Diabetes technology & therapeutics最新文献

{"title":"Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm.","authors":"Boris Kovatchev, Alberto Castillo, Elliott Pryor, Laura L Kollar, Charlotte L Barnett, Mark D DeBoer, Sue A Brown","doi":"10.1089/dia.2023.0469","DOIUrl":"10.1089/dia.2023.0469","url":null,"abstract":"<p><p><b><i>Background:</i></b> Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was to introduce a new regulatory and clinical paradigm-a Neural-Net Artificial Pancreas (NAP)-an encoding of an AID algorithm into a neural network that approximates its action and assess NAP versus the original AID algorithm. <b><i>Methods:</i></b> The University of Virginia Model-Predictive Control (UMPC) algorithm was encoded into a neural network, creating its NAP approximation. Seventeen AID users with T1D were recruited and 15 participated in two consecutive 20-h hotel sessions, receiving in random order either NAP or UMPC. Their demographic characteristics were ages 22-68 years old, duration of diabetes 7-58 years, gender 10/5 female/male, White Non-Hispanic/Black 13/2, and baseline glycated hemoglobin 5.4%-8.1%. <b><i>Results:</i></b> The time-in-range (TIR) difference between NAP and UMPC, adjusted for entry glucose level, was 1 percentage point, with absolute TIR values of 86% (NAP) and 87% (UMPC). The two algorithms achieved similar times <70 mg/dL of 2.0% versus 1.8% and coefficients of variation of 29.3% (NAP) versus 29.1 (UMPC)%. Under identical inputs, the average absolute insulin-recommendation difference was 0.031 U/h. There were no serious adverse events on either controller. NAP had sixfold lower computational demands than UMPC. <b><i>Conclusion:</i></b> In a randomized crossover study, a neural-network encoding of a complex model-predictive control algorithm demonstrated similar performance, at a fraction of the computational demands. Regulatory and clinical doors are therefore open for contemporary machine-learning methods to enter the AID field. Clinical Trial Registration number: NCT05876273.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11305265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139563014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy and Safety of Tirzepatide in Overweight and Obese Adult Patients with Type 1 Diabetes.","authors":"Satish K Garg, Halis K Akturk, Gurleen Kaur, Christie Beatson, Janet Snell-Bergeon","doi":"10.1089/dia.2024.0050","DOIUrl":"10.1089/dia.2024.0050","url":null,"abstract":"<p><p><b><i>Introduction and Objective:</i></b> Most patients with type 1 diabetes (T1D) in the United States are overweight (OW) or obese (OB), contributing to insulin resistance and suboptimal glucose control. The primary Food and Drug Administration-approved treatment for T1D is insulin, which may adversely affect weight. Tirzepatide is approved for managing type 2 diabetes, improves glucose control, facilitates weight loss, and improves cardiovascular disease outcomes. We assessed the use of tirzepatide in OW/OB subjects with T1D. <b><i>Methods:</i></b> This was a retrospective single-center real-world study in 62 OW/OB adult patients with T1D who were prescribed tirzepatide (treated group) and followed for 1 year. At least 3 months of use of tirzepatide was one of the inclusion criteria. Based on the inclusion criteria, this study represents 62 patients out of 184 prescribed tirzepatide. The control group included 37 OW/OB patients with T1D (computer frequency matched by age, duration of diabetes, gender, body mass index (BMI), and glucose control) who were not using any other weight-loss medications during the same period. The mean (±standard deviation [SD]) dose of weekly tirzepatide at 3 months was 5.6 ± 1.9 mg that increased to 9.7 ± 3.3 mg at 1 year. <b><i>Results:</i></b> The gender, mean baseline age, duration of diabetes, and glycosylated hemoglobin (HbA1c) were similar in the two groups, whereas BMI and weight were higher in the treated group. There were significantly larger declines in BMI and weight in the treated group than in controls across all time points among those in whom data were available. HbA1c decreased in the treated group as early as 3 months and was sustained through a 1-year follow-up (-0.67% at 1 year). As expected, insulin dose decreased at 3 months and throughout the study period. There were no reported hospitalizations from severe hypoglycemia or diabetic ketoacidosis. The mean glucose, time-in-range, time-above-range, SD, and coefficient of variation (continuous glucose monitoring metrics) significantly improved in the treated group. <b><i>Conclusions:</i></b> In this pilot (off label) study, we conclude that tirzepatide facilitated an average 18.5% weight loss (>46 pounds) and improved glucose control in OW/OB patients with T1D at 1 year. For safe use of tirzepatide in patients with T1D, we strongly recommend a large prospective randomized control trial in OW/OB patients with T1D.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140184015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis.","authors":"Ji Yoon Kim, Sang-Man Jin, Sarah B Andrade, Boyang Chen, Jae Hyeon Kim","doi":"10.1089/dia.2023.0513","DOIUrl":"10.1089/dia.2023.0513","url":null,"abstract":"<p><p><b><i>Background:</i></b> We used continuous glucose monitoring (CGM) data to investigate glycemic outcomes in a real-world population with type 1 diabetes (T1D) from South Korea, where the widespread use of CGM and the nationwide education program began almost simultaneously. <b><i>Methods:</i></b> Data from Dexcom G6 users with T1D in South Korea were collected between January 2019 and January 2023. Users were included if they provided at least 90 days of glucose data and used CGM at least 70% of the days in the investigational period. The relationship between CGM utilization and glycemic metrics, including the percentage of time in range (TIR), time below range (TBR), and time above range (TAR), was assessed. The study was approved by the Institutional Review Board of Samsung Medical Center (SMC 2023-05-030). <b><i>Results:</i></b> A total of 2288 users were included. Mean age was 41.5 years (57% female), with average uploads of 428 days. Mean TIR was 62.4% ± 18.5%, mean TBR <70 mg/dL was 2.6% ± 2.8%, mean TAR >180 mg/dL was 35.0% ± 19.3%, mean glucose was 168.1 ± 35.8 mg/dL, mean glucose management indicator was 7.2% ± 0.9%, and mean coefficient of variation was 36.7% ± 6.0%. Users with higher CGM utilization had higher TIR (67.8% vs. 52.7%), and lower TBR <70 mg/dL (2.3% vs. 4.7%) and TAR >180 mg/dL (30.0% vs. 42.6%) than those with low CGM utilization (<i>P</i> < 0.001 for all). Users whose data were shared with others had higher TIR than those who did not (63.3% vs. 60.8%, <i>P</i> = 0.001). <b><i>Conclusions:</i></b> In this South Korean population, higher CGM utilization was associated with a favorably higher mean TIR, which was close to the internationally recommended target. Using its remote data-sharing feature showed beneficial impact on TIR.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139562958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Macronutrient Intake and Number of Meals on Glycemic Outcomes Over 1 Year in Youth with Type 1 Diabetes.","authors":"Rebecca Ortiz La Banca Barber, Lisa K Volkening, Sanjeev N Mehta, Eyal Dassau, Lori M Laffel","doi":"10.1089/dia.2023.0464","DOIUrl":"10.1089/dia.2023.0464","url":null,"abstract":"<p><p><b><i>Objective:</i></b> Insulin bolus doses derive from glucose levels and planned carbohydrate intake, although fat and protein impact glycemic excursions. We examined the impact of macronutrients and number of daily meals/snacks on glycemic outcomes in youth with type 1 diabetes. <b><i>Methods:</i></b> Youth (<i>N</i> = 136, ages 8-17) with type 1 diabetes completed 3-day food records, wore 3-day masked continuous glucose monitoring, and had A1c measurements every 3 months for 1 year. Diet data were analyzed using Nutrition Data System for Research. Longitudinal mixed models assessed effects of macronutrient intake and number of meals/snacks on glycemic outcomes. <b><i>Results:</i></b> At baseline, youth (48% male) had mean age of 12.8 ± 2.5 years and diabetes duration of 5.9 ± 3.1 years; 73% used insulin pumps. Baseline A1c was 8.1% ± 1.0%, percent time in range 70-180 mg/dL (%TIR) was 49% ± 17%, % time below range <70 mg/dL (%TBR) was 6% ± 8%, % time above range >180 mg/dL (%TAR) was 44% ± 20%, and glycemic variability as coefficient of variation (CV) was 41% ± 8%; macronutrient intake included 48% ± 5% carbohydrate, 36% ± 5% fat, and 16% ± 2% protein. Most youth (56%) reported 3-4 meals/snacks daily (range 1-9). Over 1 year, greater carbohydrate intake was associated with lower A1c (<i>P</i> = 0.0003), more %TBR (<i>P</i> = 0.0006), less %TAR (<i>P</i> = 0.002), and higher CV (<i>P</i> = 0.03). Greater fat intake was associated with higher A1c (<i>P</i> = 0.006), less %TBR (<i>P</i> = 0.002), and more %TAR (<i>P</i> = 0.005). Greater protein intake was associated with higher A1c (<i>P</i> = 0.01). More daily meals/snacks were associated with lower A1c (<i>P</i> = 0.001), higher %TIR (<i>P</i> = 0.0006), and less %TAR (<i>P</i> = 0.0001). <b><i>Conclusions:</i></b> Both fat and protein impact glycemic outcomes. Future automated insulin delivery systems should consider all macronutrients for timely insulin provision. The present research study derived from secondary analysis of the study registered under NCT00999375.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139562859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of Two Continuous Glucose Monitoring Devices During Aerobic and High-Intensity Interval Training in Individuals with Type 1 Diabetes.","authors":"Alba Cuerda Del Pino, Rodrigo Martín-San Agustín, Alejandro José Laguna Sanz, José-Luis Díez, Ana Palanca, Paolo Rossetti, Maria Gumbau-Gimenez, F Javier Ampudia-Blasco, Jorge Bondia","doi":"10.1089/dia.2023.0535","DOIUrl":"10.1089/dia.2023.0535","url":null,"abstract":"<p><p><b><i>Background:</i></b> This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and high-intensity interval training (HIIT) in individuals with type 1 diabetes. <b><i>Methods:</i></b> Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. <b><i>Results:</i></b> Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. <b><i>Conclusions:</i></b> The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6, and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons. It was registered at clinicaltrials.gov (NCT06080542).</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139431868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycemic Outcomes Persist for up to 2 Years in Very Young Children with the Omnipod^® 5 Automated Insulin Delivery System.","authors":"Daniel J DeSalvo, Bruce W Bode, Gregory P Forlenza, Lori M Laffel, Bruce A Buckingham, Amy B Criego, Melissa Schoelwer, Sarah A MacLeish, Jennifer L Sherr, David W Hansen, Trang T Ly","doi":"10.1089/dia.2023.0506","DOIUrl":"10.1089/dia.2023.0506","url":null,"abstract":"<p><p><b><i>Background:</i></b> To evaluate the long-term safety and effectiveness of the Omnipod^® 5 Automated Insulin Delivery (AID) System in very young children with type 1 diabetes with up to 2 years of use. <b><i>Methods:</i></b> Following a 13-week single-arm, multicenter, pivotal trial that took place after 14 days of standard therapy data collection, participating children (2-5.9 years of age at study enrollment) were provided the option to continue use of the AID system in an extension phase. HbA1c was measured every 3 months, up to 15 months of total use, and continuous glucose monitor metrics were collected through the completion of the extension study (for up to 2 years). <b><i>Results:</i></b> Participants (<i>N</i> = 80) completed 18.2 [17.4, 23.4] (median [interquartile range]) total months of AID, inclusive of the 3-month pivotal trial. During the pivotal trial, HbA1c decreased from 7.4% ± 1.0% (57 ± 10.9 mmol/mol) to 6.9% ± 0.7% (52 ± 7.7 mmol/mol, <i>P</i> < 0.0001) and was maintained at 7.0% ± 0.7% (53 ± 7.7 mmol/mol) after 15 months total use (<i>P</i> < 0.0001 from baseline). Time in target range (70-180 mg/dL) increased from 57.2% ± 15.3% during standard therapy to 68.1% ± 9.0% during the pivotal trial (<i>P</i> < 0.0001) and was maintained at 67.2% ± 9.3% during the extension phase (<i>P</i> < 0.0001 from standard therapy). Participants spent a median 97.1% of time in Automated Mode during the extension phase, with one episode of severe hypoglycemia and one episode of diabetic ketoacidosis. <b><i>Conclusion:</i></b> This evaluation of the Omnipod 5 AID System indicates that long-term use can safely maintain improvements in glycemic outcomes with up to 2 years of use in very young children with type 1 diabetes. <b>Clinical Trials Registration Number:</b> NCT04476472.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139562862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes.","authors":"Simon Lebech Cichosz, Clara Bender","doi":"10.1089/dia.2023.0531","DOIUrl":"10.1089/dia.2023.0531","url":null,"abstract":"<p><p><b><i>Aims:</i></b> Diabetic ketoacidosis (DKA) is a serious life-threatening condition caused by a lack of insulin, which leads to elevated plasma glucose and metabolic acidosis. Early identification of developing DKA is important to start treatment and minimize complications and risk of death. The aim of the present study is to develop and test prediction model(s) that gives an alarm about their risk of developing elevated ketone bodies during hyperglycemia. <b><i>Methods:</i></b> We analyzed data from 138 type 1 diabetes patients with measurements of ketone bodies and continuous glucose monitoring (CGM) data from over 30,000 days of wear time. We utilized a supervised binary classification machine learning approach to identify elevated levels of ketone bodies (≥0.6 mmol/L). Data material was randomly divided at patient level in 70%/30% (training/test) dataset. Logistic regression (LR) and random forest (RF) classifier were compared. <b><i>Results:</i></b> Among included patients, 913 ketone samples were eligible for modeling, including 273 event samples with ketone levels ≥0.6 mmol/L. An area under the receiver operating characteristic curve from the RF classifier was 0.836 (confidence interval [CI] 90%, 0.783-0.886) and 0.710 (CI 90%, 0.646-0.77) for the LR classifier. <b><i>Conclusions:</i></b> The novel approach for identifying elevated ketone levels in patients with type 1 diabetes utilized in this study indicates that CGM could be a valuable resource for the early prediction of patients at risk of developing DKA. Future studies are needed to validate the results.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140058911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges of GLP Analog Use for People with Type 1 Diabetes: <i>Issues with Prior Approvals and Tips for Safer Use</i>.","authors":"Satish K Garg, Janet Snell-Bergeon, Gurleen Kaur, Christie Beatson","doi":"10.1089/dia.2024.0023","DOIUrl":"10.1089/dia.2024.0023","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140184014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship Between Lipohypertrophy, Glycemic Control, and Insulin Dosing: A Systematic Meta-Analysis.","authors":"Julia K Mader, Ricardo Fornengo, Ahmed Hassoun, Lutz Heinemann, Bernhard Kulzer, Magdalena Monica, Trung Nguyen, Jochen Sieber, Eric Renard, Yves Reznik, Przemysław Ryś, Anita Stożek-Tutro, Emma G Wilmot","doi":"10.1089/dia.2023.0491","DOIUrl":"10.1089/dia.2023.0491","url":null,"abstract":"<p><p><b><i>Background:</i></b> Lipohypertrophy is a common complication in patients with diabetes receiving insulin therapy. There is a lack of consensus regarding how much lipohypertrophy affects diabetes management. Our study aimed to assess the potential correlation between lipohypertrophy and glycemic control, as well as insulin dosing in patients with diabetes. <b><i>Methods:</i></b> We performed a systematic review followed by a meta-analysis to collect data about glycemic control and insulin dosing in diabetic patients with and without lipohypertrophy. To identify relevant studies published in English, we searched medical databases (MEDLINE/PubMed, Embase, and CENTRAL) from 1990 to January 20, 2023. An additional hand-search of references was performed to retrieve publications not indexed in medical databases. Results of meta-analyses were presented either as prevalence odds ratios (pORs) or mean differences (MDs) with 95% confidence intervals (95% CIs). This study was registered on PROSPERO (CRD42023393103). <b><i>Results:</i></b> Of the 5540 records and 240 full-text articles screened, 37 studies fulfilled the prespecified inclusion criteria. Performed meta-analyses showed that patients with lipohypertrophy compared with those without lipohypertrophy were more likely to experience unexplained hypoglycemia (pOR [95% CI] = 6.98 [3.30-14.77]), overall hypoglycemia (pOR [95% CI] = 6.65 [1.37-32.36]), and glycemic variability (pOR [95% CI] = 5.24 [2.68-10.23]). Patients with lipohypertrophy also had higher HbA1c (MD [95% CI] = 0.55 [0.23-0.87] %), and increased daily insulin consumption (MD [95% CI] = 7.68 IU [5.31-10.06]). <b><i>Conclusions:</i></b> These results suggest that overall glycemic control is worse in patients with lipohypertrophy than in those without this condition.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11058417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139431962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Glucose Monitoring Alarms in Adults with Type 1 Diabetes: User Characteristics and the Impact of Hypoglycemia and Hyperglycemia Alarm Thresholds on Glycemic Control.","authors":"Tomás González-Vidal, Diego Rivas-Otero, Pablo Agüeria-Cabal, Guillermo Ramos-Ruiz, Elías Delgado, Edelmiro Menéndez-Torre","doi":"10.1089/dia.2023.0460","DOIUrl":"10.1089/dia.2023.0460","url":null,"abstract":"<p><p><b><i>Background:</i></b> Few studies have evaluated the implications of the alarm thresholds of continuous glucose monitoring (CGM) systems for individuals with diabetes. The present study aimed to investigate the influence of hypoglycemia and hyperglycemia alarm thresholds on glycemic control in adults with type 1 diabetes (T1DM) and the characteristics of patients who use these alarms more frequently. <b><i>Methods:</i></b> This observational cross-sectional study included 873 users of the <i>FreeStyle Libre 2</i> system (501 men, median age 48 years, range 18-90 years) with T1DM from a single center. We investigated the role of demographic and metabolic factors on the use of alarms and the impact of hypoglycemia and hyperglycemia alarms and their thresholds on glycemic control. <b><i>Results:</i></b> Alarm users were older than nonusers (median age 49 vs. 43 years, respectively; <i>P</i> < 0.001). The hypoglycemia alarms were set by 76.1% of women and by 69.1% of men (<i>P</i> = 0.022). The hypoglycemia alarms reduced hypoglycemia features and glucose variability, although at the expense of shorter time in range. The higher the hypoglycemia alarm threshold, the greater these effects. The hyperglycemia alarms were effective in reducing hyperglycemia and lowering the glucose management indicator, although at the expense of a greater tendency to hypoglycemia. The lower the hyperglycemia alarm threshold, the greater these effects. <b><i>Conclusions:</i></b> CGM alarms contribute to better glycemic control. However, hypoglycemia and hyperglycemia alarms have advantages and disadvantages. Adults with T1DM should explore, under medical supervision, which alarm thresholds will best help them achieve their individual glycemic goals.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139073628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}