Insulin administration and rate of glucose appearance in people with type 1 diabetes

CTRP1: A novel player in cardiovascular and metabolic diseases

Cardiovascular diseases (CVDs) are a series of diseases induced by inflammation and lipid metabolism disorders, among others. Metabolic diseases can cause inflammation and abnormal lipid metabolism. C1q/TNF-related proteins 1 (CTRP1) is a paralog of adiponectin that belongs to the CTRP subfamily. CTRP1 is expressed and secreted in adipocytes, macrophages, cardiomyocytes, and other cells. It promotes lipid and glucose metabolism but has bidirectional effects on the regulation of inflammation. Inflammation can also inversely stimulate CTRP1 production. A vicious circle may exist between the two. This article introduces CTRP1 from the structure, expression, and different roles of CTRP1 in CVDs and metabolic diseases, to summarize the role of CTRP1 pleiotropy. Moreover, the proteins which may interact with CTRP1 are predicted through GeneCards and STRING, speculating their effects, to provide new ideas for the study of CTRP1.

The carbohydrate-insulin model: a physiological perspective on the obesity pandemic

According to a commonly held view, the obesity pandemic is caused by overconsumption of modern, highly palatable, energy-dense processed foods, exacerbated by a sedentary lifestyle. However, obesity rates remain at historic highs, despite a persistent focus on eating less and moving more, as guided by the energy balance model (EBM). This public health failure may arise from a fundamental limitation of the EBM itself. Conceptualizing obesity as a disorder of energy balance restates a principle of physics without considering the biological mechanisms that promote weight gain. An alternative paradigm, the carbohydrate-insulin model (CIM), proposes a reversal of causal direction. According to the CIM, increasing fat deposition in the body-resulting from the hormonal responses to a high-glycemic-load diet-drives positive energy balance. The CIM provides a conceptual framework with testable hypotheses for how various modifiable factors influence energy balance and fat storage. Rigorous research is needed to compare the validity of these 2 models, which have substantially different implications for obesity management, and to generate new models that best encompass the evidence.

Carbohydrate restriction for diabetes: rediscovering centuries-old wisdom

Carbohydrate restriction, used since the 1700s to prolong survival in people with diabetes, fell out of favor after the discovery of insulin. Despite costly pharmacological and technological developments in the last few decades, current therapies do not achieve optimal outcomes, and most people with diabetes remain at high risk for micro- and macrovascular complications. Recently, low-carbohydrate diets have regained popularity, with preliminary evidence of benefit for body weight, postprandial hyperglycemia, hyperinsulinemia, and other cardiometabolic risk factors in type 2 diabetes and, with more limited data, in type 1 diabetes. High-quality, long-term trials are needed to assess safety concerns and determine whether this old dietary approach might help people with diabetes attain clinical targets more effectively, and at a lower cost, than conventional treatment.

Dual-hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions

Over the last few years, technological advances have led to tremendous improvement in the management of type 1 diabetes (T1D). Artificial pancreas systems have been shown to improve glucose control compared with conventional insulin pump therapy. However, clinically significant hypoglycemic and hyperglycemic episodes still occur with the artificial pancreas. Postprandial glucose excursions and exercise-induced hypoglycemia represent major hurdles in improving glucose control and glucose variability in many patients with T1D. In this regard, dual-hormone artificial pancreas systems delivering other hormones in addition to insulin (glucagon or amylin) may better reproduce the physiology of the endocrine pancreas and have been suggested as an alternative tool to overcome these limitations in clinical practice. In addition, novel ultra-rapid-acting insulin analogs with a more physiological time-action profile are currently under investigation for use in artificial pancreas devices, aiming to address the unmet need for further improvements in postprandial glucose control. This review article aims to discuss the current progress and future outlook in the development of novel ultra-rapid insulin analogs and dual-hormone closed-loop systems, which offer the next steps to fully closing the loop in the artificial pancreas.

Fully Automated Artificial Pancreas for Adults With Type 1 Diabetes Using Multiple Hormones: Exploratory Experiments

OBJECTIVES

A fully automated insulin-pramlintide-glucagon artificial pancreas that alleviates the burden of carbohydrate counting without degrading glycemic control was iteratively enhanced until convergence through pilot experiments on adults with type 1 diabetes.

METHODS

Nine participants (age, 37±13 years; glycated hemoglobin, 7.7±0.7%) completed two 27-hour interventions: a fully automated multihormone artificial pancreas and a comparator insulin-alone artificial pancreas with carbohydrate counting. The baseline algorithm was a model-predictive controller that administered insulin and pramlintide in a fixed ratio, with boluses triggered by a glucose threshold, and administered glucagon in response to low glucose levels.

RESULTS

The baseline multihormone dosing algorithm resulted in noninferior time in target range (3.9 to 10.0 mmol/L) (71%) compared with the insulin-alone arm (70%) in 2 participants, with minimal glucagon delivery. The algorithm was modified to deliver insulin and pramlintide more aggressively to increase time in range and maximize the benefits of glucagon. The modified algorithm displayed a similar time in range for the multihormone arm (79%) compared with the insulin-alone arm (83%) in 2 participants, but with undesired glycemic fluctuations. Subsequently, we reduced the glucose threshold that triggers glucagon boluses. This resulted in inferior glycemic control for the multihormone arm (81% vs 91%) in 2 participants. Thereafter, a model-based meal-detection algorithm to deliver insulin and pramlintide boluses closer to mealtimes was added and glucagon was removed. The final dual-hormone system had comparable time in range (81% vs 83%) in the last 3 participants.

CONCLUSION

The final version of the fully automated system that delivered insulin and pramlintide warrants a randomized controlled trial.

A Meal Detection Algorithm for the Artificial Pancreas: A Randomized Controlled Clinical Trial in Adolescents With Type 1 Diabetes

OBJECTIVE

We developed a meal detection algorithm for the artificial pancreas (AP+MDA) that detects unannounced meals and delivers automatic insulin boluses.

RESEARCH DESIGN AND METHODS

We conducted a randomized crossover trial in 11 adolescents aged 12-18 years with HbA_1c ≥7.5% who missed one or more boluses in the past 6 months. We compared 1) continuous subcutaneous insulin infusion (CSII), 2) artificial pancreas (AP), and 3) AP+MDA. Participants underwent three 9-h interventions involving breakfast with a bolus and lunch without a bolus.

RESULTS

In AP+MDA, the meal detection time was 40.0 (interquartile range 40.0-57.5) min. Compared with CSII, AP+MDA decreased the 4-h postlunch incremental area under the curve (iAUC) from 24.1 ± 9.5 to 15.4 ± 8.0 h ⋅ mmol/L (P = 0.03). iAUC did not differ between AP+MDA and AP (19.6 ± 10.4 h ⋅ mmol/L, P = 0.21) or between AP and CSII (P = 0.33). The AP+MDA reduced time >10 mmol/L (58.0 ± 26.6%) compared with CSII (79.6 ± 27.5%, P = 0.02) and AP (74.2 ± 20.6%, P = 0.047).

CONCLUSIONS

The AP+MDA improved glucose control after an unannounced meal.

Modeling glucose and free fatty acid kinetics in glucose and meal tolerance test

BACKGROUND

Quantitative evaluation of insulin regulation on plasma glucose and free fatty acid (FFA) in response to external glucose challenge is clinically important to assess the development of insulin resistance (World J Diabetes 1:36-47, 2010). Mathematical minimal models (MMs) based on insulin modified frequently-sampled intravenous glucose tolerance tests (IM-FSIGT) are widely applied to ascertain an insulin sensitivity index (IEEE Rev Biomed Eng 2:54-96, 2009). Furthermore, it is important to investigate insulin regulation on glucose and FFA in postprandial state as a normal physiological condition. A simple way to calculate the appearance rate (Ra) of glucose and FFA would be especially helpful to evaluate glucose and FFA kinetics for clinical applications.

METHODS

A new MM is developed to simulate the insulin modulation of plasma glucose and FFA, combining IM-FSIGT with a mixed meal tolerance test (MT). A novel simple functional form for the appearance rate (Ra) of glucose or FFA in the MT is developed. Model results are compared with two other models for data obtained from 28 non-diabetic women (13 African American, 15 white).

RESULTS

The new functional form for Ra of glucose is an acceptable empirical approximation to the experimental Ra for a subset of individuals. When both glucose and FFA are included in FSIGT and MT, the new model is preferred using the Bayes Information Criterion (BIC).

CONCLUSIONS

Model simulations show that the new MM allows consistent application to both IM-FSIGT and MT data, balancing model complexity and data fitting. While the appearance of glucose in the circulation has an important effect on FFA kinetics in MT, the rate of appearance of FFA can be neglected for the time-period modeled.

A predictive model of subcutaneous glucose concentration in type 1 diabetes based on Random Forests

In this study, an individualized predictive model of the subcutaneous glucose concentration in type 1 diabetes is presented, which relies on the Random Forests regression technique. A multivariate dataset is utilized concerning the s.c. glucose profile, the plasma insulin concentration, the intestinal absorption of meal-derived glucose and the daily energy expenditure. In an attempt to capture daily rhythms in glucose metabolism, we also introduce a time feature in the predictive analysis. The dataset comes from the continuous multi-day recordings of 27 type 1 patients in free-living conditions. Evaluating the performance of the proposed method by 10-fold cross validation, an average RMSE of 6.60, 8.15, 9.25 and 10.83 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively, was attained.

Absorption patterns of meals containing complex carbohydrates in type 1 diabetes

AIMS/HYPOTHESIS

Successful postprandial glycaemia management requires understanding of absorption patterns after meals containing variable complex carbohydrates. We studied eight young participants with type 1 diabetes to investigate a large low-glycaemic-load (LG) meal and another eight participants to investigate a high-glycaemic-load (HG) meal matched for carbohydrates (121 g).

METHODS

On Visit 1, participants consumed an evening meal. On follow-up Visit 2, a variable-target glucose clamp was performed to reproduce glucose and insulin levels from Visit 1. Adopting stable-label tracer dilution methodology, we measured endogenous glucose production on Visit 2 and subtracted it from total glucose appearance measured on Visit 1 to obtain meal-attributable glucose appearance.

RESULTS

After the LG meal, 25%, 50% and 75% of cumulative glucose appearance was at 88 ± 21, 175 ± 39 and 270 ± 54 min (mean ± SD), whereas glucose from the HG meal appeared significantly faster at 56 ± 12, 100 ± 25 and 153 ± 39 min (p < 0.001 to 0.003), and resulted in a 50% higher peak appearance (p < 0.001). Higher apparent bioavailability by 15% (p = 0.037) was observed after the LG meal. We documented a 20 min deceleration of dietary mixed carbohydrates compared with dietary glucose for the HG meal and a twofold deceleration for the LG meal.

CONCLUSIONS/INTERPRETATION

Absorption patterns may be influenced by glycaemic load and/or meal composition, affecting optimum prandial insulin dosing in type 1 diabetes.

Estimating postprandial glucose fluxes using hierarchical Bayes modelling

A new stochastic computational method was developed to estimate the endogenous glucose production, the meal-related glucose appearance rate (R(a meal)), and the glucose disposal (R(d)) during the meal tolerance test. A prior probability distribution was adopted which assumes smooth glucose fluxes with individualized smoothness level within the context of a Bayes hierarchical model. The new method was contrasted with the maximum likelihood method using data collected in 18 subjects with type 2 diabetes who ingested a mixed meal containing [U-¹³C]glucose. Primed [6,6-²H₂]glucose was infused in a manner that mimicked the expected endogenous glucose production. The mean endogenous glucose production, R(a meal), and R(d) calculated by the new method and maximum likelihood method were nearly identical. However, the maximum likelihood gave constant, nonphysiological postprandial endogenous glucose production in two subjects whilst the new method gave plausible estimates of endogenous glucose production in all subjects. Additionally, the two methods were compared using a simulated triple-tracer experiment in 12 virtual subjects. The accuracy of the estimates of the endogenous glucose production and R(a meal) profiles was similar [root mean square error (RMSE) 1.0±0.3 vs. 1.4±0.7 μmol/kg/min for EGP and 2.6±1.0 vs. 2.9±0.9 μmol/kg/min for R(a meal); new method vs. maximum likelihood method; P=NS, paired t-test]. The accuracy of R(d) estimates was significantly increased by the new method (RMSE 5.3±1.9 vs. 4.2±1.3; new method vs. ML method; P<0.01, paired t-test). We conclude that the new method increases plausibility of the endogenous glucose production and improves accuracy of glucose disposal compared to the maximum likelihood method.

Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression

Data-driven techniques have recently drawn significant interest in the predictive modeling of subcutaneous (s.c.) glucose concentration in type 1 diabetes. In this study, the s.c. glucose prediction is treated as a multivariate regression problem, which is addressed using support vector regression (SVR). The proposed method is based on variables concerning: (i) the s.c. glucose profile, (ii) the plasma insulin concentration, (iii) the appearance of meal-derived glucose in the systemic circulation, and (iv) the energy expenditure during physical activities. Six cases corresponding to different combinations of the aforementioned variables are used to investigate the influence of the input on the daily glucose prediction. The proposed method is evaluated using a dataset of 27 patients in free-living conditions. 10-fold cross validation is applied to each dataset individually to both optimize and test the SVR model. In the case where all the input variables are considered, the average prediction errors are 5.21, 6.03, 7.14 and 7.62 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively. The results clearly indicate that the availability of multivariable data and their effective combination can significantly increase the accuracy of both short-term and long-term predictions.

Validity of triple- and dual-tracer techniques to estimate glucose appearance

The triple-tracer (TT) dilution technique has been proposed to be the gold standard method to measure postprandial glucose appearance. However, validation against an independent standard has been missing. We addressed this issue and also validated the simpler dual-tracer (DT) technique. Sixteen young subjects with type 1 diabetes (age 19.5 ± 3.8 yr, BMI 23.4 ± 1.5 kg/m(2), HbA(1c) 8.7 ± 1.7%, diabetes duration 9.0 ± 6.9 yr, total daily insulin 0.9 ± 0.2 U·kg(-1)·day(-1), mean ± SD) received a variable intravenous 20% dextrose infusion enriched with [U-(13)C]glucose over 8 h to achieve postprandial-resembling glucose excursions while intravenous insulin was administered to achieve postprandial-resembling levels of plasma insulin. Primed [6,6-(2)H(2)]glucose was infused in a manner that mimicked the expected endogenous glucose production and [U-(13)C; 1,2,3,4,5,6,6-(2)H(7)]glucose was infused in a manner that mimicked the expected glucose appearance from a standard meal. Plasma glucose enrichment was measured by gas chromatography-mass spectrometry. The intravenous dextrose infusion served as an independent standard and was reconstructed using the TT and DT techniques with the two-compartment Radziuk/Mari model and an advanced stochastic computational method. The difference between the infused and reconstructed dextrose profile was similar for the two methods (root mean square error 6.6 ± 1.9 vs. 8.0 ± 3.5 μmol·kg(-1)·min(-1), TT vs. DT, P = NS, paired t-test). The TT technique was more accurate in recovering the overall dextrose infusion (100 ± 9 and 92 ± 12%; P = 0.02). The root mean square error associated with the mean dextrose infusion profile was 2.5 and 3.3 μmol·kg(-1)·min(-1) for the TT and DT techniques, respectively. We conclude that the TT and DT techniques combined with the advanced computational method can measure accurately exogenous glucose appearance. The TT technique tends to outperform slightly the DT technique, but the latter benefits from reduced experimental and computational complexity.

Pathophysiology of postprandial hyperglycaemia in women with type 1 diabetes during pregnancy

AIMS/HYPOTHESIS

Although maternal hyperglycaemia is associated with increased risk of adverse pregnancy outcome, the mechanisms of postprandial hyperglycaemia during pregnancy are poorly understood. We aimed to describe glucose turnover in pregnant women with type 1 diabetes, according to stage of gestation (early vs late gestation).

METHODS

The rates of systemic glucose appearance (R(a)) and glucose disposal (R(d)) were measured in ten pregnant women with type 1 diabetes during early (12-16 weeks) and late (28-32 weeks) gestation. Women ate standardised meals--a starch-rich 80 g carbohydrate dinner and a sugar-rich 60 g carbohydrate breakfast--and fasted between meals and overnight. Stable-label isotope tracers ([6,6-(2)H(2)]glucose and [U-(13)C]glucose) were used to determine R(a), R(d) and glucose bioavailability. Closed-loop insulin delivery maintained stable glycaemic conditions.

RESULTS

There were no changes in fasting R(a) (10 ± 2 vs 11 ± 2 μmol kg(-1) min(-1); p = 0.32) or fasting R(d) (11 ± 2 vs 11 ± 1 μmol kg(-1) min(-1); p = 0.77) in early vs late gestation. There was increased hepatic insulin resistance (381 ± 237 vs 540 ± 242 μmol kg(-1) min(-1) × pmol/l; p = 0.04) and decreased peripheral insulin sensitivity (0.09 ± 0.04 vs 0.05 ± 0.02 μmol kg(-1) min(-1) per pmol/l dinner, 0.11 ± 0.05 vs 0.07 ± 0.03 μmol kg(-1) min(-1) per pmol/l breakfast; p = 0.002) in late gestation. It also took longer for insulin levels to reach maximal concentrations (49 [37-55] vs 71 [52-108] min; p = 0.004) with significantly delayed glucose disposal (108 [87-125] vs 135 [110-158] min; p = 0.005) in late gestation.

CONCLUSIONS/INTERPRETATION

Postprandial glucose control is impaired by significantly slower glucose disposal in late gestation. Early prandial insulin dosing may help to accelerate glucose disposal and potentially ameliorate postprandial hyperglycaemia in late pregnancy.

TRIAL REGISTRATION

ISRCTN 62568875

FUNDING

Diabetes UK Project Grant BDA 07/003551. H.R. Murphy is funded by a National Institute for Health Research (NIHR) research fellowship (PDF/08/01/036). Supported also by the Juvenile Diabetes Research Foundation (JDRF), Abbott Diabetes Care (Freestyle Navigator CGM and sensors free of charge), Medical Research Council Centre for Obesity and Related Metabolic Diseases and NIHR Cambridge Biomedical Research Centre.

Obstetric and perinatal outcomes in pregnancies complicated by Type 1 and Type 2 diabetes: influences of glycaemic control, obesity and social disadvantage

AIMS

To compare obstetric and perinatal outcomes in women with Type 1 and Type 2 diabetes and relate these to maternal risk factors.

METHODS

Prospective cohort study of 682 consecutive diabetic pregnancies in East Anglia during 2006-2009. Relationships between congenital malformation, perinatal mortality and perinatal morbidity (large for gestational age, preterm delivery, neonatal care) with maternal age, parity, ethnicity, glycaemic control, obesity and social disadvantage were examined using bivariable and multivariate models.

RESULTS

There were 408 (59.8%) Type 1 and 274 (40.2%) Type 2 diabetes pregnancies. Women with Type 2 diabetes were older (P < 0.001), heavier (P < 0.0001), more frequently multiparous (P < 0.001), more ethnically diverse (p < 0.0001) and more socially disadvantaged (P = 0.0004). Although women with Type 2 diabetes had shorter duration of diabetes (P < 0.0001) and better pre-conception glycaemic control [HbA(1c) 52 mmol/mol (6.9%) Type 2 diabetes vs. 63 mmol/l (7.9%) Type 1 diabetes; p < 0.0001), rates of congenital malformation and perinatal mortality were comparable. Women with Type 2 diabetes had fewer large-for-gestational-age infants (37.6 vs. 52.9%, P < 0.0008), fewer preterm deliveries (17.5 vs. 37.1%, P < 0.0001) and their offspring had fewer neonatal care admissions (29.8 vs. 43.2%, P = 0.001). Third trimester HbA(1c) (OR 1.35, 95% CI 1.09-1.67, P = 0.006) and social disadvantage (OR 0.80, 95% CI 0.67-0.98; P = 0.03) were risk factors for large for gestational age.

CONCLUSIONS

Despite increased age, parity, obesity and social disadvantage, women with Type 2 diabetes had better glycaemic control, fewer large-for-gestational-age infants, fewer preterm deliveries and fewer neonatal care admissions. Better tools are needed to improve glycaemic control and reduce the rates of large for gestational age, particularly in Type 1 diabetes.