A combined in vivo and in silico model shows specific predictors of individual trans-generational diabetic programming.

Diabetic pregnancies are cleary associated with maternal type 2 diabetes and metabolic syndrome as well as atherosclerotic diseases in the offspring. The global prevalence of hyperglycemia in pregnancy was estimated as 15.8% of live births to women in 2019, with an upward trend. Numerous parental risk factors as well as trans-generational mechanisms targeting the utero-placental system, leading to diabetes, dysmetabolic and atherosclerotic conditions in the next generation, seem to be involved within this pathophysiological context. To focus on the predictable impact of trans-generational diabetic programming, we studied age- and gender-matched offspring of diabetic and nondiabetic mothers. The offspring generation consists of three groups: C57BL/6-J-Ins2Akita (positive control group), wild-type C57BL/6-J-Ins2Akita (experimental group), and C57BL/6-J mice (negative control group). We undertook intraperitoneal glucose tolerance tests at 3 and 11 weeks of age. Moreover, this in vivo model was complemented by a corresponding in silico model. Although at 3 weeks of age, no significant effects could be observed, we could demonstrate at 11 weeks of age characteristic and significant differences in relation to maternal diabetic imprinting based on the in silico model-based predictors. These predictors allow the generation of a concise classification tree assigning maternal diabetic imprinting correctly in 91% of study cases. Our data show that hyperglycemic in utero milieu contributes to trans-generational diabetic programming leading to impaired glucose tolerance in the offspring of diabetic mothers early on. These observations can be clearly and early distinguished from genetically determined diabetes, for example, type 1 diabetes, in which basal glucose values are significantly raised.

Model-driven Classification of Different Diabetes Types within a Personalized Diabetes Management.

In this paper, an approach for the classification of dynamic models of diabetes mellitus is presented. The parameter vector of a personalized patient model, which has been identified e.g. by parameter estimation, is used as a classification feature. Principle component analysis and a support vector machine are used to reduce the feature space and to find a suitable classifier. The data covers type 1, type 2, and non-diabetic virtual subjects. Classification results show a good distinguishability between the classes, whereby the method may serve as a supplement in the area of model-driven diabetes management.

Modular AUV System with Integrated Real-Time Water Quality Analysis.

This paper describes the concept, the technical implementation and the practical application of a miniaturized sensor system integrated into an autonomous underwater vehicle (AUV) for real-time acquisition of water quality parameters. The main application field of the presented system is the analysis of the discharge of nitrates into Norwegian fjords near aqua farms. The presented system was developed within the research project SALMON (Sea Water Quality Monitoring and Management) over a three-year period. The development of the sensor system for water quality parameters represented a significant challenge for the research group, as it was to be integrated in the payload unit of the autonomous underwater vehicle in compliance with the underwater environmental conditions. The German company -4H- JENA engineering GmbH (4HJE), with experience in optical in situ-detection of nutrients, designed and built the measurement system. As a carrier platform, the remotely operated vehicle (ROV) "CWolf" from Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung - Institutsteil Angewandte Systemtechnik (IOSB-AST) modified to an AUV was deployed. The concept presented illustrates how the measurement system can be integrated easily into the vehicle with a minimum of hard- and software technical interfaces.

Novel individual metabolic profile characterizes the protein kinase B-alpha (pkbα-/-) in vivo model.

CONTEXT: Type 2 diabetes and associated co-morbidities run epidemic waves worldwide. Since pathophysiological constellations are individual and display a wide spread of dysmetabolic profiles personalized health care assessments start to emerge. Therefore, we established a specific in silico assessment tool targeting metabolic characterizations individually. MATERIALS AND METHODS: Values obtained from oral glucose and intraperitoneal insulin tolerance tests performed on pkbα(-/-) mice (KO) as well as age- and gender-matched controls (WT) were analysed using our established in silico model. RESULTS: Generally, male pkbα(-/-) mice (KO) presented significantly increased insulin sensitivity at an age of 6 months compared with age-matched WTs (p = 0.036). Female KO and WT groups displayed improved glucose sensitivities compared with age-matched males (for WT p ≤ 0.011). DISCUSSION AND CONCLUSION: Specific metabolic characterization should be assessed individually. Therefore, our in silico model enables novel insights inaugurating specific primary preventive strategies targeting individual metabolic profiling precisely.

Identification of tissue differentiation rates in a mechanobiological model of fracture healing.

As a basis for model-based analysis of the processes in secondary fracture healing, a dynamical model is presented that characterises the physiological status in the fracture area by the location-dependent composition of tissues. Five types of tissue are distinguished: connective tissue, cartilage, bone, haematoma and avascular bone. A rule base is given that describes dynamical tissue differentiation processes. The rules consider not only a mechanical stimulus but also osteogenic and a vasculative factors as biological stimuli. Within this model structure, it is possible, e.g., to distinguish intramembranous from endochondral ossification processes. An objective function is introduced to assess accordance between the model-based simulation results and reference healing stages. By minimising this objective function, relevant tissue differentiation rates can be determined. For a reference process of secondary fracture healing it could be shown that the intramembranous ossification rate of 0.313%/day (from connective tissue to bone) is much smaller than the endochondral ossification rate of 1.136%/day (from cartilage to bone). In order to verify the model approach, it is transferred to simulate long bone distraction. Results show that healing patterns of bone distraction can be predicted. Using this method, it is possible to identify model parameters for individual subjects. This will allow a patient-specific analysis of tissue healing processes in future.

A novel mathematical model detecting early individual changes of insulin resistance.

BACKGROUND: Insulin resistance (IR) and hyperinsulinemia as well as obesity play a key role in the metabolic syndrome (MetS), type 2 diabetes (T2D), and associated cardiovascular disease. Unfortunately, IR and hyperinsulinemia are often diagnosed late (i.e., when the MetS is already clinically evident). An earlier diagnosis of IR would be desirable to reduce its clinical consequences, in particular in view of the increasing prevalence of obesity and diabetes conditions. For this purpose, we developed a mathematical model capable of detecting early onset of IR through small variations of insulin sensitivity, glucose effectiveness, and first- or second-phase responses. MATERIALS AND METHODS: Murine models provide controlled conditions to study various stages of IR. Various degrees of hypercholesterolemia, obesity, IR, and atherosclerosis were induced in low-density lipoprotein receptor-deficient mice by feeding them cholesterol- or sucrose-rich diets. IR was assessed by oral glucose tolerance tests. Controls included animals fed exclusively, or switched back to, regular chow. A nonlinear mathematical model of the order of 5 was developed by refining Bergman's "Minimal Model" and then applied to experimental data. RESULTS: Different metabolic constellations consistently corresponded to specific and close-meshed changes in model parameters. Reduced second-phase glucose sensitivity characterized an early impaired glucose tolerance. Later stages showed an increased first-phase glucose sensitivity compensating for decreased insulin sensitivity. Finally, T2D was associated with both first- and second-phase sensitivities close to zero. CONCLUSIONS: The new mathematical model detected various insulin-sensitive or -resistant metabolic stages of IR. It can therefore be implemented for quantitative metabolic risk assessment and may be of therapeutic value by anticipating the start of therapeutic interventions.

Diabetic and metabolic programming: mechanisms altering the intrauterine milieu.

A wealth of epidemiological, clinical, and experimental studies have been linked to poor intrauterine conditions as well as metabolic and associated cardiovascular changes postnatal. These are novel perspectives connecting the altered intrauterine milieu to a rising number of metabolic diseases, such as diabetes, obesity, and hypercholesterolemia as well as the Metabolic Syndrome (Met S). Moreover, metabolic associated atherosclerotic diseases are connected to perigestational maternal health. The "Thrifty Phenotype Hypothesis" introduced cross-generational links between poor conditions during gestation and metabolic as well as cardiovascular alterations postnatal. Still, mechanisms altering the intrauterine milieu causing metabolic and associated atherosclerotic diseases are currently poorly understood. This paper will give novel insights in fundamental concepts connected to specific molecular mechanisms "programming" diabetes and associated metabolic as well as cardiovascular diseases.

Real-time state estimation and long-term model adaptation: a two-sided approach toward personalized diagnosis of glucose and insulin levels.

BACKGROUND: With continuous glucose sensors (CGSs), it is possible to obtain a dynamical signal of the patient's subcutaneous glucose concentration in real time. How could that information be exploited? We suggest a model-based diagnosis system with a twofold objective: real-time state estimation and long-term model parameter identification. METHODS: To obtain a dynamical model, Bergman's nonlinear minimal model (considering plasma glucose G, insulin I, and interstitial insulin X) is extended by two states describing first and second insulin response. Furthermore, compartments for oral glucose and subcutaneous insulin inputs as well as for subcutaneous glucose measurement are added. The observability of states and external inputs as well as the identifiability of model parameters are assessed using the empirical observability Gramian. Signals are estimated for different nondiabetic and diabetic scenarios by unscented Kalman filter. RESULTS: (1) Observability of different state subsets is evaluated, e.g., from CGSs, {G, I} or {G, X} can be observed and the set {G, I, X} cannot. (2) Model parameters are included, e.g., it is possible to estimate the second-phase insulin response gain k(G2) additionally. This can be used for model adaptation and as a diagnostic parameter that is almost zero for diabetes patients. (3) External inputs are considered, e.g., oral glucose is theoretically observable for nondiabetic patients, but estimation scenarios show that the time delay of 1 h limits application. CONCLUSIONS: A real-time estimation of states (such as plasma insulin I) and parameters (such as k(G2)) is possible, which allows an improved real-time state prediction and a personalized model.

Identifiability and online estimation of diagnostic parameters with in the glucose insulin homeostasis.

Today, diagnostic decisions about pre-diabetes or diabetes are made using static threshold rules for the measured plasma glucose. In order to develop an alternative diagnostic approach, dynamic models as the Minimal Model may be deployed. We present a novel method to analyze the identifiability of model parameters based on the interpretation of the empirical observability Gramian. This allows a unifying view of both, the observability of the system's states (with dynamics) and the identifiability of the system's parameters (without dynamics). We give an iterative algorithm, in order to find an optimized set of states and parameters to be estimated. For this set, estimation results using an Unscented Kalman Filter (UKF) are presented. Two parameters are of special interest for diagnostic purposes: the glucose effectiveness S(G) characterizes the ability of plasma glucose clearance, and the insulin sensitivity S(I) quantifies the impact from the plasma insulin to the interstitial insulin subsystem. Applying the identifiability analysis to the trajectories of the insulin glucose system during an intravenous glucose tolerance test (IVGTT) shows the following result: (1) if only plasma glucose G(t) is measured, plasma insulin I(t) and S(G) can be estimated, but not S(I). (2) If plasma insulin I(t) is captured additionally, identifiability is improved significantly such that up to four model parameters can be estimated including S(I). (3) The situation of the first case can be improved, if a controlled external dosage of insulin is applied. Then, parameters of the insulin subsystem can be identified approximately from measurement of plasma glucose G(t) only.

The Unscented Kalman Filter estimates the plasma insulin from glucose measurement.

Understanding the simultaneous interaction within the glucose and insulin homeostasis in real-time is very important for clinical treatment as well as for research issues. Until now only plasma glucose concentrations can be measured in real-time. To support a secure, effective and rapid treatment e.g. of diabetes a real-time estimation of plasma insulin would be of great value. A novel approach using an Unscented Kalman Filter that provides an estimate of the current plasma insulin concentration is presented, which operates on the measurement of the plasma glucose and Bergman's Minimal Model of the glucose insulin homeostasis. We can prove that process observability is obtained in this case. Hence, a successful estimator design is possible. Since the process is nonlinear we have to consider estimates that are not normally distributed. The symmetric Unscented Kalman Filter (UKF) will perform best compared to other estimator approaches as the Extended Kalman Filter (EKF), the simplex Unscented Kalman Filter (UKF), and the Particle Filter (PF). The symmetric UKF algorithm is applied to the plasma insulin estimation. It shows better results compared to the direct (open loop) estimation that uses a model of the insulin subsystem.