Letters to the Editor
223
Prevalence and relative risk factors of atrial fibrillation in male coal miners in North China Kanglong Yu a,1, Aijun Xing b,1, Dongmei Wang c, Shuying Qi c, Guodong Wang b, Rong Chen a, Yong Wang a, Shouling Wu b,⁎, Jiang Hong a,⁎⁎ a b c
Department of Internal Medicine, Shanghai First People's Hospital, Shanghai Jiao Tong University, Shanghai 200080, China Department of Cardiology, Kailuan General Hospital, Hebei Union University, Tangshan 063000, China Department of Cardiology, Bethune International Peace Hospital, Shijiazhuang, China
a r t i c l e
i n f o
Article history: Received 9 March 2014 Accepted 1 April 2014 Available online 12 April 2014 Keywords: Atrial fibrillation Prevalence Epidemiology Risk factors
Atrial fibrillation (AF) is one of the most common arrhythmias in clinical practice with considerable symptoms. AF impairs both functional status and the quality of life [1], and thus becomes a huge burden of healthcare costs. The prevalence of AF depends upon the population studied [1,2]. However, very few large scales of studies to date have investigated the prevalence of AF and relative risk factors in China. Thus, we performed this epidemiological study in the population of Kailuan Mining area in China to evaluate the epidemiological features of AF in such domestic population. From June, 2006 to October, 2007, we surveyed 81,103 male employees and retired employees in Kailuan Coal Mining Corporation and investigated all underlying conditions in this population. Physical examinations and questionnaires for the subjects were completed. The subjects underwent ECG and biochemical lab tests. Diagnosis of AF was made on twelve-lead ECG and history. Classification of AF [3]: ① Paroxysmal AF is defined as the one or more than 2 times AF outbreaks (including 2 times). Duration of each outbreak is less than 7 days. The other types of AF are classified as persistent/permanent AF. ② Valvular AF is defined as AF combined with rheumatic valvular diseases.③ AF with no history of heart disease or other diseases is classified as lone AF. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the institution's human research committee. Informed consent was obtained from each subject. Statistical analyses were performed with SPSS12.0. The numeral data were presented as mean ± SEM, unless specified. The measurements of hs-CRP showed a skewed distribution, and were therefore reported as medians. Comparison between groups was done with ANOVA. The categorical data were analyzed with Chi-square test. Risk factors of AF were analyzed with logistic regression analysis. Significance was recorded for P b 0.05 bilaterally. Among the subjects, 81,061 completed ECG test (response rate 99.95%), and 28.41% of them were older than 60 years, 18.59% with obesity, 9.66% with mellitus diabetes, 46.88% with hypertension, 1.40% with old myocardial infarction, and 2.50% with ischemic stroke. The mean age was 53.2 (range: 18–98) years.
⁎ Corresponding author. ⁎⁎ Corresponding author. E-mail addresses: [email protected] (S. Wu), [email protected] (J. Hong). 1 These authors contributed equally to the manuscript.
AF was detected in 398 male subjects (including 10 subjects with atrial flutter). The prevalence of AF was 0.49% and 71.61% of the AF patients were diagnosed persistent/permanent. In etiology, valvular AF accounted for 9.43%, non-valvular AF 61.56%, and lone AF 29.01% of the all AFs. Mean ventricular rate of the AF patients was 88.3 ± 19.5 (range: 44–161) bpm. On ECG findings, 22 subjects complicated with aberrant conduction, 21 with right bundle branch block, 6 with left bundle branch block, 2 with high degree block, and 1 with non-specific intraventricular block. Mean CHADS2 score in AF group was 0.73. Prevalence of ischemic stroke in AF group was significantly higher than non-AF male subjects (7.82% vs 2.50%, x2 = 42.46, P = 0.001). When analyzed by age, prevalence of AF increased significantly with age (x2 = 582.95, P = 0.001). Prevalence of AF in the patients with hypertension, obesity, or MI was remarkably higher than that of patients without those diseases (all P b 0.05). Table 1 showed that the prevalence of hypertension, UA, and hs-CRP level was significantly higher in AF group than that in non-AF group (all P b 0.01). However, both serum TC and TG levels were lower in AF group than that in non-AF group (both P b 0.01). Univariate logistic analysis revealed that age, SBP, DBP, hs-CRP, UA, TC, TG, and MI were associated with the prevalence of AF (P b 0.01). Multivariate stepwise backward logistic analysis revealed that age, SBP ≥ 140 mm Hg, UA, hs-CRP, and obesity were independent risk factors of AF while TC, TG were inversely associated with the prevalence of AF (Table 2). Previous studies indicated that prevalence of AF in general population from Asia was 0.4–1.5% [2,4,5], lower than what ESC 2010 declared (1–2%) [6]. In the present study, prevalence of AF in male population from North China is 0.49%, which is in the low limit of the reported prevalence of AF with Asians. The discrepancy of the prevalence of AF in different studies may be caused by different baseline epidemiologic characteristics of study subjects or racial difference. This study suggested that older age and hypertension were among the independent risk factors of AF which was consistent with the previous studies [7]. More recently, people show great interest in nontraditional risk factors of AF, trying to explore the possible reasons for the increasing prevalence of AF. Our data indicated that hs-CRP (OR = 1.43, P = 0.00) and obesity (OR = 1.41, P = 0.00) were strongly associated with prevalence of AF. In addition, serum UA levels in patients with AF were higher than those in non-AF subjects. The risk for AF increased by 1.07 times with elevation of serum UA level by 1 mmol (P = 0.00). The mechanism of this correlation between UA and AF is still unknown. Interestingly, our data also showed that serum TC and TG levels in AF group were lower than in non-AF group. Multivariate logistic regression analysis even revealed that TG and TC were protective factors of AF in the males. The significance of this finding needs to be elucidated by furthermore studies. The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology. We are grateful to the subjects who participated in the study, and for the physicians' assistance with subject recruitment. We thank Kailuan Coal Mining Corporation for the financial aid to perform the health check and survey. We thank Dr A. John Camm from St. George's Hospital Medical School, UK for the critical comments for this manuscript.
224
Letters to the Editor
Table 1 Comparison of biochemical data between AF and non-AF groups. Items
AF group (n = 398)
Non-AF group (n = 80,663)
x2/t
P
SBP (mm Hg) b120 120–140 ≥140 DBP (mm Hg) b80 80–90 ≥90 UA (mmol/L) TC (mmol/L) TG (mmol/L) hs-CRP (mg/L) GLU (mmol/L)
(%) 16.33 25.88 57.79 (%) 24.12 34.67 41.21 347.69 ± 104.26 4.63 ± 1.10 1.34 ± 0.93 4.16 5.39 ± 1.61
(%) 33.52 30.88 35.59 (%) 26.02 39.27 34.71 297.78 ± 89.54 4.88 ± 1.28 1.70 ± 1.43 2.40 5.45 ± 1.79
92.86
0.00
7.50
0.02
− 11.08 3.92 5.00 − 8089 0.65
0.00 0.00 0.00 0.00 0.52
SBP = systolic blood pressure, DBP = diastolic blood pressure, UA = uric acid, TC = total cholesterol, TG = total triglycide, LDL-C = low density lipoprotein cholesterol, HDLC = high density lipoprotein cholesterol, hs-CRP = high sensitive-C reactive protein (geometric mean), GLU = glucose.
Table 2 Univariate and multivariate logistic regression analysis of risk factors of AF in male. Variables
Age (years) b45 45–60 60–75 ≥75 SBP (mm Hg) b120 120–139.9 ≥140 DBP (mm Hg) b80 80–89.9 ≥90 Overweight Obesity UA TC TG hs-CRP MI
Univariate
Multivariate
β
OR
P
95% CI
β
OR
P
95% CI
– 2.33 3.69 4.62
– 10.24 39.85 101.92
– 0.00 0.00 0.00
– 4.17–25.15 16.39–96.87 41.54–250.08
– 0.54 1.20
– 1.72 3.33
– 0.00 0.00
– 1.26–2.35 2.53–4.39
– –0.05 0.25 0.20 0.43 0.01 –0.15 –0.29 0.71 1.02
– 0.953 1.28 1.22 1.54 1.01 0.86 0.75 2.03 2.75
– 0.72 0.05 0.09 0.00 0.00 0.00 0.00 0.00 0.00
– 0.73–1.24 1.00–1.65 0.97–1.53 1.18–2.01 1.01–1.06 0.80–0.93 0.67–0.84 1.74–2.38 1.63–4.63
– 2.25 3.37 4.18 – – 0.26 0.50 – – – – – 0.47 0.01 –0.30 –0.38 0.36 –
– 9.51 28.99 65.58 – – 1.30 1.65 – – – – – 1.41 1.07 0.74 0.68 1.43 –
– 0.00 0.00 0.00 – – 0.11 0.00 – – – – – 0.00 0.00 0.00 0.05 0.00 –
– 3.86–23.43 11.85–70.94 26.41–162.86 – – 0.94–1.78 1.23–2.21 – – – – – 1.20–1.61 1.04–1.10 0.67–0.81 0.60–0.77 1.22–1.69 –
References [1] Wang TJ, Larson MG, Levy D, et al. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation 2003;107(23):2920–5. [2] Zhou Z, Hu D. An epidemiological study on the prevalence of atrial fibrillation in the Chinese population of Mainland China. J Epidemiol 2008;18(5):209–16. [3] Iqbal MB, Taneja AK, Lip GY, et al. Recent developments in atrial fibrillation. BMJ 2005;330(7485):238–43. [4] Lee KS, Choi SJ, Park SH, et al. Prevalence of atrial fibrillation in middle-aged people in Korea: the Korean genome and epidemiology study. Korean Circ J 2008;38(11):601–5.
http://dx.doi.org/10.1016/j.ijcard.2014.04.002 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
[5] Yap KB, Ng TP, Ong HY. Low prevalence of atrial fibrillation in community-dwelling Chinese aged 55 years or older in Singapore: a population-based study. J Electrocardiol 2008;41(2):94–8. [6] European Heart Rhythm Association, European Association for Cardio-Thoracic Surgery, Camm AJ, et al. Guidelines for the management of atrial fibrillation: The Task Force for the management of atrial fibrillation of the European Society of Cardiology (ESC). Eur Heart J 2010;31(19):2369–429. [7] Benjamin EJ, Levy D, Vaziri SM, et al. Independent risk factors for atrial fibrillation in a population-based cohort (the Framingham heart study). JAMA 1994;271(11):840–4.
223
Prevalence and relative risk factors of atrial fibrillation in male coal miners in North China Kanglong Yu a,1, Aijun Xing b,1, Dongmei Wang c, Shuying Qi c, Guodong Wang b, Rong Chen a, Yong Wang a, Shouling Wu b,⁎, Jiang Hong a,⁎⁎ a b c
Department of Internal Medicine, Shanghai First People's Hospital, Shanghai Jiao Tong University, Shanghai 200080, China Department of Cardiology, Kailuan General Hospital, Hebei Union University, Tangshan 063000, China Department of Cardiology, Bethune International Peace Hospital, Shijiazhuang, China
a r t i c l e
i n f o
Article history: Received 9 March 2014 Accepted 1 April 2014 Available online 12 April 2014 Keywords: Atrial fibrillation Prevalence Epidemiology Risk factors
Atrial fibrillation (AF) is one of the most common arrhythmias in clinical practice with considerable symptoms. AF impairs both functional status and the quality of life [1], and thus becomes a huge burden of healthcare costs. The prevalence of AF depends upon the population studied [1,2]. However, very few large scales of studies to date have investigated the prevalence of AF and relative risk factors in China. Thus, we performed this epidemiological study in the population of Kailuan Mining area in China to evaluate the epidemiological features of AF in such domestic population. From June, 2006 to October, 2007, we surveyed 81,103 male employees and retired employees in Kailuan Coal Mining Corporation and investigated all underlying conditions in this population. Physical examinations and questionnaires for the subjects were completed. The subjects underwent ECG and biochemical lab tests. Diagnosis of AF was made on twelve-lead ECG and history. Classification of AF [3]: ① Paroxysmal AF is defined as the one or more than 2 times AF outbreaks (including 2 times). Duration of each outbreak is less than 7 days. The other types of AF are classified as persistent/permanent AF. ② Valvular AF is defined as AF combined with rheumatic valvular diseases.③ AF with no history of heart disease or other diseases is classified as lone AF. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the institution's human research committee. Informed consent was obtained from each subject. Statistical analyses were performed with SPSS12.0. The numeral data were presented as mean ± SEM, unless specified. The measurements of hs-CRP showed a skewed distribution, and were therefore reported as medians. Comparison between groups was done with ANOVA. The categorical data were analyzed with Chi-square test. Risk factors of AF were analyzed with logistic regression analysis. Significance was recorded for P b 0.05 bilaterally. Among the subjects, 81,061 completed ECG test (response rate 99.95%), and 28.41% of them were older than 60 years, 18.59% with obesity, 9.66% with mellitus diabetes, 46.88% with hypertension, 1.40% with old myocardial infarction, and 2.50% with ischemic stroke. The mean age was 53.2 (range: 18–98) years.
⁎ Corresponding author. ⁎⁎ Corresponding author. E-mail addresses: [email protected] (S. Wu), [email protected] (J. Hong). 1 These authors contributed equally to the manuscript.
AF was detected in 398 male subjects (including 10 subjects with atrial flutter). The prevalence of AF was 0.49% and 71.61% of the AF patients were diagnosed persistent/permanent. In etiology, valvular AF accounted for 9.43%, non-valvular AF 61.56%, and lone AF 29.01% of the all AFs. Mean ventricular rate of the AF patients was 88.3 ± 19.5 (range: 44–161) bpm. On ECG findings, 22 subjects complicated with aberrant conduction, 21 with right bundle branch block, 6 with left bundle branch block, 2 with high degree block, and 1 with non-specific intraventricular block. Mean CHADS2 score in AF group was 0.73. Prevalence of ischemic stroke in AF group was significantly higher than non-AF male subjects (7.82% vs 2.50%, x2 = 42.46, P = 0.001). When analyzed by age, prevalence of AF increased significantly with age (x2 = 582.95, P = 0.001). Prevalence of AF in the patients with hypertension, obesity, or MI was remarkably higher than that of patients without those diseases (all P b 0.05). Table 1 showed that the prevalence of hypertension, UA, and hs-CRP level was significantly higher in AF group than that in non-AF group (all P b 0.01). However, both serum TC and TG levels were lower in AF group than that in non-AF group (both P b 0.01). Univariate logistic analysis revealed that age, SBP, DBP, hs-CRP, UA, TC, TG, and MI were associated with the prevalence of AF (P b 0.01). Multivariate stepwise backward logistic analysis revealed that age, SBP ≥ 140 mm Hg, UA, hs-CRP, and obesity were independent risk factors of AF while TC, TG were inversely associated with the prevalence of AF (Table 2). Previous studies indicated that prevalence of AF in general population from Asia was 0.4–1.5% [2,4,5], lower than what ESC 2010 declared (1–2%) [6]. In the present study, prevalence of AF in male population from North China is 0.49%, which is in the low limit of the reported prevalence of AF with Asians. The discrepancy of the prevalence of AF in different studies may be caused by different baseline epidemiologic characteristics of study subjects or racial difference. This study suggested that older age and hypertension were among the independent risk factors of AF which was consistent with the previous studies [7]. More recently, people show great interest in nontraditional risk factors of AF, trying to explore the possible reasons for the increasing prevalence of AF. Our data indicated that hs-CRP (OR = 1.43, P = 0.00) and obesity (OR = 1.41, P = 0.00) were strongly associated with prevalence of AF. In addition, serum UA levels in patients with AF were higher than those in non-AF subjects. The risk for AF increased by 1.07 times with elevation of serum UA level by 1 mmol (P = 0.00). The mechanism of this correlation between UA and AF is still unknown. Interestingly, our data also showed that serum TC and TG levels in AF group were lower than in non-AF group. Multivariate logistic regression analysis even revealed that TG and TC were protective factors of AF in the males. The significance of this finding needs to be elucidated by furthermore studies. The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology. We are grateful to the subjects who participated in the study, and for the physicians' assistance with subject recruitment. We thank Kailuan Coal Mining Corporation for the financial aid to perform the health check and survey. We thank Dr A. John Camm from St. George's Hospital Medical School, UK for the critical comments for this manuscript.
224
Letters to the Editor
Table 1 Comparison of biochemical data between AF and non-AF groups. Items
AF group (n = 398)
Non-AF group (n = 80,663)
x2/t
P
SBP (mm Hg) b120 120–140 ≥140 DBP (mm Hg) b80 80–90 ≥90 UA (mmol/L) TC (mmol/L) TG (mmol/L) hs-CRP (mg/L) GLU (mmol/L)
(%) 16.33 25.88 57.79 (%) 24.12 34.67 41.21 347.69 ± 104.26 4.63 ± 1.10 1.34 ± 0.93 4.16 5.39 ± 1.61
(%) 33.52 30.88 35.59 (%) 26.02 39.27 34.71 297.78 ± 89.54 4.88 ± 1.28 1.70 ± 1.43 2.40 5.45 ± 1.79
92.86
0.00
7.50
0.02
− 11.08 3.92 5.00 − 8089 0.65
0.00 0.00 0.00 0.00 0.52
SBP = systolic blood pressure, DBP = diastolic blood pressure, UA = uric acid, TC = total cholesterol, TG = total triglycide, LDL-C = low density lipoprotein cholesterol, HDLC = high density lipoprotein cholesterol, hs-CRP = high sensitive-C reactive protein (geometric mean), GLU = glucose.
Table 2 Univariate and multivariate logistic regression analysis of risk factors of AF in male. Variables
Age (years) b45 45–60 60–75 ≥75 SBP (mm Hg) b120 120–139.9 ≥140 DBP (mm Hg) b80 80–89.9 ≥90 Overweight Obesity UA TC TG hs-CRP MI
Univariate
Multivariate
β
OR
P
95% CI
β
OR
P
95% CI
– 2.33 3.69 4.62
– 10.24 39.85 101.92
– 0.00 0.00 0.00
– 4.17–25.15 16.39–96.87 41.54–250.08
– 0.54 1.20
– 1.72 3.33
– 0.00 0.00
– 1.26–2.35 2.53–4.39
– –0.05 0.25 0.20 0.43 0.01 –0.15 –0.29 0.71 1.02
– 0.953 1.28 1.22 1.54 1.01 0.86 0.75 2.03 2.75
– 0.72 0.05 0.09 0.00 0.00 0.00 0.00 0.00 0.00
– 0.73–1.24 1.00–1.65 0.97–1.53 1.18–2.01 1.01–1.06 0.80–0.93 0.67–0.84 1.74–2.38 1.63–4.63
– 2.25 3.37 4.18 – – 0.26 0.50 – – – – – 0.47 0.01 –0.30 –0.38 0.36 –
– 9.51 28.99 65.58 – – 1.30 1.65 – – – – – 1.41 1.07 0.74 0.68 1.43 –
– 0.00 0.00 0.00 – – 0.11 0.00 – – – – – 0.00 0.00 0.00 0.05 0.00 –
– 3.86–23.43 11.85–70.94 26.41–162.86 – – 0.94–1.78 1.23–2.21 – – – – – 1.20–1.61 1.04–1.10 0.67–0.81 0.60–0.77 1.22–1.69 –
References [1] Wang TJ, Larson MG, Levy D, et al. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation 2003;107(23):2920–5. [2] Zhou Z, Hu D. An epidemiological study on the prevalence of atrial fibrillation in the Chinese population of Mainland China. J Epidemiol 2008;18(5):209–16. [3] Iqbal MB, Taneja AK, Lip GY, et al. Recent developments in atrial fibrillation. BMJ 2005;330(7485):238–43. [4] Lee KS, Choi SJ, Park SH, et al. Prevalence of atrial fibrillation in middle-aged people in Korea: the Korean genome and epidemiology study. Korean Circ J 2008;38(11):601–5.
http://dx.doi.org/10.1016/j.ijcard.2014.04.002 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
[5] Yap KB, Ng TP, Ong HY. Low prevalence of atrial fibrillation in community-dwelling Chinese aged 55 years or older in Singapore: a population-based study. J Electrocardiol 2008;41(2):94–8. [6] European Heart Rhythm Association, European Association for Cardio-Thoracic Surgery, Camm AJ, et al. Guidelines for the management of atrial fibrillation: The Task Force for the management of atrial fibrillation of the European Society of Cardiology (ESC). Eur Heart J 2010;31(19):2369–429. [7] Benjamin EJ, Levy D, Vaziri SM, et al. Independent risk factors for atrial fibrillation in a population-based cohort (the Framingham heart study). JAMA 1994;271(11):840–4.
