The goal of this study was to examine the sensitivity of resting glucose metabolism (FDG-PET) to detect longitudinal change in both cognitive (ADAS-cog) and functional (FAQ) measurements within AD and MCI patient populations. We used a subset of participants from the ongoing ADNI study, which provided data from multiple timepoints up to 24 months post-baseline. Overall, we found strong evidence that lower baseline FDG-PET consistently predicts subsequent cognitive decline, and that longitudinal FDG-PET is associated with concurrent cognitive decline. These relationships were similar for functional outcomes, although associations were marginal in some cases. Importantly, an analysis of the statistical power of these measures to detect attenuation in decline for a putative AD treatment () revealed that use of FDG-ROIs would require fewer AD subjects to detect attenuation in decline (101 subjects per group for 33% treatment effect) than ADAS-cog (176 subjects) and FAQ (169 subjects). Sample sizes for the MCI group were considerably higher, although FDG-ROIs again required fewer subjects than ADAS-cog. However, for the MCI group, the FAQ had the lowest sample size estimate, perhaps because MCI subjects were close to ceiling on this test, leading to reduced variability and an artifactual increase in statistical power. This suggests that the FAQ may not be optimal for capturing subtle functional change in MCI. Overall, our findings suggest that FDG-ROIs are reliable tool for detecting longitudinal change, and may exceed the power of standard clinical outcome measures.
Our finding that FDG-PET was more consistently associated with ADAS-cog than FAQ may be due to differences in test characteristics. The FAQ differs from the ADAS-cog in that it is not an index of cognitive function but instead a measure the ability to carry out daily functions. Importantly, each test requires input from a person other than the study participant, and this may introduce subjectivity; the FAQ is completed by an informant, whereas the ADAS-cog is administered by a certified tester at the study site. Furthermore, as noted above, FAQ performance may be at ceiling in cognitively normal and MCI individuals, where there is subtle or no impairment and little change over time. 27% of MCI patients (compared with 1% of AD patients) had an FAQ score of 0, indicating little or no functional impairment.
Differing associations we observed for AD and MCI groups provide insight into the sensitivity of baseline and longitudinal FDG-PET in populations with varying levels of disease severity. Consistent with previous findings (Alexander et al., 2002
), AD patients demonstrated lower FDG-PET uptake at baseline () and greater longitudinal decline than MCI or cognitively normal participants across all cognitive tests and all ROIs of interest (). For both AD and MCI groups, lower baseline FDG-ROI measurements predicted greater subsequent decline on the ADAS-cog (; ) and the FAQ (), although the latter association was marginal for the AD group. Greater longitudinal FDG-ROI decline was also associated with concurrent ADAS-cog () and FAQ decline, although the latter was marginal for the MCI group (likely due to the ceiling effect discussed above). Finally, a comparison of FDG-ROIs and ADAS-cog as predictors of FAQ decline revealed that baseline and longitudinal FDG-ROI measures were marginally or significantly associated with FAQ change in all cases. Baseline and longitudinal ADAS-cog measures were associated with FAQ change for the MCI group but not the AD group.
Parameter estimates of FDG-ROI variables were generally higher in the AD group, likely reflecting greater decline in clinical measures for that group. The MCI group, on the other hand, experienced lower levels of decline, and was more variable, with some subjects experiencing decline and others remaining relatively stable. In addition, there was a closer relationship between ADAS-cog decline and FAQ decline for the MCI group compared with the AD group (), indicating strong consistency between these measures despite the reduced variability on the FAQ.
These data extend the findings of previous studies showing the value of FDG-PET for predicting subsequent decline in MCI patients (for example, Chetelat et al., 2003
; Herholz et al., 1999
; Minoshima et al., 1997
) and normal older individuals (de Leon et al., 2001
). Few studies, however, have examined longitudinal concurrent relationships between FDG-PET and cognitive measurements. Existing large multicenter FDG-PET studies have typically focused on cross-sectional analyses and diagnostic accuracy of FDG-PET for AD, rather than longitudinal decline (Herholz et al., 2002
; Mosconi et al., 2008b
). Nonetheless, our findings are consistent with the few existing longitudinal FDG-PET studies, which is not surprising since our ROIs were based in part on coordinates cited in these studies. In voxelwise analyses, declines in AD patients (Alexander et al., 2002
) and in MCI patients who convert to AD (Drzezga et al., 2003
; Fouquet et al., 2009
) were reported in regions that overlapped with ours, as well as frontal regions. While we did identify a frontal ROI that survived our thresholding procedure during ROI generation, the region was eliminated because it was too small to give meaningful results.
Our results are also in agreement with other studies that have carried out power calculations using FDG-PET as an outcome measurement to detect clinical trial treatment effects based on data from normal individuals at genetic risk for AD (Reiman et al., 2001
; Small et al., 2000
) and in AD (Alexander et al., 2002
). The latter study was based on a similar (12 month) followup period, and it reports sample sizes (ranging from 24 – 179, depending on brain region, for a 33% treatment effect) that are less than that required for the cognitive tests they examined. However, our method differs in that we used pre-defined ROIs as opposed to a voxelwise analysis where the results depend on the AD patients in the study. Nonetheless, both studies are in agreement in suggesting that FDG-PET may be a more reliable outcome measure than cognitive tests to detect attenuation of decline in clinical trials of AD patients. For MCI subjects, sample size estimates were considerably larger than the AD group, likely due to greater variability in disease severity. Additional analyses are currently being conducted to directly compare the power of different imaging modalities (i.e., FDG-PET and structural MRI) and different voxel-based, functionally and anatomically defined ROI and whole brain image analysis methods in terms of their estimated power to detect effects of putative AD-slowing treatments in randomized clinical trials.
There are several novel features of this study that improve on previous analyses. First, we used continuous measures of cognition as predictor and outcome variables, rather than binary conversion/nonconversion status as is used frequently in longitudinal studies (de Leon et al., 2001
; Drzezga et al., 2003
). The use of continuous outcome variables measuring cognition (for example, Chetelat et al., 2005
; Herholz et al., 1999
; Jagust et al., 2006
; Mosconi et al., 2008a
) may become increasingly important as clinical trials move to enroll milder patients and measure cognitive change, rather than conversion, as an outcome. Second, our study-independent ROIs differ from other studies that used standard atlas regions (e.g. Talairach, MNI) or regions that result from a voxelwise analysis. The motivation for this approach was that it allowed us to identify critical regions with more precision than is possible using anatomically-defined regions. When hypometabolism occurs in a subregion of a large atlas ROI such as the inferior temporal gyrus, this effect may be diluted when averaged across the entire atlas-based ROI. Furthermore, in studies using voxelwise analyses, the precise location and nature of the differences is dependent on the individuals in the study and the data processing methods used. Spatial normalization procedures are highly variable, and the success of implementing these procedures successfully may introduce variability in the results. A limitation of our approach, however, is that the size and location of the most significant glucose metabolism decline for this group may not be adequately captured by the FDG-ROIs, whereas that would be optimized in a voxelwise analysis.
A final novel feature of this study was the use of the ADNI population, which made it possible to obtain serial cognitive and FDG-PET measurements acquired at a variety of sites and PET scanners up to 24 months post-baseline, which is a quantity of longitudinal data that has not been previously available. Current knowledge about cognitive and neural function in Alzheimer’s disease has been pieced together from much smaller studies, since studies incorporating multiple study sites have been rarely conducted and they are not longitudinal. The results presented here show that it is possible to successfully replicate previous findings using multisite data and to examine models that have not been previously possible due to insufficient sample sizes or study length. In addition, a multisite study raises a number of methodological questions related to image processing and statistical analysis. For example, our method of collapsing across diagnostic groups (AD + MCI subjects; AD + MCI + cognitively normal subjects) was designed to treat disease progression as a continuum as opposed to discrete diagnostic states. For these groups, we found robust relationships between FDG-ROIs and clinical/functional change, perhaps because the sample sizes were largest and the use of continuous variables allowed us to detect subtle relationships at all levels of disease severity.
In summary, we found that baseline and longitudinal FDG-ROI measures are sensitive to change in both the ADAS-cog and a test of functional competence (FAQ), validating the cognitive and functional relevance of longitudinal changes in FDG-PET measurements. Our power analysis indicated that FDG-PET may be a reliable and clinically-useful measure of decline compared with ADAS-cog, particularly in AD patients. Strong associations observed between FDG-PET and ADAS-cog, in particular, indicate that FDG-PET could be useful in clinical trials for selecting subjects who likely to decline for clinical trials, or as an outcome measurement for monitoring clinically-relevant change over time. While the ADAS-cog is frequently used as an outcome measure in clinical trials, the clinical relevance of the small margins of change that are often cited as positive results (Rogers et al., 1998
) is unclear, and it has substantial variability. The results we present here are part of an ongoing analysis of the extensive ADNI dataset that is not yet complete. Future analyses of ADNI data will address the question of role of the ApoE4 allele, which is known to play a role in FDG-PET decline (Reiman et al., 2001
), CSF biomarkers such as Aβ-42 and tau (Haense et al., 2008
), and grey matter volume, which shows substantial reductions longitudinally (Jack et al., 1999
; Mungas et al., 2005