Journal of Alzheimer’s Disease 43 (2015) 1393–1402 DOI 10.3233/JAD-140339 IOS Press

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Preclinical Cerebrospinal Fluid and Volumetric Magnetic Resonance Imaging Biomarkers in Swedish Familial Alzheimer’s Disease Steinunn Thordardottira,b,∗ , Anne Kinhult St˚ahlboma,c , Daniel Ferreirac , Ove Almkvistd , Eric Westmanc , Henrik Zetterberge,f , Maria Eriksdotterb,c , Kaj Blennowe and Caroline Graffa,b a Karolinska

Institutet, Department NVS, Division of Neurogeriatrics, Center for Alzheimer Disease Research, Huddinge, Sweden b Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden c Karolinska Institutet, Department of NVS, Division for Clinical Geriatrics, Center for Alzheimer Disease Research, Huddinge, Sweden d Karolinska Institutet, Department of NVS, Center for Alzheimer Research, Division of Translational Alzheimer Neurobiology, Huddinge, Sweden e Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, M¨olndal, Sweden f UCL Institute of Neurology, Queen Square, London, UK

Accepted 1 August 2014

Abstract. Background: It is currently believed that therapeutic interventions will be most effective when introduced at the preclinical stage of Alzheimer’s disease (AD). This underlines the importance of biomarkers to detect AD pathology in vivo before clinical disease onset. Objective: To examine the evolution of cerebrospinal fluid (CSF) biomarker and brain structure changes in the preclinical phase of familial AD. Methods: The study included members from four Swedish families at risk for carrying an APPswe, APParc, PSEN1 H163Y, or PSEN1 I143T mutation. Magnetic resonance imaging (MRI) scans were obtained from 13 mutation carriers (MC) and 20 non-carriers (NC) and analyzed using vertex-based analyses of cortical thickness and volume. CSF was collected from 10 MC and 12 NC from familial AD families and analyzed for A␤42 , total tau (T-tau) and phospho-tau (P-tau). Results: The MC had significantly lower levels of CSF A␤42 and higher levels T-tau and P-tau than the NC. There was a trend for a decrease in A␤42 15–20 years before expected onset of clinical symptoms, while increasing T-tau and P-tau was not found until close to the expected clinical onset. The MC had decreased volume on MRI in the left precuneus, superior temporal gyrus, and fusiform gyrus. Conclusions: Aberrant biomarker levels in CSF as well as regional brain atrophy are present in preclinical familial AD, several years before the expected onset of clinical symptoms. Keywords: Alzheimer’s disease, biomarkers, cerebrospinal fluid, genetics, magnetic resonance imaging, natural history studies, preclinical ∗ Correspondence to: Steinunn Thordardottir, Karolinska Institutet, Department of NVS, Center for Alzheimer Research, Division for Neurogeriatrics, 141 57 Huddinge, Sweden. Tel.: +46 702 702 066; Fax: +46 85 858 3610; E-mail: [email protected].

ISSN 1387-2877/15/$27.50 © 2015 – IOS Press and the authors. All rights reserved

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INTRODUCTION The diagnostic criteria for Alzheimer’s disease (AD) have recently been recast to include a preclinical stage, thereby underlining the importance of biomarkers to detect AD pathology in vivo before clinical disease onset [1]. Due to the low annual incidence of sporadic AD, very large cohorts of cognitively normal elderly are needed to study the preclinical stages of the disease. However, familial AD (FAD) is most often indistinguishable from sporadic AD, both clinically and neuropathologically, thus enabling individuals with FAD mutations to serve as models for sporadic disease [2]. Since the expected age at onset can be estimated, studies based on FAD are highly valuable to study the preclinical phase of AD. Currently the most promising biomarkers can be measured using positron emission tomography (PET) imaging [3], magnetic resonance imaging (MRI) [4], and cerebrospinal fluid (CSF) [5]. A hypothetical model of the temporal sequence of preclinical AD biomarker changes has been proposed by Jack et al. [6] and a few recently published studies have tested this model in FAD cohorts with somewhat varying results [7–12]. In order to understand in what time frame, before clinical onset, the AD biomarkers start to change, we measured CSF levels of A␤42 , total tau-protein (T-tau), and phospho-tau (P-tau) in FAD mutation carriers (MC) and non-carriers (NC) from the same families. We examined individuals from families carrying autosomal dominant FAD mutations in the preclinical stage of the disease, before the onset of mild cognitive impairment (MCI) and dementia. MRI measures of cortical thickness and volume as well as volumes of the hippocampus and amygdala were also analyzed. The hypothesis was that A␤42 would be decreased in the MC, years before the onset of clinical symptoms, and that T-tau and P-tau would become elevated closer to the estimated onset. Finally, brain atrophy was expected to be detected downstream of the abnormalities in CSF biomarkers. MATERIALS AND METHODS Study population Members of four Swedish families segregating four known mutations leading to autosomal dominant AD, APPswe (p.KM670/671NL) [13, 14], APParc (p.E693G) [15], PSEN1 (p.H163Y) [16, 17], and PSEN1 (p.I143T) [18], were included in the study.

The mean age of symptom onset for each family is: 54 years with a standard deviation of ± 5 years for APPswe (based on 24 affected cases), 56 ± 3 years for APParc (based on 12 affected cases), 52 ± 7 years for PSEN1 H163Y (based on 9 affected cases), and 36 ± 2 years for PSEN1 I143T (based on 5 affected cases). The age of symptom onset for each affected individual, used to calculate the mean, is defined as the age at which this person experienced the first clinically relevant cognitive symptom(s). Participants were recruited to a longitudinal clinical and experimental FAD study through the Genetics Unit providing genetic counseling at the Memory Clinic at the Karolinska University Hospital and were examined between 2006 and 2011. The FAD study is a prospective study including a thorough clinical evaluation, a comprehensive neuropsychological assessment, neuroimaging (3Tesla MRI), electroencephalography, and biochemical assessments, including collection of blood, fibroblasts, and CSF. The study subjects were a subsample of individuals without a diagnosis of MCI or dementia but at 50% risk of carrying one of the mutations (APPswe, APParc, PSEN1 H163Y, or PSEN1 I143T) leading to FAD. The distribution of MC versus NC among the study subjects from each family was as expected, but will not be revealed in detail for reasons of anonymity. The included subjects gave informed written consent to and underwent lumbar puncture and MRI (n = 20), only MRI (n = 13), or only lumbar puncture (n = 2). A total of 35 subjects underwent either one or both of the procedures, eleven from a family segregating the APPswe mutation, twelve from a family segregating the APParc mutation, nine from a family segregating a PSEN1 H163Y mutation, and three from a family segregating a PSEN1 I143T mutation. None of the members of the PSEN1 I143T family underwent a lumbar puncture, so this family is not represented in the CSF results. The subjects included in the study either contacted the Genetics Unit on their own initiative or were first approached through a relative. All of the participants received genetic counseling. The participants, clinicians, and researchers involved in the study were blind to the mutation status of the participants unless the participant opted for presymptomatic genetic testing (in a clinical genetics setting). All study procedures were approved by the Regional Ethical Review Board in Stockholm, Sweden. Cerebrospinal fluid analysis CSF samples were collected by lumbar puncture in the L3/L4 or L4/L5 interspace at variable time

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points during the day. According to current recommendations on CSF sampling and handling, there is no evidence that supports a diurnal variation of AD biomarkers [19]. The participants received premedication with 5 mg diazepam and 1 g paracetamol prior to the procedure. Immediately after collection, the CSF was centrifuged at 3000× g at +4◦ C for 10 min. The supernatant was pipetted off, aliquoted into polypropylene cryotubes, and stored at −80◦ C. All CSF samples were analyzed at the Clinical Neurochemistry Laboratory at Sahlgrenska University Hospital, Molndal, Sweden, by board-certified laboratory technicians blind to clinical data. All analytical procedures were performed according to protocols accredited by the Swedish Board for Accreditation and Conformity Assessment. CSF A␤42 was analyzed by the electrochemiluminescence technology (Meso Scale Discovery, Gaithersburg, Maryland, USA), using the MS6000 Human Abeta 3-Plex Ultra-Sensitive Kit [20]. CSF T-tau was determined using a sandwich ELISA (Innotest hTAU-Ag, Fujirebio Europe, Gent, Belgium) specifically constructed to measure all tau isoforms irrespectively of phosphorylation status, as previously described [21], while P-tau (tau phosphorylated at threonine 181) was measured using the INNOTEST® PHOSPHO-TAU 181P ELISA (Fujirebio Europe, Ghent, Belgium), as described previously in detail [22]. Magnetic resonance imaging All MRI image data sets were acquired on a Siemens whole-body clinical MRI 3T scanner (Magnetom Trio, Erlangen, Germany) equipped with a 12-channel phase-array head coil. All participants underwent the same MRI protocol. A high-resolution 3D T1-weighted MPRAGE sequence image (T1WI) was acquired in sagittal plane (TR/TE = 1780/3.42 ms, inversion time = 900 ms, 192 sagittal slices, voxel size 1 × 1 × 1 mm3 , and flip angle = 9◦ ). Full brain and skull coverage was required for the MRI datasets and detailed quality control was carried out on all MR images according to previously published quality control criteria [23]. MRI data analysis Cortical reconstruction and volumetric segmentation was performed using the FreeSurfer 5.1.0 image analysis suite (http://surfer.nmr.mgh.harvard.edu/), including removal of non-brain tissue [24], intensity normalization [25], tessellation of the gray matter

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white matter boundary, surface deformation following intensity gradients to optimally place the gray/white and gray/CSF borders at the location where the greatest shift in intensity defines the transition to the other tissue class [26, 27], registration to a spherical atlas [28], and creation of a variety of cortical and subcortical data. Results were visually inspected to ensure accuracy of registration, skull stripping, segmentation and cortical surface reconstruction. After image processing, volume measures of the hippocampus and amygdala were selected for the analysis and normalized by the subject’s intracranial volume [29]. Cortical based measures (thickness and volume) were analyzed using a vertex-by-vertex general lineal model. Maps were smoothed using a circularly symmetric Gaussian kernel across the surface with a full width at half maximum (FWHM) of 15 mm. Results were thresholded by a conventional criterion for correction for multiple comparisons using false discovery rate (FDR) [30] with a p value of 0.05. Neuropsychological assessment All of the 35 subjects included in the study underwent a neuropsychological assessment. The assessment included a series of neuropsychological tests that covered global cognitive status as well as five specific cognitive domains such as verbal, visuospatial, immediate memory, episodic memory, and attention/executive function. Here, data on the five domains will be reported. The assessment was made by means of two verbal tests (Information and Similarities), two visuospatial tests (Block Design and Rey-Osterrieth copy), two short-term memory tests (Digit and Corsi span), three episodic memory tests (Rey Auditory Verbal Learning and Retention after 30 minutes and Rey-Osterrieth Retention), and three tests of attention/executive function (Digit Symbol, Trailmaking A and B); see [31]. The test results (mean value of tests within each domain) were transformed to a mean z-score using a reference group of healthy adults [32]. All subjects underwent a Mini-Mental State Examination (MMSE) with points given on a scale from 0 to 30 [33]. The neuropsychological assessment was performed within 3 months of the other clinical examinations. Genetic analysis Apolipoprotein E The APOE genotyping was performed for SNPs rs7412 and rs429358 using TaqMan® , SNP

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Genotyping Assays (ABI, Foster City, CA, USA) according to manufacturer’s protocol. The amplified products were run on a 7500 fast Real-Time PCR System (ABI, Foster City, CA, USA). Mutation analyses in AβPP and PSEN1 Exons 16 and 17 in A␤PP were sequenced to screen for the KM670/671NL [13] and the E693G mutations [15]. To confirm the H163Y mutation in PSEN1 exon 6 was sequenced [17]. Finally, exon 5 was sequenced to confirm the I143T mutation in PSEN1 [34]. DNA was amplified using AmpliTaq Gold® PCR Master Mix (ABI, Branchburg, NJ, USA). Primer sequences and PCR conditions are available upon request. Big Dye® terminator v3.1 Cycle sequencing Kit (ABI, Austin, TX, USA) was used for sequencing. The exons in AβPP and PSEN1 were sequenced in both directions and analyzed on an ABI3100 Genetic Analyzer (ABI, Foster City, CA, USA). Statistical analysis Due to the fact that the age at symptom onset in FAD varies between families, each subject’s age relative to the mean age at symptom onset in their respective family was calculated. The time (in years) to expected age at symptom onset was calculated as the difference between the subject’s age at the time of examination and the mean age at symptom onset in their respective family (see also the section above on Study population for details). This method of calculating each subject’s years to expected symptom onset based on the mean age at onset in the family has been validated in a recent study that showed highly significant correlations between individual age at symptom onset and mean age at onset by mutation type and family [35]. Using years to expected onset therefore provides a good estimation of an individual’s stage in the development of pathology. The Mann-Whitney U test was performed comparing age, years to predicted

family specific symptom onset, MMSE scores, CSF biomarker levels, and volumes of the hippocampus and amygdala in the two samples: MC and NC. Fisher’s exact test was used to compare gender and frequency of the APOE ␧4 allele in the two groups. Exploratory Spearman correlations were performed among CSF biomarker levels, as well as between CSF biomarker levels, volume of hippocampus and amygdala, and predicted age at symptom onset. For the CSF biomarkers, one-tailed p values of 0.05 or less were considered significant as the hypothesis was that the differences in CSF biomarkers between the MC and NC were one-way. Regarding the MRI measures, two-tailed p values of 0.05 or less were considered significant. RESULTS Of the twenty-two subjects who underwent a lumbar puncture, ten were MC and twelve were NC; see Table 1 for a summary of the demographic data of the study population. There were no significant differences between MC and NC regarding gender distribution, mean age, or mean number of years to predicted family specific symptom onset. Furthermore, there was no significant difference in the prevalence of APOE ␧4 carriers and there was only one individual, a noncarrier, who was an APOE ␧4 homozygote. The mean MMSE score was not significantly different between the two groups. In the MRI group, thirteen subjects were MC and twenty were NC (see Table 1). There were no significant differences between the MC and NC regarding gender distribution, mean age, and mean number of years to the predicted family specific symptom onset or the prevalence of APOE ␧4 carriers. There was one individual in the NC group who was an APOE ␧4 homozygote. The mean MMSE score was the same in both groups. An MMSE score was missing from one subject in the MRI group who had more than 10 years left to predicted onset and was free of symptoms.

Table 1 Demographics of the study population CSF group (n = 22) MC (n = 10) NC (n = 12) Mean age, years (SD) Years to onset (SD) Male gender, n (%) Mean MMSE, score (range) Prevalence of APOE ␧4 carriers, n (%)

47.1 (9.2) 7.1 (9.4) 8 (80) 29 (26–30) 6 (60)

Standard deviations (SD) are presented in parentheses.

48.3 (11.4) 6.6 (11.7) 8 (67) 29 (27–30) 5 (42)

p-value 0.97 0.87 0.6 0.2 0.7

MRI group (n = 33) MC (n = 13) NC (n = 20) 42.5 (11.6) 9.2 (9.4) 10 (77) 29 (26–30) 6 (46)

49.5 (14.3) 3.3 (12.2) 11 (55) 29 (27–30) 6 (30)

p-value 0.2 0.3 0.3 0.5 0.3

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Table 2 Mean values of CSF and MRI biomarkers in mutation carriers and non-carriers CSF

MRI

A␤42 (ng/L) Total-tau (ng/L) Phospho-tau (ng/L) A␤42 /Phospho-tau Volume of right hippocampus (mm3 ) Volume of left hippocampus (mm3 ) Volume of right amygdala (mm3 ) Volume of left amygdala (mm3 )

Mutation carriers

Non-carriers

p-value

729 (561) 533 (330) 63 (27) 16 (17) 4403 (579) 4369 (479) 1508 (215) 1483 (231)

1687 (413) 294 (131) 43 (15) 41 (8) 4488 (462) 4357 (356) 1554 (147) 1573 (180)

0.0004 0.03 0.03 0.002 n.s. n.s. n.s. n.s.

Standard deviations are presented in parentheses. Volumetric MRI measures from each subject were normalized by the subject’s intracranial volume.

Fig. 1. Relationship between CSF biomarkers and years to symptom onset. A) Levels of CSF A␤42 in mutation carriers and non-carriers in relation to years to expected onset. Spearman correlation coefficient for the correlation between CSF A␤42 levels and years to onset in mutation carriers: r = −0.23, p = 0.5. The dotted lines represent 95% confidence intervals. B) Levels of CSF total tau-protein in mutation carriers and non-carriers in relation to years to expected onset. Spearman correlation coefficient for the correlation between CSF total tau-protein levels and years to onset in mutation carriers: r = 0.65, p = 0.06. C) Levels of CSF phospho-tau in mutation carriers and non-carriers in relation to years to expected onset. Spearman correlation coefficient for the correlation between CSF phospho-tau levels and years to onset in mutation carriers: r = 0.65, p = 0.06. D) The ratio of A␤42 to phospho-tau in CSF in mutation carriers and non-carriers in relation to years to expected onset. Spearman correlation coefficient for the correlation between CSF A␤42 /phospho-tau and years to onset in mutation carriers: r = −0.44, p = 0.2.

CSF Aβ42 , total tau, and phospho-tau Levels of A␤42 in CSF were significantly lower in the FAD MC compared with the NC (729 ng/L versus 1687 ng/L, p = 0.0004, see Table 2 for summary of the CSF analyses). There was a trend of decreasing A␤42 levels as the MC approached the family specific age

at symptom onset, with the decline appearing to start 15 to 20 years before the mean symptom onset (see Fig. 1A). T-tau and P-tau levels were significantly higher in the MC compared to NC (T-tau: 533 ng/L versus 294 ng/L, p = 0.02; P-tau: 63 ng/L versus 43 ng/L, p = 0.03), see Table 2. Furthermore, there was a trend

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of increasing T-tau and P-tau levels as the MC drew closer to the estimated time of onset (r = 0.65, p = 0.06 and r = 0.65, p = 0.06, respectively), see Fig. 1B and C. The increase in T-tau and P-tau was observed approximately 5 years before expected symptom onset. Finally, the ratio of CSF A␤42 to P-tau was calculated as it has previously been found that a combination of two biomarkers is more sensitive than any single marker alone in detecting changes indicative of the presence of disease, as well as those at high risk of imminent cognitive decline [36, 37]. The MC had a significantly lower ratio than the NC (16 versus 41, p = 0.004), see Table 2. The ratio seemed to decrease at least 20 years prior to expected symptom onset and remained low thereafter. The Spearman correlation for this decline was not significant, possibly due to the lack of linearity in the decline (Fig. 1D). One subject in the MC group was a statistical outlier, with normal values of A␤42 , T-tau, and P-tau in the CSF and normal cognition, despite having passed the expected age at symptom onset. Non-correlation analyses did not vary when excluding this outlier (data not shown). This individual was not included in the correlation calculations presented above and in Fig. 1. For confidentiality reasons, Fig. 1 shows the CSF levels for the MC as one group. However, when looking at the carriers of each of the three mutations separately, the same significant results were obtained for all three biomarkers in carriers of the APParc and the PSEN1 H163Y mutations (data not shown) when compared to all of the NC. Comparing only the APPswe carriers

with the NC, the biomarker level differences did not reach significance, despite showing the same trend as in the other MC. This is probably due to the small number of APPswe carriers and the fact that they were further from disease onset than the MC in the other families (data not shown). MRI cortical thickness and volumetric measures After FDR correction for multiple comparisons, significant clusters of reduced volume in the MC were seen in the left hemisphere, involving the precuneus, superior temporal gyrus, and fusiform gyrus (Fig. 2). There were no significant differences between the MC and NC regarding cortical thickness in either hemisphere or volume in the right hemisphere. Furthermore, the volume of the hippocampus and amygdala did not differ significantly between the two groups (Table 2). There were no significant correlations between years to onset and any of the MRI variables in the MC (data not shown), making the temporality of the observed MRI changes difficult to assess. The results above did not change when the statistical outlier (see CSF results) was excluded from the analyses. Cognitive function The z-scores for the five cognitive domains were compared between the whole MC group (excluding the outlier) and the whole NC group. There were no

Fig. 2. Regions in the left hemisphere with reduced volume in mutation carriers. 1) Left hemisphere, medial surface; 2) Left hemisphere, lateral surface; 3) Left hemisphere, ventral surface; p: posterior; a: anterior. Brains are “inflated” in the three images for a better representation of regions inside the sulci. Results were thresholded by a conventional criterion for correction for multiple comparisons using FDR with a p value of 0.05. p-values are presented in the color scale as –log10(p), where p is the significance. Red-yellow represents less volume in mutation carriers and blue represents more volume in mutation carriers. Mutation carriers had significantly decreased volumes in the left precuneus (image 1; Talairach coordinates: −19.3–72.0 25.1; cluster size: 28.57 mm2 ), left superior temporal gyrus (image 2; −48.8 3.9 −23.3; 33.28 mm2 ) and left fusiform gyrus (image 3; −40.4 −70.3 −12.6; 60.42 mm2 ), when compared with non-carriers.

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Fig. 3. The cognitive profiles for MC and NC across five domains. The cognitive profiles are presented as z-scores with correction for premorbid cognitive status as estimated by demographic characteristics. There was no significant difference between MC and NC in any of the five tested domains.

significant differences between the MC and NC regarding the mean result on the five cognitive domains, although a trend of poorer results was seen for MC versus NC in three domains. In Fig. 3, the cognitive profiles for MC and NC across five domains are presented with correction for premorbid cognitive status as estimated by demographic characteristics [38]. However, there were significant correlations between years to onset in relation to three cognitive domains in the MC: visuospatial (r = −0.54, p < 0.05), episodic memory (r = −0.70, p < 0.01), and attention/executive (r = −0.61, p < 0.05). In the NC, there was a significant correlation between years to onset and the attention/executive domain (r = −0.50, p < 0.05). DISCUSSION The current study shows that A␤42 is decreased and T-tau and P-tau are increased in CSF of individuals carrying a FAD mutation compared with non-mutation carriers from the same families, years before the predicted onset of clinical symptoms. Also, the ratio of CSF A␤42 to P-tau is significantly lower in MC compared to NC from the same families. These differences between the two groups are sufficiently robust to reach significance despite the small number of participants. Furthermore, levels of A␤42 show a trend of decreasing, and T-tau and P-tau levels a trend of increasing as the age of expected symptom onset approaches. As seen in Fig. 1, the 95% confidence intervals of the MC and NC curves for A␤42 intersect 15 to 20 years before

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expected onset, while the curves for T-tau and P-tau intersect later in the disease process, around 5 years before onset. The findings agree with the pathological changes observed on fludeoxyglucose (18 F) PET and in CSF biomarkers in previous studies involving mostly symptomatic individuals from the same Swedish FAD families as well as patients with sporadic AD [39, 40]. One of these two studies also showed a lack of binding of Pittsburgh compound B (PiB) in two APParc carriers, despite reduced glucose metabolism and reduced levels of CSF A␤42 [39]. The lack of PiB binding in the carriers of the APParc mutation is important to consider when choosing biomarker outcome measures in clinical FAD prevention trials. Using volumetric MRI, there are significant differences between MC and NC only in the volume of the left superior temporal gyrus, left precuneus, and left fusiform gyrus. This agrees well with a recent study where M¨oller et al. [41] showed that patients with early onset AD typically show gray matter atrophy in areas such as the temporal lobes, precuneus, and cingulate gyrus. The precuneus has also been shown to be a site of preferential amyloid uptake in PET studies [42, 43]. There are no significant correlations between volume or cortical thickness and years to onset in the MC. This makes it difficult to estimate the time at which the pathologic volume reduction starts in relation to expected symptom onset. With a larger sample size and longitudinal data it should be possible to better assess the temporal relationship between atrophy on MRI and the changes in CSF biomarkers. Age, gender, and APOE genotype were controlled for as confounding variables in the study design and showed no significant differences between MC and NC. For this reason, it was not necessary to enter them as covariates in the statistical analyses. Nonetheless, confirmatory analyses showed that when including gender and APOE genotype as covariates, results did not differ from what is reported here. If age was to be used as a covariate in the study, we would have taken the risk of diminishing a potential effect of the years to onset variable, which is a variable directly related to disease pathology. There are some clinical and pathological differences between AD caused by autosomal dominant mutations and sporadic AD, but generally studies in persons at risk for familial AD have replicated what is observed in the more common forms of AD, supporting the usefulness of this group in studying both familial and sporadic AD [2]. Healthy individuals carrying a mutation leading to autosomal dominant AD

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comprise a valuable research group when studying preclinical disease, as 50% of this population will eventually develop AD. However, there still is a possibility that the results may not be generalizable to individuals with sporadic disease. The known FAD mutations are thought to be 100% penetrant resulting in an inevitable development of AD for the mutation carriers. It is therefore interesting to note that one of the PSEN1 H163Y mutation carriers in the current study shows neither subjective nor objective signs of cognitive decline despite having passed the predicted age at onset. This person is an outlier in the MC group with normal levels of A␤42 , T-tau, and P-tau in the CSF. A mutation carrier from the same family died at age 67 without any clinical symptoms of dementia. As no other examples of decreased penetrance have been reported for the PSEN1 H163Y mutation, follow-up of this subject is needed in regards to this possibility. It is important to note that this particular subject’s mutation status is known to the subject and to the researchers as the subject opted for presymptomatic genetic testing. The presence of this outlier could have consequences regarding ongoing and future clinical trials in FAD populations. The design of such trials is based on expected findings related to the predicted age of symptom onset and outliers would not fit into such a model. The overall results from our study seem to be in agreement with the theoretical biomarker model proposed by Jack et al. [6], where structural MRI changes appear downstream of the changes in CSF biomarkers. However, it should be noted that the design of the current study, cross-sectional with different subjects included in the CSF and MRI analyses, is not optimal to study the temporality of biomarker changes. Previous studies in different FAD populations have shown somewhat conflicting results where atrophy on MRI sometimes coincides with CSF biomarker changes and sometimes appears later in the disease process. The temporal relationship between the different CSF biomarkers is also not clear. According to the DIAN study, CSF levels of A␤42 start to decline 25 years before symptom onset while increased levels of CSF T-tau and hippocampal atrophy can be noted 15 years before onset [7]. Fortea et al. [11] reported similar findings in CSF, with A␤42 changes preceding the elevation of T-tau, which only became apparent in symptomatic MC. Interestingly, the same group observed increased cortical thickness in the precuneus and parietotemporal areas, as well as increased caudate volumes in asymptomatic MC 9.9 years before predicted disease onset [44]. Ring-

man et al. [9] reported increases in T-tau and P-tau and decreased A␤42 20 years before onset, and the same group found no cortical thinning or hippocampal atrophy in non-demented mutation carriers [10]. Reiman et al. [8] reported a different pattern in their study with an increase in CSF A␤42 and reduced gray matter volume in the right parietal lobe on MRI 20 years before onset. Finally, a longitudinal study by Ridha et al. [12] showed differences between mutation carriers and controls in hippocampal and whole brain atrophy rates 5.5 and 3.5 years, respectively, before AD diagnosis. Despite the discrepancies described above, the results from the studies in different FAD populations seem to be more consistent than inconsistent, with abnormalities in CSF biomarkers and on MRI generally being observed in preclinical FAD. The discrepancies in the results can be due to small sample sizes and differences in the age of included subjects between studies. The studies by Fortea et al. [44] and Reiman et al. [8] mentioned above, respectively showing increased cortical thickness in MC and increased CSF A␤42 levels in MC, might reflect pathological changes appearing very early in the preclinical stage and therefore do not need to be in disagreement with results from other groups. Then there is a possibility that differences in the sequence of pathological events in different FAD mutations explain some of the variable observations. Also, the limited number of longitudinal studies makes it difficult to determine the temporal trajectories of these biomarkers with certainty. Finally, there is a lack of consensus regarding how to calculate years to expected symptom onset, which might explain some of the discrepant results observed in different FAD populations. The results of this study point out the usefulness of the CSF biomarkers A␤42 , T-tau, and P-tau in detecting preclinical AD in vivo and the power of studies on at risk FAD subjects. Given the limitations described above, our data suggests that the changes in CSF biomarkers precede atrophy on MRI by several years, at least outside specific medial temporal lobe areas in the left hemisphere. Larger studies, with repeated sampling over time and including younger subjects, further from the predicted onset of symptoms, are needed to better establish the temporal dynamics of these markers and their clinical correlates. The current study is the first study on MRI and CSF biomarkers in this population of Swedish, preclinical FAD mutation carriers. It adds to and strengthens the existing literature on preclinical biomarker changes in FAD as well as underlines that there are important differences in the sequence of pathologic events in different FAD populations. These differences need to be

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taken into account in future studies and clinical trials in FAD mutation carriers. ACKNOWLEDGMENTS The authors thank all the participants in the familial Alzheimer disease study and the staff employed at the Center for Alzheimer Disease Research at KI and the Karolinska University Hospital Memory Clinic at the Department of Geriatric Medicine, including Lena Lilius, MS, for technical assistance, Charlotte Forsell, MS, for statistical assistance, Mette Bergman, social worker, for counseling the participants and Dr. Niels Andreasen for assisting in the acquisition of CSF samples. The study was financially supported by Swedish Brain Power, The Swedish Alzheimer Foundation, The Swedish Dementia Association, the Regional Agreement on Medical Training and Clinical Research (ALF) between Stockholm County Council and Karolinska Institutet, The Swedish Research Council, Karolinska Institutet PhD-student funding and King Gustaf V and Queen Victoria’s Free Mason Foundation. Authors’ disclosures available online (http://www.jalz.com/disclosures/view.php?id=2482).

[8]

[9]

[10]

[11]

[12]

[13]

REFERENCES [1]

[2]

[3] [4] [5]

[6]

[7]

Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR, Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y, Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH (2011) Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging and the Alzheimer’s Association workgroup. Alzheimers Dement 7, 280-292. Ringman JM (2005) What the study of persons at risk for familial Alzheimer’s disease can tell us about the earliest stages of the disorder: A review. J Geriatr Psychiatry Neurol 18, 228-233. Nordberg A, Rinne JO, Kadir A, Langstrom B (2010) The use of PET in Alzheimer disease. Nat Rev Neurol 6, 78-87. Reiman EM, Jagust WJ (2012) Brain imaging in the study of Alzheimer’s disease. Neuroimage 61, 505-516. Blennow K, Hampel H, Weiner M, Zetterberg H (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 6, 131-144. Jack CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen OS, Shaw LM, Vemuri P, Wiste HJ, Weigand SD, Lesnick TG, Pankratz VS, Donohue MC, Trojanowski JQ (2013) Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. Lancet Neurol 12, 207-216. Bateman RJ, Xiong C, Benzinger TLS, Fagan AM, Goate A, Fox NC, Marcus DS, Cairns NJ, Xie XY, Blazey TM, Holtzman DM, Santacruz A, Buckles V, Oliver A, Moulder K, Aisen PS, Ghetti B, Klunk WE, McDade E, Martins RN, Masters CL, Mayeux R, Ringman JM, Rossor MN, Schofield

[14]

[15]

[16]

[17]

[18]

1401

PR, Sperling RA, Salloway S, Morris JC (2012) Clinical and Biomarker Changes in Dominantly Inherited Alzheimer’s Disease. N Engl J Med 367, 795-804. Reiman EM, Quiroz YT, Fleisher AS, Chen KW, VelezPardo C, Jimenez-Del-Rio M, Fagan AM, Shah AR, Alvarez S, Arbelaez A, Giraldo M, Acosta-Baena N, Sperling RA, Dickerson B, Stern CE, Tirado V, Munoz C, Reiman RA, Huentelman MJ, Alexander GE, Langbaum JBS, Kosik KS, Tariot PN, Lopera F (2012) Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: A case-control study. Lancet Neurol 11, 1048-1056. Ringman JM, Coppola G, Elashoff D, Rodriguez-Agudelo Y, Medina LD, Gylys K, Cummings JL, Cole GM (2012) Cerebrospinal fluid biomarkers and proximity to diagnosis in preclinical familial Alzheimer’s disease. Dement Geriatr Cogn Disord 33, 1-5. Apostolova LG, Hwang KS, Medina LD, Green AE, Braskie MN, Durron RA, Lai J, Geschwind DH, Cummings JL, Thompson PM, Ringman JM (2011) Cortical and hippocampal atrophy in patients with autosomal dominant familial Alzheimer’s disease. Dement Geriatr Cogn Disord 32, 118125. Fortea J, Llado A, Bosch B, Antonell A, Oliva R, Molinuevo JL, Sanchez-Valle R (2011) Cerebrospinal fluid biomarkers in Alzheimer’s disease families with PSEN1 mutations. Neurodegener Dis 8, 202-207. Ridha BH, Barnes J, Bartlett JW, Godbolt A, Pepple T, Rossor MN, Fox NC (2006) Tracking atrophy progression in familial Alzheimer’s disease: A serial MRI study. Lancet Neurol 5, 823-834. Mullan M, Crawford F, Axelman K, Houlden H, Lilius L, Winblad B, Lannfelt L (1992) A pathogenic mutation for probable Alzheimer’s disease in the APP gene at the Nterminus of beta-amyloid. Nat Genet 1, 345-347. Axelman K, Basun H, Winblad B, Lannfelt L (1994) A large Swedish family with Alzheimer’s disease with a codon 670/671 amyloid precursor protein mutation: A clinical and genealogical investigation. Arch Neurol 51, 11931197. Nilsberth C, Westlind-Danielsson A, Eckman CB, Condron MM, Axelman K, Forsell C, Stenh C, Luthman J, Teplow DB, Younkin SG, Naslund J, Lannfelt L (2001) The ‘Arctic’ APP mutation (E693G) causes Alzheimer’s disease by enhanced Abeta protofibril formation. Nat Neurosci 4, 887-893. Axelman K, Basun H, Lannfelt L (1998) Wide range of disease onset in a family with Alzheimer disease and a His163Tyr mutation in the presenilin-1 gene. Arch Neurol 55, 698702. Clark RF, Hutton M, Fuldner RA, Froelich S, Karran E, Talbot C, Crook R, Lendon C, Prihar G, He C, Korenblat K, Martinez A, Wragg M, Busfield F, Behrens MI, Myers A, Norton J, Morris J, Mehta N, Pearson C, Lincoln S, Baker M, Duff K, Zehr C, Pereztur J, Houlden H, Ruiz A, Ossa J, Lopera F, Arcos M, Madrigal L, Collinge J, Humphreys C, Ashworth A, Sarner S, Fox N, Harvey R, Kennedy A, Roques P, Cline RT, Phillips CA, Venter JC, Forsell L, Axelman K, Lilius L, Johnston J, Cowburn R, Viitanen M, Winblad B, Kosik K, Haltia M, Poyhonen M, Dickson D, Mann D, Neary D, Snowden J, Lantos P, Lannfelt L, Rossor M, Roberts GW, Adams MD, Hardy J, Goate A (1995) The structure of the Presenilin-1 (S182) gene and identification of 6 novel mutations in 6 early-onset AD families. Nat Genet 11, 219-222. Keller L, Welander H, Chiang HH, Tjernberg LO, Nennesmo I, Wallin AK, Graff C (2010) The PSEN1 I143T mutation in

1402

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28]

[29]

[30]

[31] [32]

S. Thordardottir et al. / Preclinical Alzheimer’s Disease Biomarkers a Swedish family with Alzheimer’s disease: Clinical report and quantification of A beta in different brain regions. Eur J Hum Genet 18, 1202-1208. Vanderstichele H, Bibl M, Engelborghs S, Le Bastard N, Lewczuk P, Molinuevo JL, Parnetti L, Perret-Liaudet A, Shaw LM, Teunissen C, Wouters D, Blennow K (2012) Standardization of preanalytical aspects of cerebrospinal fluid biomarker testing for Alzheimer’s disease diagnosis: A consensus paper from the Alzheimer’s Biomarkers Standardization Initiative. Alzheimers Dement 8, 65-73. Zetterberg H, Ruetschi U, Portelius E, Brinkmalm G, Andreasson U, Blennow K, Brinkmalm A (2008) Clinical proteomics in neurodegenerative disorders. Acta Neurol Scand 118, 1-11. Blennow K, Wallin A, Agren H, Spenger C, Siegfried J, Vanmechelen E (1995) Tau protein in cerebrospinal fluid – A biochemical marker for axonal degeneration in Alzheimer disease? Mol Chem Neuropathol 26, 231-245. Vanmechelen E, Vanderstichele H, Davidsson P, Van Kerschaver E, Van Der Perre B, Sjogren M, Andreasen N, Blennow K (2000) Quantification of tau phosphorylated at threonine 181 in human cerebrospinal fluid: A sandwich ELISA with a synthetic phosphopeptide for standardization. Neurosci Lett 285, 49-52. Simmons A, Westman E, Muehlboeck S, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Wahlund LO, Soininen H, Lovestone S, Evans A, Spenger C (2011) The AddNeuroMed framework for multi-centre MRI assessment of longitudinal changes in Alzheimer’s disease: Experience from the first 24 months. Int J Geriatr Psychiatry 26, 75-82. S´egonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B (2004) A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 1060-1075. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17, 87-97. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis I: Segmentation and surface reconstruction. Neuroimage 9, 179-194. Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97, 11050-11055. Fischl B, Sereno MI, Tootell RBH, Dale AM (1999) High-resolution inter-subject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 8, 272-284. Westman E, Aguilar C, Muehlboeck JS, Simmons A (2013) Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer’s disease and mild cognitive impairment. Brain Topogr 26, 9-23. Genovese CR, Lazar NA, Nichols T (2002) Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870-878. Lezak MD (1995) Neuropsychological Assessment (3rd edition), Oxford University Press, New York. Bergman I, Blomberg M, Almkvist O (2007) The importance of impaired physical health and age in normal cognitive aging. Scand J Psychol 48, 115-125.

[33]

[34]

[35]

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43]

[44]

Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state - Practical method for grading cognitive state of patients for clinician. J Psychiatr Res 12, 189-198. Cruts M, Backhovens H, Wang SY, Gassen GV, Theuns J, De Jonghe CD, Wehnert A, De Voecht J, De Winter G, Cras P, Bruyland M, Datson N, Weissenbach J, den Dunnen JT, Martin J-J, Hendriks L, Van Broeckhoven C (1995) Molecular genetic analysis of familial early-onset Alzheimer’s disease linked to chromosome 14q24.3. Hum Mol Genet 4, 23632372. Ryman DC, Acosta-Baena N, Aisen PS, Bird T, Danek A, Fox NC, Goate A, Frommelt P, Ghetti B, Langbaum JB, Lopera F, Martins R, Masters CL, Mayeaux RP, McDade E, Moreno S, Reiman EM, Ringman JM, Salloway S, Schofield PR, Sperling R, Tariot PN, Xiong C, Morris JC, Bateman RJ, Dominantly Inherited Alzheimer Network (2014) Symptom onset in autosomal dominant Alzheimer disease: A systematic review and meta-analysis. Neurology 83, 253-260. Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L (2006) Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: A follow-up study. Lancet Neurol 5, 228-234. Maddalena A, Papassotiropoulos A, Muller-Tillmanns B, Jung HH, Hegi T, Nitsch RM, Hock C (2003) Biochemical diagnosis of Alzheimer disease by measuring the cerebrospinal fluid ratio of phosphorylated tau protein to betaamyloid peptide42. Arch Neurol 60, 1202-1206. Almkvist O, Adveen M, Henning L, Tallberg IM (2007) Estimation of premorbid cognitive function based on word knowledge: The Swedish Lexical Decision Test (SLDT). Scand J Psychol 48, 271-279. Sch¨oll M, Almkvist O, Axelman K, Stefanova E, Wall A, Westman E, Langstrom B, Lannfelt L, Graff C, Nordberg A (2011) Glucose metabolism and PIB binding in carriers of a His163Tyr presenilin 1 mutation. Neurobiol Aging 32, 13881399. Sch¨oll M, Wall A, Thordardottir S, Ferreira D, Bogdanovic N, Langstrom B, Almkvist O, Graff C, Nordberg A (2012) Low PiB PET retention in presence of pathologic CSF biomarkers in Arctic APP mutation carriers. Neurology 79, 229-236. M¨oller C, Vrenken H, Jiskoot L, Versteeg A, Barkhof F, Scheltens P, van der Flier WM (2013) Different patterns of gray matter atrophy in early- and late-onset Alzheimer’s disease. Neurobiol Aging 34, 2014-2022. Villemagne VL, Ataka S, Mizuno T, Brooks WS, Wada Y, Kondo M, Jones G, Watanabe Y, Mulligan R, Nakagawa M, Miki T, Shimada H, O’Keefe GJ, Masters CL, Mori H, Rowe CC (2009) High striatal amyloid beta-peptide deposition across different autosomal Alzheimer disease mutation types. Arch Neurol 66, 1537-1544. Rabinovici GD, Jagust WJ (2009) Amyloid imaging in aging and dementia: Testing the amyloid hypothesis in vivo. Behav Neurol 21, 117-128. Fortea J, Sala-Llonch R, Bartr´es-Faz D, Bosch B, Llad´o A, Bargall´o N, Molinuevo JL, S´anchez-Valle R (2010) Increased cortical thickness and caudate volume precede atrophy in PSEN1 mutation carriers. J Alzheimers Dis 22, 909-922.