CRASH COURSE: CEREBROSPINAL FLUID DIAGNOSTICS FOR PSYCHIATRISTS AND NEUROLOGISTS Cerebrospinal fluid analysis for diagnosis of noninflammatory, dementive and psychiatric diseases H. Reiber1, M. Otto2 & K. Bechter3 1 CSF and Complexity Studies, University Goettingen, Germany, 2 Department of Neurology, Ulm University, Germany 3Clinic for Psychiatry and Psychotherapy II, Ulm University, Germany Abstract: CSF analysis contributes to differential diagnosis of noninflammatory diseases by: 1) exclusion of a chronic or acute inflammation. 2) detection of particular brain-derived proteins, surrogate markers, corresponding to the suggested diagnosis (tumor, dementia, brain hypoxia, hemorrhage, autoimmune disease, psychiatric disease, metabolic disorder, rhinorhea, Table 1) and 3. differential cell count in CSF. Interpretation of brain-derived proteins in CSF uses absolute concentrations (in contrast to CSF/serum quotients for bloodderived proteins) and must discriminate between different sources: Neuronal or glial proteins like NSE, or tau protein are evaluated using their absolute concentrations in CSF for maximal sensitivity without reference to QAlb. The leptomeningeal proteins like beta trace or cystatin C are evaluated as absolute concentrations with reference to QAlb. As application examples we review the group of dementive and psychiatric diseases. Alzheimer’s disease, Parkinson¢s disease dementia, Lewy-body disease and frontotemporal dementia are the major causes of neurodegenerative memory impairment and dementia. Combined analysis of Tau-Protein and Beta Amyloid 1–42 in CSF represent the classic approach, meanwhile extended with further surrogate markers. In 15% of psychiatric patients with schizophrenic or affective disorders an inflammatory process could be detected which points to a brain-organic involvement. In 24% of these patients with a psychiatric disease a moderately increased albumin quotient was observed as the only unexplained pathological sign. In psychiatric diseases it has to be regarded as a serious deficit not to make at least once a CSF analysis in the patients which could modify the diagnosis (in 6%). Introduction: The different traditions in different countries and the different judgements about the relevance of CSF analysis in the context of other clinical or imaging methods led to a wide spectrum of acknowledgement for CSF analysis. In case of infectious, acute neurological diseases (1,2) or in case of multiple sclerosis (3,4) with a chronic inflammatory process there is no controversy about the relevance of CSF analysis in Neurology (including analysis of CSF/ serum quotients QAlb, QIgG, QIgA, QIgM, cell count, oligoclonal IgG and specific antibodies). But what about psychiatric diseases or dementive processes in brain, brain trauma, autoimmune diseases with involvement of the brain? The chance for rehabilitation after neurodegenerative processes is primarily based on an early and correct diagnosis. A recent investigation (5) shows that in psychiatric diseases it has to be regarded as a serious deficit not to make at least once a CSF analysis in the patients: In 15% of psychiatric patients with schizophrenic or affective disorders an inflammatory process could be detected and in 30% an increased albumin quotient (i.e. a blood-CSF barrier
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dysfunction) which points to a brain-organic involvement and could modify the possibly wrong diagnosis in 6% of the patients. So CSF analysis must be the base for diagnostic approaches in all neurological and psychiatric diseases as CSF data pattern (1,2) can contribute to differential diagnosis of brain diseases by 1) Supporting suggested diagnosis by disease-related typical data patterns 2) Pointing to unexpected diagnosis by disease-related typical data patterns 3) Exclusion of inflammatory processes 4) Discrimination of chronic from acute diseases 5) Leading to further more specific analytical efforts. The relevance of cerebrospinal fluid and blood analysis offers a most specific and sensitive approach, but needs for reliability sophisticated, knowledge-based data interpretation. In general CSF analysis takes advantage of the intrathecal synthesis of immunoglobulins, increased release or production of marker proteins for tumor metastasis (CEA) or increased release of neuronal or glial proteins in degenerative diseases (Tau, beta amyloid, NSE, S100). In some rare cases the increased release of brain proteins (NSE) can be detected in blood. The basic issue is the difference for a source related interpretation of the proteins in CSF. After an earlier report in this journal (2) on basics in CSF analysis, focusing on blood derived proteins in CSF we focus now on the different approach for the brain-derived proteins (6,7), serving as surrogate markers for differential diagnosis of noninflammatory processes in the brain (Table 1). After reviewing the knowledge base for interpretation of brain-derived proteins we focus on two examples, the dementive and psychiatric diseases. Interpretation of blood- vs. brain-derived proteins in CSF: The understanding of the source-related dynamics of molecules in CSF with their subsequently different variability allows the rational choice between the different evaluation concepts for blood- and brain-derived proteins. Blood-derived proteins: For the primarily blood-derived proteins the discrimination of a brain-derived fraction from a bloodderived fraction in CSF is done with highest sensitivity and specificity by evaluation of CSF /serum quotients with reference to the albumin quotient in a nonlinear hyperbolic function (Fig. 1). This allows to take into account the positive rostro-caudal concentration gradients in CSF and the decreasing CSF flow rate in case of a blood CSF barrier dysfunction (8). The graphical interpretation is done with nonlinear reference ranges in quotient diagrams (Fig. 3) or by the numerical interpretation which also refers to the hyperbolic relation of proteins in CSF (1). Brain-derived proteins: A brain-derived protein in CSF is recognized by one of the following properties (Table 2) including the different source related biological coefficient of variation (CV): 1) CSF concentration > Blood concentration 2) Blood derived fraction CV in CSF) Physiology of brain-derived proteins: Proteins from neurons or glial cells like tau protein, neuron-specific enolase, S-100 protein, all enter CSF primarily in the ventricular and cisternal space. Their
2009 The Authors Journal Compilation 2009 John Wiley & Sons A/S Acta Neuropsychiatrica 2009: 21 (Supplement 2): 1–72
Downloaded from https:/www.cambridge.org/core. Kainan University, on 15 Apr 2017 at 19:29:08, subject to the Cambridge Core terms of use, available at https:/www.cambridge.org/core/terms. https://doi.org/10.1017/S0924270800032749
concentration between normal ventricular and lumbar CSF is decreasing (in contrast to blood-derived proteins), and in the case of pathologically decreasing CSF flow rate, the concentration in lumbar CSF remains invariantly constant (Fig. 2). Concentrations of the primarily leptomeningeal proteins, ß-trace protein and cystatin C, increase between normal ventricular and lumbar CSF. In the case of pathologically decreased CSF flow rate they increase linearly(Fig. 2) in lumbar CSF (concentrations of blood-derived proteins increase nonlinearly, Fig. 1). The predominantly brain-derived proteins in CSF of neuronal or glial origin are evaluated using their absolute concentrations in CSF for maximal sensitivity without reference to QAlb. The leptomeningeal proteins like beta trace or cystatin C are evaluated as absolute concentrations with reference to QAlb. Interpretation of Brain-derived proteins in blood In case of brain hypoxia with a very strong release of Neuronspecific enolase (NSE) into brain tissue the protein reaches high concentrations in blood by direct diffusion from brain tissue into blood capillaries. In contrast Beta trace protein reaches the blood by CSF bulk flow into venous blood. So the pathological increase of NSE and S100 is detectable in blood, e.g. in cases of brain hypoxia or infarction. In particular NSE has a high predictive value for clinical outcome of the patient after brain trauma or hypoxia (9). Relevance of CSF analysis for diagnosis of dementive processes: Alzheimer’s disease (AD), Parkinson¢s disease dementia (PDD)/Lewy-body disease (DLB) and frontotemporal
Fig. 1. CSF/serum quotients as a function of QA1b, a measure of CSF flow rate (7,8).
Fig. 3. CSF data of patients with schizophrenic or affective spectrum disorders in Reibergrams (1). These patients represent a subgroup with inflammatory signs in CSF (9/63) involving 3 cases with intrathecal IgG, 1 case with intrathecal IgA and 3 cases with intrathecal IgM synthesis or other signs of inflammation, like intrathecal antibodies or increased cell count in CSF (5). The graph is created with the CSF statistics tool of COMED (14). Fig. 2. CSF protein concentration as a function of QA1b.
2009 The Authors Journal Compilation 2009 John Wiley & Sons A/S Acta Neuropsychiatrica 2009: 21 (Supplement 2): 1–72
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dementia (FTD) are the major causes of neurodegenerative memory impairment and dementia. As new therapeutic agents are visible for the different diseases there is an ultimate need for an early differential diagnosis. Until recently the diagnosis of AD was made according to the McKhann criteria (10) in which cerebrospinal fluid findings were used to exclude other diseases. A recent update of the criteria originates from an international consensus group (11). In the last 10 years it was shown that a combined determination of just these few markers (tau-proteins, and abeta-peptides) is already sufficient to achieve a high degree of diagnostic certainty in the diagnosis of AD(12,13). According to the new research criteria low abeta 1–42 and high total–tau protein in CSF are suggested as additional criteria for the diagnosis for AD. As result of this there is a rising need for quality control of CSF samples and determination methods and on the knowledge of typical CSF findings in the various neurodegenerative diseases. Depending on the differential diagnostic question e.g. MCI vs. AD, depressive syndrome vs. AD or AD vs. FTD or AD vs. PDD, the used cut-off values will vary. The typical low abeta 1-42 and high tau-protein finding is especially helpful in the differential diagnosis of a depressive syndrome vs. AD, however for other differential diagnostic questions e.g. AD vs. PDD this profile will not be sufficient to differentiate between the two diseases especially not in the single patient approach (12,13). Typical findings of markers in neurodegenerative diseases are shown in Table 3. New research approaches from direct measurement of oligomers in specific diseases to proteomic expression profiles might come into clinical use (12,13). CSF analysis in psychiatric patients: In a recent study(5) the authors analysed albumin, IgG, IgA, IgM, oligoclonal IgG and specific antibodies in CSF and serum from hospitalised affective and schizophrenic spectrum disorder patients. Numerical and graphical interpretation of CSF protein data was performed in Reibergrams (1) with a new CSF statistics tool for nonlinear group analysis with reference to a large control group (14). As much as 41% of the psychiatric patients investigated (n = 63) had pathological parameters in CSF: 14% intrathecal humoral immune responses, 10% slightly increased CSF cell counts. 24% had moderate increase of the Albuminquotient QAlb (blood-CSF barrier dysfunction) as the only pathological sign with normal IgG, IgA and IgM concentrations in CSF. CSF analysis and interdisciplinary clinical approach helped to rediagnose 6% of psychiatric patients as likely representing a specific virus, streptococcus or autoimmune associated disorder with CNS involvement. Conclusions: CSF analysis is an underestimated diagnostic tool for differential diagnosis in psychiatric and noninflammatory diseases. Table 1. Disease-related relevance of marker proteins Disease
Marker proteins
Tumors
Carcino embryonic antigen, CSF IgM, tumor cells Tau-Protein, Beta Amyloid 1-42, Protein 14.3.3, NSE, S-100b Neuronspecic enolase (NSE), S100b (serial analysis in blood) Neuronspecic enolase (NSE) (serial analysis in blood) Polyspecific intrathecal antibodies (M,R,Z-antibody reaction) Erythrophages, Siderophages, Ferritin, Hemoglobin Beta trace Protein
Dementia Brain Hypoxia (9) Head brain trauma Autoimmune disease in brain (16) Hemorrhage Rhinorhea (15)
›increase; fl, decrease; M, not changed; -, negative immunoblot;+, positive immunoblot; AD, Alzheimer¢s disease, DLB, Lewy-body disease; FTD, fronto-temporal dementia; MSA, multiple system atrophy; CJD, Creutzfeldt-Jakob disease. 95% confidence interval. à Mann–Whitney U test with Exact statistical significance (P).
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Table 2. Predominantly brain-derived proteins in CSF regarding the intrathecal fraction (IF) calculated from theoretical molecular size (MW = molecular weight) dependent transfer of the blood derived fraction into CSF Proteins ß-trace protein Cystatin C Tau Protein S-100 B NSE Transthyretin
MW (kDa)
CSF Concentration
CSF:Serum Ratio
IF (%)
25 13.3 55–74 21 78 55(+21)
16.6 mg/L 3.1 mg/L 0.2 lg/L 1.5 lg/L 8 mg/L 17 mg/L
34:1 5:1 10:1 18:1 1:1 1:18
>99 >99 >99 >99 >99 ~90
Table 3. Marker proteins for differential diagnosis of dementive diseases (12). Typical constellation of laboratory findings in neurodegenerative diseases markers diagnosis
First priority totaltau
AD DLB FTD MSA CJD
›› › › › ›››
Ab
1-42
fl Mfl M Mfl Mfl
Second priority phosphotau ›› › › › M›
Ab
1-40
Third priority S-100 CSF
Mfl › Mfl Mfl
›››
Neuro filaments
14-3-3 WB
M
(+) rare +
› ›
References: 1. Reiber H & Peter JB. Cerebrospinal fluid analysis – diseaserelated data patterns and evaluation programs. J Neurol Sci 2001;184:101–122. 2. Reiber H. Basic CSF diagnostic in neuroimmunological diseases. Acta Neuropsychiatrica 2008;20(S1): 9–10. 3. Reiber H, Ungefehr St, Jacobi Chr. The intrathecal, polyspecific and oligoclonal immune response in multiple sclerosis. Multiple Sclerosis 1998;4:111–117. 4. Rostasy K, Reiber H. Clinical and neurochemical characteristics of pediatric Multiple sclerosis – CSF analysis as knowledge base for differential diagnosis and pathopysiology. Acta Neuropsychiatrica, 2009; this issue. 5. Bechter K, Reiber H, Herzog S, Fuchs D, Tumani H, Maxeiner HG. Cerebrospinal fluid analysis in affective and schizophrenic spectrum disorders. Recognition of subgroups with immune responses and blood-CSF barrier dysfunction. J Psych Res 2009; submitted. 6. Reiber H: Dynamics of brain-derived proteins in cerebrospinal fluid. Clin Chim Acta 2001;310:173–186 7. Reiber H. Proteins in cerebrospinal fluid and blood: Barriers, CSF flow rate and source-related dynamics. Restorative Neurology and Neuroscience 2003;21:79–96. 8. Reiber H. Flow rate of cerebrospinal fluid (CSF)- a concept common to normal blood-CSF barrier function and to dysfunction in neurological diseases. J Neurol Sci 1994;122:189–203. 9. Schaarschmidt H, Prange H, Reiber H. Neuron-specific enolase concentrations in blood as a prognostic parameter in cerebrovascular diseases. Stroke 1994;24:558–565. 10. McKhann G, Drachman D, Folstein M, et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDSADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984;34:939–944. 11. Dubois B, Feldman HH, Jacova C, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDSADRDA criteria. Lancet Neurol. 2007;6(8):734–746.
2009 The Authors Journal Compilation 2009 John Wiley & Sons A/S Acta Neuropsychiatrica 2009: 21 (Supplement 2): 1–72
Downloaded from https:/www.cambridge.org/core. Kainan University, on 15 Apr 2017 at 19:29:08, subject to the Cambridge Core terms of use, available at https:/www.cambridge.org/core/terms. https://doi.org/10.1017/S0924270800032749
12. Otto M, Lewczuk P, Wiltfang J. Neurochemical approaches of cerebrospinal fluid diagnostics in neurodegenerative diseases.Methods. 2008;44:289–298. 13. Jesse S, Steinacker P, Lehnert S, Gillardon F, Hengerer B, Otto M. Neurochemical approaches in the laboratory diagnosis of Parkinson’s and Parkinson’s dementia syndromes: A review. CNS Neuroscience and therapeutics 2009;1–26 (online available). 14. Reiber H, Albaum,W. Statistical evaluation of intrathecal protein synthesis in CSF/Serum quotient diagrams. Acta
Neuropsychiatrica 2008;20(S1):48–49. Free download of the CSF Statistics Tool from www.COMED-com.de. 15. Reiber H, Walther K, Althaus H. Betra-trace protein as sensitive marker for CSF Rhinorhea and CSF Otorhea. Acta Neurol Scandinavica 2003;108:359–362. 16. Graef IT, Henze T und Reiber H. Polyspezifische Immunreaktion im ZNS bei Auto-Immunerkrankungen mit ZNS-Beteiligung. Zeitschrift fu¨r a¨rztliche Fortbildung 1994;88: 587–591.
2009 The Authors Journal Compilation 2009 John Wiley & Sons A/S Acta Neuropsychiatrica 2009: 21 (Supplement 2): 1–72
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58
dysfunction) which points to a brain-organic involvement and could modify the possibly wrong diagnosis in 6% of the patients. So CSF analysis must be the base for diagnostic approaches in all neurological and psychiatric diseases as CSF data pattern (1,2) can contribute to differential diagnosis of brain diseases by 1) Supporting suggested diagnosis by disease-related typical data patterns 2) Pointing to unexpected diagnosis by disease-related typical data patterns 3) Exclusion of inflammatory processes 4) Discrimination of chronic from acute diseases 5) Leading to further more specific analytical efforts. The relevance of cerebrospinal fluid and blood analysis offers a most specific and sensitive approach, but needs for reliability sophisticated, knowledge-based data interpretation. In general CSF analysis takes advantage of the intrathecal synthesis of immunoglobulins, increased release or production of marker proteins for tumor metastasis (CEA) or increased release of neuronal or glial proteins in degenerative diseases (Tau, beta amyloid, NSE, S100). In some rare cases the increased release of brain proteins (NSE) can be detected in blood. The basic issue is the difference for a source related interpretation of the proteins in CSF. After an earlier report in this journal (2) on basics in CSF analysis, focusing on blood derived proteins in CSF we focus now on the different approach for the brain-derived proteins (6,7), serving as surrogate markers for differential diagnosis of noninflammatory processes in the brain (Table 1). After reviewing the knowledge base for interpretation of brain-derived proteins we focus on two examples, the dementive and psychiatric diseases. Interpretation of blood- vs. brain-derived proteins in CSF: The understanding of the source-related dynamics of molecules in CSF with their subsequently different variability allows the rational choice between the different evaluation concepts for blood- and brain-derived proteins. Blood-derived proteins: For the primarily blood-derived proteins the discrimination of a brain-derived fraction from a bloodderived fraction in CSF is done with highest sensitivity and specificity by evaluation of CSF /serum quotients with reference to the albumin quotient in a nonlinear hyperbolic function (Fig. 1). This allows to take into account the positive rostro-caudal concentration gradients in CSF and the decreasing CSF flow rate in case of a blood CSF barrier dysfunction (8). The graphical interpretation is done with nonlinear reference ranges in quotient diagrams (Fig. 3) or by the numerical interpretation which also refers to the hyperbolic relation of proteins in CSF (1). Brain-derived proteins: A brain-derived protein in CSF is recognized by one of the following properties (Table 2) including the different source related biological coefficient of variation (CV): 1) CSF concentration > Blood concentration 2) Blood derived fraction CV in CSF) Physiology of brain-derived proteins: Proteins from neurons or glial cells like tau protein, neuron-specific enolase, S-100 protein, all enter CSF primarily in the ventricular and cisternal space. Their
2009 The Authors Journal Compilation 2009 John Wiley & Sons A/S Acta Neuropsychiatrica 2009: 21 (Supplement 2): 1–72
Downloaded from https:/www.cambridge.org/core. Kainan University, on 15 Apr 2017 at 19:29:08, subject to the Cambridge Core terms of use, available at https:/www.cambridge.org/core/terms. https://doi.org/10.1017/S0924270800032749
concentration between normal ventricular and lumbar CSF is decreasing (in contrast to blood-derived proteins), and in the case of pathologically decreasing CSF flow rate, the concentration in lumbar CSF remains invariantly constant (Fig. 2). Concentrations of the primarily leptomeningeal proteins, ß-trace protein and cystatin C, increase between normal ventricular and lumbar CSF. In the case of pathologically decreased CSF flow rate they increase linearly(Fig. 2) in lumbar CSF (concentrations of blood-derived proteins increase nonlinearly, Fig. 1). The predominantly brain-derived proteins in CSF of neuronal or glial origin are evaluated using their absolute concentrations in CSF for maximal sensitivity without reference to QAlb. The leptomeningeal proteins like beta trace or cystatin C are evaluated as absolute concentrations with reference to QAlb. Interpretation of Brain-derived proteins in blood In case of brain hypoxia with a very strong release of Neuronspecific enolase (NSE) into brain tissue the protein reaches high concentrations in blood by direct diffusion from brain tissue into blood capillaries. In contrast Beta trace protein reaches the blood by CSF bulk flow into venous blood. So the pathological increase of NSE and S100 is detectable in blood, e.g. in cases of brain hypoxia or infarction. In particular NSE has a high predictive value for clinical outcome of the patient after brain trauma or hypoxia (9). Relevance of CSF analysis for diagnosis of dementive processes: Alzheimer’s disease (AD), Parkinson¢s disease dementia (PDD)/Lewy-body disease (DLB) and frontotemporal
Fig. 1. CSF/serum quotients as a function of QA1b, a measure of CSF flow rate (7,8).
Fig. 3. CSF data of patients with schizophrenic or affective spectrum disorders in Reibergrams (1). These patients represent a subgroup with inflammatory signs in CSF (9/63) involving 3 cases with intrathecal IgG, 1 case with intrathecal IgA and 3 cases with intrathecal IgM synthesis or other signs of inflammation, like intrathecal antibodies or increased cell count in CSF (5). The graph is created with the CSF statistics tool of COMED (14). Fig. 2. CSF protein concentration as a function of QA1b.
2009 The Authors Journal Compilation 2009 John Wiley & Sons A/S Acta Neuropsychiatrica 2009: 21 (Supplement 2): 1–72
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Downloaded from https:/www.cambridge.org/core. Kainan University, on 15 Apr 2017 at 19:29:08, subject to the Cambridge Core terms of use, available at https:/www.cambridge.org/core/terms. https://doi.org/10.1017/S0924270800032749
dementia (FTD) are the major causes of neurodegenerative memory impairment and dementia. As new therapeutic agents are visible for the different diseases there is an ultimate need for an early differential diagnosis. Until recently the diagnosis of AD was made according to the McKhann criteria (10) in which cerebrospinal fluid findings were used to exclude other diseases. A recent update of the criteria originates from an international consensus group (11). In the last 10 years it was shown that a combined determination of just these few markers (tau-proteins, and abeta-peptides) is already sufficient to achieve a high degree of diagnostic certainty in the diagnosis of AD(12,13). According to the new research criteria low abeta 1–42 and high total–tau protein in CSF are suggested as additional criteria for the diagnosis for AD. As result of this there is a rising need for quality control of CSF samples and determination methods and on the knowledge of typical CSF findings in the various neurodegenerative diseases. Depending on the differential diagnostic question e.g. MCI vs. AD, depressive syndrome vs. AD or AD vs. FTD or AD vs. PDD, the used cut-off values will vary. The typical low abeta 1-42 and high tau-protein finding is especially helpful in the differential diagnosis of a depressive syndrome vs. AD, however for other differential diagnostic questions e.g. AD vs. PDD this profile will not be sufficient to differentiate between the two diseases especially not in the single patient approach (12,13). Typical findings of markers in neurodegenerative diseases are shown in Table 3. New research approaches from direct measurement of oligomers in specific diseases to proteomic expression profiles might come into clinical use (12,13). CSF analysis in psychiatric patients: In a recent study(5) the authors analysed albumin, IgG, IgA, IgM, oligoclonal IgG and specific antibodies in CSF and serum from hospitalised affective and schizophrenic spectrum disorder patients. Numerical and graphical interpretation of CSF protein data was performed in Reibergrams (1) with a new CSF statistics tool for nonlinear group analysis with reference to a large control group (14). As much as 41% of the psychiatric patients investigated (n = 63) had pathological parameters in CSF: 14% intrathecal humoral immune responses, 10% slightly increased CSF cell counts. 24% had moderate increase of the Albuminquotient QAlb (blood-CSF barrier dysfunction) as the only pathological sign with normal IgG, IgA and IgM concentrations in CSF. CSF analysis and interdisciplinary clinical approach helped to rediagnose 6% of psychiatric patients as likely representing a specific virus, streptococcus or autoimmune associated disorder with CNS involvement. Conclusions: CSF analysis is an underestimated diagnostic tool for differential diagnosis in psychiatric and noninflammatory diseases. Table 1. Disease-related relevance of marker proteins Disease
Marker proteins
Tumors
Carcino embryonic antigen, CSF IgM, tumor cells Tau-Protein, Beta Amyloid 1-42, Protein 14.3.3, NSE, S-100b Neuronspecic enolase (NSE), S100b (serial analysis in blood) Neuronspecic enolase (NSE) (serial analysis in blood) Polyspecific intrathecal antibodies (M,R,Z-antibody reaction) Erythrophages, Siderophages, Ferritin, Hemoglobin Beta trace Protein
Dementia Brain Hypoxia (9) Head brain trauma Autoimmune disease in brain (16) Hemorrhage Rhinorhea (15)
›increase; fl, decrease; M, not changed; -, negative immunoblot;+, positive immunoblot; AD, Alzheimer¢s disease, DLB, Lewy-body disease; FTD, fronto-temporal dementia; MSA, multiple system atrophy; CJD, Creutzfeldt-Jakob disease. 95% confidence interval. à Mann–Whitney U test with Exact statistical significance (P).
60
Table 2. Predominantly brain-derived proteins in CSF regarding the intrathecal fraction (IF) calculated from theoretical molecular size (MW = molecular weight) dependent transfer of the blood derived fraction into CSF Proteins ß-trace protein Cystatin C Tau Protein S-100 B NSE Transthyretin
MW (kDa)
CSF Concentration
CSF:Serum Ratio
IF (%)
25 13.3 55–74 21 78 55(+21)
16.6 mg/L 3.1 mg/L 0.2 lg/L 1.5 lg/L 8 mg/L 17 mg/L
34:1 5:1 10:1 18:1 1:1 1:18
>99 >99 >99 >99 >99 ~90
Table 3. Marker proteins for differential diagnosis of dementive diseases (12). Typical constellation of laboratory findings in neurodegenerative diseases markers diagnosis
First priority totaltau
AD DLB FTD MSA CJD
›› › › › ›››
Ab
1-42
fl Mfl M Mfl Mfl
Second priority phosphotau ›› › › › M›
Ab
1-40
Third priority S-100 CSF
Mfl › Mfl Mfl
›››
Neuro filaments
14-3-3 WB
M
(+) rare +
› ›
References: 1. Reiber H & Peter JB. Cerebrospinal fluid analysis – diseaserelated data patterns and evaluation programs. J Neurol Sci 2001;184:101–122. 2. Reiber H. Basic CSF diagnostic in neuroimmunological diseases. Acta Neuropsychiatrica 2008;20(S1): 9–10. 3. Reiber H, Ungefehr St, Jacobi Chr. The intrathecal, polyspecific and oligoclonal immune response in multiple sclerosis. Multiple Sclerosis 1998;4:111–117. 4. Rostasy K, Reiber H. Clinical and neurochemical characteristics of pediatric Multiple sclerosis – CSF analysis as knowledge base for differential diagnosis and pathopysiology. Acta Neuropsychiatrica, 2009; this issue. 5. Bechter K, Reiber H, Herzog S, Fuchs D, Tumani H, Maxeiner HG. Cerebrospinal fluid analysis in affective and schizophrenic spectrum disorders. Recognition of subgroups with immune responses and blood-CSF barrier dysfunction. J Psych Res 2009; submitted. 6. Reiber H: Dynamics of brain-derived proteins in cerebrospinal fluid. Clin Chim Acta 2001;310:173–186 7. Reiber H. Proteins in cerebrospinal fluid and blood: Barriers, CSF flow rate and source-related dynamics. Restorative Neurology and Neuroscience 2003;21:79–96. 8. Reiber H. Flow rate of cerebrospinal fluid (CSF)- a concept common to normal blood-CSF barrier function and to dysfunction in neurological diseases. J Neurol Sci 1994;122:189–203. 9. Schaarschmidt H, Prange H, Reiber H. Neuron-specific enolase concentrations in blood as a prognostic parameter in cerebrovascular diseases. Stroke 1994;24:558–565. 10. McKhann G, Drachman D, Folstein M, et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDSADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984;34:939–944. 11. Dubois B, Feldman HH, Jacova C, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDSADRDA criteria. Lancet Neurol. 2007;6(8):734–746.
2009 The Authors Journal Compilation 2009 John Wiley & Sons A/S Acta Neuropsychiatrica 2009: 21 (Supplement 2): 1–72
Downloaded from https:/www.cambridge.org/core. Kainan University, on 15 Apr 2017 at 19:29:08, subject to the Cambridge Core terms of use, available at https:/www.cambridge.org/core/terms. https://doi.org/10.1017/S0924270800032749
12. Otto M, Lewczuk P, Wiltfang J. Neurochemical approaches of cerebrospinal fluid diagnostics in neurodegenerative diseases.Methods. 2008;44:289–298. 13. Jesse S, Steinacker P, Lehnert S, Gillardon F, Hengerer B, Otto M. Neurochemical approaches in the laboratory diagnosis of Parkinson’s and Parkinson’s dementia syndromes: A review. CNS Neuroscience and therapeutics 2009;1–26 (online available). 14. Reiber H, Albaum,W. Statistical evaluation of intrathecal protein synthesis in CSF/Serum quotient diagrams. Acta
Neuropsychiatrica 2008;20(S1):48–49. Free download of the CSF Statistics Tool from www.COMED-com.de. 15. Reiber H, Walther K, Althaus H. Betra-trace protein as sensitive marker for CSF Rhinorhea and CSF Otorhea. Acta Neurol Scandinavica 2003;108:359–362. 16. Graef IT, Henze T und Reiber H. Polyspezifische Immunreaktion im ZNS bei Auto-Immunerkrankungen mit ZNS-Beteiligung. Zeitschrift fu¨r a¨rztliche Fortbildung 1994;88: 587–591.
2009 The Authors Journal Compilation 2009 John Wiley & Sons A/S Acta Neuropsychiatrica 2009: 21 (Supplement 2): 1–72
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![[Cerebrospinal fluid diagnostics for neuroinfectious diseases].](/assets/img/hold.png)
