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Objective: This study aimed to determine the maximum safe dose of intranasal insulin administration during cardiac surgery. Methods: This open-label, Phase 1, single-center, dose-escalation clinical trial recruited patients scheduled to undergo elective cardiac surgery or major vascular surgery requiring cardiopulmonary bypass between February and September 2021. They were grouped into 5 dose-escalation cohorts and administered 0, 40, 80, 160, and 240 IU insulin (n = 6 in each group) via a metered nasal dispenser after the induction of general anesthesia. Blood samples were collected at 10-minute intervals for the first 60 minutes and at 30-minute intervals thereafter. Hypoglycemia was defined as a blood glucose level <70 mg/dL. Patient recruitment was terminated after hypoglycemia was observed in 2 patients in any of the groups. Results: In total, 27 of 29 enrolled patients were administered intranasal insulin or saline. Hypoglycemia was not observed after the administration of intranasal insulin in the 0, 40, 80, or 160 IU groups; however, it was observed in 2 of 3 patients in the 240 IU group. The serum insulin concentration was elevated in the 160-IU group, but the C-peptide concentration was not elevated in any of the groups. Conclusions: The administration of up to 160 IU intranasal insulin did not induce clinically significant hypoglycemia. However, 160 IU intranasal insulin should be administered cautiously because insulin can enter the systemic circulation in a dose-dependent manner.

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Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC 9.49±3.81 mU/L, and PGC 0.89±0.19 mmol/L. For human, the OGI with PFM has the promise to identify disturbances (95.46%±0.65% accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.

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Insulin, as a peptide hormone drug, is susceptible to changes in stability when exposed to environmental factors under storage. Proper storage according to the manufacturer's recommendations is important to maintain its potency and enable precise dosing for people with diabetes (PwD). While it is reasonable to assume that transport conditions and temperature are well controlled during the supply chain, little is known about insulin storage after dispensing and insulin potency at the moment of administration. Insulin is exposed to various environmental factors when carried by PwD and storage recommendations are often not met when it is stored in household refrigerators. It is difficult to assess changes in insulin potency in clinical practice, and there is a gap in the current scientific literature on insulin stability. Package leaflet recommendations only give limited information on the impact of improper storage conditions on insulin stability and guidelines by health organizations are inconsistent. Given the importance of precise dosing in diabetes care, there is a need for more transparency on insulin stability, awareness for proper storage among health care professionals and PwD as well as clear guidelines and practical storage recommendations from manufacturers and health organizations.

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BACKGROUND: Recent reports have suggested that insulin vials purchased in community pharmacies do not meet the minimum required intact insulin concentration (≥95 U/mL) as defined by the United States Pharmacopeia. We sought to independently obtain multidose human insulin vials from a variety of community pharmacies across the state of Washington and quantitatively measure intact insulin. METHODS: Sixty 10-mL vials of insulin (n = 30 regular human insulin and n = 30 neutral protamine Hagedorn insulin) were purchased and assayed. To ensure random selection of lots and supply chain sources, insulin samples were purchased on a variety of calendar dates from various pharmacy locations across Washington State, inclusive of both chain and independent pharmacies. All samples were assessed for intact insulin concentration via both Ultra Performance Liquid Chromatography coupled with UV detection (UPLC-UV) and Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). RESULTS: When considering all samples (N = 60), the mean concentration was 101.8 ± 4.4and 91.5 ± 1.9 U/mL as determined by UPLC-UV and UPLC-MS, respectively. Measured concentrations ranged from 90.0 to 108.4 U/mL when assayed by UV UPLC and 86.1 to 95.4 U/mL for UPLC-MS. CONCLUSION: To our knowledge, this is the first study following the report by Carter et al that assessed human insulin concentrations by both UPLC-UV and UPLC-MS. These findings are important because they demonstrate that the results obtained from these two methods differ and that the method used must be considered when interpreting findings.

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Straightforward testing procedures to enable the diagnosis of insulin dysregulation (ID) in horses that are suitable for use in daily veterinary practice are needed because of the risk that ID could result in laminitis. In our study (that included 90 horses), we compared the proportion of horses classified as ID-positive, ID-suspect, and ID-not diagnosed according to the basal insulin concentration (BIC) with the proportion of horses classified as ID-positive or ID-negative according to a practical and feasible version of an oral sugar test (OST). Furthermore, BIC, basal glucose concentration, and insulin and glucose concentration after OST were analyzed and compared. In the total study population, the OST detected significantly more ID-positive cases than the BIC, with cutoffs at equivalent specificities. Receiver operating characteristics analysis showed that at a lower cutoff, the sensitivity of the BIC could be increased, but at the cost of a significantly lower specificity. Taking this into account, we found diagnostic performance of the OST to be considerably better than the BIC and therefore considered it more recommendable for use as a screening test for ID in ambulatory practice. Furthermore, we investigated the relationship between body condition score and breed type with glucose and insulin concentration as determined after our version of the OST. For that purpose, the study group was subdivided into lean, moderate, and obese horses and "easy keeper breeds" versus "non-easy keeper breeds". Results supported the general assumption that obese horses and "easy keeper breeds" are more prone to the development of ID.

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INTRODUCTION: Standard concentration (100 units/mL) mealtime insulin is frequently used to treat patients with type 1 (T1D) and type 2 diabetes (T2D). A more concentrated version of the medication (200 units/mL) has been available in Italy since 2016. This concentrated version is bioequivalent to the standard version and delivers the same amount of medication but in half the volume of liquid. The purpose of this study was to examine patient preferences and estimate health state utilities associated with standard and concentrated rapid-acting mealtime analog insulin. METHODS: Participants with T1D and T2D in Italy valued two health states in time trade-off interviews. The descriptions of diabetes and treatment in the two health states were identical, differing only in terms of insulin concentration (e.g., half as much liquid for the same dose, less effort needed to press the injection button, and fewer injection pens required with concentrated insulin). To ensure participants understood the health states, they were shown a short video illustrating the differences between concentrations. RESULTS: A total of 217 participants completed the interviews (49.8% male; mean age 56.1 years; 109 from Milan; 108 from Rome; 12.0% T1D; 88.0% T2D). When asked which health state they preferred, 98.2% responded the concentrated version, 0.9% said the standard version, and 0.9% had no preference. Mean [standard deviation (SD)] utilities rounded to three decimals were 0.892 (0.099) for the concentrated version and 0.884 (0.101) for the standard version. The mean (SD; p value) utility difference between the standard and concentrated rapid-acting insulin was 0.007 (0.019; p < 0.0001). CONCLUSIONS: Findings from this study provide insight into patient preferences associated with concentration of rapid-acting insulin. Although the difference in utility is small, patients consistently preferred the concentrated formulation over the standard insulin, and for some patients this difference had an impact on utility valuations. These results suggest that the concentration of rapid-acting insulin should be considered because it could affect treatment preference and quality of life. FUNDING: Eli Lilly and Company.

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An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

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Background: Gestational diabetes mellitus (GDM) is a condition, in which women develop high blood sugar levels during pregnancy without having diabetes. Evidence on the effects of probiotics on the blood glucose levels of women with GDM is inconsistent. Objective: The present study aimed to investigate the effects of probiotics on the blood glucose levels of pregnant women. Methods: Online databases, such as PubMed, Cochrane, and Excerpta Medica Database (EMBASE) were searched for randomized controlled trials (RCTs) published before July 2018. Trials had to meet the inclusion criteria of our study. Methodological quality and risk bias were independently assessed by two reviewers. Data were pooled using a random effects model and were expressed as the mean difference (MD) and 95% confidence interval (CI). Heterogeneity was evaluated and quantified as I². Results: In total, 12 RCTs were included in this study. Studies have shown that the use of probiotics significantly reduced the fasting blood glucose (FBG) level (MD: -0.10 mmol/L; 95% CI: -0.19, -0.02), insulin concentration (MD: -2.24 µIU/mL; 95% CI: -3.69, -0.79), Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) score (MD: -0.47; 95% CI: -0.74, -0.21), and Homeostasis model of assessment-estimated ß cell function (HOMA-B) score (MD: -20.23; 95% CI: -31.98, -8.49) of pregnant women. In a subgroup analysis, whether the blood glucose-lowering effect of probiotics influenced the diagnosis of pregnant women with GDM was assessed. The results showed that probiotics had significantly reduced the fasting blood glucose (FBG) level (MD: -0.10 mmol/L; 95% CI: -0.17, -0.04) and HOMA-IR score (MD: -0.37; 95% CI: -0.72, -0.02) of pregnant women who were not diagnosed with GDM. Conclusion: Probiotics reduce the blood glucose level of pregnant women, especially without GDM diagnosis. However, further research using RCTs must be conducted to validate the results of the present study.

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BACKGROUND: The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. METHOD: An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. RESULTS: The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. CONCLUSIONS: The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.

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The aim of the present research was to assess factors associated with first-lactation milk yield in dairy heifers, including maternal and environmental factors, factors related to the development of the heifer and factors related to its offspring such as gender of the calf. In addition, the potential underlying mechanism, in particular metabolic adaptations, was further explored. Data on body growth, reproduction and milk yield of 74 Holstein Friesian heifers on three herds in Flanders (Belgium) were collected. At birth, body measurements of the heifers were recorded and blood samples were taken (in order) to determine basal glucose and insulin concentrations. Body measurements were assessed every 3 months until first calving, and gender and weight of their first calf were recorded. Information on fertility and milk yield of the heifer and its dam were collected from the herd databases. Daily temperature and photoperiod were recorded from the database of the Belgian Royal Meteorological Institute. Linear mixed models were run with herd as a random factor, to account for differences in herd management. Heifers grew 867±80.7 g/day during their first year of life and were inseminated at 14.8±1.34 months. First calving took place at 24.5±1.93 months, at a weight of 642±61.5 kg and heifers produced 8506±1064 kg energy corrected milk during their first 305-day lactation. Regression models revealed that none of the maternal factors such as milk yield and parity, nor the growth of the heifer during the 1st year of life were associated with milk yield during first lactation. Age, and to a lesser extent BW at first parturition were positively associated with first-lactation milk yield. In addition, the season of birth, but not calving, had a significant influence on milk yield, with winter-born heifers producing less than heifers born in any other season. The lower yielding winter-born heifers had higher insulin concentrations at birth, whereas glucose concentrations were similar, the latter being suggestive for lower insulin sensitivity of the peripheral tissues. Furthermore, environmental temperature at the end of gestation was negatively correlated with neonatal insulin concentrations. In conclusion, results of the present study suggest heifers born during the hotter months are born with a higher peripheral insulin sensitivity, finally leading to a higher first-lactation milk yield.

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Insulin pens are more accurate and easier to teach than other methods of insulin delivery. They also do not suffer from the risk of mismatch of insulin concentration and type of insulin syringe. The ISO standard used to test insulin pens, however, needs to be updated to reflect their clinical use.

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Introduction: Post-prandial hyperglycemia is an important independent risk factor in the development of cardiovascular disease in diabetes. This randomised cross-over study was conducted to compare the post-prandial glycemic and insulin responses to both high and low glycemic index (GI) meals in patients with type 2 diabetes (T2DM). Methods: A total of 41 patients with established T2DM (16 males, 25 males, Age= 55 ± 10 years and BMI = 27 ± 4kg/m²) were randomly given either a High GI or a Low GI meal in a cross-over manner. Both test meals were separated by one week washout periods. The meals contained almost the same amount of energy and macronutrients with the exception of the GI values (High GI=70 vs Low GI= 36). Venous blood was taken through an indwelling catheter periodically at 0, 30, 60, 90, 120, 150 and 180 minutes respectively. The incremental area under the curve (iAUC) was used to calculate the post-prandial glycemia and insulin excursion over the 3-hour period. Results: The low GI meal induced lower glycemic responses at times 30,60,90 and 120 minutes (mean±SE; low GI=8.1±0.4, 9.1±0.4 and 8.9±0.4 and 8.5±0.4mmol/l vs high GI=9.1±0.4, 10.7±0.4, 11.0±0.5 and 9.7±0.5mmol/l) and reduced the insulin levels at time 60,90,120 and 150 minutes (mean±SE; low GI=215.93±15.9mmol.L/minute vs high GI=419.52±32.7mmol.L/minute) and insulin (mean±SE;low GI=1439.76±226 vs high GI=2372.76±317mIU.ml/min) curves were lower after the low GI than high GI meal respectively (p<0.05). Conclusion: The low GI meal has the ability to reduce the post-prandial hyperglycemia as well insulin responses in type 2 diabetes patients.