Cumulative smoking exposure, duration of smoking cessation, and peripheral arterial disease in middle-aged and older Korean men
© Lee et al; licensee BioMed Central Ltd. 2011
Received: 1 November 2010
Accepted: 11 February 2011
Published: 11 February 2011
We investigated the association of cumulative smoking exposure and duration of smoking cessation with peripheral arterial disease (PAD).
The study population consisted of 2517 community-dwelling Korean men aged 50 years and older. Information on smoking characteristics such as smoking status, pack-years of smoking, and years since quitting smoking was collected using a standardized questionnaire. PAD was defined as an ankle-brachial index (ABI) less than 0.90 in either leg.
The odds ratio (OR, 95% confidence interval) of PAD was 2.31 (1.20-4.42) for former smokers and 4.30 (2.13-8.66) for current smokers, after adjusting for other cardiovascular risk factors. There was a significant dose-response relationship between pack-years of smoking and PAD. Compared with those who had never smoked, the multivariate-adjusted ORs of PAD for smokers of 0.1-20.0, 20.1-40.0, and >40.0 pack-years were 2.15 (1.06-4.38), 2.24 (1.08-4.65), and 2.93 (1.41-6.09), respectively. There was a significant decrease in PAD risk as the years since quitting smoking increased. The multivariate-adjusted ORs of PAD for 11-20 and ≥21 years smoking cessation were 0.41 (0.19-0.86) and 0.49 (0.24-0.98), compared with current smokers.
Cumulative smoking exposure and duration of smoking cessation were significantly associated with PAD in middle aged and older Korean men.
Peripheral arterial disease (PAD), one of the major manifestations of generalized atherosclerotic disease, results from a narrowing of arteries in the lower extremities, as a result of progressive atherosclerosis . Although PAD is a marker of coronary and cerebral atherosclerotic vascular disease, PAD is a commonly overlooked condition in primary care settings, because most patients are asymptomatic. However, people with PAD, even if asymptomatic, have an increased risk of future cardiovascular events and related mortality [2–4]. The ankle-brachial index (ABI), the ratio of systolic blood pressure in the ankle to that in the arm, is a simple, reproducible, and non-invasive test to diagnose PAD and has been used to diagnose and evaluate the severity of PAD in the lower extremities .
Cigarette smoking contributes to the constriction and damage of arteries and is a potent risk factor for PAD, by promoting endothelial dysfunction and by altering lipoprotein metabolism, coagulation, and platelet function . A number of epidemiological studies have reported an association between PAD and both current and former smoking [7–14], though few studies have examined the cumulative effects of smoking, such as pack-years exposure, on PAD . Moreover, because few studies have examined the association between smoking cessation and PAD , it remains undetermined whether the duration of smoking cessation is inversely associated with PAD in the general population.
The objective of our study was to investigate the association of cumulative cigarette smoking exposure with PAD prevalence among community-dwelling adults aged 50 years and older, and to determine whether the duration of smoking cessation was inversely associated with PAD.
The study population consisted of community-dwelling men and women aged 50 years and older who participated in the 2007-2009 baseline surveys of the Dong-gu Study . We used the national resident registration records to identify potential participants. In total, 26,323 eligible subjects aged 50 years and over who resided in one of five towns in the Dong-gu district of Gwangju Metropolitan City in South Korea were invited by telephone to participate. Total 6779 subjects were enrolled and the response rate for this study was 25.8% (21.8% for men and 29.1% for women). However, only men were included in the analysis because of the low prevalence of smoking in women (1.9% current smokers and 1.8% former smokers). Of the 2644 men, 100 subjects were excluded from the study because of incomplete medical histories, lifestyle characteristics, and anthropometric and biochemical measurements. An additional 21 subjects were excluded because of missing information on the ABI measurements. Six individuals with an ABI greater than 1.50 were further excluded from the analysis because this indicates poorly compressible leg arteries and the inability to gauge arterial perfusion accurately [17, 18]. In total, 2517 subjects were included in the analyses. This study was conducted in accordance with the Declaration of Helsinki guidelines and the study protocol was approved by the institutional review board of Chonnam National University Hospital. Participants provided written informed consent.
Definition of smoking
Participants were classified based on smoking habit information collected using a standardized questionnaire by well-trained research staff. Smoking was classified as never smokers (smoked <100 cigarettes in their lifetime and not currently smoking), former smokers (smoked ≥100 cigarettes in their lifetime and currently a non-smoker), and current smokers (smoked ≥100 cigarettes in their lifetime and currently a smoker) . Cumulative smoking exposure in former and current smokers was determined in terms of pack-years by multiplying the number of years smoked with the average number of packs per day . Based on pack-years of smoking, subjects were classified as never smokers (0.0 pack-years), light smokers (0.1-20.0 pack-years), moderate smokers (20.1-40.0 pack-years), and heavy smokers (> 40 pack-years). Based on information on years since quitting smoking, former smokers were classified as ≤3 years, 4-10 years, 11-20 years, and ≥21 years and compared with current smokers.
ABI measurement and PAD definition
ABI, an indicator of PAD, was measured using an automated, non-invasive, waveform analysis device (VP-1000, Colin Co, Komaki, Japan). The subjects were examined in the supine position after relaxing in the supine position on a bed for at least 5 min. Cuffs were wrapped around both the arms and the ankles, and electrocardiogram electrodes were placed at the left sternal border. ABI was automatically calculated by dividing the posterior tibial systolic blood pressure by the brachial systolic pressure measured by the oscillometric method with cuffs adapted to the extremities. ABI was obtained for each leg separately, and the average was used as the mean ABI. PAD, or an abnormal ABI, was defined as an ABI of lower than 0.90 in either leg [10, 19]. The absence of PAD was defined as an ABI ≥0.90 and ≤1.50 [17, 20]. An ABI less than 0.90 shows 95% sensitivity and 99% specificity for diagnosing PAD, as confirmed by angiography .
Other variables of interest
Information on the demographic characteristics, lifestyle, medical history, and medication use of each subject were assessed with a standardized questionnaire administered by trained staff. Alcohol consumption (g/day) was calculated from the average number of alcoholic beverages consumed. Physical exercise was categorized as none (0-1 time per week), irregular exercise (2-4 times per week), and regular exercise (5 or more times per week) . Body mass index was calculated as weight in kilograms divided by height in meters squared. Blood pressure was measured after at least 5 min of rest in the sitting position using a mercury sphygmomanometer. The average of three consecutive readings of systolic and diastolic blood pressure taken at 1-min intervals was used in the analysis. After a 12-h overnight fast, venous blood samples were collected. Serum total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, uric acid, and fasting blood glucose levels were measured using enzymatic methods. Low-density lipoprotein (LDL) cholesterol was calculated by the Friedewald formula. All samples were analyzed using an automatic analyzer (Hitachi 7600, Hitachi Ltd., Tokyo, Japan).
General characteristics of the study population, based on the presence or absence of PAD, were expressed as mean ± standard deviation or number (percentage), and were compared using independent t-tests for continuous variables and chi-squared tests for categorical variables. Analysis of covariance and multiple logistic regression were used to evaluate the association of smoking characteristics (e.g., smoking status, cumulative smoking exposure, and duration of smoking cessation) with mean ABI and PAD, respectively. Mean (standard error) or odds ratio (95% confidence interval) were calculated in unadjusted, age-adjusted, and multivariate-adjusted models adjusting for age, body mass index, systolic blood pressure, fasting blood glucose, total cholesterol, HDL cholesterol, uric acid, alcohol consumption, physical exercise, and use of medications for hypertension, diabetes, and hyperlipidemia. Multivariate-adjusted models of cumulative smoking exposure were further adjusted for current smoking (yes/no), and multivariate-adjusted models of duration of smoking cessation were additionally adjusted for pack-years of smoking. The trend in the association between smoking characteristics and PAD was determined after considering smoking categories as ordinal variables. All statistical analyses were performed using SPSS 15.0 and P values <0.05 were deemed to indicate statistical significance.
General characteristics of the study population with and without PAD (n = 2517)
(n = 2414)
(n = 103)
65.9 ± 7.9
70.4 ± 7.3
Body mass index, kg/m²
23.9 ± 2.8
23.4 ± 3.0
Systolic blood pressure, mmHg
124.8 ± 16.3
129.6 ± 19.5
Diastolic blood pressure, mmHg
75.7 ± 10.5
74.8 ± 10.0
Antihypertensive medications, n (%)
Fasting blood glucose, mg/dl
113.3 ± 27.2
116.8 ± 33.1
Antidiabetic medications, n (%)
Total cholesterol, mg/dl
187.7 ± 37.7
189.9 ± 38.3
LDL cholesterol, mg/dl
109.7 ± 35.7
114.0 ± 37.1
HDL cholesterol, mg/dl
49.3 ± 12.0
47.1 ± 10.0
143.7 ± 116.5
144.1 ± 96.4
Lipid-lowering medications, n (%)
Uric acid, mg/dl
5.8 ± 1.4
6.4 ± 1.9
Alcohol consumption, gram/day
14.8 ± 23.7
10.1 ± 20.0
Regular exercise, n (%)
Age at starting smoking
22.4 ± 5.6
22.9 ± 6.5
Total years of smoking, years
31.9 ± 13.8
36.8 ± 14.9
Number of cigarettes per day
17.9 ± 12.0
19.1 ± 12.7
Pack-years of smoking
21.5 ± 22.2
31.3 ± 29.7
Mean ankle-brachial index
1.147 ± 0.082
0.868 ± 0.117
Mean ankle-brachial index and odds ratio for peripheral arterial disease according to smoking status
(n = 616)
(n = 1298)
(n = 603)
Mean ankle-brachial index
Peripheral arterial diseaseb
Prevalence, n (%)
Mean ankle-brachial index and odds ratio for peripheral arterial disease according to cumulative smoking exposure
(n = 616)
Cumulative smoking exposure (pack-years of smoking)c
(n = 745)
(n = 687)
[> 40.0 pack-years]
(n = 438)
Mean ankle-brachial index
Peripheral arterial diseaseb
Prevalence, n (%)
Mean ankle-brachial index and odds ratio for peripheral arterial disease according to duration of smoking cessation
(n = 603)
Duration of smoking cessation (years since quitting smoking)c
(n = 616)
≤ 3 years
(n = 185)
(n = 398)
(n = 303)
≥ 21 years
(n = 357)
Mean ankle-brachial index
Peripheral arterial diseaseb
Prevalence, n (%)
This cross-sectional study of community-dwelling Korean men demonstrated that cumulative smoking exposure was positively associated with PAD, defined as an ABI <0.90, and that duration of smoking cessation was inversely associated with PAD, independently of conventional cardiovascular risk factors. Although, as in previous studies, current smokers had a significantly higher risk for PAD than those who never or previously smoked, pack-years of smoking and years since quitting smoking were also identified as significant risk factors that should be taken into account to properly evaluate the effect of cigarette smoking on PAD.
The ABI is considered as a surrogate marker of generalized atherosclerosis because low ABI levels have been associated with elevated risk of future coronary heart disease [3, 22], stroke [3, 23, 24], and a higher risk of all-cause and cardiovascular mortality [3, 25, 26]. A recent meta-analysis found that the predictive value of ABI for cardiovascular morbidity and mortality was similar to that of the traditional Framingham risk factors . Because ABI is simple, non-invasive, reproducible, and cost-effective , ABI measurement is routinely performed to screen for asymptomatic PAD patients. ABI is a useful tool for the prediction of cardiovascular risk because, compared with carotid intima-media thickness and coronary artery calcium, it can be performed readily in community settings and in primary care physicians' offices .
Our results are consistent with previous findings that current smoking is associated with a 1.86 to 5.48-fold increase in risk of PAD, compared with never having smoked [7–14]. Some studies have reported that current smoking is associated with a 1.60 to 2.80-fold increase in the risk of PAD, compared with never or previously smoking [29, 30]. Additionally, our findings are analogous to previous findings that former smoking is associated with a 1.02 to 1.94-fold increased risk of PAD [7, 9–11, 14]; however, generally no significant difference between former and never smoking has been found.
Although various epidemiological studies have reported an association between smoking status and PAD, the majority of studies have examined PAD risk by smoking status, finding that current smoking increased PAD risk or smoking cessation decreased PAD risk. However, the simple smoking status classification of never, former, and current smoking has been questioned on the assumption that lifetime heavy smokers who have recently stopped smoking are categorized as former smokers, whereas smokers who started smoking only a few months ago are categorized as current smokers. Thus, cumulative smoking exposure, such as pack-years, should be taken into account to properly evaluate the association between cigarette smoking and PAD. Because few studies have investigated the association between cumulative smoking exposure and PAD or between the duration of smoking cessation and PAD, the dose-response relationship between smoking habits (e.g., pack-years of smoking and years since quitting smoking) and PAD is uncertain. The Edinburgh Artery Study of 1592 adults reported that the adjusted relative risk of PAD, compared with never smoking, was 1.70 (95% CI, 0.72-3.99) for moderate smokers (≤25 pack-years) and 2.72 (95% CI, 1.13-6.53) for heavy smokers (> 25 pack-years) . Recently, a cross-sectional study of 1215 Japanese men found that mean ABI correlated inversely and linearly with pack-years of smoking and the OR (95% CI) of PAD, compared with never smoking, was 2.8 (0.8-10.2), 2.8 (0.8-10.0), and 4.2 (1.2-14.6) for <26, 26-45, and ≥45 pack-years, respectively, suggesting a linear trend . In agreement with previous studies [13, 15], we observed a significant increasing trend between pack-years of smoking and PAD, confirming that the effect of cumulative smoking exposure on PAD was a dose-response relationship. Cui et al.  found that men who had quit smoking 20 or more years ago had higher mean ABI and lower prevalence of PAD than current smokers. In our study, the risk of PAD was significantly lower with smoking cessation of over 11 years than in current smokers, whereas ≤10 years of smoking cessation was not significantly associated with PAD risk, suggesting that long-term smoking cessation is needed to diminish the effects of smoking on PAD.
Study limitations and strengths
There are several limitations to the present study. First, the cross-sectional nature limits conclusions about the direction or causality of associations observed in our study. Additional prospective studies with incident PAD are needed to confirm our findings. Second, the possible measurement errors in smoking characteristics due to the information being self-reported, without measuring biological markers, such as serum cotinine, might have attenuated the relationship between cigarette smoking and PAD. Third, unmeasured confounding variables could have affected the association of cigarette smoking and smoking cessation with PAD. Fourth, because information on environmental tobacco smoke exposure was not collected, the effect of passive smoking on PAD cannot be accessed.
Nevertheless, this study has several strengths. First, the main strength is that few studies have investigated the association between smoking characteristics (including pack-years of smoking and years since quitting smoking) and PAD risk. Our more detailed analysis, compared with previous studies, allowed us to find a significant association between smoking habits and PAD. Second, ABI was measured on both the left and right sides, and the smallest ABI was used in defining PAD. Third, a large number of community-dwelling older men participated in this study. Because subclinical atherosclerosis progresses with age, the present study population (aged 50 years and older) may help detect an association between smoking habits and PAD.
In summary, a significant dose-response association between cumulative smoking exposure and PAD prevalence was found in a community-dwelling sample of Korean men. Additionally, there was a significant inverse association between duration of smoking cessation and presence of PAD. From a public health perspective, considering the high prevalence of current smoking in Korean men, more aggressive tobacco control efforts could reduce the number of people who develop PAD.
This study was financially supported by Chonnam National University, 2007
- Newman AB, Siscovick DS, Manolio TA, Polak J, Fried LP, Borhani NO, Wolfson SK: Ankle-arm index as a marker of atherosclerosis in the Cardiovascular Health Study Cardiovascular Heart Study (CHS) Collaborative Research Group. Circulation. 1993, 88: 837-845.View ArticlePubMedGoogle Scholar
- Priollet P: Quality of life and peripheral arterial disease: perspectives for the future. Drugs. 1998, 56: 49-58. 10.2165/00003495-199856003-00006.View ArticlePubMedGoogle Scholar
- Newman AB, Shemanski L, Manolio TA, Cushman M, Mittelmark M, Polak JF, Powe NR, Siscovick D: Ankle-arm index as a predictor of cardiovascular disease and mortality in the Cardiovascular Health Study. The Cardiovascular Health Study Group. Arterioscler Thromb Vasc Biol. 1999, 19: 538-545.View ArticlePubMedGoogle Scholar
- Murabito JM, Evans JC, Larson MG, Nieto K, Levy D, Wilson PW, Framingham Study: The ankle-brachial index in the elderly and risk of stroke, coronary disease, and death: the Framingham Study. Arch Intern Med. 2003, 163: 1939-1942. 10.1001/archinte.163.16.1939.View ArticlePubMedGoogle Scholar
- Feigelson HS, Criqui MH, Fronek A, Langer RD, Molgaard CA: Screening for peripheral arterial disease: the sensitivity, specificity, and predictive value of noninvasive tests in a defined population. Am J Epidemiol. 1994, 140: 526-534.PubMedGoogle Scholar
- Lu JT, Creager MA: The relationship of cigarette smoking to peripheral arterial disease. Rev Cardiovasc Med. 2004, 5: 189-193.PubMedGoogle Scholar
- Meijer WT, Grobbee DE, Hunink MG, Hofman A, Hoes AW: Determinants of peripheral arterial disease in the elderly: the Rotterdam study. Arch Intern Med. 2000, 160: 2934-2938. 10.1001/archinte.160.19.2934.View ArticlePubMedGoogle Scholar
- Murabito JM, Evans JC, Nieto K, Larson MG, Levy D, Wilson PW: Prevalence and clinical correlates of peripheral arterial disease in the Framingham Offspring Study. Am Heart J. 2002, 143: 961-965. 10.1067/mhj.2002.122871.View ArticlePubMedGoogle Scholar
- Navas-Acien A, Selvin E, Sharrett AR, Calderon-Aranda E, Silbergeld E, Guallar E: Lead, cadmium, smoking, and increased risk of peripheral arterial disease. Circulation. 2004, 109: 3196-3201. 10.1161/01.CIR.0000130848.18636.B2.View ArticlePubMedGoogle Scholar
- Selvin E, Erlinger TP: Prevalence of and risk factors for peripheral arterial disease in the United States: results from the National Health and Nutrition Examination Survey 1999-2000. Circulation. 2004, 110: 738-743. 10.1161/01.CIR.0000137913.26087.F0.View ArticlePubMedGoogle Scholar
- Wattanakit K, Folsom AR, Selvin E, Weatherley BD, Pankow JS, Brancati FL, Hirsch AT: Risk factors for peripheral arterial disease incidence in persons with diabetes: the Atherosclerosis Risk in Communities (ARIC) Study. Atherosclerosis. 2005, 180: 389-397. 10.1016/j.atherosclerosis.2004.11.024.View ArticlePubMedGoogle Scholar
- Eason SL, Petersen NJ, Suarez-Almazor M, Davis B, Collins TC: Diabetes mellitus, smoking, and the risk for asymptomatic peripheral arterial disease: whom should we screen?. J Am Board Fam Pract. 2005, 18: 355-361. 10.3122/jabfm.18.5.355.View ArticlePubMedGoogle Scholar
- Cui R, Iso H, Yamagishi K, Tanigawa T, Imano H, Ohira T, Kitamura A, Sato S, Shimamoto T: Relationship of smoking and smoking cessation with ankle-to-arm blood pressure index in elderly Japanese men. Eur J Cardiovasc Prev Rehabil. 2006, 13: 243-248. 10.1097/01.hjr.0000209818.36067.51.View ArticlePubMedGoogle Scholar
- Ostchega Y, Paulose-Ram R, Dillon CF, Gu Q, Hughes JP: Prevalence of peripheral arterial disease and risk factors in persons aged 60 and older: data from the National Health and Nutrition Examination Survey 1999-2004. J Am Geriatr Soc. 2007, 55: 583-589. 10.1111/j.1532-5415.2007.01123.x.View ArticlePubMedGoogle Scholar
- Price JF, Mowbray PI, Lee AJ, Rumley A, Lowe GD, Fowkes FG: Relationship between smoking and cardiovascular risk factors in the development of peripheral arterial disease and coronary artery disease: Edinburgh Artery Study. Eur Heart J. 1999, 20: 344-353. 10.1053/euhj.1998.1194.View ArticlePubMedGoogle Scholar
- Lee YH, Shin MH, Kweon SS, Choi SW, Kim HY, Ryu SY, Kim BH, Rhee JA, Choi JS: Alcohol consumption and carotid artery structure in Korean adults aged 50 years and older. BMC Public Health. 2009, 9: 358-10.1186/1471-2458-9-358.View ArticlePubMedPubMed CentralGoogle Scholar
- McDermott MM, Criqui MH, Liu K, Guralnik JM, Greenland P, Martin GJ, Pearce W: The lower ankle brachial index calculated by averaging the dorsalis pedis and posterior tibial arterial pressures is most closely associated with leg functioning in peripheral arterial disease. J Vasc Surg. 2000, 32: 1164-1171. 10.1067/mva.2000.108640.View ArticlePubMedGoogle Scholar
- Lange SF, Trampisch HJ, Pittrow D, Darius H, Mahn M, Allenberg JR, Tepohl G, Haberl RL, Diehm C, getABI Study Group: Profound influence of different methods for determination of the ankle brachial index on the prevalence estimate of peripheral arterial disease. BMC Public Health. 2007, 7: 147-10.1186/1471-2458-7-147.View ArticlePubMedPubMed CentralGoogle Scholar
- Stoffers HE, Kester AD, Kaiser V, Rinkens PE, Kitslaar PJ, Knottnerus JA: The diagnostic value of the measurement of the ankle-brachial systolic pressure index in primary health care. J Clin Epidemiol. 1996, 49: 1401-1405. 10.1016/S0895-4356(96)00275-2.View ArticlePubMedGoogle Scholar
- McDermott MM, Guralnik JM, Greenland P, Pearce WH, Criqui MH, Liu K, Taylor L, Chan C, Sharma L, Schneider JR, Ridker PM, Green D, Quann M: Statin use and leg functioning in patients with and without lower-extremity peripheral arterial disease. Circulation. 2003, 107: 757-761. 10.1161/01.CIR.0000050380.64025.07.View ArticlePubMedGoogle Scholar
- Bernstein EF, Fronek A: Current status of noninvasive tests in the diagnosis of peripheral arterial disease. Surg Clin North Am. 1982, 62: 473-487.PubMedGoogle Scholar
- Abbott RD, Petrovitch H, Rodriguez BL, Yano K, Schatz IJ, Popper JS, Masaki KH, Ross GW, Curb JD: Ankle/brachial blood pressure in men >70 years of age and the risk of coronary heart disease. Am J Cardiol. 2000, 86: 280-284. 10.1016/S0002-9149(00)00914-0.View ArticlePubMedGoogle Scholar
- Abbott RD, Rodriguez BL, Petrovitch H, Yano K, Schatz IJ, Popper JS, Masaki KH, Ross GW, Curb JD: Ankle-brachial blood pressure in elderly men and the risk of stroke: the Honolulu Heart Program. J Clin Epidemiol. 2001, 54: 973-978. 10.1016/S0895-4356(01)00373-0.View ArticlePubMedGoogle Scholar
- Tsai AW, Folsom AR, Rosamond WD, Jones DW: Ankle-brachial index and 7-year ischemic stroke incidence: the ARIC study. Stroke. 2001, 32: 1721-1724.View ArticlePubMedGoogle Scholar
- Criqui MH, Langer RD, Fronek A, Feigelson HS, Klauber MR, McCann TJ, Browner D: Mortality over a period of 10 years in patients with peripheral arterial disease. N Engl J Med. 1992, 326: 381-386. 10.1056/NEJM199202063260605.View ArticlePubMedGoogle Scholar
- Vogt MT, Cauley JA, Newman AB, Kuller LH, Hulley SB: Decreased ankle/arm blood pressure index and mortality in elderly women. JAMA. 1993, 270: 465-469. 10.1001/jama.270.4.465.View ArticlePubMedGoogle Scholar
- Ankle Brachial Index Collaboration, Fowkes FG, Murray GD, Butcher I, Heald CL, Lee RJ, Chambless LE, Folsom AR, Hirsch AT, Dramaix M, deBacker G, Wautrecht JC, Kornitzer M, Newman AB, Cushman M, Sutton-Tyrrell K, Fowkes FG, Lee AJ, Price JF, d'Agostino RB, Murabito JM, Norman PE, Jamrozik K, Curb JD, Masaki KH, Rodríguez BL, Dekker JM, Bouter LM, Heine RJ, Nijpels G, Stehouwer CD, Ferrucci L, McDermott MM, Stoffers HE, Hooi JD, Knottnerus JA, Ogren M, Hedblad B, Witteman JC, Breteler MM, Hunink MG, Hofman A, Criqui MH, Langer RD, Fronek A, Hiatt WR, Hamman R, Resnick HE, Guralnik J, McDermott MM: Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA. 2008, 300: 197-208. 10.1001/jama.300.2.197.View ArticlePubMed CentralGoogle Scholar
- Khan TH, Farooqui FA, Niazi K: Critical review of the ankle brachial index. Curr Cardiol Rev. 2008, 4: 101-106. 10.2174/157340308784245810.View ArticlePubMedPubMed CentralGoogle Scholar
- Sritara P, Sritara C, Woodward M, Wangsuphachart S, Barzi F, Hengprasith B, Yipintsoi T: Prevalence and risk factors of peripheral arterial disease in a selected Thai population. Angiology. 2007, 58: 572-578. 10.1177/0003319707303652.View ArticlePubMedGoogle Scholar
- Tapp RJ, Balkau B, Shaw JE, Valensi P, Cailleau M, Eschwege E, DESIR Study Group: Association of glucose metabolism, smoking and cardiovascular risk factors with incident peripheral arterial disease: the DESIR study. Atherosclerosis. 2007, 190: 84-89. 10.1016/j.atherosclerosis.2006.02.017.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/11/94/prepub
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