The recent introduction of the CellaVision DC-1 makes it possible for small labs to implement the same digital methodology for performing blood cell differentials that is commonly used by large laboratory organizations. CellaVision recently teamed up with Alberta Public Laboratories (APL) to conduct an in-situ product evaluation assessing the utility and impact of CellaVision DC-1 in a distributed laboratory network. APL is a leading medical diagnostic laboratory serving a large catchment of Southern Alberta, Canada. CLI talked to Dr Etienne Mahe, consultant pathologist at APL, who shares here his experience of this technology.
1. Could you briefly describe your laboratory setting and specific requirements regarding hematology testing ?
Laboratory testing in Southern Alberta (and in many other jurisdictions elsewhere in the world) can easily be summarized as a “hub-and-spoke” model. We have a large central high-throughput laboratory to which geographically dispersed small referral laboratories or collection sites send specimens.
Since many of these smaller sites are at substantial distances from the central referral laboratory, the strategy in hematology has been to situate low-complexity low-throughput analysers at the spoke sites and reserve the high-throughput high-complexity infrastructure for the hub labs. In the case of peripheral smear review, CBC data are generated at the peripheral sites on low-complexity low-throughput analysers, but slides (as required) are referred to the hub for additional review and interpretation. In cases requiring pathologist review, delays of up to several days are possible, with the significant itinerant potential for delayed patient care.
2. In your view, what are the most interesting characteristics of the Cellavision DC-1 analyser and the main advantages of its technology ?
The Cellavision suite has provided our hub labs in Southern Alberta with league improvements in efficiency for high-throughput hematology testing. We employ Cellavision integrated analysers in our hub labs to perform nearly all peripheral smear manual differential and morphology review activities. We also use the Cellavision body fluid analysis features to assist with review and interpretation of most fluid specimens. The networking capabilities of the Cellavision suite have allowed for seamless data exchange between our network of hospital-based hub labs. The Cellavision suite has also allowed for improved training and quality control workflows.
The Cellavision DC-1, designed to better address the digital hematology and pathology needs of lower throughput laboratories, raised significant interest for us as a means to better improve our spoke-to-hub workflows. In particular, while our performance parameters for basic CBC resulting are reasonable, we currently experience heavy delays in the morphological review of peripheral smears by hub technologists and pathologists by virtue of transportation delays from spoke centers. The Cellavision DC-1 presents the opportunity for real-time digital interpretation of peripheral smears originating from spoke sites by expert hub lab staff, entirely negating the need for slide transport.
3. What was the aim of the product evaluation carried out by your laboratory network ? Could you briefly explain the methodology employed ?
When presented with the opportunity to test the Cellavision DC-1 instrument, we immediately wanted to prove the theoretical turn-around time benefit could be realized in our lab system. We obtained research ethics and institutional approval to perform a prospective study of turn-around times (from specimen collection at spoke sites to expert review by the hub lab), comparing a Cellavision DC-1 assisted workflow with the current standard-of-care. We assessed in comparison the reported results from morphology review using the Cellavision DC-1 assisted workflow relative to the standard-of-care workflow. We also undertook a comparison to historical turn-around time data in order to estimate the volume of cases (and hence the length of the study) required for a reasonable comparison.
4. What were the results of the evaluation and did they meet your expectations ?
Since we hope to publish our results in the future, I won’t divulge them in their totality as yet, except to say that we identified statistically significant improvements in all parameters assessed, including turn-around times, without evidence of any discordance in the quality of morphologic assessment. While we were not at all surprised to see a statistically significant difference between the workflows, we were impressed by the degree to which these improvements in turn-around time were realized, which we anticipate will mean a clinically significant improvement for labs facing similar workflow hurdles.
5. Can you tell us anything more about your experience of this technology and do you have any particular advice or recommendation for labs interested in its implementation ?
We have been working with the Cellavision suite of technologies for several years and have incorporated it into the vast majority of our routine hematology workflows. Several years ago, as part of a small implementation project, I asked a number of our technologist super users to provide their feedback on instrument usage and software usability. By far and away, the feedback was positive.
As part of our current work, we are also hoping to provide more tangible data relating to Cellavision software useability. More specifically, we have undertaken several exercises across a broad cadre of technical staff, to identify how much more time-efficient the process of technologist classification using Cellavision software is compared with manual morphology assessment. As with our turn-around time results, we will soon be reporting a significant advantage to a Cellavision based workflow.
For laboratories and laboratory networks thinking of implementing Cellavision enabled technologies, it is important to first understand the nature of your laboratory structure and its hematology workflows. For single lab sites with high-throughput, Cellavision offers a number of solutions geared to high-volume needs. Now, as we have seen, Cellavision also offers solutions for smaller low-throughput labs, especially labs frequently faced with the challenges of material referrals.
6. What do you see as the next step for your organization ?
While our data support improvements in time-based metrics using the Cellavision DC-1 in our distributed laboratory network, we are hoping next to make an economic argument to support the integration of the Cellavision suite of technologies across our hub-and-spoke network. More specifically, we are hoping to liaise with local health economics experts to prove that improvements in turn-around times (and the commensurate assumed cost reductions if materials transportation is not required) support the necessary investments in infrastructure required, as well as where such investments should be made across our network.
Dr Etienne Mahe is Clinical Assistant Professor, Department of Pathology & Laboratory Medicine, University of Calgary, and Consultant Pathologist, Division of Hematology, South Sector, Alberta Public Laboratories
Urinary kidney injury molecule-1 in renal disease
Moresco RN, Bochi GV, Stein CS, De Carvalho JAM, Cembranel BM, Bollick YS. Clin Chim Acta 2018; 487: 15–21
Kidney injury molecule-1 (KIM-1), a type l transmembrane glycoprotein, is recognized as a potential biomarker for detection of tubular injury in the main renal diseases. Urinary KIM-1 increases rapidly upon the tubular injury, and its levels are associated with the degree of tubular injury, interstitial fibrosis, and inflammation in the injured kidney. Currently, the investigation of kidney diseases is usually performed through the assessment of serum creatinine and urinary albumin. However, these biomarkers are limited for the early detection of changes in renal function. Besides, the tubular injury appears to precede glomerular damage in the pathophysiology of renal diseases. For these reasons, the search for sensitive, specific and non-invasive biomarkers is of interest. Therefore, the purpose of this article is to review the physiological mechanisms of KIM-1, as well to present clinical evidence about the association between elevated urinary KIM-1 levels and the main renal diseases such as chronic kidney disease, diabetic kidney disease, acute kidney injury, and IgA nephropathy.
Prognostic impact of tumour-infiltrating CD276/Foxp3-positive lymphocytes and associated circulating cytokines in patients undergoing radical nephrectomy for localized renal cell carcinoma
Iida K, Miyake M, Onishi K, Hori S, Morizawa Y, et al. Oncol Lett 2019;17(4): 4004–4010
Renal cell carcinoma (RCC) is an immunogenic tumour and pathological specimens generally contain large quantities of tumour-infiltrating lymphocytes (TILs). Numerous cell types and cytokines could affect the immune escape mechanism of tumour cells. The aim of the present study was to investigate the prognostic impact of TILs and the associated circulating cytokines on localized clear cell RCC following radical nephrectomy. A total of 87 patients who had undergone radical nephrectomy and were pathologically diagnosed with localized clear cell RCC were included. The present study evaluated the profile of TILs with immunohistochemical analysis of tumour specimens using a panel of antibodies [cluster of differentiation (CD)-4, CD8, CD80, CD86, CD276, and Forkhead box p3 (Foxp3)]. Counts of each TIL were compared with clinicopathological variables. Based on the results of immunohistochemical analyses, putative cytokines, including interleukin (IL)-6, IL-10, IL-17, interferon-γ, tumour necrosis factor (TNF)-α, and transforming growth factor (TGF)-β, were selected, and their levels in preoperative serum were measured by ELISA. The levels were compared with TIL counts in tumour specimens. High counts of the CD276+ and Foxp3+ TILs were identified as independent factors for poor prognosis for metastasis and local recurrence following radical nephrectomy (P=0.033 and 0.006, respectively). A high CD276+ TIL count was associated with preoperative serum levels of TNF-α and IFN-γ (P=0.027 and P=0.035, respectively), whereas a high count of Foxp3+ TILs was associated with preoperative serum levels of TGF-β (P=0.021). High levels of TNF-α and TGF-β were associated with recurrence-free survival (P=0.035 and P=0.031, respectively). Topical intra-tumoral immunoreaction and systemic immune status may be associated with patients with localized RCC. The topical induction of the CD276+ and Foxp3+ TILs was suggested to be associated with high levels of serum TNF-α and IFN-γ. Preoperative serum levels of TNF-α and TGF-β could be simple and non-invasive biomarkers for risk stratification before radical surgery.
Mesangial C4d deposition may predict progression of kidney disease in pediatric patients with IgA nephropathy
Fabiano RCG, de Almeida Araújo S, Bambirra EA, Oliveira EA, Simões E Silva AC, Pinheiro SVB. Pediatr Nephrol 2017; 32(7): 1211–1220
BACKGROUND: Data on the risk factors for chronic kidney disease in children with immunoglobulin A nephropathy (IgAN) are scarce. This study was aimed at investigating whether glomerular C4d immunostaining is a prognostic marker in pediatric IgAN.
METHODS: In this retrospective cohort study, 47 patients with IgAN biopsied from 1982 to 2010 were evaluated. Immunohistochemistry for C4d was performed in all cases. For analysis, patients were grouped according to positivity or not for C4d in the mesangial area. Primary outcome was a decline in baseline estimated glomerular filtration rate (eGFR) by 50 % or more.
RESULTS: Median follow-up was 8.3 years. Median renal survival was 13.7 years and the probability of a 50 % decline in eGFR was 13 % over 10 years. Nine children exhibited the primary outcome and four developed end-stage renal disease (ESRD). Compared with C4d-negative patients (n=37), C4d-positive patients (n=10) presented higher baseline proteinuria (1.66 ± 0.68 vs 0.47 ± 0.19 g/day/1.73 m2, P<0.001), a progressive decline in eGFR (−10.04 ± 19.38 vs 1.70 ± 18.51 mL/min/1.73 m2/year; P=0.045), and more frequently achieved the primary outcome (50.0 vs 10.8 %, P=0.013), and ESRD (30.0 vs 2.7 %, P=0.026). No difference was observed in Oxford classification variables. Baseline proteinuria, endocapillary hypercellularity and mesangial C4d deposition were associated with primary outcome in univariate analysis. Proteinuria and mesangial C4d deposition at baseline independently predicted the decline in eGFR. Renal survival was significantly reduced in C4d-positive patients (8.6 vs 15.1 years in C4d-negative patients, P<0.001).
CONCLUSIONS: In this exclusively pediatric cohort, positivity for C4d in the mesangial area was an independent predictor of renal function deterioration in IgAN.
Non-invasive biomarkers of acute rejection in kidney transplantation: novel targets and strategies
Eikmans M, Gielis EM, Ledeganck KJ, Yang J, Abramowicz D, Claas FFJ. Front Med (Lausanne) 2019; 5: 358
Kidney transplantation is considered the favoured treatment for patients suffering from end-stage renal disease, since successful transplantation is associated with longer survival and improved quality of life compared to dialysis. Alloreactive immune responses against the donor kidney may lead to acute rejection of the transplant. The current diagnosis of renal allograft rejection mainly relies on clinical monitoring, including serum creatinine, proteinuria, and confirmation by histopathologic assessment in the kidney transplant biopsy. These parameters have their limitations. Identification and validation of biomarkers, which correlate with or predict the presence of acute rejection, and which could improve therapeutic decision making, are priorities for the transplantation community. There is a need for alternative, less invasive but sensitive markers to diagnose acute graft rejection. Here, we provide an overview of the current status on research of biomarkers of acute kidney transplant rejection in blood and urine. We specifically discuss relatively novel research strategies in biomarker research, including transcriptomics and proteomics, and elaborate on donor-derived cell-free DNA as a potential biomarker.
Current measures for diagnosis and therapy of chronic kidney disease are limited. Better biomarkers are required to improve treatment by directing therapeutic intervention, tracking responses to therapy and providing greater understanding of the underlying mechanisms driving renal disease progression. We describe here the development of microRNAs as biomarkers for diabetic kidney disease, the most common etiology leading to chronic kidney disease and end-stage renal failure.
by Dr Tanya A. Smith, Dr Kate Simpson, Prof Donald J. Fraser and Dr Timothy Bowen
Diabetes, complications and biomarkers
Diabetes is a major global health challenge, with 23.1 million cases diagnosed in the US alone [1]. As described below, our laboratory is currently developing urinary microRNAs as biomarkers for diabetic kidney disease. These transcripts may also have utility as biomarkers for other complications of type 2 diabetes mellitus including diabetic retinopathy, neuropathy, cardiovascular disease, stroke, ulceration and amputation [2].
Diabetic kidney disease
Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease in the United States. Clinical presentation is characterized by proteinuria, hypertension, and progressive reduction in kidney function. DKD is a progressive condition associated with around 35% of patients with type 1 and type 2 diabetes mellitus [3]. A highly significant public health concern, DKD is currently managed by targeting cardiovascular risk reduction, blood pressure management, glycemic control (hemoglobin A1c concentration), nutritional counselling, weight loss, smoking cessation, and pharmacological inhibition of the renin–angiotensin system using angiotensin-converting enzyme inhibitors or angiotensin-2 receptor blockers [4].
Despite the stabilization of the incidence of diabetes over the past 15 years, the United States Renal Data System has demonstrated increased prevalence of end-stage renal disease attributed to diabetes. However, the disease burden is such that patients often do not survive to end-stage renal disease. There is a broad spectrum of cardiovascular complications associated with DKD of which the underlying etiology remains unclear. Cardiovascular disease is the leading cause of death in this patient group, manifesting as cerebral vascular event, sudden cardiac death, myocardial infarction and diabetic cardiomyopathy. It is, therefore, essential to identify and treat patients before irreversible organ damage to reduce the medical and economic burden of disease [4].
Existing DKD biomarkers
DKD is associated with both glomerular hyperfiltration leading to progressive albuminuria, and declining glomerular filtration rate.
Albuminuria
Proteinuria is a biomarker used widely as a proxy to assess the integrity of the glomerular filtration barrier (for detailed glomerular and nephronal physiology see [5]). Quantification of urinary albuminuria excretion is a non-invasive and inexpensive method to monitor disease. Microalbuminuria is currently the primary predictive clinical DKD marker and occurs when urinary albuminuria excretion rate reaches 30–300 mg/24 h, macroalbuminuria is reached when this rate exceeds 300 mg/24 h. In the presence of diabetes mellitus, confirmation of microalbuminuria in two separate samples taken 3–6 months apart is diagnostic of DKD. Screening for albuminuria is more commonly performed using urinary albumin-to-creatinine ratio on an isolated urine sample, and is defined as >30 mg/g.
However, albuminuria is a non-specific biomarker measurable only after kidney injury has occurred and correlates poorly with clinical disease. In addition, albuminuria may be a transient DKD feature, or may occur only when widespread glomerular damage is already present [6, 7]. Recent reports have noted that up to 25% of patients with type 2 diabetes mellitus and diminished kidney function have little or no proteinuria, despite having biopsy-proven DKD [8]. There is, therefore, a need to find sensitive and specific biomarkers to predict DKD susceptibility and progression.
Estimated glomerular filtration rate
The Kidney Disease: Improving Global Outcomes (KDIGO) [4] classification is directed at adults and children over the age of 2 years old with evidence of kidney disease. Glomerular filtration rate (GFR) is considered the best measure of kidney function. Normal GFR is quantified as 100–150 ml/min and can be determined by creatinine clearance or an estimated GFR (eGFR) calculation basis on serum creatinine, age, sex and ethnicity (Table 1).
Histological features of renal biopsies, eGFR and DKD
Histological features (see [5]) correlate with functional alterations in DKD. The Renal Pathological Society system, based on glomerular changes observed in the development of DKD, groups both type 1 and type 2 diabetes mellitus patients into the four classes described below [9].
Class I: Glomerular basement membrane thickening: isolated glomerular basement membrane thickening and only mild, non-specific changes by light microscopy that do not meet the criteria of classes II–IV.
Class II: Mesangial expansion, mild (class IIa) or severe (class IIb). Glomeruli classified as mild or severe mesangial expansion but without nodular sclerosis (Kimmelstiel–Wilson lesions) or global glomerulosclerosis in >50% of glomeruli.
Class III: Nodular sclerosis (Kimmelstiel–Wilson lesions): at least one glomerulus with nodular increase in mesangial matrix (Kimmelstiel–Wilson) without changes described in class IV.
Class IV: Advanced diabetic glomerulosclerosis. Over 50% global glomerulosclerosis with other clinical or pathologic evidence showing that sclerosis is attributable to DKD.
The need for newer biomarkers
Current biomarkers do not relate well to the above pathological classification. Many potential novel biomarkers have been tested in an attempt to improve our ability to discern underlying renal pathology non-invasively, with the aim of guiding therapy. These include urinary transferrin, serum osteopontin, urinary retinol-binding protein (RBP), serum interleukin-18, serum cystatin C, serum resistin, serum TNF-α, serum interleukin-6 and urinary neutrophil gelatinase-associated lipocalin (NGAL) [reviewed in 6]. In patients with albuminuria these markers increase significantly, but their relationships with histopathological changes, eGFR, HBA1C and blood pressure is complex.
Detection and identification of microRNAs in body fluids as kidney disease biomarkers
Members of the short single-stranded endogenous RNA transcript family known as microRNAs (miRNAs) modulate the expression of most mammalian protein coding genes, thereby influencing developmental and metabolic processes, and disease phenotypes [10]. Disease-associated changes in miRNA expression profiles have been observed in cancer, cardiovascular disease, diabetes and chronic kidney disease that is treated by dialysis or transplantation [reviewed in 11–14].
To date, the majority of miRNA biomarker analyses have focused on detection of circulating transcripts [11, 13]. By contrast, the adoption into existing treatment pathways of a miRNA biomarker test on biofluid samples that can be obtained without venipuncture promises attractive reductions in time and cost [15].
We have developed RT-qPCR-based methods for precise quantification of miRNAs in urine, peritoneal dialysis effluent and renal transplantation perfusate [15–19]. The robust recovery of miRNAs from these complex analytical matrices highlights their potential utility both as non-invasive biomarkers of occurrence and/or progression of kidney disease, and as potential targets for therapeutic intervention. We have shown association of increased miR-21 with peritoneal fibrosis [17] and transplantation outcomes [18, 19]. Analysis of the renal transplantation perfusate with which the organ is supplied between donor and recipient also identified elevated miR-21 [18].
Utility of urinary miR-29b, miR-126 and miR-155 to test for DKD
Disease biomarkers are useful only when they can inform our potential to change patient treatment. The US Food and Drug Administration recommends that a reduction in eGFR of 40% over 2–3 years is a broadly acceptable effective surrogate for confirmation of CKD [20]. However, since eGFR decline is typically very gradual over the first decade or so of disease and more rapid thereafter, a biomarker that can differentiate between later stages of CKD maybe more cost-effective in detecting quantifiable responses to therapy in clinical trials [20, 21].
We have recently shown association of elevated urinary miR-29b, miR-126 and miR-155 detection predominantly in patients with type 2 diabetes mellitus and DKD [15]. We observed upregulation of these three miRNAs in two disease cohorts, obtaining an area under the curve of 0.8 in combined receiver operating characteristic curve analysis [15]. Our markers are clustered in late-stage disease (Fig. 1) and at an 80% relative quantification threshold for each miRNA, identified 48% of DKD patients with a 3.6% false positive detection rate [15]. We are currently investigating the significance of this apparent DKD patient stratification.
Utility of urinary miR-29b, miR-126 and miR-155 to investigate DKD mechanisms
We detected increased miR-29b and miR-126 in conditioned medium from cultured glomerular endothelial cells exposed to disease-related cytokines transforming growth factor-β1 and tumour necrosis factor-α, respectively [15]. It is thus conceivable that miRNAs may travel down the nephron [5] to mediate disease-related and functional effects [22]. Our data also included evidence for decreased urinary miR-192 in DKD [15], supporting our previous finding showing downregulated miR-192 expression in renal biopsies from DKD patients [23].
Conclusion
DKD is one of the most important global health challenges. Existing biomarkers provide a non-invasive approach to diagnosis and, in late-stage disease, identify the extent of kidney damage. However, there is a lack of non-invasive measures of active disease processes. New biomarkers are, therefore, required to measure risk of progressive kidney damage and to measure responses to treatment in the individual. Successful development of such biomarkers would help to individualize treatment using existing approaches, and would greatly accelerate testing of new treatments. MicroRNAs tested in urine show promise in this area.
Acknowledgments
Supported by the National Institute for Health Research Invention for Innovation (i4i) Programme grant II-LA-0712-20003 and Kidney Research UK Project grant award RP44/2014. The Wales Kidney Research Unit is funded by core support from Health and Care Research Wales.
Disclosure
TB and DF are inventors for patent WO/2017/129977 Chronic Kidney Disease Diagnostic.
References
1. National diabetes statistics report, 2017: estimates of diabetes and its burden in the United States. Centers for Disease Control and Prevention (CDC) 2017 (https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf).
2. Wang J, Chen J, Sen S. MicroRNAs as biomarkers and diagnostics. J Cell Physiol 2016; 231(1): 25–30.
3. de Boer IH, et al. Temporal trends in the prevalence of diabetic kidney disease in the United States. JAMA 2011; 305(24): 2532–2539.
4. Levin A, et al. Kidney disease: improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Supplements 2013; 3(1): 1–150.
5. Pollak MR, et al. The glomerulus: the sphere of influence. Clin J Am Soc Nephrol 2017; 9(8): 1461–1469.
6. Al-Rubeaan K, et al. Assessment of the diagnostic value of different biomarkers in relation to various stages of diabetic nephropathy in type 2 diabetic patients. Sci Rep 2017; 7(1): 2684.
7. Alicic RZ, et al. Diabetic kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol 2017; 12(12): 2032–2045.
8. Dwyer JP, Lewis JB. Nonproteinuric diabetic nephropathy: when diabetics don’t read the textbook. Med Clin North Am 2013; 97(1): 53–58.
9. Tervaert TW, et al. Pathologic classification of diabetic nephropathy. J Am Soc Nephrol 2010; 21(4): 556–563.
10. Bartel DP. Metazoan microRNAs. Cell 2018; 173(1): 20–51.
11. Simpson K, et al. MicroRNAs in diabetic nephropathy: from biomarkers to therapy. Curr Diab Rep 2016; 16(3): 35.
12. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov 2016; 16(3): 203–222.
13. Wonnacott A, et al. MicroRNAs as biomarkers in chronic kidney disease. Curr Opin in Nephrol and Hypertens 2017; 26(6): 460–466.
14. Zhao H, et al. MicroRNAs in chronic kidney disease. Clin Chim Acta 2019; 491(4): 59–65.
15. Beltrami C, et al. Association of elevated urinary miR-126, miR-155 and miR-29b with diabetic kidney disease. Am J Pathol 2018; 188(9): 1982–1992.
16. Beltrami C, et al. Stabilization of urinary microRNAs by association with exosomes and argonaute 2 protein. Noncoding RNA 2015; 1(2): 151–165.
17. Lopez Anton M, et al. MicroRNA-21 promotes fibrogenesis in peritoneal dialysis. Am J Pathol 2017; 187(7): 1537–1550.
18. Khalid U, et al. MicroRNA-21 (miR-21) expression in hypothermic machine perfusate may be predictive of early outcomes in kidney transplantation. Clinical Transplant 2016; 30(2): 99–104.
19. Khalid U, et al. A urinary microRNA panel that is an early predictive biomarker of delayed graft function following kidney transplantation. Sci Rep 2019; 9: 3584.
20. Levey AS, et al. GFR decline as an end point for clinical trials in CKD: a scientific workshop sponsored by the National Kidney Foundation and the US Food and Drug Administration. Am J Kidney Dis 2014; 64(6): 821–835.
21. Stevens LA, et al. Surrogate end points for clinical trials of kidney disease progression. Clin J Am Soc Nephrol 2006; 1(12): 874–884.
22. Thomas MJ, et al. Biogenesis, stabilization and transport of microRNAs in kidney Health and Disease. Noncoding RNA 2018; 4(4): E30.
23. Krupa A, et al. Loss of microRNA-192 promotes fibrogenesis in diabetic nephropathy. J Am Soc Nephrol 2010; 21(3): 438–447.
The authors
Tanya A. Smith MB ChB; Kate Simpson PhD; Donald J. Fraser MB ChB, PhD; Timothy Bowen* PhD
Wales Kidney Research Unit, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
*Corresponding author
E-mail: bowent@cardiff.ac.uk
HbA1c 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:
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).
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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
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