Hyperglycemia is associated with simultaneous alterations in electrical brain activity in youths with type 1 diabetes mellitus

The Influence of COVID-19 in Endocrine Research: Critical Overview, Methodological Implications and a Guideline for Future Designs

The COVID-19 pandemic has changed many aspects of people's lives, including not only individual social behavior, healthcare procedures, and altered physiological and pathophysiological responses. As a result, some medical studies may be influenced by one or more hidden factors brought about by the COVID-19 pandemic. Using the literature review method, we are briefly discussing the studies that are confounded by COVID-19 and facemask-induced partiality and how these factors can be further complicated with other confounding variables. Facemask wearing has been reported to produce partiality in studies of ophthalmology (particularly dry eye and related ocular diseases), sleep studies, cognitive studies (such as emotion-recognition accuracy research, etc.), and gender-influenced studies, to mention a few. There is a possibility that some other COVID-19 related influences remain unrecognized in medical research. To account for heterogeneity, current and future studies need to consider the severity of the initial illness (such as diabetes, other endocrine disorders), and COVID-19 infection, the timing of analysis, or the presence of a control group. Face mask-induced influences may confound the results of diabetes studies in many ways.

The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients

The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.

Experimental increase of blood glucose alters resting state EEG measures of excitation-inhibition balance

NEW FINDINGS

What is the central question of this study? Glucose is the dominant energy source for the brain. However, little is known about how glucose metabolism impacts the coordination of network activity in the brain in healthy adults. What is the main finding and its importance? We demonstrate that both α oscillations and the aperiodic signal components of the resting EEG are modulated by experimentally elevated blood glucose concentrations. Our findings suggest that glucose increases measures associated with excitation-inhibition (E:I) balance, but that the effect on α oscillations might plateau. Understanding the relationship between glucose consumption and E:I balance is crucial to developing our understanding of how metabolism shapes human brain activity.

ABSTRACT

Brain network oscillations can be divided broadly into periodic and aperiodic signal components, which are sensitive to state-dependent changes in network coordination and excitation-inhibition (E:I) balance. We sought to address whether the dominant energy source of the brain, glucose, is implicated in the regulation of network activity and excitability. We conducted an experimenter-blind, crossover study of the effect of blood glucose level (BGL) on the resting EEG frequency spectrum. Participants consumed a glucose drink (75 g glucose) or an equivalent volume of water on two separate visits. EEG data were sampled before and ≤3 h after the drink. We found that the experimentally induced changes in BGL exhibited an inverted U-shaped relationship, with changes in the individual α frequency peak, whereas the slope of the aperiodic signal component of the frequency spectrum showed a positive linear association suggestive of greater excitation. In contrast, peak α power, which is typically associated with top-down inhibitory processes, was negatively associated with changes in BGL. Collectively, these results suggest that high BGL alters brain network coordination in the form of α oscillations and measures associated with E:I balance.

Sex-Specific Metabolic Changes in Peripheral Organs of Diabetic Mice

Diabetes mellitus (DM) can cause systemic metabolic disorders, but the impact of gender on DM-related metabolic changes is rarely reported. Herein, we analyzed metabolic alterations in the heart, liver, and kidney of male and female mice from normal to diabetes via a ¹H NMR-based metabolomics method and aimed to investigate sex-specific metabolic mechanisms underlying the onset and development of diabetes and its complications. Our results demonstrate that male mice had more significant metabolic disorders from normal to diabetes than female mice. Moreover, the kidney was found as the major organ of metabolic disorders during the development of diabetes, followed by the liver and heart. These altered metabolites were mainly implicated in energy metabolism as well as amino acid, choline, and nucleotide metabolism. Therefore, this study suggests that the kidney is the primary organ affected by diabetes in a sex-specific manner, which provides a metabolic view on the pathogenesis of diabetic kidney diseases between genders.

Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes

This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia.

Nocturnal Hypoglycemia Detection using EEG Spectral Moments under Natural Occurrence Conditions

This paper is concerned with a study of hypoglycemia under natural occurrence conditions at night time. Five adolescents with type 1 diabetes (T1D) participated in the experiments. Patients' blood glucose profiles were interpolated to estimate the intermediate values. The proposed system used spectral moments of electroencephalogram (EEG) signals from central and occipital areas as features for detecting hypoglycemia. We found that hypoglycemia could be detected non-invasively using EEG spectral moments. During hypoglycemic episodes, theta moments increased significantly (P<; 0.005) whereas beta moments decreased significantly (P<; 0.001). Based on the optimal network architecture associated with the highest log evidence, the proposed optimal Bayesian neural network resulted in a sensitivity of 82% and a specificity of 52%. In addition, the estimated blood glucose profiles showed a significant correlation (P<; 1e-6) with interpolated blood glucose values in the test set.

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia.

Hyperglycemia and Glucose Variability Are Associated with Worse Brain Function and Seizures in Neonatal Encephalopathy: A Prospective Cohort Study

OBJECTIVES

To investigate how glucose abnormalities correlate with brain function on amplitude-integrated electroencephalography (aEEG) in infants with neonatal encephalopathy.

STUDY DESIGN

Neonates born at full term with encephalopathy were enrolled within 6 hours of birth in a prospective cohort study at a pediatric academic referral hospital. Continuous interstitial glucose monitors and aEEG were placed soon after birth and continued for 3 days. Episodes of hypoglycemia (≤50 mg/dL; ≤2.8 mmol/L) and hyperglycemia (>144 mg/dL; >8.0 mmol/L) were identified. aEEG was classified in 6-hour epochs for 3 domains (background, sleep-wake cycling, electrographic seizures). Generalized estimating equations assessed the relationship of hypo- or hyperglycemia with aEEG findings, adjusting for clinical markers of hypoxia-ischemia (Apgar scores, umbilical artery pH, and base deficit).

RESULTS

Forty-five infants (gestational age 39.5 ± 1.4 weeks) were included (24 males). During aEEG monitoring, 16 episodes of hypoglycemia were detected (9 infants, median duration 77.5, maximum 220 minutes) and 18 episodes of hyperglycemia (13 infants, median duration 237.5, maximum 3125 minutes). Epochs of hypoglycemia were not associated with aEEG changes. Compared with epochs of normoglycemia, epochs of hyperglycemia were associated with worse aEEG background scores (B 1.120, 95% CI 0.501-1.738, P < .001), less sleep-wake cycling (B 0.587, 95% CI 0.417-0.757, P < .001) and more electrographic seizures (B 0.433, 95% CI 0.185-0.681, P = .001), after adjusting for hypoxia-ischemia severity.

CONCLUSIONS

In neonates with encephalopathy, epochs of hyperglycemia were temporally associated with worse global brain function and seizures, even after we adjusted for hypoxia-ischemia severity. Whether hyperglycemia causes neuronal injury or is simply a marker of severe brain injury requires further study.

Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia

Nocturnal hypoglycemia is dangerous that threatens patients because of its unclear symptoms during sleep. This paper is a study of hypoglycemia from 8 patients with type 1 diabetes (T1D) at night. O1 and O2 EEG data of the occipital lobe associated with glycemic episodes were analyzed. Frequency features were computed from Power Spectral Density using Welch's method. Centroid alpha frequency reduced significantly ($\mathrm{P}\lt 0.0001$) while centroid theta increased considerably ($\mathrm{P}\lt 0.01$). Spectral entropy of the unified theta-alpha band rose significantly ($\mathrm{P}\lt 0.005$). These occipital features acted as the input of a Bayesian regularized neural network for detecting hypoglycemic episodes. The classification results were 73% and 60% of sensitivity and specificity, respectively.

High Glucose-Induced Cardiomyocyte Death May Be Linked to Unbalanced Branched-Chain Amino Acids and Energy Metabolism

High glucose-induced cardiomyocyte death is a common symptom in advanced-stage diabetic patients, while its metabolic mechanism is still poorly understood. The aim of this study was to explore metabolic changes in high glucose-induced cardiomyocytes and the heart of streptozotocin-induced diabetic rats by ¹H-NMR-based metabolomics. We found that high glucose can promote cardiomyocyte death both in vitro and in vivo studies. Metabolomic results show that several metabolites exhibited inconsistent variations in vitro and in vivo. However, we also identified a series of common metabolic changes, including increases in branched-chain amino acids (BCAAs: leucine, isoleucine and valine) as well as decreases in aspartate and creatine under high glucose condition. Moreover, a reduced energy metabolism could also be a common metabolic characteristic, as indicated by decreases in ATP in vitro as well as AMP, fumarate and succinate in vivo. Therefore, this study reveals that a decrease in energy metabolism and an increase in BCAAs metabolism could be implicated in high glucose-induced cardiomyocyte death.

High glucose induces the aging of mesenchymal stem cells via Akt/mTOR signaling

It has previously been demonstrated that glucose is important in the process of stem cell aging. However, the mechanisms of cell senescence induced by high glucose (HG) remain to be elucidated. The preliminary study indicated that D-galactose induced mesenchymal stem cell (MSCs) aging. The present study demonstrated, following treatment with 11.0 or 22.0 mM HG for 14 days, that HG significantly promoted MSCs aging and the expression levels of phosphorylated (p-)phosphatidylinositol 3-kinase/protein kinase B (Akt) and p-mammalian target of rapamycin signaling (mTOR) in the HG groups were increased compared with the control group. However, following Akt inhibition with 1.0 or 10.0 nM MK-2206, which is an Akt-specific small molecule inhibitor, the senescence-cell value in the HG group was significantly decreased compared with the control group. These results indicated that HG induced MSCs senescence and this effect was primarily mediated via the Akt/mTOR signaling pathway.

EEG power and glucose fluctuations are coupled during sleep in young adults with type 1 diabetes

OBJECTIVE

To determine the coupling between brain activity and glucose variations during sleep in young adults with type 1 diabetes mellitus (T1DM).

METHODS

27 participants, age 18-30, wore a continuous glucose monitoring system (CGMS) and underwent in-laboratory overnight polysomnography (PSG). Quantitative electroencephalogram (qEEG) metrics were determined from the PSG and included Delta, Theta, Alpha, Sigma, Beta and Gamma Band power at 5-min intervals. Wavelet Coherence Analysis was employed to determine the time varying and frequency specific coupling between glucose and EEG Band power. ANOVA was used to compare differences across fluctuation speeds and EEG bands.

RESULTS

There was a high degree of time varying and frequency specific coupling between glucose variations and EEG power in all EEG Bands during sleep. The average number of intervals of statistically significant coherence was highest for fluctuations periods between 10 and 30min in all Bands (p<0.0001 for each). Mean significant coherence was negatively correlated with hemoglobin A1c, a marker of glycemic control.

CONCLUSIONS

The relationship between glucose and EEG power during sleep is time varying and frequency dependent in young adults with T1DM.

SIGNIFICANCE

Understanding the time varying mutual relationship between glucose changes and brain activity during sleep may have implications for disease management in T1DM.