Quantitative Brain Amyloid Measures Predict Time-to-Progression from Amnestic Mild Cognitive Impairment to Alzheimer’s Disease

Abstract

Background:

This study was designed to investigate factors that predict progression from amnestic mild cognitive impairment (aMCI) to probable Alzheimer’s disease (AD).

Objective:

We studied the usefulness of quantitative assessment of amyloid burden measured by Florbetapir PET scan.

Methods:

The study cohort consisted of aMCI participants older than 65 and those with available Florbetapir PET scan at diagnosis from the ADNI database (http://adni.loni.usc.edu). To assess the prognostic impact of amyloid burden, a staging system based on the global SUVr of the PET scan was applied. We defined the stages as: stage I, negative amyloid scan; stage II, positive amyloid in 1st tertile; stage III, positive amyloid in 2nd tertile; and stage IV, positive amyloid in 3rd tertile.

Results:

Of 250 eligible aMCI subjects (age 74.1±5.4, female n = 105), 71 (28.4%) were diagnosed with probable AD within 3 years. Higher amyloid stages showed faster cognitive decline by Kaplan-Meier analysis. In multivariate Cox analysis, with stage I as a reference, the hazard ratio (HR) increased as the stage increased: stage II (HR, 4.509; p = 0.015), stage III (HR, 7.616; p = 0.001), and stage IV (HR, 9.421; p < 0.001). Along with amyloid stage, ApoE ɛ4 (HR, 1.943; p = 0.031), score of CDR-SB (HR, 1.845; p < 0.001) and ADAS 11 (HR, 1.144; p < 0.001), and hippocampal volume (HR, 0.002; p = 0.005) were also identified as predictors of dementia progression in aMCI subjects.

Conclusions:

Large amyloid burden measured from amyloid PET scan could be a predictor of faster cognitive decline in aMCI patients.

Keywords

Alzheimer’s disease amyloid Florbetapir mild cognitive impairment positron emission tomography

INTRODUCTION

Mild cognitive impairment (MCI) is defined as the symptomatic pre-dementia stage on the continuum of cognitive decline, characterized by objective impairment in cognition that is not severe enough to require help with the usual activities of daily living [1]. Patients with MCI are at greater risk of developing dementia than the general population, but research reports significant variability [2]. Research has indicated that annual rates of MCI conversion to dementia range from 1% to 25%, depending on the country and population studied [2 –4]. Many patients with MCI (40–70%) may not progress to dementia after 10 years[5] and some MCI patients (15–20%) will have improved cognition one to two years later [4, 6].

The presence of biomarkers measuring amyloid-β (Aβ) increases the likelihood that MCI is due to Alzheimer’s disease (AD), indicating a higher risk of cognitive decline [1]. A distinct regional progression pattern of cerebral Aβ deposits has been estimated from a case series of neuropathologic examinations and forms the basis of widely-used staging schemes for the characterization of an individual’s extent of amyloid pathology at autopsy [7, 8]. Grothe et al. suggested an in vivo amyloid staging scheme using Florbetapir positron emission tomography (PET) data [9]. The amyloid PET imaging is currently used for classifying the presence of global cortical Aβ into positive or negative categories. Visual interpretation with dichotomous classification of amyloid PET image is easy, but not sufficient to reflect sequential changes in amyloid deposition.

Identification of individuals at risk for developing clinical dementia and estimation of the timing of dementia onset is a fundamental prerequisite for testing therapeutic interventions. Clinical utility of amyloid PET is still unclear and not fully investigated. The quantitative assessment of amyloid PET scan is currently not recommended for predicting MCI progression to dementia in clinical practice. Several studies have reported the prognostic impact of amyloid load measured by PET. Studies reported that C-11 PIB retention in the posterior cingulum [10] or global cortex area [11, 12] was associated with progression to dementia in MCI patients. A recent Flutemetamol PET study reported the correlation between a positive amyloid scan and rapid cognitive decline from amnestic MCI (aMCI) to probable AD [13].

In this study, we investigated the feasibility of PET-based amyloid staging for assessing the probability of progression from aMCI to clinical dementia. We explored clinical characteristics of aMCI subjects according to amyloid stratification. To identify predictive risk factors for progression of aMCI to dementia, we analyzed quantification data of amyloid PET, along with clinical factors, including age, neuropsychological testing, apolipoprotein E (ApoE) ɛ4, cerebrospinal fluid (CSF) biomarkers, and structural imaging changes.

MATERIALS AND METHODS

Alzheimer’s disease neuroimaging initiative study design

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. ADNI participants were recruited from more than 50 sites across the USA and Canada. Regional ethical committees of all institutions approved of the study, and all participants provided written informed consent. For up-to-date information, see http://www.adni-info.org.

Study subjects

The ADNI cohort in the present study consisted of aMCI participants older than 65, and those with available Florbetapir PET quantification data at a baseline visit from ADNI GO and ADNI 2. Demographic and cognitive data were downloaded in April 2017, and were collected as described (http://adni.loni.usc.edu/methods/documents/). By definition, individuals in the MCI group scored ≥24 on the Mini-Mental State Examination (MMSE) and exhibited subjective memory loss (>1 standard deviation [SD] below the normal mean of the delayed recall of the Wechsler Memory Scale Logical Memory II), received the clinical dementia rating-sum of boxes (CDR-SB) of 0.5, and preserved activities of daily living and the absence of dementia. Those who were suspected as having vascular, traumatic or inflammatory causes of aMCI, or any significant neurologic disease other than AD were excluded from the study cohort. The cognitive diagnosis was assessed at baseline and after a six-month period, then annual assessments followed. To analyze factors affecting the prognosis of aMCI patients, we divided participants into two groups, dementia and MCI, according to a subsequent diagnosis within three years. The diagnosis of probable AD was based on the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association (NINCDS/ADRDA) criteria [14]. The time to probable AD in days was measured from PET scan to the latest visit supporting a diagnosis of probable AD. Censoring time in days was measured from PET scan to the last completed follow-up visit. Follow-up diagnosis was confirmed from DXSUM_PDXCONV_ADNIALL file.

Assessment of Florbetapir PET

Brain cortical amyloid burden was measured at baseline workup using F-18 Florbetapir (also known as ¹⁸F-AV-45 and Amyvidtrademark) PET. The PET scan was acquired as the dynamic 3D scan of four 5 min frames at 50–70 minutes after injection of 370 MBq (10 mCi) of Florbetapir. To calculate the mean Florbetapir uptake within the cortical and reference regions, each PET scan was co-registered to the corresponding MRI scan of each subject. The segmentation and parcellation were performed with the FreeSurfer (version 5.3.0). The cortical target region was composed with frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal regions and the whole cerebellum was used as a reference region. The global standard uptake value ratio (SUVr) was calculated as follows: mean uptake of target region/mean uptake of whole cerebellum. The SUVr of each sub-region was calculated in the same manner. Quantification data was downloaded from LONI site via UCBERKELEYAV45. For qualitative assessment of amyloid PET, we used a cut-off of 1.11 for global SUVr, using the whole cerebellum reference region, which is equivalent to the upper 95% confidence interval (CI) above the mean of a group of young normal controls [15]. A global SUVr over 1.11 was considered as a positive amyloid scan. To investigate the clinical meaning of the quantitative assessment of amyloid PET, we created a staging system according to the global SUVr. We defined the stages as: stage I, negative amyloid scan (global SUVr < 1.11); stage II, positive amyloid in first tertile (1.11≤global SUVr < 1.30); stage III as positive amyloid in second tertile (1.30≤global SUVr < 1.50); and stage IV, positive amyloid in third tertile (1.50≤global SUVr).

ADNI clinical, CSF, and structural imaging data

Baseline variables including age, gender, education level in years, ApoE ɛ4 status and scores of MMSE, Alzheimer’s disease assessment scale-cognitive 11 (ADAS11), and CDR-SB were downloaded via ADNIMERGE data from the LONI site in April 2017. The study subject with at least one ApoE ɛ4 allele was considered to be a carrier.

The hippocampal volume was measured on the 3 Tesla (3T) MRI. Data processing was done using the FreeSurfer (version 5.1), and only values that passed all quality control standards were included in the analyses. The volume of white matter hyperintensity was measured using an automated atlas-based method based on the Bayesian approach. The white matter hyperintensity was measured using fluid attenuation inversion recovery (FLAIR) sequence on 3T MRI acquired within 1 year after baseline visit. The measured value was downloaded from the file named UCD_ADNI2_WMH. Detailed data processing methods are as described (adni.loni.usc.edu/methods/mri-analysis/).

The CSF values for Aβ₄₂, total tau protein (tTau), and phosphorylated tau protein (pTau) at baseline generated by a single lot number of the novel, fully automated, electrochemiluminescent Elecsys® immunoassays (Roche Diagnostics, Basel, Switzerland) were downloaded from a single data set (UPENNBIOMK9).

Statistical analyses

Patients were divided into two groups: converters and non-converters. Patients diagnosed with dementia within 3 years were defined as converters. Comparisons between groups of categorical variables used the Chi-square test. Comparisons according to continuous variables were performed using the Student’s t-test. Continuous variables are expressed as the mean±SD. Comparison of variables according to the amyloid staging was performed using the One-way ANOVA with post-hoc Tukey’s test. Survival (non-progression) analysis was performed using the Kaplan-Meier method and the log-rank test. Progression-free survival (PFS) was calculated from the baseline visit to the date of diagnosis of probable AD. Multivariate analysis was conducted using Cox proportional hazard regression models with forward stepwise method. The Cox proportional hazards regression model included age, ApoE ɛ4, scores of ADAS11 and CDR-SB, hippocampal volume on structural MRI, total tau protein and phosphorylated tau protein titer in CSF and amyloid PET staging. A p-value of less than 0.05 was regarded as statistically significant. SPSS for Windows (version 23.0; SPSS Inc.) was used for statistical analyses.

RESULTS

Patient characteristics

Of 894 subjects screened, 340 ADNI participants matching the inclusion criteria were enrolled into the final analysis. The mean age was 74.4±5.6 (range 65 to 91.4) years, 144 (42.3%) were female, and 157 (46.2%) had at least one ApoE ɛ4 allele. The median follow-up for the cohort was 36 months.

To analyze factors affecting the prognosis of aMCI patients, we divided participants into two groups, dementia and MCI, according to a subsequent diagnosis in three years. Of 340 participants, three-year follow-up diagnosis was available for 250 participants and 71 (28.4%) were diagnosed with dementia within three years. Factors associated with prognosis are shown in the Table 1. Patients in the dementia group were older than the MCI group (p = 0.046). The prevalence of ApoE ɛ4 carriers was higher in the dementia group (p < 0.001). Groups did not differ significantly in education level or sex. The baseline result of MMSE (p < 0.001), ADAS11 (p < 0.001), and CDR-SB (p < 0.001) were significantly worse in the dementia group than in the MCI group. The hippocampal volume measured was significantly lower in the dementia group than in the MCI group (p < 0.001), but the volume of white matter hyperintensity failed to show a significant difference between the two groups. In CSF analysis, values of Aβ₄₂ measured were significantly lower, and values of tTau and pTau measured were higher in the dementia group. Among those who progressed to dementia, 95.8% (68/71) had a positive amyloid PET scan. The progression rates were identified as 3.0% of patients with negative amyloid scans, and 45.0% of patients with positive amyloid scans.

Table 1

Demographic, cognitive, and biomarker data of subjects with amnestic mild cognitive impairment classifed by diagnosis after 3 years of follow-up

	Converters (N = 71)	Non-converters (N = 179)	Odds ratio	p
Age	75.2±4.7	73.7±5.6		0.046
Sex – Female no. (%)	29 (40.8)	76 (42.5)	0.94 (0.54–1.64)	0.816
Education (y)	16.2±2.6	16.2±2.7		0.961
ApoE ɛ4 – carrier no. (%)	54 (76.1)	62 (34.6)	5.99 (3.21–11.21)	<0.001
N europsychological test
MMSE	27.2±1.7	28.1±1.7		<0.001
ADAS11	13.8±5.1	8.2±3.3		<0.001
CDR-SB	2.1±0.9	1.2±0.7		<0.001
Structural MRI
Hippocampal volume (cm³)	6.26±0.87	7.16±1.00		<0.001
White matter hyperintensity (cm³)	9.55±11.45	8.47±9.50		0.443
C erebrospinal fluid
Amyloid-β 42 (pg/ml)	133.2±28.0	184.9±51.9		<0.001
Total-Tau (pg/ml)	128.0±60.7	76.5±45.1		<0.001
Phosphorylated-Tau (pg/ml)	60.3±25.8	35.1±19.4		<0.001
Florbetapir PET
Cortical amyloid – positive no. (%)	68 (95.8)	83 (46.4)	26.22 (7.95–86.44)	<0.001
Global SUVr	1.45±0.18	1.16±0.19		<0.001
Frontal SUVr	1.47±0.19	1.15±0.20		<0.001
Cingulate SUVr	1.55±0.20	1.25±0.20		<0.001
Parietal SUVr	1.45±0.18	1.16±0.20		<0.001
Temporal SUVr	1.36±0.19	1.07±0.17		<0.001

no., number; ApoE, Apolipoprotein E; MMSE, Mini-Mental State Examination; ADAS 11, Alzheimer’s Disease Assessment Scale-cognitive 11; CDR-SB, clinical dementia rating-sum of boxes; MRI, magnetic resonance image; SUVr, standardized uptake value ratio; MRI, magnetic resonance imaging; PET, positron emission imaging. Data are mean±SD or number (%). The Chi-square test was used to compare categorical variables, Odds ratio is calculated by the Chi-square test. The Student’s t-test was used to compare continuous variables.

Higher amyloid burden predicts rapid cognitive decline

The rates of progression to a diagnosis of dementia by 36 months were observed to be 2.2%, 15.5%, 32.9%, and 57.1% in stage I, II, III, and IV, respectively. There was no significant difference in age, sex, or education level in each stage. The prevalence of ApoE ɛ4 carriers was higher in the amyloid-positive groups than stage I (p < 0.001), and no difference was found between amyloid-positive groups. The baseline score of MMSE was lower in stage III (p = 0.033) and IV (p < 0.001), compared to the stage I. Stage IV (p = 0.031) showed lower baseline MMSE scores than stage II. Higher scores of the ADAS 11 were observed in stage III (p < 0.001) and IV (p < 0.001), than stage I. ADAS 11 scores were higher in stage IV (p = 0.001) than stage II. The CDR-SB scores in stage IV were higher than other groups (I versus IV, p < 0.001; II versus IV, p < 0.001; III versus IV p = 0.007). Lower hippocampal volumes were observed in stage III (p = 0.044) and IV (p = 0.001), compared to stage I. However, no difference was found between stage I and II. The volumes of white matter hyperintensity were not different between groups. CSF values of Aβ₄₂, tTau, and pTau showed statistical difference between amyloid-positive (stage I) and amyloid-negative (stage II, III, and IV) groups. No difference in CSF values was found between stages III and IV. Detailed results of amyloid staging are shown in a Table 2. In Kaplan-Meier estimation, subjects in higher stages with large amyloid burden showed rapid cognitive decline (p < 0.001 in all analysis except, II versus III p = 0.014; III versus IV p = 0.001) (Fig. 1). The median progression time in stage IV was 885 (95% CI, 514.2–1255.8) days.

Table 2

Demographic, cognitive, and biomarker data of study subjects with amnestic mild cognitive impairment classified by amyloid PET staging

Stage	I (n = 137)	II (n = 71)	III (n = 76)	IV (n = 56)	p
Time to dementia onset (days)	1299.4±572.9	1108.6±553.4	1029.3±554.2^a	789.5±489.1^a,b	<0.001
Global SUVr	1.01±0.06	1.22±0.06	1.39±0.05	1.61±0.11
Clinical variables
Age	73.7±5.9	74.4±5.3	75.3±5.7	74.8±5.2	0.239
Sex - Female no. (%)	61 (44.5)	26 (36.6)	32 (42.1)	25 (44.6)	0.722
Education (y)	16.3±2.5	16.1±2.9	16.0±2.7	15.7±3.0	0.551
ApoE ɛ4 - carrier no. (%)	23 (16.8)	41(57.7)^a	53(69.7)^a	40 (71.4)^a	<0.001
Neuropsychological tests
MMSE	28.4±1.5	27.9±1.9	27.8±1.7^a	27.1±1.5^a,b	<0.001
ADAS 11	8.1±3.5	9.5±4.0	10.8±5.0^a	12.3±4.6^a,b	<0.001
CDR-SB	1.3±0.8	1.4±0.9	1.5±0.8	2.0±1.0^a,b,c	<0.001
Structural MRI
Hippocampal volume (cm³)	472.25±78.32	471.80±72.10	443.04±67.96^a	426.99±68.44^a,b	<0.001
White matter hyperintensity (cm³)	5.06±5.49	6.07±6.61	7.49±9.49	5.96±5.64	0.104
C erebrospinal fluid
Amyloid-β 42 (pg/ml)	213.3±41.7	153.4±36.4^a	130.3±24.3^a,b	135.4±21.3^a,b	<0.001
Total-Tau (pg/ml)	59.3±30.2	92.5±54.1^a	108.1±54.5^a	129.1±53.2^a,b	<0.001
Phosphorylated-Tau (pg/ml)	26.8±11.9	40.4±17.3^a	56.1±27.4^a,b	60.1±19.9^a,b	<0.001

SUVr, standardized uptake value ratio; no., number; ApoE, Apolipoprotein E; MMSE, Mini-Mental State Examination; ADAS 11, Alzheimer’s Disease Assessment Scale-cognitive 11; CDR-SB, Clinical Dementia Rating-sum of boxes; MRI, magnetic resonance imaging. We defined the stages as such: stage I as negative amyloid scan (global SUVr < 1.11), stage II as positive amyloid in first tertile (1.11≤global SUVr < 1.30), stage III as positive amyloid in second tertile (1.30≤global SUVr < 1.50), and stage IV as positive amyloid in third tertile (1.50≤global SUVr). Data are mean±SD or number (%). p of the One-way ANOVA with post-hoc Tukey’s test; ^ap < 0.05 compared to stage I; ^bp < 0.05 compared to stage II; ^cp < 0.05 compared to stage III.

Fig.1

Cumulative survival (Nonprogression) probabilities over time based on Florbetapir amyloid PET staging. Scan result assessed by amyloid PET staging. Crosses are censored patients. Each vertical line marks 1 or more individuals who progressed to probable Alzheimer’s disease (pAD). The median time to progression to pAD was 885 (95% CI, 514.2–1255.8) days in stage IV but could not be estimated in the group with stage I, II, and III, owing to a progression rate of less than 50%.

Clinical variables association with cognitive decline in multivariate analysis

To identify factors associated with progression of cognitive decline in aMCI subjects, the Cox proportional hazard regression analysis was performed using variables of age, ApoE ɛ4 allele, score of ADAS11, CDR-SB, hippocampal volume on structural MRI, tTau and pTau titer in CSF, and amyloid PET staging. Titers of tTau and pTau in CSF and age were not independent factors for an association with progression to dementia. The amyloid PET staging was significantly associated with progression to dementia. With the stage I patients as a reference, the hazard ratio (HR) increased as the stage increased: the stage II (HR, 4.509; p = 0.015), stage III (HR, 7.616; p = 0.001), and stage IV (HR, 9.421; p < 0.001). The presence of the ApoE ɛ4 allele (HR, 1.943; p = 0.031), CDR-SB (HR, 1.845; p < 0.001), score of ADAS 11 (HR, 1.144; p < 0.001), and hippocampal volume (HR, 0.002; p = 0.005) were also significantly associated with progression to dementia (Table 3).

Table 3

Cox proportional hazards regression models for prediction of progression to probable Alzheimer’s disease

Model parameters Amyloid quantification	Hazard ratio	p
Stage I^*		0.001
Stage II	4.509	0.015
Stage III	7.616	0.001
Stage IV	9.421	<0.001
ApoE ɛ4	1.943	0.031
CDR-SB	1.845	<0.001
ADAS 11	1.144	<0.001
Hippocampal volume (cm³)	0.002	0.005

ApoE, Apolipoprotein E; ADAS 11, Alzheimer’s Disease Assessment Scale-cognitive 11; CDR-SB, Clinical Dementia Rating-sum of boxes. The Cox Proportional Hazards regression model included age, ApoE ɛ4 allele (considered positive at least 1 ɛ4 allele existed), scores of ADAS11 and CDR-SB, hippocampal volume on structural MRI, total tau protein and phosphorylated tau protein titer in cerebrospinal fluid and amyloid PET staging. ^*Stage I is considered as a reference.

DISCUSSION

In this study, we assessed the association of cerebral cortical amyloid density with cognitive outcome in a cohort that included individuals with aMCI. With previously validated AD related factors, including age, gender, education level and ApoE ɛ4 genotype, we assessed in vivo amyloid burden using Florbetapir PET imaging. Three years after diagnosis of aMCI, 28.4% of study subjects showed progression to dementia. The progression rates were identified in 3.0% of patients with negative amyloid scans and in 45.0% of patients with positive amyloid scans. Even among subjects showing positive amyloid scans, density of amyloid deposition showed additional prognostic value. Larger amyloid deposition was associated with shorter progression intervals from aMCI to clinical dementia.

In line with previous studies, the likelihood of progression to clinical dementia within three years was higher in the amyloid-positive group than in the negative-group of aMCI subjects (45% versus 3%). The presence of amyloid deposition in the brain indicates neuropathologic change in AD, anticipating progressive cognitive decline [16]. Currently available evidence strongly supports the position that the initiating event in AD is related to abnormal processing of Aβ peptide, ultimately leading to formation of Aβ plaques in the brain. Several amyloid PET studies have confirmed that the presence of cortical amyloid predicts the cognitive decline in MCI subjects [12 , 17].

In this study, we showed the additional prognostic impact of amyloid PET scans. Even among subjects with positive amyloid scans, large amyloid deposition was associated with rapid cognitive decline. Understanding the timeframe of cognitive decline is required to facilitate targeting a specific process in order to slow progression. The amyloid cascade hypothesis [18, 19] proposed that the presymptomatic phase is characterized by an early rise in amyloid accumulation, followed later by synaptic dysfunction, tau-mediated neuronal injury, reduction in brain volume, and finally emergence of cognitive symptoms, followed by a clinical syndrome of frank dementia [20 –22]. The protracted preclinical phase of amyloid deposition, estimated to become abnormal around 17 years before the onset of dementia, slows toward a plateau [23]. According to the amyloid cascade hypothesis, large amounts of amyloid measured from the PET scan might infer the late stage of preclinical AD. Therefore, quantification of amyloid burden in the preclinical phase of dementia could be used to predict the time remaining before clinical dementia. Previous studies have reported the association of high amyloid PET tracer retention at baseline and fast cognitive decline in MCI subjects [24, 25]. This study result supports using amyloid PET to identify patients with aMCI who are at an increased risk for relatively near-term progression to dementia.

There was no significant difference in neuropsychological tests or hippocampal volume between amyloid stage I and stage II, but stage II showed significantly worse cognitive prognosis. Stage II, by definition, is an amyloid pathology-positive group, but the amount is still relatively low. Stage II group, therefore, can be inferred to be in the early pathologic stage of AD. Hippocampal volume, which becomes abnormal at later stages, as a marker of neuronal loss [21], is interpreted as having no difference between the groups. In the current study, AD pathology may have had a decisive influence on stage II prognosis, and this interpretation is supported by the difference of CSF biomarkers between stages I and II. Only six out of 137 patients in stage I progressed to dementia during the follow-up period, and most of the patients’ cognitive function remained stable during follow-up. The cause of MCI in the stage I group, amyloid-negative by definition, is thought to be other than AD. Previous studies have shown that prognosis of MCI without amyloid pathology is better than for amyloid-positive patients [26].

Stage II and stage IV were both positive amyloid pathologies. Stage IV, in which amyloid is more frequently measured than stage II, is supposed to be the late stage of AD pathology. These groups showed significant differences in cognitive prognosis, as well as neuropsychological tests, hippocampal volumes, and CSF biomarkers. Both groups share the same underlying amyloid pathology, but can be interpreted as having different pathologic stages. These findings suggest the binary interpretation system of amyloid PET currently applied has limitations. Additional studies on more detailed amyloid PET interpretation are needed.

In multivariate regression analysis, scores of ADAS 11, CDR-SB, and the presence of ApoE ɛ4 allele showed moderate association with progression to dementia in aMCI subjects, as expected. Several studies reported a relatively high association of cognitive measures with progression to dementia in MCI [27, 28]. The ApoE ɛ4 genotype has been associated with risk of MCI and AD in the general population, but has had mixed association with conversion to AD dementia among individuals with MCI. A meta-analysis of 35 prospective cohort studies found a moderate association of ApoE ɛ4 with progression to AD, but low sensitivity (0.53) and positive predictive value (0.57) [29].

The volume of white matter hyperintensity failed to show correlation with cognitive decline in the present study. The white matter hyperintensity is considered as a risk factor for vascular dementia, rather than AD dementia. [30]. This study limited the cohort to include those without significant cerebrovascular disease (Modified Hachinski Ischemic Scale score of ≤4) and it is possible that biomarkers would have a different value in cohorts with other comorbidities affecting cognition, and future work should apply measures such as those used here to more diverse clinical populations.

This study had limitations. First, we assessed amyloid PET positivity based on automated quantification analysis. Currently, a visual assessment system for amyloid PET is recommended to confirm the presence of moderate-to-frequent amyloid neuritic plaques. Schreiber et al. found that visual and SUVr analysis of Florbetapir PET may be equivalently used to determine amyloid status for individuals with MCI [31]. Second, we used an amyloid staging system based on quantification analysis for the purpose of this research and found cut-off values for each stage. In recent years, three different F-18 labeled amyloid PET tracers have been developed and obtained clinical approval. These tracers have different chemical structures and affinity for neuritic and diffuse plaques [15]. So, the cut-off values we have identified could not be generalized. We found an association between larger amyloid burden measured on Florbetapir PET scan and faster cognitive decline. Further studies with other tracers are needed to confirm the finding. Third, we analyzed amyloid staging only considering cortical deposition according to the current reading standards for the Florbetapir [32]. Recently, there have been several studies regarding staging of amyloid PET scans using striatal tracer uptake [33, 34]. According to pathological Ab Thal staging, the deposition of Aβ plaques starts in the neocortex then extends down into subcortical structures [8]. In pathologic studies, presence of amyloid plaques in the striatum predicts higher Braak neurofibrillary tangle stage [35]. There are no reading standards to consider the striatal uptake of the Florbetapir PET scan. Future research is needed on the amyloid PET staging system that integrates the tracer binding of the cerebral cortex and striatum. Limitation of this study is a relatively lower (28.4%) progression rate of study subjects compared with previously published rates of approximately 40–50% [12, 36]. We enrolled study subjects from ADNI GO and ADNI 2 that contain substantial number of early MCI subjects in the mildest symptomatic phase of AD. This character of the study cohort appears to have influenced the results of the study. Last, this study was based on clinical diagnosis of probable AD. Although aMCI is thought to be a prodrome of AD dementia, 30% of aMCI patients who develop dementia have a primary brain pathology that is not AD [37]. Probable AD is a clinical diagnosis, and the field is moving more toward biological definitions of AD, requiring of the presence of Aβ and tau pathology, regardless of the presence of clinical symptoms [38, 39]. In fact, the diagnosis of probable AD applied here is really synonymous to a multidomain amnestic dementia, which is likely enriched in those with AD pathologic findings, but in which other pathologic characteristics may also be the primary driver. More studies with longer follow-up times, more patients, and pathologic correlation are needed to define the role of amyloid PET imaging in patients with MCI.

Conclusion

We demonstrated quantification of amyloid PET scan may provide useful clinical information in the prediction of cognitive decline in aMCI patients. In assessment of aMCI subjects, amyloid PET scan could be applied not only for the early detection of AD pathology, but risk stratification for progression of dementia. Predicting the clinical course of aMCI subjects is important in determining the timing of appropriate consultation and therapeutic intervention. The present study supports using amyloid PET to identify patients with aMCI who are at increased risk for relatively near-term progression to dementia. Large amyloid burden could be a predictor of fast cognitive decline in aMCI patients. Quantification of amyloid PET seems to be able to provide information to clinicians’ clinical consultation in a timely manner.

Footnotes

ACKNOWLEDGMENTS

Data used in preparation of this paper 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 the analysis or in the writing of this paper. A complete listing of ADNI investigators can be found at .

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. 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 National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Authors’ disclosures available online ().

References

Albert

, DeKosky

, Dickson

, Dubois

, Feldman

, Fox

, Gamst

, Holtzman

, Jagust

, Petersen

, Snyder

, Carrillo

, Thies

, Phelps

(2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 270–279.

Petersen

, Smith

, Waring

, Ivnik

, Tangalos

, Kokmen

(1999) Mild cognitive impairment: Clinical characterization and outcome. Arch Neurol 56, 303–308.

Farias

, Mungas

, Reed

, Harvey

, DeCarli

(2009) Progression of mild cognitive impairment to dementia in clinic- vs community-based cohorts. Arch Neurol 66, 1151–1157.

Plassman

, Langa

, Fisher

, Heeringa

, Weir

, Ofstedal

, Burke

, Hurd

, Potter

, Rodgers

, Steffens

, McArdle

, Willis

, Wallace

(2008) Prevalence of cognitive impairment without dementia in the United States. Ann Intern Med 148, 427–434.

Mitchell

, Shiri-Feshki

(2009) Rate of progression of mild cognitive impairment to dementia—meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand 119, 252–265.

Koepsell

, Monsell

(2012) Reversion from mild cognitive impairment to normal or near-normal cognition: Risk factors and prognosis. Neurology 79, 1591–1598.

Mirra

, Heyman

, McKeel

, Sumi

, Crain

, Brownlee

, Vogel

, Hughes

, van Belle

, Berg

(1991) The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology 41, 479–486.

Thal

, Rub

, Orantes

, Braak

(2002) Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology 58, 1791–1800.

Grothe

, Barthel

, Sepulcre

, Dyrba

, Sabri

, Teipel

, Alzheimer’s Disease Neuroimaging Initiative (2017) In vivo staging of regional amyloid deposition. Neurology 89, 2031–2038.

10.

Forsberg

, Engler

, Almkvist

, Blomquist

, Hagman

, Wall

, Ringheim

, Langstrom

, Nordberg

(2008) PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging 29, 1456–1465.

11.

Rowe

, Bourgeat

, Ellis

, Brown

, Lim

, Mulligan

, Jones

, Maruff

, Woodward

, Price

, Robins

, Tochon-Danguy

, O’Keefe

, Pike

, Yates

, Szoeke

, Salvado

, Macaulay

, O’Meara

, Head

, Cobiac

, Savage

, Martins

, Masters

, Ames

, Villemagne

(2013) Predicting Alzheimer disease with beta-amyloid imaging: Results from the Australian imaging, biomarkers, and lifestyle study of ageing. Ann Neurol 74, 905–913.

12.

Jack

Jr. , Wiste

, Vemuri

, Weigand

, Senjem

, Zeng

, Bernstein

, Gunter

, Pankratz

, Aisen

, Weiner

, Petersen

, Shaw

, Trojanowski

, Knopman

(2010) Brain beta-amyloid measures and magnetic resonance imaging atrophy both predict time-to-progression from mild cognitive impairment to Alzheimer’s disease. Brain 133, 3336–3348.

13.

Wolk

, Sadowsky

, Safirstein

, Rinne

, Duara

, Perry

, Agronin

, Gamez

, Shi

, Ivanoiu

, Minthon

, Walker

, Hasselbalch

, Holmes

, Sabbagh

, Albert

, Fleisher

, Loughlin

, Triau

, Frey

, Hogh

, Bozoki

, Bullock

, Salmon

, Farrar

, Buckley

, Zanette

, Sherwin

, Cherubini

, Inglis

(2018) Use of flutemetamol F 18-labeled positron emission tomography and other biomarkers to assess risk of clinical progression in patients with amnestic mild cognitive impairment. JAMA Neurol 75, 1114–1123.

14.

McKhann

, Drachman

, Folstein

, Katzman

, Price

, Stadlan

(1984) Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944.

15.

Landau

, Thomas

, Thurfjell

, Schmidt

, Margolin

, Mintun

, Pontecorvo

, Baker

, Jagust

, Alzheimer’s Disease Neuroimaging Initiative (2014) Amyloid PET imaging in Alzheimer’s disease: A comparison of three radiotracers. Eur J Nucl Med Mol Imaging 41, 1398–1407.

16.

Hyman

, Trojanowski

(1997) Consensus recommendations for the postmortem diagnosis of Alzheimer disease from the National Institute on Aging and the Reagan Institute Working Group on diagnostic criteria for the neuropathological assessment of Alzheimer disease. J Neuropathol Exp Neurol 56, 1095–1097.

17.

Doraiswamy

, Sperling

, Johnson

, Reiman

, Wong

, Sabbagh

, Sadowsky

, Fleisher

, Carpenter

, Joshi

, Lu

, Grundman

, Mintun

, Skovronsky

, Pontecorvo

(2014) Florbetapir F 18 amyloid PET and 36-month cognitive decline: A prospective multicenter study. Mol Psychiatry 19, 1044–1051.

18.

Hardy

, Selkoe

(2002) The amyloid hypothesis of Alzheimer’s disease: Progress and problems on the road to therapeutics. Science 297, 353–356.

19.

Karran

, Mercken

, De Strooper

(2011) The amyloid cascade hypothesis for Alzheimer’s disease: An appraisal for the development of therapeutics. Nat Rev Drug Discov 10, 698–712.

20.

Donohue

, Jacqmin-Gadda

, Le Goff

, Thomas

, Raman

, Gamst

, Beckett

, Jack

Jr , Weiner

, Dartigues

, Aisen

, Alzheimer’s Disease Neuroimaging Initiative (2014) Estimating long-term multivariate progression from short-term data. Alzheimers Dement 10, S400–410.

21.

Jack

Jr. , Knopman

, Jagust

, Shaw

, Aisen

, Weiner

, Petersen

, Trojanowski

(2010) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9, 119–128.

22.

Bateman

, Xiong

, Benzinger

, Fagan

, Goate

, Fox

, Marcus

, Cairns

, Xie

, Blazey

, Holtzman

, Santacruz

, Buckles

, Oliver

, Moulder

, Aisen

, Ghetti

, Klunk

, McDade

, Martins

, Masters

, Mayeux

, Ringman

, Rossor

, Schofield

, Sperling

, Salloway

, Morris

, Dominantly Inherited Alzheimer Network (2012) Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 367, 795–804.

23.

Villemagne

, Burnham

, Bourgeat

, Brown

, Ellis

, Salvado

, Szoeke

, Macaulay

, Martins

, Maruff

, Ames

, Rowe

, Masters

(2013) Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: A prospective cohort study. Lancet Neurol 12, 357–367.

24.

Okello

, Koivunen

, Edison

, Archer

, Turkheimer

, Nagren

, Bullock

, Walker

, Kennedy

, Fox

, Rossor

, Rinne

, Brooks

(2009) Conversion of amyloid positive and negative MCI to AD over 3 years: An 11C-PIB PET study. Neurology 73, 754–760.

25.

Doraiswamy

, Sperling

, Coleman

, Johnson

, Reiman

, Davis

, Grundman

, Sabbagh

, Sadowsky

, Fleisher

, Carpenter

, Clark

, Joshi

, Mintun

, Skovronsky

, Pontecorvo

(2012) Amyloid-beta assessed by florbetapir F 18 PET and 18-month cognitive decline: A multicenter study. Neurology 79, 1636–1644.

26.

Rice

, Bisdas

(2017) The diagnostic value of FDG and amyloid PET in Alzheimer’s disease-A systematic review. Eur J Radiol 94, 16–24.

27.

Ewers

, Walsh

, Trojanowski

, Shaw

, Petersen

, Jack

Jr , Feldman

, Bokde

, Alexander

, Scheltens

, Vellas

, Dubois

, Weiner

, Hampel

, North American Alzheimer’s Disease Neuroimaging Initiative (2012) Prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol Aging 33, 1203–1214.

28.

Gomar

, Bobes-Bascaran

, Conejero-Goldberg

, Davies

, Goldberg

, Alzheimer’s Disease Neuroimaging Initiative (2011) Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer’s disease neuroimaging initiative. Arch Gen Psychiatry 68, 961–969.

29.

Elias-Sonnenschein

, Viechtbauer

, Ramakers

, Verhey

, Visser

(2011) Predictive value of APOE-epsilon4 allele for progression from MCI to AD-type dementia: A meta-analysis. J Neurol Neurosurg Psychiatry 82, 1149–1156.

30.

Verdelho

, Madureira

, Moleiro

, Ferro

, Santos

, Erkinjuntti

, Pantoni

, Fazekas

, Visser

, Waldemar

, Wallin

, Hennerici

, Inzitari

(2010) White matter changes and diabetes predict cognitive decline in the elderly: The LADIS study. Neurology 75, 160–167.

31.

Schreiber

, Landau

, Fero

, Schreiber

, Jagust

(2015) Comparison of visual and quantitative florbetapir F 18 positron emission tomography analysis in predicting mild cognitive impairment outcomes. JAMA Neurol 72, 1183–1190.

32.

Johnson

, Sperling

, Gidicsin

, Carmasin

, Maye

, Coleman

, Reiman

, Sabbagh

, Sadowsky

, Fleisher

, Murali Doraiswamy

, Carpenter

, Clark

, Joshi

, Lu

, Grundman

, Mintun

, Pontecorvo

, Skovronsky

(2013) Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer’s disease dementia, mild cognitive impairment, and normal aging. Alzheimers Dement 9, S72–83.

33.

Hanseeuw

, Betensky

, Mormino

, Schultz

, Sepulcre

, Becker

, Jacobs

HIL

, Buckley

, LaPoint

, Vannini

, Donovan

, Chhatwal

, Marshall

, Papp

, Amariglio

, Rentz

, Sperling

, Johnson

(2018) PET staging of amyloidosis using striatum. Alzheimers Dement 14, 1281–1292.

34.

Cho

, Shin

, Jang

, Park

, Kim

, Lee

, Na

, Lockhart

, Rabinovici

, Seong

, Seo

(2018) Amyloid involvement in subcortical regions predicts cognitive decline. Eur J Nucl Med Mol Imaging 45, 2368–2376.

35.

Braak

, Braak

(1990) Alzheimer’s disease: Striatal amyloid deposits and neurofibrillary changes. J Neuropathol Exp Neurol 49, 215–224.

36.

Petersen

, Aisen

, Beckett

, Donohue

, Gamst

, Harvey

, Jack

Jr , Jagust

, Shaw

, Toga

, Trojanowski

, Weiner

(2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology 74, 201–209.

37.

Jicha

, Parisi

, Dickson

, Johnson

, Cha

, Ivnik

, Tangalos

, Boeve

, Knopman

, Braak

, Petersen

(2006) Neuropathologic outcome of mild cognitive impairment following progression to clinical dementia. Arch Neurol 63, 674–681.

38.

Dubois

, Hampel

, Feldman

, Scheltens

, Aisen

, Andrieu

, Bakardjian

, Benali

, Bertram

, Blennow

, Broich

, Cavedo

, Crutch

, Dartigues

, Duyckaerts

, Epelbaum

, Frisoni

, Gauthier

, Genthon

, Gouw

, Habert

, Holtzman

, Kivipelto

, Lista

, Molinuevo

, O’Bryant

, Rabinovici

, Rowe

, Salloway

, Schneider

, Sperling

, Teichmann

, Carrillo

, Cummings

, Jack

Jr , Proceedings of the Meeting of the International Working Group (IWG) and the American Alzheimer’s Association on “The Preclinical State of AD”; July 23, 2015; Washington DC, USA (2016) Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement 12, 292–323.

39.

Jack

Jr , Bennett

, Blennow

, Carrillo

, Feldman

, Frisoni

, Hampel

, Jagust

, Johnson

, Knopman

, Petersen

, Scheltens

, Sperling

, Dubois

(2016) A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539–547.