Hypoglycemia Research Articles

Dumping syndrome: Update on pathophysiology, diagnosis, and management.

Dumping syndrome is a complex of gastrointestinal symptoms originally studied in peptic ulcer surgery patients. At present, it is most prevalent in patients who underwent bariatric, upper gastrointestinal cancer or anti-reflux surgery. The symptom pattern comprises early and late dumping symptoms. Several management options have been reported including nutritional, pharmacological and surgical approaches. In this study, we aimed to review the current evidence on dumping syndrome definition, diagnosis and treatment, including preliminary data from newer pharmacological studies. Current pathophysiological concepts and analyses of provocative tests has led to a clear definition of dumping syndrome, including both early and late dumping symptoms. The term postbariatric hypoglycemia represents a limited focus on late dumping only. The diagnosis relies on recognition of symptoms and signs in a patient with appropriate surgical history; and can be confirmed by provocative testing or registration of spontaneous hypoglycemia. The initial treatment focuses on dietary intervention, to which meal viscosity enhancers and/or the glycosidase inhibitor acarbose can be added. The most effective therapy is the use of short- or long-acting somatostatin analogues, which is however expensive and entails side effect issues. In case of refractory hypoglycemia, diazoxide or SGLT2 inhibitors can be considered, based on limited evidence. In refractory patients, continuous enteral feeding or (rarely) surgical reinterventions have been advocated, although not supported by solid evidence. Therapies under current evaluation include the broad-spectrum somatostatin analogue pasireotide, GLP-1 receptor antagonists, GLP-1 receptor agonists and administration of stable forms of glucagon are currently under study. Dumping syndrome is a well-defined but probably under-diagnosed complication of upper gastrointestinal, especially bariatric, and surgeries. Diagnosis is confirmed by a provocative test and incremental therapies starting with diet, adding meal viscosity enhancers or glycosidase inhibitors and adding somatostatin analogues in refractory cases. A number of emerging therapies targeting intestinal propulsion, peptide hormone effects and hypoglycemic events are under evaluation.

Basal insulin analogues in type 1 diabetes – does one size fit all? Lessons from the HypoDeg trial based on single-patient outcomes

AimsIn the HypoDeg trial, a randomised crossover trial in people with type 1 diabetes prone to nocturnal severe hypoglycaemia, treatment with insulin degludec (IDeg) resulted in significantly reduced rates of nocturnal symptomatic hypoglycaemia and all-day severe hypoglycaemia compared to insulin glargine U100 (IGlar). We analysed HypoDeg data at a single-patient level to assess the proportion of participants to whom the overall result applied. MethodsPost hoc analysis using single-patient data (n=133) on nocturnal symptomatic hypoglycaemia, all-day severe hypoglycaemia and HbA1c. The outcome was classified as superior with IDeg, superior with IGlar, or similar between treatments for the three outcomes. We also assessed a composite endpoint based on these outcomes to evaluate the overall superior treatment for each participant. ResultsA higher percentage (38%) had IDeg as superior treatment compared to IGlar (15%) for nocturnal symptomatic hypoglycaemia (p < 0.001). There was no difference between the treatments for all-day severe hypoglycaemia or HbA1c. A higher percentage (44%) had a composite endpoint favouring IDeg compared to IGlar (16%) (p < 0.001). ConclusionsThe superiority of IDeg was confirmed in many of the participants. However, a minority had IGlar as superior treatment, underscoring the need for individualised basal insulin therapy in clinical practice.

Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes.

Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturnal glucose in individuals with type 1 diabetes. Methods: To identify glucose patterns, we applied a hierarchical clustering algorithm to real-time continuous glucose monitoring data obtained from 570 adult patients. Machine learning algorithms with or without pre-clustering were used for modeling. Results: Eight clusters without nocturnal hypoglycemia and six clusters with at least one low-glucose episode were identified by the cluster analysis. When forecasting time series without hypoglycemia with a prediction horizon (PH) of 15 or 30 min, gradient boosting trees (GBTs) with pre-clustering and random forest (RF) with pre-clustering outperformed algorithms based on medoids of time series clusters, the Holt model, and GBTs without pre-clustering. When forecasting time series with low-glucose episodes, a model based on the pre-clustering and GBTs provided the highest predictive accuracy at PH = 15 min, and a model based on RF with pre-clustering was the best at PH = 30 min. Conclusions: The results indicate that the clustering of glucose dynamics can enhance the efficacy of machine learning algorithms used for glucose prediction.

Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes.

Children with type 1 diabetes (T1D) frequently have nocturnal hypoglycemia, daytime physical activity being the most important risk factor. The risk for late post-exercise hypoglycemia depends on various factors and is difficult to anticipate. The availability of continuous glucose monitoring (CGM) enabled the development of various machine learning approaches for nocturnal hypoglycemia prediction for different prediction horizons. Studies focusing on nocturnal hypoglycemia prediction in children are scarce, and none, to the best knowledge of the authors, investigate the effect of previous physical activity. The primary objective of this work was to assess the risk of hypoglycemia throughout the night (prediction horizon 9 h) associated with physical activity in children with T1D using data from a structured setting. Continuous glucose and physiological data from a sports day camp for children with T1D were input for logistic regression, random forest, and deep neural network models. Results were evaluated using the F2 score, adding more weight to misclassifications as false negatives. Data of 13 children (4 female, mean age 11.3 years) were analyzed. Nocturnal hypoglycemia occurred in 18 of a total included 66 nights. Random forest using only glucose data achieved a sensitivity of 71.1% and a specificity of 75.8% for nocturnal hypoglycemia prediction. Predicting the risk of nocturnal hypoglycemia for the upcoming night at bedtime is clinically highly relevant, as it allows appropriate actions to be taken-to lighten the burden for children with T1D and their families.

Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage using various novel machine learning (ML) algorithms. Individual interviews with diabetic patients were conducted over 6 months to acquire information regarding their experience with hypoglycemic episodes. This information included warning signs of hypoglycemia, such as incoherent speech, exhaustion, weakness, and other clinically relevant cases of low blood sugar. Researchers used supervised, unsupervised, and hybrid techniques. In supervised techniques, researchers applied regression, while in hybrid classification ML techniques were used. In a 5-fold cross-validation approach, the prediction performance of seven models was examined using the area under the receiver operating characteristic curve (AUROC). We analyzed the data of 290 diabetic patients with low blood sugar episodes. Our investigation discovered that gradient boosting and neural networks performed better in regression, with accuracies of 0.416 and 0.417, respectively. In classification models, gradient boosting, AdaBoost, and random forest performed better overall, with AUC scores of 0.821, 0.814, and 0.821, individually. Precision values were 0.779, 0.775, and 0.776 for gradient boosting, AdaBoost, and random forest, respectively. AdaBoost and Gradient Boosting models, in particular, outperformed all others in predicting the probability of clinically severe hypoglycemia. These techniques enable community health nurses to predict hypoglycemia at an early stage and provide the necessary therapies to patients to prevent complications resulting from hypoglycemia.

Liraglutide for the Treatment of Severe Hypoglycemia Following Total Pancreatectomy and Islet Autotransplantation

Abstract Total pancreatectomy and islet autotransplantation (TPIAT) is an effective treatment for chronic and recurrent acute pancreatitis, and it provides a significant potential additional benefit of insulin independence. Spontaneous hypoglycemia in the absence of insulin therapy following TPIAT is a recognized complication, which has been attributed to lack of protective glucagon responses to hypoglycemia, following intrahepatic islet autotransplantation. We describe the use of liraglutide to treat spontaneous hypoglycemia following TPIAT. Continuous glucose monitoring was used to identify timing of hypoglycemia in relation to meals and monitor treatment effect. Liraglutide has been used for management of hypoglycemia following bariatric surgery, but, to our knowledge, this is the first application of its effective use to treat spontaneous severe hypoglycemia following TPIAT.

8202 Retrospective Analysis of Mixed-Meal Testing in People with Suspected Spontaneous Post-Prandial Hypoglycemia

Abstract Disclosure: N. Sankaran: None. R. Jalleh: None. C. Bischoff: None. C. Marathe: None. Introduction: While there is no consensus on the optimal confirmatory test for postprandial hypoglycemia, guidelines suggest evaluation with a mixed meal test that is most likely to provoke symptoms of hypoglycemia as nominated by the patient. However, studies evaluating post-bariatric surgery hypoglycemia indicate that meals with a lower carbohydrate content are less likely to be associated with postprandial hypoglycemia. It is hypothesized that a greater carbohydrate load may result in an excessive second-phase insulin response resulting in hypoglycemia. We evaluated the outcomes of consecutive mixed meal tests in individuals with suspected spontaneous postprandial hypoglycemia and the relationship with the percentage carbohydrate content of the test meal. Methods: We retrospectively analysed the data of 103 unselected patients who were referred to undergo a 5-hour mixed meal test following an overnight fast, between the years 2010 - 2023 at the Royal Adelaide Hospital. Correlations were determined using a linear regression model and Student’s t-test was used to compare the mean carbohydrate meal content in those with and without postprandial hypoglycemia. Outcomes: Outcomes that were measured included post-meal blood glucose, plasma insulin, plasma C-peptide levels and the macronutrient composition of the meal. Subjective reporting of symptoms during the test were also noted and correlated with the symptoms included in the Edinburgh Hypoglycemia Score. Results: 33/103 participants were excluded as they had previously undergone islet cell transplant or bariatric surgery. Out of the remaining 70 participants referred for confirmatory MMT testing, 51 were female and 19 were male. The ages of the participants ranged from 16 to 74, with the median age of 40 years. 9 participants (6 females and 3 males) had abnormal results with glucose below 3.0mmol/L. 34 participants experienced hypoglycemia with glucose levels recorded below 4.0mmol/L during the test. There was no difference in carbohydrate content consumed (61.1g, P=0.30) or percentage carbohydrate (64.5%, P=0.17) in the group without postprandial hypoglycemia compared with those with postprandial hypoglycemia (carbohydrate content 67.9 g, 69.6% carbohydrate). No correlation could be identified between the percentage of carbohydrate content of the meal with the glucose nadir (P=0.75), peak insulin concentration (P=0.99) or peak C-peptide concentration (P=0.40). The percentage of carbohydrate content of the meal was neither associated with hypoglycemia (P=0.17) or symptoms (P=0.41). Conclusion: On mixed meal testing, postprandial symptoms and hypoglycemia does not correlate with either carbohydrate load or the percentage carbohydrate content of the nominated meal. Presentation: 6/1/2024

7884 Enigmatic Spontaneous Hypoglycemia During Prolonged Complete Remission in a Child with Autoantibody-Positive Diabetes: Possible Role of Coincidental GYS2 Heterozygous Mutation

Abstract Disclosure: S.S. Sundar: None. S. Shankar: None. M.S. Vaishnav: None. L. Lekkala: None. S.S. Srikanta: None. R.B. V: None. T.-. Kamala: None. C. Siddlingappa: None. C.D. Mandyam: None. P. Ravikumar: None. K. M: None. V. Nath: None. Introduction: Complete remission duration in type 1 diabetes is highly variable (few weeks to years, average 7 months in children). Non-diabetes hypoglycemia in children occurs due to metabolic/ endocrine/ congenital disorders [insulin-mediated (hyperinsulinism) or insulin-independent]. Clinical Case 2021 Sep T1D Diagnosis 13-year boy FTLSCS; 2.86 kg. Fatigue, sleepy, early morning headaches, weight loss, polyuria, polydipsia, headache. FPG 320 mg/dL; PPPG 596 mg/dL; Ketonuria++++; C Peptide fasting 0.42 ng/ml (1.1-4.4); TSH 2.44 (0.7-6.4); S Cortisol 27.2 mcg/dL (5-23); US abdomen normal. Treatment: IV fluids/ insulin; Regular 10 u tid; Glargine 20 u Family history: Diabetes adult onset++++; Hypothyroidism++ 2021 Oct T1D remission Hypoglycemia even with Glargine 2 U/day. Insulin discontinued. FPG 88; PPBG 159 2021 Dec Endocrinology work-up Weight 41.9 kg; Height 154 cm BMI 18.0 kg/m2 FPG 88; HbA1c 6.1 % (4.8-5.6); C-peptide fasting 1.26; Blood ketone 3.35 (0.2-2.8); TSH 2.66 Autoantibodies Islet cell IFL Positive; IA2 &amp;gt;280 IU/mL (&amp;lt;28); GAD65 0.5 U/mL (&amp;lt;10); TPO negative 2021-2023 T1D prolonged remission FPG 90; PPBG 140; HbA1c 5.5 2023 Nov 5 Hypoglycemia, coma and seizures Limited breakfast; skipped lunch; vomiting. 1.15 am: Unconscious seizures. CGM unable to record. Lab BG 39; Fingerstick BG 36. IV dextrose 25%. 4 am: Regained consciousness. BG 100 to 200; HbA1c 6.4%. Insulin fasting 8.9 µU/ml (2.6-24.9); Insulin post prandial 21.6; C Peptide fasting 2.0. S Cortisol 8 am 7.6. Imaging: MRI brain abdomen pelvis normal. EEG normal 2023 Nov 9 Hyperglycemia to hypoglycemia Holiday morning: BG unexpected 306 CGM. 8.30 pm: Symptomatic hypoglycemia with sweating and light headedness; BG 52 fingerstick. IV 25% dextrose. BG 140 “Coincidental” spontaneous hypoglycemia No intake of insulin/ oral hypoglycaemics / traditional medicines. 10 pm to 3 am low CGM BG upto 60; asymptomatic. FPG 103; PPPG 146; HbA1c 6.3 %; Insulin Fasting 2.85, 3.79; C-peptide fasting 1.18, 0.94; S Cortisol 8 am 12.5; TSH 2.66 Autoantibodies Islet cell IFL Positive; IA2 &amp;gt;280 IU/mL (&amp;lt;28); GAD65 0.5 U/mL (&amp;lt;10); ZnT8 50 AU/mL (&amp;lt;10); Insulin antibodies 3.7 (&amp;lt;10). TPO negative Whole genome sequencing No monogenic/ maturity onset/ mitochondrial diabetes mutations. Hepatic glycogen synthase 2 heterozygous mutation (c.1602+1G-T) detected (Glycogen Storage Disease Type 0 Autosomal recessive: Ketotic hypoglycemia after prolonged fasting and postprandial hyperglycemia and hyperlactatemia in infancy or early childhood). Clinical Lessons Coincidental double metabolic glucose dyshomeostasis pathology? Susceptibility of heterozygous carriers of GYS2 mutations to glucose metabolism abnormalities (hypoglycemia and hyperglycemia) has been reported. Presentation: 6/2/2024

Evaluating Latent Adrenal Insufficiency in Elderly Patients with Nocturnal Hypoglycemia: A Retrospective Observational Study

ObjectiveTo evaluate the association between nocturnal hypoglycemia (NH) and latent adrenal insufficiency (LAI) among elderly aged >65 years. MethodsThis propensity-matched, retrospective observational study was conducted in an outpatient setting, assessing 1238 elderly aged >65 years between November 2017 and February 2020. Of them, 430 patients with unassessed LAI were monitored for NH using continuous glucose monitoring (CGM) with Freestyle Libre Pro®. The primary outcome was the combined prevalence of newly diagnosed and suspected LAI in patients with NH. ResultsAfter propensity score matching, 192 patients were included (96 each in the NH and non-NH group). The overall incidence rate of NH was 28.8% (124/430). The association of NH with newly diagnosed and suspected LAI was significantly higher in the NH group (26.04%, 50/192) than in the non-NH (12.5%, 24/192) (odds ratio: 3.26; 95% confidence interval: 2.59–9.06; P < 0.001). In the NH, compared with patients without LAI, those with new-diagnosed LAI had a higher incidence of hypoglycemia ConclusionsDiagnosing and treating LAI in the elderly with NH may prevent complications, including fatal diseases, and extend their life expectancy.

Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus.

To develop and test different machine learning algorithms for predicting nocturnal hypoglycaemia in patients with type 2 diabetes mellitus. A retrospective study. We collected data from dynamic blood glucose monitoring of patients with T2DM admitted to the Department of Endocrinology and Metabolism at a hospital in Shanghai, China, from November 2020 to January 2022. Patients undergone the continuous glucose monitoring (CGM) for ≥ 24 h were included in this study. Logistic regression, random forest and light gradient boosting machine algorithms were employed, and the models were validated and compared using AUC, accuracy, specificity, recall rate, precision, F1 score and the Kolmogorov-Smirnov test. A total of 4015 continuous glucose-monitoring data points from 440 patients were included, and 28 variables were selected to build the risk prediction model. The 440 patients had an average age of 62.7 years. Approximately 48.2% of the patients were female and 51.8% were male. Nocturnal hypoglycaemia appeared in 573 (14.30%) of 4015 continuous glucose monitoring data. The light gradient boosting machine model demonstrated the highest predictive performances: AUC (0.869), specificity (0.802), accuracy (0.801), precision (0.409), recall rate (0.797), F1 score (0.255) and Kolmogorov (0.603). The selected predictive factors included time below the target glucose range, duration of diabetes, insulin use before bed and dynamic blood glucose monitoring parameters from the previous day. No Patient or Public Contribution.

Perspectives and Preferences of People with Type 2 Diabetes for the Attributes of Weekly Insulin.

Daily insulin administration can be burdensome for people with type 2 diabetes (PwT2D) and can impact treatment adherence. This study investigated preferences for once-weekly, long-acting basal insulin for treatment of PwT2D. An online discrete-choice experiment was administered to PwT2D in the USA. Qualitative interviews informed the selection of six attributes: reduction in A1c level after 6months, amount of time spent in optimal blood sugar range each day, number of serious low blood sugar events, number of nighttime low blood sugar events, change in weight because of the insulin over 6months, and frequency of administration. Each participant completed eight questions offering a choice between two long-acting insulins; questions varied according to an experimental design. A fixed treatment choice question asked about preferences for daily versus weekly insulin, holding other treatment features constant. Data were analyzed using random-parameters logit models, and heterogeneity was explored through subgroup analyses. Four hundred sixty-six PwT2D completed the survey (mean age, 57; mean A1c, 7.5%; 59.0% female); 33.3% of these were currently on a basal/bolus regimen, 34.3% used basal only, and 32.4% were insulin naive. Respondents placed the most importance on avoiding a 10-pound weight change and equal importance on the largest change in the number of serious and nighttime low blood sugar events per year and achieving the longest time in range included in the choice questions. There was significant heterogeneity in preferences by experience: insulin-naive respondents had stronger preferences for scheduled and flexible weekly insulin over daily insulin; 67.6% preferred flexible weekly over daily insulin, all else being equal. PwT2D valued insulin efficacy and reducing treatment-related adverse events, with heterogeneity in the relative importance of administration frequency. All else being equal, respondents preferred weekly over daily basal insulin. These findings provide insights into the preferences of PwT2D considering weekly long-acting insulin.

Major cardiovascular and neurological events among diabetic patients with severe hypoglycaemia at a tertiary care hospital in Bangladesh

Background: Cardiovascular disease (CVD) is a major cause of death and disability among people with diabetes mellitus (DM). Hypoglycemia is a potential risk factor to augment life threatening CVD. This study intended to find out impact of severe hypoglycemia in adverse cardiovascular and neurological events among patients with DM. Methods: This cross-sectional study was conducted among diabetic patients with severe hypoglycemia (plasma glucose &lt;70 mg/dl or 3.9 mmol/l) admitted in BIRDEM General Hospital from March 2014 to April 2015. Patients were categorized into two groups depending on blood glucose values (group A &lt;2.8 and group B e”2.8) for analytical purpose. Major cardiac events including acute myocardial infarction (MI), non ST elevated MI or arrhythmia were observed along with neurological outcome including acute stroke, hypoglycemic encephalopathy and convulsion by both relevant clinical examinations and investigations as appropriate. Data were collected at discharge or death if any. Results: Among the 311 patients, 62% were in the age group of 51 to 60 years with mean age 49.02 years. Mean duration of DM was 8.5 (±5.4) years and in 85.5% cases HbA1c was e”7%. Majority was on insulin (79.41%); predominantly on premixed (33.5%) and self mixed (35.7%) regime. Meal related factors (p &lt;0.001) e.g., delayed or missed meal, insulin related factors (p&lt;0.001) e.g., miscalculation, overdosing were significant predisposing factors for developing hypoglycemia. Multivariate analysis revealed frequency of hypoglycemia was more in diabetic nephropathy (p&lt;0.01), chronic liver disease (p=0.01), gastroparesis (p=0.01), cognitive impairment (p&lt;0.001), established CVD (p=0.02) and adrenal insufficiency (p&lt;0.01). Nocturnal hypoglycemia (33.80% ) and hypoglycemic unawareness (44.23%) was also observed among the participants. Cardiovascular events were found in 98 (31.55%) individuals which included any form of arrhythmia (p=0.02), non ST elevated MI (p=0.08) and ST elevated MI (p&lt;0.008). Among the neurological events (n=67, 21.54%), hypoglycemic encephalopathy (p&lt;0.02) and acute stroke (p=0.01) were significant. Seizure happened in 15 individuals (p=0.53). Nine patients died (death rate 2.89%). Predominant cause of death was acute MI (6, 66%) and hypoglycemic encephalopathy (3, 33.33%). Conclusion: The major finding of this study suggest that severe hypoglycemia is strongly associated with fatal cardiovascular and neurological events including MI, acute stroke and hypoglycemic encephalopathy in diabetic patients predominantly treated with insulin. Mortality was found in 2.89% participants. Co morbidities including renal disease, liver disease, gastroparesis, cognitive impairment increased the vulnerability of developing severe hypoglycemia. In most of the cases, lifestyle factors are the precipitating causes of hypoglycemia, which are readily recognizable and easily modifiable. Therefore, appropriate diabetic education and periodic reevaluation of patient’s knowledge, attitude and practice towards hypoglycemia is of utmost importance to prevent this common but life-threatening complication. BIRDEM Med J 2024; 14(3): 118-123

HIT4HYPOS Continuous Glucose Monitoring Data Analysis: The Effects of High-Intensity Interval Training on Hypoglycemia in People With Type 1 Diabetes and Impaired Awareness of Hypoglycemia

Aims: To assess the impact of high-intensity interval training (HIIT) on hypoglycemia frequency and duration in people with type 1 diabetes (T1D) with impaired awareness of hypoglycemia (IAH). Methods: Post hoc analysis of four weeks of continuous glucose monitoring (CGM) data from HIT4HYPOS; a parallel-group study comparing HIIT + CGM versus no exercise + CGM in 18 participants with T1D and IAH. Results: When compared with those participating individuals not exercising, HIIT did not increase total hypoglycemia frequency, THypo(L1) 1.44 [1.00-2.77]% versus 2.53 [1.46-4.23]%; P = .335, THypo(L2) 0.25 [0.09-0.37]% versus 0.45 [0.20-0.78]%; P = .146, HIIT + CGM versus CGM, respectively, rate ( EventPerWeekHypo 5.30 [3.35-8.27] #/week vs 7.45 [3.54-10.81] #/week, P = .340) or duration ( DurationHypo 33.33 [27.60-39.10] minutes vs 39.56 [31.00-48.38] minutes; P = .219, HIIT + CGM vs CGM, respectively). There was a reduction in nocturnal hypoglycemia in those who carried out HIIT, THypo(L1) 0.50 [0.13-0.97]% versus 2.45 [0.77-4.74]%; P = .076; THypo(L2) 0.00 [0.00-0.03]% versus 0.49 [0.13-0.74]%; P = .006, HIIT + CGM versus CGM, respectively. Conclusions/interpretation: Based on CGM data collected from a real-world study of four weeks of HIIT versus no exercise in individuals with T1D and IAH, we conclude that HIIT does not increase hypoglycemia, and in fact reduces exposure to nocturnal hypoglycemia.

Public Perception of the Food Estate Program of Cassava Cultivation as a Strategic Logistic Reserve in Realizing Regional Food Security in Gunung Mas Regency, Central Kalimantan

Gestational Diabetes Mellitus (GDM) is a collection of symptoms that arise in pregnant women caused by an increase in blood glucose levels due to a progressive decrease in insulin secretion, variables that influence the incidence of gestational diabetes include age, history of diabetes mellitus, can occur at any time, but this disease usually begins to attack in the 24th week of pregnancy. The prevalence of Gestational Diabetes Mellitus in Indonesia is still relatively small, namely around 3-5%, but this figure could be higher because cases of GDM are rarely detected. Even though it can heal on its own, that doesn't mean gestational diabetes is not dangerous. If not treated properly, this disease can increase the risk of the baby being born with excess weight, being born prematurely, or being born with low blood sugar or hypoglycemia. For pregnant women, gestational diabetes has the potential to cause complications, such as preeclampsia and hypertension. In Indonesia, all pregnant women are recommended to undergo DMG screening. However, not many pregnant women do this. The aim of this research is to determine the relationship between the perceptions of pregnant women and the behavior of early detection of Gestational Diabetes Mellitus at the Plumbon health center, Indramayu Regency in 2024. This research is quantitative research with a cross-sectional study design. The respondents in this study were 107 pregnant women who lived in the working area of the Plumbon Health Center, Indramayu Regency and used health services at the health center in 2024. Samples were taken using the Accidental Sampling technique, a technique for determining samples based on chance, namely any patient who happened to meet the researcher. and data were analyzed univariate and bivariate. The results of the research show that the characteristics of respondents, namely age, education, employment and income, do not have a significant relationship with early detection behavior of GDM, however, the perception of pregnant women with behavior of early detection of Gestational Diabetes Mellitus has a significant relationship as does mother's knowledge of early detection of Gestational Diabetes Mellitus. have a significant relationship.

Assessment of Correlation Between Glycaemic Variability Indices and Measures of Hypoglycaemia in Patients with Type 2 Diabetes Mellitus

Abstract Introduction: Diabetes mellitus is a global health burden, and India is regarded as the diabetes capital of the world. Glycaemic variability (GV) is an established risk factor for hypoglycaemia (plasma glucose concentration &lt;70 mg/dL) and a notorious risk factor for diabetes complications. The primary aim of the study was to assess the correlation between the GV indices, HbA1c levels, and measures of hypoglycaemia in patients with type 2 DM (T2DM). Methods: In this cross-sectional study, continuous glucose monitoring (CGM) was done for a period of 14 days in T2DM patients (n = 50). The GV indices were determined from the readings obtained from the CGM monitor. Spearman’s rank correlation coefficient was used for correlation analyses. The area under the receiver operating characteristics (ROC) curve was used to assess the effectiveness of the various GV indices in predicting hypoglycaemia. Results: A total of 50 T2DM patients had a mean (SD) age of 61.84 ± 11.88 years. Among the GV indices, high blood glucose index (HBGI), average daily risk range (ADRR), and continuous overall net glycaemic action (CONGA) had a significantly positive correlation with HbA1c levels. Average episodes of nocturnal hypoglycaemia in 8 hours of night-time had a statistically significant negative correlation with the HbA1c levels (correlation coefficient: −0.301, P = 0.034). In addition, low blood glucose index (LBGI) was found to be the best predictor for the risk of hypoglycaemia in 24 hours and nocturnal hypoglycaemia. Conclusion: Various GV indices are associated with HbA1c levels and are better predictors of hypoglycaemia.

Exclusive Breastfeeding Rates Upon Hospital Discharge at a Tertiary Centre Prior to and During the COVID-19 Pandemic

This study aimed to describe exclusive breastfeeding (EBF) rates at discharge at Sunnybrook Health Sciences Centre and explore factors that contributed to changes in breastfeeding rates during the COVID-19 pandemic. Overall, 4762 patient charts were reviewed, 2000 from the pre-pandemic period, and 2762 from the lockdown period. Data was collected on EBF status at discharge, on maternal health history, and on infant characteristics. EBF rates fell from 75.8% to 73.85% from the pre-COVID-19 to COVID-19 period. During the pandemic, EBF was positively associated with BMI <30, spontaneous conception, and infants at risk of low blood sugar. Non-spontaneous conception was associated with lower EBF.

Optimizing Physical Activity for Glycemic Control in Type 1 Diabetes: Strategies and Risks

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition characterized by the destruction of insulin-producing beta cells in the pancreas, necessitating lifelong insulin therapy. This study explores the impact of physical activity on glycemic control and overall health in individuals with T1DM. Despite its proven benefits, including improved cardiovascular health, lower HbA1c levels, and reduced insulin requirements, over 60% of T1DM patients do not engage in regular exercise. Different types of physical activities—such as aerobic, anaerobic, and high-intensity interval training (HIIT)—affect metabolic responses differently. Aerobic exercises and HIIT have shown significant improvements in health-related quality of life (HRQoL) and reduced incidence of severe complications, such as diabetic ketoacidosis. However, exercise-induced hypoglycemia remains a significant risk, especially if exercise follows recent severe hypoglycemic episodes. Proper management strategies include adjusting insulin doses, monitoring glucose levels, and carbohydrate intake. The study highlights the importance of tailored exercise plans and preventive measures to mitigate risks such as nocturnal hypoglycemia and elevated ketones. Ultimately, effective integration of physical activity into diabetes management can lead to enhanced overall health and quality of life for individuals with T1DM.

Fabrication of Glucose Fluorescent Aptasensor Based on CdTe Quantum Dots.

Diabetes is a chronic metabolic disease characterized by high blood glucose (or blood sugar) levels, which harms the heart, blood vessels, eyes, kidneys, and nerves over time. So, it is crucial to regularly control glucose concentration in biological fluids to check its targets, reduce unpleasant symptoms of high and low blood sugar, and avoid long-term diabetes complications. This study developed a simple, rapid, sensitive, and cost-effective fluorescence system for glucose determination. The fluorescent Aptasensor was fabricated using cadmium telluride quantum dots (CdTe QDs) modified with thioglycolic acid and functionalized with thiol-glucose-aptamer through ligand exchange. The thiol-glucose-aptamer interacted directly with CdTe QDs, increasing fluorescence intensity. However, it decreased when the target molecules of glucose were introduced. The structural and morphological characteristics of the Aptasensor were confirmed by various analytical methods such as UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FESEM), energy dispersive x-ray spectroscopy (EDX), transmission electron microscopy (TEM), atomic force microscopy (AFM), and dynamic light scattering (DLS). According to the typical Stern-Volmer equation, the relationship between fluorescent quenching and target concentration was linear with a detection limit (LOD) of 0.13 ± 1.95 × 10-11 mol L-1 and a relative standard deviation (RSD) of 1.05%. The Aptasensor demonstrated high specificity towards the target and stability over 28 days. Furthermore, it detected glucose in human serum and urine with a recovery rate of up to 99.74%. The results indicate that the fluorescent Aptasensor could be valuable in developing robust sensing technology for low-concentrated analytes.

Fish-hunting cone snail disrupts prey’s glucose homeostasis with weaponized mimetics of somatostatin and insulin

Venomous animals have evolved diverse molecular mechanisms to incapacitate prey and defend against predators. Most venom components disrupt nervous, locomotor, and cardiovascular systems or cause tissue damage. The discovery that certain fish-hunting cone snails use weaponized insulins to induce hypoglycemic shock in prey highlights a unique example of toxins targeting glucose homeostasis. Here, we show that, in addition to insulins, the deadly fish hunter, Conus geographus, uses a selective somatostatin receptor 2 (SSTR2) agonist that blocks the release of the insulin-counteracting hormone glucagon, thereby exacerbating insulin-induced hypoglycemia in prey. The native toxin, Consomatin nG1, exists in several proteoforms with a minimized vertebrate somatostatin-like core motif connected to a heavily glycosylated N-terminal region. We demonstrate that the toxin’s N-terminal tail closely mimics a glycosylated somatostatin from fish pancreas and is crucial for activating the fish SSTR2. Collectively, these findings provide a stunning example of chemical mimicry, highlight the combinatorial nature of venom components, and establish glucose homeostasis as an effective target for prey capture.

Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring.

Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.