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Insulin Secretion

Insulin Secretion: The process by which insulin is released from the pancreatic beta cells in response to various stimuli, such as increased blood glucose levels.
This complex physiological mechanism involves the uptake of glucose, the generation of ATP, and the subsequent exocytosis of insulin-containing secretory vesicles.
Optimal insulin secretion is critical for maintaining glucose homeostasis and preventing metabolic disorders like diabetes.
Researchers can leverage PubCompare.ai's AI-driven platform to easily locate and compare the most effective protocols and methods for studying insulin secretion, streamlining their workflow and accelerating discovery.

Most cited protocols related to «Insulin Secretion»

1
Calculating Glucose and Insulin Indices from OGTT
The trapezoidal method was used to calculate glucose area under the curve (AUC) and insulin AUC during the OGTT. Surrogate indexes of insulin sensitivity and insulin secretion were calculated according to published formulas (Tables 1 and 2) (25 (link),30 (link)–33 (link, link, link)), using glucose and insulin concentrations at 0, 30, and 120 min.
Stančáková A., Javorský M., Kuulasmaa T., Haffner S.M., Kuusisto J, & Laakso M. (2009). Changes in Insulin Sensitivity and Insulin Release in Relation to Glycemia and Glucose Tolerance in 6,414 Finnish Men. Diabetes, 58(5), 1212-1221.
Publication 2009
Diet, Formula Glucose Insulin Insulin Secretion Insulin Sensitivity Oral Glucose Tolerance Test Trapezoid Bones
2
Progression to Prediabetes in Offspring of Diabetic Parents
Eligible participants were African American or Caucasian men and women who were biological offspring of parents with type 2 diabetes. Ethnicity was assessed by self-report of non-Hispanic White or non-Hispanic Black heritage. The parental history of type 2 diabetes is verified using a diabetes-focused medical history questionnaire. The inclusion and exclusion criteria are summarized in Table 1. Pre-existing diabetes was excluded using the 1997 ADA criterion for fasting plasma glucose (FPG) (≥126 mg/dL)13 (link) and the 1985 WHO criterion for 75-gram oral glucose tolerance test (OGTT) (2-hour plasma glucose [PG]} >200 mg/dL).14 The goal was to enroll offspring of diabetic parents who had normal fasting glucose (NFG) and/or NGT at baseline, so as to permit detection of progression to prediabetes during follow-up. Normal fasting glucose (<100 mg/dL) and IFG (100–125 mg/dL) was defined according to the 2003 revised ADA criteria12 (link) and IGT was defined by the 1985 WHO criterion (2-hour PG 140–199 mg/dL) during OGTT.14 Most exclusion criteria were chosen to reduce the risk of their confounding effects on planned assessments, body composition, insulin sensitivity and insulin secretion, among others. Because thiazide diuretics and beta-blockers can induce insulin resistance,15 (link) persons using thiazides (>25 mg/day) or beta-blockers were excluded. The study protocol was approved by the University of Tennessee Health Science Center Institutional Review Board and all participants gave written informed consent.
Dagogo-Jack S., Edeoga C., Nyenwe E., Chapp-Jumbo E, & Wan J. (2011). Pathobiology of Prediabetes in a Biracial Cohort (POP-ABC): Design and Methods. Ethnicity & disease, 21(1), 33-39.
Publication 2011
Adrenergic beta-Antagonists African American Biopharmaceuticals Body Composition Caucasoid Races Diabetes Mellitus Diabetes Mellitus, Non-Insulin-Dependent Disease Progression Ethics Committees, Research Ethnicity Glucose Hispanics Insulin Resistance Insulin Secretion Insulin Sensitivity Oral Glucose Tolerance Test Parent Plasma States, Prediabetic Thiazide Diuretics Thiazides Woman
3
Comprehensive Insulin Secretion Analysis
Insulin secretion derived from the fasting state was calculated as HOMA-B: 20·I0/(G0–3.5) with I0 = fasting insulin in µU/ml and G0 = fasting glucose in mmol/l [5] (link). All other insulin secretion indices were derived from the OGTT with insulin and C-peptide concentrations given in pmol/l, and glucose concentration given in mmol/l. AUCs of insulin, C-peptide, and glucose concentrations during the entire 120 min of the OGTT were calculated according to the trapezoid method as: 0.5·(0.5·c0+c30+c60+c90+0.5·c120) with c = concentration. AUCInsulin(0-30)/AUCGlucose(0-30) was calculated as: (I0+I30)/(G0+G30) [9] (link). AUCC-Peptide(0-30)/AUCGlucose(0-30) was calculated analogously. IGI1 was calculated as: (I30–I0)/(G30–G0) [10] (link). IGI2 was calculated as: (I30–I0)/G30[6] (link). DI oral was calculated as: IGI1/I0[8] (link). CIR was calculated as: 100·I30/[G30·(G30–3.89)] [4] (link). First-phase insulin secretion was calculated as: 1283+1.829·I30–138.7·G30+3.772·I0[7] (link). Insulin sensitivity derived from the OGTT was estimated as proposed by Matsuda and DeFronzo [22] (link): 10000/(G0·I0·Gmean·Imean)½. Fasting insulin clearance was calculated as CP0/I0 with CP0 = fasting C-peptide, insulin clearance during the OGTT was calculated as AUCC-Peptide(0-120)/AUCInsulin(0-120). Acute insulin response (AIR) derived from the IVGTT was used as gold standard for the assessment of insulin secretion and calculated as: 0.5·(0.5·I0+I2+I4+I6+I8+0.5·I10).
Herzberg-Schäfer S.A., Staiger H., Heni M., Ketterer C., Guthoff M., Kantartzis K., Machicao F., Stefan N., Häring H.U, & Fritsche A. (2010). Evaluation of Fasting State-/Oral Glucose Tolerance Test-Derived Measures of Insulin Release for the Detection of Genetically Impaired β-Cell Function. PLoS ONE, 5(12), e14194.
Publication 2010
C-Peptide Glucose Gold Insulin Insulin Secretion Insulin Sensitivity Oral Glucose Tolerance Test Peptides Trapezoid Bones
4
Genome-Wide Association Analysis of Metabolic Traits
Linear regression models were used for association of phenotypes (z-score residuals of insulin secretion and action traits) with genotypes coded additively. Discovery (stage 1) GWAS analyses were carried out using a statistical tool that was able to account for genotype uncertainty, SNPTEST [29] (link), or by using allele dosages in the linear regression model in MACH2QTL [30] (link), [31] (link), probABEL [32] , corrected for residual inflation of the test statistics using the genomic control method [33] (link). The meta-analyses of effect sizes were performed with the fixed-effect inverse-variance method using GWAMA [34] (link). The GC correction was applied only once to cohort-specific results before including them into the meta-analyses. Sex-differentiated analyses were performed using GWAMA, with an assumed heterogeneity p-value of <0.05. Effect sizes for glucose levels were estimated using a fixed-effect model using the metaphor package for R version 2.14.2 (http://www.r-project.org/).
Prokopenko I., Poon W., Mägi R., Prasad B R., Salehi S.A., Almgren P., Osmark P., Bouatia-Naji N., Wierup N., Fall T., Stančáková A., Barker A., Lagou V., Osmond C., Xie W., Lahti J., Jackson A.U., Cheng Y.C., Liu J., O'Connell J.R., Blomstedt P.A., Fadista J., Alkayyali S., Dayeh T., Ahlqvist E., Taneera J., Lecoeur C., Kumar A., Hansson O., Hansson K., Voight B.F., Kang H.M., Levy-Marchal C., Vatin V., Palotie A., Syvänen A.C., Mari A., Weedon M.N., Loos R.J., Ong K.K., Nilsson P., Isomaa B., Tuomi T., Wareham N.J., Stumvoll M., Widen E., Lakka T.A., Langenberg C., Tönjes A., Rauramaa R., Kuusisto J., Frayling T.M., Froguel P., Walker M., Eriksson J.G., Ling C., Kovacs P., Ingelsson E., McCarthy M.I., Shuldiner A.R., Silver K.D., Laakso M., Groop L, & Lyssenko V. (2014). A Central Role for GRB10 in Regulation of Islet Function in Man. PLoS Genetics, 10(4), e1004235.
Publication 2014
Alleles Genetic Heterogeneity Genome Genome-Wide Association Study Genotype Glucose Insulin Secretion Phenotype
5
Interactive Homeostatic Model of Glucose
The iHOMA2 model is shown in Fig. 1 as graphical (A), box diagrammatic (B), and mathematical (C), respectively. iHOMA2 is an integrated computer-based mathematical model of glucose and hormonal interaction under homeostatic conditions. The model, now available online at http://www.ihoma.co.uk, runs in real time with 24 operator-controlled variables (Table 1) and graphical output displays. The baseline characteristics were built from those used in the original HOMA2 model, with all of the dose-response variables now explicit. iHOMA2 runs interactively and exactly for each calculation. iHOMA2 in its default start-up setting gives identical readings to HOMA2 and can be used as a direct substitute for HOMA2 in this mode. The operator can modify each of the variables using an interactive sliding control display. The operator can control every aspect of the dose-response curve. For example, the β-cell characteristics are described by P1–P5, each of these being independently adjustable. This allows “what if” scenarios to be explored: “What would be the effect on glucose if Vmax of β-cell function were 50%?” “How might that be modified if the dose response curve were shifted to the left?” “What if autonomous insulin secretion continued at low blood glucose?” Similarly, the functions relating to the other organs and tissues involved in the glucose and hormonal compartments can be modified using sliding control displays. In the HOMA2 model, insulin sensitivity is treated as a whole-body effect, altering the liver and periphery to the same extent. In iHOMA2, this has been uncoupled and the insulin sensitivity of these organs and tissues can be modified independently. The ability to alter the 24 variables of iHOMA2 enables the modeling of known or surmised pathology and physiology and the effect of treatments both alone and in combination. The effects of the treatments on fasting glucose, insulin, β-cell function (%B), and insulin sensitivity (%S) are graphically represented in the model.
The model allows for analytical and predictive modes of use. The analytical mode allows insulin resistance and β-cell function to be read from the input of insulin and glucose in the basal state, while the predictive function shows the estimated and modeled insulin and glucose concentrations in the basal state when the β-cell function and insulin resistance parameters are set.
This article shows two detailed quantified scenarios to illustrate the interactive modalities. The first example shows that the effect of pioglitazones (thiazolidinediones) on insulin resistance can be partitioned between the liver and periphery. The second example illustrates the model’s use elucidating the effect of an SGLT2 inhibitor on glycemia. All analyses were performed using SPSS, version 19.0 (SPSS, Chicago, IL). Statistical comparisons were made using Z tests for skewness, Student independent samples t test for comparison of means, and F tests for assessment of fit of the model to the observed data (15 ).
Hill N.R., Levy J.C, & Matthews D.R. (2013). Expansion of the Homeostasis Model Assessment of β-Cell Function and Insulin Resistance to Enable Clinical Trial Outcome Modeling Through the Interactive Adjustment of Physiology and Treatment Effects: iHOMA2. Diabetes Care, 36(8), 2324-2330.
Publication 2013
Blood Glucose Glucose Homeostasis Human Body Insulin Insulin Resistance Insulin Secretion Insulin Sensitivity Liver Pancreatic beta Cells Physiology, Cell Sodium-Glucose Transporter 2 Inhibitors Student Thiazolidinediones Tissues

Most recents protocols related to «Insulin Secretion»

1
Oral Insulin Formulation with FDKP Salt
Not available on PMC !

Example 11

Capsules containing the FDKP salt and insulin are taken before a meal. The exact dosage is patient-specific, but generally on the order of approximately 10-150 units of insulin is administered per dose. The subsequent insulin absorption attenuates post-prandial blood glucose excursions. This oral insulin formulation is used to replace pre-meal insulin injections in patients with diabetes. Additionally, insulin absorbed through the gastrointestinal tract mimics endogenous insulin secretion. Endogenous insulin is secreted by the pancreas into the portal circulation. Insulin absorbed following oral administration also goes directly to the portal circulation. Thus, the oral route of insulin administration delivers insulin to its site of action in the liver, offering the potential to control glucose levels while limiting systemic exposure to insulin. Oral insulin delivery using a combination of insulin and the diacid form of FDKP is hindered by the poor solubility of the FDKP diacid in the low pH environment of the gastrointestinal tract. The FDKP salts, however, provide a local buffering effect that facilitates their dissolution in low pH.

US11872265B2. Diketopiperazine salts for drug delivery and related methods (2024-01-16). MannKind Corporation [US]. Inventors: Andrea Leone-Bay [US], Destardi Moye-Sherman [US], Bryan R. Wilson [US].
Patent 2024
3,6-bis(N-fumaryl-N(n-butyl)amino)-2,5-diketopiperazine Administration, Oral Blood Glucose Capsule Diabetes Mellitus Gastrointestinal Tract Glucose Insulin Insulin Secretion Liver Obstetric Delivery Pancreas Patients Salts Sodium Chloride
2
Abdominal Fat Distribution and Glucose Metabolism
Data were analyzed using the SPSS software package (version 24.0; SPSS Inc, Chicago, IL, USA). Continuous variables were presented as means ± standard deviation (SD) for normal distribution or median with interquartile ranges for non-normal distribution. Categorical variables were presented as frequency (percentages). The Kolmogorov-Smirnov test was used to verify the normal distribution of continuous variables. The χ² test, one-way ANOVA or Kruskal-Wallis rank sum test were used to compare differences in categorical or continuous variables across the four groups, as appropriate. Relationships between abdominal fat distribution and β-cell function were analyzed using Spearman’s correlation analysis. All the covariates were tested for collinearity; the tolerance was > 0.1, and variance inflation factor did not > 5.0. Multivariate linear regression was used to assess the association of abdominal fat distribution with insulin secretion and sensitivity. A p value < 0.05 (two-sided) was considered as statistically significant.
Huang H., Zheng X., Wen X., Zhong J., Zhou Y, & Xu L. (2023). Visceral fat correlates with insulin secretion and sensitivity independent of BMI and subcutaneous fat in Chinese with type 2 diabetes. Frontiers in Endocrinology, 14, 1144834.
Publication 2023
Abdominal Fat Hypersensitivity Immune Tolerance Insulin Secretion neuro-oncological ventral antigen 2, human Pancreatic beta Cells Physiology, Cell
3
Standardized Steamed Bread Meal Test for Beta-Cell Function
The standardized steamed bread meal was made of 100 g flour, which contained carbohydrates approximately equivalent to 75 g glucose. The Chinese Islet Beta-Cell Function Collaborative Research Group showed that standardized steamed bread meal tolerance test (BMTT) was reproducible and was better tolerated when compared to oral glucose tolerance test (OGTT) to assess β-cell function in healthy subjects (18 ). Therefore, in China, BMTT is often used in clinical practice instead of OGTT to evaluate β-cell function in patients previously diagnosed with diabetes (19 (link)). Thus, we used BMTT to assess the second phase insulin secretion (SPIS).
Blood samples were collected in the morning under fasting conditions. Glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), fasting insulin (FINS), fasting C-peptide (FCP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and high-sensitivity C-reactive protein (Hs-CRP) were measured. Post-load blood samples were collected to assess 2 h plasma glucose (PG2h), 2 h insulin (INS2h) and 2 h C-peptide (CP2h) after the patients ate a 100 g steamed bread.
Huang H., Zheng X., Wen X., Zhong J., Zhou Y, & Xu L. (2023). Visceral fat correlates with insulin secretion and sensitivity independent of BMI and subcutaneous fat in Chinese with type 2 diabetes. Frontiers in Endocrinology, 14, 1144834.
Publication 2023
BLOOD Bread C-Peptide Carbohydrates Chinese Cholesterol Cholesterol, beta-Lipoprotein C Reactive Protein Diabetes Mellitus Flour Glucose Healthy Volunteers Hemoglobin, Glycosylated High Density Lipoprotein Cholesterol Immune Tolerance Insulin Insulin Secretion Oral Glucose Tolerance Test Pancreatic beta Cells Patients Physiology, Cell Plasma Triglycerides
4
Arginine-Stimulated Insulin Secretion Assay
AST was used to assess first-phase insulin secretion (FPIS) after overnight fasting for at least eight hours. After a baseline blood sample was collected, a 10% (wt/vol.) solution of arginine hydrochloride (5 g) (Shanghai Xinyi Jinzhu Pharmaceutical Co., Ltd., Shanghai, China) was injected intravenously within 30-45 s. Blood samples were obtained at 2, 4, and 6 min after injection (17 (link)). All anti-diabetic therapy was paused during the test.
Huang H., Zheng X., Wen X., Zhong J., Zhou Y, & Xu L. (2023). Visceral fat correlates with insulin secretion and sensitivity independent of BMI and subcutaneous fat in Chinese with type 2 diabetes. Frontiers in Endocrinology, 14, 1144834.
Publication 2023
Arginine Hydrochloride BLOOD Insulin Secretion Pharmaceutical Preparations Therapeutics
5
Modeling Diabetes Progression Factors
The culprits of diabetes may vary for different subgroups of diabetic patients, which implies the distinction of possible interference factors to the glucose regulation system. Nevertheless, the underlying mechanisms through which the factors lead to dysglycemia are common. Numerous studies indicate that glycemia is primarily attributed to excess hepatic glucose output and abnormal insulin secretion and utilization [35 (link)]. Of note, beta-cell function is regulated by various mechanisms, not limited to glucose utilization [12 (link)]. Thus, confining the model for beta-cell function only with the variables of glucose and insulin may impede the study of beta-cell dysfunction. We aim to test through an in-silico approach how the T2D progression is affected by certain pathological factors. Here we propose a general form of diabetes progression model with a pathological factor X that is to be specified:
dGdt=Gin+p1(X)-f2(G)-C(I)GI,
dIdt=f1(G)p2(X)β-kI,
dβdt=(f3(I)+p3(X))β,
where X is a bounded variable with a real value; all the variables in the system are in the time scale of days: p1(X) is incorporated into Eq (1) to stand for the increased hepatic glucose production caused by the pathological factor; p2(X) integrated into Eq (2) symbolizes the impact of the factor on the insulin secretion rate; p3(X) is incorporated to Eq (3) to describe the abnormal response of beta-cells to a hostile environment that develops in a slow time scale. The exact forms of the influence functions pi(X) (i = 1, 2, 3) will be determined with X being an obesity-related factor in Section. We assume that p1(X) = 0, p2(X) = 1, and p3(X) = 0 when X = 0 so the model is in accordance with the undisturbed glucose-insulin regulatory model when no diabetogenic factors exist in normal subjects.
Yang B., Li J., Haller M.J., Schatz D.A, & Rong L. (2023). Modeling the progression of Type 2 diabetes with underlying obesity. PLOS Computational Biology, 19(2), e1010914.
Publication 2023
Diabetes Mellitus Disease Progression Factor X Glucose Hostility Insulin Insulin Secretion LEP protein, human Pancreatic beta Cells Patients Physiology, Cell

Top products related to «Insulin Secretion»

1
Rat insulin elisa kit by Mercodia
Sourced in Sweden, United States, United Kingdom, China
The Rat Insulin ELISA kit is a quantitative sandwich enzyme immunoassay designed for the measurement of insulin in rat samples. The kit utilizes a specific antibody coated on the microplate to capture insulin from the sample, which is then detected using a biotinylated detection antibody and streptavidin-HRP.
2
Bovine serum albumin (bsa) by Merck Group
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Bovine serum albumin (BSA) is a common laboratory reagent derived from bovine blood plasma. It is a protein that serves as a stabilizer and blocking agent in various biochemical and immunological applications. BSA is widely used to maintain the activity and solubility of enzymes, proteins, and other biomolecules in experimental settings.
3
Human insulin elisa kit by Mercodia
Sourced in Sweden, United States
The Human Insulin ELISA Kit is a quantitative in vitro diagnostic test used to measure human insulin levels in serum, plasma, and other biological fluids. The kit employs the enzyme-linked immunosorbent assay (ELISA) technique to detect and quantify human insulin concentrations.
4
Mouse insulin elisa kit by Mercodia
Sourced in Sweden, United States, Japan
The Mouse Insulin ELISA Kit is a quantitative sandwich enzyme-linked immunosorbent assay (ELISA) designed for the measurement of mouse insulin in serum, plasma, and cell culture supernatants. The kit utilizes a pair of highly specific antibodies directed against distinct epitopes of mouse insulin.
5
Rpmi 1640 medium by Thermo Fisher Scientific
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RPMI 1640 medium is a commonly used cell culture medium developed at Roswell Park Memorial Institute. It is a balanced salt solution that provides essential nutrients, vitamins, and amino acids to support the growth and maintenance of a variety of cell types in vitro.
6
Ultra sensitive mouse insulin elisa kit by Crystal Chem
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The Ultra Sensitive Mouse Insulin ELISA Kit is a laboratory equipment designed for the quantitative detection of mouse insulin in biological samples. It utilizes the enzyme-linked immunosorbent assay (ELISA) technique to provide a sensitive and accurate measurement of insulin levels.
7
Rpmi 1640 by Thermo Fisher Scientific
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RPMI 1640 is a common cell culture medium used for the in vitro cultivation of a variety of cells, including human and animal cells. It provides a balanced salt solution and a source of essential nutrients and growth factors to support cell growth and proliferation.

More about "Insulin Secretion"

Insulin secretion is a complex physiological process that involves the release of insulin from pancreatic beta cells in response to various stimuli, such as increased blood glucose levels.
This process is critical for maintaining glucose homeostasis and preventing metabolic disorders like diabetes.
The pathway of insulin secretion begins with the uptake of glucose by the beta cells, followed by the generation of ATP.
This triggers the exocytosis of insulin-containing secretory vesicles, leading to the release of insulin into the bloodstream.
Optimal insulin secretion is essential for regulating blood sugar levels and preventing hyperglycemia or hypoglycemia.
Researchers studying insulin secretion can leverage advanced tools and technologies to streamline their workflow and accelerate discovery.
PubCompare.ai's AI-driven platform, for example, allows scientists to easily locate and compare the most effective protocols and methods for studying insulin secretion, including those involving Rat insulin ELISA kits, Bovine serum albumin, Human insulin ELISA kits, Mouse insulin ELISA kits, RPMI 1640 medium, and Ultra Sensitive Mouse Insulin ELISA Kits.
By utilizing these resources, researchers can optimize their insulin secretion studies, leading to more reproducible and accurate results.
Additionally, the platform's advanced AI-powered comparisons can help identify the most effective methods and products, allowing researchers to make informed decisions and progress their work more efficiently.
Overall, understanding the intricacies of insulin secretion is crucial for addressing metabolic disorders and developing effective treatments.
With the help of innovative tools and technologies, researchers can enhance their insulin secretion studies and contribute to the advancement of this critical area of research.