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Rate of β-amyloid accumulation varies with baseline amyloid burden: Implications for anti-amyloid drug trials
Alzheimer’s & Dementia - (2018) 1-10
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Rate of b-amyloid accumulation varies with baseline amyloid burden: Implications for anti-amyloid drug trials Tengfei Guoa,*, Juergen Dukartb, Matthias Brendelc, Axel Romingerc, Timo Grimmerd, Igor Yakusheva,e, for the Alzheimer’s Disease Neuroimaging Initiative1 a Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universit€at M€unchen, Munich, Germany F. Hoffmann-La Roche, Pharma Research Early Development, Roche Innovation Centre Basel, Basel, Switzerland c Department of Nuclear Medicine, Ludwig-Maximilians-Universit€at M€unchen, Munich, Germany d Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technische Universit€at M€unchen, Munich, Germany e Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universit€at M€unchen, Munich, Germany b
Abstract
Introduction: This study examined a longitudinal trajectory of b-amyloid (Ab) accumulation at the predementia stage of Alzheimer’s disease in the context of clinical trials. Methods: Analyzed were baseline (BL) and 2 years’ follow-up 18F-florbetapir positron emission tomography data of 246 Ab-positive subjects with normal cognition and mild cognitive impairment. We studied the relationship between annual accumulation rates of 18F-florbetapir and BL standard uptake value ratios in whole gray matter (SUVRGM). Results: Subjects with BL SUVRGM of 0.56 to 0.92 (n 5 134) appeared to accumulate Ab approximately 1.5 times faster than remaining subjects. In subjects with SUVRGM above 0.95, most regions with the highest annual accumulation rate were outside the established set of Alzheimer’s disease typical regions. Conclusion: There are global and regional variations in annual accumulation rate at the predementia stage of Alzheimer’s disease. When taken into account, the sample size in anti-amyloid trials can be substantially reduced. Critically, treated and placebo groups should be matched for BL SUVRGM. Ó 2018 Published by Elsevier Inc. on behalf of the Alzheimer’s Association.
Keywords:
Amyloid imaging; Positron emission tomography; Florbetapir; Clinical trial; Anti-amyloid; Mild cognitive impairment; Alzheimer’s disease
1. Introduction
None of the authors has a conflict of interest with regards to the present article. Otherwise, T.G. reported having received consulting fees from Actelion, Eli Lilly, MSD, Novartis, Quintiles, and Roche Pharma; lecture fees from Biogen, Lilly, Parexel, and Roche Pharma; and grants to his institution from Actelion and Predemtec. 1 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.us c.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_Acknowledgement_List.pdf. *Corresponding author. Tel.: 149-89-4140-6397; Fax: 149-89-41404888. E-mail address: [email protected]
Positron emission tomography (PET) of b-amyloid (Ab) has been widely used as a primary or secondary end point in clinical trials with anti-amyloid therapeutics. As a matter of fact, drug trials in patients with Alzheimer’s dementia have failed to meet primary end points for clinical efficacy [1–9]. Beside the questions on ability of tested antibodies to adequately engage their targets [10], it was suggested that therapeutic interventions should be applied at earlier disease stages [11–13]. Thus, several ongoing anti-amyloid trials are testing the efficacy of potential disease-modifying drugs in amyloidpositive cognitively normal (CN) individuals [14–16] and subjects with mild cognitive impairment (MCI) [17,18]. The lateral frontal, parietal, and temporal cortices as well as the anterior and posterior cingulate regions are regarded as
https://doi.org/10.1016/j.jalz.2018.05.013 1552-5260/Ó 2018 Published by Elsevier Inc. on behalf of the Alzheimer’s Association. FLA 5.5.0 DTD JALZ2634_proof 3 July 2018 2:07 pm ce
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T. Guo et al. / Alzheimer’s & Dementia - (2018) 1-10
brain areas with a significant Ab burden in Alzheimer’s disease (AD) [19]. Of note, this regional pattern has been suggested by cross-sectional studies in patients with dementia due to AD. Thus, a standardized uptake value ratio (SUVR) in a composite cortical area averaged across these regions (referred to as AD-typical regions thereafter) has been commonly used as an end point in anti-amyloid trials [1,6,20]. However, longitudinal PET studies have produced rather inconsistent results. For instance, Sojkova et al. [21] reported the anterior cingulate region to be a fast Ab-accumulating region, whereas Villain et al. [22] did not find any Ab accumulation in this region. Both groups studied CN subjects using PET with Pittsburgh compound B (PiB). In MCI patients, a significant PiB accumulation was observed within the anterior and posterior cingulate, temporal, parietal cortices as well as in the putamen [23]. However, another research group reported a positive PiB rate also within the prefrontal cortex, insula, and occipital lobe, but not within the putamen [22]. In addition, Villemagne et al. [24] reported no significant regional PiB accumulation in Ab-positive CN and MCI subjects within 20 months, but a significant one within the orbitofrontal and dorsolateral prefrontal cortices of Ab-positive CN subjects within 38 months. The variability in the findings may be related to such factors as study duration, proportion of Ab-positive subjects at baseline (BL), and reference region. Perhaps more importantly, the study subjects may have been at different stages of the Ab trajectory. Indeed, the trajectory is not linear but seems to have a sigmoid shape with periods of accelerated Ab deposition [25,26]. Moreover, a regional pattern of longitudinal Ab accumulation is likely to be not constant over the whole trajectory. That is, some regions may accumulate Ab faster at an early stage, while others may take a lead at a later stage. These factors may be of relevance for design and analysis of anti-amyloid clinical trials. In the present study, we used a pseudo-temporal analysis [22,25,26] to explore a longitudinal trajectory of Ab accumulation at the predementia stage of AD in the context of clinical trials.
2. Methods 2.1. Participants The data were obtained from the Alzheimer’s disease Neuroimaging Initiative database (ida.loni.usc.edu). The Alzheimer’s disease Neuroimaging Initiative study was approved by institutional review boards of all participating centers, and a written informed consent was obtained from all participants or authorized representatives. Considered for the present study were Ab-positive CN, early and late MCI subjects at BL, for whom structural magnetic resonance imaging (MRI) and at least one follow-up (FU) 18F-florbetapir PET scan were available. Following recommendations of the Alzheimer’s Disease Neuroimaging Initiative, Ab positivity was defined according to the SUVR in a composite cortical region with a threshold of .1.11 [27].
2.2. PET data acquisition and analysis Details on florbetapir synthesis and image acquisition are given elsewhere (http://adni-info.org). Image analysis was performed using SPM8 (Welcome Department of Imaging Neuroscience, London, UK). Specifically, PET images were rigidly co-registered to concurrently acquired T1 MRI images to calculate a linear transformation (PET-2MRI). Individual MRI images were nonlinearly coregistered to the Montreal Neurological Institute (MNI) space MRI template, and those deformations were used to transform the co-registered PET images into the MNI space. T1 MRI images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid [28]. Then, a region-based voxel-wise partial volume correction (PVC) of PET images was performed using the PETPVC toolbox [29]. A total of 83 individual brain volumes of interests (VOIs) [30] were transferred to individual PET space using the inverse of the above transformations. Out of 83 brain VOIs, 74 VOIs are primarily GM regions. Sixty-two regions with a volume above 1 cm3, i.e. 2 x FWHM, were included in the final analysis [31]. SUVR was calculated as a ratio of regional standardized uptake value (SUV) to SUV in WM that was recommended as reference region for longitudinal 18F-florbetapir PET studies [32–36]. WM mask was defined as described elsewhere [32]. SUVR in whole brain GM (SUVRGM) was calculated as mean value of all 62 GM regions [31]. The set of AD-typical regions consisted of the frontal (8 regions), parietal (3 regions including precuneal/posterior cingulate), temporal (6 regions) lobes and the anterior cingulate region [31], i.e. 36 unilateral VOIs in total. 2.3. Pseudo-temporal image analysis Annual accumulation rate (AAR) of 18F-florbetapir was calculated as (SUVRFU-SUVRBL)/FU time, where SUVRFU and SUVRBL are SUVR of the FU and BL PET, respectively; FU time (years) is the time between two PET scans. An average AAR across all subjects was calculated for each region, the set of AD-typical regions as well as for the whole GM. To explore the variability in regional AAR as a function of total amyloid burden, an across-subject waveform for a given set of regions was calculated [31]. In analogy with previous studies [22,25,26], all groups were pooled together. AAR in the set of AD-typical regions was modeled with a restricted cubic spline with four knots to allow AAR to vary nonlinearly with BL SUVRGM [26,31]. 2.4. Implications for hypothetical anti-amyloid drug trials Two mechanisms of actions of anti-amyloid drugs were considered: (1) the drug attenuates a (further) Ab accumulation and (2) the drug reduces BL Ab burden. For each scenario, we calculated the sample size needed to detect a treatment effect in a hypothetical 24-month placebocontrolled anti-amyloid clinical trial with an 80% power
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T. Guo et al. / Alzheimer’s & Dementia - (2018) 1-10 Table 1 Demographic characteristics of subjects Group
N
FU (years)
Age (years)
Gender (f/m)
Education (years)
CN Early MCI Late MCI All
83 82 81 246
2.06 6 0.23 2.02 6 0.11 2.01 6 0.06 2.03 6 0.15
76.77 6 5.72 *73.10 6 7.27 *72.56 6 7.51 74.16 6 7.10
49/34 30/52 38/43 117/129
16.06 6 2.89 15.90 6 2.84 16.15 6 2.80 16.04 6 2.84
APOE 3 4-pos. (%) 48.19 67.07 y 66.67 60.57 y
MMSE 28.86 6 1.33 27.79 6 1.81 x 27.32 6 1.92 28.00 6 1.81 x
CDR-SB 0.19 6 0.48 1.34 6 0.79 x 1.85 6 0.94 1.12 6 1.02 x
ADAS Cog-13 10.47 6 4.93 14.34 6 5.10 x 18.39 6 7.75 14.35 6 6.84 x
Q2 Abbreviations: FU, follow-up; CN, cognitively normal; MCI, mild cognitive impairment; APOE, apolipoprotein E. NOTE. All significant differences refer to the comparison with a group of normal controls (CN): *P , .001, two-sample t-test; yP , .05, Fisher’s exact test; x P , .001, Mann–Whitney test (MMSE, CDR-SB and ADAS Cog-13 were not normally distributed).
(G Power 3.1 [37]). Furthermore, we examined how differences in BL SUVRGM between treated and placebo groups can impact PET-based end points. 2.5. Statistics Normality of distributions was tested using the D’Agostino-Pearson test and visual inspection of data histograms. Given a normal distribution of imaging variables, a (twotailed) t-test at the significance level of P , .05 was applied. Data are presented as mean 6 standard deviation. If not otherwise noted, statistical analyses were performed using Graph-Pad Prism 6 for Windows (La Jolla, CA). The consistency of regions ranking was tested using random sampling without replacement [38]. A hierarchical cluster analysis [39], two-fold cross-validation, and random sampling tests were conducted where appropriate using SPSS for Windows (version 22.0) and in-house MATLAB codes (MATLAB R2014b, The MathWorks, Natick).
According to the pseudo-trajectory, subjects with SUVRGM between 0.56 and 0.92 had on average higher AARs in AD-typical regions than the mean AAR in the whole GM (0.031 6 0.031) (Fig. 1). They are further referred to as fast accumulators (n 5 134); subjects with SUVRGM below 0.56 and above 0.92 are referred to as slow accumulators (n 5 112). The mean AAR in whole GM as threshold for separation, as well as the cutoffs of 0.56 and 0.92, appeared to be robust in the two-fold crossvalidation test (Supplementary Fig. 1). In fast accumulators, the mean AAR in AD-typical regions (0.038 6 0.033) was significantly higher (P 5 .045, one-sample t-test) than the mean AAR in the whole cohort (0.033 6 0.032). In slow accumulators, AAR in AD-typical regions (0.026 6 0.030) was significantly lower (P 5 .016). The AAR in ADtypical regions was 1.50 (P 5 .002, two-sample t-test) times higher in fast accumulators than that in slow accumulators (Fig. 2A). 3.2. Regional pattern of AAR in different phases
3. Results Demographic data of each group are summarized in Table 1.
In the phases 1 and 2, all 62 regions showed a significant increase in SUVR from BL to FU. Furthermore, all regions
3.1. Pseudo-temporal analysis The groups did not differ in respect to AAR in the set of AD-typical regions or whole GM (P . .25). In the whole cohort (n 5 246), all 62 regions showed a significant (P ,.05, paired-sample t-test) Ab accumulation over 2 years (Supplementary Table 1). Regional AARs were normally distributed (P . .10). Fig. 1 shows AAR in the set of ADtypical regions as a function of BL SUVRGM. Starting at a low BL SUVRGM, AAR increased until BL SUVRGM of 0.70. Afterward, AAR decreased reaching a plateau at 0.95. Consequently, three phases could be defined: BL SUVRGM 0.70 as phase 1, 0.70 , SUVRGM 0.95 as phase 2, and .0.95 as phase 3. These two cutoffs were confirmed as robust by the two-fold cross-validation test (Supplementary Material). Out of 246 subjects, 52, 117, and 77 subjects fall within the phases 1, 2, and 3, respectively. Notably, a very similar trajectory was obtained using CN data only (Supplementary Fig. 3).
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Fig. 1. AAR in the AD-typical regions set as a function of BL SUVRGM. Horizontal red line is the mean AAR in whole gray matter of the whole cohort. Horizontal black line is zero. From left to right, blue lines are cutoffs 0.70 and 0.95 for phase 1 and phase 2, and phase 2 and phase 3, respectively. A background red area indicates fast accumulators with the cutoffs 0.56 and 0.92. Abbreviations: AAR, annual accumulation rate; AD, Alzheimer’s disease; BL, baseline; SUVRGM, standard uptake value ratios in whole gray matter; CN, cognitively normal; MCI, mild cognitive impairment.
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T. Guo et al. / Alzheimer’s & Dementia - (2018) 1-10
Fig. 2. AAR (A) of fast accumulators and slow accumulators in the AD-typical regions set; **P ,.01 in a two-sample t-test (B) in the set of FAR and AD-typical regions in the phase 3; yyP , .01 in a paired-sample t-test. Abbreviations: AAR, annual accumulation rate; AD, Alzheimer’s disease; FAR, fast accumulating regions.
with AAR above that of the AD-typical region set (n 5 36) were part of the latter set (phase 1: n 5 25, phase 2: n 5 20). Specifically, anterior, medial, and lateral orbital, posterior cingulate, superior parietal, middle, superior, inferior frontal, and middle, inferior temporal regions showed the fastest AARs both in the phase 1 (Supplementary Table 2) and phase 2 (Supplementary Table 3). Thus, the set of AD-typical regions adequately captured the most active Ab-accumulating regions. In the phase 3, nine regions showed no significant increase in SUVR (Table 2). Twenty-five regions had a higher AAR than the AD-typical set. Among them, 10 were not part of that set. Moreover, among the top 10 fast Ab-accumulating regions, only four were part of the AD-typical region set. In contrast to the phases 1 and 2, bilateral lateral occipital lobe, postcentral, precentral, cuneus, and lingual gyrus showed a higher AAR than the set of AD-typical regions. The hierarchical cluster analysis revealed that the left middle frontal, superior frontal, postcentral, lateral anterior temporal lobe, bilateral lateral occipital lobe, and the right cuneus were within the same cluster as the top fast Ab-accumulating 2 regions. Within 5852 ðC77 Þ random samples in the random sampling test, 93.40% fell into the same top seven regions (Supplementary material), confirming the consistency of ranking. Thus, these seven regions were composed into a set of the phase 3’ fast accumulating regions (FARs). The effect size of 0.39 between AAR in FARs and AD-typical regions was not significantly lower (P 5 .299) than the mean value of 30,000 iterations in a two-fold cross-validation test (Supplementary Fig. 2). The pseudo-temporal analysis for this composited set of FARs revealed an additional acceleration in the phase 3 (Supplementary Fig. 4). AAR in these FARs (0.031 6 0.026) was 1.23 (Fig. 2B) and 1.27 times higher than that in the set of AD-typical regions (0.025 6 0.026, P 5 .001) and whole GM (0.024 6 0.026, P 5 .001), respectively. AAR in ADtypical regions was not higher than AAR in whole GM (P 5 .209). The mean BL SUVR in FARs (1.07 6 0.15) was lower (P , .001, paired-sample t-test) than that in
AD-typical regions (1.12 6 0.13). In a comparison between apolipoprotein E (APOE) 3 4 carriers and noncarriers, no significant difference in AAR in the set of AD-typical regions was found (0.032 6 0.032 vs. 0.033 6 0.033). 3.3. Implications for hypothetical anti-amyloid drug trials Table 3 summarizes the sample size per arm needed to detect a treatment effect of a drug that (1) attenuates a (further) Ab accumulation and (2) reduces BL Ab burden. Assuming a linear relationship between Ab accumulation and time within a 2 years’ period [25,26], inclusion of fast accumulators instead of the whole (unselected) cohort would reduce the sample size to treat by around 24% in the first scenario. In the second scenario, inclusion of fast accumulators would reduce the sample size to treat by 61% to 70%. The trial duration in both scenarios could be reduced as well (Supplementary Fig. 5). As compared with AD-typical regions, utilization of FARs as target region in subjects with SUVRGM .0.95 (phase 3) would reduce the sample size to treat by 36% in the first scenario (Table 3). In the second scenario, the sample size would be marginally larger. Assuming that a drug attenuates Ab accumulation by 20%, a 20% lower SUVR, i.e. attenuation, is observed in the treated group by the end of the trial (Fig. 3B), if the treated and placebo groups are matched for BL SUVRGM (Fig. 3A). Figs. 3 and 4 depict how imbalances in BL SUVRGM between treated and placebo groups impact PET-based end points of a hypothetical clinical trial, in an extreme and a regular case, respectively. In the former case, an average BL SUVRGM of a treated and placebo groups correspond to the maximal difference in AAR over the trajectory, that is, the BL SUVRGM of 0.95 and 0.70 or vice versa (Fig. 1). As shown in Fig. 3C, a false 31% ((0.029420.0426))/0.0426) attenua- Q5 tion would be detected even without drug. With a drug, a 125% ((245%2(220%))/(220%)) overestimation of a treatment effect (observed treatment effect 5 (0.0294 ! 0.820.0426)/0.0426 5 245%) would be observed
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Table 2 Ranking of annual accumulation rates in 62 regions, in the set of AD-typical regions and whole gray matter in the phase 3 Region
Mean
Middle frontal gyrus_L*,y,z Superior frontal gyrus_Lz Cuneus_R*,z Lateral remainder of occipital lobe_L*,y,z Postcentral gyrus_L*,y,z Lateral anterior temporal lobe _Lz Lateral remainder of occipital lobe_R*,y,z Middle frontal gyrus_Rz Cuneus_Lz Precentral gyrus_L*,z Superior parietal gyrus_Lz Lateral anterior temporal lobe_Rz Medial orbital gyrus_Lz Precentral gyrus_Rz Postcentral gyrus_Rz Inferiolateral remainder of parietal lobe_Rz Posterior cingulate gyrus _Lz Lingual gyrus_Lz Straight gyrus_Lz Anterior orbital gyrus_Lz Lingual gyrus_Rz Superior parietal gyrus_Rz Lateral orbital gyrus_Lz Inferiolateral remainder of parietal lobe_Lz Middle and inferior temporal gyrus_Rz AD-typical regionsz Superior frontal gyrus_Rz Middle and inferior temporal gyrus_Lz Whole gray matterz Posterior cingulate gyrus_Rz Anterior cingulate gyrus_Lz Inferior frontal gyrus_Lz Anterior orbital gyrus_Rz Posterior orbital gyrus_Lz Inferior frontal gyrus_Rz Fusiform gyrus_Lz Lateral orbital gyrus_Rz Medial orbital gyrus_Rz Posterior temporal lobe_Lz Posterior temporal lobe_R*,y,z Medial anterior temporal lobe_Lz Fusiform gyrus_Rz Posterior superior temporal gyrus_Ry,z Anterior superior temporal gyrus_Lz Straight gyrus_Ry,z Posterior orbital gyrus_Rz Posterior superior temporal gyrus_Lz Putamen_R*,y,z Putamen_L*,y,z Anterior cingulate gyrus_R*,y,z Medial anterior temporal lobe_R*,y,z Parahippocampal and ambient gyri_L*,y,z Parahippocampal and ambient gyri_R*,y,z Insula_R*,z Anterior superior temporal gyrus_R*,y,z Insula_L*,y Caudate nucleus_L*,y,z Amygdala_L*,y
SD
Volume (mm3)
0.031 0.031 0.031 0.031 0.030 0.030 0.030 0.029 0.029 0.029 0.029 0.028 0.028 0.028 0.027 0.027
0.035 0.039 0.034 0.030 0.029 0.038 0.031 0.040 0.035 0.030 0.032 0.041 0.039 0.035 0.034 0.034
44,690 46,866 9224 38,030 23,201 2856 38,789 42,929 9438 28,237 38,141 2842 4780 27,438 25,731 38,214
0.027 0.027 0.027 0.027 0.026 0.026 0.025 0.025
0.036 0.042 0.045 0.047 0.048 0.032 0.066 0.029
7159 12,333 3142 5164 11,970 37,509 3328 37,662
0.025 0.025 0.025 0.024 0.024 0.024 0.024 0.024 0.024 0.023 0.023 0.022 0.022 0.022 0.021 0.021 0.021 0.020 0.019 0.018 0.017 0.017 0.017 0.015 0.015 0.014 0.013 0.012 0.011 0.011 0.010 0.010 0.009 0.008
0.040 13,452 0.026 530,644 0.032 46,158 0.045 13,115 0.026 851,002 0.044 7557 0.037 7747 0.040 14,448 0.055 4807 0.052 4594 0.035 15,294 0.046 3486 0.061 2967 0.039 4962 0.037 48,665 0.030 47,598 0.034 4975 0.053 3479 0.037 12,468 0.048 4033 0.041 2856 0.048 4476 0.048 12,220 0.048 4063 0.050 3957 0.052 7441 0.039 5107 0.047 3595 0.046 3583 0.047 12,323 0.049 4205 0.059 12,275 0.041 3456 0.059 1120 (Continued )
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Table 2 Ranking of annual accumulation rates in 62 regions, in the set of AD-typical regions and whole gray matter in the phase 3 (Continued ) Region
Mean
SD
Thalamus_L*,y Amygdala_R*,y Thalamus_R*,y Hippocampus_L*,y Hippocampus_R*,y Caudate nucleus_R*,y
0.007 0.005 20.000 20.001 20.002 20.007
0.072 0.054 0.069 0.063 0.068 0.072
Volume (mm3) 5452 1057 5676 1626 1825 3411
Abbreviations: AD, Alzheimer’s disease; SD, standard deviation. NOTE. Regions within the set of AD-typical regions are in bold. “_L” and “_R” indicate a region in the left and right hemisphere, respectively. Marked are significant differences relative to the set of AD-typical regions y, whole gray matter * as well as between baseline and follow-up PET z (P , .05, Q3 paired-sample t-test).
(Fig. 3D). If we reverse the group order, the placebo group would accumulate Ab (SUVR increase) 45% faster than the “treated” group (without drug), i.e., a 45% false enhancement ((0.042620.0294)/0.0294) is produced (Fig. 3E). With a drug, a 180% ((16%2(220%))/(220%)) underestimation of a treatment effect (observed treatment effect 5 (0.0426 ! 0.820.0294)/0.0294 5 16%) would be observed (Fig. 3F). The regular case is arbitrarily chosen as a difference in BL SUVRGM of 0.10. For example, if an average BL SUVRGM of a treated and placebo groups are 0.80 and 0.90 or vice versa, an overestimation of 75% or underestimation of 90% is evident (Fig. 4). Simulations with BL SUVRGM of 0.60 versus 0.70 and 0.60 versus 0.80 are presented in Supplementary Figs. 6 and 7, respectively. In the latter case, an overestimation of 30% and underestimation of 35% is evident. 4. Discussion In the present study, we applied a pseudo-temporal analysis to explore the longitudinal trajectory of Ab accumulation at the predementia stage of AD in the context of clinical trials. In line with previous studies, we found the trajectory to follow an inverted U-shape [22,25,26,36] that continued into a “tail” at the late stage [22]. Thus, three phases of Ab accumulation could be defined: acceleration, deceleration, and a stable phase. As a major finding, we found a significant bias in estimating a potential drug effect, if placebo and treated groups are not matched for BL Ab burden. Given the nonlinear trajectory of Ab accumulation over time, this observation is not unexpected. This suggestion was first made in a preclinical anti-amyloid drug trial [40] but has not been tested in human so far. As shown by our simulations, a difference of 0.25 BL SUVRGM can in the extreme case produce effects that are substantially larger than the expected drug effects. But, even with a difference of 0.10 BL SUVRGM, the drug effect can be greatly overestimated or underestimated, for example, by 75 and 90%, respectively. Notably, a bias is produced not only when average AARs of two groups are
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Q6
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T. Guo et al. / Alzheimer’s & Dementia - (2018) 1-10
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Table 3 Number of subjects per arm needed to detect an Ab-modifying treatment effect in a clinical trial with an 80% power and a two-tailed a 5 0.05
20% attenuation in further SUVR increase 50% attenuation in further SUVR increase 10% decrease in SUVR from baseline 20% decrease in SUVR from baseline
Q4
Whole cohort
Fast accumulators
Slow accumulators
Phase 3
AD-typical
AD-typical
AD-typical
AD-typical
FAR
391 64 30 13
296 49 9 5
552 90 37 14
433 70 12 5
279 46 14 6
Abbreviations: AD, Alzheimer’s disease; FAR, fast accumulating region; SUVR, standardized uptake value ratio.
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Fig. 3. Simulation of a 24 months’ drug trial assuming the mean AAR of the whole cohort (A) and a 20% attenuation of a (further) SUVR increase in the ADtypical region set as a true drug effect (B). Mean BL SUVRGM are assumed to be 0.95 and 0.70 in the treated and placebo groups, respectively (C–D) or other way around (E–F). AARs corresponding to BL SUVRGM 0.70 and 0.95 were 0.0426 and 0.0294 according to the trajectory of AAR (Fig. 1) respectively. Abbreviations: AAR, Annual accumulation rate; SUVR, standard uptake value ratios; BL, baseline; SUVRGM, standard uptake value ratios in whole gray matter; AD, Alzheimer’s disease. FLA 5.5.0 DTD JALZ2634_proof 3 July 2018 2:07 pm ce
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Fig. 4. Simulation of a 24 months’ drug trial assuming the mean AAR of the whole cohort (A) and a 20% attenuation of a (further) SUVR increase in the ADtypical regions set as a true drug effect (B). Mean BL SUVRGM are assumed to be 0.90 and 0.80 in the treated and placebo groups, respectively (C–D) or other way around (E–F). AARs corresponding to BL SUVRGM 0.80 and 0.90 were 0.0388 and 0.0316, respectively, according to the trajectory of AAR (Fig. 1). Abbreviations: AAR, annual accumulation rate; SUVR, standard uptake value ratios; AD, Alzheimer’s disease; BL, baseline; SUVRGM, standard uptake value ratios in whole gray matter.
located on the accelerating or decelerating limb of the trajectory, that is, within the same phase, but also when average AARs of two groups correspond to different phases. For example, if the proportion of phase 1 subjects is substantially larger in the treated group, their on average higher AAR would cause an underestimation of a drug effect. This effect is aggravated by a (consequently) higher proportion of phase
2 subjects in the control group. If the proportion of phase 1 subjects is higher in the control group, an overestimation of a drug effect is produced. Based on the trajectory and BL SUVRGM, we separated the whole sample into subjects with the above-average AAR in whole GM, so called fast accumulators (54% of the whole cohort), and those with a below-average AAR,
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slow accumulators. Thus, from the pathophysiological perspective, fast accumulators are subjects who at this time accumulate Ab faster than an average Ab-positive subject over the period of his/her “Ab-positive life” (estimated to be 36 years, data not shown). The consistency of this intuitive cutoff was supported by a cross-validation test. Consequently, inclusion of fast accumulators in a clinical trial would allow reaching PET-based end points faster as compared with an unselected cohort of subjects. This effect can be also translated into a lower sample size to treat. In a clinical trial with a drug that attenuates a (further) Ab accumulation, inclusion of fast accumulators only would reduce the sample size by around 24%. In a trial with a drug that reduces BL Ab burden, a reduction of 61% to 70% can be achieved. From the practical perspective, data on fast accumulators can be utilized to obtain a preliminary estimation of a drug effect. As the present definition of fast accumulators is based just on a BL PET with SUVRGM being a rather robust approximation of the Ab accumulation stage, there may be some individuals with a low or even negative AAR. Yet, the proposed approach should still be effective at the group level. Regional analyses revealed that in the phases 1 and 2, most active Ab-accumulating regions were within the ADtypical set, confirming the suitability of AD-typical regions as target in anti-amyloid drug trials. In the phase 3, however, a few top FARs were not within the set of AD-typical regions. Namely, the bilateral cuneus and lateral occipital lobe, the left precentral and postcentral regions were among top 10 FARs, along with only four AD-typical regions. Thus, after reaching a certain level of Ab load, Ab accumulation in AD-typical regions continues, but at a lower rate than in the phylogenetically older primary visual and sensorimotor cortices, which at this stage become progressively affected by Ab deposition as well. We further compared the set of phase 3–specific FARs with the established set of ADtypical regions as target in a putative anti-amyloid trial. The results appeared to be dependent on the mechanism of drug action. Namely, should the drug attenuate Ab accumulation, the utilization of the above FARs would enable reducing the sample size to treat by roughly one third. Yet, the sample size would marginally increase, should the drug reduce BL Ab burden. This is due to the fact that the mean BL SUVR in FARs was lower than that in ADtypical regions. Thus, the established set of AD-typical regions does not adequately represent most active Ab-accumulating regions in the third phase of the trajectory. Theoretically, anti-amyloid therapies might be less efficient at this advanced stage of brain amyloidosis, limiting a practical implication of this finding. Nevertheless, recent studies suggested a beneficial effect of anti-amyloid monoclonal antibodies even at the stage of (mild) dementia [10]. Furthermore, there are several ongoing anti-amyloid trials in patients with MCI [17,18], a significant proportion of whom are at the late stage of Ab accumulation. Thus, our results might be of value for these studies.
The study is a subject to several limitations. First, although the cutoffs appeared to be robust according to the cross-validation test, they should be verified prospectively. Second, as is true for all semi-quantitative PET studies with SUVR as outcome measure, we assume that a hypothetical drug does not influence cerebral blood flow. Third, as we included only subjects at the predementia and preclinical disease stage, clinical relevance of the present results is yet to be proved. Yet, like it is true for anti-amyloid studies in general, a basic assumption behind our approach is that attenuation or removal of Ab is beneficial for study participants. Furthermore, we cannot rule out that in some subjects, a significant white matter disease influenced SUVR. However, in line with previous studies [41–43], we found no association between volume of white matter hyperintensities and SUVR in AD-typical regions (data not shown). Finally, we did not include PET scans with a longer than 2 years FU (available for a minority of subjects), potentially precluding a more accurate fit. Yet, a stronger impact (weight) of such data onto the slope is likely to bias it, depending on clinical entity and Ab accumulation stage of those subjects. Moreover, a common duration of antiamyloid trials is up to 2 years. Nonlinear trajectories are more likely over longer durations. Therefore, it is problematic to model linear trajectories on data with a longer FU. 5. Conclusions Several ongoing clinical trials focus on developing preventive drugs at the predementia stage of AD [14–18]. Our findings provide a meaningful reference for planning and analyzing anti-amyloid clinical trials with amyloid PET as biomarker. Acknowledgments Data collection and sharing for this project were 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.; 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
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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 (www.fnih.org). 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. Financial support of the Alzheimer Forschung Initiative e.V. (AFI) is gratefully acknowledged. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jalz.2018.05.013.
RESEARCH IN CONTEXT
1. Systematic review: A search in PubMed and on www.clinicaltrials.gov revealed a number of studies that use amyloid PET as an end point of antiamyloid drug trials. Yet, we found no report about impact of the target brain region or baseline amyloid burden on design (sample size) or analyses of such trials. 2. Interpretation: There are global and regional variations of b-amyloid accumulation rate along the natural, nonlinear trajectory of b-amyloid accumulation. Baseline amyloid burden as measured by PETenables an allocation or “staging” of a given trial subject on the trajectory. This knowledge can be used to reduce the sample size of drug trials and the likelihood of false-positive as well as false-negative drug effects. 3. Future directions: Proposed cutoffs should be validated in prospective multicenter studies. Herewith, thorough standardization of methods for PET data acquisition and analysis across centers is essential.
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Rate of b-amyloid accumulation varies with baseline amyloid burden: Implications for anti-amyloid drug trials Tengfei Guoa,*, Juergen Dukartb, Matthias Brendelc, Axel Romingerc, Timo Grimmerd, Igor Yakusheva,e, for the Alzheimer’s Disease Neuroimaging Initiative1 a Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universit€at M€unchen, Munich, Germany F. Hoffmann-La Roche, Pharma Research Early Development, Roche Innovation Centre Basel, Basel, Switzerland c Department of Nuclear Medicine, Ludwig-Maximilians-Universit€at M€unchen, Munich, Germany d Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technische Universit€at M€unchen, Munich, Germany e Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universit€at M€unchen, Munich, Germany b
Abstract
Introduction: This study examined a longitudinal trajectory of b-amyloid (Ab) accumulation at the predementia stage of Alzheimer’s disease in the context of clinical trials. Methods: Analyzed were baseline (BL) and 2 years’ follow-up 18F-florbetapir positron emission tomography data of 246 Ab-positive subjects with normal cognition and mild cognitive impairment. We studied the relationship between annual accumulation rates of 18F-florbetapir and BL standard uptake value ratios in whole gray matter (SUVRGM). Results: Subjects with BL SUVRGM of 0.56 to 0.92 (n 5 134) appeared to accumulate Ab approximately 1.5 times faster than remaining subjects. In subjects with SUVRGM above 0.95, most regions with the highest annual accumulation rate were outside the established set of Alzheimer’s disease typical regions. Conclusion: There are global and regional variations in annual accumulation rate at the predementia stage of Alzheimer’s disease. When taken into account, the sample size in anti-amyloid trials can be substantially reduced. Critically, treated and placebo groups should be matched for BL SUVRGM. Ó 2018 Published by Elsevier Inc. on behalf of the Alzheimer’s Association.
Keywords:
Amyloid imaging; Positron emission tomography; Florbetapir; Clinical trial; Anti-amyloid; Mild cognitive impairment; Alzheimer’s disease
1. Introduction
None of the authors has a conflict of interest with regards to the present article. Otherwise, T.G. reported having received consulting fees from Actelion, Eli Lilly, MSD, Novartis, Quintiles, and Roche Pharma; lecture fees from Biogen, Lilly, Parexel, and Roche Pharma; and grants to his institution from Actelion and Predemtec. 1 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.us c.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_Acknowledgement_List.pdf. *Corresponding author. Tel.: 149-89-4140-6397; Fax: 149-89-41404888. E-mail address: [email protected]
Positron emission tomography (PET) of b-amyloid (Ab) has been widely used as a primary or secondary end point in clinical trials with anti-amyloid therapeutics. As a matter of fact, drug trials in patients with Alzheimer’s dementia have failed to meet primary end points for clinical efficacy [1–9]. Beside the questions on ability of tested antibodies to adequately engage their targets [10], it was suggested that therapeutic interventions should be applied at earlier disease stages [11–13]. Thus, several ongoing anti-amyloid trials are testing the efficacy of potential disease-modifying drugs in amyloidpositive cognitively normal (CN) individuals [14–16] and subjects with mild cognitive impairment (MCI) [17,18]. The lateral frontal, parietal, and temporal cortices as well as the anterior and posterior cingulate regions are regarded as
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brain areas with a significant Ab burden in Alzheimer’s disease (AD) [19]. Of note, this regional pattern has been suggested by cross-sectional studies in patients with dementia due to AD. Thus, a standardized uptake value ratio (SUVR) in a composite cortical area averaged across these regions (referred to as AD-typical regions thereafter) has been commonly used as an end point in anti-amyloid trials [1,6,20]. However, longitudinal PET studies have produced rather inconsistent results. For instance, Sojkova et al. [21] reported the anterior cingulate region to be a fast Ab-accumulating region, whereas Villain et al. [22] did not find any Ab accumulation in this region. Both groups studied CN subjects using PET with Pittsburgh compound B (PiB). In MCI patients, a significant PiB accumulation was observed within the anterior and posterior cingulate, temporal, parietal cortices as well as in the putamen [23]. However, another research group reported a positive PiB rate also within the prefrontal cortex, insula, and occipital lobe, but not within the putamen [22]. In addition, Villemagne et al. [24] reported no significant regional PiB accumulation in Ab-positive CN and MCI subjects within 20 months, but a significant one within the orbitofrontal and dorsolateral prefrontal cortices of Ab-positive CN subjects within 38 months. The variability in the findings may be related to such factors as study duration, proportion of Ab-positive subjects at baseline (BL), and reference region. Perhaps more importantly, the study subjects may have been at different stages of the Ab trajectory. Indeed, the trajectory is not linear but seems to have a sigmoid shape with periods of accelerated Ab deposition [25,26]. Moreover, a regional pattern of longitudinal Ab accumulation is likely to be not constant over the whole trajectory. That is, some regions may accumulate Ab faster at an early stage, while others may take a lead at a later stage. These factors may be of relevance for design and analysis of anti-amyloid clinical trials. In the present study, we used a pseudo-temporal analysis [22,25,26] to explore a longitudinal trajectory of Ab accumulation at the predementia stage of AD in the context of clinical trials.
2. Methods 2.1. Participants The data were obtained from the Alzheimer’s disease Neuroimaging Initiative database (ida.loni.usc.edu). The Alzheimer’s disease Neuroimaging Initiative study was approved by institutional review boards of all participating centers, and a written informed consent was obtained from all participants or authorized representatives. Considered for the present study were Ab-positive CN, early and late MCI subjects at BL, for whom structural magnetic resonance imaging (MRI) and at least one follow-up (FU) 18F-florbetapir PET scan were available. Following recommendations of the Alzheimer’s Disease Neuroimaging Initiative, Ab positivity was defined according to the SUVR in a composite cortical region with a threshold of .1.11 [27].
2.2. PET data acquisition and analysis Details on florbetapir synthesis and image acquisition are given elsewhere (http://adni-info.org). Image analysis was performed using SPM8 (Welcome Department of Imaging Neuroscience, London, UK). Specifically, PET images were rigidly co-registered to concurrently acquired T1 MRI images to calculate a linear transformation (PET-2MRI). Individual MRI images were nonlinearly coregistered to the Montreal Neurological Institute (MNI) space MRI template, and those deformations were used to transform the co-registered PET images into the MNI space. T1 MRI images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid [28]. Then, a region-based voxel-wise partial volume correction (PVC) of PET images was performed using the PETPVC toolbox [29]. A total of 83 individual brain volumes of interests (VOIs) [30] were transferred to individual PET space using the inverse of the above transformations. Out of 83 brain VOIs, 74 VOIs are primarily GM regions. Sixty-two regions with a volume above 1 cm3, i.e. 2 x FWHM, were included in the final analysis [31]. SUVR was calculated as a ratio of regional standardized uptake value (SUV) to SUV in WM that was recommended as reference region for longitudinal 18F-florbetapir PET studies [32–36]. WM mask was defined as described elsewhere [32]. SUVR in whole brain GM (SUVRGM) was calculated as mean value of all 62 GM regions [31]. The set of AD-typical regions consisted of the frontal (8 regions), parietal (3 regions including precuneal/posterior cingulate), temporal (6 regions) lobes and the anterior cingulate region [31], i.e. 36 unilateral VOIs in total. 2.3. Pseudo-temporal image analysis Annual accumulation rate (AAR) of 18F-florbetapir was calculated as (SUVRFU-SUVRBL)/FU time, where SUVRFU and SUVRBL are SUVR of the FU and BL PET, respectively; FU time (years) is the time between two PET scans. An average AAR across all subjects was calculated for each region, the set of AD-typical regions as well as for the whole GM. To explore the variability in regional AAR as a function of total amyloid burden, an across-subject waveform for a given set of regions was calculated [31]. In analogy with previous studies [22,25,26], all groups were pooled together. AAR in the set of AD-typical regions was modeled with a restricted cubic spline with four knots to allow AAR to vary nonlinearly with BL SUVRGM [26,31]. 2.4. Implications for hypothetical anti-amyloid drug trials Two mechanisms of actions of anti-amyloid drugs were considered: (1) the drug attenuates a (further) Ab accumulation and (2) the drug reduces BL Ab burden. For each scenario, we calculated the sample size needed to detect a treatment effect in a hypothetical 24-month placebocontrolled anti-amyloid clinical trial with an 80% power
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T. Guo et al. / Alzheimer’s & Dementia - (2018) 1-10 Table 1 Demographic characteristics of subjects Group
N
FU (years)
Age (years)
Gender (f/m)
Education (years)
CN Early MCI Late MCI All
83 82 81 246
2.06 6 0.23 2.02 6 0.11 2.01 6 0.06 2.03 6 0.15
76.77 6 5.72 *73.10 6 7.27 *72.56 6 7.51 74.16 6 7.10
49/34 30/52 38/43 117/129
16.06 6 2.89 15.90 6 2.84 16.15 6 2.80 16.04 6 2.84
APOE 3 4-pos. (%) 48.19 67.07 y 66.67 60.57 y
MMSE 28.86 6 1.33 27.79 6 1.81 x 27.32 6 1.92 28.00 6 1.81 x
CDR-SB 0.19 6 0.48 1.34 6 0.79 x 1.85 6 0.94 1.12 6 1.02 x
ADAS Cog-13 10.47 6 4.93 14.34 6 5.10 x 18.39 6 7.75 14.35 6 6.84 x
Q2 Abbreviations: FU, follow-up; CN, cognitively normal; MCI, mild cognitive impairment; APOE, apolipoprotein E. NOTE. All significant differences refer to the comparison with a group of normal controls (CN): *P , .001, two-sample t-test; yP , .05, Fisher’s exact test; x P , .001, Mann–Whitney test (MMSE, CDR-SB and ADAS Cog-13 were not normally distributed).
(G Power 3.1 [37]). Furthermore, we examined how differences in BL SUVRGM between treated and placebo groups can impact PET-based end points. 2.5. Statistics Normality of distributions was tested using the D’Agostino-Pearson test and visual inspection of data histograms. Given a normal distribution of imaging variables, a (twotailed) t-test at the significance level of P , .05 was applied. Data are presented as mean 6 standard deviation. If not otherwise noted, statistical analyses were performed using Graph-Pad Prism 6 for Windows (La Jolla, CA). The consistency of regions ranking was tested using random sampling without replacement [38]. A hierarchical cluster analysis [39], two-fold cross-validation, and random sampling tests were conducted where appropriate using SPSS for Windows (version 22.0) and in-house MATLAB codes (MATLAB R2014b, The MathWorks, Natick).
According to the pseudo-trajectory, subjects with SUVRGM between 0.56 and 0.92 had on average higher AARs in AD-typical regions than the mean AAR in the whole GM (0.031 6 0.031) (Fig. 1). They are further referred to as fast accumulators (n 5 134); subjects with SUVRGM below 0.56 and above 0.92 are referred to as slow accumulators (n 5 112). The mean AAR in whole GM as threshold for separation, as well as the cutoffs of 0.56 and 0.92, appeared to be robust in the two-fold crossvalidation test (Supplementary Fig. 1). In fast accumulators, the mean AAR in AD-typical regions (0.038 6 0.033) was significantly higher (P 5 .045, one-sample t-test) than the mean AAR in the whole cohort (0.033 6 0.032). In slow accumulators, AAR in AD-typical regions (0.026 6 0.030) was significantly lower (P 5 .016). The AAR in ADtypical regions was 1.50 (P 5 .002, two-sample t-test) times higher in fast accumulators than that in slow accumulators (Fig. 2A). 3.2. Regional pattern of AAR in different phases
3. Results Demographic data of each group are summarized in Table 1.
In the phases 1 and 2, all 62 regions showed a significant increase in SUVR from BL to FU. Furthermore, all regions
3.1. Pseudo-temporal analysis The groups did not differ in respect to AAR in the set of AD-typical regions or whole GM (P . .25). In the whole cohort (n 5 246), all 62 regions showed a significant (P ,.05, paired-sample t-test) Ab accumulation over 2 years (Supplementary Table 1). Regional AARs were normally distributed (P . .10). Fig. 1 shows AAR in the set of ADtypical regions as a function of BL SUVRGM. Starting at a low BL SUVRGM, AAR increased until BL SUVRGM of 0.70. Afterward, AAR decreased reaching a plateau at 0.95. Consequently, three phases could be defined: BL SUVRGM 0.70 as phase 1, 0.70 , SUVRGM 0.95 as phase 2, and .0.95 as phase 3. These two cutoffs were confirmed as robust by the two-fold cross-validation test (Supplementary Material). Out of 246 subjects, 52, 117, and 77 subjects fall within the phases 1, 2, and 3, respectively. Notably, a very similar trajectory was obtained using CN data only (Supplementary Fig. 3).
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Fig. 1. AAR in the AD-typical regions set as a function of BL SUVRGM. Horizontal red line is the mean AAR in whole gray matter of the whole cohort. Horizontal black line is zero. From left to right, blue lines are cutoffs 0.70 and 0.95 for phase 1 and phase 2, and phase 2 and phase 3, respectively. A background red area indicates fast accumulators with the cutoffs 0.56 and 0.92. Abbreviations: AAR, annual accumulation rate; AD, Alzheimer’s disease; BL, baseline; SUVRGM, standard uptake value ratios in whole gray matter; CN, cognitively normal; MCI, mild cognitive impairment.
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Fig. 2. AAR (A) of fast accumulators and slow accumulators in the AD-typical regions set; **P ,.01 in a two-sample t-test (B) in the set of FAR and AD-typical regions in the phase 3; yyP , .01 in a paired-sample t-test. Abbreviations: AAR, annual accumulation rate; AD, Alzheimer’s disease; FAR, fast accumulating regions.
with AAR above that of the AD-typical region set (n 5 36) were part of the latter set (phase 1: n 5 25, phase 2: n 5 20). Specifically, anterior, medial, and lateral orbital, posterior cingulate, superior parietal, middle, superior, inferior frontal, and middle, inferior temporal regions showed the fastest AARs both in the phase 1 (Supplementary Table 2) and phase 2 (Supplementary Table 3). Thus, the set of AD-typical regions adequately captured the most active Ab-accumulating regions. In the phase 3, nine regions showed no significant increase in SUVR (Table 2). Twenty-five regions had a higher AAR than the AD-typical set. Among them, 10 were not part of that set. Moreover, among the top 10 fast Ab-accumulating regions, only four were part of the AD-typical region set. In contrast to the phases 1 and 2, bilateral lateral occipital lobe, postcentral, precentral, cuneus, and lingual gyrus showed a higher AAR than the set of AD-typical regions. The hierarchical cluster analysis revealed that the left middle frontal, superior frontal, postcentral, lateral anterior temporal lobe, bilateral lateral occipital lobe, and the right cuneus were within the same cluster as the top fast Ab-accumulating 2 regions. Within 5852 ðC77 Þ random samples in the random sampling test, 93.40% fell into the same top seven regions (Supplementary material), confirming the consistency of ranking. Thus, these seven regions were composed into a set of the phase 3’ fast accumulating regions (FARs). The effect size of 0.39 between AAR in FARs and AD-typical regions was not significantly lower (P 5 .299) than the mean value of 30,000 iterations in a two-fold cross-validation test (Supplementary Fig. 2). The pseudo-temporal analysis for this composited set of FARs revealed an additional acceleration in the phase 3 (Supplementary Fig. 4). AAR in these FARs (0.031 6 0.026) was 1.23 (Fig. 2B) and 1.27 times higher than that in the set of AD-typical regions (0.025 6 0.026, P 5 .001) and whole GM (0.024 6 0.026, P 5 .001), respectively. AAR in ADtypical regions was not higher than AAR in whole GM (P 5 .209). The mean BL SUVR in FARs (1.07 6 0.15) was lower (P , .001, paired-sample t-test) than that in
AD-typical regions (1.12 6 0.13). In a comparison between apolipoprotein E (APOE) 3 4 carriers and noncarriers, no significant difference in AAR in the set of AD-typical regions was found (0.032 6 0.032 vs. 0.033 6 0.033). 3.3. Implications for hypothetical anti-amyloid drug trials Table 3 summarizes the sample size per arm needed to detect a treatment effect of a drug that (1) attenuates a (further) Ab accumulation and (2) reduces BL Ab burden. Assuming a linear relationship between Ab accumulation and time within a 2 years’ period [25,26], inclusion of fast accumulators instead of the whole (unselected) cohort would reduce the sample size to treat by around 24% in the first scenario. In the second scenario, inclusion of fast accumulators would reduce the sample size to treat by 61% to 70%. The trial duration in both scenarios could be reduced as well (Supplementary Fig. 5). As compared with AD-typical regions, utilization of FARs as target region in subjects with SUVRGM .0.95 (phase 3) would reduce the sample size to treat by 36% in the first scenario (Table 3). In the second scenario, the sample size would be marginally larger. Assuming that a drug attenuates Ab accumulation by 20%, a 20% lower SUVR, i.e. attenuation, is observed in the treated group by the end of the trial (Fig. 3B), if the treated and placebo groups are matched for BL SUVRGM (Fig. 3A). Figs. 3 and 4 depict how imbalances in BL SUVRGM between treated and placebo groups impact PET-based end points of a hypothetical clinical trial, in an extreme and a regular case, respectively. In the former case, an average BL SUVRGM of a treated and placebo groups correspond to the maximal difference in AAR over the trajectory, that is, the BL SUVRGM of 0.95 and 0.70 or vice versa (Fig. 1). As shown in Fig. 3C, a false 31% ((0.029420.0426))/0.0426) attenua- Q5 tion would be detected even without drug. With a drug, a 125% ((245%2(220%))/(220%)) overestimation of a treatment effect (observed treatment effect 5 (0.0294 ! 0.820.0426)/0.0426 5 245%) would be observed
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Table 2 Ranking of annual accumulation rates in 62 regions, in the set of AD-typical regions and whole gray matter in the phase 3 Region
Mean
Middle frontal gyrus_L*,y,z Superior frontal gyrus_Lz Cuneus_R*,z Lateral remainder of occipital lobe_L*,y,z Postcentral gyrus_L*,y,z Lateral anterior temporal lobe _Lz Lateral remainder of occipital lobe_R*,y,z Middle frontal gyrus_Rz Cuneus_Lz Precentral gyrus_L*,z Superior parietal gyrus_Lz Lateral anterior temporal lobe_Rz Medial orbital gyrus_Lz Precentral gyrus_Rz Postcentral gyrus_Rz Inferiolateral remainder of parietal lobe_Rz Posterior cingulate gyrus _Lz Lingual gyrus_Lz Straight gyrus_Lz Anterior orbital gyrus_Lz Lingual gyrus_Rz Superior parietal gyrus_Rz Lateral orbital gyrus_Lz Inferiolateral remainder of parietal lobe_Lz Middle and inferior temporal gyrus_Rz AD-typical regionsz Superior frontal gyrus_Rz Middle and inferior temporal gyrus_Lz Whole gray matterz Posterior cingulate gyrus_Rz Anterior cingulate gyrus_Lz Inferior frontal gyrus_Lz Anterior orbital gyrus_Rz Posterior orbital gyrus_Lz Inferior frontal gyrus_Rz Fusiform gyrus_Lz Lateral orbital gyrus_Rz Medial orbital gyrus_Rz Posterior temporal lobe_Lz Posterior temporal lobe_R*,y,z Medial anterior temporal lobe_Lz Fusiform gyrus_Rz Posterior superior temporal gyrus_Ry,z Anterior superior temporal gyrus_Lz Straight gyrus_Ry,z Posterior orbital gyrus_Rz Posterior superior temporal gyrus_Lz Putamen_R*,y,z Putamen_L*,y,z Anterior cingulate gyrus_R*,y,z Medial anterior temporal lobe_R*,y,z Parahippocampal and ambient gyri_L*,y,z Parahippocampal and ambient gyri_R*,y,z Insula_R*,z Anterior superior temporal gyrus_R*,y,z Insula_L*,y Caudate nucleus_L*,y,z Amygdala_L*,y
SD
Volume (mm3)
0.031 0.031 0.031 0.031 0.030 0.030 0.030 0.029 0.029 0.029 0.029 0.028 0.028 0.028 0.027 0.027
0.035 0.039 0.034 0.030 0.029 0.038 0.031 0.040 0.035 0.030 0.032 0.041 0.039 0.035 0.034 0.034
44,690 46,866 9224 38,030 23,201 2856 38,789 42,929 9438 28,237 38,141 2842 4780 27,438 25,731 38,214
0.027 0.027 0.027 0.027 0.026 0.026 0.025 0.025
0.036 0.042 0.045 0.047 0.048 0.032 0.066 0.029
7159 12,333 3142 5164 11,970 37,509 3328 37,662
0.025 0.025 0.025 0.024 0.024 0.024 0.024 0.024 0.024 0.023 0.023 0.022 0.022 0.022 0.021 0.021 0.021 0.020 0.019 0.018 0.017 0.017 0.017 0.015 0.015 0.014 0.013 0.012 0.011 0.011 0.010 0.010 0.009 0.008
0.040 13,452 0.026 530,644 0.032 46,158 0.045 13,115 0.026 851,002 0.044 7557 0.037 7747 0.040 14,448 0.055 4807 0.052 4594 0.035 15,294 0.046 3486 0.061 2967 0.039 4962 0.037 48,665 0.030 47,598 0.034 4975 0.053 3479 0.037 12,468 0.048 4033 0.041 2856 0.048 4476 0.048 12,220 0.048 4063 0.050 3957 0.052 7441 0.039 5107 0.047 3595 0.046 3583 0.047 12,323 0.049 4205 0.059 12,275 0.041 3456 0.059 1120 (Continued )
5
Table 2 Ranking of annual accumulation rates in 62 regions, in the set of AD-typical regions and whole gray matter in the phase 3 (Continued ) Region
Mean
SD
Thalamus_L*,y Amygdala_R*,y Thalamus_R*,y Hippocampus_L*,y Hippocampus_R*,y Caudate nucleus_R*,y
0.007 0.005 20.000 20.001 20.002 20.007
0.072 0.054 0.069 0.063 0.068 0.072
Volume (mm3) 5452 1057 5676 1626 1825 3411
Abbreviations: AD, Alzheimer’s disease; SD, standard deviation. NOTE. Regions within the set of AD-typical regions are in bold. “_L” and “_R” indicate a region in the left and right hemisphere, respectively. Marked are significant differences relative to the set of AD-typical regions y, whole gray matter * as well as between baseline and follow-up PET z (P , .05, Q3 paired-sample t-test).
(Fig. 3D). If we reverse the group order, the placebo group would accumulate Ab (SUVR increase) 45% faster than the “treated” group (without drug), i.e., a 45% false enhancement ((0.042620.0294)/0.0294) is produced (Fig. 3E). With a drug, a 180% ((16%2(220%))/(220%)) underestimation of a treatment effect (observed treatment effect 5 (0.0426 ! 0.820.0294)/0.0294 5 16%) would be observed (Fig. 3F). The regular case is arbitrarily chosen as a difference in BL SUVRGM of 0.10. For example, if an average BL SUVRGM of a treated and placebo groups are 0.80 and 0.90 or vice versa, an overestimation of 75% or underestimation of 90% is evident (Fig. 4). Simulations with BL SUVRGM of 0.60 versus 0.70 and 0.60 versus 0.80 are presented in Supplementary Figs. 6 and 7, respectively. In the latter case, an overestimation of 30% and underestimation of 35% is evident. 4. Discussion In the present study, we applied a pseudo-temporal analysis to explore the longitudinal trajectory of Ab accumulation at the predementia stage of AD in the context of clinical trials. In line with previous studies, we found the trajectory to follow an inverted U-shape [22,25,26,36] that continued into a “tail” at the late stage [22]. Thus, three phases of Ab accumulation could be defined: acceleration, deceleration, and a stable phase. As a major finding, we found a significant bias in estimating a potential drug effect, if placebo and treated groups are not matched for BL Ab burden. Given the nonlinear trajectory of Ab accumulation over time, this observation is not unexpected. This suggestion was first made in a preclinical anti-amyloid drug trial [40] but has not been tested in human so far. As shown by our simulations, a difference of 0.25 BL SUVRGM can in the extreme case produce effects that are substantially larger than the expected drug effects. But, even with a difference of 0.10 BL SUVRGM, the drug effect can be greatly overestimated or underestimated, for example, by 75 and 90%, respectively. Notably, a bias is produced not only when average AARs of two groups are
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Q6
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Table 3 Number of subjects per arm needed to detect an Ab-modifying treatment effect in a clinical trial with an 80% power and a two-tailed a 5 0.05
20% attenuation in further SUVR increase 50% attenuation in further SUVR increase 10% decrease in SUVR from baseline 20% decrease in SUVR from baseline
Q4
Whole cohort
Fast accumulators
Slow accumulators
Phase 3
AD-typical
AD-typical
AD-typical
AD-typical
FAR
391 64 30 13
296 49 9 5
552 90 37 14
433 70 12 5
279 46 14 6
Abbreviations: AD, Alzheimer’s disease; FAR, fast accumulating region; SUVR, standardized uptake value ratio.
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Fig. 3. Simulation of a 24 months’ drug trial assuming the mean AAR of the whole cohort (A) and a 20% attenuation of a (further) SUVR increase in the ADtypical region set as a true drug effect (B). Mean BL SUVRGM are assumed to be 0.95 and 0.70 in the treated and placebo groups, respectively (C–D) or other way around (E–F). AARs corresponding to BL SUVRGM 0.70 and 0.95 were 0.0426 and 0.0294 according to the trajectory of AAR (Fig. 1) respectively. Abbreviations: AAR, Annual accumulation rate; SUVR, standard uptake value ratios; BL, baseline; SUVRGM, standard uptake value ratios in whole gray matter; AD, Alzheimer’s disease. FLA 5.5.0 DTD JALZ2634_proof 3 July 2018 2:07 pm ce
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Fig. 4. Simulation of a 24 months’ drug trial assuming the mean AAR of the whole cohort (A) and a 20% attenuation of a (further) SUVR increase in the ADtypical regions set as a true drug effect (B). Mean BL SUVRGM are assumed to be 0.90 and 0.80 in the treated and placebo groups, respectively (C–D) or other way around (E–F). AARs corresponding to BL SUVRGM 0.80 and 0.90 were 0.0388 and 0.0316, respectively, according to the trajectory of AAR (Fig. 1). Abbreviations: AAR, annual accumulation rate; SUVR, standard uptake value ratios; AD, Alzheimer’s disease; BL, baseline; SUVRGM, standard uptake value ratios in whole gray matter.
located on the accelerating or decelerating limb of the trajectory, that is, within the same phase, but also when average AARs of two groups correspond to different phases. For example, if the proportion of phase 1 subjects is substantially larger in the treated group, their on average higher AAR would cause an underestimation of a drug effect. This effect is aggravated by a (consequently) higher proportion of phase
2 subjects in the control group. If the proportion of phase 1 subjects is higher in the control group, an overestimation of a drug effect is produced. Based on the trajectory and BL SUVRGM, we separated the whole sample into subjects with the above-average AAR in whole GM, so called fast accumulators (54% of the whole cohort), and those with a below-average AAR,
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slow accumulators. Thus, from the pathophysiological perspective, fast accumulators are subjects who at this time accumulate Ab faster than an average Ab-positive subject over the period of his/her “Ab-positive life” (estimated to be 36 years, data not shown). The consistency of this intuitive cutoff was supported by a cross-validation test. Consequently, inclusion of fast accumulators in a clinical trial would allow reaching PET-based end points faster as compared with an unselected cohort of subjects. This effect can be also translated into a lower sample size to treat. In a clinical trial with a drug that attenuates a (further) Ab accumulation, inclusion of fast accumulators only would reduce the sample size by around 24%. In a trial with a drug that reduces BL Ab burden, a reduction of 61% to 70% can be achieved. From the practical perspective, data on fast accumulators can be utilized to obtain a preliminary estimation of a drug effect. As the present definition of fast accumulators is based just on a BL PET with SUVRGM being a rather robust approximation of the Ab accumulation stage, there may be some individuals with a low or even negative AAR. Yet, the proposed approach should still be effective at the group level. Regional analyses revealed that in the phases 1 and 2, most active Ab-accumulating regions were within the ADtypical set, confirming the suitability of AD-typical regions as target in anti-amyloid drug trials. In the phase 3, however, a few top FARs were not within the set of AD-typical regions. Namely, the bilateral cuneus and lateral occipital lobe, the left precentral and postcentral regions were among top 10 FARs, along with only four AD-typical regions. Thus, after reaching a certain level of Ab load, Ab accumulation in AD-typical regions continues, but at a lower rate than in the phylogenetically older primary visual and sensorimotor cortices, which at this stage become progressively affected by Ab deposition as well. We further compared the set of phase 3–specific FARs with the established set of ADtypical regions as target in a putative anti-amyloid trial. The results appeared to be dependent on the mechanism of drug action. Namely, should the drug attenuate Ab accumulation, the utilization of the above FARs would enable reducing the sample size to treat by roughly one third. Yet, the sample size would marginally increase, should the drug reduce BL Ab burden. This is due to the fact that the mean BL SUVR in FARs was lower than that in ADtypical regions. Thus, the established set of AD-typical regions does not adequately represent most active Ab-accumulating regions in the third phase of the trajectory. Theoretically, anti-amyloid therapies might be less efficient at this advanced stage of brain amyloidosis, limiting a practical implication of this finding. Nevertheless, recent studies suggested a beneficial effect of anti-amyloid monoclonal antibodies even at the stage of (mild) dementia [10]. Furthermore, there are several ongoing anti-amyloid trials in patients with MCI [17,18], a significant proportion of whom are at the late stage of Ab accumulation. Thus, our results might be of value for these studies.
The study is a subject to several limitations. First, although the cutoffs appeared to be robust according to the cross-validation test, they should be verified prospectively. Second, as is true for all semi-quantitative PET studies with SUVR as outcome measure, we assume that a hypothetical drug does not influence cerebral blood flow. Third, as we included only subjects at the predementia and preclinical disease stage, clinical relevance of the present results is yet to be proved. Yet, like it is true for anti-amyloid studies in general, a basic assumption behind our approach is that attenuation or removal of Ab is beneficial for study participants. Furthermore, we cannot rule out that in some subjects, a significant white matter disease influenced SUVR. However, in line with previous studies [41–43], we found no association between volume of white matter hyperintensities and SUVR in AD-typical regions (data not shown). Finally, we did not include PET scans with a longer than 2 years FU (available for a minority of subjects), potentially precluding a more accurate fit. Yet, a stronger impact (weight) of such data onto the slope is likely to bias it, depending on clinical entity and Ab accumulation stage of those subjects. Moreover, a common duration of antiamyloid trials is up to 2 years. Nonlinear trajectories are more likely over longer durations. Therefore, it is problematic to model linear trajectories on data with a longer FU. 5. Conclusions Several ongoing clinical trials focus on developing preventive drugs at the predementia stage of AD [14–18]. Our findings provide a meaningful reference for planning and analyzing anti-amyloid clinical trials with amyloid PET as biomarker. Acknowledgments Data collection and sharing for this project were 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.; 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
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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 (www.fnih.org). 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. Financial support of the Alzheimer Forschung Initiative e.V. (AFI) is gratefully acknowledged. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jalz.2018.05.013.
RESEARCH IN CONTEXT
1. Systematic review: A search in PubMed and on www.clinicaltrials.gov revealed a number of studies that use amyloid PET as an end point of antiamyloid drug trials. Yet, we found no report about impact of the target brain region or baseline amyloid burden on design (sample size) or analyses of such trials. 2. Interpretation: There are global and regional variations of b-amyloid accumulation rate along the natural, nonlinear trajectory of b-amyloid accumulation. Baseline amyloid burden as measured by PETenables an allocation or “staging” of a given trial subject on the trajectory. This knowledge can be used to reduce the sample size of drug trials and the likelihood of false-positive as well as false-negative drug effects. 3. Future directions: Proposed cutoffs should be validated in prospective multicenter studies. Herewith, thorough standardization of methods for PET data acquisition and analysis across centers is essential.
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