Feasibility of a New Approach to Initiate Insulin in Type 2 Diabetes.

BACKGROUND: Treatment inertia and prescription complexity are among reasons that people with type 2 diabetes (T2D) do not reach glycemic targets. This study investigated feasibility of a new approach to basal insulin initiation, where the dose needed to reach a glycemic target is estimated from two weeks of insulin and continuous glucose monitoring (CGM) data. METHODS: This was an exploratory single arm study with a maximum length of 84 days. Eight insulin naïve people with T2D, planning to initiate basal insulin, wore a CGM throughout the study period. A predetermined regime was followed for the first two weeks after which the end dose was estimated. The clinician decided whether to follow this advice and continued the titration until target was reached using a twice weekly stepwise titration algorithm. The primary outcome was the comparison between the estimated and the actual end doses. RESULTS: Median age of participants was 57 years (range: 50-77 years), duration of diabetes was 16 years (range: 5-29 years), and Bodi Mass Index (BMI) was 30.2 kg/m2 (range: 22.0-36.0 kg/m2). The median study end dose was 37 U (range: 20-123 U). The estimated end dose was smaller than or equal to the study end dose in all cases, with median error of 26.7% (range: 0.0%-75.8% underestimation). No self-monitoring of blood glucose values were below 70 mg/dL and no severe hypoglycemia occurred. CONCLUSION: While accuracy may be improved, it was found safe to predict the study end dose of insulin degludec from two weeks of data.

Rapid inflammation and early degeneration of bone and cartilage revealed in a time-course study of induced haemarthrosis in haemophilic rats.

OBJECTIVES: Detailed knowledge of the sequential cell and tissue responses following haemarthrosis is important for a deep understanding of the pathological process initiated upon extensive bleeding into the joint causing haemophilic arthropathy (HA). The underlying pathobiology driving haemarthrosis towards HA has been difficult to establish in detail, although animal models have shed light on some processes. Previous studies have focused on a single or a few distant time points and often only characterizing one tissue type of the joint. The objective of this study was, therefore, to carefully map early onset of synovitis and HA following induced haemarthrosis. METHODS: One hundred and thirty haemophilia A rats were subjected to induced haemarthrosis or a sham procedure in full anaesthesia and euthanized from 30 min to 7 days after the procedure. Pathological changes of the joints were visualized using micro-computed tomography, histology and immunohistochemistry. RESULTS: Synovitis developed within 24 h and was dominated by myeloid cell infiltrations. Cartilage and bone pathology were evident as early as 48-96 h after haemarthrosis, and the pathology rapidly progressed with extensive periosteal bone formation and formation of subchondral cysts. CONCLUSION: Fast, extensive and simultaneous cartilage and bone degeneration developed shortly after haemarthrosis, as shown by the detailed mapping of the early pathogenesis of HA. The almost immediate loss of cartilage and the pathological bone turnover suggest a direct influence of blood on these processes and are unlikely to be attributed simply to an indirect effect of inflammation.

Modelling of fasting glucose-insulin dynamics from sparse data.

With the fast growth of diabetes prevalence, the disease is now considered an epidemic. Diabetes is characterized by elevated glucose levels, that may be treated with insulin. Tight control of glucose is essential for prevention of complications and patients' well-being. In this paper we model the fasting glucose-insulin dynamics in type 2 diabetes, aiming at controlling the glucose level. Relevant clinical data are typically sparse and have a sampling period much greater than the fast dynamics in the glucose-insulin dynamics in humans. We adapt a physiological model such that important slow non-linear dynamics are identifiable and test the resulting model on deterministic simulated data and sparse, slow sampled clinical data.

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes.

BACKGROUND: Bolus calculators help patients with type 1 diabetes to mitigate the effect of meals on their blood glucose by administering a large amount of insulin at mealtime. Intraindividual changes in patients physiology and nonlinearity in insulin-glucose dynamics pose a challenge to the accuracy of such calculators. METHOD: We propose a method based on a continuous-discrete unscented Kalman filter to continuously track the postprandial glucose dynamics and the insulin sensitivity. We augment the Medtronic Virtual Patient (MVP) model to simulate noise-corrupted data from a continuous glucose monitor (CGM). The basal rate is determined by calculating the steady state of the model and is adjusted once a day before breakfast. The bolus size is determined by optimizing the postprandial glucose values based on an estimate of the insulin sensitivity and states, as well as the announced meal size. Following meal announcements, the meal compartment and the meal time constant are estimated, otherwise insulin sensitivity is estimated. RESULTS: We compare the performance of a conventional linear bolus calculator with the proposed bolus calculator. The proposed basal-bolus calculator significantly improves the time spent in glucose target ( P < .01) compared to the conventional bolus calculator. CONCLUSION: An adaptive nonlinear basal-bolus calculator can efficiently compensate for physiological changes. Further clinical studies will be needed to validate the results.

Comparison of three nonlinear filters for fault detection in continuous glucose monitors.

The purpose of this study is to compare the performance of three nonlinear filters in online drift detection of continuous glucose monitors. The nonlinear filters are the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the particle filter (PF). They are all based on a nonlinear model of the glucose-insulin dynamics in people with type 1 diabetes. Drift is modelled by a Gaussian random walk and is detected based on the statistical tests of the 90-min prediction residuals of the filters. The unscented Kalman filter had the highest average F score of 85.9%, and the smallest average detection delay of 84.1%, with the average detection sensitivity of 82.6%, and average specificity of 91.0%.

Echoes from the past: a healthy Baltic Sea requires more effort.

Integrated sediment multiproxy studies and modeling were used to reconstruct past changes in the Baltic Sea ecosystem. Results of natural changes over the past 6000 years in the Baltic Sea ecosystem suggest that forecasted climate warming might enhance environmental problems of the Baltic Sea. Integrated modeling and sediment proxy studies reveal increased sea surface temperatures and expanded seafloor anoxia (in deep basins) during earlier natural warm climate phases, such as the Medieval Climate Anomaly. Under future IPCC scenarios of global warming, there is likely no improvement of bottom water conditions in the Baltic Sea. Thus, the measures already designed to produce a healthier Baltic Sea are insufficient in the long term. The interactions between climate change and anthropogenic impacts on the Baltic Sea should be considered in management, implementation of policy strategies in the Baltic Sea environmental issues, and adaptation to future climate change.

Model-based closed-loop glucose control in type 1 diabetes: the DiaCon experience.

BACKGROUND: To improve type 1 diabetes mellitus (T1DM) management, we developed a model predictive control (MPC) algorithm for closed-loop (CL) glucose control based on a linear second-order deterministic-stochastic model. The deterministic part of the model is specified by three patient-specific parameters: insulin sensitivity factor, insulin action time, and basal insulin infusion rate. The stochastic part is identical for all patients but identified from data from a single patient. Results of the first clinical feasibility test of the algorithm are presented. METHODS: We conducted two randomized crossover studies. Study 1 compared CL with open-loop (OL) control. Study 2 compared glucose control after CL initiation in the euglycemic (CL-Eu) and hyperglycemic (CL-Hyper) ranges, respectively. Patients were studied from 22:00-07:00 on two separate nights. RESULTS: Each study included six T1DM patients (hemoglobin A1c 7.2% ± 0.4%). In study 1, hypoglycemic events (plasma glucose < 54 mg/dl) occurred on two OL and one CL nights. Average glucose from 22:00-07:00 was 90 mg/dl [74-146 mg/dl; median (interquartile range)] during OL and 108 mg/dl (101-128 mg/dl) during CL (determined by continuous glucose monitoring). However, median time spent in the range 70-144 mg/dl was 67.9% (3.0-73.3%) during OL and 80.8% (70.5-89.7%) during CL. In study 2, there was one episode of hypoglycemia with plasma glucose <54 mg/dl in a CL-Eu night. Mean glucose from 22:00-07:00 and time spent in the range 70-144 mg/dl were 121 mg/dl (117-133 mg/dl) and 69.0% (30.7-77.9%) in CL-Eu and 149 mg/dl (140-193 mg/dl) and 48.2% (34.9-72.5%) in CL-Hyper, respectively. CONCLUSIONS: This study suggests that our novel MPC algorithm can safely and effectively control glucose overnight, also when CL control is initiated during hyperglycemia.

Grey-box modelling of aeration tank settling.

A model of the concentrations of suspended solids (SS) in the aeration tanks and in the effluent from these during Aeration tank settling (ATS) operation is established. The model is based on simple SS mass balances, a model of the sludge settling and a simple model of how the SS concentration in the effluent from the aeration tanks depends on the actual concentrations in the tanks and the sludge blanket depth. The model is formulated in continuous time by means of stochastic differential equations with discrete-time observations. The parameters of the model are estimated using a maximum likelihood method from data from an alternating BioDenipho waste water treatment plant (WWTP). The model is an important tool for analyzing ATS operation and for selecting the appropriate control actions during ATS, as the model can be used to predict the SS amounts in the aeration tanks as well as in the effluent from the aeration tanks.