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Keywords:

  • biomarker;
  • low-grade inflammation;
  • prognosis;
  • risk;
  • suPAR

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Cohort description
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Abstract.  Eugen-Olsen J, Andersen O, Linneberg A, Ladelund S, Hansen TW, Langkilde A, Petersen J, Pielak T, Møller LN, Jeppesen J, Lyngbæk S, Fenger M, Olsen MH, Hildebrandt PR, Borch-Johnsen K, Jørgensen T, Haugaard SB (Copenhagen University, Hvidovre Hospital, Hvidovre; Copenhagen University Hospital, Glostrup; Copenhagen University Hospital, Copenhagen; Copenhagen University Hospital, Glostrup; Copenhagen University, Hvidovre Hospital, Hvidovre; Steno Diabetes Center, Gentofte; University of Aarhus, Aarhus; University of Copenhagen, Copenhagen; Copenhagen University, Hvidovre Hospital, Hvidovre, Denmark). Circulating soluble urokinase plasminogen activator receptor predicts cancer, cardiovascular disease, diabetes and mortality in the general population. J Intern Med 2010; 268: 296–308.

Background.  Low-grade inflammation is thought to contribute to the development of cardiovascular disease (CVD), type-2 diabetes mellitus (T2D), cancer and mortality. Biomarkers of inflammation may aid in risk prediction and enable early intervention and prevention of disease.

Objective.  The aim of this study was to investigate whether plasma levels of the inflammatory biomarker soluble urokinase plasminogen activator receptor (suPAR) are predictive of disease and mortality in the general population.

Design.  This was an observational prospective cohort study. Cohort participants were included from June 1993 to December 1994 and followed until the end of 2006.

Setting.  General adult Caucasian population.

Participants.  The MONICA10 study, a population-based cohort recruited from Copenhagen, Denmark, included 2602 individuals aged 41, 51, 61 or 71 years.

Measurements.  Blood samples were analysed for suPAR levels using a commercially available enzyme-linked immunosorbent assay. Risk of cancer (n = 308), CVD (n = 301), T2D (n = 59) and mortality (n = 411) was assessed with a multivariate proportional hazards model using Cox regression.

Results.  Elevated baseline suPAR level was associated with an increased risk of cancer, CVD, T2D and mortality during follow-up. suPAR was more strongly associated with cancer, CVD and mortality in men than in women, and in younger compared with older individuals. suPAR remained significantly associated with the risk of negative outcome after adjustment for a number of relevant risk factors including C-reactive protein levels.

Limitation.  Further validation in ethnic populations other than Caucasians is needed.

Conclusion.  The stable plasma protein suPAR may be a promising biomarker because of its independent association with incident cancer, CVD, T2D and mortality in the general population.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Cohort description
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

‘Low-grade inflammation’ (LGI) is an undefined subclinical chronic inflammatory state, which is thought to contribute to the development of cardiovascular disease (CVD) [1], type 2 diabetes mellitus (T2D) [2], cancer [3] and Alzheimer’s disease [4]. The most commonly used biomarker of LGI is C-reactive protein (CRP) measured using a high-sensitivity (hsCRP) assay; the plasma CRP concentration is associated with an increased risk of CVD [5] and, in some studies, with cancer mortality and total mortality [6].

The urokinase plasminogen activator receptor (uPAR) is expressed on a number of different cells; in particular, on vascular endothelial cells, monocytes, neutrophils and activated T-cells. It is involved in several immune functions including migration, adhesion, angiogenesis, fibrinolysis and cell proliferation [7–9]. The uPAR is released from cells during inflammatory stimulation to generate soluble uPAR (suPAR) that is a highly flexible molecule [10] with intrinsic chemotactic properties [11, 12].

Soluble urokinase plasminogen activator receptor levels are positively correlated with pro-inflammatory biomarkers such as tumour necrosis factor-α and leucocyte counts [13] and with C-reactive protein levels [14]. An elevated suPAR level is thought to reflect activation of the inflammatory and immune systems and has been associated with poor clinical outcomes in patients suffering from various infectious diseases [15–18] as well as in those with certain types of cancers [19–21].

As suPAR is likely to be a central player in the mechanisms of LGI and shows stable kinetics both in vivo [13] and in vitro [22], we investigated the potential of suPAR as a risk marker for common diseases and death in the general population. This study included 2602 men and women aged 41, 51, 61 or 71 years who were living in the community in the vicinity of Copenhagen and who were enrolled into the Danish MONICA10 cohort study in 1993–1994 to be followed for a median of 13 years.

Cohort description

  1. Top of page
  2. Abstract
  3. Introduction
  4. Cohort description
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

The Danish contribution to the international MONICA project (MONItoring trends and determinants of CArdiovascular disease), a study conducted under the auspices of the World Health Organisation (WHO), was undertaken between 1982 and 1991. The Danish MONICA1 population survey took place at the Research Center for Prevention and Health in Glostrup from 1982 to 1984 and included 4807 individuals born in 1923, 1933, 1943 or 1953 who were randomly selected from 11 municipalities within Copenhagen County [23, 24]. The participation rate was 78.7%. In 1993–1994, 2656 formerly invited individuals (55%) agreed to participate in MONICA10 (see flow chart in Fig. 1). For this, blood pressure measurements and plasma samples obtained between June 1993 and December 1994 were available from 2605 participants who completed a self-administered questionnaire on CVD risk factors, medical history and lifestyle habits including smoking and physical activity. All participants gave written consent and the study was conducted in accordance with the second Declaration of Helsinki and approved by the Ethics Committee for Copenhagen County.

image

Figure 1. Flow-chart of participation in MONICA and MONICA 10.

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Blood pressure was measured using a random-zero mercury sphygmomanometer: two measurements were taken whilst the participant was sitting down and at rest for 5 min; the mean value was used for analysis.

Laboratory measurements

Fasting concentrations of HDL cholesterol and total cholesterol were measured using enzymatic colorimetric methods (Roche, Mannheim, Germany), as previously described [25]. Fasting concentrations of blood glucose were analysed by standard methods [26]. CRP levels were measured using a particle-enhanced immunoturbidimetric hsCRP assay (Roche/Hitachi, Basel, Switzerland) with a range of 0.1–20 mg L−1 and a detection limit of 0.03 mg L−1 as previously described [27].

Plasma levels of suPAR were measured using the commercially available suPARnostic® kit, according to the manufacturer’s instructions (ViroGates, Copenhagen, Denmark). The intra-assay variation was 2.75% and inter-assay variation was 9.17%. Sixteen samples showed more than 10% variation, and were therefore re-measured. The kit standard curve was validated to measure suPAR levels between 0.6 and 22.0 ng mL−1. The technician who measured the samples (TP) and the head of the laboratory (JEO) were blinded to the identity of the patient samples. The clinical database was released by the Research Centre for Prevention and Health (AL and LNM) after having received the suPAR data. The duration of sample freezing did not appear to have a major influence on the plasma level of suPAR as indicated by the lack of correlation between suPAR levels and the date of plasma sampling from 14 June 1993 to 2 December 1994 (ρ = 0.001, P = 0.96). However, long-term freezer storage may lead to water evaporation and increased protein levels [28]. Evaporation is likely to induce nondifferential misclassification that would tend to draw the risk estimates to the null hypothesis. The median sample freezer storage duration was 13.6 years (range, 12.8–14.3).

Outcomes

Information regarding morbidity at baseline and during follow-up was obtained from the Danish National Patient Register (NPR) [29]. Mortality data were obtained from the Danish National Death Register. Participants were followed from the time of blood sampling (1993–1994) to 31 December 2006. At the time of blood sampling, the participants were 41, 51, 61 or 71 years old. We investigated four different outcomes in the study population.

  • 1
     Cancer of any kind. Both nonfatal and fatal cancers were included in the cancer end-point analysis (WHO international classification of diseases (ICD)-10 codes C00–C97) that included lung cancers (C32, C33, C34 and D38.1), gastrointestinal cancers (C00–C26), prostate cancer (C61) and breast cancer (C50). Patients with a registered diagnosis of cancer prior to the time of entry into the study were excluded from the analysis of this end-point.
  • 2
     CVD. The CVD end-point was the combination of cardiovascular death (ICD-8 codes 390–448 or ICD-10 codes I00–I79 and R95–R99), ischaemic heart disease (ICD-8 codes 410–414 or ICD-10 codes I20–I25) and stroke (ICD-8 codes 431, 433 and 434 or ICD-10 codes I61 and I63), as previously described in this cohort [30]. Patients with a prior diagnosis of myocardial infarction or stroke, or those taking digoxin or nitrates, were excluded from the analysis of this end-point.
  • 3
     T2D. Individuals with self-reported diabetes at baseline, hospitalizations with discharge diagnosis including diabetes prior to the first examination (ICD-8 code 250 and ICD-10 codes E10–E14) or a fasting plasma glucose level above 6.9 mmol L−1, or use of antidiabetic drugs at the baseline examination were all classified as prevalent cases of diabetes and consequently excluded from the analysis of this end-point. Incident cases of diabetes were defined as individuals who during the follow-up were registered for the first time in the NPR with ICD-8 code 250 or ICD-10 codes E10–E14.
  • 4
     Death from any cause. Analysis was restricted to 2362 individuals with complete information for all studied explanatory variables.

The number of individuals diagnosed with either cancer, CVD or T2D at baseline is shown in Fig. 1.

Statistical analysis

End-points are presented within birth cohort- and gender-specific suPAR-quartiles, and the association was tested with the chi-square test. A Cox proportional hazards model with age as time and delayed entry was fitted to all four end-points. The proportional hazards assumption was checked by examination and test of the Schoenfeld residuals. suPAR and CRP were estimated in birth cohorts as the Cox regression model control showed an interaction between suPAR/CRP and age at examination (nonproportionality).

Controls revealed that linear scoring of suPAR on the log hazard scale was indeed appropriate. Results are presented as hazard ratios with 95% confidence intervals and P-values. Furthermore tests for trend are presented for suPAR and CRP. Tests for departure from trend were preformed (data not shown) and were nonsignificant in all cases. Age- and gender-adjusted Spearman partial correlation was used to determine the relation between plasma levels of suPAR/CRP and selected cardiometabolic risk factors. Cumulative incidence plots were made for all disease end-points. Death was considered a competing end-point rather than censoring. Our cumulative incidence plots thus show the lifetime risk of disease, given disease-free status at the age of 41. Gender- and age (cohort)-specific suPAR quartiles were used in the cumulative incidence plots.

An analysis of death and suPAR quartiles is presented in the form of an age-specific Kaplan–Meier plot. The area between the curves for the first and fourth quartiles was calculated as an estimate of the difference in expected lifetime.

Role of the funding source.  The suPARnostic® kits were kindly donated by ViroGates A/S. ViroGates had no role in the design of the study, in the collection, analysis or interpretation of data or in the decision to submit the manuscript for publication. Copenhagen University Hospital Hvidovre holds patents on the use of suPAR in prognostics with Drs Eugen-Olsen, Andersen and Haugaard cited as inventors.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Cohort description
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Plasma suPAR levels in the MONICA10 cohort

Plasma samples from 2605 individuals were measured and suPAR was quantified in 2602 of these samples (Fig. 1); three samples were excluded because they fell below (n = 2) or above (n = 1) the validated range of the suPAR assay. The median plasma suPAR level was 4.03 ng mL−1 (range, 1.3–19.9). suPAR levels increased with age and were higher amongst women {n = 1292; median suPAR, 4.26 ng mL−1 [interquartile range (IQR), 3.60–5.13]} than men (n = 1310; median suPAR, 3.84 ng mL−1 (IQR, 3.14–4.71), P < 0.001). Smoking significantly increased suPAR in all age groups as shown in Fig. 2. Baseline cohort characteristics by quartiles of suPAR are shown in Table 1.

image

Figure 2. Box-plot of suPAR levels amongst nonsmokers (white boxes) and current smokers (grey boxes) according to age and gender. Boxes represent 25–75% percentiles and whispers are 5–95% percentiles. P < 0.05 for all age groups.

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Table 1. Baseline characteristics of participants according to interquartile range of suPAR
 suPAR quartile1st (n = 650) Range: 1.3–<3.4 ng mL−12nd (n = 651) Range: ≥3.4–≤4.0 ng mL−13rd (n = 651) Range: >4.0–≤4.9 ng mL−14th (n = 650) Range: >4.9–19.9 ng mL−1P-value
  1. Data are presented as median and 5–95% percentiles, or frequency (in percentage).

Demographic variablesFemale gender (%)34.050.356.258.2<0.0001
Age (years)51 (41–71)51 (41–71)61 (41–71)61 (41–71)<0.0001
Diagnosis of diabetes (%)1.52.64.95.9<0.0001
Treatment of hypertension (%)6.07.110.314.3<0.0001
History of CVD (%)2.96.86.09.1<0.0001
History of cancer (%)1.22.03.13.5<0.0001
Lipid variables (mmol L−1)Total cholesterol5.95 (4.55–7.86)6.08 (4.48–8.16)6.19 (4.50–8.07)6.14 (4.58–8.12)0.026
HDL cholesterol1.37 (0.93–2.09)1.45 (0.94–2.33)1.40 (0.91–2.23)1.33 (0.85–2.24)<0.0001
Triglycerides1.15 (0.61–3.06)1.10 (0.56–2.90)1.22 (0.66–3.13)1.31 (0.68–3.28)<0.0001
Metabolic variablesGlucose (mmol L−1)4.7 (4.1–5.6)4.7 (4.2–5.6)4.8 (4.1–6.4)4.8 (4.0–6.7)0.0081
Body mass index (kg m−2)25.3 (20.5–32.0)25.2 (20.7–33.8)25.7 (20.3–33.9)25.2 (19.6–34.7)0.52
Waist circumference (cm)88 (70–108)87 (69–111)87 (69–108)87 (68–111)0.58
Inflammatory variablesC-reactive protein (mg L−1)1.13 (0.23–6.79)1.50 (0.30–7.97)1.98 (0.40–13.92)3.20 (0.44–17.44)<0.0001
Leucocytes (1 × 109 cells L−1)5.4 (3.8–8.1)5.8 (3.9–8.8)6.4 (4.5–10.3)7.3 (4.6–11.3)<0.0001
Haemodynamic variablesSystolic blood pressure (mmHg)123 (102–157)128 (103–164)129 (101–163)131 (102–171)<0.0001
Diastolic blood pressure (mmHg)82 (67–101)82 (66–100)81 (65–99)81 (65–102)0.25
Smoking statusNonsmoker/ex-smoker/regular smoker (%) 75/5/2061/5/34 50/4/46 30/2/68<0.0001

Follow-up and end-points

We studied four different end-points in the population: cancer, CVD, T2D and death. Participants were followed for end-points from the time of blood sampling (1993–1994) until 31 December 2006. At the time of blood sampling, participants were 41, 51, 61 or 71 years old and were followed for a median of 12.6 years (range, 0.17–13.6).

suPAR and risk of cancer

Sixty individuals had a cancer diagnosis before blood sampling and a previous cancer analysis was associated with an elevated suPAR level (Table 1). Patients with a cancer diagnosis at baseline were excluded from further analysis (Fig. 1). During the 27 519 person-years of follow-up, 308 subjects developed cancers (Fig. 1) that were diagnosed as lung (n = 50), gastrointestinal (n = 92), prostate (n = 25), breast (n = 50) and other cancers (n = 91).

In univariate analysis, elevated baseline suPAR level was associated with an increased risk of developing cancer during follow-up. As shown in Table 2, the association between suPAR and cancer was related to age: the younger the participants, the stronger the association (p for trend = 0.008). The association between suPAR and risk of cancer remained after adjustment for gender, smoking and CRP level (Table 2).

Table 2. Age group-related hazard ratio (HR) for cancer
CancerHR (95% CI) UnivariatezPHR (95% CI) MultivariatezPHR (95% CI) MutuallyzP
  1. CRP 1–3 (mg L−1) and CRP >3 (mg L−1) are compared with CRP <1 mg L−1. E.g. CRP 1–3 41 compares CRP levels between 1 and 3 mg L−1 to below 1 mg L−1 in the group of 41-year-old subjects. Hazard ratios for suPAR are shown for each age group per 1-ng increase in suPAR concentration. When comparing the HR values, it should be noted that there are 19 steps in the suPAR model compared with two in the CRP model. Multivariate analysis was adjusted for gender and smoking status. In mutually adjusted analysis, CRP was added to multivariate analysis for suPAR, and suPAR was added for CRP. Z is the test-size.

suPAR 411.38 (1.23–1.56)5.28<0.00011.34 (1.19–1.52)4.69<0.00011.34 (1.18–1.51)4.72<0.0001
suPAR 511.28 (1.07–1.52)2.750.011.24 (1.03–1.48)2.30.021.25 (1.03–1.51)2.300.02
suPAR 611.16 (1.02–1.32)2.190.031.12 (0.98–1.28)1.680.091.10 (0.95–1.26)1.280.2
suPAR 711.09 (0.94–1.27)1.140.251.08 (0.93–1.26)1.010.311.06 (0.91–1.25)0.750.45
CRP 1–3 410.73 (0.31–1.75)−0.700.480.67 (0.28–1.6)−0.890.370.64 (0.27–1.54)−0.990.32
CRP 1–3 511.07 (0.57–2)0.200.841.02 (0.54–1.91)0.060.950.97 (0.52–1.82)0.100.92
CRP 1–3 611.37 (0.79–2.37)1.110.271.34 (0.77–2.33)1.040.301.3 (0.75–2.27)0.930.35
CRP 1–3 711.00 (0.59–1.7)0.010.990.90 (0.53–1.52)–0.410.680.88 (0.52–1.5)−0.460.65
CRP >3 411.21 (0.52–2.8)0.450.651.04 (0.45–2.42)0.10.920.92 (0.4–2.14)−0.190.85
CRP >3 511.19 (0.63–2.27)0.540.591.09 (0.57–2.09)0.270.790.92 (0.47–1.8)−0.250.8
CRP >3 611.65 (0.96–2.83)1.810.071.58 (0.92–2.72)1.660.101.49 (0.86–2.59)1.410.16
CRP >3 711.35 (0.81–2.25)1.150.251.19 (0.71–1.99)0.670.501.15 (0.68–1.95)0.520.6

With regard to CRP, univariate analysis showed a trend towards increased risk of cancer with CRP levels above 3 mg L−1 compared with CRP levels below 1 mg L−1 amongst the 61-year-old subjects (P = 0.07, Table 2). After adjustment for gender, smoking and suPAR, no significant association between CRP and development of cancer was observed (Table 2). The cumulative incidence of cancer according to age- and gender-specific suPAR quartiles is shown in Fig. 3a.

image

Figure 3. Gender- and age-specific cumulative incidence plots for cancer (a), CVD (b) and T2D (c). The Y-axis refers to the proportion of individuals having an event. The darkest grey line (1. suPAR quartile) refers to the 323 men and 327 women with suPAR in the lowest quartile (0–25%). Similarly, the lightest grey line (4. suPAR quartile) refers to the 323 men and 327 women with the highest suPAR level (75–100%).

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suPAR and risk of CVD

At the time of blood sampling, 151 individuals had experienced a CVD event and, because previous CDV diagnoses were associated with increased baseline suPAR levels (Table 1), these subjects were excluded from further CVD analysis (Fig. 1). During the 28 117 person-years of follow-up, there were 301 incident cases of CVD.

As a result of the interaction between suPAR and age, we analysed the association between suPAR level and risk of CVD within the individual age groups. Table 3 shows that an increase in suPAR level was associated with an increased risk of CVD during follow-up. After adjustment for the Framingham risk score variables (gender, smoking, total cholesterol, HDL cholesterol, systolic blood pressure, treatment for hypertension and baseline diabetes) and CRP, suPAR remained associated with the risk of CVD. It is interesting that the effect of suPAR was age related: the younger the age group, the stronger the effect (Table 3) (P for trend = 0.04). Figure 3b shows the cumulative incidence of CVD according to gender- and age-specific suPAR quartiles.

Table 3. Age group-related hazard ratio (HR) for CVD
CVDHR (95% CI) UnivariatezPHR (95% CI) MultivariatezPHR (95% CI) MutuallyzP
  1. CRP 1–3 (mg L−1) and CRP >3 (mg L−1) are compared with CRP <1 mg L−1. E.g. CRP 1–3 41 compares CRP levels between 1 and 3 mg L−1 to below 1 mg L−1 in the group of 41-year-old subjects. Hazard ratios for suPAR are shown for each age group per 1-ng increase in suPAR concentration. Multivariate analysis is adjusted for variables in Framingham risk score: gender, smoking status, total cholesterol, HDL cholesterol, systolic blood pressure, treatment for hypertension and baseline diabetes. In mutually adjusted analysis, CRP was added to multivariate analysis for suPAR, and suPAR was added for CRP.

suPAR 411.36 (1.18–1.57)4.23<0.00011.32 (1.14–1.52)3.73<0.00011.32 (1.14–1.52)3.66<0.0001
suPAR 511.31 (1.12–1.53)3.37<0.00011.25 (1.07–1.47)2.730.011.22 (1.03–1.44)2.280.02
suPAR 611.18 (1.03–1.35)2.420.021.13 (0.99–1.3)1.790.071.09 (0.95–1.26)1.210.23
suPAR 711.16 (1.08–1.24)4.07<0.00011.20 (1.11–1.29)4.63<0.00011.18 (1.09–1.27)4.09<0.0001
CRP 1–3 411.17 (0.48–2.88)0.340.730.93 (0.38–2.3)−0.150.880.89 (0.36–2.2)−0.250.80
CRP 1–3 511.66 (0.87–3.18)1.530.131.50 (0.78–2.88)1.220.221.45 (0.75–2.78)1.110.27
CRP 1–3 611.53 (0.81–2.91)1.300.191.36 (0.71–2.59)0.930.351.34 (0.7–2.55)0.880.38
CRP 1–3 711.09 (0.63–1.88)0.300.761.01 (0.58–1.76)0.030.980.97 (0.55–1.69)−0.110.91
CRP >3 411.90 (0.79–4.57)1.440.151.44 (0.6–3.47)0.810.421.24 (0.51–3)0.480.63
CRP >3 512.39 (1.26–4.52)2.670.012.04 (1.07–3.88)2.180.031.78 (0.92–3.45)1.710.09
CRP >3 612.57 (1.4–4.73)3.03<0.00012.13 (1.15–3.96)2.390.022.05 (1.1–3.86)2.240.02
CRP >3 711.72 (1.03–2.88)2.060.041.64 (0.97–2.79)1.830.071.45 (0.84–2.49)1.340.18

Amongst 51-, 61- and 71-year-old individuals, a CRP level above 3 mg L−1 compared with CRP levels below 1 mg L−1 was associated with increased risk in univariate analysis. After adjusting for the Framingham risk variables and suPAR, CRP remained significant for the 61-year-old subjects (Table 3).

suPAR and risk of T2D

At the time of inclusion, 81 participants had diabetes. Amongst the remaining 2360 individuals with complete available data, 59 developed T2D during the 27 999 person-years of follow-up (Fig. 1). A significant increase in the development of T2D was observed with increasing suPAR levels (per 1 ng) with an HR of 1.17 (95% CI, 1.04–1.31).

In the multivariate model including age, gender, fasting blood glucose and body mass index (BMI), a 1-ng increase in suPAR was associated with an HR of 1.23 (95% CI, 1.09–1.39) of developing T2D. After addition of CRP to the multivariate model, the resulting HR per 1-ng increase in suPAR was 1.18 (95% CI, 1.03–1.36).

Individuals with CRP levels of 1–3 mg L−1 had a multivariate HR of 1.11 (95% CI, 0.47–2.62) whereas those with levels above 3 mg L−1 had an HR of 2.46 (95% CI, 1.1–5.51), in both cases when compared with those with CRP levels below 1 mg L−1. When further adding suPAR to the multivariate model, the resulting HR values were 1.04 (95% CI, 0.44–2.46) and 2.09 (95% CI, 0.92–4.74) for CRP levels of 1–3 and above 3 mg L−1 respectively. As a result of the small number of events, we did not carry out specific analysis within each of the four age groups. Figure 3c shows a cumulative incidence plot of T2D according to age- and gender-specific suPAR quartiles.

Mortality

A total of 411 participants died during follow-up; 13 were lost to follow-up because of migration. In univariate analysis, a 1-ng increase in suPAR was associated with increased mortality in each of the four age groups (Table 4). After adjusting for gender, smoking, exercise, BMI, systolic blood pressure, treatment for hypertension, baseline diabetes, alcohol use and CRP level, suPAR remained significantly associated with increased risk of mortality in all age groups (Table 4).

Table 4. Age group-related hazard ratio (HR) for mortality
MortalityHR (95% CI) UnivariatezPHR (95% CI) MultivariatezPHR (95% CI) MutuallyzP
  1. CRP 1–3 (mg L−1) and CRP >3 (mg L−1) are compared with CRP <1 mg L−1. E.g. CRP 1–3 41 compares CRP levels between 1 and 3 mg L−1 to below 1 mg L−1 in the group of 41-year-old subjects year age group. Hazard ratios for suPAR are shown for each age group per 1-ng increase in suPAR concentration. Multivariate analysis was adjusted for gender, baseline diabetes, treatment for hypertension, smoking status, systolic blood pressure and alcohol use. In mutually adjusted analysis, CRP was added to multivariate analysis for suPAR, and suPAR was added for CRP.

suPAR 411.42 (1.24–1.63)5.08<0.00011.39 (1.21–1.59)4.62<0.00011.38 (1.20–1.59)4.50<0.0001
suPAR 511.53 (1.33–1.76)5.93<0.00011.44 (1.25–1.67)4.95<0.00011.44 (1.24–1.68)4.76<0.0001
suPAR 611.34 (1.22–1.47)6.06<0.00011.31 (1.18–1.45)5.16<0.00011.26 (1.13–1.41)4.14<0.0001
suPAR 711.11 (1.05–1.18)3.37<0.00011.13 (1.05–1.21)3.44<0.00011.10 (1.02–1.19)2.580.01
CRP 1–3 411.12 (0.38–3.33)0.200.840.78 (0.25–2.46)−0.430.670.80 (0.25–2.58)−0.370.71
CRP 1–3 511.00 (0.52–1.92)0.001.000.95 (0.49–1.85)−0.150.880.86 (0.44–1.67)−0.450.65
CRP 1–3 611.03 (0.60–1.77)0.120.901.12 (0.63–1.97)0.380.71.04 (0.59–1.84)0.140.89
CRP 1–3 710.77 (0.52–1.13)−1.340.180.65 (0.44–0.97)−2.090.040.64 (0.43–0.96)−2.170.03
CRP >3 411.64 (0.55–4.88)0.890.371.27 (0.43–3.79)0.430.671.18 (0.39–3.55)0.290.77
CRP >3 511.69 (0.92–3.12)1.690.091.39 (0.74–2.61)1.020.311.00 (0.52–1.92)−0.010.99
CRP >3 612.28 (1.41–3.68)3.34<0.00012.10 (1.25–3.53)2.790.011.73 (1.01–2.94)2.010.04
CRP >3 711.55 (1.09–2.19)2.460.011.37 (0.95–1.96)1.710.091.28 (0.88–1.84)1.300.19

A CRP level above 3 mg L−1 compared with a CRP level below 1 mg L−1 was associated with increased risk of mortality in both univariate and multivariate analysis for 61-year-old individuals, but not for subjects in the other age groups (Table 4).

Figure 4(a, b) show Kaplan–Meier analysis for age-specific suPAR quartiles for men and women respectively. The observed difference (area under curve) in survival for individuals in the lowest suPAR quartile compared with those in the highest suPAR quartile was 8.4 years for men and 4.7 years for women (both P < 0.0001).

image

Figure 4. Kaplan–Meier plot showing age-specific suPAR quartiles and survival for men (a) and women (b). For each age group (41, 51, 61 and 71 years), suPAR was divided into quartiles. The darkest grey line (1. suPAR quartile) refers to individuals with suPAR in the lowest quartile (0–25%). Similarly, the lightest grey line (4. suPAR quartile) refers to individuals with the highest suPAR level (75–100%).

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Association between suPAR and CRP

C-reactive protein and suPAR were positively correlated (age- and gender-adjusted Spearman partial correlation, ρ = 0.297, P < 0.0001). However, the two biomarkers were differently associated with cardio-metabolic risk factors (Table 5). As expected, BMI and waist circumference correlated significantly and positively with CRP, whereas no significant association was found with suPAR.

Table 5. Age- and gender-adjusted Spearman partial correlation coefficients between plasma levels of suPAR and CRP and selected cardio-metabolic risk factors
VariablessuPARCRP
  1. *P < 0.0001; **P < 0.001; ***P < 0.05.

Total cholesterol−0.0140.008
HDL cholesterol−0.156*−0.237*
Triglycerides0.120*0.258*
Demographic variables
Body mass index−0.0100.312*
Waist circumference0.0380.327*
Glucose0.042***0.180*
Haemodynamic variables
Systolic blood pressure−0.0020.142*
Diastolic blood pressure−0.071**0.129*

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Cohort description
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

To our knowledge, this is the first study to investigate the association between suPAR and disease development and mortality in the general population. Plasma suPAR concentration was associated with development of cancer, CVD, T2D and mortality in the general population, and these associations were robust after adjustment for a number of known risk factors for these common diseases and death.

In recent years, the role of inflammation has been addressed in various diseases, including viral and bacterial infections, cancer and T2D, as well as in acute care, and an elevation in inflammatory markers is generally associated with a negative outcome. Several studies have addressed the prognostic value of suPAR and results have shown that plasma suPAR levels are generally elevated in individuals with disease, including viral [15, 17], bacterial [31, 32] and parasitic infections [18, 33], certain cancers [34] and autoimmune diseases [14]. In all cases, those with the highest suPAR levels have increased risk of death (reviewed in [35]).

Similarly, in healthy individuals, inflammation or LGI is also associated with risk of developing disease, such as CVD. Studies such as the JUPITER trial (Justification for the Use of statins in Primary prevention: an Intervention Trial Evaluating Rosuvastatin) have indicated that amelioration of a state of LGI may be a mechanism to prevent or delay disease from occurring [36, 37]. It remains unclear whether anti-inflammatory treatment lowers suPAR levels and whether lowering of suPAR levels would indeed lower the risk of disease; however, effective treatment of infectious diseases [31, 38] and cancer [20] leads to a commensurate decrease in suPAR plasma levels.

It is not known whether suPAR, per se, adds to the inflammatory state of the individual. The suPAR molecule has intrinsic chemotactic properties and by binding to its receptor, FPRL-1, it modulates the ability of monocytes to migrate in response to other chemokines [39]. On the other hand, pro-inflammatory molecules, such as IL-1β, have been shown to increase suPAR release from endothelial cells [40]. However, this study provides no insight into whether suPAR has a causative role in disease development or whether suPAR merely reflects a disease-promoting mechanism such as inflammation.

C-reactive protein and suPAR correlated positively in this study; however, the strength of the correlation was moderate (r2∼9%) and, accordingly, suPAR and CRP levels independently predicted CVD, T2D and mortality. CRP and suPAR seem to reflect different aspects of inflammation, thus suPAR appears less related to anthropometric parameters characterizing a dysmetabolic phenotype, as demonstrated by the lack of association between suPAR and BMI or waist circumference. The predictive strength of suPAR was strongest in the younger age groups, which may indicate that suPAR is elevated early in the disease process and that young individuals are more susceptible than older subjects to the negative effect of inflammation. It may influence the results that young individuals with high levels of inflammation are less likely to reach the age of 71; hence the 71-year-old subjects are a selected group of individuals merely as a result of the fact they have reached this age.

C-reactive protein has been described as an early disease indicator in CVD disease when compared with, for example, N-terminal pro-B-type natriuretic peptide (NT-proBNP) [41]. Thus, whereas NT-proBNP seems to be of more value in elderly subjects, CRP and in particular suPAR seem to be most important in younger individuals.

C-reactive protein is a highly inducible acute-phase protein, whereas suPAR does not show major variation in blood levels. Lipopolysaccharide infusion in human subjects results in less than two-fold increase in plasma suPAR level [42] and, in contrast to many pro-inflammatory cytokines, circadian suPAR levels (measured every 20 min for 24 h) have been shown to be relatively constant [13]. The stability of the suPAR protein, both in vivo [13] and in vitro [22] may be an important quality of suPAR as a marker of the underlying low-grade inflammatory processes leading to disease. It is currently unknown whether other proteins of the plasminogen activator system, such as urokinase or plasminogen activator inhibitor-1 (PAI-1), which are useful prognostic indicators in certain cancers [43], are also associated with disease development in the general population.

Whereas multiple factors are commonly used to predict the risk of developing CVD or T2D, very few plasma biomarkers are related to the risk of developing cancer. Recently, the plasma level of YKL-40 protein was shown to be associated with development of gastrointestinal cancer in healthy individuals [44]. The discovery of biomarkers for cancer is an important step towards establishing predictive algorithms for cancer, similar to those available for CVD and T2D. Identification of individuals with increased risk of developing cancer could lead to earlier intervention and, hence, better long-term outcomes.

Preventive medicine has an impact not only at the individual level, but also economically on society in general. The present results indicate that suPAR level is associated with development of disease and its measurement may enable physicians to better target early primary prevention. However, further analysis using appropriate methods for the evaluation of each individual marker is needed to compare the contributions of suPAR and current markers used in algorithms to assess the risk of disease. Such analysis of markers associated with disease development may result in identification of parameters that can define the state of LGI.

Amongst the limitations of this study is the fact that all the participants were Caucasian and therefore further studies are needed to determine whether the association between suPAR and disease is also present in other populations. Although individuals receiving treatment with antihypertensive, lipid-lowering or antidiabetic medication were not excluded from this analysis, a separate analysis excluding these individuals did not change the conclusions of the study (data not shown). Finally, the study addressed each disease as one end-point (e.g. any cancer) and further subgroup analysis is needed to determine whether the prognostic value of suPAR is linked to specific types of disease (e.g. different cancers).

In conclusion, an elevated plasma suPAR concentration is associated with increased risk of developing cancer, CVD or T2D and with shorter life expectancy in the general population. The use of this early warning inflammatory biomarker in standard screening procedures could potentially improve our current ability to predict mortality and these major diseases.

Conflict of interest statement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Cohort description
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Jesper Eugen-Olsen is a founder, shareholder and board member of ViroGates A/S, Denmark, the company that produces the suPARnosticÒ assay. Jesper Eugen-Olsen, Ove Andersen and Steen B. Haugaard are inventors on a patent on suPAR and risk. Copenhagen University Hospital Hvidovre, Denmark, owns the patent, which is licensed to ViroGates A/S.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Cohort description
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

We are grateful to Drs. Troels Bygum Knudsen, Kristian Kofoed, Jørgen Thorball, Morten Ruhwald and Klaus Larsen for discussion, and to Anja Stausgaard for technical support.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Cohort description
  5. Results
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References