Clinical Chemistry

Little, Randie, R

doi: 10.1373/clinchem.2014.225789pmid: 24938751

Two very interesting articles on hemoglobin A1c (Hb A1c)2 method performance appear in this issue of Clinical Chemistry. Woodworth et al. (1) use Hb A1c as an example of how the risk of reporting inaccurate results can be estimated to guide individualized QC strategies. They assess the risk of reporting unreliable Hb A1c results with 6 different methods in 4 academic medical centers; 5 are laboratory instruments and 1 is a point-of-care (POC) method. Patient-weighted σ metrics were calculated on the basis of 1–2s QC rules with an average of 1 QC for every 100 samples. The authors found considerable differences in the risk of reporting unreliable results among the methods used at these sites, and that only 1 method, the Capillarys2 Flex Piercing, achieved a patient-weighted σ metric >3 at a total error allowable (TEa) of 6%. This total error limit is also used for College of American Pathologists (CAP) proficiency testing and National Glycohemoglobin Standardization Program (NGSP) certification and is considered optimal for clinical care at this time. This means that for all but the aforementioned method, “maximum QC (three levels, three times per day) should be performed to achieve the necessary error detection.” Of note was that for some methods there was a substantial amount of bias (up to 5.8% relative for values within the reference interval), and CV values were higher than the recommended 2% for either the high or low QC for 3 of the methods studied (2). The maximum number of Hb A1c results out of 100 expected to be unreliable from an out-of-control condition at TEa of 6% ranged from 0.60 to 71.48 (!), and for 2 methods the number of results expected to be unreliable was >19 of 100 even when the method would be considered in control (mainly due to high bias). The second article on Hb A1c method performance by Lenters-Westra and Slingerland (3) includes an evaluation of 7 Hb A1c POC methods. This study also finds large differences in performance among methods, with CV values varying from <1% (better than most of the laboratory methods in the Woodworth study (1)) to >3%. Precision evaluations in both studies followed the Clinical and Laboratory Standards Institute (CLSI) EP-5 protocol, so the CV values reported in the 2 studies should be directly comparable. Overall, the CV values for the POC methods overlapped those for the laboratory methods, and the CV values for the DCA Vantage (POC method) included in both studies were similar, albeit slightly above the recommended 2% for within-laboratory variability. Overall, the imprecision of all the methods evaluated in both studies varied considerably, with CV values ranging from 0.8% to 3.2% when evaluated across the measurement range. In both studies, bias was calculated on the basis of comparison to NGSP/IFCC Reference Laboratories, so the reference point in both studies is also the same. Although the way in which bias is reported is not the same (percentage bias at 2 levels vs mean absolute Hb A1c bias across the entire range), clearly, in both studies, there are large biases for some methods and almost no bias for others. All the methods evaluated in both studies were NGSP certified (on the basis of comparison data submitted by the manufacturer) at the time of each study, so one would hope that they would be able to perform at the same level in the laboratory. However, Lenters-Westra and Slingerland (3) found that 4 of the 7 methods evaluated in their laboratory would fail the certification criteria. It is worth noting that these evaluations were performed in a laboratory with experienced personnel; because these methods are all CLIA waived, there are no mandates that the end users be trained laboratory personnel or perform proficiency testing. Clearly, as noted in previous studies (4, 5), some methods that can perform well enough to pass NGSP certification when testing is performed by the manufacturer do not consistently achieve the same level of performance in the field. Also of note is that in both studies, bias rather than imprecision seemed to be the major factor when methods did not perform well; this would seem to indicate that lot-to-lot variations between reagents and/or calibrators may play a significant role. Although there were no substantial differences between tested lots for the POC methods evaluated in either of the present studies, such differences have been observed in previous studies (4, 6–9). It would have been interesting to see if the laboratory methods evaluated in the Woodworth et al. study (1) would have passed NGSP certification. Clearly, many methods, including a few POC methods, do perform well in laboratories, as seen by data from the CAP GH-2 proficiency surveys. Both articles show a wide range of performance among Hb A1c methods. The current NGSP criterion is stringent; at least 37 of 40 sample results (for samples in the 4%–10% Hb A1c range) must be within 6% of an NGSP network laboratory for a method to be certified, and method certification must be renewed annually. However, certification is performed under optimal conditions by the manufacturer, typically using 1 lot of reagents. Thus, NGSP certification shows that a Hb A1c method is capable of excellent performance, but cannot guarantee that the method will consistently achieve the same performance in the hands of end users. A method may show little bias for the lot of calibrators/reagents used during certification, but if quality assurance processes at the manufacturer level cannot consistently maintain this level of performance from 1 lot of reagents or calibrators to the next, substantial lot-to-lot variations can be observed. Proficiency testing assesses the performance of large numbers of laboratories, usually using several different lots of reagents, and thus provides a measure of both within- and between-method variability as well as bias from a reference-assigned value. An important consideration in proficiency testing, especially for accuracy-based assessments such as the CAP GH-2 Hb A1c survey, is use of matrix-appropriate materials to minimize the potential for matrix effects that can occur when processed materials (e.g., lyophilized blood) are used. The CAP survey for Hb A1c uses fresh whole blood material with NGSP value assignments. This allows for accurate evaluation of within- and between-method precision and bias. This GH2 survey is provided twice a year with Hb A1c pooled whole blood samples at 3 Hb A1c levels. The CAP LN15 Hb A1c Calibration Verification/Linearity survey is also provided twice a year with the same sample type and value assignments (6 levels are provided). The most recent GH2 survey included >3000 laboratories using >30 different methods (10). In selecting a Hb A1c method, laboratories can first determine if a method is NGSP certified (potential for optimal performance) and then evaluate CAP data (both current and past surveys) to assess how well the method actually performs in the field. If a certified method shows large CV values on >1 CAP survey, it suggests that the method will not perform well over time in an individual laboratory. Similarly, a method with a large bias on several surveys will likely show the same bias in an individual laboratory. For example, the bias observed by Woodworth et al. for the Tosoh G8 is consistent with what recent CAP surveys have shown for this method (1), and the consistent good performance of the DCA Vantage seen on several CAP surveys is also evident in both of the current studies as well as in previous observations (11). Choosing a method that is NGSP certified and has performed well on proficiency testing is very important but still does not guarantee optimal performance in every laboratory. As discussed in Woodworth et al. (1), each laboratory must develop optimal QC practices and also evaluate their method continuously. Although each laboratory may not be able to accurately calculate their risk of reporting unreliable results (as in Woodworth et al.), they must ensure that they have good QC practices, that their CV values are consistently <2%, and they have minimal bias on the basis of comparison with a reference, either by direct comparison with NGSP (e.g., individual laboratory certification and monitoring) or through the CAP GH2 and/or LN15 surveys or a comparable accuracy-based Hb A1c survey. Lenters-Westra and Slingerland (3) recommend that users of POC methods be required to perform proficiency testing; if this proved to be feasible, it would be a major step in ensuring that these methods are producing results that are consistently accurate. The fact that Hb A1c is now recommended for diagnosis of diabetes and prediabetes (12), as well as monitoring of mean glycemia, reinforces the requirement that Hb A1c results be accurate. This need for accuracy has led to the tightening of criteria for passing both NGSP certification and CAP proficiency testing. Hb A1c is an important test for the diagnosis and ongoing monitoring of a disease that is increasing in prevalence; laboratories must be vigilant in ongoing quality assurance and monitoring of their Hb A1c assays to achieve the levels of performance demanded by the clinical community to ensure optimal patient care. 2 Nonstandard abbreviations: Hb A1c glycated hemoglobin POC point-of-care TEa total allowable error CAP College of American Pathologists NGSP National Glycohemoglobin Standardization Program CLSI Clinical and Laboratory Standards Institute. " (see articles on pages 1062 and 1073) " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: None declared. " Consultant or Advisory Role: None declared. " Stock Ownership: None declared. " Honoraria: None declared. " Research Funding: None declared. " Expert Testimony: None declared. " Patents: None declared. " Other Remuneration: Roche, Bio-Rad, Arkray, and Abbott. References 1. Woodworth A , Korpi-Steiner N, Miller JJ, Rao LV, Yundt-Pacheco J, Kuchipudi L et al. Utilization of assay performance characteristics to estimate hemoglobin A1c result reliability . Clin Chem 2014 ; 60 : 1073 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Sacks DB , Arnold M, Bakris GL, Bruns DE, Horvath AR, Kirkman MS et al. Executive summary: guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus . Clin Chem 2011 ; 57 : 793 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Lenters-Westra E , Slingerland RJ. Three of 7 hemoglobin A1c point-of-care instruments do not meet generally accepted analytical performance criteria . Clin Chem 2014 ; 60 : 1062 – 72 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Lenters-Westra E , Slingerland RJ. Six of eight hemoglobin A1c point-of-care instruments do not meet the general accepted analytical performance criteria . Clin Chem 2010 ; 56 : 44 – 52 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Holmes EW , Erşahin Ç, Augustine GJ, Charnoğursky GA, Gryzbac M, Murrell JV et al. Analytic bias among certified methods for the measurement of hemoglobin A1c . Am J Clin Pathol 2008 ; 129 : 540 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Little RR , Lenters-Westra E, Rohlfing CL, Slingerland R. Point-of-care assays for hemoglobin A1c: is performance adequate? Clin Chem 2011 ; 57 : 1333 – 40 . Google Scholar Crossref Search ADS PubMed WorldCat 7. Lenters-Westra E , Slingerland RJ. Evaluation of the Quo-Test hemoglobin A1c point-of-care instrument: second chance . Clin Chem 2010 ; 56 : 1191 – 3 . Google Scholar Crossref Search ADS PubMed WorldCat 8. Lenters-Westra E , Slingerland RJ. Hemoglobin A1c point-of-care assays: a new world with a lot of consequences! J Diab Sci Technol 2009 ; 3 : 418 – 23 . Google Scholar Crossref Search ADS WorldCat 9. Dupuy AM , Badiou S, Elong-Bertard C, Bargnoux AS, Cristol JP. Analytical performance of the Axis-Shield Afinion for hemoglobin A1c measurement: impact of lot number . Clin Lab 2014 ; 60 : 369 – 76 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 10. College of American Pathologists (CAP) . Hemoglobin A1c participant summary GH2A 2014 . Northfield (IL) : CAP ; 2014 . 11. College of American Pathologists (CAP) survey data . http://www.ngsp.org/CAPdata.asp (Accessed May 2014). 12. American Diabetes Association . Standards of medical care in diabetes—2010 . Diabetes Care 2010 ; 33 Suppl 1 : S11 – 61 . Crossref Search ADS PubMed WorldCat © 2014 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Koomen, John, M

doi: 10.1373/clinchem.2014.226035pmid: 24938750

The impact of proteomics on clinical practice and laboratory medicine has been much anticipated, and many researchers have believed and predicted that the benefit would be primarily in the form of novel biomarker discovery. However, the most significant imminent improvement may be more straightforward: improved capability to measure clinical analytes and increased capacity for multiplexing to evaluate complex panels of disease biomarkers. Although discovery proteomics has revolutionized basic science in terms of defining protein complexes and molecular switching events determined by posttranslational modifications, the ongoing development of liquid chromatography–multiple reaction monitoring mass spectrometry (LC-MRM MS)2 techniques and their application to protein biomarker quantification continues to improve the capability of the clinical laboratory. Building on the long history of use in small-molecule quantification applied both to drugs and metabolites, LC-MRM MS has been used to monitor peptide surrogates for protein biomarkers of specific clinical interest such as apolipoprotein A-I (1), C-reactive protein (2), and prostate-specific antigen (3). Furthermore, peptide immunoprecipitation and mass spectrometry quantification have recently been applied to monitor thyroglobulin (4), overcoming obstacles associated with traditional antibody-based techniques. These advances can be applied to numerous other analytes relevant to human disease. One such critically important and active area of research is the measurement of immunoglobulins, both endogenous and therapeutic. As an example, the impact of this research can be made immediately in multiple myeloma (MM), which is a tumor of the immunoglobulin-producing plasma cells. Clonal expansion of the tumor cells produces a monoclonal immunoglobulin, which can be quantified as a direct biomarker of disease burden. The clinical paradigm for evaluating patients relies on evaluation of the immunoglobulin in serum and urine by protein electrophoresis and immunofixation as well as quantification by nephelometry. Other assays (e.g., serum free light chains) can also be applied in combination with these techniques. Patients are typically assessed at 2- to 4-week intervals during treatment and 1- to 4-month intervals during remission. The immunoglobulin measurements are used in patient care to evaluate disease severity, monitor response to therapy, determine when to discontinue chemotherapy, and detect disease relapse. Improvements of these approaches could be expected to significantly impact the ability to define complete responses to chemotherapy, potentially eliminate minimal residual disease (MRD), and provide earlier detection of disease relapse, opening an earlier window for patient treatment. All of these aspects could enhance the ability to treat MM patients and improve their outcomes. A method for quantification of immunoglobulins using peptides derived from tryptic digestion of the constant regions was proposed by our research team at the Moffitt Cancer Center as part of a review article on the role of quantitative proteomics in developing personalized care for cancer patients (5). This method parallels the nephelometry measurements of the total immunoglobulin concentrations (e.g., IgG, IgA, and IgM) with slightly improved sensitivity and a trade-off in precision (6). Our view was that the impact of changing the platform for this measurement from protein electrophoresis to mass spectrometry may not initially be great, because the measurements were parallel to current clinical techniques. However, as the portfolio of clinical LC-MRM MS assays increases, this approach could become useful for implementation in the clinic. Researchers at the Mayo Clinic have been working on the same problem of monitoring immunoglobulins in different disease settings, including MM, and have produced data for the feasibility and implementation of multiple mass spectrometry–based assays. These researchers have developed methods quantifying light chains using electrospray quadrupole–time-of-flight mass spectrometry, which provides a rapid analysis with improved sensitivity and molecular specificity (due to the measurement of the intact molecular weight) compared to protein electrophoresis (7). In the most recent investigation, reported in this issue of Clinical Chemistry, Ladwig et al. have evaluated quantification of IgG subclasses and compared the results to isoform-specific nephelometry in the context of immune deficiency and IgG4-related disease (8). Both this and the earlier publications illustrate methods that can be readily applied to the automated analysis of clinical samples. Their thorough and systematic approaches to testing these assays with clinical samples set a high standard and consistently illustrate the utility of quantitative mass spectrometry for assessment of protein biomarkers. Although all of the methods described above have been analogous to current clinical assays, both groups have also worked in parallel on disease-specific immunoglobulin quantification using peptides from the variable region of the monoclonal immunoglobulin secreted by MM tumor cells (6, 9). On the basis of existing literature describing proteomics experiments informed by RNA sequencing (10) and analysis of therapeutic antibodies (11, 12), proof-of-concept experiments have been performed to assess the utility of this disease-specific peptide-based approach to monitor the monoclonal immunoglobulin in serum. These methods will enter a very competitive space and must be compared to multiparameter flow cytometry (13) and genomic methods (14). However, retention of the current clinical paradigm of monitoring monoclonal immunoglobulin expression in serum has 2 benefits over genomic approaches for MRD detection using flow cytometry, allele-specific oligonucleotide PCR, or deep sequencing in serial bone marrow samples: lesser patient burden and systemic evaluation of disease. I fully expect that these methods will prove to have significant clinical value in MM. Regardless of how LC-MRM MS competes in this specific instance, the future is bright for quantitative proteomics to play a broader role in patient assessment as part of the clinical laboratory. 2 Nonstandard abbreviations: LC-MRM MS liquid chromatography–multiple reaction monitoring mass spectrometry MM multiple myeloma MRD minimal residual disease. " (see article on page 1080) " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: None declared. " Consultant or Advisory Role: None declared. " Stock Ownership: None declared. " Honoraria: None declared. " Research Funding: J.M. Koomen, National Cancer Institute (R21-CA141285) and DeBartolo Family Personalized Medicine Institute Pilot Research Award in Personalized Medicine. " Expert Testimony: None declared. " Patents: J.M. Koomen. patent number 61946629. References 1. Barr JR , Maggio VL, Patterson DG Jr, Cooper GR, Henderson LO, Turner WE et al. Isotope dilution–mass spectrometric quantification of specific proteins: model application with apolipoprotein A-I . Clin Chem 1996 ; 42 : 1676 – 82 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Kuhn E , Wu J, Karl J, Liao H, Zolg W, Guild B. Quantification of C-reactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards . Proteomics 2004 ; 4 : 1175 – 86 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Barnidge DR , Goodmanson MK, Klee GG, Muddiman DC. Absolute quantification of the model biomarker prostate-specific antigen in serum by LC-MS/MS using protein cleavage and isotope dilution mass spectrometry . J Proteome Res 2004 ; 3 : 644 – 52 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Hoofnagle AN , Becker JO, Wener MH, Heinecke JW. Quantification of thyroglobulin, a low-abundance serum protein, by immunoaffinity peptide enrichment and tandem mass spectrometry . Clin Chem 2008 ; 54 : 1796 – 804 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Koomen JM , Haura EB, Bepler G, Sutphen R, Remily-Wood ER, Benson K et al. Proteomic contributions to personalized cancer care . Mol Cell Proteomics 2008 ; 7 : 1780 – 94 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Remily-Wood ER , Benson K, Baz RC, Chen YA, Hussein M, Hartley-Brown MA et al. Quantification of peptides from immunoglobulin constant and variable regions by liquid chromatography-multiple reaction monitoring mass spectrometry for assessment of multiple myeloma patients . Proteomics Clin Appl [Epub ahead of print 2014 Apr 10] . OpenURL Placeholder Text WorldCat 7. Barnidge DR , Dasari S, Botz CM, Murray DH, Snyder MR, Katzmann JA et al. Using mass spectrometry to monitor monoclonal immunoglobulins in patients with a monoclonal gammopathy . J Proteome Res 2014 ; 13 : 1419 – 27 . Google Scholar Crossref Search ADS PubMed WorldCat 8. Ladwig PM , Barnidge DR, Snyder MR, Katzmann JA, Murray DL. Quantification of serum IgG subclasses by use of subclass-specific tryptic peptides and liquid chromatography-tandem mass spectrometry . Clin Chem 2014 ; 60 : 1080 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 9. Barnidge DR , Tschumper RC, Theis JD, Snyder MR, Jelinek DF, Katzmann JA et al. Monitoring M-proteins in patients with multiple myeloma using heavy-chain variable region clonotypic peptides and LC-MS/MS . J Proteome Res 2014 ; 13 : 1905 – 10 . Google Scholar Crossref Search ADS PubMed WorldCat 10. Evans VC , Barker G, Heesom KJ, Fan J, Bessant C, Matthews DA. De novo derivation of proteomes from transcriptomes for transcript and protein identification . Nat Methods 2012 ; 9 : 1207 – 11 . Google Scholar Crossref Search ADS PubMed WorldCat 11. Dekker LJ , Zeneyedpour L, Brouwer E, van Duijn MM, Sillevis Smitt PA, Luider TM. An antibody-based biomarker discovery method by mass spectrometry sequencing of complementarity determining regions . Anal Bioanal Chem 2011 ; 399 : 1081 – 91 . Google Scholar Crossref Search ADS PubMed WorldCat 12. Cheung WC , Beausoleil SA, Zhang X, Sato S, Schieferl SM, Wieler JS et al. A proteomics approach for the identification and cloning of monoclonal antibodies from serum . Nat Biotechnol 2012 ; 30 : 447 – 52 . Google Scholar Crossref Search ADS PubMed WorldCat 13. Paiva B , Gutiérrez NC, Rosiñol L, Vídriales MB, Montalbán MÁ, Martínez-López J et al. High-risk cytogenetics and persistent minimal residual disease by multiparameter flow cytometry predict unsustained complete response after autologous stem cell transplantation in multiple myeloma . Blood 2012 ; 119 : 687 – 91 . Google Scholar Crossref Search ADS PubMed WorldCat 14. Martinez-Lopez J , Lahuerta JJ, Pepin F, González M, Barrio S, Ayala R et al. Prognostic value of deep sequencing method for minimal residual disease detection in multiple myeloma . Blood 2014 ; 123 : 3073 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat © 2014 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Meeusen, Jeffrey, W;Lieske, John, C

doi: 10.1373/clinchem.2013.220764pmid: 24573605

Our kidneys maintain a constant internal environment and circulatory volume through a combination of filtration, selective reabsorption and secretion, and production of several key hormones. Young, healthy individuals filter >100 L of blood each day; however, their kidneys then reabsorb most of this for a mean urine output of only 1.5 L/day. This filtration process is necessary to eliminate byproducts of ongoing metabolism (so-called uremic toxins). Indeed, a minimum amount of kidney function is essential for life, and one can demonstrate increased morbidity and mortality when the glomerular filtration rate (GFR)3 chronically dips low enough. Thus, GFR is widely considered the single most important and useful indicator of overall kidney function. It is possible to directly measure GFR by administration of small molecules that are freely filtered by the kidneys and neither metabolized, secreted, nor reabsorbed. Examples include inulin, iothalamate, and iohexol, for which the renal clearance of these molecules equals GFR. However, protocols that use these exogenous agents to measure GFR require some combination of intravenous or subcutaneous administration of the marker, multiple blood draws, and carefully timed urine collections. Thus, direct measurement of GFR is not practical for most routine situations. For these reasons, several methods of GFR estimation have been developed on the basis of endogenous molecules. The best example is creatinine, a byproduct of muscle metabolism that is freely filtered, produced in a relatively consistent manner, and not reabsorbed. Although some creatinine is secreted, this amount is usually small enough that creatinine remains a useful marker of GFR. A bigger problem, however, is that muscle mass (and consequently serum creatinine) varies widely between individuals. Thus, although serum creatinine goes up as GFR goes down, the GFR for a given serum creatinine can vary widely between individuals. For example, a “normal” serum creatinine of 1.0 mg/dL (88 μmol/L) can reflect a GFR range of 20–150 mL · min−1 · (1.73 m2)−1, depending on the individual's age, sex, and ethnicity (Fig. 1). Serum creatinine and cystatin C concentrations as a function of measured GFR. Iothalmate clearance was obtained in clinical practice to measure GFR in a consecutive cohort of patients (n = 757). Fig. 1. Open in new tabDownload slide The shaded region is the 95% confidence interval of the curve fit. In general, serum creatinine and cystatin C concentrations each increase with falling GFR, but the increase is steeper and more predictable for cystatin C, especially in the GFR range of 30–60 mL · min−1 · (1.73 m2)−1. Fig. 1. Open in new tabDownload slide The shaded region is the 95% confidence interval of the curve fit. In general, serum creatinine and cystatin C concentrations each increase with falling GFR, but the increase is steeper and more predictable for cystatin C, especially in the GFR range of 30–60 mL · min−1 · (1.73 m2)−1. In 1976, Cockcroft and Gault made a seminal observation in a cohort of men when they reported that 24-h creatinine production varied in a predictable way with age and weight (1). This work yielded the Cockroft–Gault equation, still widely used today. Although this equation is a helpful tool, certainly better than use of serum creatinine alone, the Cockcroft–Gault equation yields only an estimate of creatinine clearance and not GFR. In 1999, Levey and colleagues took another seminal leap forward by relating GFR to serum creatinine and demographic factors (2). Within the large population of patients with previously diagnosed chronic kidney disease (CKD) that were studied, sex, race, and age were the most important variables that influenced serum creatinine correlations with GFR measured by iothalamate clearance. Hence, these variables were incorporated into the first estimated GFR (eGFR) tool, the widely used Modification of Diet in Renal Disease (MDRD) equation. Although the MDRD equation works very well for patients with CKD, the population in whom it was developed, it works less well for other groups. In particular, the MDRD equation underestimates GFR in healthy individuals (3). Therefore, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was subsequently developed by use of a mixed population of individuals with (approximately 70%) and without (approximately 30%) CKD. In general, the CKD-EPI equation does estimate GFR more accurately in healthy individuals, albeit at the expense of some loss in accuracy for those with CKD. The Achilles heel of any creatinine-based GFR estimate is the influence of muscle mass on creatinine production. Consequently, a search for alternative serum markers of GFR has gained increasing momentum. Cystatin C is a 13-kDa cysteine protease inhibitor produced at a constant rate by most nucleated cells. Because cystatin C is freely filtered into the urine, it has attracted great attention as a potential serum marker of GFR. Indeed, serum cystatin C concentrations have a much tighter relationship with measured GFR than serum creatinine (Fig. 1). Perhaps most importantly, cystatin C outperforms creatinine as an indicator of GFR in the clinically relevant range of 30–90 mL · min−1 · (1.73 m2)−1 that spans CKD stages 3–4. Fig. 1 shows that, if no other demographic information were available, serum cystatin C would better reflect GFR than creatinine alone. Indeed, work over the last decade has in many ways validated this use of cystatin C as an indicator of kidney function, but also pointed out some interesting nuances. Three papers in the New England Journal of Medicine highlight these issues. In 2005, Shlipak and colleagues first highlighted the prognostic value of cystatin C (4). Among 4637 participants ≥65 years of age in the prospective Cardiovascular Health Study, serum cystatin C concentrations were a strong predictor of both cardiovascular and overall mortality. Those in the highest quintile had an adjusted all-cause mortality risk of 1.77–2.58 compared to those in the lowest quintile. In contrast, relationships of mortality risk with creatinine or creatinine-based estimated GFR by quintile were weaker (1.00–1.48 and 1.21–1.78, respectively), and also J-shaped (those in the lowest quintile had a higher risk of death). Furthermore, within each quintile of creatinine the risk of death could be stratified based on cystatin C concentration (4). Thus, cystatin C seemed to impart additional and important prognostic information above and beyond creatinine. As the authors noted, other cardiovascular risk factors also correlated with cystatin C, such as C-reactive protein, HDL cholesterol, and waist-to-hip ratio. Because adipocytes are known to produce large amounts of cystatin C, it could well be that its serum concentrations integrate several signals that impact risk in addition to kidney function. As noted above, the use of equations that include demographics has revolutionized our ability to use serum creatinine to estimate GFR. In an analogous fashion, equations have been developed to estimate GFR from cystatin C. Because serum cystatin C is fairly tightly associated with GFR (Fig. 1), equations that include only cystatin C perform reasonably well. Nevertheless, the addition of demographics further improves the ability of cystatin C to estimate GFR. Recently, Inker and colleagues developed and compared the performance of equations that include cystatin C alone or creatinine plus cystatin C to estimate GFR (5). The study cohort included >6000 participants with and without CKD and excluded any transplant recipients. In general, the cystatin C equation had accuracy and precision similar to the creatinine-based equation. Thus, the use of demographics has greatly evened the playing field between the 2 biomarkers. However, the combined equation outperformed either of the single biomarker equations. This interesting observation may reflect the fact that each biomarker alone has its own set of confounders, so that averaging the 2 together in a single equation gives the best reflection of GFR, the parameter the equations are trying to capture. The authors demonstrated 1 potential use of the combined equation that has since been adopted in the 2012 Kidney Disease/Improving Global Outcomes (KDIGO) guidelines. Among participants with a creatinine-based GFR estimated between 45 and 74 mL · min−1 · (1.73 m2)−1 (just above and below the CKD stage 3 cutoff value of 60 mL · min−1 · (1.73 m2)−1), cystatin C–based or the combined equation reclassified about 20% of this subgroup, and two thirds of these were moved into the correct CKD class. Another very recent paper by Shlipak and colleagues returned to the prognostic value of cystatin C, in this case using the newly derived equations for estimating GFR (6). They performed a metaanalysis of data from >90 000 participants in 11 general population and 5 CKD cohorts. As before (4), cystatin C measurements were more strongly associated with risk of all-cause mortality compared with creatinine. Additionally, reclassification of creatinine-estimated CKD stage by cystatin C eGFR improved outcome prediction at any CKD stage. Thus, patients with higher cystatin C compared with creatinine eGFR had reduced risk, and those with lower cystatin C compared with creatinine eGFR had higher risk. There was no gold standard measure of GFR available in the metaanalysis, so we do not know if the superior prognostic value of cystatin C is related to a better approximation of GFR or instead reflects other confounding factors (e.g., obesity, inflammation, and diabetes). Where does this leave us? By use of the current generation of equations (5), creatinine and cystatin C are roughly equivalent for estimating GFR. Because creatinine is more widely available, is much cheaper, and has a much greater clinical familiarity, it is likely to be the routine biomarker of renal function for the foreseeable future. In addition, current creatinine assays are better standardized, with traceability to isotope dilution mass spectrometry. However, cystatin C has important features that make it useful in certain clinical situations. Specifically, because cystatin C is not affected by muscle metabolism, it provides a more reliable estimate of GFR among individuals with abnormal muscle mass (e.g., very elderly or malnourished individuals). Cystatin C also provides an advantage for risk stratification. This is an important feature, since measurement of cystatin C among patients with CKD established by creatinine-based eGFR equations, or in patients at high risk of CKD, could identify those persons at greatest risk of adverse outcomes. Simultaneously, those persons with a cystatin C eGFR higher than the creatinine-based values appear to have a better prognosis and might not require as intensive follow-up. In any case, current data support prospective validation of management strategies that use cystatin C and an expanding role for this analyte in routine practice. 3 Nonstandard abbreviations: GFR glomerular filtration rate CKD chronic kidney disease eGFR estimated GFR MDRD Modification of Diet in Renal Disease CKD-EPI Chronic Kidney Disease Epidemiology Collaboration KDIGO Kidney Disease/Improving Global Outcomes. " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: None declared. " Consultant or Advisory Role: None declared. " Stock Ownership: None declared. " Honoraria: None declared. " Research Funding: This work was supported by the Mayo Clinic O'Brien Urology Research Center (U54 DK100227) funded by the NIDDK; the Rare Kidney Stone Consortium (U54 DK083908), a member of the NIH Rare Diseases Clinical Research Network (RDCRN), funded by the NIDDK and the National Center For Advancing Translational Sciences (NCATS); and the Mayo Foundation. " Expert Testimony: None declared. " Patents: None declared. " Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript. References 1. Cockcroft DW , Gault MH. Prediction of creatinine clearance from serum creatinine . Nephron 1976 ; 16 : 31 – 41 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Levey AS , Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group . Ann Intern Med 1999 ; 130 : 461 – 70 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Rule AD , Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease . Ann Intern Med 2004 ; 141 : 929 – 37 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Shlipak MG , Sarnak MJ, Katz R, Fried LF, Seliger SL, Newman AB et al. Cystatin C and the risk of death and cardiovascular events among elderly persons . N Engl J Med 2005 ; 352 : 2049 – 60 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Inker LA , Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T et al. Estimating glomerular filtration rate from serum creatinine and cystatin C . N Engl J Med 2012 ; 367 : 20 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Shlipak MG , Matsushita K, Arnlov J, Inker LA, Katz R, Polkinghorne KR et al. Cystatin C versus creatinine in determining risk based on kidney function . N Engl J Med 2013 ; 369 : 932 – 43 . Google Scholar Crossref Search ADS PubMed WorldCat © 2014 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Lin, Danny C, C;Diamandis, Eleftherios, P;Januzzi, James, L;Maisel,, Alan;Jaffe, Allan, S;Clerico,, Aldo

doi: 10.1373/clinchem.2014.223057pmid: 24700774

Heart failure is a major global health problem affecting 23 million people worldwide. As more cardiac patients survive and live longer with this progressive disease, heart failure is a condition for which the prevalence will grow. Based solely on clinical presentation, heart failure can be difficult to diagnose since its presentation is complex, with signs and symptoms that are nonspecific and may not always be present. B-type natriuretic peptide (BNP)7 and N-terminal proBNP (NT-proBNP) are well established, clinically validated biomarkers that have been shown to improve the diagnostic accuracy for heart failure and provide prognostic information for risk stratification. The widespread clinical use of these biomarkers for more than a decade is reflected by their incorporation into national and international medical guidelines for heart failure, at the highest classification for recommendation. BNP is a cardiac hormone secreted by cardiomyocytes into the circulation in response to states of volume expansion and pressure overload, as is the case in heart failure. BNP's diuretic, natriuretic, and vasodilatory actions, and its protective effects on endothelial function and vascular remodeling, act to relieve the adverse consequences of heart failure. During the synthesis and processing of BNP, its 108 amino acid biologically inactive precursor, proBNP, is proteolytically cleaved to form the 32 amino acid peptide BNP and the 76 amino acid peptide NT-proBNP. While BNP is physiologically active, NT-proBNP is biologically inert. Due to its secretion at a 1:1 ratio to BNP and its longer half-life (90–120 min vs 20 min for BNP), the measurement of NT-proBNP has proven to have an essentially equivalent clinical performance to BNP as a biomarker for heart failure. In recent years, the simplistic model for the processing of BNPs has undergone a dramatic shift, with a better understanding of the complexity of their posttranslational modification and secretion. ProBNP is now known to undergo O-linked glycosylation at multiple amino acid residues within its N-terminal and central portions to give rise to glycosylated forms of NT-proBNP. In addition, proBNP itself has been shown to be present in the circulation of healthy individuals and to increase considerably in heart failure patients. Furthermore, once in circulation, BNP, NT-proBNP, and proBNP are subject to proteolytic cleavage at both their N- and C-terminal ends, giving rise to yet more molecular forms of these peptides. In this Q&A, 4 experts discuss the clinical utility of BNP testing for the diagnosis, prognosis, and guided therapy of heart failure, and the implications of the multiplicity of molecular forms of BNP, NT-proBNP, and proBNP on the measurement of these peptides and on the pathophysiology of heart failure. How are BNPs used in the clinical diagnosis of heart failure? In what clinical circumstances do BNP and NT-proBNP provide the greatest diagnostic utility? Open in new tabDownload slide Open in new tabDownload slide James L. Januzzi, Jr.: The heaviest use of the natriuretic peptides has been in the context of acutely decompensated heart failure. The tests are often used to evaluate dyspnea to correctly identify or exclude the diagnosis of decompensated heart failure as the cause of shortness of breath. They are also useful in this setting to establish the severity of heart failure, based on their concentrations, which are also very important from a prognostic setting. These uses have all been shown in prospective trials to be of substantial value to the clinician. In studies where patients were randomized to routine BNP or NT-proBNP measurement vs blinded BNP or NT-proBNP measurement, those patients evaluated with the biomarkers had more secure diagnosis, shorter hospital lengths of stay, and less money spent on their care. This informs the current support in the recent American College of Cardiology/American Heart Association (ACC/AHA) clinical practice guidelines for heart failure, where BNP and NT-proBNP received a class I, level of evidence A for diagnosis and prognosis in acute heart failure. Clearly, a role for both peptides also exists in chronic heart failure. They retain similar value for the diagnosis or exclusion of heart failure as well as prognosis. Additionally, there has been an emerging interest in using both BNP and NT-proBNP to monitor the progress of heart failure therapy, and even use them as targets for therapy. The basis for using BNP or NT-proBNP in the management/therapy of patients with heart failure is predicated on the observation that their values frequently change in response to heart failure decompensation and recompensation (rising and then falling), and therapies used to treat heart failure typically lead to parallel reductions in BNP or NT-proBNP as the patient improves. The more reduction in BNP or NT-proBNP after a therapy change, the better the prognosis. Indeed, in the hospital, it has been shown that robust reduction in either BNP or NT-proBNP in the course of acute heart failure treatment is associated with superior outcomes compared to a less significant (for example <30%) drop in the peptides. Those patients with inadequate reduction have higher rehospitalization and death rates, and therefore many clinicians have started using a robust reduction in BNP or NT-proBNP as a criterion for safe hospital discharge. In the physician's office, there has been a major focus on the use of BNP or NT-proBNP to “guide” the care of patients with heart failure. In this regard, both peptides have been studied as an addition to standard clinical judgment for the care of patients with ambulatory heart failure. In these studies, therapies were adjusted to achieve clinical goals, but also to reduce the natriuretic peptide below a target value. In trials where low targets were selected and met, biomarker guidance was typically superior to clinical judgment alone. Pivotal studies are currently examining this approach. Open in new tabDownload slide Open in new tabDownload slide Alan Maisel: Natriuretic peptides should be used in all patients with dyspnea. These peptides have excellent sensitivity and specificity, depending on the cut points one uses. Natriuretic peptides are not used by themselves but as adjuncts to history, physical exam, and other laboratory tests. Of course, the greatest utility would be in those patients for whom the doctor is unable to come to a conclusion as to whether or not heart failure is present. Open in new tabDownload slide Open in new tabDownload slide Allan S. Jaffe: It is clear that the greatest diagnostic utility for natriuretic peptides occurs in individuals in whom there is ambivalence about the diagnosis of heart failure. One of the issues in the field, however, is that different physician groups may have different degrees of expertise, and so natriuretic peptide measurement may be more helpful for those without vast experience in heart failure and be less important for those who are more familiar with this patient group. Thus, I would argue that the use of natriuretic peptides in the emergency room or in general internist offices may be different than for cardiologists and, beyond that, heart failure specialists, at least for this indication. Open in new tabDownload slide Open in new tabDownload slide Aldo Clerico: According to all of the most recent guidelines, the greatest diagnostic utility provided by the measurement of B-type natriuretic peptides is high accuracy in ruling out the diagnosis of heart failure in a patient presenting with dyspnea. The measurement of BNP or NT-proBNP usually shows a very high clinical sensitivity and negative predictive value (both >90%). However, to achieve the maximum clinical sensitivity (and therefore the best ability to correctly exclude heart failure), the cutoff value should be corrected for sex, age, and body mass index. Furthermore, although some international guidelines (i.e., those of the National Institute for Health and Clinical Excellence and the European Cardiology Society) suggest the use of a single cutoff value for all BNP immunoassays, recent studies have demonstrated that some BNP methods provide values that are about half of the others, suggesting that the cutoff values are method dependent. BNPs are not very useful for ruling-in heart failure due to their relatively low specificity and positive predictive value. In fact, the circulating concentrations of BNP and NT-proBNP may also be increased in several physiological (i.e., pregnancy and high-intensity physical exercise) and pathologic conditions (i.e., renal, liver, metabolic, endocrinological, neoplastic, and inflammatory diseases) and in response to some pharmacological agents (i.e., glucocorticoids, female sex steroid hormones, antineoplastics, and β-adrenergic agonists), drug abuse (i.e., amphetamine and alcohol), or poisoning (especially by carbon monoxide). While clinical studies have demonstrated that BNP and NT-proBNP have relatively equivalent performances as biomarkers for heart failure, are there instances where one of these biomarkers would be preferred over the other? James L. Januzzi, Jr.: Honestly, I do not envision the differences to be significant enough to mandate that one be preferred over the other. Studies have shown substantial equivalence in most groups, and even in groups where the performance of the peptides is hampered there is relatively equal influence on BNP or NT-proBNP. Where there are relatively few data in terms of comparative value, however, is in the guidance of outpatient heart failure care. In this setting, few studies have been done with BNP, so a clear need is present in this regard. Fortunately, studies are planned that should hopefully address this issue. Allan S. Jaffe: I cannot think of a situation where there are large differences in clinical performance, with the possible exception of amyloid heart disease, for which I am unaware of the data with BNP but would suspect it would work just as well as NT-proBNP. However, because the concentrations of NT-proBNP can be so high at times, especially in patients with end-stage renal disease, clinicians are unsure of how to interpret the values; with time, I think, this is something clinicians learn to cope with. The only other relevant difference that can be important is that BNP is more prone to degradation if samples are not processed expeditiously or when they are stored. Aldo Clerico: BNP is the active hormone while NT-proBNP is an inactive peptide that shows better analytical characteristics than BNP. These include lower in vivo and in vitro degradation, higher circulating concentrations, lower biological variability, and the ability to measure in specimens collected with various tube types (EDTA or heparinized plasma and serum). From a theoretical point of view, the measurement of BNP should be preferred in all physiological or pathophysiological studies when the goal is the assessment of the “true” degree of activation of the cardiac endocrine system. Unfortunately, all of the commercially available BNP immunoassays are markedly interfered with by some inactive B-type related peptide, particularly the precursor proBNP, which is probably the predominant circulating form of B-type natriuretic peptide in patients with heart failure. These interferences may greatly affect the analytical specificity of immunoassays for active peptide BNP. For these reasons, BNP and NT-proBNP usually show very similar results in patients with heart failure when assayed with commercially available methods, which are subject to interference by the precursor proBNP. The prognostic values of BNP and NT-proBNP have been well demonstrated. How best can the prognostic information provided by measurement of BNPs be used? Do you feel that the prognostic information provided by BNP and NT-proBNP has been underutilized? James L. Januzzi, Jr.: There is no question that the prognostic information from BNP or NT-proBNP may be the strongest application for either peptide and, in this regard, there is no question that they are underused. Both biomarkers have been unequivocally shown to predict outcome across a wide range of patient types, with incremental value from serial measurement. They are now the gold standard for biomarker-based prognosis, clinically and in research studies. Alan Maisel: In the emergency department, patients with high concentrations need to be admitted while those with very low concentrations can be discharged after treatment. At the time of discharge, high natriuretic peptide concentrations predict early readmission. Stable concentrations during outpatient treatment represent good prognosis. Outpatients with high concentrations should be targeted with an increased medical regimen. Yes, this is still an area that is very underutilized. Allan S. Jaffe: There is no doubt that on a statistical basis highly increased concentrations of natriuretic peptides are associated with adverse outcomes. The issue is what can or should be done about that. From my perspective, the lack of a proven treatment strategy is what has led to the underutilization of natriuretic peptides for risk stratification. From that perspective, having a baseline concentration to refer to is very helpful both to anticipate problems when concentrations are rising and to make sure that, after treatment, concentrations have returned toward baseline. I believe that in the long run the value of using natriuretic peptides to titrate therapies will be proven, but in my view the supporting data at present are not adequately robust. Aldo Clerico: In my opinion, the prognostic information provided by BNP/NT-proBNP assays is underutilized by clinicians. Several studies indicated that serial measurements in the same patient enable clinicians to more accurately assess the response to treatment and provide more prognostic information than a single measurement. Indeed, patients with acute heart failure who have significantly decreased BNP/NT-proBNP values (>30%) after treatment usually show fewer adverse events and have lower mortality rates. However, serial measurements are not currently performed in patients with heart failure because they incur additional costs and discomfort to patients and require a particular interpretation by clinicians. I hope that future guidelines will give this important point more attention. The observation that BNP and NT-proBNP concentrations are lowered as a result of treatment for heart failure has led to efforts to guide therapy using these measurements. What are the advantages of BNP- or NT-proBNP–guided therapy and what do you believe needs to be demonstrated before their routine use? James L. Januzzi, Jr.: Together with the substantial value of BNP and NT-proBNP to prognosticate and their ability to monitor heart failure care, it is natural to assume that BNP and NT-proBNP would be useful to “guide” heart failure care. The reason that the opportunity exists to add biomarkers to standard clinical care includes the fact that the ability to judge the adequacy of heart failure therapeutics may be challenging, even for the experienced physician. Additionally, there is the strong possibility that BNP or NT-proBNP are direct barometers reflecting the pathophysiology of heart failure, wherein therapies that are adjusted to reduce their values are essentially driving to the core of the abnormal biology present in each patient. Of course, there is resistance in the heart failure community, where the belief is that each patient with heart failure should be treated to the maximal degree with each and every therapy available. The reality is that this goal is rarely met and, as such, patients go undertreated. The use of biomarkers in this setting also provides an objective assessment of risk, so that the highest-risk patients can be identified. The studies that have addressed BNP- or NT-proBNP–guided care have been generally small and underpowered, although many have suggested substantial benefit from the approach as long as certain provisos are met. A low target must be selected, and this target is crucial. If a patient is treated with a goal BNP or NT-proBNP value that is too high, then the treatment will be inadequate. Those clinicians choosing a lower target value (e.g., BNP of 100 ng/L; NT-proBNP of 1000 ng/L) have been more often successful in reducing cardiovascular events when compared to clinicians who use standard management. Therapies must be adjusted to achieve that goal. Studies with low rates of therapy adjustment to achieve target BNP or NT-proBNP concentrations were not successful, but the strategy went untested! There is no such a thing as a “stable” patient with a markedly increased BNP or NT-proBNP concentration, no matter how clinically stable the patient may appear. The target must be reached: it is important to reduce BNP or NT-proBNP concentration in the course of heart failure treatment or the prognosis of the patient is not improved. As stated earlier, a multicenter, randomized National Heart, Lung and Blood Institute trial, the Guiding Evidence-Based Therapy Using Biomarker-Intensified Treatment study (GUIDE IT), is currently under way to evaluate NT-proBNP–guided care, while studies focusing on BNP are currently planned. Beyond their use in patients with ESTABLISHED heart failure, the recent St Vincent's Screening to Prevent Heart Failure (STOP-HF) and NT-proBNP Selected PreventiOn of cardiac eveNts in a populaTion of dIabetic patients without A history of Cardiac disease (PONTIAC) studies demonstrated that a BNP- or NT-proBNP– (respectively) driven strategy for “at risk” patients was superior to clinical judgment for reducing incident heart failure. This exciting finding could have even greater impacts on the care of patients if these markers could be applied in patients even before heart failure has developed. Alan Maisel: Either marker can be used to guide therapy. Randomized trials must demonstrate reduced admissions and death. Trials should attempt to drive down the natriuretic peptide concentrations to low concentrations. Actions must be taken as a result of these concentrations. Finger-stick home BNP monitoring will also allow the doctor to monitor rising natriuretic peptide concentrations. Allan S. Jaffe: It is very difficult to discern when patients with heart failure are starting to decompensate or when, after treatment, they have returned to baseline because so much of the evaluation of symptoms is subjective. Thus, objective measures are needed and natriuretic peptides can provide that objective information. I believe the mixed results of the studies reflect less aggressive treatment than is needed to cause major changes in natriuretic peptide values and thus, outcomes. Multiple publications have touted changes that are much less than one might expect from the biological variation data. Our own data suggest that much larger changes are needed to influence outcomes. Such a conclusion is also consistent with the Pro-BNP Outpatient Tailored Chronic Heart Failure Therapy (PROTECT) data from Januzzi and colleagues. Aldo Clerico: In our institution, clinicians have been routinely using the measurement of natriuretic peptides to guide therapy in patients with heart failure since the 1990s. The BNP-guided therapy allows a tailored administration of drugs according to both the activation of endocrine cardiac function as well as electrolyte and fluid balance of patients. Patients who do not respond to standard pharmacological treatment with a substantial decrease of BNP or NT-proBNP concentrations should be clinically reevaluated for possible presence of comorbidities and/or considered for alternative and more aggressive treatments. I think that some well-designed clinical trials are needed to definitively demonstrate the subset of heart failure patients that may benefit (or not) from BNP-guided therapy. Indeed, it is theoretically conceivable that a cardiovascular biomarker assay could be more useful in the early stage of heart failure, when patients are usually responsive to treatment, than in stage D of heart failure, a point at which they are refractory to standard pharmacological treatment and require specialized interventions. Patients who respond to treatment are usually younger (<70 years) and without relevant comorbidities. In addition, when designing such a trial, researchers should consider as an end point the baseline, euvolemic “dry” BNP concentration rather than a fixed concentration, given the high variability of BNP concentrations among stable patients. In some patients, a very good clinical stability is achieved in the presence of relatively increased BNP or NT-proBNP concentrations. In these patients, an aggressive therapy (e.g., diuretics) with the aim of further reducing natriuretic peptide concentrations may have detrimental results. Recent evidence has shown that the processing and secretion of BNPs is complex. Multiple variably glycosylated and proteolytically cleaved forms of BNP and NT-proBNP as well as intact proBNP are present in the circulation of healthy individuals and heart failure patients. Given that all current immunoassays cross-react with proBNP and that immunoassays for NT-proBNP do not effectively measure glycosylated forms, do you feel there is a need for next generation assays with a greater specificity? Alan Maisel: Only if the following can be demonstrated: An altered form is more prevalent in acute heart failure. An altered form might better separate heart failure with preserved ejection fraction from heart failure with reduced ejection fraction. An altered form might be used to screen asymptomatic patients for disease. Allan S. Jaffe: Present assays simply reflect how strongly this compensatory system is being stressed. The results do not provide any information concerning the functional consequences of such stimulation. In the long run, it likely would be valuable to know how much active natriuretic peptide is present and eventually to even know which forms are present. The latter has the potential to allow for targeted approaches to reduce the less active forms and to improve the functional consequences of in vivo stimulation. It may also eventually provide insights into what drugs might be necessary in individual patients to either improve processing of the natriuretic peptides or to replace them. Aldo Clerico: The NT-proBNP assay by Roche Diagnostics is not affected by glycosylated proBNP, while the commercially available methods for BNP are affected, although at different degrees of cross-reactivity. At present, the pathophysiological relevance of the glycosylated compared to nonglycosylated forms of BNPs is not well understood. Therefore, further studies are needed to better evaluate the physiological and/or clinical role of glycosylated forms of BNPs and to recommend the clinical use of immunoassays specific for glycosylated or nonglycosylated forms of BNPs. Do you feel there is a clinical value in measuring proBNP as a biomarker for heart failure and how do you think it can be integrated into current measurements for BNP or NT-proBNP? James L. Januzzi, Jr.: There are conflicting data in this regard. Some individuals have suggested the ratio of proBNP/BNP or proBNP/NT-proBNP to be more informative regarding prognosis than just BNP or NT-proBNP alone. That said, most studies do not indicate that there is a clear need, and given the huge amount of data existing that support BNP or NT-proBNP alone, it is hard to envision a change in direction at this point. Alan Maisel: If a ratio of proBNP to one of the others can be shown to reflect acuity of disease, then yes. Unfortunately, with some of the assays there are multiple binding sites of antibodies and much cross-reactivity. Allan S. Jaffe: In the few studies available, the assay for proBNP seems to provide comparable information to the other natriuretic peptides but the sampling at present is small. However, this assay is a good start on the way to trying to unravel the issues described earlier in regard to the specific forms that are present in any given patient. In our experience doing these separations with mass spectrometry, we could not find a consistent pattern, but we studied only 70 patients. One analysis has suggested that the ratio of proBNP and BNP may contain important prognostic information. However, in our data sets, such an approach has not been revealing. Aldo Clerico: There is a need for next generation assays with greater specificity for both the active peptide BNP and the precursor proBNP. From an analytical point of view, proBNP has some theoretical advantages as a biomarker (i.e., more stable molecule, higher molecular weight, lower biological variability) compared to the active hormone BNP. As a future perspective, the simultaneous measurement in the same plasma sample with 2 methods, one specific for the intact precursor proBNP and the other for the active peptide BNP, could allow a more accurate estimation of both production/secretion of B-type related peptides from cardiomyocytes and overall activity of the cardiac endocrine function compared to the single measurement of either peptide. Information obtained by contemporaneous measurement of proBNP and BNP with specific assays should likely extend our present understanding of pathophysiological mechanisms linking disease progression and cardiac endocrine dysfunction. A recent study in ambulatory patients with chronic systolic heart failure showed that the combined assessment of conventional BNP and proBNP immunoassays provides additional information in determining the risk of adverse clinical outcomes, particularly in patients with low BNP concentrations. However, clinical studies will be necessary to determine and compare the diagnostic and prognostic accuracy of specific assays for the different B-type related peptides, BNP, NT-proBNP, and intact proBNP, when used alone or in combination. It has been suggested that the increased release of biologically inactive proBNP in heart failure may reflect impaired processing of BNPs. Are there possible implications for the failing endocrine function of the heart on the pathophysiology of heart failure? James L. Januzzi, Jr.: Yes, indeed. In contrast to my ambivalence about the potential role of proBNP as a diagnostic or prognostic tool, there may be value informed by its measurement regarding the actual biology of heart failure. proBNP is released in very small amounts in healthy individuals, and its production starts to rise only as heart failure progresses. In this regard, the loss of biologically active BNP comes at a time when the heart can least sustain such a loss. Thus, understanding why proBNP cleavage is lost would be a large advance, as would be developing strategies to augment its cleavage, with an effort to clinically favor patients with very advanced heart failure. Alan Maisel: There are definite implications for heart–endocrine interactions in the pathophysiology of heart failure. We just don't yet know if inactive proBNP in heart failure may reflect impaired processing of BNPs, and if so, what the clinical implications might be. NT-proBNP is the inactive moiety that is already measured in practice. Allan S. Jaffe: There certainly is evidence to suggest that heart failure is characterized by dysfunction of the natriuretic peptide system. Whether it is primary or secondary is not clear as yet but we do know it occurs. However, measurement of the circulating convertases like corin has not been terribly illuminating to date. Aldo Clerico: A blunted natriuretic response after pharmacological doses of cardiac natriuretic hormones has been observed in experimental models and in patients with chronic heart failure, suggesting a resistance to the biological effects of these cardiac hormones. Resistance to the biological action of cardiac natriuretic peptides can be attributed to different mechanisms, acting at prereceptor, receptor, and postreceptor levels. Considering the possible causes of resistance at the prereceptor level, recent findings suggest that, in patients with heart failure, there may be insufficient posttranslational maturation of biosynthetic precursors of the BNP system. The soluble form of corin, a transmembrane serine protease able to cleave proBNP, is also capable of processing the circulating intact precursor of natriuretic hormones, indicating that the precursor proBNP may be a circulating prohormone. The peripheral processing of circulating proBNP could likely be submitted to regulatory rules, which might be impaired in patients with heart failure, opening new perspectives in the treatment of heart failure. Related to this hypothesis, some studies using quantitative mass spectral analysis reported very low circulating concentrations (or even the absence) of the active peptide BNP in patients with severe heart failure. Therefore, a novel pharmacological target may be enzymes (such as corin) that regulate the maturation of the prohormone proBNP into the active hormone (i.e., BNP). 7 Nonstandard abbreviations: BNP B-type natriuretic peptide NT-proBNP N-terminal proBNP ACC/AHA American College of Cardiology/American Heart Association GUIDE IT Guiding Evidence-Based Therapy Using Biomarker-Intensified Treatment study STOP-HF Screening to Prevent Heart Failure study PONTIAC NT-proBNP Selected PreventiOn of cardiac eveNts in a populaTion of dIabetic patients without A history of Cardiac disease study PROTECT Pro-BNP Outpatient Tailored Chronic Heart Failure Therapy study. " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: E.P. Diamandis, Clinical Chemistry, AACC. " Consultant or Advisory Role: J.L. Januzzi, Jr., Roche, Critical Diagnostics, and Sphingotec; A. Maisel, BG Medicine and Alere; A.S. Jaffe, Beckman-Coulter, Ortho Diagnostics, Alere, Roche, Radiometer, Abbott, Trinity, ET Healthcare, Amgen, and Critical Diagnostics. " Stock Ownership: A. Maisel, Critical Diagnostics. " Honoraria: A. Maisel, Alere, BG Medicine, and Critical Diagnostics; A.S. Jaffe, Radiometer. " Research Funding: Roche, Thermo Fisher, and Singulex. " Expert Testimony: None declared. " Patents: None declared. © 2014 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Roberts, Tiffany, K;Fantz, Corinne, R

doi: 10.1373/clinchem.2013.207746pmid: 25070973

CASE DESCRIPTION A 33-year-old woman presented with amenorrhea and weight gain of 27.2–31.8 kg, despite diet and exercise, as well as progressively worsening acne. Symptoms began subsequent to a spontaneous abortion 5 years earlier and had become especially concerning during the past year. There was no notable family medical history. The patient did not report taking any prescription or over-the-counter medication and denied tobacco, alcohol, or illicit drug use. She had a blood pressure of 144/86 mmHg, heart rate of 88 beats/min, temperature of 37.1 °C, and was 1.8 m tall and weighed 105.7 kg (body mass index, 33.4). The results of a physical examination were otherwise normal. Initial laboratory evaluation results included a negative point-of-care urine human chorionic gonadotropin test and concentrations within reference intervals for thyroid-stimulating hormone (TSH),2 prolactin, leutinizing hormone (LH), and follicle-stimulating hormone (FSH) (Table 1). Further testing revealed a low estradiol concentration (20 pg/mL, reference interval, 24–706 pg/mL), as well as increased total testosterone [96 ng/dL (DPC, Siemens, Malvern PA), reference interval, 10–80 ng/dL] and dehydroepiandrosterone-S (DHEA-S) (594 μg/dL, reference interval, <340 μg/dL) (Table 1). She was diagnosed with polycystic ovary syndrome (PCOS) and treated with metformin, which she did not tolerate, and local creams for her acne were ineffective. Laboratory values on the initial and return visits. Table 1. Laboratory values on the initial and return visits. . TSH, mcIU/mL . Prolactin, ng/mL . LH, mIU/mL . FSH, mIU/mL . Estradiol, pg/mL . Testosterone, ng/dL . DHEA-S, μg/dL . Reference Interval 0.34–5.60 1–24 1–105 4–22 24–706 10–80 <340 Initial Visit 4.23 15 7 7 20a 96a 594a Return Visit 3.91 25a 6 7 28 132a 812a . TSH, mcIU/mL . Prolactin, ng/mL . LH, mIU/mL . FSH, mIU/mL . Estradiol, pg/mL . Testosterone, ng/dL . DHEA-S, μg/dL . Reference Interval 0.34–5.60 1–24 1–105 4–22 24–706 10–80 <340 Initial Visit 4.23 15 7 7 20a 96a 594a Return Visit 3.91 25a 6 7 28 132a 812a a Indicates values that exceed the reference interval. Open in new tab Table 1. Laboratory values on the initial and return visits. . TSH, mcIU/mL . Prolactin, ng/mL . LH, mIU/mL . FSH, mIU/mL . Estradiol, pg/mL . Testosterone, ng/dL . DHEA-S, μg/dL . Reference Interval 0.34–5.60 1–24 1–105 4–22 24–706 10–80 <340 Initial Visit 4.23 15 7 7 20a 96a 594a Return Visit 3.91 25a 6 7 28 132a 812a . TSH, mcIU/mL . Prolactin, ng/mL . LH, mIU/mL . FSH, mIU/mL . Estradiol, pg/mL . Testosterone, ng/dL . DHEA-S, μg/dL . Reference Interval 0.34–5.60 1–24 1–105 4–22 24–706 10–80 <340 Initial Visit 4.23 15 7 7 20a 96a 594a Return Visit 3.91 25a 6 7 28 132a 812a a Indicates values that exceed the reference interval. Open in new tab Approximately 1 year later, the patient presented with continuing amenorrhea and complained of mood swings and depression as well as easy bruising and hirsutism. She was referred to an endocrinologist for further evaluation. Laboratory testing at this time included a basic metabolic panel. All measurands were within reference intervals, as were concentrations of TSH, LH, and FSH (Table 1). A random 17-hydroxyprogesterone (17-OHP) was within reference intervals, and estradiol was at the lower limit of the reference interval (28 pg/mL). Prothrombin time and partial thromboplastin time were both within reference intervals. Total testosterone (132 ng/dL) and DHEA-S (812 μg/dL) remained increased. QUESTIONS TO CONSIDER What is secondary amenorrhea and what are its most common causes? What other pathological conditions present similarly to PCOS? How can laboratory testing be used to diagnose the most common underlying causes of secondary amenorrhea? What are the possible causes of easy bruising in a patient with normal prothrombin time and partial thromboplastin time? DISCUSSION OVERVIEW OF SECONDARY AMENORRHEA Amenorrhea is classified as either primary (failure to achieve menarche) or secondary, which is the cessation of menses for 3 months or more. Secondary amenorrhea is not, in itself, a cause for concern; however, it can be a symptom of other pathological states. Secondary amenorrhea will affect approximately 5% of women of reproductive age and those who are affected often will not demonstrate an obvious etiology for their symptoms. Therefore, a systematic evaluation is required to establish a definitive diagnosis (1). The most common underlying cause of secondary amenorrhea is pregnancy. Once pregnancy is ruled out, TSH and prolactin can be measured to investigate other causes such as hypothyroidism and hyperprolactinemia. In the absence of hypothyroidism and hyperprolactinemia, secondary amenorrhea is likely due to either outflow tract obstruction or hypogonadism. The low estrogen concentrations observed in this case narrowed the differential to hypoestrogenism. Hypogonadism is further classified as either a normo-, hyper-, or hypogonadotropic state, which is differentiated on the basis of laboratory measurement of LH and FSH. In this patient, both LH and FSH were within reference intervals. Normogonadotropic hypogonadism is most frequently associated with hyperandrogenism, as demonstrated by increased testosterone concentrations in this case. Secondary amenorrhea in the background of hyperandrogenic hypogonadism can be due to PCOS, nonclassical (late-onset) congenital adrenal hyperplasia (CAH), or Cushing syndrome. PCOS is by far the most common of these 3 causes, affecting approximately 6% of women of reproductive age. The presentation may be characterized by amenorrhea, infertility, hirsutism, and metabolic disturbance often manifesting as type 2 diabetes mellitus, insulin resistance, or metabolic syndrome accompanied by obesity (2). However, before a diagnosis of PCOS is made, both CAH and Cushing syndrome should be ruled out since they will manifest similarly (3). Nonclassical CAH results most commonly from a deficiency of 21-hydroxylase that leads to accumulation of 17-OHP, which is then shunted into androgen synthesis (4). The condition is characterized by masculinization of adolescent and adult female patients, distinguishing it from classical CAH, which presents in infancy and early childhood (4). In cases in which PCOS is suspected, a random 17-OHP measurement is usually sufficient to rule out CAH before a definitive diagnosis of PCOS is made (3–4). Similarly to PCOS and CAH, Cushing syndrome can be an underlying cause of secondary amenorrhea and will present as hyperandrogenic hypogonadism. Cushing syndrome is characterized by a prolonged increase of cortisol concentrations. The most common causes of Cushing syndrome are iatrogenic, specifically the use of glucocorticoids as antiinflammatory or immunosuppressive therapies. However, Cushing syndrome can also be endogenous due to cortisol overproduction. In this case the underlying pathophysiology is further subdivided into adrenocorticotropic hormone (ACTH) dependent or ACTH independent. Cushing disease (Cushing syndrome caused by pituitary ACTH overproduction) is the cause of 80% of endogenous ACTH-dependent Cushing syndrome cases typically due to an ACTH-producing pituitary adenoma (5). The remaining 20% of ACTH-dependent cases are due to extrapituitary (ectopic) tumors (5). ACTH-independent Cushing syndrome is caused by adrenocortical hyperplasia or tumors (5). Although all cases of Cushing syndrome result from prolonged exposure to cortisol, the clinical manifestation varies widely (6). The most common presenting symptoms include truncal obesity and skin changes, including acne, purple striae, and thinning of the skin, which results in a propensity to bruising (7). Other symptoms include menstrual irregularities, hirsutism, impaired glucose metabolism and diabetes, hypertension, proximal muscle weakness and atrophy, fatigue, and neuropsychological symptoms such as depression and mood instability (5). Many patients will have only isolated symptoms, making the clinical picture unclear. Furthermore, these symptoms are very common in the general population and can be caused by many etiologies, most of which are much more common than Cushing syndrome. The consequence is that patients with Cushing syndrome are frequently misdiagnosed and treated for other conditions, often for many years, before the correct diagnosis is achieved (6). The most common screening tests for suspected Cushing syndrome are the low-dose dexamethasone suppression test and a 24-h urinary free cortisol (7). Late-night salivary cortisol measurements are also used. Cortisol production is usually suppressed at night, but not in Cushing syndrome, and salivary cortisol concentrations reflect the free plasma concentration (7). However, although saliva is easy to collect, it is not a routine matrix in most clinical laboratories and confounding factors (such as sex and age) have not been well characterized. Guidelines for the diagnosis of Cushing syndrome published by the Endocrine Society recommend using any of these 3 tests to screen patients (7). If the initial result is positive, another of the 3 screening tests should be used as confirmation. In this case, a 24-h urine free cortisol was increased (157.1 μg/dL, reference interval, ≤45 μg/dL). A low-dose (1-mg) dexamethasone suppression test was performed. Baseline cortisol was 25 μg/dL and decreased to 8 μg/dL after dexamethasone administration; however, the recommended cutoff for suppression was <5 μg/dL. Therefore, the result was considered positive and established a diagnosis of Cushing syndrome. Subsequent to the initial diagnosis, Cushing syndrome must be further subdivided into ACTH-dependent or ACTH-independent types. Lastly, to differentiate between a pituitary and extrapituitary source of ACTH, the corticotropin-releasing hormone (CRH) stimulation test is used (8). The gold standard CRH test is performed using bilateral inferior petrosal sinus sampling (9). Exaggerated increases in ACTH concentrations after CRH administration are indicative of Cushing disease. RESOLUTION OF THE CASE An increased ACTH (92 pg/mL, reference interval, 5–27 pg/mL) confirmed that the Cushing syndrome was ACTH dependent. Furthermore, a subsequent CRH-stimulation test revealed exaggerated increases in both cortisol and ACTH in peripheral blood (Fig. 1), confirming a diagnosis of Cushing disease. In Cushing disease, at least a 50% rise in ACTH and a 20% rise in cortisol 30 min after CRH administration compared to baseline have been described as criteria providing 91% sensitivity and 95% specificity for pituitary Cushing (10). In adrenal Cushing, the low ACTH and high cortisol concentrations at baseline are not affected by CRH injection. In ectopic Cushing, the high ACTH and high cortisol concentrations at baseline are usually not altered by the CRH administration. An MRI was performed and revealed a pituitary tumor measuring 0.4 cm, which was removed by transsphenoidal adenomectomy. Subsequently, both ACTH and cortisol concentrations returned to reference intervals. The patient's Cushing disease is currently in remission and her amenorrhea is resolving, with the additional symptoms of hyperandrogenic hypogonadism improving over time. Results of the CRH stimulation test. Fig. 1. Open in new tabDownload slide In this patient, ACTH concentrations increased 2-fold and cortisol concentrations increased by 50% subsequent to CRH stimulation. These results were consistent with Cushing disease. Open circles, cortisol; filled squares, ACTH. Dotted lines, reference interval for cortisol; dashed lines, reference interval for ACTH. Fig. 1. Open in new tabDownload slide In this patient, ACTH concentrations increased 2-fold and cortisol concentrations increased by 50% subsequent to CRH stimulation. These results were consistent with Cushing disease. Open circles, cortisol; filled squares, ACTH. Dotted lines, reference interval for cortisol; dashed lines, reference interval for ACTH. SUMMARY Secondary amenorrhea is a symptom that can be indicative of a more serious underlying condition. Therefore, it is critical to correctly diagnose the cause of secondary amenorrhea so that the patient can be effectively treated. Appropriate treatment of this patient for Cushing disease was delayed due to misdiagnosis, which is frequent in patients with this fairly rare condition. It is imperative that clinicians be aware of conditions that present similarly and the relevant tests to correctly differentiate between those conditions (1, 3, 7). Definitive diagnosis relies on proper laboratory evaluation, and this case illustrates that a defined, sequential approach to laboratory testing should be used to arrive at the correct diagnosis quickly and efficiently. POINTS TO REMEMBER The most common causes of secondary amenorrhea include pregnancy, hypothyroidism, and hyperprolactinemia. Laboratory evaluation of secondary amenorrhea should be done in a step-wise manner to correctly elucidate the underlying cause and should include human chorionic gonadotropin, TSH, prolactin, FSH, and LSH. Both CAH and Cushing syndrome must be ruled out as the underlying cause of secondary amenorrhea in a background of hyperandrogenic hypogonadism before PCOS can be definitively diagnosed. Low-dose dexamethasone suppression dynamic testing, 24-h urinary free cortisol, and late-night salivary cortisol concentration are the screening tests for Cushing syndrome recommended by the Endocrine Society (7). Further dynamic testing of individuals with increased ACTH is useful to differentiate Cushing disease from ectopic ACTH production. 2 Nonstandard abbreviations: TSH thyroid stimulating hormone LH leutinizing hormone FSH follicle-stimulating hormone DHEA dehydroepiandrosterone PCOS polycystic ovary syndrome 17-OHP 17-hydroxyprogesterone CAH congenital adrenal hyperplasia ACTH adrenocorticotropic hormone CRH corticotropin-releasing hormone. " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: None declared. " Consultant or Advisory Role: C.R. Fantz, Beckman Coulter. " Stock Ownership: None declared. " Honoraria: None declared. " Research Funding: None declared. " Expert Testimony: None declared. " Patents: None declared. References 1. Roberts-Wilson TK , Spencer JB, Fantz CR. Using an algorithmic approach to secondary amenorrhea: avoiding diagnostic error . Clin Chim Acta 2013 ; 423 : 56 – 61 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Trivax B , Azziz R. Diagnosis of polycystic ovary syndrome . Clin Obstet Gynecol 2007 ; 50 : 168 – 77 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Azziz R , Carmina E, Dewailly D, Diamanti-Kandarakis E, Escobar-Morreale HF, Futterweit W et al. The androgen excess and PCOS society criteria for the polycystic ovary syndrome: The complete task force report . Fertil Steril 2009 ; 91 : 456 – 88 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Huynh T , McGown I, Cowley D, Nyunt O, Leong GM, Harris M, Cotterill AM. The clinical and biochemical spectrum of congenital adrenal hyperplasia secondary to 21-hydroxylase deficiency . Clin Biochem Rev 2009 ; 30 : 75 – 86 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 5. Boscaro M , Arnaldi G. Approach to the patient with possible Cushing's syndrome . J Clin Endocrinol Metab 2009 ; 94 : 3121 – 31 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Aron DC . Cushing's syndrome: Why is diagnosis so difficult? Rev Endocr Metab Disord 2010 ; 11 : 105 – 16 . Google Scholar Crossref Search ADS PubMed WorldCat 7. Nieman L , Biller B, Findling J, Newell-Price J, Savage M, Stewart P, Montori VM. The diagnosis of Cushing's syndrome: an Endocrine Society clinical practice guideline . J Clin Endocrinol Metab 2008 ; 93 : 1526 – 40 . Google Scholar Crossref Search ADS PubMed WorldCat 8. Kola B , Grossman AB. Dynamic testing in Cushing's syndrome . Pituitary 2008 ; 11 : 155 – 62 . Google Scholar Crossref Search ADS PubMed WorldCat 9. Deipolyi AR , Hirsch JA, Oklu R. Bilateral inferior petrosal sinus sampling . J Neurointerv Surg 2012 ; 4 : 215 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 10. Chrousos GP , Schulte HM, Oldfield EH, Gold PW, Cutler GB Jr, Loriaux DL. The corticotropin-releasing factor stimulation test. An aid in the evaluation of patients with Cushing's syndrome . N Engl J Med 1984 ; 310 : 622 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat © 2014 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Loriaux, Lynn, D

doi: 10.1373/clinchem.2013.217059pmid: 25070974

This woman presented with weight gain, hirsutism, and secondary amenorrhea. Common causes of secondary amenorrhea (pregnancy, menopause, prolactinoma, over exercise, and significant weight loss) were excluded on the first visit. She was diagnosed with PCOS, a sign, not a disease. She was actually treated for metabolic syndrome, weight-related insulin resistance, but there was no evidence for that. One year later she also had depression and easy bruising, which can be caused by disordered clotting, vasculitis, and capillary fragility. The clotting cascade was within reference intervals, and the purpura was presumably nonpalpable, excluding vasculitis. Capillary fragility secondary to glucocorticoid excess should be suspected. She had 1 of 2 sensitive signs of Cushing syndrome, weight gain (90% prevalence) and acanthosis nigracans secondary to insulin resistance (88%). Specific findings are hypokalemia (96%), osteopenia (97%), and proximal muscle weakness (93%). Hypokalemia was presumably absent, but the other 2 were not mentioned. The case for Cushing syndrome rests on easy bruising and weight gain. The indicated confirmatory test is 24-h urinary free cortisol, which was 3 times the reference interval for this woman. Dexamethasone suppression was performed, but in this scenario this test has a positive predictive value of about 50% and will not help either way. The next step is plasma ACTH; it was measurable, indicating ACTH dependence. The source of ACTH must be defined; the only way to do this is inferior petrosal sinus ACTH after corticotropin-releasing hormone stimulus. A gradient, central to peripheral, of greater than 3 confirms a pituitary source with near certainty. Transphenoidal microadenomectomy is the treatment. A depressed overweight woman with an incidental pituitary adenoma (15% prevalence) could have exactly the same laboratory values. It is essential that the clinical picture for Cushing syndrome be convincing before any differential tests are ordered. I am sure that was the case. " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:No authors declared any potential conflicts of interest. © 2014 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Bertholf, Roger, L

doi: 10.1373/clinchem.2013.217067pmid: 25070975

Endocrine disorders often produce diagnostic puzzles that laboratory studies are needed to solve. This case is interesting because the initial clinical presentation conveniently fit the most likely cause, PCOS. Medical students are taught, “When you hear hoofbeats, think of horses, not zebras [attributed to Theodore E. Woodward],” so the initial diagnosis of PCOS was prudent, although it was ultimately found to be wrong. But the case also illustrates a challenge in the current climate of cost containment in healthcare: how far should clinicians go to rule out rare causes of symptoms when more common etiologies are far more likely? When the woman returned 1 year later with worsening symptoms, more extensive laboratory studies identified the underlying cause, a pituitary adenoma, and surgical excision of the tumor resolved her symptoms. Therefore, an argument could be made that the patient would have been spared an additional year of distress had the unlikely diagnosis of a pituitary adenoma been considered on her initial presentation. However, PCOS is by far the most common cause of secondary amenorrhea associated with androgen excess, affecting 5%–10% of women during their reproductive years, whereas the incidence of Cushing disease is <1 per 100 000; PCOS was several thousand times more likely than Cushing disease to cause this patient's symptoms. Given these statistics, it would seem that ordering the costly battery of laboratory tests necessary to diagnose Cushing disease (urinary free cortisol, ACTH, dexamethasone suppression, CRH stimulation test, and imaging studies) on all patients with secondary amenorrhea and androgen excess would not be cost effective. However, this case reminds us that sometimes hoofbeats are, in fact, a team of zebras. " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:No authors declared any potential conflicts of interest. © 2014 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Bauça, Josep, Miquel;Martínez-Morillo,, Eduardo;Diamandis, Eleftherios, P

doi: 10.1373/clinchem.2013.211714pmid: 24212086

Abstract BACKGROUND Cancer is a leading cause of death worldwide. The low diagnostic sensitivity and specificity of most current cancer biomarkers make early cancer diagnosis a challenging task. The comprehensive study of peptides and small proteins in a living system, known as “peptidomics,” represents an alternative technological approach to the discovery of potential biomarkers for the assessment of a wide variety of pathologies. This review examines the current status of peptidomics for several body fluids, with a focus on urine, for cancer diagnostics applications. CONTENT Several studies have used high-throughput technologies to characterize the peptide content of different body fluids. Because of its noninvasive collection and high stability, urine is a valuable source of candidate cancer biomarkers. A wide variety of preanalytical issues concerning patient selection and sample handling need to be considered, because not doing so can affect the quality of the results by introducing bias and artifacts. Optimization of both the analytical strategies and the processing of bioinformatics data is also essential to minimize the false-discovery rate. SUMMARY Peptidomics-based studies of urine and other body fluids have yielded a number of biomolecules and peptide panels with potential for diagnosing different types of cancer, especially of the ovary, prostate, and bladder. Large-scale studies are needed to validate these molecules as cancer biomarkers. Cancer is a major clinical problem worldwide. Accounting for approximately 1 in every 4 deaths, cancer represents the second leading cause of death in developed countries, after cardiovascular diseases. It is estimated that more than 1.6 million new cancer cases are diagnosed every year in the US (1). Highly heterogeneous and with only a few effective therapeutic strategies, cancer represents a challenging medical condition for both healthcare professionals and governments. The possibility of detecting cancer at early stages, before it spreads to anatomically distant tissues, has long interested physicians and scientists, because early diagnosis is a key factor for successful treatment outcomes. For instance, the overall 5-year survival rate for ovarian cancer is <40%, whereas the rate increases to 90% if it is detected in its early stages (2). Consequently, finding new tools, such as endogenous biomolecules, that could help identify patients with early-stage disease is highly desirable. As stated by the WHO in 1968, the ideal biomarker for a disease should be measurable via a simple, reliable, and affordable method and have a high diagnostic sensitivity and specificity. The biomarker should be present in higher-than-normal concentrations during early disease stages, and its concentration should reflect the extent or severity of the disease. Defining the target population for whom the test would be applied is also of major concern. Unfortunately, only a few cancer biomarkers have entered routine use. Even fewer have been approved for population screening or diagnosis (3). One of the most frequently used cancer biomarkers is prostate-specific antigen (PSA).6 Despite its widespread measurement, many issues relating to overdiagnosis and overtreatment have arisen because serum PSA also increases in benign prostatic hyperplasia and other nonmalignant diseases (4). Similarly and despite being considered the best biochemical marker for breast cancer, carbohydrate antigen 15.3 is also increased in other tumors, such as pancreatic and colorectal cancers, as well as in a number of benign pathologies. The lack of diagnostic sensitivity for this antigen, the serum concentrations of which barely increase in early-stage malignancy, is also an important limitation (5). Clearly, there is an urgent need to discover and validate new biomarkers with better performance characteristics. High-throughput technologies that generate massive quantities of data have become known as “omics,” a suffix derived from “genomics,” the comprehensive study of genes and other DNA sequences (i.e., the genome)—hence transcriptomics, proteomics, metabolomics, epigenomics, and peptidomics. Three steps are essential in the process of developing a biomarker (3): (a) the discovery of candidate molecules in defined patient groups, (b) validation of the biomarkers for their capacity to assist in disease assessment, and (c) implementation in the clinical setting. This review focuses on the current situation of cancer biomarker discovery through peptidomics. The emphasis is on urine, but this review also covers investigations of other body fluids. Technological aspects of peptidomics and their applications to different types of cancer are also reviewed. Proteomics and Peptidomics Since the dawn of the genomics and transcriptomics era, numerous efforts have been directed toward discovering biomarkers that could help in the diagnosis, prognosis, or monitoring of different diseases. The main limitation of nucleic acid–based approaches is that recognition of an inherited predisposition to disease is usually not sufficient to identify the biological processes and mechanisms by which they operate (6). This limitation can be partially alleviated with proteomics. A complete analysis of the protein content of a cell, tissue, or organism comprises all of the layers of information gathered from the genome and the transcriptome, plus posttranslational modifications (e.g., phosphorylation, glycosylation). Proteomics is the large-scale study of the full complement of proteins in a living system—their structures, their physicochemical properties, and their functions. Proteomic technologies have the potential to detect dynamic changes in the production of proteins via these technologies' integration of the proteome's genetic and epigenetic features (7). The proteome is hence much more complex than the genome or the transcriptome, and it appears to reflect actual cellular processes more accurately than the genome or the transcriptome. Proteins are the effectors of biochemical actions (Fig. 1). From a pathophysiological point of view, genetic analyses can predict the risk of developing a disease, whereas proteomic approaches have a capacity both to show when the risks become evident as a disease and to facilitate monitoring of the therapeutic response. Both the concentrations of proteins and their posttranslational modifications may be altered during disease progression (8). Application of “omics” technologies for discovering novel biomarkers. Fig. 1. Open in new tabDownload slide Peptidomics may encompass additional sources of information owing to proteolytic processing of proteins by active enzymes. Fig. 1. Open in new tabDownload slide Peptidomics may encompass additional sources of information owing to proteolytic processing of proteins by active enzymes. The peptidome constitutes the low molecular weight proteome. The term “peptidomics,” a term coined in 1996, is the systematic and comprehensive analysis of the small proteins and endogenous peptides of biological samples at a defined time. Peptidomics typically encompasses polypeptides ≤20 kDa, although no clear limit has been established. It is interesting that results obtained with the first proteomic methodologies appeared to indicate that the peptide content samples was too simple and easily cleared by the kidney to carry useful information. That turned out not to be the case, for research efforts with peptidomics have already yielded positive results. Most peptides in biological systems are not synthesized as such, but rather are derived from precursor proteins via proteolytic cleavage by endogenous peptidases in a specific or nonspecific way (Fig. 2), e.g., the activation of some zymogens of the coagulation cascade or the maturation of insulin. Other peptides are generated in situ and then traverse the endothelial vasculature, if they are sufficiently small to enter the blood passively or the wall becomes permeable owing to disease conditions (9, 10). Generation of a series of peptides from a core peptide produced via endopeptidase activity. Fig. 2. Open in new tabDownload slide Amino- and carboxypeptidases can further cleave the core peptide, generating numerous fragments, which can then be identified by mass spectrometry (12, 66). Fig. 2. Open in new tabDownload slide Amino- and carboxypeptidases can further cleave the core peptide, generating numerous fragments, which can then be identified by mass spectrometry (12, 66). Proteolytic processing has been theorized to be necessary to facilitate metabolic variation so that individuals and species may better adapt to exogenous stimuli (11). Actually, peptides in body fluids are believed to be due to an imbalance between the activities of proteases on the one hand and the actions of protease inhibitors on the other. In this way, endogenous proteases are differentially regulated in the contexts of many physiological and pathologic phenomena (12). Therefore, it is reasonable to hypothesize that studying protease activity and regulation can lead to improved detection and a deeper understanding of the molecular mechanisms of some diseases. Peptides also play central roles in healthy physiological processes (13). That is the case for many cytokines, growth factors, and some neuropeptides for which proteome mapping studies have revealed no precursor protein (14), suggesting that they are synthesized as such in the central nervous system and are not breakdown products of precursor proteins. Even peptides processed from large proteins usually show biological functions and activities different from their parent molecules (7). Given that a high percentage of proteins undergo proteolytic cleavage, identifying and characterizing their breakdown products might be of interest, because they could be even more informative than the precursor protein (10). The study of differential protease activities could be an inviting field for the application of peptidomics to medicine. For example, neoplastic processes are involved in the transformation and proliferation of certain cell types and thus alter the concentrations and activities of specific proteins and enzymes, such as proteases. Therefore, not only do proteins in the system (proteomics) become altered, but their metabolic products (peptides), which should be regarded as an extension of the proteome, also change. Peptidomics of Body Fluids The peptidome constitutes a still mostly unexplored source of biological information, and it might provide useful biomarkers for disease assessment. Peptides in body fluids are proxies for protein synthesis, processing, and degradation. Worth mentioning is that some peptide biomarkers have already entered the clinic, although none of them were discovered with contemporary peptidomic methods (15) (Table 1). The peptides most widely known are the aminoterminal propeptide of brain natriuretic peptide (which is measured in serum for assessing heart failure) and C-peptide (for monitoring endogenous insulin production in diabetes patients). Collagen N-terminal telopeptides are measured in measured in urine as biomarkers of bone turnover (16). Thus, body fluids represent attractive sources to mine for informative proteins and peptides (Table 2). Examples of current peptide biomarkers used in clinical diagnosis. Table 1. Examples of current peptide biomarkers used in clinical diagnosis. Biomarker . Fluid . Disease . NT–pro-BNPa Blood (serum) Heart failure, ventricular dysfunction Pro-GRP Blood (serum) Neuroendocrine tumors, small cell lung cancer β-CTX Blood (serum) Bone turnover PINP Blood (serum) Bone turnover Pancreatic polypeptide Blood (serum) Neuroendocrine tumors Osteocalcin Blood (serum) Osteoporosis β2-Microglobulin Blood (serum) Renal disease and inflammation Calcitonin Blood (serum) Medullary thyroid carcinoma Cystatin C Blood (serum) Renal failure C-peptide Blood (serum), urine Diabetes mellitus VIP Blood (plasma) Pancreatic tumor ANF Blood (plasma) Heart failure NTX Urine Bone turnover β-Amyloid (1–42) CSF Alzheimer disease Biomarker . Fluid . Disease . NT–pro-BNPa Blood (serum) Heart failure, ventricular dysfunction Pro-GRP Blood (serum) Neuroendocrine tumors, small cell lung cancer β-CTX Blood (serum) Bone turnover PINP Blood (serum) Bone turnover Pancreatic polypeptide Blood (serum) Neuroendocrine tumors Osteocalcin Blood (serum) Osteoporosis β2-Microglobulin Blood (serum) Renal disease and inflammation Calcitonin Blood (serum) Medullary thyroid carcinoma Cystatin C Blood (serum) Renal failure C-peptide Blood (serum), urine Diabetes mellitus VIP Blood (plasma) Pancreatic tumor ANF Blood (plasma) Heart failure NTX Urine Bone turnover β-Amyloid (1–42) CSF Alzheimer disease a NT–pro-BNP, N-terminal end of the pro–brain natriuretic peptide; pro-GRP, pro–gastrin-releasing peptide; β-CTX, cross-linked collagen type I C-terminal telopeptide; PINP, procollagen type I N-terminal propeptide; VIP, vasoactive intestinal peptide; ANF, atrial natriuretic factor; NTX, collagen type 1 N-terminal telopeptide. Open in new tab Table 1. Examples of current peptide biomarkers used in clinical diagnosis. Biomarker . Fluid . Disease . NT–pro-BNPa Blood (serum) Heart failure, ventricular dysfunction Pro-GRP Blood (serum) Neuroendocrine tumors, small cell lung cancer β-CTX Blood (serum) Bone turnover PINP Blood (serum) Bone turnover Pancreatic polypeptide Blood (serum) Neuroendocrine tumors Osteocalcin Blood (serum) Osteoporosis β2-Microglobulin Blood (serum) Renal disease and inflammation Calcitonin Blood (serum) Medullary thyroid carcinoma Cystatin C Blood (serum) Renal failure C-peptide Blood (serum), urine Diabetes mellitus VIP Blood (plasma) Pancreatic tumor ANF Blood (plasma) Heart failure NTX Urine Bone turnover β-Amyloid (1–42) CSF Alzheimer disease Biomarker . Fluid . Disease . NT–pro-BNPa Blood (serum) Heart failure, ventricular dysfunction Pro-GRP Blood (serum) Neuroendocrine tumors, small cell lung cancer β-CTX Blood (serum) Bone turnover PINP Blood (serum) Bone turnover Pancreatic polypeptide Blood (serum) Neuroendocrine tumors Osteocalcin Blood (serum) Osteoporosis β2-Microglobulin Blood (serum) Renal disease and inflammation Calcitonin Blood (serum) Medullary thyroid carcinoma Cystatin C Blood (serum) Renal failure C-peptide Blood (serum), urine Diabetes mellitus VIP Blood (plasma) Pancreatic tumor ANF Blood (plasma) Heart failure NTX Urine Bone turnover β-Amyloid (1–42) CSF Alzheimer disease a NT–pro-BNP, N-terminal end of the pro–brain natriuretic peptide; pro-GRP, pro–gastrin-releasing peptide; β-CTX, cross-linked collagen type I C-terminal telopeptide; PINP, procollagen type I N-terminal propeptide; VIP, vasoactive intestinal peptide; ANF, atrial natriuretic factor; NTX, collagen type 1 N-terminal telopeptide. Open in new tab Characteristics of body fluids for peptidomics. Table 2. Characteristics of body fluids for peptidomics. Body fluid . Invasiveness of sample-collection procedure . Relative peptide stability . Serum Minimally invasive Unstable Plasma Minimally invasive Unstable Urine Noninvasive Stable CSF Invasive Not known Saliva Noninvasive Unstable Ascitic fluid Invasive Unstable Pleural fluid Invasive Unstable Tears Noninvasive Not known Body fluid . Invasiveness of sample-collection procedure . Relative peptide stability . Serum Minimally invasive Unstable Plasma Minimally invasive Unstable Urine Noninvasive Stable CSF Invasive Not known Saliva Noninvasive Unstable Ascitic fluid Invasive Unstable Pleural fluid Invasive Unstable Tears Noninvasive Not known Open in new tab Table 2. Characteristics of body fluids for peptidomics. Body fluid . Invasiveness of sample-collection procedure . Relative peptide stability . Serum Minimally invasive Unstable Plasma Minimally invasive Unstable Urine Noninvasive Stable CSF Invasive Not known Saliva Noninvasive Unstable Ascitic fluid Invasive Unstable Pleural fluid Invasive Unstable Tears Noninvasive Not known Body fluid . Invasiveness of sample-collection procedure . Relative peptide stability . Serum Minimally invasive Unstable Plasma Minimally invasive Unstable Urine Noninvasive Stable CSF Invasive Not known Saliva Noninvasive Unstable Ascitic fluid Invasive Unstable Pleural fluid Invasive Unstable Tears Noninvasive Not known Open in new tab PEPTIDOMICS OF BLOOD Blood fluid (serum or plasma) is regarded as the most valuable specimen for biomarker elucidation (17), because blood is the transportation medium for most tissue-derived molecules in the organism. Therefore, this biofluid can reveal the pathophysiological states of a broad spectrum of tissues and organs. Compared with healthy cells, disease-affected cells within tissues could differentially harbor peptides and proteins eventually released into the interstitial fluid and later into the bloodstream. The high protein content of serum makes it an attractive fluid for peptidomics; however, proteins and peptides are present in serum over a wide and dynamic range of concentrations—>10 orders of magnitude (7). This fact represents a considerable analytical challenge, because a few high-abundance proteins (albumin, immunoglobulins, transferrin, α1-antitrypsin, haptoglobin) hamper the identification of low-abundance molecules, which are more likely to be biomarker candidates (18). Given that the composition of blood reflects the metabolic state of the entire body as it transports molecules released from virtually any tissue or organ, pathophysiological changes in any single organ could easily be missed. Because the clotting time has a substantial effect on the polypeptide content of serum, plasma is often used instead (19). Nevertheless, a few studies have demonstrated the applicability of serum peptidomic profiling to a range of medical conditions, although most of these studies have not been validated for biases and artifacts. Shen et al. (20) explored the plasma peptidome (the entire collection of protein breakdown products) in the quest for breast cancer biomarkers and detected increased concentrations of cancer-relevant protein products, including extracellular matrix components, innate immune system molecules, proteases, and protease inhibitors. In another study, Villanueva et al. (13) compared the peptidomes of patients with metastatic thyroid carcinoma with those of age- and sex-matched controls and obtained a 12-peptide signature for identifying malignancy that had a 95% diagnostic sensitivity and 95% specificity. These and similar approaches have been challenged for bias and artifacts (21, 22) (http://www.jci.org/eletters/view/26022). PEPTIDOMICS OF CEREBROSPINAL FLUID Cerebrospinal fluid (CSF) is considered an outstanding source of biomarkers for neurologic diseases. A colorless fluid produced in the choroid plexus in the brain, CSF provides mechanical protection, nutrient supply, waste product removal, and metabolite transportation. Via continual interactions, CSF contains molecules that can reflect many of the processes of the central nervous system (14). Proteomic efforts have been aimed at characterizing the CSF peptidome to discover potential biomarkers for neurodegenerative conditions, neuropsychiatric disorders, traumatic brain injury, brain tumors, and aging-related conditions (23, 24). Wijte et al. used an enhanced mass spectrometry–based approach for both free peptides and peptides bound to proteins to evaluate postmortem CSF from patients with Alzheimer disease and identified a series of candidate peptides for its diagnosis, such as VGF, nerve growth factor, and C4 complement precursor (25). Additional verification studies remain to be done. An analysis by Zougman et al. (14) revealed 391 peptides derived from 91 different proteins, and a more recent study yielded 626 unique peptide sequences of <5 kDa that were derived from 104 proteins (26). The enrichment of CSF with small peptides might be due to a higher rate of filtration from plasma compared with components of higher molecular weight. One of the major disadvantages of CSF studies is the invasive nature of the collection procedure, making it inappropriate for general screening of presumably healthy individuals or all patients with neuropathologies. PEPTIDOMICS OF SALIVA Saliva is a multifunctional body fluid secreted by the major salivary glands (parotid, submandibular, and sublingual glands) and other glands distributed in the oral cavity. It lubricates the oral cavity and participates in digestion and preventing infections (27). Saliva contains bacteria, cellular debris, crevicular fluid, and serum components, and as an alternative sample for noninvasive collection, saliva has promising, unique features (28). Proteolytic degradation occurs as soon as proteins enter the oral cavity and continues after a saliva sample is collected. This process leads to great variation in the peptide profile and thus limits the reproducibility of peptidomic analyses. Other preanalytical variables, such as sex, age, diet, and circadian rhythms, can also play important roles in the peptide composition of saliva (29). Despite these shortcomings, peptidomic analyses of saliva have been used to assess a variety of pathologies, including Sjögren syndrome, xerostomia, and diabetes (30). Studies of oral cancer (31, 32) have identified a number of peptides that are overproduced in patients with squamous cell carcinoma. Hu et al. (31) reported a combination of 5 proteins that could be used to detect oral cancer with 90% diagnostic sensitivity and 83% specificity. PEPTIDOMICS OF TEARS Tears are a complex extracellular fluid that can be assessed noninvasively. As with plasma, the concentrations of proteins and peptides in tears span several orders of magnitude (33), with low interday but remarkably pronounced interindividual variation (34). de Souza et al. (35) identified 491 proteins in tears, and a more recent study yielded 1543 proteins (33). The most comprehensive peptidomics study of tears to date characterized 30 endogenous peptides, most of which were derived from proline-rich protein 4, a protein of unknown function produced at high concentrations in lacrimal acinar cells (36). As a body fluid, tears have a composition that reflects the pathophysiological state of the underlying tissues and organs and has proved useful for assessing both ocular and systemic pathologies, such as dry eye, meibomian gland dysfunction, and Sjögren syndrome (37, 38). Urine as a Source of Biomarkers Urine is formed in the kidney by ultrafiltration of the plasma for the elimination of metabolic waste products. Because urine is stored in the bladder for several hours before elimination, proteolytic degradation by endogenous proteases has largely been considered to have been completed by the time of voiding (39). Recent findings, however, have demonstrated the presence of a wide spectrum of proteases in urine (40) that might generate various combinations of different endogenous peptides, depending on how long the urine resides in the bladder. Proteolytic degradation of a urine sample can be minimized if second morning urine samples are used, because the time between the first and second voiding can be easily monitored and standardized. Despite such variation, urine is regarded as stable body fluid, especially compared with blood, in which proteases are known to be activated during and after blood drawing and thereby generate a considerable number of breakdown products that may skew proteomic and peptidomic approaches (39, 41). Urine has long been an attractive body fluid for study, not only in biomarker research but also in clinical diagnosis, mainly because it can be obtained noninvasively in large amounts. Currently, several routinely analyzed biomolecules serve as highly discriminating markers. For example, measurements of urine catecholamines and their metabolites help in assessing pheochromocytoma and in evaluating albuminuria as an index of glomerular function. Some factors require consideration when planning experiments with urine. Urine shows high daily biological variation, reflecting the effects that many endogenous and exogenous factors have on its production and composition. Diet, exercise, and water intake are 3 major factors influencing the quality and the comparability of samples. Urine contains components released not only from the kidneys and the bladder but also from many other organs, and biological processes can have profound impacts on its fluctuating content (42). Even cardiovascular, autoimmune, and infectious diseases affect the presence and concentrations of some protein molecules (43). URINARY PEPTIDOME Despite containing very small amounts of proteins, urine samples from healthy and diseased individuals are attractive for exploring proteomic disease. In 1997, Heine et al. (44) reported the presence of 13 proteins in human urine. Since then, many other investigators have assessed the human uroproteome and have drawn different, methodology-dependent conclusions. The initial approaches with 2-dimensional electrophoresis identified 1400 spots (45), a number that increased when liquid chromatography was introduced. Adachi et al. (46) stated that urine from healthy donors contains at least 1543 different proteins, mostly extracellular and membrane bound. This finding led the authors to suggest the possibility of specific transport pathways for lysosomal and plasma membrane proteins for reaching the urine. The lower protein content of urine compared with plasma reduces the possibility for high-abundance proteins to mask potential biomarkers. Studies have also demonstrated urine to be highly enriched for small peptides (47); healthy individuals and patients with Fanconi syndrome contain a >100-fold enrichment of molecules <10 kDa, compared with higher molecular weight polypeptides, perhaps because the former pass freely through the glomerulus (48). On the other hand, low-abundance and low-mass peptides can become bound to large carrier proteins that act as harvesters in the circulation (49). The major constituents of the urinary peptidome appear to be collagen fragments, especially from the collagen α1 chain, which probably reflect the physiological turnover of tissue extracellular matrix (41). Technological Aspects of Peptidomics The discovery of novel biomarkers depends not only on the concentration of the biomarker candidate in the sample and the complexity of the matrix but also on the analytical sensitivity of the detection method and the sample-preparation steps. The design of study strategies and analyses of bioinformatics data is crucial for reproducible and unbiased results (50). Any peptidomic analysis requires a robust and comprehensive procedure. SAMPLE PREPARATION To enrich the low molecular weight components in a sample for peptidomics analyses requires sample-preparation steps different from those required for proteomic analyses (17, 51). The preanalytical phase is the most challenging. A wide range of variables, both exogenous and endogenous, can affect the results (52, 53). The considerable stability of the proteome's composition and concentrations in urine allows samples to be stored for 6 h at room temperature with little change and for years at −20 °C (39, 54). Fiedler et al. investigated the influence of many variables on final peptidomics results (55). Significant differences were observed not only between first and second morning urine samples but also between first-stream and midstream urine samples. Bacteriuria and hematuria had a great effect, even at low concentrations, on the peptide profile. Freeze–thaw cycles can influence the final results when assessing exogenous variables; thus, reproducibility is improved with once-frozen urine samples. To minimize such potentially confounding factors and preanalytical variations requires that samples be collected and handled in a standardized manner. The Human Kidney and Urine Proteome Project (http://www.hkupp.org) is an international initiative of the Human Proteome Organization to establish collection and manipulation procedures for proteomics. In Europe, the European Kidney and Urine Proteomics organization (http://www.eurokup.org) promotes interactions between scientists in the field, with the goal of improving the understanding and assessment of kidney disease through urine proteomics. Both associations have proposed recommendations for standardized urine-processing steps (with special emphasis on sample collection, centrifugation, and thawing), which should minimize biases among studies. USE OF MASS SPECTROMETRY IN PEPTIDOMICS Traditionally, hypotheses for biomarker discovery have been derived from an understanding of disease biology (56). Over the past few decades, however, many researchers have turned to mass spectrometry to discover candidate molecules that could serve as biomarkers. The advantages of mass spectrometry for identifying and quantifying peptides in complex biological samples have facilitated the development of novel biochemical approaches for diagnosis, not only of cancer but of other diseases as well. Studies of proteins and peptides have used different methodologies. Two-dimensional gel electrophoresis has been used extensively, but it is a time-consuming technique with poor interassay reproducibility, especially at low molecular weights because it cannot separate and thus distinguish molecules of <10 kDa. Capillary electrophoresis–mass spectrometry yields robust and highly reproducible analyses of low molecular weight peptides and is compatible with many volatile buffers and analytes (19, 24); however, the long processing times make this technique challenging to use for large-scale studies. One of the most suitable platforms for urine peptide profiling is SELDI and MALDI followed by mass spectrometry identification with a TOF detector. This approach focuses on peptides in the range of 1–20 kDa. Immobilization, the key step in the entire process, reduces sample complexity, but at the expense of a great loss of information (52). Finally, liquid chromatography followed by tandem mass spectrometry (LC-MS/MS) is capable of providing large amounts of information with high reproducibility. Only capillary electrophoresis and liquid chromatography are able to interface directly with tandem mass spectrometry instruments for peptidomics studies with the required depth of analysis, dynamic range, and enhanced accuracy of quantification (53). In addition, analytical methodologies that increase analytical sensitivity have been developed. One example is selected reaction monitoring, which uses a nonscanning mode of operation on an LC-MS/MS instrument (57). It increases the detection capability by 2 to 3 orders of magnitude compared with conventional scanning modes. Mass spectrometry facilitates both biomarker discovery and verification/validation. Mass spectrometers help in characterizing proteins and peptides and their modifications. One of the clearest advantages over other platforms is its capacity to qualitatively screen and analyze thousands of molecules without previous knowledge of their existence or relevance to particular pathophysiological conditions. Quantification of previously discovered candidates is essential for evaluating their diagnostic capabilities. As with proteomics, both absolute and relative quantification of peptides generally require the use of stable isotope–labeled molecules as internal standards. Their use can overcome the problems of matrix effects, variation in sample preparation, and instrument fluctuations. As outlined elsewhere, the isotopic label should be introduced into the work flow as early as possible to increase the number of steps being controlled and thereby decrease imprecision (58). This technique is fairly time-consuming and expensive, however. Label-free strategies for relative quantification are based on comparing signal intensities produced by identical peptides in different analyses and rely on the accuracy of the hypothesis that identical peptides will behave similarly across different experiments and therefore permit direct comparisons (59). With urine samples, even absolute quantification is usually uninformative, so analyte concentration is commonly corrected for creatinine or protein excretion, or it is based on a 24-h urine collection, thus reducing the dietary and exercise effects on variation in results. In contrast to transcriptomics and proteomics, no “housekeeping” peptides have been successfully identified to date (53). DATA PROCESSING AND BIOINFORMATICS Peptidomics and proteomics require considerable computing power to obtain statistically significant and reproducible data. Peptide identification is one of the most challenging aspects (60). Online databases contain peptide sequences for a variety of body fluids and a myriad of disease conditions, and they serve as universal platforms for aiding in defining and verifying candidate biomarkers (42). A substantial proportion of the human urinary proteome database is derived from studies that assessed transplantation or renal disease, whereas the data derived from studies of prostate, renal, and bladder cancers, as well as pheochromocytomas, are relatively few. The most demanding issue with peptidomics is related to the nonspecificity of the peptide ends. As Hölttä et al. have stated (26), no restrictions regarding enzyme-cleavage specificity can be applied during analyses of bioinformatics data. The consequence is a huge increase (up to 1000-fold) in the number of sequences to consider. This situation contrasts with that of proteomics, in which protease digestion (usually with trypsin) ensures specific endings for each peptide molecule. For this reason, peptidomics suffers from higher false-positive rates and less accurate results. Urine Peptidomics for Disease Diagnostics Most of the literature on urine peptidomics addresses impairment of kidney function and outcomes of kidney transplantation (43, 61). The few studies that have searched for cancer biomarkers have focused on bladder, ovarian, and prostate cancers. Genitourinary malignancies are responsible for 1 of every 6 cancer deaths in men and 1 of every 10 in women (1). OVARIAN CANCER Ovarian cancer is the deadliest gynecologic malignancy. Current diagnostic strategies are based on measuring carbohydrate antigen 125 (CA125) in serum (62) in combination with vaginal ultrasonography. Measurement of CA125 lacks diagnostic sensitivity and specificity for early diagnosis, however, and many efforts have focused on finding protein and peptide molecules that could be useful as diagnostic biomarkers. Some proteins, such as human epididymal secretory protein 4 (63) and osteopontin (64), have shown utility, although none has surpassed CA125. Using serum, one of the first peptidomics-based studies combined peaks of unknown identity, presumably representing low molecular weight polypeptides, and claimed to distinguish between individuals with no malignancy and patients with ovarian cancer (stages I–IV) with 100% sensitivity and 95% specificity (65). The results described in this report have now been invalidated because of preanalytical, analytical, and bioinformatics artifacts (66). PROSTATE CANCER Prostate cancer, the most prevalent malignancy in men, ranks second in lethality (1). Novel noninvasive markers with higher diagnostic sensitivity and specificity are needed. Although PSA-derived forms and the ribonucleic acid marker PCA3 (prostate cancer antigen 3) seem to add some degree of diagnostic specificity, they have not met expectations (67). Other protein candidates that have been suggested require large-scale validation. The first comparison of the urine proteome used 2-dimensional gel electrophoresis followed by MALDI-TOF mass spectrometry fingerprinting of voided urine samples after prostatic massage to evaluate age-matched men with benign prostatic hyperplasia (68). Calgranulin B/MRP-8 was highlighted for verification and validation. Subsequent research with urine samples has yielded additional candidate molecules, including the matrix metalloproteinases (69) and engrailed-2 (70), although none have yet been validated with large cohorts. Hypothesizing that first-void urine contains prostatic fluid, Theodorescu et al. (71) used a filter with a 20-kDa cutoff followed by capillary electrophoresis–mass spectrometry analysis and obtained a biomarker panel of 12 urinary peptides based on the results. They proposed that this peptide panel, used in combination with age, free PSA, and total PSA, could improve current diagnosis by increasing the area under the ROC curve from 0.77 (based on the free-PSA percentage and patient age) up to 0.82. Nevertheless, this peptide panel also remains to be validated. BLADDER CANCER Bladder cancer is the fifth most common cancer in Western societies. Current diagnostic strategies are based on cytoscopy and urine cytology, but these methods have high interobserver imprecision and low reproducibility. Given that the bladder is in intimate contact with urine after its production in the kidney, this body fluid has been mined heavily for both protein and peptide biomarkers that might help, not only in detecting bladder cancer, but also in distinguishing muscle-invasive from noninvasive malignancy (72, 73). A large number of peptides with different concentrations in urine samples from patients with invasive bladder cancer, compared with patients with noninvasive cancer and with controls, have been found, but most of these peptides appear to be fragments of abundant proteins. In fact, Theodorescu et al. (54) proposed a proteomic pattern of 22 polypeptides with high diagnostic sensitivity and specificity for urothelial cancer and highlighted fibrinopeptide A as a potential diagnostic biomolecule. Bryan et al. (72) identified 8 peptides with significantly different concentrations in patients with and without muscle-invasive urothelial carcinoma. Such peptides were identified as derived from albumin, fibrinogen, hemoglobin, and prealbumin—all high-abundance proteins. OTHER CANCERS Anatomically distant sites can influence urine composition. Studies of the urine peptidome have used this rationale to pursue possible markers of lung cancer (74) and gastrointestinal cancer (75). Using SELDI, Husi et al. (76) found that a nonnegligible number of the candidate proteins belonged to the family of small calcium-binding proteins, S100, which have been related to the growth of tumors of the upper gastrointestinal tract. None of the identified candidates fulfilled the requirements for a single marker, so a protein–peptide pattern served for screening and prediction of outcome. A diagnostic sensitivity of up to 98% was reported, but most of the candidates had also been described for other malignancies, compromising the pattern's specificity. If one steps back and considers these results as a whole, one sees that most of the peptide panels have not been validated properly. This lack of validation studies represents one of the major shortcomings of peptidomics for reaching the clinical setting and places the usefulness of peptidomics for cancer diagnostics under a critical eye. Translation to the Clinic Despite intensive efforts, no molecule described in any proteomics or peptidomics study has entered the clinic. For retrospective and prospective validation studies of candidate molecules and to avoid artifacts and methodology-related false-positive results, other methodologies (e.g., immunoassays) are preferred (3, 77). Sometimes initial studies based on small populations show a statistical significance that, because of bias in patient selection or other confounders, becomes lost in subsequent studies. The lessons from this experience could help in improving the planning of future strategies. Large-scale population studies are rare and carry a large financial burden. Finally, reaching statistical significance is not sufficient for candidate biomarkers. As with novel drugs, biomarkers have to show some clinical improvement over those currently in use; otherwise, they will not be adopted. Given the complexity of any biological process, a single biomarker has been widely viewed to be unlikely to discriminate a pathologic process with sufficient sensitivity and specificity. Therefore, the incorporation of combinations of multiple, independent biomarkers into a diagnostic or predictive panel may be more likely to be useful. Nevertheless, each of the individual biomarkers used in any panel must be independently verified and validated to ensure clinical utility. This requirement makes the design of large-scale validation studies even more difficult. Recently, a new perspective that transcends classic proteomics and peptidomics suggests that the study of individual or global protease activities might also yield indicators or predictors of disease (78–80). This new approach has been termed “functional peptidomics.” It relies on the fact that tumor progression and invasiveness may lead to the differential production and secretion of exoproteases; thus, the study of their functions might not only reflect the true biological/pathologic state of an organism but also overcome reproducibility problems related to preanalytical variables. This approach is still in its beginning stages, however, and conclusions about its applicability cannot yet be drawn. Future Challenges The impressive growth in high-throughput biology has dominated science during the last decade, mainly owing to the leap in the development of new technologies. Substantial efforts in proteomics have focused on the discovery and validation of sensitive and specific diagnostic biomarkers for many human pathologies. Deep biochemical and pathophysiological knowledge is critical for solving clinical questions, and every step in the procedure must be planned and executed meticulously. Standardized handling procedures are expected to aid tremendously in the generation of clinically useful and reproducible data. Peptidomics is a relatively new field, and few studies of explorations and characterization of the peptidome have yet been published. There is no strong evidence that peptidomics will yield better results than proteomics, but biological and chemical reasoning supports work in that direction. Proteomics is undoubtedly the dominant technology in the postgenomics era, and peptidomics represents a largely unexplored step forward. 6 Nonstandard abbreviations: PSA prostate-specific antigen CSF cerebrospinal fluid LC-MS/MS liquid chromatography–tandem mass spectrometry CA125 carbohydrate antigen 125. " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: E.P. Diamandis, Clinical Chemistry, AACC. " Consultant or Advisory Role: None declared. " Stock Ownership: None declared. " Honoraria: None declared. " Research Funding: None declared. " Expert Testimony: None declared. " Patents: None declared. References 1. Siegel R , Naishadham D, Jemal A. Cancer statistics, 2013 . CA Cancer J Clin 2013 ; 63 : 11 – 30 . 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Lenters-Westra,, Erna;Slingerland, Robbert, J

doi: 10.1373/clinchem.2014.224311pmid: 24865164

Abstract BACKGROUND In 2009, we investigated the conformance of 8 hemoglobin A1c (Hb A1c) point-of-care (POC) instruments. Since then, instruments have improved and new devices are available on the market. In this second study, we evaluated the performance of DCA Vantage, Afinion, InnovaStar, Quo-Lab, Quo-Test, Cobas B101, and B-analyst Hb A1c POC instruments. METHODS Clinical and Laboratory Standards Institute protocols EP-5 and EP-9 were applied to investigate imprecision, accuracy, and bias. We assessed bias using the mean of 3 certified secondary reference measurement procedures (SRMPs). Assay conformance with the National Glycohemoglobin Standardization Program (NGSP) certification criteria was also evaluated. Interference of common Hb variants was investigated for methods that could work with hemolysed material. RESULTS The total CVs for all instruments, except for the DCA Vantage at a high Hb A1c value, were ≤3.1% in SI units and ≤2.1% in Diabetes Control and Complications Trial (DCCT) units. Afinion, DCA Vantage, B-analyst, and Cobas B101 instruments passed the NGSP criteria with 2 different reagent lot numbers. Quo-Test, Quo-Lab, and InnovaStar instruments had a negative bias compared to the mean of the 3 SRMPs and failed NGSP criteria. Most of the common Hb variants did not interfere with the investigated instruments, except Hb AE for the Cobas B101. CONCLUSIONS Afinion, DCA Vantage, Cobas B101, and B-analyst instruments met the generally accepted performance criteria for Hb A1c. Quo-Test, Quo-Lab, and InnovaStar met the criteria for precision but not for bias. Proficiency testing should be mandated for users of Hb A1c POC assays to ensure quality. Point-of-care (POC)3 is the fastest growing market in clinical chemistry. Today, there are some developments to transition diagnostics from the second line (in the hospital) to the first line (in the family physician's office) or even to the zero line (in the patient's home) (1). Ensuring the quality of the POC testing instrument used is crucial and should be embedded in a chain of quality control from hospital to doctors' offices; otherwise the impact on patients will be immense, especially if these methods will be used for the diagnosis of different diseases. In 2009, we evaluated 8 different glycated hemoglobin (Hb A1c) POC instruments and came to the conclusion that, at that time, 6 of 8 Hb A1c POC instruments did not meet the generally accepted analytical performance criteria of a total CV <3.0% and a National Glycohemoglobin Standardization Program (NGSP) manufacturer certification (2–4). Since then, some manufacturers have either improved their methods or withdrawn from the market. New instruments have since come to market. This initiated a second round of evaluations of different POC instruments. We approached all manufacturers that joined the first evaluation study and asked them if they were willing to join this second round. Some were still improving their methods and not ready for this second test, and some did not want to join this second round for unknown reasons. In the meantime, manufacturers from new POC instruments approached us and asked us to do an evaluation of their methods, and eventually we had 7 Hb A1c POC instruments to evaluate. The aim of this study was to evaluate the 7 POC instruments according to the CLSI protocols and to check whether the instruments would pass the NGSP criteria with 2 different reagent lot numbers compared with 3 certified IFCC and NGSP secondary reference measurement procedures (SRMPs). Additionally, we investigated the presence of potential interference from common Hb variants on the instruments capable of analyzing frozen material, and we tried to find out whether the investigated Hb A1c POC instruments can be used for the diagnosis of diabetes. Materials and Methods The 7 POC Hb A1canalyzers evaluated in this study were The DCA Vantage™ (Siemens Medical Solutions Diagnostics), which is based on latex agglutination inhibition immunoassay methodology and provides results in 6 min. The B-analyst (Menarini Diagnostics), which is based on latex agglutination immunology turbidimetric methodology, with results available in 8 min. The Afinion™ (Alere Technologies), which is based on boronate affinity separation, with results available in 5 min. The Quo-Test (Quotient Diagnostics, an EKF Diagnostics Holding Company), which is based on boronate affinity separation and the use of fluorescence quenching, with results available in 3 min. The Quo-Lab (Quotient Diagnostics), which is based on boronate affinity separation and the use of fluorescence quenching, with results available in 3 min (this method is the same as the Quo-Test but needs some manual handling). The InnovaStar (DiaSys Diagnostics), which is based on agglutination immunoassay and provides results in 11 min. The Cobas B101 (Roche Diagnostics), which is based on latex agglutination inhibition immunoassay methodology and provides results in 5 min. DCA Vantage, Afinion, Quo-Test, and InnovaStar analyzers were also evaluated in the previous study (2). B-analyst, Quo-Lab, and Cobas B101 are new on the market. The Menarini B-analyst was evaluated at the end of 2012, and the other 6 instruments were evaluated in January 2014. All instruments were NGSP certified at the time of the evaluation (5). B-analyst and the Afinion can work only with fresh patient material and not with hemolysed or lyophilized material. To get an overall impression of performance before starting the CLSI EP-5 and EP-9 protocols, IFCC monitoring program samples were analyzed (frozen whole blood samples, n = 24; 12 samples in duplicate) on instruments specified for hemolysed material (6). For those not specified for hemolysed material, 12 fresh patient samples were run in duplicate. The familiarization study was not done with the B-analyst. The values were assigned to the IFCC monitoring samples by the IFCC network laboratories by use of the IFCC primary reference methods (n = 15) (7). Values were assigned to the fresh patient samples by the 3 SRMPs mentioned below. Precision was also calculated by analyzing the samples in duplicate. The results were sent to the manufacturers for their approval to continue with the evaluation. After obtaining manufacturer approval, we used the CLSI EP-5 protocol to further investigate assay imprecision (duplicate measurements twice per day on 2 patient samples for 20 days) (8). Aliquots were made from the patient samples and stored at minus 80 °C degrees until analysis. Controls supplied by the manufacturer were used for Afinion and B-analyst, as these methods are not specified for use with hemolysed material. Because B-analyst was evaluated 1 year earlier, we used a slightly different protocol for the imprecision study. Aside from the controls supplied by the manufacturer of the B-analyst (medium and high value), we also used a fresh sample and analyzed it twice per day in duplicate for 10 days instead of 20 days. Studies in the past have shown that better CVs are achieved with the Afinion when controls are used in an EP-5 protocol rather than patient samples (2, 4). Therefore CVs were also calculated on the basis of the duplicates of the fresh patient samples in the EP-9 protocol for all instruments. The CLSI EP-9 protocol was performed twice with 2 different reagent lot numbers, and the data were used to investigate the bias between the POC instruments and the 3 SRMPs (n = 40, 5 days, duplicate measurements) (9). Hb A1c value determination of the patient samples was performed with 3 certified SRMPs: Roche Tina-quant Gen.2 Hb A1c on Integra 800, immunoassay, IFCC and NGSP certified (Roche Diagnostics); Premier Hb9210, affinity chromatography HPLC, IFCC and NGSP certified (Trinity Biotech); and Tosoh G8, cation-exchange HPLC, IFCC certified (Tosoh Bioscience). The SRMPs have documented good results in the IFCC and NGSP monitoring programs and were calibrated by use of the IFCC secondary reference material with assigned IFCC and derived NGSP values (6, 10, 11). To check overall calibration and bias independently of the chosen SRMP, the results of the POC instruments in the EP-9 procedure were compared with the mean of the 3 SRMPs, and medical decision point (MDP) analysis was performed at an Hb A1c value of 48 mmol/mol [6.5% Diabetes Control and Complications Trial (DCCT) units]. When the 2 methods are statistically identical, the 95% CI for each y MDP includes the corresponding x MDP. The student 2-tailed t-test for paired samples was used to check for statistically significant difference between the 2 lot numbers. A P value <0.05 was considered significant. The EP-9 protocol was used to evaluate the methods against 3 different SRMPs with 2 reagent lot numbers, and the data were also used to calculate the NGSP certification criteria. Beginning in January 2014, 37 of 40 results need to be within 6% (relative) of an individual NGSP SRMP to pass certification (12). Interference from common Hb variants Hb AS, Hb AC, Hb AD, Hb AE, increased A2 (β-thalassemia), and Hb F was investigated by the instruments specified to analyze frozen, hemolysed material. Five samples of each variant with different Hb A1c values were analyzed in 1 day. The specific variants were identified/confirmed with cation-exchange HPLC (Menarini HA8180V, Diabetes Mode) and capillary electrophoresis (Sebia Capillarys 2 Flex Piercing). Percentage Hb F was determined with the Sebia Capillarys 2 Flex Piercing. Hb A1c values were assigned by use of IFCC-calibrated boronate affinity HPLC (Premier Hb9210) and, for samples with increased Hb F, Hb A1c values were assigned by use of IFCC-calibrated cation exchange HPLC (Menarini HA8180V, Diabetes Mode). A mean relative difference of >6% was considered a significant interference. The results were corrected for bias found in nonvariant samples when calculating the mean relative difference. STATISTICS Calculations were performed by use of Microsoft® Excel 2010 (Microsoft Corp.). Statistical analyses were performed by use of Analyse-It® (Analyse-It Software) and EP Evaluator Release 9 (Data Innovations) (13). For the duplicates in the EP-9 protocol, CV was calculated with the following formula: CVa=∑(Δ)2nx¯ 2×100%(1) where CVa is the analytical CV, Δ is the difference between duplicates, n is the number of duplicates, and x is the mean of the duplicates. The reference change value (RCV), which is the critical difference in the change in a patient's serial test results that can be considered significantly different at a CI of 95%, was calculated with the following formula: RCV (%)=2×1.96×[(CVa)2+(CVwp)2](2) where CVa is the analytical CV and CVwp is the intra-individual or biological (within-person) CV (CVwp) (14, 15). Results On the basis of the good results obtained in the familiarization study (deviation from the target value ≤1.9 mmol/mol, reproducibility ≤2.6% in SI units for all instruments), all manufacturers agreed to participate in the full evaluation. Table 1 shows the results of the EP-5 protocol and the CVs on the basis of the duplicate samples analyzed in EP-9. Precision ranged from 1.3% in SI units at an Hb A1c value of 71 mmol/mol (0.9% at Hb A1c value of 8.7% in DCCT units) for InnovaStar to 4.2% in SI units at an Hb A1c value of 73 mmol/mol (3.2% at Hb A1c value of 8.8% in DCCT units) for DCA Vantage. Imprecision results based on EP-5 and the duplicates in EP-9. Table 1. Imprecision results based on EP-5 and the duplicates in EP-9. Instrument . Hb A1c value sample/control . SI units (mmol/mol) . CV (%) . DCCT units (%) . CV (%) . B-analyst 29a 3.0 4.7a 2.1 61b 1.6 8.0b 1.2 92b 1.3 10.9b 1.1     Lot number 1c 1.7 1.3     Lot number 2c 1.8 1.7 Afinion 44b 2.1 6.2b 1.3 66b 1.9 8.2b 1.4     Lot number 1c 3.0 2.1     Lot number 2c 2.8 1.7 DCA Vantage 47 3.1 6.5 1.9 73 4.2 8.8 3.2     Lot number 1c 3.2 2.6     Lot number 2c 3.2 2.4 Cobas B101 46 2.8 6.3 1.8 74 1.5 8.9 1.2     Lot number 1c 1.9 1.4     Lot number 2c 1.5 1.2 InnovaStar 47 1.9 6.4 1.2 71 1.3 8.7 0.9     Lot number 1c 1.4 1.0     Lot number 2c 0.9 0.6 Quo-Test 46 2.7 6.3 1.9 71 2.0 8.6 1.6     Lot number 1c 2.8 2.1     Lot number 2c 2.5 1.7 Quo-Lab 45 3.1 6.3 2.1 70 2.2 8.6 1.7     Lot number 1c 2.0 1.5     Lot number 2c 0.9 1.6 Instrument . Hb A1c value sample/control . SI units (mmol/mol) . CV (%) . DCCT units (%) . CV (%) . B-analyst 29a 3.0 4.7a 2.1 61b 1.6 8.0b 1.2 92b 1.3 10.9b 1.1     Lot number 1c 1.7 1.3     Lot number 2c 1.8 1.7 Afinion 44b 2.1 6.2b 1.3 66b 1.9 8.2b 1.4     Lot number 1c 3.0 2.1     Lot number 2c 2.8 1.7 DCA Vantage 47 3.1 6.5 1.9 73 4.2 8.8 3.2     Lot number 1c 3.2 2.6     Lot number 2c 3.2 2.4 Cobas B101 46 2.8 6.3 1.8 74 1.5 8.9 1.2     Lot number 1c 1.9 1.4     Lot number 2c 1.5 1.2 InnovaStar 47 1.9 6.4 1.2 71 1.3 8.7 0.9     Lot number 1c 1.4 1.0     Lot number 2c 0.9 0.6 Quo-Test 46 2.7 6.3 1.9 71 2.0 8.6 1.6     Lot number 1c 2.8 2.1     Lot number 2c 2.5 1.7 Quo-Lab 45 3.1 6.3 2.1 70 2.2 8.6 1.7     Lot number 1c 2.0 1.5     Lot number 2c 0.9 1.6 a Based on 10 days instead of 20 days. b Controls from manufacturer. c Based on duplicates in EP-9. Open in new tab Table 1. Imprecision results based on EP-5 and the duplicates in EP-9. Instrument . Hb A1c value sample/control . SI units (mmol/mol) . CV (%) . DCCT units (%) . CV (%) . B-analyst 29a 3.0 4.7a 2.1 61b 1.6 8.0b 1.2 92b 1.3 10.9b 1.1     Lot number 1c 1.7 1.3     Lot number 2c 1.8 1.7 Afinion 44b 2.1 6.2b 1.3 66b 1.9 8.2b 1.4     Lot number 1c 3.0 2.1     Lot number 2c 2.8 1.7 DCA Vantage 47 3.1 6.5 1.9 73 4.2 8.8 3.2     Lot number 1c 3.2 2.6     Lot number 2c 3.2 2.4 Cobas B101 46 2.8 6.3 1.8 74 1.5 8.9 1.2     Lot number 1c 1.9 1.4     Lot number 2c 1.5 1.2 InnovaStar 47 1.9 6.4 1.2 71 1.3 8.7 0.9     Lot number 1c 1.4 1.0     Lot number 2c 0.9 0.6 Quo-Test 46 2.7 6.3 1.9 71 2.0 8.6 1.6     Lot number 1c 2.8 2.1     Lot number 2c 2.5 1.7 Quo-Lab 45 3.1 6.3 2.1 70 2.2 8.6 1.7     Lot number 1c 2.0 1.5     Lot number 2c 0.9 1.6 Instrument . Hb A1c value sample/control . SI units (mmol/mol) . CV (%) . DCCT units (%) . CV (%) . B-analyst 29a 3.0 4.7a 2.1 61b 1.6 8.0b 1.2 92b 1.3 10.9b 1.1     Lot number 1c 1.7 1.3     Lot number 2c 1.8 1.7 Afinion 44b 2.1 6.2b 1.3 66b 1.9 8.2b 1.4     Lot number 1c 3.0 2.1     Lot number 2c 2.8 1.7 DCA Vantage 47 3.1 6.5 1.9 73 4.2 8.8 3.2     Lot number 1c 3.2 2.6     Lot number 2c 3.2 2.4 Cobas B101 46 2.8 6.3 1.8 74 1.5 8.9 1.2     Lot number 1c 1.9 1.4     Lot number 2c 1.5 1.2 InnovaStar 47 1.9 6.4 1.2 71 1.3 8.7 0.9     Lot number 1c 1.4 1.0     Lot number 2c 0.9 0.6 Quo-Test 46 2.7 6.3 1.9 71 2.0 8.6 1.6     Lot number 1c 2.8 2.1     Lot number 2c 2.5 1.7 Quo-Lab 45 3.1 6.3 2.1 70 2.2 8.6 1.7     Lot number 1c 2.0 1.5     Lot number 2c 0.9 1.6 a Based on 10 days instead of 20 days. b Controls from manufacturer. c Based on duplicates in EP-9. Open in new tab Table 2 shows NGSP certification pass/fail criteria with respect to the results of the EP-9 protocol performed with fresh patient samples. The instruments were compared to the 3 SRMPs with 2 different reagent lot numbers and also with the mean 3 SRMPs (Fig. 1, A–D, and Fig. 2, A–C). B-analyst, DCA Vantage, and Afinion passed the NGSP criteria with 2 reagent lot numbers compared with the SRMPs. Cobas B101 passed the NGSP criteria with 2 lot numbers compared with the Tosoh G8 but not when compared with the Premier Hb9210 or the Roche Tina-quant Gen.2 on Integra 800. Bias for Afinion, Cobas B101, B-analyst, and the DCA Vantage was <1.0 mmol/mol compared with the mean of 3 SRMPs with 2 different reagent lot numbers. Quo-Test, Quo-Lab, and InnovaStar showed a significant bias compared with the mean of the 3 SRMPs and failed the NGSP criteria compared with the 3 SRMPs with both reagent lot numbers. EP-9 method comparison results (Deming regression lines) in DCCT units and pass/fail NGSP certification criteria.a Table 2. EP-9 method comparison results (Deming regression lines) in DCCT units and pass/fail NGSP certification criteria.a Comparison . Lot number 1 . Bias . SEEb . Samples >6% of SRMP, n . NGSP criteria . Lot number 2 . Bias . SEE . Samples >6% of SRMP, n . NGSP criteria . B-analyst (y)     vs Premier (x) y = 1.08x – 0.41 0.19 0.15 1 Pass y = 1.08x – 0.41 0.16 0.16 2 Pass     vs Tina-quant (x)c y = 1.08x – 0.39 0.19 0.18 0 Pass y = 1.08x – 0.39 0.16 0.18 1 Pass     vs Tosoh G8 (x) y = 1.08x – 0.46 0.15 0.15 0 Pass y = 1.08x – 0.46 0.11 0.15 2 Pass DCA Vantage (y)     vs Premier (x) y = 0.99x – 0.10 −0.15 0.21 3 Pass y = 1.01x – 0.15 −0.05 0.20 0 Pass     vs Tina-quant (x)c y = 1.04x – 0.35 −0.04 0.19 1 Pass y = 1.06x – 0.39 0.06 0.20 0 Pass     vs Tosoh G8 (x) y = 1.02x – 0.19 −0.06 0.20 0 Pass y = 1.04x – 0.22 0.03 0.22 1 Pass Afinion (y)     vs Premier (x)c y = 0.97x + 0.13 −0.08 0.18 0 Pass y = 0.91x + 0.60 −0.06 0.15 0 Pass     vs Tina-quant (x) y = 1.02x – 0.08 0.02 0.20 0 Pass y = 0.95x + 0.39 0.05 0.16 2 Pass     vs Tosoh G8 (x) y = 0.99x + 0.08 0.00 0.22 2 Pass y = 0.93x + 0.52 0.03 0.17 3 Pass Quo-Test (y)     vs Premier (x)c y = 1.00x – 0.29 −0.30 0.19 7 Fail y = 0.99x – 0.24 −0.32 0.16 11 Fail     vs Tina-quant (x) y = 1.05x – 0.52 −0.20 0.20 6 Fail y = 1.04x – 0.47 −0.21 0.18 5 Fail     vs Tosoh G8 (x) y = 1.02x – 0.37 −0.22 0.20 5 Fail y = 1.01x – 0.32 −0.23 0.18 5 Fail Quo-Lab (y)     vs Premier (x)c y = 0.98x – 0.20 −0.35 0.20 13 Fail y = 1.03x – 0.63 −0.39 0.19 14 Fail     vs Tina-quant (x) y = 1.03x – 0.44 −0.24 0.20 8 Fail y = 1.08x – 0.87 −0.28 0.21 12 Fail     vs Tosoh G8 (x) y = 1.00x – 0.27 −0.26 0.22 9 Fail y = 1.06x – 0.70 −0.30 0.21 15 Fail InnovaStar (y)     vs Premier (x) y = 0.89x + 0.46 −0.36 0.14 10 Fail y = 0.89x + 0.43 −0.40 0.13 15 Fail     vs Tina-quant (x)c y = 0.93x + 0.25 −0.25 0.18 8 Fail y = 0.93x + 0.21 −0.30 0.15 9 Fail     vs Tosoh G8 (x) y = 0.91x + 0.40 −0.27 0.17 9 Fail y = 0.90x + 0.37 −0.32 0.16 8 Fail Cobas B101 (y)     vs Premier (x) y = 1.06x – 0.48 −0.05 0.24 6 Fail y = 1.06x – 0.40 0.01 0.24 4 Fail     vs Tina-quant (x)c y = 1.11x – 0.74 0.06 0.23 5 Fail y = 1.10x – 0.67 0.10 0.23 5 Fail     vs Tosoh G8 (x) y = 1.09x – 0.60 0.04 0.17 2 Pass y = 1.09x – 0.52 0.09 0.17 1 Pass Comparison . Lot number 1 . Bias . SEEb . Samples >6% of SRMP, n . NGSP criteria . Lot number 2 . Bias . SEE . Samples >6% of SRMP, n . NGSP criteria . B-analyst (y)     vs Premier (x) y = 1.08x – 0.41 0.19 0.15 1 Pass y = 1.08x – 0.41 0.16 0.16 2 Pass     vs Tina-quant (x)c y = 1.08x – 0.39 0.19 0.18 0 Pass y = 1.08x – 0.39 0.16 0.18 1 Pass     vs Tosoh G8 (x) y = 1.08x – 0.46 0.15 0.15 0 Pass y = 1.08x – 0.46 0.11 0.15 2 Pass DCA Vantage (y)     vs Premier (x) y = 0.99x – 0.10 −0.15 0.21 3 Pass y = 1.01x – 0.15 −0.05 0.20 0 Pass     vs Tina-quant (x)c y = 1.04x – 0.35 −0.04 0.19 1 Pass y = 1.06x – 0.39 0.06 0.20 0 Pass     vs Tosoh G8 (x) y = 1.02x – 0.19 −0.06 0.20 0 Pass y = 1.04x – 0.22 0.03 0.22 1 Pass Afinion (y)     vs Premier (x)c y = 0.97x + 0.13 −0.08 0.18 0 Pass y = 0.91x + 0.60 −0.06 0.15 0 Pass     vs Tina-quant (x) y = 1.02x – 0.08 0.02 0.20 0 Pass y = 0.95x + 0.39 0.05 0.16 2 Pass     vs Tosoh G8 (x) y = 0.99x + 0.08 0.00 0.22 2 Pass y = 0.93x + 0.52 0.03 0.17 3 Pass Quo-Test (y)     vs Premier (x)c y = 1.00x – 0.29 −0.30 0.19 7 Fail y = 0.99x – 0.24 −0.32 0.16 11 Fail     vs Tina-quant (x) y = 1.05x – 0.52 −0.20 0.20 6 Fail y = 1.04x – 0.47 −0.21 0.18 5 Fail     vs Tosoh G8 (x) y = 1.02x – 0.37 −0.22 0.20 5 Fail y = 1.01x – 0.32 −0.23 0.18 5 Fail Quo-Lab (y)     vs Premier (x)c y = 0.98x – 0.20 −0.35 0.20 13 Fail y = 1.03x – 0.63 −0.39 0.19 14 Fail     vs Tina-quant (x) y = 1.03x – 0.44 −0.24 0.20 8 Fail y = 1.08x – 0.87 −0.28 0.21 12 Fail     vs Tosoh G8 (x) y = 1.00x – 0.27 −0.26 0.22 9 Fail y = 1.06x – 0.70 −0.30 0.21 15 Fail InnovaStar (y)     vs Premier (x) y = 0.89x + 0.46 −0.36 0.14 10 Fail y = 0.89x + 0.43 −0.40 0.13 15 Fail     vs Tina-quant (x)c y = 0.93x + 0.25 −0.25 0.18 8 Fail y = 0.93x + 0.21 −0.30 0.15 9 Fail     vs Tosoh G8 (x) y = 0.91x + 0.40 −0.27 0.17 9 Fail y = 0.90x + 0.37 −0.32 0.16 8 Fail Cobas B101 (y)     vs Premier (x) y = 1.06x – 0.48 −0.05 0.24 6 Fail y = 1.06x – 0.40 0.01 0.24 4 Fail     vs Tina-quant (x)c y = 1.11x – 0.74 0.06 0.23 5 Fail y = 1.10x – 0.67 0.10 0.23 5 Fail     vs Tosoh G8 (x) y = 1.09x – 0.60 0.04 0.17 2 Pass y = 1.09x – 0.52 0.09 0.17 1 Pass a Thirty-seven of 40 results need to be within 6% (relative) of the NGSP SRMP to pass certification. b SEE, standard error of estimate. c Same measurement principle as investigated POC method. Open in new tab Table 2. EP-9 method comparison results (Deming regression lines) in DCCT units and pass/fail NGSP certification criteria.a Comparison . Lot number 1 . Bias . SEEb . Samples >6% of SRMP, n . NGSP criteria . Lot number 2 . Bias . SEE . Samples >6% of SRMP, n . NGSP criteria . B-analyst (y)     vs Premier (x) y = 1.08x – 0.41 0.19 0.15 1 Pass y = 1.08x – 0.41 0.16 0.16 2 Pass     vs Tina-quant (x)c y = 1.08x – 0.39 0.19 0.18 0 Pass y = 1.08x – 0.39 0.16 0.18 1 Pass     vs Tosoh G8 (x) y = 1.08x – 0.46 0.15 0.15 0 Pass y = 1.08x – 0.46 0.11 0.15 2 Pass DCA Vantage (y)     vs Premier (x) y = 0.99x – 0.10 −0.15 0.21 3 Pass y = 1.01x – 0.15 −0.05 0.20 0 Pass     vs Tina-quant (x)c y = 1.04x – 0.35 −0.04 0.19 1 Pass y = 1.06x – 0.39 0.06 0.20 0 Pass     vs Tosoh G8 (x) y = 1.02x – 0.19 −0.06 0.20 0 Pass y = 1.04x – 0.22 0.03 0.22 1 Pass Afinion (y)     vs Premier (x)c y = 0.97x + 0.13 −0.08 0.18 0 Pass y = 0.91x + 0.60 −0.06 0.15 0 Pass     vs Tina-quant (x) y = 1.02x – 0.08 0.02 0.20 0 Pass y = 0.95x + 0.39 0.05 0.16 2 Pass     vs Tosoh G8 (x) y = 0.99x + 0.08 0.00 0.22 2 Pass y = 0.93x + 0.52 0.03 0.17 3 Pass Quo-Test (y)     vs Premier (x)c y = 1.00x – 0.29 −0.30 0.19 7 Fail y = 0.99x – 0.24 −0.32 0.16 11 Fail     vs Tina-quant (x) y = 1.05x – 0.52 −0.20 0.20 6 Fail y = 1.04x – 0.47 −0.21 0.18 5 Fail     vs Tosoh G8 (x) y = 1.02x – 0.37 −0.22 0.20 5 Fail y = 1.01x – 0.32 −0.23 0.18 5 Fail Quo-Lab (y)     vs Premier (x)c y = 0.98x – 0.20 −0.35 0.20 13 Fail y = 1.03x – 0.63 −0.39 0.19 14 Fail     vs Tina-quant (x) y = 1.03x – 0.44 −0.24 0.20 8 Fail y = 1.08x – 0.87 −0.28 0.21 12 Fail     vs Tosoh G8 (x) y = 1.00x – 0.27 −0.26 0.22 9 Fail y = 1.06x – 0.70 −0.30 0.21 15 Fail InnovaStar (y)     vs Premier (x) y = 0.89x + 0.46 −0.36 0.14 10 Fail y = 0.89x + 0.43 −0.40 0.13 15 Fail     vs Tina-quant (x)c y = 0.93x + 0.25 −0.25 0.18 8 Fail y = 0.93x + 0.21 −0.30 0.15 9 Fail     vs Tosoh G8 (x) y = 0.91x + 0.40 −0.27 0.17 9 Fail y = 0.90x + 0.37 −0.32 0.16 8 Fail Cobas B101 (y)     vs Premier (x) y = 1.06x – 0.48 −0.05 0.24 6 Fail y = 1.06x – 0.40 0.01 0.24 4 Fail     vs Tina-quant (x)c y = 1.11x – 0.74 0.06 0.23 5 Fail y = 1.10x – 0.67 0.10 0.23 5 Fail     vs Tosoh G8 (x) y = 1.09x – 0.60 0.04 0.17 2 Pass y = 1.09x – 0.52 0.09 0.17 1 Pass Comparison . Lot number 1 . Bias . SEEb . Samples >6% of SRMP, n . NGSP criteria . Lot number 2 . Bias . SEE . Samples >6% of SRMP, n . NGSP criteria . B-analyst (y)     vs Premier (x) y = 1.08x – 0.41 0.19 0.15 1 Pass y = 1.08x – 0.41 0.16 0.16 2 Pass     vs Tina-quant (x)c y = 1.08x – 0.39 0.19 0.18 0 Pass y = 1.08x – 0.39 0.16 0.18 1 Pass     vs Tosoh G8 (x) y = 1.08x – 0.46 0.15 0.15 0 Pass y = 1.08x – 0.46 0.11 0.15 2 Pass DCA Vantage (y)     vs Premier (x) y = 0.99x – 0.10 −0.15 0.21 3 Pass y = 1.01x – 0.15 −0.05 0.20 0 Pass     vs Tina-quant (x)c y = 1.04x – 0.35 −0.04 0.19 1 Pass y = 1.06x – 0.39 0.06 0.20 0 Pass     vs Tosoh G8 (x) y = 1.02x – 0.19 −0.06 0.20 0 Pass y = 1.04x – 0.22 0.03 0.22 1 Pass Afinion (y)     vs Premier (x)c y = 0.97x + 0.13 −0.08 0.18 0 Pass y = 0.91x + 0.60 −0.06 0.15 0 Pass     vs Tina-quant (x) y = 1.02x – 0.08 0.02 0.20 0 Pass y = 0.95x + 0.39 0.05 0.16 2 Pass     vs Tosoh G8 (x) y = 0.99x + 0.08 0.00 0.22 2 Pass y = 0.93x + 0.52 0.03 0.17 3 Pass Quo-Test (y)     vs Premier (x)c y = 1.00x – 0.29 −0.30 0.19 7 Fail y = 0.99x – 0.24 −0.32 0.16 11 Fail     vs Tina-quant (x) y = 1.05x – 0.52 −0.20 0.20 6 Fail y = 1.04x – 0.47 −0.21 0.18 5 Fail     vs Tosoh G8 (x) y = 1.02x – 0.37 −0.22 0.20 5 Fail y = 1.01x – 0.32 −0.23 0.18 5 Fail Quo-Lab (y)     vs Premier (x)c y = 0.98x – 0.20 −0.35 0.20 13 Fail y = 1.03x – 0.63 −0.39 0.19 14 Fail     vs Tina-quant (x) y = 1.03x – 0.44 −0.24 0.20 8 Fail y = 1.08x – 0.87 −0.28 0.21 12 Fail     vs Tosoh G8 (x) y = 1.00x – 0.27 −0.26 0.22 9 Fail y = 1.06x – 0.70 −0.30 0.21 15 Fail InnovaStar (y)     vs Premier (x) y = 0.89x + 0.46 −0.36 0.14 10 Fail y = 0.89x + 0.43 −0.40 0.13 15 Fail     vs Tina-quant (x)c y = 0.93x + 0.25 −0.25 0.18 8 Fail y = 0.93x + 0.21 −0.30 0.15 9 Fail     vs Tosoh G8 (x) y = 0.91x + 0.40 −0.27 0.17 9 Fail y = 0.90x + 0.37 −0.32 0.16 8 Fail Cobas B101 (y)     vs Premier (x) y = 1.06x – 0.48 −0.05 0.24 6 Fail y = 1.06x – 0.40 0.01 0.24 4 Fail     vs Tina-quant (x)c y = 1.11x – 0.74 0.06 0.23 5 Fail y = 1.10x – 0.67 0.10 0.23 5 Fail     vs Tosoh G8 (x) y = 1.09x – 0.60 0.04 0.17 2 Pass y = 1.09x – 0.52 0.09 0.17 1 Pass a Thirty-seven of 40 results need to be within 6% (relative) of the NGSP SRMP to pass certification. b SEE, standard error of estimate. c Same measurement principle as investigated POC method. Open in new tab Hb A1c results (SI units) for 2 different reagent lot numbers from (A) Afinion, (B) Cobas B101, (C) DCA Vantage, and (D) B-analyst point-of-care instruments compared to the mean Hb A1c results from 3 secondary reference measurement procedures. ——, Line of identity; -----, ± 6%. Fig. 1. Open in new tabDownload slide Open in new tabDownload slide Fig. 1. Open in new tabDownload slide Open in new tabDownload slide Hb A1c results (SI units) for 2 different reagent lot numbers from (A) InnovaStar, (B) Quo-Lab, and (C) Quo-Test point-of-care instruments compared to the mean Hb A1c results from 3 secondary reference measurement procedures. ——, Line of identity; -----, ± 6%. Fig. 2. Open in new tabDownload slide Fig. 2. Open in new tabDownload slide Statistically, we observed no difference between the 2 lot numbers for all instruments. The P values were: Afinion, 0.88; Cobas B101, 0.83; DCA Vantage, 0.69; B-analyst, 0.88; InnovaStar, 0.83; Quo-Lab, 0.88; and Quo-Test, 0.97. To determine if the evaluated POC devices are capable of diagnosing diabetes, MDP analysis was performed at an Hb A1c of 48 mmol/mol (6.5% DCCT) compared with the mean of the 3 SRMPs with 2 reagent lot numbers (Table 3). No statistical difference was measured between the mean of the 3 SRMPs and B-analyst, Cobas B101, and Afinion for either reagent lot. For DCA Vantage, no statistical difference was observed only for the second reagent lot. MDP analysis of 48 mmol/mol (95% CI) compared with the mean of the 3 SRMPs. Table 3. MDP analysis of 48 mmol/mol (95% CI) compared with the mean of the 3 SRMPs. . Lot number 1 . Lot number 2 . B-Analyst 48.1 (47.8–48.4) 47.7 (47.4–48.0) DCA Vantage 46.9 (46.4–47.4)a 47.9 (47.4–48.3) Afinion 47.5 (47.0–48.0) 48.2 (47.8–48.6) Quo-Test 45.1 (44.7–45.6)a 45.1 (44.7–45.5)a Quo-Lab 44.8 (44.3–45.3)a 44.1 (43.6–44.5)a InnovaStar 45.4 (45.0–45.7)a 44.9 (44.6–45.3)a Cobas B101 47.5 (47.0–48.0) 48.2 (47.7–48.7) . Lot number 1 . Lot number 2 . B-Analyst 48.1 (47.8–48.4) 47.7 (47.4–48.0) DCA Vantage 46.9 (46.4–47.4)a 47.9 (47.4–48.3) Afinion 47.5 (47.0–48.0) 48.2 (47.8–48.6) Quo-Test 45.1 (44.7–45.6)a 45.1 (44.7–45.5)a Quo-Lab 44.8 (44.3–45.3)a 44.1 (43.6–44.5)a InnovaStar 45.4 (45.0–45.7)a 44.9 (44.6–45.3)a Cobas B101 47.5 (47.0–48.0) 48.2 (47.7–48.7) a Significantly different from mean SRMP. Open in new tab Table 3. MDP analysis of 48 mmol/mol (95% CI) compared with the mean of the 3 SRMPs. . Lot number 1 . Lot number 2 . B-Analyst 48.1 (47.8–48.4) 47.7 (47.4–48.0) DCA Vantage 46.9 (46.4–47.4)a 47.9 (47.4–48.3) Afinion 47.5 (47.0–48.0) 48.2 (47.8–48.6) Quo-Test 45.1 (44.7–45.6)a 45.1 (44.7–45.5)a Quo-Lab 44.8 (44.3–45.3)a 44.1 (43.6–44.5)a InnovaStar 45.4 (45.0–45.7)a 44.9 (44.6–45.3)a Cobas B101 47.5 (47.0–48.0) 48.2 (47.7–48.7) . Lot number 1 . Lot number 2 . B-Analyst 48.1 (47.8–48.4) 47.7 (47.4–48.0) DCA Vantage 46.9 (46.4–47.4)a 47.9 (47.4–48.3) Afinion 47.5 (47.0–48.0) 48.2 (47.8–48.6) Quo-Test 45.1 (44.7–45.6)a 45.1 (44.7–45.5)a Quo-Lab 44.8 (44.3–45.3)a 44.1 (43.6–44.5)a InnovaStar 45.4 (45.0–45.7)a 44.9 (44.6–45.3)a Cobas B101 47.5 (47.0–48.0) 48.2 (47.7–48.7) a Significantly different from mean SRMP. Open in new tab Five frozen samples with different Hb A1c values and different Hb variants [Hb AS, Hb AC, Hb AD, Hb AE, increased A2 (β-thalassemia), and Hb F] were analyzed to investigate for the presence of potential interference. If the results of the investigated Hb variant fall within the deviation of the nonvariant samples distributed around the regression line, the investigated Hb variant can be considered as “not interfering.” Visual analysis of the graphs (see Supplemental Data for Table 4, which accompanies the online version of this article at http://www.clinchem.org/content/vol60/issue8) shows that all instruments had a negative bias at Hb F ≥6.9%. The negative bias with Hb F is directly proportional in magnitude to the percentage Hb F present in the sample. The Hb F used in this study ranged from 3.2% to 18.3%. Table 4 shows the mean relative difference of the Hb variants (n = 5 per variant) compared to the assigned value. Most of the instruments do not have interference from Hb AS, Hb AC, Hb AD, Hb AE, or increased A2, except for Cobas B101, which had interference from Hb AE (mean relative difference 17.1% increase). The mean relative difference of Hb AC for the DCA Vantage was 6.9% increase. Mean relative difference (%) of the common Hb variants (n = 5 per variant) compared to the assigned value. Table 4. Mean relative difference (%) of the common Hb variants (n = 5 per variant) compared to the assigned value. . Hb AS . Hb AC . Hb AD . Hb AE . Elevated A2 . Hb Fa . DCA Vantage 3.7 6.9 1.6 3.6 −1.4 −12.3 Cobas B101 −2.1 −1.6 3.4 17.1 2.1 −11.1 Quo-Test −5.3 −3.8 −3.6 2.5 0.4 −12.6 Quo-Lab −6.0 −1.7 −0.9 5.3 −3.6 −11.2 InnovaStar 5.5 4.4 −3.2 5.9 3.0 −18.8 . Hb AS . Hb AC . Hb AD . Hb AE . Elevated A2 . Hb Fa . DCA Vantage 3.7 6.9 1.6 3.6 −1.4 −12.3 Cobas B101 −2.1 −1.6 3.4 17.1 2.1 −11.1 Quo-Test −5.3 −3.8 −3.6 2.5 0.4 −12.6 Quo-Lab −6.0 −1.7 −0.9 5.3 −3.6 −11.2 InnovaStar 5.5 4.4 −3.2 5.9 3.0 −18.8 a HbF percentages of 3.2%, 4.2%, 6.9%, 12.0%, and 18.3%. Open in new tab Table 4. Mean relative difference (%) of the common Hb variants (n = 5 per variant) compared to the assigned value. . Hb AS . Hb AC . Hb AD . Hb AE . Elevated A2 . Hb Fa . DCA Vantage 3.7 6.9 1.6 3.6 −1.4 −12.3 Cobas B101 −2.1 −1.6 3.4 17.1 2.1 −11.1 Quo-Test −5.3 −3.8 −3.6 2.5 0.4 −12.6 Quo-Lab −6.0 −1.7 −0.9 5.3 −3.6 −11.2 InnovaStar 5.5 4.4 −3.2 5.9 3.0 −18.8 . Hb AS . Hb AC . Hb AD . Hb AE . Elevated A2 . Hb Fa . DCA Vantage 3.7 6.9 1.6 3.6 −1.4 −12.3 Cobas B101 −2.1 −1.6 3.4 17.1 2.1 −11.1 Quo-Test −5.3 −3.8 −3.6 2.5 0.4 −12.6 Quo-Lab −6.0 −1.7 −0.9 5.3 −3.6 −11.2 InnovaStar 5.5 4.4 −3.2 5.9 3.0 −18.8 a HbF percentages of 3.2%, 4.2%, 6.9%, 12.0%, and 18.3%. Open in new tab Discussion The results of this study showed that the analytical performance of POC instruments has improved considerably compared with the results of the first evaluation study in 2009 (2). In the familiarization study, all instruments showed excellent/good reproducibility in the clinically relevant range (SI units 20–86 mmol/mol, DCCT units 4.0%–10.0%) and minimal bias with frozen material. Unfortunately, the study revealed a major problem regarding calibration/standardization of 3 instruments. The results from the familiarization study were very promising for all manufacturers, and all manufacturers gave approval to continue the evaluation study. The bias found with fresh samples in the EP-9 for Quo-Test, Quo-Lab, and InnovaStar was not expected (Fig. 2, A–C). Additional experiments showed that the same samples, fresh and frozen, gave different results (see online Supplemental Data for Fig. 2, A–C). In daily life, only fresh whole blood (capillary or venous) is used when measuring Hb A1c with a POC instrument. This bias could affect the patients whose Hb A1c is analyzed by these methods by yielding a falsely low result that could possibly affect therapeutic options, which must be based on the true Hb A1c value. This, in turn, could lead to the development of complications that would have been avoided if the instrument were performing acceptably (16, 17). This shows the importance of joining external quality assessment for POC users. The fact that most POC users do not participate in external assessment schemes does not allow manufacturers the opportunity to investigate and potentially fix such problems. In the future, fresh whole blood will be required for calibration, NGSP certification, and IFCC monitoring program for Quo-Test, Quo-Lab, and InnovaStar. The mean relative difference of 6.9% for Hb AC for the DCA vantage exceeds the criterion of >6%. However, this criterion is very strict, especially if the Hb A1c values of the Hb AC samples are low. Looking at the graph, only 2 Hb AC samples exceed the 6% criterion and the other samples are within the 6% criterion, which makes it implausible that there is a real interference of Hb AC (see online Supplemental Data for Table 4). More samples with Hb AC and different Hb A1c values need to be analyzed to confirm our findings. The precision of the DCA Vantage in the first study in 2009 was 1.8% in DCCT units at an Hb A1c of 5.1% and 3.7% at an Hb A1c value of 11.2%. The CV in the high area was considered high but not clinically relevant. In the current study, we decided to take a lower Hb A1c for the high sample (73 mmol/mol, 8.8% DCCT). The CV again was too high (4.2% in SI units and 3.2% in DCCT units) compared with the general accepted performance criteria (intralaboratory CV <2% in DCCT units, <3% in SI units, and a NGSP manufacturer certification) (12, 18). The question can be raised, what does this mean for daily practice? To calculate the RCV, the CVa is needed, and also the within-person biological variation for Hb A1c (CVwp). Many articles have been published regarding CVwp for Hb A1c with different results (Rohlfing et al., CVwp ±1% (19); Braga et al., CVwp 2.5% (20); and Carlsen et al., CVwp 1.2% (21), all in DCCT units). Use of a CVwp of 1.2% in DCCT units and an analytical CV of 3.2% in DCCT units of the DCA Vantage at an Hb A1c value of 73 mmol/mol (8.8% DCCT) means that the next Hb A1c value of the patient should be <64 mmol/mol (8.0% DCCT) or >82 mmol/mol (9.6% DCCT) before one can say that there is a significant change in health status of the patient. A difference of 5 mmol/mol (0.5% DCCT) from a previous Hb A1c value is considered by most healthcare professionals as an indication to adjust therapeutic options. As the DCA Vantage is mostly used in the Netherlands at the pediatric unit for young children and teenagers with (typically) increased Hb A1c values, this could potentially lead to overtreatment of the patient. However, this situation is applicable for all patients with increased Hb A1c values. Precision studies performed on the Afinion with manufacturer-supplied controls showed lower CVs in a previous study than when fresh patient samples were used and were in accord with the CVs we calculated from the duplicates in the EP-9 protocol (4). However, the CVs were still within the acceptance limits. A limitation to this study is that we have not been able to collect fresh patient samples with different Hb variant samples with varying Hb A1c values over the clinically relevant range to investigate whether B-analyst and Afinion had interference from these Hb variants. Also, this part of the study needs to be repeated once Quo-Test, Quo-Lab, and InnovaStar have been recalibrated with fresh whole blood. In the Hb variant interference study, we used the bias obtained in the familiarization study with frozen samples to correct for the bias found in nonvariant samples. In general, caution is advised for all Hb A1c POC instruments and laboratory-based methods in the presence of Hb variants and it is not known whether a particular Hb variant interferes with the method used. The bias of the B-analyst compared with the mean of the 3 reference measurement procedures was very low (Fig. 1D) but was not in accord with the results in DCCT units (higher results in the high area than in SI units). Further investigation yielded a different master equation between IFCC and NGSP in the software of the B-analyst than that previously published (22). One could argue that using a different master equation than the published master equation would contribute to the standardization of Hb A1c worldwide. Nevertheless, the B-analyst passed the NGSP criteria when compared with 3 SRMPs with 2 reagent lot numbers. The Cobas B101 is new on the market and in general showed good performance in this study with respect to accuracy and reproducibility. However, there is some room for improvement regarding instrument calibration (Fig. 1B). The Cobas B101 passed the NGSP criteria compared with the Tosoh G8 for both reagent lot numbers but failed when compared with the Tina-quant Gen.2 on Integra 800 and the Premier Hb9210. The new NGSP criterion is quite stringent, especially for the nondiabetic patient range. Thirty-seven of 40 results need to be within 6% (relative) of the NGSP SRMP to pass certification. Six percent of a value of 5.0% DCCT is 0.30%, which is not much considering that some patient samples have an individual matrix effect with a certain method that is unrelated to the bias observed between the 2 methods. This was not the case with the Cobas B101, because most of the samples in the nondiabetic patient range were too low, which would suggest a calibration problem and not an individual patient matrix effect. The Cobas B101 showed interference from Hb AE. This is a problem if this method is used in a part of the world where the prevalence of Hb AE is high and patients are not routinely screened for this variant. The patients will get an Hb A1c value that is falsely high with possibly a more stringent therapy than is necessary. The findings for Hb F interference found in this study were consistent with those from a previous study (23). The reason for the proportional interferences with the affinity methods is probably a result of the lower glycation rate for Hb F compared with Hb A and the lack of recognition of glycated Hb F by the Hb A1c antibody with the immunoassay methods. It is reasonable to generalize this to all affinity- and immunoassay-based methods. It would be in the interest of all patients with diabetes to screen all patients at the time of diagnosis for hemoglobinopathy and thalassemia, not only to see if this variant might interfere with the Hb A1c method but also to know if the Hb variant is associated with a shorter turnover of red blood cells, which leads to a falsely low Hb A1c value (24, 25). The American Diabetes Association concluded in 2011 that POC Hb A1c assays were not sufficiently accurate at that time to use for diagnostic purposes (26). In 2014, the American Diabetes Association concluded that although POC Hb A1c assays may be NGSP certified, proficiency testing is not mandated for performing the test, so use of these assays for diagnostic purposes may be problematic (27). In reviewing CAP survey results from recent years, it seems that not all POC instruments should be excluded for use in the diagnosis of diabetes because Afinion, DCA 2000, and DCA Vantage showed excellent results, even better than some laboratory-based methods (28). This was also the conclusion of an article recently published in clinical chemistry on the basis of the results of many years of an external quality scheme in Scandinavia (29). There are no barriers for the manufacturers of POC instruments to attempt to gain FDA approval to use their instrument for the diagnosis of diabetes. However, in addition to FDA approval, we think that proficiency testing should be mandated for users of POC assays to ensure quality. The EP-9 protocol is probably not the best protocol to decide whether a POC instrument can be used for the diagnosis of diabetes, as the number of samples around the cutoff value of 48 mmol/mol (6.5% DCCT) is not sufficient. However, in an attempt to strengthen our argument, we used 3 SRMPs and calibrated with IFCC secondary reference material; therefore, the values are as close as possible to the true value and are likely more reliable than comparing with just 1 SRMP. MDP analysis showed that Afinion, DCA Vantage, Cobas B101, and B-analyst may be suitable for the diagnosis of diabetes. Statistically, the first reagent lot from the DCA Vantage differed from the mean of the SRMPs (Table 3), but clinically speaking, the difference was minimal. In conclusion, Afinion, B-analyst, and Cobas B101 met the generally accepted performance criteria for Hb A1c. DCA Vantage met the criteria in the diagnostic range but showed a high CV >64 mmol/mol (8.0% DCCT), which is the lowest Hb A1c value of the 95% CI of 73 mmol/mol (8.8% DCCT). Users of the DCA Vantage should be informed not to change therapy on the basis of a small difference between two consecutive Hb A1c values when the Hb A1c value is >64 mmol/mol (8.0% DCCT). Quo-Test, Quo-Lab, and InnovaStar are potentially good methods but need to be calibrated and certified with fresh patient samples instead of frozen material. Cobas B101 should not be used in regions where the prevalence of Hb AE is high unless the patient has been screened for this hemoglobin variant and found to be negative. 3 Nonstandard abbreviations: POC point-of-care Hb A1c glycated hemoglobin NGSP National Glycohemoglobin Standardization Program SRMP secondary reference measurement procedure MDP medical decision point DCCT Diabetes Control and Complications Trial RCV reference change value. " (see editorial on page 1031) " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: None declared. " Consultant or Advisory Role: None declared. " Stock Ownership: None declared. " Honoraria: None declared. " Research Funding: Siemens Medical Solutions Diagnostics USA, Menarini Diagnostics Italy, Alere Technologies Norway, DiaSys Germany, Quotient U.K., Roche Diagnostics Switzerland, funding and reagents provided to Isala. " Expert Testimony: None declared. " Patents: None declared. " Role of Sponsor: The manufacturers provided all instruments and reagents at no cost and financially supported this study. The evaluating protocol was designed by the authors. The manufacturers had the right to discontinue the evaluation after the familiarization study was finished. The manufacturers played no role in the review and interpretation of data or preparation or approval of manuscript and had no rights of refusal for publication of the data. Acknowledgments We thank Siemens Medical Solutions Diagnostics USA, Menarini Diagnostics Italy, Alere Technologies Norway, DiaSys Diagnostics Germany, Quotient Diagnostics United Kingdom, and Roche Diagnostics Switzerland for their financial support and for donating reagents and instruments used in this study. References 1. Fokkert M , Jonker N, Van Dijk J, Dikkeschei B, Slingerland R. Point-of-care testing in the Netherlands: current and future challenges. 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Woodworth,, Alison;Korpi-Steiner,, Nichole;Miller, James, J;Rao, Lokinendi, V;Yundt-Pacheco,, John;Kuchipudi,, Lakshmi;Parvin, Curtis, A;Rhea, Jeanne, M;Molinaro,, Ross

doi: 10.1373/clinchem.2013.220772pmid: 24815907