Abstract
Background and aims:
Identifying the association of novel plasma biomarkers with coronary artery calcium (CAC) incidence or progression may provide insights into the pathophysiology of atherogenesis and plaque formation.
Methods:
Participants of the Dallas Heart Study (DHS), a multi-ethnic cohort of ambulatory individuals at low-intermediate risk for future atherosclerotic cardiovascular disease (ASCVD), who had their blood tested for 31 biomarkers reflecting multiple pathophysiological pathways, underwent 2 serial non-contrast computed tomography assessments for CAC a median ~7 years apart. The collected biomarkers were explored for association with CAC incidence or progression using univariate and multivariate analysis.
Results:
A total of 1424 participants were included; mean age 43 years, 39% male, and nearly half African-American. Over a 7-year interval between the two CAC measurements, 340 participants (23.9%) had CAC incidence or progression, 105 (7.4%) with incident CAC, and 309 (21.7%) with CAC progression. Although several plasma biomarkers were associated with CAC incidence or progression in a univariate model, only soluble intercellular adhesion molecule- 1 (sICAM-1), related to atherosclerosis by the inflammatory pathway, remained independently associated in a multivariate model adjusted for traditional risk factors.
Conclusions:
Further studies are needed to characterize the role of sICAM-1 in CAC evolvement to establish whether it has a pivotal mechanistic contribution or is rather an innocent bystander. Alternate measures of coronary atherosclerosis may be needed to elucidate contributors to atherosclerosis incidence or progression.
Keywords: coronary artery calcium, biomarkers, atherogenesis, atherosclerosis, atherosclerotic cardiovascular disease
1. Introduction
Coronary artery calcium (CAC), measured by non-contrast cardiac computed tomography (CCT), is strongly associated with atherosclerotic cardiovascular disease (ASCVD) risk in short and long-term follow-up, independent of traditional risk factors and regardless of age, gender, and ethnicity 1–3. Furthermore, the progression of CAC over time is also associated with adverse cardiovascular (CV) outcomes 4–8.
Numerous plasma biomarkers have been linked to CAC incidence and progression including biomarkers of calcium-phosphate and lipid metabolism, inflammation, kidney function, and myocardial necrosis 9. For example, biomarkers of calcium-phosphate and lipid metabolism such as phosphate, Low-density lipoprotein, and total cholesterol levels have been independently associated with CAC incidence or progression in multivariable analyses including traditional risk factors and several biomarkers of various mechanistic pathways and 9. Similarly, low and high plasma levels of adiponectin and circulating osteoprotegerin (OPG), respectively, have been found to predict CAC progression independent of other CV risk factors 10,11. Inflammatory markers such as soluble intercellular adhesion molecule- 1 (sICAM-1), C-reactive protein (CRP), and fibrinogen have been weakly associated with CAC presence and progression 12–14 while biomarkers reflecting myocardial remodeling and injury such as cardiac troponin (T and I) have shown conflicting results 9,15,16. To date, a comprehensive evaluation of a wide array of biochemical markers and their relationships with CAC incidence and progression has not been thoroughly conducted. Identifying the association of novel biomarkers with CAC incidence and progression in the general population may provide insights into the pathophysiology of atherogenesis and plaque formation 13. In addition, the incorporation of novel biomarkers that are associated with CAC incidence and progression into existing risk calculators may improve the accuracy of ASCVD risk prediction, which is currently based only on traditional risk factors.
Given these considerations, we aimed to examine a broad panel of plasma biomarkers reflecting multiple pathophysiological pathways, including inflammation, coagulation, collagen degradation, angiogenesis, and metabolic regulation, among ambulatory individuals without prevalent ASCVD in the Dallas Heart Study (DHS) cohort, to explore their association with CAC incidence or progression to address the hypothesis that different pathophysiological mechanisms may underpin the development and/or progression of CAC in a diverse, primary prevention cohort.
2. Patients and methods
2.1. Study population and processes
The DHS is a multi-ethnic cohort study of Dallas County residents aged 18–65 years with a high proportion of African Americans set to improve the diagnosis, prevention, and treatment of heart disease. Individuals were enrolled in phase 1 of the DHS (DHS-1) from 2000 to 2002 at the University of Texas Southwestern Medical Center in Dallas, Texas. 3072 participants underwent assessments during all three segments of the DHS-1 visit including survey, laboratory, and imaging assessments. Subsequently, between 2007 and 2009, participants were invited back for a second phase, DHS-2, to complete follow-up testing that included anthropometric measurements, laboratory tests, imaging studies, and fitness assessments. The study protocol and methods have been described previously 17,18. All participants provided written informed consent and the University of Texas Southwestern Medical Center Institutional Review Board approved the protocol.
The current study includes all study participants from DHS-1 who also underwent non-contrast CCT for CAC assessment at DHS-2 (median ~7 years apart between DHS-1 and DHS-2 CAC scans) with low-intermediate risk for future ASCVD at baseline (10-year risk< 7.5% based on the pooled cohort equations 19). Participants with established ASCVD (defined as a self-reported history of myocardial infarction, revascularization, stroke, or heart failure), those with predicted 10-year ASCVD risk > 7.5%, those with missing baseline or follow-up CAC measurements, and those with missing plasma biomarkers were excluded from the study, resulting in a final study population of 1424 individuals with complete imaging and biomarker data. The primary outcome of the study was the composite of CAC incidence or progression.
2.2. Variable definitions
All variable definitions including demographic data, physical characteristics, and traditional CV risk factors have been described previously 20. Race/ethnicity, history of CVD, medication use, and smoking status were self-reported.
Hypercholesterolemia, LDL and high-density lipoprotein (HDL) cholesterol levels, and hypertension were defined using previously described clinical definitions 11. Type 2 diabetes (T2D) was defined by a fasting glucose level of at least 126 mg/dL (to convert to mmol/L, multiplied by 0.0555) or the use of hypoglycemic medications.
2.3. Biomarker measurements
Venous blood was collected in EDTA tubes and maintained at 4 C for ≤ 4 hours before centrifugation at 1430×g for 15 minutes. Plasma was then removed and frozen at −80 C until assays were performed by individuals blinded to all clinical data 17.
Thirty-one circulating biomarkers, representing various pathophysiological categories (lipids, adipokines, markers of inflammation, endothelial injury, myocyte injury and stress, and Coagulation) were included in the analyses (Supplementary Table S1 in the supplemental materials including a brief description of the biomarker, the assay used, and references to assay specifics). These biomarkers were selected based on supporting evidence from previous studies, biological plausibility, clinical relevance, and the availability of accurate assay methods (details of which have been previously reported 20).
2.4. CAC measurements
CAC measurements were performed in duplicate 1 to 2 minutes apart on an electron-beam computed tomography (EBCT) scanner (for DHS-1) and multi-detector computed tomography (MDCT, for DHS-2) 21,22. Average CAC scores were determined using the Agatston method and adjusted for attenuation differences between scanners 22,23. CAC incidence was defined as baseline CAC=0 Agatston unit (AU) and follow-up CAC>2.7 AU to account for interscan variability in CAC measurement 18. CAC progression was defined according to the “SQRT method” as the difference between the square roots of baseline and follow-up CAC scores >2.5 6,24.
2.5. Statistical analysis
Baseline characteristics of patients are presented as n (%) for categorical variables and as median (interquartile range - IQR) for continuous variables. For high-sensitivity cardiac troponin T (hs-cTnT), values less than the lower limit of detection were set at 2.5 ng/L. Non-parametric statistical methods were employed given a skewed distribution (skewness and kurtosis were all >2) examined with the Shapiro-Wilk test for normality of data. To compare patients with and without CAC incidence or progression the Mann-Whitney test was used. The association between biomarkers (exposure) and CAC incidence or progression was first examined by univariable modeling using simple logistic regression. Candidate biomarkers that were associated with the outcome with a p-value <0.05 underwent subsequent evaluation using multivariable modeling after adjustment for the traditional risk factors included in the pooled cohort equations risk calculator for ASCVD risk prediction (age, sex, diabetes, smoking, systolic blood pressure, anti-hypertensive medication use, total cholesterol, and high-density lipoprotein cholesterol) to assess their independent association with CAC incidence or progression. To account for the skewness of the biomarkers, values were presented in the logarithmic scale and the odds ratio (OR) was calculated per 1-SD increase. Biomarkers found as independently associated with CAC incidence or progression in multivariable modeling were tested for reclassification and discrimination improvement using the net reclassification improvement (NRI). All statistical analyses were performed using SAS version 9.4.
2.6. Role of the funding source
None of the funding sources had any involvement in any aspect of the study including (but not limited to) its design, conduct, data collection, data analysis, data interpretation, manuscript formation, or the decision to submit it for publication. None of the authors had been paid by a pharmaceutical company or other agency to write this article.
3. Results
A total of 1424 participants were included in the study. The mean age of study participants was 43 (±8) years, of whom 39% were male, 42% were African-American, and 21% were active smokers (Table 1). Among the study population, the median baseline CAC score was 0 (0, 2.9) and 55.6% had CAC=0. Over a seven-year interval between the two CAC measurements, 340 participants (23.9%) had CAC incidence or progression: 105 (7.4%) with incident CAC and 309 (21.7%) with CAC progression (74 had both incidence and progression); median (IQR) change in CAC was 74 (23, 194). Compared with participants without prevalent or progressive CAC, those who developed CAC or had CAC progression were more likely to be older, male, white, and had more traditional risk factors (p <0.05 for all, Table 2).
Table 1:
Demographics and baseline characteristics of patients, median biomarker levels, and CAC scores
| Characteristics | n = 1424 |
|---|---|
| Age* (years) | 43.0 (37.0–49.0) |
| Gender | |
| Male | 559 (39.3%) |
| Female | 865 (60.7%) |
| Ethnicity | |
| Non-Hispanic Black | 605 (42.5%) |
| Non-Hispanic White | 578 (40.5%) |
| Hispanic | 241 (16.9%) |
| Current smoker | 306 (21.5%) |
| BMI (kg/m2) | 28.5 (25.0–33.1) |
| Systolic BP (mmHg) | 122.0 (113.0–131.0) |
| Diastolic BP (mmHg ) | 77.3 (71.3–82.7) |
| Antihypertensive medication use | 188 (13.2%) |
| Diabetes | 60 (4.2%) |
| Total cholesterol | 177.0 (156.0–201.0) |
| CAC score (AU) ╪ | 0.0 (0.0–2.9) |
| CAC=0 (AU) ╪ | 792 (55.6%) |
| Biomarker levels | |
| Adiponectin (μg/ml) | 7.3 (4.9–10.3) |
| Cholesterol efflux capacity (%) | 1.0 (0.8–1.2) |
| CRP (mg/L) | 2.3 (1.0–5.4) |
| CXCL1 (ng/mL) | 0.0 (0.0–0.0) |
| CXCL2 (ng/mL) | 0.0 (0.0–0.2) |
| D-Dimer (μg/mL) | 0.2 (0.1–0.3) |
| sESAM (ng/mL) | 33.2 (26.4–40.4) |
| FGF21 (ng/mL) | 4.1 (4.1–4.1) |
| GDF15 (ng/mL) | 0.63 (0.47–0.84) |
| HDLc (mg/dL) | 49.0 (41.5–60.0) |
| HDLp (μmol/L) | 33.5 (29.6–38.0) |
| Hs-cTnT (ng/L) | 1.5 (1.5–1.5) |
| ICAM-1 (ng/mL) | 586.5 (432.5–791.9) |
| IL18 (mg/L) | 498.7 (348.7–726.5) |
| LDLc (mg/dL) | 105.0 (82.0–125.0) |
| LP-PLA2 (nmol/min/ml) | 186.9 (154.9–218.7) |
| MMP-9 (ng/mL) | 5.2 (3.1–9.5) |
| MPO (ng/mL) | 16.7 (12.9–20.7) |
| MPO/HDLp | 0.28 (0.2–0.39) |
| NT-pro BNP (pg/mL) | 27.7 (12.5–55.4) |
| OPG (pg/mL) | 1144.5 (838.0–1512.3) |
| Osteopontin (ng/mL) | 37.2 (24.6–55.4) |
| PIGR (ng/mL) | 38.6 (28.2–52.6) |
| PGLYRP-1 (pg/mL) | 18.9 (14.7–24.6) |
| sRAGE (ng/mL) | 1.4 (1.0–2.0) |
| sICAM-1 (ng/mL) | 1522.9 (1124.1–2129.1) |
| Total cholesterol (mg/dL) | 177.0 (156.0–201.0) |
| VCAM-1 (ng/mL) | 966.8 (721.9–1370.8) |
| VEGF-R1 (ng/mL) | 0.46 (0.38–0.55) |
| WAP4C (ng/mL) | 2.0 (2.0–2.0) |
AU, Agatston unit; BMI - body mass index; BP - blood pressure; CAC, coronary artery calcium; CRP, C- reactive protein; CXCL, C-X-C motif chemokine ligands; ESAM, Endothelial cell-selective adhesion molecule; FGF21, fibroblast growth factor21; GDF15, growth differentiation factor-15; HDLc, high density lipoprotein cholesterol; HDLp, high density lipoprotein particle number; Hs-cTnT, high-sensitivity cardiac troponin T; ICAM-1, intercellular adhesion molecule-1; IL, interleukin; LDLc, low density lipoprotein cholesterol; LP-PLA2, lipoprotein-associated phospholipase A2; MMP, matrix metalloproteinase; MPO, myeloperoxidase; MPO/HDLp, MPO indexed to HDL particle concentration; NT-proBNP, N-terminal pro-B-type natriuretic peptide; OPG, osteoprotegerin; PGLYRP-1, peptidoglycan recognition protein-1; PIGR, polymeric immunoglobulin receptor; RAGE, receotor for advanced glycation endproducts; sICAM-1, soluble intercellular adhesion molecule-1; VCAM-1, vascular cellular adhesion molecule; VEGF-R1, vascular endothelial growth factor receptor 1;
WAP4C, WAP 4 disulfide core domain protein.
Refers to the age at visit 1 of the Dallas Heart Study (DHS-1).
Refers to baseline CAC obtained at visit 3 of DHS-1.
Values are presented as median (IQR) or n (%).
Table 2:
Baseline characteristics of patients and biomarker levels by CAC incidence or progression
| CAC incidence/progression YES (n= 340) | CAC incidence/progression NO (n= 1084) | p value | |
|---|---|---|---|
| Age* (years) | 49.0 (42.5–54.0) | 41.0 (36.0–47.5) | <0.001 |
| Male | 158 (46.5%) | 401 (37%) | 0.002 |
| Non-Hispanic Black | 128 (37.7%) | 477 (44%) | 0.039 |
| Current smoker | 89 (26%) | 217 (20%) | 0.016 |
| BMI (kg/m2) | 28.9 (25.2–33.2) | 28.4 (25.0–33.0) | 0.4 |
| Systolic BP (mmHg) | 127.3 (117.0–136.5) | 120.7 (112.0–129.3) | <0.001 |
| Diastolic BP (mmHg ) | 79.3 (73.0–85.2) | 76.3 (70.7–81.7) | <0.001 |
| Anti-hypertensive medications | 89 (26.2%) | 99 (9.1%) | <0.001 |
| Diabetes | 24 (7.1%) | 36 (3.3%) | 0.003 |
| Total cholesterol | 181.0 (161.0–205.0) | 176.0 (154.0–199.0) | 0.002 |
| CAC score (AU) ╪ | 5.8 (0.0–50.4) | 0.0 (0.0–1.1) | <0.001 |
| CAC=0 (AU) ╪ | 105 (30.9%) | 687 (63.4%) | <0.001 |
| Change in CAC (AU) # | 73.9 (23.4–193.5) | 0.0 (0.0–0.0) | <0.001 |
| Biomarker levels | |||
| Adiponectin (μg/ml) | 7.4 (4.8–10.6) | 7.2 (4.9–10.1) | 0.36 |
| Cholesterol efflux capacity (%) | 1.0 (0.8–1.2) | 1.0 (0.8–1.2) | 0.18 |
| CRP (mg/L) | 2.7 (1.2–5.9) | 2.3 (0.9–5.3) | 0.02 |
| CXCL1 (ng/mL) | 0.0 (0.0) | 0.0 (0.0) | 0.91 |
| CXCL2 (ng/mL) | 0.0 (0.0–0.18) | 0.0 (0.0–0.15) | 0.03 |
| D-Dimer (μg/mL) | 0.2 (0.1–0.4) | 0.2 (0.1–0.3) | 0.95 |
| sESAM (ng/mL) | 35.4 (27.6–43.6) | 32.4 (25.9–39.5) | <0.001 |
| FGF21 (ng/mL) | 4.1 (4.1–4.1) | 4.1 (4.1–4.1) | 0.83 |
| GDF15 (ng/mL) | 0.7 (0.6–1) | 0.6 (0.5–0.8) | <0.001 |
| HDLc (mg/dL) | 48.0 (40.5–61.0) | 50.0 (42.0–59.0) | 0.5 |
| HDLp (μmol/L) | 33.9 (30.3–39.0) | 33.4 (29.4–37.6) | 0.02 |
| Hs-cTnT (ng/L) | 1.5 (1.5–3.1) | 1.5 (1.5–1.5) | 0.001 |
| ICAM-1 (ng/mL) | 608.1 (474.0–807.7) | 576.2 (418.3–783.7) | 0.02 |
| IL18 (mg/L) | 564.7 (375.8–819.6) | 481.0 (342.8–706.2) | 0.006 |
| LDLc (mg/dL) | 108.0 (83.5–127.0) | 104.0 (82.0–124.0) | 0.103 |
| LP-PLA2 (nmol/min/ml) | 194.7 (163.6–225.9) | 184.9 (153.4–217.2) | 0.003 |
| MMP-9 (ng/mL) | 5.3 (2.8–9.2) | 5.2 (3.2–9.5) | 0.29 |
| MPO (ng/mL) | 16.2 (12.8–20.6) | 16.8 (12.9–20.7) | 0.26 |
| MPO/HDLp | 0.27 (0.19–0.37) | 0.28 (0.2–0.39) | 0.13 |
| NT-pro BNP (pg/mL) | 30.6 (11.8–63.9) | 26.3 (12.6–52.0) | 0.057 |
| OPG (pg/mL) | 1169.8 (860.3–1577.4) | 1132.6 (832.0–1478.1) | 0.058 |
| Osteopontin (ng/mL) | 37.5 (24.6–55.4) | 37.2 (24.7–55.3) | 0.9 |
| PIGR (ng/mL) | 42.3 (30.8–57.9) | 36.9 (27.4–50.6) | <0.001 |
| PGLYRP-1 (pg/mL) | 19.8 (15.3–25.4) | 18.7 (14.6–24.1) | 0.053 |
| sRAGE (ng/mL) | 1.4 (0.9–1.9) | 1.4 (1.0–2.0) | 0.41 |
| sICAM-1 (ng/mL) | 1644.2 (1169.6–2325.4) | 1496.8 (1114.7–2076.3) | 0.002 |
| Total cholesterol (mg/dL) | 181.0 (161.0–205.0) | 176.0 (153.5–199.0) | 0.002 |
| VCAM-1 (ng/mL) | 1020.5 (761.4–1394.6) | 941.5 (706.1–1362.0) | 0.02 |
| VEGF-R1 (ng/mL) | 0.47 (0.39–0.55) | 0.46 (0.37–0.54) | 0.21 |
| WAP4C (ng/mL) | 2.0 (2.0–2.0) | 2.0 (2.0–2.0) | 0.23 |
AU, Agatston unit; BMI - body mass index; BP - blood pressure; CAC, coronary artery calcium; CRP, C- reactive protein; CXCL, C-X-C motif chemokine ligands; ESAM, Endothelial cell-selective adhesion molecule; FGF21, fibroblast growth factor21; GDF15, growth differentiation factor-15; HDLc, high density lipoprotein cholesterol; HDLp, high density lipoprotein particle number; Hs-cTnT, high-sensitivity cardiac troponin T; ICAM-1, intercellular adhesion molecule-1; IL, interleukin; LDLc, low density lipoprotein cholesterol; LP-PLA2, lipoprotein-associated phospholipase A2; MMP, matrix metalloproteinase; MPO, myeloperoxidase; MPO/HDLp, MPO indexed to HDL particle concentration; NT-proBNP, N-terminal pro-B-type natriuretic peptide; OPG, osteoprotegerin; PGLYRP-1, peptidoglycan recognition protein-1; PIGR, polymeric immunoglobulin receptor; RAGE, receotor for advanced glycation endproducts; sICAM-1, soluble intercellular adhesion molecule-1; VCAM-1, vascular cellular adhesion molecule; VEGF-R1, vascular endothelial growth factor receptor 1;
WAP4C, WAP 4 disulfide core domain protein.
Refers to the age at visit 1 of the Dallas Heart Study (DHS-1).
Refers to CAC obtained at visit 3 of DHS-1.
Refers to CAC obtained at DHS-2.
Values are presented as median (IQR) or n (%)
Among the 31 candidate biomarkers that were examined (Figure 1, Supplementary Table S1), several biomarkers, including growth/differentiation factor 15 (GDF-15), hs-cTnT, HDL particle number (HDLp), lipoprotein-associated phospholipase A2 (LP-PLA2), osteoprotegerin (OPG) and polymeric immunoglobulin receptor (PIGR), were significantly associated with CAC incidence or progression in univariable analysis (Table 3). sICAM-1, a biomarker related to atherosclerosis by its involvement in an inflammatory pathway and reflective of endothelial activation, was the only biomarker that was found to be independently associated with CAC incidence or progression in both the univariate (OR per 1-SD 1.20, 95% CI 1.05–1.37) and multivariate analyses after adjustment for traditional risk factors (OR 1.16, 95% CI 1.003–1.348; Table 3).
Figure 1:
Novel biomarkers by their major pathophysiological pathway CAC, coronary artery calcium; CRP, C- reactive protein; CXCL, C-X-C motif chemokine ligands; ESAM, Endothelial cell-selective adhesion molecule; FGF21, fibroblast growth factor21; GDF15, growth differentiation factor-15; HDLc, high density lipoprotein cholesterol; HDLp, high density lipoprotein particle number; Hs-cTnI, high-sensitivity cardiac troponin I; Hs-cTnT, high-sensitivity cardiac troponin T; ICAM-1, intercellular adhesion molecule-1; IL, interleukin; LDLc, low density lipoprotein cholesterol; LP-PLA2, lipoprotein-associated phospholipase A2; MMP, matrix metalloproteinase; MPO, myeloperoxidase; NT-proBNP, N-terminal pro-B-type natriuretic peptide; OPG, osteoprotegerin; PCSK9, protein convertase subtilisin/kexin type 9; PGLYRP-1, peptidoglycan recognition protein-1; PIGR, polymeric immunoglobulin receptor; RAGE, receotor for advanced glycation endproducts; sICAM-1, soluble intercellular adhesion molecule-1; VCAM-1, vascular cellular adhesion molecule; VEGF-R1, vascular endothelial growth factor receptor 1; WAP4C, WAP 4 disulfide core domain protein.
Table 3:
Standardized ORs of novel markers for annualized incident CAC or CAC progression
| Unadjusted OR per 1 SD (95% CI) | Adjusted OR per 1 SD (95% CI)* | |
|---|---|---|
| Adiponectin | 1.06 (0.94–1.20) | NS |
| Cholesterol Efflux Capacity | 1.11 (0.98–1.26) | NS |
| CRP | 1.14 (1.01–1.29) | NS |
| CXCL1 | 1.00 (0.88–1.14) | NS |
| CXCL2 | 1.03 (0.91–1.17) | NS |
| D-dimer | 0.99 (0.87–1.13) | NS |
| sESAM | 1.22 (1.07–1.39) | NS |
| FGF-21 | 0.96 (0.83–1.10) | NS |
| GDF-15 | 1.46 (1.28–1.66) | NS |
| HDLp | 1.19 (1.05–1.34) | NS |
| Hs-cTnT | 1.22 (1.09–1.37) | NS |
| ICAM-1 | 1.16 (1.004–1.288) | NS |
| IL-18 | 1.21 (1.03–1.42) | NS |
| LDLc | 1.11 (0.98–1.25) | NS |
| LP-PLA2 | 1.17 (1.02–1.33) | NS |
| MMP-9 | 0.93 (0.82–1.06) | NS |
| MPO | 0.91 (0.80–1.04) | NS |
| MPO/HDLp | 0.93 (0.81–1.06) | NS |
| NT-pro BNP | 1.12 (0.99–1.27) | NS |
| OPG | 1.15 (1.02–1.30) | NS |
| Osteopontin | 0.98 (0.87–1.12) | NS |
| PIGR | 1.25 (1.10–1.42) | NS |
| PGLYRP-1 | 1.09 (0.96–1.24) | NS |
| sRAGE | 0.94 (0.83–1.07) | NS |
| sICAM-1 | 1.20 (1.05–1.37) | 1.16 (1.003–1.348) |
| VCAM-1 | 1.15 (1.01–1.30) | NS |
| VEGF-R1 | 1.04 (0.91–1.17) | NS |
| WAP4C | 1.02 (0.89–1.15) | NS |
The values presented are in the logarithmic scale. The odds ratio (95% confidence interval) represents the odds of CAC incidence or progression per 1-SD increase in the log of the novel biomarker.
Adjusted for age, sex, race/ethnicity, diabetes, smoking, systolic blood pressure, anti-hypertensive medication use, total cholesterol, and HDL cholesterol. For acronyms refer to tables 1 and 2. NS = not significant in the adjusted model.
The addition of log sICAM-1 to a traditional risk factor model did not meaningfully change the area under the curve and modestly improved net reclassification for CAC incidence or progression (c-statistic=0.786 vs. 0.786 for sICAM-1 plus the standard model, p=0.16; NRI 0.32, p < 0.0001).
In a sensitivity analysis restricted to individuals without diabetes (n=1364), no single biomarker was independently associated with CAC incidence or progression in the multivariate analysis, including sICAM-1 (which approached significance, Supplementary Table S2). An analysis of non-statin users yielded consistent results with those of the entire cohort, with sICAM-1 remaining statistically significant (Supplementary Table S3 a,b).
4. Discussion
CAC represents the overall burden of calcified plaque in the coronary arteries and provides strong prognostic data on future coronary heart disease (CHD) events 7,8. Our findings from the DHS, a primary prevention cohort with low to intermediate CV risk, add to the accumulating data on CAC incidence or progression and its association with traditional CV risk factors 7,9,25–27, male gender, and white ethnicity 28–31. While multiple biomarkers representing various potential pathophysiological mechanisms for atherosclerosis were investigated for their association with CAC incidence and progression, the results for most were not significant and failed to demonstrate an independent association. Nevertheless, sICAM-1, an important biomarker of inflammation, was independently associated with CAC incidence and progression in both univariable and multivariable modeling (Fig. 2) albeit did not meaningfully improve its prediction.
Fig. 2:
Association with coronary artery calcium (CAC) incidence or progression among 31 plasma biomarkers from the Dallas Heart Study ASCVD, atherosclerotic cardiovascular disease; CAC, coronary artery calcium; CRP, C- reactive protein; CXCL, C-X-C motif chemokine ligands; DHS, Dallas Heart Study; ESAM, Endothelial cell-selective adhesion molecule; FGF21, fibroblast growth factor21; GDF15, growth differentiation factor-15; HDLc, high density lipoprotein cholesterol; HDLp, high density lipoprotein particle number; Hs-cTnI, high-sensitivity cardiac troponin I; Hs-cTnT, high-sensitivity cardiac troponin T; ICAM-1, intercellular adhesion molecule-1; IL, interleukin; LDLc, low density lipoprotein cholesterol; LP-PLA2, lipoprotein-associated phospholipase A2; MMP, matrix metalloproteinase; MPO, myeloperoxidase; NT-proBNP, N-terminal pro-B-type natriuretic peptide; OPG, osteoprotegerin; PCSK9, protein convertase subtilisin/kexin type 9; PGLYRP-1, peptidoglycan recognition protein-1; PIGR, polymeric immunoglobulin receptor; RAGE, receotor for advanced glycation endproducts; sICAM-1, soluble intercellular adhesion molecule-1; VCAM-1, vascular cellular adhesion molecule; VEGF-R1, vascular endothelial growth factor receptor 1; WAP4C, WAP 4 disulfide core domain protein. *Adjusted for age, sex, race/ethnicity, diabetes, smoking, systolic blood pressure, anti-hypertensive medication use, total cholesterol, and HDL cholesterol. For acronyms refer to tables 1 and 2. NS = not significant in the adjusted model.
Previous studies have identified several biomarkers as potentially implicated in CAC incidence or progression 13,32. These include markers related to lipid metabolism and atherosclerosis 33,34, coagulation and collagen degradation, inflammation 12,35, myocardial injury and necrosis 32,36, vitamin D and calcium-phosphate metabolism 9,37,38, bone mineral density 11,39 and kidney function 40.
Among the biomarkers not found to be independently associated with CAC incidence or progression in our study were lipid biomarkers such as LDL cholesterol, which was previously reported by some studies to be a marker of CAC incidence but not CAC progression 9,13,26, and HDLp. The latter could be related to specific HDL particles (those of larger size but not others) being implicated in reduced CAC formation and ASCVD risk 41,42. Similarly, Hs-cTnT and GDF-15, biomarkers of myocyte injury and remodeling, were formerly suggested as associated with CCT-diagnosed CHD and CAC 15,43, however, these studies did not account for a wide variety of other biomarkers. In addition, there may be a more robust association of hs-cTnT with total plaque burden and non-calcified plaque as compared with CAC alone and it is plausible that a larger burden of atherosclerosis is necessary to influence cardiac injury while our study included a majority of younger low to intermediate-risk individuals with a lower overall burden of coronary atherosclerosis. In line with prior studies 9,12,13,35,44–47, we found no independent association of inflammatory markers such as CRP, Interleukin-18 (IL-18), PIGR, and LP-PLA2 with CAC incidence or progression in the adjusted models accounting for traditional and other risk factors.
Despite the numerous biomarkers that have been linked to CAC, we identified only a single biomarker that remained significantly associated with CAC incidence or progression following adjustment for the baseline risk factor model: sICAM-1. This biomarker, facilitating leukocyte adhesion and migration across the endothelium, a critical step in the initiation of atherosclerosis, is involved in inflammation, arteriosclerosis, and lipid peroxidation and has been implicated in the early atherothrombotic process responsible for future myocardial infarction and CV risk 48,49. In a substudy of the Multi-Ethnic Study of Atherosclerosis (MESA) among individuals with low Framingham Risk Score, sICAM-1 was associated with incident CAC after adjusting for age but not after adjusting for traditional risk factors 13. However, in the same study, sICAM-1 was part of the three best-fit novel marker models tested to predict CAC progression from the various marker combinations, despite showing little or no improvement over the base model, consistent with our study results. In the Coronary Artery Risk Development in Young Adults (CARDIA) study, in a model with similar adjustments as in our study (with the addition of exercise, body size, and cholesterol-lowering medication) the OR of sICAM-1 for CAC progression after 20 years was significant at 1.16 - remarkably the same as found in our study after 7 years 50.
Worth noting that the association of sICAM-1 to CAC incidence or progression in our study merely approached significance once the analysis was restricted to non-diabetics. This is probably explained by the lower statistical power resulting from the exclusion of diabetic individuals from the sample size, given that the point estimate was consistent with the overall cohort albeit with a wider confidence interval and a borderline p-value (Supplementary Table S2).
Many of the aforementioned biomarkers were associated with coronary atherosclerosis and adverse clinical outcomes in prior studies despite not demonstrating an independent association with CAC in our study. This may reflect on the recognition that CAC itself is an incomplete marker of atherosclerosis and only reveals part of the story. It represents calcified plaque whereas non-calcified and soft plaques account for a considerable proportion of total plaque burden, especially in younger patients 51 (such as those included in the study). Moreover, compared to calcified plaque, non-calcified and vulnerable plaque has a more robust association with negative clinical outcomes and ASCVD risk 52. Another important issue to recognize is that CAC begets CAC, making it challenging to identify new pathways to CAC progression beyond bone mineralization biomarkers. Perhaps the better surrogate marker for adverse CV events is total plaque burden rather than the phenotype of CAC and its progression. For this purpose, CCT angiography (CCTA) may be a more appropriate modality to study the biology of athero-progression and delineating biomarker associations. Applying CCTA enables the identification of plaque type and high-risk plaque features and the quantitative assessment of atherosclerotic plaques – measures that once examined for biomarker associations may not necessarily direct to the same results 53.
This study has several strengths. First, our study is based on the DHS, a robust and highly maintained prospectively collected population-based cohort following the DHS study protocol with follow-up data updated annually. A wide array of biomarkers, one of the most comprehensive reported to date, was collected with relatively few missing values (between 0–8% of the study population). However, the study also has limitations that should be noted. There are challenges in the statistical modeling of CAC, primarily related to potential confounders not accounted for by the model; The study does not report related CV outcomes; CAC incidence and progression were combined into a single endpoint and it is possible that biological factors leading to CAC incidence differ from those of CAC progression.
4.1. Conclusion
In this multi-ethnic population-based study of low-intermediate risk primary prevention cohort, CAC incidence or progression occurred in approximately one-quarter of the participants after a 7-year follow-up. Among multiple biomarkers representing various pathophysiological pathways, sICAM-1, an endothelium-mediated pro-inflammatory marker involved in the initiation of atherosclerosis, was the only biomarker associated with CAC incidence or progression after adjustment for traditional risk factors. Further studies with alternate measures of coronary atherosclerosis may be needed to elucidate contributors to atherosclerosis incidence or progression.
Supplementary Material
Highlights.
CAC is a strong predictor of atherosclerotic cardiovascular disease.
We aimed to identify novel plasma biomarkers associated with CAC incidence or progression.
Among 31 biomarkers only sICAM-1 was independently associated with CAC evolvement.
Alternate measures of coronary atherogenesis may be needed.
Acknowledgments
This work was supported in part by grant UL1TR001105 from the National Center for Advancing Translational Sciences, National Institutes of Health, and by a joint philanthropic grant to UT Southwestern and Rabin Medical Centers for collaborative research.
Footnotes
Conflict of interest
Dr. Neeland has been a speaker/consultant for Boehringer-Ingelheim/Lilly Alliance and Bayer Pharmaceuticals and has been an advisory board member for AMRA Medical, Boehringer Ingelheim, Bayer, and Lilly. Dr. Eisen has been a speaker/consultant/ Advisory board member for Boehringer-Ingelheim, Bayer, Novo Nordisk, AstraZeneca, Pfizer, Neopharm, Medison Pharma, and Sanofi.
Dr. de Lemos reports grant support from Roche Diagnostics and Abbott Diagnostics, consulting fees from Quidel Cardiovascular, Inc., Cytokinetics and Glaxo Smith Kline, honoraria for participation in endpoint committees from Beckman Coulter and Siemens Health Care Diagnostics, and fees for participation in Data Monitoring Committees from Astra Zeneca, Novo Nordisk, Eli Lilly, Regeneron, Amgen, and Verve Therapeutics.
Credit author statement
TG contributed to the data interpretation and wrote the original draft of the manuscript. IJN contributed to the study’s conception and design. AE and IJN supervised the project administration, contributed to the data interpretation, and led the review and editing of the manuscript. PJ, AR, AK, and JADL contributed to the resources and data acquisition. Data curation and formal analysis were conducted by CA. JADL, YTB, RK, AH, GW, and AK contributed to the data interpretation and critically revised the manuscript. All authors were allowed to access the data, and they accepted responsibility to submit it for publication.
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Data sharing
De-identified data and a data dictionary can be made available to others upon reasonable request/application to access Dallas Heart Study data once the initial report is published. Requests/applications can be sent to the email address dallasheartstudy@utsouthwestern.edu. Data will be shared after the completion and approval of a proposal by the outside investigator along with a signed data access agreement. No additional documents will be available.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
De-identified data and a data dictionary can be made available to others upon reasonable request/application to access Dallas Heart Study data once the initial report is published. Requests/applications can be sent to the email address dallasheartstudy@utsouthwestern.edu. Data will be shared after the completion and approval of a proposal by the outside investigator along with a signed data access agreement. No additional documents will be available.