ObjectiveType 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 MethodsForty-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.ResultsType 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.ConclusionsType 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.

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Everyday walking is often interrupted by obstacles and changes in the environment that make gait a highly non-stationary process. This study introduces a novel measure, termed the Step Stability Index (SSI), to quantify stepping stability under non-stationary walking conditions among older adults. This index is based on the ensemble empirical mode decomposition method. We hypothesized that a higher SSI would indicate a more stable gait pattern and could be used to assess fall risk. Accelerometer-derived signals (vertical direction) were analyzed from 39 older adults with a history of 2 or more falls in the past year (i.e., fallers) and 42 older adults who reported no falls in the previous year (i.e., controls) under three walking conditions: baseline walk with and without a harness, and obstacle course with a harness. In each condition, the subjects wore a small, light-weight sensor (i.e., a 3 dimensional accelerometer) on their lower back. The SSI was significantly higher (p≤0.05) in the controls than in the fallers in all three walking conditions. The SSI was significantly (p<0.0001) lower for both the controls and the fallers during obstacle walking compared with baseline walking. This finding is consistent with a less stable step pattern during obstacle negotiation walking. The SSI was correlated with conventional clinical measures of mobility and fall risk (the correlation coefficient, r, ranged from 0.27 to 0.73, p<0.05). These initial findings suggest that SSI, an index based on the ensemble empirical mode decomposition, may be helpful for quantifying gait stability and fall risk during the challenges of everyday walking.