Cognitive impairment and decline in cognitively normal older adults with high amyloid-β: A meta-analysis

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Cognitive & Behavioral Assessment

Cognitive impairment and decline in cognitively normal older adults with high amyloid-b: A meta-analysis Q5

Q1

Jenalle E. Bakera,b,*, Yen Ying Lima,c, Robert H. Pietrzakd,e, Jason Hassenstabf,g, Peter J. Snyderh,i, Colin L. Mastersa, Paul Maruffa,b,c a

The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia b CRC for Mental Health, Carlton South, Victoria, Australia c Cogstate Ltd., Melbourne, Victoria, Australia d U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA e Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA f Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA g Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA h Department of Neurology, Warren Alpert School of Medicine, Brown University, Providence, RI, USA i Department of Neurology, Rhode Island Hospital & Alpert Medical School of Brown University, Providence, RI, USA

Abstract

Introduction: This meta-analysis aimed to characterize the nature and magnitude of amyloid (Ab)related cognitive impairment and decline in cognitively normal (CN) older individuals. Method: MEDLINE Ovid was searched from 2012 to June 2016 for studies reporting relationships between cerebrospinal fluid or positron emission tomography (PET) Ab levels and cognitive impairment (cross-sectional) and decline (longitudinal) in CN older adults. Neuropsychological data were classified into domains of episodic memory, executive function, working memory, processing speed, visuospatial function, semantic memory, and global cognition. Type of Ab measure, how Ab burden was analyzed, inclusion of control variables, and clinical criteria used to exclude participants, was considered as moderators. Random-effects models were used for analyses with effect sizes expressed as Cohen’s d. Results: A total of 37 studies met inclusion criteria contributing 30 cross-sectional (N 5 5005) and 14 longitudinal (N 5 2584) samples. Ab-related cognitive impairment was observed for global cognition (d 5 0.32), visuospatial function (d 5 0.25), processing speed (d 5 0.18), episodic memory, and executive function (both d’s 5 0.15), with decline observed for global cognition (d 5 0.30), semantic memory (d 5 0.28), visuospatial function (d 5 0.25), and episodic memory (d 5 0.24). Ab-related impairment was moderated by age, amyloid measure, type of analysis, and inclusion of control variables and decline moderated by amyloid measure, type of analysis, inclusion of control variables, and exclusion criteria used. Discussion: CN older adults with high Ab show a small general cognitive impairment and small to moderate decline in episodic memory, visuospatial function, semantic memory, and global cognition. Ó 2016 Published by Elsevier Inc. on behalf of the Alzheimer’s Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords:

Cognition; Amyloid-beta; Impairment; Decline; Meta-analysis; Preclinical Alzheimer’s disease

1. Introduction The authors list no conflicts related to the material presented in this article. *Corresponding author. Tel.: 161 3 9664 1327; Fax: 161 3 9664 1301. E-mail address: [email protected]

There is now consensus that in cognitively normal (CN) older adults, abnormal levels of amyloid-beta (Ab1) indicates that the pathophysiological process of Alzheimer’s disease

http://dx.doi.org/10.1016/j.dadm.2016.09.002 2352-8729/Ó 2016 Published by Elsevier Inc. on behalf of the Alzheimer’s Association. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). REV 5.4.0 DTD
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(AD) has begun, although it may still be up to 20 years before these individuals meet clinical criteria for dementia [1–4]. Neuroimaging and fluid biomarkers allow for in vivo measurement of Ab burden in older individuals using positron emission tomography (PET) and cerebrospinal fluid (CSF) sampling, respectively [5,6]. Studies using these techniques have shown that Ab burden increases with age, with approximately 10%–20% of CN older adults aged 60–70 years, 20%–30% of those aged 70–80 years, and 30%–40% of those aged 80–90 years being classified as Ab1 [4,7,8]. Despite their CN classification, prospective studies indicate that cognitive decline is faster and progression to a clinical diagnosis of mild cognitive impairment (MCI) or AD more rapid, in those who are Ab1 compared to matched CN adults with low Ab levels (Ab2) [2,3]. Characterizing preclinical AD is therefore important for understanding the pathogenesis of AD. Although PET Ab imaging or CSF sampling identifies reliably the presence of AD pathology in individuals with no overt symptoms, these procedures are expensive, invasive, and must occur in specialized medical centers. Ideally, sensitive and cost-effective clinical measures could be used to identify CN adults who should be referred for these more expensive and invasive testing procedures. Neuropsychological assessment may be useful in this regard, where the presence of a subtle but specific profile of cognitive dysfunction could indicate that Ab1 would be classified on CSF sampling or PET imaging. However, to date, there is no agreement on what constitutes subtle cognitive decline among individuals with Ab1. Evidence in support of a cognitive profile indicative of Ab1 comes from neuropsychological studies that use two types of experimental designs. First are studies that define Ab1-related cognitive impairment on the basis of the comparison of performance on batteries of neuropsychological tests between Ab1 CN older adults and Ab2 CN older adults at a single assessment. Such studies generally report only small and statistically nonsignificant differences in neuropsychological test performance between Ab2 and Ab1 CN older adults [9–13]. Second are studies that define Ab1-related cognitive decline by evaluating changes in performance on neuropsychological test batteries over time between Ab1 and Ab2 CN older adults. Studies using this approach have consistently found evidence of Ab1-related decline on measures of episodic memory, executive function, processing speed, visuospatial function, and language (e.g., [9,11,14–21]). However, although individual studies have identified areas of cognitive impairment and decline associated with Ab1, there is substantial variation between these studies in terms of the sample sizes enrolled, the domains of cognitive function assessed, the specific neuropsychological or cognitive tests used to measure these domains, and the statistical techniques used to compare Ab1 and Ab2 groups. Furthermore, in many studies, conclusions about the effects of Ab1 on cognition have been based only on the presence or absence of statistical significance. Consequently, small but important Ab1-related effects may have been missed when sample

sizes did not provide adequate statistical power to render such differences statistically significant. Meta-analyses of the existing literature on Ab1-related cognitive impairment, and decline could therefore provide an effective method for overcoming the different limitations of individual studies to provide reliable estimates of Ab1-related cognitive impairment and decline in preclinical AD. To date, one meta-analysis evaluated this question and concluded that associations with Ab burden were strongest for episodic memory (e.g., r 5 20.12; Hedden et al., 2013 [16]). Additionally, when combining estimates across studies that measured Ab using CSF, PET, plasma, and histopathologic methods, lower performance in executive function was also related significantly (r 5 0.08) to Ab burden. Post hoc analyses indicated that estimates of Ab1-related cognitive dysfunction were unaffected by the experimental design used, the method of determining Ab levels, or whether demographic or clinical variables were controlled statistically. Although this initial meta-analysis provides a good basis for understanding the effects of Ab on cognition in CN older adults, its conclusions are limited because a large number of studies investigating relationships between Ab1 and neuropsychological test performance have been conducted since its publication. Second, the number of studies using either cross-sectional or longitudinal designs is now sufficient to consider estimates of cognitive impairment and cognitive decline separately. Third, a broader sample of cognitive domains is now available for inclusion in meta-analyses. Finally, samples in studies using longitudinal designs have been followed for longer periods. As such, an updated metaanalysis of this literature is needed to understand the relation between Ab and cognitive impairment and decline in preclinical AD. The aim of this study was therefore to systematically review the literature on the nature and magnitude of Ab1related cognitive impairment and decline in older adults who do not meet clinical criteria for MCI or dementia.

2. Methods 2.1. Study selection 2.1.1. Inclusion/exclusion criteria Inclusion criteria for the meta-analyses were that (a) the study must include a sample of adults with an average age
60 years who did not meet clinical criteria for MCI or dementia and who had undergone assessment with standardized neuropsychological tests; (b) for each participant, Ab levels were determined using PET or CSF sampling; and (c) studies must have provided sufficient information to allow for the computation of effect sizes. Studies were excluded from the meta-analysis if they were one of a series of publications from the same specific cohort where, over time, sample sizes or the length of follow-up had increased. For studies meeting this criterion, data for the meta-analyses were taken from that study which was the

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most recent and which had the largest sample. If possible, data for any cognitive domain not reported in the chosen publication were derived from another publication from the same cohort with the next largest sample where relevant data were presented, so as to ensure as many cognitive domains as possible were represented for each cohort. Second, studies were excluded if neuropsychological data or clinical data had been used to classify CN adults “progressors/decliners” or “stable/nondecliners” independent of Ab classification. Finally, for studies that reported results from both PET and CSF sampling, we focused on PET results as indicators of Ab levels to maintain consistency across studies. 2.2. Systematic review methods Supplementary Fig. A summarizes the outcome of the systematic review process. Initially, all studies included in the meta-analysis of Hedden et al. (2013) [16] were screened via title and abstract, to determine relevance to inclusion and exclusion criteria of the current meta-analysis. Fifteen articles were excluded at this stage. A systematic electronic database search was then conducted on Medline Ovid, on the 8th of June 2016, using the subject terms for amyloid, older adults and AD, and cognition that were used in the meta-analysis by Hedden et al. (2013) [16]. The terms are described below: 1. (amyloid) AND (“Pittsburgh Compound B” OR PIB OR florbetapir OR AV-45 OR florbetaben OR flutemetamol OR PET) OR (CSF) 2. (normal OR nondemented OR aging OR older OR Alzheimer’s OR dementia OR “cognitive impairment” OR MCI) 3. (cognitive OR cognition OR memory OR executive OR speed OR visuospatial OR semantic) At this stage, 501 articles were identified to be screened via title and abstract. After removal of duplicates and review articles, 362 articles were screened via title and abstract, with 335 being excluded at this stage (Supplementary Fig. A). After this, full-text screening began of the articles identified from the Hedden et al. analysis and from those identified in the electronic search. Reference lists of articles were also screened for key citations, and newly published articles were screened for relevance. Fifty studies were excluded at this stage (see Supplementary Fig. A for details), leaving 69 studies meeting the full criteria. The largest samples from ongoing observational studies were chosen to represent the data for cognitive domains from that cohort. This process was completed separately for studies using cross-sectional or longitudinal designs. At completion, 38 studies met inclusion/exclusion criteria, consisting of 30 that used crosssectional designs and 14 that used longitudinal designs.

3

cognitive domain measured by that test according to reference frameworks from previous meta-analyses of cognition [16,22,23] and standard neuropsychological compendia [24]. The cognitive domains used and the tests classified into each of those domains are summarized in Supplementary Table A. 2.4. Statistical analysis Separate analyses were conducted for studies using crosssectional and longitudinal designs using Comprehensive Meta-Analysis, version 3.3 software (Biostat, NJ). All results are reported using a random-effects model. Effect sizes were calculated using reported statistics such as means and standard deviations (Cohen’s d) and results from analyses such as t tests, correlations, regressions (r), and linear mixedeffects models. If standard error was reported in a study, this was converted to standard deviation before the calculation of effect sizes. For each neuropsychological test or cognitive domain composite score, the sign of effect sizes was adjusted so that negative effect sizes reflected greater Ab1related impairment or decline. For studies that used more than one neuropsychological test to measure the same cognitive domain, effect sizes were averaged. All effects were weighted using inverse variance weighting based on sample size. All effect sizes were transformed into Cohen’s d, and the magnitudes were classified as small, medium, or large according to Cohen (1992) [25]. A more lenient criterion was used to assess statistical significance of heterogeneity (P , .10) due to the lack of statistical power of these tests [26]. Where statistically significant heterogeneity was identified, post hoc subgroup analyses were conducted to seek the source of this heterogeneity [27]. These analyses investigated the extent to which heterogeneity in estimated mean effect sizes arose from variance due to moderators such as: (a) the type of Ab measure (PET or CSF); (b) whether Ab burden was defined as a continuous or categorical measure; (c) whether the study had controlled statistically the effects of demographic or clinical covariates (e.g., age, education, and so forth); and (d) the clinical classification criteria used to exclude participants from CN samples (i.e. the criteria for prodromal AD or MCI used). Where group mean effect sizes were based on 10 or more samples and there was no evidence of heterogeneity, publication bias was assessed by visual inspection of funnel plots and the Egger test, with the significance level set at P , .10 [28,29]. This was done to assess whether the effect sizes of individual studies were distributed symmetrically around the overall mean effect size for the cognitive domain, or whether study size biased the results in a particular direction. 3. Results

2.3. Classification of outcome measures

3.1. Meta-analysis of studies using cross-sectional designs to measure cognitive impairment

For each study, performance data from neuropsychological tests were identified and organized according to the main

Study characteristics and individual effect sizes for studies that used cross-sectional designs are presented in

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378 (Table 1). Stratifying the studies by participants’ mean age Supplementary Table B. Estimates of effect sizes from cross379 showed that effect sizes for episodic memory and executive sectional studies were based on 30 samples providing 5005 380 function were significant only for studies using samples aged participants with study samples ranging between 23 and 381 75 years and more (P’s 5 .12 and .07 for the younger groups 564 participants. Of these, 2524 were females (50%), 1304 382 respectively), whereas visuospatial function was significant 383 Q2 carried at least one APOEε4 allele (26%), with the average 384 in the younger group only (P 5 .06 for older group). Statisages of the individual samples ranging from 60.5 to 79 years. 385 tically significant heterogeneity was detected for pooled esNineteen studies contributed data for episodic memory 386 timates of effect sizes for episodic memory. No evidence of (n 5 2886), 13 for executive function (n 5 2281), 10 for 387 publication bias was found for the remaining domains. working memory (n 5 1598), 13 for processing speed 388 (n 5 2530), nine for visuospatial function (n 5 1984), 13 389 390 for semantic memory (n 5 2585), and eight for global cogni3.3. Moderator effects on studies of cognitive impairment 391 tion (n 5 1746). 392 Table 2 shows the results of the post hoc analyses for For the majority of studies, Ab levels were determined 393 episodic memory, where significant heterogeneity was deusing PET neuroimaging (87%) with over half (63%) classi394 tected. Group mean effect sizes remained statistically signiffying Ab levels categorically (e.g., positive vs. negative or 395 icant when these accounted for data from studies that had 396 high vs. low). Demographic and clinical variables such as used PET to measure Ab levels, classified Ab as categorical, 397 age, premorbid IQ, sex, education, and APOEε4 were 398 and had controlled statistically for demographic or clinical controlled statistically in 53% of studies. The two main clin399 variables. ical criteria used to exclude participants with MCI/prodro400 mal AD were the Clinical Dementia Rating (CDR; [30]) 401 3.4. Meta-analysis of studies using longitudinal designs to 402 scale total score of 0.5 (53%) and the Petersen criteria measure cognitive decline 403 [31]. Four studies [32–35] used alternative criteria, such as 404 the Mattis Dementia Rating Scale ([36]), the Jak and Bondi Study characteristics and individual effect sizes from 405 method [37,38], and a combination of CDR 0.5 and Petersen studies using longitudinal designs included in the analysis 406 criteria. For 77% of studies, the average age of participants 407 are shown in Supplementary Table C. Estimates of effects 408 was under 75 years. from studies using longitudinal designs were based on 14 409 samples providing 2584 participants, with total samples 410 ranging from 38 to 464 CN older adults. The sample 411 3.2. Mean domain specific effect sizes of cognitive included 1295 females (50%), and 743 APOEε4 carriers 412 impairment 413 (29%), with the average age at baseline of samples ranging 414 Mean effect sizes (Cohen’s d) and associated 95% confibetween 60.5 and 78.2 years. The length of follow-up for 415 dence intervals are listed in Table 1. Fig. 1 shows the forest studies ranged between 18 months and 23 years, approxi416 plots for each cognitive domain with individual study effect mate average of 5 years. Nine studies contributed data for 417 sizes and 95% confidence intervals shown as well as the episodic memory (n 5 1781), five for executive function 418 419 mean effect size for each domain. Worse performance in (n 5 1074), three for working memory (n 5 740), six for 420 the presence of Ab1 was evident for all cognitive domains, processing speed (n 5 1568), five for visuospatial function 421 with the magnitude of effects, by convention, small. Ab1(n 5 1243), seven for semantic memory (n 5 1653), and 422 related cognitive impairment was statistically significant seven for global cognition (n 5 1396). 423 for the domains of global cognition, visuospatial function, Ab analysis was conducted via PET neuroimaging in all 424 425 processing speed, executive function, and episodic memory but one study, with 57% of studies using a categorical 426 427 Table 1 428 Summary of pooled effect sizes and heterogeneity statistics from the meta-analyses 429 430 Cross-sectional design Longitudinal design 431 Cognitive domain n Cohen’s d (95% CI) n Cohen’s d (95% CI) 432 433 y 1781 20.24 (20.44 to 20.03)*y Episodic memory 2886 20.15 (20.27 to 20.03)* 434 Executive function 2281 20.15 (20.26 to 20.05)** 1074 20.04 (20.31 to 0.22) 435 Working memory 1598 20.11 (20.24 to 0.01) 740 20.26 (20.65 to 20.13)y 436 Processing speed 2530 20.18 (20.29 to 20.07)*** 1568 20.18 (20.38 to 0.02)y 437 Visuospatial 1984 20.25 (20.35 to 20.15)*** 1243 20.25 (20.42 to 20.09)** 438 Semantic memory 2585 20.06 (20.18 to 0.06) 1653 20.28 (20.42 to 20.15)*** 439 Global cognition 1746 20.32 (20.47 to 20.17)*** 1396 20.30 (20.48 to 20.11)**y 440 441 NOTE. Cohen’s d represents the standardized difference in means between the amyloid positive and amyloid negative groups, where greater impairment is 442 represented by a negative effect. 443 *P , .05; **P , .01; ***P , .001. y 444 significant heterogeneity P . .05. REV 5.4.0 DTD
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J.E. Baker et al. / Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring - (2016) 1-13 Executive function

Episodic memory

Working memory

Amariglio et al. 2012

Amariglio et al. 2012

Doherty et al. 2015

Doherty et al. 2015

Edmonds et al. 2015

Hassenstab et al. 2016a

Hassenstab et al. 2016a Hassenstab et al. 2016b

Hassenstab et al. 2016b

Kantarci et al. 2012

Hollands et al. 2015

Lim et al. 2015

Lim et al. 2015

Lim et al. 2013

Mielke et al. 2016

Loewenstein et al. 2016 Mielke et al. 2016

Nebes et al. 2013

Nebes et al. 2013

Ossenkoppele et al. 2014

Ossenkoppele et al. 2014

Rentz et al. 2010

Rentz et al. 2010

Rolstad et al. 2011

van Harten et al. 2013

Edmonds et al. 2015

Amariglio et al. 2012 Doherty et al. 2015

Hedden et al. 2012

Hassenstab et al. 2016a

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Edmonds et al. 2015 Hassenstab et al. 2016b

2

Semantic memory Doherty et al. 2015

Aizenstein et al. 2008

Hassenstab et al. 2016a

1

Visuospatial

Processing speed

1

Cohen's d

Cohen's d

Aschenbrenner et al. 2015

0

-1

2

1

0

-1

-2

2

1

0

Cohen's d

-2

Overall

Overall

-1

Hassenstab et al. 2016a Hassenstab et al. 2016b Lim et al. 2013

Lim et al. 2013

Loewenstein et al. 2016 Hassenstab et al. 2016b

Mielke et al. 2016

Mielke et al. 2016

Lim et al. 2013

Loewenstein et al. 2016 Nebes et al. 2013

Ossenkoppele et al. 2014 Papp et al. 2015

Loewenstein et al. 2016

Oh et al. 2016

Petersen et al. 2015

Petersen et al. 2015

Mielke et al. 2016

Rentz et al. 2010

Petersen et al. 2015

Sperling et al. 2013

Rentz et al. 2010 Rolstad et al. 2011 Sperling et al. 2013

Rentz et al. 2010

van Harten et al. 2013

van Harten et al. 2013

Cohen's d

0

-1

2

1

0

Overall -1

2

1

0

-1

-2

Overall

Overall

-2

-2

Aizenstein et al. 2008 Aschenbrenner et al. 2015 Barthel et al. 2011 Doherty et al. 2015 Edmonds et al. 2015 Gidicsin et al. 2015 Lim et al. 2015 Lim et al. 2013 Loewenstein et al. 2016 Mielke et al. 2016 Oh et al. 2016 Ossenkoppele et al. 2014 Petersen et al. 2015 Rentz et al. 2010 Sojkova et al. 2011 Sperling et al. 2013 Stomrud et al. 2010 van Harten et al. 2013 Wang et al. 2015 Overall

-2

Cohen's d

Cohen's d

Global cognition Landau et al. 2012 Mielke et al. 2016 Petersen et al. 2015 Pike et al. 2011 Sperling et al. 2013 Stomrud et al. 2010 van Harten et al. 2013 Xiong et al. 2016

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1

0

-1

Overall

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Cohen's d

Fig. 1. Forest plots from the meta-analysis of studies with cross-sectional designs. Effect sizes are presented as Cohen’s d with 95% confidence intervals. The dotted lines represent no effect of amyloid on cognition. Negative values represent greater impairment in performance in the presence of high Ab. The size of the dots represents study weighting due to sample size.

classification for Ab1. Demographic and clinical variables such as baseline age and test scores, education, sex, IQ, and family history were modeled as covariates in 79% of the studies. Exclusion of participants with prodromal AD or MCI at the baseline assessment was again split between the CDR (57%) and Petersen criteria. Approximately 71% of studies used participants with average ages ,75 years.

results. Statistically significant heterogeneity was evident for the domains of episodic memory, working memory, processing speed, and global cognition (Table 1). Publication bias was unable to be assessed due to the small number of studies contributing to meta-analysis of studies using longitudinal designs [29]. 3.6. Moderator effects on studies of cognitive decline

3.5. Mean domain specific effect sizes of cognitive decline Mean effect sizes and associated 95% confidence intervals are shown in Table 1. Fig. 2 shows the forest plots representing the individual study effect sizes and 95% confidence intervals, as well as the mean effect size for each cognitive domain. Decreased performance in the presence of Ab1 was evident for all cognitive domains with the magnitude of this decline small to moderate and statistically significant for episodic memory, visuospatial function, semantic memory, and global cognition. Stratifying the samples by participants’ mean age did not change these

Results of the moderator analysis of studies with longitudinal designs are listed in Table 2. Owing to the small number of studies available for the longitudinal meta-analysis, the moderator comparisons are limited; in many cases, only one study provided data for a particular subset (e.g., only one study used CSF [39]) and only three studies contributed effect sizes toward the working memory domain [40–42]; hence, post hoc analyses were conducted only where there were sufficient samples. For episodic memory, group mean effect sizes remained statistically significant when these accounted for data from studies that

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Table 2 Pooled effect sizes and 95% confidence intervals adjusted for moderator variables Amyloid measure Cognitive domain Cross-sectional Episodic memory Longitudinal Episodic memory Processing speed Global cognition

Type of amyloid analysis

CSF

PET

Continuous

Categorical

20.07 (20.34 to 0.20)

20.17 (20.31 to 20.03)*

20.11 (20.33 to 0.12)

20.16 (20.31 to 20.02)*

— — —

20.27 (20.50 to 20.02)* — 20.31 (20.51 to 20.10)**

20.04 (20.24 to 0.16) 20.02 (20.33 to 0.29) 20.16 (20.37 to 0.05)

20.37 (20.55 to 20.18)***y 20.28 (20.52 to 20.03)* 20.43 (20.67 to 20.19)***

NOTE. Effect sizes presented are Cohen’s d, with associated 95% confidence intervals. Blank spaces represent only one study available for moderator in the domain. Control variables included clinical and demographic variables that were included as covariates in the analyses used to calculate effect sizes. *P , .05; **P , .01; ***P , .001. ygroup difference P , .05.

had classified Ab as a categorical measure and had controlled statistically for the effects of demographic and clinical characteristics. For processing speed, effect sizes remained statistically significant when these accounted for data from studies that had classified Ab as a categorical measure. For global cognition, group mean effect sizes remained statistically significant when these accounted for data from studies that had classified Ab levels categorically, and had used the CDR criteria to exclude prodromal AD or MCI at baseline. 4. Discussion The results of this meta-analysis indicate that, in CN older adults, reliable Ab1 related cognitive impairment occurs in the domains of episodic memory, executive function, processing speed, visuospatial function, and global cognition. However, for each of these cognitive domains, impairment was by convention [25], small in magnitude with effect sizes ranging from d 5 0.15 for episodic memory to d 5 0.25 for visuospatial function. Interestingly, the largest effect size observed was for measures of global cognition, computed within studies by combining data across their neuropsychological tests measuring multiple cognitive domains. Nevertheless, even with data from all tests combined in this way, the magnitude of Ab1-related impairment remained only small (d 5 0.32). No statistically significant evidence of impairment was observed for the domains of working or semantic memory and in both cases, the magnitude of impairment was smaller than that observed for the other cognitive domains (d’s , 0.12). As these estimates of cognitive impairment were based on data from 5005 subjects from 30 studies, we believe these effect sizes provide an accurate estimation of the nature and magnitude of cognitive impairment in Ab1 CN older adults. Ab1-related cognitive decline was evident in the domains of episodic memory, semantic memory, visuospatial function, and global cognition, with the magnitude of decline in these cognitive domains ranging from d 5 0.24 for episodic memory to d 5 0.30 for global cognition. Again,

the effect size for decline in global cognitive function was significant and small in magnitude (d 5 0.30). No Ab1related decline was observed for working memory, processing speed, or executive function. Given the greater resources needed to conduct prospective studies, these estimates of cognitive decline in Ab1 CN older adults were based on fewer data points than those for cognitive impairment. However, as data were obtained for 2584 subjects from 14 studies encompassing an average of approximately five years of follow-up that satisfied inclusion/exclusion criteria, therefore these estimates of cognitive decline in Ab1 CN older adults are reliable. When considered together, data from both the cross-sectional and longitudinal meta-analyses suggest that in CN older adults, Ab1 is associated with both subtle cognitive impairment and decline. There was no evidence for a specific profile of cognitive impairment associated with Ab1. In fact, measures of global cognitive function, which typically combined multiple cognitive domains, provided the largest effect sizes. In contrast, analyses of longitudinal studies indicated that Ab1 related cognitive decline manifests predominantly and moderately in episodic and semantic memory. While stratifying the samples by mean age moderated cognitive impairment in episodic memory and executive function, mean age did not influence the estimates of cognitive decline for any domain. These effects will be considered in turn. 4.1. Ab1-related cognitive impairment The results show that Ab1 is associated with moderate but nonspecific levels of cognitive impairment in CN older adults. The pattern of neuropsychological impairments detected suggests that in CN older adults, Ab1 was associated with small impairments across measures of episodic memory, executive function, processing speed, and visuospatial function. It was not associated with impairment in working memory or semantic fluency. This general decrease in attention, executive function, and memory, with relative sparing of verbal fluency is consistent with the observation that the Ab1 related impairment with the greatest magnitude was

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J.E. Baker et al. / Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring - (2016) 1-13 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846

Control variables

7

Normal criteria

Yes

No

CDR 5 0

Petersen

20.21 (20.39 to 20.03)*

20.10 (20.25 to 0.06)

20.16 (20.36 to 0.03)

20.15 (20.34 to 0.05)

20.29 (20.53 to 20.05)* 2 20.31 (20.51 to 20.10)**

20.03 (20.53 to 0.48) — —

20.28 (20.59 to 0.03) 20.18 (20.49 to 0.13) 20.38 (20.68 to 20.08)*

20.20 (20.48 to 20.07) 20.20 (20.51 to 0.11) 20.25 (20.57 to 0.07)

for global cognition, a score which in most studies was computed by combining performance across all the neuropsychological tests used in that study. The presence of this subtle and general cognitive impairment in Ab1 CN older adults indicates that even very early in the course of AD, increased deposition of Ab is related to disrupted cognitive function, although this effect is small. Estimates of Ab1 cognitive impairment from this metaanalysis suggest that the neuropsychological tests used to date in studies of preclinical AD are unlikely to be useful for identifying those CN older adults who would have abnormal Ab levels if they underwent PET or CSF assessment. For example, with a Cohen’s d of 0.30 (as observed for global cognition), 88% of the scores for Ab1 and Ab2 individuals would overlap [43]. Thus, there would be a 42% chance of picking an Ab1 individual at random that would have a higher score on the task than a similarly chosen Ab2 individual. Clearly, the sensitivity of the neuropsychological tests analyzed here is insufficient to warrant their use in distinguishing Ab1 CN older adults from Ab2 CN older adults. Zakzanis (2001) [38] suggested that the criteria for a useful clinical marker in neuropsychological disorders would be a Cohen’s d of 3 or above. This corresponds to an overlap of just 13% and ,2% chance of selecting at random an Ab1 individual that would score higher on the task than a similarly chosen Ab2 individual. Therefore, while the results from our meta-analysis confirm that Ab1 does manifest as cognitive impairment early in AD, the magnitude of this impairment is subtle and nonspecific and would therefore be unlikely to be useful clinically. The analyses indicated heterogeneity between studies that provided estimates of episodic impairment in Ab1 CN older adults. Post hoc analyses of this heterogeneity suggested it was due to methodological variation between studies. Indeed, the estimates of impairment in this domain increased when looking at subsets of studies based on moderator variables. The finding that studies using PET had greater memory impairment than those using CSF to measure Ab might be because CSF Ab42 reaches abnormal levels earlier than PiB-PET [45], and therefore,

Ab1 determined from PET occurs in those with more advanced disease. Similarly, the increased impairment in the subset of studies using CDR criteria could reflect the inclusion of individuals who would have otherwise been classified as MCI. Finally, the reduction of variance, or noise, through controlling for extraneous variables or using categorical classifications of Ab could be why an increase in impairment was seen in these subsets of studies. 4.2. Ab1-related cognitive decline The results show that Ab was associated with significant decline in the domains of episodic memory, semantic memory, visuospatial function, and global cognition. Although the magnitude of decline for each domain was small, the effect of Ab extended beyond the domain of episodic memory, and this is important for understanding the nature of cognitive change in early AD. Unexpectedly, effect sizes for Ab1-related decline in episodic memory were smaller than those in visuospatial function and semantic memory. Research from our group and others has continually identified decline in episodic memory as being central to clinical progression in early AD [18,46,47]. Furthermore, dysfunction in episodic memory has been associated with loss of hippocampal volume, in accord with models of the structural brain changes that occur early in AD [2,48–50] and with brain-behavior models of episodic memory [51,52]. The reliable decline observed here for semantic memory and visuospatial function is also consistent with the involvement of medial temporal lobe structures [53]. Consistent with the specificity of Ab1-related cognitive decline to higher cognitive functions dependent on medial temporal lobe structures was the absence of any decline in processing speed. Neuropsychological models suggest that processing speed reflects lower order or more basic cognitive functions that subserve higher processes such as working memory, executive function, and episodic memory [54]. However, this raises an issue of why Ab1 was not associated with decline in executive function, given this is dependent on the integrity of the frontal lobe [54]. One possible explanation

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Episodic memory

Semantic Memory

Cohen's d

Cohen's d

Executive function

Visuospatial

Mielke et al. 2016

Global cognition

Processing speed

Mielke et al. 2016

Mielke et al. 2016

Doraiswamy et al. 2014

Aschenbrenner et al. 2015

Ewers et al. 2012

Doraiswamy et al. 2014

Overall

Cohen's d

-2 .5

Roe et al. 2013

Overall

1. 5

Roe et al. 2013

0. 5

Petersen et al. 2015

-0 .5

Lim et al. 2013

Petersen et al. 2015

-1 .5

Mormino et al. 2014

0. 5

Cohen's d

-1 .5

Cohen's d

1. 5 1. 5

-2 .5

Overall 1. 5

Overall

0. 5

Wirth et al. 2013

-0 .5

Storandt et al. 2009

-1 .5

Roe et al. 2013 Wirth et al. 2013

0. 5

Lim et al. 2013 Petersen et al. 2015

-0 .5

Lim et al. 2013

-1 .5

Mielke et al. 2016

Resnick et al. 2010

-2 .5

1. 5

1. 5

0. 5

-0 .5

-1 .5

-2 .5

Overall

0. 5

Roe et al. 2013 Wirth et al. 2013

-0 .5

Petersen et al. 2015

-1 .5

Lim et al. 2013 Papp et al. 2015

-2 .5

Mielke et al. 2016 Aschenbrenner et al. 2015 Doraiswamy et al. 2014 Ewers et al. 2012 Lim et al. 2013 Petersen et al. 2015 Resnick et al. 2010 Roe et al. 2013 Wirth et al. 2013 Overall

Mielke et al. 2016 Doraiswamy et al. 2014

-2 .5

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-0 .5

8

Cohen's d

Fig. 2. Forest plots from the meta-analysis of studies with longitudinal designs. Effect sizes are presented as Cohen’s d with 95% confidence intervals. The dotted lines represent no effect of amyloid on cognition. Negative values represent greater decline in performance in the presence of high Ab. The domain of working memory is not included as only one study contributed data for this domain. The size of the dots represents study weighting due to sample size.

for the absence of any decline in executive function is very early AD-related neuronal loss does not involve the prefrontal cortex [55,56]. The lack of correlation between location of Ab deposition and neuronal death has been noted in many pathologic studies (see review by Musiek & Holtzman [57]). In this context, the results of this meta-analysis suggest that early AD-related cognitive declines occur predominantly in domains that rely on the normal function of the hippocampus and entorhinal cortex, which are areas that are most sensitive to early neuronal death in AD [58]. Thus, in preclinical AD, despite the widespread deposition of Ab [59], neuronal disruption and cognitive decline remain relatively specific.

Although some neuropsychological studies have concluded that decline in executive function does occur in preclinical AD and can also predict clinical progression to MCI and AD [15,60], the neuropsychological tests classified as measuring executive function have varied between studies. For example, some studies include measures of working memory [11,61,62] or tasks of verbal and semantic fluency [13,15] as part of the domain of their executive function composite scores. Previous research suggests that a specific component of executive function, verbal switching, and inhibition is more sensitive to executive dysfunction before dementia than other measures, such as planning [63,64]. In

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accord with previous meta-analyses and standard neuropsychological models, the current study classified tasks of semantic and verbal fluency as representing semantic memory, whereas working memory was classified as a domain separate from executive function. Thus, the definition of executive function used here reflected tests of planning, error monitoring, and inhibition. Therefore, should the data from the present study be considered in the context of a broader definition of executive function, one possible conclusion is that Ab1-related decline in executive function in preclinical AD does not manifest in areas of inhibition, planning, error monitoring, or working memory but rather in semantic and phonemic fluency. This absence of any impairment in semantic and phonemic fluency from the analysis of crosssectional designs reinforces the subtlety of the impairment and shows that repeated assessment is required for it to become evident. The lower than expected estimates of Ab1-related cognitive decline in episodic memory might have been due to the substantial heterogeneity between studies for measures of this domain (Table 1). First, given the centrality of memory dysfunction to early AD, all but one study measured episodic memory (i.e., [65]). This resulted in many more measures of episodic memory being included in the current metaanalysis than measures of other cognitive domains. Thus, there was also the potential for greater variation to be included in estimates of mean effect size. Methodologic differences between studies might also account for some of this heterogeneity. For example, estimates of decline in episodic memory were larger in the subgroups of studies that used PET imaging, categorical Ab levels, statistically controlled for extraneous variables and in those which used the CDR criteria to exclude subjects. As discussed, the reason for a stronger effect under these conditions could be that the samples contain more individuals at later stages of preclinical AD. It is interesting to note that the largest increase in effect size could be attained if including only those studies which used both PET imaging and categorically defined Ab levels (n 5 8; d 5 0.45). While, as mentioned, PiB-PET becomes abnormal much later than CSF Ab42, adding the additional constraint of using a categorical classification for Ab increased the effect above that of just PET alone. 4.3. Comparison with previous meta-analysis The current results extend conclusions about Ab1-related cognitive impairment and decline drawn from a previous meta-analysis [16]. Unlike for the current meta-analysis, Hedden et al. (2013) concluded that the greatest cognitive dysfunction in Ab1 CN older adults occurred in the domain of episodic memory (r 5 0.12). The additional domains of Ab1-related cognitive impairment and decline identified in the present study is likely to have occurred because of the comparatively greater number of participants and studies now available and because the Hedden et al. study did not combine data from studies with cross-sectional and those

9

with longitudinal designs. The inclusion/exclusion criteria for the current meta-analysis were also slightly different to that used previously. For example, the present study did not include data from histopathologic studies as the prevalence of comorbidities in these populations, and the substantial time between clinical assessment and death can reduce the reliability of clinicopathologic correlations [66]. The present study also excluded studies that classified Ab from plasma, given that as yet there are no reliable blood-based biomarkers of CNS Ab [67]. Most importantly, the current results were based on a much larger sample than the previous study. For example, 59% of studies included in the current meta-analysis were published after the period covered by Hedden et al. Furthermore, the length of prospective studies is now greater, enabling more reliable estimates of Ab1-related cognitive decline. Thus, the findings from the present study provide a current and reliable foundation for understanding the nature and magnitude of cognitive impairment and decline in Ab1 CN older adults. 4.4. Limitations and directions for future research Although the estimates of Ab1-related cognitive impairment and decline were based on large sample sizes measuring multiple cognitive domains, there are some important caveats on our conclusions. This study did not consider relationships between tau and cognitive impairment or decline. Given that post mortem studies of AD report that cognitive dysfunction is related more strongly to NFT burden than to amyloid burden [66], it is likely that the addition of levels of tau to meta-analyses will provide greater explanation of variability in cognitive outcomes. Second, as discussed above, there was bias toward the measurement of episodic memory in the studies included. Third, although we identified no evidence of publication bias in studies using cross-sectional designs, the limited number of studies using longitudinal designs meant that publication bias was unable to be assessed. This is indicative of the small number of independent, ongoing observational cohort studies. Furthermore, results from the same cohort are often published in multiple articles, so although there may be a wealth of articles reporting on cognitive decline, only a few can be chosen to represent the cohort. Importantly, 20% of the references that were screened in full were excluded due to not including sufficient information for effect size elicitation, and this lead to exclusion of information from some very important and influential studies in the area. While reporting of means and standard deviations is not always appropriate, the explicit reporting of effect sizes and confidence intervals around these should be standard practice, as basing conclusions solely on the absence or presence of statistical significance is inherently flawed [68]. Fourth, the small sample sizes for some estimates may have reduced the reliability of moderator analyses. Although 60% of studies using PET neuroimaging

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classified Ab burden using a dichotomous outcome (e.g., low/negative or high/positive) in studies using the [11C] PiB-PET tracer, and calculating burden using distribution volume ratios, the criterion score used to define abnormality has ranged between 1.06 [35] and 1.80 [69]. Additionally, the neocortical areas contributing to these estimates of Ab burden varied slightly. Recent recommendations outlined by Dubois et al. (2016) [64] highlighted the large methodological variation between studies with regard to their PET and CSF analyses, stating that differences in thresholds established, protocols and procedures used, and reference and target regions used are possible confounding variables. These caveats notwithstanding, the conclusions here that Ab is associated with small impairments in episodic memory, executive function, processing speed, visuospatial function, and global cognition, and with greater decline in episodic and semantic memory, visuospatial function, and global cognition, are based on a large sample that can be considered to provide a good representation of a preclinical AD population, with an average age of 70 years, approximately equal numbers of males and females, and the number of APOEε4 carriers consistent with population prevalence rates [71]. Although these estimates can be considered representative of what would be seen in a preclinical AD population, it is clear that the effect of Ab on cognition in this early stage is very subtle, and that current neuropsychological measures do not possess the sensitivity to reliably detect Ab1 related cognitive dysfunction. Development or refinement of neuropsychological measures and procedures sensitive to early neurological changes will ultimately provide the sensitivity and specificity that is required to enable the use of cognitive outcomes as clinical markers of disease. This may mean focusing on tools that have characteristics such as the ability for use with increased frequency of testing, sensitivity to other biological markers of disease in addition to amyloid (such as tau or other markers of neurodegeneration) and which provide evidence of reliable change specific to clinical groups such as those with MCI and AD. The estimates generated here do provide a foundation for determination of the extent to which new tests of cognition or behavioral assays can be used to identify Ab in CN adults.

Acknowledgments The authors acknowledge the financial support of the CRC for Mental Health. The Cooperative Research Centre (CRC) programme is an Australian Government Initiative. Y.Y.L is funded by the NHMRC-ARC Dementia Research Development Fellowship.

Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.dadm.2016.09.002.

RESEARCH IN CONTEXT

1. Systematic review: The authors conducted metaanalyses of the published literature examining relationships between amyloid levels and cognition in cognitively normal older adults using online database Medline OVID. Estimates of cognitive impairment from studies using cross-sectional designs and cognitive decline from studies using longitudinal designs were obtained. 2. Interpretation: The findings indicate that subtle yet statistically significant cognitive impairment and cognitive decline occur in cognitively normal older people with high levels of amyloid. Cognitive impairment is general in nature and small in magnitude, whereas moderate cognitive decline manifests primarily in episodic memory, semantic memory, and visuospatial function. 3. Future directions: The estimates of amyloid-related cognitive impairment and cognitive decline developed from the current meta-analyses provide a strong foundation for the design of studies that seek to improve the detection of amyloid in cognitively normal older adults through the development of new approaches to assessment. They also provide a basis for computing statistical power for secondary prevention clinical trials where slowing cognitive decline may determine the effectiveness of experimental drugs designed to reduce amyloid burden.

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[28] Egger M, Davey Smith G, Schneider M, Minder C. Bias in metaanalysis detected by a simple, graphical test. BMJ 1997;315:629–34. [29] Sterne JAC, Gavaghan D, Egger M. Publication and related bias in meta-analysis: Power of statistical tests and prevalence in the literature. J Clin Epidemiol 2000;53:1119–29. [30] Morris JC. The Clinical Dementia Rating: Current version and scoring rules. Neurology 1993;43:2412–4. [31] Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: Clinical characterization and outcome. Arch Neurol 1999;56:303–9. [32] Edmonds EC, Delano-Wood L, Galasko DR, Salmon DP, Bondi MW. Subtle cognitive decline and biomarker staging in preclinical Alzheimer’s disease. J Alzheimers Dis 2015;47:231–42. [33] Oh H, Steffener J, Razlighi QR, Habeck C, Stern Y. b-amyloid deposition is associated with decreased right prefrontal activation during task switching among cognitively normal elderly. J Neurosci 2016; 36:1962–70. [34] Loewenstein DA, Curiel RE, Greig MT, Bauer RM, Rosado M, Bowers D, et al. A novel cognitive stress test for the detection of preclinical Alzheimer’s disease: Discriminative properties and relation to amyloid load. Am J Geriatr Psychiatry 2016;24:804–13. [35] Sojkova J, Zhou Y, An Y, Kraut MA, Ferrucci L, Wong DF, et al. Longitudinal patterns of b-amyloid deposition in nondemented older adults. Arch Neurol 2011;68:644–9. [36] Mattis S. Dementia Rating Scale (DRS). Odessa: FL: Psychological Assessment Resources; 1988. [37] Bondi MW, Edmonds EC, Jak AJ, Clark LR, Delano-Wood L, McDonald CR, et al. Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and prediction of progression. J Alzheimers Dis 2014;42:275–89. [38] Jak AJ, Bondi MW, Delano-Wood L, Wierenga C, Corey-Bloom J, Salmon DP, et al. Quantification of five neuropsychological approaches to defining mild cognitive impairment. Am J Geriatr Psychiatry 2009;17:368–75. [39] Ewers M, Insel P, Jagust WJ, Shaw L, Trojanowski JQ, Aisen P, et al. CSF biomarker and PIB-PET-derived beta-amyloid signature predicts metabolic, gray matter, and cognitive changes in nondemented subjects. Cereb Cortex 2012;22:1993–2004. [40] Storandt M, Mintun MA, Head D, Morris JC. Cognitive decline and brain volume loss as signatures of cerebral amyloid-b peptide deposition identified with Pittsburgh compound B: Cognitive decline associated with Ab deposition. Arch Neurol 2009;66:1476–81. [41] Lim YY, Ellis KA, Pietrzak RH, Ames D, Darby D, Harrington K, et al. Stronger effect of amyloid load than APOE genotype on cognitive decline in healthy older adults. Neurology 2012;79:1645–52. [42] Mielke MM, Machulda MM, Hagen CE, Christianson TJ, Roberts RO, Knopman DS, et al. Influence of amyloid and APOE on cognitive performance in a late middle-aged cohort. Alzheimers Dement 2016; 12:281–91. [43] Magnusson K. Interpreting Cohen’s d effect size: An interactive visualization. Available at: http://rpsychologist.com/d3/cohend/; 2014. Accessed June 1, 2016. [44] Zakzanis KK. Statistics to tell the truth, the whole truth, and nothing but the truth: Formulae, illustrative numerical examples, and heuristic interpretation of effect size analyses for neuropsychological researchers. Arch Clin Neuropsychol 2001;16:653–67. Q4 [45] Xiong C, Jasielec MS, Weng H, Fagan AM, Benzinger TLS, Head D, et al. Longitudinal relationships among biomarkers for Alzheimer disease in the Adult Children Study. Neurology 2016;86:1–8. [46] Pietrzak RH, Lim YY, Ames D, Harrington K, Restrepo C, Martins RN, et al. Trajectories of memory decline in preclinical Alzheimer’s disease: Results from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing. Neurobiol Aging 2015; 36:1231–8. [47] Hassenstab J, Ruvolo D, Jasielec M, Xiong C, Grant E, Morris JC. Absence of practice effects in preclinical Alzheimer’s disease. Neuropsychology 2015;29:940–8.

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REV 5.4.0 DTD
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18 October 2016
7:47 am
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[15] Papp KV, Mormino EC, Amariglio RE, Munro C, Dagley A, Schultz AP, et al. Biomarker validation of a decline in semantic processing in preclinical Alzheimer’s disease. Neuropsychology 2016;30:624–30. [16] Amariglio RE, Becker JA, Carmasin J, Wadsworth LP, Lorius N, Sullivan C, et al. Subjective cognitive complaints and amyloid burden in cognitively normal older individuals. Neuropsychologia 2012; 50:2880–6. [17] Hedden T, Mormino EC, Amariglio RE, Younger AP, Schultz AP, Becker JA, et al. Cognitive profile of amyloid burden and white matter hyperintensities in cognitively normal older adults. J Neurosci 2012; 32:16233–42. [18] Petersen RC, Wiste HJ, Weigand SD, Rocca WA, Roberts RO, Mielke MM, et al. Association of elevated amyloid levels with cognition and biomarkers in cognitively normal people from the community. JAMA Neurol 2015;73:85–92. [19] Kantarci K, Lowe V, Przybelski SA, Weigand SD, Senjem ML, Ivnik RJ, et al. APOE modifies the association between Ab load and cognition in cognitively normal older adults. Neurology 2012; 78:232–40. [20] Mielke MM, Machulda MM, Hagen CE, Christianson TJ, Roberts RO, Knopman DS, et al. Influence of amyloid and APOE on cognitive performance in a late middle-aged cohort. Alzheimers Dement 2016; 12:281–91. [21] Rentz DM, Locascio JJ, Becker JA, Moran EK, Eng E, Buckner RL, et al. Cognition, reserve, and amyloid deposition in normal aging. Ann Neurol 2010;67:353–64. [22] Loewenstein DA, Curiel RE, Greig MT, Bauer RM, Rosado M, Bowers D, et al. A novel cognitive stress test for the detection of preclinical Alzheimer’s disease: Discriminative properties and relation to amyloid load. Am J Geriatr Psychiatry 2016;24:804–13. [23] Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K, et al. Cerebral amyloid-b PET with florbetaben (18F) in patients with Alzheimer’s disease and healthy controls: A multicentre phase 2 diagnostic study. Lancet Neurol 2011;10:424–35. [24] Nebes RD, Snitz BE, Cohen AD, Aizenstein HJ, Saxton JA, Halligan EM, et al. Cognitive aging in persons with minimal amyloid-beta and white matter hyperintensities. Neuropsychologia 2013;51:2202–9. [25] Aizenstein HJ, Nebes RD, Saxton JA, Price JC, Mathis CA, Tsopelas ND, et al. Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol 2008;65:1509–17. [26] Doherty BM, Schultz SA, Oh JM, Koscik RL, Dowling NM, Barnhart TE, et al. Amyloid burden, cortical thickness, and cognitive function in the Wisconsin Registry for Alzheimer’s Prevention. Alzheimers Dement (Amst) 2015;1:160–9.

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[27] Aschenbrenner AJ, Balota DA, Fagan AM, Duchek JM, Benzinger TLS, Morris JC. Alzheimer disease cerebrospinal fluid biomarkers moderate baseline differences and predict longitudinal change in attentional control and episodic memory composites in the Adult Children Study. J Int Neuropsychol Soc 2015;21:573–83. [28] Xiong C, Jasielec MS, Weng H, Fagan AM, Benzinger TLS, Head D, et al. Longitudinal relationships among biomarkers for Alzheimer disease in the Adult Children Study. Neurology 2016;86:1–8. [29] Hassenstab J, Chasse R, Grabow P, Benzinger TLS, Fagan AM, Xiong C, et al. Certified normal: Alzheimer’s disease biomarkers and normative estimates of cognitive functioning. Neurobiol Aging 2016;43:23–33. [30] Wang L, Benzinger TL, Hassenstab J, Blazey T, Owen C, Fagan AM, et al. Spatially distinct atrophy is linked to b-amyloid and tau in preclinical Alzheimer disease. Neurology 2015;84:1254–60. [31] Ewers M, Insel P, Jagust WJ, Shaw L, Trojanowski JQ, Aisen P, et al. CSF biomarker and PIB-PET-derived beta-amyloid signature predicts metabolic, gray matter, and cognitive changes in nondemented subjects. Cereb Cortex 2012;22:1993–2004. [32] Lim YY, Ellis KA, Pietrzak RH, Ames D, Darby D, Harrington K, et al. Stronger effect of amyloid load than APOE genotype on cognitive decline in healthy older adults. Neurology 2012;79:1645–52. [33] Doraiswamy P, Sperling RA, Johnson K, Reiman EM, Wong TZ, Sabbagh MN, et al. Florbetapir F 18 amyloid PET and 36-month cognitive decline: A prospective multicenter study. Mol Psychiatry 2014; 19:1044–51. [34] Wirth M, Oh H, Mormino EC, Markley C, Landau SM, Jagust WJ. The effect of amyloid b on cognitive decline is modulated by neural integrity in cognitively normal elderly. Alzheimers Dement 2013; 9:997–1003. [35] Resnick SM, Sojkova J, Zhou Y, An Y, Ye W, Holt DP, et al. Longitudinal cognitive decline is associated with fibrillar amyloid-beta measured by [11C]PiB. Neurology 2010;74:807–15. [36] Mormino E, Betensky RA, Hedden T, Schultz AP, Amariglio RE, Rentz DM, et al. Synergistic Effect of b-Amyloid and Neurodegeneration on Cognitive Decline in Clinically Normal Individuals. JAMA Neurol 2014;71:1379. [37] Roe CM, Fagan AM, Grant EA, Hassenstab J, Dreyfus DM, Sutphen CL, et al. Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later. Neurology 2013; 80:1784–91. [38] Storandt M, Mintun MA, Head D, Morris JC. Cognitive decline and brain volume loss as signatures of cerebral amyloid-b peptide deposition identified with Pittsburgh compound B: Cognitive decline associated with Ab deposition. Arch Neurol 2009;66:1476–81.

REV 5.4.0 DTD
DADM122_proof
18 October 2016
7:47 am
ce

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