Elsevier

Neurobiology of Aging

Volume 32, Issue 7, July 2011, Pages 1207-1218
Neurobiology of Aging

Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI

https://doi.org/10.1016/j.neurobiolaging.2009.07.002Get rights and content

Abstract

The Functional Activities Questionnaire (FAQ) and Alzheimer's Disease Assessment Scale—cognitive subscale (ADAS-cog) are frequently used indices of cognitive decline in Alzheimer's disease (AD). The goal of this study was to compare FDG-PET and clinical measurements in a large sample of elderly subjects with memory disturbance. We examined relationships between glucose metabolism in FDG-PET regions of interest (FDG-ROIs), and ADAS-cog and FAQ scores in AD and mild cognitive impairment (MCI) patients enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Low glucose metabolism at baseline predicted subsequent ADAS-cog and FAQ decline. In addition, longitudinal glucose metabolism decline was associated with concurrent ADAS-cog and FAQ decline. Finally, a power analysis revealed that FDG-ROI values have greater statistical power than ADAS-cog to detect attenuation of cognitive decline in AD and MCI patients. Glucose metabolism is a sensitive measure of change in cognition and functional ability in AD and MCI, and has value in predicting future cognitive decline.

Introduction

Although cognitive tests are used frequently as outcome measures in clinical trials, there are a number of limitations associated with their use (Visser, 2006). The Alzheimer's Disease Assessment Scale (ADAS-cog) is the standard for measuring decline in clinical trials for mild to moderate AD, but several factors limit the utility of this test in a clinical setting. First, the symptomatic significance of improvement or decline on clinical tests has not been well established, making it difficult to set a standard for what is meant by meaningful improvement in order to evaluate potential disease treatments. For example, there is not strong evidence that ADAS-cog performance correlates with measures that are clinically meaningful for patients, such as performance of everyday tasks and social activities (Winblad et al., 2001). Second, scores are highly variable when measured longitudinally (Doraiswamy et al., 2001), perhaps due to the influence of factors like test administrator biases, practice effects, and time of day of testing. Finally, the neurobiological mechanisms that underlie test performance are not well understood, and this complicates the selection of a clinical test that is aligned with biological indicators of disease state.

An optimal outcome measure, then, would reflect clinically significant patient function, provide reliable measurements with minimal variability, and track a physiologically relevant disease process. FDG-PET is a candidate measure, in that cerebral glucose metabolism is largely a measure of synaptic activity (Sokoloff, 1981) and loss of synapses is an early feature of AD that explains the mechanism of progressive cognitive decline (Terry et al., 1991). Patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) show well-documented patterns of reduced [18F]fluorodeoxyglucose uptake (FDG-PET) at rest in a network of parietal, posterior cingulate, temporal, and frontal regions (Herholz et al., 2002). While there are few existing longitudinal FDG-PET studies in AD and MCI (Alexander et al., 2002, Drzezga et al., 2003), there is some evidence that FDG-PET accurately predicts subsequent decline (Anchisi et al., 2005, Minoshima et al., 1997) and conversion to AD (Chetelat et al., 2003, Drzezga et al., 2003). However, these studies have relatively small sample sizes and have not established strong evidence for longitudinal associations between existing cognitive measures and FDG-PET.

The goal of this study was to examine the potential for use of FDG-PET as a biomarker in clinical trials of putative therapeutic treatments. Validation of FDG-PET for this purpose would require (1) evidence that longitudinal measurements are feasible in a multicenter clinical trial setting, (2) that FDG-PET accurately tracks AD progression, and (3) that FDG-PET provides adequate statistical power (e.g. required number of subjects per treatment arm).

Our FDG-PET measure was mean glucose metabolism uptake in a set of regions of interest (FDG-ROIs) developed a priori and chosen because they have been frequently cited as demonstrating hypometabolism in AD in comparable studies. Our clinical measurements included the ADAS-cog (Rosen et al., 1984) and the Functional Activities Questionnaire (FAQ). We chose the FAQ because it is more closely tied to functionally relevant abilities, such as accomplishing everyday tasks required to live independently (Pfeffer et al., 1982), than the ADAS-cog. The statistical approach employed mixed effects models, which are used frequently to examine factors predicting longitudinal decline in AD by accounting for differences in individual starting points, missing data, and different numbers of visits across participants (Mungas et al., 2005, Pavlik et al., 2006). Here, we used these models to determine whether baseline and longitudinal FDG-PET measurements were associated with decline in ADAS-cog and FAQ. In addition, because of its functional relevance, changes in FAQ scores over successive assessments served as an outcome variable with which to compare the FDG-PET and ADAS-cog to one another as independent predictors. Finally, we compared the statistical power of FDG-ROIs to ADAS-cog and FAQ as potential outcome measurements in a clinical trial of a putative treatment for AD symptoms. We carried out all analyses for an MCI group, as well as an AD group, in order to examine the relationship between FDG-PET and clinical measures within a population that is more diverse and less impaired than AD subjects. Furthermore, since MCI is considered a transitional phase into AD, our analysis for the MCI group allowed us to determine whether FDG-PET is associated with cognitive changes that precede AD diagnosis. For all analyses, we used subject data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), an ongoing multisite imaging study with a large elderly participant population with a range of cognitive impairment.

Section snippets

The Alzheimer's Disease Neuroimaging Initiative (ADNI)

ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies, and non-profit organizations as a $60 million, 5-year public–private partnership. The primary goal of ADNI is to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be

Demographic, clinical, and neuroimaging data

Demographic, clinical, and neuroimaging summary data for each group is summarized in Table 1. Means (±SD) are shown for baseline clinical tests and FDG-ROI values (Table 1B) and for annual change in the same clinical tests and FDG-ROIs (Table 1C).

AD, MCI, and cognitively normal participant groups do not differ in age or gender ratios (Table 1B). However, AD patients had a lower education level than both MCI (t = 3.18, p = 0.002) and cognitively normal (t = 2.89, p = 0.004) groups. In addition, AD

Discussion

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 time-points up to 24 months post-baseline. Overall, we found strong evidence that lower baseline FDG-PET consistently predicts subsequent cognitive decline, and that

Conflict of interest

There are no potential or actual conflicts of interest.

Acknowledgement

This study was supported by NIH grant U01 AG024904.

References (33)

  • J. Ashburner et al.

    Unified segmentation

    Neuroimage

    (2005)
  • K. Herholz et al.

    Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET

    Neuroimage

    (2002)
  • L. Mosconi et al.

    Hippocampal hypometabolism predicts cognitive decline from normal aging

    Neurobiol. Aging

    (2008)
  • G.E. Alexander et al.

    Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's Disease Treatment Studies

    Am. J. Psychiatry

    (2002)
  • D. Anchisi et al.

    Heterogeneity of brain glucose metabolism in mild cognitive impairment and clinical progression to Alzheimer disease

    Arch. Neurol.

    (2005)
  • G. Chetelat et al.

    Mild cognitive impairment: can FDG-PET predict who is to rapidly convert to Alzheimer's disease?

    Neurology

    (2003)
  • G. Chetelat et al.

    FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment

    Neurocase

    (2005)
  • M.J. de Leon et al.

    Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro-2-deoxy-D-glucose/positron-emission tomography (FDG/PET)

    Proc. Natl. Acad. Sci. U.S.A.

    (2001)
  • P. Diggle et al.

    Analysis of Longitudinal Data

    (2002)
  • P.M. Doraiswamy et al.

    The Alzheimer's Disease Assessment Scale: evaluation of psychometric properties and patterns of cognitive decline in multicenter clinical trials of mild to moderate Alzheimer's disease

    Alzheimer Dis. Assoc. Disord.

    (2001)
  • A. Drzezga et al.

    Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer's disease: a PET follow-up study

    Eur. J. Nucl. Med. Mol. Imaging

    (2003)
  • M. Fouquet et al.

    Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer's disease

    Brain

    (2009)
  • R. Gould et al.

    Rate of cognitive change in Alzheimer's disease: methodological approaches using random effects models

    J. Int. Neuropsychol. Soc.

    (2001)
  • C. Haense et al.

    CSF total and phosphorylated tau protein, regional glucose metabolism and dementia severity in Alzheimer's disease

    Eur. J. Neurol.

    (2008)
  • K. Herholz et al.

    Impairment of neocortical metabolism predicts progression in Alzheimer's disease

    Dement. Geriatr. Cogn. Disord.

    (1999)
  • C.R. Jack et al.

    Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment

    Neurology

    (1999)
  • Cited by (0)

    1

    Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu\ADNI). 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. ADNI investigators include (complete listing available at http://www.loni.ucla.edu\ADNI\Collaboration\ADNI_Manuscript_Citations.pdf).

    View full text