Impact of High Intensity Interval Training Using Elastic Bands on Glycemic Control in Adults with Type 1 Diabetes: A Pilot Study

[EN] High intensity interval training (HIIT) using elastic bands is easy to do, but no data on its impact on glycemic control in people with type 1 diabetes (T1D) are available. Six males with T1D performed three weekly sessions of HIIT using elastic bands for 12 weeks. Each session consisted of eight exercises. Glycemic control was evaluated by using intermittent scanning continuous glucose monitoring two weeks before study onset (baseline) and during the intervention period in the first two (first stage) and last two weeks (last stage). In the 24 h post-exercise, time-in-range (70-180 mg/dL) was reduced from baseline to the end of the study (67.2% to 63.0%), and time-above-range (>180 mg/dL) seemed to increase from baseline across the study (20.8% -> 27.5% -> 22.1%, from baseline -> first -> last stage), but did not show any statistical significance. Time in hypoglycemia (either < 70 mg/dL or <54 mg/dL) did not show statistically significant differences. This study shows that a HIIT program with elastic bands is safe and effective to perform in T1D patients, keeping blood glucose levels in a safe range.This study was funded by MINECO DPI2016-78831-C2-1-R, Agencia Estatal de Investigacion (PID2019-107722RB-C21/AEI/10.13039/501100011033), FEDER funds from EU, and the Vicerectorate of Research, Innovation and Technology Transference from the Universitat Politecnica de Valencia grant number PAID-06-18. This study was also supported by the official funding agency for biomedical research of the Spanish government, Institute of Health Carlos III (ISCIII) through CIBEROBN CB12/03/30038, and CIBERDEM CB17/08/00004, which is co-funded by the European Regional Development Fund.Martín-San Agustín, R.; Laguna Sanz, AJ.; Bondía Company, J.; Roche, E.; Benítez Martínez, JC.; Ampudia-Blasco, FJ. (2020). Impact of High Intensity Interval Training Using Elastic Bands on Glycemic Control in Adults with Type 1 Diabetes: A Pilot Study. Applied Sciences. 10(19). https://doi.org/10.3390/app10196988101

Basal plus basal-bolus approach in type 2 diabetes

This is a copy of an article published in the Diabetes Technology and Therapeutics © 2011 [copyright Mary Ann Liebert, Inc.]; Diabetes Technology and Therapeutics is available online at: http://online.liebertpub.com.[EN] Type 2 diabetes is characterized by insulin resistance and progressive b-cell deterioration. As b-cell function declines, most patients with type 2 diabetes treated with oral agents, in monotherapy or combination, will require insulin therapy. Addition of basal insulin (glargine, detemir, or NPH/neutral protamine lispro insulin) to previous treatment is accepted as the simplest way to start insulin therapy in those patients. But even when basal insulin is adequately titrated, some patients will also need prandial insulin to achieve or maintain individual glycemic targets over time. Starting with premixed insulin is an effective option, but it is frequently associated with increased hypoglycemia risk, ¿xed meal schedules, and weight gain. As an alternative, a novel approached known as ``basal plus strategy¿¿ has been developed. This approach considers the addition of increasing injections of prandial insulin, beginning with the meal that has the major impact on postprandial glucose values. Finally, if this is not enough intensi¿cation to basal¿bolus will be necessary. In reducing hyperglycemia, this modality still remains the most effective option, even in people with type 2 diabetes. This article will review the currently evidence on the basal plus strategy and also its progression to basal¿bolus therapy. In addition, practical recommendations to start and adjust basal plus therapy will be provided.F.J.A.-B. has received honoraria as speaker and/or consultant from Abbott, AstraZeneca, Bristol-Myers Squibb, Glaxo-SmithKline, LifeScan, Lilly, Madaus, MannKind Corp., Medtronic, Menarini, Merch Farma y Quimica, SA, MSD, Novartis, Novo Nordisk, Pfizer, Roche, sanofi-aventis, Schering-Plough, and Solvay. In addition, F.J.A.-B. has participated in clinical trials supported totally or partially by AstraZeneca, Glaxo-SmithKline, LifeScan, Lilly, MSD, Novo Nordisk, Pfizer, sanofi-aventis, and Servier. P. R. has no potential conflicts of interest to declare. J.F.A. has received honoraria as speaker and/or consultant form AstraZeneca, Ferrer, Glaxo-SmithKline, Laboratorios Dr. Esteve, Lilly, MSD, and Solvay.Ampudia-Blasco, J.; Rossetti ., P.; Ascaso, JF. (2011). Basal plus basal-bolus approach in type 2 diabetes. Diabetes Technology & Therapeutics. 13:75-83. doi:10.1089/dia.2011.0001S75831

Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing - the iBolus - in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial

This is a copy of an article published in the Diabetes Technology and Therapeutics © 2012 [copyright Mary Ann Liebert, Inc.]; Diabetes Technology and Therapeutics is available online at: http://online.liebertpub.com.[EN] Objective: Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. Subjects and Methods: An individual patient¿s model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t 0) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG0¿5h) and variability as the individual coef¿cient of variation (CV) of AUC-PG0¿5h. The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t 0 (PGt0), and insulin dose to AUC-PG0¿5h and its CV was also investigated. Results: AUC-PG0¿5h was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5¿127.5 vs. 689.2¿180.7 mg/dL$h) or 100-g (752.1¿237.7 vs. 760.0¿263.2 mg/dL$h) CHO meals. A multiple regression analysis revealed a signi¿cant model only for the tBolus, with PGt0 being the best predictor of AUC-PG0¿5h explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7¿15.3% vs. 10.1¿12.5%) but independent of the factors studied. Conclusions: A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identi¿ed and analyzed for further optimization of postprandial glycemic control.We are grateful to Mrs. Sara Correa, Fundacion INCLIVA-Hospital Clinico Universitario de Valencia, and Mrs. Geles Viguer, Hospital Clinico Universitario de Valencia, for their invaluable help in conducting the study. We also thanks Dr. Carmine Fanelli, University of Perugia, Dr. Howard Zisser, Sansum Diabetes Research Institute, and Prof. Alberto Ferrer, Universitat Politecnica de Valencia, for their suggestions on study design and data analysis. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007/2013) under grant agreement 252085 and from the Spanish Ministry of Science under grants DPI2010-20764-C02-01 and DPI2011-28112-C04-01.Rossetti, P.; Ampudia Blasco, FJ.; Laguna Sanz, AJ.; Revert Tomás, A.; Vehí Casellas, J.; Ascaso, JF.; Bondía Company, J. (2012). Evaluation of a novel continuous glucose monitoring-based method for mealtime insulin dosing - the iBolus - in subjects with type 1 diabetes using continuous subcutaneous insulin infusion therapy: a randomized controlled trial. Diabetes Technology & Therapeutics. 14(11):1043-1052. https://doi.org/10.1089/dia.2012.0145S10431052141

Patient-reported outcomes in adults with type 1 diabetes in global real-world clinical practice: The SAGE study

AimsTo conduct a secondary analysis of the SAGE study to evaluate the association between glycaemic control and patient-reported outcomes (PROs), in adults with type 1 diabetes (T1DM) across different age groups and regions.Materials and methodsSAGE was a multinational, cross-sectional, observational study in adults with T1DM. Data were collected at a single visit, analysed according to predefined age groups (26-44, 45-64, and ≥65 years), and reported across different regions. PRO questionnaires were applied to assess hypoglycaemia fear (Hypoglycemia Fear Survey-II), diabetes-related distress (Problem Areas In Diabetes questionnaire), insulin treatment satisfaction (Insulin Treatment Satisfaction Questionnaire), and diabetes-specific quality of life (QoL; Audit of Diabetes-Dependent Quality of Life). Multivariable analysis was performed to evaluate the relationship between glycated haemoglobin (HbA1c) target achievement (less than 7% and individualised targets) with PRO scores.ResultsThe PRO scores showed relatively low levels of diabetes-related emotional distress and fear of hypoglycaemia, moderate to high treatment satisfaction, and low diabetes-related impact on QoL. Results were generally comparable across age groups with some regional variability. Achievement of the HbA1c less than 7% target was associated with less worry about hypoglycaemia, lower diabetes-related emotional distress, higher insulin treatment satisfaction, and higher QoL. Achievement of individualised HbA1c targets was associated with lower diabetes-related emotional distress and higher insulin treatment satisfaction.ConclusionsBetter glycaemic control was most closely associated with low emotional distress due to diabetes and high patient-reported insulin treatment satisfaction

Risk and impact of herpes zoster on patients with diabetes: A population-based study, 2009–2014

Aims: This study was designed to assess the impact of diabetes on the risk and severity of herpes zoster (HZ), and the impact of HZ on diabetes. It focused primarily on immunocompetent patients aged ≥ 50 years who would be eligible for preventive vaccination. Methods: Using population and healthcare databases of Valencia Region (Spain), a retrospective cohort of all subjects ≥ 50 years was followed up between 2009 and 2014. HZ and diabetes were defined using ICD-9 codes. We compared the incidence of HZ between non-diabetes and diabetes groups and healthcare resource consumption due to HZ in the 6 months following HZ diagnosis using different statistical generalized linear models (GLM). We also compared resources consumption due to diabetes treatment and haemoglobinA1c(HbA1c) levels before and after HZ. Results: The cohort consisted of 2,289,485 individuals ≥ 50 years old, 397,940 of whom had diabetes. HZ incidence rate was 9.3 cases/1000 persons with diabetes-year (95% CI: 9.1–9.4). Incidence increased with age in all groups. The risk of HZ increased in the diabetes group compared to the non-diabetes group (RR 1.2, 95% credibility interval [CrI] 1.17–1.22). Patients with diabetes utilized more health care resources due to their HZ episodes than patients without diabetes. In 24% of well controlled patients with diabetes (HbA1C levels ≤ 6.5%), HbA1C increased after HZ. Conclusions: Diabetes increased by 20% the risk of HZ. HZ contributed to the deterioration of glycaemic control and higher healthcare resource consumption in people with diabetes, becoming a priority population for HZ immunization

Experimental blood glucose interval identification of patients with type 1 diabetes

[EN] Many problems are confronted when characterizing a type 1 diabetic patient such as model mismatches, noisy inputs, measurement errors and huge variability in the glucose profiles. In this work we introduce a new identification method based on interval analysis where variability and model imprecisions are represented by an interval model as parametric uncertainty. The minimization of a composite cost index comprising: (1) the glucose envelope width predicted by the interval model, and (2) a Hausdorff-distance-based prediction error with respect to the envelope, is proposed. The method is evaluated with clinical data consisting in insulin and blood glucose reference measurements from 12 patients for four different lunchtime postprandial periods each. Following a “leave-one-day-out” cross-validation study, model prediction capabilities for validation days were encouraging (medians of: relative error = 5.45%, samples predicted = 57%, prediction width = 79.1 mg/dL). The consideration of the days with maximum patient variability represented as identification days, resulted in improved prediction capabilities for the identified model (medians of: relative error = 0.03%, samples predicted = 96.8%, prediction width = 101.3 mg/dL). Feasibility of interval models identification in the context of type 1 diabetes was demonstrated.The research leading to these results has received funding from the Spanish Ministry of Science and Innovation under grant DPI2010-20764-C02-01, the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement FP7-PEOPLE-2009-IEF, Ref 252085 and the GeneralitatValenciana through Grant GV/2012/085. The authors acknowledge the collaboration of Sara Correa, Geles Viguer and Pepa Gabaldón from the Diabetes Reference Unit in the Clinic University Hospital of Valencia, and the selfless participation of all the patients involved in the experiments from which data were obtained.Laguna Sanz, AJ.; Rossetti, P.; Ampudia Blasco, FJ.; Vehí, J.; Bondía Company, J. (2014). Experimental blood glucose interval identification of patients with type 1 diabetes. Journal of Process Control. 24(1):171-181. https://doi.org/10.1016/j.jprocont.2013.09.015S17118124

Impact of High Intensity Interval Training Using Elastic Bands on Glycemic Control in Adults with Type 1 Diabetes: A Pilot Study

High intensity interval training (HIIT) using elastic bands is easy to do, but no data on its impact on glycemic control in people with type 1 diabetes (T1D) are available. Six males with T1D performed three weekly sessions of HIIT using elastic bands for 12 weeks. Each session consisted of eight exercises. Glycemic control was evaluated by using intermittent scanning continuous glucose monitoring two weeks before study onset (baseline) and during the intervention period in the first two (first stage) and last two weeks (last stage). In the 24 h post-exercise, time-in-range (70&ndash;180 mg/dL) was reduced from baseline to the end of the study (67.2% to 63.0%), and time-above-range (&gt;180 mg/dL) seemed to increase from baseline across the study (20.8% &rarr; 27.5% &rarr; 22.1%, from baseline &rarr; first &rarr; last stage), but did not show any statistical significance. Time in hypoglycemia (either &lt; 70 mg/dL or &lt;54 mg/dL) did not show statistically significant differences. This study shows that a HIIT program with elastic bands is safe and effective to perform in T1D patients, keeping blood glucose levels in a safe range

Conversaciones y reacciones en relación con la hipoglucemia grave (CRASH): resultados de España de una encuesta global en personas con diabetes tipo 1 o diabetes tipo 2 tratada con insulina y cuidadores

Introduction: Information on experience/management of severe hypoglycaemic events (SHEs) among people with insulin-treated diabetes (PWD) and caregivers (CGs) providing care to PWD was sought. Materials and methods: An online cross-sectional survey was conducted in eight countries. Inclusion criteria: PWD (aged ≥ 18 years; self-reported type 1 [T1D] or insulin-treated type 2 [T2D] diabetes; experienced ≥1 SHE [hypoglycaemia requiring external assistance] in past 3 years); CGs (layperson aged ≥18 years; caring for PWD meeting all criteria above except age [≥4 years]). This descriptive analysis provides data from Spain. SHE-associated data relate to the most recent SHE. Results: Across all groups (T1D PWD, n = 106; T2D PWD, n = 88, T1D CG, n = 87; T2D CG, n = 96), 76–89% reported that the SHE occurred at home; most common cause was eating less than planned (38–53%). Most usual action during the SHE was to intake carbohydrates (67–84%); glucagon use was low (9–36%). Discussion of the SHE with their healthcare provider (HCP) was reported by 70–75% of PWD. During the SHE, 35–69% of PWD/CGs reported feeling scared, unprepared and/or helpless. Conclusions: Most SHEs occurred outside the healthcare setting; treatment therefore depends greatly on CGs. SHEs have a negative emotional impact on PWD/CGs, underscoring the need for HCPs to discuss SHEs with PWD/CGs, and to provide tools and strategies to prevent and effectively manage SHEs

A Decision Support Tool for Appropriate Glucose-Lowering Therapy in Patients with Type 2 Diabetes

Abstract Background: Optimal glucose-lowering therapy in type 2 diabetes mellitus requires a patient-specific approach. Although a good framework, current guidelines are insufficiently detailed to address the different phenotypes and individual needs of patients seen in daily practice. We developed a patient-specific decision support tool based on a systematic analysis of expert opinion. Materials and Methods: Based on the American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) 2012 position statement, a panel of 12 European experts rated the appropriateness (RAND/UCLA Appropriateness Method) of treatment strategies for 930 clinical scenarios, which were permutations of clinical variables considered relevant to treatment choice. These included current treatment, hemoglobin A1c difference from individualized target, risk of hypoglycemia, body mass index, life expectancy, and comorbidities. Treatment options included addition of a second or third agent, drug switches, and replacement by monotherapies if the patient was metformin-intolerant. Treatment costs were not considered. Appropriateness (appropriate, inappropriate, uncertain) was based on the median score and expert agreement. The panel recommendations were embedded in an online decision support tool (DiaScope(®); Novo Nordisk Health Care AG, Zürich, Switzerland). Results: Treatment appropriateness was associated with (combinations of) the patient variables mentioned above. As second-line agents, dipeptidyl peptidase-4 inhibitors were considered appropriate in all scenarios, followed by glucagon-like peptide-1 receptor agonists (50%), insulins (33%), and sulfonylureas (25%), but not pioglitazone (0%). Ratings of third-line combinations followed a similar pattern. Disagreement was highest for regimens including pioglitazone, sulfonylureas, or insulins and was partly due to differences in panelists' opinions and in drug availability and reimbursement across European countries (although costs were disregarded in the rating process). Conclusions: A novel decision support tool based on the ADA/EASD 2012 position statement and a systematic analysis of expert opinion has been developed to help healthcare professionals to individualize glucose-lowering therapy in daily clinical situations.status: publishe