Disease-associated variations in DNA methylation profiles hold significant potential for diagnostic and research applications. Unfortunately, scant and degraded samples often limit the analyses which can be performed. To overcome these issues, we developed Mass Spectrometry Minimal Methylation Classifier (MS-MIMIC) to identify then reliably analyse disease-specific DNA methylation profiles. The technique has now been validated in a cohort of pediatric medulloblastoma cases.
by Dr Ben Chaffey, Dr Debbie Hicks, Dr Edward Schwalbe and Prof. Steve Clifford
Background
Altered DNA methylation patterns have emerged as valuable biomarkers of disease pathogenesis, showing clear potential in diagnostics, sub-classification and prediction of therapeutic response/ disease course [1–7]. However, clinical assessment of these altered patterns can be problematic, with sample materials often being degraded/scant, such as formalin-fixed paraffin-embedded (FFPE) tissue and core biopsies, and certain platforms, such as DNA methylation microarrays, having a requirement for batched assessments of relatively large numbers of samples. This compromises generation of data from real-world samples within clinically meaningful timeframes, hampering translation of research findings into routine practice.
In this project, we focused on developing a new DNA methylation state assay to provide molecular subgrouping of cases of medulloblastoma (Fig. 1), the most frequently occurring malignant brain tumour in children. This disease has an approximate incidence of 1.5 cases per million, rising to 6 per million in children aged 1–9 years. It also occurs in adults, although in this group it is around ten times less common [8].
Although rates of survival to 5 years and beyond following diagnosis are around 65–70%, medulloblastoma still causes around 10% of all childhood cancer deaths. Initial treatment generally consists of complete or near complete surgical resection followed by adjuvant treatment with both post-operative radiotherapy and chemotherapy. Despite the fact that survival rates have improved over past decades, the delivery of individualized therapies based on patient-specific disease-risk profiles remains a major goal; intensified treatment for poor-risk disease, while reducing therapy for favourable-risk cases, with the overall aim of maximizing survival while minimizing late effects [9].
Medulloblastomas can be placed into one of four distinct subgroups, which are defined by specific methylomic, transcriptomic and genomic features. These are WNT, SHH, Group 3 and Group 4 [10]. Each group displays characteristic clinical and pathological features, drug targets and outcomes, and contributes significantly to the 2016 World Health Organization (WHO) classification of brain tumours [11]. Molecular subgrouping is, therefore, an important step in determining the most appropriate course of treatment and follow-up for individual patients [12].
Mass spectrometry minimal methylation classifier (MS-MIMIC) assay
The assay we have developed and validated, MS-MIMIC, is a novel polymerase chain reaction (PCR)-based assay for the multiplexed assessment of multiple signature CpG loci. We first identified a DNA methylation signature of 17 CpG loci using genome-scale Illumina 450k DNA methylation microarray data from 220 medulloblastoma cases. The 50 most discriminatory CpG loci for each molecular subgroup (200 loci in total) were considered as candidates for inclusion in the signature set. These were triaged using a 10-fold cross validated classification fusion algorithm, followed by a reiterative primer design process where amenability to primer design and multiplex bisulfite PCR was assessed in silico before finally undergoing in vitro PCR validation.
Candidate signature CpG loci were then analysed by a specific custom iPLEX assay [13] (Agena Bioscience). In this method (displayed schematically in Fig. 2), methylation-dependent SNPs representative of CpG methylation status are induced by initial treatment of DNA with sodium bisulfite [14] followed by multiplexed target-region amplification PCR, then single base extension and termination of target-specific probe oligonucleotides. The products of this reaction are analysed using MALDI-ToF (matrix-assisted laser desorption and ionisation – time of flight) mass spectrometry (MassARRAY System, Agena Bioscience). Each potential CpG locus variant yields a product with a unique and characteristic mass, enabling their rapid and unambiguous identification. MALDI-ToF analysis of single base variants is widely used to provide clinical DNA diagnostics in related genotyping applications [15], and is the key technical innovation which enables the robust assessment of medulloblastoma molecular subgroup, especially for samples which are refractory to analysis using conventional DNA methylation-array based methods.
Using these techniques, we generated an optimal, multiply-redundant 17-CpG locus signature and a robust assay for its detection.
A Support Vector Machine (SVM) classifier for the signature was then developed, using the existing 450k DNA methylation array data as a training set. SVM is a supervised machine learning technique commonly used in multiple areas of data analysis, including analysis of microarray data [16], making it well-suited to this application. Crucially, it returns a probability of group membership, enabling the assessment of confidence of subgroup assignment.
Next, we assessed MS-MIMIC performance against Illumina 450k methylation microarrays using an independent validation cohort of 106 medulloblastoma DNA samples which contained examples of all four medulloblastoma subgroups. These samples were also derived from tissue which reflected different clinical fixation methods commonly in use; fresh-frozen biopsies (n=40), FFPE tumour section (n=39), or FFPE-derived nuclear preparations [17] (n=27) produced by cytospin, a pre-analytical method that uses centrifugation to create a monolayer of cells for analysis on a slide from a low-concentration cellular suspension sample [18]. In this validation cohort, MS-MIMIC faithfully recapitulated DNA methylation array molecular subgroup assignments.
Quality control measures for CpG locus-specific assay failure were established; up to six failed CpG loci per sample were tolerated within the multiply-redundant signature/classifier, without impacting performance. Forty-three out of 106 validation cohort samples were affected by at least one locus failure, reflecting the damaged nature of DNA generally obtained from some of these samples. Five out of 106 samples had more than seven failed CpGs and were deemed not classifiable (NC). Molecular subgroup classifications were then compared, with MS-MIMIC classifications showed complete concordance with the reference subgroup, as determined by DNA methylation array [10]. Furthermore, CpG-level methylation estimates (β-values) were equivalent between methods (R2 = 0.79). As anticipated, fresh-frozen derivatives performed best (n=39/40; 98% successfully subgrouped), with 91% success (n=56/61) using FFPE-derived DNA from tumour sections and cytospin preparations (Fig. 3a–c).
Application of MS-MIMIC to the HIT-SIOP-PNET4 clinical trials cohort
Following successful assay development and validation, we next wished to test MS-MIMIC methylation signature detection in limited, poor quality, archival, clinical biopsies. Analysis of remnant material from the HIT-SIOP-PNET4 cohort [17] offered the first opportunity to determine the potential utility of molecular subgroup status to predict disease outcome in a clinical trial of risk-factor negative, ‘standard risk’ (SR) medulloblastoma. Only FFPE sections (n=42/153 available tumour samples) and cytospin nuclear preparations (approximately 30 000 nuclei isolated and centrifuged onto microscope slides; n=111/153) remained from this study archive and all DNA preparations fell below quality and quantity thresholds (>200 ng double-stranded DNA (dsDNA)) required for methylation profiling using conventional research methods (Illumina 450k and MethylationEpic arrays [16]). Using MS-MIMIC, 70% (107/153) of samples were successfully subgrouped, and subgroup assignments and β-value estimations were consistent across duplicate determinations. Assay performance was equivalent across the input DNA range (<2 ng (limit of detection) to 100 ng dsDNA (41.4 ng median DNA input).
Reasons for assay failure included unsuccessful bisulfite conversion/PCR (6%; 9/153), and inability to classify due to assay QC failure (24%; 37/153). These findings from HIT-SIOP-PNET4 reveal important subgroup-dependent molecular pathology in SR medulloblastoma. Group 4 was most common (n=62; 58%), with approximately equivalent numbers of WNT (18/170; 16%), SHH (17/107; 16%) and Group 3 (10/107; 9%) tumours observed. The majority (11/13) of events (defined as disease recurrence or progression following treatment) affected Group 4 patients [82% 5-year progression-free survival (PFS)], with >95% PFS in non-Group 4 patients. Subgroup assignment will thus be essential to inform future clinical and research studies in SR medulloblastoma.
Discussion
Detection of disease-specific variations in DNA methylation patterns has great potential for both supporting biomedical research and improving the quality of care that is delivered to patients. MS MIMIC has so far only been applied to medulloblastoma but this approach has clear potential for use in other cancers [7] and in other diverse settings, for example smoking [1], obesity [2], human fetal alcohol spectrum disorder [3] and aging [4].
Key resources which must be available for development of an MS-MIMIC assay for a given condition are a suitable collection of data concerning disease-state specific methylation patterns obtained using an array system such as those mentioned above, samples with which to perform assay validation, bioinformatics knowledge and support to create, optimize and operate the disease-specific SVM classifier system, plus access to a MassARRAY System for analysis. MS-MIMIC is discussed in greater detail in Schwalbe et al., 2017 [19].
References
1. Besingi W, Johansson A. Smoke-related DNA methylation changes in the etiology of human disease. Hum Mol Genet 2014; 23: 2290–2297.
2. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, Tsai PC, Ried JS, Zhang W et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 2017; 541: 81–86.
3. Portales-Casamar E, Lussier AA, Jones MJ, MacIsaac JL, Edgar RD, Mah SM, Barhdadi A, Provost S, Lemieux-Perreault LP et al. DNA methylation signature of human fetal alcohol spectrum disorder. Epigenetics Chromatin 2016; 9: 1–20.
4. Ong ML, Holbrook JD. Novel region discovery method for Infinium 450 K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways. Aging Cell 2014; 13: 142–155.
5. Mehta D, Klengel T, Conneely KN, Smith AK, Altmann A, Pace TW, Rex-Haffner M, Loeschner A, Gonik M et al. Childhood maltreatment is associated with distinct genomic and epigenetic profiles in posttraumatic stress disorder. Proc Natl Acad Sci USA 2013; 110: 8302–8307.
6. Bacalini MG, Gentilini D, Boattini A, Giampieri E, Pirazzini C, Giuliani C, Fontanesi E, Scurti M, Remondini D et al. Identification of a DNA methylation signature in blood cells from persons with Down Syndrome. Aging 2015; 7: 82–96.
7. Sturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT, Konermann C, Pfaff E, Tönjes M, Sill M et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 2012; 22: 425–437.
8. Smoll NR, Drummond KJ. The incidence of medulloblastomas and primitive neurectodermal tumours in adults and children. J Clin Neurosci 2012; 19: 1541–1544.
9. Pizer BL, Clifford SC. The potential impact of tumour biology on improved clinical practice for medulloblastoma: progress towards biologically driven clinical trials. British Journal Of Neurosurgery 2009; 23: 364–375.
10. Taylor MD, Northcott PA, Korshunov A, Remke M, Cho YJ, Clifford SC, Eberhart CG, Parsons DW, Rutkowski S et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol 2012; 123, 465–472.
11. Louis DN, Cavenee WK, Ohgaki H, Wiestler OD. WHO classification of tumours of the central nervous system, 4th edn. pp.184–200. IARC Press, 2016.
12. Schwalbe EC, Williamson D, Lindsey JC, Hamilton D, Ryan SL, Megahed H, Garami M, Hauser P, Dembowska-Baginska B et al. DNA methylation profiling of medulloblastoma allows robust subclassification and improved outcome prediction using formalin-fixed biopsies. Acta Neuropathol 2013; 125: 359–371.
13. Gabriel S, Ziaugra L, Tabbaa D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Current Protocols in Human Genetics, Chapter 2: Unit 2.12. Wiley & Sons 2009.
14. Wang RY, Gehrke CW, Ehrlich M. Comparison of bisulfite modification of 5-methyldeoxycytidine and deoxycytidine residues. Nucleic Acids Res 1980; 8: 4777–4790.
15. Griffin TJ, Smith LM. Single-nucleotide polymorphism analysis by MALDI-ToF mass spectrometry. Trends Biotechnol 2000; 18: 77–84.
16. Hovestadt V, Remke M, Kool M, Pietsch T, Northcott PA, Fischer R, Cavalli FM, Ramaswamy V, Zapatka M et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol 2013; 125: 913–916.
17. Clifford SC, Lannering B, Schwalbe EC, Hicks D, O’Toole K, Nicholson SL, Goschzik T, Zur Mühlen A, Figarella-Branger D et al. Biomarker-driven stratification of disease-risk in non-metastatic medulloblastoma: Results from the multicentre HIT-SIOP-PNET4 clinical trial. Oncotarget 2015; 6: 38827–38839.
18. Koh CM. Preparation of cells for microscopy using cytospin. Meth Enzymol 2013; 533: 235–240.
19. Schwalbe EC, Hicks D, Rafiee G, Bashton M, Gohlke H, Enshaei A, Potluri S, Matthiesen J, Mather M et al. Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures. Sci Rep 2017; 7: 13421.
The authors
Ben Chaffey1 PhD, Debbie Hicks2 PhD, Edward Schwalbe3 PhD, Steve Clifford2* PhD
1NewGene Ltd, International Centre for Life, Newcastle-upon-Tyne, UK
2Wolfson Childhood Cancer Research Centre, Northern Institute for Cancer Research, Newcastle University, Newcastle-upon-Tyne, UK
3Northumbria University, Newcastle-upon-Tyne, UK
*Corresponding author
E-mail: steve.clifford@ncl.ac.uk
Calprotectin use in primary care. Are testing criteria being followed?
, /in Featured Articles /by 3wmediaIn 2013, the National Institute for Health and Care Excellence (NICE) published guidelines recommending the use of fecal calprotectin as a screening test for differentiating between inflammatory bowel disease and irritable bowel syndrome. Since then, despite a relatively slow uptake with only a few laboratories offering the test, fecal calprotectin has garnered much interest with more hospitals incorporating it into their pathology services. Therefore, the question of how well primary care is using the service arises. This, along with a brief historical context of inflammatory bowel disease and its diagnosis together with implications of calprotectin testing are discussed here.
by Dr Benjamin Palmer, Dr Wisam Jafar and Steven McCann
Historical context
Inflammatory bowel disease (IBD) is a term used to describe two relapsing chronic gastrointestinal disorders: ulcerative colitis (UC) and Crohn’s disease (CD). Both cause considerable morbidity amongst young patients in whom they occur more frequently [1]. With an estimated prevalence of 2.5–3.0 million people affected in Europe the incidence of IBD is increasing [1]. Although the etiology of IBD is unknown several theories have been put forward the most important and frequently cited elements being: gut microbiota; genetic pre-disposition; environment and immune dysregulation [2]. The two main types of IBD seen in the clinical setting are UC and CD: definitive diagnosis and confirmation is provided by endoscopy and histology. However, in 5% of cases a definitive diagnosis cannot be established: in such cases patients are diagnosed with inflammatory bowel disease unclassified (IBDU) [3, 4].
The gastrointestinal tract is colonized by a large number of diverse bacteria living in symbiosis with their host (microbiota) [5]. Disruption to the diversity and quality of the gut microbiota (dysbiosis) is now implicated in the pathogenesis of both UC and CD [2, 5, 6]. Other important participants are cytokines: as their release causes intestinal inflammation they have been implicated in some of the clinical symptoms such as diarrhea [7–9].
Historically, the two types of IBD were differentiated by the different cytokines and mature CD4+ T helper (Th) cells found elevated in tissue biopsies. UC was characterized by elevated levels of Th2 cells and interleukin (IL)-4, -5, -10 and -13 cytokines, whereas CD was defined by high levels of Th1 cells and the interferon gamma (IFNγ) cytokine [3]. However, new emerging information suggests that it is not as cut and dried as this: emphasis has now shifted towards such pathways as IL-23/IL-17 activation of more pathogenic Th17 cells playing a more significant role in the pathophysiology of not just IBD but other inflammatory diseases [10].
Complicating the clinical scenario is the high prevalence (around 10−20% of the UK population) of an unrelated functional bowel disorder called irritable bowel syndrome (IBS): the true prevalence is thought to be higher as it is suspected that many people may not seek medical advice [11]. Although it does not cause serious morbidities, IBS manifests with symptoms similar to those of IBD, making diagnosis difficult. Unlike IBD, it is not associated with inflammation and, hence, this provides a means of making a differential diagnosis.
Until recently, the diagnosis of IBD was made by clinical evaluation and a combination of biochemical and mainly endoscopic investigations that often involved repeat consultations and testing [3]. This, taken together with an ageing population, an increased public awareness of bowel cancer and the introduction of national screening campaigns has led to increased service demands in endoscopy. The fact that fecal markers, such as calprotectin, are secreted by inflamed intestinal mucosa has led to the development of laboratory assays that can detect gastrointestinal inflammation. It was because of these challenges and recent developments that led the National Institute for Health and Care Excellence (NICE) to publish guidelines on the use of fecal calprotectin in differentiating IBD from IBS in adults provided cancer is not suspected and that local reporting guidelines along with appropriate quality assurance procedures are in place [12]. A cut-off of 50 μg/g is recommended in which all patients with a calprotectin level ≥50 μg/g may be suggestive of IBD and should be referred to Gastroenterology, whereas a calprotectin level <50 μg/g is unlikely to be caused by IBD and should, therefore, be managed with a presumptive diagnosis of IBS [12]. One key study used by NICE in recommending this threshold was carried out by Tibble et al. in which 602 outpatients with lower gastrointestinal tract symptom were included [13]. In this study the authors clearly demonstrated that the IBD group of patients differed significantly (P=0.001–<0.0001) from the IBS group of patients and that a cut-off level of 10 mg/L (equivalent to 50 μg/g) provided optimal diagnostic performance [13]. However, the cut-off level of any test is dependent on the method used and, therefore, each laboratory should establish their method-specific cut-off level [12].
IBD pathway and audit outcome
Following the recommendations of NICE, fecal calprotectin testing was incorporated into the pathology services at Stepping Hill hospital on 1 November 2014. The test is available for patients from primary care between 20 and 40 years of age who are presenting with abdominal pain or discomfort, bloating or altered bowel habits for 6 months or longer. Patients with red flag symptoms (anemia, abdominal mass, gastrointestinal infection, rectal bleeding, unexplained weight loss or a family history of ovarian or bowel cancer) should be referred directly to Gastroenterology (Fig. 1).
If the calprotectin test is negative the patient should be managed by primary care with a presumptive diagnosis of IBS; if the test is positive the patient should be referred to Gastroenterology. Then in 2016 a 1-year retrospective audit was carried out with the aim of determining how well primary care was adhering to the clinical pathway [14]. In order to achieve this, a questionnaire was designed and sent to all primary care surgeries in the catchment area for each patient who received calprotectin testing (n=587): the responses (n=217) to these questionnaires were then compared to the IBD pathway [14].
The outcome of this audit revealed that most areas of the IBD pathway were not being adhered to and, therefore, GP re-education and training was needed; a similar finding to another hospital [10]. The worst area of non-conformance was to ensure that patients had had signs/symptoms for at least 6 months: 69% of requests were not compliant [14]. Exclusion of gastrointestinal infection was second (63%), followed by ensuring age was 20–40 years (48%) [14]. Of the 216 questionnaires returned, 35% of patients had had red flag signs/symptoms at the time of the request [14]. Alarmingly, rectal bleeding was the most frequently encountered, followed by anemia, unexplained weight loss and a family history of bowel/ovarian cancer [14]. None of the patients had abdominal mass [14]. Conversely, high compliance was observed for the withdrawal of non-steroidal inflammatory drugs (NSAIDs) and antibiotics before testing [14]. Overall, only 24 requests (11%) were fully compliant for all criteria of the IBD clinical pathway [14].
Patient/clinician considerations
Unlike other fecal markers such as elastase, calprotectin is much less stable (up to 3 days at room temperature) and, therefore, it is important that patient samples are sent to the laboratory within 72 hours of collection [16]. It is still the case that samples are received by the laboratory with no date or time of collection and these are consequently rejected for analysis. Considering that calprotectin is elevated in gastrointestinal infection, in order for the test to be of clinical use, it is imperative that this is excluded.
The age restriction imposed on calprotectin testing is important and is based on the fact that IBS is more common in younger adults, that the prevalence of IBS decreases with increasing age and that new onset of symptoms after 50 years is uncommon [11, 17]. By focusing on this age group a large number of patients who do not require diagnostic testing will be excluded and, therefore, they will not add to the delay that current IBD sufferers face in receiving a colonoscopy.
Patients >40 years of age should be referred to Gastroenterology without delay, as the risk of developing colorectal cancer increases with age: colorectal cancer is the third most commonly diagnosed cancer in the UK and one of the major complications of IBD [18].
Since IBD is a relapsing disease the clinician should be aware that screening results from some patients with IBD may be negative, particularly if the disease is in a period of remittance and, therefore, the presentation of the patient, not the test result, should be the ultimate deciding factor over whether to refer. Finally, underpinning this is the need for local laboratories to determine their own method-specific cut-off values using evidence-based medicine. As with all screening tests the aim should be to optimize the test’s ability to exclude disease (IBS) so that fewer patients without disease are referred.
References
1. Trbojevic Akmacic I, Ventham NT, Theodoratou E, Vuckovic F, Kennedy NA, Krištic J, Nimmo ER, Kalla R, Drummond H, et al. Inflammatory bowel disease associates with proinflammatory potenial of the immunoglobulin G glycome. Inflamm Bowel Dis 2015; 21: 1237–1247.
2. de Souza HS, Fiocchi C. Immunopathogenesis of IBD: current state of the art. Nat Rev Gastroenterol Hepatol 2016; 186: 13–27.
3. Odze R. Diagnostic problems and advances in inflammatory bowel disease. Mod Pathol 2003; 16(4): 347–358.
4. Magro F, Giochetti P, Eliakim R, Ardissone S, Armuzzi A, Barreiro-de Acosta M, Burisch J, Gecse KB, Hart AL, et al. Third European evidence-based consensus on diagnosis and management of ulcerative colitis. Part1: definitions, diagnosis, extra-intestinal manifestations, pregnancy, cancer surveillance, surgery and ileo-anal pouch disorders. J Crohns Colitis 2017; 11(6): 649–670.
5. Salim SY, Söderholm JD. Importance of disrupted intestinal barrier in inflammatory diseases. Inflamm Bowel Dis 2011; 17(1): 362–381.
6. Ni J, Wu GD, Alenberg L, Tomov VT. Gut microbiota and IBD: causation or correlation? Nat Rev Gastroenterol Hepatol 2017; 14(10): 573–584.
7. Neurath MF. Cytokines in inflammatory bowel disease. Nat Rev Immunol 2014; 14(5): 329–342.
8. Műzes G, Molnár B, Tulassay Z, Sipos F. Changes of the cytokine profile in inflammatory bowel diseases. World J Gastroenterol 2012; 18(41): 5848–5861.
9. Ohama T, Hori M, Sato K, Ozaki H, Karaki H. Chronic treatment with interleukin-1β attenuates contractions by decreasing the activities of CPI-17 and MYPT-1 in intestinal smooth muscle. J Biol Chem 2003; 278(49): 48794–48804.
10. Iwakura Y, Ishigame H. The IL-23/IL-17 axis in inflammation. J Clin Invest 2006; 116(5): 1218–1222.
11. National Institute for Health and Care Excellence (NICE). Irritable bowel syndrome in adults: diagnosis and management of irritable bowel syndrome in primary care. NICE Clinical Guideline 61, 2008 (https://www.nice.org.uk/guidance/cg61).
12. NICE. Faecal calprotectin diagnostic tests for inflammatory diseases of the bowel. NICE Diagnostics Guidance 11, 2013 (https://www.nice.org.uk/guidance/dg11).
13. Tibble J, Teahon K, Thjodleifsson B, Roseth A, Sigthorsson G, Bridger S, Foster R, Sherwood R, Fagerhol M, Bjarnason I. A simple method for assessing intestinal inflammation in Crohn’s disease. Gut 2000; 47: 506–513.
14. Palmer B, McCann S. A one year retrospective audit on calprotectin: how well is primary care adhering to the pathway for inflammatory bowel disease. Poster presented at Focus 2017, the Association of Clinical Biochemistry national annual meeting.
15. Turvill J. Evaluation of guidelines for the use of faecal calprotectin testing in primary care. NICE 2015. (https://www.nice.org.uk/sharedlearning/evaluation-of-guidelines-for-the-use-of-faecal-calprotectin-testing-in-primary-care).
16. Tøn H1, Brandsnes, Dale S, Holtlund J, Skuibina E, Schjønsby H, Johne B. Improved assay for faecal calprotectin. Clin Chim Acta 2000; 292: 41–54.
17. Halland M, Saito YA. Irritable syndrome: new and emerging treatments. BMJ 2015; 350: h1622.
18. Adelstein B, Macaskill P, Chan SF, Katelaris PH, Irwig L. Most bowel cancer symptoms do not indicate colorectal cancer and polyps: a systematic review. BMC gastroenterol 2011; 11: 65–74.
The authors
Benjamin Palmer*1 PhD, MRSC; Wisam Jafar2 MBChB, MRCP(gastro), MSc, MA, FRCP; Steven McCann2 MSc, FRCPath
1Betsi Cadwaladr Health Board, Glan Clwyd Hospital, Rhyl, Denbighshire, Wales, UK
2Stockport NHS Foundation Trust, Stockport, Cheshire, UK
*Corresponding author
E-mail: Ben.palmer@wales.nhs.uk
Multiplex determination of ANA and cytoplasmic antibodies according to ICAP
, /in Autoimmunity & Allergy, Featured Articles /by 3wmediaby Dr Jacqueline Gosink The international consensus on standardized nomenclature of human epithelial cell (HEp-2 cell) patterns in indirect immunofluorescence (ICAP, www.anapatterns.org) defines fifteen nuclear patterns, nine cytoplasmic patterns and five mitotic patterns which are relevant for the diagnosis of various autoimmune diseases. Furthermore, the consensus stipulates that autoantibodies detected by indirect immunofluorescence on HEp-2 […]
Adrenal cortex antibodies
, /in Autoimmunity & Allergy, Featured Articles /by 3wmediaby Dr Petraki Munujos The detection of anti-adrenal cortex antibodies, also known as 21-hydroxylase or 21-OH antibodies, is an aid in the diagnosis and treatment of autoimmune adrenalitis. Far from being outdated, indirect immunofluorescence is a major analytical procedure used in the autoimmune laboratory for the measurement of these autoantibodies. Several techniques can be currently […]
Chest pain and elevated cardiac troponin: a typical case of myocardial infarction? Perhaps not…
, /in Featured Articles /by 3wmediaChest pain is a common presentation in Emergency Medicine and can cause a diagnostic dilemma. Does the patient need urgent admission for percutaneous coronary intervention following a myocardial infarction? Or will discharge with reassurance and a bottle of antacids sort out their indigestion? Measurement of cardiac troponin has become an essential part of emergency assessment of chest pain. Despite this, clinicians and laboratory professionals should be conscious that elevated cardiac troponin is not inevitably indicative of cardiac pathology – and in some cases, may be deceptive…
by Ceri Parfitt and Dr Christopher Duff
Troponin and assessment of acute chest pain
The Universal Definition of Myocardial Infarction and Injury has incorporated measurement of cardiac troponin (cTn) since 2000, with the most recent revision in 2012 [1]. In the UK, the National Institute of Health and Care Excellence (NICE) the ‘Chest pain of recent onset: assessment and diagnosis’ pathway [2] is well established in clinical practice, and Emergency Department clinicians are confident in the use of cTn assays to identify patients with acute coronary syndrome (ACS).
The troponin complex comprises three regulatory proteins – troponin C, troponin T and troponin I – essential for skeletal and cardiac muscle contraction, through regulation of actin/myosin filaments. Cardiac-specific isoforms of both troponin T (cTnT) and troponin I (cTnI) have been identified. cTn complexes can be detected in the blood within 2–3 hours of myocardial damage, peak within 24 hours and persist for 1–2 weeks [3, 4]. This characteristic pattern of release and specificity for cardiac injury has cemented cTn as the biomarker of choice for diagnosis of ACS, replacing creatinine kinase, aspartate transaminase and lactate dehydrogenase. Measurement of cTn is a standard assay available on an urgent basis in the majority of modern clinical laboratories. Both cTnT and cTnI can be measured by two-site ‘sandwich’ immunoassay, based on formation of complexes of cTn, a ‘capture’ anti-cTn antibody and a ‘label’ anti-cTn antibody (Fig. 1a). Owing to patent regulations, Roche Diagnostics is the sole distributor of cTnT immunoassays, whereas various companies offer cTnI immunoassays on several different platforms. Although results cannot be directly compared, the two assays largely provide the same diagnostic information.
A patient presents with chest pain and raised troponin
Despite the widespread use of cTn in Emergency Departments worldwide, clinicians may often be unaware of spurious causes of raised cTn. Application of the pathway without considering patient history and previous presentations can potentially lead to invasive, unnecessary (and expensive) investigations. We recently encountered an example of this in our local Emergency Department. A 34-year-old male with no significant cardiac risk factors repeatedly presented to the Emergency Department complaining of chest pain over a period of 3 years, resulting in more than 40 individual analyses of cTnI (using the Siemens TnI-Ultra assay on ADVIA Centaur XP). The results were variable, but always elevated (48–4030 ng/L, decision limit 40 ng/L). The patient underwent thorough investigation, but no cardiac pathology was ever identified. The Cardiology Department contacted the laboratory to discuss the apparent mismatch between patient presentation and biochemical findings.
Has there been a laboratory error?
Spurious results can arise for a number of reasons, including mislabelled samples, sample contamination, poor sample quality, analyser malfunction, calibration/quality control errors and transcription errors [5]. Where possible, these factors are minimized by stringent sampling techniques and robust laboratory procedures, or can be identified through the technical and clinical validation processes, and highlighted to the clinical team by laboratory staff. In many cases, a simple repeat sample is sufficient to confirm/rule out error. However, clinicians should always be vigilant for results that are not consistent with patient presentation. The persistent nature of the elevated cTnI in this case – over a period of 3 years – rules out sporadic errors, such as mislabelled samples or analyser malfunction. Furthermore, no other patients with unexpectedly high cTnI concentrations were discussed with the laboratory. These factors strongly suggested that the solution to the mystery lay within the patient, rather than in management of their samples.
Alternative causes of elevated troponin: cardiac and non-cardiac
Although elevated cTn is typically associated with myocardial infarction, other cardiac pathologies also cause cTn release into the circulation through direct or hypoxic myocyte damage. These include aortic dissection, pericarditis, myocarditis, acute heart failure and cardiac contusion following trauma [4, 6]. Unsurprisingly, cardiac interventions such as defibrillator shocks, coronary angioplasty, percutaneous coronary intervention and open heart surgery are also associated with variable increase in cTn [6]. Chemotherapy agents, including anthracyclines, alkylating agents, anti-metabolites and anti-microtubules are all associated with toxic damage to myocytes [4].
In addition to cardiac sources of cTn, non-cardiac sources must also be considered. A significant proportion (estimated at 36–85 %) of patients admitted to critical care units for sepsis/systemic inflammatory response syndromes (SIRS) have elevated cTn concentrations. In these patients, cTn release is thought to be a consequence of oxygen supply-demand mismatch in the myocardium: although oxygen demand is increased, due to fever and tachycardia, systemic hypoxemia reduces oxygen supply due to respiratory failure, circulatory dysfunction and hypotension [7, 8]. Patients with end-stage renal disease (ESRD) are often noted to have chronically elevated cTn. The underlying cause for this is unclear, but various hypotheses have been presented, including reduced renal clearance of cTn microfragments, sub-clinical cardiac disease, and metabolic abnormalities [9]. cTn concentrations are also found to be elevated in patients with acute pulmonary embolism. Again, the cause has not been conclusively established, but it is believed that abrupt increases in pulmonary artery pressure leads to dilatation of the right ventricle, with associated myocyte injury [4].
For most patients, any of these situations can be identified by examining the patient records, or through further cardiac investigations. In the case presented here, after thorough analysis of the patient’s history, and given the resolutely normal cardiac investigations, we concluded that it was unlikely that the elevation in cTn was due to pathological factors, whether cardiac or non-cardiac. This led us to examine the local cTnI assay – and the patient’s samples – in more detail, to ascertain the cause of the spurious results.
Could immunoassay interference be involved?
Non-pathological causes of false positives in cTn analysis include human anti-mouse antibodies (HAMA), rheumatoid factor (RF) and heterophilic antibodies [10–12]. HAMA are characterized by strong avidity to well-defined antigens. They are thought to develop following exposure to mouse immunoglobulins, which may occur through monoclonal antibody therapy, vaccination, infection, blood transfusion, or simply through animal handling [10]. In contrast, heterophilic antibodies are endogenous antibodies present in serum/plasma produced against poorly defined antigens. They often have weak affinity, but multiple specificities. Heterophilic antibodies may develop following infection and are thought to affect up to 30% of the population [10, 12, 13]. RFs are endogenous human antibodies with heterophilic activity, often arising following autoimmune disease [12].
Genuine assay interference is less commonly encountered these days, with the development of highly specialized commercial assays. Most modern immunoassays contain agents able to block low concentrations of interfering proteins. However, the fact that interference is less frequently observed may lead laboratory staff members to believe that interference is less prevalent in the general population. This is not the case. In fact, the prevalence of assay interference may be increasing, as immunotherapy and the use of radiolabelled antibodies are established in routine practice [13]. Although heterophilic antibodies usually have little clinical significance in vivo, their ability to interfere with two-site immunoassays can have major effects on patient management. The magnitude of interference varies between samples and may even vary within a patient over time [13]. The mechanism for antibody interference is complex and has not been fully elucidated, but is likely to involve ‘bridge’ formation between the capture antibody and label antibody (Fig. 1b) [13, 14]. Several standard techniques are available to investigate assay interference [12, 13]. In this case, we used dilution studies, polyethylene glycol precipitation, heterophilic antibody blocking tubes and an alternative assay method. In all cases, samples from the patient were compared with a control sample with a similar cTnI concentration, from a patient with confirmed ACS.
Investigations into immunoassay interference
Serial dilution of samples should produce a linear decrease in concentration if interfering species are absent. However, if interfering substances are present, then non-linear recovery following dilution is commonly observed, as the interferent is ‘diluted out’. Both the patient’s serum and control serum were diluted with saline, and cTnI was measured in each dilution. As expected, the cTnI concentration in the control sample decreased proportionally to the dilution factor. In contrast, any dilution of the patient sample resulted in undetectable cTnI, consistent with presence of an interfering species (Fig. 2a). Immunoglobulins can be removed by treatment with polyethylene glycol (PEG). PEG forms poorly soluble complexes with immunoglobulins, which can be precipitated then removed by centrifugation, before the sample is re-analysed. In the patient sample, PEG precipitation resulted in undetectable levels of cTnI. Levels were also reduced in a control sample (62% recovery), though it is not uncommon to see at least some reduction in recovery using this approach, regardless of whether interfering immunoglobulins are present or not (Fig. 2b). Heterophilic blocking reagents prevent non-analyte mediated bridging of antibodies by heterophilic interference. Incubation of a patient sample in a heterophilic blocking tube (Scantibodies Laboratory Inc.) before cTnI analysis resulted in 4% recovery of cTnI, suggesting analytical interference. In the control sample, 94% of cTnI was recovered (Fig. 2c). Finally, a patient sample was sent to a referral laboratory for analysis of cTnT, as opposed to cTnI. A cTnT concentration below the decision limit (99th percentile) was obtained (cTnI result: 398 ng/L; cTnT result: 6 ng/L) (Fig. 3).
As initial investigations strongly suggested the presence of an assay interferent, further work was conducted to identify the precise nature of the interfering species. Two patient samples with elevated cTnI were analysed for presence of HAMAs, which was negative in both cases. This was not unexpected, given that the patient had no history of exposure to animal proteins in either a therapeutic or a social setting. The same sample was tested for RFs, using the local assay (Orgentec RF IgM), which returned a slightly elevated result. This slightly elevated concentration is not considered clinically significant, but may contribute to interference in the assay. Thus, heterophilic antibody interference remains the most likely cause of cTnI elevation in this patient. Further investigations have been limited, however, by recently decreasing concentrations of cTnI in this patient. Unpredictable variation in heterophilic antibody activity is a known phenomenon however, and the laboratory continues to oversee samples received from the patient.
Outcome and conclusions
The patient continues to regularly attend the Emergency Department with chest pain. No cardiac pathology has ever been identified in this patient and his chest pain has been attributed to a combination of gastro-oesophageal reflux disease and health anxiety. Senior review is now required in the Emergency Department before the patient is investigated or admitted. If ACS is ever suspected in this patient, the laboratory is able to pre-treat samples with heterophilic blocking tubes to provide an estimate of cTnI concentration, with referral for cTnT analysis required for a definitive result. This alternate pathway ensures that the patient is protected in case of a genuine cardiac event.
This case demonstrates that clinicians must be aware of assay interference, and highlights the benefit of discussing patients with the laboratory when test results do not correlate with clinical presentation. Laboratory staff members are rarely able to visit patients and, thus, are not in a position to suspect interference without input from the clinical team. In this case, failure to identify the underlying cause of elevated cTnI at an early stage resulted in a number of unnecessary and invasive investigations, significant costs to the NHS, and continued anxiety to the patient.
References
1. Thygesen K, Alpert JS, Jaffe AS, Simoons ML, Chaitman BR, et al. Third universal definition of myocardial infarction. Eur Heart J 2012; 33: 2551–2567.
2. National Institute of Health and Care Excellence (NICE). Chest pain of recent onset: assessment and diagnosis. NICE clinical guideline 95, 2010 (https://www.nice.org.uk/guidance/cg95).
3. Wu AHB. Analytical issues for clinical use of cardiac troponin. In: Morrow DA (ed.) Contemporary cardiology: cardiovascular biomarkers: pathophysiology and disease management. Humana 2006, pp 27–40.
4. Korff S, Katus HA, Giannitsis E. Differential diagnosis of elevated troponins. Heart 2006; 92: 987–993.
5. Plebani M. The detection and prevention of errors in laboratory medicine. Ann Clin Biochem 2010; 47: 101–110.
6. Sara JDS, Holmes DR, Jaffe AS. Fundamental concepts of effective troponin use: important principles for internists. Am J Med 2015; 128: 111–119.
7. Ver Elst KM, Spapen HD, Nguyen DN, Garbar C, Huyghens LP, Gorus FK. Cardiac troponins I and T are biological markers of left ventricular dysfunction in septic shock 2000; 46: 650–657.
8. Spies C, Haude V, Fitzner R, Schroder K, Overbeck M, Runkel N, Schaffartzik W. Serum cardiac troponin T as a prognostic marker in early sepsis. Chest 1998; 113: 1055–1063.
9. Jaffe AS. The 10 commandments of troponin with special reference to high sensitivity assays. Heart 2011; 97: 940–946.
10. Lippi G, Aloe R, Meschi T, Borghi L, Cervellin G. Interference from heterophilic antibodies in troponin testing. Case report and systematic review of the literature. Clin Chim Acta 2013; 426: 79–84.
11. Makaryus AN, Markaryus MN, Hassid B. Falsely elevated cardiac troponin I levels. Clin Cardiol 2007; 30: 92–94.
12. Zaidi A, Cowell R. False positive cardiac troponin elevation due to heterophile antibodies: more common than we recognise? BMJ Case Rep 2010; 2010: bcr1120092477.
13. Ismail AAA, Walker PL, Cawood ML, Barth JH. Interference in immunoassay is an underestimated problem. Ann Clin Biochem 2002; 39: 266–373.
14. Kaplan IV, Levinson SS. When is a heterophile antibody not a heterophile antibody? When it is an antibody against a specific immunogen. Clin Chem 1999; 45: 616–618.
The authors
Ceri Parfitt MSc, Christopher Duff* PhD
Department of Clinical Biochemistry, Pathology Directorate, Royal Stoke University Hospital, University Hospitals of North Midlands NHS Trust, Staffordshire, UK
*Corresponding author
E-mail: Chris.duff@uhnm.nhs.uk
MTHFR, hyperhomocysteinemia, CAD and T2DM
, /in Featured Articles /by 3wmediaIndividuals with type 2 diabetes mellitus (T2DM) are at increased risk of coronary artery disease (CAD). The C677T mutation of the methylenetetrahydrofolate reductase (MTHFR) gene is associated with elevated plasma levels of homocysteine. The association of the MTHFR gene and the level of homocysteine with development of CAD has been studied in various population groups, including patients with T2DM, but the results have been variable. In practice, plasma homocysteine may be ordered as part of a screen for people with CAD or stroke, or who are at high risk for CAD or stroke but no other known risk factors. Testing of C677T polymorphism with or without elevated homocysteine is not recommended and has no clinical utility.
by Prof. Bakri Saeed and Dr Nisreen Mohammed
Type 2 diabetes mellitus and coronary artery disease
Type 2 diabetes mellitus (T2DM) is a major health problem throughout the world. It is a polygenic and multifactorial disease that is a major risk factor for cardiovascular disease. Cardiovascular disease (CVD) comprises coronary artery disease (CAD), also referred to as coronary heart disease (CHD), or ischemic heart disease (IHD), and cerebrovascular disease.
CAD due to atherosclerosis is a cause of significant morbidity and mortality, and is the leading cause of death worldwide. There are several risk factors for CAD. The well-stablished risk factors for CAD include diabetes mellitus, hypertension, smoking and dyslipidemia. There is growing interest in emerging risk factors for improved understanding of the mechanisms that underline cardiovascular disorders and CAD.
T2DM increases the risk for CAD by 2–4-fold compared to people without diabetes. CVD accounts for about 70% of deaths in people with diabetes. Identification and management of risk factors for CAD is an important aspect of management of diabetes mellitus.
Hyperhomocysteinemia and MTHFR polymorphism
Homocysteine is a sulfur-containing amino acid formed from demethylation of methionine. Methionine is the precursor to S-adenosyl methionine (SAMe) and is one of the essential amino acids. SAMe is a major methyl donor and is involved in numerous biological reactions. Homocysteine is metabolized by either remethylation to methionine or transsulfuration to cystathionine. The former reaction is catalysed by the vitamin B12-dependent methionine synthase. The latter reaction is catalysed by the enzyme cystathionine beta-synthase, which requires vitamin B6.
The methyl donor in the remethylation of homocysteine to methionine is 5-methyltetrahydrofolate. The 5,10-methylene-tetrahydrofolate reductase (MTHFR) enzyme catalyses the reduction of 5,10-methylene-tetrahydrofolate to 5-methyltetrahydrofolate. The enzyme requires B2 (riboflavin) as a cofactor (Fig. 1).
Therefore, hyperhomocysteinemia can result from reduced activity of the enzymes involved in homocysteine metabolism or from deficiency of the vitamins which are needed as cofactors in homocysteine metabolic reactions: folate, vitamin B6 and vitamin B12.
Several mutations in the MTHFR gene have been identified and some of them affect the activity of the enzyme. The commonest MTHFR gene mutation is a cytosine-to-thymidine substitution at nucleotide 677 (C677T), which changes alanine into valine, resulting in a thermolabile enzyme with impaired enzymatic activity and leading to hyperhomocysteinemia.
There are two copies of each gene. Therefore, an individual can be homozygous for the mutated gene or can be heterozygous, having one copy of the C677T variant and one normal copy. The C677T homozygous variant enzyme is thermolabile and demonstrates 70% reduced enzyme activity in vitro. The heterozygous C677T MTHFR enzyme has 35% reduced activity in vitro.
Worldwide, the frequency of MTHFR gene mutations varies among racial and ethnic groups, in Africa MTHFR gene polymorphism is markedly low (below 10%) for the C677T allele. In the European and Asian population, estimates of 18.6% and 20.8% were reported [1].
Association with CAD
In recent years hyperhomocysteinemia has been implicated as a risk factor for CAD, independent of other known risk factors. The primary mechanism by which homocysteine promotes atherosclerosis is by impairing endothelial function, which initiates the chain of events resulting in atherosclerotic plaque formation.
Numerous studies looked into the possible association between MTHFR genotypes and plasma homocysteine levels and the incidence of different MTHFR genotypes and hyperhomocysteinemia in CAD patients [2–5]. The results of these studies have been controversial. Several studies have shown the link between the MTHFR C677T gene polymorphism and the risk for CAD but many other studies failed to show association between MTHFR genotypes and plasma homocysteine levels and their role in CAD.
Previous studies in T2DM patients were also controversial. MTHFR polymorphism and hyperhomocysteinemia were shown to be predictors of cardiovascular events among diabetic patients [6, 7], whereas other studies failed to show a role for MTHFR polymorphic variants and homocysteine in increasing susceptibility to cardiovascular disease [8, 9].
Our study
We recently screened 226 consecutive patients with T2DM, <60 years of age, diagnosed according to WHO criteria. Of these, 113 had CAD confirmed by angiography and electrocardiography (ECG) and 113 had no evidence of CAD [10]. PCR and restriction fragment length polymorphism (RFLP) using Hinf1 restriction enzyme were used to determine MTHFR genotypes.
In our study, the T allele had a significant effect on homocysteine level (P value <0.05) and showed strong association with CAD among T2DM patients (odds ratio 6.2, P <0.0001).
Our study indicates that the C677T polymorphism of the MTHFR gene is associated with hyperhomocysteinemia, and the two are independently associated with the presence of CAD in patients with T2DM.
Reasons for controversy
The outcome of these numerous studies and meta-analysis remained contradictory. There was no agreement on the association between MTHFR genotypes and plasma homocysteine levels or the incidence of different MTHFR genotypes and hyperhomocysteinemia in CAD patients.
Plasma homocysteine levels are dependent on interacting nutritional and genetic factors. Some studies suggested that people homozygous for MTHFR C667T polymorphism tend to have hyperhomocysteinemia in the context of low folic acid levels. Supplementation with the vitamins involved in homocysteine metabolism was found to lower plasma homocysteine levels.
Therefore, geographic heterogeneity, nutritional and environmental factors could affect the relationship between MTHFR genotypes and CVD risk in different populations.
Practical points
Homocysteine may be ordered as part of a screen for people with or at high risk of CAD or stroke, especially if there is family history of CAD or stroke but no other known risk factors, such as diabetes, smoking, hypertension, or dyslipidemia. Routine screening of homocysteine, like that of cholesterol, has not been recommended.
Plasma homocysteine concentration may be elevated in B12 and folate deficiency and its measurement has been suggested to give an early indicator of deficiency.
In new-born testing, greatly increased concentrations of homocysteine in the urine and blood suggests a diagnosis of homocystinuria and indicates the need for confirmation of the cause of raised levels.
Most laboratories report normal homocysteine levels in the blood between 5 and 15 µmol/L. Any measurement above 15 µmol/L is considered high.
However, it should be noted that normal levels will vary between ethnic groups and populations. Homocysteine levels increase with age, are lower in pregnancy and are influenced by drugs. These factors should be taken into consideration when interpreting results.
Testing of C677T polymorphism with or without elevated homocysteine is not recommended in patients with CAD or other diseases where MTHFR variants have been implicated, such as thrombophilia or recurrent pregnancy loss.
References
1. Schneider JA, Rees DC, Liu YT, Clegg JB. Worldwide distribution of a common methylenetetrahydrofolate reductase mutation. Am J Hum Genet 1998; 62: 1258–1260.
2. Chehadeh SWEH, Jelinek HF, Al Mahmeed WA, Tay GK, Odama UO, Elghazali GE, et al. Relationship between MTHFR C677T and A1298C gene polymorphisms and complications of type 2 diabetes mellitus in an Emirati population. Meta gene 2016; 9: 70–75.
3. Bickel C, Schnabel R, Zengin E, Lubos E, Rupprecht H, Lackner K, et al. Homocysteine concentration in coronary artery disease: Influence of three common single nucleotide polymorphisms. Nutr Metab Cardiovascular Dis 2017; 27(2): 168–175.
4. Yilmaz H, Isbir S, Agachan B, Ergen A, Farsak B, Isbir T. C677T mutation of methylenetetrahydrofolate reductase gene and serum homocysteine levels in Turkish patients with coronary artery disease. Cell Biochem Funct 2006; 24(1): 87–90.
5. Meisel C, Cascorbi I, Gerloff T, Stangl V, Laule M, Müller JM, et al. Identification of six methylenetetrahydrofolate reductase (MTHFR) genotypes resulting from common polymorphisms: impact on plasma homocysteine levels and development of coronary artery disease. Atherosclerosis 2001; 154(3): 651–658.
6. Lewis SJ, Ebrahim S, Smith GD. Meta-analysis of MTHFR 677C→T polymorphism and coronary heart disease: does totality of evidence support causal role for homocysteine and preventive potential of folate? BMJ 2005; 331(7524): 1053–1058.
7. Bennouar N, Allami A, Azeddoug H, Bendris A, Laraqui A, El Jaffali A, et al. Thermolabile methylenetetrahydrofolate reductase C677T polymorphism and homocysteine are risk factors for coronary artery disease in Moroccan population. J Biomed Biotechnol 2007(1); 80687.
8. Bahadır A, Eroz R, Türker Y. Does the MTHFR C677T gene polymorphism indicate cardiovascular disease risk in type 2 diabetes mellitus patients? Anatolian J Cardiol 2015; 15(7): 524–530.
9. Rahimi Z, Nomani H, Mozafari H, Vaisi-Raygani A, Madani H, Malek-Khosravi S, et al. Factor V G1691A, prothrombin G20210A and methylenetetrahydrofolate reductase polymorphism C677T are not associated with coronary artery disease and type 2 diabetes mellitus in western Iran. Blood Coagul Fibrinolysis 2009; 20(4): 252–256.
10. Mohammed NO, Ali IA, Elamin BK and Saeed BO. The association of methylenetetrahydrofolate reductase gene polymorphism and hyperhomocysteinaemia with coronary artery disease in Sudanese patients with type 2 diabetes. Poster at Focus 2017, Association of Clinical Biochemistry annual meeting.
The authors
Bakri Osman Saeed*1 PhD, MD, FRCPath, FRCP; Nisreen Osman Mohamed2 PhD
1Faculty of Medicine, Sudan International University, Khartoum, Sudan
2Ahfad Centre for Science and Technology, Ahfad University for Women, Khartoum, Sudan
*Corresponding author
E-mail: saeedbakri@hotmail.com
Early risk assessment of Type 2 Diabetes Mellitus through the use of the biomarker adiponectin
, /in Featured Articles /by 3wmediaThe prevalence of T2DM has reached epidemic levels, affecting about 7% of the U.S. population, and is growing.
The Randox automated immunoturbidimetric adiponectin test offers an improved method for assessing T2DM risk, with a convenient format for routine clinical use. The Randox Adiponectin assay is available for use on most biochemistry analysers, including the RX series.
Background
The prevalence of type 2 diabetes mellitus (T2DM) has reached epidemic levels, affecting ~7% of the U.S. population, and current epidemiological trends indicate that the prevalence will continue to increase dramatically (Blonde, 2007).
The global prevalence of diabetes among adults over 18 years of age has risen from 4.7% in 1980 to 8.5% in 2014 (WHO, 2016). About 422 million people worldwide have diabetes (WHO, 2016). Furthermore, the prevalence of diabetes is growing most rapidly in low and middle-income countries (WHO, 2016).
Millions more people are also at risk. One in three adults have prediabetes, and 9 out of 10 of those with prediabetes don’t know they have it (CDC, 2016).
Early risk assessment is vital for a number of reasons. Diabetes is one of the leading causes of death in the world – in 2012 it was the direct cause of 1.5 million deaths (WHO, 2016). 50% of people with diabetes die of CVD (WHO, 2016). Additionally, diabetes is the leading cause of newly diagnosed adult blindness for people between the ages of 20 and 74 (NIDDK, 2016).
Economically, diabetes and its complications bring about substantial economic loss to people with diabetes and their families, and to health systems and national economies through direct medical costs and loss of work and wages. While the major cost drivers are hospital and outpatient care, a contributing factor is the rise in cost for analogue insulins 1 which are increasingly prescribed despite little evidence that they provide significant advantages over cheaper human insulins (WHO, 2016).
Traditional methods for diabetes risk assessment
Non-biochemical methods for assessing a patient’s risk of developing T2DM traditionally take into account gender; age; family history of T2DM; BMI waist size; and high blood pressure to give a risk score. Other factors which health services may take into account include ethnicity (UK NHS); history of gestational diabetes (GDM) (American Diabetes Association (ADA)); physical activity (ADA and Finnish Diabetes Association (FDA)); blood glucose history (FDA) and diet (FDA).
It is widely recognized that people who are overweight are at higher risk of developing T2DM. However, assessing those who are overweight can be challenging. Studies have shown that measuring waist circumference alone measures total abdominal fat reliably, but its association with visceral fat depends on visceral fat/ subcutaneous fat ratios that vary by gender and ethnicity (Grundy et al, 2013). Body mass index (BMI) (weight kg / height m2) is another common method of determining which patients are classed as overweight or obese, however it has limitations in measuring athletes and varies in reliability based on age, sex, and race.
Furthermore, it has been found that risk prediction for T2DM and cardiovascular disease (CVD) remains suboptimal even after the introduction of global risk assessment by various scores. This has prompted the search for additional biomarkers (Herder et al, 2011).
The most commonly used biochemical method of assessing risk of T2DM is measuring fasting plasma glucose (FPG); however, the specificity of this test is poor (Genuth et al, 2003; Nichols et al, 2007). Although many individuals are identified as having impaired fasting glucose (IFG), their absolute risk of conversion to diabetes is only 5–10% per year (Gerstein et al, 2007). The oral glucose tolerance test (OGTT) is more accurate for risk assessment. However, it is rarely used in practice because it is unpleasant for the patient and requires 2 hours to perform. Another challenge is that by the time glucose regulation is abnormal, the underlying disease has been progressing for many years, and complications have already occurred in a significant number of individuals (Wong et al, 2008). Thus, the rationale of using one variable to assess risk is questionable, when the risk of harm actually varies based on a range of variables and would be better assessed using a multivariable individualized risk score (Mohamed and Evans, 2008).
Given the limitations of the OGTT, FPG, and indexes that the clinician must calculate, it is clear that an improved method for assessing T2DM risk, with a convenient format for routine clinical use, would enable physicians to accurately evaluate more individuals (Kolberg et al, 2009).
Clinical significance
A number of recent, key publications have advocated the testing of adiponectin for T2DM risk assessment in clinical settings.
Implications for clinicians
Adiponectin measurement is not yet a routinely run test in the majority of laboratories worldwide, and it is therefore not available for many clinicians to request. Yet the clinical implications of this becoming widely available could be extremely valuable.
When risk via adiponectin measurement is identified, lifestyle modification to reduce visceral fat should become a primary measure for the prevention of the development of cardiovascular diseases as well as its risks including T2DM in metabolic syndrome with visceral fat accumulation (metabolic syndrome in the narrow sense) through the improvement of adiponectin production (Matsuzawa, 2014).
Randox automated adiponectin assay
The Randox automated immunoturbidimetric adiponectin test offers an improved method for assessing T2DM risk, with a convenient format for routine clinical use, to enable physicians to accurately evaluate at-risk individuals. Randox is presently the only diagnostic manufacturer who has a globally available automated biochemistry test for adiponectin measurement to assess T2DM risk.
The Randox Adiponectin assay is now available for use on most globally available clinical chemistry analysers, including the RX series.
The RX series combines robust hardware and intuitive software with the extensive RX series test menu, and the full range of routine and novel diabetes and cardiac risk tests, including the adiponectin assay. Renowned for quality and reliability, the RX series is suitable for clinical, research, education and pharmaceutical settings. This extensive dedicated test menu of high quality reagents guarantees excellence in results ensuring unrivalled precision and accuracy, reducing costly test re-runs or misdiagnosis and offering complete confidence in results. The series includes the RX misano, RX monaco, RX daytona+, RX imola and RX modena.
Contact us now for further information; for a quotation for the adiponectin kit on any third party clinical chemistry analyser; or to enquire about any analyser in the RX series range. Please contact marketing@randox.com
Randox Laboratories Ltd
55 Diamond Rd
Crumlin, Co. Antrim BT29 4QY
U.K. www.randox.com
Scientific Literature Review: Cancer
, /in Featured Articles /by 3wmediaCirculating or tissue micro-RNAs and extracellular vesicles as potential lung cancer biomarkers: a systematic review
Song Y, Yu X, Zang Z, Zhao G. Int J Biol Markers 2017; doi: 10.5301/ijbm.5000307 [Epub ahead of print]
For both lung cancer patients and clinical physicians, tumor biomarkers for more efficient early diagnosis and prediction of prognosis are always wanted. Biomarkers in circulating serum, including microRNAs (miRNAs) and extracellular vesicles, hold the greatest possibilities to partially substitute for tissue biopsy. In this systematic review, studies on circulating or tissue miRNAs and extracellular vesicles as potential biomarkers for lung cancer patients were reviewed and are discussed. Furthermore, the target genes of the miRNAs indicated were identified through the miRTarBase, while the relevant biological processes and pathways of miRNAs in lung cancer were analysed through MiRNA Enrichment Analysis and Annotation (MiEAA). In conclusion, circulating or tissue miRNAs and extracellular vesicles provide us with a window to explore strategies for diagnosing and assessing prognosis and treatment in lung cancer patients.
Back to the future: routine morphological assessment of the tumour microenvironment is prognostic in stage II/III colon cancer in a large population-based study
Hynes SO, Coleman HG, Kelly PJ et al. Histopathology 2017; 71(1): 12–26
AIMS: Both morphological and molecular approaches have highlighted the biological and prognostic importance of the tumour microenvironment in colorectal cancer (CRC). Despite this, microscopic assessment of the tumour microenvironment has not been adopted into routine practice. The study aim was to identify those tumour microenvironmental features that are most likely to provide prognostic information and be feasible to use in routine pathology reporting practice.
METHODS AND RESULTS: On the basis of existing evidence, we selected specific morphological features relating to peritumoral inflammatory and stromal responses, agreed criteria for scoring, and assessed these in representative hematoxylin and eosin (H&E)-stained whole tumour sections from a population-based cohort of 445 stage II/III colon cancer cases. Moderate/severe peritumoral diffuse lymphoid inflammation and Crohn’s disease-like reaction were associated with significantly reduced risks of CRC-specific death [adjusted hazard ratio (HR) 0.48, 95% confidence interval (CI) 0.31–0.76, and HR 0.60, 95% CI 0.42–0.84, respectively]. The presence of >50% tumour stromal percentage, as assessed by global evaluation of tumour area, was associated with a significantly increased risk of CRC-specific death (HR 1.60 95% CI 1.06–2.41). A composite ‘fibroinflammatory score’ (0–3), combining dichotomized scores of these three features, showed a highly significant association with survival outcomes. Those with a score of ≥2 had an almost 2.5-fold increased risk of CRC-specific death (HR 2.44, 95% CI 1.56–3.81) as compared with those scoring zero. These associations were stronger in microsatellite instability (MSI)-high tumours, potentially identifying a subset of MSI-high colon cancers that lack characteristic morphological features and have an associated worse prognosis.
CONCLUSIONS: In summary, reporting on H&E staining of selected microscopic features of the tumour microenvironment, independently or in combination, offers valuable prognostic information in stage II/III colon cancer, and may allow morphological correlation with developing molecular classifications of prognostic and predictive relevance.
METHODS: Serum samples were collected from 60 patients with primary colorectal cancer, 40 patients with colorectal polyps and 50 healthy controls. Serum miR-135a-5p expression levels were detected by reverse transcription quantitative real-time quantitative polymerase chain reaction. Serum carcinoembryonic antigen and carbohydrate antigen 199 concentrations were detected by MODULAR ANALYTICS E170.
RESULTS: The relative expression level of serum miR-135a-5p in colorectal cancer patients, colorectal polyps patients and healthy controls was 2.451 (1.107, 4.413), 0.946 (0.401, 1.942) and 0.949 (0.194, 1.415), respectively, indicating that it was significantly higher in colorectal cancer patients than that in the other two groups (U = 351.0, 313.0, both P <0.001). Additionally, it was significantly correlated with different degrees of tumour differentiation (U = 215.0, P = 0.029) and different tumour stages (U = 202.0, P = 0.013). There was no significant correlation between the relative expression of serum miR-135a-5p and carcinoembryonic antigen (r2 = 0.023, P = 0.293) or carbohydrate antigen 199 (r2 = 0.067, P = 0.068) in colorectal cancer patients. Compared with colorectal polyps group, AUCROC of serum miR-135a-5p in colorectal cancer group was 0.832 with 95% CI 0.73-0.93; compared with healthy control group, AUCROC was 0.875 with 95% CI 0.80-0.95.
CONCLUSION: Serum miR-135a-5p expression in colorectal cancer patients was higher than that in patients with colorectal polyps and healthy controls, suggesting that serum miR-135a-5p may prove to be an important biomarker for auxiliary diagnosis of colorectal cancer.
AFM and QCM-D as tools for the distinction of melanoma cells with a different metastatic potential
Sobiepanek A, Milner-Krawczyk M, Lekka M, Kobiela T. Biosens Bioelectron 2017; 93: 274–281
Malignant melanoma is one of the most dangerous skin cancer originating from melanocytes. Thus, an early and proper melanoma diagnosis influences significantly the therapy efficiency. The melanoma recognition is still difficult, and generally, relies on subjective assessments. In particular, there is a lack of quantitative methods used in melanoma diagnosis and in the monitoring of tumour progression. One such method can be the atomic force microscopy (AFM) working in the force spectroscopy mode combined with quartz crystal microbalance (QCM), both applied to quantify the molecular interactions. In our study we have compared the recognition of mannose type glycans in melanocytes (HEMa-LP) and melanoma cells originating from the radial growth phase (WM35) and from lung metastasis (A375-P). The glycosylation level on their surfaces was probed using lectin concanavalin A (Con A) from Canavalia ensiformis. The interactions of Con A with surface glycans were quantified with both AFM and QCM techniques that revealed the presence of various glycan structural groups in a cell-dependent manner. The Con A – mannose (or glucose) type glycans present on WM35 cell surface are rather short and less ramified while in A375-P cells, Con A binds to long, branched mannose and glucose types of oligosaccharides.
Cervical cancer screening tests: HPV testing will replace Pap smear
, /in Featured Articles /by 3wmediaIn the 1980s, cervical screening tests were introduced for the detection of abnormal cervical cells (the cytology-based Pap smear test). Since then there has been a reduction in the number of cervical cancer cases by about 7% each year. Under current guidelines in the UK, women are offered 12 tests per lifetime, with frequency based on age: every 3 years for 25–49-year-olds, every 5 years for 50–64-year-olds, and only in certain circumstances for women over 65.
We have been aware for some time that certain high-risk types of human papillomavirus (HPV) are the causative agents of virtually all cases of cervical cancer and a new cervical test procedure is set to be introduced in England by 2019 that will first test samples for HPV and then only check for abnormal cells if the virus is found. Primary HPV testing has a higher sensitivity, lower false-negative rate and is more cost-effective than cytology, thus allowing further resources and cytology-based tests to be reserved for the closer follow-up of women who test positive for high-risk HPV types.
This change is being introduced at around the same time that the first women to be vaccinated against HPV are about to enter the screening system. In the UK, vaccination of girls against HPV began in 2008 using Gardasil, which protects against HPV 16 and 18 as well as 6 and 11 (responsible for approximately 90% of cases of benign genital warts) and dramatically reduces the risk of cervical cancer. Gardasil-9 offers protection against nine HPV types, adding 31, 33, 45, 52 and 58 to the four mentioned above, but is currently only available privately in the UK. Recent research by Landy et al. in the International Journal of Cancer (2017; doi: 10.1002/ijc.31094) suggests that with the use of primary HPV testing, the screening programme should be personalized based on vaccination status, with perhaps as few as two lifetime tests needed for women who have received the nonavalent vaccine, three for the quadrivalent vaccine and seven for unvaccinated women.
However, the researchers also note that with many fewer tests, it is crucial that participation in screening is high; however, recent figures revealed that less than three-quarters of women take up screening invitations. Perhaps this would improve if the method of sample collection was changed from a cervical smear to a urine-based HPV test.
Rapid and reliable detection of medulloblastoma-associated DNA methylation patterns: MS-MIMIC
, /in Featured Articles /by 3wmediaDisease-associated variations in DNA methylation profiles hold significant potential for diagnostic and research applications. Unfortunately, scant and degraded samples often limit the analyses which can be performed. To overcome these issues, we developed Mass Spectrometry Minimal Methylation Classifier (MS-MIMIC) to identify then reliably analyse disease-specific DNA methylation profiles. The technique has now been validated in a cohort of pediatric medulloblastoma cases.
by Dr Ben Chaffey, Dr Debbie Hicks, Dr Edward Schwalbe and Prof. Steve Clifford
Background
Altered DNA methylation patterns have emerged as valuable biomarkers of disease pathogenesis, showing clear potential in diagnostics, sub-classification and prediction of therapeutic response/ disease course [1–7]. However, clinical assessment of these altered patterns can be problematic, with sample materials often being degraded/scant, such as formalin-fixed paraffin-embedded (FFPE) tissue and core biopsies, and certain platforms, such as DNA methylation microarrays, having a requirement for batched assessments of relatively large numbers of samples. This compromises generation of data from real-world samples within clinically meaningful timeframes, hampering translation of research findings into routine practice.
In this project, we focused on developing a new DNA methylation state assay to provide molecular subgrouping of cases of medulloblastoma (Fig. 1), the most frequently occurring malignant brain tumour in children. This disease has an approximate incidence of 1.5 cases per million, rising to 6 per million in children aged 1–9 years. It also occurs in adults, although in this group it is around ten times less common [8].
Although rates of survival to 5 years and beyond following diagnosis are around 65–70%, medulloblastoma still causes around 10% of all childhood cancer deaths. Initial treatment generally consists of complete or near complete surgical resection followed by adjuvant treatment with both post-operative radiotherapy and chemotherapy. Despite the fact that survival rates have improved over past decades, the delivery of individualized therapies based on patient-specific disease-risk profiles remains a major goal; intensified treatment for poor-risk disease, while reducing therapy for favourable-risk cases, with the overall aim of maximizing survival while minimizing late effects [9].
Medulloblastomas can be placed into one of four distinct subgroups, which are defined by specific methylomic, transcriptomic and genomic features. These are WNT, SHH, Group 3 and Group 4 [10]. Each group displays characteristic clinical and pathological features, drug targets and outcomes, and contributes significantly to the 2016 World Health Organization (WHO) classification of brain tumours [11]. Molecular subgrouping is, therefore, an important step in determining the most appropriate course of treatment and follow-up for individual patients [12].
Mass spectrometry minimal methylation classifier (MS-MIMIC) assay
The assay we have developed and validated, MS-MIMIC, is a novel polymerase chain reaction (PCR)-based assay for the multiplexed assessment of multiple signature CpG loci. We first identified a DNA methylation signature of 17 CpG loci using genome-scale Illumina 450k DNA methylation microarray data from 220 medulloblastoma cases. The 50 most discriminatory CpG loci for each molecular subgroup (200 loci in total) were considered as candidates for inclusion in the signature set. These were triaged using a 10-fold cross validated classification fusion algorithm, followed by a reiterative primer design process where amenability to primer design and multiplex bisulfite PCR was assessed in silico before finally undergoing in vitro PCR validation.
Candidate signature CpG loci were then analysed by a specific custom iPLEX assay [13] (Agena Bioscience). In this method (displayed schematically in Fig. 2), methylation-dependent SNPs representative of CpG methylation status are induced by initial treatment of DNA with sodium bisulfite [14] followed by multiplexed target-region amplification PCR, then single base extension and termination of target-specific probe oligonucleotides. The products of this reaction are analysed using MALDI-ToF (matrix-assisted laser desorption and ionisation – time of flight) mass spectrometry (MassARRAY System, Agena Bioscience). Each potential CpG locus variant yields a product with a unique and characteristic mass, enabling their rapid and unambiguous identification. MALDI-ToF analysis of single base variants is widely used to provide clinical DNA diagnostics in related genotyping applications [15], and is the key technical innovation which enables the robust assessment of medulloblastoma molecular subgroup, especially for samples which are refractory to analysis using conventional DNA methylation-array based methods.
Using these techniques, we generated an optimal, multiply-redundant 17-CpG locus signature and a robust assay for its detection.
A Support Vector Machine (SVM) classifier for the signature was then developed, using the existing 450k DNA methylation array data as a training set. SVM is a supervised machine learning technique commonly used in multiple areas of data analysis, including analysis of microarray data [16], making it well-suited to this application. Crucially, it returns a probability of group membership, enabling the assessment of confidence of subgroup assignment.
Next, we assessed MS-MIMIC performance against Illumina 450k methylation microarrays using an independent validation cohort of 106 medulloblastoma DNA samples which contained examples of all four medulloblastoma subgroups. These samples were also derived from tissue which reflected different clinical fixation methods commonly in use; fresh-frozen biopsies (n=40), FFPE tumour section (n=39), or FFPE-derived nuclear preparations [17] (n=27) produced by cytospin, a pre-analytical method that uses centrifugation to create a monolayer of cells for analysis on a slide from a low-concentration cellular suspension sample [18]. In this validation cohort, MS-MIMIC faithfully recapitulated DNA methylation array molecular subgroup assignments.
Quality control measures for CpG locus-specific assay failure were established; up to six failed CpG loci per sample were tolerated within the multiply-redundant signature/classifier, without impacting performance. Forty-three out of 106 validation cohort samples were affected by at least one locus failure, reflecting the damaged nature of DNA generally obtained from some of these samples. Five out of 106 samples had more than seven failed CpGs and were deemed not classifiable (NC). Molecular subgroup classifications were then compared, with MS-MIMIC classifications showed complete concordance with the reference subgroup, as determined by DNA methylation array [10]. Furthermore, CpG-level methylation estimates (β-values) were equivalent between methods (R2 = 0.79). As anticipated, fresh-frozen derivatives performed best (n=39/40; 98% successfully subgrouped), with 91% success (n=56/61) using FFPE-derived DNA from tumour sections and cytospin preparations (Fig. 3a–c).
Application of MS-MIMIC to the HIT-SIOP-PNET4 clinical trials cohort
Following successful assay development and validation, we next wished to test MS-MIMIC methylation signature detection in limited, poor quality, archival, clinical biopsies. Analysis of remnant material from the HIT-SIOP-PNET4 cohort [17] offered the first opportunity to determine the potential utility of molecular subgroup status to predict disease outcome in a clinical trial of risk-factor negative, ‘standard risk’ (SR) medulloblastoma. Only FFPE sections (n=42/153 available tumour samples) and cytospin nuclear preparations (approximately 30 000 nuclei isolated and centrifuged onto microscope slides; n=111/153) remained from this study archive and all DNA preparations fell below quality and quantity thresholds (>200 ng double-stranded DNA (dsDNA)) required for methylation profiling using conventional research methods (Illumina 450k and MethylationEpic arrays [16]). Using MS-MIMIC, 70% (107/153) of samples were successfully subgrouped, and subgroup assignments and β-value estimations were consistent across duplicate determinations. Assay performance was equivalent across the input DNA range (<2 ng (limit of detection) to 100 ng dsDNA (41.4 ng median DNA input).
Reasons for assay failure included unsuccessful bisulfite conversion/PCR (6%; 9/153), and inability to classify due to assay QC failure (24%; 37/153). These findings from HIT-SIOP-PNET4 reveal important subgroup-dependent molecular pathology in SR medulloblastoma. Group 4 was most common (n=62; 58%), with approximately equivalent numbers of WNT (18/170; 16%), SHH (17/107; 16%) and Group 3 (10/107; 9%) tumours observed. The majority (11/13) of events (defined as disease recurrence or progression following treatment) affected Group 4 patients [82% 5-year progression-free survival (PFS)], with >95% PFS in non-Group 4 patients. Subgroup assignment will thus be essential to inform future clinical and research studies in SR medulloblastoma.
Discussion
Detection of disease-specific variations in DNA methylation patterns has great potential for both supporting biomedical research and improving the quality of care that is delivered to patients. MS MIMIC has so far only been applied to medulloblastoma but this approach has clear potential for use in other cancers [7] and in other diverse settings, for example smoking [1], obesity [2], human fetal alcohol spectrum disorder [3] and aging [4].
Key resources which must be available for development of an MS-MIMIC assay for a given condition are a suitable collection of data concerning disease-state specific methylation patterns obtained using an array system such as those mentioned above, samples with which to perform assay validation, bioinformatics knowledge and support to create, optimize and operate the disease-specific SVM classifier system, plus access to a MassARRAY System for analysis. MS-MIMIC is discussed in greater detail in Schwalbe et al., 2017 [19].
References
1. Besingi W, Johansson A. Smoke-related DNA methylation changes in the etiology of human disease. Hum Mol Genet 2014; 23: 2290–2297.
2. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, Tsai PC, Ried JS, Zhang W et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 2017; 541: 81–86.
3. Portales-Casamar E, Lussier AA, Jones MJ, MacIsaac JL, Edgar RD, Mah SM, Barhdadi A, Provost S, Lemieux-Perreault LP et al. DNA methylation signature of human fetal alcohol spectrum disorder. Epigenetics Chromatin 2016; 9: 1–20.
4. Ong ML, Holbrook JD. Novel region discovery method for Infinium 450 K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways. Aging Cell 2014; 13: 142–155.
5. Mehta D, Klengel T, Conneely KN, Smith AK, Altmann A, Pace TW, Rex-Haffner M, Loeschner A, Gonik M et al. Childhood maltreatment is associated with distinct genomic and epigenetic profiles in posttraumatic stress disorder. Proc Natl Acad Sci USA 2013; 110: 8302–8307.
6. Bacalini MG, Gentilini D, Boattini A, Giampieri E, Pirazzini C, Giuliani C, Fontanesi E, Scurti M, Remondini D et al. Identification of a DNA methylation signature in blood cells from persons with Down Syndrome. Aging 2015; 7: 82–96.
7. Sturm D, Witt H, Hovestadt V, Khuong-Quang DA, Jones DT, Konermann C, Pfaff E, Tönjes M, Sill M et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 2012; 22: 425–437.
8. Smoll NR, Drummond KJ. The incidence of medulloblastomas and primitive neurectodermal tumours in adults and children. J Clin Neurosci 2012; 19: 1541–1544.
9. Pizer BL, Clifford SC. The potential impact of tumour biology on improved clinical practice for medulloblastoma: progress towards biologically driven clinical trials. British Journal Of Neurosurgery 2009; 23: 364–375.
10. Taylor MD, Northcott PA, Korshunov A, Remke M, Cho YJ, Clifford SC, Eberhart CG, Parsons DW, Rutkowski S et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol 2012; 123, 465–472.
11. Louis DN, Cavenee WK, Ohgaki H, Wiestler OD. WHO classification of tumours of the central nervous system, 4th edn. pp.184–200. IARC Press, 2016.
12. Schwalbe EC, Williamson D, Lindsey JC, Hamilton D, Ryan SL, Megahed H, Garami M, Hauser P, Dembowska-Baginska B et al. DNA methylation profiling of medulloblastoma allows robust subclassification and improved outcome prediction using formalin-fixed biopsies. Acta Neuropathol 2013; 125: 359–371.
13. Gabriel S, Ziaugra L, Tabbaa D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Current Protocols in Human Genetics, Chapter 2: Unit 2.12. Wiley & Sons 2009.
14. Wang RY, Gehrke CW, Ehrlich M. Comparison of bisulfite modification of 5-methyldeoxycytidine and deoxycytidine residues. Nucleic Acids Res 1980; 8: 4777–4790.
15. Griffin TJ, Smith LM. Single-nucleotide polymorphism analysis by MALDI-ToF mass spectrometry. Trends Biotechnol 2000; 18: 77–84.
16. Hovestadt V, Remke M, Kool M, Pietsch T, Northcott PA, Fischer R, Cavalli FM, Ramaswamy V, Zapatka M et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol 2013; 125: 913–916.
17. Clifford SC, Lannering B, Schwalbe EC, Hicks D, O’Toole K, Nicholson SL, Goschzik T, Zur Mühlen A, Figarella-Branger D et al. Biomarker-driven stratification of disease-risk in non-metastatic medulloblastoma: Results from the multicentre HIT-SIOP-PNET4 clinical trial. Oncotarget 2015; 6: 38827–38839.
18. Koh CM. Preparation of cells for microscopy using cytospin. Meth Enzymol 2013; 533: 235–240.
19. Schwalbe EC, Hicks D, Rafiee G, Bashton M, Gohlke H, Enshaei A, Potluri S, Matthiesen J, Mather M et al. Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures. Sci Rep 2017; 7: 13421.
The authors
Ben Chaffey1 PhD, Debbie Hicks2 PhD, Edward Schwalbe3 PhD, Steve Clifford2* PhD
1NewGene Ltd, International Centre for Life, Newcastle-upon-Tyne, UK
2Wolfson Childhood Cancer Research Centre, Northern Institute for Cancer Research, Newcastle University, Newcastle-upon-Tyne, UK
3Northumbria University, Newcastle-upon-Tyne, UK
*Corresponding author
E-mail: steve.clifford@ncl.ac.uk
Point-of-care glucose meters: useful in a neonatal setting
, /in Featured Articles /by 3wmediaPoint-of-care glucose meters are used in a variety of settings to monitor glucose concentration in whole blood. Comparability between the results from these meters and results issued on plasma samples was examined by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), which in 2006 recommended that all glucose results should be reported as a plasma concentration. The group advised that a conversion factor of 1.11 be used to convert whole blood results to plasma equivalence. As neonatal hematocrit differs from that seen in adults, the IFCC recommendation is not appropriate in neonatal samples. It was decided to review this recommendation.
by Mary Stapleton and Ruth O’Kelly
Introduction
Neonates may be at risk of hypoglycemia in the first few hours and days after birth, the cause of which may be attributed to the stress of extra-uterine life [1]. However, it may also signal an underlying pathology, and prolonged episodes of hypoglycemia have been described as a cause of neurodevelopmental morbidity [2]. Identification of hypoglycemic episodes is, therefore, considered to be vital in the neonatal period, but the population in question often includes extremely premature and small infants. By regularly using point-of-care (POC) devices to measure glucose in this cohort of patients, it is hoped to obtain useful results while avoiding unnecessary blood loss.
In instances where glucose results obtained on POC devices do not fit the clinical picture, a fluoride-preserved sample may be sent for plasma analysis.
Discrepancies between POC whole blood and laboratory plasma results may be a cause of lack of confidence in bedside technology. There are several causes of such discrepancies, and while literature has suggested that hypoglycemia is missed by using POC devices, the role of glycolysis as a pre-analytical factor is starting to be recognized [3]. The second possible cause is that differing sample types are measured and unlikely to be comparable. In 2006, the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) published a recommendation that manufacturers of POC devices were to report glucose concentration as though it were a plasma sample rather than whole blood. A conversion factor of 1.11 was calculated to equate the results from the two sample types (whole blood × 1.11 = plasma) [4].
The aim of this study was to perform glucose measurements in neonatal and adult whole blood and plasma samples by a laboratory method and a POC method without a plasma correction factor. By comparing results, it was hoped to determine the appropriateness of the plasma conversion factor as recommended by the IFCC.
Methods
The HemoCue 201+ POC methodology that was used to analyse whole blood samples consists of an analyser and measuring cuvette containing dried reagents. The cuvette serves as a pipette, reaction chamber and measuring vessel. Analysis of plasma for glucose concentration was performed on an automated chemistry platform (Beckman Coulter AU640) using a hexokinase method in a laboratory accredited to ISO 15189 standards.
Samples for plasma glucose analysis were obtained in tubes containing fluoride as an antiglycolytic agent. When measuring glucose in the POC device, an aliquot of sample was taken from the sample in the blood tube before separation.
Statistical analysis, using Bland–Altman analysis to compare results by two different methods, was performed using Analyse-It software for Microsoft Excel (Analyse-It Software Ltd).
Study 1
Fluoride-stabilized plasma samples from 25 neonates (aged 3 days or less) received into the laboratory for routine glucose estimation were included in the study. An aliquot was taken from each sample before centrifugation and analysis, and glucose determination by POC was performed on a HemoCue 201+ analyser located in the laboratory.
Study 2
Fluoride plasma samples from pregnant women (n = 34) were also analysed for whole blood and plasma glucose in the same manner described in study 1.
Study 3
A portion of patients who were a part of the study had a sample sent for full blood count (FBC) analysis on the same day of the glucose request. Results were subdivided into greater and less than the median result for both hematocrit and mean corpuscular volume (MCV). These were then reviewed against the reported glucose concentrations.
Results
Studies 1 and 2
No significant difference was noted between neonatal samples analysed (Table 1, Fig. 1) (bias, 0.05mmol/L). However, a significant difference (P<0.0001) was noted between the two methods when samples had been obtained from adult patients (Table 2, Fig. 2) (bias, 0.6mmol/L).
Study 3
A standard calculation for determining the percentage of water in blood was reviewed (Equation 1). The data obtained from the FBC samples was used to propose plasma conversion factors for both adult and neonatal patients (Table 3). It was assumed that the median hematocrit in a healthy, non-pregnant adult is 0.43 L/L, with a resulting calculated conversion factor (CCF) of 1.11.
Discussion
This study investigated the reported difference between samples analysed for glucose using POC meters in a ward setting and those samples received for glucose analysis in a central laboratory. It may be seen that there is good correlation between POC and laboratory analyser methods in samples obtained from neonates.
This correlation was not seen in the set of adult samples analysed, and an average difference of up to 10% in results was reported from the two methods. By applying a plasma equivalence factor of 1.11 to the whole blood results from adults as recommended by the IFCC in 2006, the difference in results from adult patients could be explained.
The IFCC equivalence factor based on the hematocrit in neonates is 1.15, but this study confirms that the neonatal samples did not require this factor. POC glucose measurements in the HemoCue device include a cell lysis step and thus whole blood (intra-and extra-cellular) glucose is measured. However, neonatal blood is recognized as containing resistant cells and cells may not fully lyse causing the measured glucose to reflect extra-cellular glucose similar to plasma measurements.
In a previous study [5], Vadasdi and Jacobs compared heparinized samples from neonates that were analysed on the HemoCue immediately before centrifugation and assayed by the laboratory method. No significant difference was found between the mean values of the two methods over a hematocrit range of 0.185–0.72. Our study agrees with these findings.
Vadasdi and Jacobs suggested that the effect of hematocrit was decreased significantly by the hemolysis step in the cuvette. It is recognized that HemoCue POC meters are not affected by hematocrit [4, 5], which is why this meter is frequently used in a neonatal setting. Vadasdi and Jacobs also suggested that because the MCV (which describes the size of the red cells) is greater than seen in adults, there is less of a dilutional effect due to membrane proteins after lysis. Our study showed that the mean MCV in neonates was greater than seen in our adult (pregnant) subjects.
Conclusion
Laboratory measurements for glucose are usually performed on plasma samples while POC measurements are performed on whole blood. A difference in results may be expected as whole blood glucose is known to be approximately 11% lower than plasma glucose due to lower volume of water in the erythrocytes.
The difference between plasma and whole blood glucose in adults was similar to the recommended IFCC “plasma equivalent factor” of 1.11. The lack of difference between plasma and whole blood glucose in neonatal samples may be explained by the increased MCV or the presence of resistant red cells that may not undergo lysis in the POC device.
Many modern POC devices for measuring glucose now include the IFCC plasma conversion factor and such results should be carefully interpreted.
References
1. World Health Organization. Hypoglycaemia of the newborn. Review of the literature. WHO/CHD/97.1, 1997.
2. Lucas A, Morley R, Cole TJ. Adverse neurodevelopmental outcome of moderate neonatal hypoglycaemia. BMJ 1988; 297(6659): 1304–1308.
3. Stapleton M, Daly N, O’Kelly R, Turner MJ. Time and temperature affect glycolysis in blood samples regardless of fluoride- based preservatives: a potential underestimation of diabetes. Ann Clin Biochem 2017; 54: 671–676.
4. D’Orazio P, Burnett RW, Fogh-Anderson N, Jacobs E, Kuwa K, Külpmann WR, Larsson L, Lewenstam A, Maas AH, et al. Approved IFCC recommendation on reporting results for blood glucose: International Federation of Clinical Chemistry and Laboratory Medicine Scientific Division, Working Group on Selective Electrodes and Point of Care Testing (IFCC-SD-WG-SEPOCT). Clin Chem Lab Med 2006; 44: 1486–1490.
5. Vadasdi E, Jacobs E. HemoCue β-glucose photometer evaluated for use in a neonatal intensive care unit. Clin Chem 1993; 39(11): 2329–2332.
The authors
Mary Stapleton* FRCPath; Ruth O’Kelly FRCPath
Biochemistry Department, Coombe Women & Infants University Hospital, Dublin, Ireland
*Corresponding author
E-mail: mary.stapleton@nhs.net