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Prognostic value of suPAR and hs-CRP on cardiovascular disease
Atherosclerosis xxx (2018) 1e7
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Prognostic value of suPAR and hs-CRP on cardiovascular disease € ga Diederichsen a, *, Søren Zo € ga Diederichsen b, Hans Mickley a, Marie Zo c Flemming Hald Steffensen , Jess Lambrechtsen d, Niels Peter Rønnow Sand e, f, Kent Lodberg Christensen g, Michael Hecht Olsen h, i, j, Axel Diederichsen a, i, j, Mette Hjortdal Grønhøj a a
Department of Cardiology, Odense University Hospital, J.B. Winsløws Vej 4, DK-5000 Odense C, Denmark Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej i, DK-2100 Copenhagen, Denmark Department of Cardiology, Sygehus Lillebælt Vejle, Kabbeltoft 25, DK-7100 Vejle, Denmark d Department of Cardiology, Svendborg Hospital, Valdemarsgade 53, DK-5700 Svendborg, Denmark e Department of Cardiology, Hospital of South West Denmark, Finsensgade 35, DK-6700 Esbjerg, Denmark f Institute of Regional Health Services Research, University of Southern Denmark, Winsløwparken 19, c., DK-5000 Odense C, Denmark g Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark h Cardiology Section, Department of Internal Medicine, Holbæk Hospital, Smedelundsgade 60, DK-4300 Holbæk, Denmark i Centre for Individualized Medicine in Arterial Diseases (CIMA), Odense University Hospital, J.B. Winsløws Vej 4, DK-5000 Odense C, Denmark j Cardiovascular Centre of Excellence (CAVAC), University of Southern Denmark, Denmark b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 October 2017 Received in revised form 10 January 2018 Accepted 18 January 2018 Available online xxx
Background and aims: Studies have shown that soluble urokinase Plasminogen Activator Receptor (suPAR) and CRP (both inflammatory markers) and coronary artery calcification (CAC) are independent risk predictors for cardiovascular (CV) disease. The aim of this study is to assess whether suPAR and CRP have an increased predictive prognostic value beyond the traditional CV risk factors and the CAC score. Methods: A population sample of 1179 subjects, free of CV disease was included. The subjects underwent traditional CV risk evaluation, CAC assessment and blood sampling for suPAR and CRP. CV events were extracted from The Danish National Patient Register after 6.5 years. The additive values of suPAR and CRP were evaluated by unadjusted Kaplan Meier analysis, adjusted hazard ratio and ROCAUC models. Results: 1179 participants (47.6% males, mean age 55 years) were included. 73 events occurred. In Kaplan Meier analyses, suPAR and CRP were significantly associated with CV events (p ¼ 0.03 and p ¼ 0.002). Adjusted for the CV risk factors and the CAC score, the hazard ratios for suPAR and CRP were 1.17 (95% confidence interval [CI] 1.01e1.34) and 1.04 (95% CI 1.01e1.06), respectively. suPAR was associated with a substantial risk among women (2.03; 95% CI 1.45e2.84) and 60-year-old subjects (1.44; 95% CI 1.09 e1.90). By ROCAUC, neither suPAR nor CRP provided significant estimates (0.7100 and 0.7054) compared to the traditionally CV risk factors (0.6952, p ¼ 0.24 and p ¼ 0.16) and CAC score (0.7481, p ¼ 0.33 and p ¼ 0.32). Conclusions: Adjusted for traditional CV risk factors and CAC score, suPAR and CRP were of minor importance in risk prediction. © 2018 Elsevier B.V. All rights reserved.
Keywords: Cardiovascular disease Atherosclerosis Cardiovascular risk factors Coronary artery calcification Inflammatory markers Soluble urokinase plasminogen activator receptor High-sensitive C-reactive-protein
1. Introduction Despite massive improvement in diagnostics and treatment of atherosclerotic related diseases, atherosclerosis remains a major cause of morbidity and mortality [1]. Initiating primary medical
* Corresponding author. E-mail address: [email protected] (M.Z. Diederichsen).
prevention of cardiovascular disease (CVD) in the general population rests on the assessment of established cardiovascular (CV) risk factors (like age, gender, smoking, blood pressure and cholesterol) included in comprehensive risk assessment algorithms like HeartSCORE or Framingham, from Europe and America, respectively. However, such models have shortcomings, especially since the majority of manifest CVD occurs in the very large group of people with low to moderate risk [2e4]. Further, the algorithms have been claimed inadequate because of their general rather than individual
https://doi.org/10.1016/j.atherosclerosis.2018.01.029 0021-9150/© 2018 Elsevier B.V. All rights reserved.
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M.Z. Diederichsen et al. / Atherosclerosis xxx (2018) 1e7
approach and inability to assess risk in women correctly [2,5]. Thus, there is a need for further tools to perform more precise and individualized risk assessment, especially in people with low to moderate CV risk. Coronary artery calcification (CAC), measured by cardiac noncontrast computed tomography (CT), is an important late marker of atherosclerosis, and has shown to improve the accuracy and risk classification beyond the conventional CV risk assessment tools [6,7]. In continuation of this, current guidelines on CVD prevention from Europe and America provides Class IIb recommendations for the use of CAC in asymptomatic adults at intermediate risk [1,8]. Even though the predictive ability of CAC is evident, the applicability of CAC scoring as a screening tool in the general population remains controversial and routine screening with imaging modalities to predict future CV events is generally not recommended in clinical practice [1]. Therefore, easier accessible tools are wanted. The early phases in formation of atherosclerotic plaques are thought be influenced by inflammation [9]. This has led to the suggestion of inflammatory biomarkers as potential tools to individualize the risk assessment. Most commonly known is C-reactive protein (CRP), which has shown consistency across large prospective studies as a risk factor of similar importance as traditional CV risk factors [1,10]. A novel and promising inflammatory biomarker linked to CVD is soluble urokinase Plasminogen Activator Receptor (suPAR) [11]. suPAR is the solvent form of urokinase-type plasminogen activator receptor (uPAR), which is a membrane-bound protein present on immune cells. During the inflammatory response suPAR is cleaved and released of uPAR [12]. The aim of the present work is to clarify if inflammation markers (suPAR and CRP) improve the assessment above and beyond the traditional risk factors and CAC scoring. 2. Patients and methods 2.1. Study design and population This is a follow-up of the DanRisk study [13]. Enrollment and baseline examination were carried out in 2009e2010. 1825 men and women (born in either 1949 (aged 60 years) or 1959 (aged 50 years)), from the general population and living in the Region of Southern Denmark, were randomly sampled from national registries and invited to take part in a CV risk screening including a cardiac CT-scan in one of four regional centres (Odense, Esbjerg, Svendborg or Vejle). Participants with known CVD, defined as diagnosis or surgical procedure of substantial CVD before examination (see below) or atrial fibrillation, were excluded from this study. 2.2. Data collection Baseline examination and non-contrast CT scans have been described in details previously [14]. Through linkage to The Danish Central Person Register, The Danish Register of Causes of Death and The Danish National Patient Register, a register-based follow-up of the cohort was carried out in April 2016. Data on vital status or date of death and underlying cause of death were collected in the first mentioned databases, while diagnoses and surgical procedures were collected in the later one. CV events were defined to be the first listed primary diagnosis or surgical procedure indicating substantial CVD or death due to CVD. Substantial CVD included myocardial infarction, coronary revascularization procedures, stroke, ventricular arrhythmias, cardiac arrest, heart failure, heart valve surgery, significant aortic disease (including dissection, ruptured aneurysms, thromboses or embolisms or corresponding aortic surgery) and significant peripheral artery disease (including
thromboses or embolisms or corresponding surgery).
2.3. Biochemical analyses At screening examination, blood samples were drawn and stored at 80 C, and from these samples suPAR and CRP were measured in 2012 and 2010, respectively [15]. Blood levels of suPAR were measured using frozen EDTA-plasma samples stored at 80 C and with no prior thawing. The samples were analyzed using the commercial available CE/IVD approved suPARnostic® ELISA standard kit, according to the instructions of the manufacturer (Virogates, Birkeroed, Denmark). High-sensitive CRP was measured using frozen lithium-heparin plasma samples stored at 80 C and with no prior thawing. The samples were assayed with an ultra-high sensitivity latex-based immunoassay (CRP Vario, Sentinel diagnostics, Milan, Italy) with an Architect 8000c analyzer (Abbott Laboratories, Copenhagen, Denmark).
2.4. Statistical analyses Continuous variables are presented as arithmetic mean ± standard deviation (SD) or median with 25th and 75th percentiles. Categorical variables are presented by frequencies and respective percentages. The normally distributed continuous variables were compared with Student's t-test whereas skewed distributed continuous variables were analyzed using Mann-Whitney U test. Comparing categorical variables Chi-square test or Fisher's exact test were used as appropriate. Tertiles of suPAR were defined and unadjusted Kaplan Meier analysis and log-rank test were performed to evaluate the association between suPAR and CV event. Likewise, definitions and analyses were done for CRP. Cox proportional hazards analysis was used to evaluate the association between suPAR and CRP, respectively, and CV events in a multivariate setting. Covariates were gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus, body mass index (BMI) and CAC score. suPAR and CRP was used as continuous variables, whereas CAC score was used categorically with four commonly seen categories [16]: 0, 1e99, 100e399, 400 U, corresponding to no, low, moderate and severe coronary atherosclerosis, or as a continuous variable. Area under the receiver operator characteristic curve (ROCAUC) was estimated in several models (including the traditionally CV risk factors (gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus and BMI), CAC categories, suPAR and CRP) in the differentiation event versus no event. Finally, the ability of each biomarker to improve prognostication was further tested with the continuous net reclassification improvement [17]. Analyses were performed with IBM SPSS Statistics 22 (IBM, Armonk, NY, USA), and STATA SE 14 (StataCorp, College Station, TX, USA). A two-sided p-value < 0.05 was considered statistically significant.
2.5. Ethics The study was conducted in accordance with the Second Helsinki Declaration and was approved by the Regional Scientific Ethical Committee for Southern Denmark (project-ID S20080140). Written informed consent was obtained from all participants.
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M.Z. Diederichsen et al. / Atherosclerosis xxx (2018) 1e7
3. Results 3.1. Study population 1825 individuals were invited. Of these, 1257 subjects (68.9%) responded and accepted the invitation to participate. 30 were excluded due to known CVD, 9 due to participant-reported history of atrial fibrillation, 6 participants (none of these had events) due to incomplete CAC scoring, and 33 participants (3 of these had events) due to missing blood samples. The remaining 1179 participants were included, see Fig. 1. 3.2. Descriptive analysis Baseline characteristics are shown in Table 1. The distribution between genders and proportions of 50-year-old and 60-year-old participants was equal. Follow up was 6.5 years, and baseline characteristics were stratified by event status. Overall, 73 events (event rate 6.2%) occurred from baseline to follow up. Myocardial infarction, coronary revascularization procedures, stroke and CV mortality accounted for 57 of the events. Participants experiencing CV events were more likely to be male, 60 years of age, activly smoking, with higher blood pressure and in lipid lowering treatment. Additionally, the event group was characterized by having higher suPAR and CRP values, as well as higher CAC scores. suPAR and CRP were correlated (r ¼ 0.1840, p < 0.0001). 3.3. Event analysis The effect of both suPAR and CRP was tested in different models; including and excluding traditionally CV risk factors and CAC score. Kaplan Meier curves are shown for suPAR and CRP tertiles (Fig. 2). Unadjusted suPAR and CRP tertiles were significantly associated with CV events (p ¼ 0.03 and p ¼ 0.002, respectively).
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The suPAR values for the tertiles were 0.9e2.4, 2.4e3.0 and 3.0e16.6 nmol/L, with 20, 18 and 35 events in each, respectively. Accordingly, the event rates were 27.4%, 24.7% and 47.9%. Corresponding results for CRP were: values for tertiles 0.03e0.9, 0.9e2.37 and 2.37e94.22 mg/L, with 13, 23 and 37 events and event rates 17.8%, 31.5% and 50.7%. In Cox analyses adjusted for traditionally risk factors, suPAR was significantly associated with an increased risk of CV events (hazard ratios (HR) ¼ 1.21; 95% confidence interval (CI) 1.05e1.39). Likewise, CRP was significantly associated (HR ¼ 1.02; 95% CI 1.004e1.05), Table 2. By adding the CAC score, both biomarkers were independently associated with CV events. Interactions were found between suPAR and both gender and age, whereas the same only were present with CRP and age. Stratification for age and gender and adjusted for all of the above risk factors, including CAC score category, showed that suPAR and CRP were significantly associated with CV events among the 60-year-old subjects (HR ¼ 1.44; 95% CI 1.09e1.90 and HR ¼ 1.04; 95% CI 1.02e1.07). suPAR was also associated with events among women (HR ¼ 2.03; 95% CI 1.45e2.84), while CRP was associated with events among both men and women (HR ¼ 1.03; 95% CI 1.006e1.06 and HR ¼ 1.05; 95% CI 1.002e1.10), Table 2. Cox regression using CAC score as a continuous variable led to similar results. Changing the endpoint from substantial CVD to a combined endpoint of “Myocardial infarction, coronary revascularization procedures, stroke and CV mortality” did not make any difference in the HRs. Including renal function in Cox regression did not change the results. Among participants with HeartSCORE below 5%, 41 of 912 had events. suPAR was significantly higher among those with event compared to those without event (3.3 nmol/L versus 2.8 nmol/L, p ¼ 0.002). By contrast, CRP was not significantly different (4.4 mg/L versus 2.8 mg/L, p ¼ 0.06). Adjusted HRs were similar in the entire group. By ROCAUC, neither suPAR nor CRP provided stronger estimates (0.7577; 95% CI 0.6954e0.8200 and 0.7545; 95% CI 0.6910e0.8180) compared to the model including traditional risk factors and CAC score category (0.7481; 95% CI 0.6849e0.8113) (p ¼ 0.33 and p ¼ 0.32, respectively), as well as compared to the model including traditional risk factors alone, Table 3. This was also true including both biomarkers. Including renal function did not change the results. No substantial change was found if participants with low and high HeartSCORE were excluded (Supplementary Table A). Opposed to suPAR, CRP did improve the continuous net reclassification in a model including traditional risk factors and CAC score category (suPAR: 0.188 (95% CI (0.198)-0.507) and CRP: 0.324 (95% CI 0.029e0.573)), but not in a model only including the traditional risk factors (suPAR: 0.191 (95% CI (0.119)-0.536) and CRP: 0.244 (95% CI (0.039)-0.563)). Including renal function did not change the results. 4. Discussion
Fig. 1. Exclusion and inclusion criteria.
The relation between inflammatory markers, CAC score and clinical events have been described previously. In this study, we include suPAR, a new promising inflammatory biomarker. The main finding of the present study is that elevated suPAR and CRP were associated CV risk. suPAR were of particular importance among women, while both suPAR and CRP had predictive value among the 60 years old. Traditionally, CV risk assessment is based on age, gender, smoking status, blood pressure and cholesterol, but the association between inflammation and CV events has been suggested to improve risk prediction. CRP has been heavily debated. Measurement of CRP may be considered for clinical decision making
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Table 1 Baseline characteristics.
All Gender Age Smoking status
Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Antihypertensive treatment Hypertension Total cholesterol (mmol/l) LDL-C (mmol/l) HDL-C (mmol/l) Triglycerides (mmol/l) Lipid lowering treatments Dyslipidemia Diabetes BMI (kg/m2) Family history of CVD HeartSCORE SCORE < 1% SCORE 1% < 5% SCORE 5% suPAR (nmol/L) hs-CRP (mg/L) CAC score 0 AU 1e99 AU 100e399 AU 400 AU
Female 50 years Current Former Never
Median
Median
All
With event
Without event
1179 618 (52.4%) 578 (49.0%) 300 (25.4%) 394 (33.4%) 485 (41.1%) 136 (±19) 82 (±10) 242 (20.5%) 587 (49.8%) 5.5 (±1.0) 3.2 (2.8e3.6) 1.5 (1.3e1.7) 1.3 (1.0e1.6) 134 (11%) 932 (79%) 58 (4.9%) 26.9 (±4.6) 273 (23.2%) 2.4 (1.2e4.7) 255 (22%) 657 (56%) 267 (23%) 2.6 (2.4e3.0) 1.5 (0.9e2.4) 0 (0e24) 646 (54.8%) 358 (30.4%) 113 (9.6%) 62 (5.3%)
73 (6.19%) 29 (39.7%) 22 (30.1%) 30 (41.1%) 26 (35.6%) 17 (23.3%) 144 (±20) 87 (±12) 23 (31.5%) 48 (65.8%) 5.5 (±1.1) 3.3 (2.8e3.8) 1.3 (1.2e1.5) 1.3 (1.2e1.7) 15 (21%) 62 (85%) 6 (8.2%) 27.3 (±4.8) 21 (28.8%) 4.3 (2.1e8.9) 5 (7%) 36 (49%) 32 (44%) 2.9 (2.6e3.5) 2.7 (1.3e4.1) 52 (1e245) 17 (23.3%) 25 (34.2%) 14 (19.2%) 17 (23.3%)
1106 (93.8%) 589 (53.3%) 556 (50.3%) 270 (24.4%) 368 (33.3%) 468 (42.3%) 136 (±18) 82 (±10) 219 (19.8%) 539 (48.7%) 5.5 (±1.0) 3.2 (2.8e3.6) 1.5 (1.3e1.7) 1.3 (1.0e1.6) 119 (11%) 870 (79%) 52 (4.7%) 26.9 (±4.6) 252 (22.8%) 2.3 (1.1e4.5) 250 (23%) 621 (56%) 235 (21%) 2.6 (2.4e2.9) 1.4 (0.9e2.3) 0 (0e19) 629 (56.9%) 333 (30.1%) 99 (9.0%) 45 (4.1%)
p-valuea 0.025 <0.001 0.001
<0.001 0.001 0.016 0.005 0.841 0.749 0.061 0.134 0.011 0.202 0.166 0.479 0.241 <0.001 <0.001
0.003 <0.001 <0.001 <0.001
Values are presented as frequency (percent), mean (standard deviation) or median (quartiles 1e3), where appropriate. AU, Agatston units; CAC, coronary artery calcification; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol. a 2-sided p-values for difference between subjects with and without event, compared by Chi-square test, t-tests or Mann-Whitney U tests, where appropriate.
according to the ACC/AHA Guidelines [8], but has not gained impasse in clinic practice in Europe [1]. In a substudy from MultiEthnic Study of Atherosclerosis (MESA) (1330 individuals, mean age 63.8 years, male 67%, exclusion of diabetes mellitus), a minor but significant association between CRP and events after 7.6 years of follow up was found [18]. Our study adds to this, since we found a small but significant predictive value of CRP in the entire population, in particular among the 60-year-old subjects. However, a large meta-analysis concluded that the relevance of measuring CRP in relation to CVD is unclear [10]. In 2010, Eugen-Olsen et al. were the first to describe suPAR as a marker of CV risk and mortality [11]. In the Danish MONICA cohort with 2304 participants, it was found that suPAR significantly correlated with events in three out of four age groups by adjusted hazard ratios (HR ¼ 1.13e1.32). Later, Persson et al. (5166 participants) demonstrated that suPAR was associated with an increased incidence of CVD [19]. This is consistent with the findings of our study. Interestingly, we found no association in men, whereas a strong association between suPAR and clinical events in females was observed. suPAR was associated with a substantial risk of events among the 60-year-old subjects, but not in the 50-year-old ones. In conclusion, suPAR seems to be of particular importance in women and persons of 60 years of age. CAC has proven to be a solid predictor for CVD risk. In the MESA study with 6726 participants and 10.2 years of follow up, the ROCAUC improved from 0.7600 to 0.8136 [20], and in the Heinz Nixdorf Recall study (HNR) (4129 participants, 5.1 years follow up), the ROCAUC increased from 0.681 to 0.749 [6]. Finally, a newer metaanalysis found the pooled change in the ROCAUC 0.063 [21]. In this study, the ROCAUC increased from 0.6952 to 0.7481. While there are no differences in gender distribution, there are minor differences between the former studies and this study: age, exclusion criteria, definition of event and variables selections. The mean age in the
present study was 55 years, compared to 62 years and 61 years in the MESA and HNR cohorts. In contrast to other studies, we also excluded self-reported atrial fibrillation. Events differ as MESA and HNR only include coronary heart disease (myocardial infarction, resuscitated cardiac arrest, and fatal coronary heart disease, further MESA included revascularization), while we used a broader definition of CVD. Models differ slightly. As standard models, all studies include age, gender, smoking habits, blood pressure and cholesterol. Additionally, MESA included family history, while we included BMI. In conclusion, CAC score has been shown to individualize the risk of CVD and reduce the effects of demographic risk factors [20]. In this study, we wanted to assess whether suPAR and/or CRP improved the predictive ability over the traditionally risk factors and CAC score. Adding CRP to the Framingham risk variables and CAC did not contribute to coronary event prediction in previous studies [22e24]. However, in our fully adjusted Cox regression models, not only suPAR, but also CRP were significantly associated with CV events. Both were of importance among the 60-year-old subjects, with 44% and 4% increased risk for suPAR and CRP, respectively. Additionally, suPAR was of major importance among women e with 103% increased risk. This might be of clinical value, as the traditional risk factors are known to underestimate CV risk in women. However, suPAR and CRP did not provide significant increment in ROCAUC. It has been suggested that suPAR and CRP represent two different inflammatory response systems, and a previous work has shown and debated the superiority of suPAR compared to CRP [5,11,25]. It is established that the immunologic process is involved in plaque deposition and further vascular remodeling, and suPAR and CRP might be markers of this, but definitive evidence is lacking. Previously, an association between CAC score and suPAR was found
Please cite this article in press as: M.Z. Diederichsen, et al., Prognostic value of suPAR and hs-CRP on cardiovascular disease, Atherosclerosis (2018), https://doi.org/10.1016/j.atherosclerosis.2018.01.029
c
Adjusted for gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus and BMI. Adjusted for gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus, BMI and CAC score (categorically). Adjusted for gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus, BMI and CAC score (continuous). a
b
1.12 1.04 1.10 1.42 (0.87e1.44) (0.82e1.33) (0.85e1.43) (1.09e1.85) 1.05 (0.80e1.36) 0.99 (0.76e1.29) 1.04 (0.79e1.38) 1.96 (1.41e2.71) 2.03 (1.45e2.84) 1.94 (1.41e2.67)
1.02 1.04 1.03 1.04 1.05 1.04 1.02 1.03 1.02 1.03 1.04 1.03 1.02 1.04 1.03 (1.004e1.05) (1.01e1.06) (1.003e1.05) (0.996e1.09) (1.002e1.10) (0.995e1.09) (0.997e1.17) (1.006e1.06) (0.997e1.05) (0.96e0.11) (0.97e1.11) (0.96e1.11) (1.002e1.05) (1.02e1.07) (1.001e1.05) CRP
HRb
1.17 1.20 (1.01e1.34) (1.04e1.38) suPAR 1.21 (1.05e1.39)
HRb HRa HRc HRb HRa HRa
HRc
Female (n ¼ 618)
The prospective design and use of the validated Danish National Patient Register strengthens the results, as well as the extensive collection of data at baseline of all participants. It is an important limitation that suPAR and CRP might be elevated due to non-cardiovascular reasons [11,12,29]. Besides, suPAR might be falsely lowered by external factors [30]. The down side of CAC is that the absence does not exclude atherosclerosis, since noncontrast CT scanning does not detect incipient atherosclerosis such as fatty streaks and lipid deposits [31]. This could be an advantage to the inflammation markers. The study population is mainly Caucasian, which questions the usefulness of the results in other ethnicities. The event analysis is weakened by the modest number of women and younger participants with clinical events.
All (n ¼ 1179)
4.1. Strengths and limitations
Table 2 Adjusted hazard ratios for incident CV event in relation to baseline suPAR or CRP.
among women, while in two studies there was no significant association between CRP and CAC score [15,26]. Additionally, in a recent publication, we have showed that neither suPAR nor CRP had any predictive value concerning neither CAC development or progression [27]. Whether measurement of suPAR and CRP provides consistent, clinically meaningful incremental predictive value in risk stratification and reclassification beyond conventional factors remains debatable, but there is a lack of clear consensus regarding the optimal clinical use [28]. The current European guidelines do not recommend routine assessment of circulating biomarkers (Class III recommendations) [1].
HRa
HRb
HRc
HRa
HRb
HRc
60 years (n ¼ 601) 50 years (n ¼ 578) Male (n ¼ 561)
Fig. 2. Kaplan-Meier event free survival curves for tertiles of (A) suPAR and (B) CPR.
1.44 1.40 (1.09e1.90) (1.07e1.82)
5
HRc
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Table 3 Areas under the curve for cardiovascular risk factors, CAC categories, suPAR and CRP.
CV risk factors (age, gender, smoking status, hypertension, dyslipidemia, diabetes mellitus and BMI) Model A: Including CV risk factors and CAC categories Model B: Including CV risk factors and suPAR Model C: Including CV risk factors, CAC categories and suPAR Model D: Including CV risk factors and CRP Model E: Including CV risk factors, CAC categories and CRP Model F: Including CV risk factors, CAC categories, suPAR and CRP
AUC
95% CI
p-value
0.6952 0.7481 0.7100 0.7577 0.7054 0.7545 0.7621
0.6317e0.7587 0.6849e0.8113 0.6474e0.7726 0.6954e0.8200 0.6420e0.7689 0.6910e0.8180 0.6999e0.8242
Reference 0.0104 0.2445 0.0052 0.1558 0.0069 0.0033
Reference 0.3297 0.3229 0.1800
CAC, coronary artery calcification; CV, cardiovascular.
Information bias could be introduced, as selected data is relying on participant-statements and register data. Selection bias might be introduced since one third of the invited subjects did not participate or were excluded. Consequently, the study population might not represent the intended population. In particular, participants with clinical events, but missing blood samples (n ¼ 3) would have been preferable to include, to potentially strengthened the estimates. However, these problems are inevitable in prospective studies. Finally, treatment of hypertension, diabetes and hypercholesterolemia after screening examination will most likely decrease the event rate, and this way affect the results. 4.2. Conclusion In agreement with a previous work, suPAR and CRP were markers of CVD. In this study, we also included CAC score, and in this context, the discriminative ability of the biomarkers were diminished, but still of significant value in several models. The importance of suPAR was notable in females and among persons at age of 60. Our results are promising, but at present, suPAR and CRP are not ready for clinical use in risk stratification, and should be further assessed. Conflict of interest The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript. Financial support The Danish Heart Foundation, Region of Southern Denmark, Odense University Hospital, Odense Patient data Explorative Network, University of Southern Denmark, the Bønnelykke Foundation, the A.P. Moller and Chastine Mc-Kinney Moller Foundation for general purposes, the Aase and Ejnar Danielsens Foundation and the Herta Christensens Foundation. The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication. Author contributions € ga Diederichsen, Michael Hecht Olsen, Study design: Marie Zo Hans Mickley, Axel Diederichsen, Mette Hjortdal Grønhøj. Data collection: Mette Hjortdal Grønhøj, Jess Lambrechtsen, Niels Peter Rønnow Sand, Flemming Hald Steffensen, Axel Diederichsen. €ga Diederichsen, Søren Zo € ga DieData analysis: Marie Zo derichsen, Kent Lodberg Christensen, Axel Diederichsen, Mette Hjortdal Grønhøj. € ga Diederichsen. Manuscript writing: Marie Zo €ga Diederichsen, Søren Zo € ga Manuscript revision: Marie Zo
Diederichsen, Hans Mickley, Flemming Hald Steffensen, Jess Lambrechtsen, Niels Peter Rønnow Sand, Kent Lodberg Christensen, Michael Hecht Olsen, Axel Diederichsen, Mette Hjortdal Grønhøj. All authors had full access to all of the data (including statistical reports and tables) in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.atherosclerosis.2018.01.029. References [1] Authors/Task Force Members M.F. Piepoli, A.W. Hoes, S. Agewall, C. Albus, C. Brotons, et al., 2016 european guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the european society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the european association for cardiovascular prevention & rehabilitation (EACPR), Atherosclerosis 252 (2016 Sep) 207e274. [2] J. Yeboah, T.S. Polonsky, R. Young, R.L. McClelland, J.C. Delaney, F. Dawood, et al., Utility of nontraditional risk markers in individuals ineligible for statin therapy according to the 2013 american College of cardiology/american heart association cholesterol guidelines, Circulation 132 (10) (2015 Sep 8) 916e922. [3] M. Naghavi, From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies, Part II. Circulation 108 (15) (2003 Oct 14) 1772e1778. [4] M.W. Cavanaugh-Hussey, J.D. Berry, D.M. Lloyd-Jones, Who exceeds ATP-III risk thresholds? Systematic examination of the effect of varying age and risk factor levels in the ATP-III risk assessment tool, Prev. Med. 47 (6) (2008 Dec) 619e623. [5] S. Lyngbæk, J.L. Marott, T. Sehestedt, T.W. Hansen, M.H. Olsen, O. Andersen, et al., Cardiovascular risk prediction in the general population with use of suPAR, CRP, and Framingham Risk Score, Int. J. Cardiol. 167 (6) (2013 Sep) 2904e2911. € hlenkamp, S. Moebus, A. Schmermund, N. Lehmann, A. Stang, et [6] R. Erbel, S. Mo al., Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis, J. Am. Coll. Cardiol. 56 (17) (2010 Oct) 1397e1406. [7] T.S. Polonsky, R.L. McClelland, N.W. Jorgensen, D.E. Bild, G.L. Burke, A.D. Guerci, et al., Coronary artery calcium score and risk classification for coronary heart disease prediction, Jama 303 (16) (2010) 1610e1616. [8] D.C. Goff, D.M. Lloyd-Jones, G. Bennett, S. Coady, R.B. D'Agostino, R. Gibbons, et al., 2013 ACC/AHA guideline on the assessment of cardiovascular risk, J. Am. Coll. Cardiol. 63 (25) (2014 Jul) 2935e2959. [9] P. Libby, P.M. Ridker, G.K. Hansson, Leducq transatlantic Network on atherothrombosis. Inflammation in atherosclerosis: from pathophysiology to practice, J. Am. Coll. Cardiol. 54 (23) (2009 Dec 1) 2129e2138. [10] Collaboration ERF, et al., C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis, Lancet 375 (9709) (2010) 132e140. [11] J. Eugen-Olsen, O. Andersen, A. Linneberg, S. Ladelund, T.W. Hansen, A. Langkilde, et al., Circulating soluble urokinase plasminogen activator receptor predicts cancer, cardiovascular disease, diabetes and mortality in the general population: suPAR: a novel risk marker for major disease and mortality, J. Intern. Med. 268 (3) (2010 Jun 15) 296e308. [12] M. Thunø, B. Macho, J. Eugen-Olsen, suPAR: the molecular crystal ball, Dis. Markers 27 (3e4) (2009) 157e172. [13] A.C. Diederichsen, N.P. Sand, B. Nørgaard, J. Lambrechtsen, J.M. Jensen, H. Munkholm, et al., Discrepancy between coronary artery calcium score and HeartScore in middle-aged Danes: the DanRisk study, Eur. J. Prev. Cardiol. 19 (3) (2012 Jun) 558e564.
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M.Z. Diederichsen et al. / Atherosclerosis xxx (2018) 1e7 [14] J. Lambrechtsen, O. Gerke, K. Egstrup, N.P. Sand, B.L. Nørgaard, H. Petersen, et al., The relation between coronary artery calcification in asymptomatic subjects and both traditional risk factors and living in the city centre: a DanRisk substudy, J. Intern. Med. 271 (5) (2012 May) 444e450. [15] M.H. Sørensen, O. Gerke, J. Eugen-Olsen, H. Munkholm, J. Lambrechtsen, N.P.R. Sand, et al., Soluble urokinase plasminogen activator receptor is in contrast to high-sensitive C-reactive-protein associated with coronary artery calcifications in healthy middle-aged subjects, Atherosclerosis 237 (1) (2014 Nov) 60e66. [16] M.J. Budoff, K. Nasir, R.L. McClelland, R. Detrano, N. Wong, R.S. Blumenthal, et al., Coronary calcium predicts events better with absolute calcium scores than age-sex-race/ethnicity percentiles: MESA (Multi-Ethnic study of atherosclerosis), J. Am. Coll. Cardiol. 53 (4) (2009 Jan 27) 345e352. [17] M.J. Pencina, R.B. D'Agostino, E.W. Steyerberg, Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers, Stat. Med. 30 (1) (2011 Jan 15) 11e21. [18] J. Yeboah, R.L. McClelland, T.S. Polonsky, G.L. Burke, C.T. Sibley, D. O’leary, et al., Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals, Jama 308 (8) (2012) 788e795. [19] M. Persson, G. Ostling, G. Smith, V. Hamrefors, O. Melander, B. Hedblad, et al., Soluble urokinase plasminogen activator receptor: a risk factor for carotid plaque, stroke, and coronary artery disease, Stroke 45 (1) (2014 Jan 1) 18e23. [20] R.L. McClelland, N.W. Jorgensen, M. Budoff, M.J. Blaha, W.S. Post, R.A. Kronmal, et al., 10-Year coronary heart disease risk prediction using coronary artery calcium and traditional risk factors, J. Am. Coll. Cardiol. 66 (15) (2015 Oct) 1643e1653. [21] W.T. Qureshi, J.S. Rana, J. Yeboah, U. bin Nasir, M.H. Al-Mallah, Risk stratification for primary prevention of coronary artery disease: roles of C-Reactive protein and coronary artery calcium [cited 2017 Apr 16], Curr Cardiol Rep [Internet] 17 (12) (2015 Dec). Available from: http://link.springer.com/10. 1007/s11886-015-0666-9. [22] Y. Arad, K.J. Goodman, M. Roth, D. Newstein, Guerci AD. Coronary Calcification, Coronary Disease Risk Factors, C-Reactive Protein, Atherosclerotic Cardiovascular, Disease events, J. Am. Coll. Cardiol. 46 (1) (2005 Jul) 158e165. [23] R. Park, R. Detrano, M. Xiang, P. Fu, Y. Ibrahim, L. LaBree, et al., Combined use of computed tomography coronary calcium scores and C-reactive protein
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Contents lists available at ScienceDirect
Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Prognostic value of suPAR and hs-CRP on cardiovascular disease € ga Diederichsen a, *, Søren Zo € ga Diederichsen b, Hans Mickley a, Marie Zo c Flemming Hald Steffensen , Jess Lambrechtsen d, Niels Peter Rønnow Sand e, f, Kent Lodberg Christensen g, Michael Hecht Olsen h, i, j, Axel Diederichsen a, i, j, Mette Hjortdal Grønhøj a a
Department of Cardiology, Odense University Hospital, J.B. Winsløws Vej 4, DK-5000 Odense C, Denmark Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej i, DK-2100 Copenhagen, Denmark Department of Cardiology, Sygehus Lillebælt Vejle, Kabbeltoft 25, DK-7100 Vejle, Denmark d Department of Cardiology, Svendborg Hospital, Valdemarsgade 53, DK-5700 Svendborg, Denmark e Department of Cardiology, Hospital of South West Denmark, Finsensgade 35, DK-6700 Esbjerg, Denmark f Institute of Regional Health Services Research, University of Southern Denmark, Winsløwparken 19, c., DK-5000 Odense C, Denmark g Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark h Cardiology Section, Department of Internal Medicine, Holbæk Hospital, Smedelundsgade 60, DK-4300 Holbæk, Denmark i Centre for Individualized Medicine in Arterial Diseases (CIMA), Odense University Hospital, J.B. Winsløws Vej 4, DK-5000 Odense C, Denmark j Cardiovascular Centre of Excellence (CAVAC), University of Southern Denmark, Denmark b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 October 2017 Received in revised form 10 January 2018 Accepted 18 January 2018 Available online xxx
Background and aims: Studies have shown that soluble urokinase Plasminogen Activator Receptor (suPAR) and CRP (both inflammatory markers) and coronary artery calcification (CAC) are independent risk predictors for cardiovascular (CV) disease. The aim of this study is to assess whether suPAR and CRP have an increased predictive prognostic value beyond the traditional CV risk factors and the CAC score. Methods: A population sample of 1179 subjects, free of CV disease was included. The subjects underwent traditional CV risk evaluation, CAC assessment and blood sampling for suPAR and CRP. CV events were extracted from The Danish National Patient Register after 6.5 years. The additive values of suPAR and CRP were evaluated by unadjusted Kaplan Meier analysis, adjusted hazard ratio and ROCAUC models. Results: 1179 participants (47.6% males, mean age 55 years) were included. 73 events occurred. In Kaplan Meier analyses, suPAR and CRP were significantly associated with CV events (p ¼ 0.03 and p ¼ 0.002). Adjusted for the CV risk factors and the CAC score, the hazard ratios for suPAR and CRP were 1.17 (95% confidence interval [CI] 1.01e1.34) and 1.04 (95% CI 1.01e1.06), respectively. suPAR was associated with a substantial risk among women (2.03; 95% CI 1.45e2.84) and 60-year-old subjects (1.44; 95% CI 1.09 e1.90). By ROCAUC, neither suPAR nor CRP provided significant estimates (0.7100 and 0.7054) compared to the traditionally CV risk factors (0.6952, p ¼ 0.24 and p ¼ 0.16) and CAC score (0.7481, p ¼ 0.33 and p ¼ 0.32). Conclusions: Adjusted for traditional CV risk factors and CAC score, suPAR and CRP were of minor importance in risk prediction. © 2018 Elsevier B.V. All rights reserved.
Keywords: Cardiovascular disease Atherosclerosis Cardiovascular risk factors Coronary artery calcification Inflammatory markers Soluble urokinase plasminogen activator receptor High-sensitive C-reactive-protein
1. Introduction Despite massive improvement in diagnostics and treatment of atherosclerotic related diseases, atherosclerosis remains a major cause of morbidity and mortality [1]. Initiating primary medical
* Corresponding author. E-mail address: [email protected] (M.Z. Diederichsen).
prevention of cardiovascular disease (CVD) in the general population rests on the assessment of established cardiovascular (CV) risk factors (like age, gender, smoking, blood pressure and cholesterol) included in comprehensive risk assessment algorithms like HeartSCORE or Framingham, from Europe and America, respectively. However, such models have shortcomings, especially since the majority of manifest CVD occurs in the very large group of people with low to moderate risk [2e4]. Further, the algorithms have been claimed inadequate because of their general rather than individual
https://doi.org/10.1016/j.atherosclerosis.2018.01.029 0021-9150/© 2018 Elsevier B.V. All rights reserved.
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approach and inability to assess risk in women correctly [2,5]. Thus, there is a need for further tools to perform more precise and individualized risk assessment, especially in people with low to moderate CV risk. Coronary artery calcification (CAC), measured by cardiac noncontrast computed tomography (CT), is an important late marker of atherosclerosis, and has shown to improve the accuracy and risk classification beyond the conventional CV risk assessment tools [6,7]. In continuation of this, current guidelines on CVD prevention from Europe and America provides Class IIb recommendations for the use of CAC in asymptomatic adults at intermediate risk [1,8]. Even though the predictive ability of CAC is evident, the applicability of CAC scoring as a screening tool in the general population remains controversial and routine screening with imaging modalities to predict future CV events is generally not recommended in clinical practice [1]. Therefore, easier accessible tools are wanted. The early phases in formation of atherosclerotic plaques are thought be influenced by inflammation [9]. This has led to the suggestion of inflammatory biomarkers as potential tools to individualize the risk assessment. Most commonly known is C-reactive protein (CRP), which has shown consistency across large prospective studies as a risk factor of similar importance as traditional CV risk factors [1,10]. A novel and promising inflammatory biomarker linked to CVD is soluble urokinase Plasminogen Activator Receptor (suPAR) [11]. suPAR is the solvent form of urokinase-type plasminogen activator receptor (uPAR), which is a membrane-bound protein present on immune cells. During the inflammatory response suPAR is cleaved and released of uPAR [12]. The aim of the present work is to clarify if inflammation markers (suPAR and CRP) improve the assessment above and beyond the traditional risk factors and CAC scoring. 2. Patients and methods 2.1. Study design and population This is a follow-up of the DanRisk study [13]. Enrollment and baseline examination were carried out in 2009e2010. 1825 men and women (born in either 1949 (aged 60 years) or 1959 (aged 50 years)), from the general population and living in the Region of Southern Denmark, were randomly sampled from national registries and invited to take part in a CV risk screening including a cardiac CT-scan in one of four regional centres (Odense, Esbjerg, Svendborg or Vejle). Participants with known CVD, defined as diagnosis or surgical procedure of substantial CVD before examination (see below) or atrial fibrillation, were excluded from this study. 2.2. Data collection Baseline examination and non-contrast CT scans have been described in details previously [14]. Through linkage to The Danish Central Person Register, The Danish Register of Causes of Death and The Danish National Patient Register, a register-based follow-up of the cohort was carried out in April 2016. Data on vital status or date of death and underlying cause of death were collected in the first mentioned databases, while diagnoses and surgical procedures were collected in the later one. CV events were defined to be the first listed primary diagnosis or surgical procedure indicating substantial CVD or death due to CVD. Substantial CVD included myocardial infarction, coronary revascularization procedures, stroke, ventricular arrhythmias, cardiac arrest, heart failure, heart valve surgery, significant aortic disease (including dissection, ruptured aneurysms, thromboses or embolisms or corresponding aortic surgery) and significant peripheral artery disease (including
thromboses or embolisms or corresponding surgery).
2.3. Biochemical analyses At screening examination, blood samples were drawn and stored at 80 C, and from these samples suPAR and CRP were measured in 2012 and 2010, respectively [15]. Blood levels of suPAR were measured using frozen EDTA-plasma samples stored at 80 C and with no prior thawing. The samples were analyzed using the commercial available CE/IVD approved suPARnostic® ELISA standard kit, according to the instructions of the manufacturer (Virogates, Birkeroed, Denmark). High-sensitive CRP was measured using frozen lithium-heparin plasma samples stored at 80 C and with no prior thawing. The samples were assayed with an ultra-high sensitivity latex-based immunoassay (CRP Vario, Sentinel diagnostics, Milan, Italy) with an Architect 8000c analyzer (Abbott Laboratories, Copenhagen, Denmark).
2.4. Statistical analyses Continuous variables are presented as arithmetic mean ± standard deviation (SD) or median with 25th and 75th percentiles. Categorical variables are presented by frequencies and respective percentages. The normally distributed continuous variables were compared with Student's t-test whereas skewed distributed continuous variables were analyzed using Mann-Whitney U test. Comparing categorical variables Chi-square test or Fisher's exact test were used as appropriate. Tertiles of suPAR were defined and unadjusted Kaplan Meier analysis and log-rank test were performed to evaluate the association between suPAR and CV event. Likewise, definitions and analyses were done for CRP. Cox proportional hazards analysis was used to evaluate the association between suPAR and CRP, respectively, and CV events in a multivariate setting. Covariates were gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus, body mass index (BMI) and CAC score. suPAR and CRP was used as continuous variables, whereas CAC score was used categorically with four commonly seen categories [16]: 0, 1e99, 100e399, 400 U, corresponding to no, low, moderate and severe coronary atherosclerosis, or as a continuous variable. Area under the receiver operator characteristic curve (ROCAUC) was estimated in several models (including the traditionally CV risk factors (gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus and BMI), CAC categories, suPAR and CRP) in the differentiation event versus no event. Finally, the ability of each biomarker to improve prognostication was further tested with the continuous net reclassification improvement [17]. Analyses were performed with IBM SPSS Statistics 22 (IBM, Armonk, NY, USA), and STATA SE 14 (StataCorp, College Station, TX, USA). A two-sided p-value < 0.05 was considered statistically significant.
2.5. Ethics The study was conducted in accordance with the Second Helsinki Declaration and was approved by the Regional Scientific Ethical Committee for Southern Denmark (project-ID S20080140). Written informed consent was obtained from all participants.
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3. Results 3.1. Study population 1825 individuals were invited. Of these, 1257 subjects (68.9%) responded and accepted the invitation to participate. 30 were excluded due to known CVD, 9 due to participant-reported history of atrial fibrillation, 6 participants (none of these had events) due to incomplete CAC scoring, and 33 participants (3 of these had events) due to missing blood samples. The remaining 1179 participants were included, see Fig. 1. 3.2. Descriptive analysis Baseline characteristics are shown in Table 1. The distribution between genders and proportions of 50-year-old and 60-year-old participants was equal. Follow up was 6.5 years, and baseline characteristics were stratified by event status. Overall, 73 events (event rate 6.2%) occurred from baseline to follow up. Myocardial infarction, coronary revascularization procedures, stroke and CV mortality accounted for 57 of the events. Participants experiencing CV events were more likely to be male, 60 years of age, activly smoking, with higher blood pressure and in lipid lowering treatment. Additionally, the event group was characterized by having higher suPAR and CRP values, as well as higher CAC scores. suPAR and CRP were correlated (r ¼ 0.1840, p < 0.0001). 3.3. Event analysis The effect of both suPAR and CRP was tested in different models; including and excluding traditionally CV risk factors and CAC score. Kaplan Meier curves are shown for suPAR and CRP tertiles (Fig. 2). Unadjusted suPAR and CRP tertiles were significantly associated with CV events (p ¼ 0.03 and p ¼ 0.002, respectively).
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The suPAR values for the tertiles were 0.9e2.4, 2.4e3.0 and 3.0e16.6 nmol/L, with 20, 18 and 35 events in each, respectively. Accordingly, the event rates were 27.4%, 24.7% and 47.9%. Corresponding results for CRP were: values for tertiles 0.03e0.9, 0.9e2.37 and 2.37e94.22 mg/L, with 13, 23 and 37 events and event rates 17.8%, 31.5% and 50.7%. In Cox analyses adjusted for traditionally risk factors, suPAR was significantly associated with an increased risk of CV events (hazard ratios (HR) ¼ 1.21; 95% confidence interval (CI) 1.05e1.39). Likewise, CRP was significantly associated (HR ¼ 1.02; 95% CI 1.004e1.05), Table 2. By adding the CAC score, both biomarkers were independently associated with CV events. Interactions were found between suPAR and both gender and age, whereas the same only were present with CRP and age. Stratification for age and gender and adjusted for all of the above risk factors, including CAC score category, showed that suPAR and CRP were significantly associated with CV events among the 60-year-old subjects (HR ¼ 1.44; 95% CI 1.09e1.90 and HR ¼ 1.04; 95% CI 1.02e1.07). suPAR was also associated with events among women (HR ¼ 2.03; 95% CI 1.45e2.84), while CRP was associated with events among both men and women (HR ¼ 1.03; 95% CI 1.006e1.06 and HR ¼ 1.05; 95% CI 1.002e1.10), Table 2. Cox regression using CAC score as a continuous variable led to similar results. Changing the endpoint from substantial CVD to a combined endpoint of “Myocardial infarction, coronary revascularization procedures, stroke and CV mortality” did not make any difference in the HRs. Including renal function in Cox regression did not change the results. Among participants with HeartSCORE below 5%, 41 of 912 had events. suPAR was significantly higher among those with event compared to those without event (3.3 nmol/L versus 2.8 nmol/L, p ¼ 0.002). By contrast, CRP was not significantly different (4.4 mg/L versus 2.8 mg/L, p ¼ 0.06). Adjusted HRs were similar in the entire group. By ROCAUC, neither suPAR nor CRP provided stronger estimates (0.7577; 95% CI 0.6954e0.8200 and 0.7545; 95% CI 0.6910e0.8180) compared to the model including traditional risk factors and CAC score category (0.7481; 95% CI 0.6849e0.8113) (p ¼ 0.33 and p ¼ 0.32, respectively), as well as compared to the model including traditional risk factors alone, Table 3. This was also true including both biomarkers. Including renal function did not change the results. No substantial change was found if participants with low and high HeartSCORE were excluded (Supplementary Table A). Opposed to suPAR, CRP did improve the continuous net reclassification in a model including traditional risk factors and CAC score category (suPAR: 0.188 (95% CI (0.198)-0.507) and CRP: 0.324 (95% CI 0.029e0.573)), but not in a model only including the traditional risk factors (suPAR: 0.191 (95% CI (0.119)-0.536) and CRP: 0.244 (95% CI (0.039)-0.563)). Including renal function did not change the results. 4. Discussion
Fig. 1. Exclusion and inclusion criteria.
The relation between inflammatory markers, CAC score and clinical events have been described previously. In this study, we include suPAR, a new promising inflammatory biomarker. The main finding of the present study is that elevated suPAR and CRP were associated CV risk. suPAR were of particular importance among women, while both suPAR and CRP had predictive value among the 60 years old. Traditionally, CV risk assessment is based on age, gender, smoking status, blood pressure and cholesterol, but the association between inflammation and CV events has been suggested to improve risk prediction. CRP has been heavily debated. Measurement of CRP may be considered for clinical decision making
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Table 1 Baseline characteristics.
All Gender Age Smoking status
Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Antihypertensive treatment Hypertension Total cholesterol (mmol/l) LDL-C (mmol/l) HDL-C (mmol/l) Triglycerides (mmol/l) Lipid lowering treatments Dyslipidemia Diabetes BMI (kg/m2) Family history of CVD HeartSCORE SCORE < 1% SCORE 1% < 5% SCORE 5% suPAR (nmol/L) hs-CRP (mg/L) CAC score 0 AU 1e99 AU 100e399 AU 400 AU
Female 50 years Current Former Never
Median
Median
All
With event
Without event
1179 618 (52.4%) 578 (49.0%) 300 (25.4%) 394 (33.4%) 485 (41.1%) 136 (±19) 82 (±10) 242 (20.5%) 587 (49.8%) 5.5 (±1.0) 3.2 (2.8e3.6) 1.5 (1.3e1.7) 1.3 (1.0e1.6) 134 (11%) 932 (79%) 58 (4.9%) 26.9 (±4.6) 273 (23.2%) 2.4 (1.2e4.7) 255 (22%) 657 (56%) 267 (23%) 2.6 (2.4e3.0) 1.5 (0.9e2.4) 0 (0e24) 646 (54.8%) 358 (30.4%) 113 (9.6%) 62 (5.3%)
73 (6.19%) 29 (39.7%) 22 (30.1%) 30 (41.1%) 26 (35.6%) 17 (23.3%) 144 (±20) 87 (±12) 23 (31.5%) 48 (65.8%) 5.5 (±1.1) 3.3 (2.8e3.8) 1.3 (1.2e1.5) 1.3 (1.2e1.7) 15 (21%) 62 (85%) 6 (8.2%) 27.3 (±4.8) 21 (28.8%) 4.3 (2.1e8.9) 5 (7%) 36 (49%) 32 (44%) 2.9 (2.6e3.5) 2.7 (1.3e4.1) 52 (1e245) 17 (23.3%) 25 (34.2%) 14 (19.2%) 17 (23.3%)
1106 (93.8%) 589 (53.3%) 556 (50.3%) 270 (24.4%) 368 (33.3%) 468 (42.3%) 136 (±18) 82 (±10) 219 (19.8%) 539 (48.7%) 5.5 (±1.0) 3.2 (2.8e3.6) 1.5 (1.3e1.7) 1.3 (1.0e1.6) 119 (11%) 870 (79%) 52 (4.7%) 26.9 (±4.6) 252 (22.8%) 2.3 (1.1e4.5) 250 (23%) 621 (56%) 235 (21%) 2.6 (2.4e2.9) 1.4 (0.9e2.3) 0 (0e19) 629 (56.9%) 333 (30.1%) 99 (9.0%) 45 (4.1%)
p-valuea 0.025 <0.001 0.001
<0.001 0.001 0.016 0.005 0.841 0.749 0.061 0.134 0.011 0.202 0.166 0.479 0.241 <0.001 <0.001
0.003 <0.001 <0.001 <0.001
Values are presented as frequency (percent), mean (standard deviation) or median (quartiles 1e3), where appropriate. AU, Agatston units; CAC, coronary artery calcification; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol. a 2-sided p-values for difference between subjects with and without event, compared by Chi-square test, t-tests or Mann-Whitney U tests, where appropriate.
according to the ACC/AHA Guidelines [8], but has not gained impasse in clinic practice in Europe [1]. In a substudy from MultiEthnic Study of Atherosclerosis (MESA) (1330 individuals, mean age 63.8 years, male 67%, exclusion of diabetes mellitus), a minor but significant association between CRP and events after 7.6 years of follow up was found [18]. Our study adds to this, since we found a small but significant predictive value of CRP in the entire population, in particular among the 60-year-old subjects. However, a large meta-analysis concluded that the relevance of measuring CRP in relation to CVD is unclear [10]. In 2010, Eugen-Olsen et al. were the first to describe suPAR as a marker of CV risk and mortality [11]. In the Danish MONICA cohort with 2304 participants, it was found that suPAR significantly correlated with events in three out of four age groups by adjusted hazard ratios (HR ¼ 1.13e1.32). Later, Persson et al. (5166 participants) demonstrated that suPAR was associated with an increased incidence of CVD [19]. This is consistent with the findings of our study. Interestingly, we found no association in men, whereas a strong association between suPAR and clinical events in females was observed. suPAR was associated with a substantial risk of events among the 60-year-old subjects, but not in the 50-year-old ones. In conclusion, suPAR seems to be of particular importance in women and persons of 60 years of age. CAC has proven to be a solid predictor for CVD risk. In the MESA study with 6726 participants and 10.2 years of follow up, the ROCAUC improved from 0.7600 to 0.8136 [20], and in the Heinz Nixdorf Recall study (HNR) (4129 participants, 5.1 years follow up), the ROCAUC increased from 0.681 to 0.749 [6]. Finally, a newer metaanalysis found the pooled change in the ROCAUC 0.063 [21]. In this study, the ROCAUC increased from 0.6952 to 0.7481. While there are no differences in gender distribution, there are minor differences between the former studies and this study: age, exclusion criteria, definition of event and variables selections. The mean age in the
present study was 55 years, compared to 62 years and 61 years in the MESA and HNR cohorts. In contrast to other studies, we also excluded self-reported atrial fibrillation. Events differ as MESA and HNR only include coronary heart disease (myocardial infarction, resuscitated cardiac arrest, and fatal coronary heart disease, further MESA included revascularization), while we used a broader definition of CVD. Models differ slightly. As standard models, all studies include age, gender, smoking habits, blood pressure and cholesterol. Additionally, MESA included family history, while we included BMI. In conclusion, CAC score has been shown to individualize the risk of CVD and reduce the effects of demographic risk factors [20]. In this study, we wanted to assess whether suPAR and/or CRP improved the predictive ability over the traditionally risk factors and CAC score. Adding CRP to the Framingham risk variables and CAC did not contribute to coronary event prediction in previous studies [22e24]. However, in our fully adjusted Cox regression models, not only suPAR, but also CRP were significantly associated with CV events. Both were of importance among the 60-year-old subjects, with 44% and 4% increased risk for suPAR and CRP, respectively. Additionally, suPAR was of major importance among women e with 103% increased risk. This might be of clinical value, as the traditional risk factors are known to underestimate CV risk in women. However, suPAR and CRP did not provide significant increment in ROCAUC. It has been suggested that suPAR and CRP represent two different inflammatory response systems, and a previous work has shown and debated the superiority of suPAR compared to CRP [5,11,25]. It is established that the immunologic process is involved in plaque deposition and further vascular remodeling, and suPAR and CRP might be markers of this, but definitive evidence is lacking. Previously, an association between CAC score and suPAR was found
Please cite this article in press as: M.Z. Diederichsen, et al., Prognostic value of suPAR and hs-CRP on cardiovascular disease, Atherosclerosis (2018), https://doi.org/10.1016/j.atherosclerosis.2018.01.029
c
Adjusted for gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus and BMI. Adjusted for gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus, BMI and CAC score (categorically). Adjusted for gender, age, smoking status, hypertension, dyslipidemia, diabetes mellitus, BMI and CAC score (continuous). a
b
1.12 1.04 1.10 1.42 (0.87e1.44) (0.82e1.33) (0.85e1.43) (1.09e1.85) 1.05 (0.80e1.36) 0.99 (0.76e1.29) 1.04 (0.79e1.38) 1.96 (1.41e2.71) 2.03 (1.45e2.84) 1.94 (1.41e2.67)
1.02 1.04 1.03 1.04 1.05 1.04 1.02 1.03 1.02 1.03 1.04 1.03 1.02 1.04 1.03 (1.004e1.05) (1.01e1.06) (1.003e1.05) (0.996e1.09) (1.002e1.10) (0.995e1.09) (0.997e1.17) (1.006e1.06) (0.997e1.05) (0.96e0.11) (0.97e1.11) (0.96e1.11) (1.002e1.05) (1.02e1.07) (1.001e1.05) CRP
HRb
1.17 1.20 (1.01e1.34) (1.04e1.38) suPAR 1.21 (1.05e1.39)
HRb HRa HRc HRb HRa HRa
HRc
Female (n ¼ 618)
The prospective design and use of the validated Danish National Patient Register strengthens the results, as well as the extensive collection of data at baseline of all participants. It is an important limitation that suPAR and CRP might be elevated due to non-cardiovascular reasons [11,12,29]. Besides, suPAR might be falsely lowered by external factors [30]. The down side of CAC is that the absence does not exclude atherosclerosis, since noncontrast CT scanning does not detect incipient atherosclerosis such as fatty streaks and lipid deposits [31]. This could be an advantage to the inflammation markers. The study population is mainly Caucasian, which questions the usefulness of the results in other ethnicities. The event analysis is weakened by the modest number of women and younger participants with clinical events.
All (n ¼ 1179)
4.1. Strengths and limitations
Table 2 Adjusted hazard ratios for incident CV event in relation to baseline suPAR or CRP.
among women, while in two studies there was no significant association between CRP and CAC score [15,26]. Additionally, in a recent publication, we have showed that neither suPAR nor CRP had any predictive value concerning neither CAC development or progression [27]. Whether measurement of suPAR and CRP provides consistent, clinically meaningful incremental predictive value in risk stratification and reclassification beyond conventional factors remains debatable, but there is a lack of clear consensus regarding the optimal clinical use [28]. The current European guidelines do not recommend routine assessment of circulating biomarkers (Class III recommendations) [1].
HRa
HRb
HRc
HRa
HRb
HRc
60 years (n ¼ 601) 50 years (n ¼ 578) Male (n ¼ 561)
Fig. 2. Kaplan-Meier event free survival curves for tertiles of (A) suPAR and (B) CPR.
1.44 1.40 (1.09e1.90) (1.07e1.82)
5
HRc
M.Z. Diederichsen et al. / Atherosclerosis xxx (2018) 1e7
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6
M.Z. Diederichsen et al. / Atherosclerosis xxx (2018) 1e7
Table 3 Areas under the curve for cardiovascular risk factors, CAC categories, suPAR and CRP.
CV risk factors (age, gender, smoking status, hypertension, dyslipidemia, diabetes mellitus and BMI) Model A: Including CV risk factors and CAC categories Model B: Including CV risk factors and suPAR Model C: Including CV risk factors, CAC categories and suPAR Model D: Including CV risk factors and CRP Model E: Including CV risk factors, CAC categories and CRP Model F: Including CV risk factors, CAC categories, suPAR and CRP
AUC
95% CI
p-value
0.6952 0.7481 0.7100 0.7577 0.7054 0.7545 0.7621
0.6317e0.7587 0.6849e0.8113 0.6474e0.7726 0.6954e0.8200 0.6420e0.7689 0.6910e0.8180 0.6999e0.8242
Reference 0.0104 0.2445 0.0052 0.1558 0.0069 0.0033
Reference 0.3297 0.3229 0.1800
CAC, coronary artery calcification; CV, cardiovascular.
Information bias could be introduced, as selected data is relying on participant-statements and register data. Selection bias might be introduced since one third of the invited subjects did not participate or were excluded. Consequently, the study population might not represent the intended population. In particular, participants with clinical events, but missing blood samples (n ¼ 3) would have been preferable to include, to potentially strengthened the estimates. However, these problems are inevitable in prospective studies. Finally, treatment of hypertension, diabetes and hypercholesterolemia after screening examination will most likely decrease the event rate, and this way affect the results. 4.2. Conclusion In agreement with a previous work, suPAR and CRP were markers of CVD. In this study, we also included CAC score, and in this context, the discriminative ability of the biomarkers were diminished, but still of significant value in several models. The importance of suPAR was notable in females and among persons at age of 60. Our results are promising, but at present, suPAR and CRP are not ready for clinical use in risk stratification, and should be further assessed. Conflict of interest The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript. Financial support The Danish Heart Foundation, Region of Southern Denmark, Odense University Hospital, Odense Patient data Explorative Network, University of Southern Denmark, the Bønnelykke Foundation, the A.P. Moller and Chastine Mc-Kinney Moller Foundation for general purposes, the Aase and Ejnar Danielsens Foundation and the Herta Christensens Foundation. The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication. Author contributions € ga Diederichsen, Michael Hecht Olsen, Study design: Marie Zo Hans Mickley, Axel Diederichsen, Mette Hjortdal Grønhøj. Data collection: Mette Hjortdal Grønhøj, Jess Lambrechtsen, Niels Peter Rønnow Sand, Flemming Hald Steffensen, Axel Diederichsen. €ga Diederichsen, Søren Zo € ga DieData analysis: Marie Zo derichsen, Kent Lodberg Christensen, Axel Diederichsen, Mette Hjortdal Grønhøj. € ga Diederichsen. Manuscript writing: Marie Zo €ga Diederichsen, Søren Zo € ga Manuscript revision: Marie Zo
Diederichsen, Hans Mickley, Flemming Hald Steffensen, Jess Lambrechtsen, Niels Peter Rønnow Sand, Kent Lodberg Christensen, Michael Hecht Olsen, Axel Diederichsen, Mette Hjortdal Grønhøj. All authors had full access to all of the data (including statistical reports and tables) in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.atherosclerosis.2018.01.029. References [1] Authors/Task Force Members M.F. Piepoli, A.W. Hoes, S. Agewall, C. Albus, C. Brotons, et al., 2016 european guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the european society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the european association for cardiovascular prevention & rehabilitation (EACPR), Atherosclerosis 252 (2016 Sep) 207e274. [2] J. Yeboah, T.S. Polonsky, R. Young, R.L. McClelland, J.C. Delaney, F. Dawood, et al., Utility of nontraditional risk markers in individuals ineligible for statin therapy according to the 2013 american College of cardiology/american heart association cholesterol guidelines, Circulation 132 (10) (2015 Sep 8) 916e922. [3] M. Naghavi, From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies, Part II. Circulation 108 (15) (2003 Oct 14) 1772e1778. [4] M.W. Cavanaugh-Hussey, J.D. Berry, D.M. Lloyd-Jones, Who exceeds ATP-III risk thresholds? Systematic examination of the effect of varying age and risk factor levels in the ATP-III risk assessment tool, Prev. Med. 47 (6) (2008 Dec) 619e623. [5] S. Lyngbæk, J.L. Marott, T. Sehestedt, T.W. Hansen, M.H. Olsen, O. Andersen, et al., Cardiovascular risk prediction in the general population with use of suPAR, CRP, and Framingham Risk Score, Int. J. Cardiol. 167 (6) (2013 Sep) 2904e2911. € hlenkamp, S. Moebus, A. Schmermund, N. Lehmann, A. Stang, et [6] R. Erbel, S. Mo al., Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis, J. Am. Coll. Cardiol. 56 (17) (2010 Oct) 1397e1406. [7] T.S. Polonsky, R.L. McClelland, N.W. Jorgensen, D.E. Bild, G.L. Burke, A.D. Guerci, et al., Coronary artery calcium score and risk classification for coronary heart disease prediction, Jama 303 (16) (2010) 1610e1616. [8] D.C. Goff, D.M. Lloyd-Jones, G. Bennett, S. Coady, R.B. D'Agostino, R. Gibbons, et al., 2013 ACC/AHA guideline on the assessment of cardiovascular risk, J. Am. Coll. Cardiol. 63 (25) (2014 Jul) 2935e2959. [9] P. Libby, P.M. Ridker, G.K. Hansson, Leducq transatlantic Network on atherothrombosis. Inflammation in atherosclerosis: from pathophysiology to practice, J. Am. Coll. Cardiol. 54 (23) (2009 Dec 1) 2129e2138. [10] Collaboration ERF, et al., C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis, Lancet 375 (9709) (2010) 132e140. [11] J. Eugen-Olsen, O. Andersen, A. Linneberg, S. Ladelund, T.W. Hansen, A. Langkilde, et al., Circulating soluble urokinase plasminogen activator receptor predicts cancer, cardiovascular disease, diabetes and mortality in the general population: suPAR: a novel risk marker for major disease and mortality, J. Intern. Med. 268 (3) (2010 Jun 15) 296e308. [12] M. Thunø, B. Macho, J. Eugen-Olsen, suPAR: the molecular crystal ball, Dis. Markers 27 (3e4) (2009) 157e172. [13] A.C. Diederichsen, N.P. Sand, B. Nørgaard, J. Lambrechtsen, J.M. Jensen, H. Munkholm, et al., Discrepancy between coronary artery calcium score and HeartScore in middle-aged Danes: the DanRisk study, Eur. J. Prev. Cardiol. 19 (3) (2012 Jun) 558e564.
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