Abstract
Aims
To investigate whether the adverse impact of lower educational attainment on mortality risk in patients with coronary artery disease (CAD) is mediated by the activation of inflammatory and immune pathways, estimated as elevated soluble urokinase plasminogen activator receptor levels.
Methods and results
In 3164 patients undergoing coronary angiography, we investigated multivariable associations between suPAR and educational attainment and assessed the relationship between a lower educational level (defined as a high-school degree or less as the highest educational qualification) and outcomes using Cox proportional hazard and Fine and Gray’s subdistribution competing risk models. The potential mediating effect through suPAR and high-sensitivity C-reactive protein (hs-CRP) was assessed using mediation analysis. A total of 1814 patients (57.3%) had achieved a higher (≥college) education level and 1350 patients (42.7%) a lower (≤high school) education level. Soluble urokinase plasminogen activator receptor levels were 9.0% [95% confidence interval (CI) 6.3–11.8, P ≤ 0.0001] higher in patients with lower educational qualifications than in those with higher educational qualifications after covariate adjustment. Lower educational attainment was associated with a higher risk of cardiovascular death after adjustment for demographic, clinical, and behavioural covariates, including CAD severity and heart failure history, medication use, and hs-CRP levels [hazard ratio 1.26 (95% CI 1.02–1.55, P = 0.03)]. However, after adjustment for suPAR levels, the effect of a lower educational level on cardiovascular death became insignificant. Values were similar for all-cause death. Soluble urokinase plasminogen activator receptor levels mediated 49% and hs-CRP levels 17% of the cardiovascular death risk attributable to lower educational attainment.
Conclusion
Circulating suPAR levels importantly mediate the effects of lower educational attainment on mortality, indicating the importance of systemic inflammation and immune dysregulation as biologic mediators of adverse social determinants of health.
Keywords: suPAR, Biology of adversity, CAD outcomes, Social determinants of health, Education
Graphical Abstract
Graphical Abstract.
See the editorial comment for this article ‘Soluble urokinase plasminogen activator receptor (suRAR) levels: underscoring the link between low educational attainment and death among coronary artery disease patients', by O.R. Coelho-Filho and R.D. Santos, https://doi.org/10.1093/eurjpc/zwad346.
Introduction
Social determinants of health (SDoH) have a significant impact on cardiovascular morbidity and mortality, but the underlying mechanisms that ultimately lead to socioeconomic inequities in coronary heart disease (CHD) burden and outcomes remain insufficiently understood.1–4 Chronic socioeconomic stress can result from individual and environmental factors such as poor education, low income, unemployment, discrimination, noise pollution, and food insecurity.5 A lower socioeconomic status (SES) may engender biological effects through unfavourable behavioural patterns that ultimately lead to adverse health outcomes.6–8 In this study, we have demonstrated that lower educational attainment is an independent predictor of cardiovascular and all-cause mortality in patients with CHD.9 It was previously assumed that the effect of lower SES on health, which is intimately associated with lower educational attainment, is associated with stress10 and that inflammation might be the biological mechanism that leads to a higher risk for CHD events.11 Although some studies have shown that social stress due to marginalization or social hardship is associated with systemic inflammation, measured as higher circulating levels of C-reactive protein (CRP) and interleukin-6 (IL-6), there is considerable heterogeneity between these studies, suggesting that the overall impact of inflammation may be limited.12,13
Soluble urokinase plasminogen activator receptor (suPAR) is a three-domain highly glycosylated phosphatidylinositol-linked membrane protein cleaved from bound urokinase-type plasminogen activator receptor (uPAR) that is present on immune, endothelial, and smooth muscle cells, fibroblasts, and others.14 Soluble urokinase plasminogen activator receptor levels are elevated in a variety of conditions and signify the activation of immune and inflammatory pathways. We have previously shown that higher suPAR levels are an independent determinant of a faster decline in renal function, incident chronic kidney disease, and acute kidney injury,15,16 and are also associated with the presence and severity of CHD and incident myocardial infarction (MI) or death,17 findings also supported by other investigators.18 In addition, suPAR levels are higher and prognostic in those with sepsis,19 complications from Type 1 diabetes,20 chronic liver disease,21 and in the critically ill.22 Therefore, suPAR appears to have a distinct and additive value in predicting a variety of negative health outcomes compared with other inflammatory markers of disease risk such as CRP.15,18,23–25 Recent studies have also shown that there is an increase in salivary suPAR levels following acute psychological stress,25 and plasma levels are higher in those experiencing stressful life events.26,27
Our aim in this study was to examine (i) the association between suPAR levels and educational attainment in patients with coronary artery disease (CAD) and (ii) whether the impact of lower education on long-term outcomes in this population is mediated by elevated suPAR levels, independent of traditional risk factors and inflammatory markers such as high-sensitivity (hs)-CRP. We hypothesized that suPAR levels would be elevated in vulnerable populations with lower educational attainment and mediate the higher risk of adverse events in this population.
Methods
Study population
The Emory Cardiovascular Biobank is an ongoing prospective cohort study of patients undergoing elective or emergent left heart catheterization across three Emory Healthcare sites in Atlanta, GA, USA.28 Subjects with congenital heart disease, severe valvular heart disease, severe anaemia, recent blood transfusion, myocarditis, active inflammatory disease, cancer, or <18 years of age are not enrolled. Participants provide written informed consent, and the study is approved by the institutional review board at Emory University.
Information on demographic characteristics, medical history, medication use, and lifestyle factors were obtained through the questionnaires and review of medical records. Prevalence of cardiovascular risk factors such as hypertension and diabetes was determined by the examining physician. Medical records were reviewed to confirm self-reported medical history. Weight and height were measured, and body mass index (BMI) was calculated (kg/m2). Education level was assessed by self-report, and patients were categorized based on their highest educational qualification ranging from elementary or middle school, some high school, high-school graduate, some college, college graduate, to graduate education or degree.
Follow-up of the patients was conducted by blinded personnel via telephone interview, chart review, Social Security Death Index, and State records, as described previously.28 Two blinded physicians independently adjudicated the cause of death. Cardiovascular death was defined as death from MI, heart failure, stroke, pulmonary embolism, or as a complication during any cardiovascular-related procedure. All-cause death was defined as the total number of deaths from any cause within follow-up.
Sample collection and laboratory measurements
Our cohort was recruited between April 2004 and May 2015. Arterial blood samples were obtained before cardiac catheterization. Serum and plasma were stored at −80°C.
Plasma levels of suPAR were measured by using the suPARnostic kit (ViroGates, Birkerød, Denmark). It has a lower detection limit of 100 pg/mL, and the intra- and inter-assay variations are 2.75 and 9.17%, respectively.15 Concentrations of hs-CRP were measured using the particle-enhanced immunoturbidimetry assay (FirstMark, a division of GenWay Biotech, San Diego, CA, USA) and Abbott Architect platform (Abbott Laboratories, Abbott Park, IL, USA) with a lower detection limit of 0.03 mg/L. Total cholesterol, HDL-cholesterol, and triglycerides were assayed (Beckman Coulter Inc., Brea, CA, USA) and LDL-cholesterol (mg/dL) was calculated using Friedewald formula.29 The estimated glomerular filtration rate (eGFR; mL/min/1.73 m2) was computed using the Chronic Kidney Disease Epidemiology Collaboration equation.30
Statistical analysis
Participants were assigned as achieving a low educational level when they had a high-school degree or less as their highest qualification and a higher educational qualification when they had a college education or higher.
To compare baseline characteristics between the two groups, two sample t-tests for normally distributed continuous variables, Wilcoxon rank-sum test for abnormally distributed continuous variables, and χ2 test for categorical variables were used where appropriate. The suPAR levels were illustrated using a box plot across six groups of education levels, including elementary or middle school, some high school, high-school graduate, some college, college graduate, graduate education, or degree.
Log 2 transform was performed for suPAR and hs-CRP values to achieve normality before modelling. We assessed multivariate associations between circulating suPAR levels and demographic and clinical characteristics such as age, sex, race (White vs. non-White), education (high vs. low), smoking, diabetes, hypertension, BMI, total cholesterol, LDL-C, HDL-C, triglycerides, hs-CRP, eGFR, ejection fraction (EF), and obstructive CAD defined as ≥50% stenosis in ≥1 epicardial artery, using linear regression. Cox proportional hazard models were used to investigate the association between educational qualification and risk of all-cause death with stepwise adjustment for the aforementioned covariates, history of MI, heart failure, percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), and use of aspirin daily, P2Y12-receptor inhibitors, statins, beta-blockers, angiotensin antagonists, and alcohol consumption, and by finally additionally adjusting for hs-CRP or suPAR levels. For the outcome of cardiovascular death, Fine and Gray’s subdistribution competing risk models were used, treating the non-cardiovascular death events as competing risk.31 Mediation analyses were performed to assess the total effect of educational qualification on mortality, and the indirect effect mediated through hs-CRP and suPAR using accelerated failure time models under exponential distribution to compare the survival time by education groups.32 Statistical significance was defined as P < 0.05. Analyses were conducted using SAS, version 9.4 (SAS Institute) and R, version 4.0.2 (https://www.R-project.org/).
Results
Baseline characteristics by education level are displayed in Table 1. Patients with a lower educational qualification are more likely to be female, have higher rates of diabetes, previous MI, and obstructive CAD, are more likely to be on a P2Y12-inhibitor, be current or former smokers, have a lower eGFR, higher levels of suPAR, hs-CRP, total cholesterol, LDL-C, and triglycerides, lower HDL-C levels, and consume alcohol less frequently. All-cause mortality rates were 34.1 and 25.7% (P < 0.001) in the groups with lower and higher educational qualifications, respectively. Cardiovascular death also occurred significantly more often in the low education group than in the high education group.
Table 1.
Baseline characteristics by education level
| High school or lessa | College attendeesa | P-value | |
|---|---|---|---|
| n (%) | 1350 (42.67) | 1814 (57.33) | — |
| Age (years) | 64.8 ± 11.6 | 64.2 ± 12.1 | 0.17 |
| Male (%) | 837 (62) | 1322 (72.88) | <0.001 |
| White (%) | 1050 (77.78) | 1429 (78.78) | 0.5 |
| Hypertension (%) | 1085 (80.37) | 1431 (78.89) | 0.31 |
| Diabetes (%) | 529 (39.19) | 642 (35.39) | 0.03 |
| History of MI (%) | 395 (29.52) | 463 (25.79) | 0.02 |
| Heart failure (%) | 466 (34.52) | 575 (31.7) | 0.1 |
| Previous PCI (%) | 701 (51.93) | 907 (50) | 0.28 |
| Previous CABG (%) | 360 (26.67) | 510 (28.11) | 0.37 |
| Obstructive CADb (%) | 1061 (78.59) | 1362 (75.08) | 0.02 |
| EF (%) | 52.7 ± 12.5 | 53.2 ± 11.9 | 0.2 |
| Aspirin | 1123 (83.19) | 1513 (83.41) | 0.87 |
| P2Y12-receptor inhibitor | 772 (57.19) | 944 (52.04) | 0.004 |
| Statin | 1048 (77.63) | 1443 (79.55) | 0.19 |
| Beta-blocker | 992 (73.48) | 1277 (70.4) | 0.06 |
| ACE-inhibitor or ARB | 847 (62.74) | 1121 (61.8) | 0.59 |
| Current smoking (%) | 142 (10.52) | 111 (6.12) | <0.001 |
| Former smoking (%) | 828 (61.33) | 1113 (61.36) | <0.001 |
| Alcohol consumption | 0.002 | ||
| No current drinking | 886 (69.71) | 1075 (63.2) | |
| <8 drinks per week | 318 (25.02) | 503 (29.57) | |
| 8–14 drinks per week | 41 (3.23) | 79 (4.64) | |
| >14 drinks per week | 26 (2.05) | 44 (2.59) | |
| BMI (kg/m2) | 29.32 ± 5.8 | 29.51 ± 5.86 | 0.37 |
| eGFR (mL/min/1.73 m2) | 69.64 ± 23.76 | 71.36 ± 22.25 | 0.04 |
| Total cholesterol (mg/dL) | 167.55 ± 43.5 | 162.96 ± 42.6 | 0.003 |
| LDL-cholesterol (mg/dL) | 96.32 ± 37.64 | 92.35 ± 36.51 | 0.003 |
| HDL-cholesterol (mg/dL) | 41.79 ± 13.37 | 43 ± 13.61 | 0.01 |
| Triglycerides (mg/dL) | 149.33 ± 92.97 | 139.57 ± 100.66 | 0.005 |
| Biomarkers | |||
| SuPAR (pg/mL) (median, IQR) | 3182 (2475–4234) | 2798 (2204–3605) | <0.001 |
| Hs-CRP (mg/L) (median, IQR) | 3.3 (1.4–8.6) | 2.4 (1.0–6.8) | <0.001 |
| Events | |||
| Time to death (years) | 8.04 ± 4 | 8.22 ± 3.74 | 0.18 |
| All-cause death (%) | 460 (34.07) | 467 (25.74) | <0.001 |
| Cardiovascular death (%) | 213 (15.78) | 209 (11.52) | <0.001 |
Mean ± standard deviation shown unless stated.
MI, myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass graft; CAD, coronary artery disease; EF, ejection fraction; ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor Type 1 antagonists; BMI, body mass index; eGFR, estimated glomerular filtration rate; IQR, interquartile range.
aHigh school or less indicates elementary/middle school, some high school, or high-school graduate as having the highest educational qualification. College attendees have some college education, a college degree, graduate education, or a degree as the highest educational qualification.
bObstructive CAD defined as ≥50% stenosis in ≥1 epicardial artery.
Higher suPAR levels were associated with female sex, white race, diabetes, lower EF, lower eGFR, lower HDL-C, and higher hs-CRP levels (Table 2). The relationship between the six categories of educational attainment and suPAR levels is shown in Figure 1. Soluble urokinase plasminogen activator receptor levels were 9.0% [95% confidence interval (CI) 6.3–11.8, P ≤ 0.0001] higher in patients having a lower (≤high school) educational qualification than in those having a higher (≥college) educational qualification after adjustment for age, sex, race (White vs. non-White), hypertension, diabetes, obstructive CAD, EF, smoking, BMI, eGFR, hs-CRP, total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglyceride levels (Table 2). High-sensitivity CRP levels were also 9.7% (95% CI 0.2–20.1, P = 0.04) higher in the group with a lower education level than in the group with a higher education level after covariate adjustment, including suPAR.
Table 2.
Multivariable associations between soluble urokinase plasminogen activator receptor levels and the demographic, clinical, and behavioural characteristics of the cohort
| % Estimates | |
|---|---|
| Age | −0.004 (−0.1, 0.1) P = 0.99 |
| Male sex | −13 (−15.5, −10.4) P < 0.0001 |
| White race | 4 (0.8, 7.4) P = 0.01 |
| Educationa | 9 (6.3, 11.8) P < 0.0001 |
| Hypertension | −0.4 (−3.5, 2.8) P = 0.79 |
| Diabetes | 7.5 (4.6, 10.4) P < 0.0001 |
| Previous MI | −0.07 (−3.1, 3.1) P = 0.97 |
| Obstructive CADb | 0.8 (−2.2, 3.9) P = 0.6 |
| EF | −0.2 (−0.3, −0.1) P < 0.0001 |
| Current/former smoker | 1.2 (−1.5, 3.9) P = 0.38 |
| Alcohol consumption | −0.8 (−2.6, 1.1) P = 0.4 |
| BMI | −0.1 (−0.3, 0.1) P = 0.38 |
| eGFR | −0.8 (−0.9, −0.8) P < 0.0001 |
| Hs-CRPc | 0.05 (0.04, 0.06) P < 0.0001 |
| Total cholesterol | −0.07 (−0.2, 0.08) P = 0.37 |
| LDL-cholesterol | −0.003 (−0.2, 0.2) P = 0.97 |
| HDL-cholesterol | −0.2 (−0.4, −0.03) P = 0.02 |
| Triglycerides | −0.004 (−0.03, 0.03) P = 0.8 |
MI, myocardial infarction; CAD, coronary artery disease; EF, ejection fraction; BMI, body mass index; eGFR, estimated glomerular filtration rate.
aEducation low vs. high. Low educational qualification is defined as elementary/middle school, some high school, or high-school graduate as having the highest educational qualification. High educational qualification is defined as some college education, a college degree, graduate education, or a degree as the highest educational qualification.
bObstructive CAD defined as ≥50% stenosis in ≥1 epicardial artery.
cThe association between 1% higher in hs-CRP and suPAR levels in %.
Figure 1.
Plasma levels of the soluble urokinase plasminogen activator receptor through six different categories of educational attainment assessed in the Emory Cardiovascular Biobank. Based on the linear regression model, every one level higher of education is associated with lower levels of soluble urokinase plasminogen activator receptor by 5.3% (95% CI −6.3, −4.3%), P ≤ 0.0001.
Lower educational attainment was associated with a higher risk of all-cause death [hazard ratio (HR) 1.36 (95% CI 1.20–1.55, P ≤ 0.0001)] and cardiovascular death [HR 1.39 (95% CI 1.15–1.68, P = 0.0008)]. After adjustment for demographic, clinical, and behavioural covariates, CAD and heart failure history, as well as medication use, a lower educational level was associated with a 23 and 28% higher risk of all-cause and cardiovascular death, respectively. After additionally adjusting for hs-CRP levels, a lower education level was associated with a 23% higher risk for all-cause death and a 26% higher risk for cardiovascular death. After adjustment for suPAR levels, the effect of lower educational attainment became insignificant for both adverse outcomes (Table 3 and Figure 2).
Table 3.
Association between educational attainment and incident adverse outcomes
| Outcomes: low vs. high educationa | Unadjusted | Model 1 | Model 2 | Model 3 |
|---|---|---|---|---|
| Death | 1.36 (1.20–1.55); P ≤ 0.0001 | 1.23 (1.08–1.41); P = 0.0026 | 1.23 (1.08–1.42); P = 0.0028 | 1.14 (1.00–1.31); P = 0.06 |
| Cardiovascular death | 1.39 (1.15–1.68); P = 0.0008 | 1.28 (1.04–1.58); P = 0.02 | 1.26 (1.02–1.55); P = 0.03 | 1.18 (0.96–1.46); P = 0.12 |
Model 1: adjusted for age, sex, race (White vs. non-White), diabetes, hypertension, history of MI, heart failure, previous PCI, previous CABG, EF, obstructive CAD defined as ≥50% stenosis in ≥1 epicardial artery, smoking, use of aspirin daily, P2Y12-receptor inhibitors, statins, beta-blockers, angiotensin antagonists, alcohol consumption, BMI, eGFR, total cholesterol, LDL-C, HDL-C, and triglycerides. Model 2: adjusted for covariates in Model 1 and hs-CRP. Model 3: adjusted for covariates in Model 1 and suPAR.
aEducation low vs. high. Low educational qualification is defined as elementary/middle school, some high school, or high-school graduate as the highest educational qualification. High educational qualification is defined as some college education, a college degree, graduate education, or a degree as the highest educational qualification.
Figure 2.
Effect of a low educational qualification, defined as elementary/middle school, some high school, or high-school graduate as having the highest educational qualification, on adverse outcomes. Reference high educational qualification defined as some college education, a college degree, graduate education, or a degree as the highest educational qualification. Model 1: adjusted for age, sex, race (White vs. non-White), diabetes, hypertension, history of myocardial infarction, heart failure, previous percutaneous coronary intervention, previous coronary artery bypass graft, ejection fraction, obstructive coronary artery disease defined as ≥50% stenosis in ≥1 epicardial artery, smoking, use of aspirin daily, P2Y12-receptor inhibitors, statins, beta-blockers, angiotensin antagonists, alcohol consumption, body mass index, estimated glomerular filtration rate, total cholesterol, LDL-C, HDL-C, and triglycerides. Model 2: adjusted for covariates in Model 1 and high-sensitivity C-reactive protein. Model 3: adjusted for covariates in Model 1 and soluble urokinase plasminogen activator receptor.
A 100% higher suPAR level was associated with a higher risk of cardiovascular and all-cause death after full covariate adjustment, including low educational level and hs-CRP [HR 2.01 (95% CI 1.66–2.43, P < 0.0001) and HR 2.11 (95% CI 1.86–2.39, P < 0.0001), respectively]. A 100% higher hs-CRP level was also associated with an increased risk for both adverse outcomes [HR 1.07 (95% CI 1.01–1.13, P = 0.02) and HR 1.11 (95% CI 1.07–1.15, P < 0.0001)] after full covariate adjustment, including low educational level and suPAR.
In the mediation analysis, the mean survival time ratio of low education compared with higher education was 71% for all-cause death and 68% for cardiovascular death. Soluble urokinase plasminogen activator receptor levels mediated 49% of the cardiovascular death risk due to lower educational attainment and 50% of the risk for all-cause death, which was more than hs-CRP, which mediated 17% for these adverse outcomes (P < 0.0001; Table 4 and Graphical Abstract).
Table 4.
Effect of low education on survival mediated by soluble urokinase plasminogen activator receptor and high-sensitivity C-reactive protein
| Outcome | Educationa | Educationb mediated by suPAR | Proportion mediated by suPAR (%) | Educationb mediated by hs-CRP | Proportion mediated by hs-CRP (%) |
|---|---|---|---|---|---|
| Death | 71% (62–82%); P < 0.0001 | 83% (80–87%); P < 0.0001 | 50 | 94% (92–97%); P < 0.0001 | 17 |
| Cardiovascular Death | 68% (56–83%); P < 0.0001 | 81% (77–86%); P < 0.0001 | 49 | 93% (91–96%); P < 0.0001 | 17 |
Low educational qualification is defined as elementary/middle school, some high school, or high-school graduate as the highest educational qualification. High educational qualification is defined as some college education, a college degree, graduate education, or a degree as the highest educational qualification.
aTotal effect of low education (reference high education) on mean survival time. Estimates of percentages indicate the mean survival time ratio of low education compared with higher education.
bNatural indirect effect of low education (reference high education) on mean survival time, mediated by suPAR or hs-CRP. Estimates of percentages indicate the mean survival time ratio of low education compared with higher education.
Discussion
It is well recognized that SDoH, and particularly educational attainment, influence the risk of development of cardiovascular disease and its associated mortality.9,33,34 Increased cardiovascular risk in people with a lower SES has been attributed to a higher prevalence of behavioural and other risk factors, poor healthcare access, greater exposure to high-risk environments, and other factors.5 However, none of these factors fully account for the higher risk and mortality in this population.35 Moreover, the biologic pathways through which this risk is mediated are still being explored. Previous studies have suggested that higher inflammation, assessed as elevated levels of hs-CRP and IL-6, may be partly responsible for mediating the higher risk in socioeconomically vulnerable populations,36 although these findings remain controversial.12 In a population of patients with CHD, we confirm the role of inflammation, measured as elevated hs-CRP levels, as a biologic determinant of the impact of poor educational attainment on long-term mortality. The novel findings from our study pertain to the important role of suPAR as a biological mediator of the effects of educational qualification on cardiovascular and all-cause mortality. The impact of suPAR was significantly higher than for hs-CRP levels. In fact, after adjusting for suPAR levels, the independent effect of educational attainment on cardiovascular and all-cause mortality was no longer significant. Thus, in addition to inflammation, immune and renal dysregulation, which are encapsulated in suPAR values,37 are mediators of the impact of lower educational attainment on adverse outcomes.
Socioeconomic adversity has previously been associated with activation of the sympatho-adrenomedullary and hypothalamic–pituitary–adrenal axes, glucocorticoid receptor resistance, catecholamine signalling pathways, telomere shortening, impairment and shift in subsets of critical immune cells, and others.10,36,38 The mechanisms that lead to increases in suPAR levels when these pathways are upregulated need to be further investigated. Our findings are in line with previous investigations that demonstrated the effects of hs-CRP, suPAR, and the educational level on mortality risk in people with CAD.9,39,40 However, we now demonstrate the important mediation of the effects of lower educational attainment on adverse outcomes through suPAR and hs-CRP.
The strengths of our study include the large size of the cohort and an extended follow-up. Limitations include the fact that overall, our population had higher average educational levels than the national average reported by the US Census Bureau.9 However, there were large numbers of participants within each category of educational qualification. Educational attainment was the only individual-level determinant of SES that was available for this cohort. Whether the impact of other measures of SES such as income and occupational status on health outcomes is also associated with systemic inflammation and immune dysregulation needs to be studied. As our cohort is recruited from patients undergoing left heart catheterization with CHD from a single centre, our results may not be generalizable to the general population without CHD.
Contributor Information
David Füller, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA; Brandenburg Medical School (Theodor Fontane), Brandenburg an der Havel, Germany.
Chang Liu, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Yi-An Ko, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Ayman A Alkhoder, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Shivang R Desai, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Zakaria Almuwaqqat, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Shivani A Patel, Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Kiran Ejaz, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Tanveer Kauser, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Mohamed Afif Martini, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Zain Alvi, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Puja K Mehta, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Laurence S Sperling, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Arshed A Quyyumi, Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Emory University School of Medicine, 1462 Clifton Road NE, Suite 507, Atlanta, GA 30322, USA.
Author contributions
D.F., C.L., and A.A.Q. conceptualized the study and contributed to the methodology. D.F. was responsible for drafting the original manuscript. C.L. conducted the formal analysis, participated in the investigation, and critically reviewed and edited the manuscript. Y.-A.K., A.A.A., Z.Alm., K.E., T.K., M.A.M., and Z.Alv. curated the data and reviewed and edited the manuscript. Y.-A.K., A.A.A., K.E., and A.A.Q. managed project administration tasks. S.R.D., S.A.P., P.K.M., and L.S.S. contributed to the investigation and reviewed and edited the manuscript. A.A.Q. acquired funding, was responsible for the project administration, participated in the investigation, and reviewed and edited the manuscript.
Disclosures
The suPARnostic kits were donated by ViroGates, Birkerød, Denmark, and hs-CRP measurements were conducted by FirstMark, a division of GenWay Biotech, San Diego, CA, USA, and Abbott Laboratories, Abbott Park, IL, USA. Neither ViroGates, FirstMark, or Abbott Laboratories had any role in data analysis or manuscript preparation.
Funding
A.A.Q. has been supported by National Institutes of Health grants P01HL154996-01A1, R33HL138657-05, U54AG062334-01, P30DK111024-07S2, R61HL154116-01, R01HL109413-07, R01HL166004-01, 15SFCRN23910003, 5P01HL086773-09, 5P01HL101398-05, and 1P20HL113451-04.
Data availability
The data that support the findings of this study are available from the corresponding author on reasonable request.
References
- 1. Galea S, Tracy M, Hoggatt KJ, DiMaggio C, Karpati A. Estimated deaths attributable to social factors in the United States. Am J Public Health 2011;10:1456–1465. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Füller D, Jaehn P, Andresen-Bundus H, Pagonas N, Holmberg C, Christ M, et al. Impact of the educational level on non-fatal health outcomes following myocardial infarction. Curr Probl Cardiol 2022;47:101340. [DOI] [PubMed] [Google Scholar]
- 3. Essien UR, Kornej J, Johnson AE, Schulson LB, Benjamin EJ, Magnani JW. Social determinants of atrial fibrillation. Nat Rev Cardiol 2021;18:763–773. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Stringhini S, Carmeli C, Jokela M, Avendaño M, Muennig P, Guida F, et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1·7 million men and women. Lancet 2017;389:1229–1237. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Kaplan GA, Keil JE. Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 1993;88:1973–1998. [DOI] [PubMed] [Google Scholar]
- 6. Kanjilal S, Gregg EW, Cheng YJ, Zhang P, Nelson DE, Mensah G, et al. Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971–2002. Arch Intern Med 2006;166:2348–2355. [DOI] [PubMed] [Google Scholar]
- 7. Tandon PS, Kroshus E, Olsen K, Garrett K, Qu P, McCleery J. Socioeconomic inequities in youth participation in physical activity and sports. Int J Environ Res Public Health 2021;18:6946. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Lam CSP. The socioeconomics of hypertension. Hypertension 2011;58:140–141. [DOI] [PubMed] [Google Scholar]
- 9. Kelli HM, Mehta A, Tahhan AS, Liu C, Kim JH, Dong TA, et al. Low educational attainment is a predictor of adverse outcomes in patients with coronary artery disease. J Am Heart Assoc 2019;8:e013165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Baum A, Garofalo J, Yali AM. Socioeconomic status and chronic stress: does stress account for SES effects on health? Ann N Y Acad Sci 1999;896:131–144. [DOI] [PubMed] [Google Scholar]
- 11. Wirtz PH, von Känel R. Psychological stress, inflammation, and coronary heart disease. Curr Cardiol Rep 2017;19:111. [DOI] [PubMed] [Google Scholar]
- 12. Muscatell KA, Brosso SN, Humphreys KL. Socioeconomic status and inflammation: a meta-analysis. Mol Psychiatry 2020;25:2189–2199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Neergheen VL, Topel M, Van Dyke ME, Sullivan S, Pemu PE, Gibbons GH, et al. Neighborhood social cohesion is associated with lower levels of interleukin-6 in African American women. Brain Behav Immun 2019;76:28–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Thunø M, Macho B, Eugen-Olsen J. suPAR: the molecular crystal ball. Dis Markers 2009;27:504294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Hayek SS, Sever S, Ko Y-A, Trachtman H, Awad M, Wadhwani S, et al. Soluble urokinase receptor and chronic kidney disease. N Engl J Med 2015;373:1916–1925. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Hayek SS, Leaf DE, Samman Tahhan A, Raad M, Sharma S, Waikar SS, et al. Soluble urokinase receptor and acute kidney injury. N Engl J Med 2020;382:416–426. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Eapen DJ, Manocha P, Ghasemzadeh N, Patel RS, Kassem HA, Hammadah M, et al. Soluble urokinase plasminogen activator receptor level is an independent predictor of the presence and severity of coronary artery disease and of future adverse events. J Am Heart Assoc 2014;3:e001118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Eugen-Olsen J, Andersen O, Linneberg A, Ladelund S, Hansen TW, Langkilde A, et al. Circulating soluble urokinase plasminogen activator receptor predicts cancer, cardiovascular disease, diabetes and mortality in the general population. J Intern Med 2010;268:296–308. [DOI] [PubMed] [Google Scholar]
- 19. Koch A, Voigt S, Kruschinski C, Sanson E, Dückers H, Horn A, et al. Circulating soluble urokinase plasminogen activator receptor is stably elevated during the first week of treatment in the intensive care unit and predicts mortality in critically ill patients. Crit Care 2011;15:R63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Theilade S, Lyngbæk S, Hansen TW, Eugen-Olsen J, Fenger M, Rossing P, et al. Soluble urokinase plasminogen activator receptor levels are elevated and associated with complications in patients with type 1 diabetes. J Intern Med 2015;277:362–371. [DOI] [PubMed] [Google Scholar]
- 21. Zimmermann HW, Koch A, Seidler S, Trautwein C, Tacke F. Circulating soluble urokinase plasminogen activator is elevated in patients with chronic liver disease, discriminates stage and aetiology of cirrhosis and predicts prognosis. Liver Int 2012;32:500–509. [DOI] [PubMed] [Google Scholar]
- 22. Rasmussen LJH, Ladelund S, Haupt TH, Ellekilde GE, Eugen-Olsen J, Andersen O. Combining national early warning score with soluble urokinase plasminogen activator receptor (suPAR) improves risk prediction in acute medical patients: a registry-based cohort study. Crit Care Med 2018;46:1961–1968. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Botha S, Fourie CMT, Schutte R, Eugen-Olsen J, Pretorius R, Schutte AE. Soluble urokinase plasminogen activator receptor as a prognostic marker of all-cause and cardiovascular mortality in a black population. Int J Cardiol 2015;184:631–636. [DOI] [PubMed] [Google Scholar]
- 24. Lyngbæk S, Marott JL, Sehestedt T, Hansen TW, Olsen MH, Andersen O, et al. Cardiovascular risk prediction in the general population with use of suPAR, CRP, and Framingham risk score. Int J Cardiol 2013;167:2904–2911. [DOI] [PubMed] [Google Scholar]
- 25. Fernandez-Botran R, Szabo YZ, Lyle KB, Newton TL. The levels of soluble urokinase plasminogen activator receptor (suPAR) in saliva are influenced by acute stress. Biol Psychol 2021;165:108147. [DOI] [PubMed] [Google Scholar]
- 26. Rasmussen LJH, Moffitt TE, Arseneault L, Danese A, Eugen-Olsen J, Fisher HL, et al. Association of adverse experiences and exposure to violence in childhood and adolescence with inflammatory burden in young people. JAMA Pediatr 2020;174:38–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Ventorp F, Gustafsson A, Träskman-Bendz L, Westrin Å, Ljunggren L. Increased soluble urokinase-type plasminogen activator receptor (suPAR) levels in plasma of suicide attempters. PLoS One 2015;10:e0140052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Ko Y-A, Hayek S, Sandesara P, Samman Tahhan A, Quyyumi A. Cohort profile: the Emory Cardiovascular Biobank (EmCAB). BMJ Open 2017;7:e018753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972;18:499–502. [PubMed] [Google Scholar]
- 30. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF III, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999;94:496–509. [Google Scholar]
- 32. Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods 2013;18:137–150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Salomaa V, Niemelä M, Miettinen H, Ketonen M, Immonen-Räihä P, Koskinen S, et al. Relationship of socioeconomic status to the incidence and prehospital, 28-day, and 1-year mortality rates of acute coronary events in the FINMONICA myocardial infarction register study. Circulation 2000;101:1913–1918. [DOI] [PubMed] [Google Scholar]
- 34. Peltonen M, Rosén M, Lundberg V, Asplund K. Social patterning of myocardial infarction and stroke in Sweden: incidence and survival. Am J Epidemiol 2000;151:283–292. [DOI] [PubMed] [Google Scholar]
- 35. Schultz WM, Kelli HM, Lisko JC, Varghese T, Shen J, Sandesara P, et al. Socioeconomic status and cardiovascular outcomes. Circulation 2018;137:2166–2178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Baumer Y, Powell-Wiley TM. Interdisciplinary approaches are fundamental to decode the biology of adversity. Cell 2021;184:2797–2801. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Hayek SS, Koh KH, Grams ME, Wei C, Ko Y-A, Li J, et al. A tripartite complex of suPAR, APOL1 risk variants and αvβ3 integrin on podocytes mediates chronic kidney disease. Nat Med 2017;23:945–953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Powell-Wiley TM, Baumer Y, Baah FO, Baez AS, Farmer N, Mahlobo CT, et al. Social determinants of cardiovascular disease. Circ Res 2022;130:782–799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Bogaty P, Boyer L, Simard S, Dauwe F, Dupuis R, Verret B, et al. Clinical utility of C-reactive protein measured at admission, hospital discharge, and 1 month later to predict outcome in patients with acute coronary disease: the RISCA (recurrence and inflammation in the acute coronary syndromes) study. J Am Coll Cardiol 2008;51:2339–2346. [DOI] [PubMed] [Google Scholar]
- 40. Sommerer C, Zeier M, Morath C, Reiser J, Scharnagl H, Stojakovic T, et al. Soluble urokinase plasminogen activation receptor and long-term outcomes in persons undergoing coronary angiography. Sci Rep 2019;9:475. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author on reasonable request.