Cross-section analysis of coal workers’ pneumoconiosis and higher brachial-ankle pulse wave velocity within Kailuan study

The present case-control study included participants with CWP from the Kailuan cohort study, which recruited 101,510 employees and retirees of the Kailuan (Group) Co. Ltd., a large coal company located in Tangshan City, Hebei province, China, from June 2006 to October 2007. Details of this prospective cohort study were described previously [18‐20]. The study followed the guidelines of the Helsinki Declaration and was approved by the Ethics Committees of Kailuan General Hospital and Beijing Chaoyang Hospital, China. Written informed consent was obtained from all the participants.

A total of 16,185 coal workers were included at baseline and followed up with an examination for pneumoconiosis every 2–3 years. As of December 31, 2010, 1,806 cases of CWP were diagnosed, which were all in male subjects. Some subjects were excluded from further analysis due to a failure to participate in the 2010–2011 resurvey due to limitations of activity (n = 441), death during the 2010–2011 resurvey (n = 123), age greater than 90 years (n = 22), refusal to undergo baPWV measurements (n = 166), incomplete baPWV data (n = 14), or a history of stroke, transient ischaemic attack, and/or coronary disease (n = 33). The present study therefore included 1,007 cases with CWP. This investigation also included 1,007 healthy controls from the Kailuan cohort study, all of which lacked a history of stroke, transient ischaemic attack, and coronary disease. These controls were matched to the cases based on age (±1 year), gender, systolic blood pressure (±5 mmHg), and previous history of hypertension.

All of the enrolled CWP cases had physical examination cards and detailed records of their occupational history including CDE, individual medical and CWP diagnosis records, and measurements of dust concentrations in the subjects’ workplaces, which were obtained from personnel files in the human resources section of the Kailuan Colliery Group. The diagnosis of CWP was based on the Diagnostic Criteria of Pneumoconiosis and corresponding standard videos of pneumoconiosis in China [21]. CWP was classified as stage I, stage II, or stage III according to the size, profusion, and distribution range of opacities, as previously reported [22].

Estimates of CDE were derived from each coal miner’s work history up until the time of study enrolment. Work histories included job titles and calendar years for each coal worker’s full duration of employment. CDE was calculated from a job-exposure matrix as follows: the duration of exposure in years was multiplied by the dust concentration at the same time in every period of dust exposure for each subject [22]. CDE is given in milligrams per cubic metre-years. Job title-specific exposure estimates were obtained from the Department of Dust Detection and Monitoring of the Kailuan Colliery Group. Dust was sampled randomly twice per month in the tunnelling, mining, combining, and helping areas. The dust concentration and free silica content were measured using the gravimetric method and the pyrophosphate method, respectively, which are national standard methods [23‐25]. These numerical data were collected to calculate the geometric means of each area yearly, which were then used to calculate the CDE for each coal worker.

During the resurvey in 2010–2011, all of the participants underwent a clinical examination, laboratory tests, and baPWV measurements. Structured interviews based on a standardized questionnaire were conducted by trained investigators. The questionnaire included information on the subject’s demographics, history of occupational exposure, medical disorders, and traditional cardiovascular risk factors including age, smoking, body mass index, hypertension, diabetes and dyslipidaemia. Body mass index was calculated as body weight (kg) divided by the square of body height (m²). Current smokers were defined as subjects who had smoked at least 100 cigarettes during their lifetime and, at the time of the interview, reported smoking every day or some days.

In 2010–2011, all of the participants underwent baPWV measurements using an automatic arteriosclerosis detection device (BP-203RPE III; Omron Healthcare Co., Japan) in the supine position after at least 5 min of rest. BaPWV was calculated as the distance between the two sites divided by the pulse transit time, which was defined as the time interval between the wave front of the brachial waveform and that of the ankle waveform. The distance between the sampling points was calculated automatically according to the subject’s height. The maximum value of the bilateral baPWV was used in the present analysis.

Blood samples were obtained after at least 8 h of fasting and were analysed within 4 h. Fasting blood glucose levels were measured using the hexokinase/glucose-6-phosphate-dehydrogenase method. Total cholesterol and triglycerides were measured enzymatically (inter-assay coefficient of variation, 10%; Mind Bioengineering Co. Ltd., Shanghai, China). All biochemical variables were measured using an auto-analyser (Hitachi 747; Hitachi, Tokyo, Japan) at the central laboratory of Kailuan General Hospital.

Statistical analyses were carried out using commercially available software (SAS software version 9.3; SAS Institute Inc., Cary, NC, USA). The continuous variables were described as the mean ± standard deviation and compared using a two-sample Student’s t-test or one-way analysis of variance for the normally distributed data. For skewed distributions, the data are presented as the median (with interquartile ranges) and compared using a Student’s t-test or one-way analysis of variance after log transformation. The categorical variables were described as percentages and compared using the chi-squared test. All tests were two-tailed and P < 0.05 was considered to be statistically significant.

Multivariate logistic regression analyses were used to explore the association of increased baPWV with CWP and its potential risk factors after adjusting for potential confounding factors. Increased baPWV was defined as a value greater than the median baPWV of the study population. Based on similar multivariate adjustments, the odds ratios (ORs) for increased baPWV were calculated for four subject subgroups: without hypertension or CWP, with CWP only, with hypertension only, and with hypertension and CWP. Similar analyses of the relationship between increased baPWV and CDE were conducted among the CWP cases categorized into four subgroups according to the CDE quartile using the trend test.

The demographic and clinical characteristics of the CWP cases and healthy controls are presented in Table 1. All of the participants were male, and the mean age of the CWP cases was 65.2 years, which was similar to that of the controls (P = 0.853). There were no significant between-group differences in body mass index (P = 0.134), systolic and diastolic blood pressure (P = 0.508 and P = 0.108, respectively), low-density lipoprotein cholesterol (P = 0.640), and hypertension (P = 0.646). The CWP cases had higher heart rates (P < 0.001), fasting blood glucose levels (P = 0.003), and triglyceride levels (P = 0.001), but lower total cholesterol (P < 0.001) and high-density lipoprotein cholesterol (P = 0.033) levels than the controls. Significantly fewer CWP cases were smokers (P < 0.001) and they were more likely to have diabetes mellitus (P = 0.051) than the controls. The CWP cases were categorized as follows: stage I, 980 cases (97.3%); stage II, 22 cases (2.2%); and stage III, 5 cases (0.5%). The CWP cases had higher baPWV values than the controls (P = 0.006) and a higher proportion of the CWP cases (P = 0.016) had increased baPWV (≥1687 cm/s according to the median baPWV of the population) than the controls (Table 1).

In the logistic regression models shown in Table 2, the CWP cases had a higher risk of increased baPWV than the controls OR 1.24 (95% CI 1.05–1.48). After the multivariate adjustment for age, current smoking status, body mass index, heart rate, hypertension, diabetes mellitus, total cholesterol, and low-density lipoprotein cholesterol, the association remained significant OR 1.43 (95% CI 1.11–1.83).

A further stratification analysis of the potential risk factors for baPWV (Table 3) showed that in both the CWP and control groups, age (≥60 years), heart rate, and hypertension were positively associated with increased baPWV (all P < 0.001). In addition, increased baPWV was also positively associated with diabetes mellitus (P = 0.012) and negatively associated with current smoking status (P < 0.001) in the control group.

All of the participants were categorized into four subgroups (Table 4), and the subgroup of subjects without CWP or hypertension was employed as a reference in the subsequent calculations of the ORs of increased baPWV. We detected a gradually increasing association strength across the four subgroups (P for trend <0.001) as follows: CWP only, OR 1.20 (95% CI 0.90–1.61); hypertension only, OR 2.54 (95% CI 1.95–3.30); and both CWP and hypertension, OR 3.34 (95% CI 2.56–4.37). The trend persisted after the multivariate adjustment (P for trend, <0.001), and the multivariate-adjusted ORs were 1.18 (95% CI 0.85–1.16), 1.55 (95% CI 1.92–3.41), and 2.80 (95% CI 2.08–3.75).

To explore the association between long-term CDE and increased baPWV (Fig. 1), all of the CWP cases were categorized into four subgroups according to the CDE quartile. We observed that the risk of increased baPWV gradually increased across the CDE quartiles, independent of traditional cardiovascular risk factors. The multivariate-adjusted ORs (compared with the cases in the first quartile) for the cases in the second, third, and fourth CDE quartiles were 1.02 (95% CI 0.61–1.70), 1.40 (95% CI 0.87–2.24), and 2.23 (95% CI 1.30–3.83), respectively. This trend was statistically significant (P for trend, 0.02). The adjusted variables included age, current smoking status, body mass index, heart rate, hypertension, diabetes mellitus, total cholesterol, and low-density lipoprotein cholesterol.

Inflammation is one plausible mechanism. Atherosclerosis, a pathological process of CVD, is now generally accepted to be an inflammatory disorder of the arterial wall [31]. Inflammation may also contribute to baPWV elevation. For example, Saijo et al. reported a significant, progressive increase in baPWV with high-sensitivity C-reactive protein levels in male subjects after controlling for traditional cardiovascular risk factors such as age, body mass index, systolic blood pressure, heart rate, smoking, past history of hypertension, hyperlipidaemia, and diabetes [32]. Andoh et al. also determined that baPWV is significantly associated with the serum levels of high-sensitivity C-reactive protein [33]. Moreover, CWP is caused by the long-term inhalation and deposition of coalmine dust, which triggers a persistent inflammatory response and the induction of pro-inflammatory and pro-fibrotic mediators, which eventually results in irreversible lung damage [34, 35]. Respirable silica particles can initiate inflammation of the cardiovascular system via the direct effects of fine particulates that cross the pulmonary epithelium into the cardiovascular system [36] or via indirect effects mediated by the inflammatory response.

Evidence of the association between increased systemic inflammation and CWP is limited. Zhai et al. reported that the serum levels of cytokines such as interleukin 6 were associated with CWP in a Chinese sample [37]. Lee et al. suggested that high serum levels of interleukin 8 in Korean subjects were associated with CWP and those of serum tumour necrosis factor α were associated with the progression of CWP at the 1-year follow-up [38], but not at the 3-year follow-up [39]. In addition, studies of Chinese CWP cases revealed associations between CWP and genetic polymorphisms related to inflammatory markers such as E-selectin [40] or the inflammasome (nod-like receptor protein 3) [41]. Combining these previous data with the findings of the current investigation, it is logical to hypothesize that long-term CDE can instigate the inflammation response and damage arterial walls, which leads to atherosclerosis and cardiovascular events. This hypothesis needs to be validated using large-scale prospective cohort studies.

Several limitations of this study should be considered when interpreting the data. First, in light of the small sample size of the participants with CWP in Stage II and III (2.2% and 0.3%, respectively), we could not explore the association of the severity of CWP with baPWV. Second, we were unable to elucidate the role of inflammation in the observed relationship between CWP and baPWV. Third, increased arterial stiffness is thought to increase the risk of CVD; however, baPWV only reflects the stiffness of middle-sized to large arteries and is closely correlated with carotid-femoral pulse wave velocity, which is a gold standard for the assessment of large-artery stiffness. Most importantly, the case-control design of our study hindered us from evaluating the prognostic significance of increased baPWV in CWP cases. In addition, coal dust is a mixture and other components such as polycyclic aromatic hydrocarbons might have cardiovascular effects. It is also possible that other co-exposures in the mines might contribute to the development of cardiovascular diseases. Therefore, longitudinal studies of factors that affect cardiovascular prognosis as well as studies investigating the exposure to the components of coal dust and other co-exposures in the mines are necessary in order to establish a better understanding of the risk factors of arterial stiffness and cardiovascular risk in patients with CWP.