Individuals 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

Circulating 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.

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

Current emergency department strategies are aimed at reliably excluding myocardial infarction as soon as possible through clinical assessment and time-dependent measurement of high-sensitivity cardiac troponin. Point-of-care cardiac troponin methods have evolved, but can they be used to support the early rule-in or rule-out strategies for myocardial infarction?

by Dr Martha E. Lyon and Dr Andrew W. Lyon

Introduction
Significant attention has recently focused on early rule-in and rule-out strategies to detect non-ST-segment elevation (NSTEMI) acute myocardial infarction in the emergency department (ED) [1]. In 2015, the European Society of Cardiology (ESC) introduced guidelines for the management of acute coronary syndrome in patients without ST-segment elevation [2]. This guideline included interpretative algorithms to rule in or rule out acute myocardial infarction (AMI) based on clinical symptoms, high-sensitivity cardiac troponin (hs-cTn) concentrations at specific thresholds and changes in hs-cTn over intervals of 1 or 3 hours (Fig. 1) [2]. Importantly, the guidelines also highlighted the time-dependent uncertainty of using low concentration cut-offs in patients presenting early after the onset of pain with the following comment, “Only applicable if chest pain onset <3 h.” hs-cTn methods used in hospital clinical laboratories are expected to have an imprecision of ≤10% at the 99th percentile of a healthy population and allow for the detection of at least 50% and ideally >95% of healthy individuals [3]. The analytical qualities of the high-sensitivity methodology enable excellent diagnostic performance, that being a 99% clinical sensitivity and negative predictive value. However, it should be acknowledged that the prevalence of AMI in a specific population and the clinical sensitivity of the cardiac troponin test will influence the calculation of the negative predictive value [1]. Physicians will need to confirm that clinical trial populations are representative of their local population in order to verify the applicability of the diagnostic performance of the hs-cTn method [1].

Many studies with hs-cTn methods have investigated the derivation of upper reference limits, rates of change in hs-cTn concentration to detect AMI, assay imprecision at the 99th percentile concentration and performance characteristics of commercial assays with various interpretative thresholds [2, 4, 5]. Additional factors such as hemolysis, anticoagulant-bias, and within-subject variation will cause bias and imprecision in method results [6, 7]. An understanding of the interaction between these factors is incomplete because of the limited sample size in many clinical studies and the poor correlation between commercial assays [8]. In an initial attempt to understand this complex and complicated interaction, we used computer simulation models to predict the influence of method bias and imprecision on the rates of misclassification at low interpretative thresholds to rule out AMI and at thresholds near or exceeding the overall 99th percentile to rule in AMI. We found that at low thresholds, only method bias and not imprecision influenced the rate of misclassification whereas both method bias and imprecision would affect the early rule-in for AMI [9].

Point-of-care (POC) cardiac troponin devices
Short turnaround testing (STAT) in central hospital laboratories commonly employs the expectation of 1-hour turnaround time once the specimen has arrived in the laboratory. The ability to consistently meet the earlier time points, as outlined in the guideline algorithms, will represent a logistic challenge for many hospitals. POC methods provide an appealing alternative to central laboratory assessment, in particular for the initial rule-in when elevated cTn levels are present at Time 0 hr as well as sequential monitoring. However, prior to implementing a POC troponin method, comparison studies between the POC method and central laboratory cardiac troponin methods need to be conducted to assure concordance of the results. Several studies have reported a significant gap in the analytical sensitivity between hs-cTn and POC troponin methods [10, 11]. In 2015, Amundson and Apple described the analytical performance characteristics of POC cardiac troponin methods from nine different manufacturers [12]. These characteristics included clinical sensitivity and specificity, analytical imprecision, specimen type and preparation as well as the method principle of analysis. Although each of the devices provided different qualities, it was determined that an imprecision of ≤20% at the 99th percentile was paramount to limit both false-positive and false-negative results [12].

Accurate and precise measurement of cardiac troponin is essential for the consistent identification of NSTEMI patients with acute coronary syndrome. Currently, significant variation exists between clinical laboratory hs-cTn methods that could influence the clinical care provided to patients [8]. This also represents a challenge to the adoption of POC cTn technology.

Simulation models
Computer simulation model utility has been demonstrated with investigations of the impact of method bias and imprecision on potential clinical risk of insulin dosing errors with glucose meters [13], warfarin dosing errors with POC international normalized ratio (INR) devices [14] and with early rule-in and rule-out of AMI misclassification rates with cTn methods [9]. Clinical studies are challenging to conduct with NSTEMI patients presenting early in an emergency department because of variation in disease prevalence, poor correlation between cTn analytical methods and uncertainty in the time of pain onset. Low prevalence and variation in disease over time are common problems with other clinical laboratory biomarkers such as anti-viral antibodies as well as first-trimester pregnancy screening methods [15, 16]. One solution to this concern has been to generate finite mixture models of biomarker distributions to predict biomarker assay performance [17]. These simulated databases have been used to understand the relationship between clinical risk and assay characteristics and to extend use of the statistical information gathered in small clinical trials.

Simulation model investigation of the utility of POC cardiac troponin testing
Recognizing the lack of hs-cTn method standardization, the low incidence of NSTEMI AMI and the high cost of conducting clinical trials, few studies have assessed the utility of POC cardiac troponin methods to rule-in or rule-out AMI. To overcome these limitations, we recently used a simulation model to predict the diagnostic performance of two POC troponin methods (Radiometer AQT90 and Roche cobas h 232) relative to a hs-cTnT method (Roche cobas 6000/Elecsys) using an Emergency Department patient database proportionately expanded to n=10 000 in a finite mixture model. This study was presented at the 2017 Annual Conference for the American Association of Clinical Chemistry and Canadian Society of Clinical Chemists [18]. Finite mixture analysis of the 0-hr data obtained from the ROMI trial (n=1137 Optimal Troponin Cut-Offs for acute coronary syndrome by Roche hs-cTnT) enabled derivation of a simulation data set (n=10 000) troponin test results. Published regression equations were used to convert the hs-cTNT results into simulated AQT90 and h 232 cTnT results [19, 20]. Clinical sensitivity, specificity, positive and negative predicative values were calculated using the simulated hs-cTnT, AQT90 and h 232 data for AMI diagnosis using the limit of detection for the assays (Table 1).

The Roche hs-cTnT in this simulated data set achieved both sensitivity and negative predictive values above 99%. The predicted performance of the Radiometer AQT90 cTnT POC assay approached the estimates for hs-cTnT, suggesting POC methods are emerging that could be used both for AMI rule-in and AMI rule-out. The high limit of detection of the h 232 POC method limited its sensitivity and negative predictive values.

Conclusions
Rapid and reliable measurements of cardiac troponins are ongoing analytical challenges in laboratory medicine because this clinical tool is being used both to rule in and rule out NSTEMI. Clinical trials will be required to prospectively measure the diagnostic utility of high-sensitivity central laboratory and novel POC cTn methods, but simulation studies provide useful predictions. Our recent simulation study predicted we are now in an age where POC cTn methods are approaching analytical performance necessary to effectively rule in and rule out NSTEMI.

References
1. Morrow DA. Clinician’s guide to early rule-out strategies with high sensitivity cardiac troponin. Circulation 2017; 135: 1612–1616.
2. Roffi M, Patrono C, Collet JP, Mueller C, Valgimigli M, Andreotti F, Bax JJ, Borger MA, Brotons C et al. 2015 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: task force for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J 2016; 37: 267–315.
3. Jarolim P. High sensitivity cardiac troponin assays in the clinical laboratories. Clin Chem Lab Med 2015; 53: 635–652.
4. Wildi K, Gimenez MR, Twerenbold R, Reichlin T, Jaeger C, Heinzelmann A, Arnold C, Nelles B, Druey S et al. Misdiagnosis of myocardial infarction related to limitations of the current regulatory approach to define clinical decision values for cardiac troponin. Circulation 2015; 131: 2032–2040.
5. Love SA, Sandoval Y, Smith SW, Nicholson J, Cao J, Ler R, Schulz K, Apple FS. Incidence of undetectable, measurable, and increased cardiac troponin I concentrations above the 99th percentile using a high-sensitivity vs a contemporary assay in patients presenting to the emergency department. Clin Chem 2016; 62: 1115–1119.
6. Krintus M, Kozinski M, Boudry P, Capell NE, Koller U, Lackner K, Lefèvre G, Lennartz L, Lotz J et al. European multicenter analytical evaluation of the Abbott ARCHITECT STAT high sensitive troponin I immunoassay. Clin Chem Lab Med 2014; 52: 1657–1665.
7. Ryan JB, Wallace J, Sies CV, Florkowski CM, George PM. Evaluation of Abbott Architect high-sensitivity troponin I assay for haemolysis interference. Pathology 2015; 47: 716–718.
8. Ungerer JPJ, Tate J, Pretorius JC. Discordance with 3 cardiac troponin I and T assays: implications for the 99th percentile cut-off. Clin Chem 2016; 62: 1106–1114.
9. Lyon AW, Kavsak P, Lyon OAS, Worster A, Lyon ME. Simulation models of misclassification error for single thresholds of high-sensitivity cardiac troponin I due to assay bias and imprecision. Clin Chem 2017; 63: 585–592.
10. Palamalai V, Murakami MM, Apple FS. Diagnostic performance of four point of care troponin I assays to rule in and rule out acute myocardial infarction. Clin Biochem 2013; 46: 1631–1635.
11. Bruins Slot MHE, van der Heijden GJMG, Stelpstra SD, Hoes AW, Rutten FH. Point-of-care tests in suspected acute myocardial infarction: A systematic review. Int J Cardiol 2013; 168: 5255–5262.
12. Amundson BE, Apple FS. Cardiac troponin assays: a review of quantitative point-of-care devices and their efficacy in the diagnosis of myocardial infarction. Clin Chem Lab Med 2015; 53: 665–676.
13. Karon BS, Boyd JC, Klee GG. Glucose meter performance criteria for tight glycemic control estimated by simulation modeling. Clin Chem 2010; 56: 1091–1097.
14. Lyon ME, Sinha R, Lyon OAS, Lyon AW. Application of a simulation model to estimate treatment error and clinical risk derived from point-of-care INR device analytic performance. J Appl Lab Med 2017; 2: 25–32.
15. Harelip P, Williams D, Dezateux C, Tookey PA, Peckham CS. Analysis of rubella antibody distribution from newborn dried blood spots using finite mixture models. Epidemiol Infect 2008; 136: 1698–1706.
16. Wright D, Abele H, Baker A, Kagan KO. Impact of bias in serum free beta-human chorionic gonadotroponin and pregnancy-associated plasma protein-A multiples of the median levels on first-trimester screening of trisomy 21. Ultrasound Obstet Gynecol 2011; 38: 309–313.
17. Deb P, Trivedi PK. Demand for medical care by the elderly: a finite mixture approach. J Appl Econ 1997; 12: 313–326.
18. Lyon ME, Kavsak PA, Worster A, Lyon AW. Simulation models to rule out acute myocardial infarction with two point-of-care testing devices and a high sensitivity cardiac troponin T method. Poster Presentation. AACC/CSCC Annual Meeting, San Diego, CA, USA, 2017.
19. Bertsch T, Chapelle JP, Dempfle CE, Giannitsis E, Schwab M, Zerback R. Multicentre analytical evaluation of a new point-of-care system for the determination of cardiac and thromboembolic markers. Clin Lab 2010; 56: 37–49.
20. Le Goff C, Evrards S, Brevers E, Kaux JF, Cavalier E. Evaluation of troponin T on AQT90 and Cobas 8000 as a rule-in/-out tool in an emergency ward. Poster Presentation, EuroMed Lab Conference 2014.

The authors
Martha E. Lyon* PhD, DABCC, FACB and Andrew W. Lyon PhD
Department of Pathology & Laboratory Medicine, Division of Clinical Biochemistry, Saskatoon Health Region, Saskatoon, Saskatchewan, Canada

*Corresponding author
E-mail: martha.lyon@saskatoonhealthregion.ca

The need to differentiate patients with advanced liver disease from those with earlier stage, or more benign diseases for optimal management and allocation of resources is an ever present challenge. In this article we discuss our experiences of using the aspartate transaminase (AST) : alanine transaminase (ALT) ratio as part of a pathway to screen patients for referral to secondary care.

by Dr Raphael Buttigieg and Dr Sara Jenks

Introduction
Deaths from liver disease in Scotland are on the increase [1]. More often than not patients are picked up at a late stage of their disease with significant fibrosis and/or cirrhosis already present. As a result there is a need to try to identify patients with progressive disease earlier on in the course of their illness.

Abnormal liver function tests (LFTs) are frequently picked up on general screening blood samples done in primary care. The degree of abnormality correlates poorly with the extent of liver disease. The gold standard test for liver disease diagnosis and staging is considered to be a liver biopsy; however, there are many other considerations to this invasive procedure including clinical risk, technical ability of person doing the biopsy, inter-pathologist variation in scoring and others. These limitations have led to the development of non-invasive methods for the assessment of liver fibrosis. Although there have been suggestions by different groups regarding the appropriate use of non-invasive fibrosis scoring systems, no one guideline is currently in use.

Non-invasive methods rely on two different approaches [2]:
(a) A biomarker-based approach using serum samples. Advantages are their high applicability (>95%) and good inter-laboratory reproducibility.
(b) A physical approach based on the measurement of liver stiffness. Liver stiffness corresponds to an intrinsic physical property of liver parenchyma. Physical approaches include transient elastography such as FibroScan^® and MR elastography.

Because of noted variations in care, as well as to ensure appropriate referrals, NHS Lothian made a guideline for GPs in 2013. At the time, based on the best available clinical evidence, the aspartate transaminase (AST) : alanine transaminase (ALT) ratio was chosen as a scoring system to guide referrals, which was developed in recognition that as liver fibrosis develops, the normal ratio tends to reverse. An abnormal AST:ALT ratio can, thus, be used to pick up patients who should be referred to secondary care for further investigation, as well as closer monitoring and treatment.

However, other biomarker-based fibrosis risk scores have also been developed [2], which have been used for this purpose including the Fibrosis-4 (FIB-4) [3], NAFLD (non-alcoholic fatty liver disease) fibrosis score, and APRI (AST to platelet ratio index), which may have a better performance than the AST:ALT ratio [4]. Each of these has been validated for different liver diseases – and in many cases different cut-off points are recommended for diagnosis of advancing fibrosis based on the likely primary pathology involved in the individual patient. For example, alcohol use in itself will raise the AST and, thus, the same AST:ALT ratio is likely to indicate more advanced fibrosis in someone with HCV-related liver disease than in alcoholic liver disease with ongoing ethanol excess. This adds to the complexity of using any one score in a guideline to ensure the right balance between sensitivity and specificity.

A final consideration to note is that specifically for NAFLD/non-alcoholic steatohepatitis (NASH), the continued development of pharmaceuticals for the prevention of disease progression means that, once again, the threshold for diagnosis may need to change as therapies to target earlier stages become available [5].

This article will discuss our guideline (Fig. 1) and conclusions drawn from an audit of its use.

Method
A list of all the requests for a AST:ALT ratio in a 6-month period in NHS Lothian was obtained from laboratory records – in terms of date of request, patient name and CHI number (unique patient identifier) (n=874). These records were encoded into a spreadsheet and a plan for analysis made.
Following this, various data were audited retrospectively from the patient electronic record. Individual notes and files were not used because of the logistical difficulty in analysing large numbers of case notes.
Of the total number of ratios (n=874) requested in the 6-month period, 49 were elevated at >1.0 and 295 were normal at ≤1.0; 530 ratio requests were cancelled due to ALT being within the reference range.

Results
The various aspects of the referral process from primary to secondary care were audited with the following aims.

1. To identify all the abnormal ratios in a 6-month period (n=49) (Table 1).

2. To identify all the ratios in the same 6-month period that were in the range 0.8–1.0 (n=53) (Table 2).
This was carried out to assess whether there should be concern about the ratio producing false negative results, and how useful it was to actually exclude liver disease. We thus audited all patients with a borderline ratio of 0.8–1.0.
Additionally, we asked if the FIB-4 score or APRI score was used, would this have affected referral?

3. To identify the first 50 individuals in a 6-month period tested with an ALT level of 40–49 on whom the AST:ALT ratio had been cancelled (n=50) (Table 3).
Although an upper limit of 50 is taken for the normal range of ALT, there is evidence that even at levels below this a certain amount of liver inflammation is present, and, thus, different health boards use other values – such as an upper limit of normal of 40.

This last part of the project set out to identify people with a borderline abnormal ALT of 40–49, and assess whether using different scores – such as the FIB-4 or APRI scores would potentially label these individuals as having liver disease and needing to be referred

Limitations of our study
Most of these patients had a very short-term follow-up, which in many cases did not allow proper determination of their disease severity, as well as assessment of long-term mortality/morbidity risk using different scoring systems.

Secondly, we were unable to compare scores to a gold standard as in many cases a liver biopsy had not been carried out. Transient elastography and hyaluronic acid testing had been carried out in a selection of patients which allowed further characterization of fibrosis staging; however, it is appreciated that neither of these are the gold standard.

Conclusions and considerations
The AST:ALT ratio is a good test for assessing whether people should be referred to secondary care or not. This conclusion is based on the fact that many patients who were referred with a positive ratio were seen in secondary care and kept under review. However, better tests are needed to further assess their stage of disease, ideally non-invasively.

The FIB-4 (possibly in association with further tests below) may be a more sensitive/specific score to be used in diagnosing patients; however, cut-off points would need to be determined to guide the most effective use of available resources in primary and secondary care. As can be seen in the second group FIB-4 and the APRI were raised in patients which would not have been picked up by the AST:ALT ratio, which thus increases pick-up. Another consideration is, as previously mentioned, that the AST:ALT ratio tends to be raised in patients drinking excessive ethanol, even if their disease is not very advanced. Since in our cohort alcohol use was very prevalent, other scores may possibly be better suited.
The plan from now is to adopt a pathway of cascading lab tests based on patients’ alcohol consumption, BMI/metabolic syndrome markers, LFT results and automatic scoring with interpretation will be issued to GPs. Also possible is further testing – either in the community or in secondary care to further guide patients in different scoring groups – including either transient elastography (FibroScan), or further biochemical testing. NHS Lothian currently uses hyaluronic acid, and this may be a way of further classifying/evaluating people in ‘intermediate’ categories. The elastography (FibroScan) test could be another option and this is the current recommendation in the current NICE guidelines.

For any further information please feel free to contact the authors:
Raphael Buttigieg: ST3 Chemical Pathology and Metabolic Medicine, NHS Greater Glasgow and Clyde, UK; raphael.buttigieg@nhs.net.
Sara Jenks: Consultant in Chemical Pathology and Metabolic Medicine, NHS Lothian, Edinburgh, UK; sjenks@nhs.net.

References
1. Gray L, Leyland AH. Alcohol. The Scottish Health Survey 2014: Volume 1: Main report (http://www.gov.scot/Publications/2015/09/6648/318753)
2. European Association for Study of Liver. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. J Hepatol 2015; 63(1): 237.
3. McPherson S, Anstee QM, Henderson E, Day CP, Burt AD. Are simple noninvasive scoring systems for fibrosis reliable in patients with NAFLD and normal ALT levels? Eur J Gastroenterol Hepatol 2013; 25(6): 652–658.
4. Parkes J, Guha IN, Harris S, Rosenberg WM, Roderick PJ. Systematic review of the diagnostic performance of serum markers of liver fibrosis in alcoholic liver disease. Comp Hepatol 2012; 11(1): 5.
5. Dyson JK, Anstee QM, McPherson S. Non-alcoholic fatty liver disease: a practical approach to diagnosis and staging. Frontline gastroenterology 2014; 5(3):211–218.

The authors
Raphael Buttigieg*¹ Sara Jenks^2
1Department of Clinical biochemistry, Glasgow Royal Infirmary, NHS Greater Glasgow and Clyde, UK
²Department of Clinical biochemistry, NHS Lothian, Royal Infirmary of Edinburgh, Edinburgh, UK

*Corresponding author
E-mail: raphael.buttigieg@nhs.net