Kidney disease is one of the most life-threatening complications of diabetes and as the global incidence of diabetes soars, largely due to the dramatic increase in type 2 diabetes (T2DM), there will be a seismic shift in the number of patients in need of treatment through dialysis or transplant. Since up to 40% of diabetic patients develop symptoms of diabetic kidney disease (DKD), accurate and early identification of which patients are at the highest risk of progression from DKD to end stage renal disease (ESRD) will enable early initiation of protective renal therapies with subsequent reduction in healthcare costs and improved patient outcomes.
The cytokine TNFα, part of the Tumour Necrosis Factor (TNF) superfamily that plays a key role in homeostasis, has been implicated in the pathogenesis of diabetic kidney disease for over 20 years [1]. Researchers conclude that the elevated levels seen in diabetic patients could be the result of a TNFα driven dysregulation of the inflammatory/apoptotic pathways, which leads to kidney injury. The spotlight has recently shifted onto the TNF α receptors, Tumour Necrosis Factor Receptor 1 (TNFR1) and Tumour Necrosis Factor Receptor 2 (TNFR2), after a number of studies showed how elevated levels of these proteins were a predictor of progressive kidney disease.
In this article we look at the development of an In-Vitro Diagnostic test (IVD), the ‘EKF sTNFR1 Test’. This has been developed by EKF Diagnostics to measure levels of TNFR1 in plasma or serum in light of scientific evidence that this robust biomarker provides valuable prognostic information for diabetic patients at risk of progressive renal decline and ESRD.
The scientific evidence for the involvement of TNF receptors in kidney disease
Cytokine TNFα is a transmembrane protein generated by many cells, including lipocytes, endothelial cells and leukocytes. After processing by TNFα-converting enzyme (TACE), the soluble form of TNFα is cleaved from transmembrane TNFα and mediates its biological activities through binding the receptors TNFR1 and TNFR2 either in their transmembrane or soluble forms to activate inflammatory and stress response pathways (Figure 1). Transmembrane TNF-α also binds to TNFR1 and TNFR2 so that both transmembrane and soluble TNF-α can mediate downstream signalling events (apoptosis, cell proliferation and cytokine production).
In 2009, at the Joslin Diabetes Center, USA (the world’s largest diabetes research centre and an affiliate of the Harvard Medical School), researchers found that the presence of circulating soluble TNF receptors (sTNFR1 and sTNFR2) were strongly correlated with decreased renal function, or glomerular filtration rate (GFR). The research threw up questions about why these soluble receptors were indicative of renal disease. Were they playing an active part in causing disease, or were they just the by-product of the process? Elevations in circulating sTNFR1 have previously been reported in a wide variety of clinical conditions including cancer, congestive heart failure, rheumatoid arthritis, neurological diseases and infection; so what was their role in kidney disease?
Interestingly, as Niewczas et al. [2] pointed out, the decline of renal function was occurring in T1DM patients who had normal albumin excretion levels. This gave a clue to the researchers that the concentrations of these receptors were not merely markers of the injury leading to ESRD but were also involved in the inception of renal function decline, playing a part in inflammation and apoptosis.
1n 2012, the Joslin researchers published two further studies, on Type 1 and Type 2 diabetes cohorts, [3,4] and found that TNF receptor levels were robust predictors of progressive decline in GFR. The results showed that Type 1 Diabetes patients who had normal renal function at the onset, but TNFR2 levels in the highest quartile had a 60% cumulative incidence of reaching stage 3 Chronic Kidney Disease (CKD) with subsequent risk of progression to ESRD (compared to less than 20% in the lowest three quartiles) (Figure 2).
Most significantly, in Type 2 Diabetes patients with evidence of overt Kidney Disease (as evidence by elevated levels of albumin excretion levels) at the onset of the study, those with levels of TNFR1 in the fourth quartile had an 80% chance of developing renal disease over the twelve year period (compared to less than 20% of those in the lower three quartiles) (Figure 3).
These studies revealed that elevated TNF Receptor levels were a robust predictor of progressive disease in both Type 1 diabetes and Type 2 diabetes. In both studies, the levels of the TNFα levels also tended to predict progressive kidney disease, but less strongly than the TNF receptor levels. The data provided further evidence that inflammation in general, and the TNFα signalling pathway in particular, plays a role in kidney disease.
TNF receptors (TNFR1 and TNFR2) and their role in the disease process
So how are circulating TNFR receptors associated with early GFR reduction and kidney damage? It is known that the 55 kD TNFR1 and 75 kD TNFR2 receptors play a crucial part in apoptosis, survival and key aspects of the inflammatory and immune response. TNFR1 is abundant on all nucleated cells, but TNFR2 expression is restricted mainly to endothelial cells and leukocytes although this varies between normal and diseased tissues. Circulating TNFR1 in the plasma is released by two mechanisms: the inducible cleavage of the 34 kD TNFR1 extracellular domain by an enzyme known as ADAM17 and the constitutive release of a full-length 55 kD TNFR1 within exosome-like vesicles.
It is not-well understood whether the same mechanisms apply to TNFR2 release, or how this process is regulated and the biology of the soluble forms remain largely undiscovered. What is understood, however, is that in plasma, TNF receptors block TNFα from binding its target cell surface receptor and can therefore cause a prolonged and delayed effect of the cytokine. How subsequent damage occurs to the kidney is not well known, however sTNFRs have been shown to be involved in tubulointerstitial fibrosis, the characteristic tissue scaring that leads to kidney disease [5].
Seeing into the future: a powerful diagnostic test for DKD
The diagnosis of DKD is conventionally made by assessment of overall GFR and the presence of kidney damage is ascertained by either kidney biopsy or other markers of kidney damage such as microalbuminuria or proteinuria (collectively known as albuminuria – a condition where protein is lost in the urine). GFR is estimated in clinical practice using readily calculated equations that adjust serum creatinine values (measurement of the by-product of muscle metabolism cleared by the kidneys) to age, sex, and ethnicity. However, while laboratory tests which assess both serum creatinine and albuminuria are inexpensive and readily available, these parameters have a low predictive value.
In 2012, EKF Diagnostics signed an exclusive licence agreement for novel kidney biomarker technology that focused on sTNFR1 and sTNFR2. This was developed by a team led by Prof. Andrzej Krolewski, MD, PhD, Head of Section on Genetics and Epidemiology at the Joslin Diabetes Center, Professor of Medicine at Harvard Medical School. Prof. Krowlewski was recently awarded the American Diabetes Association’s 2014 Kelly West Award in Epidemiology for services to diabetes epidemiology.
EKF Diagnostics has worked in partnership with Joslin and other key diabetes research centres to further validate the clinical utility of the markers and develop its first IVD product, the sTNFR1 test kit. The sTNFR1 test is an easy-to-use, microtitre plate ELISA-based assay requiring minimal training, which uses standard laboratory equipment and monoclonal antibodies to analyse just 50 µL of blood serum or plasma. Accurate and reliable results are obtained in a few hours and the standard assay format means that the test requires minimal training.
Julian Baines, Group Chief Executive Officer of EKF Diagnostics highlights the benefits of the test, “Our new sTNFR1 test adds greatly to information provided by standard clinical tests and provides valuable long term prognostic information for progressive renal decline to ESRD with the potential to streamline diabetic patient management, reduce time and costs and improve patient outcomes.”
Further evidence for the use of sTNFRs for the early prediction of DKD
A number of high impact studies published this year have independently corroborated the original research by the Joslin Diabetes Center. This newly published data from eminent European research centres in France (SURDIAGNE Study Group) and Finland (FinnDiane Study Group) add to the expanding data set underpinning the value of sTNFR1/2 biomarkers.
In the FinnDiane cohort study of over 400 subjects with Type 1 Diabetes followed over an average of 9 years, researchers found that, “Circulating levels of sTNFR1 were independently associated with incidence of ESRD. This association was reported as both significant and biologically plausible and demonstrated added value of sTNFR1 as a biomarker” [6].
In France, Saulnier et al. [7] found results from a study of n=522 Type 2 Diabetes patients with DKD were in accordance with published data, showing a deleterious effect of TNFR1 serum concentrations on renal outcomes.
Further evidence continues to mount for how TNFR biomarkers could be used to improve diabetic patient management and outcomes through early intervention. Lopes-Virella et al. [8] have shown in a large cohort of type 1 diabetes patients, followed for six years, how high levels of sTNFR1 and sTNFR2 can predict progression to macroalbuminuria in patients completely free of disease at baseline. TNFR biomarkers can also help doctors to stratify patients with early kidney disease according to the risk of ESRD. Skupien et al [9] show a strong association between a single baseline measurement of TNFR2 serum concentration combined with measurement of HbA1c levels and the future rate of renal function decline in T1DM patients with proteinuria. Identifying patients at highest risk can ensure they are enrolled in therapeutic programmes to delay the rapid decline in renal function.
The future management of kidney disease
Recent statistics show that 25-40% of patients with diabetes are at significant risk of progression to ESRD and cardiovascular morbidity and mortality [10]. The global increase in the incidence in Type 2 diabetes will put more pressure on healthcare systems making it imperative to identify patients at risk of progressive diabetic kidney disease, and initiate protective renal and cardiovascular therapies. Improving outcomes for chronic kidney disease in diabetic patients also has an important impact on mortality; for example, compared with non-diabetic individuals, patients with Type 1 diabetes have no increase in mortality in absence of DKD [11]. There is now solid evidence that sTNFR1 and sTNFR2 can be useful as biomarkers to predict the progression of kidney disease – and not just in patients with diabetes: recent research in Sweden has shown how circulating sTNFRs are relevant biomarkers for kidney damage and dysfunction in elderly individuals in a community setting [12].
Current treatments for CKD, such as control of hypertension and lifestyle interventions (weight loss, diet control, smoking cessation), can reduce the risk of progression to ESRD; therefore, an advanced knowledge of disease risk up to 10 years in advance that the sTNFR1 test kit can provide would be an extremely valuable tool to effectively prevent or reduce morbidity and mortality. Significantly, the sTNFR1 test is also contributing to the development of new targeted therapies aimed at delaying or halting decline in renal function.
References
1. Hasegawa G et al. Possible role of tumor necrosis factor and interleukin-1 in the development of diabetic nephropathy. Kidney Int. 1991; 40: 1007 –1012.
2. Niewczas MA et al. Serum concentrations of markers of TNF alpha and Fas-mediated pathways and renal function in nonproteinuric patients with type 1 diabetes. Clin J Am Soc Nephrol. 2009; 4: 62-70.
3. Ghoda T et al. Circulating TNF receptors 1 and 2 predict stage 3 CKD in Type 1 diabetes. J Am Soc Nephrol. 2012; 23: 516-24.
4. Niewczas MA et al. Circulating TNF receptors 1 and 2 predict ESRD in Type 2 Diabetes. J Am Soc Nephrol. 2012; 23: 507-15.
5. Guo G et al. Role of TNFR1 and TNFR2 receptors in tubulointerstitial fibrosis of obstructive nephropathy. Am J Physiol. 1999; 277: F766–F772.
6. Forsblom C et al. Added Value of Soluble Tumor Necrosis Factor Alpha Receptor-1 as a Biomarker of ESRD Risk in Patients With Type 1 Diabetes. Diabetes Care 2014; 37: 1–9.
7. Saulnier et al. Association of Serum Concentration of TNFR1 With All-Cause Mortality in Patients With Type 2 Diabetes and Chronic Kidney Disease: Follow-up of the SURDIAGENE Cohort Published online before print March 12, 2014, doi: 10.2337/dc13-2580.
8. Lopes-Virella MF et al. Baseline markers of inflammation are associated with progression to macroalbuminuria in type 1 diabetic subjects. Diabetes Care 2013; 36: 2317-23. doi: 10.2337/dc12-2521.
9. Skupien et al. Synergism between circulating tumor necrosis factor receptor 2 and HbA1c in determining renal decline during 5-18 years of follow-up in patients with type 1 diabetes and proteinuria. In press: Accepted for publication in Diabetes Care, April 22, 2014.
10. MacIsaac RJ. Markers of and Risk Factors for the Development and Progression of Diabetic Kidney Disease.American Journal of Kidney Diseases 2014; 63: S39–S62.
11. Orchard TJ et al. In the absence of renal disease, 20 year mortality risk in type 1 diabetes is comparable to that of the general population: a report from the Pittsburgh Epidemiology of Diabetes Complications Study. Diabetologia 2010; 53: 2312– 2319.
12. Carlsson AC et al. Soluble TNF Receptors and Kidney Dysfunction in the Elderly. J Am Soc Nephrol. 2014; 25: 1313-1320.
The author
Fergus Fleming
EKF Diagnostic Holdings Plc
Cardiff, UKwww.ekf-diagnostic.com
Smoking is a major cause of morbidity and mortality worldwide. The adverse health effects of chronic cigarette smoke exposure are widely known. Active smoking increases the risk of developing several pathologies including pulmonary disease, cardiovascular disease and cancer. Importantly, the sequelae of smoking also extend to non-smokers via frequent passive inhalation. Accurate measures of cigarette smoke exposure then are required to draw meaningful conclusions about the healthcare risks to both smokers and non-smokers. Cotinine is the major primary metabolite of nicotine and is the biochemical marker of choice for measuring exposure to cigarette smoke.
by Dr A. Dunlop, Dr B. L. Croal and J. Allison
Background
Chronic exposure to tobacco products is amongst the leading causes of preventable morbidity and mortality worldwide, being responsible for approximately 6 million deaths per annum [1]. Typically this involves inhalation of cigarette smoke which contains in excess of 5000 different chemicals; many of these are known toxins and carcinogens [2]. Upon inhalation of cigarette smoke, nicotine is transported to the lungs within tar droplets, dissolving in the alveolar fluid, and is then absorbed into the bloodstream. Following entry into the pulmonary circulation, nicotine quickly travels to the brain – within a matter of seconds – and exerts its pharmacological effects [3].
Nicotine is the addictive component of tobacco products, stimulating dopamine release in the brain and leading to heightened feelings of pleasure and reward [4]. In active smokers this nicotine dependence sustains chronic exposure to the toxins present in cigarette smoke [5]. Active smokers are therefore at increased risk of developing multiple pathologies including pulmonary disease, cardiovascular disease and cancer [6, 7]. Importantly, non-smokers are also at increased risk via involuntary or passive/second-hand smoke (SHS) exposure [8, 9]. Children are particularly susceptible to involuntary exposure, mainly occurring in enclosed spaces such as the parental home/car, via maternal smoking or passive exposure during pregnancy [10]. The adverse health effects of SHS exposure in children include increased risk of miscarriage, sudden infant death syndrome, lower respiratory tract infections, asthma and invasive meningococcal disease [10].
In addition, an emerging area of interest surrounds involuntary exposure via so-called third-hand smoke (THS). THS is a term used to describe the deposits of tobacco smoke that accumulate on surfaces, objects and in dust particles, persisting long after the dispersal of cigarette smoke. There is some evidence to suggest that atmospheric reactions may lead to re-release of smoke-derived toxins into the environment [11]. However, the health risks of THS are not yet known and remain the subject of ongoing research [12].
Assessing cigarette smoke exposure
The healthcare risks associated with cigarette smoking and SHS exposure ensure that smoking status should always be included in any routine clinical assessment. Monitoring of smoking status may also be indicated in specific circumstances, such as epidemiological studies, smoking cessation programmes, lung transplant patients, employee and health/life insurance screening. The most convenient and cost-effective means of assessing cigarette smoke exposure is by self-report. This may occur either during face-to-face consultation with healthcare professionals or often as part of a generic healthcare questionnaire. However self-report is frequently unreliable in estimating smoking status [13].
Moreover, the risk and extent of SHS exposure to non-smokers cannot be adequately assessed using these methods. For example, self-report cannot reliably quantify exposure in those who co-habit and/or socialise with smokers nor can it inform on fetal exposure in maternal smoking. Consequently, cigarette smoke exposure should be accurately quantified by measuring biomarkers to draw meaningful conclusions between smoking status and health outcomes [14, 15].
Biomarkers of cigarette smoke exposure
Numerous biomarkers have been examined in the analysis of cigarette smoke exposure, e.g. carbon monoxide, carboxyhaemoglobin, thiocyanate and polycyclic aromatic hydrocarbons [4]. However, many are non-specific for tobacco use and contribution from other environmental or dietary sources can cause interference [4]. In contrast, nicotine is a more specific marker of cigarette smoke exposure, being derived solely from tobacco [3]. Biochemical measurements of nicotine and its metabolites then are typically used to provide reliable measures of cigarette smoke exposure. Nicotine largely undergoes hepatic metabolism (with a half-life of approximately 2 h) and the plasma of active smokers typically contains 10–50 ng/mL of nicotine [3]. Cotinine is the major breakdown product of nicotine accounting for around 80% of all metabolites [3]. The half-life of cotinine, at around 16 h, is substantially longer than nicotine and plasma levels in active smokers are approximately 250–300 ng/mL [4]. Consequently, cotinine is the preferred biomarker for measuring cigarette smoke exposure.
Quantifying cotinine in biological matrices
A variety of methods have been developed for quantification of cotinine in several biological matrices including urine, blood, saliva and hair [14, 15]. There is good agreement between cotinine levels in plasma/serum and saliva, whilst levels in urine are typically higher [15].
Immunoassay methods have traditionally been used for the detection of cotinine in urine, offering rapid turnaround with minimal sample preparation. In addition, commercially available immunoassay kits are easily integrated into most core automated analysers available in modern clinical laboratories. However, reagent costs are typically high and it would be fair to say that immunoassays may be susceptible to cross-reactivity with other nicotine and cotinine-derived metabolites and thus may be of questionable accuracy [16, 17].
Gas chromatography–mass spectrometry (GC-MS) methods are also available; although sample preparation is typically labour intensive and time consuming, proving impractical for high sample throughput. Not surprisingly, liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods have emerged as the sine qua non for quantification of cotinine in biological fluids.
LC-MS/MS analyses
Liquid chromatography–tandem mass spectrometry (LC-MS/MS) affords the requisite specificity and sensitivity to detect and quantify cotinine at levels encountered throughout the spectrum of cigarette smoke exposure. The majority of recently published methods now routinely quote lower limits of quantification (LLOQ) in the region of <0.5 ng/mL, in both plasma/serum and in urine [15]. Cut-points to distinguish smokers from non-smokers have been variously proposed from 12 ng/mL down to 3 ng/mL, depending on the population [15]. Nevertheless, regular active smokers can be expected to have serum/plasma cotinine levels in marked excess of 100 ng/mL, although non-smokers are usually comfortably below 10 ng/mL.
The majority of LC-MS/MS methods for cotinine have been developed in-house, an important advantage compared with immunoassay techniques. This not only affords flexibility in the choice of matrix to be analysed but also permits the inclusion of more than one analyte in the assay. Thus nicotine, cotinine and various metabolites thereof may be detected in a multiplexed assay. Published guidelines are also widely available to assist in the development and validation of LC-MS/MS methods [18]. The advent of enhanced chromatographic separation techniques, such as ultra-performance liquid chromatography (UPLC), has significantly shortened run times thereby facilitating higher sample throughput. Development of uncomplicated sample preparation procedures has further simplified analyses.
For example, in our own laboratory we recently developed a rapid and straightforward UPLC-MS/MS protocol for the determination of cotinine in plasma (Fig. 1) [19]. Analytical run time was 4 min per sample with a LLOQ of 0.2 ng/mL and the assay was linear from 0.5 to 1000 ng/mL; comfortably covering the concentration range of active and non-smokers (Fig. 2). A simple 5-step automated SPE process was also developed, permitting minimal sample handling and using only water and methanol, both cheap and readily available. To date we have successfully deployed this method for the analyses of two large patient cohorts (each comprising several hundred samples) associated with independent epidemiological studies.
Although the initial outlay for equipment is high, thereafter LC-MS/MS assays can be run relatively cheaply using readily available inexpensive solvents. Furthermore, sample preparation procedures can usually be streamlined/simplified and therefore easily adapted for high-throughput analyses [19]. Matrix effects, chiefly ion suppression, are a particular disadvantage of LC-MS/MS techniques; however careful consideration and troubleshooting during method development can often overcome this issue [20].
Conclusions and future directions
Despite widespread awareness of the adverse effects of tobacco use and increasing public health initiatives to combat this, cigarette smoking continues to be a major global cause of morbidity and mortality and is likely to remain so for the foreseeable future. Accurate quantification of cigarette smoke exposure via biomarkers is therefore an important measure in stratifying the risk of both active and non-smokers.
The need to quantify ever decreasing amounts of nicotine, cotinine and their metabolites in monitoring exposure to tobacco products ensures that LC-MS/MS techniques and modifications thereof remain at the forefront of detection methods in this field. Similarly, as new biomarkers become available which inform on the detrimental health effects of smoking these methods are ideally placed to keep pace, both in research and in clinical laboratories.
The recent emergence of electronic cigarette devices (e-cigarettes) is currently the subject of much debate. E-cigarettes typically deliver nicotine in a vapour generated via heating a liquid that also contains propylene glycol and other additives e.g. flavouring [21]. Exponents propose e-cigarettes as a safer alternative to smoking associated with tobacco combustion and promote the benefits for smoking cessation. However, some healthcare professionals believe that while e-cigarettes are safer, they may still act as a gateway or as a way of prolonging or even enhancing dependency on nicotine. In addition, the long-term health effects of these products are unknown, as is the need to monitor biomarkers such as nicotine and/or cotinine in so-called ‘e-smokers’.
References
1. WHO report on the global tobacco epidemic 2013. http://www.who.int/tobacco/global_report/2013/en/
2. Talhout R, et al. Hazardous compounds in tobacco smoke. Int J Environ Res Public Health 2011; 8(2): 613–628.
3. Hukkanen J, et al. Metabolism and disposition kinetics of nicotine. Pharmacol Rev. 2005; 57(1): 79–115.
4. Benowitz NL, et al. Nicotine chemistry, metabolism, kinetics and biomarkers. Handb Exp Pharmacol. 2009; 192(192): 29–60.
5. Berrendero F, et al. Neurobiological mechanisms involved in nicotine dependence and reward: participation of the endogenous opioid system. Neurosci Biobehav Rev. 2010; 35(2): 220–231.
6. Doll R, et al. Mortality in relation to smoking: 50 years’ observations on male British doctors. BMJ (Clinical research ed.). 2004; 328(7455): 1519.
7. Jha P. Avoidable global cancer deaths and total deaths from smoking. Nature reviews.Cancer. 2011; 9(9): 655–664.
8. Scientific Committee on Tobacco and Health. Secondhand smoke: Review of evidence since 1998. Update of evidence on health effects of secondhand smoke. Department of Health, UK 2004. http://www.smokefreeengland.co.uk/files/scoth_secondhandsmoke.pdf
9. Vardavas CI, Panagiotakos DB. The causal relationship between passive smoking and inflammation on the development of cardiovascular disease: a review of the evidence. Inflamm Allergy Drug Targets 2009; 8(5): 328–333.
10. Action on Smoking and Health. Research Report. Secondhand smoke: the impact on children. March 2014. http://www.ash.org.uk/files/documents/ASH_596.pdf
11. Sleiman M, et al. Formation of carcinogens indoors by surface-mediated reactions of nicotine with nitrous acid, leading to potential thirdhand smoke hazards. PNAS 2010; 107(15): 6576–6581.
12. Matt GE, et al. Thirdhand tobacco smoke: emerging evidence and arguments for a multidisciplinary research agenda. Environ Health Perspect. 2011; 119(9): 1218–1226.
13. Connor Gorber S, et al. The accuracy of self-reported smoking: a systematic review of the relationship between self-reported and cotinine-assessed smoking status. Nicotine Tob Res. 2009; 11(1): 12–24.
14. Florescu A, et al. Methods for quantification of exposure to cigarette smoking and environmental tobacco smoke: focus on developmental toxicology. Ther Drug Monitg. 2009; 31(1): 14–30.
15. Avila-Tang E, et al. Assesing secondhand smoke using biological markers. Tob Control 2013; 22: 164–171.
16. Schepers G, Walk RA. Cotinine determination by immunoassays may be influenced by other nicotine metabolites. Arch Toxicol. 1988; 62(5): 395–397.
17. Tate J, Ward G. Interferences in immunoassay. Clin Biochem Rev. 2004; 25(2): 105–120.
18. Honour JW. Development and validation of a quantitative assay based on tandem mass spectrometry. Ann Clin Biochem. 2001; 48(2): 97–111.
19. Dunlop AJ, et al. Determination of cotinine by LC-MS-MS with automated solid-phase extraction. J Chromatogr Sci. 2014; 52(4): 351–356.
20. Matuszewski BK, et al. Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS. Anal Chem. 2003; 75(13): 3019–3030.
21. Grana R, et al. E-cigarettes: a scientific review. Circulation 2014; 129(19): 1972–1986.
The authors
Allan Dunlop1* PhD, Bernard Croal2 MD and James Allison2 BSc
1Department of Clinical Biochemistry Laboratory, Southern General Hospital, Glasgow G51 4TF, UK
2Department of Clinical Biochemistry, Aberdeen Royal Infirmary, Aberdeen AB25 2ZD, UK
*Corresponding author
E-mail: allandunlop@nhs.net
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