American Journal of Epidemiology Advance Access originally published online on December 15, 2005
American Journal of Epidemiology 2006 163(4):334-341; doi:10.1093/aje/kwj051
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Original Contribution |
Hemostatic Factors, Inflammatory Markers, and Progressive Peripheral Atherosclerosis
The Edinburgh Artery Study
1 Wolfson Unit for Prevention of Peripheral Vascular Diseases, School of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom
2 Department of General Practice and Primary Care, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen, United Kingdom
3 Division of Cardiovascular and Medical Sciences, University of Glasgow and Royal Infirmary, Glasgow, United Kingdom
Correspondence to Ioanna Tzoulaki, Wolfson Unit for Prevention of Peripheral Vascular Diseases, School of Medicine and Veterinary Medicine, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, United Kingdom (e-mail: I.Tzoulaki{at}sms.ed.ac.uk).
Received for publication June 20, 2005. Accepted for publication October 6, 2005.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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The interplay between inflammatory and hemostatic mechanisms may play a crucial role in the development and progression of atherosclerosis. The authors evaluated the separate and joint associations of hemostatic and inflammatory variables on peripheral atherosclerotic progression in the Edinburgh Artery Study, a population cohort study of 1,592 men and women aged 55–74 years that started in 1987. Levels of fibrinogen, fibrin D-dimer, von Willebrand factor, tissue plasminogen activator antigen, factor VII, prothrombin fragment 1 + 2, urinary fibrinopeptide A, C-reactive protein, and interleukin-6 were measured at baseline. Arm and ankle blood pressures were measured, and atherosclerotic progression was assessed by computing ankle brachial index (ABI) at baseline (1,582 participants) and after 12 years of follow-up (813 participants). Fibrinogen (p = 0.05) and D-dimer (p
0.05) were significantly associated with ABI change independently of baseline ABI and cardiovascular disease risk factors. However, these associations were no longer significant when analyses were adjusted for either C-reactive protein or interleukin-6. Moreover, subjects with higher levels of both D-dimer and interleukin-6 at baseline had the greatest ABI decline. In conclusion, fibrinogen and D-dimer, but not other hemostatic factors, were associated with progressive peripheral atherosclerosis. Since D-dimer and fibrinogen are acute phase reactants, these data support the hypothesis that inflammation is more related to atherosclerosis than is hypercoagulation.
arteriosclerosis; cohort studies; hemostasis; inflammation; peripheral vascular diseases
Abbreviations: ABI, ankle brachial index; HDL, high density lipoprotein; SD, standard deviation
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Plasma levels of several hemostatic factors have been associated with cardiovascular events in numerous prospective epidemiologic studies (1
). Meta-analyses have shown that fibrinogen (2), fibrin D-dimer (3), tissue plasminogen activator antigen (4), and von Willebrand factor (5) are significantly associated with an increased risk of coronary heart disease. Other hemostatic variables, including prothrombin fragment 1 + 2, fibrinopeptide A, and factor VII, have been related to cardiovascular disease, but the results were inconsistent (1).
The role of hemostatic factors in the early (asymptomatic) stages of atherosclerosis is not well established. Furthermore, the true relation between elevated levels of hemostatic factors and progression of atherosclerosis remains a matter of controversy. Elevated levels of hemostatic factors may represent activated coagulation and impaired fibrinolysis in the arterial wall (6). On the other hand, many such factors are acute phase reactant proteins (1), so their increased blood levels in persons with cardiovascular disease (clinical or subclinical) may result from inflammatory stimuli. In turn, inflammatory markers, such as C-reactive protein and interleukin-6, have been associated with cardiovascular disease and atherosclerotic development and progression in many epidemiologic studies (7, 8).
In the Edinburgh Artery Study, a population-based cohort study, our aim was to analyze the complex relations between inflammation, coagulation, and atherosclerosis. We tested associations between several hemostatic factors (fibrinogen, D-dimer, tissue plasminogen activator antigen, von Willebrand factor, factor VII, prothrombin fragment 1 + 2, and fibrinopeptide A) and progressive atherosclerosis, as measured by the change in ankle brachial index (ABI), over a period of 12 years. The ABI is a reliable marker of peripheral and generalized atherosclerosis in populations (9). We also compared, for the first time, the aforementioned hemostatic markers with levels of C-reactive protein and interleukin-6 as markers of progressive peripheral atherosclerosis and investigated whether they had an additive effect on disease prognosis.
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MATERIALS AND METHODS |
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Cohort examination
The Edinburgh Artery Study began in 1987 as a cross-sectional survey of 809 men and 783 women aged 55–74 years. This population, which was almost exclusively of White ethnicity, was selected at random, in 5-year age bands, from 11 general practices serving a range of socioeconomic and geographic areas throughout the city. The response rate was 65 percent, and follow-up of a sample of nonresponders showed no substantial bias. Details on the study recruitment and examination process have been published elsewhere (10
). Ethics committee approval was given for this study, and informed consent was obtained from each subject. Subjects completed a self-administered questionnaire at baseline that contained validated questions regarding personal characteristics, social class, intermittent claudication and angina (Rose questionnaire (11)), medical history, smoking, and diet. A similar modified questionnaire was repeated at the year 12 examination.
Subjects were invited to undergo a comprehensive clinical examination at baseline and 12 years after the study commenced. Clinical measurements were conducted by trained research staff during each examination. A 12-lead electrocardiogram was taken and coded independently by two observers using the Minnesota Code (12). Standing height (without shoes) was measured to the nearest 5 mm using a free-standing metal ruler on a heavy base. Weight (without shoes and outer clothing) was measured to the nearest 100 g on digital scales (Soehnle Professional GmbH & Company, Murrhardt, Germany). Body mass index was calculated as weight in kilograms divided by the square of height in meters.
Systolic and diastolic (phase V) blood pressures were recorded in the right arm only, after 10 minutes' rest in the supine position, using a stethoscope. The femoral, posterior tibial, and dorsalis pedis pulses were palpated in both legs. Ankle systolic blood pressures were measured first in the right leg and then in the left leg at the posterior tibial artery, using a Sonicaid Doppler ultrasound probe (Sonicaid Ltd., Chichester, United Kingdom) and a random-zero sphygmomanometer with the cuff placed proximal to the malleoli. The pulse was located with the Doppler probe, and the cuff was inflated until the pulse was no longer audible. The cuff was then deflated, and the pressure was noted when the pulse reappeared. If the posterior tibial pulse was not detectable, the dorsalis pedis pulse was used wherever possible. ABI was calculated by dividing the ankle systolic pressure by the brachial systolic pressure. The lower of the two leg indices was used in the analysis as indicative of worse disease. In the current analyses, three and eight subjects at baseline and 12 years, respectively, had ABI values above 1.50 and were excluded because of probable arterial rigidity.
At baseline, a fasting 20-ml sample of venous blood was taken for estimation of biochemical, inflammatory, and hemostatic factors. All blood samples were centrifuged within 2 hours of collection and were stored the same day at –40°C. Each variable was assayed for the whole population at the same time. Tests for serum total cholesterol, high density lipoprotein (HDL) cholesterol, and blood glucose were performed on a COBAS BIO analyzer (Hoffmann-La Roche Ltd., Basel, Switzerland) using standard kits. The total:HDL cholesterol ratio was calculated by dividing the total cholesterol value by the HDL cholesterol value. Diabetes status was assessed in a number of ways. At the baseline examination, a blood sample was taken for measurement of blood glucose, and then each subject not known to be diabetic consumed 75 g of glucose in the form of 335 ml of Solripe Gluctoza Health Drink (Strathmore Mineral Water Company, Forfar, United Kingdom). A second blood glucose specimen was taken 2 hours after the oral glucose load. In addition, at the baseline and year 12 examinations, self-reported diabetes status and use of insulin injections and tablets for diabetes were recorded. Subjects were classified as suffering from diabetes if 1) they had been told by a doctor that they suffered from diabetes and were receiving treatment or 2) the glucose concentration of their 2-hour blood sample was greater than or equal to 11.1 mmol/liter.
C-reactive protein was measured immunologically using a high-sensitivity assay in a BN ProSpec nephelometer (Dade Behring, Milton Keynes, United Kingdom). Plasma levels of interleukin-6, von Willebrand factor, D-dimer, and tissue plasminogen activator antigen were measured using high-sensitivity enzyme-linked immunosorbent assay kits from R&D Systems (Oxford, United Kingdom), Dako Denmark A/S (Glostrup, Denmark), Antigenics, Inc. (Parsippanny, New Jersey), and Biopool AB (Umeå, Sweden), respectively (13, 14). Fibrinogen was measured in citrated plasma by means of a thrombin-clotting turbidimetric method in a centrifugal analyzer (15). Urinary fibrinopeptide A was measured by radioimmunoassay (16); plasma factor VII was measured by functional antigenic assay (17); and plasma prothrombin fragment 1 + 2 was measured by enzyme-linked immunosorbent assay (Dade Behring, Marburg, Germany). Some values for these markers were missing because of decreasing availability of plasma samples and were considered "data missing at random." Internal quality control plasma was included in the assay for each marker. In addition, marker measurements that fell outside the 95 percent normal range were repeated.
Statistical methods
Data were analyzed using the SPSS software package, version 12.0 (SPSS, Inc., Chicago, Illinois). Pack-years of smoking were calculated as years of cigarette smoking multiplied by the average number of packs smoked per day, with the value zero entered for lifelong nonsmokers. The distribution of pack-years was skewed, and square-root transformation was used in all analyses. Distributions of D-dimer, prothrombin fragment 1 + 2, urinary fibrinopeptide A, C-reactive protein, and interleukin-6 were logarithmically transformed, and distributions of von Willebrand factor, tissue plasminogen activator antigen, and factor VII were square-root-transformed. Data for all transformed variables were approximately normally distributed. Physical activity was coded as a four-group categorical variable (no activity, light activity, moderate activity, and strenuous activity) as previously described (18). Ninety-eight subjects with C-reactive protein levels above 10 mg/liter and 11 subjects with interleukin-6 levels above 100 pg/ml were excluded from all analyses, because these levels indicate the presence of acute inflammatory disease. Atherosclerotic progression was determined according to mean change in ABI, calculated as ABI at 12 years minus ABI at baseline. At both baseline and 12 years, the lower leg index was used to calculate ABI.
Pearson's correlation coefficient was used to test associations between hemostatic factors and ABI at baseline and between hemostatic factors and inflammatory markers. Linear regression was used to test associations between each hemostatic factor and ABI change at 12 years. Analyses were initially adjusted for baseline ABI and were further adjusted for risk factors: age, sex, pack-years of smoking, diabetes, total:HDL cholesterol ratio, body mass index, and physical activity. Further adjustments for alcohol use or baseline cardiovascular disease did not meaningfully change the results, and those findings are not reported here. Finally, we entered all hemostatic variables and baseline risk factors simultaneously into the linear regression model for ABI change after 12 years. We used linear regression analysis for ABI change, with fibrinogen, D-dimer, C-reactive protein, and interleukin-6 as predictor variables. We investigated different models with combinations of these four variables as independent variables, and we looked for the model with the highest R2. There was no evidence of interaction between age, sex, baseline peripheral arterial disease status, or baseline cardiovascular disease status and levels of hemostatic or inflammatory factors in any analysis. We also tested regression models without adjustment for baseline ABI. In these models, the regression coefficients were essentially unchanged, but the confidence intervals were wider (data not shown). We tested for collinearity and examined residual plots to check the assumptions of the regression analysis.
Finally, we divided subjects into three equal groups (tertiles) according to their baseline levels of D-dimer and into three other groups according to their baseline levels of interleukin-6. Cutoff points for interleukin-6 and D-dimer tertiles were 1.65 pg/ml and 2.96 pg/ml and 67 ng/ml and 107 ng/ml, respectively. We then categorized subjects into four mutually exclusive groups depending on their baseline levels of D-dimer and interleukin-6: both D-dimer and interleukin-6 levels in the top tertile; a D-dimer level in the top tertile but not interleukin-6 level; an interleukin-6 level in the top tertile but not D-dimer level; and, finally, neither interleukin-6 level nor D-dimer level in the top tertile. The trend between these groups and ABI change was tested for statistical significance using analysis of covariance adjusted for baseline ABI and for risk factors. The assumption of homogeneity of variance was examined in all analyses. Throughout all analyses, a two-sided p value less than or equal to 0.05 was taken to denote statistical significance.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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A total of 1,592 subjects were examined at baseline, and 837 participated in the year 12 follow-up (88 others completed the questionnaire only). Up to the year 12 examination, the total number of known deaths was 485 (30.5 percent), of which 207 (42.7 percent) were due to cardiovascular disease. Among the 270 participants who did not undergo the follow-up clinical examination, 105 refused, 74 were too frail, 69 did not reply, 21 could not be contacted, and one was lost to follow-up. The mean age of the baseline population was 64.8 years (standard deviation (SD), 5.7), and 50.9 percent of the subjects were male. Six percent suffered from diabetes mellitus, 26 percent were self-reported current cigarette smokers, and 36 percent were ex-smokers. Among smokers, the median number of pack-years of smoking was 25 (interquartile range, 12–39). The mean total cholesterol level was 7.03 mmol/liter (SD, 1.33), the mean HDL cholesterol level was 1.44 mmol/liter (SD, 0.41), and the mean body mass index was 25.6 (SD, 3.9). The risk factor profile of participants at the year 12 examination (data not shown) was only marginally better than that of all subjects at baseline.
Median values of hemostatic factors measured at baseline are listed in table 1. Of these, fibrinogen, tissue plasminogen activator antigen, von Willebrand factor, and D-dimer were significantly (p 0.01) and inversely associated with baseline ABI (table 1). Valid ABI measurements were available for 1,582 subjects (99 percent of participants) at baseline and for 813 subjects (97.1 percent) at the year 12 follow-up. The mean ABI at baseline was 1.03 (SD, 0.18); at 12 years, it had decreased to 1.00 (SD, 0.19). The change in ABI from baseline to 12 years was significant (p 0.01), with a mean value of –0.07 (SD, 0.18). In addition, at baseline, the 73 (4.6 percent) subjects who had symptomatic peripheral arterial disease (defined as intermittent claudication) had a mean ABI of 0.82 (SD, 0.27), whereas the rest of the cohort had a mean ABI of 1.04 (SD, 0.17).
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We performed separate linear regression analyses for ABI change from baseline to 12 years with each hemostatic factor used as a predictor variable (table 2). To allow for comparisons between markers, table 2 presents the effect of the mean difference between the top and bottom tertiles of each marker on ABI change. Fibrinogen (p
0.001) and D-dimer (p 0.001) were significantly associated with ABI change in analyses adjusted for baseline ABI and baseline age. However, D-dimer had a greater effect, with a mean difference between the top and bottom tertiles of –0.016 (95 percent confidence interval: –0.027, –0.005). Moreover, in analyses adjusted for baseline ABI and conventional risk factors (age, sex, body mass index, diabetes, pack-years of smoking, total:HDL cholesterol ratio, and physical activity), D-dimer remained significantly and independently associated with ABI change (p = 0.003). The effect of fibrinogen was reduced slightly and had borderline significance (p = 0.05) in this risk-factor-adjusted analysis.
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Next, we examined the corresponding effect of all hemostatic factors on ABI change at 12 years with all hemostatic factors entered simultaneously into the linear regression model (data not shown). In this analysis, D-dimer (p = 0.015) and fibrinogen (p = 0.025) were significantly and independently associated with ABI change. Nevertheless, when we further adjusted for known risk factors, only D-dimer was significantly associated with ABI change independently of other blood factors and of cardiovascular disease risk factors. The mean difference in ABI change between the top and bottom tertiles of D-dimer in this final analysis was –0.021 (95 percent confidence interval: –0.038, –0.004).
We then compared D-dimer and fibrinogen with the inflammatory markers interleukin-6 and C-reactive protein as predictors of ABI change. Levels of C-reactive protein and interleukin-6 were measured at baseline and had median values of 2.17 mg/liter (interquartile range, 1.42–3.60) and 1.77 pg/ml (interquartile range, 0.87–3.75), respectively (table 1). D-dimer was correlated significantly with interleukin-6 (r = 0.33, p 0.01) and C-reactive protein (r = 0.26, p 0.01). Fibrinogen had higher correlations with C-reactive protein and interleukin-6 than did D-dimer (for both C-reactive protein and interleukin-6, r = 0.48, p < 0.001). Table 3 (model 1) shows the separate effect of each of these four variables on ABI change and the corresponding R2 in models adjusted for baseline ABI and age and further adjusted for cardiovascular disease risk factors. Interleukin-6 had marginally the greatest individual effect, explaining 22.6 percent of the variance in ABI change.
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We then adjusted these variables for each other and examined all possible combinations of them as predictors of ABI change. Some differences in sample size may have slightly influenced the effect sizes and significance levels. Table 3 lists only selected combinations and shows that the joint effect of C-reactive protein and D-dimer on ABI change increased the variance explained by the model from 17.9 percent (when D-dimer was assessed alone) to 23.0 percent (model 2). However, both C-reactive protein and D-dimer failed to retain their significant associations when they were adjusted for each other in risk-factor-adjusted analysis. Fibrinogen did not add to any other variable in any analysis. On the other hand, the combined effect of interleukin-6 and D-dimer further increased the R2 to 23.6 percent (model 3). This was the highest R2 observed from all models that we examined, despite the fact that D-dimer was not significantly associated with ABI change in this model. All four variables explained 23.3 percent of the variance in ABI change after 12 years of follow-up in risk-factor-adjusted analysis (model 4). Interleukin-6 was the only one of the four variables that was significantly (p = 0.03) associated with ABI change independently of the other three variables and of cardiovascular disease risk factors.
Finally, the combined effect of D-dimer and interleukin-6 on ABI change was investigated further. Participants were divided into four mutually exclusive groups according to their baseline levels of D-dimer and interleukin-6: both interleukin-6 and D-dimer levels in the top tertile (171 subjects); interleukin-6 level in the top tertile and D-dimer level in the middle or bottom tertile (180 subjects); D-dimer level in the top tertile and interleukin-6 level in the middle or bottom tertile (170 subjects); and, finally, both D-dimer and interleukin-6 levels in the middle or bottom tertile (506 subjects). Figure 1 presents the unadjusted mean ABI change (±1 standard error) in each of these four groups. A significant trend, with subjects in the top tertiles of both D-dimer and interleukin-6 having the greatest ABI decline, was found in analysis adjusted for baseline ABI (p = 0.004) and further adjusted for cardiovascular disease risk factors (p = 0.04).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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In this population-based follow-up study, we have shown weak associations for fibrinogen and stronger associations for D-dimer with atherosclerotic progression, independently of other hemostatic factors and conventional risk factors. However, fibrinogen and D-dimer failed to retain significant associations with ABI change when results were adjusted for interleukin-6 levels. In fact, interleukin-6 was a better single predictor of progression of peripheral atherosclerosis than were the other markers studied here.
Other markers of coagulation and fibrinolysis did not show any significant associations with ABI decline. In addition, factor VII, urinary fibrinopeptide A, and prothrombin fragment 1 + 2 were not even significantly correlated with baseline ABI. Several previous investigators (19–22), as well as previous reports from the Edinburgh Artery Study (16, 23–25), have also failed to find independent associations between these markers and baseline ABI, peripheral arterial disease development, or carotid artery intima-media thickness. Meta-analyses of prospective studies of von Willebrand factor (26, 27) and tissue plasminogen activator antigen (4) have observed associations with risk of coronary heart disease, but their causality remains to be proven.
Fibrinogen (a circulating glycoprotein with important roles in both hemostasis and inflammation) and, to a lesser extent, D-dimer (the cross-linked degradation product of fibrin), are by far the most studied hemostatic factors in relation to cardiovascular disease (1–3). Increased fibrinogen and D-dimer levels have been previously associated with the presence of peripheral arterial disease (25, 28–34) and with disease severity as measured by ABI (16, 20, 21, 35), as well as carotid artery intima-media thickness and carotid artery stenosis (19, 22, 23, 36). Interestingly, our results revealed that fibrinogen and D-dimer were associated with ABI change independently of other hemostatic factors. However, adjustment for conventional risk factors had a considerable impact on their association with ABI change, suggesting that the association between these factors and atherosclerosis is partly due to interrelations with these risk factors. In particular, fibrinogen was greatly influenced and had marginal significance (p = 0.05) after risk factor adjustments.
Moreover, D-dimer and fibrinogen did not significantly predict ABI change independently of either C-reactive protein or interleukin-6 in risk-factor-adjusted analyses. C-reactive protein and interleukin-6 are two well-studied inflammatory markers that have been associated with cardiovascular disease in many epidemiologic studies. In the Edinburgh Artery Study, they have both been shown to predict ABI decline after 12 years independently of conventional risk factors (8). Interleukin-6, in particular, had the greatest independent effect on ABI change in comparison with C-reactive protein and other inflammatory markers, including intercellular adhesion molecule-1, vascular cell adhesion molecule-1, and E-selectin (8). In the present study, interleukin-6 had the greatest independent effect on ABI change as compared with hemostatic factors. On the other hand, D-dimer added to the R2 value of the model with interleukin-6 when predicting ABI change but failed to reach statistical significance. Similarly, subjects with high levels of both D-dimer and interleukin-6 had a worse disease outcome (greater ABI decline) than those who had one or none of these factors at high levels. Evidence for an additive effect between these two markers has been previously reported (37).
In this study, in accordance with a previous report on coronary heart disease (38), fibrinogen and D-dimer but not other markers of coagulation and fibrinolysis showed associations with peripheral atherosclerosis. There was also evidence for an additive effect between D-dimer and the proinflammatory cytokine interleukin-6. The most probable interpretation of these data is that inflammation rather than coagulation is more related to atherosclerotic progression. The proinflammatory cytokine interleukin-6 is thought to promote atherogenesis through several pathways. It induces C-reactive protein and fibrinogen production and increases endothelial cell adhesiveness. In addition, it has prohemostatic properties, such as activation of tissue factor and von Willebrand factor production, a decrease in anticoagulant levels, and production of more thrombogenic platelets. On the other hand, fibrinogen and D-dimer could give positive feedback to this atherosclerotic process by inducing interleukin-6 production. Therefore, elevated levels of interleukin-6 and acute phase reactants (C-reactive protein, fibrinogen, and D-dimer) might reflect the extensive interaction between inflammation and coagulation which, after repeated cycles, leads to atherosclerotic progression.
Some limitations of this study need to be addressed. Firstly, the generalizability of our results to other ethnic groups and ages is unknown. Moreover, the participants who did not survive to the year 12 follow-up measurement of ABI had more severe atherosclerosis at baseline and poorer health. Although this limited the range of baseline ABIs and thus the range of atherosclerotic disease at baseline, it would not be expected to have affected the validity of this analysis. Another limitation is that we did not have data with which to adjust for measurement error in ABI; however, this is likely to have led to underestimation of the strength of the reported associations. In addition, the changes in ABI throughout the 12 years of the study predicted by the markers examined here were very small, and their clinical significance remains unknown. In addition, the hemostatic and inflammatory markers were measured only once, and thus intraindividual variation could not be taken into account. Nevertheless, this would also have tended to result in underestimation of the true effect. Not all subjects had measurements for all of the hemostatic and inflammatory markers studied here, but the missing data were considered missing at random (because of attrition of stored plasma samples with repeated assays) and hence were unlikely to have biased our results. Furthermore, we did not adjust our analysis for aspirin or statin use at baseline. However, at the time of baseline examination (1987–1988), very few members of the Edinburgh population took aspirin for the prevention of cardiovascular disease, and statins had not yet been introduced. Finally, despite the prospective design of the present study and the biologic mechanisms proposed, the directions of the associations described here and the exact causal pathways through which these markers relate to atherosclerosis remain unknown.
In conclusion, in this population-based prospective study, fibrin D-dimer was associated with peripheral atherosclerotic progression independently of cardiovascular disease risk factors but not independently of interleukin-6. In contrast, interleukin-6 was a better marker of atherosclerotic progression in the lower limbs than the hemostatic markers studied here and was little affected by adjustment for either fibrinogen or D-dimer. There was also some evidence for an additive role of interleukin-6 and D-dimer in progressive atherosclerosis (as well as in risk of coronary heart disease) (37), which merits further investigation. Further studies are required to validate these results and to establish whether or not these associations are of causal significance.
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ACKNOWLEDGMENTS |
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The authors acknowledge financial support from the British Heart Foundation.
The authors thank Professor Joan Dawes for assay of urinary fibrinopeptide A and all staff and general practitioners involved in the Edinburgh Artery Study. Ioanna Tzoulaki was supported by the Scottish Executive (Chief Scientist Office).
Conflict of interest: none declared.
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