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Association between hematocrit in late adolescence and subsequent myocardial infarction in Swedish men
International Journal of Cardiology 168 (2013) 3588–3593
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Association between hematocrit in late adolescence and subsequent myocardial infarction in Swedish men☆ Fredrik Toss a,b,c,⁎,1, Anna Nordström a,b,1, Peter Nordström a,c,1 a b c
Department of Surgical and Perioperative Sciences, Sports Medicine, Sweden Department of Community Medicine and Rehabilitation, Rehabilitation Medicine, Sweden Department of Community Medicine and Rehabilitation, Geriatric Medicine, Sweden
a r t i c l e
i n f o
Article history: Received 6 November 2012 Received in revised form 18 March 2013 Accepted 4 May 2013 Available online 2 June 2013 Keywords: Hematocrit Blood rheology Erythrocyte sedimentation rate Risk factor Adolescence Myocardial infarction
a b s t r a c t Background: Hematocrit is an independent predictor of cardiovascular risk in middle and old age, but whether hematocrit is also a predictor at younger ages is presently not known. In this study, we examined whether hematocrit measured in adolescence was associated with the risk of myocardial infarction later in life. Methods: During Swedish national conscription tests conducted between 1969 and 1978, the hematocrit was measured in 417,099 young Swedish men. The cohort was followed for subsequent myocardial infarction events through December 2010. Associations between hematocrit and myocardial infarction were accessed using Cox regression models. Results: During a median follow-up period of 36 years, 9322 first-time myocardial infarctions occurred within the study cohort. After adjusting for relevant confounders and potential risk factors for myocardial infarction, men with a hematocrit ≥ 49% had a 1.4-fold increased risk of myocardial infarction compared with men with a hematocrit ≤ 44%. This relationship was dose dependent (p b 0.001 for trend) and remained consistent throughout the follow-up period. Conclusions: In this cohort of young Swedish men, hematocrit was associated with the risk of myocardial infarction later in life after controlling for other coronary risk factors. The study findings indicate that hematocrit may aid future risk assessments in young individuals. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Ischemic heart disease (IHD) is the most common cause of death and constitutes an enormous burden at the individual and societal levels [1,2]. In recent decades, we have gained a significant understanding of the pathophysiology of IHD, which has led to major improvements in treatment and prevention. In the United States, the age-adjusted mortality rate from IHD fell by more than 40% between 1980 and 2000. A large proportion (~44%) of this decline was attributed to improvements in cardiovascular risk factors, in particular to significant reductions in systolic blood pressure, cholesterol level, and smoking prevalence during this period [3]. Several cardiovascular risk scoring systems have been developed to guide clinical decision making [4–6]. These systems are generally based on 10-year risk estimates and provide a basis for discussions with patients about lifestyle improvements and preventive medical therapies. ☆ Conflicts of interest: None. ⁎ Corresponding author at: Department of Community Medicine and Rehabilitation, Geriatric Medicine, Umeå University, 90187 Umeå, Sweden. Tel.: +46 738 520826; fax: +46 90 135692. E-mail address: [email protected] (F. Toss). 1 This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. 0167-5273/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2013.05.065
In younger individuals, 10-year risk estimates are often low despite considerable long-term risk [7]. More research on the long-term effects of cardiovascular risk factors, particularly at younger ages, is thus warranted. The hematocrit is not regarded as a classical cardiovascular risk factor, but several longitudinal studies have found an association between high hematocrit and prospective IHD [8–15]. Thus far, studies have been restricted to middle-aged and elderly individuals and have documented a tendency toward stronger associations in middle-aged than in older patients. Given a causal relationship between hematocrit and IHD, one may therefore speculate that the association is even stronger in young individuals, in whom interfering factors such as pre-existing disease and medical interventions are less prevalent. In the present study, we followed a nationwide population-based cohort of young Swedish men with the aim of investigating the association between hematocrit and the risk of subsequent myocardial infarction (MI). 2. Methods 2.1. Study sample The subjects considered for inclusion in the present study were Swedish men who underwent conscription testing between September 1969 and December 1977. During this period, military conscription was mandatory for men in Sweden and generally
F. Toss et al. / International Journal of Cardiology 168 (2013) 3588–3593 occurred at 18–19 years of age. Exemptions from conscription were rare and were given only to subjects with a documented chronic disease or severe disability [16,17]. The sample of subjects considered for inclusion (n = 433,567) represented approximately 97% of all Swedish men born between 1950 and 1959.
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All statistical tests were two-tailed and were performed using SPSS software (version 19.0 for PC; SPSS Inc., Chicago, IL, USA). A p-value b0.05 was considered to indicate statistical significance. 2.7. Ethics statement
2.2. Baseline examinations Prior to conscription, all subjects underwent a 2-day-long, highly standardized battery of tests. Height (cm) and weight (kg) were measured and body mass index (BMI, kg/m2) was calculated. Systolic and diastolic blood pressures were measured using a mercury sphygmomanometer while subjects were in the supine position, with an appropriately sized cuff placed at the heart level. All subjects rested for 5–10 min prior to blood pressure measurements. Physicians examined all conscripts and diagnosed any disorder according to the International Classification of Diseases, version 8 (ICD-8) criteria. Venous blood samples were taken for the analysis of hematocrit and erythrocyte sedimentation rate (ESR). The hematocrit is defined as the ratio of the volume occupied by red blood cells (RBCs) relative to the volume of whole blood. Hematocrit analyses were performed using the microhematocrit method and were consistent with the guidelines of the National Committee for Clinical Laboratory Standards [18]. The ESR is defined as the distance that a column of anticoagulated blood falls over a 1-hour period and provides an estimate of RBC aggregability. ESR analyses were performed using the Westergren method, which is considered to be the reference method [19]. 2.3. Socioeconomics The educational background of each individual was categorized into four groups: completion of 8 or 9 years of elementary school, or 2 or 3+ years of high school. Each subject's annual income at 20 years after conscription was used as an approximation of financial status. Socioeconomic information was acquired from the Statistics Sweden [20]. 2.4. Follow-up and outcome parameters Using the unique personal identification numbers assigned to all Swedish citizens, record linkage was performed to obtain information from the Statistics Sweden database [20], National Hospital Discharge Register (HDR) [21], National Cause of Death Register, and National Registry of Emigration [22]. The individual study endpoint was 31 December 2010, date of MI, date of death, or date of emigration, whichever came first. Information on MI diagnoses was obtained from the HDR. Since 1987 the HDR has covered all public inpatient care in Sweden and is administered by the Center for Epidemiology at the National Board of Health and Welfare in Sweden. Diagnoses were recorded using the ICD-9 (1987–1997) and ICD-10 (1998–2010). The diagnosis codes 410 (ICD-9) and I21 (ICD-10) were used to define MI events. A validation study performed on 713 patients diagnosed with MIs within the HDR found that 86% of patients fulfilled the ICD criteria. Another 9% were classified as having had a possible MI [23]. The Swedish MI statistics are thus generally considered to be of high quality and well suited for epidemiological research. 2.5. Exclusion criteria To reduce errors due to random misclassification and missing measurement values, exclusion limits for hematocrit (b30% or >60%), ESR (b1 or >98 mm/h), systolic blood pressure (b80 or >190 mm Hg), diastolic blood pressure (b30 or >120 mm Hg), and BMI (b15 or >40 kg/m2) were applied. Because the ICD-9 was available for the registration of MIs beginning in 1987, subjects who died or emigrated before 1987 were excluded from the study. Subjects who were excluded due to an endpoint prior to 1987 were older and had lower systolic blood pressures and BMIs compared with non-excluded subjects (p b 0.05 for all). A total of 16,468 subjects were excluded, leaving 417,099 subjects available for further analyses. 2.6. Statistical analyses Differences in baseline characteristics between subjects with prospective MI and subjects free of MI were analyzed using Student's t-test for independent samples. Cox regression models were used to investigate the relationship between hematocrit and the risk of MI. We first performed survival analyses in which hematocrit was treated as a continuous variable. Potential non-linear associations were assessed by classifying the subjects into quintiles of hematocrit. To evaluate the effects of cardiovascular risk factors on the hematocrit–MI association, we considered six sets of covariates (Table 2). The fully adjusted model (model F) included adjustments for age, year and place of conscription, education, annual income, BMI, systolic blood pressure, ESR, and the 10 most common diagnoses at baseline. The possibility of interactions among these variables and the hematocrit–MI relationship was tested by adding an interaction term for each variable into model F. The proportional hazard assumption was verified graphically by cumulative incidence curves (Fig. 2). Information on smoking was available for subjects who performed conscription tests between 1969 and 1970 (n = 21,966). Smoking was treated as a dichotomous variable (previous/current smoker or non-smoker) and the effect of smoking was analyzed by incorporating this variable into model F.
The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology. The study protocol was approved a priori by the local ethics committee of Umeå University and by the National Board of Health and Welfare in Sweden.
3. Results This historic cohort study included 417,099 young Swedish men who were followed for MI events after baseline measurement. During the follow-up period (median, 36 years; range, 9–41 years), 9322 subjects (2.2%) suffered an MI (median age, 51 years; range, 28–59 years). These individuals were on average shorter and heavier and had less education and higher blood pressures, hematocrit, and ESRs compared with MI-free subjects (p b 0.01 for all). 3.1. Hematocrit and cardiovascular risk factors Partial correlations adjusted for age and place and year of conscription showed that hematocrit was positively associated with BMI and systolic and diastolic blood pressures (r = 0.12, p b 0.001 for all), and negatively associated with ESR (r = −0.31, p b 0.001), education level (r = −0.05, p b 0.001), and annual income (r = −0.02, p b 0.001). Table 1 shows the baseline characteristics of the cohort according to hematocrit level. 3.2. Hematocrit and MI Hematocrit was normally distributed, with a mean value of 47% and a standard deviation (SD) of 2% (Fig. 1). In the unadjusted model, one SD increase in hematocrit was associated with an 18% increase in MI risk [hazard ratio (HR) = 1.18, p b 0.001, 95% confidence interval (CI) = 1.16–1.21]. In the fully adjusted model (model F), the association was slightly attenuated but remained statistically significant (HR = 1.13, p b 0.001, 95% CI = 1.10–1.15). No indication of non-linearity was found and the increase in risk appeared to be continuous throughout the studied hematocrit range (p for trend b 0.001 in models A–F; Table 2). Adjustments for cardiovascular disease (CVD) risk factors and socioeconomic factors had slight attenuating effects, whereas adjusting for ESR slightly strengthened the hematocrit–MI association. Adjustments for the 10 most common diseases at baseline had virtually no impact on the final results. We also explored the possibility of interactions between the hematocrit–MI relationship and BMI, systolic blood pressure, ESR, smoking, year and place of conscription, and disease at baseline. The effects of these factors were similar at all levels, with no evidence of interaction (p > 0.05). Furthermore, we found no indication of timedependent effects on the hematocrit–MI relationship (Fig. 2). 3.3. Hematocrit, smoking, and MI risk In a sub-cohort of 21,966 individuals, smoking was strongly associated with MI risk (relative risk = 2.07, p b 0.001, 95% CI = 1.77–2.44), but only moderately associated with hematocrit (r = 0.04, p b 0.001). Additional adjustment for smoking had very little impact on the hematocrit– MI relationship in the fully adjusted model (model F: HR = 1.11 vs. 1.09, p b 0.05 for both). 4. Discussion In the present study, we found that hematocrit measured in adolescence was associated with MI risk throughout a time period spanning
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Table 1 Baseline characteristics by quintiles of hematocrit. Values are presented as mean (standard deviation) or percentage. Hematocrit groups
Age (years) Weight (kg) Height (cm) Body mass index (kg/m2) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Hematocrit (%) Erythrocyte sedimentation rate (mm/h) Disease diagnoses (%) Impaired hearing Lumbago Hay fever Cold Asthma Gastritis Atopic dermatitis Drug dependence Alcoholism Diabetes Annual income (Swedish kronor × 100,000)b Education (%) 8 years of elementary school 9 years of elementary school 2 years of high school >2 years of high school a b c
≤44% (n = 86,450)
45% (n = 62,400)
46–47% (n = 124,198)
48% (n = 62,683)
≥49% (n = 81,368)
18.4 (0.6) 66.5 (9.0) 179 (6) 20.8 (2.4) 126 (11) 70 (9) 43 (1) 5 (5)
18.5 (0.6)a 67.4 (9.2)a 179 (6)a 21.1 (2.5)a 127 (11)a 70 (9)a 45 (0)a 4 (4)a
18.5 (0.7)a 68.0 (9.6)a 179 (6)a 21.3 (2.6)a 128 (11)a 71 (9)a 47 (0)a 3 (3)a
18.5 (0.7)a 68.6 (9.9)a 179 (6) 21.5 (2.7)a 129 (11)a 72 (9)a 48 (0)a 3 (3)a
18.6 (0.7)a 69.5 (10.4)a 179 (6) 21.8 (2.9)a 130 (11)a 73 (9)a 50 (1)a 2 (2)a
10.0 6.4 5.1 2.1 1.7 1.2 0.8 0.6 0.4 0.0 1.6 (0.8)
9.7 6.8a 5.2 2.2 1.8 1.4a 0.7 0.6 0.4 0.0 1.6 (0.8)a
9.9 7.0a 5.3 2.2 1.9a 1.4a 0.8 0.6 0.4 0.0 1.6 (0.8)a
9.8 7.0a 5.2 2.3a 2.2a 1.5a 0.8 0.6 0.5a 0.1 1.6 (0.8)a
9.9 7.7a 5.2 2.3a 2.4a 1.7a 0.8 0.7a 0.6a 0.1a 1.6 (0.8)a
c
c
c
c
22.9 36.3 12.1 28.7
23.6 36.3 12.1 28.0
24.6 36.7 11.9 26.8
26.2 37.3 11.3 25.2
22.0 36.2 12.3 29.5
p b 0.05 vs. control group (group 1). Annual income 20 years after conscription. Distribution differs significantly from control group.
more than 3 decades. The association was dose-dependent and largely independent of other factors assessed at baseline. Previous studies conducted in middle-aged populations have also reported an increased risk of coronary artery disease (CAD) at the upper end of the hematocrit distribution [8–15]. Some studies found indications of a dose–response relationship [11–13,15], and others reported very small risk differences at the lower end of the hematocrit distribution [8–10]. In contrast to the results of this study, a few previous studies have also reported an increased risk associated
Fig. 1. The distribution of hematocrit within the study cohort.
with low hematocrit [14,24]. Importantly and unlike previous studies, in this study we followed a generally healthy population that was very young at baseline. Pre-existing vascular disease is virtually nonexistent at the age of 18–19 years, and close to all subjects in this study had hematocrits within the normal physiological range. We thus suspect that the J- and U-shaped associations in the hematocrit–MI relationship observed in some previous studies may be attributed to factors other than a true hematocrit effect. In particular, pre-existing subclinical disease may result in a greater tendency toward U-shaped associations because subjects with CVD often have both lower and higher hematocrits than healthy individuals [8,25]. As atherosclerosis, the underlying mechanism of CVD, is a progressive disorder that builds over time, the prevalence of pre-existing disease will increase with the age of the studied population. Furthermore, because hematocrit declines in later life [10] whereas the risk of IHD increases, previous research has suggested that a true linear relationship may have a U-shaped appearance in a mixed-age population [26]. As this study included men of very similar ages, our results are less likely to have been influenced by this type of statistical confounding. The dose–response relationship and the consistency of findings throughout the extensive follow-up period strengthen the hypothesized cause–effect relationship between hematocrit and MI. Hematocrit may promote MI risk through several plausible mechanisms, discussed below. Firstly, several observations have indicated that high hematocrit may increase the risk of thrombus formation. Patients with polycythemia vera have abnormally high RBC production and are at an increased risk of arterial and venous thrombi [27,28]. Lowering the hematocrit by phlebotomy significantly reduces this risk [29]. The risk of thrombotic complications is also increased in subjects living at high altitudes, where low oxygen pressure leads to elevated hematocrit levels [30]. In experimental settings, an increase in hematocrit results in a higher concentration of platelets near the vessel wall; RBCs may also have a direct chemical effect on platelet reactivity through adenosine diphosphate release [31]. At intermediate and high shear rates (resembling
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Table 2 Relative risks and 95% confidence intervals of myocardial infarction by hematocrit group. Hematocrit (%)
≤44 45
No. of men
No. of MIs (%)
86,450 62,400
1574 (1.8) 1218 (2.0)
124,198
2785 (2.2)
48
62,683
1439 (2.3)
≥49
81,368
2306 (2.8)
46–47
Adjusted relative risk A
B
C
D
E
F
1 1.08 (1.00–1.16) 1.25 (1.17–1.33) 1.28 (1.19–1.37) 1.58 (1.48–1.69) p b 0.001
1 1.07 (1.00–1.16) 1.23 (1.15–1.31) 1.25 (1.17–1.35) 1.54 (1.44–1.64) p b 0.001
1 1.04 (0.96–1.12) 1.16 (1.09–1.24) 1.16 (1.08–1.24) 1.36 (1.28–1.45) p b 0.001
1 1.04 (0.96–1.12) 1.16 (1.09–1.24) 1.16 (1.08–1.24) 1.36 (1.27–1.45) p b 0.001
1 1.03 (0.95–1.11) 1.14 (1.07–1.22) 1.13 (1.05–1.21) 1.32 (1.23–1.41) p b 0.001
1 1.05 (0.97–1.13) 1.18 (1.11–1.25) 1.17 (1.09–1.26) 1.38 (1.29–1.48) p b 0.001
MI, myocardial infarction. (A) Unadjusted; (B) adjusted for age, year and place of conscription; (C) adjusted for (B) and education, annual income, and body mass index; (D) adjusted for (C) and disease diagnoses; (E) adjusted for (D) and systolic blood pressure; (F) adjusted for (E) and erythrocyte sedimentation rate.
flow conditions in coronary arteries), platelet adhesion and thrombus formation have been found to be directly proportional to hematocrit concentration [32,33]. Computer modeling has also suggested that RBCs have a mechanical effect on thrombi formation by promoting the horizontal spreading of the thrombus [34]. Secondly, hematocrit may increase MI risk through an effect on blood pressure. Hematocrit increases blood viscosity exponentially, accounting for a large proportion of total blood viscosity [35]. When other parameters remain unchanged, increased viscosity will result in elevated blood pressure because blood viscosity is directly proportional to flow resistance [36]. This proposed mechanism is consistent with the positive correlation between hematocrit and blood pressure observed in the present study and in previous reports [8,9,12,13,15,37]. A third possible explanation implicates the relationship between hematocrit and other CVD risk factors. Indeed, in the present study we found significant positive correlations between hematocrit and smoking, blood pressure, and BMI. These findings are in agreement with previous data, as other studies have consistently reported positive associations between hematocrit and CVD risk factors [8,9,11–14,38]. Adjusting for these factors in multivariate analyses attenuated, but did not eliminate,
the hematocrit–MI association. This finding is in agreement with most previous research [8–10,15,24], and suggests an effect of hematocrit that is independent of other established risk factors. Given this posited independent effect, lowering hematocrit to reduce MI risk is of potential interest. A substantial part of the hematocrit is naturally determined by genetic factors, but environmental influences appear to be surprisingly strong, accounting for 35–60% of the total variance [39,40]. Behavioral changes could thus theoretically reduce the hematocrit. Although smoking cessation [41,42], weight loss [42], and physical exercise [43] are associated with a lower hematocrit, the effects of changes in these parameters on the hematocrit appears to be modest. Despite the difficulty of efficiently altering the hematocrit, the findings of this study have potential health implications. In recent decades, efforts to reduce the burden of CVD have emphasized the importance of calculating short-term (generally 10-year) risks of CVD [4–6]. However, many younger individuals who are considered to be at low short-term CVD risk are still at a conceivable lifetime risk [7]. The relatively high life time risk of MI, the potentially severe health consequences of an event and the fact that feasible actions can be
Fig. 2. Cumulative incidence of myocardial infarction (MI) by hematocrit group.
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taken to reduce MI risk all favors early risk assessment. A recent meta-analysis elegantly showed that despite available medical treatment against many CVD risk factors, baseline risk factor burden still translate into considerable differences in life time CVD risk [44]. These findings highlight the need for primordial and primary prevention early in life. Blood pressure [16], BMI [17] and ESR [45] measured during adolescence have been reported to be long-term predictors of CAD, and we showed in the present study that hematocrit provides additional prognostic information. Given our findings, hematocrit measures may thus complement other variables in efforts to identify young individuals with considerable long-term CVD risk. 4.1. Study strengths and limitations This analysis was performed using data from a large populationbased cohort of similar age that was carefully characterized at baseline using a widely accepted method of hematocrit measurement. The primary outcome measure was MI events, which the HDR has documented with high sensitivity and specificity. Because the study cohort included men of very similar ages, the results may not be generalizable to women or to men in other age groups. Furthermore, we cannot rule out the potential confounding of our analysis by unaccounted risk factors. A major limitation of this study was thus the lack of data on baseline lipid levels, pulmonary function, and other potentially important factors. Other studies have reported relatively low correlations between hematocrit and these risk factors, including total cholesterol (r = 0.08–0.25) [12,13,38,46], triglycerides (r = 0.08–0.15) [13,38,46], and forced expiratory volume− 1(r = − 0.06–− 0.09) [13,38], but interpretation of the present results should be made keeping their potential effects in mind. Furthermore, hematocrit was measured only at baseline in this study. Previous studies have reported considerable intra-individual variability over time [15], and regression dilution might have resulted in underestimation of the actual association between hematocrit and MI risk [47]. 4.2. Conclusions This study showed that a single measurement of hematocrit in adolescence was associated with MI risk during a follow-up period of more than 35 years. The hematocrit provides prognostic information that is independent of traditional CVD risk factors and may potentially aid future risk assessments in young individuals. Acknowledgments This work was supported by the Västerbotten County Council. References [1] Department of Health Statistics and Informatics WHO. Causes of death in 2008; 2011. [2] Organization WH. Global Atlas on cardiovascular disease prevention and, control; 2011. [3] Ford ES, Ajani UA, Croft JB, et al. Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. N Engl J Med 2007;356:2388–98. [4] D'Agostino Sr RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008;117: 743–53. [5] Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002;106:3143–421. [6] Conroy RM, Pyorala K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003;24:987–1003. [7] Berry JD, Liu K, Folsom AR, et al. Prevalence and progression of subclinical atherosclerosis in younger adults with low short-term but high lifetime estimated risk for cardiovascular disease: the coronary artery risk development in young adults study and multi-ethnic study of atherosclerosis. Circulation 2009;119:382–9.
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F. Toss et al. / International Journal of Cardiology 168 (2013) 3588–3593 a 20-year follow-up of a male cohort: the Tromso Study 1974–1995. Eur J Epidemiol 2006;21:493–9. [43] Romain AJ, Brun JF, Varlet-Marie E, Raynaud de Mauverger E. Effects of exercise training on blood rheology: a meta-analysis. Clin Hemorheol Microcirc 2011;49: 199–205. [44] Berry JD, Dyer A, Cai X, et al. Lifetime risks of cardiovascular disease. N Engl J Med 2012;366:321–9. [45] Toss F, Nordstrom A, Nordstrom P. Inflammation in young adulthood is associated with myocardial infarction later in life. Am Heart J 2013;165:164–9.
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Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Association between hematocrit in late adolescence and subsequent myocardial infarction in Swedish men☆ Fredrik Toss a,b,c,⁎,1, Anna Nordström a,b,1, Peter Nordström a,c,1 a b c
Department of Surgical and Perioperative Sciences, Sports Medicine, Sweden Department of Community Medicine and Rehabilitation, Rehabilitation Medicine, Sweden Department of Community Medicine and Rehabilitation, Geriatric Medicine, Sweden
a r t i c l e
i n f o
Article history: Received 6 November 2012 Received in revised form 18 March 2013 Accepted 4 May 2013 Available online 2 June 2013 Keywords: Hematocrit Blood rheology Erythrocyte sedimentation rate Risk factor Adolescence Myocardial infarction
a b s t r a c t Background: Hematocrit is an independent predictor of cardiovascular risk in middle and old age, but whether hematocrit is also a predictor at younger ages is presently not known. In this study, we examined whether hematocrit measured in adolescence was associated with the risk of myocardial infarction later in life. Methods: During Swedish national conscription tests conducted between 1969 and 1978, the hematocrit was measured in 417,099 young Swedish men. The cohort was followed for subsequent myocardial infarction events through December 2010. Associations between hematocrit and myocardial infarction were accessed using Cox regression models. Results: During a median follow-up period of 36 years, 9322 first-time myocardial infarctions occurred within the study cohort. After adjusting for relevant confounders and potential risk factors for myocardial infarction, men with a hematocrit ≥ 49% had a 1.4-fold increased risk of myocardial infarction compared with men with a hematocrit ≤ 44%. This relationship was dose dependent (p b 0.001 for trend) and remained consistent throughout the follow-up period. Conclusions: In this cohort of young Swedish men, hematocrit was associated with the risk of myocardial infarction later in life after controlling for other coronary risk factors. The study findings indicate that hematocrit may aid future risk assessments in young individuals. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Ischemic heart disease (IHD) is the most common cause of death and constitutes an enormous burden at the individual and societal levels [1,2]. In recent decades, we have gained a significant understanding of the pathophysiology of IHD, which has led to major improvements in treatment and prevention. In the United States, the age-adjusted mortality rate from IHD fell by more than 40% between 1980 and 2000. A large proportion (~44%) of this decline was attributed to improvements in cardiovascular risk factors, in particular to significant reductions in systolic blood pressure, cholesterol level, and smoking prevalence during this period [3]. Several cardiovascular risk scoring systems have been developed to guide clinical decision making [4–6]. These systems are generally based on 10-year risk estimates and provide a basis for discussions with patients about lifestyle improvements and preventive medical therapies. ☆ Conflicts of interest: None. ⁎ Corresponding author at: Department of Community Medicine and Rehabilitation, Geriatric Medicine, Umeå University, 90187 Umeå, Sweden. Tel.: +46 738 520826; fax: +46 90 135692. E-mail address: [email protected] (F. Toss). 1 This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. 0167-5273/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2013.05.065
In younger individuals, 10-year risk estimates are often low despite considerable long-term risk [7]. More research on the long-term effects of cardiovascular risk factors, particularly at younger ages, is thus warranted. The hematocrit is not regarded as a classical cardiovascular risk factor, but several longitudinal studies have found an association between high hematocrit and prospective IHD [8–15]. Thus far, studies have been restricted to middle-aged and elderly individuals and have documented a tendency toward stronger associations in middle-aged than in older patients. Given a causal relationship between hematocrit and IHD, one may therefore speculate that the association is even stronger in young individuals, in whom interfering factors such as pre-existing disease and medical interventions are less prevalent. In the present study, we followed a nationwide population-based cohort of young Swedish men with the aim of investigating the association between hematocrit and the risk of subsequent myocardial infarction (MI). 2. Methods 2.1. Study sample The subjects considered for inclusion in the present study were Swedish men who underwent conscription testing between September 1969 and December 1977. During this period, military conscription was mandatory for men in Sweden and generally
F. Toss et al. / International Journal of Cardiology 168 (2013) 3588–3593 occurred at 18–19 years of age. Exemptions from conscription were rare and were given only to subjects with a documented chronic disease or severe disability [16,17]. The sample of subjects considered for inclusion (n = 433,567) represented approximately 97% of all Swedish men born between 1950 and 1959.
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All statistical tests were two-tailed and were performed using SPSS software (version 19.0 for PC; SPSS Inc., Chicago, IL, USA). A p-value b0.05 was considered to indicate statistical significance. 2.7. Ethics statement
2.2. Baseline examinations Prior to conscription, all subjects underwent a 2-day-long, highly standardized battery of tests. Height (cm) and weight (kg) were measured and body mass index (BMI, kg/m2) was calculated. Systolic and diastolic blood pressures were measured using a mercury sphygmomanometer while subjects were in the supine position, with an appropriately sized cuff placed at the heart level. All subjects rested for 5–10 min prior to blood pressure measurements. Physicians examined all conscripts and diagnosed any disorder according to the International Classification of Diseases, version 8 (ICD-8) criteria. Venous blood samples were taken for the analysis of hematocrit and erythrocyte sedimentation rate (ESR). The hematocrit is defined as the ratio of the volume occupied by red blood cells (RBCs) relative to the volume of whole blood. Hematocrit analyses were performed using the microhematocrit method and were consistent with the guidelines of the National Committee for Clinical Laboratory Standards [18]. The ESR is defined as the distance that a column of anticoagulated blood falls over a 1-hour period and provides an estimate of RBC aggregability. ESR analyses were performed using the Westergren method, which is considered to be the reference method [19]. 2.3. Socioeconomics The educational background of each individual was categorized into four groups: completion of 8 or 9 years of elementary school, or 2 or 3+ years of high school. Each subject's annual income at 20 years after conscription was used as an approximation of financial status. Socioeconomic information was acquired from the Statistics Sweden [20]. 2.4. Follow-up and outcome parameters Using the unique personal identification numbers assigned to all Swedish citizens, record linkage was performed to obtain information from the Statistics Sweden database [20], National Hospital Discharge Register (HDR) [21], National Cause of Death Register, and National Registry of Emigration [22]. The individual study endpoint was 31 December 2010, date of MI, date of death, or date of emigration, whichever came first. Information on MI diagnoses was obtained from the HDR. Since 1987 the HDR has covered all public inpatient care in Sweden and is administered by the Center for Epidemiology at the National Board of Health and Welfare in Sweden. Diagnoses were recorded using the ICD-9 (1987–1997) and ICD-10 (1998–2010). The diagnosis codes 410 (ICD-9) and I21 (ICD-10) were used to define MI events. A validation study performed on 713 patients diagnosed with MIs within the HDR found that 86% of patients fulfilled the ICD criteria. Another 9% were classified as having had a possible MI [23]. The Swedish MI statistics are thus generally considered to be of high quality and well suited for epidemiological research. 2.5. Exclusion criteria To reduce errors due to random misclassification and missing measurement values, exclusion limits for hematocrit (b30% or >60%), ESR (b1 or >98 mm/h), systolic blood pressure (b80 or >190 mm Hg), diastolic blood pressure (b30 or >120 mm Hg), and BMI (b15 or >40 kg/m2) were applied. Because the ICD-9 was available for the registration of MIs beginning in 1987, subjects who died or emigrated before 1987 were excluded from the study. Subjects who were excluded due to an endpoint prior to 1987 were older and had lower systolic blood pressures and BMIs compared with non-excluded subjects (p b 0.05 for all). A total of 16,468 subjects were excluded, leaving 417,099 subjects available for further analyses. 2.6. Statistical analyses Differences in baseline characteristics between subjects with prospective MI and subjects free of MI were analyzed using Student's t-test for independent samples. Cox regression models were used to investigate the relationship between hematocrit and the risk of MI. We first performed survival analyses in which hematocrit was treated as a continuous variable. Potential non-linear associations were assessed by classifying the subjects into quintiles of hematocrit. To evaluate the effects of cardiovascular risk factors on the hematocrit–MI association, we considered six sets of covariates (Table 2). The fully adjusted model (model F) included adjustments for age, year and place of conscription, education, annual income, BMI, systolic blood pressure, ESR, and the 10 most common diagnoses at baseline. The possibility of interactions among these variables and the hematocrit–MI relationship was tested by adding an interaction term for each variable into model F. The proportional hazard assumption was verified graphically by cumulative incidence curves (Fig. 2). Information on smoking was available for subjects who performed conscription tests between 1969 and 1970 (n = 21,966). Smoking was treated as a dichotomous variable (previous/current smoker or non-smoker) and the effect of smoking was analyzed by incorporating this variable into model F.
The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology. The study protocol was approved a priori by the local ethics committee of Umeå University and by the National Board of Health and Welfare in Sweden.
3. Results This historic cohort study included 417,099 young Swedish men who were followed for MI events after baseline measurement. During the follow-up period (median, 36 years; range, 9–41 years), 9322 subjects (2.2%) suffered an MI (median age, 51 years; range, 28–59 years). These individuals were on average shorter and heavier and had less education and higher blood pressures, hematocrit, and ESRs compared with MI-free subjects (p b 0.01 for all). 3.1. Hematocrit and cardiovascular risk factors Partial correlations adjusted for age and place and year of conscription showed that hematocrit was positively associated with BMI and systolic and diastolic blood pressures (r = 0.12, p b 0.001 for all), and negatively associated with ESR (r = −0.31, p b 0.001), education level (r = −0.05, p b 0.001), and annual income (r = −0.02, p b 0.001). Table 1 shows the baseline characteristics of the cohort according to hematocrit level. 3.2. Hematocrit and MI Hematocrit was normally distributed, with a mean value of 47% and a standard deviation (SD) of 2% (Fig. 1). In the unadjusted model, one SD increase in hematocrit was associated with an 18% increase in MI risk [hazard ratio (HR) = 1.18, p b 0.001, 95% confidence interval (CI) = 1.16–1.21]. In the fully adjusted model (model F), the association was slightly attenuated but remained statistically significant (HR = 1.13, p b 0.001, 95% CI = 1.10–1.15). No indication of non-linearity was found and the increase in risk appeared to be continuous throughout the studied hematocrit range (p for trend b 0.001 in models A–F; Table 2). Adjustments for cardiovascular disease (CVD) risk factors and socioeconomic factors had slight attenuating effects, whereas adjusting for ESR slightly strengthened the hematocrit–MI association. Adjustments for the 10 most common diseases at baseline had virtually no impact on the final results. We also explored the possibility of interactions between the hematocrit–MI relationship and BMI, systolic blood pressure, ESR, smoking, year and place of conscription, and disease at baseline. The effects of these factors were similar at all levels, with no evidence of interaction (p > 0.05). Furthermore, we found no indication of timedependent effects on the hematocrit–MI relationship (Fig. 2). 3.3. Hematocrit, smoking, and MI risk In a sub-cohort of 21,966 individuals, smoking was strongly associated with MI risk (relative risk = 2.07, p b 0.001, 95% CI = 1.77–2.44), but only moderately associated with hematocrit (r = 0.04, p b 0.001). Additional adjustment for smoking had very little impact on the hematocrit– MI relationship in the fully adjusted model (model F: HR = 1.11 vs. 1.09, p b 0.05 for both). 4. Discussion In the present study, we found that hematocrit measured in adolescence was associated with MI risk throughout a time period spanning
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Table 1 Baseline characteristics by quintiles of hematocrit. Values are presented as mean (standard deviation) or percentage. Hematocrit groups
Age (years) Weight (kg) Height (cm) Body mass index (kg/m2) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Hematocrit (%) Erythrocyte sedimentation rate (mm/h) Disease diagnoses (%) Impaired hearing Lumbago Hay fever Cold Asthma Gastritis Atopic dermatitis Drug dependence Alcoholism Diabetes Annual income (Swedish kronor × 100,000)b Education (%) 8 years of elementary school 9 years of elementary school 2 years of high school >2 years of high school a b c
≤44% (n = 86,450)
45% (n = 62,400)
46–47% (n = 124,198)
48% (n = 62,683)
≥49% (n = 81,368)
18.4 (0.6) 66.5 (9.0) 179 (6) 20.8 (2.4) 126 (11) 70 (9) 43 (1) 5 (5)
18.5 (0.6)a 67.4 (9.2)a 179 (6)a 21.1 (2.5)a 127 (11)a 70 (9)a 45 (0)a 4 (4)a
18.5 (0.7)a 68.0 (9.6)a 179 (6)a 21.3 (2.6)a 128 (11)a 71 (9)a 47 (0)a 3 (3)a
18.5 (0.7)a 68.6 (9.9)a 179 (6) 21.5 (2.7)a 129 (11)a 72 (9)a 48 (0)a 3 (3)a
18.6 (0.7)a 69.5 (10.4)a 179 (6) 21.8 (2.9)a 130 (11)a 73 (9)a 50 (1)a 2 (2)a
10.0 6.4 5.1 2.1 1.7 1.2 0.8 0.6 0.4 0.0 1.6 (0.8)
9.7 6.8a 5.2 2.2 1.8 1.4a 0.7 0.6 0.4 0.0 1.6 (0.8)a
9.9 7.0a 5.3 2.2 1.9a 1.4a 0.8 0.6 0.4 0.0 1.6 (0.8)a
9.8 7.0a 5.2 2.3a 2.2a 1.5a 0.8 0.6 0.5a 0.1 1.6 (0.8)a
9.9 7.7a 5.2 2.3a 2.4a 1.7a 0.8 0.7a 0.6a 0.1a 1.6 (0.8)a
c
c
c
c
22.9 36.3 12.1 28.7
23.6 36.3 12.1 28.0
24.6 36.7 11.9 26.8
26.2 37.3 11.3 25.2
22.0 36.2 12.3 29.5
p b 0.05 vs. control group (group 1). Annual income 20 years after conscription. Distribution differs significantly from control group.
more than 3 decades. The association was dose-dependent and largely independent of other factors assessed at baseline. Previous studies conducted in middle-aged populations have also reported an increased risk of coronary artery disease (CAD) at the upper end of the hematocrit distribution [8–15]. Some studies found indications of a dose–response relationship [11–13,15], and others reported very small risk differences at the lower end of the hematocrit distribution [8–10]. In contrast to the results of this study, a few previous studies have also reported an increased risk associated
Fig. 1. The distribution of hematocrit within the study cohort.
with low hematocrit [14,24]. Importantly and unlike previous studies, in this study we followed a generally healthy population that was very young at baseline. Pre-existing vascular disease is virtually nonexistent at the age of 18–19 years, and close to all subjects in this study had hematocrits within the normal physiological range. We thus suspect that the J- and U-shaped associations in the hematocrit–MI relationship observed in some previous studies may be attributed to factors other than a true hematocrit effect. In particular, pre-existing subclinical disease may result in a greater tendency toward U-shaped associations because subjects with CVD often have both lower and higher hematocrits than healthy individuals [8,25]. As atherosclerosis, the underlying mechanism of CVD, is a progressive disorder that builds over time, the prevalence of pre-existing disease will increase with the age of the studied population. Furthermore, because hematocrit declines in later life [10] whereas the risk of IHD increases, previous research has suggested that a true linear relationship may have a U-shaped appearance in a mixed-age population [26]. As this study included men of very similar ages, our results are less likely to have been influenced by this type of statistical confounding. The dose–response relationship and the consistency of findings throughout the extensive follow-up period strengthen the hypothesized cause–effect relationship between hematocrit and MI. Hematocrit may promote MI risk through several plausible mechanisms, discussed below. Firstly, several observations have indicated that high hematocrit may increase the risk of thrombus formation. Patients with polycythemia vera have abnormally high RBC production and are at an increased risk of arterial and venous thrombi [27,28]. Lowering the hematocrit by phlebotomy significantly reduces this risk [29]. The risk of thrombotic complications is also increased in subjects living at high altitudes, where low oxygen pressure leads to elevated hematocrit levels [30]. In experimental settings, an increase in hematocrit results in a higher concentration of platelets near the vessel wall; RBCs may also have a direct chemical effect on platelet reactivity through adenosine diphosphate release [31]. At intermediate and high shear rates (resembling
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Table 2 Relative risks and 95% confidence intervals of myocardial infarction by hematocrit group. Hematocrit (%)
≤44 45
No. of men
No. of MIs (%)
86,450 62,400
1574 (1.8) 1218 (2.0)
124,198
2785 (2.2)
48
62,683
1439 (2.3)
≥49
81,368
2306 (2.8)
46–47
Adjusted relative risk A
B
C
D
E
F
1 1.08 (1.00–1.16) 1.25 (1.17–1.33) 1.28 (1.19–1.37) 1.58 (1.48–1.69) p b 0.001
1 1.07 (1.00–1.16) 1.23 (1.15–1.31) 1.25 (1.17–1.35) 1.54 (1.44–1.64) p b 0.001
1 1.04 (0.96–1.12) 1.16 (1.09–1.24) 1.16 (1.08–1.24) 1.36 (1.28–1.45) p b 0.001
1 1.04 (0.96–1.12) 1.16 (1.09–1.24) 1.16 (1.08–1.24) 1.36 (1.27–1.45) p b 0.001
1 1.03 (0.95–1.11) 1.14 (1.07–1.22) 1.13 (1.05–1.21) 1.32 (1.23–1.41) p b 0.001
1 1.05 (0.97–1.13) 1.18 (1.11–1.25) 1.17 (1.09–1.26) 1.38 (1.29–1.48) p b 0.001
MI, myocardial infarction. (A) Unadjusted; (B) adjusted for age, year and place of conscription; (C) adjusted for (B) and education, annual income, and body mass index; (D) adjusted for (C) and disease diagnoses; (E) adjusted for (D) and systolic blood pressure; (F) adjusted for (E) and erythrocyte sedimentation rate.
flow conditions in coronary arteries), platelet adhesion and thrombus formation have been found to be directly proportional to hematocrit concentration [32,33]. Computer modeling has also suggested that RBCs have a mechanical effect on thrombi formation by promoting the horizontal spreading of the thrombus [34]. Secondly, hematocrit may increase MI risk through an effect on blood pressure. Hematocrit increases blood viscosity exponentially, accounting for a large proportion of total blood viscosity [35]. When other parameters remain unchanged, increased viscosity will result in elevated blood pressure because blood viscosity is directly proportional to flow resistance [36]. This proposed mechanism is consistent with the positive correlation between hematocrit and blood pressure observed in the present study and in previous reports [8,9,12,13,15,37]. A third possible explanation implicates the relationship between hematocrit and other CVD risk factors. Indeed, in the present study we found significant positive correlations between hematocrit and smoking, blood pressure, and BMI. These findings are in agreement with previous data, as other studies have consistently reported positive associations between hematocrit and CVD risk factors [8,9,11–14,38]. Adjusting for these factors in multivariate analyses attenuated, but did not eliminate,
the hematocrit–MI association. This finding is in agreement with most previous research [8–10,15,24], and suggests an effect of hematocrit that is independent of other established risk factors. Given this posited independent effect, lowering hematocrit to reduce MI risk is of potential interest. A substantial part of the hematocrit is naturally determined by genetic factors, but environmental influences appear to be surprisingly strong, accounting for 35–60% of the total variance [39,40]. Behavioral changes could thus theoretically reduce the hematocrit. Although smoking cessation [41,42], weight loss [42], and physical exercise [43] are associated with a lower hematocrit, the effects of changes in these parameters on the hematocrit appears to be modest. Despite the difficulty of efficiently altering the hematocrit, the findings of this study have potential health implications. In recent decades, efforts to reduce the burden of CVD have emphasized the importance of calculating short-term (generally 10-year) risks of CVD [4–6]. However, many younger individuals who are considered to be at low short-term CVD risk are still at a conceivable lifetime risk [7]. The relatively high life time risk of MI, the potentially severe health consequences of an event and the fact that feasible actions can be
Fig. 2. Cumulative incidence of myocardial infarction (MI) by hematocrit group.
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taken to reduce MI risk all favors early risk assessment. A recent meta-analysis elegantly showed that despite available medical treatment against many CVD risk factors, baseline risk factor burden still translate into considerable differences in life time CVD risk [44]. These findings highlight the need for primordial and primary prevention early in life. Blood pressure [16], BMI [17] and ESR [45] measured during adolescence have been reported to be long-term predictors of CAD, and we showed in the present study that hematocrit provides additional prognostic information. Given our findings, hematocrit measures may thus complement other variables in efforts to identify young individuals with considerable long-term CVD risk. 4.1. Study strengths and limitations This analysis was performed using data from a large populationbased cohort of similar age that was carefully characterized at baseline using a widely accepted method of hematocrit measurement. The primary outcome measure was MI events, which the HDR has documented with high sensitivity and specificity. Because the study cohort included men of very similar ages, the results may not be generalizable to women or to men in other age groups. Furthermore, we cannot rule out the potential confounding of our analysis by unaccounted risk factors. A major limitation of this study was thus the lack of data on baseline lipid levels, pulmonary function, and other potentially important factors. Other studies have reported relatively low correlations between hematocrit and these risk factors, including total cholesterol (r = 0.08–0.25) [12,13,38,46], triglycerides (r = 0.08–0.15) [13,38,46], and forced expiratory volume− 1(r = − 0.06–− 0.09) [13,38], but interpretation of the present results should be made keeping their potential effects in mind. Furthermore, hematocrit was measured only at baseline in this study. Previous studies have reported considerable intra-individual variability over time [15], and regression dilution might have resulted in underestimation of the actual association between hematocrit and MI risk [47]. 4.2. Conclusions This study showed that a single measurement of hematocrit in adolescence was associated with MI risk during a follow-up period of more than 35 years. The hematocrit provides prognostic information that is independent of traditional CVD risk factors and may potentially aid future risk assessments in young individuals. Acknowledgments This work was supported by the Västerbotten County Council. References [1] Department of Health Statistics and Informatics WHO. Causes of death in 2008; 2011. [2] Organization WH. Global Atlas on cardiovascular disease prevention and, control; 2011. [3] Ford ES, Ajani UA, Croft JB, et al. Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. N Engl J Med 2007;356:2388–98. [4] D'Agostino Sr RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008;117: 743–53. [5] Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002;106:3143–421. [6] Conroy RM, Pyorala K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003;24:987–1003. [7] Berry JD, Liu K, Folsom AR, et al. Prevalence and progression of subclinical atherosclerosis in younger adults with low short-term but high lifetime estimated risk for cardiovascular disease: the coronary artery risk development in young adults study and multi-ethnic study of atherosclerosis. Circulation 2009;119:382–9.
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