1
|
Royston C, Bergford S, Calhoun P, Sibayan J, Ruan Y, Boughton C, Wilinska ME, Hovorka R. Safety of Options to "Boost" (Enhancing Insulin Infusion Rates) and "Ease-Off" (Reducing Insulin Infusion Rates) in CamAPS FX Hybrid Closed-Loop System: A Real-World Analysis. Diabetes Technol Ther 2024. [PMID: 39146468 DOI: 10.1089/dia.2024.0298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
The usage and safety of the Boost and Ease-off features in the CamAPS FX hybrid closed-loop system were analyzed in a retrospective analysis of real-world data from 7,464 users over a 12-month period. Boost was used more frequently than Ease-off, but for a shorter duration per use. Mean starting glucose was above range for Boost (229 ± 51 mg/dL), and within range for Ease-off (114 ± 29 mg/dL). Time spent below 70 mg/dL was low during Boost periods [median (interquartile range; IQR) 0.0% (0.0, 0.5%)], and lower than during no Boost periods [2.1% (1.2, 3.4%)], while time spent above 180 mg/dL was lower during Ease-off periods (15 ± 14%) compared with no Ease-off periods (25 ± 12%). There were no episodes of severe hypoglycemia or diabetic ketoacidosis attributed to Boost or Ease-off use. Boost and Ease-off allow users to engage safely with CamAPS FX to manage their glucose levels during periods of more-than-usual and less-than-usual insulin needs.
Collapse
Affiliation(s)
- Chloë Royston
- Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | | | - Peter Calhoun
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Judy Sibayan
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Yue Ruan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Charlotte Boughton
- Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | - Malgorzata E Wilinska
- Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| | - Roman Hovorka
- Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
2
|
Li S, Tao J, Tang J, Chu Y, Wu H. Digital therapeutics as an emerging new therapy for diabetes mellitus: potentials and concerns. Endocr Connect 2024; 13:EC-24-0219. [PMID: 38963663 PMCID: PMC11378137 DOI: 10.1530/ec-24-0219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/04/2024] [Indexed: 07/05/2024]
Abstract
The global burden of controlling and managing diabetes mellitus (DM) is a significant challenge. Despite the advancements in conventional DM therapy, there remain hurdles to overcome, such as enhancing medication adherence and improving patient prognosis. Digital therapeutics (DTx), an innovative digital application, has been proposed to augment the traditional disease management workflow, particularly in managing chronic diseases like DM. Several studies have explored DTx, yielding promising results. However, certain concerns about this innovation persist. In this review, we aim to encapsulate the potential of DTx and its applications in DM management, thereby providing a comprehensive overview of this technique for public health policymakers.
Collapse
Affiliation(s)
| | - Jincheng Tao
- J Tao, Department of Medical Informatics, Nantong University Medical School, Nantong, China
| | - Jie Tang
- J Tang, Department of Medical Informatics, Nantong University Medical School, Nantong, China
| | - Yanting Chu
- Y Chu, Department of Medical Informatics, Nantong University Medical School, Nantong, China
| | - Huiqun Wu
- H Wu, Department of Medical Informatics, Nantong University Medical School, Nantong, China
| |
Collapse
|
3
|
Annuzzi G, Apicella A, Arpaia P, Bozzetto L, Criscuolo S, De Benedetto E, Pesola M, Prevete R. Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI. IEEE J Biomed Health Inform 2024; 28:3123-3133. [PMID: 38157465 DOI: 10.1109/jbhi.2023.3348334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.
Collapse
|
4
|
Oliva Morgado Ferreira R, Trevisan T, Pasqualotto E, Schmidt P, Pedrotti Chavez M, Figueiredo Watanabe JM, van de Sande-Lee S. Efficacy of the hybrid closedloop insulin delivery system in children and adolescents with type 1 diabetes: a meta-analysis with trial sequential analysis. ARCHIVES OF ENDOCRINOLOGY AND METABOLISM 2024; 68:e230280. [PMID: 38602747 PMCID: PMC11081057 DOI: 10.20945/2359-4292-2023-0280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/11/2023] [Indexed: 04/12/2024]
Abstract
The aim of this study was to assess the efficacy and safety of hybrid closed-loop (HCL) systems for insulin delivery in children and adolescents with type 1 diabetes (T1D). We searched Embase, PubMed, and Cochrane Library for randomized controlled trials (RCTs) published until March 2023 comparing the HCL therapy with control therapies for children and adolescents with T1D. We computed weighted mean differences (WMDs) for continuous outcomes and risk ratios (RRs) with 95% confidence intervals (CIs) for binary endpoints. Four RCTs and 501 patients were included, of whom 323 were randomized to HCL therapy. Compared with control therapies, HCL significantly improved the period during which glucose level was 70-180 mg/dL (WMD 10.89%, 95% CI 8.22-13.56%) and the number of participants with glycated hemoglobin (HbA1c) level < 7% (RR 2.61, 95% CI 1.29-5.28). Also, HCL significantly reduced the time during which glucoselevel was > 180 mg/dL (WMD-10.46%, 95% CI-13.99 to-6.93%) and the mean levels of glucose (WMD-16.67 mg/dL, 95% CI-22.25 to-11.09 mg/dL) and HbA1c (WMD-0.50%, 95% CI-0.68 to-0.31). There were no significant differences between therapies regarding time during which glucose level was < 70 mg/dL or <54 mg/dL or number of episodes of ketoacidosis, hyperglycemia, and hypoglycemia. In this meta-analysis, HCL compared with control therapies was associated with improved time in range and HbA1c control in children and adolescents with T1D and a similar profile of side effects. These findings support the efficacy of HCL in the treatment of T1D in this population.
Collapse
Affiliation(s)
| | - Talita Trevisan
- Clínica particular, Talita Trevisan Endocrinologia, Itajaí, SC, Brasil
| | - Eric Pasqualotto
- Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil
| | - Pedro Schmidt
- Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil
| | | | | | | |
Collapse
|
5
|
Nimri R, Phillip M, Clements MA, Kovatchev B. Closed-Loop Control, Artificial Intelligence-Based Decision-Support Systems, and Data Science. Diabetes Technol Ther 2024; 26:S68-S89. [PMID: 38441444 DOI: 10.1089/dia.2024.2505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Affiliation(s)
- Revital Nimri
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Moshe Phillip
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Mark A Clements
- Division of Pediatric Endocrinology, Children's Mercy Hospitals and Clinics, Kansas City, MO, USA
| | - Boris Kovatchev
- Center for Diabetes Technology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| |
Collapse
|
6
|
Renard E, Joubert M, Villard O, Dreves B, Reznik Y, Farret A, Place J, Breton MD, Kovatchev BP. Safety and Efficacy of Sustained Automated Insulin Delivery Compared With Sensor and Pump Therapy in Adults With Type 1 Diabetes at High Risk for Hypoglycemia: A Randomized Controlled Trial. Diabetes Care 2023; 46:2180-2187. [PMID: 37729080 DOI: 10.2337/dc23-0685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/06/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVE Assess the safety and efficacy of automated insulin delivery (AID) in adults with type 1 diabetes (T1D) at high risk for hypoglycemia. RESEARCH DESIGN AND METHODS Participants were 72 adults with T1D who used an insulin pump with Clarke Hypoglycemia Perception Awareness scale score >3 and/or had severe hypoglycemia during the previous 6 months confirmed by time below range (TBR; defined as sensor glucose [SG] reading <70 mg/dL) of at least 5% during 2 weeks of blinded continuous glucose monitoring (CGM). Parallel-arm, randomized trial (2:1) of AID (Tandem t:slim ×2 with Control-IQ technology) versus CGM and pump therapy for 12 weeks. The primary outcome was TBR change from baseline. Secondary outcomes included time in target range (TIR; 70-180 mg/dL), time above range (TAR), mean SG reading, and time with glucose level <54 mg/dL. An optional 12-week extension with AID was offered to all participants. RESULTS Compared with the sensor and pump (S&P), AID resulted in significant reduction of TBR by -3.7% (95% CI -4.8, -2.6), P < 0.001; an 8.6% increase in TIR (95% CI 5.2, 12.1), P < 0.001; and a -5.3% decrease in TAR (95% CI -87.7, -1.8), P = 0.004. Mean SG reading remained similar in the AID and S&P groups. During the 12-week extension, the effects of AID were sustained in the AID group and reproduced in the S&P group. Two severe hypoglycemic episodes occurred using AID. CONCLUSIONS In adults with T1D at high risk for hypoglycemia, AID reduced the risk for hypoglycemia more than twofold, as quantified by TBR, while improving TIR and reducing hyperglycemia. Hence, AID is strongly recommended for this specific population.
Collapse
Affiliation(s)
- Eric Renard
- Department of Endocrinology and Diabetology, Montpellier University Hospital, Montpellier, France
- Department of Physiology, Institute of Functional Genomics, CNRS, INSERM, University of Montpellier, France
| | - Michael Joubert
- Diabetes Care Unit, Caen University Hospital, Caen, France
- University of Caen Normandy, University of Caen, Caen, France
| | - Orianne Villard
- Department of Endocrinology and Diabetology, Montpellier University Hospital, Montpellier, France
- Department of Physiology, Institute of Functional Genomics, CNRS, INSERM, University of Montpellier, France
| | - Bleuenn Dreves
- Diabetes Care Unit, Caen University Hospital, Caen, France
- University of Caen Normandy, University of Caen, Caen, France
| | - Yves Reznik
- Diabetes Care Unit, Caen University Hospital, Caen, France
- University of Caen Normandy, University of Caen, Caen, France
| | - Anne Farret
- Department of Endocrinology and Diabetology, Montpellier University Hospital, Montpellier, France
- Department of Physiology, Institute of Functional Genomics, CNRS, INSERM, University of Montpellier, France
| | - Jerome Place
- Department of Physiology, Institute of Functional Genomics, CNRS, INSERM, University of Montpellier, France
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - Boris P Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| |
Collapse
|
7
|
Benhamou PY, Adenis A, Lablanche S, Franc S, Amadou C, Penfornis A, Kariyawasam D, Beltrand J, Charpentier G. First Generation of a Modular Interoperable Closed-Loop System for Automated Insulin Delivery in Patients With Type 1 Diabetes: Lessons From Trials and Real-Life Data. J Diabetes Sci Technol 2023; 17:1433-1439. [PMID: 37449762 PMCID: PMC10658690 DOI: 10.1177/19322968231186976] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND DBLG1 (Diabeloop Generation 1) stands as one of the five commercially available closed-loop solution worldwide for patients with type 1 diabetes as of 2023. Our aim was to provide an overview of all data obtained with this system regarding outcomes and populations, with an emphasis on interoperability. METHODS This report includes all available sources of data (three randomized control trials and five surveys on real-life data). Collection ran from March 3, 2017 to April 30, 2022. RESULTS We gathered data from 6859 adult patients treated with closed-loop from three to 12 months. Overall, all sources of data showed that time in range (TIR) 70 to 180 mg/dL, starting from 47.4% to 56.6%, improved from 12.2 to 17.3 percentage points. Time in hypoglycemia was reduced by 48% in average (range: 26%-70%) and reached a level of 1.3% in the largest and most recent cohort. In patients with excessive time in hypoglycemia at baseline (≥5%), closed-loop allowed a reduction in time below range (TBR) by 59%. The comparison of days with declared physical activity versus days without physical activity did not show differences in TBR. The improvement in TIR observed with three different pump systems (Vicentra Kaleido, n = 117; Sooil Dana-I, n = 84; and Roche Insight, n = 6684) ranged from 15.4 to 17.3 percentage points. DISCUSSION These data obtained in different European countries were consistent throughout all reports, showing that this closed-loop system is efficient (high improvement in TIR), safe (remarkably low level of TBR), and interoperable (three pump settings so far).
Collapse
Affiliation(s)
- Pierre-Yves Benhamou
- Department of Endocrinology, Grenoble
University Hospital, Grenoble Alpes University, INSERM U1055, Laboratory of
Fundamental and Applied Bioenergetics, Grenoble, France
- Endocrinology, Centre Hospitalier
Universitaire Grenoble Alpes, Grenoble Alpes University, Grenoble, France
| | | | - Sandrine Lablanche
- Department of Endocrinology, Grenoble
University Hospital, Grenoble Alpes University, INSERM U1055, Laboratory of
Fundamental and Applied Bioenergetics, Grenoble, France
| | - Sylvia Franc
- Center for Study and Research for
Improvement of the Treatment of Diabetes, Bioparc-Genopole Evry-Corbeil, Evry,
France
- Department of Diabetes and
Endocrinology, Sud-Francilien Hospital, Corbeil-Essonnes, France
- Department of Endocrinology,
Diabetology & Metabolic Diseases, Sud-Francilien Hospital, Paris-Saclay
University, Corbeil-Essonnes, France
| | - Coralie Amadou
- Department of Endocrinology,
Diabetology & Metabolic Diseases, Sud-Francilien Hospital, Paris-Saclay
University, Corbeil-Essonnes, France
| | - Alfred Penfornis
- Department of Endocrinology,
Diabetology & Metabolic Diseases, Sud-Francilien Hospital, Paris-Saclay
University, Corbeil-Essonnes, France
| | - Dulanjalee Kariyawasam
- Paediatric Endocrinology, Diabetology,
Gynaecology Department, Necker-Enfants Malades University Hospital, Assistance
Publique des Hôpitaux de Paris-Centre, Paris, France
- Paris Cite University, Paris,
France
| | - Jacques Beltrand
- Paediatric Endocrinology, Diabetology,
Gynaecology Department, Necker-Enfants Malades University Hospital, Assistance
Publique des Hôpitaux de Paris-Centre, Paris, France
- Paris Cite University, Paris,
France
| | - Guillaume Charpentier
- Center for Study and Research for
Improvement of the Treatment of Diabetes, Bioparc-Genopole Evry-Corbeil, Evry,
France
- Department of Diabetes and
Endocrinology, Sud-Francilien Hospital, Corbeil-Essonnes, France
| |
Collapse
|
8
|
Newman C, Hartnell S, Wilinska M, Alwan H, Hovorka R. Real-World Evidence of the Cambridge Hybrid Closed-Loop App With a Novel Real-Time Continuous Glucose Monitoring System. J Diabetes Sci Technol 2023:19322968231187915. [PMID: 37503893 DOI: 10.1177/19322968231187915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
We evaluated the performance of the interoperable Cambridge hybrid closed-loop app with FreeStyle Libre 3 glucose sensor, and YpsoPump insulin pump in a real-world setting. Data from 100 users (63 adults [mean ± SD age 41.9 ± 14.0 years], 15 children [8.6 ± 5.2 years)] and 22 users of unreported age) for a period of 28 days were analyzed. Time in range (3.91- 10.0mmol/L) was 72.6 ± 11.1% overall. Time below range (<3.9mmol/L) was 3.1% (1.4-5.1) (median [interquartile range]). Auto-mode was active for 95.8% (91.8-97.9) of time. This real-world analysis suggests that the performance of Cambridge hybrid closed-loop app with this glucose sensor is comparable to other commercially available hybrid closed-loop systems.
Collapse
Affiliation(s)
- Christine Newman
- Wolfson Diabetes and Endocrinology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Sara Hartnell
- Wolfson Diabetes and Endocrinology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Malgorzata Wilinska
- Wellcome-MRC Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Heba Alwan
- Wellcome-MRC Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, UK
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Roman Hovorka
- Wellcome-MRC Institute of Metabolic Science-Metabolic Research Laboratories, University of Cambridge, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| |
Collapse
|
9
|
Estremera E, Beneyto A, Cabrera A, Contreras I, Vehí J. Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107568. [PMID: 37137221 DOI: 10.1016/j.cmpb.2023.107568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/13/2023] [Accepted: 04/24/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND AND OBJECTIVES Recent advances in Automated Insulin Delivery systems have been shown to dramatically improve glycaemic control and reduce the risk of hypoglycemia in people with type 1 diabetes. However, they are complex systems that require specific training and are not affordable for most. Attempts to reduce the gap with closed-loop therapies using advanced dosing advisors have so far failed, mainly because they require too much human intervention. With the advent of smart insulin pens, one of the main constraints (having reliable bolus and meal information) disappears and new strategies can be employed. This is our starting hypothesis, which we have validated in a very demanding simulator. In this paper, we propose an intermittent closed-loop control system specifically intended for multiple daily injection therapy to bring the benefits of artificial pancreas to the application of multiple daily injections. METHODS The proposed control algorithm is based on model predictive control and integrates two patient-driven control actions. Correction insulin boluses are automatically computed and recommended to the patient to minimize the duration of hyperglycemia. Rescue carbohydrates are also triggered to avoid hypoglycemia episodes. The algorithm can adapt to different patient lifestyles with customizable triggering conditions, closing the gap between practicality and performance. The proposed algorithm is compared with conventional open-loop therapy, and its superiority is demonstrated through extensive in silico evaluations using realistic cohorts and scenarios. The evaluations were conducted in a cohort of 47 virtual patients. We also provide detailed explanations of the implementation, imposed constraints, triggering conditions, cost functions, and penalties for the algorithm. RESULTS The in-silico outcomes combining the proposed closed-loop strategy with slow-acting insulin analog injections at 09:00 h resulted in percentages of time in range (TIR) (70-180 mg/dL) of 69.5%, 70.6%, and 70.4% for glargine-100, glargine-300, and degludec-100, respectively, and injections at 20:00 h resulted in percentages of TIR of 70.5%, 70.3%, and 71.6%, respectively. In all the cases, the percentages of TIR were considerably higher than those obtained from the open-loop strategy, being only 50.7%, 53.9%, and 52.2% for daytime injection and 55.5%, 54.1%, and 56.9% for nighttime injection. Overall, the occurrence of hypoglycemia and hyperglycemia was notably reduced using our approach. CONCLUSIONS Event-triggering model predictive control in the proposed algorithm is feasible and may meet clinical targets for people with type 1 diabetes.
Collapse
Affiliation(s)
- Ernesto Estremera
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
| | - Aleix Beneyto
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
| | - Alvis Cabrera
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
| | - Iván Contreras
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
| | - Josep Vehí
- Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain.
| |
Collapse
|