Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Nov 29.
Published in final edited form as: JAMA Psychiatry. 2018 Jan 1;75(1):84–95. doi: 10.1001/jamapsychiatry.2017.3391

Association of Cerebral Amyloid-β Aggregation With Cognitive Functioning in Persons Without Dementia

Willemijn J Jansen 1, Rik Ossenkoppele 1, BettyM Tijms 1, Anne M Fagan 1, Oskar Hansson 1, William E Klunk 1, Wiesje M van der Flier 1, Victor L Villemagne 1, Giovanni B Frisoni 1, Adam S Fleisher 1, Alberto Lleó 1, Mark A Mintun 1, Anders Wallin 1, Sebastiaan Engelborghs 1, Duk L Na 1, Gäel Chételat 1, José Luis Molinuevo 1, Susan M Landau 1, Niklas Mattsson 1, Johannes Kornhuber 1, Osama Sabri 1, Christopher C Rowe 1, Lucilla Parnetti 1, Julius Popp 1, Tormod Fladby 1, William J Jagust 1, Pauline Aalten 1, Dong Young Lee 1, Rik Vandenberghe 1, Catarina Resende de Oliveira 1, Elisabeth Kapaki 1, Lutz Froelich 1, Adrian Ivanoiu 1, Tomasz Gabryelewicz 1, Marcel M Verbeek 1, Páscual Sanchez-Juan 1, Helmut Hildebrandt 1, Vincent Camus 1, Marzena Zboch 1, David J Brooks 1, Alexander Drzezga 1, Juha O Rinne 1, Andrew Newberg 1, Alexandre de Mendonça 1, Marie Sarazin 1, Gil D Rabinovici 1, Karine Madsen 1, Milica G Kramberger 1, Agneta Nordberg 1, Vincent Mok 1, Barbara Mroczko 1, David A Wolk 1, Philipp T Meyer 1, Magda Tsolaki 1; Amyloid Biomarker Study Group1, Philip Scheltens 1, Frans R J Verhey 1, Pieter Jelle Visser 1
PMCID: PMC5786156  NIHMSID: NIHMS927770  PMID: 29188296

Abstract

IMPORTANCE

Cerebral amyloid-β aggregation is an early event in Alzheimer disease (AD). Understanding the association between amyloid aggregation and cognitive manifestation in persons without dementia is important for a better understanding of the course of AD and for the design of prevention trials.

OBJECTIVE

To investigate whether amyloid-β aggregation is associated with cognitive functioning in persons without dementia.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study included 2908 participants with normal cognition and 4133 with mild cognitive impairment (MCI) from 53 studies in the multicenter Amyloid Biomarker Study. Normal cognition was defined as having no cognitive concerns for which medical help was sought and scores within the normal range on cognitive tests. Mild cognitive impairment was diagnosed according to published criteria. Study inclusion began in 2013 and is ongoing. Data analysis was performed in January 2017.

MAIN OUTCOMES AND MEASURES

Global cognitive performance as assessed by the Mini-Mental State Examination (MMSE) and episodic memory performance as assessed by a verbal word learning test. Amyloid aggregation was measured with positron emission tomography or cerebrospinal fluid biomarkers and dichotomized as negative (normal) or positive (abnormal) according to study-specific cutoffs. Generalized estimating equations were used to examine the association between amyloid aggregation and low cognitive scores (MMSE score ≤27 or memory z score≤−1.28) and to assess whether this association was moderated by age, sex, educational level, or apolipoprotein E genotype.

RESULTS

Among 2908 persons with normal cognition (mean [SD] age, 67.4 [12.8] years), amyloid positivity was associated with low memory scores after age 70 years (mean difference in amyloid positive vs negative, 4% [95% CI, 0%–7%] at 72 years and 21% [95% CI, 10%–33%] at 90 years) but was not associated with low MMSE scores (mean difference, 3% [95% CI, −1% to 6%], P = .16). Among 4133 patients with MCI (mean [SD] age, 70.2 [8.5] years), amyloid positivity was associated with low memory (mean difference, 16% [95% CI, 12%–20%], P < .001) and low MMSE (mean difference, 14% [95% CI, 12%–17%], P < .001) scores, and this association decreased with age. Low cognitive scores had limited utility for screening of amyloid positivity in persons with normal cognition and those with MCI. In persons with normal cognition, the age-related increase in low memory score paralleled the age-related increase in amyloid positivity with an intervening period of 10 to 15 years.

CONCLUSIONS AND RELEVANCE

Although low memory scores are an early marker of amyloid positivity, their value as a screening measure for early AD among persons without dementia is limited.

Cerebral amyloid-β aggregation is an early pathologic event in Alzheimer disease (AD), starting 2 to 3 decades before dementia onset.1,2 Approximately 25% of cognitively normal elderly individuals and 50% of patients with mild cognitive impairment (MCI) have biomarker evidence of amyloid pathology.1,3 These persons are at increased risk for developing AD-type dementia,1 but the extent to which amyloid-β aggregation is associated with cognition in persons without dementia is unclear. Understanding the association between amyloid pathology and cognitive functioning is important for a better understanding of the course of AD and for the design of AD-prevention trials.

Longitudinal cohort studies4–6 have revealed an association between the presence of amyloid pathology and long-term cognitive decline. To assess the role of cognitive screening as a tool to enrich AD clinical trials, the cross-sectional association also needs to be established. However, findings from cross-sectional studies in cognitively normal individuals4,7,8 and patients with MCI7,9,10 have been inconsistent. This variability may be caused by differences among studies in demographic and genetic factors, disease stage and reserve capacity, and methodologic approaches. Effects are mainly observed in the episodic memory and global cognition domains and tend to be small,11 indicating that large samples are needed to investigate the amyloid-cognition association.

We previously estimated the prevalence of amyloid positivity in persons without dementia and, in the present study, investigated the association between amyloid positivity and cognitive scores in this population by using individual participant data from 53 studies included in the multicenter Amyloid Biomarker Study.1 We also examined whether age, cognitive status, sex, educational level, and apolipoprotein E (APOE) genotype modify the association between amyloid status and global cognition or episodic memory; estimated temporal associations among amyloid positivity, low memory scores, and AD-type dementia; and tested the usefulness of cognitive scores as a screening instrument for amyloid positivity.

Methods

Participants

Participants were recruited from studies that participated in the multicenter Amyloid Biomarker Study1 on establishing the prevalence of amyloid pathology measured with a positron emission tomography (PET) or cerebrospinal fluid (CSF) biomarker. For details on study selection and data collection, see Jansen et al.1 Study inclusion began in 2012 and is ongoing. At time of analysis (January 2017), we included participant-level data from 2908 participants with normal cognition and 4133 patients with MCI from 53 studies that had data available on the Mini-Mental State Examination (MMSE) and/or an episodic memory score. Participants with normal cognition had no cognitive concerns for which medical help was sought and scored within the normal range on cognitive tests. The diagnosis of MCI was made according to published criteria, including subjectively experienced and objectively verified decline in memory or another cognitive domain. Characteristics of the included studies are given in eTable 1 in the Supplement. All participants gave written informed consent to participate, and data were deidentified. Study protocols were approved by the local ethics committees of all centers participating in the Amyloid Biomarker Study.

Cognitive Tests

Global cognition was assessed with the MMSE.12 The MMSE scores were available from 53 studies comprising a total of 2885 participants with normal cognition (38 studies) and 4133 patients with MCI (48 studies). A low MMSE score was defined as 27 or less.

Episodic memory was assessed with verbal word learning tests. Data were available from 31 studies including 2010 participants with normal cognition (21 studies) and 2615 patients with MCI(26 studies). Most studies provided raw scores (28 studies) that were transformed into z scores using the mean and SD of the center-specific group of cognitively unimpaired individuals or, in the absence of such a group, using published test-specific means. Three studies provided z scores that were calculated similarly. Episodic memory was assessed using 12 different verbal memory tests of immediate or delayed recall. When multiple memory test scores were provided for a participant, the delayed recall score was chosen. Tests of delayed recall were used for 2809 participants (21 studies) and tests of immediate recall for 1816 participants (10 studies). See eTable 2 in the Supplement for an overview of the memory tests used in each study. In our analyses, low episodic memory performance was defined as a z score of −1.28 or less, capturing performance in the 10th percentile or lower of the population mean.

Amyloid Assessment

In the sample selected for these analyses, amyloid pathology was assessed with amyloid-PET in 1224 participants with normal cognition (21 studies) and 956 patients with MCI (23 studies) and by amyloid-β1–42 level in CSF in 1684 participants with normal cognition (19 studies) and 3177 with MCI (29 studies). Measurement details have been described previously.1 The PET and CSF biomarkers were dichotomized as negative (normal) or positive (abnormal) according to study-specific cutoffs or visual reads.

Data Availability

Information on the level of education was available for 2558 participants (88.0%) with normal cognition (memory score: n = 1973; MMSE: n = 2536) and 3270 patients (79.1%) with MCI (memory score: n = 2456; MMSE: n = 3264). Information on APOE-ε4 carrier status (yes or no) was available for 2400 participants (82.5%) with normal cognition (memory score: n = 1764; MMSE: n = 2379) and 3292 patients (79.7%) with MCI (memory score: n = 2217; MMSE: n = 3286). The APOE genotype was available for 2347 participants (80.7%) with normal cognition (memory score: n = 1763; MMSE: n = 2326) and 3019 patients (73.0%) with MCI (memory score: n = 2215; MMSE: n = 3013).

Temporal Association Among Amyloid Positivity, Low Memory Scores, and AD-Type Dementia

To provide an estimation of the temporal associations among amyloid positivity, low memory scores, and AD-type dementia, we compared age-specific frequency estimates of low memory scores and amyloid positivity in participants with normal cognition with age-specific prevalence data of AD-type dementia in the general population (adopted from Jansen et al1).

Cognitive Screening as an Indication of Amyloid Status

To investigate the screening potential of low cognitive scores, we calculated the odds of low cognitive performance for amyloid positivity and examined improvement in area under the receiver operating characteristic curve when cognitive scores were added to a model with age and APOE-ε4 carrier status.

Statistical Analysis

Differences in clinical and demographic characteristics between the amyloid-positive and amyloid-negative subgroups were analyzed using analyses of variance for continuous variables and χ2 tests for categorical variables. Dichotomized MMSE and memory scores (low vs normal) were used as outcome variables in generalized estimating equations. Generalized estimating equations allow for the analysis of binary correlated data such that participant-level data from all studies could be modeled simultaneously while accounting for the clustering of participants within studies. We assumed a logit link function for binary outcome with an exchangeable correlation structure to account for within-study correlation. Age was included as a continuous measure and was centered at the median. Educational level was dichotomized at the median (high [≥14 years] vs moderate to low [<14 years]). For each outcome measure, we performed the following 6 models. The first model included amyloid pathology (present or absent), cognitive status (normal cognition or MCI), age, interactions among these 3 variables as predictors, and sex and educational level as covariates. In the second, third, and fourth models, we added sex (model 2), educational level (model 3), and APOE-ε4 carrier status (model 4) with up to 3-way interactions of these variables with amyloid pathology, cognitive status, and age. In the fifth model, we entered all variables and included up to 3-way interactions among age, sex, educational level, APOE-ε4 carrier status, cognitive status, and amyloid pathology using a forward selection method. In the sixth model, we examined whether APOE genotype (coded as ε4ε4, ε2ε4/ε3ε4, ε3ε3, or ε2ε2/ε2ε3) modified the association among amyloid, age, and cognition while correcting for sex and educational level. Terms were retained in the equation in case of a significant Wald statistic (P < .05). For an overview of significant terms in each of the models tested, see eTable 3 in the Supplement. We report estimates corrected for age, sex, and educational level in the text. Models unadjusted for sex and educational level yielded similar results and were used to display estimates in Figures 1, 2, 3 and Table 1 and Table 2. Associations did not change after correcting for multiple comparisons with the Bonferroni method. Secondary analyses are described in eAppendix 1 and eFigure3 in the Supplement. Analyses were conducted with SPSS statistical software, version 22.0 (IBM Corp), with a significance level set at P < .05 for unpaired, 2-sided tests.

Figure 1. Frequencies of Low Memory and Low Mini-Mental State Examination (MMSE) Scores.

Figure 1

Open in a new tab

A and B, Model with terms for age, amyloid status, and cognitive status and up to 3-way interactions among these terms (3-way interaction P = .002). C, and D, Model with terms for age, amyloid status, cognitive status, and interactions between amyloid status and age (P = .03) and between amyloid status and cognitive status (P < .001). Adjustment for sex and educational level did not change either model. Low score was defined as 10th percentile or lower of z scores for the memory score and 27 or lower for MMSE scores. Shading indicated 95% CIs.

Figure 2. Frequency of Low Memory Score According to Apolipoprotein E (APOE) Genotype.

Figure 2

Open in a new tab

Model with terms for age, amyloid status, cognitive status, and up to 3-way interactions among these terms (3-way interaction P = .007), APOE genotype, and interactions among APOE genotype, amyloid status, and cognitive status (P = .02). Adjustment for sex and educational level did not change the model. Low score was defined as 10th percentile or lower of z scores.

Figure 3. Prevalence of Amyloid Positivity and Low Memory Score in Participants With Normal Cognition and of Alzheimer Disease (AD)–Type Dementia in the General Population.

Figure 3

Open in a new tab

Prevalence estimates of amyloid positivity and low memory score were derived from separate models including age as a predictor. Prevalence estimates of AD-type dementia were derived from a meta-analysis of 14 studies.1 Low score was defined as 10th percentile or lower of z scores.

Table 1.

Study Sample Characteristics by Cognitive Status and Amyloid Statusa

Characteristic Normal Cognition P Value MCI P Value
Amyloid Negative
(n = 2166)		 Amyloid Positive
(n = 742)		 Amyloid Negative
(n = 2000)		 Amyloid Positive
(n = 2133)
Age, mean (SD), y 65.6 (13.2) 72.9 (9.5) <.001 68.6 (8.9) 71.7 (7.9) <.001
Age groups, y
  <40 127 (5.9) 2 (0.3) NA 1 (0.1) 0 NA
  40–49 91 (4.2) 10 (1.3) 29 (1.5) 8 (0.4)
  50–59 311 (14.4) 55 (7.4) 309 (15.5) 147 (6.9)
  60–69 714 (33.0) 180 (24.3) 719 (36.0) 654 (30.7)
  70–79 714 (33.0) 322 (43.4) 736 (36.8) 1004 (47.1)
  80–89 195 (9.0) 160 (21.6) 200 (10.0) 308 (14.4)
  ≥90 14 (0.6) 13 (1.8) 6 (0.3) 11 (0.5)
Women 1231/2154 (57.1) 397/742 (53.5) .08 898/2000 (45.0) 1013/2133 (47.5) .11
Educational level
  Mean (SD), y 14.2 (3.7) 14.7 (3.7) .001 11.8 (4.2) 12.7 (4.4) <.001
  High level (≥14 y) 1107/1906 (58.1) 430/652 (66.0) <.001 536/1597 (33.6) 732/1673 (43.8) <.001
MMSE score
  Mean (SD) 29.1 (1.2) 28.9 (1.3) .001 27.2 (2.3) 26.4 (2.6) <.001
  MMSE score ≤27 208/2148 (9.7) 88/737 (11.9) .08 938/1999 (46.9) 1312/2128 (61.7) <.001
Memory z score
  Mean (SD) 0.04 (0.95) −0.16 (1.07) <.001 −1.27 (1.58) −1.78 (1.47) <.001
  Memory z score ≤1.28 130/1480 (8.9) 76/530 (14.3) <.001 594/1256 (47.3) 878/1359 (64.6) <.001
APOE-ε4 positive 402/1746 (23.0) 324/654 (49.5) <.001 421/1510 (27.9) 1134/1782 (63.5) <.001
APOE genotype
  ε2ε2/ε2ε3 263/1713 (15.4) 26/634 (4.1) NA 172/1407 (12.2) 61/1612 (3.8) NA
  ε3ε3 1052/1713 (61.4) 293/634 (46.2) 829/1407 (58.9) 513/1612 (31.8)
  ε2ε4/ε3ε4 376/1713 (21.9) 263/634 (41.5) 365/1407 (25.9) 753/1612 (46.7)
  ε4ε4 22/1713 (1.3) 52/634 (8.2) 41/1407 (2.9) 285/1612 (17.7)
Open in a new tab

Abbreviations: APOE, apolipoprotein E; MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination; NA, not applicable.

a

Data are presented as number/total number (percentage) of study participants unless otherwise indicated. Amyloid-negative and amyloid-positive subgroups were compared among participants with normal cognition and among patients with MCI with t tests or χ2 tests.

Table 2.

Estimated Frequencies of Low Cognitive Performance According to Amyloid Status and Agea

Age, y Frequency, % (95% CI)
Memory MMSE
Normal Cognition MCI Normal Cognition MCI
Amyloid Negative Amyloid
Positive		 P Value Amyloid Negative Amyloid
Positive		 P Value Amyloid Negative Amyloid
Positive		 P Value Amyloid Negative Amyloid
Positive		 P Value
50 4.9 (2.1–11.1) 2.7 (1.2–6.1) .17 29.1 (17.0–45.1) 50.4 (40.1–60.6) <.001 6.5 (4.7–9.0) 9.3 (6.3–13.4) .09 30.2 (23.4–38.0) 50.0 (42.9–57.1) <.001
55 5.9 (2.9–11.8) 4.0 (2.0–7.7) .24 34.8 (22.8–49.0) 55.3 (45.9–64.2) <.001 8.0 (5.9–10.8) 10.6 (7.4–15.0) .11 35.0 (28.4–42.3) 53.7 (47.3–60.0) <.001
60 7.1 (3.8–12.9) 5.9 (3.4–9.9) .47 40.9 (29.6–53.2) 60.0 (51.5–68.0) <.001 9.8 (7.3–13.0) 12.1 (8.6–16.7) .15 40.2 (33.9–46.8) 57.4 (51.7–62.9) <.001
65 8.5 (4.9–14.5) 8.6 (5.6–12.9) .97 47.3 (37.0–57.8) 64.6 (56.6–71.9) <.001 11.9 (9.0–15.6) 13.8 (10.0–18.7) .24 45.6 (39.6–51.6) 61.0 (55.9–65.9) <.001
70 10.2 (6.0–16.8) 12.4 (8.9–17.2) .20 53.8 (44.3–63.0) 68.9 (61.1–75.8) <.001 14.4 (10.9–18.8) 15.7 (11.5–21.0) .43 51.0 (45.3–56.7) 64.5 (59.8–69.0) <.001
75 12.2 (7.0–20.3) 17.7 (12.9–23.8) .002 60.1 (50.7–68.9) 72.9 (65.0–79.7) <.001 17.3 (13.0–22.7) 17.7 (13.1–23.5) .80 56.5 (50.8–62.0) 67.9 (63.5–72.0) <.001
80 14.4 (7.8–25.1) 24.5 (17.3–33.6) <.001 66.2 (56.2–74.9) 76.6 (68.4–83.2) <.001 20.7 (15.3–27.3) 20.0 (14.9–26.4) .73 61.8 (55.9–67.3) 71.1 (66.7–75.1) <.001
85 17.0 (8.5–21.3) 32.9 (22.0–46.1) <.001 71.7 (60.7–80.7) 79.9 (71.4–86.4) .004 24.5 (17.9–32.6) 22.6 (16.8–29.5) .39 66.8 (60.6–72.5) 74.1 (69.6–78.0) .001
90 20.0 (9.0–38.6) 42.6 (27.0–59.7) <.001 76.7 (64.6–85.6) 82.9 (74.1–89.1) .07 28.8 (20.7–38.5) 25.3 (18.9–33.0) .22 71.5 (64.9–77.3) 76.8 (72.3–80.9) .03
Open in a new tab

Abbreviations: MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination.

a

Data are estimated probabilities uncorrected for sex, educational level, and apolipoprotein E ε4 carrier status. The model for memory included terms for age, amyloid status, cognitive status, and interactions among these terms (3-way interaction P = .002). The model for MMSE included terms for age, amyloid status, cognitive status, and interactions between amyloid status and age (P = .02), and between amyloid status and cognitive status (P < .001). Adjustment for sex and educational level did not change the models. Low score was defined as 10th percentile or lower of z scores for the low memory score and 27 or lower for low MMSE scores.

Results

A total of 2908 persons with normal cognition (mean [SD] age, 67.4 [12.8] years; 1628 of 2896 participants [56.2%] were female) and 4133 patients with MCI (mean [SD] age, 70.2 [8.5] years; 1911 female [46.2%]) participated in the study. Sample characteristics of the participants with normal cognition and MCI according to amyloid status are given in Table 1. The amyloid-positive and amyloid-negative groups differed on all variables except sex in participants with normal cognition and MCI and except MMSE score in participants with normal cognition.

Association Between Amyloid Status and Episodic Memory Score

Amyloid positivity was differentially associated with low memory scores across age and diagnostic groups (3-way interaction; β = −0.54 [95% CI, −0.097 to −0.011]). In participants with normal cognition, low memory scores were more frequent in amyloid-positive than in amyloid-negative participants but only after 70 years of age (mean difference for amyloid-positive vs amyloid-negative participants at 72 years of age, 4% [95% CI, 0%–7%], P = .04; at 90 years of age, 21% [95% CI, 10%–33%], P < .001). At 80 years of age, the frequency of low memory scores in amyloid-positive participants with normal cognition was almost double that of their amyloid-negative counterparts (25% vs 14%) (Figure 1A and Table 2). In patients with MCI, amyloid-positive participants more often had low memory scores than amyloid negative participants (mean difference, 16% [95% CI, 12%–20%], P < .001) (Figure 1B and Table 2), although this difference decreased with advancing age.

In participants with normal cognition and patients with MCI, sex, educational level, and APOE-ε4 carrier status were each associated with memory scores but did not moderate the association between amyloid status and memory scores (eFigure 1A in the Supplement). A low memory score was more frequent in men than in women (mean difference, 8% [95% CI, 3%–14%], P = .004), in participants with low rather than high educational level (mean difference, 8% [95% CI, 5%–11%], P < .001), and in APOE-ε4 carriers than in noncarriers (mean difference, 8% [95% CI, 4%–11%], P < .001).

Furthermore, APOE genotype was associated with low memory score independent of amyloid status, cognitive status, or age (Figure 2). Participants with the APOE-ε4ε4 genotype most often had a low memory score (mean difference vs ε2ε4/ε3ε4, 10% [95% CI, 4%–16%], P < .001; vs ε3ε3, 16% [95% CI, 9%–24%], P < .001; vs ε2ε2/ε2ε3, 21% [95% CI, 9%–24%], P < .001), followed by participants with the APOE-ε2ε4/ε3ε4 genotype (mean difference vs ε3ε3, 6% [95% CI, 3%–9%], P < .001; vs ε2ε2/ε2ε3, 11% [95% CI 4%–17%], P = .001) and the APOE-ε3ε3 and APOE-ε2ε2/ε2ε3 genotypes (mean difference, 5% [95% CI, −1% to 10%], P = .09).

Association Between Amyloid Status and MMSE Score

The association between amyloid positivity and a low score on the MMSE was dependent on age (β = 0.023 [95% CI, 0.009–0.037, P = .001 for interaction) and cognitive status (β = 0.348 [95% CI, 0.032–0.665], P = .03 for interaction; β = 0.006 [95% CI, −0.033 to 0.044], P = .10 for 3-way interaction). In participants with normal cognition, amyloid positivity was not associated with low MMSE scores at any age (mean difference, 3% [95% CI, −1% to 6%], P = .16) (Figure 1C and Table 2). In patients with MCI, the frequency of low MMSE scores was greater in amyloid-positive compared with amyloid-negative patients at all ages (mean difference, 14% [95% CI, 12%–17%], P < .001) (Figure 1D and Table 2).

When adjusting for sex, educational level, and APOE-ε4 carrier status, none of these factors modulated the association between amyloid status and MMSE score in participants with normal cognition or patients with MCI. APOE-ε4 carrier-ship was more often associated with low MMSE scores regardless of cognitive status and age (mean difference, 4% [95% CI, 2%–7%], P < .001). Sex (β = −0.444 [95% CI, −0.681 to −0.207; P<.001 for interaction) and educational level (β = −0.554 [95% CI, −0.874 to −0.234], P<.001 for interaction) had an association with MMSE score that was dependent on cognitive status (eFigure 1B in the Supplement).

In addition, APOE genotype was associated with low MMSE score (P = .002) (eFigure 2 in the Supplement) independent of amyloid status, cognitive status, and age. Participants with the APOE-ε4ε4 genotype most often had a low MMSE score (mean difference vs ε2ε4/ε3ε4, 6% [95% CI, 0%–12%], P = .06; vs ε3ε3, 10% [95% CI, 3%–16%], P = .005; vs ε2ε2/ε2ε3, 10% [95% CI, 3%–16%], P = .005) followed by participants with the APOE-ε2ε4/ε3ε4 genotype (mean difference vs ε3ε3, 4% [95% CI, 1%–6%], P = .008; vs ε2ε2/ε2ε3, 4% [95% CI, 1%–8%], P = .14) and the APOE-ε3ε3 and APOE-ε2ε2/ε2ε3 genotypes (mean difference, 0% [95% CI, −4% to 4%], P = .99).

Amyloid Positivity, Low Memory Scores, and AD-Type Dementia

In participants with normal cognition, the age-related increase in low memory scores paralleled the age-related increase in amyloid positivity with an intervening period of 10 to 15 years. Subsequently, the age-related increase in low memory score was paralleled by an age-related increase in prevalence of AD-type dementia 10 to 15 years later (Figure 3).

Cognitive Screening as an Indication of Amyloid Status

The odds ratio of low memory score for amyloid positivity varied depending on age from 1.06 to 1.80 for participants with normal cognition and from 1.47 to 2.81 for patients with MCI. The odds ratio of low MMSE score varied from 0.77 to 1.19 for participants with normal cognition and from 1.32 to 2.15 for patients with MCI (eTable 4 in the Supplement). The receiver operating characteristic curve analyses showed that low memory or MMSE scores did not add to the estimation of amyloid positivity above that of age and APOE-ε4 carrier status (eTable 5 in the Supplement).

Discussion

In this cross-sectional analysis of adults without dementia enrolled from multiple studies, amyloid pathology was associated with low memory scores among cognitively normal individuals older than 70 years and in patients with MCI until old age, whereas it was associated with low MMSE scores in patients with MCI only. However, a low cognitive score had limited value as a screening measure for early AD. The association between amyloid pathology and cognition existed independent of sex, educational level, and APOE genotype, but these factors contributed to individual estimates of cognitive level. We further observed 10- to 15-year intervals between the onset of amyloid positivity and emergence of low memory scores in persons with normal cognition and between the age-related increase in low memory scores and prevalence of AD-type dementia.

Of note, the proportion of low memory scores in amyloid-positive participants with normal cognition increased rapidly after the age of 70 years. Possible explanations for this age association are that more extensive amounts of amyloid deposition are needed before becoming clinically manifest, that other pathology is also present after this age, or that cognitive vulnerability to amyloid pathology may be higher at older age. This finding agrees with a recent population-based study13 in cognitively normal individuals aged 50 to 69 years in which amyloid-PET levels were unrelated to cross-sectional and longitudinal measures of episodic memory. Previous contrasting findings from cross-sectional studies4,7,8 that examined the amyloid-cognition association may therefore, in addition to the previously identified task complexity,14 be explained by age of the individuals in the study sample.

We found no association of amyloid pathology with low MMSE score in participants with normal cognition. This result contrasts with a meta-analysis of biomarker and neuropathologic studies.11 However, that meta-analysis also included global cognition tests that have a larger memory component than the MMSE.

In patients with MCI, amyloid-positive patients had (depending on age) low memory and MMSE scores 5% to 20% more often than amyloid-negative patients. Unlike in participants with normal cognition, frequencies of low cognitive scores in amyloid-positive and amyloid-negative patients were more similar at older ages in patients with MCI. This finding is in line with a study in persons with dementia,15 suggesting that neuropathologic changes other than amyloid may contribute to cognitive impairment at more advanced age in patients with MCI but not in persons with normal cognition.

Sex and amyloid pathology independently predicted cognitive performance, suggesting that amyloid-related cognitive decline is equally prevalent in men and women, as has also been suggested in a large cross-sectional study16 of cognitively normal individuals from the general population not included in our study. High educational level was also associated with fewer cognitive deficiencies independent of amyloid status. This finding is consistent with earlier studies17,18 showing that cognitive reserve helps to preserve cognition in pathologic conditions and aging. Our study suggests that a high educational level does not counteract the effect of amyloid pathology on cognition but instead delays its expression, which agrees with earlier studies.19,20

Each APOE-ε4 allele increased the risk of low cognitive performance independent of amyloid status. Previous longitudinal studies examining the association among APOE-ε4, amyloid positivity, and cognition have had mixed results and identified mediating8,21 or interactive effects.22–24 Remarkably, our results indicate that the APOE genotype was also associated with cognitive performance in amyloid-negative participants. This finding may relate to subthreshold pathologic aggregation25,26 or to the association of APOE-ε4 genotype with non–amyloid-dependent processes in the brain that are associated with cognitive functioning.27

This study has implications for understanding the individual contributions of amyloid pathology and various AD risk factors to cognitive decline and the time course of AD. Therefore, these findings are important for the design of secondary AD prevention trials. Because amyloid positivity has become a requirement for enrollment in many AD prevention trials, cognitive testing would be an inexpensive and noninvasive alternative to screen for amyloid positivity. Our study showed that a low cognitive score for screening of amyloid positivity had only limited utility. Cognitive screening efficiency may be improved by more sensitive neuropsychological tests or test norms based on cognitively normal persons without amyloid pathology. The latter is supported by our finding that amyloid pathology appeared to explain a substantial part of the so-called age-related memory decline (Figure 3). However, cognitive measures can serve as an additional source of enrichment to optimize clinical trial design as has already been implemented in a prevention study.28

Limitations

This study has several methodologic limitations. There were several sources of variance among the pooled studies, including differences in amyloid assessment methods, participant selection, and other aspects of study design. However, the association of these differences with amyloid positivity was modest,1 and the association of amyloid status with cognitive performance was independent of biomarker modality. Furthermore, the type of memory test differed across studies but did not affect the association between amyloid and cognitive performance. Another potential limitation is that amyloid pathology might be associated with decline in other cognitive domains, such as executive functioning,8,11 that were not assessed in this study. Furthermore, the power to detect an association between amyloid positivity and MMSE score in participants with normal cognition was limited by the restricted range of MMSE scores in this group. In addition, because our findings were based on research or memory clinic populations, results may not be generalizable to the overall population or other settings. Last, our findings are based on cross-sectional data, which might not accurately reflect individual trajectories of amyloid-related cognitive decline.

Conclusions

Although low memory scores are an early marker of amyloid positivity, their value as a screening measure for early AD among persons without dementia is limited.

Supplementary Material

Supplemental

Key Points.

Question

Is cerebral amyloid-β aggregation, a key characteristic of Alzheimer disease, associated with cognitive functioning in persons without dementia?

Findings

In this cross-sectional, multicenter study of 7041 persons without dementia, amyloid-positive persons with normal cognition had low episodic memory scores almost twice as often as persons without amyloid aggregation after the age of 70 years. Low memory scores emerged 10 to 15 years after the onset of amyloid positivity.

Meaning

Alzheimer disease manifests through low memory scores in elderly persons with normal cognition, but a low memory score has limited value as a screening tool for early Alzheimer disease.

Acknowledgments

Dr Aarsland reports receiving research support or honoraria from AstraZeneca, H. Lundbeck, Novartis Pharmaceuticals, and GE Health. Dr Alexander reports being an employee of Roche Products. Dr Barthel reports receiving speaker, consultant honoraria, and travel expenses from Piramal Imaging (Berlin) and personal fees from Siemens Healthcare. Dr Blennow reports receiving personal fees (advisory boards or consulting) from Roche Diagnostics, IBL International, Novartis, Fujirebio Europe, and Eli Lilly and Company and is a cofounder of Brain Biomarker Solutions in Gothenburg AB, a GU Venture-based platform company at the University of Gothenburg. Dr Chen reports receiving grants from the National Institutes of Health (NIH). Dr Drzezga reports receiving speaker honoraria and consulting fees from GE Healthcare, Avid Radiopharmaceuticals/Eli Lilly and Company, and Piramal. Dr Fagan reports receiving grants from the NIH, DiamiR, Fred Simmons and Olga Mohan, and Charles and Joanne Knight Alzheimer’s Research Initiative of the Washington University Knight Alzheimer’s Disease Research Center and personal fees from IBL International, Roche, and AbbVie. Dr Fladby reports having a patent on methods and compositions for monitoring phagocytic activity (PCT/US2011/062233) pending. Dr Fleisher reports having been a full-time employee of the Banner Alzheimer’s Institute; being a full-time employee of Eli Lilly and Company; maintaining a voluntary faculty appointment at the University of California, San Diego; having been a member of data and safety monitoring boards for Merck, Pfizer, and the National Institute of Aging (NIA); receiving grant funding from the NIA and Avid Radiopharmaceuticals; and having been a consultant for Eli Lilly and Company, Grifols, Avid Radiopharmaceuticals, and Siemens Imaging. Dr van der Flier reports receiving grants from Boehringer Ingelheim, Piramal Imaging, and Roche. Dr Förster reports receiving personal fees (consultancy) from Piramal, Bayer, and GE. Dr Frisoni reports receiving grants and/or personal fees from Eli Lilly and Company, Bristol-Myers Squibb, Bayer, Lundbeck, Elan, AstraZeneca, Pfizer, Taurx, Wyeth, GE, Baxter, Avid Radiopharmaceuticals, Roche, Piramal, and the Alzheimer’s Association. Dr Gordon reports being a salaried employee of Boehringer Ingelheim Pharmaceuticals. Dr Grimmer reports receiving personal fees from Eli Lilly and Company.

Dr Hampel reports support from the AXA Research Fund, the Fondation Université Pierre et Marie Curie, and the Fondation pour la Recherche sur Alzheimer, Paris, France; receiving grants, personal fees, and/or nonfinancial support from Boehringer-Ingelheim, Bristol-Myers Squibb, Elan, Novartis, Eisai, Pfizer, Sanofi, Roche, GE Healthcare, Avid Radiopharmaceuticals, Eli Lilly and Company, GlaxoSmithKline Biologicals, Jung Diagnostics, and Cytox; and having a patent on method for predicting whether subjects with mild cognitive impairment will develop Alzheimer’s disease pending, a patent on 3-hydroxykynurenin in serum as diagnostic marker for Alzheimer-type dementia pending, a patent on neurodegenerative markers for 10 psychiatric conditions pending, a patent on plasma Aβ42/40 ratio for early and differential diagnosis of Alzheimer disease pending, a patent in liquor diagnostics in vitro procedures for the diagnosis of dementia and neuroinflammatory disease pending, and a patent on in vitro procedures for the diagnosis of neurodegenerative diseases pending. Dr Hansson reports receiving research support from GE Healthcare, Avid Radiopharmaceuticals, and Hoffmann-La Roche. Dr Hellwig reports receiving grants from GE Healthcare and Medical Faculty, University of Freiburg. Dr Jagust reports receiving personal fees from Banner Alzheimer Institute/Genentech, Synarc/Bioclinica, and Novartis. Dr Jansen reports receiving research support from Biogen. Dr Kapaki reports receiving grants from the European Union (European Regional Development Fund) and Greek national funds through the Operational Program “Competitiveness and Entrepreneurship” of the National Strategic Reference Framework Research Funding Program: Joint Programming Neurodegenerative Disease, “Biomarkers for Alzheimer’s Disease and Parkinson’s Disease.”

Dr Klunk reports being a co-inventor of the amyloid imaging tracer PiB and having a financial interest in the license agreement (PiB intellectual property is owned by the University of Pittsburgh, and GE Healthcare holds a license agreement with the University of Pittsburgh based on the PiB technology described in this article. Dr Klunk receives inventors share payments from the University of Pittsburgh based on income from that license). Dr Koglin reports receiving personal fees from employment at Piramal Imaging.

Dr Kornhuber reports receiving grants from the German Federal Ministry of Education and Research (grant 01GI0420 from the Kompetenznetz Demenzen and German Federal Ministry of Education and Research and grant 01GI1007A from the Frontotemporo-Lobar Degeneration Consortium) and having a patent on diagnosis of Alzheimer’s disease (PCT/ EP2004/003963) issued, a patent on large Aβ-peptide binding particles in diagnosis and therapy of Alzheimer’s dementia (EP 1811304 A1) issued, a patent on immunoglobulin-bound Ab-peptides and immunoglobulins-binding Ab-peptides in diagnosis and therapy of Alzheimer’s dementia (WO2007/082750 A1) issued, a patent on methods of differentially diagnosing dementias (EP 2437067A2) issued, and a patent on new formulations for diagnosis of Alzheimer’s disease pending. Dr Van Laere reported having received grants through KU Leuven from Merck, Janssen Pharmaceuticals, UCB, Novartis, Pfizer, and GE Healthcare. Dr Landau reported having received grants from the NIH and personal fees from Biogen Idec, Genentech, and Synarc. Dr Lleo reports receiving grants PI10/ 01878, PI13/01532, PI11/242, and PI11/3035 from Instituto de Salud Carlos III (Fondo de Investigación Sanitario and the CIBERNED program). Dr Madsen reports receiving grants from the Lundbeck Foundation, Danish Medical Research Council, and Rigshospitalet. Dr Meyer reportd receiving money from GE Healthcare for an ongoing research study. Dr Mintun reports being an employee of Avid Radiopharmaceuticals. Dr Morris reports receiving grants P50AG005681, P01AG003991, P01AG026276, and U19AG032438 from the NIH. Dr Mroczko reports receiving grants and personal fees from the Leading National Research Centre (KNOW), Medical University of Bialystok, Poland, and consultation and/or lecture honoraria from Roche, Cormay, and Biameditek. Dr Novak reports being an employee of and holding stock in Janssen Research and Development. Dr Paraskevas reports receiving grants from the European Union (European Regional Development Fund) and Greek national funds through the Operational Program “Competitiveness and Entrepreneurship” of the National Strategic Reference Framework Research Funding Program: Joint Programming Neurodegenerative Disease, “Biomarkers for Alzheimer’s Disease and Parkinson’s Disease.”

Dr Perera reports research support from grant ES/L002825/1 by the British Cross-research Council Lifelong Health and Wellbeing Programme under Extending Working Lives as part of an interdisciplinary consortium on Wellbeing, Health, Retirement and the Lifecourse and receiving funding from the National Institute for Health Research Biomedical Research Centre and Dementia Biomedical Research Unit at South London and Maudsley NHS Foundation Trust and King’s College London. Dr Peters reports receiving grants and/or personal fees from Eli Lilly and Company, Roche, Genentech, Lundbeck, Affiris, Piramal, Novartis, and Trx-Pharmaceuticals. Dr Popp reports receiving grant 320030L_141179 from the Swiss National Science Foundation and from the Nestlé Institute of Health Sciences.

Dr Rabinovici reports receiving grants from Avid Radiopharmaceuticals and personal fees from GE Healthcare and Piramal. Dr Rinne reports receiving grants from Sigrid Juselius Foundation and Turku University Hospital. Dr Rot reports receiving grants from EU Joint Programme–Neurodegenerative Disease Research–Biomarkers for Alzheimer Disease and Parkinson Disease. Dr Rowe reports receiving grants from Avid Radiopharmaceuticals, Piramal Imaging, AstraZeneca, GE Healthcare, Avid Radiopharmaceuticals/Eli Lilly and Company, Navidea, Australian Commonwealth Scientific Industrial and Research Organization, National Health and Medical Research Council, Alzheimer’s Association, and an anonymous foundation and having had a patent licensed for positron emission tomography image processing. Dr Sabri reports receiving grants and/or personal fees from Piramal Imaging, Bayer Healthcare, and Siemens Healthcare. Dr Sarazin reports receiving personal lecture fees from Novartis and Allianz. Dr Scheltens reports receiving grants from GE Healthcare, Piramal, and Merck paid to his institution.

Dr Soininen reports receiving grants from the Academy of Finland, European Union 7ThFP 601055 VPH-DARE, Kuopio University Hospital VTR, and University of Eastern Finland.

Dr Teunissen reports being a member of the international advisory board at Innogenetics and Roche and having research contracts at Probiodrug, Boehringer, Roche, EIP Pharma, and IBL.

Dr Vandenberghe reports receiving clinical trial agreements with GE Health Care, Merck, Forum, and Roche; grants from Research Foundation–Flanders (FWO) and KU Leuven; and nonfinancial support from GEHC. Dr Verbeek reports serving on an advisory board for Roche. Dr Verhey reports receiving compensation as a speaker and consultant for Nutricia Advanced Medical Food. Dr Visser reports receiving research support from Biogen, grants from the European Federation of Pharmaceutical Industries and Associations (EFPIA) Innovative Medicines Initiative Joint Undertaking, EU Joint Programme–Neurodegenerative Disease Research, ZonMw, and Bristol-Myers Squibb; having served as member of the advisory board of Roche Diagnostics; and having received nonfinancial support from GE Healthcare. Dr Vos reports receiving research support from ZonMW and from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement 115372, resources that are composed of financial contributions from EU FP7 (FP7/2007–2013) and in-kind contributions from EFPIA. Dr Waldemar reports being a board member of the Lundbeck Foundation. Dr Anders Wallin reports receiving speakers’ bureau fees from Esai and Triolab and serving on the advisory board for Nutrica and Esai. Dr Wolk reports receiving personal fees from GE Healthcare and Piramal Pharma and grants from Avid Radiopharmaceuticals. Dr Zetterberg reports being a cofounder of Brain Biomarker Solutions in Gothenburg AB, a GU Venture-based platform company at the University of Gothenburg. The authors received compensation (ie, salary) as employees of their respective organizations.

Funding/Support: 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. BIOMARKAPD is an EU JPND project. The project is supported through national funding organizations under the aegis of JPND. In the Netherlands, this is ZonMw. The Development of Screening Guidelines and Criteria for Predementia Alzheimer’s Disease (DESCRIPA) study was funded by grant QLRT-2001-2455 from the European Commission within the fifth framework program. The European Beta Amyloid Oligomers in the Early Diagnosis of AD and as Marker for Treatment Response (EDAR) study was funded by contract 37670 from the European Commission as part of the sixth framework program. This research was performed within the framework of the Center for Translational Molecular Medicine, grant 02N-101 from project Leiden Alzheimer-Research Nederland (LeARN). The Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) study was funded in part by the study partners (Australian Commonwealth Scientific Industrial and Research Organization, Edith Cowan University, Mental Health Research Institute, Alzheimer’s Australia, National Ageing Research Institute, Austin Health, CogState, Hollywood Private Hospital, and Sir Charles Gardner Hospital). The study also received support from the National Health and Medical Research Council and the Dementia Collaborative Research Centres program and ongoing funding from the Science and Industry Endowment Fund. Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (NIH grant U01 AG024904) and the US Department of Defense (ADNI grant W81XWH-12-2 -0012). ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, BioClinica, Biogen Idec, Bristol-Myers Squibb, Eisai, Elan Pharmaceuticals, Eli Lilly and Company, Hoffmann-La Roche and its affiliated company Genentech, GE Healthcare, Innogenetics, IXICO; Janssen Alzheimer Immunotherapy Research & Development, Johnson & Johnson Pharmaceutical Research & Development, Medpace, Merck, Meso Scale Diagnostics, NeuroRx Research, Novartis Pharmaceuticals, Pfizer, Piramal Imaging, Servier, Synarc, and Takeda Pharmaceuticals. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the NIH. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The Dementia Competence Network (DCN) has been supported by grant 01GI0420 from the German Federal Ministry of Education and Research: Kompetenznetz Demenzen. Additional funding related to the randomized clinical trials came from Janssen-Cilag and Merz Pharmaceuticals. The latter funds were exclusively used for personnel, pharmaceuticals, blistering and shipment of medication, and monitoring and as capitation fees for recruiting centers. Funding for the St Louis contribution was provided by grants P50 AG005681, P01 AG003991, and P01 AG026276 from the National Institute on Aging; Fred Simmons and Olga Mohan; and the Charles and Joanne Knight Alzheimer’s Research Initiative of the Washington University Knight Alzheimer’s Disease Research Center. The Tours study received financial support from grant PHRC-N 2008 1004 from the French Ministry of Health and the EC-FP6-project DiMI, LSHB-CT-2005-512146. The Caen study was funded by Agence Nationale de la Recherche, Programme Hospitalier de Recherche Clinique, Région Basse Normandie, and Institut National de la Santé et de la Recherche Médicale (Inserm). The research leading to the Munich contribution to the Mattsson multicenter study has received funding from the grant ANR-10-IAIHU-06 from the program “Investissements d’avenir” (Agence Nationale de la Recherche-10-IA Agence Institut Hospitalo-Universitaire-6). The study from Pittsburgh was supported by grants P50 AG005133, RF1 AG025516, and P01 AG025204 from the NIH. The New York contributions to the Mattsson multicenter study were in part supported by grants P30 AG008051, R01 AG13616, R01 AG022374, and R01 AG12101 from the NIH. Data from Brescia in this article were collected by Translational Outpatient Memory Clinic (TOMC) working group at IRCCS Fatebenefratelli in Brescia, Italy. Contributors to the TOMC are G. Amicucci, S. Archetti, L. Benussi, G. Binetti, L. Bocchio-Chiavetto, C. Bonvicini, E. Canu, F. Caobelli, E. Cavedo, E. Chittò, M. Cotelli, M. Gennarelli, S. Galluzzi, C. Geroldi, R. Ghidoni, R. Giubbini, U. P. Guerra, G. Kuffenschin, G. Lussignoli, D. Moretti, B. Paghera, M. Parapini, C. Porteri, M. Romano, S. Rosini, I. Villa, R. Zanardini, and O. Zanetti. The JPND Project is supported in Italy by the Italian Ministry of Health. The assembling of the TU Munich data set was supported in part by grants HE 4560/1-2, DR 445/3-1, and DR 445/4-1 (Dr Drzezga) to the German Research Foundation (Deutsche Forschungsgemeinschaft) and by a KKF grant for clinical research of the Technische Universität München (Drs Drzezga and Gabryelewicz). The Florbetaben phase 2 study from which data were derived for this multicenter evaluation was sponsored by Bayer Healthcare/Piramal Imaging (Berlin, Germany). This work was supported by the University of Antwerp Research Fund, the Alzheimer Research Foundation, the Research Foundation Flanders, the Agency for Innovation by Science and Technology, the Belgian Science Policy Office Interuniversity Attraction Poles program, and the Flemish Government–initiated Methusalem excellence grant. The study in Hong Kong received funding support from Therese Pei Fong Chow Research Centre for Prevention of Dementia and Lui CheWoo Institute of Innovative Medicine. The JPND–BIOMARKAPD Project was supported in Portugal by the Fundação para a Ciência e Tecnologia through grant JPND/0005/2011. The Coimbra center was funded by Project PIC/IC/83206/2007 da Fundação para a Ciência e Tecnologia – Portugal. The study from Lund (the Swedish BioFINDER study) was supported by the European Research Council, Swedish Research Council, the Swedish Brain Foundation, the Swedish Alzheimer Foundation, the Marianne and Marcus Wallenberg Foundation, and the Swedish Federal Government under the ALF Agreement.

Role of the Funder/Sponsor: The funders 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.

Additional Contributions: Multicenter studies involved in this project included the following: European Medical Information Framework–Alzheimer Disease, BIOMARKAPD, ADNI, AIBL study, Avid Radiopharmaceuticals multicenter study for the AV45-A17 Study Group, DESCRIPA study, DCN; EDAR study, Florbetaben phase 2 multicenter study, LeARN project, Mattsson et al (2009) multicenter study, and multicenter study by UK Hospitals and University Hospital of Turku. A portion of the data used in preparation of this article were obtained from the 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 report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgment_List.pdf. Additional information is available in eAppendix 2 in the Supplement.

Appendix

Amyloid Biomarker Study Group Authors

Dag Aarsland, MD, PhD; Daniel Alcolea, MD; Myriam Alexander, PhD; Ina S. Almdahl, MD; Steven E. Arnold, MD; Ines Baldeiras, PhD; Henryk Barthel, MD, PhD; Bart N.M. van Berckel, MD, PhD; Kaj Blennow, MD, PhD; Mark A. van Buchem, MD, PhD; Enrica Cavedo, PhD; Kewei Chen, PhD; Elena Chipi, PhD; Ann D. Cohen, PhD; Stefan Förster, MD, PhD; Juan Fortea, PhD; Kristian S. Frederiksen, MD, PhD; Yvonne Freund-Levi, MD, PhD; Olymbia Gkatzima, MSc; Mark Forrest Gordon, MD; Timo Grimmer, MD, PhD; Harald Hampel, MD, PhD; Lucrezia Hausner, MD, PhD; Sabine Hellwig, MD; Sanna-Kaisa Herukka, PhD; Peter Johannsen, MD, PhD; Aleksandra Klimkowicz-Mrowiec, MD, PhD; Sebastian Köhler, PhD; Norman Koglin, PhD; Koen van Laere, MD, PhD, DrSc; Mony de Leon, MD, PhD; Viviana Lisetti, MSc; Wolfgang Maier, MD, PhD; Jan Marcusson, MD, PhD; Olga Meulenbroek, PhD; Hanne M. Møllergård, PhD; John C. Morris, MD, PhD; Arto Nordlund, PhD; Gerald P. Novak, MD; George P. Paraskevas, MD, PhD; Gayan Perera, PhD; Oliver Peters, MD; Inez H. G. B. Ramakers, PhD; Lorena Rami, MSc; Eloy Rodríguez-Rodríguez, MD, PhD; Catherine M. Roe, PhD; Uros Rot, MD, PhD; Eckart Rüther, MD, PhD; Isabel Santana, MD, PhD; Johannes Schröder, MD, PhD; Sang W. Seo, MD, PhD; Hilkka Sorininen, MD, PhD; Luiza Spiru, MD, PhD; Erik Stomrud, MD, PhD; Hanne Struyfs, MSc; Charlotte E. Teunissen, PhD; Stephanie J. B. Vos, PhD; Linda J. C. van Waalwijk van Doorn, MSc; Gunhild Waldemar, MD, DMsc; Åsa K. Wallin, MD, PhD; Jens Wiltfang, MD, PhD; Henrik Zetterberg, MD, PhD.

Affiliations of Amyloid Biomarker Study Group Authors

Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, the Netherlands (Kohler, Ramakers, Vos); Department of Radiology and Nuclear Medicine, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands (van Berckel); Knight Alzheimer’s Disease Research Center, Department of Neurology, Washington University School of Medicine, St Louis, Missouri (Morris, Roe); Clinical Memory Research Unit, Clinical Sciences Malmö, Lund University, Lund, Sweden (Stomrud, Wallin); Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Cohen); Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy (Cavedo); Banner Alzheimer’s Institute, Phoenix, Arizona (Chen); Neurology Department, Hospital de Sant Pau, Barcelona, Spain (Alcolea, Fortea); Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden (Blennow, Nordlund, Zetterberg); Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium (Struyfs); Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (Seo); Alzheimer’s Disease and Other Cognitive Disorders Unit, IDIBAPS, Clinic University Hospital, Barcelona, Spain (Rami); Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany (Barthel); Section of Neurology, Center for Memory Disturbances, University of Perugia, Perugia, Italy (Chipi, Lisetti); Department of Neurology, Akershus University Hospital, Lørenskog, Norway (Almdahl, Møllergård); Center for Neuroscience and Cell Biology, Faculty of Medicine, Centro Hospitalar e Universitário de Coimbra, Portugal (Baldeiras, Santana); First Department of Neurology, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece (Paraskevas); Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany (Hausner); Departments of Neurology and Laboratory Medicine, Donders Institute for Brain, Cognition and Behaviour, Radboud Alzheimer Center, Radboud University Medical Center, Nijmegen, the Netherlands (van Waalwijk van Doorn); Neurology Service, Universitary Hospital Marqués de Valdecilla, IDIVAL, Santander, Spain (Rodríguez-Rodríguez); Center for Cognitive Impairments, University Medical Centre Ljubljana, Ljubljana, Slovenia (Rot); Department of Neurology, University of Pennsylvania, Philadelphia (Arnold); Third Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece (Gkatzima); Center for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Roche Products, Welwyn Garden City, United Kingdom (Alexander, Perera); Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden (Blennow, Zetterberg); Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (van Buchem); Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Stijmsalpêtrière, Boulevard de l'hôpital, F-75013, Paris, France (Cavedo, Hampel); AXA Research Fund & UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie Paris 06, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière, Paris, France (Cavedo, Hampel); Department of Nuclear Medicine, Technische Universitaet München, Munich, Germany (Förster, Waldemar); Danish Dementia Research Center, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark (Frederiksen); Department of Geriatrics, Karolinska University Hospital Huddinge, Section of Clinical Geriatrics, Institution of NVS, Karolinska Institutet, Stockholm, Sweden (Freund-Levi); Boehringer Ingelheim Pharmaceuticals, Ridgefield, Connecticut (Gordon); Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar der Technischen Universitaet München, Munich, Germany (Grimmer); Department of Psychiatry, Alzheimer Memorial Center and Geriatric Psychiatry Branch, Ludwig-Maximilian University, Munich, Germany (Hampel); Center of Geriatrics and Gerontology, University Hospital Freiburg, Freiburg, Germany (Hellwig); Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland (Herukka, Sorininen); Memory Clinic, Danish Dementia Research Center, Rigshospitalet, Copenhagen, Denmark (Johannsen); Jagiellonian University College of Medicine, Krakow, Poland (Klimkowicz-Mrowiec); Piramal Imaging GmbH, Berlin, Germany (Koglin); Department of Imaging and Pathology, Catholic University Leuven, Leuven, Belgium (van Laere); School of Medicine, Center for Brain Health, New York University, New York (de Leon); Department of Psychiatry and Psychotherapy, University of Bonn, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany (Maier); Geriatric Medicine, Department of Clinical and Experimental Medicine, University of Linköping, Linköping, Sweden (Marcusson); Department of Geriatric Medicine, Radboud Alzheimer Center, Radboud University Medical Center, Nijmegen, the Netherlands (Meulenbroek); Janssen Research and Development, Titusville, New Jersey (Novak); Department of Psychological Medicine, Institute of Psychiatry, Kings College London, London, United Kingdom (Perera); Department of Psychiatry and Psychotherapy, Charité Berlin, German Center for Neurodegenrative Diseases (DZNE), Berlin, Germany (Peters); Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University, Göttingen, Germany (Rüther, Wiltfang); Sektion Gerontopsychiatrie, Universität Heidelberg, Heidelberg, Germany (Schröder); Department of Geriatrics-Gerontology-Gerontopsychiatry, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania (Spiru); Neurochemistry Laboratory and Biobank, Department of Clinical Chemistry, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands (Teunissen); UCL Institute of Neurology, Queen Square, London, United Kingdom (Zetterberg).

Footnotes

Author Contributions: Drs Jansen and Visser had full access to all 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: Jansen, Visser.

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

Drafting of the manuscript: Jansen, Visser.

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

Statistical analysis: Jansen, Tijms, Visser.

Obtained funding: All authors.

Administrative, technical, or material support: All authors.

Study supervision: Visser.

Conflict of Interest Disclosures: No other disclosures were reported.

References

  • 1.Jansen WJ, Ossenkoppele R, Knol DL, et al. Amyloid Biomarker Study Group. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA. 2015;313(19):1924–1938. doi: 10.1001/jama.2015.4668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Fagan AM, Xiong C, Jasielec MS, et al. Dominantly Inherited Alzheimer Network. Longitudinal change in CSF biomarkers in autosomal-dominant Alzheimer’s disease. Sci Transl Med. 2014;6(226):226ra30. doi: 10.1126/scitranslmed.3007901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Jack CR, Jr, Wiste HJ, Weigand SD, et al. Age-specific population frequencies of cerebral β-amyloidosis and neurodegeneration among people with normal cognitive function aged 50–89 years: a cross-sectional study. Lancet Neurol. 2014;13(10):997–1005. doi: 10.1016/S1474-4422(14)70194-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Sperling RA, Johnson KA, Doraiswamy PM, et al. AV45-A05 Study Group. Amyloid deposition detected with florbetapir F 18 ((18)F-AV-45) is related to lower episodic memory performance in clinically normal older individuals. Neurobiol Aging. 2013;34(3):822–831. doi: 10.1016/j.neurobiolaging.2012.06.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Vos SJ, Xiong C, Visser PJ, et al. Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study. Lancet Neurol. 2013;12(10):957–965. doi: 10.1016/S1474-4422(13)70194-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Burnham SC, Bourgeat P, Doré V, et al. AIBL Research Group. Clinical and cognitive trajectories in cognitively healthy elderly individuals with suspected non-Alzheimer’s disease pathophysiology (SNAP) or Alzheimer’s disease pathology: a longitudinal study. Lancet Neurol. 2016;15(10):1044–1053. doi: 10.1016/S1474-4422(16)30125-9. [DOI] [PubMed] [Google Scholar]
  • 7.Rowe CC, Ellis KA, Rimajova M, et al. Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging. 2010;31(8):1275–1283. doi: 10.1016/j.neurobiolaging.2010.04.007. [DOI] [PubMed] [Google Scholar]
  • 8.Petersen RC, Wiste HJ, Weigand SD, et al. Association of elevated amyloid levels with cognition and biomarkers in cognitively normal people from the community. JAMA Neurol. 2016;73(1):85–92. doi: 10.1001/jamaneurol.2015.3098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Pike KE, Savage G, Villemagne VL, et al. Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer’s disease. Brain. 2007;130(pt 11):2837–2844. doi: 10.1093/brain/awm238. [DOI] [PubMed] [Google Scholar]
  • 10.Forsberg A, Engler H, Almkvist O, et al. PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging. 2008;29(10):1456–1465. doi: 10.1016/j.neurobiolaging.2007.03.029. [DOI] [PubMed] [Google Scholar]
  • 11.Hedden T, Oh H, Younger AP, Patel TA. Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology. 2013;80(14):1341–1348. doi: 10.1212/WNL.0b013e31828ab35d. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–198. doi: 10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
  • 13.Mielke MM, Machulda MM, Hagen CE, et al. Influence of amyloid and APOE on cognitive performance in a late middle-aged cohort. Alzheimers Dement. 2016;12(3):281–291. doi: 10.1016/j.jalz.2015.09.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Rentz DM, Amariglio RE, Becker JA, et al. Face-name associative memory performance is related to amyloid burden in normal elderly. Neuropsychologia. 2011;49(9):2776–2783. doi: 10.1016/j.neuropsychologia.2011.06.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ossenkoppele R, Jansen WJ, Rabinovici GD, et al. Amyloid PET Study Group. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA. 2015;313(19):1939–1949. doi: 10.1001/jama.2015.4669. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Jack CR, Jr, Wiste HJ, Weigand SD, et al. Age, sex, and APOE ε4 effects on memory, brain structure, and β-amyloid across the adult life span. JAMA Neurol. 2015;72(5):511–519. doi: 10.1001/jamaneurol.2014.4821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Wilson RS, Boyle PA, Yu L, Barnes LL, Schneider JA, Bennett DA. Life-span cognitive activity, neuropathologic burden, and cognitive aging. Neurology. 2013;81(4):314–321. doi: 10.1212/WNL.0b013e31829c5e8a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Vemuri P, Lesnick TG, Przybelski SA, et al. Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly. Brain. 2015;138(Pt 3):761–771. doi: 10.1093/brain/awu393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Amieva H, Mokri H, Le Goff M, et al. Compensatory mechanisms in higher-educated subjects with Alzheimer’s disease: a study of 20 years of cognitive decline. Brain. 2014;137(Pt 4):1167–1175. doi: 10.1093/brain/awu035. [DOI] [PubMed] [Google Scholar]
  • 20.Aguirre-Acevedo DC, Lopera F, Henao E, et al. Cognitive decline in a Colombian kindred with autosomal dominant Alzheimer disease: a retrospective cohort study. JAMA Neurol. 2016;73(4):431–438. doi: 10.1001/jamaneurol.2015.4851. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Roe CM, Fagan AM, Grant EA, et al. Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later. Neurology. 2013;80(19):1784–1791. doi: 10.1212/WNL.0b013e3182918ca6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Mormino EC, Betensky RA, Hedden T, et al. Alzheimer’s Disease Neuroimaging Initiative; Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing; Harvard Aging Brain Study. Amyloid and APOE ε4 interact to influence short-term decline in preclinical Alzheimer disease. Neurology. 2014;82(20):1760–1767. doi: 10.1212/WNL.0000000000000431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Lim YY, Villemagne VL, Pietrzak RH, et al. Australian Imaging, Biomarkers and Lifestyle (AIBL) Research Group. APOE ε4 moderates amyloid-related memory decline in preclinical Alzheimer’s disease. Neurobiol Aging. 2015;36(3):1239–1244. doi: 10.1016/j.neurobiolaging.2014.12.008. [DOI] [PubMed] [Google Scholar]
  • 24.Seo EH, Kim SH, Park SH, Kang SH, Choo IH Alzheimer’s Disease Neuroimaging Initiative. Independent and interactive influences of the APOE genotype and beta-amyloid burden on cognitive function in mild cognitive impairment. J Korean Med Sci. 2016;31(2):286–295. doi: 10.3346/jkms.2016.31.2.286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Yu L, Boyle PA, Leurgans S, Schneider JA, Bennett DA. Disentangling the effects of age and APOE on neuropathology and late life cognitive decline. Neurobiol Aging. 2014;35(4):819–826. doi: 10.1016/j.neurobiolaging.2013.10.074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Rebeck GW, Reiter JS, Strickland DK, Hyman BT. Apolipoprotein E in sporadic Alzheimer’s disease: allelic variation and receptor interactions. Neuron. 1993;11(4):575–580. doi: 10.1016/0896-6273(93)90070-8. [DOI] [PubMed] [Google Scholar]
  • 27.Dowell NG, Evans SL, Tofts PS, King SL, Tabet N, Rusted JM. Structural and resting-state MRI detects regional brain differences in young and mid-age healthy APOE-e4 carriers compared with non-APOE-e4 carriers. NMR Biomed. 2016;29(5):614–624. doi: 10.1002/nbm.3502. [DOI] [PubMed] [Google Scholar]
  • 28.Sperling RA, Rentz DM, Johnson KA, et al. The A4 study: stopping AD before symptoms begin? Sci Transl Med. 2014;6(228):228fs13. doi: 10.1126/scitranslmed.3007941. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental
NIHMS927770-supplement-Supplemental.pdf (930.1KB, pdf)

Data Availability Statement

Information on the level of education was available for 2558 participants (88.0%) with normal cognition (memory score: n = 1973; MMSE: n = 2536) and 3270 patients (79.1%) with MCI (memory score: n = 2456; MMSE: n = 3264). Information on APOE-ε4 carrier status (yes or no) was available for 2400 participants (82.5%) with normal cognition (memory score: n = 1764; MMSE: n = 2379) and 3292 patients (79.7%) with MCI (memory score: n = 2217; MMSE: n = 3286). The APOE genotype was available for 2347 participants (80.7%) with normal cognition (memory score: n = 1763; MMSE: n = 2326) and 3019 patients (73.0%) with MCI (memory score: n = 2215; MMSE: n = 3013).

RESOURCES