among persons with normal weight, normal renal function (eGFR N 90 mL/min/1.73 m2) and those without comorbidities such as hypertension, or metabolic syndrome. These findings were similar to previous studies, which found that the association was more pronounced for individuals without hypertension [8] and metabolic syndrome [9]. Although the underlying pathophysiological mechanisms are unclear, it is possible that the presence of CHD risk factors would cover up the effect of SUA on risk of CHD among high-risk persons and leave the pernicious effects prominent in relatively healthy adults. Several potential mechanisms might explain the association between uric acid and CHD incidence. First, uric acid has been associated with oxidative stress and insulin resistance as it may enhance lipid peroxidation and platelet adhesiveness [37–39]. Second, uric acid could induce vascular inflammation, as a significant association between SUA and several inflammation markers was found in epidemiological studies [40,41]. Third, high uric acid levels could lead to endothelial dysfunction [42,43], stimulating vascular smooth cell proliferation and reducing nitric oxide production [44,45]. In addition, it has been demonstrated that uric acid levels were closely related to arterial intima-media thickness, a precursor to atherosclerosis [46]. There were several limitations in the present this study. Firstly, like almost all of the previous studies, serum uric acid was measured only once at baseline, so we were unable to account for within-individual variability in the present study. Secondly, participants included in our study were middle-aged and older Chinese free of cardiovascular disease, stroke, cancer and renal disease, thus the generalizability of our findings to the general populations of younger age, or other ethnicities and different health conditions needs to be further verified. In addition, although a variety of potential confounding factors were accounted for in multivariate analysis, the possibility of residual and unmeasured confounders could not be ruled out in observational studies.
5. Conclusion In summary, our findings confirm hyperuricemia as an independent risk factor and showed a positive dose-response relationship between SUA levels and CHD incidence. This association might be modified by comorbidities such as hypertension, overweight, and metabolic syndrome. Author contributions Authors X. Lai, X. Zhang take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. Concept and design: H. Yang, T. Wu, X. Zhang. Acquisition, analysis or interpretation of data: all authors. Drafting of the manuscript: X. Lai, L. Yang, and X. Zhang. Critical revision of the manuscript for important intellectual content: X. Lai, L. Yang, S. Légaré, F. Angileri, X. Zhang. Statistical analysis: X. Lai, X. Zhang. Administrative, technical, or material support: X. Li, X. Min, C. Zhang, C. Xu, M. He, J. Yuan.
Source(s) of funding This work was supported by the Natural National Scientific Foundation of China (81373093, 81230069, and 81390542), 111 Project (No. B12004); and the Program for Changjiang Scholars; Innovative Research Team in University of Ministry of Education of China (No. IRT1246); China Medical Board (No. 12-113).
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Conflict of interest None.
Acknowledgements The authors would like to thank all study subjects for participating in the present Dongfeng-Tongji Cohort study as well as volunteers for assisting in collecting the sample, questionnaire data and clinic data.
References [1] J. Wang, T. Qin, J. Chen, Y. Li, L. Wang, H. Huang, et al., Hyperuricemia and risk of incident hypertension: a systematic review and meta-analysis of observational studies, PLoS One 9 (2014), e114259. [2] T. Wang, Y. Bi, M. Xu, Y. Huang, Y. Xu, X. Li, et al., Serum uric acid associates with the incidence of type 2 diabetes in a prospective cohort of middle-aged and elderly Chinese, Endocrine 40 (2011) 109–116. [3] T.Y. Yu, J.H. Jee, J.C. Bae, S.M. Jin, J.H. Baek, M.K. Lee, et al., Serum uric acid: A strong and independent predictor of metabolic syndrome after adjusting for body composition, Metab. Clin. Exp. 65 (2016) 432–440. [4] N.F. Chu, D.J. Wang, S.H. Liou, S.M. Shieh, Relationship between hyperuricemia and other cardiovascular disease risk factors among adult males in Taiwan, Eur. J. Epidemiol. 16 (2000) 13–17. [5] M. Li, W. Hou, X. Zhang, L. Hu, Z. Tang, Hyperuricemia and risk of stroke: a systematic review and meta-analysis of prospective studies, Atherosclerosis 232 (2014) 265–270. [6] A.H. Wu, J.D. Gladden, M. Ahmed, A. Ahmed, G. Filippatos, Relation of serum uric acid to cardiovascular disease, Int. J. Cardiol. (2015). [7] E. Krishnan, J.F. Baker, D.E. Furst, H.R. Schumacher, Gout and the risk of acute myocardial infarction, Arthritis Rheum. 54 (2006) 2688–2696. [8] M.J. Bos, P.J. Koudstaal, A. Hofman, J.C. Witteman, M.M. Breteler, Uric acid is a risk factor for myocardial infarction and stroke: the Rotterdam study, Stroke 37 (2006) 1503–1507. [9] S.Y. Chuang, J.H. Chen, W.T. Yeh, C.C. Wu, W.H. Pan, Hyperuricemia and increased risk of ischemic heart disease in a large Chinese cohort, Int. J. Cardiol. 154 (2012) 316–321. [10] I. Holme, A.H. Aastveit, N. Hammar, I. Jungner, G. Walldius, Uric acid and risk of myocardial infarction, stroke and congestive heart failure in 417,734 men and women in the Apolipoprotein MOrtality RISk study (AMORIS), J. Intern. Med. 266 (2009) 558–570. [11] S.Y. Kim, J.P. Guevara, K.M. Kim, H.K. Choi, D.F. Heitjan, D.A. Albert, Hyperuricemia and coronary heart disease: a systematic review and meta-analysis, Arthritis Care Res. 62 (2010) 170–180. [12] F. Braga, S. Pasqualetti, S. Ferraro, M. Panteghini, Hyperuricemia as risk factor for coronary heart disease incidence and mortality in the general population: a systematic review and meta-analysis, Clin. Chem. Lab. Med. 54 (2016) 7–15. [13] C. Meisinger, W. Koenig, J. Baumert, A. Doring, Uric acid levels are associated with all-cause and cardiovascular disease mortality independent of systemic inflammation in men from the general population: the MONICA/KORA cohort study, Arterioscler. Thromb. Vasc. Biol. 28 (2008) 1186–1192. [14] H.M. Storhaug, J.V. Norvik, I. Toft, B.O. Eriksen, M.L. Lochen, S. Zykova, et al., Uric acid is a risk factor for ischemic stroke and all-cause mortality in the general population: a gender specific analysis from The Tromso Study, BMC Cardiovasc. Disord. 13 (2013) 115. [15] A.D. Liese, H.W. Hense, H. Lowel, A. Doring, M. Tietze, U. Keil, Association of serum uric acid with all-cause and cardiovascular disease mortality and incident myocardial infarction in the MONICA Augsburg cohort. World Health Organization Monitoring Trends and Determinants in Cardiovascular Diseases, Epidemiology 10 (1999) 391–397. [16] B.F. Culleton, M.G. Larson, W.B. Kannel, D. Levy, Serum uric acid and risk for cardiovascular disease and death: the Framingham Heart Study, Ann. Intern. Med. 131 (1999) 7–13. [17] J.T. Moriarity, A.R. Folsom, C. Iribarren, F.J. Nieto, W.D. Rosamond, Serum uric acid and risk of coronary heart disease: Atherosclerosis Risk in Communities (ARIC) Study, Ann. Epidemiol. 10 (2000) 136–143. [18] P.E. Puddu, M. Lanti, A. Menotti, M. Mancini, A. Zanchetti, M. Cirillo, et al., Serum uric acid for short-term prediction of cardiovascular disease incidence in the Gubbio population Study, Acta Cardiol. 56 (2001) 243–251. [19] J.G. Wheeler, K.D. Juzwishin, G. Eiriksdottir, V. Gudnason, J. Danesh, Serum uric acid and coronary heart disease in 9,458 incident cases and 155,084 controls: prospective study and meta-analysis, PLoS Med. 2 (2005) e76. [20] M. Li, X. Hu, Y. Fan, K. Li, X. Zhang, W. Hou, et al., Hyperuricemia and the risk for coronary heart disease morbidity and mortality a systematic review and dose-response meta-analysis, Sci. Rep. 6 (2016) 19520. [21] R. Liu, C. Han, D. Wu, X. Xia, J. Gu, H. Guan, et al., Prevalence of hyperuricemia and gout in mainland China from 2000 to 2014: a systematic review and metaanalysis, Biomed. Res. Int. 2015 (2015) 762820. [22] Y. Zhu, B.J. Pandya, H.K. Choi, Prevalence of gout and hyperuricemia in the US general population: the National Health and Nutrition Examination Survey 2007-2008, Arthritis Rheum. 63 (2011) 3136–3141.
304
X. Lai et al. / International Journal of Cardiology 224 (2016) 299–304
[23] K. Nagahama, K. Iseki, T. Inoue, T. Touma, Y. Ikemiya, S. Takishita, Hyperuricemia and cardiovascular risk factor clustering in a screened cohort in Okinawa, Japan, Hypertens. Res. 27 (2004) 227–233. [24] F. Wang, J. Zhu, P. Yao, X. Li, M. He, Y. Liu, et al., Cohort profile: the Dongfeng-Tongji cohort study of retired workers, Int. J. Epidemiol. 42 (2013) 731–740. [25] L. Yang, H. Yang, M. He, A. Pan, X. Li, X. Min, et al., Longer sleep duration and midday napping are associated with a higher risk of CHD incidence in middle-aged and older Chinese: the Dongfeng-Tongji Cohort Study, Sleep 39 (2016) 645–652. [26] N. Schlesinger, Dietary factors and hyperuricaemia, Curr. Pharm. Des. 11 (2005) 4133–4138. [27] R. Villegas, Y.B. Xiang, T. Elasy, W.H. Xu, H. Cai, Q. Cai, et al., Purine-rich foods, protein intake, and the prevalence of hyperuricemia: the Shanghai Men's Health Study, Nutr. Metab. Cardiovasc. Dis. 22 (2012) 409–416. [28] Q. Su, B. Yu, H. He, Q. Zhang, G. Meng, H. Wu, et al., Nut consumption is associated with depressive symptoms among Chinese adults, Depress. Anxiety (2016). [29] L. Meng, D. Chen, Y. Yang, Y. Zheng, R. Hui, Depression increases the risk of hypertension incidence: a meta-analysis of prospective cohort studies, J. Hypertens. 30 (2012) 842–851. [30] K.B. Lam, C.Q. Jiang, G.N. Thomas, T. Arora, W.S. Zhang, S. Taheri, et al., Napping is associated with increased risk of type 2 diabetes: the Guangzhou Biobank Cohort Study, Sleep 33 (2010) 402–407. [31] W. Feng, X. He, M. Chen, S. Deng, G. Qiu, X. Li, et al., Urinary metals and heart rate variability: a cross-sectional study of urban adults in Wuhan, China, Environ. Health Perspect. 123 (2015) 217–222. [32] K.G. Alberti, P. Zimmet, J. Shaw, Metabolic syndrome—a new world-wide definition. A consensus statement from the International Diabetes Federation, Diabet. Med. 23 (2006) 469–480. [33] Y.C. Ma, L. Zuo, J.H. Chen, Q. Luo, X.Q. Yu, Y. Li, et al., Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease, J. Am. Soc. Nephrol. 17 (2006) 2937–2944. [34] World Health Organisation, International statistical classification of diseases and related health problems, 10th revision, http://apps.who.int/classifications/icd10/ browse/2013/en2013 (Accessed Oct 10,2013).
[35] V. Lohsoonthorn, B. Dhanamun, M.A. Williams, Prevalence of hyperuricemia and its relationship with metabolic syndrome in Thai adults receiving annual health exams, Arch. Med. Res. 37 (2006) 883–889. [36] I. Sari, S. Akar, B. Pakoz, A.R. Sisman, O. Gurler, M. Birlik, et al., Hyperuricemia and its related factors in an urban population, Izmir, Turkey, Rheumatol. Int. 29 (2009) 869–874. [37] Y.Y. Sautin, T. Nakagawa, S. Zharikov, R.J. Johnson, Adverse effects of the classic antioxidant uric acid in adipocytes: NADPH oxidase-mediated oxidative/nitrosative stress, Am. J. Physiol. Cell Physiol. 293 (2007) C584–C596. [38] M.A. Yu, L.G. Sanchez-Lozada, R.J. Johnson, D.H. Kang, Oxidative stress with an activation of the renin-angiotensin system in human vascular endothelial cells as a novel mechanism of uric acid-induced endothelial dysfunction, J. Hypertens. 28 (2010) 1234–1242. [39] J.X. Zhang, Y.P. Zhang, Q.N. Wu, B. Chen, Uric acid induces oxidative stress via an activation of the renin-angiotensin system in 3T3-L1 adipocytes, Endocrine 48 (2015) 135–142. [40] C. Ruggiero, A. Cherubini, A. Ble, A.J. Bos, M. Maggio, V.D. Dixit, et al., Uric acid and inflammatory markers, Eur. Heart J. 27 (2006) 1174–1181. [41] A.Y. Tiong, D. Brieger, Inflammation and coronary artery disease, Am. Heart J. 150 (2005) 11–18. [42] J. Kanellis, D.H. Kang, Uric acid as a mediator of endothelial dysfunction, inflammation, and vascular disease, Semin. Nephrol. 25 (2005) 39–42. [43] P. Li, L. Zhang, M. Zhang, C. Zhou, N. Lin, Uric acid enhances PKC-dependent eNOS phosphorylation and mediates cellular ER stress: A mechanism for uric acid-induced endothelial dysfunction, Int. J. Mol. Med. 37 (2016) 989–997. [44] W.S. Waring, S.R. Maxwell, D.J. Webb, Uric acid concentrations and the mechanisms of cardiovascular disease, Eur. Heart J. 23 (2002) 1888–1889. [45] J. Dawson, T. Quinn, M. Walters, Uric acid reduction: a new paradigm in the management of cardiovascular risk? Curr. Med. Chem. 14 (2007) 1879–1886. [46] T. Montalcini, G. Gorgone, C. Gazzaruso, G. Sesti, F. Perticone, A. Pujia, Relation between serum uric acid and carotid intima-media thickness in healthy postmenopausal women, Intern. Emerg. Med. 2 (2007) 19–23.