The authors have declared that no competing interests exist.
Critically revised the manuscript: MPHvdW MMJvG CJHvdK RMAH EJMF CDAS. Read and approved the manuscript: BCTvB IF MPHvdW MMJvG CJHvdK RMAH EJMF CDAS CGS. Conceived and designed the experiments: IF MMJvG CJHvdK RMAH EJMF CDAS CGS. Performed the experiments: BCTvB IF MPHvdW. Analyzed the data: BCTvB IF MPHvdW. Contributed reagents/materials/analysis tools: IF MMJvG CJHvdK RMAH EJMF CDAS CGS. Wrote the paper: BCTvB IF CGS.
In terms of time, effort and quality, multiplex technology is an attractive alternative for well-established single-biomarker measurements in clinical studies. However, limited data comparing these methods are available.
We measured, in a large ongoing cohort study (n = 574), by means of both a 4-plex multi-array biomarker assay developed by MesoScaleDiscovery (MSD) and single-biomarker techniques (ELISA or immunoturbidimetric assay), the following biomarkers of low-grade inflammation: C-reactive protein (CRP), serum amyloid A (SAA), soluble intercellular adhesion molecule 1 (sICAM-1) and soluble vascular cell adhesion molecule 1 (sVCAM-1). These measures were realigned by weighted Deming regression and compared across a wide spectrum of subjects’ cardiovascular risk factors by ANOVA.
Despite that both methods ranked individuals’ levels of biomarkers very similarly (Pearson’s r all≥0.755) absolute concentrations of all biomarkers differed significantly between methods. Equations retrieved by the Deming regression enabled proper realignment of the data to overcome these differences, such that intra-class correlation coefficients were then 0.996 (CRP), 0.711 (SAA), 0.895 (sICAM-1) and 0.858 (sVCAM-1). Additionally, individual biomarkers differed across categories of glucose metabolism, weight, metabolic syndrome and smoking status to a similar extent by either method.
Multiple low-grade inflammatory biomarker data obtained by the 4-plex multi-array platform of MSD or by well-established single-biomarker methods are comparable after proper realignment of differences in absolute concentrations, and are equally associated with cardiovascular risk factors, regardless of such differences. Given its greater efficiency, the MSD platform is a potential tool for the quantification of multiple biomarkers of low-grade inflammation in large ongoing and future clinical studies.
Biomarker measurements representing low-grade inflammation have gained increasing importance in the management and understanding of cardiovascular disease (CVD)
Among a large variety of potential biomarkers
Traditionally, well-established analytical methods have enabled the analysis of single biomarkers of low-grade inflammation in one run. However, obtaining multiple biomarkers based on many single-biomarker measurements is very labor intensive, expensive and requires (relatively) large sample volumes. These limitations hamper an efficient multiple biomarker approach, particularly in large observational cohort or clinical trial studies. An attractive solution to these limitations is the simultaneous, and thus more efficient, measurement of a set of low-grade inflammatory biomarkers in one run. Such methods have recently become available with the use of multi-array platforms, such as the Luminex® and the MesoScaleDiscovery® (MSD) platforms and provide the tools necessary for efficient multiple biomarker detection. However, it remains to be established to what extent biomarker concentrations, as measured with these multi-array platforms, are comparable to well-established single-biomarker measurements. Although some cross-validation studies have been performed, most have not focused on biomarkers of low-grade inflammation
Therefore, introducing a multi-array platform in the context of an ongoing longitudinal cohort study poses some challenges
In view of these considerations, we compared the performance of a 4-plex multi-array electrochemiluminescense detection platform of low-grade inflammatory biomarkers (CRP, SAA, sICAM-1 and sVCAM-1) of MSD with that of well-established single-biomarker measurements, in a large ongoing cohort study of individuals with a wide spectrum of cardiovascular risk factors (RFs) known to be associated with low-grade inflammation.
The study was approved by the Medical Ethical Committee of the Maastricht University and all individuals gave written informed consent.
The Cohort on Diabetes and Atherosclerosis Maastricht (CODAM) is a prospective cohort study that was originally designed to study the effects of obesity, glucose and lipid metabolism, lifestyle and genetics on cardiovascular complications, as described in detail elsewhere
At baseline (i.e. CODAM-1), biomarkers of low-grade inflammation were assessed by single-biomarker techniques. At follow-up (i.e. CODAM-2), the single-biomarker techniques were replaced by the multi-array platform of MSD. To ensure comparability between methods, biomarkers of low-grade inflammation were also reassayed by the multi-array platform of MSD in all samples from the baseline examination (i.e. CODAM-1); at the time of these measurements, baseline samples had thus been stored for ∼7 years. The present cross-validation study reports on individuals’ paired data on biomarkers during the baseline examination (CODAM-1) and thus is a cross-sectional method comparison study. Method comparison for each biomarker was conducted on paired data, which were available for CPR in 566 individuals, for SAA in 563 individuals, for sICAM-1 in 566 individuals and for sVCAM-1 in 567 individuals and full paired data on all four inflammatory biomarkers were available in 550 individuals.
The CODAM study population is characterized by a wide spectrum of conditions known to be associated with low-grade inflammation
Individuals were asked to stop their lipid-lowering medication 14 days prior to the blood withdrawals and all other medication on the day before. After an overnight fast (duration of at least 10 hours) blood was drawn from the anticubital vein and collected in EDTA polypropylene tubes for plasma and in clot activator containing polypropylene tubes for serum. EDTA tubes were centrifuged at 3000 rpm for 15 min at 4°C, and plasma was immediately divided into 1 ml aliquots and stored in −80°C freezers until further analysis. Tubes with cloth activator were left 20 minutes before centrifugation at 3000 rpm for 15 min at 20°C, and serum was immediately divided into 1 ml aliquots and stored in −20°C freezers until analysis
CRP was measured in a single measurement in serum with a high-sensitivity, immunoturbidimetric assay (detection range 100 ng/ml to 20000 ng/ml, i.e. factor 200) (Latex, Roche Diagnostics Netherlands BV, Almere, The Netherlands,
The 4-plex multi-array electrochemiluminescence platform of MesoScaleDiscovery (detection range 0.008 ng/ml to 1000 ng/ml, i.e. factor 125000) (MesoScaleDiscovery, Gaithersburg, MD, USA,
Variation between production lots of multi-array plates could influence biomarker measurements. We have evaluated the possible effect of lot-to-lot variation in the current 4-plex assay using additional data of previous studies
Absolute concentrations of each biomarker as measured by the single-biomarker techniques and the multi-array platform were examined on all paired samples from the CODAM study baseline examination (n = 566 for CRP, n = 563 for SAA, n = 566 for sICAM-1 and n = 567 for sVCAM-1, after exclusion of erroneous outliers
We anticipated that absolute biomarker concentrations, as obtained by either single- or multi-array methods, would differ due to a lack of standardization. Realignment of the data would, therefore, be necessary to enable direct comparison of absolute concentrations. For that purpose we used equations derived from Deming regression analyses to realign the data as obtained by one to the other method.
To examine the levels of agreement and verify the absence of systematic error
We used ANOVA to investigate the extent to which biomarker concentrations, either assessed by the single-biomarker techniques or the multi-array platform, increased across categories of glucose metabolism (i.e. NGM, IGM and DM2), weight (i.e. normal weight, overweight and obesity), number of traits of the metabolic syndrome (0–1 RFs, 2 RFs and ≥3 RFs) and smoking status (never, ex- and current-smoker), by appreciation of the group effects. ANOVA for repeated measures were subsequently used to ascertain whether such patterns of associations were similar between methods, by appreciation of group-by-method effects (the P-values of which should then be ≥0.05). In these analyses, (non-aligned) individual biomarker data, which are expressed in different scale units, were first standardized to comparable units by calculation of Z-scores as follows: (the individuals’ value – the population mean) \ the population SD. Per definition, each Z-score has a mean of 0, a SD of 1, and the same distribution as the absolute biomarker concentration (i.e. the ranking of individuals in the population remains the same). This thus enabled a direct comparison of the magnitude of relative differences in each biomarker by RF categories. All comparisons included adjustments for sex, age, eGFR and prior CVD and were conducted among individuals with complete paired data on all four biomarkers (n = 550).
All analyses were performed with the use of the Statistical Package for Social Sciences (SPSS Inc, version 15.0, Chicago, Illinois, USA,
CRP (mg/l) | SAA (mg/l) | sICAM-1 (µg/l) | sVCAM-1 (µg/l) | |||||
Immunoturbidimetry | Multi-array | ELISA | Multi-array | ELISA | Multi-array | ELISA | Multi-array | |
Total population (n = 550) | 2.6 [1.4–4.5] | 1.9 [0.9–3.8] | 7.0 [4.0–13.8] | 1.3 [0.8–2.2] | 350±91 | 219±54 | 476±121 | 339±75 |
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NGM (n = 291) | 2.2 [1.3–3.7] | 1.6 [0.9–3.1] | 6.2 [3.7–11.8] | 1.2 [0.7–2.0] | 338±85 | 210±50 | 463±121 | 331±73 |
IGM (n = 122) | 2.8 [1.5–4.8] | 2.1 [1.0–3.9] | 8.0 [4.5–14.9] | 1.3 [0.9–2.6] | 354±90 | 220±48 | 467±105 | 338±68 |
DM2 (n = 137) | 3.2 [1.9–5.7] | 2.4 [1.3–5.3] | 8.1 [4.7–15.5] | 1.5 [1.0–2.6] | 373±100 | 237±63 | 510±127 | 356±82 |
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Normal weight (n = 100) | 1.4 [0.9–3.0] | 1.0 [0.5–2.4] | 5.3 [3.0–12.0] | 1.0 [0.6–1.9] | 326±99 | 205±50 | 454±130 | 323±70 |
Overweight (n = 283) | 2.2 [1.4–3.8] | 1.6 [0.9–3.1] | 6.4 [4.2–12.6] | 1.2 [0.8–2.0] | 341±79 | 214±47 | 467±114 | 334±73 |
Obese (n = 167) | 3.6 [2.3–6.0] | 3.0 [1.6–5.4] | 8.6 [5.2–15.8] | 1.6 [1.0–2.7] | 380±98 | 238±62 | 505±121 | 357±76 |
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0–1 risk factor (n = 134) | 1.5 [0.9–3.1] | 1.0 [0.5–2.4] | 5.6 [3.4–11.6] | 1.1 [0.7–1.8] | 317±75 | 198±42 | 447±106 | 322±65 |
2 risk factors (n = 119) | 2.4 [1.3–3.8] | 1.8 [0.9–3.2] | 6.5 [4.0–14.7] | 1.3 [0.8–2.5] | 334±80 | 205±41 | 464±117 | 324±67 |
≥3 risk factors |
3.0 [1.8–5.2] | 2.4 [1.3–4.7] | 7.7 [4.5–14.2] | 1.4 [0.9–2.4] | 372±96 | 235±58 | 494±126 | 352±79 |
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Never (n = 161) | 2.1 [1.3–3.4] | 1.5 [0.9–2.7] | 6.1 [3.8–13.6] | 1.3 [0.8–2.1] | 331±82 | 211±49 | 485±125 | 337±75 |
Ex-smoker (n = 278) | 2.6 [1.4–4.6] | 2.0 [0.9–3.9] | 6.8 [4.2–12.6] | 1.3 [0.8–2.0] | 347±88 | 217±57 | 481±117 | 344±73 |
Current (n = 111) | 3.2 [1.5–5.5] | 2.4 [1.0–5.3] | 9.4 [4.5–15.6] | 1.5 [0.9–2.7] | 385±101 | 237±50 | 450±122 | 327±78 |
Data are means ± SD or medians [interquartile range].
NGM, normal glucose metabolism: defined as fasting plasma glucose <6.1 mmol/l and 2-hour post-load plasma glucose <7.8 mmol/l; IGM, impaired glucose metabolism: includes impaired fasting plasma glucose (between 6.1 mmol/l and 7.0 mmol/l) and/or impaired glucose tolerance (2-hour post-load plasma glucose between 7.8 and 11.1 mmol/l); DM2, diabetes mellitus type 2 (fasting plasma glucose ≥7.0 mmol/l and/or 2-hour post-load plasma glucose ≥11.1 mmol/l);
Categorized on the basis of individuals’ body mass index (BMI) as: normal (if BMI 18.5–24.9 kg/m2); overweight (if BMI ≥25.0 and <29.9 kg/m2), and obese (if BMI ≥30 kg/m2);
Metabolic syndrome status was defined according to the revised NCEP-ATPIII definition (American Heart Association/National Heart, Lung and Blood Institute);
any 3 out of the following traits/risk factors reflect the presence of the syndrome: elevated waist circumference (≥102 cm in men, ≥88 cm in women); reduced HDL-cholesterol (<1.03 mmol/l in men, <1.29 mmol/l in women, and/or specific drug treatment); elevated triglycerides (≥1.7 mmol/l and/or specific drug treatment); elevated blood pressure (systolic/diastolic ≥130/85 mm Hg and/or anti-hypertensive treatment); and elevated fasting plasma glucose (≥5.6 mmol/l and/or glucose-lowering treatment); CRP, C-reactive protein; SAA, serum amyloid A; sICAM-1, soluble intercellular adhesion molecule 1; sVCAM-1, soluble vascular cell adhesion molecule 1.
Despite the very high Pearson’s correlation coefficients (i.e. 0.994 for CRP, 0.758 for SAA, 0.816 for sICAM-1 and 0.755 for sVCAM-1) absolute concentrations of biomarkers as obtained by single-biomarker vs. multi-array techniques differed considerably. Indeed, weighted Deming regression analyses for all biomarkers showed significant constant (intercepts) and proportional (slopes) bias between methods such that the absolute mean concentrations of all four biomarkers were lower when measured with the multi-array platform than with the single-biomarker techniques (
(
Realignment of the data as obtained by different methods was therefore conducted with the use of the coefficients retrieved from the Deming regression models (
(
Regression equations (weighted Deming) |
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Variable | N pairs | Y | X | intercept | slope | Sylx | ||
α | 95% CI | β | 95% CI | |||||
CRP (mg/l) | 566 | CRPMA | CRPITB | −0.33 | −0.35; −0.31 | 0.93 | 0.91; 0.94 | 0.102 |
SAA (mg/l) | 563 | SAAMA | SAAELISA | 0.47 | 0.05; 0.90 | 0.14 | 0.07; 0.20 | 0.239 |
sICAM-1 (µg/l) | 566 | sICAM-1MA | sICAM-1ELISA | 36.01 | 24.63; 47.39 | 0.53 | 0.49; 0.56 | 0.104 |
sVCAM-1 (µg/l) | 567 | sVCAM-1MA | sVCAM-1ELISA | 77.83 | 58.90; 96.75 | 0.55 | 0.51; 0.59 | 0.124 |
CRP (mg/l) | 566 | CRPITB | CRPMA | 0.35 | 0.34; 0.37 | 1.08 | 1.06; 1.10 | 0.110 |
SAA (mg/l) | 563 | SAAELISA | SAAMA | −3.46 | −7.37; 0.45 | 7.33 | 4.40; 10.25 | 1.751 |
sICAM-1 (µg/l) | 566 | sICAM-1ELISA | sICAM-1MA | −68.49 | −94.71; −42.27 | 1.90 | 1.77; 2.03 | 0.197 |
sVCAM-1 (µg/l) | 567 | sVCAM-1ELISA | sVCAM-1MA | −141.52 | −186.54; −96.49 | 1.82 | 1.68; 1.96 | 0.225 |
Data are intercepts (α) and (slopes) β of the Deming regression equation, which all differed significantly from 0 and 1, respectively, as indicated by their 95% CI; rejection of the hypothesis that α = 0 means that the two methods differ at least by a constant amount; rejection of the hypothesis that β = 1 implies that there is a proportional difference between methods; Sylx are standard deviations of the residuals;
Upper panel: these equations are used as cross-validation equations to realign single-biomarker data (ITB, immunoturbidimetry; or ELISA) to multi-array (MA) data.
Lower panel: these equations are used as cross-validation equations to realign multi-array (MA) data to single-biomarker data (ITB, immunoturbidimetry; or ELISA).
CRP, C-reactive protein; SAA, serum amyloid A; sICAM-1, soluble intercellular adhesion molecule 1; sVCAM-1, soluble vascular cell adhesion molecule 1.
Concentrations of all biomarkers, as measured by single-biomarker or multi-array methods (expressed as Z-scores), increased significantly across categories of glucose metabolism, weight, metabolic syndrome and smoking status (all P-trends ≤0.028, except for sVCAM-1 and smoking status), independently of sex, age, eGFR and prior CVD (
Log |
Log |
sICAM-1 | sVCAM-1 | ||||||||||||||
ITB | Multi-array | ELISA | Multi-array | ELISA | Multi-array | ELISA | Multi-array | ||||||||||
mean | 95% CI | mean | 95% CI | mean | 95% CI | mean | 95% CI | mean | 95% CI | mean | 95% CI | mean | 95% CI | mean | 95% CI | ||
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NGM (n = 291) | −0.13 | −0.24; −0.01 | −0.13 | −0.24; −0.02 | −0.11 | −0.23; 0.00 | −0.09 | −0.20; 0.02 | −0.11 | −0.23; 0.00 | −0.14 | −0.26; −0.03 | −0.09 | −0.20; 0.02 | −0.08 | −0.20; 0.03 | |
IGM (n = 122) | 0.06 | −0.12; 0.23 | 0.06 | −0.11; 0.24 | 0.11 | −0.06; 0.28 | 0.001 | −0.17; 0.17 | 0.05 | −0.13; 0.22 | 0.01 | −0.16; 0.19 | −0.09 | −0.26; 0.09 | −0.02 | −0.19; 0.15 | |
DM2 (n = 137) | 0.21 | 0.05; 0.38 | 0.22 | 0.05; 0.39 | 0.14 | −0.02; 0.31 | 0.20 | 0.03; 0.36 | 0.20 | 0.03; 0.37 | 0.29 | 0.13; 0.46 | 0.26 | 0.10; 0.42 | 0.20 | 0.03; 0.36 | |
P-value for linear trend | 0.001 | 0.001 | 0.014 | 0.005 | 0.003 |
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0.001 | 0.007 | |||||||||
P-value interaction group*method |
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Normal weight (n = 100) | −0.53 | −0.71; −0.34 | −0.55 | −0.74; −0.37 | −0.28 | −0.47; −0.09 | −0.28 | −0.47; −0.09 | −0.24 | −0.44; −0.05 | −0.25 | −0.44; −0.06 | −0.17 | −0.36; 0.02 | −0.20 | −0.39; −0.006 | |
Overweight (n = 283) | −0.06 | −0.17; 0.05 | −0.05 | −0.16; 0.06 | −0.04 | −0.15; 0.07 | −0.07 | −0.18; 0.04 | −0.09 | −0.20; 0.03 | −0.09 | −0.20; 0.02 | −0.09 | −0.20; 0.03 | −0.07 | −0.19; 0.04 | |
Obese (n = 167) | 0.41 | 0.27; 0.56 | 0.42 | 0.28; 0.56 | 0.23 | 0.09; 0.38 | 0.28 | 0.14; 0.43 | 0.29 | 0.14; 0.44 | 0.30 | 0.15; 0.45 | 0.24 | 0.10; 0.39 | 0.24 | 0.10; 0.39 | |
P-value for linear trend |
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0–1 risk factors (n = 134) | −0.39 | −0.56; −0.23 | −0.43 | −0.59; −0.26 | −0.17 | −0.34; 0.00 | −0.16 | −0.32; 0.01 | −0.33 | −0.49; −0.16 | −0.36 | −0.52; −0.20 | −0.19 | −0.36; −0.03 | −0.18 | −0.34; −0.01 | |
2 risk factors (n = 119) | −0.09 | −0.26; 0.08 | −0.09 | −0.26; 0.08 | −0.02 | −0.19; 0.16 | −0.002 | −0.18; 0.17 | −0.18 | −0.35; −0.001 | −0.26 | −0.43; −0.09 | −0.12 | −0.29; 0.05 | −0.21 | −0.38; −0.03 | |
≥3 risk factors (n = 297) | 0.21 | 0.10; 0.32 | 0.23 | 0.12; 0.34 | 0.08 | −0.03; 0.20 | 0.07 | −0.04; 0.18 | 0.22 | 0.11; 0.33 | 0.27 | 0.16; 0.38 | 0.14 | 0.02; 0.25 | 0.16 | 0.05; 0.27 | |
P-value for linear trend |
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Never (n = 161) | −0.23 | −0.39; −0.08 | −0.24 | −0.40; −0.09 | −0.17 | −0.33; −0.02 | −0.14 | −0.29; 0.02 | −0.22 | −0.38; −0.07 | −0.18 | −0.34; −0.03 | 0.07 | −0.08; 0.22 | −0.03 | −0.18; 0.13 | |
Ex-smoker (n = 278) | 0.07 | −0.05; 0.18 | 0.06 | −0.06; 0.18 | 0.03 | −0.08; 0.15 | −0.001 | −0.12; 0.11 | −0.02 | −0.14; 0.10 | −0.03 | −0.14; 0.09 | 0.01 | −0.10; 0.13 | 0.05 | −0.07; 0.16 | |
Current (n = 111) | 0.18 | −0.01; 0.36 | 0.20 | 0.01; 0.38 | 0.17 | −0.02; 0.35 | 0.20 | 0.02; 0.38 | 0.38 | 0.19; 0.56 | 0.33 | 0.14; 0.51 | −0.13 | −0.31; 0.05 | −0.08 | −0.26; 0.11 | |
P-value for linear trend |
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Data are means of standardized biomarker concentrations (Z-scores); all data are adjusted for sex, age, estimated glomerular filtration rate and prior CVD;
NGM, normal glucose metabolism: IGM, impaired glucose metabolism; DM2, diabetes mellitus type 2; CRP, C-reactive protein; SAA, serum amyloid A; sICAM-1, soluble intercellular adhesion molecule 1; sVCAM-1, soluble vascular cell adhesion molecule 1; ITB, immunoturbidimetry.
These results did not materially change, when the analyses were repeated excluding individuals with CRP values >10 mg/l, likely to indicate an acute inflammatory response
A key step in biochemical tests comparison is to ascertain whether the level of agreement between methods is acceptable from a clinical standpoint
CRP | Multi-array platform | Multi-array platformrealigned to Immunoturbidimetry | |||||||||||
Level | <1 mg/l(%) | 1–3 mg/l (%) | >3 mg/l(%) | Total(%) | Concordance(%) | κ | <1 mg/l(%) | 1–3 mg/l (%) | >3 mg/l(%) | Total(%) | Concordance(%) | κ | |
Immunoturbidimetry | <1 mg/l |
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0.0 | 0.0 | 12.9 | 0.641 |
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0.4 | 0.0 | 12.9 | 0.946 | ||
1–3 mg/l | 15.1 |
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0.0 | 46.4 | 0.5 |
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0.4 | 46.4 | |||||
>3 mg/l | 0.0 | 8.2 |
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40.7 | 0.0 | 2.0 |
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40.7 | |||||
Total | 28.0 | 39.5 | 32.5 | 100.0 | 13.1 | 47.8 | 39.1 | 100.0 | |||||
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Immunoturbidimetry realigned to multi-array | <1 mg/l |
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0.4 | 0.0 | 26.4 | 0.931 | – | – | – | – | – | ||
1–3 mg/l | 2.0 |
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0.7 | 40.4 | – | – | – | – | |||||
>3 mg/l | 0.0 | 1.5 |
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33.3 | – | – | – | – | |||||
Total | 28.0 | 39.5 | 32.5 | 100.0 | – | – | – | – | |||||
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– |
κ, Cohen’s kappa (measure of agreement for categorical data); –, not applicable.
The present study has three main findings. First, the absolute concentrations of CRP, SAA, sICAM-1 and sVCAM-1 differed significantly between the single-biomarker techniques and the multi-array platform of MSD. Second, equations retrieved by weighted Deming regression enabled proper realignment of the data to overcome these absolute differences. Finally, the overall pattern of associations between levels of the individual biomarkers with glucose metabolism, weight, metabolic syndrome and smoking status did not differ by method of detection. This is the first study that has examined and cross-validated, in a large ongoing cohort study, measurements of biomarkers of low-grade inflammation by means of single-biomarker techniques and the multi-array platform of MSD.
Our results are in line with a previous study, which suggested that data measured with single-biomarker techniques and data measured with the multi-array platform cannot be combined without appropriate realignment of the data as this would distort epidemiological associations
Another option to directly compare individual biomarker levels between methods (but also between clinical studies) is by transformation of data to Z-scores, especially if realignment equations are lacking. By Z-score transformation, between-subjects ranking in terms of their biomarkers levels are preserved within the population. The present study shows that Z-scores of CRP, SAA, sICAM-1, sVCAM-1 differed across categories of glucose metabolism, weight, metabolic syndrome and smoking status in a similar fashion irrespective of the method of detection. Although it is evident that a high correlation between assays will result in identical associations, these results, illustrate and emphasize that, despite absolute differences, the relative differences are comparable between the single-biomarker techniques and the multi-array platform.
Taken together, our findings suggest that the multi-array platform of MSD could potentially replace the single-biomarker techniques for the detection of multiple biomarkers in large ongoing and future clinical studies aiming at the investigation of the role of low-grade inflammation in the etiology of CVD, though careful validation would be required.
Furthermore, the multi-array platform of MSD has several practical advantages over the well-established single-biomarker techniques for biomarker detection, although CRP assays are generally automated
The present study has some limitations. First, with the single-biomarker techniques, CRP was measured in serum and SAA, sVCAM-1 and sICAM-1 were measured in plasma, whereas with the multi-array platform all biomarkers were measured in plasma. This may, in part, explain the differences between methods in absolute concentrations of CRP, since a different matrix might effect detection. Furthermore, the measurement of biomarkers by the single-biomarker techniques and the multi-array platform were performed ∼7 years apart, which could also have contributed to an underestimation of absolute biomarker concentrations by the multi-array platform. However, because storage time of samples was the same for all study individuals, if anything: 1) this underestimation was likely systematic and properly incorporated in the realignment equations; and 2) could not have affected the relative differences in biomarkers across different levels of subjects’ cardiovascular RFs. Second, we showed realignment equations to enable transition of ‘old’ to ‘new’ methods within our ongoing cohort study (and vice versa). However, the results were shown in detail for single-biomarker data realigned to multi-array data. This way of presentation facilitates future comparisons of those biomarkers measured with the multi-array platform at follow-up examinations within this ongoing cohort study. However, any other cohort study should calculate realignment equations within their own data. These may be susceptible to lot-to-lot variation, although in our laboratory the lot-to-lot variation between multi-array assays was low for most of the biomarkers. Nevertheless, the measured concentrations will always depend on the standards provided by the commercial kits (for both the single biomarker and multi-array techniques), which have not been satisfactorily standardized internationally
In conclusion, multiple biomarker detection by the 4-plex multi-array platform of MSD including CRP, SAA, sICAM-1 and sVCAM-1 shows comparable results with well-established single-biomarker techniques, despite differences in absolute concentrations. Subjects’ risk-level assignment therefore depends on the method used. It is, however, uncertain which method is superior in risk prediction. Nevertheless, these biomarkers of low-grade inflammation are associated with glucose metabolism, weight, metabolic syndrome and smoking status, irrespective of the method of detection. In terms of time, effort and quality, this multi-array platform of MSD is an attractive alternative for single-biomarker measurements. Therefore, this platform is a potential tool for the quantification of multiple biomarkers of low-grade inflammation using small sample volume in one single run in large ongoing and future clinical studies.