Journal of Diabetes Science and Technology最新文献

{"title":"Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring.","authors":"Bernhard Kulzer, Guido Freckmann, Ralph Ziegler, Oliver Schnell, Timor Glatzer, Lutz Heinemann","doi":"10.1177/19322968241267823","DOIUrl":"10.1177/19322968241267823","url":null,"abstract":"<p><p>Nocturnal hypoglycemia is a common acute complication of people with diabetes on insulin therapy. In particular, the inability to control glucose levels during sleep, the impact of external factors such as exercise, or alcohol and the influence of hormones are the main causes. Nocturnal hypoglycemia has several negative somatic, psychological, and social effects for people with diabetes, which are summarized in this article. With the advent of continuous glucose monitoring (CGM), it has been shown that the number of nocturnal hypoglycemic events was significantly underestimated when traditional blood glucose monitoring was used. The CGM can reduce the number of nocturnal hypoglycemia episodes with the help of alarms, trend arrows, and evaluation routines. In combination with CGM with an insulin pump and an algorithm, automatic glucose adjustment (AID) systems have their particular strength in nocturnal glucose regulation and the prevention of nocturnal hypoglycemia. Nevertheless, the problem of nocturnal hypoglycemia has not yet been solved completely with the technologies currently available. The CGM systems that use predictive models to warn of hypoglycemia, improved AID systems that recognize hypoglycemia patterns even better, and the increasing integration of artificial intelligence methods are promising approaches in the future to significantly minimize the risk of a side effect of insulin therapy that is burdensome for people with diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of a Novel Continuous Glucose Monitoring Device in People With Diabetes.","authors":"Julia K Mader, Delia Waldenmaier, Wiebke Mueller-Hoffmann, Katrin Mueller, Michael Angstmann, Gerhard Vogt, Cosima C Rieger, Manuel Eichenlaub, Thomas Forst, Guido Freckmann","doi":"10.1177/19322968241267774","DOIUrl":"10.1177/19322968241267774","url":null,"abstract":"<p><strong>Background: </strong>In this multicenter study, performance of a novel continuous glucose monitoring (CGM) system was evaluated.</p><p><strong>Methods: </strong>Adult participants with diabetes were included in the study. They each wore three sensors of the CGM system on the upper arms for up to 14 days. During four in-clinic visits, frequent comparison measurements with capillary blood glucose (BG) samples were performed. The primary endpoint was the 20/20 agreement rate (AR): the percentage of CGM readings within ±20 mg/dL (at BG values <100 mg/dL) or ±20% (at BG values ≥100 mg/dL) of the comparator. Further evaluations included mean absolute relative difference (MARD) and 20/20 AR in different BG ranges and across the wear time.</p><p><strong>Results: </strong>Data from 48 participants and 139 sensors were analyzed. During in-clinic sessions the 20/20 AR was 90.5% and the MARD was 9.2%. For BG ranges <70, 70-180, and >180 mg/dL, 20/20 AR was 94.3%, 89.0%, and 92.5%, respectively. At the beginning, middle, and end of sensor wear time, 20/20 AR was 92.8%, 91.5%, and 85.9%, respectively. The 14-day survival probability was 82.4%. Pain and bleeding after sensor insertion were within the expected range. Based on the study outcome, the use of the device is regarded as safe.</p><p><strong>Conclusions: </strong>The system showed a good performance compared to capillary BG measurements. This level of accuracy could be shown over the entire measurement range, especially in the low glycemic range, and the whole wear time of the sensors. The results of this study are supporting a non-adjunctive use of the device.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic Accuracy of Perception Threshold Tracking in the Detection of Small Fiber Damage in Type 1 Diabetes.","authors":"Johan Røikjer, Suganthiya Santhiapillai Croosu, Benn Falch Sejergaard, Tine Maria Hansen, Jens Brøndum Frøkjær, Chris Bath Søndergaard, Ioannis N Petropoulos, Rayaz A Malik, Esben Nielsen, Carsten Dahl Mørch, Niels Ejskjaer","doi":"10.1177/19322968231157431","DOIUrl":"10.1177/19322968231157431","url":null,"abstract":"<p><strong>Aim: </strong>An objective assessment of small nerve fibers is key to the early detection of diabetic peripheral neuropathy (DPN). This study investigates the diagnostic accuracy of a novel perception threshold tracking technique in detecting small nerve fiber damage.</p><p><strong>Methods: </strong>Participants with type 1 diabetes (T1DM) without DPN (n = 20), with DPN (n = 20), with painful DPN (n = 20) and 20 healthy controls (HCs) underwent perception threshold tracking on the foot and corneal confocal microscopy. Diagnostic accuracy of perception threshold tracking compared to corneal confocal microscopy was analyzed using logistic regression.</p><p><strong>Results: </strong>The rheobase, corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), and corneal nerve fiber length (CNFL) (all <i>P</i> < .001) differed between groups. The diagnostic accuracy of perception threshold tracking (rheobase) was excellent for identifying small nerve fiber damage, especially for CNFL with a sensitivity of 94%, specificity 94%, positive predictive value 97%, and negative predictive value 89%. There was a significant correlation between rheobase with CNFD, CNBD, CNFL, and Michigan Neuropathy Screening Instrument (all <i>P</i> < .001).</p><p><strong>Conclusion: </strong>Perception threshold tracking had a very high diagnostic agreement with corneal confocal microscopy for detecting small nerve fiber loss and may have clinical utility for assessing small nerve fiber damage and hence early DPN.</p><p><strong>Clinical trials: </strong>NCT04078516.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10769607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance.","authors":"Camilla Heisel Nyholm Thomsen, Stine Hangaard, Thomas Kronborg, Peter Vestergaard, Ole Hejlesen, Morten Hasselstrøm Jensen","doi":"10.1177/19322968221145964","DOIUrl":"10.1177/19322968221145964","url":null,"abstract":"<p><strong>Background: </strong>Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not reach glycemic targets. This emphasizes a need for methods supporting efficient and individualized basal insulin titration of people with T2D. However, no systematic review of basal insulin dose guidance for people with T2D has been found.</p><p><strong>Objective: </strong>To provide an overview of basal insulin dose guidance methods that support titration of people with T2D and categorize these methods by characteristics, effect, and user experience.</p><p><strong>Methods: </strong>The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies about basal insulin dose guidance, including adults with T2D on basal insulin analogs published before September 7, 2022, were included. Joanna Briggs Institute critical appraisal checklists were applied to assess risk of bias.</p><p><strong>Results: </strong>In total, 35 studies were included, and three categories of dose guidance were identified: paper-based titration algorithms, telehealth solutions, and mathematical models. Heterogeneous reporting of glycemic outcomes challenged comparison of effect between the three categories. Few studies assessed user experience.</p><p><strong>Conclusions: </strong>Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10421073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thirteen Years JDST: A Quite Nice Ride.","authors":"Lutz Heinemann","doi":"10.1177/19322968241267855","DOIUrl":"10.1177/19322968241267855","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Glycemic Control Using a Bluetooth®-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144 000 People With Diabetes.","authors":"Mike Grady, Hilary Cameron, Elizabeth Holt","doi":"10.1177/19322968221148764","DOIUrl":"10.1177/19322968221148764","url":null,"abstract":"<p><strong>Background: </strong>The OneTouch Verio Flex® (OTVF) blood glucose (BG) meter features a ColorSure® Range Indicator. Diabetes management is enhanced by connecting the meter to the OneTouch Reveal® (OTR) mobile app. We sought to provide real-world evidence (RWE) that combining both devices improves glycemic control.</p><p><strong>Methods: </strong>Anonymized glucose and app analytics were extracted from a server from over 144 000 people with diabetes (PWDs). Data from their first 14 days using OTVF and OTR were compared with 14 days prior to 90- and 180-day timepoints using paired within-subject differences.</p><p><strong>Results: </strong>In people with type 1 diabetes (PwT1D) or people with type 2 diabetes (PwT2D), readings in-range (RIR) improved by +6.1 (54.5% to 60.6%) and +11.9 percentage points (68.2% to 80.1%), respectively, over 180 days, and hyperglycemia was reduced by -6.6 (40.5% to 33.9%) and -12.0 (30.3% to 18.3%). In total, 35% of PwT1D and 40% of PwT2D improved RIR by >10 percentage points. People with type 1 diabetes spending two to four sessions or 10 to 20 minutes per week on the app improved RIR by +5.1 and 7.0, respectively. People with type 2 diabetes spending two to four sessions or 10 to 20 minutes per week on the app improved RIR by +11.6 and 12.0, respectively. In PwT1D or PwT2D, mean BG reduced by -11.4 and -19.5 mg/dL, respectively, from baseline to 180 days, with no clinically meaningful changes in percentage of hypoglycemic readings. All glycemic changes were statistically significant (<i>P</i> < .0005 level).</p><p><strong>Conclusion: </strong>Real-world data from over 144 000 PWDs demonstrated improved percentage readings in-range and reduced hyperglycemia in PWDs using the OneTouch Verio Flex blood glucose meter and OneTouch Reveal app.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10632287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vacuum-Assisted Needle-Free Capillary Blood Sampling.","authors":"Michael Hoffman, James McKeage, Bryan Ruddy, Poul Nielsen, Andrew Taberner","doi":"10.1177/19322968231161361","DOIUrl":"10.1177/19322968231161361","url":null,"abstract":"<p><strong>Background: </strong>Poor glycemic management persists among people practicing insulin therapy in relation to type 1 and 2 diabetes despite a clear relationship with negative health outcomes. Skin penetration by jet injection has recently been shown as a viable method for inducing blood release from fingertips. This study examines the use of vacuum to enhance the volume of blood released and quantifies any dilution of the collected blood.</p><p><strong>Methods: </strong>A single-blind crossover study involving 15 participants, each receiving four different interventions, was conducted wherein each participant served as their own control. Each participant experienced fingertip lancing and fingertip jet injection, both with and without applied vacuum. Participants were divided into three equal groups to explore different vacuum pressures.</p><p><strong>Results: </strong>This study found that glucose concentration in blood collected under vacuum following jet injection and lancing were equivalent. We found that applying a 40 kPa vacuum following jet injection produced a 35-fold increase in the collected volume. We determined the limited extent to which the injectate dilutes blood collected following jet injection. The mean dilution of blood collected by jet injection was 5.5%. We show that jet injection is as acceptable to patients as lancing, while being equally suited for conducting glucose measurements.</p><p><strong>Conclusions: </strong>Vacuum significantly enhances the volume of capillary blood released from the fingertip without any difference in pain. The blood collected by jet injection with vacuum is equivalent to that from lancing for glucose measurement purposes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9126294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Hybrid Closed Loop in Adult Population With Type 1 Diabetes: A Substudy From the ADAPT Randomized Controlled Trial in Users of Real-Time Continuous Glucose Monitoring.","authors":"Tim van den Heuvel, Ralf Kolassa, Winfried Keuthage, Jens Kroeger, Roseline Ré, Simona de Portu, Linda Vorrink, John Shin, Javier Castañeda, Robert Vigersky, Ohad Cohen","doi":"10.1177/19322968231161320","DOIUrl":"10.1177/19322968231161320","url":null,"abstract":"<p><strong>Background: </strong>This analysis reports the findings from a predefined exploratory cohort (cohort B) from the ADAPT (ADvanced Hybrid Closed Loop Study in Adult Population with Type 1 Diabetes) study. Adults with type 1 diabetes (T1D) with suboptimal glucose control were randomly allocated to an advanced hybrid closed-loop (AHCL) system or multiple daily injections of insulin (MDI) plus real-time continuous glucose monitoring (RT-CGM).</p><p><strong>Methods: </strong>In this prospective, multicenter, exploratory, open-label, randomized controlled trial, 13 participants using MDI + RT-CGM and with HbA1c ≥8.0% were randomized to switch to AHCL (n = 8) or continue with MDI + RT-CGM (n = 5) for six months. Prespecified endpoints included the between-group difference in mean change from baseline in HbA1c, CGM-derived measures of glycemic control, and safety.</p><p><strong>Results: </strong>The mean HbA1c level decreased by 1.70 percentage points in the AHCL group versus a 0.60 percentage point decrease in the MDI + RT-CGM group, with a model-based treatment effect of -1.08 percentage points (95% confidence interval [CI] = -2.17 to 0.00 percentage points; <i>P</i> = .0508) in favor of AHCL. The percentage of time spent with sensor glucose levels between 70 and 180 mg/dL in the study phase was 73.6% in the AHCL group and 46.4% in the MDI + RT-CGM group; model-based between-group difference of 28.8 percentage points (95% CI = 12.3 to 45.3 percentage points; <i>P</i> = .0035). No diabetic ketoacidosis or severe hypoglycemia occurred in either group.</p><p><strong>Conclusions: </strong>In people with T1D with HbA1c ≥8.0%, the use of AHCL resulted in improved glycemic control relative to MDI + RT-CGM. The scale of improvement suggests that AHCL should be considered as an option for people not achieving good glycemic control on MDI + RT-CGM.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9611027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Glucose Values: A New Era for Continuous Glucose Monitoring.","authors":"Bernhard Kulzer, Lutz Heinemann","doi":"10.1177/19322968241271925","DOIUrl":"10.1177/19322968241271925","url":null,"abstract":"<p><p>The last 25 years of CGM have been characterized above all by providing better and more accurate glucose values in real time and analyzing the measured glucose values. Trend arrows are the only way to look into the future, but they are often too imprecise for therapy adjustment. While AID systems provide algorithms to use glucose values for glucose control, this has not been possible with stand-alone CGM systems, which are most used by people with diabetes. By analyzing the measured values with algorithms, often supported by AI, this should be possible in the future. This provides the user with important information about the further course of the glucose level, such as during the night. Predictive approaches can be used by next-generation CGM systems. These systems can proactively prevent glucose events such as hypo- or hyperglycemia. With the Accu-Chek® SmartGuide Predict app, an integral part of a novel CGM system, and the Glucose Predict (GP) feature, people with diabetes have the first commercially available CGM system with predictive algorithms. It characterizes the CGM systems of the future, which not only analyze past values and current glucose values in the future, but also use these values to predict future glucose progression.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Care Providers Can Deliver Personalized Precision Diabetes Care and Adopt GRI If It is Incorporated into a Centralized Platform.","authors":"Ralph Oiknine, Kay Broschat, Maria Weir, Stephen Von Rump","doi":"10.1177/19322968241257005","DOIUrl":"10.1177/19322968241257005","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}