Association between CSF biomarkers, hippocampal volume and
cognitive function in patients with amnestic mild cognitive
impairment (MCI)
Pradeep J. Nathan
a,b,*,1, Yen Ying Lim
c,**,1, Rosemary Abbott
d, Samantha Galluzzi
e,
Moira Marizzoni
e, Claudio Babiloni
f,g, Diego Albani
h, David Bartres-Faz
i, Mira Didic
j,k,
Lucia Farotti
l, Lucilla Parnetti
l, Nicola Salvadori
l, Bernhard W. Müller
m,
Gianluigi Forloni
h, Nicola Girtler
n, Tilman Hensch
o, Jorge Jovicich
p, Annebet Leeuwis
q,
Camillo Marra
r, José Luis Molinuevo
s, Flavio Nobili
n, Jeremie Pariente
t, Pierre Payoux
t,
Jean-Philippe Ranjeva
j,k, Elena Rolandi
e, Paolo Maria Rossini
r, Peter Schönknecht
o,
Andrea Soricelli
u, Magda Tsolaki
v, Pieter Jelle Visser
q, Jens Wiltfang
m,w,
Jill C. Richardson
x, Régis Bordet
y, Olivier Blin
z, Giovanni B. Frisoni
e,aa, on behalf of the
PharmaCog Consortium
aHeptares Therapeutics Ltd, Cambridge, UK
bBrain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
cThe Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia dCambridge Cognition, Cambridge, UK
eLab Alzheimer’s Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy fDepartment of Physiology and Pharmacology, University of Rome“La Sapienza”, Rome, Italy
gIRCCS San Raffaele Pisana of Rome, Italy
hDepartment of Neuroscience, Mario Negri Institute for Pharmacological Research, Milano, Italy
iDepartment of Psychiatry and Clinical Psychobiology, Faculty of Medicine, University of Barcelona and Institut d’Investigacions Biomèdiques August Pi i
Sunyer (IDIBAPS), Barcelona, Catalunya, Spain
jAix-Marseille Université, INSERM, INS UMR_S 1106, Marseille, France
kService de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, France
lClinica Neurologica, Centro Disturbi della Memoria, Università di Perugia, Ospedale Santa Maria della Misericordia, Perugia, Italy mDepartment of Psychiatry and Psychotherapy, LVR-Hospital Essen, Faculty of Medicine, University of Duisburg-Essen, Essen, Germany nDepartment of Neuroscience (DINOGMI), Clinical Neurology, University of Genoa, Genoa, Italy
oDepartment of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany pCenter for Mind/Brain Sciences, University of Trento, Rovereto, Trento, Italy
qDepartment of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, the Netherlands
rDepartment of Gerontology, Neurosciences and Orthopedics, Institute of Neurology, Catholic University, Policlinic A. Gemelli Foundation, Rome, Italy sAlzheimer’s Disease Unit and Other Cognitive Disorders Unit, Hospital Clínic de Barcelona, and Institut d’Investigacions Biomèdiques August Pi i Sunyer
(IDIBAPS), Barcelona, Catalunya, Spain
tINSERM, Imagerie cérébrale et handicaps neurologiques UMR 825, Toulouse, France uSDN Istituto di Ricerca Diagnostica e Nucleare, Napoli, Italy
v3rd Neurologic Clinic, Medical School, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece wDepartment of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg-August-University, Goettingen, Germany xNeurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, UK
yUniversity of Lille, Inserm, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
zMediterranean Institute of Cognitive Neurosciences (INCM), UMR-CNRS (6193), Aix Marseille University, Marseille, France
aaMemory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
* Corresponding author at: Heptares Therapeutics BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire, AL7 3AX, UK. Tel.:þ44 (0) 1707 448037.
** Corresponding author at: 155 Oak Street, Parkville, Victoria 3051, Australia. Tel.:þ61 3 9389 2909; fax: þ61 3 9035 8642.
E-mail addresses:pradeep.nathan@heptares.com(P.J. Nathan),yen.lim@florey. edu.au(Y.Y. Lim).
1 Jointfirst authors.
Contents lists available atScienceDirect
Neurobiology of Aging
j o u r n a l h o me p a g e : w w w . e l s e v i e r . c o m / l o ca t e / n e u a g i n g
0197-4580/$ e see front matter Ó 2017 Elsevier Inc. All rights reserved.
a r t i c l e i n f o
Article history: Received 15 August 2016
Received in revised form 8 January 2017 Accepted 10 January 2017
Available online 18 January 2017
Keywords:
Mild cognitive impairment Prodromal Alzheimer’s disease Cognitive assessment CANTAB CSF Amyloid Tau Hippocampal volume
a b s t r a c t
Few studies have examined the relationship between CSF and structural biomarkers, and cognitive function in MCI. We examined the relationship between cognitive function, hippocampal volume and cerebrospinalfluid (CSF) Ab42and tau in 145 patients with MCI. Patients were assessed on cognitive tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB), the Geriatric Depression Scale and the Functional Activities Questionnaire. Hippocampal volume was measured using magnetic resonance imaging (MRI), and CSF markers of Ab42, tau and p-tau181were also measured. Worse per-formance on a wide range of memory and sustained attention tasks were associated with reduced hippocampal volume, higher CSF tau and p-tau181and increased tau/Ab42ratio. Memory tasks were also associated with lower ability to conduct functional activities of daily living, providing a link between AD biomarkers, memory performance and functional outcome. These results suggest that biomarkers of Ab and tau are strongly related to cognitive performance as assessed by the CANTAB, and have implications for the early detection and characterization of incipient AD.
Ó 2017 Elsevier Inc. All rights reserved.
1. Introduction
There is now general consensus that Alzheimer’s disease (AD) has a long prodromal phase, initially characterized by synaptic dysfunction due to amyloid precursors (dimers, oligomers) then by the gradual accumulation of beta-amyloid (A
b
) plaques and neurofibrillary tangles which lead to neuronal death and the emergence of impairment in cognitive function and functional activities of daily living (Jack and Holtzman, 2013; McKhann et al., 2011). Efforts to characterize this prodromal phase have resulted in the clinical entity of amnestic mild cognitive impairment (MCI), which is defined by the presence of a subjective memory complaint, an objective memory impairment that has been matched for age (e.g., performance i.e., 1.5 standard deviations less than normative controls) and the relative preservation of activities of daily living (Petersen, 2004; Winblad et al., 2004). However, MCI remains a largely heterogeneous condition with a range of different under-lying neurodegenerative mechanisms that result in different forms of dementia, of which AD is only one.Recent advances in neuroimaging and fluid biomarkers have allowed for the detection and early characterization of AD patho-logical markers in vivo, and may help in the etiologic definition of MCI. Neuroimaging studies conducted in a wide range of laboratories have now consistently shown that the majority of patients who meet clinical classification of dementia of the Alzheimer’s type have abnormal accumulation of A
b
(Ab
þ) in the brain (Jack et al., 2008; Rowe et al., 2010). Similarly, studies examining cerebrospinalfluid assays of patients with AD have shown significantly decreased levels of Ab
42. These studies suggest that Ab
biomarkers have a highsensitivity for identifying individuals with AD. However, the sensi-tivity of A
b
for the identification of the prodromal, or MCI stage of the disease is less clear, particularly as only 50%e60% of older adults who met clinical criteria for amnestic MCI were also Ab
þ (Fleisher et al., 2011; Johnson et al., 2013; Ong et al., 2013). An important limita-tion of these studies, however, has been the dichotomizalimita-tion of Ab
levels into positive and negative (e.g., standardized uptake value ratio scores that are higher or lower than the 1.5 threshold on a positron emission tomography scan using the Pittsburgh compound B radiotracer). As only very subtle differences in cognitive perfor-mance are likely to be observed when samples are divided into Ab
þ and Ab
, one approach has been to understand the relationship between cognitive performance and AD biomarkers instead. A sec-ond issue is that these studies have only focused on the relationship between Ab
and cognitive performance. As neurodegeneration and markers of neuronal injury such as tau, phosphorylated tau (p-tau) and hippocampal volume may be more closely related to cognitivefunction, particularly episodic memory (Bennett et al., 2004; Han et al., 2012), it is necessary to examine the relationships between cognitive function and other markers of AD beyond that of A
b
, particularly in the prodromal or MCI stage of the disease (Mormino et al., 2008).The overarching aim of this study was to examine the rela-tionship between performance on a well-established computerized cognitive test battery (Cambridge Neuropsychological Test Auto-mated Battery [CANTAB]) and AD-related biomarkers including hippocampal volume and cerebrospinalfluid (CSF) biomarkers of A
b
and tau in a group of well characterized patients with amnestic MCI. Our first hypothesis was that performance on measures of memory would be associated with reduced hippocampal volume, increased levels of CSF tau and p-tau, particularly given the early distribution of tau in the temporal cortex. As previous studies have reported relationships between Ab
and cognitive function, partic-ularly memory and executive function (Pike et al., 2007), our second hypothesis was that lower performance on measures of memory and executive function would be associated with lower levels of CSF Ab
42.2. Methods 2.1. Participants
A total of 145 older adults with MCI were recruited from the IMI Pharmacog’s WP5 (European ADNI) study, a prospective study with multiple centers in Europe. The process of recruitment and enrollment has been described in detail previously (Galluzzi et al., 2016). Briefly, the inclusion criteria included: (1) age between 55e90 years; (2) presence of a subjective memory complaint that has been verified by a family relative; (3) at least 1 standard de-viation deficit in a measure of episodic memory (Logical Memory II subscaleedelayed paragraph recall); (4) a MinieMental State Examination score between 24 and 30; (5) a Clinical Dementia Rating (CDR) scale score of 0.5 (with a score of 0.5 for the memory subscale); (6) a clinical diagnosis of amnestic MCI, but preserva-tion of general cognitive and funcpreserva-tional performance sufficient to not meet clinical criteria for AD; (7) Geriatric Depression Scale (GDS) scale score<6; and (8) Hachinski Modified Ischemic Scale score 4. Participants were excluded if there was a history of significant neurological or psychiatric illness, ferromagnetic im-plants, and/or devices that may preclude them from magnetic resonance imaging (MRI), brain malformations or other conditions that may complicate lumbar punctures, and use of the following medications: antidepressants with anticholinergic properties,
regular use of narcotic analgesics, use of neuroleptics with anti-cholinergic properties, and use of antiParkinsonian medications. The demographic and clinical characteristics of participants are summarized inTable 1.
All participants provided written informed consent prior to participating in the study. The study was reviewed and approved by the Ethics Committee of the coordinating site (Comitato Etico delle Istituzioni Ospedaliere Cattoliche) and then by Ethics Committees of all other sites (Galluzzi et al., 2016).
2.2. Assessments
2.2.1. CANTAB cognitive battery
All participants underwent cognitive evaluation using the CANTAB battery of computerized cognitive tests (Egerházi et al., 2007). The tests in this battery included the motor control task (practice task only), reaction time (RTI) task, delayed matching to sample (DMS) task, paired associate learning (PAL) task, spatial working memory (SWM) task, rapid visual information processing (RVP) task, pattern recognition memory (PRM) task (immediate and delayed), and spatial recognition memory (SRM) task. The key outcome measures selected for the analysis were; RTI (median reaction time), DMS (% correct [all delays]), PAL (total errors adjusted), SWM (between errors and strategy score), RVP (A0), PRM (% correct immediate and delayed), and SRM (% correct). These outcome variables were selected as they have been shown to be sensitive in detecting cognitive impairment in Alzheimer’s disease (Swainson et al., 2001).
2.2.2. Neuropsychiatric and functional measures
All participants completed the GDS. Participants’ caregivers completed the Functional Activities Questionnaire (FAQ), where informants rated the participants’ ability to conduct a range of activities of daily living. Participants’ caregivers also participated in a detailed interview with a trained clinician to complete the Neuropsychiatric Inventory Questionnaire (NPIQ).
2.2.3. Biochemical analysis
CSF from each participant was preprocessed, frozen, and stored at each site according to a standardized protocol developed by Mario Negri Institute of Milan (Italy) which is in line with the Alzheimer’s Association’s quality control program for CSF biomarkers. At the central site in Milan, dedicated single parameter colorimetric ELISA kits (Innogenetics, Belgium) were used to quantify the amyloid-
b
peptide 1e42 (Ab
42). Levels of tau and p-tau at residue 181 (p-tau181)in the CSF were also quantified using ELISA kits. Each sample was
assessed in duplicate. A sigmoidal standard curve was plotted to allow quantitative expression (picograms/milliliter) of measured light absorbance. Of note, CSF samples of 2 patients were excluded from all statistical analyses as the value of measured proteins resulted below the limit of detection. While this report considered levels of CSF biomarkers as a continuous variable, we have also dichotomized participants into groups A
b
þ (<550 pg/mL) and Ab
(>550 pg/mL), consistent with previous reports (Fagan et al., 2007). 2.2.4. Neuroimaging analysisAll MRI scans were performed on 3.0 Tesla machines. Details of scanners used at each site have been detailed previously (Galluzzi et al., 2016; Jovicich et al., 2013; Marizzoni et al., 2015). All partic-ipants underwent MRI scans during the screening phase of the study, between January 2012 and July 2013. All analyses were performed at the central study site (IRCCS Institute of San Giovanni di Dio, Brescia) as previously described (Jovicich et al., 2013; Marizzoni et al., 2015). Briefly, structural T1 images were visually inspected for quality assurance prior to analyses to ensure that there were no gross partial brain coverage errors and no major visible artefacts, including motion, wrap around, radio frequency interference, and signal intensity or contrast inhomogeneities. Within session T1 images were averaged and were processed using the FreeSurfer v5.1.0 (Dale et al., 1999; Fischl et al., 2002; Fischl et al., 2004; Reuter et al., 2012) on the neuGRID platform. 2.3. Data analysis
To determine differences in cognitive performance between A
b
þ and Ab
groups, a series of t tests were conducted. To determine the relationship between cognitive performance and biomarkers of AD, a series of linear mixed models using SAS software (v9.4) were con-ducted, with the relevant biomarker as afixed effect, and site as a random effect. All statistical models were adjusted for age, years of education, testing site, and evaluated for outliers. An adjustment for multiple comparisons was not conducted because (1) we had prior hypotheses pertaining to the relations between AD biomarkers and the cognitive outcomes reported here; (2) the cognitive variables were likely to be related; and (3) effect size statistics (e.g., r2and Cohen’s d) were used to quantify the magnitude of relations between outcome measures, thus lowering the likelihood of over-interpreting our results. Finally, with multiple tests for each biomarker with 9 cognitive variables, we would also expect less than 1 case per anal-ysis to be significant by chance at p ¼ 0.05.3. Results
3.1. Clinical and demographic differences
The clinical and demographic data for the overall group and the A
b
þ and Ab
groups are shown inTable 1. The Ab
þ group had lower MinieMental State Examination scores than the Ab
group at baseline, and the Ab
þ group had a significant higher proportion of apolipoprotein E (APOE)ε4 carriers than the Ab
group. No other clinical or demographic characteristic was statistically significant between Ab
þ and Ab
groups.3.2. Relationships between cognitive performance and markers of A
b
, tau, and brain volumeHigher CSF levels of tau and p-tau181were associated with worse
performance on“visuospatial” memory tests including associative memory and cued recall (PAL), visual episodic memory (PRM delayed), SWM, and SRM (Table 2,Figs. 1and2). Higher CSF levels of tau were also associated with worse performance on sustained attention (RVP) Table 1
Demographic and clinical characteristics Outcome variable Overall (n¼ 145), Mean (SD) Abþ (n ¼ 55), Mean (SD) Ab (n ¼ 90), Mean (SD) Range p
Age (y) 68.2 (7.3) 69.8 (6.7) 68.8 (7.7) 50e84 0.40 GDS 2.6 (1.9) 2.7 (1.9) 2.6 (1.9) 0e10 0.69 NPIQ 8.8 (10.3) 9.6 (11.1) 8.3 (9.7) 0e48 0.48 FAQ 2.5 (2.3) 2.6 (2.5) 2.5 (2.3) 0e11 0.67 MMSE 26.6 (1.9) 26.0 (1.8) 27.0 (1.8) 23e30 0.01
Percentage (%) Percentage (%) Percentage (%)
APOE (%ε4) 37.3 52.7 27.8 0.00
Sex (% Male) 42.8 42.2 43.6 0.87 Education
(%10 y)
53.1 45.5 56.8 0.15
Bold values are significant at the p < 0.05 level.
Key: APOE, apolipoprotein E (gene); FAQ, Functional Activities Questionnaire; GDS, Geriatric Depression Scale; MMSE, MinieMental Status Examination; NPIQ, Neuropsychiatric Inventory Questionnaire.
(Table 2). Lower levels of A
b
42was associated with worse performanceon visual episodic memory (PRM delayed), working memory (SWM and DMS) and recognition memory (SRM) (Table 2). Interestingly, higher tau/A
b
42ratio was associated with worse performance on alltests of memory, including episodic memory (PAL and PRM delayed), working memory (SWM and DMS), recognition memory (SRM and PRM immediate), and sustained attention (RVP).
Associations were also found between reduced hippocampal volume and memory including episodic memory (PAL and PRM delayed), working memory (SWM), recognition memory (PRM immediate), and sustained attention (RVP) (Table 2).
For comparison, we also examined relationships between biomarkers and cognition as dichotomous groups. When partic-ipants were divided into A
b
þ and Ab
groups, the Ab
þ group showed worse performance relative to the Ab
group on recog-nition and working memory tasks (i.e., SRM and DMS) (Table 3), with these differences, by convention, large in magnitude (i.e., Cohen’s d ¼ 0.61e0.67), with a moderate difference identified for SWM errors (d ¼ 0.30). Groups did not differ on any measure of attention (i.e., RTI) or PAL (PAL total errors), with any differences between groups small in magnitude (d< 0.30;Fig. 3).Table 2
Relationship between each cognitive task and each biomarker
Outcome variable Memory tasks Executive function,
SWM strategy Sustained attention, RVP Processing speed, RTI Episodic memory, PAL errors
Working memory Recognition memory SWM errors DMS SRM PRM immediate PRM delayed Baseline model Random effects (site) ICC 0.15 0.03 0.00 0.03 0.00 0.12 0.00 0.16 0.06 Tau t 2.51 2.21 0.84 2.58 1.36 2.07 0.19 2.03 0.42 R2 0.06 0.05 0.01 0.06 0.02 0.04 0.00 0.04 0.00 p 0.014 0.030 0.402 0.011 0.178 0.041 0.849 0.045 0.676 ICC 0.15 0.01 0.00 0.02 0.00 0.15 0.01 0.16 0.06 P-Tau t 2.00 2.12 0.92 2.06 0.37 0.95 0.91 1.63 0.19 R2 0.04 0.05 0.01 0.04 0.00 0.01 0.01 0.03 0.00 p 0.048 0.037 0.370 0.042 0.713 0.346 0.367 0.106 0.850 ICC 0.16 0.02 0.00 0.03 0.00 0.13 0.01 0.17 0.06 Ab42 t 1.50 2.56 3.35 2.75 1.15 2.16 0.41 1.80 0.22 R2 0.02 0.07 0.09 0.07 0.01 0.04 0.00 0.03 0.00 p 0.138 0.012 0.001 0.007 0.251 0.033 0.681 0.075 0.826 ICC 0.14 0.02 0.00 0.04 0.00 0.15 0.01 0.15 0.06 Tau/Ab42 t 2.96 2.77 2.17 3.77 2.09 2.39 0.14 2.66 0.07 R2 0.08 0.08 0.04 0.11 0.04 0.05 0.00 0.07 0.00 p 0.004 0.007 0.037 <0.001 0.039 0.019 0.886 0.009 0.948 ICC 0.17 0.01 0.00 0.03 0.00 0.16 0.01 0.16 0.06 Left HV t 4.29 2.23 0.84 1.47 1.22 2.40 2.37 1.45 0.09 R2 0.18 0.07 0.40 0.03 0.02 0.06 0.07 0.03 0.00 p <0.001 0.029 0.402 0.147 0.225 0.019 0.021 0.151 0.927 ICC 0.10 0.00 0.02 0.02 0.00 0.15 0.00 0.13 0.03 Right HV t 4.89 2.00 1.58 1.24 2.03 1.92 1.78 2.54 0.33 R2 0.22 0.05 0.03 0.02 0.05 0.04 0.04 0.08 0.00 p <0.001 0.049 0.118 0.220 0.046 0.058 0.079 0.013 0.740 ICC 0.10 0.00 0.01 0.02 d 0.13 0.01 11.56 0.03 Baseline model BIC 1155.5 744.1 1053.0 989.2 1032.7 1055.7 485.9 333.0 1367.7 Tau BIC 1129.3 722.5 1022.3 961.6 1009.0 1030.2 481.1 342.2 1337.6 P-Tau BCC 1119.6 722.7 1022.4 963.8 1010.3 1033.7 480.4 340.9 1337.6 Ab42 BCC 1115.9 740.3 1044.8 984.5 1034.0 1053.8 488.3 333.8 1370.2 Tau/Ab42 BIC 1115.1 719.8 1011.1 947.7 998.0 1021.4 481.1 341.0 1326.6 Left HV BIC 904.9 583.9 834.4 786.0 827.3 839.2 384.8 288.4 1073.9 Right HV BIC 900.4 584.8 832.9 786.4 824.7 841.1 389.2 292.4 1073.8
t, R2, and p indicatefixed effects of biomarker.
Bold values are significant at the p < 0.05 or p < 0.001 level; all values have been adjusted for age, years of education, and testing site.
Key: BIC, Bayesian information criteria; DMS, delayed matching to sample; FAQ, Functional Activities Questionnaire; GDS, Geriatric Depression Scale; HV, hippocampal volume; ICC, intraclass correlation coefficient (random effect of site); NPIQ, Neuropsychiatric Inventory Questionnaire; PAL, paired associate learning; PRM, pattern recognition memory; p-tau, phosphorylated tau; RTI, reaction time; RVP, rapid visual processing; SRM, spatial recognition memory; SWM, spatial working memory.
3.3. Relationships between cognitive performance and neuropsychiatric and functional measures
There were significant associations between tasks of memory with FAQ, but not the GDS or NPIQ (Table 4). Specifically, asso-ciations were observed between episodic memory (PAL, PRM delayed), working memory (DMS), and ratings on the FAQ. There was also a small but significant association between GDS scores and performance on the task of sustained attention (RVP) (Table 4).
4. Discussion
This is the first study examining the relationship between cognitive function as assessed by the CANTAB battery and both structural and CSF biomarkers relevant to prodromal AD. Ourfirst hypothesis, that performance on measures of memory would be associated with reduced hippocampal volume, and increased levels of CSF tau and p-tau, was supported. Worse performance on CANTAB tasks measuring “visuospatial memory” including associative memory and cued recall (e.g., PAL), episodic memory (e.g., PRM delayed recall), spatial recognition memory (e.g., SRM),
and spatial working memory (e.g., SWM) were significantly asso-ciated with higher CSF levels of tau and p-tau181 (Table 2). This
relationship between memory and tau is consistent with previous studies (Bennett et al., 2004; Ingelsson et al., 2004), and is also consistent with our observations that performance on these tasks were also significantly associated with lower hippocampal volume (Table 2), with PAL accounting for approximately 20% of the vari-ance. These results suggest that performance on tasks of memory, particularly PAL, is strongly associated with hippocampal volume, and suggests a role for the PAL task in characterizing hippocampal-dependent memory impairment in the early stages of AD. The latter is of great significance, given that reduction in hippocampal volume is a recognized biomarker of AD (Frisoni et al., 2010), the rate of hippocampal atrophy is among the most sensitive markers of disease progression in AD (Frisoni et al., 2010; Jack et al., 2004) and one of the core biomarkers in the revised National Institute on Aging-Alzheimer’s Association (NIA-AA) diagnostic criteria for AD (Jack et al., 2012). Hippocampal atrophy has been qualified by the European Medicines Agency as a biomarker for enrichment in regulatory clinical trials in the early stages of AD (CHMP, 2011) and is currently being used as a secondary outcome in several clinical trials with candidate disease modification drugs.
Fig. 1. Relationship between performance on the spatial working memory (SWM) task and hippocampal volume, CSF Ab42, and CSF tau levels. Model predicted scores adjusted for
The second hypothesis, that lower performance on measures of memory and executive function would be associated with lower levels of CSF A
b
42, was partially supported. Worse performance ontasks of working (i.e., SWM, DMS) and recognition memory (i.e., SRM, PRM) from the CANTAB battery were significantly associated with lower CSF levels of A
b
42 (Table 2), and these were, byconvention, of a moderate magnitude; however, there were no
associations between executive function (i.e., as measured by the SWM task) and CSF A
b
42levels (Table 2). However, in this study,only 1 measure of executive function (SWM strategy) was included, and as the domain of executive function can be divided into several subdomains, such as planning, response, inhibition, and manipu-lation, it is possible that a relationship may exist only with execu-tive processes that also overlap with memory processes, such as Fig. 2. Relationship between performance on the paired associate learning (PAL) task and hippocampal volume, CSF Ab42, and CSF tau levels. Model predicted scores adjusted for age
and education.
Table 3
Differences between Abþ and Ab groups on each cognitive outcome measure
Outcome variable Overall (n¼ 145), Mean (SD) Abþ (n ¼ 55), Mean (SD) Ab (n ¼ 90), Mean (SD) p PAL total errors (adjusted) (ve) 55.3 (26.6) 59.4 (25.3) 52.3 (27.2) 0.14
SWM between errors (Lve) 27.7 (8.3) 29.6 (7.1) 26.3 (8.4) 0.04
DMS % correct (all delays) (Dve) 67.9 (16.3) 62.5 (16.6) 72.0 (14.9) <0.01
SRM % correct (Dve) 63.7 (13.5) 58.7 (13.6) 67.3 (12.3) <0.01
PRM immediate % correct (þve) 77.6 (15.2) 75.5 (14.5) 79.2 (15.6) 0.17 PRM delayed % correct (þve) 65.3 (17.9) 63.5 (17.4) 66.6 (18.3) 0.34
SWM strategy (ve) 19.9 (2.4) 20.2 (2.0) 19.7 (2.7) 0.43
RVP A0(þve) 0.8 (0.1) 0.8 (0.1) 0.8 (0.1) 0.33
RTI (ms) 417.9 (96.4) 414.5 (97.7) 420.5 (96.0) 0.74
þve indicates that higher score ¼ better performance; ve indicates that higher score ¼ worse performance. Bold values are significant at the p < 0.05 level; all values have been adjusted for age and years of education.
Key: DMS, delayed matching to sample; PAL, paired associate learning; PRM, pattern recognition memory; RTI, reaction time; RVP, rapid visual processing; SRM, spatial recognition memory; SWM, spatial working memory.
working memory. In support, previous studies from the Australian Imaging, Biomarkers and Lifestyle study have observed that A
b
þ older adults with amnestic MCI performed worse than Ab
olderadults with amnestic MCI on a computerized task of working memory from the Cogstate Brief Battery (Lim et al., 2013c) but not on neuropsychological pen and paper tasks such as the Rey Fig. 3. Magnitude of difference between Ab MCI and Abþ MCI groups on a series of cognitive measures from the CANTAB computerized test battery (“0” line represents Ab MCI and error bars represent 95% confidence intervals). Abbreviations: CANTAB, Cambridge Neuropsychological Test Automated Battery; MCI, mild cognitive impairment.
Table 4
Relationship between each cognitive task and neuropsychiatric measure
Outcome variable Memory tasks Executive function,
SWM strategy Sustained attention, RVP Processing speed, RTI Episodic memory, PAL errors
Working memory Recognition memory SWM errors DMS SRM PRM immediate PRM delayed Baseline model Random effects (site) ICC 0.15 0.03 0.00 0.03 0.00 0.12 0.00 0.16 0.06 GDS t 0.85 0.38 1.18 1.11 1.28 0.33 0.18 2.02 0.32 R2 0.01 0.00 0.01 0.00 0.02 0.00 0.00 0.04 0.00 p 0.396 0.702 0.242 0.589 0.202 0.740 0.854 0.046 0.749 ICC 0.14 0.02 0.00 0.03 0.00 0.12 0.00 0.08 0.05 FAQ t 3.70 1.59 2.20 1.15 1.19 2.41 0.45 1.27 0.34 R2 0.12 0.03 0.05 0.02 0.01 0.06 0.00 0.02 0.00 p <0.001 0.115 0.030 0.253 0.239 0.018 0.652 0.208 0.734 ICC 0.11 0.03 0.00 0.03 0.00 0.11 0.01 0.12 0.08 NPIQ t 1.90 0.63 1.15 0.54 0.51 1.70 0.46 0.62 0.30 R2 0.04 0.01 0.01 0.00 0.00 0.03 0.00 0.00 0.00 p 0.060 0.529 0.255 0.589 0.610 0.092 0.650 0.535 0.768 ICC 0.19 0.04 0.00 0.03 0.01 0.14 0.02 0.13 0.08 Baseline model BIC 1155.5 744.1 1053.0 989.2 1032.7 1055.7 485.9 333.0 1367.7 GDS BIC 1154.7 746.5 1054.2 990.5 1033.7 1058.2 488.4 287.8 1370.2 FAQ BIC 1031.3 671.0 938.2 842.9 925.5 950.5 422.1 295.3 1229.0 NPIQ BIC 1022.9 665.4 928.9 873.0 911.9 939.6 433.1 293.1 1206.2
t, R2, and p indicatefixed effects of biomarker.
Bold values are significant at the p < 0.05 or p < 0.001 level; all values have been adjusted for age, years of education, and testing site.
Key: BIC, Bayesian information criteria; DMS, delayed matching to sample; FAQ, Functional Activities Questionnaire; GDS, Geriatric Depression Scale; ICC, intraclass correlation coefficient (random effect of site); NPIQ, Neuropsychiatric Inventory Questionnaire; PAL, paired associate learning; PRM, pattern recognition memory; RTI, reaction time; RVP, rapid visual processing; SRM, spatial recognition memory; SWM, spatial working memory.
Complex Figure Test and or tests of verbalfluency (Ellis et al., 2013; Lim et al., 2014b). The hypothesis that only executive processes that also have a memory load are affected in the early AD process, can be tested in future studies that have a wider range of executive func-tion measures in their cognitive battery.
Interestingly, worse performance on all tests of memory including episodic memory (PAL), working memory (SWM, DMS), and recognition memory (SRM, PRM) were associated with a higher tau/A
b
42ratio (Table 2). Previously, pathological studies and CSFstudies have shown that in AD, cognitive impairment and synaptic loss are associated more strongly with the presence and number of neurofibrillary tangles, rather than amyloid plaques (Bennett et al., 2004; Giannakopoulos et al., 2003; Ingelsson et al., 2004). However, neuroimaging studies of preclinical and prodromal AD have re-ported that higher cortical A
b
load is associated with greater rates of cognitive decline and progression to AD (Lim et al., 2014a; Rowe et al., 2013). The results of this study suggest that while there are independent associations between CSF Ab
42 as well as tau andp-tau, and cognitive function in MCI, these associations become stronger, affecting broader cognitive domains when levels of both CSF tau and A
b
42are considered together.Worse performance on the CANTAB task of sustained attention (i.e., RVP) was also similarly associated with higher CSF levels of tau and p-tau, and tau/A
b
42 ratio, and lower hippocampal volume(Table 2). Previous studies have not reported any relationship between tasks of complex or simple attention and any biomarker of AD pathophysiology (e.g., A
b
levels or hippocampal volume), with most studies only noticing any deterioration in attentional function when individuals progress to meet a clinical diagnosis of AD. One possible reason why the CANTAB sustained attention task may be sensitive to subtle impairments in this prodromal stage of AD is because in addition to probing sustained attention, the task involves aspects of working memory (e.g., remembering the sequences 3-5-7, 2-4-6, and 4-6-8 in order to respond accurately) (van der Wardt et al., 2015). Further, brain imaging studies have shown that successful performance on the CANTAB task of sustained attention is associated with activation within the classical attentional network, including the frontal, parietal, and occipital gyrus, as well as deactivation within the temporal and para-hippocampal gyrus regions (Coull et al., 1996), which are known to be affected by both Ab
and tau pathology (Brier et al., 2016). As neurodegenerative mechanisms leading to cognitive decline severely impact on brain connectivity (initially by synaptic dysfunction), then also by degeneration of cortico-cortical con-nections, it is not surprising that highly specialized functions which involve large networks linking adjacent and remote neuronal assemblies “around” a given function, are precociously involved (D’Amelio and Rossini, 2012).While the focus of this study was to examine the relationship between AD-related biomarkers and cognitive function, for completeness, it is important to compare thefindings examining associations with biomarkers as continuous variables with impairment in cognition in dichotomized groups (i.e., A
b
þ vs Ab
). The latter findings have been previously reported in brief (Galluzzi et al., 2016), but we further discuss the significance of thesefindings here. In comparison to the Ab
MCI group, the Ab
þ MCI group showed significantly worse performance, of a moderate magnitude, on measures of working memory (SWM and DMS) and recognition memory (SRM) (Fig. 3, Table 3). These findings are consistent with the findings observed when examined as a continuous variable with worse performance on SWM, DMS, and SRM associated with lower CSF levels of Ab
42. The substantialdef-icits (dz 0.6) observed in the A
b
þ MCI group on memory tasks including the DMS and SRM suggest that despite all participants meeting criteria for amnestic MCI (and showing impairment onepisodic memory), the A
b
þ MCI group also showed additional deficits in other types of memory probing a wider neural network beyond the hippocampus/temporal cortex. This is consistent with previous reports from our group (Galluzzi et al., 2016), and the Australian Imaging, Biomarkers and Lifestyle study that have also observed that in older adults with amnestic MCI, Ab
þ groups per-formed worse than Ab
groups on tasks of language and working memory (Lim et al., 2013b). It is also important to note that no differences between Ab
þ MCI and Ab
MCI groups were observed on the hippocampal-dependent task of episodic memory (i.e., PAL). One hypothesis for this is that as all participants had met clinical criteria for amnestic MCI with objective and subjective episodic memory deficits at screening, it is unlikely that any further deficits in episodic memory would be evident.Cognitive tasks that were associated with structural and CSF biomarkers were also associated with functional outcome. In particular, significant associations between memory tasks and ratings on the FAQ (Table 4) were observed. Specially, associations were found between hippocampal (PAL, PRM) and frontal cortex (DMS)edependent memory tasks and FAQ. The worse performance on these tasks that is associated with lower ability to conduct activities of daily living is consistent with the notion that the preservation of memory function is highly involved in an in-dividual’s ability to conduct and manage such activities of daily living (Perneczky et al., 2006; Royall et al., 2005). Thesefindings are of importance given the need to identify cognitive measures related to functional outcomes for use in clinical trials in prodromal AD and the requirements placed by regulatory bodies to demonstrate the benefit of new treatments on functional outcome.
When interpreting the results of this study, there are several caveats to consider. First, the clinicopathologic inferences that we have made in this study between measures of cognitive function and AD biomarkers were based on cross-sectional data. As AD is a neurodegenerative disease, the measurement of change in cognitive function and AD biomarkers over time may improve our understanding of the relationship between AD biomarkers and cognitive function in this early stage of the disease. Second, we have only explored the relationship between AD biomarkers and cogni-tive function, without taking into consideration other potential moderating factors, such as the APOEε4 allele. As previous studies in cognitively normal older adults have suggested that some of the heterogeneity in the relationship between A
b
and memory may be explained by the APOEε4 allele (Kantarci et al., 2012; Lim et al., 2013a), it will be important for future studies to extend these investigations into MCI populations. Finally, this study did not recruit cognitively normal older adults, and as such, a character-ization of the current MCI group to a matched healthy control sample was not conducted. We were also unable to directly deter-mine whether relations between each AD biomarker and cognitive function as assessed by the CANTAB were similarly present in cognitively normal older adults.These caveats notwithstanding, the results of this study show that the strong association between CSF markers of neuronal injury such as tau and p-tau181and memory processes that tap
into both frontal and hippocampal networks, accords with neuropathological studies that have demonstrated strong relationships between neurofibrillary tangles and cognitive func-tion (Bennett et al., 2004). It also highlights that while cognitive function, particularly memory, is closely associated to markers of neurodegeneration and neuronal injury (e.g., loss of hippocampal volume), they are also highly associated with levels of A
b
. This is consistent with previous biomarker studies that have also shown strong relationships between cortical amyloid levels and cognitive function in both cognitively normal older adults and in MCI pop-ulations. Finally, thefindings also raise an important distinction inthe classification of patients with MCI due to AD, and individuals with MCI due to a non-AD pathophysiological process. While clinical subclassifications such as “amnestic” and “nonamnestic” MCI have been previously used to make this distinction (Petersen, 2004), the results of this study have shown that even with careful clinical classification of individuals, there remain a substantial proportion of A
b
negative older adults who have been clinically classified as amnestic MCI. As such, the presence of an abnormal biomarker of Ab
and an abnormal biomarker of tau, in conjunction with the presence of objective cognitive impairment, particularly in memory and executive function, may provide the greatest confidence in identifying individuals who may be at highest risk of progressive cognitive decline (Albert et al., 2011; Dubois and Albert, 2004; Shaw et al., 2009).Disclosure statement
R. A. is an employee of Cambridge Cognition, the company that provides the CANTAB cognitive test battery. J. R. is an employee of GlaxoSmithKline. F. N. has received fees from Eli Lilly & Co in 2014e2015 for amyloid imaging reading courses. P. J. V. has received grants from EU/EFPIA Innovative Medicines Initiative Joint Undertaking, EU Joint ProgrammeeNeurodegenerative Disease Research (JPND) and ZonMw, during the conduct of this study; he also received grants from Bristol-Myers Squibb, nonfinancial support from GE Healthcare and other support from Roche Diagnostics which is of no relevance to the submitted work. All other authors have no conflict of interests to disclose.
Acknowledgements
The research leading to the current results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) for the Innovative Medicine Initiative under grant agreement no 115009 (prediction of cognitive properties of new drug candidates for neurodegenerative diseases in early clinical development, PharmaCog).
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