Dataset Information

Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes.

ABSTRACT:

Objective

Type 2 diabetes mellitus (DM) accelerates brain aging and cognitive decline. Complex interactions between hyperglycemia, glycemic variability and brain aging remain unresolved. This study investigated the relationship between glycemic variability at multiple time scales, brain volumes and cognition in type 2 DM.

Research design and methods

Forty-three older adults with and 26 without type 2 DM completed 72-hour continuous glucose monitoring, cognitive tests and anatomical MRI. We described a new analysis of continuous glucose monitoring, termed Multi-Scale glycemic variability (Multi-Scale GV), to examine glycemic variability at multiple time scales. Specifically, Ensemble Empirical Mode Decomposition was used to identify five unique ultradian glycemic variability cycles (GVC1-5) that modulate serum glucose with periods ranging from 0.5-12 hrs.

Results

Type 2 DM subjects demonstrated greater variability in GVC3-5 (period 2.0-12 hrs) than controls (P<0.0001), during the day as well as during the night. Multi-Scale GV was related to conventional markers of glycemic variability (e.g. standard deviation and mean glycemic excursions), but demonstrated greater sensitivity and specificity to conventional markers, and was associated with worse long-term glycemic control (e.g. fasting glucose and HbA1c). Across all subjects, those with greater glycemic variability within higher frequency cycles (GVC1-3; 0.5-2.0 hrs) had less gray matter within the limbic system and temporo-parietal lobes (e.g. cingulum, insular, hippocampus), and exhibited worse cognitive performance. Specifically within those with type 2 DM, greater glycemic variability in GVC2-3 was associated with worse learning and memory scores. Greater variability in GVC5 was associated with longer DM duration and more depression. These relationships were independent of HbA1c and hypoglycemic episodes.

Conclusions

Type 2 DM is associated with dysregulation of glycemic variability over multiple scales of time. These time-scale-dependent glycemic fluctuations might contribute to brain atrophy and cognitive outcomes within this vulnerable population.

SUBMITTER: Cui X

PROVIDER: S-EPMC3901681 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes.

Cui Xingran X Abduljalil Amir A Manor Brad D BD Peng Chung-Kang CK Novak Vera V

PloS one 20140124 1

<h4>Objective</h4>Type 2 diabetes mellitus (DM) accelerates brain aging and cognitive decline. Complex interactions between hyperglycemia, glycemic variability and brain aging remain unresolved. This study investigated the relationship between glycemic variability at multiple time scales, brain volumes and cognition in type 2 DM.<h4>Research design and methods</h4>Forty-three older adults with and 26 without type 2 DM completed 72-hour continuous glucose monitoring, cognitive tests and anatomica ...[more]

PMID: 24475100

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Project description:Background: Voxel-based morphometry (VBM) has been widely used to investigate structural alterations in amnesia mild cognitive impairment (aMCI). However, inconsistent results have hindered our understanding of the exact neuropathology related to aMCI. Objectives: Our aim was to systematically review the literature reporting VBM on aMCI to elucidate consistent gray matter alterations, their functional characterization, and corresponding co-activation patterns. Methods: The PubMed, Web of Science, and EMBASE databases were searched for VBM studies on aMCI published from inception up to June 2020. Peak coordinates were extracted from clusters that showed significant gray matter differences between aMCI patients and healthy controls (HC). Meta-analysis was performed using seed-based d mapping with the permutation of subject images (SDM-PSI), a newly improved meta-analytic method. Functional characterization and task-based co-activation patterns using the BrainMap database were performed on significant clusters to explore their functional roles. Finally, VBM was performed based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to further support the findings. Results: A total of 31 studies with 681 aMCI patients and 837 HC were included in this systematic review. The aMCI group showed significant gray matter atrophy in the left amygdala and right hippocampus, which was consistent with results from the ADNI dataset. Functional characterization revealed that these regions were mainly associated with emotion, cognition, and perception. Further, meta-regression analysis demonstrated that gray matter atrophy in the left inferior frontal gyrus and the left angular gyrus was significantly associated with cognitive impairment in the aMCI group. Conclusions: The findings of gray matter atrophy in the left amygdala and right hippocampus are highly consistent and robust, and not only offer a better understanding of the underlying neuropathology but also provide accurate potential biomarkers for aMCI.

Project description:In contrast to the idea that hippocampal and amygdala volume loss occur in late phases of neurodegeneration, recent contributions point to the relevance of preexisting structural deficits that are associated with aging and are independent of amyloid deposition in preclinical Alzheimer disease cases. The present work explores GM hippocampal and amygdala volumes in elderly controls displaying the first signs of cognitive decline. 455 subjects (263 females), including 374 controls (228 females) and 81 middle cognitive impairment subjects (35 females), underwent two neuropsychological evaluations (baseline and 18 months follow-up) and a MRI-T1 examination (only baseline). Clinical assessment included Mini-Mental State Examination (MMSE), Clinical Dementia Rating scale, Hospitalized Anxiety and Depression scale, the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological battery and RI-48 Cued Recall Test (RI-48) for episodic memory. Based on their cognitive performance, we defined the controls as stable controls (sCON) and deteriorating controls (dCONs). Analyses included volumetric assessment, shape analyses and linear regressions between GM volume loss and differences in clinical scores between baseline and follow-up. Significant GM volume decrease in hippocampus bilaterally and right amygdala was found in dCON compared to sCON (p < 0.05). Lower right amygdala volumes were measured in mild cognitive impairment (MCI) compared to sCON (p < 0.05). Shape analyses revealed that atrophy was more pronounced at the superior- posterior lateral side of the hippocampus and amygdala. Significant correlations were found between GM volume of left hippocampus and the delta of MMSE and RI-48 scores in dCON and MCI groups separately. Decreased hippocampal and right amygdala volumes precede the first signs of cognitive decline in healthy elderly controls at the pre-MCI state. Left hippocampus volume may also predict short-term changes of overall cognition in these vulnerable cases.

Dataset Information

Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes.

Objective

Research design and methods

Results

Conclusions

Publications

Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes.

Similar Datasets

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