Pub Date : 2024-09-23DOI: 10.1177/19322968241274800
Ryan Pai, Souptik Barua, Bo Sung Kim, Maya McDonald, Raven A Wierzchowska-McNew, Amruta Pai, Nicolaas E P Deutz, David Kerr, Ashutosh Sabharwal
Background: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices' impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment.
Objective: We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D).
Methods: Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants' CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D.
Results: Our algorithm's estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise (P > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set (P = .005) but not in the validation T2D data set (P = .18).
Conclusions: We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.
{"title":"Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes.","authors":"Ryan Pai, Souptik Barua, Bo Sung Kim, Maya McDonald, Raven A Wierzchowska-McNew, Amruta Pai, Nicolaas E P Deutz, David Kerr, Ashutosh Sabharwal","doi":"10.1177/19322968241274800","DOIUrl":"10.1177/19322968241274800","url":null,"abstract":"<p><strong>Background: </strong>Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices' impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment.</p><p><strong>Objective: </strong>We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D).</p><p><strong>Methods: </strong>Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants' CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D.</p><p><strong>Results: </strong>Our algorithm's estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise (<i>P</i> > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set (<i>P</i> = .005) but not in the validation T2D data set (<i>P</i> = .18).</p><p><strong>Conclusions: </strong>We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241274800"},"PeriodicalIF":4.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288368","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}
Pub Date : 2024-09-23DOI: 10.1177/19322968241273845
Catriona M Farrell, Giacomo Cappon, Daniel J West, Andrea Facchinetti, Rory J McCrimmon
Aims: To assess the impact of high-intensity interval training (HIIT) on hypoglycemia frequency and duration in people with type 1 diabetes (T1D) with impaired awareness of hypoglycemia (IAH).
Methods: Post hoc analysis of four weeks of continuous glucose monitoring (CGM) data from HIT4HYPOS; a parallel-group study comparing HIIT + CGM versus no exercise + CGM in 18 participants with T1D and IAH.
Results: When compared with those participating individuals not exercising, HIIT did not increase total hypoglycemia frequency, THypo(L1) 1.44 [1.00-2.77]% versus 2.53 [1.46-4.23]%; P = .335, THypo(L2) 0.25 [0.09-0.37]% versus 0.45 [0.20-0.78]%; P = .146, HIIT + CGM versus CGM, respectively, rate (EventPerWeekHypo 5.30 [3.35-8.27] #/week vs 7.45 [3.54-10.81] #/week, P = .340) or duration (DurationHypo 33.33 [27.60-39.10] minutes vs 39.56 [31.00-48.38] minutes; P = .219, HIIT + CGM vs CGM, respectively). There was a reduction in nocturnal hypoglycemia in those who carried out HIIT, THypo(L1) 0.50 [0.13-0.97]% versus 2.45 [0.77-4.74]%; P = .076; THypo(L2) 0.00 [0.00-0.03]% versus 0.49 [0.13-0.74]%; P = .006, HIIT + CGM versus CGM, respectively.
Conclusions/interpretation: Based on CGM data collected from a real-world study of four weeks of HIIT versus no exercise in individuals with T1D and IAH, we conclude that HIIT does not increase hypoglycemia, and in fact reduces exposure to nocturnal hypoglycemia.
{"title":"HIT4HYPOS Continuous Glucose Monitoring Data Analysis: The Effects of High-Intensity Interval Training on Hypoglycemia in People With Type 1 Diabetes and Impaired Awareness of Hypoglycemia.","authors":"Catriona M Farrell, Giacomo Cappon, Daniel J West, Andrea Facchinetti, Rory J McCrimmon","doi":"10.1177/19322968241273845","DOIUrl":"10.1177/19322968241273845","url":null,"abstract":"<p><strong>Aims: </strong>To assess the impact of high-intensity interval training (HIIT) on hypoglycemia frequency and duration in people with type 1 diabetes (T1D) with impaired awareness of hypoglycemia (IAH).</p><p><strong>Methods: </strong>Post hoc analysis of four weeks of continuous glucose monitoring (CGM) data from HIT4HYPOS; a parallel-group study comparing HIIT + CGM versus no exercise + CGM in 18 participants with T1D and IAH.</p><p><strong>Results: </strong>When compared with those participating individuals not exercising, HIIT did not increase total hypoglycemia frequency, <i>T<sub>Hypo(L1)</sub></i> 1.44 [1.00-2.77]% versus 2.53 [1.46-4.23]%; <i>P</i> = .335, <i>T<sub>Hypo(L2)</sub></i> 0.25 [0.09-0.37]% versus 0.45 [0.20-0.78]%; <i>P</i> = .146, HIIT + CGM versus CGM, respectively, rate (<i>EventPerWeek<sub>Hypo</sub></i> 5.30 [3.35-8.27] #/week vs 7.45 [3.54-10.81] #/week, <i>P</i> = .340) or duration (<i>Duration<sub>Hypo</sub></i> 33.33 [27.60-39.10] minutes vs 39.56 [31.00-48.38] minutes; <i>P</i> = .219, HIIT + CGM vs CGM, respectively). There was a reduction in nocturnal hypoglycemia in those who carried out HIIT, <i>T<sub>Hypo</sub></i><sub>(L1)</sub> 0.50 [0.13-0.97]% versus 2.45 [0.77-4.74]%; <i>P</i> = .076; <i>T<sub>Hypo</sub></i><sub>(L2)</sub> 0.00 [0.00-0.03]% versus 0.49 [0.13-0.74]%; <i>P</i> = .006, HIIT + CGM versus CGM, respectively.</p><p><strong>Conclusions/interpretation: </strong>Based on CGM data collected from a real-world study of four weeks of HIIT versus no exercise in individuals with T1D and IAH, we conclude that HIIT does not increase hypoglycemia, and in fact reduces exposure to nocturnal hypoglycemia.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241273845"},"PeriodicalIF":4.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288370","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}
Pub Date : 2024-09-20DOI: 10.1177/19322968241280096
Carsten Wridt Stoltenberg, Stine Hangaard, Ole Hejlesen, Thomas Kronborg, Peter Vestergaard, Morten Hasselstrøm Jensen
Background and aims: Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support. Thus, the aim of this study was to develop a model to predict people with type 2 diabetes not achieving HbA1c target after initiating fast-acting insulin.
Methods: Data from a randomized controlled trial (NCT01819129) of participants with type 2 diabetes initiating fast-acting insulin were used. Data included demographics, clinical laboratory values, self-monitored blood glucose (SMBG), health-related quality of life (SF-36), and body measurements. A logistic regression was developed to predict HbA1c target nonachievers. A potential of 196 features was input for a forward feature selection. To assess the performance, a 20-repeated stratified 5-fold cross-validation with area under the receiver operating characteristics curve (AUROC) was used.
Results: Out of the 467 included participants, 98 (21%) did not achieve HbA1c target of ≤7%. The forward selection identified 7 features: baseline HbA1c (%), mean postprandial SMBG at all meals 3 consecutive days before baseline (mmol/L), sex, no ketones in urine, baseline albumin (g/dL), baseline low-density lipoprotein cholesterol (mmol/L), and traces of protein in urine. The model had an AUROC of 0.745 [95% CI = 0.734, 0.756].
Conclusions: The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control.
{"title":"Prediction of People With Type 2 Diabetes Not Achieving HbA1c Target After Initiation of Fast-Acting Insulin Therapy: Using Machine Learning Framework on Clinical Trial Data.","authors":"Carsten Wridt Stoltenberg, Stine Hangaard, Ole Hejlesen, Thomas Kronborg, Peter Vestergaard, Morten Hasselstrøm Jensen","doi":"10.1177/19322968241280096","DOIUrl":"10.1177/19322968241280096","url":null,"abstract":"<p><strong>Background and aims: </strong>Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support. Thus, the aim of this study was to develop a model to predict people with type 2 diabetes not achieving HbA1c target after initiating fast-acting insulin.</p><p><strong>Methods: </strong>Data from a randomized controlled trial (NCT01819129) of participants with type 2 diabetes initiating fast-acting insulin were used. Data included demographics, clinical laboratory values, self-monitored blood glucose (SMBG), health-related quality of life (SF-36), and body measurements. A logistic regression was developed to predict HbA1c target nonachievers. A potential of 196 features was input for a forward feature selection. To assess the performance, a 20-repeated stratified 5-fold cross-validation with area under the receiver operating characteristics curve (AUROC) was used.</p><p><strong>Results: </strong>Out of the 467 included participants, 98 (21%) did not achieve HbA1c target of ≤7%. The forward selection identified 7 features: baseline HbA1c (%), mean postprandial SMBG at all meals 3 consecutive days before baseline (mmol/L), sex, no ketones in urine, baseline albumin (g/dL), baseline low-density lipoprotein cholesterol (mmol/L), and traces of protein in urine. The model had an AUROC of 0.745 [95% CI = 0.734, 0.756].</p><p><strong>Conclusions: </strong>The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241280096"},"PeriodicalIF":4.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288371","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}
Pub Date : 2024-09-16DOI: 10.1177/19322968241279553
Johan Røikjer, Mette Krabsmark Borbjerg, Trine Andresen, Rocco Giordano, Claus Vinter Bødker Hviid, Carsten Dahl Mørch, Pall Karlsson, David C Klonoff, Lars Arendt-Nielsen, Niels Ejskjaer
Background: Diabetic peripheral neuropathy (DPN) is a prevalent and debilitating complication of diabetes, often leading to severe neuropathic pain. Although other diabetes-related complications have witnessed a surge of emerging treatments in recent years, DPN has seen minimal progression. This stagnation stems from various factors, including insensitive diagnostic methods and inadequate treatment options for neuropathic pain.
Methods: In this comprehensive review, we highlight promising novel diagnostic techniques for assessing DPN, elucidating their development, strengths, and limitations, and assessing their potential as future reliable clinical biomarkers and endpoints. In addition, we delve into the most promising emerging pharmacological and mechanistic treatments for managing neuropathic pain, an area currently characterized by inadequate pain relief and a notable burden of side effects.
Results: Skin biopsies, corneal confocal microscopy, transcutaneous electrical stimulation, blood-derived biomarkers, and multi-omics emerge as some of the most promising new techniques, while low-dose naltrexone, selective sodium-channel blockers, calcitonin gene-related peptide antibodies, and angiotensin type 2 receptor antagonists emerge as some of the most promising new drug candidates.
Conclusion: Our review concludes that although several promising diagnostic modalities and emerging treatments exist, an ongoing need persists for the further development of sensitive diagnostic tools and mechanism-based, personalized treatment approaches.
{"title":"Diabetic Peripheral Neuropathy: Emerging Treatments of Neuropathic Pain and Novel Diagnostic Methods.","authors":"Johan Røikjer, Mette Krabsmark Borbjerg, Trine Andresen, Rocco Giordano, Claus Vinter Bødker Hviid, Carsten Dahl Mørch, Pall Karlsson, David C Klonoff, Lars Arendt-Nielsen, Niels Ejskjaer","doi":"10.1177/19322968241279553","DOIUrl":"10.1177/19322968241279553","url":null,"abstract":"<p><strong>Background: </strong>Diabetic peripheral neuropathy (DPN) is a prevalent and debilitating complication of diabetes, often leading to severe neuropathic pain. Although other diabetes-related complications have witnessed a surge of emerging treatments in recent years, DPN has seen minimal progression. This stagnation stems from various factors, including insensitive diagnostic methods and inadequate treatment options for neuropathic pain.</p><p><strong>Methods: </strong>In this comprehensive review, we highlight promising novel diagnostic techniques for assessing DPN, elucidating their development, strengths, and limitations, and assessing their potential as future reliable clinical biomarkers and endpoints. In addition, we delve into the most promising emerging pharmacological and mechanistic treatments for managing neuropathic pain, an area currently characterized by inadequate pain relief and a notable burden of side effects.</p><p><strong>Results: </strong>Skin biopsies, corneal confocal microscopy, transcutaneous electrical stimulation, blood-derived biomarkers, and multi-omics emerge as some of the most promising new techniques, while low-dose naltrexone, selective sodium-channel blockers, calcitonin gene-related peptide antibodies, and angiotensin type 2 receptor antagonists emerge as some of the most promising new drug candidates.</p><p><strong>Conclusion: </strong>Our review concludes that although several promising diagnostic modalities and emerging treatments exist, an ongoing need persists for the further development of sensitive diagnostic tools and mechanism-based, personalized treatment approaches.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241279553"},"PeriodicalIF":4.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288366","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}
Pub Date : 2024-09-10DOI: 10.1177/19322968241278374
Parizad Avari, Yi Cai, Vivek Verma, Monika Reddy, Madhavi Srinivasan, Nick Oliver
The adoption of diabetes technology for the management of type 1 and insulin-treated type 2 diabetes has greatly increased. The annual volume of discarded continuous glucose monitoring (CGM) devices, considering only Dexcom and Freestyle Libre brands, totals more than 153 million units and Omnipod® contributes an additional estimated 43.8 million units.Although these technologies are clinically effective, their environmental impact is unknown. Batteries are a pivotal, yet often overlooked, component in diabetes technologies and can exert a detrimental impact on the environment.In this commentary article, we explore the environmental impact of batteries used in diabetes devices. Furthermore, we highlight various strategies, including recycling of used batteries and alternative design approaches, that may reduce the environmental burden, as they become the ubiquitous standard of care for people with diabetes.
{"title":"Batteries Within Diabetes Devices: A Narrative Review on Recycling, Environmental, and Sustainability Perspective.","authors":"Parizad Avari, Yi Cai, Vivek Verma, Monika Reddy, Madhavi Srinivasan, Nick Oliver","doi":"10.1177/19322968241278374","DOIUrl":"10.1177/19322968241278374","url":null,"abstract":"<p><p>The adoption of diabetes technology for the management of type 1 and insulin-treated type 2 diabetes has greatly increased. The annual volume of discarded continuous glucose monitoring (CGM) devices, considering only Dexcom and Freestyle Libre brands, totals more than 153 million units and Omnipod<sup>®</sup> contributes an additional estimated 43.8 million units.Although these technologies are clinically effective, their environmental impact is unknown. Batteries are a pivotal, yet often overlooked, component in diabetes technologies and can exert a detrimental impact on the environment.In this commentary article, we explore the environmental impact of batteries used in diabetes devices. Furthermore, we highlight various strategies, including recycling of used batteries and alternative design approaches, that may reduce the environmental burden, as they become the ubiquitous standard of care for people with diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241278374"},"PeriodicalIF":4.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288365","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}
Pub Date : 2024-09-10DOI: 10.1177/19322968241264747
Matthew W Segar, Kershaw V Patel, Neil Keshvani, Vaishnavi Kannan, Duwayne Willett, David C Klonoff, Ambarish Pandey
Background: Sodium glucose cotransporter 2 inhibitors (SGLT2i) prevent heart failure (HF) in patients with type 2 diabetes mellitus (T2DM) but prescription rates are low. The effect of an electronic health record (EHR) alert notifying providers of patients' estimated risk of developing HF on SGTL2i prescriptions is unknown.
Methods: This was a pragmatic, randomized clinical trial that compared an EHR alert and usual care among patients with T2DM and no history of HF or SGLT2i use at a single center. The EHR alert notified providers of their patient's HF risk and recommended HF prevention strategies. Randomization was performed at the provider level across general and subspecialty internal medicine as well as family medicine outpatient clinics. The primary outcome was proportion of SGLT2i prescriptions within 30 days. Proportion of natriuretic peptide (NP) tests within 90 days was also assessed.
Results: A total of 1524 patients (median age 75 years, 45% women, 23% Black) were enrolled between September 28, 2021, and April 29, 2022 from 189 outpatient clinics. SGLT2i were prescribed to 1.2% (9/780) of patients in the EHR alert group and 0% (0/744) of those in the usual care group (P value = 0.009). Natriuretic peptide testing was performed within 90 days among 10.8% (84/780) of patients in the EHR alert group and 7.3% (54/744) of patients in the usual care group (P value = 0.02).
Conclusions: In a single-center trial with low overall SGLT2i use, an EHR alert incorporating HF risk information significantly increased SGLT2i prescriptions and NP testing although the absolute rates were low.
{"title":"Electronic Health Record Alert With Heart Failure Risk and Sodium Glucose Cotransporter 2 Inhibitor Prescriptions in Diabetes: A Randomized Clinical Trial.","authors":"Matthew W Segar, Kershaw V Patel, Neil Keshvani, Vaishnavi Kannan, Duwayne Willett, David C Klonoff, Ambarish Pandey","doi":"10.1177/19322968241264747","DOIUrl":"10.1177/19322968241264747","url":null,"abstract":"<p><strong>Background: </strong>Sodium glucose cotransporter 2 inhibitors (SGLT2i) prevent heart failure (HF) in patients with type 2 diabetes mellitus (T2DM) but prescription rates are low. The effect of an electronic health record (EHR) alert notifying providers of patients' estimated risk of developing HF on SGTL2i prescriptions is unknown.</p><p><strong>Methods: </strong>This was a pragmatic, randomized clinical trial that compared an EHR alert and usual care among patients with T2DM and no history of HF or SGLT2i use at a single center. The EHR alert notified providers of their patient's HF risk and recommended HF prevention strategies. Randomization was performed at the provider level across general and subspecialty internal medicine as well as family medicine outpatient clinics. The primary outcome was proportion of SGLT2i prescriptions within 30 days. Proportion of natriuretic peptide (NP) tests within 90 days was also assessed.</p><p><strong>Results: </strong>A total of 1524 patients (median age 75 years, 45% women, 23% Black) were enrolled between September 28, 2021, and April 29, 2022 from 189 outpatient clinics. SGLT2i were prescribed to 1.2% (9/780) of patients in the EHR alert group and 0% (0/744) of those in the usual care group (<i>P</i> value = 0.009). Natriuretic peptide testing was performed within 90 days among 10.8% (84/780) of patients in the EHR alert group and 7.3% (54/744) of patients in the usual care group (<i>P</i> value = 0.02).</p><p><strong>Conclusions: </strong>In a single-center trial with low overall SGLT2i use, an EHR alert incorporating HF risk information significantly increased SGLT2i prescriptions and NP testing although the absolute rates were low.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241264747"},"PeriodicalIF":4.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288367","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}
Pub Date : 2024-09-10DOI: 10.1177/19322968241271304
David C Klonoff, Cindy N Ho, Alessandra Ayers, Aiman Abdel-Malek
{"title":"FDA Interoperability Designation-Creating Options for People With Diabetes and Pump Companies: Regulatory, Technological, and Commercial Perspectives.","authors":"David C Klonoff, Cindy N Ho, Alessandra Ayers, Aiman Abdel-Malek","doi":"10.1177/19322968241271304","DOIUrl":"10.1177/19322968241271304","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241271304"},"PeriodicalIF":4.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288369","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}
Pub Date : 2024-09-01Epub Date: 2023-03-15DOI: 10.1177/19322968231161317
Carol J Levy, Rodolfo J Galindo, Christopher G Parkin, Jacob Gillis, Nicholas B Argento
Gestational diabetes mellitus (GDM) is a common metabolic disease of pregnancy that threatens the health of several million women and their offspring. The highest prevalence of GDM is seen in women of low socioeconomic status. Women with GDM are at increased risk of adverse maternal outcomes, including increased rates of Cesarean section delivery, preeclampsia, perineal tears, and postpartum hemorrhage. However, of even greater concern is the increased risk to the fetus and long-term health of the child due to elevated glycemia during pregnancy. Although the use of continuous glucose monitoring (CGM) has been shown to reduce the incidence of maternal and fetal complications in pregnant women with type 1 diabetes and type 2 diabetes, most state Medicaid programs do not cover CGM for women with GDM. This article reviews current statistics relevant to the incidence and costs of GDM among Medicaid beneficiaries, summarizes key findings from pregnancy studies using CGM, and presents a rationale for expanding and standardizing CGM coverage for GDM within state Medicaid populations.
{"title":"All Children Deserve to Be Safe, Mothers Too: Evidence and Rationale Supporting Continuous Glucose Monitoring Use in Gestational Diabetes Within the Medicaid Population.","authors":"Carol J Levy, Rodolfo J Galindo, Christopher G Parkin, Jacob Gillis, Nicholas B Argento","doi":"10.1177/19322968231161317","DOIUrl":"10.1177/19322968231161317","url":null,"abstract":"<p><p>Gestational diabetes mellitus (GDM) is a common metabolic disease of pregnancy that threatens the health of several million women and their offspring. The highest prevalence of GDM is seen in women of low socioeconomic status. Women with GDM are at increased risk of adverse maternal outcomes, including increased rates of Cesarean section delivery, preeclampsia, perineal tears, and postpartum hemorrhage. However, of even greater concern is the increased risk to the fetus and long-term health of the child due to elevated glycemia during pregnancy. Although the use of continuous glucose monitoring (CGM) has been shown to reduce the incidence of maternal and fetal complications in pregnant women with type 1 diabetes and type 2 diabetes, most state Medicaid programs do not cover CGM for women with GDM. This article reviews current statistics relevant to the incidence and costs of GDM among Medicaid beneficiaries, summarizes key findings from pregnancy studies using CGM, and presents a rationale for expanding and standardizing CGM coverage for GDM within state Medicaid populations.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1198-1207"},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10210482","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}
Pub Date : 2024-09-01Epub Date: 2024-05-27DOI: 10.1177/19322968241257003
Laurel H Messer, John B Welsh, Steph Habif, Jordan E Pinsker, Tomas C Walker
{"title":"Regarding Singh et al, \"Effects, Safety, and Treatment Experience of Advanced Hybrid Closed-Loop Systems in Clinical Practice Among Adults Living With Type 1 Diabetes\".","authors":"Laurel H Messer, John B Welsh, Steph Habif, Jordan E Pinsker, Tomas C Walker","doi":"10.1177/19322968241257003","DOIUrl":"10.1177/19322968241257003","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1265-1266"},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141154844","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}
Pub Date : 2024-09-01Epub Date: 2024-08-19DOI: 10.1177/19322968241269927
Timor Glatzer, Christian Ringemann, Daniel Militz, Wiebke Mueller-Hoffmann
The recently CE-marked continuous real-time glucose monitoring (rtCGM) solution Accu-Chek® (AC) SmartGuide Solution was developed to enable people with diabetes mellitus (DM) to proactively control their glucose levels using predictive technologies. The comprehensive solution consists of three components that harmonize well with each other. The CGM device is composed of a sensor applicator and a glucose sensor patch whose data are transferred to the connected smartphone by Bluetooth® Low Energy. The user interface of the CGM solution is powered by the AC SmartGuide app delivering current and past glucose metrics, and the AC SmartGuide Predict app providing a glucose prediction suite enabled by artificial intelligence (AI). This article describes the innovative CGM solution.
最近获得 CE 认证的连续实时血糖监测(rtCGM)解决方案 Accu-Chek® (AC) SmartGuide Solution 是为糖尿病(DM)患者利用预测技术主动控制血糖水平而开发的。该综合解决方案由三个相互协调的组件组成。CGM 设备由传感器涂抹器和葡萄糖传感器贴片组成,其数据通过蓝牙® 低能耗传输到连接的智能手机。CGM 解决方案的用户界面由 AC SmartGuide 应用程序和 AC SmartGuide Predict 应用程序提供支持,前者提供当前和过去的血糖指标,后者提供人工智能(AI)支持的血糖预测套件。本文介绍了创新型 CGM 解决方案。
{"title":"Concept and Implementation of a Novel Continuous Glucose Monitoring Solution With Glucose Predictions on Board.","authors":"Timor Glatzer, Christian Ringemann, Daniel Militz, Wiebke Mueller-Hoffmann","doi":"10.1177/19322968241269927","DOIUrl":"10.1177/19322968241269927","url":null,"abstract":"<p><p>The recently CE-marked continuous real-time glucose monitoring (rtCGM) solution Accu-Chek® (AC) SmartGuide Solution was developed to enable people with diabetes mellitus (DM) to proactively control their glucose levels using predictive technologies. The comprehensive solution consists of three components that harmonize well with each other. The CGM device is composed of a sensor applicator and a glucose sensor patch whose data are transferred to the connected smartphone by Bluetooth® Low Energy. The user interface of the CGM solution is powered by the AC SmartGuide app delivering current and past glucose metrics, and the AC SmartGuide Predict app providing a glucose prediction suite enabled by artificial intelligence (AI). This article describes the innovative CGM solution.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1004-1008"},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004341","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}