%0 Journal Article %J J Alzheimers Dis %D 2018 %T Longitudinal Changes in Serum Glucose Levels are Associated with Metabolic Changes in Alzheimer's Disease Related Brain Regions. %A Burns, Christine M %A Kaszniak, Alfred W %A Chen, Kewei %A Lee, Wendy %A Bandy, Daniel J %A Caselli, Richard J %A Reiman, Eric M %K Aged %K Alzheimer Disease %K Apolipoprotein E4 %K Blood Glucose %K Brain %K Brain Mapping %K Female %K Fluorodeoxyglucose F18 %K Heterozygote %K Humans %K Longitudinal Studies %K Male %K Middle Aged %K Positron-Emission Tomography %K Prospective Studies %K Radiopharmaceuticals %X

BACKGROUND: The association between longitudinal changes in serum glucose level and longitudinal changes in [18F] Fluorodeoxyglucose-PET (FDG PET) measurements of Alzheimer's disease (AD) risk are unknown.

OBJECTIVE: To investigate whether variation in serum glucose levels across time are associated with changes in FDG PET measurements of cerebral metabolic rate for glucose (rCMRgl) in brain regions preferentially affected by Alzheimer's disease (AD).

METHODS: Participants are a subset of a prospective cohort study investigating FDG PET, apolipoprotein E (APOE) ɛ4, and risk for AD which includes data from baseline, interim, and follow up visits over 4.4±1.0-years. An automated brain-mapping algorithm was utilized to characterize and compare associations between longitudinal changes in serum glucose levels and longitudinal changes in rCMRgl.

RESULTS: This study included 80 adults aged 61.5±5 years, including 38 carriers and 42 non-carriers of the APOE ɛ4 allele. Longitudinal increases in serum glucose levels were associated with longitudinal CMRgl decline in the vicinity of parietotemporal, precuneus/posterior cingulate, and prefrontal brain regions preferentially affected by AD (p < 0.05, corrected for multiple comparisons). Findings remained significant when controlled for APOE ɛ4 status and baseline and advancing age.

CONCLUSIONS: Additional studies are needed to clarify and confirm the relationship between longitudinal changes in peripheral glucose and FDG PET measurements of AD risk. Future findings will set the stage on the use of FDG PET in the evaluation of possible interventions that target risk factors for the development of AD.

%B J Alzheimers Dis %V 62 %P 833-840 %8 2018 %G eng %N 2 %1 http://www.ncbi.nlm.nih.gov/pubmed/29480176?dopt=Abstract %R 10.3233/JAD-170767 %0 Journal Article %J J Alzheimers Dis %D 2016 %T Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers. %A Xu, Lele %A Wu, Xia %A Li, Rui %A Chen, Kewei %A Long, Zhiying %A Zhang, Jiacai %A Guo, Xiaojuan %A Yao, Li %K Aged %K Aged, 80 and over %K Alzheimer Disease %K Aniline Compounds %K Biomarkers %K Brain %K Cognitive Dysfunction %K Disease Progression %K Ethylene Glycols %K Female %K Fluorodeoxyglucose F18 %K Humans %K Image Processing, Computer-Assisted %K Magnetic Resonance Imaging %K Male %K Positron-Emission Tomography %K Predictive Value of Tests %K Psychiatric Status Rating Scales %K Sensitivity and Specificity %X

For patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimer's disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).

%B J Alzheimers Dis %V 51 %P 1045-56 %8 2016 %G eng %N 4 %1 http://www.ncbi.nlm.nih.gov/pubmed/26923024?dopt=Abstract %R 10.3233/JAD-151010