J Neural Transm (2009) 116:1163–1167 DOI 10.1007/s00702-009-0277-y
DEMENTIAS - ORIGINAL ARTICLE
Neurochemical dementia diagnostics: a simple algorithm for interpretation of the CSF biomarkers Piotr Lewczuk Æ Ru¨diger Zimmermann Æ Jens Wiltfang Æ Johannes Kornhuber
Received: 29 April 2009 / Accepted: 17 July 2009 / Published online: 4 August 2009 Ó Springer-Verlag 2009
Abstract Cerebrospinal fluid (CSF)-based neurochemical dementia diagnostics (NDD) is a well-established diagnostic tool for neurodegenerative disorders, including Alzheimer’s disease (AD). However, the direct comparison of the concentrations of the biomarkers between laboratories is often very misleading, due to relatively high interlaboratory discrepancies of normal/abnormal ranges (cutoff values). Therefore, an interpretation tool might be useful for centers performing NDD to facilitate a standardized, diagnostic-oriented reporting of the data on biomarkers. In this study, we present a simple, easy-to-implement algorithm allowing diagnostic-relevant categorization of patients according to the outcome of the CSF NDD results and, correspondingly, enabling reporting of the data to clinicians in a clear and easy-to-follow form. The algorithm is flexible and cutoff values independent, meaning each laboratory can easily supplement it with the cutoff values and normal/abnormal ranges according to the needs; the only prerequisite is to perform the standard CSF NDD assays (amyloid b peptides and Tau/pTau). Keywords Alzheimer’s disease Neurodegeneration Cerebrospinal fluid Interpretation algorithm Biomarkers
P. Lewczuk R. Zimmermann J. Kornhuber (&) Department of Psychiatry and Psychotherapy, Universita¨tsklinikum Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany e-mail:
[email protected] P. Lewczuk e-mail:
[email protected] J. Wiltfang Department of Psychiatry and Psychotherapy, University of Duisburg-Essen, Essen, Germany
Introduction Since neurodegeneration, including Alzheimer’s disease (AD), strongly affects populations of all industrialized countries, it is obvious that more and more laboratories worldwide establish methods and protocols to improve AD diagnosis. Among accepted biomarkers, cerebrospinal fluid (CSF) concentrations of amyloid b peptides (Ab peptides) and Tau protein(s) along with hyperphosphorylated forms of the latter have been proven to fulfill the criteria for a valid diagnostic test (The Working Group on: ‘‘Molecular and Biochemical Markers of Alzheimer’s Disease’’ 1998). This is not surprising because these molecules are directly involved in the pathologic events of the disease, namely deposition of senile plaques and formation of neurofibrillary tangles, respectively. CSF concentration of Ab peptides ending at the amino acid position 42 (Ab42) is consequently found to be decreased, and Tau/phospho-Tau proteins are increased in AD (Welge et al. 2009 and reviewed in Lewczuk and Wiltfang 2008). Due to many reasons, among them relatively high interlaboratory imprecision of the measurements of biomarkers (Lewczuk et al. 2006), laboratory-specific cutoff values must be elaborated and used in each diagnostic center, even when the same assays are performed. Discrepancies in the protocols of collection and storage of the CSF samples make the direct comparison of the results even more complex. Therefore, we postulate that physicians ordering laboratory analysis obtain not only ‘raw’ concentrations, but also an integrated laboratory report including diagnostic-relevant interpretation of the biomarkers’ constellation. To facilitate this task, we have elaborated an interpretation algorithm that might be easily implemented by other interested groups.
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all biomarkers of the Aβ group are normal
Amyloid β Peptides
Fig. 1 Interpretation of the concentrations of CSF biomarkers starts c with the score = 0 and, depending on the results, can increase up to 4 points. With positive answers for the questions in the algorithm’s nodes, the interpreter follows green arrows, with negative answers, red ones. Finally, the score obtained at the end of the interpretation determines the categorization of the patient into one of the groups listed in the insert. In special cases, an additional comment is given: a the results are in a border zone (marginally increased concentration of Tau/pTau or decreased Ab42 concentration/Ab42/40 ratio) and patients should be treated cautiously. This comment is usually given when the results are altered by not more than the assay’s imprecision; b discrepancy between Ab42 concentration and Ab42/40 concentration ratio (usually in case of very low or very high total Ab peptides CSF load); c in case of very high Tau concentration, rapidly progressing neurodegeneration must be taken into account (for example Creutzfeldt-Jakob disease should be considered as the differential diagnosis); the value presented here is the cutoff of our laboratory and can be easily adjusted
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at least one exceeds border zone
Aβ42 fits AβR
+0
+1
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+2
+2
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all biomarkers of the Tau/pTau group are normal
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Tau>1200pg/mL
Assays
Algorithm The algorithm for interpreting the outcome of the analysis of the biomarkers is presented in Fig. 1. Each analysis starts with the score equal to 0. Depending on the concentration of the biomarkers, the score can increase and the final sum (the lowest, 0 points; the highest, 4 points) defines the categorization of a given patient into one of the four diagnostic groups, which is eventually presented to the physician on the CSF integrated report.
at least one exceeds border zone +0
+1
a
+2
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pTau normal or in border zone
+1
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Rapidly progressing neurodegeneration (e.g. CJD)
Biomarkers in the CSF are analyzed according to the protocols described elsewhere (Lewczuk et al. 2004a, b). Briefly, we measure CSF concentrations of two forms of Ab peptides ending at the C-terminus of 42: Ab1-42 (the assay of Innogenetics, Ghent, Belgium, specific for both the C- and N-termini) and Abx-42 (the assay of The Genetics, Zu¨rich, Switzerland, unspecific for the N-terminus, but specific for the C-terminus), as well as Abx-40 (The Genetics), total Tau (Innogenetics) and Tau phosphorylated at the position 181 (pTau181, Innogenetics). Apolipoprotein E (APOE) genotyping is performed with the assay of Innogenetics.
Tau/pTau
Assays and the interpretation algorithm
END: SCORING SCORE: 0: no evidence of organic CNS disease 1: improbably AD 2-3: possibly AD 4: probably AD
Interpretation of the amyloid b results CSF concentration of Ab peptides ending at the amino acid position of 42 is decreased in AD and, as recently observed, Ab42/40 concentration ratio (AbR) seems to even better reflect AD pathology than Ab42 alone, especially in cases with extremely low or extremely high total Ab concentrations (Wiltfang et al. 2007). In such cases, a discrepancy can be observed between Ab1-42 and/or
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Abx-42 ‘raw’ concentration and the Ab42/40 ratio, and this requires a corresponding comment on the CSF report. Nevertheless, our current opinion is that the decreased Ab1-42 or Abx-42 cannot exclude Ab pathology even if the Ab42/40 ratio is normal. Certainly more experimental work is required to properly weigh the role of Ab42/40 in the interpretation of the Ab pathology in AD.
Neurochemical dementia diagnostics
Interpretation of the Tau/pTau181 results Increased Tau concentration in the CSF is quite a sensitive biomarker of a neurodegenerative process, however, unspecific for any given disorder, especially AD (Itoh et al. 2001). A very high CSF concentration of Tau points to ‘rapidly progressing neurodegeneration’, which means that Creutzfeldt-Jakob disease (CJD) must be taken into consideration as differential diagnosis (Otto et al. 2002). On the other hand, even very high CSF Tau concentrations cannot exclude AD, although it seems to be less probable in such cases compared to those when Tau is moderately increased. Therefore, although in cases with CSF Tau concentration higher than 1,200 pg/mL (or any other laboratory-specific cutoff value), interpretation is directed into the block of ‘rapidly progressing neurodegeneration’, AD cannot be completely excluded. pTau181 is more specific for AD; however, a mild-to-moderate increase of the CSF concentration of phosphorylated Tau protein can be observed in rapidly progressing neurodegeneration, too (Buerger et al. 2006), and hence AD cannot be definitely confirmed even if the CSF pTau concentration is very high.
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interpreted as ‘suspected rapidly progressing neurodegeneration, improbable AD’ (1 point), but this same concentration of Tau accompanied by pathologic Ab concentrations/ratio would shift the interpretation to ‘possible’ (2–3 points) or even ‘probable’ AD (4 points) depending on whether pTau was normal or not, respectively. Validation of the algorithm To validate the algorithm, we prepared a Microsoft Excelbased macro executing the algorithm’s rules and compared the interpretations of 100 reports performed by an expert clinical neurochemist with the interpretations delivered by the software. We found 85% of the diagnostic group categorizations identical, whereby the remaining 15% differed slightly by one diagnostic category (for example ‘possibly AD’ vs. ‘probably’ or ‘improbably AD’ vs. ‘no evidence of organic CNS disorder’). In all five cases with very high Tau concentration, both the software and the human expert delivered interpretation of ‘suspected rapidly progressing neurodegeneration’.
Border zones Discussion Each laboratory analysis is characterized by a certain margin of imprecision and, as a matter of fact, restrictive application of mathematical rules on how to interpret a given value in relation to a defined cutoff can bring about serious misinterpretations. The presented algorithm deals with this problem by knots ‘border zones’: their role is to correctly interpret for example the results within the assay’s imprecision. Neglecting such rules in the interpretation process easily leads to overinterpretation of only slightly increased Tau/pTau or only slightly decreased Ab peptides/ratio: Tau increase by 1% or so is indeed ‘increased’, but such a result must not be reported without a comment. The definition of border zones certainly depends on the laboratory’s experience, assay performance and so on; in our case, it is usually 5–10% of the cutoff. Diagnosis-relevant categorization of patients: AD and ‘rapidly progressing neurodegeneration’ Results of the CSF analysis with both groups of biomarkers (Tau/pTau and Ab) in pathologic ranges are interpreted as ‘probable AD’ (4 points in the interpretation diagram). Results of the CSF analysis with all biomarkers in normal ranges are interpreted as ‘no evidence of organic CNS disease’ (0 points). Results in-between, either with normal Tau/pTau and abnormal Ab, or vice versa, with pathologic Tau/pTau and normal Ab, are interpreted as ‘possible AD’ (2–3 points). The isolated very high concentration of Tau is
Relatively high inter-laboratory imprecision of the concentrations of the NDD biomarkers (Lewczuk et al. 2006) excludes simple copying and pasting of the cutoff values from one center to another, and lack of any systematic quality control program for NDD biomarkers makes the situation even more difficult (Verwey et al. 2009). Moreover, similar to neurochemical diagnosis of virtually all diseases of the nervous system, central and peripheral, CSF-based AD diagnosis is more precise when more biomarkers are taken into account. Currently, no single CSF biomarker is pathognomonic for AD. This, in turn, calls for interpretation algorithms that could be easily implemented in clinical neurochemistry laboratories to enable comparison of the results among centers. The reported algorithm is cutoff value-independent, which means that each laboratory performing analyses presented here (Tau/pTau and Ab) can, or actually even should, establish its own cutoff values. For this reason, we purposely do not present any concrete numbers, apart of the Tau cutoff for rapid neurodegeneration, which, of course, can be easily modified to fit a laboratory’s experience. We feel that this is the most important difference compared to the ‘dementia markers calculator’ distributed by Innogenetics, where the cutoff values for the assays of the company are fixed according to the experience we have in our laboratory in Erlangen (Dr. M von Darl, personal information). Such an approach has certain advantages too, for
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example it is easy to use; however, we feel that the algorithm with unfixed normal/abnormal ranges is easier to implement if the cutoff values in a given center differ from those suggested by the manufacturer. Similarly, it must be stressed that the algorithm presented in this report is assayunspecific, which means that the assays of biomarkers of the Ab and Tau groups, as used currently in our laboratory, can be replaced by other assays or even other analytical platforms. The only prerequisite is that Ab peptide ending at the amino acid position of 42 is measured along with its ratio to Ab ending at the position of 40, Tau, and phosphorylated form of the latter. Since no data currently exists to show whether one of the biomarkers, or a group of biomarkers, has stronger impact on the AD diagnosis than the others, we treat them equally (equal number of points for altered Tau/pTau and Ab). Nevertheless, if convincing evidences are published showing that one of the groups of biomarkers is more important that the other, in terms of specificity and sensitivity, this algorithm can be easily modified by adjusting the number of points given for this biomarker. Similarly, for the current version of the interpretation algorithm, only established and generally accepted routine NDD biomarkers were considered (CSF amyloid b peptides, Tau and pTau proteins). Currently, we do not consider these ‘candidate biomarkers’ that, although promising, still need more studies to be validated in terms of diagnosing patients. Good examples for these might be CSF soluble amyloid precursor proteins (Lewczuk et al. 2008; Portelius et al. 2009), decreased phospholipase A2 activity in the CSF of AD patients (Smesny et al. 2008) or alterations in the concentration of amyloid b peptides in the blood (Graff-Radford et al. 2007; Hansson et al. 2008). Currently, discrepant results are presented in literature regarding the question whether APOE genotype affects the metabolism of AD biomarkers (Strittmatter et al. 1993), which would mean that normal/abnormal ranges should be defined considering the APOE status of a given patient (Arai et al. 1995; Blomberg et al. 1996; Golombowski et al. 1997). Since it is currently unclear if APOE genotype should influence the CSF biomarkers cutoffs, we currently use one set of cutoffs for all possible APOE genotypes. Nevertheless, the algorithm presented here can be easily adjusted to meet the needs of different cutoffs for different APOE genotypes, if it turns out to be necessary. Validation of the algorithm with an MS Excel-based macro showed not only high compatibility with the interpretations done by a clinical neurochemist, dealing with ‘border zone’ cases perhaps even more objectively, but also proved that this algorithm can easily be implemented as a computer software to further simplify interpretation of NDD results. In comparison with other diagnostic tools, the sensitivity of the CSF NDD methods, including their
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interpretation according to the currently suggested algorithm, seems to be higher than, for example, neuroimaging techniques (Weih et al. 2009), which further validates it as a useful diagnostic support tool. Further verification procedures, especially comparison with postmortem analysis, would certainly further validate the algorithm. However, it must be treated carefully for two reasons: (a) neither Ab plaques nor Tau/pTau tangles are pathognomonic for Alzheimer’s disease, and (b) there is usually substantial time difference between the diagnostic lumbar puncture and the autopsy, and hence neuropathology does not necessarily reflect the stage of the disease when the CSF biomarkers were analyzed, usually years previously. Currently we are working on the integration of the software into a laboratory database. In conclusion, we present a flexible, easy-to-implement algorithm to interpret Ab and Tau/pTau CSF concentrations in the context of neurochemical dementia diagnostics. We hope that this algorithm would help to standardize diagnosis-oriented interpretation of the CSF NDD biomarkers. Acknowledgments We thank all the technical coworkers of the Laboratory for Clinical Neurochemistry and Neurochemical Dementia Diagnostics at the Department of Psychiatry and Psychotherapy, Erlangen, for years of performing NDD analysis with excellent quality. A part of this work was presented at the 9th International Conference on Alzheimer’s and Parkinson’s Diseases, Prague, 2009. PL and RZ are supported by Fond fu¨r Forschung und Lehre am Klinikum Erlangen (ELAN; grant No.: 08.12.11.1). PL is a consultant of Innogenetics.
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