Multi-Scale Glycemic Variability: A Link to Gray Matter Atrophy and Cognitive Decline in Type 2 Diabetes

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

Complexity-Based Measures Inform Effects of Tai Chi Training on Standing Postural Control: Cross-Sectional and Randomized Trial Studies

Background: Diminished control of standing balance, traditionally indicated by greater postural sway magnitude and speed, is associated with falls in older adults. Tai Chi (TC) is a multisystem intervention that reduces fall risk, yet its impact on sway measures vary considerably. We hypothesized that TC improves the integrated function of multiple control systems influencing balance, quantifiable by the multi-scale “complexity” of postural sway fluctuations. Objectives: To evaluate both traditional and complexity-based measures of sway to characterize the short- and potential long-term effects of TC training on postural control and the relationships between sway measures and physical function in healthy older adults. Methods: A cross-sectional comparison of standing postural sway in healthy TC-naïve and TC-expert (24.5±12 yrs experience) adults. TC-naïve participants then completed a 6-month, two-arm, wait-list randomized clinical trial of TC training. Postural sway was assessed before and after the training during standing on a force-plate with eyes-open (EO) and eyes-closed (EC). Anterior-posterior (AP) and medio-lateral (ML) sway speed, magnitude, and complexity (quantified by multiscale entropy) were calculated. Single-legged standing time and Timed-Up–and-Go tests characterized physical function. Results: At baseline, compared to TC-naïve adults (n = 60, age 64.5±7.5 yrs), TC-experts (n = 27, age 62.8±7.5 yrs) exhibited greater complexity of sway in the AP EC (P = 0.023), ML EO (P<0.001), and ML EC (P<0.001) conditions. Traditional measures of sway speed and magnitude were not significantly lower among TC-experts. Intention-to-treat analyses indicated no significant effects of short-term TC training; however, increases in AP EC and ML EC complexity amongst those randomized to TC were positively correlated with practice hours (P = 0.044, P = 0.018). Long- and short-term TC training were positively associated with physical function. Conclusion: Multiscale entropy offers a complementary approach to traditional COP measures for characterizing sway during quiet standing, and may be more sensitive to the effects of TC in healthy adults. Trial Registration ClinicalTrials.gov NCT0134036

Group differences of regional GM volumes in left hemisphere and their relationship with Multi-Scale GV.

<p>‘*’ indicates significant differences between the type 2 DM group (white) and controls (grey) in GM volumes (One-Way ANOVA); regional GM volumes in left hemisphere were correlated with Multi-Scale GV for diabetics and/or controls, blue indicates positive correlation, red indicates negative correlation with each GVC, G' = gyrus, ‘#’ indicates we found similar relationship between Multi-Scale GV and GM volumes in the right hemisphere (<i>r²</i> = 0.26–074, <i>P</i><0.05). The bar graphs are presented as mean ± SEM.</p

Day and night Multi-Scale Glycemic Variability in older adults with and without type 2 DM As compared to controls, the type 2 DM group had greater variability during the day in GVC_2–5, and night GVC_3–5.

<p>At night, glycemic variability declined in type 2 DM in GVC_2–4 and in controls in GVC₃. ‘*’ (<i>P</i> = 0.002) and ‘‡’ (<i>P</i><0.0001) indicate significant differences between diabetics/day and controls/day; ‘∥∥’ (<i>P</i><0.0001) indicates significant differences between diabetics/night and controls/night; ‘†’ (<i>P</i> = 0.003) and ‘§’ (<i>P</i><0.0001) indicates significant differences between diabetics/day and diabetics/night; ‘¶’ (<i>P</i> = 0.028) indicates significant difference between control/day and control/night. All the <i>P</i> values were obtained by ANOVA. Results are presented as mean ± SEM.</p

Examples of least squares models indicating negative relationships between Multi-Scale GV and regional GM volumes as well as cognitive performance.

<p>(A) relationship between GVC₂ and GM volume in the left insular cortex; (B) relationship between GVC₁ and GM volume in the right fusiform gyrus; (C) relationship between GVC₂ and GM volume in the left cingulate gyrus; (D) relationship between GVC₂ and overall cognitive performance (composite T score) (diabetics: triangles; controls: circles). We presented <i>r²</i> for the entire model adjusted for age and sex and group, and <i>P</i> values for the specific effect of Multi-scale GV.</p

Characteristics of the study cohort.

<p>Data are presented as mean ± standard deviation (SD). P values were obtained by One-Way ANOVA to compare group means and using Wilcoxon Test for not normally distributed variables. The variables analyzed using Wilcoxon Test are Age, Sex, Race, Education, Hypertension, Microalbumin (urine), Cholesterol-to-HDL ratio, Triglycerides, Total number of Hypoglycemic Events, Average duration of Hypoglycemic Events, and Hematocrit, and other variables were analyzed using One-Way ANOVA. MMSE: Mini-Mental State Examination.</p

Complexity-based and Traditional measures of sway for the Randomized to Tai-Chi and Randomized to usual care groups across visits.

a<p>values provided are mean ± standard deviation. ^bvalues are estimated slope (lower 95% confidence interval, upper 95% confidence interval). ^cp-values for testing for an effect of age indicated by p-age. ^dp-values for comparing mean rates of change among Tai-Chi vs. usual care groups indicated by p-group*time.</p><p>Complexity-based and Traditional measures of sway for the Randomized to Tai-Chi and Randomized to usual care groups across visits.</p