Benchmarking biomarker-based criteria for Alzheimer's disease: Data from the Swedish Dementia Registry, SveDem

Alzheimer’s & Dementia - (2015) 1-10

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Benchmarking biomarker-based criteria for Alzheimer’s disease: Data from the Swedish Dementia Registry, SveDem Christoffer Ros
ena,*, Bahman Farahmandb,c, Tobias Skillb€acka, Katarina N€aggad, Niklas Mattssona,e, Lena Kilanderf, Dorota Religac,g, Anders Wimog, Kaj Blennowa, Bengt Winbladc,g, Henrik Zetterberga,h, Maria Eriksdotterb,c a

Clinical Neurochemistry Laboratory, Department of Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, M€olndal, Sweden b Center for Alzheimer Research, Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska Institutet, Stockholm, Sweden c Department of Geriatric Medicine, Karolinska University Hospital, Huddinge, Sweden d Clinical Memory Research Unit, Department of Clinical Sciences Malm€o, Lund University, Malm€o, Sweden e Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, University of California San Francisco, San Francisco, CA, USA f Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala, Sweden g Center for Alzheimer Research, Division for Neurogeriatrics, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska Institutet, Huddinge, Sweden h Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK

Abstract

Introduction: New research guidelines for the diagnosis of Alzheimer’s disease (AD) include biomarker evidence of amyloid-b (Ab) and tau pathology. The aim of this study was to investigate what proportion of AD patients diagnosed in clinical routine in Sweden that had an AD-indicative cerebrospinal fluid (CSF) biomarker profile. Methods: By cross-referencing a laboratory database with the Swedish Dementia Registry (SveDem), 2357 patients with data on CSF Ab and tau biomarkers and a clinical diagnosis of AD with dementia were acquired. Results: Altogether, 77.2% had pathologic Ab42 and total tau or phosphorylated tau in CSF. These results were stable across age groups. Female sex and low mini-mental state examination score increased the likelihood of pathologic biomarkers. Conclusion: About a quarter of clinically diagnosed AD patients did not have an AD-indicative CSF biomarker profile. This discrepancy may partly reflect incorrect (false positive) clinical diagnosis or a lack in sensitivity of the biomarker assays. Ó 2015 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved.

Keywords:

Alzheimer’s disease; Diagnostic criteria; Diagnosis; Cerebrospinal fluid; Biomarkers

1. Background Patients with Alzheimer’s disease (AD) are routinely diagnosed by clinical criteria, commonly the International Classification of Diseases (ICD-10) or the National

The authors report no conflicts of interest. *Corresponding author. Tel.: 146-0708-540533; Fax: 146-3141-9289. E-mail address: [email protected]

Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria [1,2]. However, the “gold standard” diagnostic method for AD is to perform an autopsy to find neuropathologic AD changes at a sufficient degree to confer a diagnosis. These hallmarks include extracellular senile plaques, consisting of amyloid-b (Ab) protein, intracellular neurofibrillary tangles, consisting of phosphorylated tau (P-tau) protein,

http://dx.doi.org/10.1016/j.jalz.2015.04.007 1552-5260/Ó 2015 The Alzheimer’s Association. Published by Elsevier Inc. All rights reserved.

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and loss of neurons with gross atrophy [3]. These pathologies can also be estimated in living patients using biomarkers, including magnetic resonance imaging (MRI) measurements of hippocampal atrophy, positron emission tomography (PET) measurements (of metabolism and Ab pathology), and cerebrospinal fluid (CSF; Ab42, P-tau, and total tau [T-tau]) analyses [3]. Most of clinically diagnosed AD patients have a CSF profile with lower levels of Ab42 and higher levels of P-tau and T-tau compared with cognitively normal controls [4]. Updated diagnostic guidelines for research have therefore included CSF biomarkers to provide in vivo evidence of AD neuropathology [5–8]. CSF biomarkers are also increasingly used in clinical practice in several countries. It has been shown that clinicians are influenced by the results of CSF AD biomarkers when diagnosing AD patients [9]. However, more research is needed before the biomarkers are implemented in clinical diagnostic criteria [6]. The recently published updated diagnostic criteria from the International Working Group (IWG-2) for New Research Criteria for the Diagnosis of AD propose that AD can be diagnosed when the specific clinical phenotype of episodic memory impairment (or variant phenotypes) is present together with low levels of CSF Ab42 and high levels of either Ttau or P-tau [10]. In this article, we operationalize biomarker-informed AD criteria by examining CSF biomarker data in a large set of clinically diagnosed AD patients in the Swedish Dementia Registry (SveDem). Specifically, we (1) quantified the proportion of patients who had an AD-indicative CSF biomarker panel with pathologic Ab42 and T-tau or P-tau, (2) quantified the proportions of patients who were positive for other combinations of biomarkers (i.e., single biomarkers, Ab42 and T-tau, or Ab42 and P-tau) and investigated if these results were different in patients that were lumbar punctured after diagnosis, (3) tested for differences in biomarker positivity between early-onset AD (EOAD), late-onset AD (LOAD), and different age groups within LOAD, (4) investigated the effects of sex, age, and mini-mental state examination (MMSE) score as predictors of a pathologic biomarker profile, and (5) used the coefficient of variation (CV) of the laboratory assays to investigate the uncertainties in the biomarker results in a separate analysis of the proportion of patients fulfilling the biomarker criteria.

2. Methods 2.1. Patient group For this study, we extracted available data from measurements in clinical practice of CSF P-tau, T-tau, and Ab42 from the local laboratory database at the Sahlgrenska University Hospital, between January 1, 2005 and June 7, 2012. The data was cross-referenced with the SveDem registry by the use of the patients’ unique personal identification numbers, which resulted in a cohort of 2357 patients with AD (345

with EOAD, defined as age of onset less than 65 years, and 2012 with LOAD) with CSF biomarker data. The SveDem registry started in 2007 and is a national quality registry for improvement of the quality of diagnostic workup, treatment, and care of patients with dementia in Sweden. Newly diagnosed dementia patients enter the registry. Diagnosis and other characteristics, including demographic factors, diagnostic procedures and MMSE scores, are recorded and updated every year. The coverage is about 95% of specialized memory clinics and about 70% of primary health care units [11]. Patients with EOAD and LOAD are diagnosed according to the ICD-10 [1]. ICD codes used were F000 (EOAD) and G300 (LOAD). The ICD-10 guidelines do not include CSF biomarkers for the diagnosis of EOAD or LOAD. It is not known whether the EOAD patients in the study have sporadic or familial AD, but among EOAD, autosomal dominant transmission is only apparent in around 10% of the cases [12]. The diagnostics in SveDem have been described in detail before [13,14]. Most of the patients in the study were diagnosed at memory clinics; however, 4.7% were diagnosed at specialized primary health care units. Cases where CSF sampling was done more than 1 year before or after clinical diagnosis were excluded (n 5 245) and not used in any analysis, to avoid bias caused by dynamic changes in CSF biomarker levels [15,16]. Demographics and biomarker levels are summarized in Table 1. 2.2. CSF biomarkers The Clinical Neurochemistry Laboratory at Sahlgrenska University Hospital performs analysis of the biomarkers Ab42, T-tau, and P-tau in clinical practice since the beginning of the 2000s. The laboratory analyzes CSF samples from patients undergoing investigation for dementia from all over Sweden. The concentrations of Ab42, P-tau, and T-tau are measured using INNOTEST enzyme-linked immunosorbent assays, as previously described [17–19]. The same measurement techniques were used during the entire study period. The test results are stored in a local laboratory database. A description of the longitudinal stability of the biomarker assays can be found in Appendix A. The procedures for maintaining long-term stability of the biomarker measurements have been described before [20]. The following cutoffs for pathologic biomarker levels were used: Ab42 ,550 pg/mL, T-tau .350 pg/mL, and P-tau .60 pg/mL. These were the optimal cut points to differentiate AD from other disorders and cognitively normal controls in an earlier study from our group [21]. By accounting for the CVs of the biomarker assays, reference limits for a gray zone with uncertain biomarker results were calculated by applying a 95% confidence interval (CI) around the biomarker cutoffs. This was done to investigate the uncertainties in the biomarker measurements and specifies what values that can be said to be above or below the cut point with certainty. Values within this CI are in the gray zone. The gray zone limits were 424–636 pg/mL

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Table 1 Demographics and biomarker levels of AD patients EOAD

LOAD

Variable

,65 y

65–74 y

75–84 y


85 y

Overall

Sex, F/M Age (y), mean (std) MMSE, median (IQR) Aß42 (pg/mL), median (IQR) T-tau (pg/mL), median (IQR) P-tau (pg/mL), median (IQR)

217/128 60 (4) 23* (6) 360 (170) 663 (440) 89 (43)

561/318 70 (3) 24y (6) 360 (167) 670 (450) 89 (44)

601/351 79 (3) 23y (6) 363 (172) 670 (400) 93 (39)

117/64 87 (2) 22 (6) 380 (185) 646 (419) 89 (42)

1496/861 73 (8) 23 (6) 360 (169) 670 (430) 91 (42)

Abbreviations: AD, Alzheimer’s disease; EOAD, early-onset Alzheimer’s disease; LOAD, late-onset Alzheimer’s disease; F, females; M, males; std, standard deviation; MMSE, mini-mental state examination; IQR, interquartile range; Ab42, amyloid b 1-42; T-tau, total tau; P-tau, phosphorylated tau. *P , .001 versus LOAD .84 years. y P , .0001 versus LOAD .84 years.

for Ab42, 280–420 pg/mL for T-tau, and 48–72 pg/mL for Ptau. The procedure for calculating these reference limits is shown in Appendix B.

Foundation for Statistical Computing, Vienna, Austria [http://www.R-project.org/]). 3. Results

2.3. Statistics Biomarkers were used alone and in prespecified combinations. The tested combinations were positive Ab42 and T-tau, positive Ab42 and P-tau, and the AD-indicative combination with positive Ab42 and either T-tau or P-tau, as suggested by the CSF IWG-2 criteria. For the combinations of Ab42 with Ttau or Ab42 with P-tau, individuals with pathologic levels on only one biomarker were classified as normal. For the ADindicative combination, individuals had to be positive on both CSF Ab42 and a tau biomarker (CSF T-tau and/or Ptau) to be classified as pathologic. Similar analyses were also performed accounting for the gray zone limits of the biomarkers. The definitions of the biomarker groups in this analysis are given in Appendix B. We tested for differences in biomarker levels between diagnostic groups (EOAD and LOAD) and between different age groups within LOAD (65–74 years, 75–84 years, and
85 years). Group comparisons of single biomarkers and MMSE (as continuous data) were made using Kruskal-Wallis test, followed by the Mann-Whitney U test for two-group comparisons when relevant because the data were skewed. No corrections for multiple comparisons were used. The c2 test was used to investigate relationships between diagnostic groups (EOAD vs. LOAD, or age groups within LOAD) and biomarker profiles (dichotomous data, pathologic vs. normal). Logistic regression was used to simultaneously model the effects of patient group, MMSE, age, and sex as predictors of a pathologic biomarker profile. MMSE was included both as a categorical and continuous variable. The categories were divided using the cutoffs of MMSE ,15, 15–19, 20–24 and .24 and consisted of 160, 406, 866, and 925 individuals, respectively. Correlations were computed with the Spearman correlation coefficient test. A P-value ,.05 was considered statistically significant. Statistics and graphs were made using SAS 9.3 (SAS Institute Inc, Cary, NC, USA) and R, version 3.1.0 (R

Study demographics are listed in Table 1. There were no significant differences between the groups (EOAD vs. LOAD and different LOAD age groups) in CSF biomarker levels. LOAD patients aged .84 years had significantly lower MMSE than EOAD patients and the other LOAD groups. There were no overall significant differences in biomarker levels between men and women and no correlations between CSF biomarkers and age. MMSE score was weakly correlated with age (r 5 20.07, P , .001), Ab42 (r 5 0.09, P , .001), T-tau (r 5 20.1, P , .001), and Ptau (r 5 20.04, P , .05) with lower MMSE score in people who were older, had low CSF Ab42, high CSF T-tau, or high CSF P-tau. The correlations between biomarkers and MMSE are shown in Fig. 1. There was a strong correlation between T-tau and P-tau (r 5 0.88, P , .001), but no significant correlations between Ab42 and T-tau or P-tau (data not shown). 3.1. CSF biomarker profiles The proportions of patients with pathologic biomarker profiles based on the prespecified cutoffs in the overall population, and among EOAD patients and in the LOAD groups are shown in Fig. 2A and 3, respectively. In the overall population, 84.8% had pathologic levels of Ab42, 89.5% had pathologic levels of T-tau, and 84.7% had pathologic levels of P-tau. When combining biomarkers, 76.7% had a pathologic profile of Ab42 and T-tau, 72.7% had a pathologic profile of Ab42 and P-tau, and 77.2% of the patients had a positive AD-indicative profile. Among the LOAD patients aged 65–74 years, a significantly lower proportion of patients had pathologic T-tau levels compared with the patients aged 75–84 and
85 years, and a significantly lower proportion had pathologic P-tau levels compared with LOAD patients aged 75–84 years. Otherwise, the proportions of subjects with pathologic biomarker profiles were overall similar across EOAD and the LOAD groups.

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Fig. 1. Scatter plots showing the relationship between MMSE and biomarker levels. The red line shows the linear regression of the relationship. The spearman rank correlation test showed significant correlations between MMSE and all three biomarkers: Ab42 (r 5 0.09, P , .001), T-tau (r 5 20.1, P , .001), and P-tau (r 5 20.04, P , .05). Abbreviations: MMSE, mini-mental state examination; Aß42, amyloid ß 1-42; T-tau, total tau; P-tau, phosphorylated tau.

3.2. Gray zone values due to the CVs of the biomarker assays The overall proportion of individuals classified with pathologic, gray zone, and normal biomarker levels when accounting for the CVs of the biomarker assays are given in Fig. 2B. For the AD-indicative profile, these proportions are 57.3%, 32.5%, and 10.2%, respectively.

3.3. Impact of time of lumbar puncture A total of 142 AD patients (24 with EOAD and 118 with LOAD) patients underwent lumbar puncture after or at the same date as the clinical diagnosis was made. This group had significantly lower proportions of pathologic biomarker levels than 2215 AD patients (321 with EOAD and 1894 with LOAD) undergoing lumbar puncture before the date of clinical diagnosis. These findings are presented in Fig. 4. 3.4. Logistic regression

Fig. 2. Bar plot showing percentages with pathologic biomarkers in the overall population (A). Bar plot showing percentages with pathologic biomarkers, where the CVs of the biomarker assays have been accounted for (B). The gray zone is defined as 95% CI around the biomarker cutoff. The term “normal” includes all individuals who are not pathologic for the given biomarker or biomarker combination. Abbreviations: CV, coefficient of variation; CI, confidence interval; Aß42, amyloid ß 1-42; T-tau, total tau; P-tau, phosphorylated tau; AD, Alzheimer’s disease.

Logistic regression was used to simultaneously test different predictors (age, sex, and MMSE) of pathologic CSF profiles (Table 2). One logistic regression model was built for each CSF biomarker or combination (6 models in total). In these analyses, EOAD, male sex, and high MMSE (.24) were the reference categories. Female sex increased the risk of having pathologic levels in most models. For example, for the AD-indicative profile female sex had an odds ratio of 1.35 (P 5 .0009, females had 35% greater odds of having a pathologic AD-indicative biomarker profile compared to men). Age had no significant effect on the likelihood of pathologic biomarkers. Lower MMSE scores were associated with higher risk of pathologic levels in most models (models for Ab42, Ab42 and P-tau, Ab42 and T-tau, and the AD-indicative profile). For example, for the AD-indicative profile MMSE ,15 had an odds ratio of 2.06 (the odds for a pathologic AD-indicative profile was increased with 106% compared with patients with MMSE .24). We also tested a logistic regression model with

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Fig. 3. Bar plots showing percentages with pathologic biomarkers in (A) EOAD patients, (B) LOAD patients aged 65–74 years, (C) LOAD patients aged 75– 84 years, and (D) LOAD patients aged
85 years. The term “normal” includes all individuals who are not pathologic for the given biomarker or biomarker combination. Abbreviations: EOAD, early-onset Alzheimer’s disease; LOAD, late-onset Alzheimer’s disease; Aß42, amyloid ß 1-42; T-tau, total tau; P-tau, phosphorylated tau; AD, Alzheimer’s disease.

MMSE as a continuous predictor, as opposed to categories of MMSE ranges. This reduced the Akaike information criterion (AIC) compared with using categorical MMSE, delta AIC 5 4, suggesting that the model fit was improved [22]. In the model with continuous MMSE, a one point increase in MMSE was associated with an odds ratio of 0.96 (P 5 .0001) for a pathologic AD-indicative profile (the

odds for having a pathologic AD-indicative biomarker profile increased by 4% for each point lower MMSE score). Furthermore, when the four MMSE groups were used as a continuous variable in the logistic regression, an increase in MMSE group (i.e., from a group with lower MMSE to a group with higher MMSE) was associated with an odds ratio of 0.83 (P 5 .0008). 4. Discussion

Fig. 4. Bar plots showing percentages with pathologic biomarkers in (A) patients who were lumbar punctured before diagnosis (n 5 142), and (B) patients who were lumbar punctured after or at the same date as diagnosis was made (n 5 2215). The term “normal” includes all individuals who are not pathologic for the given biomarker or biomarker combination. All data are included (both EOAD and LOAD). Abbreviations: Aß42, amyloid ß 142; AD, Alzheimer’s disease; T-tau, total tau; P-tau, phosphorylated tau; AD, Alzheimer’s disease.

We have evaluated CSF biomarkers for in vivo evidence of AD neuropathology in 2357 clinically diagnosed AD dementia patients who underwent CSF sampling in clinical practice. The major finding was that the AD-indicative biomarker profile with positivity for Ab42 and T-tau or P-tau was fulfilled only by 77.2% of the clinically diagnosed AD patients. Using a combination of positive Ab42 and T-tau, 76.7% were classified as pathologic, whereas 72.7% were pathologic when using a combination of Ab42 and P-tau. When accounting for variability of the biomarker assays, 32.5% had biomarker results in the gray zone for the AD-indicative profile. Our second major finding was that the CSF biomarkers and the proportions of subjects with pathologic biomarker profiles were overall similar across the study groups (EOAD and LOAD divided in three different age groups). Our third major finding was that in a logistic regression model, female sex and lower MMSE were associated with higher likelihood of pathologic biomarker profiles. Our first major finding was that about a quarter of the subjects did not fulfill the AD-indicative biomarker combination for AD. This is in line with previous clinicopathologic

Abbreviations: Ab42, amyloid b 1-42; P-tau, phosphorylated tau; T-tau, total tau; AD, Alzheimer’s disease; OR, odds ratio; CI, confidence interval; MMSE, mini-mental state examination. NOTE. The models included all data (both EOAD and LOAD). Reference for age is ,65 years. Reference for MMSE is MMSE .24.

1.353 (1.111–1.649) 0.831 (0.616–1.121) 0.995 (0.737–1.343) 1.070 (0.681–1.682) 2.056 (1.298–3.257) 1.282 (0.970–1.695) 1.371 (1.099–1.711) .0012 .3639 .8013 .6632 .0014 .0635 .0067 1.382 (1.136–1.682) 0.872 (0.649–1.172) 1.039 (0.773–1.396) 1.104 (0.707–1.726) 2.117 (1.337–3.352) 1.301 (0.985–1.717) 1.354 (1.087–1.687) .0015 .1476 .6465 .5791 .0021 .1020 .0022 1.352 (1.122–1.630) 0.810 (0.610–1.077) 0.936 (0.704–1.243) 0.889 (0.587–1.347) 1.927 (1.268–2.926) 1.244 (0.958–1.616) 1.387 (1.125–1.709) .0013 .4717 .0838 .0994 .1488 .7873 .3348 1.551 (1.188–2.026) 0.868 (0.591–1.276) 1.426 (0.954–2.133) 1.764 (0.898–3.467) 1.586 (0.848–2.964) 1.053 (0.722–1.537) 1.162 (0.857–1.575) .0012 .0606 .8193 .5055 .1803 .9166 .1628 1.457 (1.160–1.831) 0.711 (0.498–1.015) 0.959 (0.667–1.377) 0.839 (0.500–1.407) 1.410 (0.853–2.331) 1.017 (0.739–1.400) 1.204 (0.928–1.563) 1.195 (0.948–1.506) 0.921 (0.644–1.318) 0.866 (0.609–1.232) 0.744 (0.451–1.226) 2.555 (1.412–4.624) 1.306 (0.944–1.806) 1.397 (1.080–1.808) Female sex Age 65–74 y Age 75–84 y Age .84 y MMSE ,15 MMSE 15–19 MMSE 20–24

.1307 .6525 .4233 .2458 .0019 .1068 .0108

P-value P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) OR (95% CI) Category

AD profile Ab42 and T-tau Ab42 and P-tau T-tau P-tau Ab42

Table 2 Logistic regression models for predicting pathologic biomarker levels

.0027 .2245 .9721 .7691 .0021 .0806 .0052

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studies on AD, which have showed that clinical evaluation alone has a limited diagnostic accuracy for AD. A review in 2001 showed sensitivity and specificity figures around 80% and 70%, respectively, compared with neuropathologic diagnosis [23]. Data comparing clinical and neuropathologic diagnosis on 919 autopsied subjects from the National Alzheimer’s Coordinating Center showed sensitivity between 70.9% and 87.3%, and specificity between 44.3% and 70.8%, depending on the clinical and neuropathologic criteria used [24]. Recently, a high proportion of clinically diagnosed AD patients enrolled in clinical trials on Ab disease-modifying drugs were noted to have negative amyloid PET scans (up to 36% of patients among APOE ε4 noncarriers) [25]. These numbers, which are in agreement with the findings in our study, further increase concerns about the accuracy of a diagnosis of AD based on pure clinical criteria because enrollment of patients without underlying AD pathology in trials reduces the power to identify drug effects. However, faulty clinical diagnoses may not be the only reason for the biomarker results in our study. As discussed in the following text, biomarker assays for AD neuropathology do not have 100% sensitivity, and thus, a lack in sensitivity could also explain the picture. Also, as demonstrated by the proportion of individuals in the gray zone, lack of accuracy of the biomarker assays could be an important factor. In addition, there may be other pathologic processes contributing to AD that the studied biomarkers not optimally represent. There is an ongoing search for new biomarkers for AD, which has resulted in several candidate biomarkers [4]. If successfully validated in future studies, they may help to better characterize AD and reduce some of the limitations with the current biomarkers. The accuracy of CSF biomarkers for AD has been assessed using neuropathologic diagnosis as standard of truth. Studies have shown sensitivity and specificity over 80 % for the use of CSF T-tau, P-tau, and Ab42 in the differentiation of autopsy-confirmed AD and (often non-autopsy confirmed) healthy controls [26–28]. One study found that the ratio of P-tau to Ab42 identified AD-associated neuropathologic changes with a sensitivity of 91.6% and a specificity of 85.7% [29]. Note that about 20–30% of cognitively healthy controls have Ab pathology, which limits the maximum possible specificity for CSF biomarkers in all studies comparing AD patients with controls. It has also been shown that CSF biomarkers could improve diagnostic accuracy in clinically uncertain cases [30]. In addition, the concordance between CSF Ab42 measurements and amyloid PET is very high, also with CSF analyses performed in clinical routine, supporting the accuracy of this biomarker to identify brain amyloid deposition [20,31]. These results indicate relevance and potential clinical usefulness of the biomarkers. Albeit not being required for diagnosis, measurement of CSF biomarkers is a common diagnostic workup for dementia patients in Sweden because more than 40% of all patients referred to memory clinics have had lumbar puncture performed [32]. Nevertheless, more

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studies are required to validate the biomarkers, before they are included in routine clinical practice [6,10]. Clinically diagnosed AD patients who do not fulfill the criteria for pathologic CSF AD biomarkers constitute an interesting group. Given the high correlation between CSF AD biomarkers and presence of AD neuropathology, it is possible that most of these individuals do not suffer from AD. Thus, implementation of CSF biomarker criteria in the diagnostic process may reduce the number of misdiagnosed patients in clinical practice. Of note is that these patients examined in specialized memory clinics by experienced clinicians have a sufficiently strong clinical phenotype of AD to result in an AD diagnosis may be reflecting other pathogenic mechanisms important for AD pathology. The clinicians in this study have probably correctly identified patients with dementia, but plaques and tangles may only explain a proportion of the cases, as inferred from the biomarkers. The dementias could be multifactorial, with plaques and tangles playing a role, but not explaining the entire clinical picture. Further studies (including autopsy studies) are needed to better characterize this patient group and distinguish AD from important differential diagnoses, including, for example, Lewy body dementia, vascular dementia, and frontotemporal lobe dementia. Note that the currently used clinical diagnostic criteria for AD require dementia to be present for an AD diagnosis. In contrast, IWG-2 (and other biomarker-based criteria) [6–8,10] acknowledges that AD forms a continuum with an extended stage of pathologic changes before clinical dementia. Thus, using biomarker-based criteria would probably increase the number of AD patients diagnosed in early disease stage. The new biomarker-informed criteria for AD do not specify cutoffs for the individual biomarkers. Biomarker measurements differ between assays and laboratories [33]. Therefore, for biomarkers to be used globally in clinical research and routine, standardization of their measurement and analysis is essential. Several global standardization initiatives are already ongoing [33–35]. Bearing the variability of measurements in mind, a strength of this study is that all biomarker measurements were conducted in the same laboratory, with an elaborate system for ascertaining longitudinal biomarker assay stability. In spite of these efforts, the CVs for the biomarker assays were around 10%. As shown here, this can have a large impact on the interpretation of the biomarker result of an individual because the gray zone with uncertain biomarker values becomes wide. This highlights the need for better biomarker assays with lower CVs before strict cut points for the CSF biomarkers are implemented in clinical practice. Because this study investigated biomarker profiles in patients with clinically diagnosed AD, the results can not be directly coupled to the IWG-2 AD criteria (which require the presence of a certain memory profile). However, the findings still have impact for IWG-2 and other diagnostic criteria that include biomarkers (such as the recommendations from the National Institute on Aging-Alzheimer’s Association

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[6]). Validation studies using clinical cohorts are an important part in the implementation of biomarkers for AD. One study on the Dubois criteria from 2007 showed that 55% of the patients with a clinical AD diagnosis fulfilled the criteria of pathologic episodic memory and
1 pathologic biomarker (by MRI, single-photon emission computed tomography [SPECT], or CSF) [36]. However, only a part of the patients had CSF results available, which makes comparisons to our study difficult. Furthermore, in the revised IWG criteria, MRI and SPECT examination are not considered as biomarkers of diagnosis, but biomarkers of disease progression [10]. Nevertheless, future studies that evaluate biomarker criteria for AD could benefit from including patients with results from several biomarker modalities (such as MRI, PET, and CSF biomarkers), to compare different biomarkers for AD. Our second major finding was that the CSF biomarkers and the proportions of subjects with pathologic biomarker profiles were overall similar across the study groups. This is in agreement with previous results that have showed similar CSF biomarker levels in AD independent of age of onset [37]. No significant differences were found between the proportion of patients that had pathologic P-tau in LOAD patients aged 65–74 years and EOAD (which had a proportion of pathological patients that was even higher than that observed in LOAD patients aged 75–84 years). A possible explanation could be the number of patients analyzed (n 5 345 vs. n 5 952). Our third major finding was that in a logistic regression model, female sex and lower MMSE were associated with higher likelihood of pathologic biomarker profiles. Previous studies on sex differences in the risk for AD have given mixed results, with some showing increased risk for females and others showing no sex differences [38]. One study found that female sex was associated with elevated CSF T-tau levels in patients with various dementias and other neurologic diseases [29]. Here, we show that females with clinically diagnosed AD have significantly higher likelihood for pathologic levels of T-tau and P-tau and a trend toward pathologic levels of Ab42 compared to males. Low levels of Ab42 and high levels of T-tau and/or P-tau have previously been associated with increased rate of cognitive decline in AD [39,40]. In other studies, elevated levels of P-tau and/or T-tau, but not levels of Ab42, were associated with longitudinal decline in MMSE [41,42]. One study found that CSF Ab42 and P-tau/T-tau ratio were cross-sectionally associated with MMSE score at baseline, but not with longitudinal change [43]. In this study, the most prominent association between low MMSE score and pathologic biomarker levels in the logistic regression was seen for Ab42. Further studies are needed to clarify these discrepancies. The correlation analyses revealed weak, but significant correlations between biomarker levels and MMSE. No correlations between Ab42 and T-tau or P-tau were found, which is consistent with previous findings

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[44,45]. In the logistic regression, there are significant Pvalues for the highest and lowest MMSE in different models but not with the middle MMSE (15–19). This might be a power issue because the middle group is less than half the size of the higher group. Also the upper 95% CIs for these two groups are the same, and there was a significant trend when using the four MMSE categories as a continuous variable in the logistic regression. This study has several limitations. The patients in our study were diagnosed according to the ICD-10 criteria, which are similar to the NINCDS-ADRDA criteria. Although these criteria do not implement CSF biomarkers for diagnosis, a limitation of the study is that the clinicians had the biomarker results at hand and may have been influenced by them when making the diagnosis. It has been shown that clinicians diagnosing AD patients were affected by the results of CSF AD biomarkers [9]. This is supported by the fact that the percentage of patients with a pathologic biomarker profile was higher in the patients where lumbar puncture was performed before diagnosis. This suggests that our results may actually underestimate the difference between clinical diagnosis and biomarker informed diagnosis, giving further support to the use of CSF biomarkers for AD diagnosis. However, it is not known why some subjects were lumbar punctured after diagnosis. This adds to the heterogeneity of the subject group, and the lower proportion of pathologic biomarker levels in subjects with lumbar puncture after diagnosis could be due to a referral bias. Another limitation is that CSF sampling on the subjects in our study may have been biased toward patients with an unclear clinical picture, reducing the generalizability to the entire AD population. However, in Sweden CSF sampling is common in clinical practice for investigation of cognitive decline, especially at specialized memory clinics (as mentioned previously, more than 40% of the patients in memory clinics are lumbar punctured). Although, without knowing more about the 60% without CSF biomarkers, it is hard to draw conclusions about generalizability. 4.1. Conclusion About a quarter of patients diagnosed with AD in clinical practice in Sweden did not fulfill the AD-indicative CSF biomarker combination, meaning that they did not have pathologic CSF levels of Ab42 and tau biomarkers. This may reflect a false clinical AD diagnosis, lack in sensitivity of the biomarker assays or that other pathologic mechanisms also contribute to AD. Implementation of biomarkerinformed criteria may reduce the estimated number of patients with dementia due to AD. Acknowledgments The authors are grateful to SveDem (www.svedem.se) for providing data for this study as well as thank all participants

in SveDem (patients, caregivers, and staff). This study was supported financially by the Swedish Research Council, the Torsten S€oderberg Foundation at the Royal Swedish Academy of Sciences, the Knut and Alice Wallenberg Foundation, the Emil and Maria Palm Foundation, the Royal and Hvitfeldt foundation, the Alzheimer Foundation, the Swedish association of local authorities and regions, Swedish State Support for Clinical Research, the Swedish Brain Power network, KI Foundations, Svenska L€akares€allskapet, and BIOMARKAPD in the frame of JPND. Ethics: The study was approved by the Regional Ethical Review Board of the University of Gothenburg. Role of the funding source: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

RESEARCH IN CONTEXT

1. Systematic review: The authors reviewed the literature using PubMed. A few small studies have evaluated previous cerebrospinal fluid (CSF) biomarker criteria for Alzheimer’s disease (AD). 2. Interpretation: For the use of biomarker-informed diagnostic AD criteria in clinical practice, validation in clinical cohorts is mandatory. In this study, a large proportion of the clinically diagnosed AD patients lacked a pathologic CSF AD profile. Whether this discrepancy may represent misdiagnosis or lack in sensitivity of the studied CSF biomarkers remains to be determined. 3. Future directions: Future validation studies, including studies with neuropathologic confirmation of diagnosis, are needed before the new biomarkerinformed diagnostic criteria for AD could be used routinely in clinical practice.

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Appendix A The laboratory in M€ olndal uses a system for internal control of the assays. Internal control samples are measured at least twice a week. The samples that are analyzed are kept frozen in aliquots and reused until depleted and are thereafter replaced. For each control sample, a coefficient of variation (CV) of all measurements was calculated, and the average of these values was used for the representation of a CV for each analysis. As every measurement made on each of the internal control samples (i.e., covering the entire study period from 2007–2012) was included in the calculation of the CVs, the CVs are from interassay measurements, from different plates, days, and lot numbers. The control samples for T-tau included six with low values and seven with high values. The CVs for the lowvalue control samples were 8.04%, 8.69%, 10.94%, 8.66%, 9.67%, 9.88%, and 9.01%, giving a mean CV of 9.27%. The CVs for the high-value control samples were 19.41%, 4.57%, 7.56%, 13.95%, 10.73%, and 9.62%, yielding a mean CV of 10.97%. The total average of both high and low value CVs for T-tau was 10.35%. The control samples for P-tau included seven with low values and seven with high values. The CVs for the lowvalue control samples were 9.06%, 8.22%, 9.20%, 11.79%, 8.54%, 11.68%, and 11.04%, giving a mean CV of 9.94%. The CVs for the high-value control samples were 9.80%, 15.49%, 8.91%, 9.30%, 8.47%, 8.85%, and

9.38%, yielding a mean CV of 10.03%. The total average of both high and low value CVs for P-tau was 9.98%. The control samples for Ab42 included seven with low values and seven with high values. The CVs for the lowvalue control samples were 8.34%, 9.10%, 9.01%, 12.29%, 12.10%, 11.00%, and 10.52%, giving a mean CV of 10.34%. The CVs for the high-value control samples were 6.24%, 8.81%, 9.41%, 10.12%, 12.82%, 11.76%, and 11.48%, yielding a mean CV of 10.09%. The total average of both high and low value CVs for Ab42 was 10.21%. No longitudinal drift was noticed for any of the analyses. Appendix B The gray zone limits for the biomarker assays were calculated by applying a 95 % confidence interval (CI) to the biomarker cutoff. The CV was approximately 10% for each biomarker assay. The CI for Ab42 was achieved by multiplying the CV by the biomarker cutoff and multiply by two (to get two standard deviations). This value was then added and subtracted to the biomarker cutoff to get upper and lower confidence limits, as shown in the following text. Calculating 95% CI around the cutoff for Ab42, 1. Two standard deviations: 530 pg/mL ! 0.10 ! 2 5 106 pg/mL. 2. Upper confidence limit: 530 1 106 5 636 pg/mL. 3. Lower confidence limit: 530 2 106 5 424 pg/mL. Confidence limits for T-tau and P-tau were calculated in a similar fashion. For Ab42, values below the interval were considered pathologic and values above it normal. For Ttau and P-tau, values above the interval were considered pathologic and values below it normal. For the combination of Ab42 and T-tau or Ab42 and P-tau, the labels pathologic, gray zone, and remainder were assigned in the following way: 1. 2. 3. 4.

Both pathologic 5 pathologic. One pathologic and the other gray zone 5 gray zone. Both gray zone 5 gray zone. All other combinations 5 normal.

For the AD-indicative CSF biomarker profile, labels were assigned in the following way: 1. Pathologic Ab42 and T-tau or P-tau 5 pathologic. 2. Pathologic Ab42 while both T-tau and P-tau gray zone or one normal 5 gray zone. 3. Gray zone Ab42 and one at least one tau biomarker not normal 5 gray zone. 4. All other combinations 5 normal.