451 research outputs found

    A greater proportion of participants with type 2 diabetes achieve treatment targets with insulin degludec/liraglutide versus insulin glargine 100 units/mL at 26 weeks. DUAL VIII, a randomized trial designed to resemble clinical practice

    Get PDF
    This report presents the efficacy and safety of insulin degludec/liraglutide (IDegLira) versus insulin glargine 100 units/mL (IGlar U100) as initial injectable therapy at 26 weeks in the 104-week DUAL VIII durability trial (NCT02501161). Participants (N = 1012) with type 2 diabetes (T2D) uncontrolled on oral antidiabetic drugs (OADs) were randomized 1:1 to open-label IDegLira or IGlar U100. Visits were scheduled at weeks 1, 2, 4 and 12, and every 3 months thereafter. After 26 weeks, glycated haemoglobin (HbA1c) reductions were greater with IDegLira versus IGlar U100 (−21.5 vs. –16.4 mmol/mol [−2.0 vs. –1.5%]), as was the percentage of participants achieving HbA1c <53 mmol/mol (78.7% vs. 55.7%) and HbA1c targets without weight gain and/or hypoglycaemia. Estimated treatment differences for insulin dose (−13.01 U) and body weight change (−1.57 kg) significantly favoured IDegLira. The hypoglycaemia rate was 44% lower with IDegLira versus IGlar U100. Safety results were similar. In a trial resembling clinical practice, more participants receiving IDegLira than IGlar U100 met treatment targets, supporting use of IDegLira as an initial injectable therapy for people with T2D uncontrolled on OADs and eligible for insulin initiation

    Real-Life Effectiveness of iGlarLixi (Insulin Glargine 100 U/ml and Lixisenatide) in People with Type 2 Diabetes (T2D) According to Baseline HbA1c and BMI

    Get PDF
    Introduction: This study aimed to evaluate the effect of baseline body mass index (BMI) and glycated hemoglobin (HbA1c) on the effectiveness and safety of initiating iGlarLixi (insulin glargine 100 U/ml and lixisenatide) in people with type 2 diabetes (T2D) in routine clinical practice. Methods: We pooled patient-level data from 1406 people with inadequately controlled T2D, initiating a 24-week iGlarLixi treatment. Analysis sets were based on baseline BMI and HbA1c. In the BMI set, 894 (64%) people had a BMI ≥30 kg/m2 and 510 (36%) a BMI<30 kg/ m2; in the HbA1c set, 615 (44%) people had an HbA1c >9%, 491 (35%) between 8 and 9%, and 298 (21%)<8%. Results: After initiating iGlarLixi, HbA1c decreased in all participants, with the greatest least-squares mean reduction at 2.15% from baseline to week 24 in those with baseline HbA1c>9% (using a mixed model for repeated measures). Overall, mean ± standard deviation body weight decreased by 1.9±4.8 kg, with the most prominent loss of 2.6±4.9 kg recorded in people presenting with obesity. Reported hypoglycemia rates were low across all groups. Conclusions: Initiation of iGlarLixi in people with uncontrolled T2D is effective and safe in clinical practice, across different baseline HbA1c and BMI categories

    Effectiveness and Safety of iGlarLixi (Insulin Glargine 100 U/mL Plus Lixisenatide) in Type 2 Diabetes According to the Timing of Daily Administration: Data from the REALI Pooled Analysis

    Get PDF
    INTRODUCTION: iGlarLixi (insulin glargine 100 U/mL plus lixisenatide) has demonstrated glycaemic efficacy and safety in adults with inadequately controlled type 2 diabetes mellitus (T2DM). Per the European Medicines Agency's product label, iGlarLixi should be injected once a day within 1 h prior to a meal, preferably the same meal every day when the most convenient meal has been chosen. It is however unknown whether iGlarLixi administration timing affects glycaemic control and safety, as clinical trial evidence is mainly based on pre-breakfast iGlarLixi administration. Therefore, we assessed the effectiveness and safety of iGlarLixi in clinical practice, according to its administration timing. METHODS: Data were pooled from two prospective observational studies including 1303 European participants with T2DM inadequately controlled on oral antidiabetic drugs with or without basal insulin who initiated iGlarLixi therapy for 24 weeks. Participants were classified into four subgroups based on daily timing of iGlarLixi injection: pre-breakfast (N = 436), pre-lunch (N = 262), pre-dinner (N = 399), and those who switched iGlarLixi injection time during the study (N = 206). RESULTS: No meaningful differences in baseline characteristics were observed between the study groups. Least-squares mean reductions in haemoglobin A1c (HbA1c) from baseline to week 24 were substantial in all groups, with the numerically largest decrease observed in the pre-breakfast group (1.57%) compared with the pre-lunch (1.27%), pre-dinner (1.42%), or changed injection time (1.33%) groups. Pre-breakfast iGlarLixi injection also resulted in a numerically greater proportion of participants achieving HbA1c < 7.0% at week 24 (33.7% versus 19.0% for pre-lunch, 25.6% pre-dinner, and 23.2% changed injection time). iGlarLixi was well tolerated across all groups, with low rates of gastrointestinal disorders and hypoglycaemia. Mean body weight decreased similarly in all groups (by 1.3-2.3 kg). CONCLUSION: iGlarLixi was effective and safe regardless of its daily administration time. However, pre-breakfast iGlarLixi injection resulted in a more effective glycaemic control

    iGlarLixi (insulin glargine 100 U/ml plus lixisenatide) is effective and well tolerated in people with uncontrolled type 2 diabetes regardless of age: A REALI pooled analysis of prospective real-world data

    Get PDF
    AIM: To evaluate the effectiveness and safety in routine clinical practice of insulin glargine/lixisenatide (iGlarLixi) in people with type 2 diabetes (T2D) according to age. METHODS: Patient-level data were pooled from 1316 adults with T2D inadequately controlled on oral antidiabetic drugs with or without basal insulin who initiated iGlarLixi for 24 weeks. Participants were classified into age subgroups of younger than 65 years (N = 806) and 65 years or older (N = 510). RESULTS: Compared with participants aged younger than 65 years, those aged 65 years or older had a numerically lower mean body mass index (31.6 vs. 32.6 kg/m2 ), a longer median diabetes duration (11.0 vs. 8.0 years), were more likely to receive prior basal insulin (48.4% vs. 43.5%) and had a lower mean HbA1c (8.93% [74.10 mmol/mol] vs. 9.22% [77.28 mmol/mol]). Similar and clinically relevant reductions in HbA1c and fasting plasma glucose from baseline to week 24 of iGlarLixi therapy were observed regardless of age. At 24 weeks, least-squares adjusted mean (95% confidence interval [CI]) change in HbA1c from baseline was -1.55% (-1.65% to -1.44%) in those aged 65 years or older and -1.42% (-1.50% to -1.33%) in those aged younger than 65 years (95% CI: -0.26% to 0.00%; P = .058 between subgroups). Low incidences of gastrointestinal adverse events and hypoglycaemic episodes were reported in both age subgroups. iGlarLixi decreased mean body weight from baseline to week 24 in both subgroups (-1.6 kg in those aged ≥ 65 years and -2.0 kg in those aged < 65 years). CONCLUSIONS: iGlarLixi is effective and well tolerated in both younger and older people with uncontrolled T2D

    Expert Opinion on Current Trends in the Use of Insulin in the Management of People with Type 2 Diabetes from the South-Eastern European Region and Israel

    Get PDF
    Despite the availability of various antihyperglycaemic therapies and comprehensive guidelines, glycaemic control in diabetes management has not improved significantly during the last decade in the real-world clinical setting. Treatment inertia arising from a complex interplay among patient-, clinician- and healthcare-system-related factors is the prime reason for this suboptimal glycaemic control. Also, the key factor leading to inadequate glycaemic levels remains limited communication between healthcare professionals (HCPs) and people with type 2 diabetes (PwT2D). Early insulin administration has several advantages including reduced glucotoxicity, high efficacy and preserved β-cell mass/function, leading to lowering the risk of diabetes complications. The current publication is based on consensus of experts from the South-Eastern European region and Israel who reviewed the existing evidence and guidelines for the treatment of PwT2D. Herein, the experts emphasised the timely use of insulin, preferably second-generation basal insulin (BI) analogues and intensification using basal-plus therapy, as the most-potent glucose-lowering treatment choice in the real-world clinical setting. Despite an increase in the use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs), the experts urged timely insulin initiation for inadequate glycaemic control in PwT2D. Furthermore, the combination of BI and GLP-1 RA addressing both fasting plasma glucose and post-prandial excursions as a free- or fixed-ratio combination was identified to reduce treatment complexity and burden. To minimise discontinuation and improve adherence, the experts reiterated quality, regular interactions and discussions between HCPs and PwT2D/carers for their involvement in the diabetes management decision-making process. Clinicians and HCPs should consider the opinions of the experts in accordance with the most recent recommendations for diabetes management

    Laparoscopic sleeve gastrectomy without over-sewing of the staple line is effective and safe

    Full text link
    INTRODUCTION: Laparoscopic sleeve gastrectomy (LSG) is a bariatric procedure with very good long-term weight-reducing and metabolic effects. AIM: Here we report 6 years’ experience with LSG performed in morbidly obese patients by one surgical team focusing on the impact of the degree of sleeve restriction and safety of the procedure without over-sewing the staple line. MATERIAL AND METHODS: From 2006 to 2012, 207 morbid obese patients with average age of 43.4 years and average body mass index 44.9 kg/m(2) underwent LSG without over-sewing the staple line. The complete 5- and 3-year follow-up is recorded in 59 and 117 patients with prospective data collection at 3, 6, 9, 12, 18, 24, 36, 42 and 60 months after LSG. Group 1 patients operated in 2006–2008 had smaller sleeve restriction. Group 2 patients operated in 2009–2012 had major sleeve restriction. All procedures were performed without over-sewing of the staple line. RESULTS: The average %EBMIL (excess body mass index loss) in group 1 patients with minor sleeve restriction reached 54.1% and average %EWL (excess weight loss) was 50.8% while in group 2 with major sleeve restriction the average %EBMIL reached 69.7% and average %EWL was 66.8%. Final weight reduction was significantly higher in group 2 patients compared to group 1 patients with smaller sleeve restriction. Out of 49 patients with preoperatively diagnosed T2DM (type 2 diabetes mellitus) was completely resolved in 70.8%. Pre-operatively diagnosed hypertension normalized in 64.2%, improved in 23.2%, and remained unchanged in 12.6% of patients. CONCLUSIONS: Carefully performed LSG without over-sewing the staple line is feasible and safe. A better weight-reducing effect was present in patients with major sleeve restriction

    Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

    Full text link
    [EN] This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.The Czech partners were supported by DROIKEM000023001 and RVOVFN64165. No funding was received to support this research work by the Spanish partners.Cuesta Frau, D.; Novák, D.; Burda, V.; Molina Picó, A.; Vargas-Rojo, B.; Mraz, M.; Kavalkova, P.... (2018). Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics. Entropy. 20(11):1-18. https://doi.org/10.3390/e20110871S118201

    Clinical Evaluation of Subcutaneous Lactate Measurement in Patients after Major Cardiac Surgery

    Get PDF
    Minimally invasive techniques to access subcutaneous adipose tissue for glucose monitoring are successfully applied in type1 diabetic and critically ill patients. During critical illness, the addition of a lactate sensor might enhance prognosis and early intervention. Our objective was to evaluate SAT as a site for lactate measurement in critically ill patients. In 40 patients after major cardiac surgery, arterial blood and SAT microdialysis samples were taken in hourly intervals. Lactate concentrations from SAT were prospectively calibrated to arterial blood. Analysis was based on comparison of absolute lactate concentrations (arterial blood vs. SAT) and on a 6-hour lactate trend analysis, to test whether changes of arterial lactate can be described by SAT lactate. Correlation between lactate readings from arterial blood vs. SAT was highly significant (r2 = 0.71, P < .001). Nevertheless, 42% of SAT lactate readings and 35% of the SAT lactate trends were not comparable to arterial blood. When a 6-hour stabilization period after catheter insertion was introduced, 5.5% of SAT readings and 41.6% of the SAT lactate trends remained incomparable to arterial blood. In conclusion, replacement of arterial blood lactate measurements by readings from SAT is associated with a substantial shortcoming in clinical predictability in patients after major cardiac surgery

    Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

    Get PDF
    [EN] Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal¿jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel¿Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave¿One¿Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.The Czech clinical partners were supported by DRO IKEM 000023001 and RVO VFN 64165. The Czech technical partners were supported by Research Centre for Informatics grant numbers CZ.02.1.01/0.0/16 - 019/0000765 and SGS16/231/OHK3/3T/13-Support of interactive approaches to biomedical data acquisition and processing. No funding was received to support this research work by the Spanish and British partnersCuesta Frau, D.; Novák, D.; Burda, V.; Abasolo, D.; Adjei, T.; Varela, M.; Vargas, B.... (2019). Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods. Complexity. 2019. https://doi.org/10.1155/2019/6070518S2019Kassirer, J. P., & Angell, M. (1998). Losing Weight — An Ill-Fated New Year’s Resolution. New England Journal of Medicine, 338(1), 52-54. doi:10.1056/nejm199801013380109Van Gaal, L., & Dirinck, E. (2016). Pharmacological Approaches in the Treatment and Maintenance of Weight Loss. Diabetes Care, 39(Supplement 2), S260-S267. doi:10.2337/dcs15-3016De Jonge, C., Rensen, S. S., Verdam, F. J., Vincent, R. P., Bloom, S. R., Buurman, W. A., … Greve, J. W. M. (2015). Impact of Duodenal-Jejunal Exclusion on Satiety Hormones. Obesity Surgery, 26(3), 672-678. doi:10.1007/s11695-015-1889-yMuñoz, R., Dominguez, A., Muñoz, F., Muñoz, C., Slako, M., Turiel, D., … Escalona, A. (2013). Baseline glycated hemoglobin levels are associated with duodenal-jejunal bypass liner-induced weight loss in obese patients. Surgical Endoscopy, 28(4), 1056-1062. doi:10.1007/s00464-013-3283-yOgata, H., Tokuyama, K., Nagasaka, S., Ando, A., Kusaka, I., Sato, N., … Yamamoto, Y. (2007). Long-range Correlated Glucose Fluctuations in Diabetes. Methods of Information in Medicine, 46(02), 222-226. doi:10.1055/s-0038-1625411Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831DeFronzo, R. A. (2004). Pathogenesis of type 2 diabetes mellitus. Medical Clinics of North America, 88(4), 787-835. doi:10.1016/j.mcna.2004.04.013Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical Review Letters, 88(17). doi:10.1103/physrevlett.88.174102Bian, C., Qin, C., Ma, Q. D. Y., & Shen, Q. (2012). Modified permutation-entropy analysis of heartbeat dynamics. Physical Review E, 85(2). doi:10.1103/physreve.85.021906Zhao, L., Wei, S., Zhang, C., Zhang, Y., Jiang, X., Liu, F., & Liu, C. (2015). Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects. Entropy, 17(12), 6270-6288. doi:10.3390/e17096270Weinstein, R. L., Schwartz, S. L., Brazg, R. L., Bugler, J. R., Peyser, T. A., & McGarraugh, G. V. (2007). Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System: Comparison with frequent laboratory reference measurements. Diabetes Care, 30(5), 1125-1130. doi:10.2337/dc06-1602Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043Cuesta-Frau, D., Miró-Martínez, P., Oltra-Crespo, S., Jordán-Núñez, J., Vargas, B., González, P., & Varela-Entrecanales, M. (2018). Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures. Entropy, 20(11), 853. doi:10.3390/e20110853Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., Jordán–Núñez, J., Vargas, B., & Vigil, L. (2018). Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Computer Methods and Programs in Biomedicine, 165, 197-204. doi:10.1016/j.cmpb.2018.08.018Cuesta–Frau, D., Varela–Entrecanales, M., Molina–Picó, A., & Vargas, B. (2018). Patterns with Equal Values in Permutation Entropy: Do They Really Matter for Biosignal Classification? Complexity, 2018, 1-15. doi:10.1155/2018/1324696Mayer, C. C., Bachler, M., Hörtenhuber, M., Stocker, C., Holzinger, A., & Wassertheurer, S. (2014). Selection of entropy-measure parameters for knowledge discovery in heart rate variability data. BMC Bioinformatics, 15(S6). doi:10.1186/1471-2105-15-s6-s2Sheng Lu, Xinnian Chen, Kanters, J. K., Solomon, I. C., & Chon, K. H. (2008). Automatic Selection of the Threshold Value rr for Approximate Entropy. IEEE Transactions on Biomedical Engineering, 55(8), 1966-1972. doi:10.1109/tbme.2008.919870Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism, 101(4), 1490-1497. doi:10.1210/jc.2015-4035Cuesta, D., Varela, M., Miró, P., Galdós, P., Abásolo, D., Hornero, R., & Aboy, M. (2007). Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy. Medical & Biological Engineering & Computing, 45(7), 671-678. doi:10.1007/s11517-007-0200-3Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1), 61-68. doi:10.1016/j.medengphy.2008.04.005Xiao-Feng, L., & Yue, W. (2009). Fine-grained permutation entropy as a measure of natural complexity for time series. Chinese Physics B, 18(7), 2690-2695. doi:10.1088/1674-1056/18/7/011Fadlallah, B., Chen, B., Keil, A., & Príncipe, J. (2013). Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information. Physical Review E, 87(2). doi:10.1103/physreve.87.02291
    corecore