Over the past 10 years, the group has received more than 2 million euros from competitive funding for research projects related to technologies for diabetes. The main projects are:
TAILOR – Patient-TAILOREd solutions for blood glucose control in type 1 diabetes. (2020-2023) Reference: PID2019-107722RB-C22. Funded by MICINN. 211.508,00 €. P.I. Josep Vehí. Other researchers: Ningsu Luo, Joaquim Armengol, Remei Calm. Coordinated project (UdG, UPV).
The TAILOR project is a multidisciplinary project in the areas of biomedical engineering, systems engineering and control, and endocrinology, addressing new technological challenges for automated glucose control in type 1 diabetes.
In the design of any system for glucose control in type 1 diabetes, maximization of perceived usefulness and ease of use must be pursued to cover the different psychosocial profiles of patients to whom the technology is addressed. This is still an unmet need in the artificial pancreas, where attrition of use has been recently reported during the post-market surveillance for the first commercial hybrid system.
Motivated by this fact, the project TAILOR addresses new technological challenges for automated glucose control in type 1 diabetes aiming at the improvement of glycemic control while matching the patients’ expected interaction with the system, yielding to patient-tailored solutions that maximize perceived usefulness and ease of use to foster the adoption of the technology. As ultimate goal, technological progress to widen the access of technological solutions to all patients that might benefit from them is pursued.
To achieve this goal, either in the artificial pancreas or new technological solutions for insulin pen users integrating a continuous glucose monitor (MDI+CGM), devices should be flexible enough to accommodate to different users, widening its target population. This implies that freedom should be given to the patient regarding management of meals and exercise (with/without announcement, carbs/glucagon) and use of extra wearables (always/sometimes/never) while improving glycemic control with respect to current therapy. To this end, learning tools for a better management of variability and system personalization will be key.
This subproject will address the following challenges:
- Improvement of glycemic control by means of better system personalization through the integration of learning tools into control algorithms to cope with patient’s variability. The behavior of the glucoregulatory system can vary very quickly, especially in hypoglycemic patients or in patients with high glycemic variability. System adaptation to personalize the controller’s tuning to the patient’s current metabolic condition is key for the performance of an AP system. Complexity of the cohorts at hand imposes advanced learning techniques for patient’s condition classification, detection and triggering of suitable adaptation laws.
- Alleviation of decision making burden and improvement of glycemic control in sensor-augmented MDI users. Technically, incorporation of CGM into MDI allows to “close the loop”, analogously to an artificial pancreas, although with distinctive features: (1) they are “human-inthe- loop” systems, that is, all actions require human intervention. Automatic features are then relegated to dosing computations, which can be based on CGM data, glucose predictions and estimations of insulin-on-board, affecting disturbances, insulin sensitivity, etc. in the same way an artificial pancreas does; (2) actions are not periodical (every 5 minutes), but event-driven (at meals) or self-driven (at any moment when a correction may be needed); (3) the above means that reliable long-term glucose predictors may be necessary; (4) modulation of long-acting basal insulin is difficult, limiting insulin actions to “above-basal actions”. In our opinion, conceptualization of MDI+CGM as an event-driven/self-driven closed-loop system can allow a rich transfer of technologies from artificial pancreas research, bringing significant improvements in glucose control for the big majority of patients using insulin pens.
VaNeSA – Modelling and Control of Non-invasive Vagus Nerve Stimulation for autoimmune disease (2021-2024). Reference: PID2020-117171RA-I00. Funded by MICINI.117.370 €. P.I. Ivan Contreras
Autoimmune disease is characterized by the failure of an organism to tolerate its own cells and tissues, resulting in an aberrant immune response by lymphocytes and/or antibodies. This leads to pathological changes and dysfunction of the tissue that is the target of the self-directed immune response. Autoimmune diseases can be systemic or can affect specific organs or body systems including the endocrine, gastro-intestinal and liver, rheumatological, and neurological systems. Patients with autoimmune diseases such as type 1 diabetes (T1D) and systemic lupus erythematosus (SLE) have been found to suffer from dysautonomia, which manifests as an imbalance in activity/reactivity of the sympathetic and parasympathetic divisions of the autonomic nervous system (ANS). This autonomic imbalance is related to an increased risk of developing cardiovascular disease, which is a major cause of morbidity and mortality in patients with T1D and SLE.
To restore balance to the ANS, improve patient outcomes and improve patients quality of life, non-invasive vagus nerve stimulation (nVNS) has been proposed. Further investigation of the nVNS parameters is required to enable appropriate administration to patients with autoimmune diseases such as T1D and SLE. As the ultimate goal, a Mobile-based nVNS platform will be developed with analysis and prediction tools to personalize nVNS administration thus, optimizing the desired therapeutic outcomes.
This project aims to:
1) Collect data via an exploratory study
2) Analyze clinical markers to distinguish useful biomarkers of disease and physiological signals for our purposes
3) Create computational models to understand how these clinical markers change dynamically with the delivery of nVNS for a deepened
understanding of the characteristic responses
4) Predict each subject responses to nVNS for improved treatment outcomes
5) Optimize nVNS waveform parameters for the personalized treatment of autoimmune disease
6) Develop a decision support system for patients
7) Bundle the developed tools into a mobile-based nVNS platform, and finally
8) Clinically validate the developed nVNS platform.
LEAP – Machine learning tool minimizing the risk of hypoglycemia to support insulin therapies in type 1 diabetes: from the lab to a product prototype (2021-2023). Reference: PDC2021-121470-C22. Funded by MICIN – 69.000 €. P.I. Josep Vehí
This project aims at advancing Technological Readiness Level of results from the research team in the scope of learning-based prediction systems to support insulin therapies in type 1 diabetes, addressing both machine learning tools for prediction-based decision support (Subproject 2 by University of Girona, UdG) and glucose prediction without manual input data collection (Subproject 1 by Universitat Politècnica de València, UPV). As an outcome, a product prototype and demonstrator per subproject will be built.
Personalización de un sistema de páncreas artificial en pacientes con diabetes tipo 1 y alto riesgo cardiovascular – FIS (2020-2022). Reference: PI19/01865. Funded by MICIN. Project owned by the Hospital Clinic de Barcelona, outsorced to MiceLab-Univeristy of Girona for the clinical trial.
The presence of repeated severe/non-severe hypoglycemic episodes and hypoglycemia unawareness may be associated with a higher cardiovascular risk (CVR) in patients with type 1 diabetes (T1D). It has also been described that there are two main groups of T1D patients at high hypoglycemia risk. This includes patients with tight glycemic control manifested by lower HbA1c (reduced glucose variability and higher incidence of severe/non severe hypoglycaemia and hypoglycemia unawareness, HYPO GROUP). It also includes patients with higher HbA1c values, higher frequency of hypoglycemia, and increased glucose variability (VARIABILITY GROUP). Both groups are typically excluded from current trials dealing with new therapies.
The objectives of the project are:
1) To evaluate the CVR profile (carotid/femoral ultrasound, blood analysis to study inflammatory markers and endothelial dysfunction markers) of both groups of patients and to compare them with 2 cohorts of previously studied patients (HYPO GROUP 2007, T1D CONTROL GROUP);
2) To analyze the continuous glucose profile/physical activity of both groups during a month to finally apply machine learning techniques to these results
3) To study the efficacy and safety of an artificial pancreas system (AP, jAP system) on this population of patients with a greater hypoglycemia risk through a pilot study. The ultimate goal is to achieve sufficient knowledge to develop personalised AP systems in different groups of patients within the next future.
Hypoglycemia Minimizer. (2019-2020) Reference: 2018 PROD 00041. Funded by the European Regional Development Fund (ERDF) of the European Union under the FEDER Operational Program of Catalonia 2014-2020. 99.530,10 €. P.I. Josep Vehí. Other researchers: Iván Contreras.
The HYPOGLYCEMIA MINIMIZER is a software package that can be embedded in any diabetes management app or system. It combines four machine learning approaches to assess the risk of having a hypoglycemic event:
- grammatical evolution for the mid-term continuous prediction of blood glucose levels,
- support vector machines to predict hypoglycemia during postprandial periods,
- artificial neural networks to predict hypoglycemia overnight, and
- data mining to profile diabetes management scenarios.
The HYPOGLYCEMIA MINIMIZER combines these four methods to help the patient to take decisions before an insulin injection. The system provides advanced calculations that minimize the risks, alarms and warnings, reducing to less than half the number of hypoglycemic events. It can be adapted for any pump and for MDI and it is also compatible with any CGM currently in the market, or even could also work without a CGM.
At the end of the project we will have a technologically and clinically validated complete prototype. The clinical trial will include patients in real-life everyday situations for sufficient time to prove their efficiency.
mSAFE-AP – Solutions for the improvement of efficiency and safety of the artificial pancreas by fault-tolerant multivariable control architectures. (2016-2019) Reference: DPI2016-78831-C2-2-R. Funded by MICINN. 284.350,00 €. P.I. Josep Vehí. Other researchers: Remei Calm, Joaquim Armengol. Coordinated project (UdG, UPV).
As artificial pancreas systems move closer to commercialization, it is necessary to face the new challenges which involve intensive and prolonged use of a complex system, with multiple components with different operational lifetime, managed by humans with minimal training and tendency to non-adherence.
The constant challenges of efficiency and safety enter a new dimension when considering long periods of time, in which many actions must be carried by the patient without supervision. It is thus necessary to provide the artificial pancreas with new mechanisms for patient monitoring and detection/prediction of risks, acting accordingly on the system to ensure its safety.
In this context, techniques based on reconfigurable fault-tolerant control are considered as a good candidate to address these problems and will be explored in this proposal. Additionally, the complexity of the problem suggests the need for more degrees of freedom than those given by a single-input-singleoutput control architecture, based solely on glucose measurement and insulin infusion, leading to multivariable control approaches for the artificial pancreas. However, this involves additional challenges, as misinterpretations by the control system of additional signals, that need to be treated properly in the fault mitigation modules.
The overall objective of this project is the design of an efficient and safe artificial pancreas in normal free-living use, by means of new multivariable reconfigurable fault-tolerant control architectures. Specifically, the proposal will address:
- (1) The use of additional inputs aside glucose measurements, either from monitoring devices or new patient interventions, for the improvement of performance under exercise and mixed meals of varying nutritional composition with alcohol intake. Techniques based on slidingmode reference conditioning will be at the core of the developments due to its success in improving postprandial control in a recent controlled study by the group. A metabolic study analyzing the glycemic impact of alcohol and meal composition will be carried out.
- (2) The use of additional control actions, aside insulin infusion, for the minimization of hypoglycemia risk under challenging scenarios with fast glucose drop such as exercise.
Dual-hormone systems with coordinated insulin and glucagon infusion will be explored and compared in a clinical study to rescue carbs warnings.
- (3) The development of new tools for patient’s supervision, including the classification and detection of free-living scenarios, with the extension of the concept of fault beyond instrumentation to patient’s anomalous metabolic states and human factors, as well as prediction of risks based on stochastic and hybrid modelling.
As a result of the project, a new smartphone-based multivariable reconfigurable fault-tolerant artificial pancreas system will be built including the methods developed and evaluated in the last phase of the project in a pilot 3-month outpatient study.
CIBERDEM – Incorporation of new groups to the CIBER consortium. (2018-) Reference: CB17/08/00004. Funded by MINECO – Instituto de Salud Carlos III. P.I. Jorge Bondia. Co-PI. Josep Vehí.
The research will be conducted on artificial pancreas and related technologies, such as continuous glucose monitoring and automatic patient monitoring using artificial intelligence techniques.
The development and validation of new artificial pancreas systems to improve the efficiency and safety of food intake and exercise is proposed as a general objective. The complexity of the glycemic effect of intakes and exercise suggests the need for more degrees of freedom than current systems based solely on the measurement of glucose and insulin infusion. The use of additional signals to glucose, for example from physical activity monitors, to measure or estimate such disturbances and facilitate the automatic monitoring of the patient with the characterization of glycemic patterns and risk situations, as well as the incorporation of new counterregulatory actuation signals for greater safety in the face of hypoglycaemia, especially in the case of exercise, can be fundamental in overcoming the limitations of current systems in a second generation of artificial pancreas. This gives rise, in terms of control engineering, to new multivariable architectures of the artificial pancreas.
The annual organization of a Multidisciplinary Workshop on New Technologies for Diabetes is proposed. This is intended to establish a scientific-technological and innovation forum for interaction between clinical teams, engineering teams and industry, in various areas related to new technologies applied to diabetes, which catalyzes multidisciplinary research in the area and transfer activities, strengthening the various groups of CIBERDEM.
SAFE-AP – New methods for the efficiency and safety of home artificial pancreas. (2014-2016) Reference: DPI 2013-46982-C2-2-R. MINECO. 204.180,00 €. P.I. Josep Vehí. Other researchers: Remei Calm, Joaquim Armengol, Ningsu Luo. Coordinated project (UdG, UPV).
The aim of SAFE-AP project is to develop methods and tools to improve efficiency and robustness of closed-loop glucose control and continuous glucose monitoring in face of daily life disturbances, in particular meals and exercise. Patient health monitoring and assessment tools are also addressed for the detection of anomalous patient behaviours compromising patient’s safety when using the artificial pancreas at home. Improved models of hypoglycaemia are also being developed.
TecnioSpring – SMART-Diabetes: The Self-Management Assistant and Remote Treatment for Diabetes. (2016-2018) Reference: FP7-PEOPLE-2012-COFUND. European Commission. 96.951,64 €. P.I. Josep Vehí.
A platform integrating existing physiological, psychological and epidemiological models to enable accurate predictions of the health status of the patients, predicting models for the interaction between diabetes and co-morbidities, and personalised strategies to facilitate the behaviour change for a healthier life-style.
DECIPHER – Distributed European Community Individual Patient Healthcare Electronic Record. (2015-2016) Reference: FP7 – 288028. European Commission. 27.830,00 €. P.I: Josep Vehí. Coordinated project (UdG, SocialDiabetes).
The project aims to develop a mobile solution which enables secure cross-border access to existing patient healthcare portals. The new user-friendly application acquired through pre-commercial public procurement (PCP) will enable efficient and safe medical care of mobile patients in EU member states. The objective of the project is to develop a solution addressing the management of patients with chronic long term conditions.
Control of blood glucose in the postprandial period in type 1 Diabetes. Safety and efficacy of a new algorithm in closed loop (artificial pancreas). (2014-2016) Research grant Josep Font 2014. 107.000,00 €. Hospital Clínic Universitari de Barcelona. P.I. Ignacio Conget. Josep Vehí, research team member.
VITALITY – Monitoring & Managing your Health and Well-being. (2011-2014) Reference: IPT-2011-1194-900000. European Commission (ITEA 2 ip10002). MINECO (programa INNPACTO). 158.827,86 €. P.I. Josep Vehí. Other researchers: Remei Calm. Coordinated project.
The objective of VITALITY is to provide a socio-health care solution that provides with a fast response time high quality consolidated data from different applications and sensors, focusing on interoperability and standardization.
CLOSEDLOOP4MEALS – New strategies for post-prandial glycaemic control using insulin pump therapy in type 1 diabetes. (2011-2013) Reference: DPI2010-20764-C02-02. Spanish Ministry of Education and Science. 223.850,00 €. P.I. Josep Vehí. Other researchers: Remei Calm, Joaquim Armengol. Coordinated project (UdG, UPV).
CLOSEDLOOP4MEALS is a research project aiming at the development of new strategies for postprandial glucose control in type 1 diabetes and in-clinic validation. Topics like glucoregulatory modelling, model individualization tools, prediction under intra-patient variability, accuracy improvement of continuous glucose monitoring, robust control algorithms design are addressed to advance towards a safe and efficient artificial pancreas for postprandial control.