Average Glucose Levels Research Articles

Determining how the gestational changes, including low hemoglobin influence the hba1c and estimated average glucose relationship

ABSTRACT Background: Gestational Diabetes Mellitus (GDM) affects up to 14% of pregnancies globally, with accurate glycemic control critical for preventing adverse outcomes. While HbA1c is a standard marker for long-term glucose control, its reliability in pregnant women with anemia is debated. This study explores the association between anemia and glycemic indicators (Glycosylated hemoglobin HbA1c, Random plasma glucose (rPG), and Estimated average glucose (eAG) in pregnant women with GDM. Additionally, it examined the correlation between average glucose levels or eAG, measured by random plasma glucose rPG, and HbA1C. Methods: A prospective case-control study was carried out over eight months at the Departments of Chemical Pathology and Obstetrics and Gynecology, Dow University of Health Sciences (DUHS). Pregnant women in their second trimester after being diagnosed for GDM through oral glucose tolerance test were classified into anemic (hemoglobin < 10.5 g/dL) and non-anemic groups. Blood samples were collected and analyzed for HbA1c, hemoglobin, and random plasma glucose levels. Data were processed using SPSS (version-26) different variables compared by using independent t-test and correlation analyses at a significance level of p < 0.05. Results: Mean values for eAG, HbA1C, and rPG were 174.22 ± 24.489 mg/dl, 8.342 ± 1.6152%, and 166.21 ± 21.478 mg/dl, respectively. Anemia was associated with significantly higher HbA1C (p < 0.001), eAG (p < 0.001), and rPG (p = 0.004). A strong positive correlation was observed between eAG and rPG (r = 0.958, p < 0.01), while eAG showed a moderate correlation with HbA1C (r = 0.501, p < 0.01). In contrast, the correlation between HbA1C and rPG was weaker (r = 0.331, p < 0.01). Conclusion: eAG proved to be a good indicator for assessing glycemic control in pregnant non-anemic women with GDM. Anemia significantly affected glycemic metrics, leading to higher HbA1C and glucose levels.

Metabolic impact of residual C-peptide secretion in type 1 diabetes mellitus.

This study aimed to evaluate the association of detectable C-peptide levels with various continuous glucose monitoring (CGM) metrics and diabetes complications in patients with type 1 diabetes mellitus (T1DM). Retrospective, descriptive study of 112 patients with T1DM undergoing intensive insulin therapy, categorized according to fasting C-peptide level into undetectable (<0.05 ng/mL) and detectable (≥0.05 ng/mL) groups. The patients' median age at diagnosis was 22 (12-34) years and the median T1DM duration was 18.5 (12-29) years. Patients with detectable versus undetectable C-peptide levels were older (27.5 [16.5-38.5] versus 17.5 [9.8-28.8] years, respectively, p = 0.002) and had shorter disease duration (14 [9-24] versus 20 [14-32] years, respectively, p = 0.004). After adjustment for covariates (sex, disease duration, body mass index, and use of continuous subcutaneous insulin infusion), detectable C-peptide level was associated with lower time above range (TAR; aβ -11.03, p = 0.002), glucose management indicator (GMI, aβ -0.55, p = 0.024), and average glucose (aβ -14.48, p = 0.045) and HbA1c (aβ -0.41, p = 0.035) levels. Patients with detectable versus those with undetectable C-peptide level had significantly higher time in range (TIR) before (β = 7.13, p = 0.044) and after (aβ = 11.42, p = 0.001) adjustments. Detectable C-peptide level was not associated with lower time below range (TBR), coefficient of variation (CV), or prevalence of chronic microvascular and macrovascular complications. Persistent C-peptide secretion in patients with T1DM was associated with significantly better metabolic control reflected by different glucose metrics, namely, TIR, TAR, GMI, and HbA1c.

Insulin requirement trajectories during COVID-19 versus non-COVID-19 critical illness-A retrospective cohort study.

The glycemic response to critical COVID-19 remains uncertain. We aimed to assess the association between COVID-19, insulin requirements, glycemic control, and mortality in intensive care unit (ICU) patients. We conducted a retrospective observational study of 350 COVID-19 patients and 1067 non-COVID-19 patients admitted to the ICU. Insulin requirement was defined as the total units of exogenous insulin required to cover one gram of administered carbohydrates (insulin-to-carbohydrate ratio, ICR). We used multivariable generalized linear mixed-model (GLMM) analysis to assess the association of the interaction between COVID-19 and ICU-day with daily ICR, adjusted for fixed and time-dependent covariates. Glycemic control was assessed after stratification on diabetes and COVID-19. We used multivariable logistic regression analysis to assess the association between ICR and 90-day mortality. The mean (95% CI) of the mean daily ICR among patients without diabetes was 0.09 (0.08-0.11) U/g and 0.15 (0.11-0.18) U/g in the non-COVID-19 group and COVID-19 group (p = .01), respectively. In diabetes patients, the corresponding ICRs were 0.52 (0.43-0.62) U/g and 0.59 (0.50-0.68) U/g (p = .32). In multivariable GLMM analysis, the interaction between COVID-19 and ICU-day was independently associated with ICR (risk estimate 1.22, 95% CI 1.15-1.31, p < .001). COVID-19 was associated with higher hypoglycemia prevalence irrespective of diabetes status, higher average glucose levels, more pronounced glucose variability, and a lower proportion of glucose values within target range among patients without diabetes. On multivariable logistic regression analysis, the adjusted odds ratio for 90-day mortality was 1.77 (95% CI 0.94-3.34, p = .076) per one unit increase in mean ICR. In our cohort of ICU patients, COVID-19 was associated with higher daily insulin requirements per gram of administered carbohydrates, and worse glycemic control. We found no robust association between ICR and increased odds of death at 90 days.

6370 New-Onset Autoimmune Diabetes Following COVID-19 Infection and its Implications for Identical Twin Siblings

Abstract Disclosure: G. Wintermyer: None. S. Gillis-Funderburk: None. COVID-19 patients face many post-recovery complications,including autoimmune disorders. Late autoimmune diabetes ofadults (LADA) is one such disorder. Studies suggest that SARS-CoV may trigger proinflammatory cytokines that damage beta cellsand impair insulin secretion. Here, we explore a case of new-onsetLADA in a patient after recovery from COVID-19. Further, asidentical twins of LADA patients have a higher risk of developingit, we discuss how we approached the patient’s identical twin.A 29-year-old female with history of obesity (BMI 32.3) presentedto our endocrinology clinic. She complained of difficulty losingweight and fatigue and reported having COVID-19 three monthsearlier. She also mentioned having polyuria. She underwent labtests, including CBC, CMP, TSH, A1C, and a lipid panel. The testresults were within normal limits, except for her A1C of 11.3%.She was started on metformin and weekly Semaglutide injections.Given her recent COVID-19 history, she was assessed for LADA.Anti-GAD-65 and ZNT8 antibodies were 120 U/mL and 150U/mL, respectively. Serum C-peptide and IA-antibodies werewithin normal ranges. The patient informed us of her twin sister’sconcurrent COVID-19 infection. Due to the risk of LADA in hertwin sister, we recommended an evaluation by our clinic. Duringthe sister’s first visit, her BMI was 37.5, and she asked for weightloss medication. Comprehensive testing, including LADAantibodies, was conducted, with results within normal ranges andA1C level of 4.9%. The sister was also started on Semaglutide forweight management.The patient had quarterly follow-up appointments at the clinic.After a year, her A1C and continuous glucose monitoring machine(CGM) readings showed improvement. Her BMI decreased to 24.She never required insulin therapy, as her diabetes was well-managed and her average monthly glucose level on GCM readings were 125, 123 and 119 mg/dL in three different periods. Antibodieswere retested. This time IA-2 antibody was also elevated.Similarly, her twin sister continued to follow-up at our clinic. Thesister achieved significant weight loss without complications. HerLADA antibodies remained negative, and her A1C remainednormal. Clinicians should be vigilant for post-COVID LADA. In this case,Semaglutide aided weight loss, which benefits diabetesmanagement. Ongoing studies explore the potential of GLP-1medications in early-stage LADA. Historically, LADA patientswere started on insulin. However, CGM has enabled close glucosemonitoring, offering management of LADA with non-insulinmedications. It is also important to remember that autoimmunedisease risk is elevated in identical twins. In this case, although thetwin sister did not develop antibodies, the risk remains. Regularevaluation of high-risk patients is crucial for early detection ofantibodies, facilitating timely treatment to prevent adverseoutcomes. Presentation: 6/3/2024

The Effect of COVID-19 Lockdown Among Adolescents with Type 1 Diabetes: A Systematic Review and Meta-Analysis.

The aim of this study was to assess how the lockdown of the COVID-19 pandemic had affected the glycaemic control of adolescents aged 10-19 with type 1 diabetes. A comprehensive search of literature was performed in PubMed, Scopus, Web of Science, and ProQuest. Published articles up to September 2022 were included. The Glucose Monitoring Index (GMI) and HbA1c level were defined as outcome variables. Average glucose level was found to be a common variable in both HbA1c levels and GMI; therefore, HbA1c and GMI were converted to average glucose (mg/dL) using appropriate formulas. Studies reported the outcomes in two or three periods (pre-lockdown, lockdown, and post-lockdown) were included in the analysis. A paired wise meta-analysis was performed among the studies that reported all three periods. Homogeneity across studies was assessed using I2 statistic. Fourteen studies were included in the study. The pooled average glucose during the lockdown decreased to 166.9 mg/dL (95% CI, 153.78, 180.02) from 205.793 mg/dL (95% CI, 188.412, 223.173) during the pre-lockdown period, then it increased to 204.23 mg/dL (95% CI, 186.17, 222.29) during the post-lockdown period. A paired wise meta-analysis indicated a reduction in average glucose levels. However, it was not statistically significant, possibly due to the small number of studies that reported data from all three periods. Although the descriptive analysis of our study showed that the lockdown had affected (decreased) the average glucose level among adolescents with type 1 diabetes, this was not statistically significant in the pooled analysis.

A-037 A1c by separation and enzymatic methods: what's the difference?

Abstract Background Accurate values for hemoglobin A1c (HbA1c) are crucial for diagnosing and monitoring diabetes mellitus. Two common methods to measure HbA1c are capillary electrophoresis (CE) and immunoassay (IA). Many methods used to measure HbA1c are prone to interferences from hemoglobin variants and abnormalities affecting the red blood cell’s (RBC) turnover. Consequently, the average estimated glucose levels using the HbA1c value can be unreliable in diabetics with hemoglobinopathies and other RBC lifetime disorders. Separation methods such as CE can capture hemoglobinopathy information, while IA methods may not. We performed this study in a population with relatively high levels of hemoglobin disorders to evaluate the performance of HbA1c testing on the Alinity platform (IA) against the Sebia Capillarys 3 Tera instrument (CE). Methods Samples were identified retrospectively based on a search of the Electronic Medical Record for all patients with an ordered and resulted HbA1c and complete blood count in the Montefiore Medical Center the day prior to sampling. Routine HbA1c testing was performed by an Abbott Immunoassay method on the Abbott Alinity Instrument and CBC results were obtained from a Sysmex XN2100 instrument. Records were compared by patient name, medical record number, and sample collection time to identify all patients with CBC and HbA1c drawn within one hour of each other. Those samples were retrieved from storage (a 4° automated refrigerator) within 48 hours of draw and tested via the Sebia Capillarys 3 Tera instrument using HBA1c kits. Results In this large representative population study (N=2075), there was overall excellent agreement between IA and CE. The correlation coefficient (R2) of the whole dataset was 0.989, with an average bias of 2.7% between the two methods. Additional analysis was performed on those with possible RBC turnover disorder (patients with RBC counts below 4x106/uL or above 6x106/uL, MCVs above 96 fL or below 80 fL, RDW above 17%, or abnormal A2 value: total of 638 samples), those with a hemoglobin variant as detected by the Capillarys 3 instrument (162 samples), or with a measurable hemoglobin F value detected by the Capillarys 3 instrument (37 samples). Samples were sorted into these groups independently and thus there was significant overlap between groups, notably with 23 of 37 samples with elevated HbF also having a hemoglobin variant. In both the RBC disorder and hemoglobinopathies group, similar low bias and high correlations were seen. The elevated F samples showed a clear negative bias (5.6%) for all patients on the IA method as compared with CE. The IA method states that samples with HbF above 5% should not be tested. However, as this is not a separation method, there is no way to test samples for HbF before analysis, and bias was seen for samples below 5%. Conclusions The IA and CE methods showed similar performance across patients regardless of RBC lifetime or hemoglobin disorder status, though presence of HbF is a potential interference and should be considered. Further questions remain for these instruments in terms of correlation with average glucose levels as calculated through CGM instruments.

Evaluating HbA1c-to-average glucose conversion with patient-specific kinetic models for diverse populations.

The discrepancy between estimated glycemia from HbA1c values and actual average glucose (AG) levels has significant implications for treatment decisions and patient understanding. Factors contributing to the gap include red blood cell (RBC) lifespan and glucose uptake into the RBC. Personalized models have been proposed to enhance AG prediction accuracy by considering interpersonal variation. This study contributes to our understanding of personalized models for estimating AG from HbA1c. Utilizing data from seven studies (340 participants), including Hispanic/Latino populations with or at risk of non-insulin-treated type 2 diabetes (T2D), we examined kinetic features across cohorts. Additionally, the study simulated scenarios to understand data requirements for improving accuracy. Personalized approaches improved agreement between AG estimations and CGM-AG, particularly with four or more weeks of training CGM data. A multiple linear regression model using kinetic parameters and added clinical features was shown to improve the accuracy of personalized models further. As CGM usage extends beyond type 1 diabetes, there is growing interest in leveraging CGM data for clinical decision-making. Patient-specific models offer a valuable tool for managing glycemic status in patients with discordant HbA1c and AG values.

Relationship Between Average Glucose Levels and HbA1c Differs Across Racial Groups: A Substudy of the GRADE Randomized Trial.

To determine whether the relationship between average glucose (AG) levels and hemoglobin A1c (HbA1c) differs across racial/ethnic groups. We performed a prospective substudy of GRADE, a comparative effectiveness randomized trial conducted in 36 centers in the U.S. A total of 1,454 of the 5,047 participants in the GRADE cohort, including 534 non-Hispanic White (NHW), 389 non-Hispanic Black (NHB), and 327 Hispanic White patients and 204 patients of other racial/ethnic backgrounds, were included in the substudy. Continuous glucose monitoring (CGM) performed for 10 days was used to calculate AG10. Immediately after CGM, HbA1c and glycated albumin were measured. Fasting plasma glucose (FPG) and glucose area under the curve (AUC) were derived from a 75-g oral glucose tolerance test. The relationship between AG10 and HbA1c was significantly different for NHB compared with NHW patients and those of other racial/ethnic groups. HbA1c levels were 0.2-0.6 percentage points higher in NHB than in NHW patients for AG10 levels from 100 to 250 mg/dL. For an HbA1c of 7%, AG10 was 11 mg/dL higher for NHW than for NHB patients. Similar findings were observed across races for relationships of FPG and AUC with HbA1c and for glucose measurements with glycated albumin levels. Differences in the relationship between AG10 and HbA1c across racial groups remained after adjustments for any demographic or other differences between racial/ethnic subgroups. The relationship between several measures of glucose with HbA1c and glycated albumin consistently differed across races. These findings should be considered in setting treatment goals and diagnostic levels.

Data Management Analysis for Predicting Stroke using RapidMiner

Stroke are known as the second most leading cause of death. Because of this, data mining techniques are already being used to predict patients that may have stroke. Therefore, we are doing a study to try using data mining techniques using RapidMiner to find information or patterns regarding stroke from a dataset obtained from Kaggle. Three data mining techniques are used in this study, that is classification using decision trees, association rule using FP-Growth algorithm, and clustering technique using k-Means algorithm. Using RapidMiner, we are able to process the dataset using the operators provided in the application. As the result, we found out that due to an unbalanced data, the decision tree model made were only able to predict 68,75% patients as having stroke. With the association rule technique, we found out that most attributes in the dataset does not really associated with each other. With the clustering technique, we were able to group up patients and found out that most patients that have stroke are averaged in the age of 58, with 31 bmi, and 201 average glucose level.

An explainable Artificial Intelligence software system for predicting diabetes

Implementing diabetes surveillance systems is paramount to mitigate the risk of incurring substantial medical expenses. Currently, blood glucose is measured by minimally invasive methods, which involve extracting a small blood sample and transmitting it to a blood glucose meter. This method is deemed discomforting for individuals who are undergoing it. The present study introduces an Explainable Artificial Intelligence (XAI) system, which aims to create an intelligible machine capable of explaining expected outcomes and decision models. To this end, we analyze abnormal glucose levels by utilizing Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN). In this regard, the glucose levels are acquired through the glucose oxidase (GOD) strips placed over the human body. Later, the signal data is converted to the spectrogram images, classified as low glucose, average glucose, and abnormal glucose levels. The labeled spectrogram images are then used to train the individualized monitoring model. The proposed XAI model to track real-time glucose levels uses the XAI-driven architecture in its feature processing. The model's effectiveness is evaluated by analyzing the performance of the proposed model and several evolutionary metrics used in the confusion matrix. The data revealed in the study demonstrate that the proposed model effectively identifies individuals with elevated glucose levels.

The Effect of Healthy Boba Pearl Drink on Post-Prandial Glucose

This study was a randomized controlled trial using a repeated experimental design with a pre-test and a post-test control group. The study subjects were women of childbearing age, aged 20‒30 years, with no diagnosed chronic diseases and with baseline fasting blood glucose levels less than 125 mg/dL. Participants were randomly assigned to treatment and control groups. The treatment group received 350 mL of healthy boba drink, while the control group received 350 mL of commercial boba drink. The healthy boba drink was made by mixing 67 mL of fresh milk, 133 mL of soy milk, 1 g of stevia sweetener, 100 g of red dragon fruit, and 40 g of healthy boba pearls. It contained 215 kcal of energy, 8.82 g of protein, 8.90 g of fat, 30.78 g of carbohydrate, and 1,808 g of fiber. Each group had an initial fasting blood glucose levels measurement before the intervention, and postprandial glucose levels were measured at the end of the intervention (one time of intervention). The collected data were analyzed univariately to analyze the effect of treatment on postprandial glucose using the independent t-test. The result indicated that the subjects in the treatment group and 96.2% of the subjects in the control group had fasting glucose levels less than 126 mg/dL. Both participants in the control and treatment groups had postprandial glucose levels less than 200 mg/dL. The mean glucose level was 89.49 mg/dL in the treatment group and 92.57 mg/dL in the control group. The study results showed that the treatment group that consumed the healthy boba drink had a lower average postprandial glucose level than the control group. The statistical test results showed that there was a significant effect of healthy boba drink consumption on postprandial glucose levels in the treatment group (p&lt;0.000). It is concluded that the healthy boba drink intervention had a significant effect (p&lt;0.000) on lowering blood glucose by 5.82 mg/dL after the initial treatment. The results of this study are a major first step for future work to develop a healthier boba drink.

Glucometric parameter changes in patients with type 2 diabetes during ramadan fasting: A prospective comparative real-world study

BackgroundThis study assessed glucometric changes in Type 2 diabetes mellitus (T2DM) patients before, during, and after Ramadan fasting using an intermittently scanned continuous glucose monitoring system (isCGMS). MethodsThis prospective comparative study included T2DM patients aged 30–70 years who were receiving nonintensive insulin in Riyadh, Saudi Arabia. In addition to the baseline characteristics, glycated hemoglobin (HbA1c) and ambulatory glucose profile (AGP)-derived metric data were collected at three specific points: pre-, during-, and post-Ramadan. Self-care activities during Ramadan were evaluated using the Diabetes Self-Management Questionnaire (DSMQ). ResultsOverall, a total of 93 T2DM patients were enrolled in the study. Their mean age ±SD age was 47.9 ± 7.5 years, and 51.6 % of them were males. Compared with pre- and post-Ramadan, there was a significant decrease in HbA1c (p < 0.001 for both periods), average glucose level (p = 0.001 and p = 0.026, respectively), glucose variability (p = 0.043 and p = 0.005, respectively), and % time above the range of 181–250 mg/dL (p < 0.001 for both periods), as well as a significant increase in % time in target (70–180 mg/dL) during Ramadan (p < 0.001 for both periods). However, the % time below 54 mg/dL was slightly greater during Ramadan than both pre- and post-Ramadan (p < 0.001 and p = 0.002, respectively). Furthermore, 32.3 % reported inadequate self-care behaviors during Ramadan. ConclusionsRamadan fasting could improve glucose levels in T2DM patients who were not on intensive insulin, with a relatively low incidence of hypoglycemia.

Continuous Glucose Monitoring in Intensive Care Unit Patients with Diabetes: Insights and Outcomes from a Single-center Study in India

Abstract Introduction: The use of continuous glucose monitoring (CGM) for in-hospital glucose control has been widely studied. However, its application in intensive care unit (ICU) settings is debatable owing to its perceived limitations. In ICU patients with diabetes, glycemic excursions, including frequent hypoglycemic episodes, are more prevalent. This retrospective study was undertaken to evaluate the utility of CGM in the management of diabetes patients admitted to the ICU. Methods: We collected data of patients with diabetes mellitus who were admitted to the ICU at our institution and were advised CGM by the intensivist. A retrospective analysis was conducted, examining demographic, clinical, and laboratory data, along with glucose meter readings and CGM data. The results are presented as numbers, percentages, or median values, as appropriate. Results: Out of 65 patients in the ICU, the median values of percentage time in range, time below range, and time above range were 59%, 2%, and 36%, respectively. The median HbA1C and capillary glucose on admission were 8.8% and 246 mg/dl, respectively. During the ICU stay, the CGM derived average glucose level was 167 mg/dl (percent coefficient of variation [%CV] =25.96) and the glucose management indicator was 7%. In total, 65 episodes of hypoglycemia were detected, all confirmed by capillary glucose tests. A total of 27 patients experienced hypoglycemia, of which 12 had nocturnal hypoglycemia. Conclusion: The use of CGM in the ICU improved glycemic control and helped to detect hypoglycemia, particularly nocturnal with acceptably low glycemic variability as measured by percentage CV. Further studies are required to corroborate and confirm these findings.

The Effect of Sargassum fusiforme and Fucus vesiculosus on Continuous Glucose Levels in Overweight Patients with Type 2 Diabetes Mellitus: A Feasibility Randomized, Double-Blind, Placebo-Controlled Trial.

Brown seaweed is promising for the treatment of type 2 diabetes mellitus (T2DM). Its bioactive constituents can positively affect plasma glucose homeostasis in healthy humans. We investigated the effect of the brown seaweeds Sargassum (S.) fusiforme and Fucus (F.) vesiculosus in their natural form on glucose regulation in patients with T2DM. We conducted a randomized, double-blind, placebo-controlled pilot trial. Thirty-six participants with T2DM received, on a daily basis, either 5 g of dried S. fusiforme, 5 g of dried F. vesiculosus, or 0.5 g of dried Porphyra (control) for 5 weeks, alongside regular treatment. The primary outcome was the between-group difference in the change in weekly average blood glucose levels (continuous glucose monitoring). The secondary outcomes were the changes in anthropometrics, plasma lipid levels, and dietary intake. The data were analyzed using a linear mixed-effects model. The change in weekly average glucose levels was 8.2 ± 2.1 to 9.0 ± 0.7 mmol/L (p = 0.2) in the S. fusiforme group (n = 12) and 10.1 ± 3.3 to 9.2 ± 0.7 mmol/L (p = 0.9) in the F. vesiculosus group (n = 10). The between-group difference was non-significant. Similarly, no between-group differences were observed for the changes in the secondary outcomes. A daily intake of 5 g of fresh, dried S. fusiforme or F. vesiculosus alongside regular treatment had no differential effect on weekly average blood glucose levels in T2DM.

A Novel Gene ARHGAP44 for Longitudinal Changes in Glycated Hemoglobin (HbA1c) in Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Offspring Study (FOS).

Glycated hemoglobin (HbA1c) indicates average glucose levels over three months and is associated with insulin resistance and type 2 diabetes (T2D). Longitudinal changes in HbA1c (ΔHbA1c) are also associated with aging processes, cognitive performance, and mortality. We analyzed ΔHbA1c in 1,886 non-diabetic Europeans from the Long Life Family Study to uncover gene variants influencing ΔHbA1c. Using growth curve modeling adjusted for multiple covariates, we derived ΔHbA1c and conducted linkage-guided sequence analysis. Our genome-wide linkage scan identified a significant locus on 17p12. In-depth analysis of this locus revealed a variant rs56340929 (explaining 27% of the linkage peak) in the ARHGAP44 gene that was significantly associated with ΔHbA1c. RNA transcription of ARHGAP44 was associated with ΔHbA1c. The Framingham Offspring Study data further supported these findings on the gene level. Together, we found a novel gene ARHGAP44 for ΔHbA1c in family members without T2D. Follow-up studies using longitudinal omics data in large independent cohorts are warranted.

Predictive modelling and identification of key risk factors for stroke using machine learning

Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

Oral administration of proniosomal glibenclamide formulation protects testicular tissue from hyperglycemia fluctuations and ROS via Nrf2/HO-1 pathway

Type 2 diabetes causes high blood sugar due to insulin malfunction and is linked to male infertility. Using proniosomes can enhance the effectiveness of Glibenclamide, a medication that stimulates insulin secretion. In our study, male rats with diabetes were treated with GLB with or without proniosomal for 14 days. Proniosomal formulations maintained glucose levels prevented weight loss and showed normal testicular tissue.GLB-proniosomal reduces ROS caused by T2DM through Nrf2, HO-1 pathway and increases CAT, SOD, and GSH production in response to insulin and glucose uptake. The reference and proniosomal treatments showed CAT and SOD significant enzymatic elevation compared to the positive and negative control. CAT significantly correlated with Gpx4 expression with P = 0.0169 and r = 0.98; similarly, the enzymatic activity of SOD also showed a positive correlation between the average glucose levels (r = 0.99 and P = 0.0037). Intestinally, GSH analysis revealed that only proniosomal-GLB samples are significantly elevated from the positive control, with a P value of 0.0210. The data showed proniosomal-GLB was more effective than pure GLB, confirmed by higher Nrf2 (2.050 folds), HO-1 (2.148 folds), and GPx4 (1.9 folds) transcript levels relative to the control with less sample diversity compared to the reference samples, indicating proniosomal stabilized GLB in the blood.Administering GLB and proniosomes formulation has effectively restored testicular function and sperm production in diabetic rats by regulating ROS levels and upregulating anti-ROS in response to glucose uptake. These findings may lead to better treatments for diabetic patients who have infertility issues.

Functional efficiency of pancreatic cell-engineered construct in an animal experimental model for type I diabetes

The creation of a cell-engineered pancreatic construct (CEPC) from islets of Langerhans and biocompatible matrix carrier (framework/scaffold), which imitates the native microenvironment of pancreatic tissue, is an approach to the treatment of type I diabetes mellitus (T1D).The objective of this work is to conduct a comparative analysis of the functional efficacy of CEPC and isolated rat islets of Langerhans after intraperitoneal administration into rats with experimental T1D.Materials and method. T1D was induced in rats by injecting low-dose (15 mg/ kg) streptozotocin (STZ) for 5 days. CEPC samples were created using viable and functional allogeneic isolated islets of Langerhans and tissue-specific scaffold obtained by decellularization of human pancreatic fragments. The rats received intraperitoneal injection of allogeneic islets of Langerhans (experimental group 1, n = 4) and CEPC (experimental group 2, n = 4). Control group rats received no treatment (n = 4). Blood glucose levels in the rats were measured, and the pancreas and kidneys of the experimental animals were examined histologically. The follow-up period for all animals continued for 10 weeks. Results. In experimental group 1, on day 7 after injection of Langerhans islets, glycemia decreased significantly from 28.2 ± 4.2 mmol/L to 13.4 ± 2.6 mmol/L. This fall persisted for 7 weeks, following which blood sugar increased to nearly their initial levels (prior to islets administration). In experimental group 2, on day 7 after CEPC administration, there was a more noticeable drop in blood sugar levels from 25.8 ± 5.1 mmol/L to 6.3 ± 2.7 mmol/L compared to experimental group 1. By the 10th week of the experiment, the average glucose level was two times lower than it was at the beginning. Blood glucose levels dropped more sharply in the CEPC group than in the islet group (by 75.6% and 52.5%, respectively).Conclusion. In T1D rats, CEPC has a more potent antidiabetic effect than islets of Langerhans. Thus, it has been shown that a tissue-specific scaffold may be used to create bioartificial pancreas in order to increase the functional efficiency of islets.

Impact of early-morning administration of rapid-acting insulin on the increase in blood glucose levels related to the dawn phenomenon in individuals with type 1 diabetes.

An early-morning elevation of blood glucose levels known as the dawn phenomenon and consequent postbreakfast hyperglycemia occur in some individuals with type 1 diabetes (T1D). Whereas insulin pump therapy can mitigate this phenomenon, some individuals prefer or are limited to alternative treatments. We have now assessed the effectiveness of early-morning administration of rapid-acting insulin for amelioration of the dawn phenomenon in individuals with T1D. Thirteen individuals with T1D who experienced the dawn phenomenon as determined by continuous glucose monitoring (CGM) and who received a small dose of rapid-acting insulin on waking were included in this retrospective study. We evaluated the change in sensor glucose levels during a 2-h period from before to after breakfast consumed at 0700h. The change in blood glucose levels during additional time intervals, average daily sensor glucose values, CGM indices, and insulin dose were also evaluated. The early-morning administration of 0.5-1 unit of rapid-acting insulin was associated with a significant reduction in 2-h glucose variability between before (0700h) and after breakfast from a median of 90.7-51.0mg/dL. The glucose variability from 0300 to 0700 or 0900h was also significantly decreased, from 67.7 to 29.0mg/dL and from 172.5 to 78.3mg/dL, respectively. Average sensor glucose levels throughout the day were significantly reduced (from 192.7 to 156.7mg/dL), as was the daily total insulin dose. Early-morning administration of rapid-acting insulin effectively managed the dawn phenomenon and subsequent postbreakfast hyperglycemia in individuals with T1D. The online version contains supplementary material available at 10.1007/s13340-024-00709-6.

Stroke Data Analysis and Prediction

This paper explores the impact of strokes on society and emphasizes collaborative efforts to enhance stroke management. By leveraging technology and medical records, caregivers gain insights into relevant risk factors for stroke prediction. This paper systematically examines various components in electronic health records for effective stroke forecasting. Employing diverse statistical methods and principal component analysis, we identify the most significant factors for stroke prediction. Using statistical methods and principal component analysis, we identify age, heart disease, average glucose level, and hypertension as crucial factors. Our findings indicate that age, heart disease, average glucose level, and hypertension emerge as the most critical factors for detecting stroke in patients. Furthermore, a perceptron neural network utilizing these attributes achieves superior accuracy and lower miss rates compared to other methods on a balanced dataset. Keywords: Age, heart disease, glucose level, hypertension.