The authors have declared that no competing interests exist.
Edited and proofread manuscript: CAA KMH AC DS RJG FAS TEV JWW. Conceived and designed the experiments: RWB AK MJL RJG FAS TEV JWW. Performed the experiments: RWB AK AC DS. Analyzed the data: RWB AK CAA KMH KLF. Contributed reagents/materials/analysis tools: RWB AK RJG FAS TEV JWW. Wrote the paper: RWB AK MJL KLF.
Multiplexing arrays increase the throughput and decrease sample requirements for studies employing multiple biomarkers. The goal of this project was to examine the performance of Multiplex arrays for measuring multiple protein biomarkers in saliva and serum. Specimens from the OsteoPerio ancillary study of the Women’s Health Initiative Observational Study were used. Participants required the presence of at least 6 teeth and were excluded based on active cancer and certain bone issues but were not selected on any specific condition. Quality control (QC) samples were created from pooled serum and saliva. Twenty protein markers were measured on five multiplexing array panels. Sample pretreatment conditions were optimized for each panel. Recovery, lower limit of quantification (LLOQ) and imprecision were determined for each analyte. Statistical adjustment at the plate level was used to reduce imprecision estimates and increase the number of usable observations. Sample pre-treatment improved recovery estimates for many analytes. The LLOQ for each analyte agreed with manufacturer specifications except for MMP-1 and MMP-2 which were significantly higher than reported. Following batch adjustment, 17 of 20 biomarkers in serum and 9 of 20 biomarkers in saliva demonstrated acceptable precision, defined as <20% coefficient of variation (<25% at LLOQ). The percentage of cohort samples having levels within the reportable range for each analyte varied from 10% to 100%. The ratio of levels in saliva to serum varied from 1∶100 to 28∶1. Correlations between saliva and serum were of moderate positive magnitude and significant for CRP, MMP-2, insulin, adiponectin, GM-CSF and IL-5. Multiplex arrays exhibit high levels of analytical imprecision, particularly at the batch level. Careful sample pre-treatment can enhance recovery and reduce imprecision. Following statistical adjustments to reduce batch effects, we identified biomarkers that are of acceptable quality in serum and to a lesser degree in saliva using Multiplex arrays.
Accurate and reliable measurement of inflammatory biomarkers is critical to assessing inflammatory mechanisms involved in many diseases including periodontal disease. Periodontitis is a good model for studying these biomarker issues because although the etiology of periodontitis is bacterial, the pathogenesis is clearly inflammatory
Multiplex array platforms and associated reagent kits have been developed which assay for a large number of analytes and have the ability to rapidly process multiple specimens. These systems are more cost-effective and increase the throughput and decrease the sample amounts compared with traditional EIA and ELISA. With applications ranging from protein to nucleic acids multiplex assays add value in their ability to screen multiple biomarkers where there is no know correlate or identify complex and dynamic biosignatures that offer better differentiation than any single biomarker can afford. Bead-based flow cytometric multiplex arrays are commonly used and commercially available for the detection of proteins. The technique utilizes microsphere beads, coated with monoclonal antibodies against specific proteins, to measure analyte concentrations in body fluids, cell extracts and culture supernatants
A large number of studies have reported inflammatory biomarker concentrations in samples tested using multiplex arrays with little apparent attention in the manuscripts to quality control (QC) performance. Few reports have been published on methodological limitations and imprecision estimates of this technique in blood serum and plasma
This investigation is part of a larger study funded under the ARRA program by NIDCR to characterize biomarkers of inflammation in both serum and saliva, determine the extent that serum and saliva measures correlate, and to determine associations of serum and salivary markers with clinical periodontal disease and bone density measures in an established and well-characterized cohort of postmenopausal women. Serum and saliva samples were previously collected as part of two completed studies on osteoporosis and periodontal disease (OsteoPerio Studies) that were ancillary to the Buffalo center of the national Women’s Health Initiative Observational Study (WHI-OS). Participants for the OsteoPerio studies were recruited from 2,249 postmenopausal women ages 53–84 years who enrolled in the WHI-OS at the University at Buffalo clinical center of the WHI. The baseline OsteoPerio study enrolled 1,362 women and 1,025 of these women were reexamined five years later through a second examination. The OsteoPerio baseline visit corresponded with the 3rd annual visit of the WHI-OS. The OsteoPerio studies included questionnaires on demographics, lifestyle, and medical history; dual energy x-ray absorptiometry (DXA) scans for measuring bone density; and a comprehensive clinical dental exam with oral radiographs
Collection of serum and saliva was completed as part of the OsteoPerio studies. In brief, participants came to the Buffalo WHI clinic in the morning and provided a fasting saliva and blood sample. All samples were collected, processed and stored using standardized protocols.
Saliva samples were collected in the clinic prior to blood draw, eating/drinking or dental examination. Participants provided 5 ml of saliva in a pre-marked collection tube. Saliva collection was completed in 10 minutes or less. Those with difficulty producing enough saliva were offered the option to chew a sterile rubber band to help stimulate saliva production. Samples were transferred into 0.5 ml cryogenic storage straws, which were sealed and placed in −80°C freezers for 24 hours prior to long-term submersion in liquid nitrogen (−196°C).
Fasting blood samples were collected at the same visit by venipuncture after the saliva collection and prior to the dental examination. A 10cc tube without anticoagulant was used for serum collection. The tube was placed in darkness for 30 minutes to allow a clot to form and centrifuged at 1500×g for 15 minutes. The serum portion was removed, transferred to 0.5 ml straws, sealed and placed in −80°C freezers for 24 hours prior to long-term submersion in liquid nitrogen (−196°C).
For the purpose of this study, quality control (QC) specimens were created from serum and saliva samples obtained at a single visit from 24 individual volunteers, using a protocol identical to that used for participant samples. Samples were centrifuged and pooled into a single sterile flask. The pooled specimens were then centrifuged again and portioned into 0.5 ml cryogenic storage straws (125 serum and 125 saliva straws), heat sealed and placed stored in liquid nitrogen (−196°C).
For analysis, cryogenic straws of all samples were retrieved from liquid nitrogen, placed on dry ice and shipped to a single research laboratory facility (The Forsyth Institute, Boston, MA). They remained in −80°C freezers until the time of testing.
Samples were sent in batches that included at least two serum and saliva QC samples. The samples were assembled and sent in blinded fashion as related to health outcomes and any personal information. The samples from one individual who had two time points were sent in a single batch to be assayed on the same plate. The order of samples on the plate was pre-determined for the laboratory to follow. All samples were blinded to the laboratory personnel by use of unique sample identification numbers. The present study includes stored serum samples from 910 women at baseline and from 410 women at follow-up, among these were 1133 paired saliva/serum samples (725 baseline and 408 follow-up pairs).
Multiplexed sandwich immunoassays, based on flowmetric Luminex™ xMAP technology, were conducted at The Forsyth Institute (Cambridge, MA). Assays were carried out on a Luminex 100 Bio-Plex Platform. Immediately prior to the initiation of study measurements the Bio-Plex platform underwent a complete on-site maintenance cycle and operational qualification by Luminex field engineers. Daily and weekly performance qualification was continuously verified by Forsyth Institute technicians during the seven week analytical period.
Assay kits provided by the commercial vendors consisted of 5 panels: 1) “
Single lot numbers of each kit were purchased in bulk in order to minimize analytical variability. Reagents provided in these kits included beads, monoclonal antibodies, standards, assay diluents, biotin-conjugated secondary antibodies, biotin diluent, streptavidin conjugated to the fluorescent protein, R-phycoerythrin (streptavidin-RPE), streptavidin-RPE diluent, washing buffer concentrates, and incubation buffer concentrates as well as the 96-well filter plates.
Samples were thawed directly on the day of analysis. Working wash solutions were prepared from concentrates on a daily basis. Protein standards were prepared, within one hour of beginning the assay, by reconstituting the standard in assay diluent and performing serial dilutions according to manufacturer specifications. To prepare beads for the multiplex assays, each analyte bead solution was mixed with wash solution or bead diluents in an aluminum foil-wrapped test tube as the beads are light-sensitive.
Bead solution, incubation buffer, assay diluents, samples, standards and blanks were pipetted in designated wells using negative volume displacement precision pipettes (Rainin Instrument LLS, Woburn, MA). Plates were incubated at 4°C, overnight, on an orbital shaker (IKA Werke, Staufen, Germany) set to 600 rpm in order to keep beads suspended. After washing, diluted biotinylated detector antibody was added into each well, followed by incubation and washing. Streptavidin-PRE solution was added into each well after washing; the instrument was calibrated, a standard curve was created, and the observed concentrations of samples were calculated.
Statistical analyses were performed to summarize data descriptively and included means, standard deviations and percent coefficient of variation (%CV; relative standard deviation). Pearson correlations between serum and saliva measures were performed on log-transformed data so as to approximately normalize the population frequency distributions of the measurements. In our initial processing of these data we did consider other measures of association. As our log transformed concentrations are nearly normally distributed, and as we intend to use linear regression (and to adjust for other covariates) in other analyses, we chose to use the Pearson correlation (which has close ties to multivariate normality and linear models) to summarize the association between the serum and saliva concentrations. Substantively similar results were obtained with Kendall’s tau correlation. Statistical analyses were performed using SAS V.9.2 (Carey, NC). Further calculations and statistical procedures are described where relevant below.
We determined the single most appropriate minimum required dilution (MRD) for each multiplex panel which would allow a maximum number of samples to generate measurements within the linear calibration range
We performed recovery studies by standard additions methodology
During initial method validation it was apparent that normal serum and saliva levels were near or below the lower limit of quantification (LLOQ) for some analytes. To establish the LLOQ for each analyte, empirical LLOQ determinations were performed as we have described previously in other studies
For each analyte, the known concentrations (and dilutions thereof) were plotted on the x-axis and compared with the %CV of six replicates on the y-axis using SigmaPlot ver. 9.01. The nonlinear trendline was plotted and fitted using a 3-parameter, exponential decay model (
Each QC sample was assayed in duplicate on each multiplex plate and for each of the 5 panels. Daily batches consisted of 2 plates of serum samples and two plates of corresponding saliva samples measured over 7 consecutive weeks. Average within-run CV was calculated from at least 4 replicates of each QC specimen per batch. Unadjusted between-run CV was calculated from at least 28 replicates across all 7 batches.
Intra-assay (within-run, plate-specific) and inter-assay (between-run, plate-to-plate) imprecision as well as trending of the data was evaluated across the study using QC specimens which were analyzed in multiple replicates within each plate. We calculated plate-specific means and %CVs for the sample cohort and QC measurements as well as means and CVs for all plates within the sample set. To characterize the variability of sample levels relative to the imprecision of QC samples we calculated the ratio of analytical-to-inter-individual variability (A/I) defined as the %CV of QC measurements divided by the %CV of all sample measurements.
There was significant plate-to-plate variation in the mean analyte concentrations of participant samples and QC materials. Quantitative and categorical demographic variables were tested across plates by ANOVA F-test and the chi-square test of independence, respectively. As a means of filtering batch-to-batch imprecision the conversion from fluorescent intensity (FI) values to observed concentration (OC) values was followed by an adjustment process at the plate level using the QC replicates. Adjustments were separate for each analyte. After adjustment, the plate means of the log OC of the QC replicates are equal. Briefly, the batch adjustment procedure for each plate and each analyte was to (1) convert all OC to log scale; (2) compute grand mean and plate means of QC replicates; (3) compute residuals (plate means - grand mean); (4) subtract residuals from each log OC to produce `adjusted log OC’; and, (5) exponentiate to produce an `adjusted between Run CV’.
Following this batch adjustment we examined each plate by traditional QC algorithms. We generated Levey-Jennings type plate-to-plate plots of QC values across 7 batches of 4 plates per day (two containing serum samples and two containing homologous saliva samples), constituting 28 total plates. Run acceptability was based upon conventional Westgard rule interpretation
Serum | Saliva | |||
Panels | Dilution | Diluent | Dilution | Diluent |
MMP | 1∶10 | 1 mM NA in 0.05% Tween in assay diluent | 1∶10 | assay diluent |
Bone | None | None | None | None |
hs10-Plex | 1∶2 | 25 mM EDTA in 0.05% Tween in assay diluent | 1∶2 | assay diluent |
Obesity | 1∶500 | assay diluent | None | None |
2-Plex | None | 25 mM EDTA in 0.05% Tween in assay diluent | None | assay diluent |
MMP Panel: MMP-2, MMP-8 and MMP-9; Bone Panel: OPG, Leptin, PTH and Insulin; 10-Plex: IL-1β, IL-10,
IL-6, GM-CSF, IL-5, IFN-γ, TNF-α, IL-2, IL-4 and IL-8; Obesity Panel: Adiponectin and CRP; 2-Plex Panel: TNF-α and MCP-1.
For the 10-plex panel in serum the first two-fold dilution (1∶2) of samples was observed to increase the measured concentration of IL-1β, IL-6, IL-10, IFN-γ, GM-CSF and IL-8 by a factor of 2–4 while further two-fold serial dilutions (1∶4 to 1∶64) resulted in decreasing concentrations.
Initial 1∶2 dilution causes a 3 fold increase in observed concentration followed by 50% decreases with each successive serial dilution.
10-PlexAnalyte | Neat Serum | 25 mM EDTA/0.05% Tween-20(1∶2 dilution) |
IL-1β | 12% | 46% |
IL-10 | 2% | 117% |
IL-6 | 16% | 94% |
GM-CSF | 17% | 72% |
IL-5 | 22% | 168% |
IFNG-γ | 34% | 51% |
TNF-α | 15% | 19% |
IL-2 | 30% | 63% |
IL-4 | 22% | 54% |
IL-8 | 34% | 107% |
EDTA, ethylenediaminetetraacetic acid disodium salt.
Panel | Analyte | CalibrationRange | Unit | KitLLOQ | EmpiricalLLOQ | Serum %Recovery | Saliva % Recovery |
Bone | Insulin | 0.08–250 | ng/mL | 0.05 |
|
125 | 184 |
Leptin | 0.016–300 | ng/mL | 0.12 |
|
7 | 59 | |
OPG | 0.50–8500 | pg/mL | 1.42 |
|
156 | 36 | |
PTH | 0.55–9800 | pg/mL | 0.3 |
|
88 | 38 | |
MMP | MMP-1 | 9.13–6800 | pg/mL | 6.3 | 37 | 58 | 42 |
MMP-2 | 9.7–55,000 | pg/mL | 7.5 | 400 | ND | 102 | |
MMP-8 | 12.5–80,000 | pg/mL | 5.0 |
|
20 | ND | |
MMP-9 | 8.1–47,900 | pg/mL | 11.0 |
|
107 | ND | |
Obesity | Adiponectin | 0.37–270 | ng/mL | 0.0198 |
|
ND | 92 |
CRP | 0.03–21 | ng/mL | 0.0019 |
|
ND | 56 | |
10 Plex | GM-CSF | 0.20–387 | pg/mL | <1.0 | 1.1 | 72 | 56 |
IFNγ | 0.10–145 | pg/mL | <1.0 |
|
51 | 168 | |
IL-10 | 0.20–485 | pg/mL | <1.0 |
|
117 | 8 | |
IL-1β | 0.10–211 | pg/mL | <1.0 |
|
46 | 85 | |
IL-2 | 0.10–273 | pg/mL | <1.0 |
|
63 | 128 | |
IL-4 | 0.20–417 | pg/mL | <1.0 |
|
54 | 112 | |
IL-5 | 0.20–393 | pg/mL | <1.0 |
|
168 | 70 | |
IL-6 | 0.10–133 | pg/mL | <1.0 |
|
94 | 116 | |
IL-8 | 0.20–356 | pg/mL | <1.0 |
|
107 | ND | |
TNFα | 0.10–212 | pg/mL | <1.0 |
|
19 | 83 | |
4-Plex | MCP-1 | 6.2–2100 | pg/mL | 0.5 |
|
ND | ND |
TNFα | 2.3–4500 | pg/mL | 0.3 |
|
100.4 | 93.24 |
ND, not determined as baseline sample level was>highest calibrator.
all replicate samples achieved CV <20% and therefore the manufacturer’s stated LLOQ is accepted.
In saliva 10-plex panels, two-fold dilution generated similar increases in measured analyte concentration and % recovery; however, inclusion of additives in the assay sample diluents did not further improve recovery and were therefore not used (data not shown). For the remaining panels, dilution/additive used (
The x-axis is analyte concentration and the y-axis is the coefficient of variation of six replicate measurements of authentic serum spiked with analyte by standard additions methodology.
Serum | Saliva | ||||||||||
Panel | Analyte | N | Average Within run CV | Unadjusted Between Run CV | Adjusted Between Run CV | AI | N | Average Within run CV | Unadjusted Between Run CV | Adjusted Between Run CV | AI |
Bone | Insulin | 32 | 5.80 | 30.19 |
|
0.06 | 31 | 20.33 | 38.20 |
|
0.13 |
MMP | MMP_8 | 32 | 7.17 | 31.66 |
|
0.07 | 31 | 10.91 | 25.55 |
|
0.14 |
MMP | MMP-2 | 32 | 8.69 | 25.26 |
|
0.26 | 20 | 17.79 | 48.73 |
|
0.16 |
MMP | MMP-9 | 30 | 7.24 | 25.33 |
|
0.13 | 31 | 8.29 | 14.19 |
|
0.16 |
4Plex | TNF-α | 28 | 10.62 | 84.78 |
|
0.21 | 29 | 14.57 | 88.61 |
|
0.11 |
Bone | Leptin | 32 | 7.79 | 26.32 |
|
0.10 | 31 | 29.38 | 43.00 | 24.42 | 0.39 |
Bone | PTH | 30 | 10.13 | 36.40 |
|
0.08 | 29 | 27.78 | 54.29 | 29.46 | 0.24 |
4Plex | MCP-1 | 30 | 4.36 | 23.69 |
|
0.26 | 29 | 13.39 | 18.61 |
|
0.13 |
Bone | OPG | 30 | 10.01 | 35.10 |
|
0.29 | 31 | 16.21 | 56.52 |
|
0.15 |
10Plex | TNF-α | 30 | 14.88 | 53.32 |
|
0.04 | 30 | 46.54 | 55.54 | 73.52 | 0.41 |
10Plex | IL-10 | 32 | 16.52 | 29.31 |
|
0.03 | 30 | 39.54 | 51.03 | 109.38 | 0.20 |
10Plex | IL-4 | 30 | 14.85 | 72.03 |
|
0.04 | 30 | 40.05 | 52.20 | 98.64 | 0.23 |
10Plex | IL-6 | 30 | 16.46 | 46.54 |
|
0.03 | 30 | 38.98 | 58.39 | 55.49 | 0.19 |
10Plex | Adiponectin | 32 | 21.30 | 34.38 |
|
0.30 | 26 | 8.13 | 40.41 |
|
0.04 |
10Plex | IL-2 | 30 | 20.05 | 36.13 |
|
0.04 | 30 | 38.51 | 45.11 | 107.35 | 0.23 |
10Plex | IL-5 | 30 | 20.80 | 31.10 |
|
0.03 | 30 | 47.91 | 58.15 | 119.11 | 0.22 |
10Plex | IL-8 | 32 | 22.68 | 38.66 |
|
0.15 | – | – | – | – | – |
Obesity | CRP | 30 | 25.32 | 36.02 |
|
0.25 | 24 | 23.10 | 115.51 |
|
0.09 |
10Plex | IFN-γ | 30 | 24.58 | 103.45 | 25.22 | 0.05 | 30 | 42.47 | 72.56 | 94.13 | 0.13 |
10Plex | GM-CSF | 30 | 30.28 | 64.34 | 31.57 | 0.08 | 30 | 42.14 | 49.52 | 102.09 | 0.22 |
10Plex | IL-1β | 29 | 41.05 | 89.40 | 48.58 | 0.25 | – | – | – | – | – |
CV, % coefficient of variation; AI, ratio of analytical imprecision (CV of QC materials) over interindividual variation (CV of all participant values) with a target value of <0.25; QC data for IL-1β and IL-8 were insufficient in saliva, no data is reported; Bolded Adjusted Between Run CV indicate analytes that are in the acceptable range (<25%) for imprecision.
The 10-plex analytes in our serum QC materials were repeatedly measured below the manufacturer’s LLOQ (<1.0 pg/mL) yet still above the lowest calibrator. The imprecision estimates described above were therefore generated at the extreme low range of the assay and should be interpreted carefully because the high CVs demonstrate the expected loss of precision when quantifying samples at the extreme ends of the assay’s range
The ratio of analytical-to-inter-individual variability (A/I ratio) calculated for each analyte provides a point of comparison for the amount of analytical imprecision relative to the inter-individual variation for each analyte. Studies of biological variability considered minimal analytical imprecision to be less than half intraindividual variation and require that intraindividual variation be less than half interindividual variation in order for a biomarker to be minimally useful in distinguishing longitudinal differences within a person or distinguishing person-to-person differences within a population
Analyte | Insulin | Leptin | OPG | PTH | MMP-2 | MMP-8 | MMP-9 | Adiponectin | CRP |
(Unit) | (ng/mL) | (ng/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (ng/mL) | (ng/mL) |
Total N Tested | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 |
Total N Pass QC | 1320 | 1320 | 1226 | 1226 | 1320 | 1320 | 1226 | 1320 | 1226 |
% below | 2.5 | 0.61 | 0.08 | 0 | 1.59 | 1.89 | 0.49 | 0.24 | |
% above | 0 | 0 | 0 | 0 | 0.61 | 0 | 1.88 | 0.83 | 13.7 |
N | 1287 | 1312 | 1225 | 1226 | 1290 | 1295 | 1196 | 1307 | 1055 |
Mean(SD) | 0.191(0.211) | 4.24(4.02) | 334.9(140.5) | 32.14(43.69) | 192,490(47,910) | 7,910(7,776) | 146,937(83,364) | 19,829(11,440) | 2,908(2,362) |
Min | 0.054 | 0.16 | 7.38 | 0.903 | 3,618 | 637.2 | 381.5 | 42.39 | 12.94 |
25th percentile | 0.109 | 1.63 | 246.1 | 21.72 | 163,222 | 3,440 | 85,674 | 12,123 | 1,056 |
Median | 0.14 | 3.16 | 301.1 | 27.92 | 194,419 | 5,690 | 129,839 | 16,984 | 2,238 |
75th percentile | 0.196 | 5.57 | 396.1 | 35.56 | 222,013 | 9,217 | 188,153 | 24,690 | 4,217 |
Max | 3.84 | 50.77 | 1,369 | 1,243 | 386,209 | 113,240 | 533,844 | 111,687 | 12,847 |
Total N Tested; number of participant samples sent for testing, Total N pass QC; number of participant samples after removal of failed QC batches,
% above/below; percent of samples to pass QC but fall above or below the quantifiable range of the assay based on
Analyte | GM-CSF | IFN-γ | IL-10 | IL-1β | IL-2 | IL-4 | IL-5 | IL-6 | IL-8 | TNF-α1 | MCP-1 | TNF-α2 |
(Unit) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) |
Total N Tested | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 | 1320 |
Total N Pass QC | 1226 | 1226 | 1320 | 1109 | 1226 | 1226 | 1226 | 1226 | 1320 | 1226 | 1226 | 1138 |
% below | 60.44 | 33.77 | 17.05 | 33.27 | 4.32 | 64.68 | 4.08 | 0 | 0 | 0 | 3.16 | |
% above | 0.73 | 0 | 0 | 0 | 0 | 0.08 | 0 | 0.24 | 0 | 0.16 | 0 | 0 |
N | 476 | 812 | 1095 | 740 | 1173 | 1225 | 433 | 1173 | 1320 | 1224 | 1226 | 1102 |
Mean(SD) | 33.34(80.01) | 0.746(3.34) | 10.89(45.84) | 1.32(2.35) | 2.79(11.94) | 5.73(21.39) | 2.70(10.64) | 4.33(20.31) | 13.12(17.57) | 4.16(14.83) | 143.3(66.49) | 1.72(0.758) |
Min | 1.46 | 0.111 | 0.276 | 0.083 | 0.164 | 0.39 | 0.254 | 0.162 | 0.592 | 0.181 | 1.24 | 0.56 |
25th percentile | 3.46 | 0.223 | 0.598 | 0.37 | 0.44 | 1.26 | 0.454 | 0.515 | 6.51 | 0.935 | 99.34 | 1.27 |
Median | 7.23 | 0.316 | 1.01 | 0.622 | 0.681 | 1.68 | 0.657 | 0.877 | 9.28 | 1.43 | 134.5 | 1.59 |
75th percentile | 21.1 | 0.486 | 2.7 | 1.25 | 1.39 | 2.82 | 1.28 | 1.89 | 13.82 | 2.64 | 179.1 | 1.98 |
Max | 888 | 86.5 | 633.5 | 28.45 | 241.4 | 360.2 | 129.5 | 379 | 322.6 | 350.3 | 471.6 | 13.14 |
Total N Tested; number of participant samples sent for testing, Total N pass QC; number of participant samples after removal of failed QC batches,
% above/below; percent of samples to pass QC but fall above or below the quantifiable range of the assay based on
1– TNF-α assayed as part of the 10-Plex.
2– TNF-α assayed with MCP-1.
Analyte | Insulin | Leptin | OPG | PTH | MMP-2 | MMP-8 | MMP-9 | Adiponectin | CRP |
(Unit) | (ng/mL) | (ng/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (ng/mL) | (ng/mL) |
Total N Tested | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 |
Total N Pass QC | 1133 | 1133 | 1133 | 1059 | 820 | 1133 | 1133 | 1062 | 968 |
% below | 5.83 | 9.89 | 0.35 | 2.83 | 83.29 | 0.44 | 0.09 | 0.47 | 2.58 |
% above | 0 | 0 | 0 | 0 | 0 | 6.47 | 31.71 | 1.6 | 0.1 |
N | 1067 | 1021 | 1129 | 1029 | 136 | 1050 | 768 | 1040 | 942 |
Mean(SD) | 0.484(0.579) | 0.362(0.205) | 167.6169.9) | 21.425.7) | 5,6975,840) | 214,728179,177) | 224,126122,263) | 22.8132.3) | 0.9921.66) |
Min | 0.037 | 0.06 | 2.84 | 0.449 | 2,134 | 1,259 | 254.9 | 0.068 | 0.002 |
25th percentile | 0.146 | 0.208 | 72.43 | 4.87 | 2,840 | 79,866 | 125,687 | 6.13 | 0.154 |
Median | 0.278 | 0.321 | 115.5 | 11.2 | 3,487 | 161,302 | 214,383 | 11.8 | 0.438 |
75th percentile | 0.622 | 0.467 | 203.7 | 27.8 | 6,015 | 296,368 | 317,275 | 25.2 | 1.12 |
Max | 5.31 | 1.57 | 2,293 | 185.6 | 33,807 | 1,049,129 | 532,426 | 276.7 | 25.9 |
Total N Tested; number of participant samples sent for testing, Total N pass QC; number of participant samples after removal of failed QC batches
% above/below; percent of samples to pass QC but fall above or below the quantifiable range of the assay based on
Analyte | GM-CSF | IFN-γ | IL-10 | IL-1β | IL-2 | IL-4 | IL-5 | IL-6 | IL-8 | TNF-a1 | MCP-1 | TNF-a2 |
(Unit) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) | (pg/mL) |
Total N Tested | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 | 1133 |
Total N Pass QC | 1133 | 1133 | 1133 | 1133 |
1133 | 1133 | 1133 | 1133 | 1133 |
1133 | 1039 | 1042 |
% below | 74.03 | 37.99 | 32.36 | 0.53 | 5.21 | 2.3 | 68.55 | 0.71 | 0 | 3.18 | 0.29 | 5.37 |
% above | 0.88 | 1.5 | 0.18 | 11.58 | 0.53 | 0.97 | 0.09 | 1.86 | 81.79 | 2.74 | 3.37 | 0 |
N | 284 | 685 | 763 | 994 | 1067 | 1094 | 355 | 1102 | 206 | 1065 | 1001 | 986 |
Mean(SD) | 39.77(100.2) | 21.48(134.3) | 11.8(55.0) | 61.3(69.5) | 9.64(44.8) | 26.0(111.3) | 17.1(60.9) | 14.2(40.7) | 413.2(188.5) | 20.6(36.8) | 374.1(360.3) | 5.27(7.17) |
Min | 1 | 0.073 | 0.178 | 0.48 | 0.119 | 0.35 | 0.178 | 0.106 | 0.74 | 0.132 | 1.77 | 0.526 |
25th percentile | 3.61 | 0.213 | 0.706 | 21.1 | 0.711 | 2.25 | 0.682 | 1.59 | 254 | 4.3 | 143.6 | 1.97 |
Median | 8.15 | 0.836 | 1.62 | 38.1 | 1.32 | 4.98 | 1.84 | 4.14 | 436.1 | 8.96 | 244.7 | 3.32 |
75th percentile | 27.04 | 4.61 | 3.93 | 73.0 | 3.45 | 11.63 | 5.05 | 10.46 | 578.2 | 19.01 | 471.3 | 5.83 |
Max | 799.5 | 2,425 | 669.1 | 421.6 | 872.1 | 1,854 | 480.4 | 621.6 | 705.4 | 411.1 | 2,303 | 86.88 |
Total N Tested; number of participant samples sent for testing, Total N pass QC; number of participant samples after removal of failed QC batches.
% above/below; percent of samples to pass QC but fall above or below the quantifiable range of the assay based on
1– TNF-α assayed as part of the 10-Plex.
2– TNF-α assayed with MCP-1.
- the QC material was insufficient for evaluation of these analytes, all data within analytic range is reported and has not been adjusted by batch.
For saliva (
Panel | Analyte | N | Medianof Ratios | Pearsoncorrelationr | LCL | UCL |
Bone | Insulin | 1047 | 1.82 | 0.29 | 0.23 | 0.34 |
Leptin | 1015 | 0.10 | 0.02 | –0.04 | 0.08 | |
OPG | 1054 | 0.38 | 0.12 | 0.06 | 0.18 | |
PTH | 955 | 0.40 | 0.01 | –0.05 | 0.08 | |
MMP | MMP-2 | 134 | 0.02 | 0.25 | 0.08 | 0.40 |
MMP-8 | 1035 | 29.62 | 0.06 | 7.0E−04 | 0.12 | |
MMP-9 | 696 | 1.69 | –0.03 | –0.11 | 0.04 | |
Obesity | Adiponectin | 1030 | 6.7E−04 | 0.31 | 0.25 | 0.36 |
CRP | 781 | 1.5E−04 | 0.66 | 0.53 | 0.62 | |
10-plex | GM-CSF | 113 | 0.97 | 0.27 | 0.08 | 0.43 |
IFN-γ | 416 | 2.64 | –0.01 | –0.11 | 0.09 | |
Il-10 | 639 | 1.35 | 0.06 | –0.01 | 0.14 | |
Il-1β |
559 | 62.57 | –0.03 | –0.23 | 0.17 | |
Il-2 | 957 | 1.78 | 0.07 | 3.1E−03 | 0.13 | |
Il-4 | 1025 | 2.57 | –0.04 | –0.10 | 0.03 | |
Il-5 | 113 | 3.07 | 0.20 | 0.01 | 0.37 | |
Il-6 | 979 | 4.22 | 0.05 | –0.01 | 0.11 | |
Il-8 |
206 | 46.17 | –0.33 | –0.51 | –0.10 | |
TNF-α | 997 | 5.29 | 8.0E−03 | –0.05 | 0.07 | |
4-plex | MCP-1 | 919 | 1.89 | 0.03 | –0.04 | 0.09 |
TNF-α | 887 | 2.17 | 0.02 | –0.05 | 0.08 |
All values are batch adjusted.
Median of Ratios – median of ratios computed for each participant.
Saliva values for these analytes are not adjusted due to insufficient QC data.
Pearson Correlation of natural log adjusted values, LCL, UCL lower and upper 95% confidence interval.
Our goal in this study was to use multiplex technology to simultaneously measure a relatively large set of protein biomarkers in serum and homologous saliva. We developed sample dilution and pre-treatments that improved recovery estimates for many analytes. We confirmed the lower limit of quantification for each analyte. We determined that 17 of 20 biomarkers in serum and 9 of 20 biomarkers in saliva demonstrated acceptable precision. We examined a large cohort of well defined specimens and determined the percentage of cohort samples having levels within the reportable range. Finally, we determined the ratio of levels in saliva to serum, and assessed correlations between saliva and serum.
Before initiating analysis of participant samples, we attempted to characterize the performance characteristics of these methods, guided by the kit manufacturer’s protocols. These initial efforts indicated poor performance in many of the assays we evaluated. We therefore undertook efforts to optimize these assays. In order to bring endogenous analyte levels into the analytical range of the assays, we established minimum required dilutions (MRD) ranged from 1∶1 (no dilution) to 1∶500 in serum while saliva MRDs ranged from 1∶1 to 1∶10. Upon dilution we identified significant matrix effects for many analytes and therefore, different pre-treatment diluents were selected to minimized these effects and improve the recovery of the analytes. Matrix effects are interferences in the measurement of a target analyte caused by non-analyte components of complex milieus such as serum and saliva. Matrix effects are an especially critical issue when multiplexing analytes since interfering substances such as non-specific sample proteins may affect high abundance targets differently than targets with lower concentrations. While the paradigm dictates that matrix effects may be more of a problem in serum with its high protein concentration, our results suggest that saliva poses the same limitation. Indeed, salivary components are well known to interact with other components to form so called “heterotypic complexes”
We have shown here that careful determination of MRDs and pretreatment/dilution with various diluents can reduce matrix effects and increase the recoveries as shown in
We evaluated imprecision of study materials using coefficients of variation (CVs) as a measure of variability. Most analytes had acceptable levels of within-run imprecision for QC materials (within plate %CV <20%); however, between-run (plate-to-plate) imprecision was >20% for all analytes. We tested whether this variation could have been due to differences in demographic qualities across plates, and could not identify any factors to explain the differences that were found. Plate specific cohort means co-varied with QC material measurement and conventional QC algorithms rejected more than half of all cohort data. We therefore applied statistical adjustment which considered some of the plate-to-plate variability and increased the number of usable observations. When doing studies with large numbers of samples over time, such batch adjustments may be necessary. After adjustment, traditional application of Westgard rules eliminated only the most errant results. In serum, 17 out of 20 analytes showed acceptable performance defined as adjusted between-run CV of <20% (<25% at LLOQ). MMP-2, MCP-1, OPG and adiponectin each had acceptable imprecision in serum and saliva; however, the ratio of analytical imprecision to interindividual variation indicated that these analytes may be of limited value as biomarkers in a population based or diagnostic setting. Fewer analytes were acceptable in saliva using this method with 9 out of 20 meeting acceptability criteria. Of those saliva analytes rejected, we found that IL-8 and IL-1β were present at significantly higher levels in saliva than in serum. The single MRD therefore yielded insufficient dilution for these analytes and a large proportion of the measurements (IL-8 in particular) were above the linear range of the assay. We suggest that in future assessments of saliva that these cytokines be carefully considered in larger plexed panels where these problems may occur and tested separately so that a more suitable dilution can be achieved without detriment to other 10-plex analytes.
There are many different vendors and platforms now available for multiplex assays. The design, nature of quality control material and imprecision estimates from these sources vary greatly, and reports are almost exclusively about serum or plasma. Hsu et al reported interassay imprecision for a panel of cytokine analytes that ranged from 10.2–19.8%
In addition to imprecision problems, recovery studies suggest that sample matrix effects can result in significant inaccuracy. This phenomena was also previously observed in a study using a more limited set of analytes and a similarly modified diluent improved the accuracy of the spike recovery for two different multiplex platforms
One of the aims of the larger grant was to compare analyte measures in saliva and serum samples collected at the same time among a defined set of participants in order to determine correlations between biomarker concentrations in saliva and serum. Saliva has been proposed as a convenient medium for monitoring local and systemic inflammatory processes
Strengths of the current study include the large, well-characterized cohort of postmenopausal women. Given their age, these participants provide a great opportunity to study a broad range of biomarkers and will, in further analyses, allow us to explore the biomarkers according to personal characteristics in various levels of health and disease. Importantly, the serum and saliva were obtained from each individual participant at one visit using standardized protocols including careful handling and processing of samples for immediate freezing. A single laboratory completed all bioassays in serum and saliva and attempts to control variation by using the same lot numbers for each biomarker assay kit was another strength. There were a large number of participants assayed and many had measures available at two time points.
There are several limitations of the current study. First, there is a lack of repeat measurements of all samples due to cost. Use of QC replicates allowed us to examine this issue, but further replicates would have been useful. Second, we were not able to perform direct comparison using more traditional assays such as ELISA due to sample limitations and cost. We do have information on some traditional markers (i.e., CRP by nephelometry and insulin by chemiluminescent immunoassay) that will be available for exploration in future analyses. Direct comparison between multiplex and traditional ELISA however is difficult to accomplish. A number of published studies have compared these two methods and it is apparent that certain elements of these assays are pivotal in obtaining similar results from both assays