Pancreatic Insulin Production Research Articles

INTEGRATED EDUCATION ON HEALTHY FAMILY MANAGEMENT ON THE STATUS OF DIABETIC MELLITUS PATIENTS

Insufficient pancreatic insulin production or ineffective use by the body is the main cause of diabetes mellitus. Insulin sugar is regulated by hormones. In the initial survey, five diabetes mellitus sufferers (50%) were asked and, five of them did not understand the problem, underwent routine physical examinations, but did not regularly take the prescribed medication because they lacked family support and did not understand how serious the disease was. Objective: To determine the health status of diabetes mellitus patients in the Guntur 2 Community Health Center working area and analyze healthy family management with integrated education. Method: Purposive sampling was used in a quasi-experimental two-group pre-procedure with a sample size of 20 participants. The health status of the respondents was recorded in the observation sheet after the integrated education on diabetes mellitus was carried out as stated in the Healthy Family Assistance Book. Statistical test using the Paired T-test Results: Independent sample test results were obtained with a p-value of 0.003 and paired T-test findings with p-values of 0.001 and 0.05 respectively. Conclusion: Diabetes mellitus sufferers in the Guntur II Health Center Working Area have significantly different health statuses.

Effects Of Phytochemicals on Type 2 Diabetes Mellitus -A Short Review

Diabetes mellitus (DM) is the most prominent cause of mortality globally. It is a severe metabolic disorder marked by high blood glucose levels and insufficient insulin generation and activity. Type 2 Diabetes Mellitus (T2DM) prevalence has risen dramatically due to population aging, obesity, and modern lifestyles. T2DM is distinguished by impaired beta pancreatic cell function and insulin production. Numerous studies have found that diabetes mellitus is related to increased free radical production and decreased antioxidant potential. Phytochemicals are essential in improving insulin sensitivity, which has free radical scavenging and antioxidant properties. Several phytoactive compounds, such as flavonoids, prophenylphenols, vicine, charantin, lignans, glycosides, and karavilosides, are also found to combat the complications of diabetes. The primary focus of this research will be the connection between T2DM and the preventative roles of several phytochemicals on diabetes through their antioxidant characteristics. These phytochemicals and photo sourcesmay be employed to discover and create novel anti-diabetic medicines

FoxO1 as a tissue-specific therapeutic target for type 2 diabetes.

Forkhead box O (FoxO) proteins are transcription factors that mediate many aspects of physiology and thus have been targeted as therapeutics for several diseases including metabolic disorders such as type 2 diabetes mellitus (T2D). The role of FoxO1 in metabolism has been well studied, but recently FoxO1's potential for diabetes prevention and therapy has been debated. For example, studies have shown that increased FoxO1 activity in certain tissue types contributes to T2D pathology, symptoms, and comorbidities, yet in other tissue types elevated FoxO1 has been reported to alleviate symptoms associated with diabetes. Furthermore, studies have reported opposite effects of active FoxO1 in the same tissue type. For example, in the liver, FoxO1 contributes to T2D by increasing hepatic glucose production. However, FoxO1 has been shown to either increase or decrease hepatic lipogenesis as well as adipogenesis in white adipose tissue. In skeletal muscle, FoxO1 reduces glucose uptake and oxidation, promotes lipid uptake and oxidation, and increases muscle atrophy. While many studies show that FoxO1 lowers pancreatic insulin production and secretion, others show the opposite, especially in response to oxidative stress and inflammation. Elevated FoxO1 in the hypothalamus increases the risk of developing T2D. However, increased FoxO1 may mitigate Alzheimer's disease, a neurodegenerative disease strongly associated with T2D. Conversely, accumulating evidence implicates increased FoxO1 with Parkinson's disease pathogenesis. Here we review FoxO1's actions in T2D conditions in metabolic tissues that abundantly express FoxO1 and highlight some of the current studies targeting FoxO1 for T2D treatment.

IoMT innovations in diabetes management: Predictive models using wearable data

Diabetes Mellitus (DM) represents a metabolic disorder characterized by consistently elevated blood glucose levels due to inadequate pancreatic insulin production. Type 1 DM (DM1) constitutes the insulin-dependent manifestation from disease onset. Effective DM1 management necessitates daily blood glucose monitoring, pattern recognition, and cognitive prediction of future glycemic levels to ascertain the requisite exogenous insulin dosage. Nevertheless, this methodology may prove imprecise and perilous. The advent of groundbreaking developments in information and communication technologies (ICT), encompassing Big Data, the Internet of Medical Things (IoMT), Cloud Computing, and Machine Learning algorithms (ML), has facilitated continuous DM1 management monitoring. This investigation concentrates on IoMT-based methodologies for the unbroken observation of DM1 management, thereby enabling comprehensive characterization of diabetic individuals. Integrating machine learning techniques with wearable technology may yield dependable models for forecasting short-term blood glucose concentrations. The objective of this research is to devise precise person-specific short-term prediction models, utilizing an array of features. To accomplish this, inventive modeling strategies were employed on an extensive dataset comprising glycaemia-related biological attributes gathered from a large-scale passive monitoring initiative involving 40 DM1 patients. The models produced via the Random Forest approach can predict glucose levels within a 30-minute horizon with an average error of 18.60 mg/dL for six-hour data, and 26.21 mg/dL for a 45-minute prediction horizon. These findings have also been corroborated with data from 10 Type 2 DM patients as a proof of concept, thereby demonstrating the potential of IoMT-based methodologies for continuous DM monitoring and management. The integration of innovative biological signal sensors and the application of transformative trends in ICT can offer a novel perspective on DM treatment, ensuring precise and secure glucose level management.

SAT155 Insulin Receptor Autoantibody Syndrome (Iras): A Rare Occurrence In Lymphoproliferative Disease

Abstract Disclosure: C. Villatoro Santos: None. E.A. Oral: None. Insulin receptor antibody syndrome (IRAS) may present as fasting hypoglycemia, high serum insulin, and serum autoantibodies to insulin receptor at low titers. We present a case of a patient with multiple myeloma (MM) who developed hypoglycemia suspected to be related to insulin receptor antibodies. The patient is a 60-year-old male with history of prediabetes, MM, anemia, and thrombocytopenia treated with carfilzomib, dexamethasone 20 mg weekly, and prior radiation to the right pelvis, who presented to the hospital for increased right hip pain and cough. Patient reported 4 days of markedly decreased appetite with poor intake. He noted increased chronic right hip and low back pain. On exam, he was cachectic, had hyperpigmentation under bilateral eyes, and muscle wasting. Hypoglycemia as low as 38 mg/dL (70-180 mg/dL) was noted. MM studies on hospital day 4 showed increased kappa-M protein and kappa-free light chains with worsening anemia and thrombocytopenia, concerning for progression of his disease. He later developed recurrent hypoglycemia; endocrinology was consulted on hospital day 6. He also had hyponatremia and borderline low blood pressure. Cortisol on hospital day 7 at 9:00 am was 15.4 μg/dL (5.3-22.5 μg/dL) in the setting of normoglycemia. On hospital day 9, hematology started dexamethasone 40 mg daily for 3 days for MM relapse, which resulted in marked improvement in his mental status, appetite, and resolution of hypoglycemia. On hospital day 11, he started weekly cyclophosphamide, bortezomib, plus dexamethasone for 3 weeks. Results for hypoglycemia workup returned on hospital day 12 from samples of Day 7: glucose 53 mg/dL (70-180 mg/dL), C-peptide 0.2 ng/mL (1-5.2 ng/mL), insulin 14.6 μU/mL (1-21 μU/mL), insulin antibodies undetectable, negative serum screen for oral hypoglycemic agents. His course was complicated by sepsis with gram-negative bacteremia treated with antibiotics and stress dose hydrocortisone. He was discharged to rehab on hospital day 34 on dexamethasone 4 mg daily. Glucose, c-peptide and insulin levels could be consistent with exogenous hyperinsulinemic hypoglycemia but may also represent delayed insulin clearance due to insulin receptor antibodies and autocrine suppression of pancreatic insulin production in a patient with no access to insulin. Therefore, the most likely diagnosis was IRAS. The insulin antibody radioactive essay was negative for native IgG and IgM antibodies; testing for insulin receptor antibodies is in progress as a clinical test is unavailable. Treatment is common with high doses of steroids. IRAs can be seen in patients with lymphoproliferative disorders and would respond to steroid treatment for hospital management. Definitive treatment should be tailored with oncology guidance. There is a need for an accessible clinical test for IRAS in the US. Presentation: Saturday, June 17, 2023

Factors Associated with Adherence to Oral Antidiabetic Drugs among Patients with type 2 Diabetes Mellitus attending Diabetic Clinic at Jinja Regional Referral Hospital Jinja District

Diabetes Mellitus (DM) is a metabolic illness that causes excessive blood sugar levels as a result of either impaired pancreatic insulin production, insulin resistance, or both. People with diabetes mellitus die prematurely, and DM is a global burden. Adherence to drug regimens is essential in the management of diabetes mellitus, in addition to lifestyle changes. However, patients with diabetes are more likely not to take their medications as prescribed, and poor medication compliance jeopardizes patient safety and treatment efficacy, increasing mortality and morbidity. This study aimed to determine oral diabetes medication adherence and its associated factors among type two diabetes patients (T2DM) at Jinja Regional Referral Hospital. An institutional-based cross-sectional study of 136 T2DM patients was undertaken at Jinja Regional Referral Hospital in Jinja district. A convenience sampling technique was used to recruit participants, and the research was carried out from April 2022 to October 2022. The Morisky Medication Adherence Scale was used to assess medication adherence. STATA version 15.1 was used to analyze the data. To discover independent determinants of T2DM adherence, logistic regressions were used. To declare statistical significance, a P-value of less than 0.05 was employed. This study enrolled 136 type 2 diabetes patients, with a response rate of 100%. Overall, 58.33% of T2DM patients showed good medication adherence. T2DM patients who took both oraland injectable anti-diabetic medications (AOR = 1.98, 95% CI: 1.16-3.39), received the prescribedanti-diabetic medication from the hospital (AOR = 0.51, 95% CI: 0.32-0.80), had their own glucometer at home (AOR = 0.30, 95% CI: 0.16-0.54), and had good diabetes-related knowledgewere found to be more adherent to oral antidiabetic medication. At Jinja Regional Referral Hospital, there is a low prevalence of oral anti-diabetic drug adherence among patients with T2DM. Significant independent predictors of anti-diabetic treatment adherence included the type of medication that T2DM patients were taking, the ability of patients to receive their prescriptions from the hospital, having a glucometer at home for glucose monitoring, and having sound knowledge of diabetes. Patients should therefore receive more education on the importance of medication adherence, and as well be encouraged to purchase personal glucometers. Keywords: Diabetes Mellitus, Blood sugar levels, Insulin, Medication adherence, Oral antidiabetic.

Multi-regulator of EZH2-PPARs Therapeutic Targets: A Hallmark for Prospective Restoration of Pancreatic Insulin Production and Cancer Dysregulation.

The unexpected rise in cancer and diabetes statistics has been a significant global threat, inciting ongoing research into various biomarkers that can act as innovative therapeutic targets for their management. The recent discovery of how EZH2-PPARs' regulatory function affects the metabolic and signalling pathways contributing to this disease has posed a significant breakthrough, with the synergistic combination of inhibitors like GSK-126 and bezafibrate for treating these diseases. Nonetheless, no findings on other protein biomarkers involved in the associated side effects have been reported. As a result of this virtual study, we identified the gene-disease association, protein interaction networks between EZH2-PPARs and other protein biomarkers regulating pancreatic cancer and diabetes pathology, ADME/Toxicity profiling, docking simulation and density functional theory of some natural products. The results indicated a correlation between obesity and hypertensive disease for the investigated biomarkers. At the same time, the predicted protein network validates the link to cancer and diabetes, and nine natural products were screened to have versatile binding capacity against the targets. Among all natural products, phytocassane A outperforms the standard drugs' (GSK-126 and bezafibrate) in silico validation for drug-likeness profiles. Hence, these natural products were conclusively proposed for additional experimental screening to complement the results on their utility in drug development for diabetes and cancer therapy against the EZH2-PPARs' new target.

Bisphenol F induced hyperglycemia via activation of oxidative stress-responsive miR-200 family in the pancreas

Bisphenol F (BPF), BPS and BPAF are gaining popularity as main substitutes to BPA, but there is no clear evidence that these compounds disrupt glycemic homeostasis in the same way. In this study, four bisphenols were administered to C57BL/6 J mice, and showed that the serum insulin was elevated in the BPA and BPS exposed mice, whereas BPF exposed mice exhibited lower serum insulin and higher blood glucose. BPF decreased oxidized glutathione/reduced glutathione ratio (GSSG/GSH) and N6–methyladenosine (m6A) levels, which was responsible for pancreatic apoptosis in mice. Additionally, the downregulation of Nrf2 and the aberrant regulation of the p53-lncRNA H19 signaling pathway further increased miR-200 family in the BPF-exposed pancreas. The miR-200 family directly suppressed Mettl14 and Xiap by targeting their 3' UTR, leading to islet apoptosis. Antioxidant treatment not only elevated m6A levels and insulin contents but also suppressed the miR-200 family in the pancreas, ultimately improving BPF-induced hyperglycemia. Taken together, miR-200 family could serve as a potential oxidative stress-responsive regulator in the pancreas. And moreover, we demonstrated a novel toxicological mechanism in that BPF disrupted the Keap1-Nrf2 redox system to upregulate miR-141/200b/c which controlled pancreatic insulin production and apoptosis via Mettl14 and Xiap, respectively. As the major surrogates of BPA in various applications, BPF was also diabetogenic, which warrants attention in future research.

Stacking Ensemble Learning-Based Convolutional Gated Recurrent Neural Network for Diabetes Miletus

Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure. It causes hyperglycemia and chronic multiorgan dysfunction, including blindness, renal failure, and cardiovascular disease, if left untreated. One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test, this procedure involves extracting blood quite frequently, which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring. Existing methods used for diabetes classification have less classification accuracy and suffer from vanishing gradient problems, to overcome these issues, we proposed stacking ensemble learning-based convolutional gated recurrent neural network (CGRNN) Metamodel algorithm. Our proposed method initially performs outlier detection to remove outlier data, using the Gaussian distribution method, and the Box-cox method is used to correctly order the dataset. After the outliers’ detection, the missing values are replaced by the data’s mean rather than their elimination. In the stacking ensemble base model, multiple machine learning algorithms like Naïve Bayes, Bagging with random forest, and Adaboost Decision tree have been employed. CGRNN Meta model uses two hidden layers Long-Short-Time Memory (LSTM) and Gated Recurrent Unit (GRU) to calculate the weight matrix for diabetes prediction. Finally, the calculated weight matrix is passed to the softmax function in the output layer to produce the diabetes prediction results. By using LSTM-based CG-RNN, the mean square error (MSE) value is 0.016 and the obtained accuracy is 91.33%.

Regulation of Pancreatic TXNIP-Insulin Expression Levels after Bariatric Surgery Using Diabetic Rodent Model.

The purpose of this study was to investigate the effect of bariatric surgery on pancreatic thioredoxin-interacting protein (TXNIP) and insulin expression levels. The research question is does bariatric surgery induce changes in the pancreatic TXNIP level, given that TXNIP has been proposed as a key glucose control factor? Using nondiabetic and diabetic rats, we investigated whether our streptozotocin-induced diabetic rat models exhibited changes in pancreatic TXNIP regulation. Following this confirmation, we randomly divided the diabetic rats into the following three groups: the gastric bypass group (n = 16), pair-fed group (n = 10), and sham group (n = 10). Preoperatively and 3 weeks postoperatively, all the rats underwent an oral glucose tolerance test, insulin tolerance test, and blood sampling procedures for hormonal analysis. The TXNIP messenger ribonucleic acid (mRNA) and protein expression levels were significantly lower in the gastric bypass group than in the other groups. Regarding the gastric bypass group, the pancreatic mRNA expression levels of microRNA-204 (miR-204) and MafA were significantly lower and higher, respectively, than in the other groups. Furthermore, the levels of pancreatic insulin expression at the mRNA and protein levels were also significantly higher in the gastric bypass group than in the other groups. Bariatric surgery significantly improved glucose control and regulated the pancreatic insulin production pathways of TXNIP, miR-204, and MafA. The regulation of TXNIP, miR-204, and MafA might play an important role in the mechanism of diabetes remission following bariatric surgery.

The Role of Pancreatic Fatty Acid Synthesis in Islet Morphology and Function after Caloric Restriction or Roux-En-Y Gastric Bypass Surgery in Mice.

Both caloric restriction (CR) and Roux-en-Y gastric bypass (RYGB) are practical interventions for type 2 diabetes mellitus (T2DM), while the molecular mechanisms of CR and RYGB regarding glycemic control are still poorly understood. Here, we explore the effects and underlying mechanisms of CR and RYGB on β-cell area and function. Average islet size was measured by histological analysis. The pancreatic lipid content was detected by using a commercial lipid assay kit. The expression levels of lipogenic transcription factors and enzymes in mouse pancreas were determined by quantitative PCR, Western blot, and immunofluorescence. CR decreased the mean size of islets and pancreatic insulin production in both regular diet-fed and high-fat diet-fed mice. Increased β-cell apoptosis was detected in the calorie-restricted mice. Interestingly, the lipogenic transcription factors and enzymes such as SREBP1c, PPARγ, FASN and ACC were upregulated in the pancreas after CR. In contrast to CR, RYGB decreased the apoptosis of β-cells and the expression of fatty acid synthase. Pancreatic fatty acid synthesis is critical to the β-cell function after CR and RYGB.

A proposed approach for diabetes diagnosis using neuro-fuzzy technique

Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most significant challenges in the healthcare unit is disease diagnosis. Traditional techniques of disease diagnosis are manual and prone to inaccuracy. This paper proposed an approach for diagnosing diabetes using the adaptive neuro-fuzzy inference system (ANFIS) based on Pima Indians diabetes dataset (PIDD). The three stages of the proposed approach are pre-processing classification and evaluation. Normalization, imputation, and anomaly detection are part of the pre-processing stage. The pre-processing was done by normalizing the data, replacing the missing values, and using the local outlier factor (LOF) technique. In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. Finally, the evaluation procedures use the last stage’s sensitivity, specificity, and accuracy metrics. The obtained classification accuracy was 92.77%, and it seemed rather promising compared to the other classification applications for this topic found in the literature.

PSUN374 Casting a Wide NET: A Case Report of Recurrent Hypoglycemia and 'Malignant' Insulinoma

Abstract Introduction Insulinomas are rare neuroendocrine tumors, with an incidence of 4 cases per one million patient-years. Malignant insulinomas are even rarer, with a paucity of data for specific treatment modalities. Surgery, the mainstay of treatment, needs to be determined by the disease extent prior to resection. Here, we describe a patient who presented to a Federally Qualified Health Center with a diagnosis of hypoglycemia and the barriers related to the evaluation and treatment of his insulinoma. Case Presentation A 40-year-old Hispanic man with no past medical history presented to the clinic with an 8-year history of syncopal episodes. Continuous glucose monitoring revealed nocturnal hypoglycemia with BG <50 mg/dL, but he discontinued its use due to frequent low blood glucose alerts. Physical exam was notable for central obesity and acanthosis nigricans. Labs showed a fasting glucose of 63 mg/dL, proinsulin 419.9 (<18.8 pmol/L), insulin 11.3 (<19.6 uIU/ml), C-peptide 2.24 (0.80-3.85 ng/mL), A1C 4.3%, and negative urine sulfonylurea screen. A diagnosis of insulinoma was suspected and he was started on acarbose. CT abdomen showed a centrally calcified, peripherally enhancing 24 mm lesion in the pancreatic head. 68Ga-DOTATATE PET/CT scan was recommended for further evaluation but was denied by his insurance. Biopsy of the mass using endoscopic ultrasound (EUS) was positive for neuroendocrine tumor with metastasis to a peripancreatic lymph node. Given the concern for metastatic insulinoma, he underwent a pancreaticoduodenectomy. Surgical pathology confirmed the diagnosis of insulinoma but without evidence of tumor in the lymph nodes. The most likely explanation for the tumor cells previously noted in the lymph node was the passage of the needle through the tumor during biopsy. The postoperative course was complicated by hyperglycemia requiring insulin infusion and a pancreatic fistula. The patient was followed in clinic where his repeat proinsulin level was normal. He had resolution of his hypoglycemic episodes, his fistula healed, and he is back at work. Discussion Our case reinforces the need for an accurate diagnosis and localization of neuroendocrine tumors prior to surgical intervention. A preoperative 68Ga-DOTATATE PET/CT would have demonstrated an absence of tumor outside the pancreatic mass and prompted a less invasive surgical approach. Because of insurance denial, the EUS biopsy findings suggesting metastatic insulinoma could not be confirmed and he underwent a pancreaticoduodenectomy. Patients with metastatic insulinoma are at risk for recurrence of hypoglycemia in the postoperative period. Persistence of postoperative hyperglycemia suggests either new-onset diabetes due to insulin deficiency or resistance and suppression of residual endogenous insulin secretion as a result of insulinoma-induced chronic hyperinsulinemia. Decreases in insulin requirements post-operatively can be explained by recovery of remnant pancreatic insulin production after alleviation of chronic suppression, and reduction in insulin resistance induced by surgery. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Zinc Oxide Nanoparticles and Synthesized Pyrazolopyrimidine Alleviate Diabetic Effects in Rats Induced by Type II Diabetes.

Diabetes mellitus (DM) is a category of metabolic illness characterized by high blood sugar levels and insufficient pancreatic insulin production or activity within the body. The most common type of diabetes is type II diabetes, which is a metabolic condition characterized by insulin resistance and pancreatic islet β-cell failure, resulting in hyperglycemia. The goal of this study was to examine the anti-diabetic implications of zinc oxide nanoparticles (ZnO NPs) and/or pyrazolopyrimidine in type II diabetic rats. Rats with a weight of 150 ± 20 g were used. Animals were divided into five groups as follows: group 1: control, group 2: type II diabetic rats, group 3: diabetic rats received ZnO NPs (10 mg/kg/orally/day), group 4: diabetic rats received pyrazolopyrimidine (5 mg/kg/orally/day), and group 5: diabetic rats received ZnO NPs (10 mg/kg/orally/day) + pyrazolopyrimidine (5 mg/kg/orally/day), respectively, for 30 days. The results indicated that serum glucose, total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein-cholesterol (LDL-c), very low-density lipoprotein-cholesterol (VLDL-c), malondialdehyde, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha PGC-1α mRNA expressions were increased in the diabetic group versus the control group, while serum insulin, high-density lipoprotein-cholesterol (HDL-c), superoxide dismutase (SOD), and carnitine palmitoyltransferase 1A (CPT1A) mRNA expression levels were decreased. These parameters were reserved in the treated groups (ZnO NPs, pyrazolopyrimidine, and ZnO NPs + pyrazolopyrimidine). This study proved that ZnO NPs and pyrazolopyrimidine had an ameliorative effect on blood glucose levels, antioxidant status, lipid profile, liver function enzymes, and mRNA expression of hepatic genes.

TAZ promotes PDX1-mediated insulinogenesis.

Transcriptional co-activator with PDZ-binding motif (TAZ) is a key mediator of the Hippo signaling pathway and regulates structural and functional homeostasis in various tissues. TAZ activation is associated with the development of pancreatic cancer in humans, but it is unclear whether TAZ directly affects the structure and function of the pancreas. So we sought to identify the TAZ function in the normal pancreas. TAZ defect caused structural changes in the pancreas, particularly islet cell shrinkage and decreased insulin production and β-cell markers expression, leading to hyperglycemia. Interestingly, TAZ physically interacted with the pancreatic and duodenal homeobox 1 (PDX1), a key insulin transcription factor, through the N-terminal domain of TAZ and the homeodomain of PDX1. TAZ deficiency decreased the DNA-binding and transcriptional activity of PDX1, whereas TAZ overexpression promoted PDX1 activity and increased insulin production even in a low glucose environment. Indeed, high glucose increased insulin production by turning off the Hippo pathway and inducing TAZ activation in pancreatic β-cells. Ectopic TAZ overexpression along with PDX1 activation was sufficient to produce insulin in non-β-cells. TAZ deficiency impaired the mesenchymal stem cell differentiation into insulin-producing cells (IPCs), whereas TAZ recovery restored normal IPCs differentiation. Compared to WT control, body weight increased in TAZ-deficient mice with age and even more with a high-fat diet (HFD). TAZ deficiency significantly exacerbated HFD-induced glucose intolerance and insulin resistance. Therefore, TAZ deficiency impaired pancreatic insulin production, causing hyperglycemia and exacerbating HFD-induced insulin resistance, indicating that TAZ may have a beneficial effect in treating insulin deficiency in diabetes.

Nutrient Regulation of Pancreatic Islet β-Cell Secretory Capacity and Insulin Production.

Pancreatic islet β-cells exhibit tremendous plasticity for secretory adaptations that coordinate insulin production and release with nutritional demands. This essential feature of the β-cell can allow for compensatory changes that increase secretory output to overcome insulin resistance early in Type 2 diabetes (T2D). Nutrient-stimulated increases in proinsulin biosynthesis may initiate this β-cell adaptive compensation; however, the molecular regulators of secretory expansion that accommodate the increased biosynthetic burden of packaging and producing additional insulin granules, such as enhanced ER and Golgi functions, remain poorly defined. As these adaptive mechanisms fail and T2D progresses, the β-cell succumbs to metabolic defects resulting in alterations to glucose metabolism and a decline in nutrient-regulated secretory functions, including impaired proinsulin processing and a deficit in mature insulin-containing secretory granules. In this review, we will discuss how the adaptative plasticity of the pancreatic islet β-cell’s secretory program allows insulin production to be carefully matched with nutrient availability and peripheral cues for insulin signaling. Furthermore, we will highlight potential defects in the secretory pathway that limit or delay insulin granule biosynthesis, which may contribute to the decline in β-cell function during the pathogenesis of T2D.

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.

Delay-Independent regulation of blood glucose for type-1 diabetes mellitus patients via an observer-based predictor feedback approach by considering quantization constraints

Diabetes, as a widespread chronic disease, is caused by the increase of blood glucose concentration (BGC) due to pancreatic insulin production failure and/or insulin resistance in the body. The artificial pancreas (AP) known as a closed-loop insulin delivery control system consists of a glucose sensor for BGC measurement, a control algorithm for calculation of exogenous insulin delivery rate (IDR), and an insulin infusion pump. The AP provides a closed-loop glucose-insulin regulatory system for type-1 diabetes mellitus (T1DM) patients in order to effectively reduce the high BGC level. In this paper, we aim to design a controller in order to regulate the BGC of T1DM patients at its basal value with considering meal disturbance in the system. We confront with different problems for glucose control such as sampled and quantized glucose measurement by the sensor, quantized IDR by the insulin infusion pump, and an unknown input delay of the insulin infusion pump as the actuator of the control system. The bergman minimal model (BMM) as the most famous model for describing the glucose-insulin regulatory system is reviewed and the delay-independent observer for the estimation of the system’s state vector from the sensor output (sampled and quantized BGC) is considered. Moreover, the observer works on the basis of an event-triggered strategy in order to avoid unnecessary signal updating. At the end, the IDR calculated by the proposed delay-independent truncated predictor output feedback controller (based on the observer’s output) is delivered to the T1DM patient. A sufficient condition on the insulin infusion pump delay’s upper bound value in order to guarantee the ultimate boundedness of the glucose-insulin regulatory system under the mentioned constraints is derived. Also, the nonoccurrence of Zeno behavior in the proposed control system is proved. Numerical simulation results confirm the effectiveness of the designed controller in the presence of meal disturbance, unknown input delay, and quantization errors for blood glucose regulation of the glucose-insulin regulatory system in T1DM patients. Moreover, a pre-clinical verification of the applicability of the proposed glucose controller is carried out on a large-scale and multi-compartmental model that is the base of the UVA/PADOVA Type-1 Diabetes Simulator.

Micro-RNA Implications in Type-1 Diabetes Mellitus: A Review of Literature.

Type-1 diabetes mellitus (T1DM) is one of the most well-defined and complex metabolic disorders, characterized by hyperglycemia, with a constantly increasing incidence in children and adolescents. While current knowledge regarding the molecules related to the pathogenesis and diagnosis of T1DM is vast, the discovery of new molecules, such as micro ribonucleic acids (micro-RNAs, miRNAs), as well as their interactions with T1DM, has spurred novel prospects in the diagnosis of the disease. This review aims at summarizing current knowledge regarding miRNAs’ biosynthesis and action pathways and their role as gene expression regulators in T1DM. MiRNAs follow a complex biosynthesis pathway, including cleaving and transport from nucleus to cytoplasm. After assembly of their final form, they inhibit translation or cause messenger RNA (mRNA) degradation, resulting in the obstruction of protein synthesis. Many studies have reported miRNA involvement in T1DM pathogenesis, mainly through interference with pancreatic b-cell function, insulin production and secretion. They are also found to contribute to β-cell destruction, as they aid in the production of autoreactive agents. Due to their elevated accumulation in various biological specimens, as well as their involvement in T1DM pathogenesis, their role as biomarkers in early preclinical T1DM diagnosis is widely hypothesized, with future studies concerning their diagnostic value deemed a necessity.

Probable Dapagliflozin-Associated Acute Pancreatitis.

Pharmacy and Therapeutics| September 28 2021 Probable Dapagliflozin-Associated Acute Pancreatitis Ashlee D. Barrett; Ashlee D. Barrett 1Advanced Vital Care Pharmacy: Home Infusion and Infusion Services, Amarillo, TX2Southwestern Oklahoma State University College of Pharmacy, Weatherford, OK Search for other works by this author on: This Site PubMed Google Scholar Mason A. Pitts; Mason A. Pitts 2Southwestern Oklahoma State University College of Pharmacy, Weatherford, OK3Stillwater Medical Center, Stillwater, OK Search for other works by this author on: This Site PubMed Google Scholar Elizabeth M. Myers; Elizabeth M. Myers 2Southwestern Oklahoma State University College of Pharmacy, Weatherford, OK4Jackson County Memorial Hospital, Altus, OK Search for other works by this author on: This Site PubMed Google Scholar Jeremy L. Johnson 0000-0003-4001-5883 Jeremy L. Johnson 5Department of Pharmacy Practice, Southwestern Oklahoma State University College of Pharmacy, Weatherford, OK6Department of Internal Medicine, Oklahoma State University Center for Health Sciences, Tulsa, OK Corresponding author: Jeremy L. Johnson, jeremy.johnson@swosu.edu Search for other works by this author on: This Site PubMed Google Scholar Corresponding author: Jeremy L. Johnson, jeremy.johnson@swosu.edu Diabetes Spectr 2022;35(2):232–238 https://doi.org/10.2337/ds21-0025 PubMed: 35668880 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Cite Icon Cite Get Permissions Citation Ashlee D. Barrett, Mason A. Pitts, Elizabeth M. Myers, Jeremy L. Johnson; Probable Dapagliflozin-Associated Acute Pancreatitis. Diabetes Spectr 15 May 2022; 35 (2): 232–238. https://doi.org/10.2337/ds21-0025 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsDiabetes Spectrum Search Advanced Search Type 2 diabetes directly affects 34.2 million Americans (1). Combined pathophysiologic mechanisms of impaired pancreatic insulin production and release, resistance to insulin by target tissues, and dysregulation of hepatogluconeogenesis lead to hyperglycemia (2). Long-term complications of chronically uncontrolled type 2 diabetes include both microvascular and macrovascular disease. The effects of diabetes on the vasculature increase the risk of death from heart disease by two to four times; as a result, >68% of people ≥65 years of age with diabetes die of some form of heart disease and 16% die of stroke (3). The prevalence of heart failure in patients with type 2 diabetes is four times that in the general population, ranging between 9 and 22% (4). Additionally, 38.6% experience end-stage renal disease (2). Many drug classes are now available to treat type 2 diabetes. One of the newest classes,... You do not currently have access to this content.