Dementia is one of the leading causes of disability in old age and places a huge burden on society. The growing prevalence of dementia calls for accurate, more accessible biomarkers to facilitate clinical diagnosis and prognosis. Peripheral mediums, such as blood and blood derivates (i.e. plasma and platelets), are currently being investigated for their potential as biomarkers of dementia subtypes. There is a lack of reproducibility in dementia biomarker studies, likely because of unaccounted factors such as age, ethnicity and gender, which is stalling their translation from research to the clinical setting. However, several blood-based biomarkers have been consistently reported from plasma and blood cells, including amyloid and tau protein, clusterin and immunoglobulins, as well as α-synuclein. This review highlights the need for further validation of the current blood-based dementia biomarkers for their routine clinical use.
by Oluwatomi E. S. Akingbade and Prof. Elizabeta B. Mukaetova-Ladinska
Introduction
The increasing incidence of neurodegenerative diseases, such as dementia, legitimizes the search for readily accessible biological markers. At present, there are 50 million people worldwide living with dementia, with 10 million new cases reported each year [1]. Dementia care costs are as high as £26 billion a year [1], with the likelihood of increasing further owing to higher numbers of people living with dementia. The increasing incidence of dementia calls for more efficient diagnosis. Currently, dementia diagnosis relies on extensive clinical assessments, facilitated by invasive (i.e. lumbar puncture) and technical (i.e. MRI) testing, all costly and inefficient in keeping up with the growing number of dementia patients.
The definitive diagnosis of dementia is done neuropathologically, and besides the clinical evidence of dementia, is based on the characteristic hallmarks of plaques and tangles (for Alzheimer’s disease (AD), the most common form of dementia [2]), Lewy bodies (for dementia with Lewy bodies (DLB)), and vascular changes (for vascular dementia). Some of the molecular substrates of the characteristic neuropathological dementia features have been taken forward both in neuroradiological (i.e. β-amyloid radiotracers) and biochemical assessments [i.e. amyloid-β (Aβ42), total tau and phosphorylated tau181 measurements in the cerebrospinal fluid (CSF)] [3]. However, their costs, limited access and invasive approach, as well as involvement in secondary inflammatory processes in dementia [4], are restricting their wider clinical utility. Thus, there is a need for the development of less invasive and more cost-effective peripheral biomarkers to facilitate the clinical diagnosis of dementia.
Unlike CSF, blood and blood derivates (platelets and plasma) are easily accessible in the clinical setting and the potential of using them to corroborate dementia diagnosis will likely lead to earlier, and more accurate dementia diagnoses. Although the blood–central nervous system barrier provides a physiological and physical barrier, changes in peripheral fluids and organs have been identified in people with dementia. Notably, erythrocyte [5] and platelet [6] physiology and function are largely effected in dementia. Proteins in peripheral organs are also being explored: i.e. α-synuclein, p53 protein, tau and amyloid in the skin, kidneys and liver; tau protein in the testes; Aβ1–42 and acetylcholinesterase in serous fluid, as well as amyloid and tau proteins in the gastrointestinal tract. Explorative studies continue to report that peripheral components are influenced in dementia disease. Reoccurring peripheral proteins of interest in the search for dementia biomarkers include α-synuclein, Immunoglobulin G (IgG), Aβ precursor protein (AβPP), clusterin (all found in both platelets and plasma) and myeloperoxidase (MPO, present in plasma only).
Clinical relevance of potential blood-based dementia biomarkers
Peripheral Aβ and AβPP
Aβ plaques in the brain are key pathological hallmarks of AD, and lower Aβ CSF level is a known marker in autopsy-confirmed AD subjects [22]. Aβ is also reported in the periphery, i.e. blood and skin. In a healthy population, plasma Aβ40, Aβ42 and Aβ40/42 ratio levels were significantly higher in older participants than in the younger ones [23]. In the Rotterdam Study Cohort [21], lower levels of plasma Aβ1–28 and Aβ40–42 were linked to higher risks of dementia in older age [7]. In platelets, the AβPP ratio [upper (130kDa) to lower (106–110kDa) immunoreactive bands] has been investigated (Table 1). Thus, there are differences in platelet AβPP ratios of those with cognitive deficits pertaining to dementia (i.e. very mild AD [10]; mild AD [10]; and AD [9–11]) and control patients. However, there are conflicting reports on the differences between the AβPP ratio in patients with varying severity of cognitive deficit with some studies [9,10]. Additionally, some studies have reported that increased AβPP levels in platelets correlated with higher Mini-Mental State Examination (MMSE) [11, 12] and Cambridge Cognition Examination (CAMCOG) scores [11]. Whether or not peripheral measurements of Aβ can be used as a pre-symptomatic marker of dementia (and/or the associated cognitive decline) remains uncertain. However, the studies mentioned in this review provide a consistent foundation for future studies to standardize measurement techniques that can be used in the clinical setting.
Peripheral α-synuclein
Studies have consistently reported the potential of α-synuclein as a biomarker of dementia – particularly in DLB as α-synuclein is reported to be a key constituent of Lewy bodies [24]. About 95 % of the α-synuclein in blood is predominantly found in erythrocytes, with the levels in plasma being extremely low. It is only recently that an assay was developed that is sensitive enough to detect the low levels of α-synuclein in plasma – reported in the range of 2.1–19.4 µg/L [25]. However, platelet α-synuclein levels in dementia have been reported to be similar among control and AD subjects [12] (Table 1). The utility of α-synuclein as a dementia biomarker is somewhat questionable, largely owing to its unknown physiological function in the human brain, as well as its involvement in Lewy body formation, which can be present in both normal aging as well as in a number of synucleinopathies, including Parkinson’s disease, multiple system atrophy, DLB, amyotrophic lateral sclerosis, etc, [26].
Blood clusterin
Increased plasma clusterin levels were reported to be indicative of both brain atrophy in AD patients [27] and an increased risk of dementia in older people [13]. Indeed. elevated levels of plasma clusterin have been reported as a risk factor for dementia [13, 28]. One study identified that lower MMSE cognitive scores in an AD population were associated with higher levels of plasma clusterin, whereas AD patients with higher cognitive scores had lower plasma clusterin levels [15]. Furthermore, longitudinal assessments identified that increased plasma clusterin concentrations are related to cognitive decline in mild cognitive impairment [28]. Interestingly, platelet levels of clusterin appear to be similar in both control and AD subjects [12, 16], but the ratio of platelet and plasma clusterin is positively correlated to specific neuropsychiatric inventory sub-categories, in particular agitation, apathy, motor aberrant behaviour and irritability seen in AD subjects [12] (Table 1).
Peripheral immunoglobulins
Increased IgG levels in plasma of AD subjects has been reported [17] with no link to disease progression. In an independent study, plasma IgG levels remained unaltered but increased in the platelet samples of the same AD subjects when compared to IgG levels in their control counterparts [12]. Inconsistent results found in IgG in plasma may be due to experimental constraints and, therefore, the potential of IgGs to form part of dementia diagnosis should not be ruled out. A recent study has reported that electrochemical techniques can be used to detect immunoglobulin in the plasma in the pg/mL range [29] – such sensitivity will likely aid in more consistent findings in plasma IgG studies in dementia.
Peripheral tau protein
CSF tau (and its ratio to Aβ) has been extensively investigated in relation to dementia. A recent literature review has addressed the limited clinical value of CSF tau biomarker studies [30], attesting to the need to address the uncertainty behind CSF tau as a suitable peripheral biomarker for dementia. More recently studies have focused on the presence of tau in the periphery. In plasma, high levels of tau were weakly associated with AD and were longitudinally associated with increased brain atrophy and poor cognition [18]. Platelet tau protein showed a more complex relationship than the reported plasma tau protein levels; studies have also reported a higher ratio between high to low molecular weight tau ratio in AD patients when compared to controls [19, 31] – with acknowledgement of no significant differences found between low or high molecular weight tau protein levels in the platelets of control and AD patients [19] (Table 1). Another study, reported a negative correlation between total tau and phosphorylated tau in the platelets of AD participants [20]. All in all, investigation into peripheral tau as a biomarker of dementia (and even specific dementia subtypes) is still in the early stages and requires further investigation before it can be considered for use in clinical diagnosis.
Summary and conclusions
Historically, the search for peripheral, blood-based dementia biomarkers has focused largely on the protein profiles of plasma and serum in dementia patients. This review, based on 13 dementia biomarker studies, has shown that proteins in the periphery are influenced by neurodegeneration in dementia. Namely, increased concentrations of Aβ40, Aβ42 and clusterin in plasma were all shown to be indicative of an increased risk of developing dementia in the elderly. Most recently, increased levels of plasma neurofilament light chain were reported to be closely related to amyloid processing in both mild cognitive impairment and AD, and to correlate with poor cognitive performance and AD-related brain atrophy and hypometabolism [32]. Changes in proteins in platelets were shown to coincide with cognitive decline in dementia: lower levels of AβPP and higher levels of clusterin were present in those with poorer performance in cognitive tests. Interestingly, increased plasma levels of tau protein were associated with brain atrophy while total and phosphorylated tau levels in platelets were negatively correlated. These findings provide encouraging evidence for the measurable presence of blood-based proteins that are closely linked to AD hallmarks that have the potential to be used in routine clinical setting not only for diagnosis, but also for severity and progression of the dementia process. The reproducibility and causes of possible heterogeneity, i.e. age, ethnicity, co-morbidity and genetics, that may influence protein expression at the periphery will need to be explored further before these biomarkers can be used routinely in the clinic, and their accuracy for distinct dementia subtypes will also need to be determined.
References
1. Dementia. World Health Organization 2017 (http://www.who.int/en/news-room/fact-sheets/detail/dementia).
2. Types of dementia. Alzheimer’s Research UK 2018 (https://www.alzheimersresearchuk.org/about-dementia/types-of-dementia/).
3. Andreasen N, Vanmechelen E, Vanderstichele H, Davidsson P, Blennow K. Cerebrospinal fluid levels of total-tau, phospho-tau and A beta 42 predicts development of Alzheimer’s disease in patients with mild cognitive impairment. Acta Neurol Scand Suppl 2003; 179: 47–51.
4. Veitinger M, Varga B, Guterres SB, Zellner M. Platelets, a reliable source for peripheral Alzheimer’s disease biomarkers? Acta Neuropathol Commun 2014; 2: 65.
5. Stevenson A, Lopez D, Khoo P, Kalaria RN, Mukaetova-Ladinska EB. Exploring erythrocytes as blood biomarkers for Alzheimer’s disease. J Alzheimers Dis 2017; 60(3): 845–857.
6. Akingbade OES, Gibson C, Kalaria RN, Mukaetova-Ladinska EB. Platelets: peripheral biomarkers of dementia? J Alzheimers Dis 2018; 63(4): 1235–1259.
7. Hilal S, Wolters FJ, Verbeek MM, Vanderstichele H, Kamran Ikram M, Stoops E, Ikram MA, Vernooij MW. Plasma amyloid-β levels, cerebral atrophy and risk of dementia: a population-based study. Alzheimers Res Ther 2018; 10: 63 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026500/).
8. Tang K, Hynan LS, Baskin F, Rosenberg RN. Platelet amyloid precursor protein processing: a bio-marker for Alzheimer’s disease. J Neurol Sci 2006; 240(1–2): 53–58.
9. Borroni B, Colciaghi F, Corsini P, Akkawi N, Rozzini L, Del Zotto E, et al. Early stages of probable Alzheimer disease are associated with changes in platelet amyloid precursor protein forms. Neurol Sci 2002; 23(5): 207–210.
10. Padovani A, Borroni B, Colciaghi F, Pettenati C, Cottini E, Agosti C, Talarico G, Cattabeni F, Lenzi GL, et al. Abnormalities in the pattern of platelet amyloid precursor protein forms in patients with mild cognitive impairment and Alzheimer disease. Arch Neurol 2002 Jan; 59(1): 71–75.
11. Zainaghi IA, Forlenza O V, Gattaz WF. Abnormal APP processing in platelets of patients with Alzheimer’s disease: correlations with membrane fluidity and cognitive decline. Psychopharmacology (Berl) 2007; 192(4): 547–553.
12. Mukaetova-Ladinska EB, Abdel-All Z, Dodds S, Andrade J, Alves da Silva J, Kalaria RN, O’Brien JT. Platelet immunoglobulin and amyloid precursor protein as potential peripheral biomarkers for Alzheimer’s disease: findings from a pilot study. Age Ageing 2012; 41(3): 408–412.
13. Weinstein G, Beiser AS, Preis SR, Courchesne P, Chouraki V, Levy D, Seshadri S. Plasma clusterin levels and risk of dementia, Alzheimer’s disease, and stroke. Alzheimer’s Dement 2016; 3: 103–109.
14. Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data. Prev Med 1975; 4(4): 518–525.
15. Hsu J-L, Lee W-J, Liao Y-C, Wang S-J, Fuh J-L. The clinical significance of plasma clusterin and Abeta in the longitudinal follow-up of patients with Alzheimer’s disease. Alzheimers Res Ther 2017 Nov; 9(1): 91.
16. Mukaetova-Ladinska EB, Abdel-All Z, Andrade J, Alves da Silva J, O’Brien JT, Kalaria RN. Plasma and platelet clusterin ratio is altered in Alzheimer’s disease patients with distinct neuropsychiatric symptoms: findings from a pilot study. Int J Geriatr Psychiatry 2015; 30(4): 368–375.
17. Bosman GJ, Van der Linden PA, Bartholomeus IG, De Man AJ, De Grip WJ, Van Kalmthout PJ. Erythrocyte aging in the demented elderly: a fluctuating process? Mech Ageing Dev 1998; 100(1): 53–58.
18. Mattsson N, Zetterberg H, Janelidze S, Insel PS, Andreasson U, Stomrud E, Palmqvist S2, Baker D2, Tan Hehir CA, et al. Plasma tau in Alzheimer disease. Neurology 2016; 87(17): 1827–1835.
19. Slachevsky A, Guzmán-Martínez L, Delgado C, Reyes P, Farías GA, Muñoz-Neira C, Bravo E, Farías M, Flores P, et al. Tau platelets correlate with regional brain atrophy in patients with Alzheimer’s disease. J Alzheimers Dis 2017; 55(4): 1595–1603.
20. Mukaetova-Ladinska EB, Abdell-All Z, Andrade J, da Silva JA, Boksha I, Burbaeva G, Kalaria RN, O’Brien JT. Platelet tau protein as a potential peripheral biomarker in Alzheimer’s disease: an explorative study. Curr Alzheimer Res 2018; 15(9): 800–808.
21. Hofman A, Brusselle GGO, Darwish Murad S, van Duijn CM, Franco OH, Goedegebure A, Ikram MA, Klaver CC, Nijsten TE, et al. The Rotterdam Study: 2016 objectives and design update. Eur J Epidemiol 2015; 30(8): 661–708.
22. Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, Blennow K, Soares H, Simon A, et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 2009; 65(4): 403–413.
23. Kiko T, Nakagawa K, Satoh A, Tsuduki T, Furukawa K, Arai H, Miyazawa T. Amyloid beta levels in human red blood cells. PLoS One 2012; 7(11): e49620.
24. Koehler NKU, Stransky E, Shing M, Gaertner S, Meyer M, Schreitmuller B, Leyhe T, Laske C, Maetzler W, et al. Altered serum IgG levels to alpha-synuclein in dementia with Lewy bodies and Alzheimer’s disease. PLoS One 2013; 8(5): e64649.
25. Fjorback AW, Varming K, Jensen PH. Determination of alpha-synuclein concentration in human plasma using ELISA. Scand J Clin Lab Invest 2007; 67(4): 431–435.
26. Spillantini MG, Schmidt ML, Lee VM-Y, Trojanowski JQ, Jakes R, Goedert M. α-Synuclein in Lewy bodies. Nature 1997; 388: 839 (http://dx.doi.org/10.1038/42166).
27. Thambisetty M, Simmons A, Velayudhan L, Hye A, Campbell J, Zhang Y, Wahlund LO, Westman E, Kinsey A, et al. Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Arch Gen Psychiatry 2010 Jul; 67(7): 739–748.
28. Jongbloed W, van Dijk KD, Mulder SD, van de Berg WDJ, Blankenstein MA, van der Flier W, Veerhuis R. Clusterin levels in plasma predict cognitive decline and progression to Alzheimer’s disease. J Alzheimers Dis 2015; 46(4): 1103–1110.
29. Garyfallou G-Z, Ketebu O, Sahin S, Mukaetova-Ladinska EB, Catt M, Yu EH. Electrochemical detection of plasma immunoglobulin as a biomarker for Alzheimer’s disease. Sensors 2017; 17(11): doi: 10.3390/s17112464.
30. Ritchie C, Smailagic N, Noel-Storr AH, Ukoumunne O, Ladds EC, Martin S. CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane database Syst Rev 2017; 3: CD010803.
31. Neumann K, Farías G, Slachevsky A, Perez P, Maccioni RB. Human platelets tau: a potential peripheral marker for Alzheimer’s disease. J Alzheimers Dis 2011; 25(1): 103–109.
32. Mattsson N, Andreasson U, Zetterberg H, Blennow K. Association of plasma neurofilament light with neurodegeneration in Patients With Alzheimer disease. JAMA Neurol 2017; 74(5): 557–566.
The authors
Oluwatomi E. S. Akingbade1,2 and Elizabeta B. Mukaetova-Ladinska*2,3 MD, PhD, MRCPsych
1School of Life Sciences, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
2Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
3Evington Centre, Leicester General Hospital, Leicester, UK
*Corresponding author
E-mail: eml12@le.ac.uk
More hygiene with the VACUETTE® Super-T Disposable Tourniquet
, /in Featured Articles /by 3wmediaDiaSys’ System Solutions
, /in Featured Articles /by 3wmediaWakey Wakey
, /in Featured Articles /by 3wmediaAdvancing biochemistry testing with Randox third party reagents
, /in Featured Articles /by 3wmediaSniffing out malaria
, /in Featured Articles /by 3wmediaThe global eradication of malaria is a challenging, although perhaps not impossible, aim: Paraguay was recently declared malaria-free and, according to the World Health Organization, Algeria, Argentina and Uzbekistan are likely to be declared malaria free by the end of the year. Several tactics are being used to prevent infection, including preventing mosquito bites by sleeping under insecticide-soaked nets, spraying and draining standing water to control mosquito numbers, and CRISPR techniques to generate malaria-resistant mosquitos are being tested. A number of antimalarial medications are available for treating infection and it is advised that malaria infection is confirmed before starting treatment in order to limit the spread of drug resistance. Early and accurate diagnosis of malaria is essential for the best treatment outcomes and diagnosis can be achieved in several ways. Microscopy is the gold standard, but requires trained and experienced personnel, takes days and can still be inadequate and inaccessible in many remote/poor areas. PCR is another lab-based technique that allows detection and identification of the Plasmodium species and is useful where there is no access to microscopy or the microscopy results are unclear, as well as in cases of mixed infection. However, the cost of these tests is prohibitive in many regions with endemic malaria. Rapid diagnostic tests, using finger-prick blood samples, are specifically designed for use in remote, poor areas, detect malaria antigens and provide results within 30 minutes. One difficulty, however, is the identification and treatment of the small number of infected but symptomless carriers of the disease, who, if left untreated, can provide a continuing source of infection. According to research presented recently at the annual meeting of the American Association of Tropical Medicine & Hygiene, we might soon have a fast and non-invasive way of detecting these symptomless carriers. People infected with malaria give off an aroma that is imperceptible to humans but is very attractive to mosquitos. A pilot study by Steve Lindsay at Durham University and colleagues has shown that it is possible to train dogs to detect the same aroma and to discriminate between malaria-infected and -uninfected people with a reasonable degree of diagnostic accuracy, recognizing socks worn by children with malaria about 70% of the time and socks worn by uninfected children about 90% of the time. The next stage is to test the dogs with people instead of just socks. If successful, the potential exists to use information learnt from the dogs to create a bioelectronic nose for malaria, in the same way that medical detection dogs are already being used to aid the development of such a device for the detection of cancer.
Multiplex specific IgE detection in allergy diagnostics
, /in Autoimmunity & Allergy, Featured Articles /by 3wmediaby Dr Jacqueline Gosink In vitro determination of specific IgE is nowadays a central pillar of allergy diagnostics. Specific IgE against up to 54 allergens can be investigated in parallel using the EUROLINE immunoblot system. Individual EUROLINE profiles are targeted to specific indications, encompassing food, inhalation, atopy, insect venoms and pediatrics, while diverse region-specific profiles […]
MicroRNAs show potential as molecular biomarkers for graft-versus-host disease
, /in Featured Articles /by 3wmediaGraft-versus-host disease is a serious complication following hematopoietic stem cell transplantation (HSCT), with a high mortality rate. A clearer understanding of the molecular pathogenesis may allow robust biomarker identification and improved therapeutic options. MicroRNAs (miRNAs) are short non-coding regulatory RNAs that are expressed in both tissue and body fluids, and show great potential as clinically translatable biomarkers. Here we discuss the field of miRNA biomarker discovery in the setting of HSCT.
by Dr Rachel E. Crossland and Prof. Anne M. Dickinson
Allogeneic hematopoietic stem cell transplant and graft-versus-host disease
Allogeneic hematopoietic stem cell transplant (allo-HSCT) is a curative treatment for many blood cancers. It is based on the transplant of hematopoietic blood and marrow stem cells from related or unrelated donors, and over 17 000 allo-HSCT transplants a year are carried out in Europe. The therapy is curative due to the properties of subsets of donor-derived lymphocytes, including T-cells and natural killer cells, that are able to eradicate residual malignancy due to their ‘graft-versus-leukemia’ (GvL) effects. However, T-cells can also give rise to a life-threatening complication, called graft-versus-host disease (GvHD).
GvHD affects 40–70 % of HSCT patients, and severe disease is associated with 40–60 % mortality. The pathology of GvHD is not completely understood, but has been generally attributed to three main stages:
Acute GvHD (aGvHD) typically occurs within the first 100 days following transplantation and primarily presents in the skin, liver and gastrointestinal tract as an erythematous maculopapular rash, elevated bilirubin, and diarrhoea and vomiting, respectively [1]. Chronic GvHD (cGvHD) has a more delayed onset, and is a multi-organ allo- and auto-immune disorder that most frequently affects the skin, lung, mouth, liver, eye, joints and gastrointestinal tract causing a plethora of co-morbidities including cardiovascular, gastrointestinal, hepatic, pulmonary, endocrine, bone and joint disorders, infections and secondary malignancies. GvHD is commonly treated with immunosuppressants, which increase the patient’s susceptibility to life-threatening infections. Therefore, survival rates after allo-HSCT have not improved for over two decades, owing to major complications such as infections, GvHD and relapse of malignant disease. To date, GvHD can be well characterized by established and clinically validated GvHD grading scales and measurements of the National Institute of Health (NIH) Consensus classification. However, there is a lack of understanding of the immunobiology and metabolic triggers that cause the development and further perpetuation of GvHD, especially cGvHD and subsequent co-morbidity.
GvHD and biomarkers
Biomarkers are being increasingly used in the prediction, prognosis and diagnosis of diseases and are now being validated for prediction of outcome in patients with GvHD. Predicting and preventing GvHD would allow clinicians to develop of risk-adapted clinical protocols, encourage a curative GvL response and improve outcomes, including transplant survival rates and long-term complications. However, despite the frequency and significance of GvHD, there are currently no early diagnostic or predictive markers that have been validated for use in clinic. This may be attributed to a lack of understanding of the molecular pathobiology of aGvHD on a systemic level. Determining the molecular pathways involved at initiation of aGvHD will identify novel targets for therapeutic intervention, and these factors may have the potential to act as biomarkers for aGvHD.
MicroRNAs as biomarkers
MicroRNAs (miRNAs) represent a promising source of biomarkers for GvHD because they play critical roles in the development and function of the immune system and in transplant biology (Fig. 1). MiRNAs represent a family of small (19–24 nucleotide) non-coding RNAs, which affect the regulation of gene expression in eukaryotic cells by binding to the 3´-untranslated region of target messenger RNAs [2]. They are predicted to target around 50 % of all genes and play an important role in fundamental cellular processes such as development, stem cell division, apoptosis and cancer. MiRNAs represent ideal candidates for biomarker identification in GvHD as they can be assessed using accurate and sensitive technology (e.g. NanoString/qRT-PCR), quantified in bodily fluids that require minimally invasive sample collection (e.g. serum/urine) and further investigated for biological function (e.g. target protein identification) that may expand upon our understanding of GvHD pathology. Although the field of GvHD-related miRNA research is in its infancy, recent studies have demonstrated an emerging role for miRNAs as GvHD biomarkers.
MiRNAs as biomarkers for GvHD
MiR-155 was one of the first miRNAs to be associated with the regulation of aGvHD. This miRNA is a critical regulator of inflammation, as well as adaptive and innate immune responses. In 2012, Ranganathan et al. demonstrated upregulation of miR-155 in the T-cells of mice and patients developing aGvHD following HSCT [3]. Serum expression levels also correlated with GvHD severity, and serum IFN-gamma, IL-17 and IL-9 levels, suggesting the potential of miR-155 as a biomarker for aGvHD diagnosis, and as a therapeutic target. It has since been demonstrated that miR-155 expression in both donor CD8+ T-cells and conventional CD4+ CD25− T-cells is pivotal for aGvHD pathogenesis, and drives a pro-inflammatory Th1 phenotype in donor T-cells [4].
MiR-146 is increasingly being recognized as a ‘fine-tuner’ of cell function and differentiation in both innate and the adaptive immunity. MiR-146a controls innate immune cell and T-cell responses, and directly targets two adapter proteins in the nuclear factor-kappa B (NF-κB) activation pathway; tumour necrosis factor (TNF) receptor-associated factor 6 (TRAF6) and IL-1 receptor-associated kinase 1 (IRAK1) [5]. In addition, the survival and maturation of human plasmacytoid dendritic cells that are involved in GvHD can be regulated by miR-146a. With regard to GvHD, miR-146a has been shown to be upregulated in the T-cells of nice developing aGvHD, and transplanting miR-146a–/– T-cells causes increased GvHD severity, elevated TNF serum levels and reduced survival [6]. Interestingly, Stickel et al. observed downregulation of miR-146a shortly following allo-HCT in mice (day 2), followed by upregulation in T-cells later in the aGvHD reaction (days 6 and 12), which they hypothesized may be a rescue mechanism to counteract inflammation [6]. Expression of miR-146a has since been identified to show a statistical interaction with expression of miR-155 in the peripheral blood of allo-HSCT patients before disease onset, and this interaction was predictive of aGvHD incidence, further implicating its potential as a GvHD biomarker [7].
Serum expression of miR-29a has recently been implicated as a potential biomarker for GvHD. Ranganathan et al. showed in two independent cohorts that miR-29a is significantly upregulated in allo-HSCT patients at aGvHD onset compared with non-aGvHD patients, and as early as 2 weeks before symptomatic disease onset compared to time-matched controls [8]. Further investigation into the function of miR-29a showed that it binds to and activates dendritic cells, via toll-like receptor (TLR)7 and TLR8, resulting in the activation of the NF-κB pathway and secretion of pro-inflammatory cytokines. Treatment with locked nucleic acid anti-miR-29a significantly improved survival in a mouse model of aGvHD, while retaining GvL effects [8].
In 2013 an elegant study by Xiao et al. investigated miRNA expression profiles in the plasma of patients with aGvHD, compared to patients with no aGvHD, using a qRT-PCR array to include 345 miRNAs [9]. The study identified a final signature of four miRNAs (miR-423, miR-199-3p, miR-93*, and miR-377) that significantly predicted for aGvHD at 6 weeks post-HSCT, before the onset of symptoms. Furthermore, the model was associated with disease severity and poor overall survivall [9]. Gimondi et al. have also profiled circulating miRNA expression using a qRT-PCR platform, based on samples collected 28 days post-HSCT [10]. They detected 27 miRNAs that could collectively discriminate between aGvHD and non-aGvHD. MiR-194 and miR-518f were significantly upregulated in patients who later developed aGvHD, and these miRNAs were predicted to target critical pathways implicated in aGvHD pathogenesis [10]. Our laboratory has used NanoString technology to comprehensively profile the expression of n=799 mature miRNAs in the serum of patients who had undergone HSCT, to identify miRNAs with altered expression at aGvHD diagnosis (Fig. 2) [11]. Assessment of selected miRNAs was also replicated in independent cohorts of serum samples taken at aGvHD diagnosis and before disease onset to assess their prognostic potential. Expression analysis identified 61 miRNAs that were differentially expressed at aGvHD diagnosis, and miR-146a, miR-30b-5p, miR-374-5p, miR-181a, miR-20a, and miR-15a were significantly verified in an independent cohort. MiR-146a, miR-20a, miR-18, miR-19a, miR-19b, and miR-451 were also differentially expressed 14 days post-HSCT, before the onset of symptoms, in patients who later developed aGvHD. High miR-19b expression was associated with improved overall survival, whereas high miR-20a and miR-30b-5p were associated with lower rates of non-relapse mortality and improved overall survival [11]. Collectively, these miRNA profiling studies highlight that circulating biofluid miRNAs show altered expression at aGvHD onset and have the capacity to act as independent markers for prediction, prognosis and diagnosis of GvHD.
Future directions
Despite greater recognition of the potential for miRNAs as clinically adaptable biomarkers, they have not yet reached translation to the clinic. This is predominantly because of the lack of reproducibility and independent validation to date. Indeed, owing to the high degree of variability in factors when designing and performing miRNA profiling experiments, which may be attributed to clinical (patient characteristics, sampling time points and type of body fluid analysed), technical (sample preparation, miRNA profiling platform and spectrum of miRNAs profiled) and analytical (normalization strategy) factors, progress has been slow in realizing their full potential. Despite contradictory research results on the biological basis of GvHD, low patient cohorts in single transplant centre studies, insufficient characterization of GvHD and lack of understanding and knowledge of GvHD’s impact on the immune system, miRNA biomarkers continue to show promise, but many studies are still in their infancy. Future progress relies on collaboration between research groups, focusing on standardization of the samples, protocols and technologies used, which will greatly improve the reproducibility of findings allowing for extended validation of miRNAs of interest. The ultimate aim will be to diagnose GvHD and predict outcome before the onset of clinical symptoms, allowing for earlier therapy and personalized treatments and leading to reduced mortality and morbidity outcomes.
References
1. Shlomchik WD. Graft-versus-host disease. Nat Rev Immunol 2007; 7(5): 340–352.
2. Stefan LA, Phillip DZ. Diversifying microRNA sequence and function. Nature Reviews Mol Cell Biol 2013; 14(8): 475–488.
3. Ranganathan P, Heaphy CEA, Costinean S, Stauffer N, Na C, Hamadani M, Santhanam R, Mao C, Taylor PA, et al. Regulation of acute graft-versus-host disease by microRNA-155. Blood 2012; 119(20): 4786–4797.
4. Zitzer NC, Snyder K, Meng X, Taylor PA, Efebera YA, Devine SM, Blazar BR, Garzon R, Ranganathan P. MicroRNA-155 modulates acute graft-versus-host disease by impacting T cell expansion, migration, and effector function. J Immunol 2018; 200(12): 4170–4179.
5. Taganov KD, Boldin MP, Chang KJ, Baltimore D. NF-kappaB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci USA 2006; 103(33): 12481–12486.
6. Stickel N, Prinz G, Pfeifer D, Hasselblatt P, Schmitt-Graeff A, Follo M, Thimme R, Finke J, Duyster J, et al. MiR-146a regulates the TRAF6/TNF-axis in donor T cells during GvHD. Blood 2014; 124(16): 2586–2595.
7. Atarod S, Ahmed MM, Lendrem C, Pearce KF, Cope W, Norden J, Wang XN, Collin M, Dickinson AM. miR-146a and miR-155 expression levels in acute graft-versus-host disease incidence. Frontiers in immunology. 2016; 7: 56.
8. Ranganathan P, Ngankeu A, Zitzer NC, Leoncini P, Yu X, Casadei L, Challagundla K, Reichenbach DK, Garman S, et al. Serum miR-29a is upregulated in acute graft-versus-host disease and activates dendritic cells through TLR binding. J Immunol 2017; 198(6):2500–2512.
9. Xiao B, Wang Y, Li W, Baker M, Guo J, Corbet K, Tsalik EL, Li QJ, Palmer SM, et al. Plasma microRNA signature as a noninvasive biomarker for acute graft-versus-host disease. Blood 2013; 122(19): 3365–33675.
10. Gimondi S, Dugo M, Vendramin A, Bermema A, Biancon G, Cavane A, Corradini P, Carniti C. Circulating miRNA panel for prediction of acute graft-versus-host disease in lymphoma patients undergoing matched unrelated hematopoietic stem cell transplantation. Exp Hematol 2016; 44(7): 624–634.e1.
11. Crossland RE, Norden J, Juric MK, Green K, Pearce KF, Lendrem C, Greinix HT, Dickinson AM. Expression of serum microRNAs is altered during acute graft-versus-host disease. Front immunol 2017; 8: 308.
The authors
Rachel E. Crossland* PhD and Anne M. Dickinson PhD
Haematological Sciences, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK
*Corresponding author
E-mail: Rachel.crossland@ncl.ac.uk
Twitter: @RECrossland
Variation in measuring and reporting serum indices and the effect on clinical practice
, /in Featured Articles /by 3wmediaThe presence of hemoglobin (hemolysis), bilirubin (icterus) and lipids (lipemia) in serum can affect the measurement and reporting of many clinical chemistry laboratory test results. This is not only in the validity of the test results themselves, but also whether a result is, or should be, reported. Quality assurance of serum indices is not as rigorous as for other clinical chemistry assays. This article highlights the variation in practice which is ultimately affecting the patient and their care pathway.
by Dr Rachel Marrington and Finlay MacKenzie
Introduction
There are a number of interferences that affect the analytical accuracy of clinical chemistry assays. These can be both endogenous and exogenous and can cause falsely elevated or falsely reduced results that can have serious clinical consequences. The most widely recognized endogenous interference is hemolysis. Hemolysis occurs when red blood cells are ruptured either in vivo, or in vitro, and their contents are released into blood plasma. There can be increased values [potassium, aspartate aminotransferase (AST)] and decreased values (sodium) because of the concentration gradient between the cells and plasma. Hemoglobin and other intracellular components can interfere with chemical reactions, and also hemoglobin absorbs light at 415 nm and, therefore, can cause an apparent increase in concentration for assays using this wavelength of absorbance [1]. Icterus is caused by elevated bilirubin present in the serum resulting in a yellow coloured serum, and is most frequently caused from liver disease. Lipemia is due to the presence of a high concentration of lipids in the blood, which cause light scattering in spectrophotometric assays.
Although most laboratory staff are fully aware of what the measurement of hemolysis, icterus and lipemia (HIL) indices is meant to achieve, there is a bit of a ‘black hole’ in how their instruments have been set up. Most automated clinical chemistry analysers automatically measure the presence of the serum indices of HIL by absorbance alone and report a number with no units to the user. The manufacturer’s engineers are usually the people who set the analysers up but unfortunately there appears to be a variety of ways to do this, and because there is no ‘correct’ way, differences have inadvertently crept in. Manufacturers use different paired wavelengths for the determination of the serum indices, and have different methods of reporting – numerical (e.g. 1, 2, 3) and category/non-numerical (e.g. +++) [2]. Automatic algorithms, which differ between manufacturer, will either allow, or not allow an individual result to be reported.
A typical large laboratory in the UK analyses approximately 6000 specimens for serum indices daily. Although internal quality control (IQC) is carried out on all clinical chemistry ‘real’ assays prior to results being released, it is not routine for laboratories to apply IQC procedures to serum indices. Laboratories accredited to ISO15189:2012 are accredited at the reportable test level, and as serum indices are not reported, these are not always covered within the scope of accreditation. There is, therefore, a large scope for errors within a laboratory’s serum indices to impact on analytical results.
EQA scheme design
Birmingham Quality has established an external quality assessment (EQA) scheme for serum indices – UK NEQAS for Serum Indices. The scheme not only looks at hemolysis, icterus and lipemia as individual analytes but also systematically looks at the impact that these serum indices have on particular analytes, which changes from month to month, and is called ‘Analyte X’. Laboratories are asked whether they would report the result for the measured analyte based on the serum indices. This scheme is available worldwide but the majority of participants are UK based.
The innovative report style from Birmingham Quality allows two data presentations from the same analyte – standard report format for numerical (index) data, and pie charts for the category data (Fig. 1a and 1b, respectively). Method mean concentrations are used as the target value for numerical results and the consensus category for individual manufacturers.
HIL performance
By their very nature, indices have no real ‘unit’ associated with them. That said, they do have a tenuous linkage with concentrations of hemoglobin, bilirubin and triglyceride. The usual units for these analytes in the UK are g/L, µmol/L and mmol/L and many instruments are indeed set up in these units. Similarly, many are set up in mg/dL, which though perfectly common in the US and the non-UK, non-Scandinavian, European countries, are never used in the UK but yet have representation here. Now although this doesn’t particularly matter to an individual laboratory, we have the unintended situation of two machines in the same laboratory having been set up in different units.
Hemolysis
Semi-quantitative hemolysis results are correlated to an approximate hemoglobin concentration, and, as such, the units are g/L, mg/dL or µmol/L.
Hemolysed specimens are prepared endogenously by allowing serum to remain on red blood cells for a period of time, or by the addition of exogenous material. There is generally good agreement within a method and the between method imprecision [percent coefficient of variation (%CV)] is fairly consistent across the concentration range 0.5–10 g/L at approximately 5 % for Abbott and Roche, and approximately 12 % for Siemens and Ortho J&J.
Icterus
Semi-quantitative icterus results are correlated to an approximate bilirubin concentration, and, as such, the units are µmol/L or mg/dL.
Icteric specimens are prepared as either endogenous, or by the addition of exogenous material. The between method imprecision varies between manufacturer and is likely to reflect the differences in measurement approach. Differences between methods have been observed. For example, an unspiked sample was distributed which had endogenous elevated triglycerides (specimen 111C in Fig. 1). Two methods – Beckman Synchron and Beckman AU Olympus – have identified a significant amount of icterus present, whereas other major methods haven’t. Analytically this is because of secondary interference due to overlapped absorbance not being corrected. Clinically this means that there is the potential that results would not be reported because of an incorrect icterus result being reported on a lipemic sample, when analytically they may be valid for icterus.
Lipemia
Lipemia results are roughly correlated to triglyceride concentration and in most cases are calibrated/anchored to intralipid concentration, and reported as g/L, mg/dL or mmol/L. Many manufacturers use more than one ‘unit’ for serum indices. For hemolysis and icterus the numerical results are obviously different; however, for lipemia there is only a factor of 1.13 between g/L and mmol/L. As serum indices are usually reported without a unit there is the potential for error if the units that are measured are not the same as when interpreted.
Lipemia shows higher between method imprecision %CVs on all methods. Specimens are either distributed with endogenously elevated lipids, or intralipid is added. Both cases show similarly high %CV, which is likely to be due to differences in the light scattering of turbid samples. However, there is also the possibility that although participants are advised to mix EQA material prior to analysis, they may not always. This problem is not unique to EQA specimens, as a delay in analysis of separated clinical specimens, or ‘add-on’ tests means that lipemic material could have separated before being sampled.
Analyte X
Analyte X is a unique and sophisticated concept where the laboratory is challenged for a specified different analyte every month. The participant reports the value obtained for this variable analyte and also an interpretation of whether they would report that result for analyte X based on the serum indices they obtained. This allows an assessment of the impact of serum indices on the numerical result of the analyte, and the participant can directly compare different methods. The interpretative element demonstrates for the first time the variation in clinical practice.
Significant differences in practice for the interpretation of serum indices both within and between manufacturers have been observed. For example, three specimens of the same base material were distributed with varying degrees of hemolysis for the analysis of total protein (Fig. 2). Specimen 106A was slightly hemolysed (overall consensus mean 0.7 g/L) and specimen 106C was grossly hemolysed (overall consensus mean 4.7 g/L). Beckman AU, Roche and Siemens did not show any significant change in the total protein result reported for all three specimens; however, Abbott and Ortho J&J both showed an increase in total protein as the amount of hemolysis increased. All methods showed a mixture of whether a laboratory would or would not report the total protein result, even with the grossly hemolysed specimen (Fig. 2b). This shows differences in algorithms being used even within manufacturers. Approximately 20 % of Abbott participants and approximately 25% of Ortho J&J participants would report an elevated total protein result in the presence of gross hemolysis. The consequences of this erroneously high total protein result could lead to additional testing. This would not only cause the clinician to unnecessarily waste time, but could also cause concern for the patient.
Discussion
With the increase of tracked automation, separated specimens are no longer ‘handled’ by the operator, and, therefore, no visual inspection takes place. The laboratory is entirely reliant on the use of algorithms based on absorbance of the specimen to decide whether individual analytical results should be reported or not. Serum indices are usually only measured once. This may be soon after a specimen has been centrifuged or sometime later. It is known that lipemic specimens ‘separate’ over time; therefore, any delay in analysis, or the addition of any subsequent ‘add-on’ tests, may result in the sampling of an incorrect portion of serum. Serum indices are not usually reported to a clinician, and may be presented on the analyser only as a number with no units. Therefore, there is a reliance by the laboratory on the manufacturer that the numbers correlate with the correct cut-off values for particular analytes. Data from the UK NEQAS for Serum Indices Scheme has shown variation within a manufacturer on the reporting of analytes based on the HIL result; therefore, either laboratories are changing cut-offs, or manufacturers are not setting laboratories up the same way. Manufacturers extensively test their assays for interferences prior to being released, and inform laboratories of this in their kit inserts. The laboratory is responsible for verifying that these levels of interference are suitable for their requirements.
Hemolysed, icteric or lipemic specimens either result in patients not receiving results, and, therefore, needing to be re-bled, or incorrect results being reported for specific analytes. Either way, the patient’s care is affected. Hemolysis is considered one of the most common interferents and the incidence with which a laboratory receives hemolysed specimens varies widely and is dependent on how specimens are collected [2]. A study of the incidence of hemolysed samples in an Emergency Medicine department in the UK concluded that 10.7 % of specimens were hemolysed over the seven-day sampling period. [3] Overall incidence data for specimens/tests rejected because of hemolysis, icterus or lipemia is not available; however, the impact on the laboratory, clinician and patient is likely to be significant.
Conclusion
The UK NEQAS for Serum Indices Scheme has shown that there is generally good analytical performance within individual manufacturers for hemolysis and icterus. Lipemia shows more variation in results. Variations are observed between manufacturers and in the application of the serum index to interpretation of a clinical chemistry result.
Automation has allowed the clinical chemistry analysis a more rapid throughput; however, human contact with specimens has now been reduced, which has increased reliance on computer algorithms. The UK NEQAS for Serum Indices Scheme has, and continues to show, variation in practice which consequently affects patient care both in terms of repeat testing and the validity of results.
References
1. Thomas L. Haemolysis as influence and interference factor. eJIFCC 2002; 13(4): http://www.ifcc.org/media/477061/ejifcc2002vol13no4pp095-098.pdf.
2. Farrell CJL, Carter AC. Serum indices: managing assay interference. Ann Clin Biochem 2016; 53: 527–538.
3. Berg JE, Ahee P, Berg JD. Variation in phlebotomy techniques in emergency medicine and the incidence of haemolysed samples. Ann Clin Biochem 2011; 48: 562–565.
The authors
Rachel Marrington* PhD, FRCPath and Finlay MacKenzie MSc (Director and consultant clinical scientist)
Birmingham Quality (UK NEQAS), Queen Elizabeth Hospital Birmingham, Birmingham, UK
*Corresponding author
E-mail: rachel.marrington@uhb.nhs.uk
Point-of-care testing for HbA1c: clinical need and analytical quality
, /in Featured Articles /by 3wmediaHbA1c plays an essential role in the diagnosis and management of people with diabetes. Point-of-care testing for HbA1c offers a wealth of opportunities to provide a rapid, accurate and easy to access tool for healthcare professionals, with performance of some devices matching or even outperforming routine laboratory instruments.
by Dr Emma English, Larissa-Nele Schaffert and Dr Erna Lenters-Westra
Introduction
Diabetes mellitus (DM) represents a major health problem of the 21st century, causing severe long-term damage to the cardiovascular and nervous system as well as the eyes and kidneys. The International Diabetes Federation (IDF) estimates that currently 425 million people globally, have diabetes. Regions such as Africa are predicted to see an increase in diabetes cases of over 150 % by the year 2045, representing a huge burden on already limited health resources [1].
Hemoglobin A1c (HbA1c) has traditionally been used to monitor glycemic control in patients with diabetes. Multiple large-scale studies have demonstrated the benefit of lowering HbA1c values in reducing microvascular and macrovascular complications. HbA1c is formed by glycation of the N-terminal valine of the beta chain of hemoglobin, which is a non-enzymatic reaction occurring within red blood cells, resulting in an increased negative charge of the molecule. The more glucose that is present in the blood stream during the lifetime of the red blood cells (around 100–120 days), the higher the concentration of HbA1c.
In 2011 the World Health Organization (WHO) advocated the use of HbA1c for the diagnosis of type 2 DM (T2DM) and this has been implemented in a number of countries worldwide. The threshold for diagnosing T2DM was determined as 48 mmol/mol (6.5 %) HbA1c, although this value has not been universally accepted [2].
The typical clinical procedure to assess patients with suspected diabetes will often involve a risk score to assess risk factors for diabetes such as age, family history and BMI and if this is elevated an HbA1c test may be requested. The testing process involves at least two appointments with a GP/practice nurse: (1) blood samples being taken during the first visit, and (2) 1–2 weeks later results being discussed with the patient, after laboratory analysis. If elevated HbA1c levels are found and there are no other symptoms then a repeat HbA1c test would normally be undertaken, adding to the length of time taken to reach a diagnosis.
Why are HbA1c point-of-care tests useful?
There are a number of potential benefits to using point-of-care testing (POCT) for HbA1c. The timely identification of disease is a key advantage of POCT as it provides immediate results at the time of patient consultation; this enables decisions to be made at the earliest possible opportunity, potentially resulting in fewer patient visits. It should be noted, however, that there are currently no guidelines supporting the use of POCT devices for the diagnosis of diabetes. In addition to potential use for diagnosis, the regular monitoring of people with diabetes may be more effectively facilitated with POCT devices, especially in rural or hard-to-reach environments. The patient may have their HbA1c levels tested upon arrival at clinic and the results will be available at the consultation, saving the need for a pre-visit. Alternatively the analysis may be undertaken during the consultation itself and the analysis time can be used to perform other measurements, such as blood pressure, or provide an opportunity for the clinician to engage in patient education.
The Noklus programme is an excellent example of where POCT has been shown to be effective. Owing to its geography, Norway has a low population density, resulting in many patients having to travel long distances to access primary healthcare provision. Repeated visits to the healthcare providers are time consuming and costly and ideally avoided. The use of POCT could mitigate some of the need to travel; indeed Norway has been using HbA1c POCT for more than 17 years for monitoring patients with diabetes and for the last two years, it has been used for the diagnosis of T2DM [3]. Recently Noklus have expanded activities to include the use of pharmacies to identify those at risk of diabetes and to test for diabetes using a POCT device, demonstrating a clearly expanding role for POCT [4].
The area where the diabetes disease burden is increasing at the fastest rate is in sub-Saharan Africa. Current estimates predict a threefold increase in cases over the next 25 years with four out of five diabetes-related deaths occurring in those of working age below 60 years [1]. This is a high priority region for early identification of disease and early intervention to limit progression of complications, as the costs associated with diabetes care are beyond the reach of many countries in this region. With two-thirds of those with diabetes unaware that they have the disease, access to rapid, easy-to-use and portable HbA1c devices is needed. POCT devices are likely to play a crucial role in the identification and monitoring of people with diabetes in Africa, especially as the current laboratory infrastructure is unlikely to meet this need [5].
HbA1c measurement
Analytical methods are based on either differences in structure, or charge of the glycated versus non-glycated hemoglobin. The main methods used for POCT are:
Hemoglobin species (HbA1c and HbA0) are separated according to the difference in isoelectric point, by employing differences in ionic interactions between the hemoglobin in the blood sample and the cation exchange groups on the column resin surface.
The immunoassay method uses antibodies that bind to the N-terminal glycated tetrapeptide or hexapeptide group of the HbA1c, forming immunocomplexes that can be detected and measured using a turbidimeter or a nephelometer.
Affinity chromatography is a separation technique based on structural differences between glycated versus non-glycated hemoglobin, which utilizes m-aminophenylboronic acid and its specific interactions with the glucose adduct of glycated hemoglobin.
Enzymatic quantification of HbA1c is based on cleavage of the beta chain of hemoglobin by specific proteases to liberate peptides, which then further react to produce a measurable signal.
Most POCT devices for HbA1c use a drop of capillary whole blood, collected via the finger-prick procedure. Following application to the test cartridge, the sample is analysed within a few minutes, although some methods require additional preparation steps. Details of current devices are available from manufacturers and in Schaffert et al. [6].
Quality criteria for HbA1c POCT
WHO guidance states that HbA1c may be used for diagnosis of T2DM provided “stringent quality assurance tests are in place and assays are standardized to criteria aligned to the international reference values”[2]. For laboratory-based methods, the quality standards for HbA1c as a diagnostic tool and HbA1c as a monitoring tool are the same. Quality targets vary, depending on the organization or body giving the guidance; however, the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) recently proposed the use of sigma metrics to define and set quality targets that can be adjusted depending on the specific requirements of the system/setting being assessed [7]. Currently there is no further additional guidance that specifically relates to quality targets for POC devices for HbA1c. What is essential for high quality in POCT is a robust quality framework, see Figure 1.
Assessing the quality of POC devices
There are several ways in which the quality of an analytical device can be assessed. A common approach is a laboratory evaluation following standardized protocols, such as the Clinical & Laboratory Standards Institute (CLSI) guidelines. To meet WHO criteria, such evaluations should be undertaken using samples targeted to the Reference Measurement Procedure (RMP), which for HbA1c is the IFCC RMP. The results of the evaluation will provide a set of performance figures for that instrument. In order to interpret these values, quality targets or criteria also need to be applied. In 2015, HbA1c was one of the first analytes for which such quality criteria have been set and these criteria are based on sigma metrics [7]. A significant number of method evaluations for HbA1c POC devices have been undertaken in recent years and the findings of these have been summarized in a recent systematic review and meta-analysis [8]. More recently sigma metrics have been applied alongside CLSI guidance [9].
Another approach is to evaluate external quality assessment (EQA) data, which provides a ‘real world’ perspective on method performance. A recent large-scale study by the IFCC demonstrated that performance of HbA1c testing varies between countries and between manufacturers but also showed that performance can vary between countries with a single manufacturer and method type [10].
HbA1c POCT myths and facts
There is often controversy around hot topics such as the use of HbA1c testing for the diagnosis of T2DM and in particular the use of POC for diagnosis; however, there are some key messages to consider:
Summary
HbA1c POC devices play a valuable role in tackling the global diabetes epidemic, offering rapid and accurate test results, which have the potential to improve patient care and timeliness of diagnosis and treatment changes during monitoring of glycemic control. Quality guidelines are the same for POCT devices as laboratory devices and many POCT devices perform as well as laboratory instruments. Essential to all high quality testing is a robust EQA scheme and adequate training for all users.
References
1. IDF Diabetes Atlas, 8th edn. International Diabetes Federation (IDF) 2017 (http://www.diabetesatlas.org).
2. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus: abbreviated report of a WHO consultation. World Health Organization 2011 (http://www.who.int/diabetes/publications/report-hba1c_2011.pdf).
3. Skeie S, Thue G, Sandberg S. Use and interpretation of HbA1c testing in general practice. Implications for quality of care. Scand J Clin Lab Invest 2000; 60(5): 349–356.
4. Risøy AJ, Kjome RLS, Sandberg S, Sølvik UØ. Risk assessment and HbA1c measurement in Norwegian community pharmacies to identify people with undiagnosed type 2 diabetes – A feasibility study. PLoS One 2018; 13(2): e0191316.
5. Atun R, Davies JI, Gale EAM, Bärnighausen T, Beran D, Kengne AP, Levitt NS, Mangugu FW, Nyirenda MJ, et al. Diabetes in sub-Saharan Africa: from clinical care to health policy. Lancet Diabetes Endocrinol 2017; 5(8): 622–667.
6. Schaffert L-N, English E, Heneghan C, Price CP, Van den Bruel A, Plüddemann A. Point-of-care HbA1c tests – diagnosis of diabetes. Horizon Scan Report 0044. National Institute for Health Research 2016 (https://www.community.healthcare.mic.nihr.ac.uk/reports-and-resources/horizon-scanning-reports/point-of-care-hba1c-tests-diagnosis-of-diabetes).
7. Weykamp C, John G, Gillery P, English E, Ji L, Lenters-Westra E, Little RR, Roglic G, Sacks DB, et al. Investigation of 2 models to set and evaluate quality targets for HbA1c: biological variation and sigma-metrics. Clin Chem 2015; 61(5): 752–759.
8. Hirst JA, McLellan JH, Price CP, English E, Feakins BG, Stevens RJ, Farmer AJ. Performance of point-of-care HbA1c test devices: implications for use in clinical practice – a systematic review and meta-analysis. Clin Chem Lab Med 2017; 55(2): 167–180.
9. Lenters-Westra E, English E. Evaluation of four HbA1c point-of-care devices using international quality targets: are they fit for the purpose? J Diabetes Sci Technol 2018; 12(4): 762–770.
10. EurA1c Trial Group. EurA1c: the European HbA1c trial to investigate the performance of HbA1c assays in 2166 laboratories across 17 countries and 24 manufacturers by use of the IFCC model for quality targets. Clin Chem 2018; 64(8): 1183–1192.
11. Lenters-Westra E, Slingerland RJ. Three of 7 hemoglobin A1c point-of-care instruments do not meet generally accepted analytical performance criteria. Clin Chem 2014; 60(8): 1062–1072.
12. Lenters-Westra E, English E. Understanding the use of sigma metrics in hemoglobin A1c analysis. Clin Lab Med 2017 Mar; 37(1): 57–71.
13. The use of POCT HbA1c devices in the NHS Diabetes Prevention Programme: recommendations from an expert working group commissioned by NHS England. NHS England Publications Gateway Reference 05139. NHS 2016 (https://www.england.nhs.uk/wp-content/uploads/2016/07/poct-paper.pdf)
The authors
Emma English*1 PhD, Larissa-Nele Schaffert2 BSc and Dr Erna Lenters-Westra3,4 PhD
1Faculty of Medicine and Health, University of East Anglia, Norwich Research Park, UK
2School of Medicine, University of Nottingham, Nottingham, UK
3Department of Clinical Chemistry, Isala, Zwolle, The Netherlands
4European Reference Laboratory for Glycohemoglobin, Location Isala, Zwolle, The Netherlands
*Corresponding author
E-mail: emma.english@uea.ac.uk
Blood-based peripheral biomarkers for dementia: are we any closer to their use in the clinical setting?
, /in Featured Articles /by 3wmediaDementia is one of the leading causes of disability in old age and places a huge burden on society. The growing prevalence of dementia calls for accurate, more accessible biomarkers to facilitate clinical diagnosis and prognosis. Peripheral mediums, such as blood and blood derivates (i.e. plasma and platelets), are currently being investigated for their potential as biomarkers of dementia subtypes. There is a lack of reproducibility in dementia biomarker studies, likely because of unaccounted factors such as age, ethnicity and gender, which is stalling their translation from research to the clinical setting. However, several blood-based biomarkers have been consistently reported from plasma and blood cells, including amyloid and tau protein, clusterin and immunoglobulins, as well as α-synuclein. This review highlights the need for further validation of the current blood-based dementia biomarkers for their routine clinical use.
by Oluwatomi E. S. Akingbade and Prof. Elizabeta B. Mukaetova-Ladinska
Introduction
The increasing incidence of neurodegenerative diseases, such as dementia, legitimizes the search for readily accessible biological markers. At present, there are 50 million people worldwide living with dementia, with 10 million new cases reported each year [1]. Dementia care costs are as high as £26 billion a year [1], with the likelihood of increasing further owing to higher numbers of people living with dementia. The increasing incidence of dementia calls for more efficient diagnosis. Currently, dementia diagnosis relies on extensive clinical assessments, facilitated by invasive (i.e. lumbar puncture) and technical (i.e. MRI) testing, all costly and inefficient in keeping up with the growing number of dementia patients.
The definitive diagnosis of dementia is done neuropathologically, and besides the clinical evidence of dementia, is based on the characteristic hallmarks of plaques and tangles (for Alzheimer’s disease (AD), the most common form of dementia [2]), Lewy bodies (for dementia with Lewy bodies (DLB)), and vascular changes (for vascular dementia). Some of the molecular substrates of the characteristic neuropathological dementia features have been taken forward both in neuroradiological (i.e. β-amyloid radiotracers) and biochemical assessments [i.e. amyloid-β (Aβ42), total tau and phosphorylated tau181 measurements in the cerebrospinal fluid (CSF)] [3]. However, their costs, limited access and invasive approach, as well as involvement in secondary inflammatory processes in dementia [4], are restricting their wider clinical utility. Thus, there is a need for the development of less invasive and more cost-effective peripheral biomarkers to facilitate the clinical diagnosis of dementia.
Unlike CSF, blood and blood derivates (platelets and plasma) are easily accessible in the clinical setting and the potential of using them to corroborate dementia diagnosis will likely lead to earlier, and more accurate dementia diagnoses. Although the blood–central nervous system barrier provides a physiological and physical barrier, changes in peripheral fluids and organs have been identified in people with dementia. Notably, erythrocyte [5] and platelet [6] physiology and function are largely effected in dementia. Proteins in peripheral organs are also being explored: i.e. α-synuclein, p53 protein, tau and amyloid in the skin, kidneys and liver; tau protein in the testes; Aβ1–42 and acetylcholinesterase in serous fluid, as well as amyloid and tau proteins in the gastrointestinal tract. Explorative studies continue to report that peripheral components are influenced in dementia disease. Reoccurring peripheral proteins of interest in the search for dementia biomarkers include α-synuclein, Immunoglobulin G (IgG), Aβ precursor protein (AβPP), clusterin (all found in both platelets and plasma) and myeloperoxidase (MPO, present in plasma only).
Clinical relevance of potential blood-based dementia biomarkers
Peripheral Aβ and AβPP
Aβ plaques in the brain are key pathological hallmarks of AD, and lower Aβ CSF level is a known marker in autopsy-confirmed AD subjects [22]. Aβ is also reported in the periphery, i.e. blood and skin. In a healthy population, plasma Aβ40, Aβ42 and Aβ40/42 ratio levels were significantly higher in older participants than in the younger ones [23]. In the Rotterdam Study Cohort [21], lower levels of plasma Aβ1–28 and Aβ40–42 were linked to higher risks of dementia in older age [7]. In platelets, the AβPP ratio [upper (130kDa) to lower (106–110kDa) immunoreactive bands] has been investigated (Table 1). Thus, there are differences in platelet AβPP ratios of those with cognitive deficits pertaining to dementia (i.e. very mild AD [10]; mild AD [10]; and AD [9–11]) and control patients. However, there are conflicting reports on the differences between the AβPP ratio in patients with varying severity of cognitive deficit with some studies [9,10]. Additionally, some studies have reported that increased AβPP levels in platelets correlated with higher Mini-Mental State Examination (MMSE) [11, 12] and Cambridge Cognition Examination (CAMCOG) scores [11]. Whether or not peripheral measurements of Aβ can be used as a pre-symptomatic marker of dementia (and/or the associated cognitive decline) remains uncertain. However, the studies mentioned in this review provide a consistent foundation for future studies to standardize measurement techniques that can be used in the clinical setting.
Peripheral α-synuclein
Studies have consistently reported the potential of α-synuclein as a biomarker of dementia – particularly in DLB as α-synuclein is reported to be a key constituent of Lewy bodies [24]. About 95 % of the α-synuclein in blood is predominantly found in erythrocytes, with the levels in plasma being extremely low. It is only recently that an assay was developed that is sensitive enough to detect the low levels of α-synuclein in plasma – reported in the range of 2.1–19.4 µg/L [25]. However, platelet α-synuclein levels in dementia have been reported to be similar among control and AD subjects [12] (Table 1). The utility of α-synuclein as a dementia biomarker is somewhat questionable, largely owing to its unknown physiological function in the human brain, as well as its involvement in Lewy body formation, which can be present in both normal aging as well as in a number of synucleinopathies, including Parkinson’s disease, multiple system atrophy, DLB, amyotrophic lateral sclerosis, etc, [26].
Blood clusterin
Increased plasma clusterin levels were reported to be indicative of both brain atrophy in AD patients [27] and an increased risk of dementia in older people [13]. Indeed. elevated levels of plasma clusterin have been reported as a risk factor for dementia [13, 28]. One study identified that lower MMSE cognitive scores in an AD population were associated with higher levels of plasma clusterin, whereas AD patients with higher cognitive scores had lower plasma clusterin levels [15]. Furthermore, longitudinal assessments identified that increased plasma clusterin concentrations are related to cognitive decline in mild cognitive impairment [28]. Interestingly, platelet levels of clusterin appear to be similar in both control and AD subjects [12, 16], but the ratio of platelet and plasma clusterin is positively correlated to specific neuropsychiatric inventory sub-categories, in particular agitation, apathy, motor aberrant behaviour and irritability seen in AD subjects [12] (Table 1).
Peripheral immunoglobulins
Increased IgG levels in plasma of AD subjects has been reported [17] with no link to disease progression. In an independent study, plasma IgG levels remained unaltered but increased in the platelet samples of the same AD subjects when compared to IgG levels in their control counterparts [12]. Inconsistent results found in IgG in plasma may be due to experimental constraints and, therefore, the potential of IgGs to form part of dementia diagnosis should not be ruled out. A recent study has reported that electrochemical techniques can be used to detect immunoglobulin in the plasma in the pg/mL range [29] – such sensitivity will likely aid in more consistent findings in plasma IgG studies in dementia.
Peripheral tau protein
CSF tau (and its ratio to Aβ) has been extensively investigated in relation to dementia. A recent literature review has addressed the limited clinical value of CSF tau biomarker studies [30], attesting to the need to address the uncertainty behind CSF tau as a suitable peripheral biomarker for dementia. More recently studies have focused on the presence of tau in the periphery. In plasma, high levels of tau were weakly associated with AD and were longitudinally associated with increased brain atrophy and poor cognition [18]. Platelet tau protein showed a more complex relationship than the reported plasma tau protein levels; studies have also reported a higher ratio between high to low molecular weight tau ratio in AD patients when compared to controls [19, 31] – with acknowledgement of no significant differences found between low or high molecular weight tau protein levels in the platelets of control and AD patients [19] (Table 1). Another study, reported a negative correlation between total tau and phosphorylated tau in the platelets of AD participants [20]. All in all, investigation into peripheral tau as a biomarker of dementia (and even specific dementia subtypes) is still in the early stages and requires further investigation before it can be considered for use in clinical diagnosis.
Summary and conclusions
Historically, the search for peripheral, blood-based dementia biomarkers has focused largely on the protein profiles of plasma and serum in dementia patients. This review, based on 13 dementia biomarker studies, has shown that proteins in the periphery are influenced by neurodegeneration in dementia. Namely, increased concentrations of Aβ40, Aβ42 and clusterin in plasma were all shown to be indicative of an increased risk of developing dementia in the elderly. Most recently, increased levels of plasma neurofilament light chain were reported to be closely related to amyloid processing in both mild cognitive impairment and AD, and to correlate with poor cognitive performance and AD-related brain atrophy and hypometabolism [32]. Changes in proteins in platelets were shown to coincide with cognitive decline in dementia: lower levels of AβPP and higher levels of clusterin were present in those with poorer performance in cognitive tests. Interestingly, increased plasma levels of tau protein were associated with brain atrophy while total and phosphorylated tau levels in platelets were negatively correlated. These findings provide encouraging evidence for the measurable presence of blood-based proteins that are closely linked to AD hallmarks that have the potential to be used in routine clinical setting not only for diagnosis, but also for severity and progression of the dementia process. The reproducibility and causes of possible heterogeneity, i.e. age, ethnicity, co-morbidity and genetics, that may influence protein expression at the periphery will need to be explored further before these biomarkers can be used routinely in the clinic, and their accuracy for distinct dementia subtypes will also need to be determined.
References
1. Dementia. World Health Organization 2017 (http://www.who.int/en/news-room/fact-sheets/detail/dementia).
2. Types of dementia. Alzheimer’s Research UK 2018 (https://www.alzheimersresearchuk.org/about-dementia/types-of-dementia/).
3. Andreasen N, Vanmechelen E, Vanderstichele H, Davidsson P, Blennow K. Cerebrospinal fluid levels of total-tau, phospho-tau and A beta 42 predicts development of Alzheimer’s disease in patients with mild cognitive impairment. Acta Neurol Scand Suppl 2003; 179: 47–51.
4. Veitinger M, Varga B, Guterres SB, Zellner M. Platelets, a reliable source for peripheral Alzheimer’s disease biomarkers? Acta Neuropathol Commun 2014; 2: 65.
5. Stevenson A, Lopez D, Khoo P, Kalaria RN, Mukaetova-Ladinska EB. Exploring erythrocytes as blood biomarkers for Alzheimer’s disease. J Alzheimers Dis 2017; 60(3): 845–857.
6. Akingbade OES, Gibson C, Kalaria RN, Mukaetova-Ladinska EB. Platelets: peripheral biomarkers of dementia? J Alzheimers Dis 2018; 63(4): 1235–1259.
7. Hilal S, Wolters FJ, Verbeek MM, Vanderstichele H, Kamran Ikram M, Stoops E, Ikram MA, Vernooij MW. Plasma amyloid-β levels, cerebral atrophy and risk of dementia: a population-based study. Alzheimers Res Ther 2018; 10: 63 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026500/).
8. Tang K, Hynan LS, Baskin F, Rosenberg RN. Platelet amyloid precursor protein processing: a bio-marker for Alzheimer’s disease. J Neurol Sci 2006; 240(1–2): 53–58.
9. Borroni B, Colciaghi F, Corsini P, Akkawi N, Rozzini L, Del Zotto E, et al. Early stages of probable Alzheimer disease are associated with changes in platelet amyloid precursor protein forms. Neurol Sci 2002; 23(5): 207–210.
10. Padovani A, Borroni B, Colciaghi F, Pettenati C, Cottini E, Agosti C, Talarico G, Cattabeni F, Lenzi GL, et al. Abnormalities in the pattern of platelet amyloid precursor protein forms in patients with mild cognitive impairment and Alzheimer disease. Arch Neurol 2002 Jan; 59(1): 71–75.
11. Zainaghi IA, Forlenza O V, Gattaz WF. Abnormal APP processing in platelets of patients with Alzheimer’s disease: correlations with membrane fluidity and cognitive decline. Psychopharmacology (Berl) 2007; 192(4): 547–553.
12. Mukaetova-Ladinska EB, Abdel-All Z, Dodds S, Andrade J, Alves da Silva J, Kalaria RN, O’Brien JT. Platelet immunoglobulin and amyloid precursor protein as potential peripheral biomarkers for Alzheimer’s disease: findings from a pilot study. Age Ageing 2012; 41(3): 408–412.
13. Weinstein G, Beiser AS, Preis SR, Courchesne P, Chouraki V, Levy D, Seshadri S. Plasma clusterin levels and risk of dementia, Alzheimer’s disease, and stroke. Alzheimer’s Dement 2016; 3: 103–109.
14. Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data. Prev Med 1975; 4(4): 518–525.
15. Hsu J-L, Lee W-J, Liao Y-C, Wang S-J, Fuh J-L. The clinical significance of plasma clusterin and Abeta in the longitudinal follow-up of patients with Alzheimer’s disease. Alzheimers Res Ther 2017 Nov; 9(1): 91.
16. Mukaetova-Ladinska EB, Abdel-All Z, Andrade J, Alves da Silva J, O’Brien JT, Kalaria RN. Plasma and platelet clusterin ratio is altered in Alzheimer’s disease patients with distinct neuropsychiatric symptoms: findings from a pilot study. Int J Geriatr Psychiatry 2015; 30(4): 368–375.
17. Bosman GJ, Van der Linden PA, Bartholomeus IG, De Man AJ, De Grip WJ, Van Kalmthout PJ. Erythrocyte aging in the demented elderly: a fluctuating process? Mech Ageing Dev 1998; 100(1): 53–58.
18. Mattsson N, Zetterberg H, Janelidze S, Insel PS, Andreasson U, Stomrud E, Palmqvist S2, Baker D2, Tan Hehir CA, et al. Plasma tau in Alzheimer disease. Neurology 2016; 87(17): 1827–1835.
19. Slachevsky A, Guzmán-Martínez L, Delgado C, Reyes P, Farías GA, Muñoz-Neira C, Bravo E, Farías M, Flores P, et al. Tau platelets correlate with regional brain atrophy in patients with Alzheimer’s disease. J Alzheimers Dis 2017; 55(4): 1595–1603.
20. Mukaetova-Ladinska EB, Abdell-All Z, Andrade J, da Silva JA, Boksha I, Burbaeva G, Kalaria RN, O’Brien JT. Platelet tau protein as a potential peripheral biomarker in Alzheimer’s disease: an explorative study. Curr Alzheimer Res 2018; 15(9): 800–808.
21. Hofman A, Brusselle GGO, Darwish Murad S, van Duijn CM, Franco OH, Goedegebure A, Ikram MA, Klaver CC, Nijsten TE, et al. The Rotterdam Study: 2016 objectives and design update. Eur J Epidemiol 2015; 30(8): 661–708.
22. Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, Blennow K, Soares H, Simon A, et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 2009; 65(4): 403–413.
23. Kiko T, Nakagawa K, Satoh A, Tsuduki T, Furukawa K, Arai H, Miyazawa T. Amyloid beta levels in human red blood cells. PLoS One 2012; 7(11): e49620.
24. Koehler NKU, Stransky E, Shing M, Gaertner S, Meyer M, Schreitmuller B, Leyhe T, Laske C, Maetzler W, et al. Altered serum IgG levels to alpha-synuclein in dementia with Lewy bodies and Alzheimer’s disease. PLoS One 2013; 8(5): e64649.
25. Fjorback AW, Varming K, Jensen PH. Determination of alpha-synuclein concentration in human plasma using ELISA. Scand J Clin Lab Invest 2007; 67(4): 431–435.
26. Spillantini MG, Schmidt ML, Lee VM-Y, Trojanowski JQ, Jakes R, Goedert M. α-Synuclein in Lewy bodies. Nature 1997; 388: 839 (http://dx.doi.org/10.1038/42166).
27. Thambisetty M, Simmons A, Velayudhan L, Hye A, Campbell J, Zhang Y, Wahlund LO, Westman E, Kinsey A, et al. Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Arch Gen Psychiatry 2010 Jul; 67(7): 739–748.
28. Jongbloed W, van Dijk KD, Mulder SD, van de Berg WDJ, Blankenstein MA, van der Flier W, Veerhuis R. Clusterin levels in plasma predict cognitive decline and progression to Alzheimer’s disease. J Alzheimers Dis 2015; 46(4): 1103–1110.
29. Garyfallou G-Z, Ketebu O, Sahin S, Mukaetova-Ladinska EB, Catt M, Yu EH. Electrochemical detection of plasma immunoglobulin as a biomarker for Alzheimer’s disease. Sensors 2017; 17(11): doi: 10.3390/s17112464.
30. Ritchie C, Smailagic N, Noel-Storr AH, Ukoumunne O, Ladds EC, Martin S. CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane database Syst Rev 2017; 3: CD010803.
31. Neumann K, Farías G, Slachevsky A, Perez P, Maccioni RB. Human platelets tau: a potential peripheral marker for Alzheimer’s disease. J Alzheimers Dis 2011; 25(1): 103–109.
32. Mattsson N, Andreasson U, Zetterberg H, Blennow K. Association of plasma neurofilament light with neurodegeneration in Patients With Alzheimer disease. JAMA Neurol 2017; 74(5): 557–566.
The authors
Oluwatomi E. S. Akingbade1,2 and Elizabeta B. Mukaetova-Ladinska*2,3 MD, PhD, MRCPsych
1School of Life Sciences, Queen’s Medical Centre, University of Nottingham, Nottingham, UK
2Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
3Evington Centre, Leicester General Hospital, Leicester, UK
*Corresponding author
E-mail: eml12@le.ac.uk