Plasma Metal Concentrations and Incident Coronary Heart Disease in Chinese Adults: The Dongfeng-Tongji Cohort
2017; National Institute of Environmental Health Sciences; Volume: 125; Issue: 10 Linguagem: Inglês
10.1289/ehp1521
ISSN1552-9924
AutoresYu Yuan, Yang Xiao, Wei Feng, Yiyi Liu, Yanqiu Yu, Lue Zhou, Gaokun Qiu, Hao Wang, Bing Liu, Kang Liu, Handong Yang, Xiulou Li, Xinwen Min, Ce Zhang, Chengwei Xu, Xiaomin Zhang, Meian He, Frank B. Hu, An Pan, Tangchun Wu,
Tópico(s)Trace Elements in Health
ResumoVol. 125, No. 10 ResearchOpen AccessPlasma Metal Concentrations and Incident Coronary Heart Disease in Chinese Adults: The Dongfeng-Tongji Cohortis companion ofAssessing a Medley of Metals: Combined Exposures and Incident Coronary Heart Disease Yu Yuan, Yang Xiao, Wei Feng, Yiyi Liu, Yanqiu Yu, Lue Zhou, Gaokun Qiu, Hao Wang, Bing Liu, Kang Liu, Handong Yang, Xiulou Li, Xinwen Min, Ce Zhang, Chengwei Xu, Xiaomin Zhang, Meian He, Frank B. Hu, An Pan, and Tangchun Wu Yu Yuan Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Yang Xiao Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Wei Feng Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Yiyi Liu Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Yanqiu Yu Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Lue Zhou Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Gaokun Qiu Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Hao Wang Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Bing Liu Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Kang Liu Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Handong Yang Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Xiulou Li Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Xinwen Min Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Ce Zhang Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Chengwei Xu Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Xiaomin Zhang Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Meian He Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China , Frank B. Hu Department of Cardiovascular Diseases, Dongfeng Central Hospital, Hubei University of Medicine, Shiyan, China , An Pan Address correspondence to T. Wu, or A. Pan, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hongkong Rd., Wuhan 430030, Hubei, China. Telephone: +86-27-83692347. Email: E-mail Address: [email protected] or E-mail Address: [email protected] Department of Nutrition and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA , and Tangchun Wu Address correspondence to T. Wu, or A. Pan, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hongkong Rd., Wuhan 430030, Hubei, China. Telephone: +86-27-83692347. Email: E-mail Address: [email protected] or E-mail Address: [email protected] Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Published:19 October 2017CID: 107007https://doi.org/10.1289/EHP1521Cited by:4AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Circulating metals from both the natural environment and pollution have been linked to cardiovascular disease. However, few prospective studies have investigated the associations between exposure to multiple metals and incident coronary heart disease (CHD).Objectives:We conducted a nested case–control study in the prospective Dongfeng-Tongji cohort, to investigate the prospective association between plasma metal concentrations and incident CHD.Methods:A total of 1,621 incident CHD cases and 1,621 controls free of major cardiovascular disease at baseline and follow-up visits were matched on age (±5 years) and sex. We measured baseline fasting plasma concentrations of 23 metals and used conditional logistic regression models to estimate odds ratios (ORs) of CHD for metal concentrations categorized according to quartiles in controls.Results:Five metals (titanium, arsenic, selenium, aluminum, and barium) were significantly associated with CHD based on trend tests from single-metal multivariable models adjusted for established cardiovascular risk factors. When all five were included in the same model, adjusted ORs for barium and aluminum were close to the null, whereas associations with titanium, arsenic, and selenium were similar to estimates from single-metal models, and ORs comparing extreme quartiles were 1.32 (95% CI: 1.03, 1.69; p-trend=0.04), 1.78 (95% CI: 1.29, 2.46; p-trend=0.001), and 0.67 (95% CI: 0.52, 0.85; p-trend=0.001), respectively.Conclusions:Our study suggested that incident CHD was positively associated with plasma levels of titanium and arsenic, and inversely associated with selenium. Additional research is needed to confirm these findings in other populations. https://doi.org/10.1289/EHP1521IntroductionExposure to metals from both the natural environment and pollution may influence the development of chronic diseases, including cardiovascular disease (CVD). As ubiquitous components of the natural environment as well as of pollutants, multiple metals coexist in the ecosystem and reach the public through ambient air, drinking water, food, medications, and consumer products (Nordberg et al. 2014; Saper et al. 2008). Several prospective studies have evaluated associations between cardiovascular outcomes and exposures to single metals, such as arsenic (Argos et al. 2010; Moon et al. 2013; Wu et al. 2015), lead (Menke et al. 2006; Weisskopf et al. 2009), and selenium (Rayman 2012; Zhang et al. 2016). In a prospective case–control study nested in the Health Effects of Arsenic Longitudinal Study (HEALS) cohort of Bangladeshi adults exposed to high levels of arsenic via drinking water, researchers found positive associations between well-water arsenic and fatal and nonfatal CVD (Wu et al. 2015). The Strong Heart Study (SHS) further confirmed the association between urinary arsenic levels and incident CVD and coronary heart disease (CHD) among American Indians with low to moderate exposure levels (Moon et al. 2013). Baseline blood lead concentrations were significantly associated with CVD mortality during follow-up in a representative sample of U.S. adults [National Health and Nutrition Examination Survey III (NHANES III Study), USA] (Menke et al. 2006), whereas weak positive associations were reported between blood lead and CVD mortality (38 deaths) and ischemic heart disease mortality (based on 17 deaths) among men in the Normative Aging Study (Weisskopf et al. 2009). On the other hand, a meta-analysis of prospective studies showed a significant inverse association between selenium status and CVD risk within a narrow selenium range, whereas a null effect of selenium supplementation on CVD was observed in randomized controlled trials (RCTs) (Zhang et al. 2016). This finding might be due to a potential U-shaped association between selenium and CVD and differences in exposure levels among various study populations (Zhang et al. 2016). Limited information about associations between exposures to other metals and CVD is available. For example, Lind et al. (2012) reported an inverted U-shaped association between blood aluminum levels and the prevalence of carotid artery plaques in elderly residents of Uppsala, Sweden. Peters et al. (2013) reported that cardiovascular mortality was associated with the duration of aluminum dust inhalation in underground gold miners, but no difference in CVD mortality was found between miners with any vs. no aluminum dust exposure.In addition, although humans are exposed to many metals in daily life, few studies have examined the relationships between exposure to multiple metals and cardiovascular risk, particularly in China, where air pollution (Rich et al. 2012) and water pollution (Tan et al. 2015) are major public health concerns (Zhang et al. 2010). Given that CHD remains a leading cause of death and disease both in China and globally (Murray and Lopez 2013; Wilkins et al. 2012), even a modest increase in CHD would translate into a large burden of increasing morbidity and mortality. Therefore, we applied a novel method to measure 23 plasma metals using inductively coupled plasma mass spectrometry (ICP-MS) in a cohort of Chinese adults, and estimated the associations between plasma metal levels and incident CHD.MethodsStudy Design and ParticipantsWe used data from the Dongfeng-Tongji (DFTJ) cohort (Wang et al. 2013), an ongoing prospective study in Shiyan, China. Shiyan is an inland city located in central China. In comparison with other cities, there is no clear evidence suggesting special natural and anthropogenic sources of metals in this city. Dongfeng Motor Corporation (DMC) is one of the largest auto manufacturers in China. In 2008, the Retirement Office and the Social Insurance Center of DMC provided a list of all living retired employees (n=31,000). They were invited to participate in the study, and 27,009 were enrolled (a response rate of 87%), completed questionnaires, underwent physical examinations, and provided blood specimens during September 2008–June 2010. Most of the employees who were invited but not enrolled (approximately 3,000 out of 3,991) had relocated to other cities and could not be reached (Wang et al. 2013). The participants were invited to a follow-up examination in 2013 with a follow-up rate of 96.2% (n=25,978).Ascertainment of CHD CasesIncident CHD events were defined as first occurrence of nonfatal myocardial infarction (MI), fatal CHD, stable and unstable angina, or coronary revascularization (coronary artery bypass graft or percutaneous transluminal coronary angioplasty) during follow-up, as recommended in guidelines for observational research from the American Heart Association (Luepker et al. 2003). All participants were covered by the health-care service system of the Dongfeng Corporation and thus could to be tracked for morbidity and mortality records. Possible cases were initially identified through review of medical insurance documents, hospital records, and death certificates up to 31 December 2013 and were adjudicated by an expert panel of physicians. The medical records were available for all participants with diagnosed diseases and cover the entire follow-up period. CHD was diagnosed following the World Health Organization criteria using clinical symptoms, cardiac enzymes, and electrocardiograms or by coronary angiography (stenosis ≥50% in at least one major coronary artery) (American Heart Association 1979). We included only definite or probable MIs, where definite MI was defined by diagnostic ECG or enzymes, and probable MI was defined by positive ECG findings in the presence of ischemic signs or symptoms or equivocal diagnostic enzymes (Luepker et al. 2003). Fatal CHD cases were identified by death certificates with International Classification of Diseases (ICD) codes (ICD-9 410–414 and ICD-10 I20–I25). Stable angina was defined as angina without alteration of frequency or pattern for six weeks before hospitalization; unstable angina included angina occurring at rest and prolonged, new onset of serious angina, or deteriorating angina before hospitalization (Cannon et al. 2001). Revascularization was verified through hospital records. At baseline, we excluded participants with self-reported CHD or diagnosed CHD with medical records (prevalent CHD cases, n=4,492), participants with self-reported stroke (n=1,144) or cancer (n=1,472), and participants with abnormal results on resting ECG (n=315). A total of 1,962 incident CHD cases were identified from the remaining participants during the follow-up until the end of 2013. Incident CHD cases were excluded if their diagnosis date was unknown, or if they were diagnosed <1 year after completing their baseline survey (n=226). We also excluded 48 cases with missing data for covariates (38 had missing baseline blood pressure data, and 10 had missing BMI values) and cases with insufficient plasma samples for metal measurements (n=67). Consequently, a total of 1,621 incident CHD cases were included in the analysis. Each incident case was matched to one control that was randomly selected from study participants who were CVD-free at the end of follow-up, had complete covariate data and sufficient samples for metals analysis, and had at least as much follow-up time as the matched cases. Cases and controls were matched for sex and age (within five years).Metals Correlation StudyPrevious studies have measured metal concentrations in plasma (Zhang et al. 2016), whole blood (Menke et al. 2006), or urine (Moon et al. 2013) as internal biomarkers to characterize exposures to different metals, and it is not clear whether plasma was an appropriate biological matrix for all metals that we examined. For example, it has been recommended that human exposure to chromium be characterized based on concentrations in both plasma and erythrocytes (Paustenbach et al. 1997) because concentrations in plasma represent chromium (III) only, whereas chromium (VI) is present primarily in erythrocytes (Wiegand et al. 1988). Whole blood has been proposed as the best matrix for characterizing iron exposure, because 60–70% of total body iron is present in hemoglobin in circulating erythrocytes (Nordberg et al. 2014). Similarly, cadmium in blood is mainly concentrated in the blood cells, with low levels in human plasma (Nordberg et al. 2014). Therefore, we conducted a correlation substudy to compare the concentrations of each metal in plasma, whole blood, and urine samples. Those samples were collected from a separate population of 94 healthy volunteers living in Shiyan who were free of CVD, cancer, and diabetes, and who completed questionnaires and physical examinations that were similar to questionnaires and examinations completed by the nested case–control study population. Our intention was to use the findings of this analysis to support the use of plasma concentrations to characterize exposures, or to identify metals for which plasma concentrations might not be a reliable biomarker of exposure based on low correlations with concentrations measured in whole blood or urine.Metals Variability StudyWe also performed a separate study to evaluate the inter- and intra-individual variability of plasma metals by comparing plasma metal concentrations measured at baseline (in 2008) and at a follow-up visit (in 2013) in a separate group of 138 cohort members who were free of CVD, cancer, and diabetes at the baseline and follow-up visits.Written informed consent was obtained from each participant, including participants in the correlation and variability studies, as well as the primary case–control study, and all parts of the study were approved by the Ethics and Human Subject Committees of Tongji Medical College.Metals ExposurePeripheral venous EDTA blood specimens were collected after overnight fasting. The samples were centrifuged and frozen within two hours of collection, and stored at −80°C. We measured case and control specimens in random order, with laboratory personnel blinded to the case–control status. Similarly, samples from the correlation and variability studies were measured separately and in random order. Plasma concentrations of 23 metals were determined by Agilent 7700x ICP-MS with an octopole-based collision/reaction cell (Agilent Technologies), following previously reported protocols (Cesbron et al. 2013). In the metal correlation study, similar methods were used to measure metals in the whole blood (Cesbron et al. 2013) and urine (Feng et al. 2015). The urinary metals concentrations were standardized on creatinine.For quality-control purposes, we measured metals in standard reference materials once in every 20 samples [specifically, 1640a (Trace Elements in Natural Water from the National Institute of Standards and Technology) and certified reference materials (ClinChek® human plasma controls for trace elements no. 8,883 and no. 8,884; Recipe Chemicals)] and confirmed that values measured in the reference materials were within the recommended range for each metal. For titanium, rubidium, and tungsten, which did not have certified reference samples available, we assayed a spiked pooled plasma specimen (gathered randomly from 100 specimens). The spike recovery values of the three metals were 82.9–105.8%. Intra-assay and interassay CVs for all plasma metals were <10% (see Table S1).Metal concentrations that were below the limit of detection (LOD) (listed in Table S1) were imputed with a value equal to the half of the detection limit. More than half of the participants [98.3%, 80.2%, and 54.0%, respectively (see Table S1)] had plasma tungsten, tin, and uranium concentrations LOD, and to metals that plasma was deemed to be a reliable biospecimen matrix based on the correlation substudy and prior information. All models were adjusted for baseline values of potential confounders that were selected a priori: body mass index (BMI, continuous), smoking (current, former, and never), smoking pack-years, alcohol consumption (current, former, and never), education (≤primary school, middle school, ≥high school), physical activity (yes or no), family history of CHD (any or none), hypertension (yes or no, as defined above), hyperlipidemia (yes or no), diabetes (yes or no), and eGFR. Linear trend p-values were derived by modeling the median value of each metal quartile as a continuous variable in adjusted models.After deriving trend p-values for each individual metal, we derived corresponding q-values for a False Discovery Rate (FDR) <0.05 [using software published by Pike (2011)] to identify a subset of the 20 metals for simultaneous evaluation in a multiple-metal model that included the same covariates as the single-metal models. We also performed sensitivity analyses by additionally adjusting the multiple-metal models for each participant's most recent occupation (categorized into six groups), and for dietary consumption of meats, vegetables, fruits, beans, eggs, and dairy, with each food group modeled using a separate dichotomous variable based on consumption ≥5 times per week or <5 times per week.The subset of metals that were significantly associated with CHD in the primary multiple-metals model (p-trend<0.05) underwent additional evaluation. First, we estimated associations between the metals and CHD using a multiple-metal model with each ln-transformed metal modeled using restricted cubic splines with knots at the 20th, 40th, 60th, and 80th percentiles of its distribution; the reference value (OR=1) set at the 10th percentile (Moon et al. 2013); and the measured concentration replaced with the mean concentration ±3 SD for all observations with measured concentrations above this value. We also performed a sensitivity analysis of restricted cubic splines with knots at the 5th, 35th, 65th, and 95th percentiles.Stratified analyses were performed by including metals in the multiple-metal model and corresponding dichotomous variables for baseline characteristics, including age (<65, ≥65 years), sex, BMI (<25.0, ≥25.0 kg/m2), smoking status (never-smokers, ever-smokers), presence of hypertension, and diabetes (yes, no), as well as renal function (eGFR <90, ≥90mL/min per 1.73 m2). In addition, we evaluated the joint associations between metals that were significant in the multiple-metal model, and the individual metal was dichotomized as low (Q1+Q2) and high (Q3+Q4), and a four-category variable was created for two metals (i.e., low/low, high/low, low/high, and high/high) with low/low group as the reference. To determine whether the pairwise interactions were significantly different from expectations for multiplicative risks, we derived likelihood ratio test p-values, comparing the fit of models with jointly categorized exposures to corresponding models with lower-order terms for each metal only. Finally, we compared the baseline characteristics according to the quartiles of titanium using linear regression for continuous variables and chi-square tests for categorical variables. The purpose of this analysis was to explore the potential correlations between titanium and participants' characteristics because there was limited information about the sources of exposure to this metal, whereas it showed significant association with CHD (as described in the "Results" section). Analyses were conducted using SAS (version 9.3; SAS Institute Inc.) and R software (version 3.3.2; R Core Team), and a two-sided p<0.05 was considered statistically significant.ResultsCharacteristics of the Study PopulationIn comparison with the controls, CHD cases were more likely to have hypertension, hyperlipidemia, and diabetes at baseline; they had slightly but significantly higher BMIs, higher pack-years among ever-smokers, and lower eGFRs; in addition, they were less likely to have never smoked or to have a high-school education (Table 1). No significant differences were found for alcohol intake status, physical activity levels, or family history of heart disease between the cases and controls. Concentrations of aluminum, titanium, manganese, copper, zinc, arsenic, strontium, barium, and lead were significantly higher in cases than controls, whereas selenium concentrations were significantly lower (Table 2). Among the 20 metals with less than 12% of samples below the LOD (see Table S1), most of them were significantly but modestly correlated with each other (Spearman's rank correlation coefficients −0.08∼0.72; see Table S2 and Figure S1).Table 1 Basic characteristics of study participants at baseline.Table 1 lists variables in the first column; the corresponding values for controls (n equals 1621), cases (n equals 1621), and their p-values are listed in the other columns.VariablesControls (n=1,621)Cases (n=1,621)p-ValueaAge (years)65.6±7.465.7±7.30.72Male sex, n (%)789 (48.7)789 (48.7) BMI (kg/m2)24.23±3.2524.80±3.31<0.001Smoking status, n (%) Current smoker305 (18.8)344 (21.2) Former smoker179 (11.0)203 (12.5)0.06 Never smoker1137 (70.1)1074 (66.3) Pack-years among ever-smokersb29.1±20.331.8±22.60.04Alcohol intake status, n (%) Current drinker360 (22.2)361 (22.3) Former drinker78 (4.8)70 (4.3)0.80 Never drinker1183 (73.0)1190 (73.4) Education level, n (%) Primary school or below512 (31.6)543 (33.5) Middle school563 (34.7)597 (36.8)0.05 High school or beyond546 (33.7)481 (29.7) Physical activity (yes), n (%)c1466 (90.4)1437 (88.6)0.10CHD family history, n (%)77 (4.8)78 (4.8)0.93Hypertension, n (%)d699 (43.1)1074 (66.3)<0.001
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