Alzheimer’s & Dementia 11 (2015) 1306-1315

Featured Article

CSF biomarkers for the differential diagnosis of Alzheimer’s disease: A large-scale international multicenter study Michael Ewersa,*, Niklas Mattssonb,c, Lennart Minthond,e, Jos
e L. Molinuevof, Anna Antonellf, Julius Poppg,h, Frank Jesseng, Sanna-Kaisa Herukkai, Hilka Soinineni, Walter Maetzlerj,k, Thomas Leyhel, Katharina B€ urgera, Miyako Taniguchim, n o,p p Katsuya Urakami , Simone Lista , Bruno Dubois , Kaj Blennowc, Harald Hampelo,p a

Institute for Stroke and Dementia Research, Klinikum der Universit€at M€unchen, Ludwig-Maximilian-University, Munich, Bayern, Germany b Department of Radiology, University of California San Francisco, San Francisco, CA, USA c Institute of Neuroscience & Physiology, Department of Psychiatry & Neurochemistry, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, M€olndal, Sweden d Clinical Memory Research Unit, Department of Clinical Sciences Malm€o, Lund University, Lund, Sweden e Neuropsychiatric Clinic, Malm€o University Hospital, Malm€o, Sweden f Alzheimer’s disease and other cognitive disorders unit, Neurology Service, ICN Hospital Clinic i Universitari and Pasqual Maragall Foundation, Barcelona, Spain g Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Nordrhein-Westfalen, Germany h Department of Psychiatry, University Hospital of Lausanne, Lausanne, Waadt, Switzerland i Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland j Hertie Institute for Clinical Brain Research, Department of Neurodegeneration, Center of Neurology, University of Tuebingen, Tuebingen, Germany k DZNE, German Center for Neurodegenerative Diseases, Tuebingen, Germany l Department of Psychiatry and Psychotherapy, University Hospital, T€ubingen, Germany m Center of Old Age Psychiatry, Psychiatric University Hospital, Basel, Switzerland n Department of Biological Regulation, School of Health Science, Tottori University Faculty of Medicine, Yonago, Japan o AXA Research Fund & UPMC Chair, Paris, France p Sorbonne Universit
es, Universit
e Pierre et Marie Curie, Paris 06, Institut de la M
emoire et de la Maladie d’Alzheimer & Institut du Cerveau et de la Moelle
epiniere (ICM), D
epartement de Neurologie, H^opital de la Piti
e-Salp
etriere, Paris, France

Abstract

Introduction: The aim of this study was to test the diagnostic value of cerebrospinal fluid (CSF) beta-amyloid (Ab1–42), phosphorylated tau, and total tau (tau) to discriminate Alzheimer’s disease (AD) dementia from other forms of dementia. Methods: A total of 675 CSF samples collected at eight memory clinics were obtained from healthy controls, AD dementia, subjective memory impairment, mild cognitive impairment, vascular dementia, Lewy body dementia (LBD), fronto-temporal dementia (FTD), depression, or other neurological diseases. Results: CSF Ab1–42 showed the best diagnostic accuracy among the CSF biomarkers. At a sensitivity of 85%, the specificity to differentiate AD dementia against other diagnoses ranged from 42% (for LBD, 95% confidence interval or CI 5 32–62) to 77% (for FTD, 95% CI 5 62–90). Discussion: CSF Ab1–42 discriminates AD dementia from FTD, but shows significant overlap with other non-AD forms of dementia, possibly reflecting the underlying mixed pathologies. Ó 2015 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved.

Keywords:

Biomarker; Cerebrospinal fluid; CSF; Differential diagnosis; Classification; Dementia; Alzheimer’s disease; Vascular dementia; Lewy body dementia; Fronto-temporal dementia

*Corresponding author. Tel.: 149-89-4400-46221; Fax: 149-89-440046113.

E-mail address: [email protected]

http://dx.doi.org/10.1016/j.jalz.2014.12.006 1552-5260/Ó 2015 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved.

M. Ewers et al. / Alzheimer’s & Dementia 11 (2015) 1306-1315

1. Introduction

2. Materials and methods

The operational diagnosis of Alzheimer’s disease (AD) dementia is currently undergoing a major change from a fragmented purely clinically defined entity [1] toward a dimensional clinico-biologically informed classification as recently outlined by several expert working groups such as the International Working Group and the National Institute on Aging-Alzheimer’s Association [2–6]. The major biochemical measurement candidates for the definition of such biomarker informed diagnosis of AD include, among others, cerebrospinal fluid (CSF) markers of core AD pathology, that is, beta-amyloid (Ab1–42) to measure the deposition of Ab [7], and CSF total tau (tau) and hyperphosphorylated tau (p-tau181) to assess neurofibrillary tangles and neuronal loss in the brain [8–10]. These biomarkers have been extensively studied in patients with AD dementia and mild cognitive impairment (MCI) [11], and have been widely considered as core feasible candidate biomarkers for the diagnosis of AD dementia at an early stage. However, CSF biomarkers are not yet recommended for the clinical use and are presently restricted to research purposes only. A major current limitation of CSF biomarkers is the uncertainty concerning the reliability of CSF measurement across different clinical centers. Recent multicenter studies suggest that CSF markers may demonstrate substantial variability when the CSF was sampled or analyzed at different centers [12–17]. CSF biomarker measurement has been reported to be sensitive to a variety of factors related to handling and storage of CSF samples [18–20]. Thus, multicenter validation of the reliability of quantitative cut-off points of CSF markers for the diagnosis of AD dementia is an urgent need for the implementation of CSF biomarkers in worldwide clinical diagnostic praxis. A second major outstanding question concerning the utility of CSF biomarkers is their differential diagnostic (classificatory) value. Quantitative CSF cut-off points have been proposed [21–24], including those resulting from postmortem verified cases with AD pathology [21]. Previous studies which assessed the diagnostic accuracy of CSF biomarkers of AD for the differential diagnosis suggested that CSF biomarker levels in AD dementia can substantially overlap with those observed in other forms of dementia [25–28]. However, earlier studies often pooled different non-AD forms of dementia together for comparison with AD dementia [23], or considered only a subset of core candidate biomarkers [27–29]. The major aim of this study was to test the multicenter diagnostic value of the core CSF biomarkers alone and in combination for the differential diagnosis of AD dementia based on (a) a priori previously reported cutoff values and (b) optimized cross-validated cut-off values.

2.1. Subjects

1307

A total of 675 CSF samples, collected at eight expert academic university-based memory clinics between the years 2000 and 2009, were included in this study. Subjects included 55 healthy controls (HC) subjects, 167 patients with AD dementia, 172 subjects with MCI, 22 subjects with subjective memory impairment (SMI), 69 patients with vascular dementia (VD), 26 patients with Lewy body dementia (LBD), 39 patients with fronto-temporal dementia (FTD), 39 patients with depression, and 86 patients with other neurological disorders (OND, see Fig. 1 for flow diagram of sample inclusion in this study and Table 1 for demographic statistics). The MCI subjects were clinically followed up for a mean period of 3.8 years (range 1– 7.4 years). The MCI group included 99 subjects with amnestic MCI who remained stable for a mean of 4.1 years (ranging between 1.1 and 7.4 years), 56 amnestic MCI subjects who converted to AD dementia and mixed AD/VD dementia (pooled MCI-AD converter group) during a period of 3.2 years (ranging between 1 and 6.7 years), and 17 amnestic MCI subjects who converted to dementias other than AD dementia (mean follow-up duration: 4.2 years, ranging between 1.6 and 6.2 years; see Results for further details). The follow-up duration did not differ between the MCI groups (F 5 2.6, P . .05). The OND group included patients with amyotrophic lateral sclerosis (n 5 2), multiple system atrophy (n 5 1), normal pressure hydrocephalus (n 5 25), progressive supranuclear palsy (n 5 4), other neurological syndromes without dementia (n 5 39), Parkinson’s disease dementia (n 5 10), and Parkinson’s disease without dementia (n 5 5). The patients with Parkinson’s disease were included in the OND group as the sample size of that diagnostic group was considered too small to be included as a diagnostic group on its own. The predictive value for the conversion from MCI to AD was not examined in this study but has been described for those CSF samples from MCI subjects who were recruited from Malmoe [30]. The diagnostic criteria for the different forms of dementia [31-36] are described in Supplementary Material. The number of subjects of each diagnosis per center is displayed in Supplementary Table 1. The general inclusion criterion was an age greater than 55 years. The exclusion criterion was evidence of stroke. Although originally the design of the study was such that only MCI subjects with a minimum of 2 years of followup duration were meant to be included in the study, de facto many centers included also MCI subjects who were followed up for less than 2 years. To avoid a drop in the sample size and thus statistical power, MCI subjects with a minimum follow-up duration of 1 year were included as well. However, similar results in terms of diagnostic accuracy were observed for subjects of a minimum of 2 years when

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N = 20 Failed CSF measurement (n = 8) MMSE < 24 in HC (n = 8) Incomplete data (n = 4)

Eligible subjects N = 695 Exclude

CSF biomarkers N = 675 Reference standard

AD N = 167

HC N = 55

SMI N = 22

OND N = 86

MCI N = 172

VD N = 69

LBD N = 26

FTD N = 39

Depr. N = 39

Follow-up Diagnosis

MCI N = 99

AD N = 56

Non-AD N = 17

Fig. 1. Flow diagram showing the stream of cerebrospinal fluid sample inclusion as the index test for the evaluation of the accuracy of the differential diagnosis of Alzheimer’s disease dementia against other types of dementias, mild cognitive impairment or depression.

compared with the results reported for the larger sample of MCI subjects with a minimum of 1-year follow-up (see Results). The control subjects were recruited by several strategies at the different clinics, including relatives of patients or clinical controls (exclusion of vascular events, lumbar disc problems, headache, dizziness). 2.2. CSF biomarker measurement CSF was collected by lumbar puncture (LP) in the L3-4 or L4-5 interspace. No serious adverse events were reported. All CSF samples collected at the different centers were stored in tubes and shipped frozen on dry ice to the central study laboratory of Clinical Neurochemistry of the University of Gothenburg, Sweden (PI Kaj Blennow) where they were stored at 280 C until biomarker measurement at the end of 2009. The CSF concentrations of Ab1–42, p-tau181, and total tau were measured in the CSF samples using the Inno-Bia AlzBio3 kit on the multiplex Luminex xMAP platform (Innogenetics, Zwijndrecht, Belgium) as previously described in detail [37]. Experienced and certified laboratory technicians who were blinded to clinical diagnosis and other clinical information performed the analyses. To ascertain a high analytical performance of the AlzBio3 assay, two internal quality control (QC) samples (aliquots of pooled CSF) were run on each plate. The between-day coefficient of variation for the high and low internal QC samples, analyzed on

13 different Luminex plates were 12.5% and 11.7% for Ab1–42, 8.8% and 6.2% for total tau, and 6.1% and 6.4% for p-tau181. 2.3. Statistics In a first step, all CSF markers were adjusted for age through regression analyses to enable age-independent assessment of the diagnostic value of each CSF biomarker. Gender was not associated with any of the CSF biomarkers when assessed in the HC and AD samples or across the whole sample and was therefore not statistically corrected for. Center effects of each age-adjusted CSF biomarker index (Ab1–42, tau, and p-tau181 and biomarker ratios including tau/Ab1–42 ratio and the p-tau181/Ab1–42 ratio) were assessed in a pooled group of HC and SMI subjects, with center as the independent variable and the CSF biomarker as the dependent variable in Kruskal-Wallis rank sum tests. Next, the diagnostic accuracy of single CSF biomarkers was tested. This was done in three steps including (1) the application of a priori defined cut-offs established in postmortem validated AD dementia patients and living HC subjects, using the same Luminex xMAP technology that was used in this study [21], (2) cut-offs for AD dementia versus HC defined by the Youden method based on the samples in this study, that is, cut-offs chosen to maximize

Abbreviations: CSF, cerebrospinal fluid; HC, healthy controls; AD, Alzheimer’s disease; SMI, subjective memory impairment; OND, other neurological disorders; MCI, mild cognitive impairment; VD, vascular dementia; LBD, Lewy body dementia; FTD, fronto-temporal dementia; f, female; m, male; MMSE, Mini-Mental State Examination; CSF, cerebrospinal fluid; Ab, beta-amyloid; p-tau181, phosphorylation tau.

39 39 69 (56–80) 66 (56–80) 22/17 23 (5–30) 25 (18–29) 260 (94–208) 291 (93–411) 26 (7–95) 26 (7–76) 98 (32–291) 68 (23–128) 0.12 (0.03–0.45) 0.1 (.03–0.82) 0.4 (0.14–161) 0.25 (0.08–1.04) 26 75 (66–75) 11/15 20 (8–28) 203 (120–323) 30 (8–85) 106 (33–373) 0.16 (0.04–0.44) 0.59 (0.1–1.88) 69 75 (61–92) 32/37 22 (6–30) 211 (86–404) 29 (6–96) 87 (14–299) 0.17 (0.03–0.7) 0.5 (0.06–2.65) 17 71 (59–83) 9/8 27 (24–30) 219 (116–342) 33 (9–68) 82 (29–190) 0.17 (0.04–0.44) 0.39 (0.13–1.23) 56 73 (55–87) 18/38 26 (22–30) 158 (78–373) 56 (10–118) 128 (26–324) 0.38 (0.07–0.96) 0.93 (0.14–3.24) 55 74 (55–99) 21/34 29 (26–30) 280 (140–496) 32 (6–82) 93 (32–325) 0.12 (0.04–0.5) 0.37 (0.1–1.2) Sample size Age Gender (m/f) MMSE CSF Ab1–42 CSF p-tau181 CSF Tau CSF p-tau181/Ab1–42 CSF Tau/Ab1–42

167 75 (56–87) 81/86 20 (6–29) 158 (38–454) 53 (12–155) 137 (10–771) 0.38 (0.05–1.18) 0.95 (0.06–5.20)

22 67 (55–81) 15/7 28 (24–30) 265 (134–351) 25 (9–70) 86 (30–401) 0.1 (0.03–0.4) 0.37 (0.11–2.3)

86 72 (55–92) 31/55 25 (9–30) 239 (13–454) 28 (4–91) 68 (8–252) 0.14 (0.02–0.47) 0.37 (0.09–1.54)

99 70 (56–85) 45/54 28 (21–30) 235 (79–373) 35 (11–126) 83 (10–297) 0.17 (0.05–0.72) 0.4 (0.06–1.66)

LBD VD MCI-other MCI-AD MCI stable OND SMI AD HC

Diagnostic group

Measure

Table 1 Descriptive statistics of demographic and CSF biomarker indices by diagnostic group

FTD

Depr

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Y 5 sensitivity 1 specificity 2 100 (in percentage), with a 95% confidence interval (CI) based on the sum of the bootstrapped 95% CI (n 5 2000) of the sensitivity and specificity, and (3) cut-offs to achieve a sensitivity of 85% for AD dementia or prodromal AD, testing the specificity for the discrimination against other types of diagnoses. The best prediction model that included multiple CSF biomarkers was determined in stepwise forward logistic regression models (comparing AD dementia vs. HC) based on the maximum likelihood method, using the likelihood ratio test as the measure of goodness of fit. All analyses were done with the statistical software library R (version 2.12.1, freely available at http://www.r-project.org/) [38,39] or SPSS Version 22. 3. Results Descriptive statistics of the CSF markers and demographic variables for each diagnostic group are displayed in Table 1. 3.1. Multicenter effects on CSF biomarker measurements To assess whether CSF markers including Ab1–42, ptau181, and tau significantly differed between centers, we tested center effects on the age-adjusted biomarker levels in the pooled controls groups including HC and SMI. No significant center effect was detected for any of the age-adjusted CSF markers, including CSF Ab1–42 (Kruskal-Wallis c2 5 8.7, df 5 5, P 5 .12), CSF p-tau181 (Kruskal-Wallis c2 5 4.1, df 5 5, P 5 .53), and CSF tau (Kruskal-Wallis c2 5 2.9, df 5 5, P 5 .71), shown in Fig. 2. 3.2. Diagnostic value and cut-off for each CSF biomarker To test the use of standard cut-off points of the CSF markers across different studies, we compared the diagnostic accuracy of the cut-off points a priori established in postmortem verified AD samples [21], with the cut-off points derived post hoc on the basis of CSF measures of this study for the discrimination between AD dementia and HC subjects. Table 2 displays the sensitivity, specificity, and Youden index of the a priori and the post-hoc determined cut-off values for each CSF biomarker index. The Youden indices of the a priori and post-hoc determined cut-off values were highly similar for all three CSF biomarker measures including Ab1–42 (Youden index 5 61% for a priori cut-off and 68% for post hoc cut-off), p-tau181 (Youden index 5 27% for a priori cut-off and 31% for post-hoc cut-off), and tau (Youden index 5 23% for both cut-offs). The 95% CIs of the Youden indices were overlapping between the a priori and the optimized cut-off values for each CSF biomarker index, indicating that the a priori cutoff values yielded a similar diagnostic accuracy when compared with the Youden-index based cut-off values derived post-hoc from this study samples. Because no agespecific norms have yet been established for CSF biomarkers

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Table 2). The results were not significantly different when compared with adjusted CSF values (Table 2). 3.3. Differential diagnostic accuracy for combinations of CSF biomarkers

Fig. 2. Box plot of age-adjusted cerebrospinal fluid (CSF) levels of betaamyloid (Ab1–42) (A), phosphorylated tau (B), and tau (C) within a pooled group of healthy control (HC) (n 5 55) and subjective memory impairment (SMI) subjects (n 5 22) displayed for each of the six centers who contributed CSF samples to these control groups (centers 5 and 7 did not). For two out of eight centers, no CSF measures for these HC and SMI groups were available.

and clinicians may refer to cut-off values regardless of age, we also computed the cut-off values and diagnostic accuracy for age-unadjusted CSF biomarkers (Supplementary

In the next step, we determined whether the combination of CSF biomarker indices improves on the performance of single CSF biomarker indices. For the comparison between AD dementia and HC, the best model included the combination of CSF Ab1–42 and CSF p-tau181/Ab1–42 ratio, when compared with each single biomarker model. The logistic regression equation for this model was y 5 3.17 2 0.02 * CSF Ab1–42 1 5.9 * CSF p-tau181/CSF Ab1–42. For this combined biomarker model, we evaluated the specificity at sensitivity of 85% to discriminate AD dementia from the other diagnostic groups (cut-off point of predicted probability 5 0.73). The combined model improved numerically the specificity when compared with CSF Ab1–42 alone (cut-off ,202 pg/ml). CSF Ab1-42 was the marker that achieved the highest classification accuracy as a single marker (Table 2). For the discrimination of prodromal AD vs. HC, the logistic regression equation was y 5 1.709 2 0.02 * CSF Ab1–42 1 7.826 * CSF p-tau181/CSF Ab1–42. At a sensitivity of 85% to detect prodromal AD, the cut-off value for CSF Ab1–42 alone was 213 pg/ml, and for the predicted probability derived from the combined model the cut-off was 0.49. The results are displayed in Table 3. For the discrimination of AD dementia vs. VD, the multimarker model showed an improved specificity that reached 59% (95% CI 48–71) compared with the specificity of 46% (95% CI: 35–58%) for CSF Ab1–42 alone. For LBD, the specificity improved from 42% (95% CI: 32–62%) for CSF Ab1– 42 alone to 54% (95% CI: 35–73) for the multimarker model. However, in both cases the 95% CI still overlapped with the 50% boundary, rendering the specificity nonsignificantly different from chance level. Next, we computed the positive predictive value (PPV) and negative predictive value (NPV) value of CSF Ab1–42 and the combined model (i.e., predicted probabilities of the logistic regression model CSF Ab1–42 1 CSF p-tau181/ CSF Ab1–42). For CSF Ab1–42 (cut-off ,220 pg/ml), the PPV was 0.57, and the NPV was 0.84. When assuming the proportion of cases with AD dementia among elderly demented subjects to be 70%, the PPV was 0.81 and the NPV was 0.63. For the predicted probabilities of the combined model (cut-off .0.73), the PPV was 0.66 and the NPV was 0.86. When taking the prevalence of AD among subjects with dementia into account, the PPV of the combined model was 0.86 and the NPV was 0.67. To test how the CSF Ab1–42 and the multimarker model separate prodromal AD from the other diagnosis, we computed the specificities at the fixed sensitivity of 85% to separate those MCI subjects who converted to AD dementia during the follow-up period (prodromal AD) from the other groups. The 95% CI of the specificities overlapped when

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Table 2 Sensitivity, specificity, and Youden Index (95% CI) for the post-hoc (in current sample) and a priori (previously published) [21] cut-off values of each biomarker applied to the comparison of AD vs. HC CSF index

Ab1–42

Type of cut-off

Current sample

A priori

Current sample

A priori

Current sample

Cut-off (pg/ml) Sensitivity (%) Specificity (%) Youden Index (%)

187 79 (72–85) 89 (80–96) 68 (52–81)

192 81 (75–87) 80 (69–89) 61 (44–76)

51 47 (40–54) 84 (73–93) 31 (13–47)

23 85 (80–90) 42 (29–55) 27 (9–45)

114 47 (40–56) 76 (65–87) 23 (5–43)

p-tau181

T-tau (pg/ml)

CSF p-tau181/Ab1–42

CSF tau/Ab1–42

A priori

Current sample

A priori

Current sample

A priori

93 59 (51–65) 64 (51–75) 23 (2–40)

0.24 68 (61–75) 93 (85–98) 61 (46–73)

0.1 90 (85–94) 47 (35–60) 37 (32–54)

0.59 60 (53–68) 87 (78–95) 47 (31–63)

0.39 75 (69–81) 65 (53–78) 40 (22–59)

Abbreviations: AD, Alzheimer’s disease; Ab, beta-amyloid; HC, healthy controls; CSF, cerebrospinal fluid; p-tau181, phosphorylation tau.

using AD dementia or, alternatively, prodromal AD as the target group (Table 3). 4. Discussion The major findings of this study are: (1) no significant multicenter variability in the core CSF biomarkers was detected when assessed in the control group, (2) a priori determined cut-off values and post-hoc derived cut-off values of single CSF markers showed comparable diagnostic accuracies for the AD vs. HC differentiation, and (3) CSF Ab1–42 alone or in combination with the CSF p-tau181/ Ab1–42 ratio could distinguish FTD from AD dementia but showed a substantial overlap between AD dementia and other forms of dementia including LBD and VD. The current results on the multicenter variability of CSF measures in HC and SMI subjects suggest that there are no significant center effects of CSF markers. The fact that the measurements of the CSF biomarkers were conducted at a central laboratory may have reduced methodological variance and multicenter variability considerably. Similar to the design of our current multicenter initiative, previous multicenter investigations including the “Alzheimer’s Disease Neuroimaging Initiative” (ADNI) [40] and the “Development of screening guidelines and criteria for predementia Alzheimer’s disease” (DESCRIPA) study [14] used a study design where

CSF is obtained at different centers but analyzed at a single central expert laboratory. Both multicenter studies reported good diagnostic accuracy for the distinction between AD and HC. A large recent multicenter study reported a significant multicenter CSF variability in the AD vs. HC comparison [12]. Notably, CSF measurement variability was considerably larger when multicenter CSF samples were analyzed at different laboratories rather than a single laboratory [12]. Systematic investigations of CSF measurement variability among laboratories, using a common CSF pool analyzed at different laboratories, have shown that the interlaboratory coefficient of variation fluctuated up to 30% depending on the CSF biomarker [41]. This suggests that laboratory-specific measurement variability may contribute a substantial portion of multicenter variability that may be reduced when using expert centralized single-laboratory CSF analysis [19,41]. However, measurement variability stemming from circumstances such as the use of different types of testtubes, or differences in handling and storage at different sites may still remain a significant source of variability [19]. To monitor the analytical variability and address the influence of potential confounding factors, international consortia–including the Alzheimer’s Association quality control (QC) program for CSF biomarkers–have been established (for review see [42]). Standard operating procedures for LP, CSF handling/analysis have been

Table 3 Bootstrapped median percentage of specificity (95% CI) for the distinction of AD dementia or prodromal AD against each of the other diagnostic groups, estimated at a fixed sensitivity of 85% AD dementia

Prodromal AD

Reference vs. control group

Ab1–42

Multimarker model

Ab1–42

Multimarker Model

HC SMI OND VD LBD FTD Depression

76 (64–87) 82 (64–95) 66 (56–76) 46 (35–58) 42 (32–62) 77 (62–90) 87 (74–97)

85 (75–95) 86 (73–100) 70 (59–79) 59 (48–71) 54 (35–73) 79 (67–92) 95 (87–100)

75 (64–85) 77 (59–95) 59 (49–77) 39 (29–51) 35 (15–54) 74 (62–86) 85 (72–75)

89 (80–96) 90 (77–100) 73 (64–83) 61 (49–73) 54 (35–73) 79 (67–92) 95 (87–100)

Abbreviations: AD, Alzheimer’s disease; HC, healthy controls; SMI, subjective memory impairment; OND, other neurological disorders; VD, vascular dementia; LBD, Lewy body dementia; FTD, fronto-temporal dementia. NOTE. Predictors were either a single biomarker (beta-amyloid or Ab1–42) and the multi-marker prediction model (cerebrospinal fluid Ab1–42 1 p-tau181/ Ab1–42). Cut-off values were determined separately to reach a sensitivity of 85% to detect either AD dementia or prodromal AD.

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proposed [16,43,44] to reduce the measurement variability to levels seen for other commonly used biochemical markers, such as cholesterol or troponin, which would be valuable for the clinical utility of CSF biomarkers in dementia diagnosis. For the reliability of CSF cut-off values to distinguish AD dementia from HC, we found similar diagnostic accuracy when compared between the previously derived a priori cut-off value [21] and the post hoc cut-off value for CSF Ab1–42. The absolute values of the a priori cut-off value of CSF Ab1–42 as determined by Shaw et al. [21] (i.e., 192 pg/ml) and the current post-hoc determined cut-off value were comparable, suggesting a convergence of cutoff values of Ab1–42 across studies for the identification of AD dementia. For the CSF p-tau181 and tau measures, relatively large difference were observed between the a priori and post hoc determined optimal cut-off values. Note, however, that the overall classification accuracy as measured by the Youden index was comparable between the a priori and post-hoc determined cut-off values, although the sensitivity and specificity seemed sometimes quite variable or even flipped when compared between both cut-off values. This is because the Youden index does not favor sensitivity or specificity but just reflects the overall classification accuracy. Therefore, we also presented the performance of the biomarkers at cut-off values for a fixed sensitivity of 85% to better appreciate that variability in specificity between biomarkers. In summary, these results suggest that the standardization of cut-off points remains a crucial issue in future studies [45], likely requiring guidelines for uniform ways of collecting and handling CSF samples as is currently addressed in the Alzheimer’s Association QC program for CSF biomarkers [16]. Our results on the differential diagnostic utility of core feasible hypothesis-based CSF biomarkers demonstrated that the combination of CSF p-tau181/Ab1–42 ratio was the model showing the best diagnostic accuracy for the discrimination between AD-type dementia and other types or causes of a dementia syndrome. Such a combination of CSF biomarkers improved the specificity of the best single CSF biomarker including CSF Ab1–42 and showed excellent diagnostic specificity for the discrimination between AD dementia and SMI or depression. It should be noted, however, that in this study we found only a numeric advantage of the combined Ab plus p-tau/Ab ratio when compared with Ab alone for distinguishing different clinically diagnosed types of dementia. The results were similar when testing the discrimination between prodromal AD (MCI-AD progressors) vs. the other diagnostic groups, suggesting generalizability of the differential diagnostic performance of the core feasible CSF biomarkers toward the early prodromal stage of AD. Although the differentiation of AD dementia against SMI or depression was within a clinically useful range, low specificity was observed for the differentiation between AD dementia and some other types of dementia. This is unlikely to be majorly due to multicenter variability because simi-

larly to our multicenter study, a recent large monocenter study reported also that 47% of LBD patients showed CSF profile of AD [25]. A CSF biomarker profile of AD dementia was also found in up to 50% of VD patients in a previous study [29], and few studies have reported a somewhat better specificity of CSF biomarkers of AD [25]. Rather, the mixed results on the differential diagnosis for condition such as LBD and VD may reflect converging pathophysiological mechanisms and pathways at this late clinical stage. Neuropathological and neuroimaging studies have revealed Ab and tau-pathology in LBD patients [46,47]. For VD, recent amyloid positron emission tomography (PET) imaging studies reported AD-like levels of Ab deposition in the brains of 30% of patients with subcortical VD [48]. These results suggest that CSF Ab1–42 may detect such increased levels of Ab pathology in these other clinically diagnosed forms of dementia, limiting the differential diagnostic value of such biomarkers, and potentially reflecting the dynamic overlap in the etiology of different types of biologically late-stage neurodegenerative and brain diseases with associated dementia syndromes [49]. In that sense, the future introduction of additional CSF biomarkers, such as a-synuclein, will be of value to optimize the differential diagnosis of primary degenerative dementias. Previous CSF studies in cases with post-mortem confirmed that diagnosis have demonstrated that CSF markers show a distinct profile of relatively higher Ab1–42 but lower p-tau181 and tau levels in frontotemporal lobar degeneration (FTLD) compared with AD dementia [50,51]. The diagnostic accuracy of CSF Ab1–42 and CSF p-tau181 obtained in living subjects, showed excellent diagnostic accuracy (95%) for distinguishing post-mortem confirmed FTLD vs. AD dementia [50]. When discriminating the clinical diagnosis of AD dementia vs. FTD, the diagnostic accuracy of the best model (CSF tau and p-tau181) dropped to below 80% [50] comparable with the current results. Together, these results suggest that current clinical diagnostic categories do not adequately reflect the underlying mixed pathology in a substantial portion of subjects. Future studies, therefore, are needed to refine diagnostic classification and biomarkers to match dementia symptoms to underlying mixed profiles of pathologies. A combination of different types of CSF biomarkers such as CSF Ab1–42 and the ratio of CSF p-tau/Ab1–42 could be especially beneficial—compared with unitary CSF biomarkers— for identifying diverse pathological profiles, which could be assessed in post-mortem confirmed diagnosis. For the interpretation of the current results, some caveats should be taken into account. The limited diagnostic accuracy of the CSF markers reported here may to some degree reflect the variability of the accuracy of the clinically diagnosis (reference standard). Confirmation of the diagnosis in post-mortem autopsy studies is surely of value to further test the diagnostic accuracy of the current CSF biomarker candidates. Multicenter variability may have been due to biological variability, preanalytical (handling and storage of samples), analytical (assay measurement), or other

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unidentified sources of bias. An analysis of specific preanalytical procedures of handling and storage rather than “center” as potential sources of variability in CSF measurements may have been a more sensitive way to explain nondisease related variability in CSF measurements. Thus, a systematic identification of methods of CSF measurement variability is desirable to control specific major factors of CSF measurement alterations in future studies. An analysis of specific preanalytical procedures of handling and storage rather than “center” as potential sources of variability in CSF measurements may have been a more sensitive way to explain nondisease related variability in CSF measurements. Acknowledgments JLM has provided scientific advice or has been an investigator or data monitoring board member for consultancy fees from Pfizer, Eisai, MSD, Merz, Janssen-Cilag, Novartis, Lundbeck, Roche, Bayer, Bristol-Myers Squibb, GE Health Care, Avid, GlaxoSmithKline, and Fujirebio-Europe (previously Innogenetics). HS and SKH were supported by University of Eastern Finland for UEFBRAIN consortium and EVO funding 5772708. WM receives funding from the European Union (SENSE-PARK, no. 288557, 2011-2014; Moving beyond, no. 316639, 2012-2016) and from the local University. He received funding from the Robert Bosch foundation (2008-2011) and speaker honoraria from GlaxoSmithKline (2011). HH is supported by the AXA Research Fund, the Fondation Universit
e Pierre et Marie Curie and the Fondation pour la Recherche sur Alzheimer, Paris, France. The research leading to these results has received funding from the program “Investissements d’avenir” ANR-10IAIHU-06. HH was supportd by grants of the KatharinaHardt-Foundation, Bad Homburg, Germany. HH declares no competing financial interests related to the present article. During the last 36 months he has received lecture honoraria and/or research grants and/or travel funding and/or participated in scientific advisory boards and/or as a consultant to diagnostic, biotechnology and pharmaceutical companies involved in the manufacture and marketing of biomarkers and/or diagnostics and/or drugs or medicinal products for cognitive impairment and Alzheimer’s disease including Boehringer-Ingelheim, Bristol-Myers Squibb, Elan Corporation, Novartis, Eisai Inc., Pfizer, Sanofi-Aventis, Roche Pharmaceuticals and Diagnostics, GE Healthcare, Avid, Eli Lilly and Company, GlaxoSmithKline-Biologicals, Jung-Diagnostics, Cytox. He is co-inventor in pending patent submissions relating to biological markers and/or diagnostics and has not received any royalties. BD reports personal fees from Eli Lilly and grants from Pfizer and Roche. All other authors report nothing to disclose and no conflicts of interest. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jalz.2014.12.006.

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RESEARCH IN CONTEXT

1. Systematic review: The research diagnosis of AD that was recently proposed by a joint NIA and Alzheimer’s Association work group incorporates biomarker-based measures of AD-specific brain changes in addition to clinical/neuropsychological assessment. Among the major biomarker candidates are CSF Ab1–42, total tau, and p-tau. The clinical diagnostic utility of such biomarkers depends on both the sensitivity to detect AD dementia and the specificity to differentiate AD-type impairment from other types of dementia. 2. Interpretation: The current results of multicenter assessed CSF measures suggest limited utility of these biomarkers to discriminate AD dementia from a variety of other types of dementias. On a more general note, the overlap in CSF biomarker profiles between different types of dementia likely reflects an overlap in the primary pathologies. 3 Future directions: A multidimensional classification scheme that recognizes the contribution of specific pathologies to different subtypes of clinical syndromes may be ultimately necessary to replace the current broad diagnostic categories of psychiatric diseases.

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