Arthritis Care & Research Vol. 68, No. 5, May 2016, pp 660–666 DOI 10.1002/acr.22830 C 2016, American College of Rheumatology V
ORIGINAL ARTICLE
Ideal Cardiovascular Health Metrics and Incident Hyperuricemia ZHENG LI,1 LINGMIN MENG,2 ZHE HUANG,3 LIUFU CUI,2 WEIJUAN LI,4 JINGSHENG GAO,2 ZHANQI WANG,5 RUI ZHANG,6 JING ZHOU,1 GE ZHANG,5 SHUOHUA CHEN,2 XIAOMING ZHENG,2 HONGLIANG CONG,7 XIANG GAO,8 AND SHOULING WU2
Objective. Hyperuricemia has been shown to be associated with increased risks of gout and cardiovascular diseases. We prospectively investigated the association between the American Heart Association (AHA) ideal cardiovascular health metrics, including smoking, body mass index, dietary intake, physical activity, blood pressure, total cholesterol, and fasting blood glucose, and the risk of developing hyperuricemia. Methods. We included 77,787 Chinese adults, ages ‡18 years (60,951 men and 16,836 women), without hyperuricemia at the baseline (2006) in this study. Information on the cardiovascular health metrics at baseline was collected. Incident hyperuricemia cases were identified by elevated serum uric acid concentrations, which were repeatedly assessed in 2006, 2008, 2010, and 2012, respectively. Cox regression was used to calculate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) for incident hyperuricemia according to the baseline ideal cardiovascular health metrics. Results. We observed an inverse relation between the greater numbers of ideal cardiovascular health metrics at baseline and lower risks of developing hyperuricemia during 6 years of followup. After adjusting for age, sex, alcohol consumption, and other potential confounders, the HRs for incident hyperuricemia were 0.95, 0.84, 0.72, and 0.64 (95% CIs 0.58–0.70, P for trend < 0.0001) for participants who met 2, 3, 4, and 5–7 metrics, respectively, compared with those who met 0–1 cardiovascular health metrics. Conclusion. Greater cardiovascular health metrics were associated with lower risk of hyperuricemia in this Chinese population, suggesting that the modifiable construct defined by the AHA could be of significance in reducing the risk of developing hyperuricemia-related diseases, such as gout.
Gout is a disease that results from chronic elevation of uric acid (UA) levels above the saturation point for monosodium urate crystal formation. Further, an association between gout and hypertension, cardiovascular diseases, diabetes mellitus, and kidney disease has been observed since the late 19th century. However, the association between UA and cardiovascular disease was largely ignored until the
1950s and 1960s (1). Since then, a large number of epidemiologic studies have reported that elevated serum UA levels were associated with a higher risk of a wide variety of cardiovascular conditions, including hypertension (2–4), coronary artery disease, (4–7), congestive heart failure (7–9), metabolic syndrome (3,10,11), kidney disease (3,4,6), cerebrovascular disease (7,12), and vascular dementia (13,14). Therefore, it is important to understand potential risk factors for UA elevation in the general population.
Supported by the National Natural Science Foundation of China (grants 81170244 and 81170090), and the Tianjin Municipal Science and Technology Commission, Science and Technology Support Program (grant 12ZCZDSY03200). 1 Zheng Li, MD, Jing Zhou, MD: Tianjin Medical University, Tianjin, China, and Chinese Medicine Hospital, North China University of Science and Technology, Tangshan, China; 2 Lingmin Meng, MD, Liufu Cui, MD, Jingsheng Gao, MD, Shuohua Chen, MD, Xiaoming Zheng, MD, PhD, Shouling Wu, MD, PhD: Kailuan Hospital, North China University of Science and Technology, Tangshan, China; 3Zhe Huang, MD: Kailuan Hospital, North China University of Science and Technology, Tangshan, China, and Tianjin Medical University, Tianjin, China; 4Weijuan Li, MD: Jacobi Medical Center, Albert Einstein College of Medicine, Morris Park, Bronx, New York; 5Zhanqi
Wang, MD, Ge Zhang, MD: Tianjin Medical University, Tianjin, China; 6Rui Zhang, MD: Tianjin Medical University and Tianjin Chest Hospital, Tianjin, China; 7Hongliang Cong, MD, PhD: Tianjin Chest Hospital, Tianjin, China; 8Xiang Gao, MD, PhD: Pennsylvania State University, University Park. Drs. Z. Li and Meng contributed equally to this work. Address correspondence to Xiang Gao, MD, PhD, Department of Nutritional Science, Pennsylvania State University, 109 Chandlee Laboratory, University Park, PA 16802 (e-mail:
[email protected]) or Shouling Wu, MD, Department of Cardiology, Kailuan Hospital Affiliated to Hebei United University, No. 57 Xinhua Road (East), Tangshan 063000, China (e-mail:
[email protected]). Submitted for publication September 14, 2015; accepted in revised form December 22, 2015.
INTRODUCTION
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Health Metrics and Incident Hyperuricemia
Significance & Innovations We conducted the first prospective study to investigate the relationship between the American Heart Association (AHA) ideal cardiovascular health (CVH) metrics and the risk of developing hyperuricemia during 6 years of followup in 77,787 Chinese participants in the ongoing Kailuan Study. Participants with better CVH status, as assessed by the AHA CVH metrics, had significantly lower risk of developing hyperuricemia. Our study suggests that the AHA-recommended CVH metrics could be of significance in reducing the risk of hyperuricemia-related diseases, such as gout.
The American Heart Association (AHA) recently set a goal to improve the cardiovascular health (CVH) of Americans by 20% by 2020 (15). In order to accurately measure progress toward this goal, the AHA defined 7 behaviors and risk factors (smoking status, physical activity, cholesterol level, healthy diet score, body mass index [BMI], blood pressure [BP], and fasting blood glucose level) as health metrics and created 3 stages for each metric to reflect poor, intermediate, and ideal CVH status. It has been shown that ideal CVH metrics (nonsmoking, BMI ,25 kg/m2, physical activity at goal levels, a healthy diet, untreated total cholesterol ,200 mg/dl, untreated systolic BP ,120 mm Hg, diastolic BP ,80 mm Hg, and untreated fasting blood glucose ,100 mg/dl) are associated with lower risks of cardiovascular events, cancer, and mortality (16–19). Although previous studies have shown the association between partial components of the CVH metrics (e.g., BMI and BP) and elevated UA concentration, it remains unknown whether the risk of developing hyperuricemia is influenced by the overall CVH profile. Information about this relationship would provide insights into how the CVH metrics promoted by the AHA are associated with gout and other UArelated diseases. We therefore conducted a prospective analysis to investigate the relationship between ideal CVH metrics and the development of hyperuricemia during 6 years of followup in 77,787 Chinese participants in the ongoing Kailuan Study.
661 used for all the measurements and were administered by trained field workers (i.e., physicians and nurses). The study was performed based on Helsinki Declaration guidelines and was approved by the Ethics Committee of the Kailuan General Hospital. In the current study, we excluded 23,723 participants, including 6,143 participants due to incomplete data on health behaviors or health factors at baseline, 1,284 participants due to missing serum UA data, 8,308 participants due to baseline hyperuricemia (1,386 women and 6,922 men), 563 participants due to estimated glomerular filtration rate (eGFR) ,30 ml/minute/1.73 m2, and 7,425 participants due to missing followup surveys, leaving 77,787 participants for further analyses (Figure 1). Assessment of CVH metrics at baseline. The CVH metrics included 7 components (smoking status, physical activity level, cholesterol level, healthy diet score, BMI, BP, and fasting blood glucose level). Because the primary aim of the current analysis was to evaluate the association between AHA-recommended ideal CVH metrics and hyperuricemia risk, we scored each component as 0 (non-ideal status) or 1 (ideal status), based on the AHA recommendation. The number of ideal CVH metrics was used as primary exposure. The total scores therefore ranged from 0 (worst) to 7 (best). During the interview, participants’ height was measured by the trained field workers to the nearest 0.1 cm using height meter of platform scales. Weight was measured to the nearest 0.1 kg using the weight meter of platform scales. BMI was calculated as body weight (kg) divided by the square of height (m2). Systolic and diastolic BP was measured on the left arm to the nearest 2 mm Hg using a mercury sphygmomanometer with a cuff of appropriate size following the standard recommended procedures. The BPs were measured 3 times in the sitting position; the average of the 3 readings was used for data analysis (22,23). Information on the use of antihypertensive and lipid-lowering medications and hypoglycemic treatment was collected by questionnaires during the survey. BMI was defined as ideal if it was ,25 kg/m2. BP was defined as ideal if systolic BP was ,120 mm Hg and
PATIENTS AND METHODS Study design and population. The Kailuan Study was a prospective cohort study of cardiovascular, cerebrovascular, and related disease risk factors based on the Kailuan community population living in Tangshan city, which was started in June 2006, and described in detail elsewhere (20,21). There are 11 hospitals responsible for the health care of the Kailuan community (22). A total of 101,510 participants (81,110 men and 20,400 women, ages 18–98 years) were recruited in the Kailuan Study and underwent questionnaire assessment, clinical examination, and laboratory assessment at the baseline and every 2 years afterward. Standard protocols were
Figure 1. Flowchart of the study. eGFR 5 estimated glomerular filtration rate.
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Table 1.
Participant characteristics according to the number of ideal CVH metrics at baseline (2006–2007)* Number of ideal CVH metrics
Men Women Age, years Uric acid, mg/dl Education Illiteracy/primary Middle school College or above Income† ,¥600/month ¥600–800/month $¥800/month Occupation White collar Blue collar Alcohol consumption ,1 time/day $1 times/day hsCRP (mg/liter) eGFR, ml/minute/1.73m2
0–1 (n 5 10,390)
2 (n 5 18,905)
3 (n 5 23,860)
4 (n 5 17,151)
5–7 (n 5 7,481)
9,614 (15.8) 776 (4.6) 50.9 6 10.1 4.9 6 1.1
16,291 (26.7) 2,614 (15.5) 51.5 6 10.8 4.8 6 1.1
19,160 (31.4) 4,700 (27.9) 50.8 6 11.8 4.6 6 1.1
12,115 (19.9) 5,036 (29.9) 49.5 6 12.8 4.4 6 1.1
3,771 (6.2) 3,710 (22.0) 46.2 6 13.7 4.2 6 1.1
1,196 (16.2) 8,657 (13.3) 525 (9.8)
1,973 (26.7) 16,036 (24.7) 881 (16.5)
2,243 (30.3) 20,275 (31.2) 1,330 (24.9)
1,433 (19.4) 14,383 (22.1) 1,325 (24.8)
547 (7.4) 5,650 (8.7) 1,280 (24.0)
3,867 (16.9) 4,889 (11.1) 1,619 (15.0)
5,960 (26.1) 10,442 (23.7) 2,483 (23.0)
6,578 (28.8) 14,318 (32.5) 2,946 (27.3)
4,388 (19.2) 10,474 (23.8) 2,281 (21.1)
2,068 (9.0) 3,930 (8.9) 1,478 (13.7)
640 (10.9) 9,738 (13.6)
1,132 (19.3) 17,747 (24.7)
1,522 (26) 22,298 (31.1)
1,415 (24.2) 15,709 (21.9)
1,147 (19.6) 6,316 (8.8)
7,070 (11.0) 3,317 (24.4) 2.4 6 7.4 84.5 6 24.1
14,686 (22.9) 4,201 (30.9) 2.3 6 6.1 83.3 6 25.9
20,167 (31.4) 3,682 (27.1) 2.1 6 6.5 82.9 6 23.7
15,321 (23.9) 1,820 (13.4) 2.0 6 5.2 84.0 6 27.4
6,904 (10.8) 575 (4.2) 1.8 6 5.1 85.8 6 22.7
* Values are the mean 6 SD or the number (%). CVH 5 cardiovascular health; hsCRP 5 high-sensitivity C-reactive protein; eGFR 5 estimated glomerular filtration rate. † Average monthly income of every family member.
diastolic BP was ,80 mm Hg, without taking antihypertensive medications. Blood samples were collected from the antecubital vein in the morning after an overnight fast. Concentrations of fasting blood glucose and total cholesterol were determined by the professional laboratory technicians via standard methods, which were described previously (24). Fasting blood glucose was defined as ideal if ,100 mg/dl without hypoglycemic treatment; total cholesterol was defined as ideal if the untreated total cholesterol level was ,200 mg/dl. Otherwise, they were defined as non-ideal. Information on smoking status, salt intake, and physical activity was collected via questionnaires. Because the questionnaire did not include dietary questions in 2006, salt intake was used as a surrogate for overall diet quality. This is supported by the fact that salt intakes have consistently been found to be associated with risk of cardiovascular disease in previous epidemiologic studies (25,26). The ideal diet was defined as a daily salt consumption of ,6 gm, as described previously (24,27,28). Ideal smoking status was defined as having never smoked; ideal physical activity was defined as moderate or vigorous physical activity for .80 minutes/week. Serum UA concentration assessment. Serum UA concentrations were measured in 2006, 2008, 2010, and 2012 using an oxidase method by the Hitachi 7600 automatic biochemical analyzer at the central laboratory in the Kailuan General Hospital. The UA kit was provided by Shanghai KEHUA Bio-Engineering (Share). The coefficient of variations of serum UA assessment was #6.0% for both within and between groups. Hyperuricemia was defined as $7 mg/dl
(416 mmoles/liter) in men and $6mg/dl (357 mmoles/liter) in women (29). Assessment of potential confounders. Data on demographic variables (e.g., age, sex, education level, occupation, and income) were collected using questionnaires. We also collected information on alcohol consumption by asking the average frequency and amount of alcoholic beverages recently consumed. We found a strong relationship between greater frequency of alcohol consumption and higher blood high-density lipoprotein (HDL) cholesterol concentrations in 97,742 Kailuan participants with both alcohol and cholesterol data available; age- and sexadjusted mean HDL cholesterol was 59.5 mg/dl for those with no alcohol intake and 62.2 mg/dl for those consuming alcohol $1 times per day (P , 0.0001 for trend). Blood concentrations of high-sensitivity C-reactive protein (hsCRP) and creatinine were determined by the professional laboratory physicians via standard methods. We then calculated the eGFR using the Chronic Kidney Disease Epidemiology Collaboration 2-level race equation (30). Statistical analyses. Data management and statistical analyses were performed using SPSS, version 13.0, for Windows. Continuous variables were described by means 6 SDs and compared using the analyses of variance. Values (e.g., CRP level) with skewed distribution were analyzed after log conversion. Categorical variables were described by percentages and compared using the chi-square tests. The number of ideal CVH metrics was computed. Cox proportional hazard regression was used to calculate the hazard ratios (HRs)
Health Metrics and Incident Hyperuricemia
Table 2.
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Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for hyperuricemia according to the number of ideal CVH metrics*
Number of ideal CVH metrics 0–1 2 3 4 5–7 P for trend
No.
Cases
10,390 18,905 23,860 17,151 7,481
1,847 2,900 3,079 1,791 727
Model 1 HR (95% CI)
Model 2 HR (95% CI)
Model 3 HR (95% CI)
1 (reference) 0.93 (0.88–0.99) 0.82 (0.78–0.87) 0.70 (0.66–0.75) 0.63 (0.57–0.68) , 0.001
1 (reference) 0.95 (0.89–1.01) 0.85 (0.80–0.90) 0.72 (0.68–0.77) 0.63 (0.58–0.69) , 0.001
1 (reference) 0.95 (0.89–1.01) 0.84 (0.80–0.90) 0.72 (0.68–0.77) 0.64 (0.58–0.70) , 0.001
* CVH 5 cardiovascular health. Model 1: adjusted for age, sex, and baseline uric acid; model 2: further adjusted for education (illiteracy/primary, high school, or college or above), income (,¥600/month, ¥600–800/month, or $¥800/month), occupation (white collar or blue collar), and alcohol consumption (,1 time/day or $1 times/day); model 3: further adjusted for high-sensitivity C-reactive protein and estimated glomerular filtration rate.
and 95% confidence intervals (95% CIs) of the risk of developing hyperuricemia according to the number of baseline ideal CVH metrics. All the analyses were performed after adjusting for age, sex, baseline serum UA level, education level, occupation, income, alcohol consumption, eGFR, and CRP level, because they could be associated with both CVH metrics and UA concentrations. We tested interactions between CVH metrics and age (,60 years versus $60 years), sex, and alcohol consumption ($1 times/day versus ,1 time/day) after controlling for aforementioned covariates. To examine the robustness of our results, we also conducted a sensitivity analysis by excluding participants who reported use of antihypertensive medications, since they could have effects on UA status. All statistical tests were 2-sided, and P values less than 0.05 were considered statistically significant.
RESULTS Participants with a greater number of ideal CVH metrics were younger and more likely to be women, and had higher social economic status (i.e., white collar occupation and higher education level and income) (Table 1) compared to those with lower CVH scores.
Table 3.
After 6 years of followup, we identified 10,344 new-onset hyperuricemia cases. A greater number of ideal CVH metrics was associated with a lower risk of developing hyperuricemia (Table 2). After adjusting for age, sex, baseline serum UA level, and other covariates, the HRs for hyperuricemia with adherence to 0–1 (reference), 2, 3, 4, and 5–7 ideal heath metrics were 1 (reference), 0.95, 0.84, 0.72, and 0.64 (95% CI 0.58–0.70, P , 0.001 for trend), respectively (Table 2). Similar results were generated when participants with use of antihypertensive medications were excluded: adjusted HRs across 5 CVH metrics categories were 1 (reference), 0.97, 0.90, 0.77, and 0.70 (95% CI 0.64–0.77, P , 0.001 for trend), respectively. The association between CVH metrics and hyperuricemia risk was more pronounced in women, relative to men (P , 0.0001 for interaction) (Table 3), and was more pronounced in people who drank alcohol ,1 times/day, relative to participants with frequent alcohol consumption (P , 0.0001 for interaction). We did not observe a significant interaction between age and the number of ideal CVH metrics, in relation to hyperuricemia risk (P , 0.64 for interaction) (Table 3). For each individual component of the CVH metrics, significant associations were observed between ideal status
Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for hyperuricemia according to the number of ideal CVH metrics, stratified by sex and alcohol consumption* Men†
0–1 2 3 4 5–7 P for trend P for interaction
Women†
Alcohol ‡1 times/day‡
Alcohol
