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Figure 1.
Flow Diagram of Participant Selection for Dementia Syndromes
Flow Diagram of Participant Selection for Dementia Syndromes

MCI indicates mild cognitive impairment. MEDLINE and Web of Science databases were searched from January 2004 to April 2015.

Figure 2.
Prevalence of Amyloid Positivity on PET According to Age for the Different Dementia Diagnostic Groups
Prevalence of Amyloid Positivity on PET According to Age for the Different Dementia Diagnostic Groups

PET indicates positron emission tomography. The curves were plotted using the point estimates generated by generalized estimating equations and are within the age limits of the diagnostic groups. The models were adjusted for study effects. The 95% CIs are presented in Table 2 and eFigure 3 in the Supplement.

Figure 3.
Relative Odds of Non–Alzheimer Dementias vs Alzheimer Dementia
Relative Odds of Non–Alzheimer Dementias vs Alzheimer Dementia

AD indicates Alzheimer disease. The curves were plotted using the point estimates generated by generalized estimating equations and represent odds ratios of amyloid positivity for the different non–AD dementia syndromes (with patients with AD dementia as the reference group) as a function of age. The models include amyloid status on PET (positive or negative), age (as a continuous variable), and an interaction between amyloid status and age. The curves are within the age limits of the diagnostic groups.

Table 1.  
Participant Characteristics in Each Dementia Diagnostic Groupa
Participant Characteristics in Each Dementia Diagnostic Groupa
Table 2.  
Prevalence Estimates According to Age, Diagnosis, and APOE ε4 Status
Prevalence Estimates According to Age, Diagnosis, and APOE ε4 Status
Table 3.  
Observed Probabilities of Amyloid Positivity on PET Across Diagnostic and Age Groupsa
Observed Probabilities of Amyloid Positivity on PET Across Diagnostic and Age Groupsa
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Original Investigation
May 19, 2015

Prevalence of Amyloid PET Positivity in Dementia SyndromesA Meta-analysis

Author Affiliations
  • 1Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
  • 2Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
  • 3Memory and Aging Center, University of California, San Francisco
  • 4Helen Wills Neuroscience Institute, University of California, Berkeley
  • 5Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, the Netherlands
  • 6Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, the Netherlands
JAMA. 2015;313(19):1939-1950. doi:10.1001/jama.2015.4669
Abstract

Importance  Amyloid-β positron emission tomography (PET) imaging allows in vivo detection of fibrillar plaques, a core neuropathological feature of Alzheimer disease (AD). Its diagnostic utility is still unclear because amyloid plaques also occur in patients with non–AD dementia.

Objective  To use individual participant data meta-analysis to estimate the prevalence of amyloid positivity on PET in a wide variety of dementia syndromes.

Data Sources  The MEDLINE and Web of Science databases were searched from January 2004 to April 2015 for amyloid PET studies.

Study Selection  Case reports and studies on neurological or psychiatric diseases other than dementia were excluded. Corresponding authors of eligible cohorts were invited to provide individual participant data.

Data Extraction and Synthesis  Data were provided for 1359 participants with clinically diagnosed AD and 538 participants with non–AD dementia. The reference groups were 1849 healthy control participants (with amyloid PET) and an independent sample of 1369 AD participants (with autopsy data).

Main Outcomes and Measures  Estimated prevalence of positive amyloid PET scans according to diagnosis, age, and apolipoprotein E (APOE) ε4 status, using the generalized estimating equations method.

Results  The likelihood of amyloid positivity was associated with age and APOE ε4 status. In AD dementia, the prevalence of amyloid positivity decreased from age 50 to 90 years in APOE ε4 noncarriers (86% [95% CI, 73%-94%] at 50 years to 68% [95% CI, 57%-77%] at 90 years; n = 377) and to a lesser degree in APOE ε4 carriers (97% [95% CI, 92%-99%] at 50 years to 90% [95% CI, 83%-94%] at 90 years; n = 593; P < .01). Similar associations of age and APOE ε4 with amyloid positivity were observed in participants with AD dementia at autopsy. In most non–AD dementias, amyloid positivity increased with both age (from 60 to 80 years) and APOE ε4 carriership (dementia with Lewy bodies: carriers [n = 16], 63% [95% CI, 48%-80%] at 60 years to 83% [95% CI, 67%-92%] at 80 years; noncarriers [n = 18], 29% [95% CI, 15%-50%] at 60 years to 54% [95% CI, 30%-77%] at 80 years; frontotemporal dementia: carriers [n = 48], 19% [95% CI, 12%-28%] at 60 years to 43% [95% CI, 35%-50%] at 80 years; noncarriers [n = 160], 5% [95% CI, 3%-8%] at 60 years to 14% [95% CI, 11%-18%] at 80 years; vascular dementia: carriers [n = 30], 25% [95% CI, 9%-52%] at 60 years to 64% [95% CI, 49%-77%] at 80 years; noncarriers [n = 77], 7% [95% CI, 3%-18%] at 60 years to 29% [95% CI, 17%-43%] at 80 years.

Conclusions and Relevance  Among participants with dementia, the prevalence of amyloid positivity was associated with clinical diagnosis, age, and APOE genotype. These findings indicate the potential clinical utility of amyloid imaging for differential diagnosis in early-onset dementia and to support the clinical diagnosis of participants with AD dementia and noncarrier APOE ε4 status who are older than 70 years.

Introduction

More than 35 million people worldwide experience dementia, with Alzheimer disease (AD) hallmark pathologies amyloid-β plaques and neurofibrillary tangles as the most common cause.1 Accurately determining the cause of dementia during life is essential to developing and implementing disease-specific therapies. However, a diagnosis based on clinical criteria alone has limited capacity to determine the histopathological cause of dementia. For example, the clinical diagnosis of probable AD shows only modest sensitivity (71%-81%) and specificity (approximately 70%) against postmortem examination,2,3 which potentially confounds clinical trials in AD.4,5 Development of amyloid-β–specific positron emission tomography (PET) tracers69 now enable human in vivo detection of fibrillar amyloid-β in neuritic plaques. Incorporating amyloid imaging into the diagnostic workup can lead to change in diagnosis,1012 increased diagnostic confidence,11 and altered patient management.10,12 Approval by the US Food and Drug Administration (FDA) for [18F]florbetapir (in 2012), [18F]flutemetamol (in 2013), and [18F]florbetaben (in 2014) supports potential application of amyloid imaging in clinical practice.13

However, the clinical utility of amyloid imaging is potentially limited by a proportion of patients with non–AD dementia and cerebral amyloid-β plaques.14,15 To correctly interpret the clinical significance of amyloid PET results, clinicians need to understand the prevalence of amyloid positivity across different types of dementia and how this is associated with demographic, genetic, and cognitive factors. Most amyloid PET studies to date come from single centers with modest sample sizes.16 Therefore, we conducted an individual participant meta-analysis to estimate the prevalence of amyloid positivity in a large sample encompassing a variety of dementia syndromes and to evaluate relationships between amyloid PET positivity and age, sex, education, global cognition, and the AD risk-allele apolipoprotein E (APOE) ε4. We also compared the prevalence of amyloid positivity between participants with dementia and participants who were cognitively healthy, and tested associations of amyloid prevalence with age and APOE genotype in an independent autopsy sample of participants with AD.

Methods
Study Selection

Informed consent was obtained from all participants or their assigned surrogate decision makers, and the institutional review boards for human research of the participating centers approved all studies. The MEDLINE and Web of Science databases were searched from January 2004 (when the first human amyloid PET study was published with carbon 11–labeled Pittsburgh Compound B [{11C}PIB]6) to April 7, 2015, on amyloid PET studies in patients with dementia. The search terms used were PET and amyloid or abeta or PET tracer (ie, PIB, Pittsburgh, florbetapir, AV-45, florbetaben, or flutemetamol). Due to its affinity to both amyloid and tau pathology, 2-(1-{6-[(2-fluorine 18–labeled fluoroethyl)methylamino]-2-napthyl}ethylidene) malononitrile ([18F]FDDNP) was not included.17 The search resulted in 3250 studies. Titles and abstracts were reviewed and 227 relevant full-text articles were retrieved to assess their eligibility. Studies were excluded if they presented case reports, included duplicate participants, or involved neurological or psychiatric diseases other than dementia. The search identified 40 unique cohorts. We asked 37 study contact persons to provide participant-level data on amyloid status, age, sex, education, APOE ε4 status,18 Mini-Mental State Examination (MMSE) score, and Clinical Dementia Rating (CDR) scale score (3 cohorts published their studies after our inclusion stop in April 2014). Eight contact persons declined or did not respond, leaving participant-level data from 29 cohorts for analysis (Figure 1). Seven cohorts provided additional unpublished participant-level data, acquired using peer-reviewed clinical and PET procedures (eTable 1A in the Supplement). Only 1 cohort provided data that were not yet published in a peer-reviewed journal (n = 37, participants with dementia only). Following the same procedure, we selected 1849 healthy control participants from 23 cohorts (eFigure 1 in the Supplement), defined as participants who performed cognitive testing within normal limits and without any major neurological or psychiatric disorder.19 The quality of primary reports from each cohort was systematically assessed by examining the setting, generalizability, selection, measurements, reference, bias, participant flow, descriptives, outcome, and dichotomization using combined STROBE20 and QUADAS21 guidelines (eTable 2A and eTable 2B in the Supplement). All cohorts reported their studies following the STROBE and QUADAS guidelines, although bias could not be assessed in 17 of 29 dementia cohorts and 13 of 23 control cohorts.

Data Collection and Operationalization

Information on study procedures, extracted from the publications or provided by the study contact person, was used to create a common set of variables.

Participants

Participants met diagnostic criteria for AD (including the atypical variants posterior cortical atrophy and logopenic-variant primary progressive aphasia), frontotemporal dementia (including behavioral, semantic, and progressive nonfluent variants), dementia with Lewy bodies (DLB), vascular dementia, and corticobasal syndrome. All diagnoses were made clinically without using amyloid PET or cerebrospinal fluid biomarker information. Detailed characteristics for each study are in eTable 1 in the Supplement. For an indirect comparison between in vivo and postmortem prevalence of amyloid positivity, the National Alzheimer’s Coordinating Center (NACC) database22 provided autopsy data of participants who were clinically diagnosed with probable AD dementia at their last visit. Participants who met the Consortium to Establish a Registry for Alzheimer’s Disease criteria23 for definite, probable, or possible AD (indicating presence of moderate to frequent neuritic plaques) were considered amyloid positive.

PET Procedures

The PET scans were dichotomized (amyloid positive or negative) using quantitative thresholds or visual reads according to the method used at the study site. Detailed PET procedures for all participating cohorts are presented in eTable 1 in the Supplement.

APOE Genotype and Clinical Measures

Information on APOE genotype was available for 1370 participants (72.2%). The MMSE24 (measure of global cognition) was available for 1817 participants with dementia (95.8%) and the CDR scale25 (indicator of disease severity based on caregiver information) was available for 1329 participants with dementia (70.0%). Participants with missing data for any of those variables did not differ in amyloid positivity compared with participants with complete data sets.

Statistical Analysis

We conducted a meta-analysis with individual participant data. Baseline characteristics were compared using analysis of variance and Fisher exact tests where appropriate. Generalized estimating equations (GEE, using SPSS software [IBM], version 21.0) were used to estimate probabilities for amyloid positivity on PET and odds ratios. Generalized estimating equations was the method of choice for the study as it allows analysis of binary-correlated data, such that participant-level data from all cohorts can be modeled while simultaneously accounting for participants within studies. A logit link function for binary outcome with an exchangeable correlation structure was assumed to account for within-study correlation. Analyses were conducted using the total study population, unless specified otherwise.

The main analyses were performed with diagnosis, age, sex, and APOE genotype as independent variables. Age was entered as a continuous measure centered at the median. We tested 2-way and 3-way interactions between variables, and these terms were retained in the model if they appeared significant by the Wald statistical test (indicated in Table footnotes and Figure legends). The GEE method derived unstandardized βs, and standard errors (SE) of the main effect were reported. Estimated probabilities and 95% CIs from the GEE analysis were used in Tables and Figures. These GEE probabilities were compared with the observed probabilities to determine the goodness-of-fit between actual data and the smoothed GEE estimates. The relationship between amyloid positivity on PET and MMSE scores was examined using general linear mixed models including education as an additional covariate.

The degree of heterogeneity across cohorts was assessed in several ways. In the total sample, the random intercept variance related to a study was estimated in a random effect analysis with age, APOE ε4 carriership, and interactions by the “xtmelogit” function from STATA (StataCorp), version 12.0. This variance was expressed as an intraclass correlation coefficient. For each diagnostic group, we assessed heterogeneity within 10-year strata using the I2 statistic26 generated by a random-effects meta-analysis in STATA. An I2 statistic value greater than 50% indicates substantial heterogeneity.26 Across the age range, study variability was visualized by plotting prevalence estimates for each AD and frontotemporal dementia cohort that contained at least 5 participants.

Significance level was set at a 2-sided P value less than .05. All reported P values were not corrected for multiple comparisons. Secondary analyses using Bonferroni correction were also conducted, and results for which interpretation changed are noted. R (R Foundation for Statistical Computing), version 3.1.2, and GraphPad Prism (GraphPad Software), version 6.0 were used for the Figures.

Results

The study included 1897 participants with a clinical diagnosis of dementia (AD, 1359 participants; frontotemporal dementia, 288 participants; DLB, 51 participants; vascular dementia, 138 participants; corticobasal syndrome, 61 participants) and 1849 healthy control participants with PET data (Table 1). From the NACC database, 1369 participants with AD dementia and autopsy data were included (eTable 2 in the Supplement). Amyloid positivity refers to positive (abnormal) amyloid PET scans or presence of moderate-to-frequent plaques on neuropathological examination.

Prevalence of Amyloid Positivity According to Diagnosis, Age, and APOE

In AD dementia, the mean prevalence of amyloid positivity was 88% (95% CI, 85% to 90%, Figure 2A). The prevalence decreased with age from 93% (95% CI, 90% to 95%) at age 50 to 79% (95% CI, 73% to 85%) at age 90 (β for change in GEE estimated prevalence of amyloid positivity per year, −0.032 [95% CI, −.050 to −.014], P < .001). This association differed according to APOE ε4 status (Figure 2C and Figure 2D). In APOE ε4 carriers, the prevalence remained at least 90% regardless of age, whereas the prevalence in noncarriers declined from 86% (95% CI, 73% to 94%) at age 50 years to 68% (95% CI, 57% to 77%) at age 90 years (β, −0.034 [95% CI, −.058 to −.010], P < .01). Similar associations were found for age and APOE ε4 with amyloid positivity as assessed using neuropathological criteria in an independent cohort of AD dementia participants with autopsy data (Figure 2B). The mean prevalence estimate for the autopsy data was 85% (95% CI, 82% to 87%), with stable estimates across age in APOE ε4 carriers and a decreasing prevalence with increasing age in noncarriers.

Mean amyloid positivity in non–AD dementias was highest in DLB (51% [95% CI, 33% to 69%]), followed by vascular dementia (30% [95% CI, 21% to 42%]) and frontotemporal dementia (12% [95% CI, 8% to 18%]). In these dementias, amyloid positivity increased with age (β, 0.042 [95% CI, .012 to .071], P < .01), Figure 2A and Table 2). The rate of increase was independent of APOE genotype but APOE ε4 carriers had higher overall mean prevalence estimates than noncarriers (18% [95% CI, 8% to 28%]) (Figure 2C and Figure 2D). In participants with corticobasal syndrome, the overall prevalence of amyloid positivity was 38% (95% CI, 23% to 54%), which decreased with age (β, −0.073 [95% CI, −.130 to −.016], P < .05), independent of APOE ε4 status. This analysis was no longer statistically significant after Bonferroni correction (P = .15). Repeating all analyses above using only participant data from published cohorts (28 of 29 cohorts) yielded essentially the same results (eTable 3 in the Supplement).

The prevalence of amyloid positivity was not significantly associated with sex in both AD (women, 89% [95% CI, 86% to 91%]; men, 86% [95% CI, 83% to 89%], β for change in GEE estimated prevalence of amyloid positivity for men vs women, −0.287 [95% CI, −.620 to .046], P = .09) and non–AD dementias (women, 26% [95% CI, 19% to 34%); men, 21% [95% CI, 15% to 29%], β, −0.134 [95% CI, −.447 to .299], P = .54). Years of education was also not associated with the prevalence of amyloid positivity in AD (β for change in GEE estimated prevalence of amyloid positivity per year of education, 0.016 [95% CI, −0.31 to .063], P = .51) and non–AD dementias (β, 0.025 [95% CI, −.038 to .088], P = .44).

For comparison with the GEE estimated probabilities for amyloid positivity on PET, the observed probabilities are provided in Table 3. Estimates of overall amyloid positivity in different subtypes of AD and frontotemporal dementia are provided in eTable 4 in the Supplement.

Amyloid Positivity Prevalence Relative to Controls

The mean prevalence of amyloid positivity was higher in the total group of participants with AD (β for difference in GEE estimate compared with the control group, 3.215 [95% CI, 3.013 to 3.417], P < .001), DLB (β, 1.231 [95% CI, .663 to 1.799], P < .001), and corticobasal syndrome (β, 0.787 [95% CI, .250 to 1.324], P < .001), similar in those with vascular dementia (β, 0.090 [95% CI, −.294 to .475], P = .65), and lower in those with frontotemporal dementia (β, −0.691 [95% CI, −1.065 to −.318], P < .001) compared with cognitively normal participants (Figure 2A and Figure 2D).

Amyloid Positivity as Discriminator Between Clinical Dementia Syndromes

Figure 3 displays the odds ratios for discrimination of AD from non–AD participants using amyloid PET. Odds ratios decreased in all non–AD dementias with increasing age, except for corticobasal syndrome participants.

Association of Amyloid Positivity With Global Cognition

Amyloid positivity was associated with lower MMSE scores in both AD dementia (amyloid positive, 21.2 [95% CI, 20.2 to 22.2]; amyloid negative, 22.2 [95% CI, 20.9 to 23.4]; P < .05) and non–AD dementia (amyloid positive, 20.6 [95% CI, 19.2 to 21.9]; amyloid negative, 23.2 [95% CI, 22.2 to 24.3]; P < .001). Among non–AD dementias, the association between MMSE scores and amyloid status was significant for DLB (amyloid positive, 19.6 [95% CI, 17.3 to 21.9]; amyloid negative, 25.3 [95% CI, 22.9 to 27.8]; P < .001), and vascular dementia (amyloid positive, 19.5 [95% CI, 15.9 to 23.1]; amyloid negative, 22.3 [95% CI, 18.9 to 25.7]; P < .05; no longer significant after Bonferroni correction [P = .07]), but not for frontotemporal dementia (amyloid positive, 22.4 [95% CI, 20.3 to 24.4]; amyloid negative, 23.9 [95% CI, 23.0 to 24.8]; P = .17) and CBS (amyloid positive, 21.6 [95% CI, 18.5 to 24.7]; amyloid negative, 23.0 [95% CI, 20.8 to 25.2]; P = .48).

PET Tracers and Procedures

In most participants, [11C]PIB was used (n = 1330), followed by [18F]florbetapir (n = 328), [18F]flutemetamol (n = 120), and [18F]florbetaben (n = 119). On post hoc analyses, there were no significant differences in prevalence of amyloid positivity between [11C]PIB and [18F]florbetapir (eTable 6 in the Supplement, [18F]flutemetamol and [18F]florbetaben were excluded from this analysis due to their sample size). The method of assessment (visual reads [n = 1123] or quantitative thresholds [n = 774]) and type of data acquisition (static [n = 1318] or dynamic [n = 579]) were not associated with the prevalence of amyloid positivity either.

Assessment of Study-Related Heterogeneity

In the total study population, the intraclass correlation coefficient for study-related random intercept variance was 0.046, indicating minor heterogeneity across cohorts. Within age and diagnostic groups, heterogeneity was not substantial according to the I2 statistic, except for the vascular dementia group with participants older than 80 years (eTable 7 in the Supplement). Upon visual inspection, variability in prevalence estimates as a function of age in cohorts with at least 5 participants was limited (eFigure 3 in the Supplement).

Discussion

The main findings of this individual participant meta-analysis were that the prevalence of amyloid on PET decreased with age in participants diagnosed with AD (greatest in APOE ε4 noncarriers) and increased with age in most non–AD dementias. The convergence of amyloid positivity across dementias with increasing age suggests that amyloid imaging might have the potential to be most helpful for differential diagnosis in early-onset dementia, particularly if the goal is to rule-in AD dementia. However, the high concordance between PET and pathology suggests that amyloid imaging might have the potential to be used to rule-out AD dementia regardless of age. Furthermore, amyloid in non–AD dementia may be clinically important as amyloid positivity was associated with worse global cognition. Data from this study may inform research into the clinical application of amyloid PET and highlight the necessity of biomarker-based participant selection for clinical trials.

A negative amyloid PET scan was observed in 12% of clinically diagnosed AD dementia participants and was most common in older APOE ε4 noncarriers. The latter finding is consistent with 2 recent phase 3 trials with humanized anti–amyloid-β monoclonal antibodies.4,5 The “AD phenocopy” was most prevalent in older and APOE ε4 negative participants and may best be explained by a mix of age-related pathologies (eg, hippocampal sclerosis, argyrophilic grain disease, or tangle-predominant dementia2729) that preferentially target the limbic system, resulting in a memory-predominant presentation that may be mistaken for AD, as well as false-negative PET scans. False-negative PET scans may reflect insensitivity to detect advanced amyloid pathology, possibly caused by distinct conformations of amyloid plaques, amyloid deposition in reference regions, or severe neurodegeneration. This is likely only a partial explanation because, with a few exceptions,30,31 PET and neuropathological assessments correspond well,32 and the independent samples of AD dementia participants with autopsy or PET showed similar prevalence estimates. Alternatively, elderly people may develop AD dementia in the presence of a lower amyloid burden (potentially not captured by PET) due to age-related diminished resilience (cognitive reserve theory33) or the cumulative effect of comorbid pathologies (double-hit hypothesis34). Future studies with antemortem amyloid PET and postmortem neuropathological examination are needed to identify which proportion of amyloid negative PET scans can be attributed to clinical misclassification or to false-negative PET findings in patients with clinical AD dementia.

In participants with frontotemporal dementia, vascular dementia, and DLB, the prevalence of amyloid positivity increased with age. A proportion of these participants may have been clinically misdiagnosed, with AD as the pathological substrate for their dementia.2 Another explanation is that amyloid is present as secondary pathology whereas the clinical syndrome is driven by non–AD pathologies.15,35,36 The finding that the prevalence of amyloid positivity increases with presence of the 2 major risk factors for sporadic AD, aging and APOE ε4 genotype, supports the latter interpretation. The advent of novel tau PET tracers3739 could provide further clues when 2 pathologies manifest simultaneously, because prominent neocortical tau pathology is typically absent in patients with DLB, vascular dementia, and some frontotemporal dementia subtypes.

In corticobasal syndrome the prevalence of amyloid positivity decreased with age. Corticobasal syndrome is a clinically and pathologically heterogeneous entity including motor, behavioral, and cognitive features.40 Corticobasal syndrome is mostly associated with underlying 4-repeat tauopathy (corticobasal degeneration or progressive supranuclear palsy), but up to 25% of patients have AD as the primary pathology at autopsy.41,42 This study suggests that AD may be the causative pathology in young corticobasal syndrome patients, whereas a primary tauopathy becomes more likely with increasing age.

This study underscores that clinical diagnosis, age, and APOE status are crucial factors when ordering and interpreting clinical amyloid PET scans. The likelihood of detecting incidental amyloid pathology increased with advancing age in both controls and non–AD dementia patients. In line with recently proposed appropriate use criteria,43 this suggests that amyloid imaging might be particularly helpful for differential diagnosis in early-onset dementia. In contrast, the convergence between AD and non–AD dementia participants with age warrants careful interpretation of positive amyloid PET scans in older patients. Also, amyloid imaging does not seem justified in APOE ε4 carriers to confirm their clinical diagnosis of AD dementia, as the prevalence of amyloid positivity remained around 90% regardless of age. In noncarriers, however, an amyloid PET scan may be informative in patients older than 70 years as the prevalence declined to 78% and further decreased to 68% at age 90. Although not recommended for routine diagnostic assessment,44 knowledge of APOE status may be helpful when considering amyloid assessment in clinical practice.

There are a number of limitations that need to be considered in interpreting this study. First is its limited generalizability as participants were highly educated (mean, 14.3 years of education [SD, 3.6]) and relatively small proportions of AD (15.9%) and non–AD (10.9%) dementia participants were older than 80 years (this age range represents the largest segment in the community). This meta-analysis reflects a collection of studies conducted in research memory clinics or focused epidemiological studies with limits on age and medical comorbidities. Furthermore, data on race/ethnicity would have been informative because previous studies have reported differences in the prevalence of APOE ε4 and its association with cognitive decline between white patients and black patients.4547

Second, we pooled data from a large number of cohorts, which may have introduced bias due to differences in study designs. However, there was limited evidence for heterogeneity across cohorts (eFigure 3 and eTable 7 in the Supplement). Third, due to the absence of histopathological data in participants with amyloid PET, it remains unknown whether the clinical diagnoses were correct and what type of pathologies underlie non–AD diagnoses, particularly in amyloid positive participants. Fourth, differences in acquisition methods did not allow for harmonized PET data analysis across cohorts, so that we adopted the methodology as specified by the different study sites. This lack of standardization was addressed by adjusting all analyses for study effects. Also, post hoc analyses showed no significant differences for assessment methods or acquisition modus (eTable 6 in the Supplement).

Fifth, 70% of participants underwent [11C]PIB imaging, although, from 2012 to 2014, the FDA approved three 18F-labeled PET tracers for clinical use.13 Although the number of 18F-labeled amyloid PET scans was relatively small, comparable prevalence estimates between [11C]PIB and [18F]florbetapir suggests that present findings are coherent across tracers.48 Sixth, although by design this is, to our knowledge, the largest amyloid PET study in patients with dementia, sample sizes in some non–AD dementia groups were relatively small and resulted in wide CIs. In particular, the prevalence estimates at the lower and higher age extremes in models that include both age and APOE genotype in non–AD dementias should be interpreted with caution.

Conclusions

Among participants with dementia, the prevalence of amyloid positivity was associated with clinical diagnosis, age, and APOE genotype. These findings indicate the potential clinical utility of amyloid imaging for differential diagnosis in early-onset dementia and to support the clinical diagnosis of patients with AD dementia and noncarrier APOE ε4 status who are older than 70 years.

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Article Information

Corresponding Author: Rik Ossenkoppele, PhD, Department of Neurology and Alzheimer Center, VU University Medical Center Amsterdam, the Netherlands (r.ossenkoppele@vumc.nl).

The Amyloid PET Study Group includes Sander C. J. Verfaillie, MSc; Marissa D. Zwan, MSc; Sofie M. Adriaanse, MSc; Adriaan A. Lammertsma, PhD; Frederik Barkhof, MD, PhD; William J. Jagust, MD; Bruce L. Miller, MD; Howard J. Rosen, MD; Susan M. Landau, PhD; Victor L. Villemagne, MD, PhD; Christopher C. Rowe, MD, PhD; Dong Y. Lee, MD, PhD; Duk L. Na, MD, PhD; Sang W. Seo, MD, PhD; Marie Sarazin, MD, PhD; Catherine M. Roe, PhD; Osama Sabri, MD, PhD; Henryk Barthel, MD, PhD; Norman Koglin, MD, PhD; John Hodges, MD, PhD; Cristian E. Leyton, MD, PhD; Rik Vandenberghe, MD, PhD; Koen van Laere, MD, PhD; Alexander Drzezga, MD, PhD; Stefan Forster, MD, PhD; Timo Grimmer, MD, PhD; Pascual Sánchez-Juan, MD; Jose M. Carril, MD; Vincent Mok, MD, PhD; Vincent Camus, MD, PhD; William E. Klunk, MD; Ann D. Cohen, PhD; Philipp T. Meyer, MD, PhD; Sabine Hellwig, MD, PhD; Andrew Newberg, MD; Kristian S. Frederiksen, MD, PhD; Adam S. Fleisher, MD; Mark A. Mintun, MD; David A. Wolk, MD; Agneta Nordberg, MD, PhD; Juha O. Rinne, MD, PhD; Gaël Chételat, PhD; Alberto Lleo, MD, PhD; Rafael Blesa, MD, PhD; Juan Fortea, MD, PhD; Karine Madsen, MD, PhD; Karen M. Rodrigue, PhD; David J. Brooks, MD, PhD.

Affiliations of The Amyloid PET Study Group: Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands (Verfaillie, Zwan, Adriaanse); Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands (Verfaillie, Zwan, Adriaanse, Lammertsma, Barkhof); Memory and Aging Center, University of California, San Francisco (Miller, Rosen); Helen Wills Neuroscience Institute, University of California, Berkeley (Jagust, Landau); Lawrence Berkeley National Laboratory, University of California, Berkeley (Jagust, Landau); Department of Nuclear Medicine and Centre for PET, Austin Health, Melbourne, Australia (Villemagne, Rowe); Department of Neuropsychiatry, College of Medicine, Seoul National University, South Korea (Lee); Department of Neurology, Sungkyunkwan University, Seoul, South Korea (Na, Seo); Neurologie de la Mémoire et du Langage, Sorbonne Paris Cité, INSERM UMR S894, Centre Hospitalier Sainte Anne, Université Paris Descartes, France (Sarazin); Department of Neurology, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, Missouri (Roe); Department of Nuclear Medicine, University of Leipzig, Germany (Sabri, Barthel); Piramal Imaging, Berlin, Germany (Koglin); Neuroscience Research Australia, Sydney, Australia (Hodges, Leyton); Laboratory for Cognitive Neurology and Alzheimer Research Center, Katholieke Universiteit Leuven, Catholic University Leuven, Belgium (Vandenberghe, van Laere); Department of Nuclear Medicine, University of Cologne, Germany (Drzezga); Department of Nuclear Medicine, Technische Universitaet Muenchen, Munich, Germany (Forster); Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar der Technische Universitaet Muenchen, Munich, Germany (Grimmer); IFIMAV and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Marqués de Valdecilla University Hospital, Cantabria, Spain (Sánchez-Juan); Department of Nuclear Medicine, Marqués de Valdecilla University Hospital, University of Cantabria, Spain (Carril); Department of Medicine and Therapeutics, Chinese University of Hong Kong, Shatin, China (Mok); INSERM U930 and Université François Rabelais de Tours, Centre Hospitalier Régional Universitaire Hôpitaux de Tours, France (Camus); Alzheimer’s Disease Research Center, University of Pittsburgh, Pittsburgh, Pennsylvania (Klunk, Cohen); Department of Nuclear Medicine, University Hospital Freiburg, Germany (Meyer); Centre for Geriatrics and Gerontology, University Hospital Freiburg, Germany (Hellwig); Myrna Brind Center of Integrative Medicine, Thomas Jefferson University and Hospital, Philadelphia, Pennsylvania (Newberg); Danish Dementia Research Center, Department of Neurology, Righospitalet, University of Copenhagen, Denmark (Frederiksen); The Banner Alzheimer’s Institute, Phoenix, Arizona (Fleisher); Avid Radiopharmaceuticals, Philadelphia, Pennsylvania (Mintun); Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania (Wolk); Center for Alzheimer Research, Translational Alzheimer Neurobiology, Karolinska Institutet, Stockholm, Sweden (Nordberg); Turku PET Centre and Division of Clinical Neuroscience, Turku University Hospital, University of Turku, Finland (Rinne); Institut National de la Santé et de la Recherche Medicale, Caen, France (Chételat); Department of Neurology, Universitat Autònoma de Barcelona, Spain (Lleo, Blesa, Fortea); Neurobiology Research Unit, Copenhagen University Hospital, Denmark, Germany (Madsen); Center for Longevity, The University of Texas at Dallas (Rodrigue); University of Texas Southwestern Medical Center, Dallas (Brooks); Division of Neuroscience and Medical Research Council Clinical Sciences Centre, Imperial College London, United Kingdom (Brooks).

Author Contributions: Drs Ossenkoppele and Visser had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Ossenkoppele, Jansen, Visser.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Ossenkoppele, Jansen, Rabinovici, Visser.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Ossenkoppele, Jansen, Knol, Visser.

Obtained funding: Rabinovici, van der Flier, Lammertsma, Jagust, Miller, Villemagne, Rowe, Lee, Na, Seo, Sarazin, Roe, Sabri, Barthel, Koglin, Hodges, Leyton, Vandenberghe, van Laere, Drzezga, Forster, Grimmer, Sánchez-Juan, Carril, Mok, Camus, Klunk, Cohen, Meyer, Hellwig, Newberg, Frederiksen, Fleisher, Mintun, Wolk, Nordberg, Rinne, Chételat, Lleo, Blesa, Fortea, Madsen, Rodrigue, Brooks, van Berckel, Scheltens, Visser.

Administrative, technical, or material support: All authors.

Study supervision: Visser.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.Dr Rabinovici reports receiving grant funding from Avid Radiopharmaceuticals and personal fees GE Healthcare and Piramal. Dr van der Flier reports receiving grant funding from Boehringer Ingelheim, Piramal, and Roche Europe BV. Dr Scheltens reports receiving grant funding from GE Healthcare, Piramal, and Merck. Dr Zwan reports grant funding from GE Healthcare. Dr Visser reports receiving grants from European Union/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative Joint Undertaking, European Union Joint Programme—Neurodegenerative Disease Research, Bristol-Myers Squibb, and ZonMw; and other support from Roche Diagnostics and GE Healthcare. Dr Lammertsma reports receiving grant funding from the International Stichting Alzheimer Onderzoek, Center for Translational Molecular Medicine, Philips, and Avid and personal fees from Philips. Dr Barkhof reports receiving consulting fees for Bayer Schering Pharma, sanofi-aventis, Biogen, TEVA, Merck Serono, Novartis, Roche, Synthon BV, Jansen Research, Genzyme, grants from the Dutch Multiple Sclerosis Society, and speaker fees from Serono Symposia and Medscape. Dr Jagust reports receiving personal fees from Banner Alzheimer Institute/Genentech, Synarc/Bioclinica, and Novartis. Dr Miller reports receiving grant support from the National Institutes of Health/National Institute on Aging (NIH/NIA) and the Centers for Medicare & Medicaid Services (CMS) as grants for the Memory and Aging Center; serving as medical director for the John Douglas French Foundation, scientific director for the Tau Consortium, director/medical advisory board of the Larry L. Hillblom Foundation, and the scientific advisory board member for the National Institute for Health Research Cambridge Biomedical Research Centre and its subunit, the Biomedical Research Unit in Dementia (UK). Dr Landau reports receiving grant funding from NIH and personal fees from Biogen Idec, Genentech, and Synarc. Dr Rowe reports receiving research grants in the last 2 years from Piramal, GE Healthcare, Avid/Lilly, Navidia, and AstraZeneca; giving talks at educational meetings arranged by Piramal and GE Healthcare; and receiving personal fees from Roche. Dr Sarazin reports receiving personal fees from Novartis and Allianz. Dr Sabri reports receiving grant funding from Pirimal Imaging, Bayer HealthCare, and personal fees from Bayer HealthCare and GE Healthcare. Dr Barthel reports receiving personal fees from Piramal Imaging and Siemens Healthcare. Dr Koglin reports receiving personal fees from Piramal Imaging, which is marketing Neuraceq ([F18]florbetaben) as an amyloid-β PET imaging agent. Dr Vandenberghe reports receiving support from GEHC, Merck, Forum and Roche; grant funding from Research Foundation—Flanders (FWO) and KU Leuven; and nonfinancial support from GEHC. Dr Van Laere reports receiving grants from Merck, Janssens Pharmaceuticals, and GE Healthcare. Dr Drzezga reports receiving speaker honoraria and consulting fees from GE Healthcare, AVID/Lilly, and Piramal and grant funding from the German Research Foundation. Dr Forster reports receiving personal fees from Piramal, Bayer, and GE Healthcare. Dr Grimmer reports receiving personal fees from Eli Lilly. Dr Camus reports receiving grants from French Ministry of Health. Dr Klunk reports being co-inventor of the amyloid imaging tracer PiB, which is owned by the University of Pittsburgh and licensed to GE Healthcare; and receiving payments from the University of Pittsburgh from that license. Dr Meyer reports grants from GE Healthcare. Dr Hellwig reports grant funding from GE Healthcare and the University of Freiburg. Dr Fleisher reports receiving support from Eli Lilly, Merck, and Pfizer; grant support from National Institute of Aging, Avid Radiopharmaceuticals, and Eli Lilly; personal fees from Grifols, Avid Radiopharmaceuticals, and Siemens; and being a full-time employee of the Banner Alzheimer's Institute and a full-time employee of Eli Lilly at the time of submission; maintaining a voluntary faculty appointment at the University of California, San Diego. Dr Mintun reports being an employee of Avid Radiopharmaceuticals. Dr Wolk reports consulting for GE Healthcare and being a site private investigator for a clinical trial supported by AVID/Lilly. Dr Rinne reports receiving grant funding from Sigrid Juselius Foundation and Turku University Hospital. Dr Lleo reports grant funding from Instituto de Salud Carlos III and the CIBERNED program. Dr Fortea reports receiving grant funding from Instituto de Salud Carlos III. Dr Madsen reports receiving grant funding from the Lundbeck Foundation, the Dagmar Marshalls Fond, and the Danish Medical Council. The authors received salary as employees of their respective organizations. Dr Brooks reports receiving consulting fees from GE Healthcare. No other disclosures are reported.

Funding/Support: The National Alzheimer's Coordinating Center (NACC) database is funded by grant U01 AG016976 from the National Institute on Aging. The Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health, grant U01 AG024904) is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences; AstraZeneca; Bayer HealthCare; BioClinica; Biogen Idec; Bristol-Myers Squibb; Eisai; Elan Pharmaceuticals; Eli Lilly; F. Hoffmann-La Roche and its affiliated company Genentech; GE Healthcare; Innogenetics, NV; IXICO; Janssen Alzheimer Immunotherapy Research and Development; Johnson & Johnson Pharmaceutical Research and Development; Medpace; Merck and Company; Meso Scale Diagnostics; Novartis Pharmaceuticals; Pfizer; Servier; Synarc; and Takeda Pharmaceutical. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of California, Los Angeles. Funding for the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study was provided in part by the study partners (Australian Commonwealth Scientific Industrial and Research Organization [CSIRO], Edith Cowan University [ECU], Mental Health Research Institute [MHRI], Alzheimer’s Australia [AA], National Ageing Research Institute [NARI], Austin Health, CogState, Hollywood Private Hospital, Sir Charles Gardner Hospital). The study also received support from the National Health and Medical Research Council (NHMRC) and the Dementia Collaborative Research Centres program (DCRC2), as well as ongoing funding from the Science and Industry Endowment Fund (SIEF). The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement 115372, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The florbetaben phase 2 study, from which data were derived for this multicenter evaluation, was sponsored by Bayer Healthcare/Piramal Imaging (Berlin, Germany). The assembling of the TU Munich data set was supported in part by the German research foundation (Deutsche Forschungsgemeinschaft; HE 4560/1-2, DR 445/3-1 and DR 445/4-1 to Dr Drzezga), and by a KKF-grant for clinical research of the Technische Universität München (to Drs Drzezga and Grimmer). The amyloid PET studies of The Chinese University of Hong Kong are supported by Health and Health Services Research Fund (06070231) and seeding money from Lui Che Woo Institute of Innovative Medicine. Funding for this study was provided by the National Institute on Aging (P50 AG005681, P01 AD 003991, and P01 AD 026276); Fred Simmons and Olga Mohan, and the Joanne Knight Alzheimer’s Research Initiative of the Washington Knight Alzheimer’s Disease Research Center. The study in Tours was financially supported by the French Ministry of Health (grant PHRC-N 2008 1004) and the EC-FP6-project (DiMI, LSHB-CT-2005-512146). Data from the University of Pennsylvania was supported by the Pennsylvanian Department of Health (4100037703). Data from Washington University in St Louis was supported by grants NIH NCRR UL1RR024992, NINDS NS075321, the American Parkinson Disease Association (APDA) Advanced Research Center for Parkinson Disease at Washington University in St. Louis, and the Greater St Louis Chapter of the APDA. This research was performed within the framework of The Center for Translational Molecular Medicine (CTMM; http://www.ctmm.nl), project LeARN (grant 02N-101). The Caen study was funded by Agence Nationale de la Reserche, Programme Hospitalier de Reserche Clinique, Region Basse Normandie, and Institut National de la Sante et de la Reserche Medicale (INSERM).

Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The authors’ respective organizations were given the opportunity to review the manuscript for medical and scientific accuracy as well as intellectual property considerations.

Disclaimer: Any views expressed in this publication represent the personal opinions of the authors, and not those of their respective employer.

Additional Contributions: Multicenter studies involved in this study: Alzheimer’s Disease Neuroimaging Initiative (ADNI); Australian Imaging, Biomarkers & Lifestyle (AIBL) study; Avid Pharmaceuticals multicenter study for the AV45-A17 Study Group; Florbetaben (FBB) Phase 2 multicenter study; Leiden Alzheimer-Research Nederland (LeARN) project; Multicenter study by UK Hospitals and University Hospital of Turku. Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this article. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Additional information is available in the Supplement.

Correction: This article was corrected for typographical errors on June 11, 2015.

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