Atherosclerosis 213 (2010) Contents lists available at ScienceDirect. Atherosclerosis

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1 Atherosclerosis 213 (2010) Contents lists available at ScienceDirect Atherosclerosis journal homepage: Inflammatory markers, lipoprotein components and risk of major cardiovascular events in 65,005 men and women in the Apolipoprotein MOrtality RISk study (AMORIS) Ingar Holme a,, Are H. Aastveit b, Niklas Hammar c,d, Ingmar Jungner e, Göran Walldius f a Department of Preventive Cardiology, Centre of Preventive Medicine, Oslo University Hospital, Ulleval, N-0407 Oslo, Norway b Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Aas, Norway c Department of Epidemiology, Institute of Environmental Medicine, Karolinska institutet, Stockholm, Sweden d AstraZeneca R&D, Södertälje, Sweden e Department of Medicine, Clinical Epidemiological Unit, Karolinska Institutet, and CALAB Research, Stockholm, Sweden f Department of Medicine, and Department of Epidemiology, Institute of Environmental Medicine, Karolinska institutet, Stockholm, Sweden article info abstract Article history: Received 10 March 2010 Received in revised form 28 June 2010 Accepted 3 August 2010 Available online 19 August 2010 Keywords: Inflammation Lipoproteins Apolipoproteins Acute myocardial infarction Ischemic stroke Heart failure Background and aims: In contrast to lipoprotein components, few studies have analysed the importance of a combination of commonly available inflammatory markers as predictors of major cardiovascular events (MACE) in large healthy populations. We examined summary scores of inflammation and compared their predictive strength with that of lipoproteins in the Apolipoprotein MOrtality RISk (AMORIS) Study. Method and results: Using data from AMORIS and the Swedish hospital discharge and mortality registers, a prospective cohort study of 65,050 subjects with mean follow-up time of 11.8 years, we studied the association between lipoproteins, inflammatory markers and risk of acute myocardial infarction (AMI), stroke and heart failure. An inflammatory score was measured as the number of inflammatory variables (white blood cell count, haptoglobin and in a subgroup CRP) in their upper quartile or as a continuous summary score. All analyses were conducted with multivariate Cox proportional hazards analysis. The inflammatory scores added predictive information over and above classical lipids such as total cholesterol and triglycerides. Compared to the apolipoprotein B (apob)/apolipoprotein A-1 (apoa-1) ratio, a stronger marker of CVD risk than conventional lipids, the inflammatory score added some discrimination value measured by net reclassification improvement, but added more within higher risk strata. No statistically significant biological interaction was found between lipoproteins and inflammatory markers. Conclusion: The inflammation score and lipoproteins, including apob and apoa-i, carry important and at least additive predictive information for risk of MACE. Routinely used markers of inflammation could be used in daily medical practice to assess cardiovascular risk Elsevier Ireland Ltd. All rights reserved. 1. Introduction Several markers of inflammation have been associated with the increased risk of cardiovascular disease (CVD) [1 3]. In particular, white blood cell count (WBC) has been studied extensively in the context of CVD [4 6]. During the last decade, a variety of new inflammatory markers have emerged, including high sensitive C-reactive protein (hscrp) [7], circulating proinflammatory cytokines, adhesion molecules, lipoprotein associated phospholipase A2 and others [1 3,8,9]. However, many of these markers cannot be used as a first screening tool for CVD risk assessment Corresponding author. Tel.: ; fax: addresses: ingar.holme@uus.no, ingar.holme@nih.no, Ingar.holme@ulleval.no (I. Holme). as they are not yet available in routine laboratory practice. Therefore, it is still of interest to assess the predictive information of the more commonly used markers of inflammation, such as WBC. In the Apolipoprotein MOrtality RISk (AMORIS) study, we measured routine markers of inflammation (WBC and non-high sensitive CRP) as well as a more modern marker, haptoglobin (Hp) known to be related to inflammation [10], in a large series of healthy subjects from the Stockholm area whose morbidity and mortality information was followed for 12 years in the Swedish Hospital Discharge and Cause of Death Registries [11,12]. We aimed to assess the predictive strength of each individual marker in the context of CVD risk, as well as the predictive value of a combination of these markers by using summary scores. Moreover, a possible biologic interaction of these markers with that of other important biomarkers of CVD risk including lipo- and apolipoprotein components was evaluated. This is of importance for the current medical practice of /$ see front matter 2010 Elsevier Ireland Ltd. All rights reserved. doi: /j.atherosclerosis

2 300 I. Holme et al. / Atherosclerosis 213 (2010) CVD risk assessment as it has, to our knowledge, not been established yet. 2. Methods 2.1. Study population and data collection The AMORIS study ( ), contains 351,487 men and 338,101 women with age ranging from <20 to >80 years, mainly from the greater Stockholm area. They were either healthy individuals referred for clinical laboratory testing as part of health check-ups, or were outpatients referred for laboratory testing. All laboratory analyses were done at the Central Automation Laboratory (CALAB), Stockholm. No individuals were inpatients at the time their blood samples were analyzed and none were excluded from the database for any possible manifestation of disease or because of treatment. A more detailed description of the AMORIS study is given elsewhere [13 15]. Incident cases of ischemic stroke (in the remaining called stroke) (ICD-7: 332, ICD-8: ; ICD-9: , and ICD-10: I63), acute myocardial infarction (ICD 8 and ICD 9: 410 and ICD 10: I 21), and heart failure (ICD-7: 422, 434.1, 434.2, and 782.4; ICD-8: 427.0, 428, 429, 782.4; ICD-9: 428; ICD 10: I50) as well as cause of death were ascertained by record linkage to the Swedish Hospital Discharge register and the Swedish Cause of Death register, respectively. The validity of the Swedish Hospital Discharge and Cause of Death data for these diagnoses has been evaluated previously [16,17]. Persons with non-fatal stroke, acute myocardial infarction (AMI) or heart failure (HF) as primary or secondary diagnosis prior to blood sampling were excluded. The endpoint of interest was a composite called Major (atherosclerotic) Cardiovascular Event (MACE). Follow-up time was defined as the time from blood sampling until the date of MACE (stroke, AMI, or HF, whichever came first), death, or study closing date (31 December 2002) and was on average 11.8 years. The analyses for this study were based on 65,050 persons aged years who were free of registered stroke, AMI or HF at time of blood sampling and who had measurements of WBC and Hp, as well as total cholesterol (TC) and triglycerides (TG) recorded at the same date. In a subgroup analysis, consisting of 26,571 (41%) subjects also non-high sensitive CRP was measured and in another subgroup (n = 12,910), measurements of apolipoprotein B (apob) and apolipoprotein A-1 (apoa-1) were also available. Since prescriptions could vary widely in number of variables asked for between subjects, joint measurements were often available only in more limited number of subjects. In addition, all variables including the inflammatory and lipoproteins had to be taken at the first visit to the physician. WBC was analysed with routinely used hematology analysers (Coulter STKS). Hp was measured with an immunoturbidimetric method (reagents from Orion Diagnostics, Finland), applied to an automated Hitachi-analyser (Boehringer Mannheim, Germany). The total imprecision, calculated with the coefficient of variation (CV), was 5.6% at Hp level 1.1 g/l. CRP was analysed by an established turbidimetric assay (reagents from Orion, Finland) using fully automated multichannel analyzers (an AutoChemist-PRISMA, New Clinicon, Stockholm, Sweden, and DAX 96, Technicon Instruments Corporation, Tarrytown, NY, USA, ). More modern inflammatory markers such as high-sensitivity (hs) CRP, interleukin-6 or interleukin-8 were not available during the whole period of blood sampling collection. The following information was collected for lipids and lipoproteins. Total cholesterol (TC) and triglycerides (TG) were measured enzymatically as described previously [18,19]. In the subgroup with available measurements of apob and apoa-1 low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C) were calculated [13] and the validation procedures have been reported previously [11,20]. To assess diabetes, we also used information on glucose levels, which was measured enzymatically with a glucose-oxidase/peroxidase method. ApoB and apoa-1 were measured by immunoturbidimetric methods with CV <5% [18,19]. All methods were fully automated with automatic calibration and laboratory facilities accredited [19]. Hypertension status was not recorded in these subjects. However, based on the hospital discharge data 620 (1.0%) subjects were hospitalized at any time prior to blood sampling with a primary or secondary diagnosis of hypertension (ICD-8: ; ICD-9: ; ICD-10: I10 I15). Since these are only hospitalized cases of hypertension, this information most likely accounts only for a small proportion of all subjects with hypertension in the study population. Diabetes was considered present in those with fasting glucose >7.0 mmol/l at baseline or with a hospital discharge diagnosis of diabetes (ICD-7: 260; ICD-8: ; ICD-9: 250; ICD-10: E10 E14) prior to blood sampling, accounting for 2731 (4.2%) subjects classified as diabetics. Height and weight were not mandatory information on the lab-referral form and was missing for the majority of subjects (>80%), and was not used for analyses in this study, since joint measurements of all variables then would have limited statistical power too much Statistical analysis First, we calculated an inflammatory score (IS), ranging from 0 to 2, as the number of inflammatory markers in the upper quartile of the distribution among WBC and Hp. Differences of other biomarkers by IS were evaluated with an analysis of variance with linear contrasts for continuous variables and Chi-square tests of trend for categorical variables. We then analysed the association between time from blood sampling to first event of MACE and IS with Cox models. Hazard ratios (HR) were calculated with IS = 0 as reference category and HR for continuous variables were expressed with a one standard deviation (SD) difference, including 95% confidence intervals (95% CI). Differences in hazard ratios were evaluated with Z-tests. IS and quartiles of TC or TG were cross-classified (11 different categories) in a Cox proportional hazards model with the lowest combined category as the reference. All models were adjusted for age, gender, hospital diagnosed hypertension and diabetes status. These variations in relative risks were also illustrated graphically. The natural logarithm (Ln) of the 2 continuous inflammatory markers, which were not highly correlated (r = 0.34), were used in a multiple Cox proportional regression analysis of MACE. The statistical information in this model is completely contained in the cross product sum of estimated regression coefficient (b) and level of variable for each subject (b 1 Ln WBC + b 2 Ln Hp). This sum we called the continuous inflammation score (CIS) and had the potential of being a better predictor than IS, since no categorization at upper quartile was done initially. The score was also categorized into quartiles. Then it was used for comparison of association strength with lipoproteins, both by quartile and in a continuous mode. In a subgroup (n = 26,549) non-high sensitive CRP was measured and a summary score based on WBC, Hp and CRP was also created by the same method as described above and CRP was categorized into four groups: 1, 2 7, 8 10, 11+ mg/l. To assess the biological interaction between IS, TC, and TG for predicting CVD risk, three different measures of synergy were calculated by using logistic regression. Synergy can in this case be defined as a combined effect that is larger than the added effects of each marker studied. The markers were IS (dichotomized as 0 or 1), TG and TC (dichotomized as >/< median), respectively, and the outcome was MACE [21]. The first measure of interaction estimates the relative excess risk due to interaction (RERI), the second rep-

3 I. Holme et al. / Atherosclerosis 213 (2010) Table 1 Descriptive characteristics (mean, SD) by inflammation score (IS) a, adjusted for age and gender. Risk factors IS: number of inflammatory markers (Inflammation Score) Total Subject number 43,403 16, ,005 Age 51.2 (13.9) 52.2 (14.4) 54.4 (13.9) 51.7 (14.0) Gender (% females) Haptoglobin g/l 0.97 (0.20) 1.25 (0.33) 1.67 (0.37) 1.09 (0.33) WBC 10 9 /L 5.59 (1.09) 8.06 (3.08) 9.78 (3.37) 6.59 (2.46) TC mmol/l 5.64 (1.07) 5.76 (1.17) 5.86 (1.22) 5.73 (1.15) TG mmol/l 1.22 (0.94) 1.48 (1.17) 1.68 (1.28) 1.32 (1.05) Diabetes, n (%) 1425 (3.3) 900 (5.5) 406 (7.8) 2731 (4.2) Hypertension, n (%) 337 (0.8) 203 (1.2) 80 (1.5) 620 (1.0) AMI, n (%) 1912 (4.4) 1193 (7.3) 544 (10.4) 3649 (5.6) Stroke, n (%) 1578 (3.6) 758 (4.6) 327 (6.3) 2663 (4.1) HF, n (%) 1436 (3.3) 897 (5.5) 357 (6.8) 2690 (4.1) MACE, n (%) 4132 (9.5) 2328 (14.1) 996 (19.1) 7456 (11.5) SD = standard deviation; WBC = white blood cell count; TC = total serum cholesterol; TG = serum triglycerides; AMI = acute myocardial infarction; HF = congestive heart failure; stroke = ischemic stroke; MACE = major cardiovascular events. a Based on WBC and haptoglobin. resent the attributable proportion due to interaction (AP), and the third is called the synergy index (S). This index calculates the ratio of the joint effect of IS, TC and TG, respectively, to the theoretical joint effects, under the assumption of independence [22]. If there is no biological interaction or synergism, RERI and AP are equal to 0 and S is equal to 1. The analyses were repeated in the subgroup with measurements for apob and apoa-1, but the apob/apoa-1 ratio was used instead of TC. To assess the discrimination ability of the studied markers, we calculated the net reclassification improvement proportions (NRI) of MACE predictions comparing the CIS (based on WBC, Hp and CRP) to the apob/apoa-1 ratio [23] in a subgroup of n = 12,910 subjects. In order to calculate the NRI, the probabilities of MACE were calculated in a logistic regression model including CIS and the basic set of risk factors (age, gender, hospital recorded hypertension, and diabetes). In a second step, the apob/apoa-1 ratio was added in the logistic regression model. The probabilities of MACE were then divided into quartiles (cut-off: 3, 8, and 20%) for each model (with and without the apob/apoa-1 ratio) and cross-classified for MACE cases and subjects not experiencing a MACE, respectively. The number of MACE cases being reclassified to a higher risk category minus those reclassified to a lower was calculated as a proportion of all MACE cases. Correspondingly, for subjects not experiencing a MACE the proportion of subjects being reclassified to a lower risk category minus those reclassified to a higher was calculated. The sum of these proportions is defined as the NRI for adding apob/apoa-1 to CIS over and above the basic set of risk factors. The 95% CI were calculated by the sum of two independent binomial proportions. All analyses were conducted with SAS version Ethics and approvals This study complied with the Declaration of Helsinki and the ethics review board of the Karolinska Institutet approved the study. 3. Results A total of 43,403 (67%) persons had a IS equal to 0, whereas 16,377 (25%) had a IS of 1, and 5225 (8%) had a IS of 2. All participant characteristics are described by IS in Table 1. Apart from age and gender, a positive trend was observed between all biomarkers measured and IS (P for trend <0.001), including diabetes and hospital recorded hypertension. Multivariate adjusted HRs for the association between inflammatory markers, lipids, lipoproteins and risk of AMI, stroke, HF, and MACE are shown in Table 2. Having at least one inflammatory marker in the upper quartile (IS 1) was associated with an increased risk of AMI, stroke, HF and MACE. Moreover, a positive trend by values of IS was observed (e.g. HR for MACE: 1.33 (95% CI: ), 1.62 (95% CI: ) for IS = 1 and 2, respectively). When looking at categories of IS or quartiles of CIS, a slightly stronger association was observed for AMI than for stroke or HF. High levels of TC were strongest associated with the risk of AMI, whereas TG was almost equally associated with each CVD risk studied. Additional adjustments for TG and TC of the association between CIS and risk of CVD did not alter the findings. WBC was stronger as risk factor than Hp for AMI and HF, but not for stroke, to which WBC seemed to associate equally strong as the combined inflammatory score. It can also be seen that CIS is more strongly associated to the endpoints than IS, since HR for quartile 4 of CIS is generally higher than the category 2 of IS, despite that only 8% of subjects were in this category vs. 25% of CIS. We also conducted an analysis stratified by gender for each association studied in Table 2. Gender differences were minor and no clear pattern was found (results not shown). In a subgroup of 26,571 subjects CRP was combined with WBC and Hp to create a summary score based on three inflammatory markers, Table 3. The addition of CRP to the other two factors did not substantially change the risk gradients Subgroup analysis with apolipoproteins In a subgroup of 12,910 subjects apob and apoa-1 were measured in addition to the other factors. Table 4 shows the multivariate adjusted HRs for the association between inflammatory markers, lipoproteins, and risk of MACE. Significant risk increases were particularly seen for increasing levels of IS, CIS, TG, apob/apoa-1 ratio, apob, HDL-C (risk decrease), and non-hdl-c. The association between CIS and MACE remained after adjustment for apob/apoa-1 (HR: 1.19 (95% CI: )). Differences by gender were small and not statistically significant (results not shown) Biological interaction between IS and lipoproteins In the total study group, the measures of synergism between IS and TC suggested a biological interaction (RERI: 0.11 (95% CI: to 0.239), AP: (95% CI: to 0.129), and S: (95% CI: )). Fig. 1 depicts incident MACE by a crossclassification of quartiles of TC and IS. A 3-fold increase in MACE risk was found in those having two inflammation markers and TC levels in the fourth quartile as compared to those with IS equal

4 302 I. Holme et al. / Atherosclerosis 213 (2010) Table 2 Hazard ratios (HR) with 95% confidence intervals of incident AMI, stroke, HF and MACE (major cardiovascular events) in relation to lipids, inflammatory markers, and clinical characteristics, adjusted for age, gender, hospital recorded hypertension and diabetes. a. Risk factors AMI (n = 3649) Stroke (n = 2663) HF (n = 2690) MACE (n = 7456) HR 95% CI HR 95% CI HR 95% CI HR 95% CI TC (per SD) TG (per SD) IS Diabetes Hypertension (yes/no) Quartiles of inflammatory score (CIS) Q Q Q Inflammatory score (CIS) (per SD) TC, TG adjusted Quartiles of WBC Q Q Q Log WBC (per SD) TC, TG adjusted Quartiles of haptoglobin Q Q Q Log haptoglobin (per SD) TC, TG adjusted a See footnote of Table 1; HR = hazard ratio; CI = confidence interval; CIS = Continuous Inflammation Score; IS = Inflammation Score. Adjusted only for age and gender. Based on WBC and haptoglobin. to zero and TC levels in the lowest quartile. Similar analyses were conducted for IS and TG, but no strong biological interaction was found (RERI: (95% CI: to 0.152), AP: (95% CI: to 0.087), and S: (95% CI: )). There was an almost 3-fold increase in risk of MACE comparing the combined highest exposure category with the reference category (figure not shown). In the subgroup with measurements for apob and apoa-1, the same type of analysis was conducted to study the biological interaction between CIS and the apob/apoa-1 ratio. The mea- sures of synergy suggested a possible biological interaction (RERI: (95% CI: to 0.470), AP: (95% CI: to 0.254), and S: (95% CI: )). Fig. 2 shows incident MACE by a cross-classification of quartiles of apob/apoa-1 and values of CIS. We observed a 2.9-fold increase in MACE risk when comparing the highest combined group to the lowest combined group. Similar results were found for an analysis of biological interaction between CIS and TG (RERI: (95% CI: to 0.225), AP: (95% CI: to 0.133), and S: (95% CI: )). Fig. 1. Hazard ratio of major cardiovascular events (MACE) by categories of IS (inflammation score based on number of inflammatory markers) and quartiles of total cholesterol, adjusted for age, gender, hospital recorded hypertension and diabetes. Fig. 2. Hazard ratio of major cardiovascular events (MACE) by CIS (summary continuous inflammation score) and quartiles of apob/apoa-1, adjusted for age, gender, hospital recorded hypertension and diabetes.

5 I. Holme et al. / Atherosclerosis 213 (2010) Table 3 Hazard ratio (95% CI) of incident AMI, stroke, HF and MACE (major cardiovascular disease) separately in relation to lipids, inflammatory markers, and clinical characteristics, adjusted for age, gender, hospital recorded hypertension and diabetes. a. Risk factors AMI (n = 1363) Stroke (n = 1021) HF (n = 1036) MACE (n = 2829) HR 95% CI HR 95% CI HR 95% CI HR 95% CI TC (per SD) TG (per SD) IS 0 (13,670/1079) (8854/1053) (4025/697) Diabetes Hypertension (yes/no) Quartiles of inflammatory score (CIS) Q Q Q Inflammatory score (CIS) (per SD) TC, TG adjusted Quartiles of WBC Q Q Q Log WBC (per SD) TC, TG adjusted Quartiles of haptoglobin Q Q Q Log haptoglobin (per SD) TC, TG adjusted Groups of CRP Log CRP (per SD) TC, TG adjusted a See footnote of Table 1; HR = hazard ratio; CI = confidence interval; CRP = C-reactive protein; CIS = Continuous Inflammation Score; IS = Inflammation Score. Adjusted only for age and gender. Based on three factors: WBC, haptoglobin and CRP Reclassification of risk in a subgroup comparing CIS with apob/apoa-1 Table 5A shows the classifications of MACE cases and subjects not experiencing a MACE according to four risk categories based on a multivariate model of CIS (WBC, Hp and CRP) with and without apob/apoa-1. Among the MACE cases in risk category 1 9/53 (17.0%) net proportion was reclassified, whereas the net proportions were 29/166 (17.5%) and 52/386 (13.5%) for categories 2 and 3, respectively. Among subjects not experiencing a MACE the net proportions reclassified downwards within categories 2 4 were 249/3257 (7.6%), 77/2430 (3.2%), 199/1714 (11.6%), respectively. The global NRI was 7.2% (95% CI: ). In Table 5B the same analysis was conducted, but now the categories were based on a multivariate model of apob/apoa-1 with and without addition of CIS. Among the MACE cases in risk category 1 6/48 (12.5%) net proportion was reclassified upwards, whereas the net proportions were 12/159 (7.5%), 27/279 (9.7%), within categories 2 3, respectively. Among subjects not experiencing a MACE the net proportions reclassified downwards within categories 2 4 were 111/2956 (3.8%), 87/2426 (3.6%) and 163/1746 (9.3%), respectively. The global NRI was 1.6% (95% CI: %). 4. Discussion In this study we demonstrated that summary scores (IS and CIS) of inflammation based on levels of WBC, and Hp, were strong predictors for the composite endpoint MACE. Moderate heterogeneity was observed for the associations between the inflammatory scores and different CVD risks studied, pointing to stronger relationships to AMI and to WBC as risk factor. There was a linear increase in risk by increasing levels of IS or CIS, but CIS associated more strongly to endpoints than IS. There were indications, but no clear evidence, that inflammation and dyslipidemia act synergistically on the risk of developing MACE. In the subgroup with measurements of apob and apoa-1, a 2.9-fold increased risk was found for those with values in the upper quartile of CIS and apob/apoa-1. Moreover, apob/apoa-1 added more net reclassification improvements to CIS than the opposite. However, in high-risk groups that require the clinician to make precise treatment decisions, sizeable amounts of reclassifications were indicated by adding CIS to apob/apoa-1. Previous studies have shown that acute phase reactants, such as the combination of fibrinogen, 1 -antitrypsin, Hp, ceruluplasmin and orosomucoid in their upper quartile add predictive information to the prediction of cardiac events and ischemic stroke as well as hypercholesterolemia [24]. We used a different set of inflammatory

6 304 I. Holme et al. / Atherosclerosis 213 (2010) Table 4 Hazard ratios (HR) and 95% confidence intervals of incident MACE per category of categorical inflammation scores and per SD of continuous risk factors in a subgroup with apolipoprotein measurements, adjusted for age, gender, hospital recorded hypertension and diabetes (N = 12,910 subjects and n = 1490 MACE). a. Risk factors HR 95% CI IS 0 (10,663/868) 1 1 (9594/1246) (6109/1168) Quartiles of inflammatory score (CIS) Q1 1 Q Q Q Inflammatory score (CIS) Adjusted for apob/apoa TC TG LDL-C HDL-C Non-HDL-C apo B apoa apob/apoa TC/HDL-C Log WBC Adjusted for apob/apoa Log CRP Adjusted for apob/apoa Log Haptoglobin Adjusted for apob/apoa a See footnotes of Tables 1 and 2; CIS = Continuous Inflammation Score; IS = Inflammation Score; LDL-C = low density lipoprotein cholesterol; HDL-C = high density lipoprotein cholesterol; Non-HDL = TC minus HDL-C; apob = apolipoprotein B; apoa-1 = apolipoprotein A-1; WBC = white blood cell count; CRP = C-reactive protein. Based on three factors: WBC, haptoglobin and CRP. markers in the AMORIS study, but our findings were comparable in the sense that the inflammatory markers and first and foremost WBC seem to contain substantial predictive information on a broad (atherosclerotic) cardiovascular endpoint such as MACE. More recently, hscrp has been considered as one of the major synthesizing markers of inflammation [25]. Moreover, it has been found to be a powerful and independent marker of CVD. However, over and above hscrp, WBC has been shown to be a strong and independent predictor of CVD mortality in patients with acute coronary syndromes [5]. These associations are thought to be mediated through the association between serum uric acid and monocytes and neutrophils [26]. Recently, it has been shown that the predic- Table 5A Classifications of MACE cases and subjects without MACE according to 4 risk categories by a model of both CIS and apob/apoa-1 (columns) and without apob/apoa-1 (rows), adjusted for age, gender, hospital based hypertension and diabetes. a. Risk categories by CIS (%) Risk categories by CIS and apob/apoa Total MACE cases Total Subjects without MACE Total ,350 a See footnotes of Tables 1, 3, 4; MACE = major cardiovascular events; CIS = summary continuous inflammation score (WBC; Hp, CRP). Table 5B Classifications of MACE cases and subjects without MACE according to 4 risk categories by a model of both CIS and apob/apoa-1 (columns) and without CIS (rows), adjusted for age, gender, hospital based hypertension and diabetes. a. Risk categories by apob/apoa-1 (%) Risk categories by CIS and apob/apoa Total MACE cases Total Subjects without MACE Total ,350 a See footnotes of Tables 1, 3, 4; MACE = major cardiovascular events; CIS = summary continuous inflammation score (WBC; Hp, CRP). tive value of hscrp is varying for different types of CVD [27]. For instance, the predictive value of hscrp for risk of coronary artery disease has been found to be rather small [27,28]. In the AMORIS study it was unfortunately not possible to study hscrp because at the time of blood sampling only a non-hscrp measurement method was available and we demonstrated that this measurement did not associate to CVD to the same extent as WBC or Hp. It has been shown that the strength of the association between inflammatory markers and risk of CVD may vary between different study populations. For instance, Ridker et al. has found steep gradients in a nested case control study of a subgroup of the Womens Health Initiative (WHI) (HR: 4.4 (95% CI: ), comparing extreme quintiles) [7], whereas we found a much lower gradient (HR: for the different endpoints comparing extreme quartiles) in the AMORIS study. In addition, the WHI study reported much higher predictive values of lipoproteins such as for LDL, apob, and non-hdl cholesterol compared to those for women in our AMORIS study. The use of different endpoints may be one explanation for these discrepancies, since the HR related to 1 SD increase of apob in AMORIS was 1.40 for AMI, but only 1.23 for the MACE [14]. Thus, like for hscrp, the strength of the association between inflammatory markers and CVD risk is likely to vary due to differences in CVD endpoint as well as study population [29] Study strengths and limitations The major strength of this analysis is the large number of major cardiovascular endpoints in AMORIS. This population consists of non-hospitalized individuals referred for laboratory testing. It is a fact that major drops in number of subjects may take place in AMORIS when several variables are to be included simultaneously. However, special runs on single factor analyses in larger populations do not give substantially different estimates of HR when compared to those found when joint variable analyses are made on a more limited number of subjects, so severe selections are probably not involved. During the study period the all cause mortality was about 14% lower in the AMORIS population than in the general population of Stockholm county, when taking age, gender, and calendar year into account [11]. This healthy cohort effect may influence the generalizability of the results, but not the internal validity, i.e. the hazard ratios given for different levels of, e.g. lipids. In addition, the impact on the generalizability is likely to be minor since it has been shown that the AMORIS cohort is similar to the general working population of Stockholm county in terms of socioeconomic class and ethnicity. A major limitation of this study is the lack of the more specific hscrp (which at the time of the study

7 I. Holme et al. / Atherosclerosis 213 (2010) was not available) measurements and other important risk factors such as smoking, and hypertension. However, adjustment for these factors has not fully explained the association between elevated inflammatory markers and cardiac events in previous studies [28]. Whereas the extra adjustments over and above age and gender for factors such as lipoproteins, smoking, diabetes, body mass index and blood pressure attenuated the regression estimates by about 33% in a large meta-analysis (27), in our Table 4 we noticed that extra adjustments for apolipoproteins and hospital hypertension and diabetes attenuated the estimates by about 25%. The use of hospital hypertension in our study is a major underestimation as it most likely only accounts for a small proportion of all subjects with hypertension in the study population. The quality of AMI classifications in the Swedish Hospital Discharge Register has been evaluated several times and was shown to be of good quality for epidemiological purposes [17]. The quality of routine stroke diagnoses in Sweden has been evaluated less extensively and with less consistent results [16]. For HF it is known from a Danish study that the specificity is high but the sensitivity is low, so that an underreporting of less severe cases may have occurred in our study [30]. 5. Conclusions The investigated inflammatory markers added predictive information for the risk of MACE, independent of the associations of lipids and lipoproteins such as apob and apoa-1. We found indications of synergistic effects between inflammation and dyslipidemia for incident MACE but no firm evidence. Future guidelines for risk assessment of CVD could include both summary scores of inflammatory markers as well as lipids and lipoproteins, under the assumption that standardized procedures for these factors can be developed. Funding This work was supported by grants from the Gunnar and Ingmar Jungner Foundation for Laboratory Medicine, Stockholm. Conflict of interest None declared. References [1] Marcovina SM, Crea F, Davignon J, et al. Biochemical and bioimaging markers for risk assessment and diagnosis in major cardiovascular diseases: a road to integration of complementary diagnostic tools. Review. J Intern Med 2007;261: [2] Zethelius B, Berglund L, Sundström J, et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N Engl J Med 2008;358: [3] Melander O, Newton-Cheh C, Almgren P, et al. Novel and conventional biomarkers for prediction of incident cardiovascular events in the community. J Am Med Assoc 2009;302: [4] Rana JS, Boekholt SM, Ridker PM, et al. Differential leucocyte count and the risk of future coronary artery disease in healthy men and women: the EPIC-Norfolk Prospective Population Study. J Intern Med 2007;262: [5] Li C, Engström G, Hedblad B. Leukocyte count is associated with incidence of coronary events, but not with stroke: a prospective cohort study. Atherosclerosis 2010;209: [6] Turner SJ, Ketch TR, Gandhi SK, Sane DC. Routine hematologic clinical tests as prognostic markers in patients with acute coronary syndromes. Am Heart J 2008;155: [7] Ridker PM, Hennekens CH, Buring JE, Rifai N. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med 2000;342: [Correction: N Engl J Med 2000;343:512]. [8] Pearson TA, Mensah GA, Alexander RW, et al. 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