{"@context":{"@language":"en","Affiliation":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","AggregatedSourceRepository":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","Citation":"https:\/\/open.library.ubc.ca\/terms#identifierCitation","Contributor":"http:\/\/purl.org\/dc\/terms\/contributor","Creator":"http:\/\/purl.org\/dc\/terms\/creator","DateAvailable":"http:\/\/purl.org\/dc\/terms\/issued","DateIssued":"http:\/\/purl.org\/dc\/terms\/issued","Description":"http:\/\/purl.org\/dc\/terms\/description","DigitalResourceOriginalRecord":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","FullText":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","Genre":"http:\/\/www.europeana.eu\/schemas\/edm\/hasType","IsShownAt":"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt","Language":"http:\/\/purl.org\/dc\/terms\/language","PeerReviewStatus":"https:\/\/open.library.ubc.ca\/terms#peerReviewStatus","Provider":"http:\/\/www.europeana.eu\/schemas\/edm\/provider","Publisher":"http:\/\/purl.org\/dc\/terms\/publisher","PublisherDOI":"https:\/\/open.library.ubc.ca\/terms#publisherDOI","Rights":"http:\/\/purl.org\/dc\/terms\/rights","RightsURI":"https:\/\/open.library.ubc.ca\/terms#rightsURI","ScholarlyLevel":"https:\/\/open.library.ubc.ca\/terms#scholarLevel","Subject":"http:\/\/purl.org\/dc\/terms\/subject","Title":"http:\/\/purl.org\/dc\/terms\/title","Type":"http:\/\/purl.org\/dc\/terms\/type","URI":"https:\/\/open.library.ubc.ca\/terms#identifierURI","SortDate":"http:\/\/purl.org\/dc\/terms\/date"},"Affiliation":[{"@value":"Medicine, Faculty of","@language":"en"},{"@value":"Other UBC","@language":"en"},{"@value":"Non UBC","@language":"en"},{"@value":"Pediatrics, Department of","@language":"en"}],"AggregatedSourceRepository":[{"@value":"DSpace","@language":"en"}],"Citation":[{"@value":"Diagnostics 8 (1): 13 (2018)","@language":"en"}],"Contributor":[{"@value":"University of British Columbia. Food, Nutrition and Health Program","@language":"en"},{"@value":"Children's Hospital (Vancouver, B.C.). Research Institute","@language":"en"}],"Creator":[{"@value":"Karakochuk, Crystal D.","@language":"en"},{"@value":"Henderson, Amanda M.","@language":"en"},{"@value":"Samson, Kaitlyn L. I.","@language":"en"},{"@value":"Aljaadi, Abeer M.","@language":"en"},{"@value":"Devlin, Angela M.","@language":"en"},{"@value":"Becquey, Elodie","@language":"en"},{"@value":"Wirth, James P.","@language":"en"},{"@value":"Rohner, Fabian","@language":"en"}],"DateAvailable":[{"@value":"2019-07-03T18:02:42Z","@language":"en"}],"DateIssued":[{"@value":"2018-02-02","@language":"en"}],"Description":[{"@value":"Recently, a multiplex ELISA (Quansys Biosciences) was developed that measures ferritin, soluble transferrin receptor (sTfR), retinol-binding protein (RBP), C-reactive protein (CRP), \u03b11-acid glycoprotein (AGP), thyroglobulin, and histidine-rich protein 2. Our primary aim was to conduct a method-comparison study to compare five biomarkers (ferritin, sTfR, RBP, CRP, and AGP) measured with the Quansys assay and a widely-used s-ELISA (VitMin Lab, Willstaett, Germany) with use of serum samples from 180 women and children from Burkina Faso, Cambodia, and Malaysia. Bias and concordance were used to describe the agreement in values measured by the two methods. We observed poor overall agreement between the methods, both with regard to biomarker concentrations and deficiency prevalence estimates. Several measurements were outside of the limit of detection with use of the Quansys ELISA (total n = 42 for ferritin, n = 2 for sTfR, n = 0 for AGP, n = 5 for CRP, n = 22 for RBP), limiting our ability to interpret assay findings. Although the Quansys ELISA has great potential to simplify laboratory analysis of key nutritional and inflammation biomarkers, there are some weaknesses in the procedures. Overall, we found poor comparability of results between methods. Besides addressing procedural issues, additional validation of the Quansys against a gold standard method is warranted for future research.","@language":"en"}],"DigitalResourceOriginalRecord":[{"@value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/70886?expand=metadata","@language":"en"}],"FullText":[{"@value":"diagnosticsArticleComparison of a New Multiplex Immunoassay forMeasurement of Ferritin, Soluble TransferrinReceptor, Retinol-Binding Protein, C-Reactive Proteinand \u03b11-Acid-glycoprotein Concentrations against aWidely-Used s-ELISA MethodCrystal D. Karakochuk 1,2,* ID , Amanda M. Henderson 2,3, Kaitlyn L. I. Samson 1,2,Abeer M. Aljaadi 1,2, Angela M. Devlin 2,3, Elodie Becquey 4, James P. Wirth 5 andFabian Rohner 5 ID1 Food, Nutrition, and Health, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;kaitlyn.samson@ubc.ca (K.L.I.S.); aaljaadi@bcchr.ca (A.M.A.)2 BC Children\u2019s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada; ahenderson@bcchr.ca (A.M.H.);adevlin@bcchr.ca (A.M.D.)3 Department of Paediatrics, University of British Columbia, Vancouver, BC V6H 3V4, Canada4 International Food Policy Research Institute, Washington, DC 20005, USA; e.becquey@cgiar.org5 GroundWork, Fl\u00e4sch 7306, Switzerland; james@groundworkhealth.org (J.P.W.);fabian@groundworkhealth.org (F.R.)* Correspondence: crystal.karakochuk@ubc.ca; Tel.: +1-604-822-0421Received: 10 January 2018; Accepted: 31 January 2018; Published: 2 February 2018Abstract: Recently, a multiplex ELISA (Quansys Biosciences) was developed that measures ferritin,soluble transferrin receptor (sTfR), retinol-binding protein (RBP), C-reactive protein (CRP), \u03b11-acidglycoprotein (AGP), thyroglobulin, and histidine-rich protein 2. Our primary aim was to conduct amethod-comparison study to compare five biomarkers (ferritin, sTfR, RBP, CRP, and AGP) measuredwith the Quansys assay and a widely-used s-ELISA (VitMin Lab, Willstaett, Germany) with useof serum samples from 180 women and children from Burkina Faso, Cambodia, and Malaysia.Bias and concordance were used to describe the agreement in values measured by the twomethods. We observed poor overall agreement between the methods, both with regard to biomarkerconcentrations and deficiency prevalence estimates. Several measurements were outside of the limitof detection with use of the Quansys ELISA (total n = 42 for ferritin, n = 2 for sTfR, n = 0 for AGP, n = 5for CRP, n = 22 for RBP), limiting our ability to interpret assay findings. Although the Quansys ELISAhas great potential to simplify laboratory analysis of key nutritional and inflammation biomarkers,there are some weaknesses in the procedures. Overall, we found poor comparability of resultsbetween methods. Besides addressing procedural issues, additional validation of the Quansys againsta gold standard method is warranted for future research.Keywords: \u03b11-acid glycoprotein; C-reactive protein; ELISA; ferritin; immunoassay; inflammation;micronutrient; retinol-binding protein; soluble transferrin receptor1. IntroductionMicronutrient deficiencies are thought to be widespread globally but particularly affect certainsubgroups in low- and middle-income countries, such as young children and women of child-bearingage [1]. The measurement of nutritional biomarkers such as iron, vitamin A, iodine, folate, vitaminB12, and zinc, is an essential component of assessing nutritional status at the population-level.Diagnostics 2018, 8, 13; doi:10.3390\/diagnostics8010013 www.mdpi.com\/journal\/diagnosticsDiagnostics 2018, 8, 13 2 of 16Such biomarker analyses are done in a laboratory, either in the country where samples were collectedor internationally. In either case, acceptable precision and accuracy of laboratory analysis is ofprimary importance in obtaining reliable estimates of nutritional status. In addition, affordability isan important criterion for selecting laboratory tests to be done in a survey or study, in particular inresource-constrained settings. Other factors include sample throughput, complexity of the method,technical expertise required for operation and maintenance, and quantity of blood sample availablefor analysis.Often in large micronutrient surveys conducted in low- and middle-income countries,the identification of a suitable laboratory to measure selected biomarkers with adequate qualityand affordable cost is not straightforward. When biological samples cannot be exported, stakeholdersmust identify a laboratory with sufficient expertise in-country, and it can be challenging to find alaboratory that has the needed capacity at affordable costs. When exporting samples is possible,affordability is also often a challenge.In the recent past years, the VitMin Lab in Germany (led by Dr. Juergen Erhardt) has automatedand scaled up an in-house sandwich ELISA (s-ELISA) [2], enabling the analysis of large numbers ofsamples at a very competitive cost. This laboratory offers the analysis of ferritin, soluble transferrinreceptor (sTfR), retinol-binding protein (RBP), C-reactive protein (CRP), and \u03b11-acid-glycoprotein(AGP). It has demonstrated high performance in external quality control schemes [2]. Because thislaboratory offers outstanding quality at a low price, many groups working in the field of nutrition relyheavily on it for analysis of said biomarkers. These biomarkers are ideal for assessing iron and vitaminA status (ferritin, sTfR, RBP) and adjusting for their response to inflammatory triggers [3]. This strongperformance and a low price has: (a) resulted in a high demand for the services of the single personoperating the VitMin Lab; and (b) forced the laboratory to restrict the number of samples acceptedper project to a somewhat arbitrary number of 3000 samples in order to cope with the huge demand.Repeated attempts of the laboratory to establish the methodology in other laboratories for analysinglarge number of samples have not yet been successful [4]. In addition, to our knowledge, the range ofaforementioned biomarkers cannot currently be measured on a single commercially-available clinicalanalyser, further complicating the issue. Several models are available that measure all but either AGPor RBP, necessitating employment of several methods\/analysers. Thus, while the VitMin Lab is aviable and reliable solution at present, it has the drawbacks of: (a) dependency on a single laboratory;(b) limitations in the number of samples that can be analysed; and (c) the permission and resourcesto export samples from the country of origin to Germany. Therefore, there is a need to identifyalternative options.PATH, in collaboration with Quansys (Quansys Biosciences, Logan, UT, USA), has developeda multiplex ELISA (Q-Plex\u2122 Human Micronutrient Assay) that measures the same five biomarkersas those measured by the VitMin Lab, as well as thyroglobulin and histidine-rich protein 2 (HRP2; abiomarker to detect recent or current malaria parasitemia). The multiplex platform is relatively easyto use and requires less sophisticated laboratory knowledge and skills than other methods, whichwould make it suitable for use in different contexts, including those in low- and middle-incomecountries. Additionally, although the multiplex reader is relatively expensive, the cost per sample ofthe disposable microplates is similar to that of the VitMin Lab.As a primary goal, we conducted a method-comparison study to compare five biomarkers(ferritin, sTfR, RBP, CRP, and AGP) measured by the VitMin Lab and using the Quansys assay withuse of serum blood sampled from women and children from three different regions. As a secondarygoal, we compared a sub-sample of children with confirmed qualitative malaria testing to the Quansysassay to determine its diagnostic ability to identify children with malaria.Diagnostics 2018, 8, 13 3 of 162. Materials and Methods2.1. Description of the ELISA MethodsThe VitMin lab uses a s-ELISA that concurrently measures ferritin, sTfR, RBP, AGP, and CRP in a384-well plate with capture antibody. Capture antibodies used were: ferritin (Code A0133, Dako), sTfR(Cat. No 4Tr26; Clone 23D10, Hytest), RBP (Code A0040, Dako), CRP (Code A0073, Dako, Denmark).Detection antibodies were: anti-ferritin-horseradish peroxidase (Code P0145, Dako), anti-sTfR-horseradish peroxidase (Cat. No. 4Tr26-c; Clone 13E4, Hytest), anti-RBP-anti-ferritin-horseradishperoxidase (Code P0304, Dako) and anti-CRP-anti-ferritin-horseradish peroxidase [2]. Serum controls(Liquicheck, Bio-Rad) were used as standards for the calibration curves [2]. Quality control (QC)samples were prepared from serum samples with a low and high content of analytes [2]. Full detailson the s-ELISA assay can be found elsewhere [2].The Quansys Q-Plex ELISA is a quantitative chemiluminescent assay that concurrently measuresthe same five analytes as Erhardt\u2019s s-ELISA, as well as thyroglobulin and HRP2, in a 96-well plate.Each well concurrently measures the seven analytes along with a positive control. Binding of detectionmoieties is measured via the chemiluminescence produced by streptavidin horseradish peroxidase inthe presence of a luminol-based substrate [5]. Capture and detector mouse anti-human IgA antibodieswere used for ferritin (F23, F31, and 4F23, Hytest, Turku, Finland). Capture antibodies used for theother analytes included: sTfR (4Tr26 13E4, Hytest), RBP (RBP, 4RBP2, RB42, Hytest), CRP (4C28C6, Hytest), and AGP (GW22927F, Sigma Aldrich) [5]. Purified ferritin (RP-87068, ThermoFisher,Waltham, MA, USA), sTfR (8Tr56, Hytest), CRP (8C72, Hytest), AGP (G9885, Sigma Aldrich, St. Louis,MO, USA), and RBP (8RF9, Hytest) antigens were used [5]. Serum controls (Liquicheck, Bio-Rad,Hercules, CA, USA) were used as standards for the calibration curves [5]. Full details on the QuansysQ-Plex ELISA assay can be found elsewhere [5].2.2. Description of the Gold Standard Malaria TestQualitative malaria diagnosis was conducted by detecting the histidine-rich protein 2 (HRP2)antigen of Plasmodium falciparum using a commercial kit (SD BIOLINE Malaria Ag P.f, StandardDiagnostics, Gyeonggi-do, Republic of Korea) and was considered as the gold standard method (in themethod comparison to the Quansys s-ELISA method) for detecting malarial infection that occurred inthe last month [6].2.3. Studied PopulationA summary of the origin of the 180 serum samples for the primary method comparison analysis isdescribed in Table 1. The 60 samples from Burkina Faso children were included in the malaria indicatormethod-comparison in a separate analysis. Ethics approval was obtained from the International FoodPolicy Research Institute\u2019s IRB (2014-7-PHND-M) and the National Ethics Committee of Burkina Faso(2014-02-015)for use of the Burkina Faso samples; from the University of British Columbia ClinicalResearch Ethics Board in Canada (H12-00451) and the National Ethics Committee for Health Researchin Cambodia (010-NECHR) for use of the Cambodia samples; and from the University of BritishColumbia Clinical Research Ethics Board in Canada (H15-00521) and the Ethics Committee for Researchinvolving Human Subjects at the University Putra Malaysia in Malaysia (UPM\/TNCPI\/RMC\/1.4.18.1[JKEUPM]\/F2) for use of the Malaysian samples.Diagnostics 2018, 8, 13 4 of 16Table 1. Summary of the origin of the 180 serum samples included for method-comparison analysis.Country Population Group Total n Available 1 Total n Analysed 2Burkina Faso Children aged 21\u201324 month 2100 60Cambodia Non-pregnant women aged 18\u201345 year 809 60Malaysia Non-pregnant women aged 18\u201345 year 200 601 Numbers are estimates, as some samples had insufficient volumes; 2 Purposively selected samples from allavailable samples in order to represent a range of values that were low, near the cut-off for deficiency, and high.2.4. Blood Collection ProceduresIn Burkina Faso, a capillary blood sample was collected from the finger into silica-coated bloodcollection tubes (Microvette 300, Sarstedt, N\u00fcmbrecht, Germany) and stored and transported colduntil later centrifugation on the same day at the district health centre in Fada-N\u2019Gourma (East Regionof Burkina Faso). Samples were stored frozen (\u221220 \u25e6C) until shipment to the VitMin lab (July 2016)and use on the Quansys platform (June 2017).In Cambodia, a 3-h fasting venous blood sample was collected from women in the morning into a3.5 mL trace element-free tube (Becton Dickinson, Franklin Lakes, NJ, USA) and transported on iceuntil time of processing (within ~4\u20136 h) at the National Institute of Public Health Laboratory in PhnomPenh, Cambodia. Samples were stored at \u221270 \u25e6C until shipment to Canada. In Canada, samples werestored at \u221280 \u25e6C until time of shipment to Erhardt\u2019s VitMin lab for the s-ELISA (June 2015) and untiltime of Quansys ELISA (June 2017).In Malaysia, a 10-h overnight fasting venous blood sample was collected from women in themorning into a 3.5 mL trace element-free tube (Becton Dickinson, Franklin Lakes, NJ, USA) andtransported on ice until time of processing (within ~3\u20134 h) at the Faculty of Medicine and HealthSciences, University of Putra, Malaysia. Samples were stored at \u221220 \u25e6C until shipment to Canada viaWorld Courier (Stamford, CT, USA). In Canada, samples were stored at \u221280 \u25e6C until time of shipmentto Erhardt\u2019s VitMin lab for the s-ELISA (October 2016) and until time of Quansys ELISA (June 2017).2.5. Data and Statistical AnalysisBiomarkers concentrations were reported as mean \u00b1 SD or median (IQR) depending onthe distribution (normal or skewed, respectively). Commonly-used cut-offs were used to definenutritional deficiencies were as follows: ferritin <12 \u00b5g\/L for children and <15 \u00b5g\/L for women [7],sTfR <8.3 mg\/L [8], and RBP <0.7 \u00b5mol\/L [9]. Acute inflammation was defined as CRP >5 mg\/Land chronic inflammation as AGP >1 mg\/L [10]. HRP2 >0.92 \u00b5g\/L was used to indicate a positivemalaria diagnosis in the Quansys ELISA (personal communication, A. Tyler, Quansys). All valueswere unadjusted (e.g., no adjustments for inflammation or other factors).Bland and Altman\u2019s bias and limits of agreement (95% CI) were used to describe the agreementin values measured by the two methods [11,12]. Bias was defined as the difference in meansbetween the two measures. Limit of agreement plots were also generated for visual interpretation.Lin\u2019s concordance coefficient was also calculated as a measure of reproducibility between the twomethods [13]. Concordance measures the departure of the measured values from a 45\u25e6 line of perfectconcordance. We also estimated linear trend equations in the form (y = ax + b) for each populationgroup for the comparison between each of the two methods. Two-sided p-values < 0.05 indicatedstatistical significance. Stata software version SE\/13.1 for Mac (Stata Corp., College Station, TX, USA)was used for analyses.3. Results3.1. Quality ControlQuality control data for the s-ELISA are described as overall co-efficients of variation (CV, %) ofthe different analytes for each population group (with expected concentrations). Calibration curvesDiagnostics 2018, 8, 13 5 of 16were adjusted with a control sample, measured in 10 wells with Biorad Liquichek controls in 3 differentconcentrations in 6 wells on each plate. CV\u2019s for samples from the three countries were <3.2% forFerritin, <4.3% for sTfR, <4.0% for RBP, <6.2% for CRP and <10.1% for AGP. The Quansys softwareprovides the following quality control data in each measured plate (Table 2).Table 2. Quality control data for the Quansys enzyme-linked immunosorbent assay (ELISA) for eachof the five assays conducted (total n = 180).Analyte ULOQ 1 LLOQ 2 LOD 3LowestValue (<)Total nof LowValuesHighestValue (>)Total nof HighValuesAssay 1 Ferritin 110.16 1.39 0.067 <13.94 11\/39 - -n = 40STfR 118.49 0.17 0.065 - - - -AGP 0.34 0.00059 0.00061 - - - -CRP 17.87 0.029 0.0084 <0.29 3\/40 - -RBP 0.39 0.0016 0.00053 - - >3.9 2\/40HRP2 1.03 0.0015 0.00026 <0.015 40\/40 - -Assay 2 Ferritin 112.43 0.38 0.18 <3.84 3\/40 - -n = 40STfR 118.27 0.17 0.001 <1.70 2\/40 - -AGP 0.36 0.00053 0.00021 - - - -CRP 6.97 0.029 0.0065 <0.29 2\/40 - -RBP 0.39 0.0015 0.00027 - - >3.86 2\/40HRP2 1.03 0.004 0.00092 <0.040 40\/40 - -Assay 3 Ferritin 111.94 1.52 0.081 <15.16 14\/40 - -n = 40STfR 116.62 0.16 0.84 - - - -AGP 0.37 0.00059 0.0004 - - - -CRP 19.86 0.03 0.001 - - - -RBP 1.06 0.0043 0.001 - - - -HRP2 1.05 0.0014 0.0056 <0.014 29\/40 >10.45 8\/40Assay 4 Ferritin 112.31 1.43 0.065 <14.31 9\/20 - -n = 20STfR 115.36 0.16 0.026 - - - -AGP 0.35 0.0015 0.00056 - - - -CRP 18.45 0.26 0.0018 <0.26 - - -RBP 0.11 0.0016 0.0016 - - >1.08 18\/20HRP2 1.02 0.0015 0.00065 <0.015 16\/20 - -Assay 5 Ferritin 113.16 0.4 0.24 <3.97 5\/40 - -n = 40STfR 117.21 1.36 0.008 - - - -AGP 0.36 0.0014 0.00081 - - - -CRP 18.74 0.027 0.01 - - - -RBP 0.38 0.0042 0.002 - - - -HRP2 1.05 0.0045 0.000034 <0.045 26\/40 >10.51 6\/401 ULOQ: the highest standard curve point that can still be used for quantification; the highest concentrationof an analyte that can be accurately measured; 2 LLOQ: the lowest point standard curve point that can still beused for quantification; the lowest concentration of an analyte that can be accurately measured; 3 LOD: limit ofdetection, the lowest concentration level that can be determined to be statistically different from a blank at a 99%confidence level.The total number of samples that were outside of the LOD range in the Quansys ELISA were:n = 35\/179 (20%) for ferritin, n = 2\/180 (<1%) for sTfR, n = 0\/180 (0%) for AGP, n = 7\/180 (4%) forCRP, and n = 4\/180 (2%) for RBP. One sample failed the s-ELISA analysis for ferritin in the Cambodianpopulation; thus, only n = 59 samples were available for comparison in this group.3.2. Characterisitcs of the Women and Children Included in the AnalysisA total of 180 individuals were included in the analysis from three countries: Burkina Faso,Cambodia, and Malaysia. Table 3 presents the mean or median concentrations of eachDiagnostics 2018, 8, 13 6 of 16nutritional biomarker and the deficiency prevalence rates of women and children included in themethod-comparison analyses.Ferritin: Prevalence of iron deficiency based on low ferritin concentrations (<12 and <15 \u00b5g\/Lfor children and women, respectively) varied between the s-ELISA and Quansys ELISAs in BurkinaFaso children (17\u201332%), Cambodian women (0\u201323%), and Malaysian women (6\u201318%). Prevalencealso varied between the Quansys ELISAs in Burkina Faso and Malaysia regardless of whether ornot measurements outside of the limit of detection (LOD) were excluded or included (17\u201322% and6\u201310%, respectively).STfR: Prevalence of iron deficiency based on sTfR (>8.3 mg\/L) varied between the twomethods in Burkina Faso children (75\u2013100%), Cambodian women (35\u201350%), and Malaysian women(8\u201317%). Prevalence also varied between the Quansys ELISAs in Malaysia regardless whether or notmeasurements outside of the LOD were excluded or included (10\u201317%).AGP: Prevalence of chronic inflammation (>1 mg\/L) varied between the two methods inCambodian women (10\u201315%) and Malaysian women (3\u201325%), but was relatively similar in BurkinaFaso children (65\u201367%).CRP: Prevalence of acute inflammation (>5 mg\/L) varied between the two methods in BurkinaFaso children (42\u201358%) and Malaysian women (10\u201334%), but was relatively similar in Cambodianwomen (7\u20138%).RBP: Prevalence of vitamin A deficiency based on RBP concentrations (<0.7 \u00b5mol\/L) variedbetween the two methods in Burkina Faso children. No differences in vitamin A deficiency wereobserved in Cambodian and Malaysian women in either the s-ELISA or Quansys ELISA (2% and 0%prevalence, respectively).Diagnostics 2018, 8, 13 7 of 16Table 3. Nutritional biomarkers and deficiency prevalence of the women and children based on s-ELISA and Quansys measurements both within and outside of thelimit of detection (LOD) range 1.Burkina Faso Children Cambodian Women Malaysian Womens-ELISA n = 60 Quansys_EXCL2 n = 47\u201360Quansys_INCL3 n = 60 s-ELISA n = 60Quansys_EXCL2 n = 39\u201360Quansys_INCL3 n = 59\u201360 s-ELISA n = 60Quansys_EXCL2 n = 51\u201360Quansys_INCL3 n = 60Ferritin, \u00b5g\/L 20.5 (8.9, 44.8) 34.6 (16.9, 91.0) 22.5 (14.5, 62.6) 29.3 (16.9, 59.8) 54.9 (31.0, 105.7) 31.0 (14.3, 73.5) 47.1 (23.2, 81.9) 62.6 (29.7, 118.6) 54.6 (17.8, 96.0)<12 or <15 \u00b5g\/L 4, n (%) 19\/60 (32%) 8\/47 (17%) 13\/60 (22%) 14\/60 (23%) 0\/39 (0%) 0\/59 (0%) 11\/60 (18%) 3\/51 (6%) 6\/60 (10%)STfR, mg\/L 10.4 (8.3, 15.8) 30.7 (22.8, 49.5) 30.7 (22.8, 49.5) 6.6 (4.8, 9.6) 8.5 (6.6, 14.6) 8.5 (6.6, 14.6) 5.2 (4.2, 6.0) 4.7 (3.6, 5.8) 4.7 (3.6, 5.8)>8.3 mg\/L, n (%) 45\/60 (75%) 60\/60 (100%) 60\/60 (100%) 21\/60 (35%) 30\/60 (50%) 30\/60 (50%) 5\/60 (8%) 10\/58 (17%) 10\/60 (10%)AGP, g\/L 1.25 (0.86, 1.82) 1.25 (0.97, 1.56) 1.25 (0.97, 1.56) 0.57 (0.44, 0.76) 0.58 (0.50, 0.69) 0.58 (0.50, 0.69) 0.57 (0.49, 0.72) 0.81 (0.65, 0.99) 0.81 (0.65, 0.99)>1 g\/L, n (%) 39\/60 (65%) 40\/60 (67%) 40\/60 (67%) 9\/60 (15%) 6\/60 (10%) 6\/60 (10%) 2\/60 (3%) 15\/60 (25%) 15\/60 (25%)CRP, mg\/L 3.2 (0.9, 9.2) 7.3 (2.0, 16.3) 7.3 (2.0, 16.3) 0.4 (0.2, 0.8) 1.4 (0.9, 3.0) 1.4 (0.7, 2.8) 0.78 (0.35, 2.16) 3.0 (1.3, 8.5) 2.7 (0.9, 8.3)>5 g\/L, n (%) 25\/60 (42%) 35\/60 (58%) 35\/60 (58%) 4\/60 (7%) 10\/57 (18%) 10\/60 (17%) 6\/60 (10%) 19\/56 (34%) 19\/60 (32%)RBP, \u00b5mol\/L, mean \u00b1 SD 0.83 \u00b1 0.29 0.88 \u00b1 0.33 0.88\u00b1 0.33 1.62 \u00b1 0.62 1.51 \u00b1 0.59 1.59 \u00b1 0.72 1.44 \u00b1 0.32 1.97 \u00b1 0.63 2.03 \u00b1 0.71<0.7, \u00b5mol\/L, n (%) 12\/60 (20%) 23\/60 (38%) 23\/60 (38%) 1\/60 (2%) 1\/58 (2%) 1\/60 (2%) 0\/60 (0%) 0\/58 (0%) 0\/60 (0%)1 Values are median (interquartile range; IQR) unless otherwise indicated. AGP, \u03b11-acid-glycoprotein; CRP, C-reactive protein; LOD, limit of detection; RBP, retinol-binding protein;SD, standard deviation; sTfR, soluble transferrin receptor; 2 Excludes values of measurement for the Quansys ELISA that were outside of the LOD range; 3 Includes all values ofmeasurement for the Quansys ELISA, however, for values outside of the LOD range, values were included in the analysis at the value of the lowest or highest value of the LOD (e.g., for aferritin value measured as <1.5 \u00b5g\/L on the Quansys ELISA, we included in the analysis as a value of 1.5 \u00b5g\/L); 4 Ferritin <12 \u00b5g\/L for children and <15 \u00b5g\/L for women.Diagnostics 2018, 8, 13 8 of 163.3. Trend Estimates for Each Analyte for Each Population Group and the Pooled PopulationWe estimated linear trend equations in the form (y = ax + b; y representing the Quansys method) foreach population group for the comparison between each of the two methods (Table 4). Equations werecalculated for each analyte using all samples within the LOD range in the Quansys ELISA, with theexception of ferritin, for which we calculated two trend equations for (1) samples within the LODrange; and (2) all samples within and outside of the LOD range.Table 4. Linear trend equations for each population group for the comparison between each of thetwo methods.Burkina Faso Cambodia Malaysia Pooled 1Ferritin_EXCL 2 1.58x \u2212 4.66 2.26x \u2212 57.54 2.84x \u2212 79.32 2.25x \u2212 42.79Ferritin_INCL 3 1.55x \u2212 2.78 2.00x \u2212 30.93 2.56x \u2212 54.31 2.08x \u2212 27.59STfR 2 1.75x + 17.19 2.07x + 4.55 1.62x \u2212 3.57 2.48x \u2212 3.04RBP 2 0.97x + 0.07 0.96x \u2212 0.02 1.55x \u2212 0.25 1.12x + 0.04CRP 2 1.05x + 3.76 0.66x + 2.13 1.47x + 2.22 0.99x + 2.88AGP 2 0.54x + 0.56 0.56x + 0.26 0.86x + 0.30 0.64x + 0.361 Pooled results are trend estimates for Burkina Faso, Cambodia, and Malaysia combined; 2 Excludes values ofmeasurement for the Quansys ELISA that were outside of the LOD range; 3 Includes all values of measurement forthe Quansys ELISA (within\/outside of the LOD range).3.4. Method-Comparisons between the Two Methods for Each AnalyteBias, limits of agreement, and correlation coefficients of ferritin, sTfR, AGP, CRP, and RBPconcentrations comparing the s-ELISA and the Quansys ELISA kit are described in Table 5. Results areshown for all samples, as well as only for those within the LOD range in the Quansys ELISA.Ferritin: Agreement between the two methods was poor in the Burkina Faso, Cambodian andMalaysian populations (bias ranged from 14.7 to 29.3 \u00b5g\/L, and concordance ranged from 0.40 to 0.62).Bias did not improve when the samples outside of the LOD range were excluded.STfR: Agreement between the two methods was poor in the Burkina Faso children, (bias of26.5 mg\/L). Conversely, agreement was low in the Cambodian and Malaysian women (bias of 5.1 and0.1 mg\/L), respectively. Concordance ranged from 0.09 to 0.62 in the Burkina Faso and Cambodianwomen, but was higher (0.78) in the Malaysian women. Bias did not improve when samples outside ofthe LOD range were excluded.AGP: Agreement between the two methods varied among the three populations (0.1\u20130.2 g\/L inall three groups). Concordance ranged from 0.41 to 0.85. No samples were outside of the LOD rangeon the Quansys assay.CRP: Agreement between the two methods varied among the three populations (1.4\u20133.1 mg\/L inall three groups). Concordance was relatively good in all three populations and ranged from 0.74 to0.79. Bias did not change when the samples outside of the LOD range were excluded.RBP: Agreement between the two methods varied from \u22120.04 to 0.6 in the three populations.Concordance ranged from 0.38 to 0.83. Bias did not improve when the samples outside of the LODrange were excluded (although only 2 samples were excluded for this reason).Figures 1\u20135 present the bias and limit of agreement plots for each of the five analytes in eachpopulation group for samples within the LOD range (excludes all values of measurement for theQuansys ELISA that were outside of the LOD range).Diagnostics 2018, 8, 13 9 of 16Table 5. Bias, limits of agreement, and correlation coefficients of ferritin, soluble transferrin receptor (sTfR), \u03b11-acid glycoprotein (AGP), C-reactive protein (CRP),and retinol-binding protein (RBP) concentrations comparing the s-ELISA and the Quansys ELISA kit.ParticipantsAll Samples 1 Only Samples Within LOD Range 2Total, All Bias Limits ofAgreementPearson\u2019sCoefficientConcordance(95% CI)WithinRange 1 BiasLimits ofAgreementPearson\u2019sCoefficientConcordance(95% CI)n mean \u00b1 SD \u00b11.96 SD r \u03c1c (\u00b11.96 SD) n mean \u00b1 SD \u00b11.96 SD r \u03c1c (\u00b11.96 SD)Ferritin, \u00b5g\/LBurkina Faso children 60 14.7 \u00b1 36.0 \u221255.8, 85.3 0.81 0.62 (0.51, 0.71) 47 17.8 \u00b1 40.1 \u221260.7, 96.3 0.78 0.57 (0.43, 0.68)Cambodian women 3 59 18.3 \u00b1 84.4 \u2212147.1, 183.7 0.81 0.55 (0.45, 0.63) 39 27.1 \u00b1 103.0 \u2212174.8, 229.0 0.80 0.48 (0.36, 0.59)Malaysian women 60 29.3 \u00b1 104.6 \u2212175.8, 234.3 0.76 0.40 (0.31, 0.48) 51 34.1 \u00b1 112.9 \u2212187.3, 255.4 0.76 0.36 (0.27, 0.45)STfR, mg\/LBurkina Faso children 60 26.5 \u00b1 21.8 \u221216.1, 69.1 0.42 0.09 (0.03, 0.14) 60 26.5 \u00b1 21.8 \u221216.1, 69.1 0.42 0.09 (0.03, 0.14)Cambodian women 60 5.2 \u00b1 10.4 \u221215.2, 25.6 0.91 0.62 (0.54, 0.69) 60 5.2 \u00b1 10.4 \u221215.2, 25.6 0.91 0.62 (0.54, 0.69)Malaysian women 60 0.1 \u00b1 3.3 \u22126.3, 6.5 0.92 0.78 (0.72, 0.83) 58 0.2 \u00b1 3.3 \u22126.3, 6.6 0.91 0.78 (0.72, 0.83)AGP, g\/LBurkina Faso children 60 \u22120.1 \u00b1 0.4 \u22120.9, 0.8 0.67 0.65 (0.49, 0.77) 60 \u22120.1 \u00b1 0.4 \u22120.9, 0.8 0.67 0.65 (0.49, 0.77)Cambodian women 60 \u22120.04 \u00b1 0.2 \u22120.5, 0.4 0.94 0.82 (0.77, 0.86) 60 \u22120.04 \u00b1 0.2 \u22120.5, 0.4 0.94 0.82 (0.77, 0.86)Malaysian women 60 0.2 \u00b1 0.2 \u22120.1, 0.6 0.66 0.41 (0.27, 0.54) 60 0.2 \u00b1 0.2 \u22120.1, 0.6 0.66 0.41 (0.27, 0.54)CRP, mg\/LBurkina Faso children 60 4.1 \u00b1 5.5 \u22126.7, 14.8 0.83 0.73 (0.61, 0.82) 60 4.1 \u00b1 5.5 \u22126.7, 14.8 0.83 0.73 (0.61, 0.82)Cambodian women 60 1.4 \u00b1 4.0 \u22126.5, 9.3 0.83 0.79 (0.68, 0.86) 57 1.5 \u00b1 4.1 \u22126.6, 9.6 0.83 0.79 (0.68, 0.86)Malaysian women 60 3.1 \u00b1 2.9 \u22122.6, 8.7 0.95 0.74 (0.65, 0.81) 56 3.3 \u00b1 2.9 \u22122.4, 8.9 0.95 0.73 (0.64, 0.80)RBP, \u00b5mol\/LBurkina Faso children 60 0.04 \u00b1 0.2 \u22120.3, 0.4 0.85 0.83 (0.74, 0.90) 60 0.04 \u00b1 0.2 \u22120.3, 0.4 0.85 0.83 (0.74, 0.90)Cambodian women 60 \u22120.04 \u00b1 0.4 \u22120.8, 0.8 0.83 0.82 (0.72, 0.89) 58 \u22120.05 \u00b1 0.4 \u22120.8, 0.7 0.74 0.72 (0.58, 0.82)Malaysian women 60 0.6 \u00b1 0.5 \u22120.3, 1.6 0.79 0.38 (0.27, 0.48) 58 0.5 \u00b1 0.4 \u22120.3, 1.4 0.79 0.40 (0.28, 0.50)1 Includes all values of measurement for the Quansys ELISA, however, values outside of the LOD range were included in the analysis at the value of the lowest or highest value of the LOD(e.g., for ferritin measured as <1.5 \u00b5g\/L on the Quansys ELISA, we included in the analysis as a value of 1.5 \u00b5g\/L). AGP, \u03b11-acid-glycoprotein; CI, confidence interval; CRP, C-reactiveprotein; LOD, limit of quantification; RBP, retinol-binding protein; SD, standard deviation; sTfR, soluble transferrin receptor; 2 Excludes values of measurement for the Quansys ELISA thatwere outside of the LOD range; 3 n = 1 sample failed the ferritin Quansys ELISA assay (was reported as incalculable). 3.5. Bland-Altman plots comparing the s-ELISA and the QuansysELISA kit for samples within the LOD range (excludes values for the Quansys ELISA that were outside of the LOD range).Diagnostics 2018, 8, 13 10 of 16Diagnostics 2018, 8, x FOR PEER REVIEW  9 of 15  (a) (b) (c)Figure 1. Ferritin concentrations for (a) Burkina Faso children (n = 47); (b) Cambodian women (n = 39); and (c) Malaysian women (n = 51). QS: ferritin results obtained on the Quansys ELISA; VM: ferritin results obtained from the VitMin lab. (a) (b) (c)Figure 2. STfR concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 60); and (c) Malaysian women (n = 58). Figure 1. Ferritin concentrations for (a) Burkina Faso children (n = 47); (b) Cambodian women (n = 39); and (c) Malaysian women (n = 51). QS: ferritin results obtainedon the Quansys ELISA; VM: ferritin results obtained from the VitMin lab.Diagnostics 2018, 8, x FOR PEER REVIEW  9 of 15  (a) (b) (c)Figure 1. Ferritin concentrations for (a) Burkina Faso children (n = 47); (b) Cambodian women (n = 39); and (c) Malaysian women (n = 51). QS: ferritin results obtained on the Quansys ELISA; VM: ferritin results obtained from the VitMin lab. (a) (b) (c)Figure 2. STfR concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 60); and (c) Malaysian women (n = 58). Figure 2. STfR concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 60); and (c) Malaysian women (n = 58).Diagnostics 2018, 8, 13 11 of 16Diagnostics 2018, 8, x FOR PEER REVIEW  10 of 15  (a) (b) (c)Figure 3. AGP concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 60); and (c) Malaysian women (n = 60).  (a) (b) (c)Figure 4. CRP concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 57); and (c) Malaysian women (n = 56). Figure 3. AGP concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 60); and (c) Malaysian women (n = 60).Diagnostics 2018, 8, x FOR PEER REVIEW  10 of 15  (a) (b) (c)Figure 3. AGP concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 60); and (c) Malaysian women (n = 60).  (a) (b) (c)Figure 4. CRP concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 57); and (c) Malaysian women (n = 56). Figure 4. CRP concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 57); and (c) Malaysian women (n = 56).Diagnostics 2018, 8, 13 12 of 16Diagnostics 2018, 8, x FOR PEER REVIEW  11 of 15 (a) (b) (c)Figure 5. RBP concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 58); and (c) Malaysian women (n = 58). Figure 5. RBP concentrations for (a) Burkina Faso children (n = 60); (b) Cambodian women (n = 58); and (c) Malaysian women (n = 58).Diagnostics 2018, 8, 13 13 of 163.5. Malaria TestingThe Quansys ELISA measures HRP2 as a biomarker measuring malarial infection. We comparedHRP2 concentrations in 60 Burkina Faso children with completed qualitative testing of malariaparasitemia (Table 6). Considering the qualitative method as the reference, the specificity and sensitivityof the HRP2 biomarker measured on the Quansys ELISA to identify children with malaria was asfollows: 72% sensitivity (true-positive rate) and 80% specificity (true-negative rate). The resultingKappa coefficient is 0.680 (95% CI 0.488, 0.872), which is considered to indicate \u2018good agreement\u2019.Table 6. Accuracy of the HRP2 biomarker measured on the Quansys ELISA to identify Burkina Fasochildren with malaria.Malaria Diagnosis Confirmed by the Rapid Diagnostic Test (Reference)Quansys HRP2Yes\u2014Malaria No\u2014Malaria TotalPositive (HRP2 > 1) True-positive18\/25 (72%)False-positive7\/35 (20%) 25Negative (HRP2 < 1) False-negative7\/25 (28%)True-negative28\/35 (80%) 35Total 25 354. DiscussionThis study compares a newly established method (Quansys ELISA) with s-ELISA results froma single laboratory (VitMin lab) which has over the recent years been widely used in the field ofsurveys assessing vitamin A and iron deficiencies. As such, this study is not a method validationper se, but rather an evaluation to determine if results from a newly established method can be usedinterchangeably with those from an established lab. This is an important distinction to keep in mind,as for both the newly established method and the established approach, validation studies have beenconducted [2,5]. Further, it is noteworthy that although the Quansys ELISA platform is on the market,the producer of the platform continues to further improve the analytical performance. As such, resultspresented here are of somewhat transient nature.Results from our comparison almost consistently indicate poor overall agreement of the twomethods, both with regard to absolute biomarker concentrations and deficiency prevalence estimates.For most of the analytes, there is a relatively wide scattering between the two methods as can be seenfrom the Bland-Altmann plots. Moreover, as can be seen in Figures 1\u20135 in the results section, there isalso a systematic shift for several biomarkers, in particular for ferritin, sTfR, and CRP, and to someextent RBP. Such bias may be a result of different affinity of the antibodies in the two methods [14]and as such, if proving to be constant, could be justifiably adjusted, in particular if absolute truevalues could be established to adjust for. However, this shift is not consistent across the samples fromthe three countries such that from this work, no factor-adjustment to render the two methods morecomparable can be proposed without reservation. Further, in comparison with a recently publishedreport comparing the Quansys method to other methods based on samples from pregnant women inNiger [5], the shift varies again, despite some rough qualitative agreement (above or below the line ofequality): the slopes (y = Quansys, x = comparing method) are 1.88x in that work vs. 2.08x for pooledFerritin in our work, 1.70x vs. 2.48x for sTfR, 0.67x vs. 0.99x for CRP, and 0.47x vs. 0.63x for AGP;for RBP, this qualitative trend does not hold true: 0.84x vs. 1.12x.The observed lack of concordance may be attributed to the sample preparation. In 2017,the Quansys assay was validated using a Nigerian cohort of heparinized plasma samples [15]. The factthat serum and capillary samples were used in this study could account for some of the observed assaydifferences. Further studies would be warranted to further understand the impact of sample collectionmethods on both the s-ELISA and Quansys multiplex assay.Diagnostics 2018, 8, 13 14 of 16With regard to malaria parasitemia, 80% percent of the cases were truly positive and 72% weretruly negative, when using the on-site rapid diagnostic test kit as the reference method. This results ina Kappa coefficient indicating good agreement. However, Kappa statistics are being used primarilyfor inter-observer comparability and as such, are tending to overestimate the comparability ofbiochemical methods.In terms of handling the Quansys ELISA, our impression is that the platform is relatively easy touse with minimal additional training for trained lab technicians. Instructions on assay preparationand completion are well-described in the assay handbook (available online and inside the kit). The kitcontains the 96-well plate and all required reagents (calibrator, detection mix, substrate, samplediluents, and wash buffer). The Q-View\u2122 software (Quansys Biosciences, Logan, UT, USA) isuser-friendly and tutorials are available online to provide the user with additional guidance if needed.Despite the simple handling, there were some challenges faced and these revolve in particular aroundthe platform\u2019s automatic calculation of the LOD: this calibration is done for each new plate anda built-in algorithm fits a curve to the measured concentration of the calibrators. Particularly forferritin, this led to difficulty in interpreting deficiency status on two plates, where the fitted curveresulted in estimations of ferritin concentrations below the threshold for defining iron deficiency inchildren (two plates produced n = 14\/40 and n = 9\/40 ferritin values defined as <15.16 \u00b5g\/L and<14.13 \u00b5g\/L, respectively; both below or just around the cut-off for iron deficiency of <12 \u00b5g\/L and<15 \u00b5g\/L, see Table 2). For these n = 23 samples, we could not confirm if children had a ferritinconcentration below the 12 \u00b5g\/L cut-off for deficiency. Through discussions with technical staff ofQuansys, we identified options to \u2018manually\u2019 adjust the calibration curve and lower the LOD. However,we decided against presenting results of such manual adjustments, since this comparison was meantto be done for routine analyses and not a research-context. Instead, we present the result including andexcluding samples where these challenges were faced. As previously mentioned, Quansys is workingon improving the calibration curve and introducing quality control samples to reduce such issues.As observed in several of the Bland-Altman plots, there appeared to be poorer agreement betweenvalues at the higher end (this was especially the case for ferritin (see Figure 1)). We recognize thatmost assays are developed with the goal to achieve high accuracy of measurement near the thresholdthat defines deficiency (for ferritin, ~12\u201315 \u00b5g\/L). This is intended to optimize diagnostic accuracywith use of ferritin measurements. However, this is likely achieved with the consequence of decreasedaccuracy at higher values (e.g., ferritin > 150 \u00b5g\/L). We considered whether or not we should excludehigher values of each analyte because of the poor agreement observed among values at the higher end.However, it was challenging to arbitrarily estimate what these higher values should be. This is alsobecause the higher end threshold would likely vary from assay to assay, and would also depend onthe controls used in each assay (which differ from lot to lot). Without a consensus on what higher endthresholds should be, and if we should exclude those values for the purposes of a method-comparison,we decided not to exclude any values. However, we raise this issue as one that requires furtherattention and solution, and note that the agreement between the methods in our study would likelyhave been higher if we excluded those higher (potentially inaccurate) values.The strength of our comparison is that we had samples over a wide concentration range of mostanalytes available, samples from both women and children and from Asia and Africa. Although thisis not an exhaustive representation, the samples certainly provide a certain heterogeneity in theirorigins. One possible limitation is that samples were analysed at different time points after obtainingthem and during that time, they were stored under different conditions. This may have affected theanalytes (concentration or chemical form) and thereby affected comparability. All analytes used in thiswork are proteins and are considered relatively stable for an extended period of time if kept frozen at\u221280 \u25e6C. For example, ferritin is very stable and serum can be frozen at \u221220 to \u221270 \u25e6C for several yearswithout affecting sample quality [16]. Samples from Malaysia and Cambodia followed this standard,but this was not the case for Burkina Faso, where samples were stored at \u221220 \u25e6C for about 2 monthsand from then onwards at \u221240 \u25e6C. Further, the time between the two analyses was less than one yearDiagnostics 2018, 8, 13 15 of 16for samples from Malaysia and Burkina Faso, while it was two years for those from Cambodia, thus,we believe degradation of sample poses a very minor risk. Moreover, we suspect that degradation wasnot an issue as the Quansys concentrations tend to be higher than those obtained from the VitMin lab.The other important limitation is that this study is not using a method performance validation againsta gold standard, but merely compares one newly-developed method with an established method.Thus, it would be inappropriate to say that one method is wrong and the other is right, both may haveshifts from the true concentrations of the analytes. Yet, the VitMin Lab method has undergone severalcritical assessments and mostly yielded good scores [2,14]; plus, it is so widely used that it is almostsetting a benchmark for new methods to be compared against.In our prior work, we measured serum ferritin concentrations using four different immunoassays,including Erhardt\u2019s s-ELISA, in Cambodian women (n = 420) and Congolese children (n = 226) [14].We observed differences in mean serum ferritin concentrations across assays, which were likely aninherent reflection of the different ferritin isoforms, antibodies, and calibrators used by these assaysand labs [14]. Despite the differences in ferritin concentrations, iron deficiency prevalence was similar(and low) across the different ferritin methods [14]. Similarly, we suspect that some of the observeddifferences in ferritin concentrations are likely due to the different analytical (isoforms and antibodies)and calibration techniques between the two methods.In conclusion, we think it would be important to identify and publish the upper thresholds forboth methods used here, so that users can easily understand where the methods\u2019 limitations are interms of accurate measurement, although we recognize this is difficult without the use of standardcontrols across all methods. With regard to the Quansys ELISA, although it has great potential tosimplify laboratory analysis of ferritin, sTfR, RBP, CRP and AGP, there are still some weaknesses inthe procedures that will need to be eliminated for routine use, in particular the automated calibrationcurves. In our use, it sometimes led to measured values below the established thresholds definingdeficiency, in particular with ferritin. It would further be helpful if the test kits included certified qualitycontrol materials that would enable the user to spot inconsistencies relatively easily in routine use.An important caveat is that the systematic shift between the two methods is not constant for a givenanalyte in the samples from the three population in our study. More research is needed to establishsuch adjustment factors, if the comparability cannot be improved by changing the concentration of thecapture antibodies.In its present form, the Quansys platform may be of interest to laboratories that have methodsin place for analysing these analytes at small-scale and are looking for higher throughput options.In such a context, the laboratory may be able to cross-compare results on a regular basis and detectinconsistencies originating from the Quansys platform. For routine use in settings where such qualitycontrol options are not easily available, we conclude from our data that the risk of obtaining biasedresults is currently too high.Acknowledgments: Funding was provided by the University of British Columbia, Vancouver, Canada.Helen Keller International co-contributed the Burkina Faso Samples. Quansys loaned the analyser for thestudy and provided in-kind kits. We did not receive funds for covering the costs to publish in open access.Author Contributions: Fabian Rohner, James P. Wirth and Crystal D. Karakochuk conceived and designedthe experiments; Amanda M. Henderson and Kaitlyn L. I. Samson performed the Quansys s-ELISA;Crystal D. Karakochuk and Fabian Rohner analysed the data; Angela M. Devlin provided the lab space andbasic equipment for the analyses; Abeer M. Aljaadi contributed data for analysis; Elodie Becquey contributedpreviously collected data for analysis; Crystal D. Karakochuk and Fabian Rohner wrote the paper; all authorscontributed to revision of the paper to the final stage.Conflicts of Interest: The authors declare no conflict of interest.References1. Bailey, R.L.; West, K.P.; Black, R.E. The epidemiology of global micronutrient deficiencies. Ann. Nutr. Metab.2015, 66, 22\u201333. [CrossRef] [PubMed]Diagnostics 2018, 8, 13 16 of 162. Erhardt, J.G.; Estes, J.E.; Pfeiffer, C.M.; Biesalski, H.K.; Craft, N.E. Combined measurement of ferritin, solubletransferrin receptor, retinol binding protein, and C-reactive protein by an inexpensive, sensitive, and simplesandwich enzyme-linked immunosorbent assay technique. J. Nutr. 2004, 134, 3127\u20133132. [CrossRef][PubMed]3. Supplement\u2014Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA).Am. J. Clin. Nutr. 2017, 106, 327S\u2013330S. Available online: https:\/\/academic.oup.com\/ajcn\/issue\/106\/suppl_1(accessed on 1 January 2018).4. Erhardt, J.G. 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This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http:\/\/creativecommons.org\/licenses\/by\/4.0\/).","@language":"en"}],"Genre":[{"@value":"Article","@language":"en"}],"IsShownAt":[{"@value":"10.14288\/1.0379719","@language":"en"}],"Language":[{"@value":"eng","@language":"en"}],"PeerReviewStatus":[{"@value":"Reviewed","@language":"en"}],"Provider":[{"@value":"Vancouver : University of British Columbia Library","@language":"en"}],"Publisher":[{"@value":"Multidisciplinary Digital Publishing Institute","@language":"en"}],"PublisherDOI":[{"@value":"10.3390\/diagnostics8010013","@language":"en"}],"Rights":[{"@value":"CC BY 4.0","@language":"en"}],"RightsURI":[{"@value":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","@language":"en"}],"ScholarlyLevel":[{"@value":"Faculty","@language":"en"}],"Subject":[{"@value":"\u03911-acid glycoprotein","@language":"en"},{"@value":"C-reactive protein","@language":"en"},{"@value":"ELISA","@language":"en"},{"@value":"Ferritin","@language":"en"},{"@value":"Immunoassay","@language":"en"},{"@value":"Inflammation","@language":"en"},{"@value":"Micronutrient","@language":"en"},{"@value":"Retinol-binding protein","@language":"en"},{"@value":"Soluble transferrin receptor","@language":"en"}],"Title":[{"@value":"Comparison of a New Multiplex Immunoassay for Measurement of Ferritin, Soluble Transferrin Receptor, Retinol-Binding Protein, C-Reactive Protein and \u03b11-Acid-glycoprotein Concentrations against a Widely-Used s-ELISA Method","@language":"en"}],"Type":[{"@value":"Text","@language":"en"}],"URI":[{"@value":"http:\/\/hdl.handle.net\/2429\/70886","@language":"en"}],"SortDate":[{"@value":"2018-02-02 AD","@language":"en"}],"@id":"doi:10.14288\/1.0379719"}