Associations between different FDG PET indices and clinical ratings in cognitively unimpaired older adults. (31st December 2021)

• Record Type: Journal Article
• Authors: Protas, Hillary D.; Chen, Kewei; Chen, Yinghua; Luo, Ji; Raichle, Marcus E.; Morris, John C.; Benzinger, Tammie L.S.; Goyal, Manu S.; Vlassenko, Andrei G.; Reiman, Eric M.; Su, Yi
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.055696
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: FDG PET measurements of cerebral glucose hypometabolism in brain regions that are preferentially affected by Alzheimer's disease (AD) are known to be associated with clinical decline. However, commonly used FDG PET indices such as the standard uptake value ratio (SUVR) lack the sensitivity to detect early metabolic changes associated with cognitive decline. In this study we examine whether the full quantitative FDG net uptake rate (Ki) can track metabolic change in cognitive unimpaired elderly (CU) and compare that with indices that do not require full dynamic acquisition including the AD‐related hypometabolic convergence index (HCI), the AD‐related statistical region of interest (sROI), and regional SUVRs from known AD affected brain regions, e.g. precuneus (SUVRpre ), posterior cingulate (SUVRpc ) and hippocampus(SUVRhipp ). Method: Dynamic FDG PET images were acquired between 0 and 60 min after intravenous radiotracer administration in 75 subjects (all with CDR=0, age 67±7 years) from Washington University cohort. Full quantification of FDG PET was achieved using an image‐derived arterial input function to estimate hippocampal Ki. A Patlak model with reference region was used to estimate the hippocampal relative uptake rate Ri. HCI, sROI, SUVRpre, SUVRpc, SUVRhipp were also estimated using the last 30 minutes of data from each dynamic scan. Using Pearson correlation (r), we examined MMSE association with Ki, Ri, SUVRpre, SUVRpc, SUVRhipp, HCI and sROI each separately. Using statistical Steiger test, we compared the difference of these correlation strengths. Result: Lower hippocampal Ki and AD‐related sROI FDG measurements were significantly correlated with lower MMSE in CU older adults (r=0.31, p=0.007, r=0.27, p=0.019), but not Ri, HCI, SUVRpre, SUVRpc, and SUVRhipp (r=0.09, ‐0.06, 0.13, 0.12, 0.03, p>0.28). MMSE correlations with hippocampal Ki and AD‐related sROI measurements were not significantly different (p=0.74, Steiger test). Among the FDG indices that did not require full dynamic scan, sROI also had the strongest correlation with Ki (r=0.39, p<0.001). Conclusion: Among the tested FDG PET indicators of AD, lower Hippocampal Ki and AD‐related sROI measurements were associated with lower clinical ratings in CU older adults. Additional studies are needed to clarify the extent to which they predict subsequent cognitive decline.