The two aims of this article are to review some current methods of early diagnosis of Alzheimer’s disease (AD) and to discuss a new integration method proposed in Kong et al. [1]. We focus on how to combine the whole genome single nucleotide polymorphism (SNP) data and high-dimensional whole-brain imaging data to offer predictive values to identify subjects at risk for progressing to AD.
by Dr Dehan Kong, Prof. Kelly S. Giovanello, Eunjee Lee, Prof. P. Murali Doraiswamy and Prof. Hongtu Zhu
Introduction
Mild cognitive impairment (MCI), which commonly arises as a result of underlying neurodegenerative pathology, is a clinical syndrome characterized by insidious onset and gradual progression. Recently, much research has focused on delineating a set of biomarkers that provide evidence of such neurodegenerative pathology in living individuals and increasing attention has been devoted to combine the information from imaging, genetic, clinical, behavioural and fluid data to predict the conversion from MCI to AD. In this article, we first review some of the literature on MCI-to-AD conversion and their limitations. Subsequently, we review the results in Kong et al. [1]. According to the best of our knowledge, it is the first paper that addresses the question of how to combine the whole genome single nucleotide polymorphism (SNP) data and high-dimensional whole-brain imaging data to offer predictive values to identify subjects at risk for progressing to AD.
Methods for predicting progression from MCI to AD
There are several studies assessing the relative importance of different modalities in predicting the diagnostic change from MCI to AD by using a small subset of biosignatures [2–6]. For example, Cui et al. simultaneously examined multiple features from different modalities of data [2]. In particular, they combined structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid biomarkers and neuropsychological measures to access an optimal set of predictors of conversion from MCI to AD. Their findings suggested that structural changes within the medial temporal lobe (MTL), particularly the hippocampus, and the performance on cognitive tests that rely on MTL integrity provided strong prediction for MCI-to-AD conversion.
Recent studies focused on the analysis of longitudinal data to assess the dynamic changes of various biomarkers associated with the MCI-to-AD conversion. A prominent neural correlate of MCI–AD is volume loss within the MTL, especially within the hippocampus and entorhinal cortex [7, 8], with increasing atrophy in these structures from normal aging to MCI to AD [9, 10]. The importance of assessing MTL changes in tracking the progression of MCI to AD has been highlighted in various longitudinal studies of individuals with MCI-AD conversion. For instance, an increased likelihood of progressing to clinical dementia has been linked with documented diminished baseline hippocampal and entorhinal volumes in several studies [11, 12].
The aforementioned studies focused on the question of whether the MCI subjects progress to AD or not, i.e. treating the conversion as a binary response. However, an important question remains, namely, how can we predict the time to conversion in MCI individuals, as well as determine the early markers of conversion? In Tarbert et al., the authors used 148 MCI subjects to identify the most predictive neuropsychological measures [13]. In Li et al., the authors used 139 MCI subjects from the Alzheimer’s disease neuroimaging initiative phase 1 (ADNI-1) to evaluate the prediction power of brain volume, ventricular volume, hippocampus volume, apolipoprotein E (APOE) gene status, cerebrospinal fluid (CSF) biomarkers, and behavioural scores [14]. They found that baseline volumetric MRI and behavioural scores were selectively predictive, and their model can achieve a moderately accurate prediction with the value of an area under the curve of 0.757 at 36 months. In Da et al., the authors used 381 MCI subjects from ADNI-1 to evaluate how several biomarkers for predicting MCI-to-AD conversion including spatial patterns of brain atrophy, Alzheimer’s disease assessment scale-cognitive subscale (ADAS-Cog) score, APOE genotype, and cerebrospinal fluid (CSF) biomarkers [15]. They have found that a combination of spatial patterns of brain atrophy and ADAS-Cog score offers a good predictive power of conversion from MCI to AD.
To the best of our knowledge, none of the previous studies have leveraged both genome-wide association study (GWAS) SNP data as well as high-dimensional whole-brain imaging data to examine their combined value in identifying subjects at greatest risk for progressing to AD.
Predicting AD using combined imaging–whole genome SNP data
In Kong et al., the authors focused on the MCI patients and combined information from whole brain MR imaging and whole genome data to predict the time to onset of AD in a 48-month national study of subjects at risk [1]. This study considered 343 subjects with MCI enrolled in ADNI-1. The patients were followed over 48 months, with 150 participants progressing to AD. The data can be treated as time-to-event data before those MCI subjects without conversion are censored data. One of the most popular models for the time-to-event data is the Cox proportional hazards model. The authors used this model to account for the covariates that are associated with the time of the events, i.e. conversion from MCI to AD.
The candidate covariates include demographic covariates (age, gender, handedness, mean education length, retirement percentage, and three dummy variables for the marital status), the APOE4 genotype, the AD assessment scale-cognitive subscale (ADAS-Cog) score, the hippocampus surface data, the region of interest (ROI) volume data, the chromosome-wise information and the significant SNP information. For each subject, the radial distance was obtained from the baseline hippocampal surfaces data for each subject, which yielded two sets of 15 000 dimensional vectors denoting the surface data from both parts of the hippocampus. For better illustration, we have plotted the hippocampus surface in Figure 1.
In Kong et al., the authors treated each part of the hippocampi as a functional predictor, and applied functional principal component analysis, and selected seven functional principal component scores for each functional predictor, which explain approximately 70% of the variance [1]. These functional principal component scores were taken as the summary measures for the hippocampus surface and put into the Cox regression model as predictors. For the chromosome-wise information, they extracted the top two principal components of the SNP data along each chromosome as predictors. For the significant SNP information, the top 101 significant SNPs were picked up using a kernel machine method, and then their top five principal components (PCs) were calculated and been used as predictors.
Specifically, they considered three candidate models. The first model is to fit a Cox regression model with demographic, clinical and ADAS-Cog score as predictors as well as APOE. This model did not include any other imaging and genetic data. The second model is to fit a Cox regression model with demographic, imaging and chromosome-wise predictors, but without the ADAS-Cog score and significant SNPs information. As a comparison, they also considered the genetic information from genome-wide association analysis (GWAS). The third model is to fit a Cox regression model with demographic, imaging and significant SNP information, but without the ADAS-Cog score and chromosome-wise information.
They compared the predictive value of the first and second model, and receiver operating characteristic (ROC) analysis indicated that the first model had a lower predictive value at 48 months than the second model. For the first model, they only identified APOE4 and ADAS-Cog score as the significant predictors. For the second model, they found that combining full genetic SNP and high-dimensional imaging data had a much higher predictive value. In particular, SNPs on chromosomes 2, 10, 11, 15, 17 and 18, APOE4 genotype, surface morphology data of both hippocampi and volumes of hippocampus, amygdala and thalamus contributed significantly. The findings support those from previous MRI studies of volumetric hippocampal changes in prodromal AD and extend them by finding that the possible prognostic value of combining information from high-dimensional imaging and genetics may be superior to that provided by routine clinical cognitive testing data. The findings also confirm the association between APOE4 status and AD, and identify additional new markers on chromosomes 2, 10, 11, 15, 17 and 18 as significant predictors for conversion. For the third model, they found that the predictive value is lower than that of the second model. This finding indicates that using the chromosome-wise information instead of the traditional significant SNPs information would be favoured. They suggest it may be due to the pitfalls of prediction using significant SNPs [16].
There are some limitations to their analysis. First, there are no test data for this study, and their findings are based on the interval cross validation. Second, they did not include measures of pathology in the models, as cerebrospinal fluid and amyloid-PET were available only in a small subset of individuals in ADNI-1. It would be beneficial to combine the data from ADNI-GO and ADNI-2 for future research.
Summary
In this article, we first reviewed some current methods of diagnosis of AD, and discussed their limitations. Then we reviewed the method and findings in Kong et al. and discussed the novelty and advantages of their study and how their proposal can be used for early diagnosis of AD by combined imaging–whole genome SNP data [1].
Acknowledgements
This material was based upon work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. The research of Dr Zhu was supported by NSF grants SES-1357666 and DMS-1407655 and NIH grants MH086633, T32MH106440, and 1UL1TR001111. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and NSF.
References
1. Kong D, Giovanello KS, Wang Y, Lin W, Lee E, Fan YD Doraiswamy PM, Zhu H, Alzheimer’s Neuroimaging Initiative. Predicting Alzheimer’s disease using combined imaging-whole genome SNP data. J Alzheimer’s Dis. 2015; 46(3): 695–702.
2. Cui Y, Liu B, Luo S, Zhen X, Fan M, Liu T, Zhu W, Park. M, Jiang T, Jin SE. Identification of conversion from Mild Cognitive Impairment to Alzheimer’s disease using multivariate predictors. PLoS One 2011; 6: e21896.
3. Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging 2011; 32: 2322.e19–2322.e27.
4. Dickerson BC, Wolk DA, Alzheimer’s Disease Neuroimaging Initiative. Biomarker-based prediction of progression in MCI: comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau. Front Aging Neurosci. 2013; 5: 1–9.
5. Young J, Modat M, Cardoso MJ, Mendelson A, Cash D, Ourselin S. Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neuroimage Clin. 2013; 2: 735–745.
6. Zhang D, Shen D. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLoS One 2012; 7(3): e33182.
7. Dickerson BC, Goncharova I, Sullivan MP, Forchetti C, Wilson RS, Bennett DA, Beckett LA, deToledo-Morrell L. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging 2001; 22: 747–754.
8. Xu Y, Jack CR, O’Brien PC, Kokmen E, Smith GE, Ivnik RJ, Boeve BF, Tangalos RG, Petersen RC. Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology 2000; 54: 1760–1767.
9. Du AT, Schuff N, Amend D, Laakso MP, Hsu YY, Jagust WJ, et al. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2001; 71: 441–447.
10. Pennanen C, Kivipelto M, Tuomainen S, Hartikainen P, Hanninen T, Laakso MP, et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging. 2004; 25: 303–310.
11. Jack CR, Petersen RC, Xu YC, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Waring SC, Tangalos EG, Kokmen E. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 1999; 52: 1397–1403.
12. Killiany RJ, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F, et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer’s disease. Ann Neurol. 2000; 47:430–439.
13. Tabert MH, Manly JJ, Liu X, Pelton GH, Rosenblum S, Jacobs M, Zamora D, Goodkind M, Bell K, Stern Y, Devanand DP. Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment. Arch Gen Psychiatry. 2006; 63(8): 916–24.
14. Li S, Okonkwo O, Albert M, Wang M-C. Variation in variables that predict progression from MCI to AD dementia over duration of follow-up. American J Alzheimer’s Dis. 2013; 1: 12–28.
15. Da X, Toledo JB, Zee J, Wolk DA, Xie SX, Ou Y, Shacklett A, Parmpi P, Shaw L, Trojanowski JQ, Davatzikos C, Alzheimer’s Neuroimaging Initiative. Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. Neuroimage Clin. 2014; 4: 164–173.
16. Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME and Visscher PM. Pitfalls of predicting complex traits from SNPs. Nat Rev Genet 2013; 14(7): 507–15.
The authors
Dehan Kong1 PhD, Kelly S. Giovanello2,3 PhD, Eunjee Lee4 MS, P. Murali Doraiswamy5 MD, Hongtu Zhu*1,3,6 PhD
1 Department of Biostatistics, University of North Carolina (UNC), NC, USA
2 Department of Psychology, UNC, NC, USA
3 Biomedical Research Imaging Center, UNC, NC, USA
4 Department of Statistics, UNC, NC, USA
5 Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
6 Department of Radiology, UNC, NC, USA
*Corresponding author
E-mail: htzhu@email.unc.edu
Overview of biomarkers for predicting Alzheimer’s disease
, /in Featured Articles /by 3wmediaThe two aims of this article are to review some current methods of early diagnosis of Alzheimer’s disease (AD) and to discuss a new integration method proposed in Kong et al. [1]. We focus on how to combine the whole genome single nucleotide polymorphism (SNP) data and high-dimensional whole-brain imaging data to offer predictive values to identify subjects at risk for progressing to AD.
by Dr Dehan Kong, Prof. Kelly S. Giovanello, Eunjee Lee, Prof. P. Murali Doraiswamy and Prof. Hongtu Zhu
Introduction
Mild cognitive impairment (MCI), which commonly arises as a result of underlying neurodegenerative pathology, is a clinical syndrome characterized by insidious onset and gradual progression. Recently, much research has focused on delineating a set of biomarkers that provide evidence of such neurodegenerative pathology in living individuals and increasing attention has been devoted to combine the information from imaging, genetic, clinical, behavioural and fluid data to predict the conversion from MCI to AD. In this article, we first review some of the literature on MCI-to-AD conversion and their limitations. Subsequently, we review the results in Kong et al. [1]. According to the best of our knowledge, it is the first paper that addresses the question of how to combine the whole genome single nucleotide polymorphism (SNP) data and high-dimensional whole-brain imaging data to offer predictive values to identify subjects at risk for progressing to AD.
Methods for predicting progression from MCI to AD
There are several studies assessing the relative importance of different modalities in predicting the diagnostic change from MCI to AD by using a small subset of biosignatures [2–6]. For example, Cui et al. simultaneously examined multiple features from different modalities of data [2]. In particular, they combined structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid biomarkers and neuropsychological measures to access an optimal set of predictors of conversion from MCI to AD. Their findings suggested that structural changes within the medial temporal lobe (MTL), particularly the hippocampus, and the performance on cognitive tests that rely on MTL integrity provided strong prediction for MCI-to-AD conversion.
Recent studies focused on the analysis of longitudinal data to assess the dynamic changes of various biomarkers associated with the MCI-to-AD conversion. A prominent neural correlate of MCI–AD is volume loss within the MTL, especially within the hippocampus and entorhinal cortex [7, 8], with increasing atrophy in these structures from normal aging to MCI to AD [9, 10]. The importance of assessing MTL changes in tracking the progression of MCI to AD has been highlighted in various longitudinal studies of individuals with MCI-AD conversion. For instance, an increased likelihood of progressing to clinical dementia has been linked with documented diminished baseline hippocampal and entorhinal volumes in several studies [11, 12].
The aforementioned studies focused on the question of whether the MCI subjects progress to AD or not, i.e. treating the conversion as a binary response. However, an important question remains, namely, how can we predict the time to conversion in MCI individuals, as well as determine the early markers of conversion? In Tarbert et al., the authors used 148 MCI subjects to identify the most predictive neuropsychological measures [13]. In Li et al., the authors used 139 MCI subjects from the Alzheimer’s disease neuroimaging initiative phase 1 (ADNI-1) to evaluate the prediction power of brain volume, ventricular volume, hippocampus volume, apolipoprotein E (APOE) gene status, cerebrospinal fluid (CSF) biomarkers, and behavioural scores [14]. They found that baseline volumetric MRI and behavioural scores were selectively predictive, and their model can achieve a moderately accurate prediction with the value of an area under the curve of 0.757 at 36 months. In Da et al., the authors used 381 MCI subjects from ADNI-1 to evaluate how several biomarkers for predicting MCI-to-AD conversion including spatial patterns of brain atrophy, Alzheimer’s disease assessment scale-cognitive subscale (ADAS-Cog) score, APOE genotype, and cerebrospinal fluid (CSF) biomarkers [15]. They have found that a combination of spatial patterns of brain atrophy and ADAS-Cog score offers a good predictive power of conversion from MCI to AD.
To the best of our knowledge, none of the previous studies have leveraged both genome-wide association study (GWAS) SNP data as well as high-dimensional whole-brain imaging data to examine their combined value in identifying subjects at greatest risk for progressing to AD.
Predicting AD using combined imaging–whole genome SNP data
In Kong et al., the authors focused on the MCI patients and combined information from whole brain MR imaging and whole genome data to predict the time to onset of AD in a 48-month national study of subjects at risk [1]. This study considered 343 subjects with MCI enrolled in ADNI-1. The patients were followed over 48 months, with 150 participants progressing to AD. The data can be treated as time-to-event data before those MCI subjects without conversion are censored data. One of the most popular models for the time-to-event data is the Cox proportional hazards model. The authors used this model to account for the covariates that are associated with the time of the events, i.e. conversion from MCI to AD.
The candidate covariates include demographic covariates (age, gender, handedness, mean education length, retirement percentage, and three dummy variables for the marital status), the APOE4 genotype, the AD assessment scale-cognitive subscale (ADAS-Cog) score, the hippocampus surface data, the region of interest (ROI) volume data, the chromosome-wise information and the significant SNP information. For each subject, the radial distance was obtained from the baseline hippocampal surfaces data for each subject, which yielded two sets of 15 000 dimensional vectors denoting the surface data from both parts of the hippocampus. For better illustration, we have plotted the hippocampus surface in Figure 1.
In Kong et al., the authors treated each part of the hippocampi as a functional predictor, and applied functional principal component analysis, and selected seven functional principal component scores for each functional predictor, which explain approximately 70% of the variance [1]. These functional principal component scores were taken as the summary measures for the hippocampus surface and put into the Cox regression model as predictors. For the chromosome-wise information, they extracted the top two principal components of the SNP data along each chromosome as predictors. For the significant SNP information, the top 101 significant SNPs were picked up using a kernel machine method, and then their top five principal components (PCs) were calculated and been used as predictors.
Specifically, they considered three candidate models. The first model is to fit a Cox regression model with demographic, clinical and ADAS-Cog score as predictors as well as APOE. This model did not include any other imaging and genetic data. The second model is to fit a Cox regression model with demographic, imaging and chromosome-wise predictors, but without the ADAS-Cog score and significant SNPs information. As a comparison, they also considered the genetic information from genome-wide association analysis (GWAS). The third model is to fit a Cox regression model with demographic, imaging and significant SNP information, but without the ADAS-Cog score and chromosome-wise information.
They compared the predictive value of the first and second model, and receiver operating characteristic (ROC) analysis indicated that the first model had a lower predictive value at 48 months than the second model. For the first model, they only identified APOE4 and ADAS-Cog score as the significant predictors. For the second model, they found that combining full genetic SNP and high-dimensional imaging data had a much higher predictive value. In particular, SNPs on chromosomes 2, 10, 11, 15, 17 and 18, APOE4 genotype, surface morphology data of both hippocampi and volumes of hippocampus, amygdala and thalamus contributed significantly. The findings support those from previous MRI studies of volumetric hippocampal changes in prodromal AD and extend them by finding that the possible prognostic value of combining information from high-dimensional imaging and genetics may be superior to that provided by routine clinical cognitive testing data. The findings also confirm the association between APOE4 status and AD, and identify additional new markers on chromosomes 2, 10, 11, 15, 17 and 18 as significant predictors for conversion. For the third model, they found that the predictive value is lower than that of the second model. This finding indicates that using the chromosome-wise information instead of the traditional significant SNPs information would be favoured. They suggest it may be due to the pitfalls of prediction using significant SNPs [16].
There are some limitations to their analysis. First, there are no test data for this study, and their findings are based on the interval cross validation. Second, they did not include measures of pathology in the models, as cerebrospinal fluid and amyloid-PET were available only in a small subset of individuals in ADNI-1. It would be beneficial to combine the data from ADNI-GO and ADNI-2 for future research.
Summary
In this article, we first reviewed some current methods of diagnosis of AD, and discussed their limitations. Then we reviewed the method and findings in Kong et al. and discussed the novelty and advantages of their study and how their proposal can be used for early diagnosis of AD by combined imaging–whole genome SNP data [1].
Acknowledgements
This material was based upon work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. The research of Dr Zhu was supported by NSF grants SES-1357666 and DMS-1407655 and NIH grants MH086633, T32MH106440, and 1UL1TR001111. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and NSF.
References
1. Kong D, Giovanello KS, Wang Y, Lin W, Lee E, Fan YD Doraiswamy PM, Zhu H, Alzheimer’s Neuroimaging Initiative. Predicting Alzheimer’s disease using combined imaging-whole genome SNP data. J Alzheimer’s Dis. 2015; 46(3): 695–702.
2. Cui Y, Liu B, Luo S, Zhen X, Fan M, Liu T, Zhu W, Park. M, Jiang T, Jin SE. Identification of conversion from Mild Cognitive Impairment to Alzheimer’s disease using multivariate predictors. PLoS One 2011; 6: e21896.
3. Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging 2011; 32: 2322.e19–2322.e27.
4. Dickerson BC, Wolk DA, Alzheimer’s Disease Neuroimaging Initiative. Biomarker-based prediction of progression in MCI: comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau. Front Aging Neurosci. 2013; 5: 1–9.
5. Young J, Modat M, Cardoso MJ, Mendelson A, Cash D, Ourselin S. Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neuroimage Clin. 2013; 2: 735–745.
6. Zhang D, Shen D. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PLoS One 2012; 7(3): e33182.
7. Dickerson BC, Goncharova I, Sullivan MP, Forchetti C, Wilson RS, Bennett DA, Beckett LA, deToledo-Morrell L. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging 2001; 22: 747–754.
8. Xu Y, Jack CR, O’Brien PC, Kokmen E, Smith GE, Ivnik RJ, Boeve BF, Tangalos RG, Petersen RC. Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology 2000; 54: 1760–1767.
9. Du AT, Schuff N, Amend D, Laakso MP, Hsu YY, Jagust WJ, et al. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2001; 71: 441–447.
10. Pennanen C, Kivipelto M, Tuomainen S, Hartikainen P, Hanninen T, Laakso MP, et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging. 2004; 25: 303–310.
11. Jack CR, Petersen RC, Xu YC, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Waring SC, Tangalos EG, Kokmen E. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 1999; 52: 1397–1403.
12. Killiany RJ, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F, et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer’s disease. Ann Neurol. 2000; 47:430–439.
13. Tabert MH, Manly JJ, Liu X, Pelton GH, Rosenblum S, Jacobs M, Zamora D, Goodkind M, Bell K, Stern Y, Devanand DP. Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment. Arch Gen Psychiatry. 2006; 63(8): 916–24.
14. Li S, Okonkwo O, Albert M, Wang M-C. Variation in variables that predict progression from MCI to AD dementia over duration of follow-up. American J Alzheimer’s Dis. 2013; 1: 12–28.
15. Da X, Toledo JB, Zee J, Wolk DA, Xie SX, Ou Y, Shacklett A, Parmpi P, Shaw L, Trojanowski JQ, Davatzikos C, Alzheimer’s Neuroimaging Initiative. Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. Neuroimage Clin. 2014; 4: 164–173.
16. Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME and Visscher PM. Pitfalls of predicting complex traits from SNPs. Nat Rev Genet 2013; 14(7): 507–15.
The authors
Dehan Kong1 PhD, Kelly S. Giovanello2,3 PhD, Eunjee Lee4 MS, P. Murali Doraiswamy5 MD, Hongtu Zhu*1,3,6 PhD
1 Department of Biostatistics, University of North Carolina (UNC), NC, USA
2 Department of Psychology, UNC, NC, USA
3 Biomedical Research Imaging Center, UNC, NC, USA
4 Department of Statistics, UNC, NC, USA
5 Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
6 Department of Radiology, UNC, NC, USA
*Corresponding author
E-mail: htzhu@email.unc.edu
Developments in cerebrospinal fluid biomarkers for Alzheimer’s disease
, /in Featured Articles /by 3wmediaAlzheimer’s disease (AD) is the most common cause of dementia. It is characterized by accumulation of β-amyloid (Aβ) peptides and tau proteins, loss of neurons and cognitive decline. It is difficult to diagnose AD in the early stages by clinical examination. Cerebrospinal fluid (CSF) biomarkers can be used to overcome this and could be useful in clinical practice, research and in trials of novel treatments.
by Philip Insel, Dr Rik Ossenkoppele and Dr Niklas Mattsson
Introduction
Alzheimer’s disease (AD) is a neurodegenerative disease that leads to cognitive impairment and ultimately dementia. The majority of the 45 million dementia patients world-wide have dementia due to AD [1]. In terms of brain pathology, AD is characterized by progressive accumulation of extracellular deposits of β-amyloid (Aβ) peptides in plaques and intracellular deposits of tau proteins in neurofibrillary tangles. The abnormal metabolism of Aβ and tau is believed to lead to impaired brain function, loss of synapses and neurons, and cognitive decline. The cognitive domain that is most severely impaired in most AD patients is episodic memory, but other domains such as language, visuo-spatial performance, behaviour and executive function may also become affected. In a subset of patients, deficits in non-memory domains are the dominating (early) features, as we will further discuss below.
Stages of Alzheimer’s disease
There is an increasing awareness among researchers and clinicians that AD starts many years before the onset of dementia. The first stage is thought to be asymptomatic, when pathologies accumulate silently in the brains of affected individuals for years or even decades before clinical symptoms emerge [2]. This is followed by an early clinical stage, which is characterized by objective cognitive impairment but still preserved function. This is often called mild cognitive impairment (MCI) due to AD, or prodromal AD [3]. The final stage is the classic stage, where AD has caused sufficient functional impairment for the patient to qualify for a dementia diagnosis.
Previously the procedure to diagnose AD was based solely on clinical examination and neuropsychological testing of the patient and interviews with proxies and caregivers. These methods are hampered by low sensitivity in the early stages of the disease and low specificity in the late stages of the disease. With the development of novel imaging and biochemistry technologies it is now possible to diagnose AD with the aid of direct evidence of relevant molecular pathologies in the brain. This is a conceptual leap that has revolutionized clinical AD research and is rapidly transforming clinical practice and clinical trial design. In this article we will discuss this development, with a focus on cerebrospinal fluid biomarkers for AD. Key points are summarized in Table 1.
The first steps
The amino acid sequences of Aβ and tau were identified in the mid-1980s [4, 5]. Early on it was suggested that a test for AD could be developed based on serum measurements of Aβ [4], but it was the discovery in the early 1990s that Aβ is secreted into the CSF that boosted the modern development of biochemical AD markers [6]. In the mid-1990s immunoassays (ELISAs) were developed for CSF Aβ1-42 (the prominent Aβ peptide isoform in Aβ plaques, typically reduced in AD patients compared to controls [7]), total-tau (T-tau, increased in AD patients compared to controls [8, 9]), and phosphorylated-tau (P-tau, increased in AD patients compared to other neurological diseases and controls [8]).
A window into the brain
Traditionally, AD could only be diagnosed at the dementia stage, and definite diagnosis was only possible through post-mortem analysis of brain tissue. The CSF biomarkers Aβ1-42, T-tau and P-tau (and imaging technologies not covered in this article) have made it possible to approach identification of AD brain pathology in living patients. Several studies have found that CSF Aβ1-42 is strongly related to the presence of brain Aβ pathology, quantified at autopsy [10] or in vivo using positron emission tomography imaging with tracers specific for fibrillar Aβ [11]. Likewise, although with less strong associations, CSF P-tau correlates with neocortical tangle pathology [12, 13], whereas CSF T-tau is more non-specifically increased in a number of neurological diseases, with the magnitude of the increase correlating with the size of the damaged tissue and the clinical outcome [14, 15]. CSF biomarkers enable detection of AD pathology in patients in early stages of the disease, when only mild symptoms or no clinical symptoms are present. As clinical diagnosis alone is inadequate in those early disease stages, biomarkers may be critical for an accurate diagnosis. CSF biomarkers may also increase the diagnostic accuracy of the diagnosis in advanced clinical stages, by providing biological evidence of AD related pathology. This helps in identifying the clinical syndrome and differentiating it from other neurological diseases.
Challenges
The field is rapidly advancing to overcome some hurdles that have prevented widespread implementation of CSF biomarkers. Problems with between-laboratory and between-assay variability in measurements have been noticed [16] and are being tackled by the development of certified reference procedures (based on selected reaction monitoring mass spectrometry [17]) and certified reference materials (created by the Institute of Reference Materials and Methods [18]). Development of fully automated assays will further bring down the variability and facilitate implementation of CSF biomarkers outside of expert centres (see www.neurochem.gu.se/TheAlzAssQCprogram for updated comparisons of different assay systems in a global quality control programme). In some countries a remaining obstacle is the unwillingness of medical practitioners to perform lumbar punctures, especially outside of highly specialized clinics. More training is needed to increase the familiarity of doctors with this procedure, and more education is needed to inform staff and patients that the procedure is safe. Headache is the only common complication (2–5 % incidence), but this is usually benign and treatable by common analgesics. Severe complications are extremely rare.
Alzheimer’s disease variants
CSF biomarkers of Aβ and tau may be particularly helpful to assist the diagnostic process in patients with a non-amnestic presentation of AD who may show substantial clinical overlap with patients experiencing non-AD types of dementia. Recently, there has been an increased awareness of these atypical presentations such as posterior cortical atrophy (PCA, ‘visual variant AD’ [19]), logopenic variant primary progressive aphasia (‘language variant AD’ [20]), and the behavioural/dysexecutive variant of AD [21]. As previous studies with small sample sizes have yielded conflicting results, we performed a study in 176 patients selected for abnormal CSF Aβ biomarkers to assess whether CSF T-tau and P-tau differ between atypical variants of AD [22]. Bootstrapping showed that the prevalence of abnormal T-tau and P-tau was ~80–90%, roughly equally distributed across AD phenotypes. This suggests that CSF T-tau and P-tau are equally useful in all clinical phenotypes of AD, which is compatible with current National Institute on Aging–Alzheimer’s Association (NIA-AA) and International Working Group for New Research Criteria for the Diagnosis of AD (IWG-2) diagnostic criteria.
Biomarkers in clinical trials
Biomarker measurement is a recent addition to AD clinical trials. The use of biomarkers is thought to improve trial design both in terms of subject selection and measurement of disease progression. It becomes particularly important in trials of disease-modifying treatments to recruit only those subjects with the target pathology of the therapy. Several recent failed trials may have been hindered by the inclusion of subjects without the underlying pathology [23]. Biomarkers are also less affected by measurement error compared with clinical outcomes and thus offer certain advantages in measuring progression over time, especially in early stages of disease [24]. In these early stages of the disease, before the onset of clinical symptoms, the ability of biomarkers to predict future pathology and cognitive decliners will aid in identifying those most in need of treatment. This will become especially important if intervention in the earliest stages of the disease, prior to substantial neurodegeneration, offers the best chance of a treatment to be effective.
Early treatment trials of anti-Aβ therapies employ thresholds to ensure recruitment of subjects with a minimal level of Aβ pathology. This threshold is frequently taken to be the level of amyloid pathology that most accurately distinguishes cases of AD from cognitively-normal controls [25]. However, if earlier treatment has a higher likelihood of success, identifying subjects with normal amyloid levels who are likely to have elevated levels in the future may be a further step toward early intervention. A recent study demonstrated that amyloid-negative subjects with low levels of CSF Aβ1-42 were much more likely to become amyloid-positive in the near term [26]. Individuals with CSF Aβ1-42 levels in the low normal range may be optimal candidates for early intervention trials aimed at halting further Aβ accumulation.
Conclusions
CSF biomarkers have helped to transform the diagnosis of AD from a clinical diagnosis to a biomarker-informed diagnosis based on molecular evidence of the underlying neuropathology. This has implications for research, where CSF biomarkers enable researchers to characterize subjects at all levels of cognitive function, in clinical practice, where CSF biomarkers aid doctors in diagnosis of AD versus other causes of cognitive impairment, and in the design of clinical trials, where CSF biomarkers may be used to enrich study populations and construct sensitive measures of outcomes to increase study power.
References
1. Alzheimer’s disease International: World Alzheimer Report 2015 (http://www.alz.co.uk/research/world-report-2015).
2. Jansen WJ, Ossenkoppele R, Knol DL, Tijms BM, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis.
JAMA 2015; 313: 1924–1938.
3. Albert MS, DeKosky ST, Dickson D, Dubois B, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011; 7: 270–279.
4. Glenner GG, Wong CW. Alzheimer’s disease: initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochem Biophys Res Commun. 1984; 120: 885–890.
5. Grundke-Iqbal I, Iqbal K, Quinlan M, Tung YC, et al. Microtubule-associated protein tau. A component of Alzheimer paired helical filaments. J Biol Chem. 1986; 261: 6084–6089.
6. Seubert P, Vigo-Pelfrey C, Esch F, Lee M, et al. Isolation and quantification of soluble Alzheimer’s beta-peptide from biological fluids. Nature 1992; 359: 325–327.
7. Motter R, Vigo-Pelfrey C, Kholodenko D, Barbour R, et al. Reduction of beta-amyloid peptide42 in the cerebrospinal fluid of patients with Alzheimer’s disease. Ann Neurol. 1995; 38: 643–648.
8. Blennow K, Wallin A, Agren H, Spenger C, et al. Tau protein in cerebrospinal fluid: a biochemical marker for axonal degeneration in Alzheimer disease? Mol Chem Neuropathol. 1995; 26: 231–245.
9. Vandermeeren M, Mercken M, Vanmechelen E, Six J, et al. Detection of tau proteins in normal and Alzheimer’s disease cerebrospinal fluid with a sensitive sandwich enzyme-linked immunosorbent assay. J Neurochem. 1993; 61: 1828–1834.
10. Strozyk D, Blennow K, White LR, Launer LJ. CSF Abeta 42 levels correlate with amyloid-neuropathology in a population-based autopsy study. Neurology. 2003; 60: 652–656.
11. Fagan AM, Mintun MA, Mach RH, Lee SY, et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol. 2006; 59: 512–519.
12. Tapiola T, Alafuzoff I, Herukka SK, Parkkinen L, et al. Cerebrospinal fluid {beta}-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch Neurol. 2009; 66: 382–389.
13. Seppala TT, Nerg O, Koivisto AM, Rummukainen J, et al. CSF biomarkers for Alzheimer disease correlate with cortical brain biopsy findings. Neurology. 2012; 78: 1568–1575.
14. Hesse C, Rosengren L, Andreasen N, Davidsson P, et al. Transient increase in total tau but not phospho-tau in human cerebrospinal fluid after acute stroke. Neurosci Lett. 2001; 297: 187–190.
15. Otto M, Wiltfang J, Tumani H, Zerr I, et al. Elevated levels of tau-protein in cerebrospinal fluid of patients with Creutzfeldt-Jakob disease. Neurosci Lett. 1997; 225: 210–212.
16. Mattsson N, Andreasson U, Persson S, Carrillo MC, et al. CSF biomarker variability in the Alzheimer’s Association quality control program. Alzheimers Dement J Alzheimers Assoc. 2013; 9: 251–261.
17. Leinenbach A, Pannee J, Dülffer T, Huber A, et al. Mass Spectrometry-Based Candidate Reference Measurement Procedure for Quantification of Amyloid-β in Cerebrospinal Fluid. Clin Chem. 2014; 60: 987–994.
18. Mattsson N, Zetterberg H. What is a certified reference material? Biomark Med. 2012; 6: 369–370.
19. Crutch SJ, Lehmann M, Schott JM, Rabinovici GD, et al. Posterior cortical atrophy. Lancet Neurol. 2012; 11: 170–178.
20. Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, et al. Classification of primary progressive aphasia and its variants. Neurology 2011; 76: 1006–1014.
21. Ossenkoppele R, Pijnenburg YAL, Perry DC, Cohn-Sheehy BI, et al. The behavioural/dysexecutive variant of Alzheimer’s disease: clinical, neuroimaging and pathological features. Brain J Neurol. 2015; 138: 2732–2749.
22. Ossenkoppele R, Mattsson N, Teunissen CE, Barkhof F, et al. Cerebrospinal fluid biomarkers and cerebral atrophy in distinct clinical variants of probable Alzheimer’s disease. Neurobiol Aging 2015; 36: 2340–2347.
23. Karran E, Hardy J. Antiamyloid therapy for Alzheimer’s disease—are we on the right road? N Eng J Med. 2014; 370: 377–378.
24. Hendrix SB. Measuring clinical progression in MCI and pre-MCI populations: Enrichment and optimizing clinical outcomes over time. Alzheimer’s Res Ther. 2012; 4: 24.
25. Shaw LM, Vanderstichele H, Knapik‐Czajka M, Clark CM, et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol. 2009; 65: 403–413.
26. Mattsson N, Insel PS, Donohue M, Jagust W, et al. Predicting reduction of cerebrospinal fluid β-amyloid 42 in cognitively healthy controls. JAMA Neurol. 2015; 72: 554–560.
The authors
Philip Insel1,2,3 MSs; Rik Ossenkoppele4,5 PhD; Niklas Mattsson*1 MD, PhD
1 Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden
2 Center for Imaging of Neurodegenerative Diseases, Department of Veterans Affairs Medical Center, San Francisco, CA, USA
3 Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
4 Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
5 Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
*Corresponding author
E-mail: Niklas.mattsson@med.lu.se
A success story for more than 35 years: Gonotec GmbH
, /in Featured Articles /by 3wmediaWhen founding the company GONOTEC GmbH in 1979, electronics engineer Harald Göritz and chemist Klaus Noack could not possibly imagine that their target to develop, produce and market analytical measuring instruments for medical and chemical application would be as successful as it turned out to be. Both founders of the company could already look back to decades of experience in this field.
It all started with a cryoscopic osmometer for clinical application: the OSMOMAT 030. At the first exhibitions he visited, Klaus Noack was surprised by the high level of interest generated by the instrument. There was no other way than to expand production to cope with the increasing demand for this osmometer, which offered a complete ease of handling, previously unattained, resulting in a growing number of sales.
Inspired by this success, a new osmometer was developed by Klaus Noack to complete the osmometer line for medical application: the colloid osmometer OSMOMAT 050. Even though the market for this instrument, basically used in intensive care units, was not as big as for the OSMOMAT 030, the product also contributed to the further success of GONOTEC GmbH.
In the middle of the 80’s, development started on a new range of instruments, namely the chemical osmometers. The aim was the development of instruments for the determination of molar masses based on osmotic parameter for chemical application that also offer easy handling for the user.
The three osmometers, vapor pressure osmometer OSMOMAT 070, membrane osmometer OSMOMAT 090 and cryoscopic osmometer OSMOMAT 010 complement one another due to their different measuring methods in the determination of the range of molar masses up to 2,000,000 Dalton.
In 2001 the general management of GONOTEC was taken over by Jan Celinsek, who worked already with GONOTEC since 1991.
In 2003 a new model in the osmometer family was launched into the market: the OSMOMAT auto, which is also characterized by extreme reliability and easy handling, thus fitting perfectly into the already well known GONOTEC osmometer line.
In 2009 GONOTEC moved to new premises with lots of space for new ideas! In the same year, the chloridmeter CM20 was launched, followed in 2013 by the next generation of osmometers: the OSMOMAT 3000 and the OSMOMAT 3000 basic, both replacing the well known OSMOMAT 030.
To this day, GONOTEC is no large, anonymous concern but still a medium-sized, private company, owned by Klaus Noack, the founder. However, we became a global player with customers in more than 60 countries.
One of the most valuable resources GONOTEC always had was its permanent staff. Once people start working for GONOTEC they stay with the company as they are proud of their work. The same applies to the numerous number of dealers all over the world. The cooperation between the agents and GONOTEC is like in a family; constant trainings at the company headquarters improve this special relationship between agent and manufacturer. GONOTEC products do deserve the description “Made in Germany”, as the whole production is in one location. It is easy for external persons visiting the company to see an osmometer being manufactured from the very beginning to its finishing and perfect functioning.
Since GONOTEC was able to export the company’s philosophy by means of the highest quality standards and competence as well as constant assistance to customers and agents, it is looking optimistically into the future. Our company’s philosophy is a promise to all our customers and potential customers.
Ultrasensitive colorimetric detection of HIV-1 p24
, /in Featured Articles /by 3wmediaTo reduce the window period for HIV-1 infection, a method for detecting trace amounts of HIV-1 p24 in blood is needed. We developed a simple de novo ultrasensitive colorimetric ELISA by adding a thio-NAD cycling solution to the standard ELISA. The limit of detection for p24 was 0.005 IU (i.e. attomoles) per assay by the ultrasensitive colorimetric ELISA.
by Dr A. Nakatsuma, M. Kaneda, H. Kodama, M. Morikawa, S. Watabe, et al.
Background
During the window period between infection with human immunodeficiency virus type 1 (HIV-1) and the appearance of detectable antibodies to HIV-1, the infection cannot be diagnosed. Attempts to shorten this period have been made using a fourth-generation immunoassay that detects both HIV-1/2 IgG/M and HIV-1 p24 antigens [1]. However, most of the commercially available detection systems for fourth-generation immunoassays use chemiluminescent measurement and thus require specialized, highly expensive automated measurement equipment. For this reason, fourth-generation immunoassays are performed only at diagnostics companies and hub hospitals. To overcome this limitation and to test many samples simultaneously, there is need of an immunoassay with increased sensitivity for the HIV-1 p24 antigen that nonetheless uses a common enzyme and does not require any specialized instruments.
In 2010, French health authorities mandated a limit of detection of at least 2 IU/mL of HIV-1 p24 antigen for a Conformité Européenne (CE)-marked HIV antigen/antibody assay [2]. According to this mandate, commercially available assay kits were manufactured to detect p24 antigen with limits of detection ranging from 0.505 to 1.901 IU/mL and from 11.9 to 33.5 pg/mL [2]. Units of pg/mL are used for the Société Française de Tranfusion Sanguine (SFTS) standard (i.e. recombinant proteins), versus IU/mL for the WHO (World Health Organization) standard. As 1 IU/mL is estimated to be equivalent to 10 pg/mL and MW = 24 000 for p24, the best sensitivity in these kits is 0.505 IU/mL, which is ~2 × 10−16 moles/mL.
To date, numerous methods have been proposed for the detection of p24 antigen. However, the limit of detection of p24 antigen is not expected to overcome the sensitivity of 10−17 to 10−18 moles/mL. In addition, we have to note that HIV testing of many samples requires not only ultrasensitive HIV-1 p24 detection but also rapidity, a reasonable cost, and a simple protocol without the requirement of special equipment. In the present review, we introduce a de novo ultrasensitive colorimetric enzyme-linked immunosorbent assay (ELISA) for HIV-1 p24 [3].
Mechanism of ultrasensitive colorimetric ELISA
Watabe and colleagues developed an ultrasensitive ELISA to measure trace amounts of proteins by combining a conventional ELISA with thionicotinamide-adenine dinucleotide (thio-NAD) cycling [4]. Their rationale was that although proteins cannot be amplified by polymerase chain reaction (PCR) in the manner of nucleic acids, a detectable signal for proteins can be amplified. Thus, their ultrasensitive ELISA (Fig. 1) employs a sandwich method using a primary and a secondary antibody for antigens. An androsterone derivative, 3α-hydroxysteroid, is produced by the hydrolysis of 3α-hydroxysteroid 3-phosphate with alkaline phosphatase linked to the secondary antibody. This 3α-hydroxysteroid is oxidized to a 3-ketosteroid by 3α-hydroxysteroid dehydrogenase (3αHSD) with a cofactor thio-NAD. By the opposite reaction, the 3-ketosteroid is reduced to a 3α-hydroxysteroid by 3α-HSD with a cofactor NADH. During this cycling reaction, thio-NADH accumulates in a quadratic function-like fashion. Accumulated thio-NADH can be measured directly at an absorbance of 400 nm without any interference from other cofactors.
This method enables the detection of a target protein with ultrasensitivity (10−19 moles/assay) by measuring the cumulative quantity of thio-NADH by a colorimetric method without the use of any special instruments for the measurements of fluorescence, luminescence or radio isotopes [4]. Further, we should note that this ultrasensitive method will allow a technician to detect trace amounts of proteins simply by applying thio-NAD cycling reagents to the conventional ELISA system. We therefore applied this ultrasensitive ELISA to the detection of HIV-1 p24 antigen in blood [3].
Sensitivity and stability of the ultrasensitive colorimetric ELISA for HIV-1 p24
A typical linear calibration curve for HIV-1 p24 antigen provided by the ultrasensitive ELISA coupled with thio-NAD cycling was y = 0.27x + 0.019, R2 = 0.99 in the range of 0.1‒1.0 IU/mL. The limit of detection of p24 was 0.0055 IU/assay (i.e. ~2 × 10−18 moles/assay). These findings indicate that the ultrasensitive colorimetric ELISA succeeds in detecting p24 at the attomole level [3]. Because this measurement system employs a 50 µL solution for each assay, the detection limit corresponded to 0.1 IU/mL, or 10−17 moles/mL. Therefore, even in terms of the concentration per mL, our detection limit is less than one-tenth of that required by the French health authorities [2]. The coefficient of variation was 8% for 1 IU/mL.
Spike-and-recovery test using serum
We attempted to perform spike-and-recovery tests in which the HIV-1 p24 antigen was added to the control serum. Because our results demonstrated that the ratio was about 100% for 0.5 IU/mL of HIV-1 p24, which was less than the value (2 IU/mL) required for a CE-marked HIV antigen/antibody assay (see Background), the ultrasensitive method was judged to sufficiently detect IV-1 p24 antigen in human blood obtained from patients in the very early period after infection.
Detection of HIV-1 p24 in the early stages of infection
It is important to diagnose primary HIV-1 infection and begin antiretroviral treatment as early as possible. Most HIV-1/2 antibody diagnostic tests detect the antibodies for the antigens of HIV-1 gp41 and HIV-2 gp36, which are highly conservative transmembrane proteins. These tests are quick and easy, and thus have been widely used in many clinics and public health centres. However, when only the antibody diagnostic tests are used, there is a long delay (generally a 28-day window period) before diagnosis is possible [5]. Further, HIV-1/2 antibody tests in children younger than 18 months tend to be especially inaccurate as a result of the continued presence of maternal antibodies [6]. To shorten the delay and to validate HIV tests, the HIV-1 p24 antigen, the concentration of which is expected to increase before antibodies emerge, should be detectable in trace amounts. HIV-1 p24 in blood emerges transiently in the very early period after infection, and then its concentration quickly returns to the basal level [5]. An HIV-1 p24 test is, therefore, very useful as a screening test in the early stage of infection.
Closing the gap on PCR-based nucleic acid testing (NAT)
Generally, the gold standard for diagnosing HIV-1 is PCR-based nucleic acid testing (NAT) [7], but this method is expensive and has infrastructure requirements, a long measuring time, and high complexity, thereby limiting its usefulness for large numbers of samples. There is also the issue that much of the world lacks access to reliable NAT, and thus in many geographic regions the policy is to simply wait until symptoms develop. Use of ultrasensitive detection of HIV-1 p24 antigen for early diagnosis would be a simple and reasonable alternative to NAT, such as for monitoring HIV treatment and protecting the blood supply. Accordingly, it is time to reconsider whether NAT should be the gold standard for diagnosing HIV-1. Barletta et al. claimed that the target protein (i.e. HIV-1 p24 antigen) is present in the virion in much higher numbers than viral RNA copies (approximately 3000 HIV-1 p24 antigen molecules versus 2 RNA copies per virion) [8]. The 10−18 moles/assay value in our present results corresponds to 106 protein molecules/assay, or ~103 RNA copies/assay. Although under laboratory conditions a real-time PCR (i.e. NAT) can detect on the order of 101 RNA copies/assay, the limitation of detection is usually in the order of 102 RNA copies/assay [9]. Hence, the ultrasensitive ELISA coupled with thio-NAD cycling for HIV-1 p24 is closing in on the detection limit obtained by NAT, with a margin of difference of only one order of magnitude.
Conclusion
The ultrasensitive ELISA coupled with thio-NAD cycling is a very convenient method for the early testing of HIV-1 infection because it requires only the addition of a thio-NAD cycling solution to the usual ELISA without the use of any specialized measuring equipment. Consequently, the present method could be widely used as a powerful tool to test many samples simultaneously.
References
1. George CRR, Robertson PW, Lusk MJ, Whybin R, Rawlinson W. Prolonged second diagnostic window for human immunodeficiency virus type 1 in a fourth-generation immunoassay: Are alternative testing strategies required? J Clin Microbiol. 2014; 52: 4105–4108.
2. Ly TD, Plantier JC, Leballais L, Gonzalo S, Lemée V, Laperche S. The variable sensitivity of HIV Ag/Ab combination assays in the detection of p24Ag according to genotype could compromise the diagnosis of early HIV infection. J Clin Virol. 2012; 55: 121–127.
3. Nakatsuma A, Kaneda M, Kodama H, Morikawa M, Watabe S, Nakaishi K, Yamashita M, Yoshimura T, Miura T, Ninomiya M, Ito E. Detection of HIV-1 p24 at attomole level by ultrasensitive ELISA with thio-NAD cycling. PLoS One 2015; 10: e0131319.
4. Watabe S, Kodama H, Kaneda M, Morikawa M, Nakaishi K, Yoshimura T. Ultrasensitive enzyme-linked immunosorbent assay (ELISA) of proteins by combination with the thio-NAD cycling method. BIOPHYSICS. 2014; 10: 49–54.
5. World Health Organization (WHO). HIV/AIDS Fact sheet No 360. WHO 2015; http://www.who.int/mediacentre/factsheets/fs360/en/
6. Zijenah LS, Tobaiwa O, Rusakaniko S, Nathoo KJ, Nhembe M, Matibe P, Katzenstein DA. Signal-boosted qualitative ultrasensitive p24 antigen assay for diagnosis of subtype C HIV-1 infection in infants under the age of 2 years. J Acquir Immune Defic Syndr. 2005; 39: 391–394.
7. Patel P, Mackellar D, Simmons P, Uniyal A, Gallagher K, Bennett B, Sullivan TJ, Kowalski A, Parker MM, LaLota M, Kerndt P, Sullivan PS; Centers for Disease Control and Prevention Acute HIV Infection Study Group. Detecting acute human immunodeficiency virus infection using 3 different screening immunoassays and nucleic acid amplification testing for human immunodeficiency virus RNA, 2006-2008. Arch Intern Med. 2010; 170: 66–74.
8. Barletta JM, Edelman DC, Constantine NT. Lowering the detection limits of HIV-1 viral load using real-time immuno-PCR for HIV-1 p24 antigen. Am J Clin Pathol. 2004; 122: 20–27.
9. Wagatsuma A, Sadamoto H, Kitahashi T, Lukowiak K, Urano A, Ito E. Determination of the exact copy numbers of particular mRNAs in a single cell by quantitative real-time RT-PCR. J Exp Biol. 2005; 208: 2389–2398.
The authors
Akira Nakatsuma1 PhD, PhC; Mugiho Kaneda1 BAgr; Hiromi Kodama1 MAgr; Mika Morikawa1,2 BASc; Satoshi Watabe3 BPha; Kazunari Nakaishi2; Masakane Yamashita4 PhD; Teruki Yoshimura5 PhD, PhC; Toshiaki Miura6 PhD, PhC; Masaki Ninomiya1 PhD, PhC; Etsuro Ito*1 PhD
1 Kagawa School of Pharmaceutical Sciences, Tokushima Bunri University, Sanuki, Japan
2 TAUNS Laboratories, Inc., Izunokuni, Japan
3 BL Co., Ltd., Numazu, Japan
4 Faculty of Science, Hokkaido University, Sapporo, Japan
5 Faculty of Pharmaceutical Sciences, Health Sciences University of Hokkaido, Ishikari-Tobetsu, Japan
6 Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan
*Corresponding author
E-mail: eito@kph.bunri-u.ac.jp
Dilution testing as a novel alternative for confirmation of HIV rapid diagnostic testing in resource-limited settings
, /in Featured Articles /by 3wmediaRapid diagnostic testing enables life-saving scale up of HIV diagnosis but is vulnerable to false positive results. Confirmation testing can be impractical or cost prohibitive in resource-limited settings. Retesting a diluted blood sample is evaluated and proposed, at a proof of concept level, as a simple cost-effective HIV confirmation methodology.
by Derryck Klarkowski and Dr Erwan Piriou
Background
The diagnosis of HIV infection in developed countries is based on initial screening for HIV antibodies, and if detected, confirmation with nucleic acid testing (NAT) [1]. This ensures high sensitivity and specificity. However, the current World Health Organization (WHO) HIV Testing Services Guidelines do not include specific confirmation testing for the diagnosis of HIV across large population groups in resource-limited settings [2]. Instead WHO recommends that diagnosis be made on the basis of rapid diagnostic tests (RDTs) only (or equivalent enzyme immune assay tests) requiring a minimum of two positive test results, using test devices from different sources, for a positive diagnosis (or three in low prevalence settings) [2]. Although the WHO strategy has enabled life-saving scale-up of HIV diagnosis the significant compromise is that without confirmation there is a risk that patients/clients can be falsely diagnosed as HIV positive [3]. This is also well demonstrated in the study discussed now in this article where the WHO RDT algorithm resulted in 6.8% false positive results (n = 2897). Incorrect HIV diagnosis can have devastating consequences for the individual as well as wasting often over-stretched resources required for treatment and care.
Médecins Sans Frontières (MSF) has strongly advocated for the use of serological HIV confirmation testing in resource-limited settings when it is impractical to perform NAT [4–6]. Commercial confirmation kits are available that detect individual specific HIV antibodies, such as gp40, gp120, p24 and p32, that significantly increase the accuracy of testing at a considerably lower cost than NAT, and this type of confirmation testing can be performed by non-specialized laboratories. The downside, however, is that commercial confirmation kits nevertheless add cost, albeit reduced compared to NAT, and logistical complications that restrict their widespread use.
To address this, MSF has recently published a simplified confirmation approach based on antibody dilution requiring only the use of an additional routinely used RDT test device [7]. This study has been published as a ‘proof of concept’ paper and requires further testing across different settings for refinement before it can be generally recommended.
Causes of false positive HIV antibody detection
As with all tests, false positive HIV RDTs can be caused by user error (clerical mistakes, incorrect test performance, misinterpretation and cross-contamination). Other causes for HIV tests include nonspecific IgG binding [2], cross reactivity [2, 5], contaminating proteins [2] and pseudo-antigens created during the manufacturing process [5]. However, a key additional vulnerability for HIV antibody detection testing is that all commonly available HIV RDTs share a common gp41 detection antigen. Therefore, a cross-reactive antibody interacting with gp41 will act as a pan-cross-reactive antibody across multiple test devices [5].
The WHO algorithm is based on the assumption that HIV RDTs that use different antigen preparations are independent and, therefore, by requiring two positive tests (at a prevalence >5%) before reporting HIV positivity, the algorithm assumes that the second test confirms the result of the initial screening test [2]. However, in one MSF published study 50% of false positive samples had cross-reactive anti-gp41 activity, identified by Western blot (WB), that was the likely cause of the double false positive reactions with the two independent RDTs used in the testing algorithm [4].
MSF has proposed that early-immune-response broad-specificity polyclonal B-lymphocyte antibodies are a potential source of HIV RDT cross-reactive interfering antibodies [4]. These antibodies are likely to have increased frequency and intensity in resource-limited settings because of the higher prevalence of concomitant infections [8–10]. Additionally, displaced populations and individuals, such as caused by oppression, conflict and famine, are likely to have a greater vulnerability to cross-reacting infections than stable communities.
Theoretical basis for dilution methodology
Confirmation by dilution is based on the established sensitive/less sensitive (S/LS) methodology developed to identify recent HIV infection for the purposes of incidence surveys [11–15]. This methodology is based on the principle that HIV antibody titres increase over a period of several months after initial infection. Samples are initially tested using a high sensitivity HIV enzyme immunoassay (EIA) and if reactive are then further tested by the same EIA assay but using a diluted sample and reduced incubation time to reduce sensitivity. Samples testing positive on the sensitive (S) test but negative on the less sensitive (LS) test are designated a recent infection. The methodology has been successfully extended to the use of RDTs [13–15]. Confirmation by dilution adapts the S/LS principle to differentiate between high titre true HIV antibodies and low titre cross-reacting antibodies.
One postulated source of cross-reactive antibodies are broad spectrum antibodies produced in the early immune response to a wide range of infectious disease antigens, and these antibodies can cause nonspecific cross reactivity in HIV serological testing [5]. In proposing dilution as a methodology to confirm HIV infection, we postulate that cross-reacting antibodies will have a low titre relative to specific HIV antibodies.
Cross-reacting antibodies can generally be expected to have low avidity, as has been demonstrated by work in blood donors [16] and in MSF findings [4]. This will result in weakly positive results that can provide an alert for the tester; however, manufacturers generally state that any positive test line independent of strength should be interpreted as a positive result. Cross-reactive antibodies can also have high avidity as shown in a previous MSF publication where 7 of 24 (29.2%) false positive samples (total sample size 229) had strongly positive test lines in two RDTs but had a low titre relative to the confirmed true HIV antibodies [4].
The use of dilution as a supplementary confirmatory test by using antibody relative titres has been previously reported by Urwijitaroon et al. [17]. In another study, 41 samples were found positive using the HIV RDT Determine™ and 23 were negative on dilution [18]. Only 1 of these 23 samples was confirmed to be positive using serological confirmation (INNO-LIA™).
Field testing
A study was conducted at two sites in north western Ethiopia in programmes covering both residents and seasonal migrant workers. Seasonal workers are transient and, as postulated by MSF, may potentially have a higher risk of false positivity caused by cross-reacting antibodies [4, 5].
The study recruited 2897 individuals, and 265 (9.1%) samples tested as positive using two HIV RDTs from different manufacturers and would have been interpreted as HIV positive using the WHO algorithm [2]. Of the negative samples, 229 (approximately every 11th sample) were selected as a control. All algorithm-positive and negative control samples were further tested by dilution in situ, and additional confirmation testing performed by reference laboratories using WB and NAT for indeterminate WB samples.
All negative samples were confirmed as negative (100% sensitivity). However, 18/265 (6.8%) algorithm ‘positive’ samples were identified as HIV negative (false positive) by either WB or NAT.
Dilution testing was performed by titrating the patient’s plasma using confirmed seronegative plasma from healthy blood donors using a micropipette. Ten microlitres of patient plasma was first diluted 1 : 10 in 90 µL of negative plasma followed by a serial 4-fold dilution from 1:40 to 1 : 10,240. Testing was performed using Determine™ HIV-1/2 (Alere Laboratories, Japan) following manufacturer’s instructions. Tests were interpreted as positive if there was any colouration of the test line and the highest dilution that gave a positive result was recorded. Where the lowest dilution (1 : 10) was negative, the sample was reported as negative.
Findings and conclusion
In this study, based on a specific population group over a specific time period, repeating the RDT test using the sample diluted 1 : 160 identified all false positive results and misidentified one true positive (see Table 1). However, there is a safety net that any sample with a reactive HIV RDT test that is not resolved as a true positive at the time of testing is not reported as negative but as inconclusive [2]. The patient/client is advised that testing has been inconclusive and testing should be repeated at a later time; WHO recommends retesting after 14 days. This allows time for true HIV antibodies to increase in titre.
The discriminatory threshold dilution may vary between different settings. In an earlier MSF study, a dilution of 1 : 1000 differentiated 229 true HIV positive from 27 HIV false positive samples (unpublished data, for further details see Klarkowski et al. [4]).
One strength of this MSF study is that NAT testing was available to resolve indeterminate WB samples which made it possible to rule out early seroconversion as a potential cause of false positive results. The limitation is that the findings are restricted to a single cohort with a single RDT and should be viewed as a ‘proof of concept’. More experience is needed in different settings and by different workers before the dilution methodology can be considered for potential scale up. It is proposed that the methodology has potential for use as a supplementary test in a confirmatory algorithm, whereby double positive RDT results are tested by dilution, with positive results above a determined threshold confirming HIV infection. Dilution results below the threshold would require further testing, such as repeat testing at a later time or NAT, to rule out false negative results either due to seroconversion or misclassification by the lower sensitivity dilution test.
References
1. Centers for Disease Control and Prevention and Association of Public Health Laboratories. Laboratory testing for the diagnosis of HIV infection: updated recommendations. 2014; http://stacks.cdc.gov/view/cdc/23447.
2. World Health Organization. Consolidated guidelines on HIV testing services. 2015; http://www.who.int/hiv/pub/guidelines/hiv-testing-services/en/.
3. Johnson C, Fonner V, Sands A, Tsui S, Ford N, Wong V, Obermeyer C, Baggaley R. Annex 14 A report on the misdiagnosis of HIV status. In: World Health Organization. Consolidated Guidelines on HIV Testing Services. 2015; http://www.who.int/hiv/pub/guidelines/hiv-testing-services/en/.
4. Klarkowski DB, Wazome JM, Lokuge KM, Shanks L, Mills CF, O’Brien DP. The evaluation of a rapid in situ HIV confirmation test in a programme with a high failure rate of the WHO HIV two-test diagnostic algorithm. PLoS One 2009; 4(2): e4351.
5. Klarkowski D, O’Brien DP, Shanks L, Singh KP. Causes of false positive HIV rapid diagnostic test results. Expert Rev Anti-infect Ther. 2013; 12(1): 49-62
6. Shanks L, Klarkowski D, O’Brien DP. False positive HIV diagnoses in resource limited settings: operational lessons learned for HIV programmes. PLoS ONE 2013; 8(3): e59906.
7. Shanks L, Siddiqui MR, Abebe A, Piriou E, Pearce N, Ariti C, Masiga J, Muluneh L, Wazome J, Ritmeijer K, Klarkowski D. Dilution testing using rapid diagnostic tests in a HIV diagnostic algorithm: a novel alternative for confirmation testing in resource limited settings. Virol J. 2015; 12: 75.DOI 10.1186/s12985-015-0306-4
8. Messele T, Abdulkadir M, Fontanet AL, Petros B, Hamann D, Koot M, Roos MT, Schellekens PT, Miedema F, Rinke de Wit TF. Reduced naive and increased activated CD4 and CD8 cells in healthy adult Ethiopians compared with their Dutch counterparts. Clin Exp Immunol. 1999; 115(3): 443–50.
9. Clerici M, Butto S, Lukwiya M, Saresella M, Declich S, Trabattoni D, Pastori C, Piconi S, Fracasso C, Fabiani M, Ferrante P, Rizzardini G, Lopalco L. Immune activation in Africa is environmentally-driven and is associated with upregulation of CCR5. Italian-Ugandan AIDS Project. AIDS 2000; 14(14): 2083–2092.
10. Clerici M, Declich S, Rizzardini G. African enigma: key player in human immunodeficiency virus pathogenesis in developing countries? Clin Diagn Lab Immunol. 2001; 8(5): 864–866.
11. World Health Organization Technical Working Group on HIV Incidence Assays. When and how to use assays for recent infection to estimate HIV incidence at a population level. 2011; http://www.who.int/diagnostics_laboratory/hiv_incidence_may13_final.pdf 2011.
12. Constantine NT, Sill AM, Jack N, Kreisel K, Edwards J, Cafarella T, Smith H, Bartholomew C, Cleghorn FR, Blattner WA. Improved classification of recent HIV-1 infection by employing a two-stage sensitive/less-sensitive test strategy. J Acquir Immune Defic Syndr. 2003; 32: 94–103.
13. Soroka SD, Granade TC, Candal D, Parekh BS. Modification of rapid human immunodeficiency virus (HIV) antibody assay protocols for detecting recent HIV seroconversion. Clin Diagn Lab Immunol. 2005; 12: 918–21.
14. Kshatriya R, Cachafeiro AA, Kerr RJS, Nelson JA, Fiscus SA. Comparison of two rapid human immunodeficiency virus (HIV) assays, Determine™ HIV-1/2 and OraQuick Advance Rapid HIV-1/2, for detection of recent HIV seroconversion. J Clin Microbiol. 2008; 46(10): 3482–3483.
15. Girardi SB, Barreto AM, Barreto CC, Proietti AB, Carvalho SM, Loureiro P, Sabino EC. Evaluation of rapid tests for human immunodeficiency virus as a tool to detect recent seroconversion. Braz J Infect Dis. 2012; 16(5): 452–456.
16. Bouillon M, Aubin E, Roberge C, Bazin R, Lemieux R. Reduced frequency of blood donors with false-positive HIV-1 and -2 antibody EIA reactivity after elution of low-affinity nonspecific natural antibodies. Transfusion 2002; 42(8): 1046–1052.
17. Urwijitaroon Y, Barusrux S, Romphruk A, Puapairoj C, Thongkrajai P. Anti-HIV Antibody Titer: An Alternative Supplementary Test for Diagnosis of HIV-1 Infection. Asian Pac J Allergy Immunol. 1997; 15:193–198.
18. Duedu KO, Hayford AA and Sagoe KW. Misclassification of recent HIV-1 seroconversion in sub-Saharan Africa using the sensitive/less sensitive technique. Virol J. 2011; 8: 176.
The authors
Derryck Klarkowski* MAppSc, Erwan Piriou PhD
Médecins Sans Frontières, Amsterdam, The Netherlands
*Corresponding author
E-mail: derryck.klarkowski@gmail.com
Advanced automation system drives lab efficiency while optimizing use of staff time
, /in Featured Articles /by 3wmediaFollowing the trend in the US, pressure is mounting on big European hospital labs to consolidate their operations and boost workflow performances while at the same time making better use of their staff. The Aptio Automation system from Siemens Healthcare Diagnostics goes a long way to fulfill these objectives. Here we take a look at three specific examples of hospitals that have integrated the system and how it has helped both their clinical and operational effectiveness
National Health Service (NHS) Tayside
NHS Tayside serves a population of 480,000 through a network of 22 hospitals/infirmaries and 69 general-practice sites that rely on two laboratories. The Blood Sciences Laboratory is located at the 900-bed Ninewells Hospital in Dundee, one of the UK’s major teaching hospitals. Here, Aptio Automation merges the three former individual labs onto a single track, providing a full complement of pre- and post-analytical sample-processing modules along with comprehensive analytics. The efficiencies gained have empowered the Ninewells Hospital laboratory to take on 73% of the testing that historically had been conducted at the 260-bed Perth Royal Infirmary (PRI), enabling the smaller PRI laboratory to focus exclusively on acute admissions and inpatient testing. Ninewells now handles 100% of the general practice testing in the entire region.
“Underpinning all of our actions is the commitment to reduce waste and variation—and most of all, to prevent harm to patients,” says Dr. Bill Bartlett, Tayside’s joint clinical director of diagnostics. “Aptio Automation is helping us enable our vision of cost-effective, patient-focused care.”
Using Aptio Automation, Tayside now processes 7,000 tubes per day—a 20% increase in the workload of its main laboratory with no additional staff. Decreased TAT across the board drove a 61% improvement in the TAT for add-on tests – all with the high-quality results derived from the consistency and standardization enabled by automation. Increased capacity has even enabled Tayside to introduce new testing protocols that improve the quality of care and can save the hospital money.
While Tayside staff had ideas about what they needed and wanted to do, Siemens gave them data-driven information to guide their decision making. “Siemens’ expertise and consultative approach was paramount to the success of this project, from beginning to end,” says Dr. Bartlett. “We relied on them to evaluate workloads from a variety of locations and to recommend the optimal mix of instruments to support peak loads. They devised the final track layout for the new space and helped optimize the use of automation to best manage the workflow.”
Carlos Haya Hospital Malaga
Rising along Spain’s Mediterranean coast, Malaga has a population of approximately 600,000 through tourism, construction, and technology services. The healthcare needs of locals and tourists are met by the 1,100-bed Universitario Carlos Haya Hospital. Part of the government-run Andalusian Public Health System, Carlos Haya is a regional institution with four hospitals: the General Hospital, Civil Hospital, CARE Joseph Estrada, and Hospital Materno Infantil. The latter, in addition to providing healthcare for mothers and infants, houses the core laboratory that serves as the reference lab for all four hospitals. The Carlos Haya General Hospital also operates a biochemistry lab and its own emergency lab.
Hospitals in Spain are classified into three tiers, depending on the complexity of the diseases they can treat. Materno Infantil holds the highest rating, Tier 3, which means it handles the most difficult cases. Its core lab provides testing across a wide spectrum of disease states, performing seven million tests a year for approximately 660,000 patients.
The great leap forward occurred in 2012, with the implementation of Siemens Aptio Automation integrated with the Siemens CentraLink Data Management System. The solution delivers extensive automation, customization, and traceability, while eliminating the need for third-party informatics software.
“Aptio Automation strengthened our preanalytical and analytical phases, giving us more capacity, more versatility, and overall, more possibility,” says Dr. Manuel Rodriguez, who is responsible for biochemistry and automation labs. “CentraLink, meanwhile, makes it much easier to manage quality. It facilitates sample follow-up and management of repetitions. With CentraLink control over instrument alarms, we gain comprehensive information on the situation of a particular sample. What’s more, the solution is simple, problem-free, and easy to use.”
Hospital Clinic de Barcelona (CDB)
In 2001, the Hospital Clinic de Barcelona in Spain was among the first healthcare providers in the world to create an automated core laboratory. Today the laboratory is gaining even greater efficiencies with Aptio Automation connecting analytical systems across all four core laboratory disciplines: clinical chemistry, immunoassay, hematology, and hemostasis.
Over the course of its 13-year journey with Siemens, the laboratory has been able to consolidate instruments, integrate and automate STAT testing, reallocate staff to higher-value responsibilities, and save upwards of €600,000 in tube costs – all while increasing clinicians’ trust in the laboratory to support excellence in patient care.
In 2000, Hospital Clinic de Barcelona established the Biomedical Diagnostic Centre (CDB) to provide high-quality, comprehensive service in all areas of laboratory medicine and to be a reference for excellence in the related specialties. The CDB uses a client-focused model to optimize the use of resources while ensuring advanced technological development in healthcare and research.
The CDB is divided into five specialty departments and an operative core laboratory where high-volume automated testing is performed. In total, the CDB is composed of 100 staff specialists from the various laboratory areas along with 300 professionals representing pathology, biochemistry, molecular genetics, hemotherapy and hemostasis, immunology, and microbiology specialties.
The CDB recently began a project to create a Molecular Biology Core Laboratory, which will integrate the most frequently tested molecular technologies.
“Economic pressure is an ongoing fact of life,” says Dr. Aurea Mira, director of the CDB. “Lab automation allows us to improve workflows, optimize human and technology resources, and save money. It also raises hospital awareness of the lab as a provider of fast, accurate testing that supports good clinical outcomes.”
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