Table 1.
Characteristics of the study cohort.
Figure 1.
The Multi-Scale Glycemic Variability method applied to 3-day continuous glucose monitoring (CGM).
The decomposition is based on the EEMD (Ensemble Empirical Mode Decomposition) technique. (A) The original CGM time-series from a representative control subject (male, 72 years old, HbA1c = 5.2%, SD (standard deviation) = 10.44, MAGE (mean average glycemic excursions) = 31.61) is decomposed into five glycemic variability cycles (GVCs) that are each characterized by fluctuations within a specific frequency band. The bold black lines along the X-axis denote sleep periods defined by actigraphy and patient records. (B) Comparison of raw CGM signals and selected GVCs between the control subject in (A) and a representative patient with type2 DM (male, 62 years old, HbA1c = 9.4%, SD = 71.36, MAGE = 161.85). The shading areas denote sleep for the type 2 DM patient, while the bold black lines along the X-axis denote sleep for the control subject.
Table 2.
Comparisons of glycemic measures and glycemic variability measures between diabetic and control groups y.
Figure 2.
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 GVC2–5, and night GVC3–5.
At night, glycemic variability declined in type 2 DM in GVC2–4 and in controls in GVC3. ‘*’ (P = 0.002) and ‘‡’ (P<0.0001) indicate significant differences between diabetics/day and controls/day; ‘∥∥’ (P<0.0001) indicates significant differences between diabetics/night and controls/night; ‘†’ (P = 0.003) and ‘§’ (P<0.0001) indicates significant differences between diabetics/day and diabetics/night; ‘¶’ (P = 0.028) indicates significant difference between control/day and control/night. All the P values were obtained by ANOVA. Results are presented as mean ± SEM.
Figure 3.
Relationships between the fourth glycemic variability cycle (GVC4) and conventional measures of glycemic control.
The degree of glycemic variability within GVC4 was highly correlated with SD (A) and MAGE (B), but the areas under the curves of GVC4 and GVC5 were greater than SD and MAGE (C). The degree of glycemic variability within GVC4 was highly correlated several markers of glucose control including HbA1c (D). As with GVC4 (the cycle linked with meal intake), the example in this figure, similar relationships were observed for all other GVC cycles. The r2 and P values represent the least square model fit.
Figure 4.
The brain regions associated with Multi-Scale GV.
Higher glycemic variability of GVC1–3 (period 0.5–2 hours) were associated with lower gray matter (GM) volume (red color; both hemispheres in the cingulate gyrus, hippocampal gyrus, middle and inferior temporal gyrus, insular cortex, the left superior parietal gyrus and right fusiform gyrus), greater GM volume (blue color; the bilateral supramarginal gyrus, left angular gyrus and left middle orbitofrontal gyrus), and greater cerebrospinal fluid (CSF) in the right lingual gyrus (green color).
Figure 5.
Group differences of regional GM volumes in left hemisphere and their relationship with Multi-Scale GV.
‘*’ 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 (r2 = 0.26–074, P<0.05). The bar graphs are presented as mean ± SEM.
Figure 6.
Examples of least squares models indicating negative relationships between Multi-Scale GV and regional GM volumes as well as cognitive performance.
(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 r2 for the entire model adjusted for age and sex and group, and P values for the specific effect of Multi-scale GV.