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

Recognition of Emotional States Using Multiscale Information Analysis of High Frequency EEG Oscillations

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell&rsquo;s circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51&ndash;100Hz) of EEG signals rather than low frequency oscillations (0.3&ndash;49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals

A dataset on 24-h electrocardiograph, sleep and metabolic function of male type 2 diabetes mellitus

This dataset provides a collection of 24 h electrocardiograph (ECG) signals, ECG analysis results based on circadian rhythm and R-peak detection, results of sleep quality assessment and clinical indicators of metabolic function acquired from 60 male type 2 diabetes mellitus (T2DM) inpatients. Upon admission, a fasting blood draw and urinary sample were obtained the next morning for routine glucose, lipid and renal panels. Subjects were also involved in investigation for diabetic complications. On the second day of hospitalization, subjects were monitored in hospital for 24-h ECG starting at 10 pm. Subjective sleep quality was assessed by Pittsburgh Sleep Quality Index and a brief sleep log was used to record sleep duration for the studied night. Objective sleep quality and sleep staging were assessed by cardiopulmonary coupling analysis. This dataset could be utilized to conduct conjoint research on the relationships among sleep, metabolic function, and function of cardiovascular system and autonomic nervous system derived from ECG analysis in T2DM, and further investigate the information in ECG signals based on circadian rhythm and physiological status, providing new insights into long term physiological signal processing

Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device

The demand for non-laboratory and long-term EEG acquisition in scientific and clinical applications has put forward new requirements for wearable EEG devices. In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep&rsquo;s circuit, and the high correlation coefficients (&gt;0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 &plusmn; 0.02, Berger effect was observed (p &lt; 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements

Potential options for managing LOX+ ER− breast cancer patients

Overexpression of lysyl oxidase (LOX) is often observed in estrogen receptor negative (ER–) breast cancer patients with bone metastasis. In the present bioinformatics study, we observed that LOX is a prognostic factor for poor progression free survival in patients with ER– breast cancer. LOX overexpression was positively correlated with resistance to radiation, doxorubin and mitoxantrone, but negatively correlated with resistance to bisphosphonate, PARP1 inhibitors, cisplatin, trabectedin and gemcitabine. LOX overexpression was also associated with EMT and stemness of cancer cells, which leads to chemotherapeutic resistance and poor outcome in ER– patients. Although we suggest several therapeutic interventions that may help in the management of LOX+ ER– breast cancer patients, experiments to validate the function of LOX in ER– breast cancer are still needed

Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach

Atrial fibrillation (AF) is an abnormal rhythm of the heart, which can increase heart-related complications. Paroxysmal AF episodes occur intermittently with varying duration. Human-based diagnosis of paroxysmal AF with a longer-term electrocardiogram recording is time-consuming. Here we present a fully automated ensemble model for AF episode detection based on RR-interval time series, applying a novel approach of information-based similarity analysis and ensemble scheme. By mapping RR-interval time series to binary symbolic sequences and comparing the rank-frequency patterns of m-bit words, the dissimilarity between AF and normal sinus rhythms (NSR) were quantified. To achieve high detection specificity and sensitivity, and low variance, a weighted variation of bagging with multiple AF and NSR templates was applied. By performing dissimilarity comparisons between unknown RR-interval time series and multiple templates, paroxysmal AF episodes were detected. Based on our results, optimal AF detection parameters are symbolic word length m = 9 and observation window n = 150, achieving 97.04% sensitivity, 97.96% specificity, and 97.78% overall accuracy. Sensitivity, specificity, and overall accuracy vary little despite changes in m and n parameters. This study provides quantitative information to enhance the categorization of AF and normal cardiac rhythms

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

The brain regions associated with Multi-Scale GV.

<p>Higher glycemic variability of GVC_1–3 (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).</p

Relationships between the fourth glycemic variability cycle (GVC₄) and conventional measures of glycemic control.

<p>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 <i>r²</i> and <i>P</i> values represent the least square model fit.</p