7. Diabetes Technology: <i>Standards of Care in Diabetes—2024</i>

,; ElSayed, Nuha A.; Aleppo, Grazia; Bannuru, Raveendhara R.; Bruemmer, Dennis; Collins, Billy S.; Ekhlaspour, Laya; Hilliard, Marisa E.; Johnson, Eric L.; Khunti, Kamlesh; Lingvay, Ildiko; Matfin, Glenn; McCoy, Rozalina G.; Perry, Mary Lou; Pilla, Scott J.; Polsky, Sarit; Prahalad, Priya; Pratley, Richard E.; Segal, Alissa R.; Seley, Jane Jeffrie; Stanton, Robert C.; Gabbay, Robert A.

doi:10.2337/dc24-s007

Cited by 87 publications

(8 citation statements)

References 281 publications

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“…In addition, greater adherence to CGM was associated with significant and clinically meaningful improvements in A1C [ 13 ]. This supports the ADA’s Standards of Care in Diabetes which recommends CGM for adults with diabetes using intensive insulin therapy to manage their diabetes [ 14 ]. Users’ higher adherence to rtCGM is relevant to the recommendation that CGM devices should be used as close to daily as possible for maximal benefit [ 14 ].…”

Section: Discussionsupporting

confidence: 59%

Association Between Adherence, A1C Improvement, and Type of Continuous Glucose Monitoring System in People with Type 1 Diabetes or Type 2 Diabetes Treated with Intensive Insulin Therapy

Nemlekar,

Hannah,

Green

et al. 2024

Diabetes Ther

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Introduction Use of continuous glucose monitoring (CGM) systems by people with diabetes is associated with improved glycemic outcomes, including lower glycated hemoglobin (A1C). Less is known about adherence to CGM systems, whether glycemic outcomes are impacted by levels of adherence, or whether adherence rates differ between types of CGM systems—intermittently scanned CGM (isCGM) or real-time CGM (rtCGM). Methods A retrospective analysis of de-identified US administrative health claims and linked laboratory data was conducted using the Merative™ MarketScan ® Research Database. The cohort included CGM-naïve people with type 1 diabetes (T1D) or type 2 diabetes treated with intensive insulin therapy (T2D-IIT) who initiated rtCGM or isCGM between August 1, 2019 and March 31, 2021 (defined as the index date). Adherence was calculated over a 12-month period using the proportion of days covered (PDC) with PDC ≥ 0.8 defined as adherent. A1C values were obtained within 6 months of the index date. Results A total of 7669 individuals were identified. Subgroups included T1D using isCGM ( n = 1578), T1D using rtCGM ( n = 1244), T2D-IIT using isCGM ( n = 3567), and T2D-IIT using rtCGM ( n = 1280). After 12 months, PDC was 0.71 (0.30)–0.72 (0.31) (mean(SD)) for T1D and T2D-IIT rtCGM users and 0.55 (0.34)–0.56 (0.34) for T1D and T2D-IIT isCGM users. The proportion of adherent users (PDC ≥ 0.8) was 56.8–59.7% for rtCGM users and 36.3–37.6% for isCGM users. Overall, regardless of diabetes type, the odds of adherence were over two times higher for rtCGM users compared to isCGM users. For those with available A1C information (T1D n = 213; T2D-IIT n = 346), independent of CGM type, adherence to CGM was associated with a greater reduction in A1C and more people reaching A1C targets of < 7.0% or < 8.0%. Conclusion For people with T1D or T2D-IIT, higher adherence to CGM is associated with greater reductions in A1C, and higher adherence rates were observed with rtCGM systems than with isCGM systems.

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Section: Discussionsupporting

confidence: 59%

Association Between Adherence, A1C Improvement, and Type of Continuous Glucose Monitoring System in People with Type 1 Diabetes or Type 2 Diabetes Treated with Intensive Insulin Therapy

Nemlekar,

Hannah,

Green

et al. 2024

Diabetes Ther

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“…While these devices are increasingly used, practice guidelines do not recommend their long-term use for most individuals with DM2. 39 Accordingly, a key goal of our study will be to explore the ability to predict PPGR response without reliance on CGM data or with very limited blood sugar data from patients.…”

Section: Discussionmentioning

confidence: 99%

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India

Choudhry,

Priyadarshini,

Swamy

et al. 2024

Preprint

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Background: Type 2 diabetes (DM2) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the world's most populous country, India. Nutrition is a critical and evidence-based component of effective blood sugar control and most dietary advice emphasizes carbohydrate and calorie reduction. Emerging global evidence demonstrates marked inter-individual differences in post-prandial glucose response (PPGR) although no such data exists in India and prior studies have primarily evaluated PPGR variation in individuals without diabetes. Methods: This prospective cohort study seeks to characterize the PPGR variability in Indians with diabetes and to identify factors associated with these differences. Adults with type 2 diabetes and a hemoglobin A1c >7 are being enrolled from 14 sites around India. Subjects wear a continuous glucose monitor, eat a series of standardized meals, and record all free-leaving foods, activities, and medication use for a 14-day period. The study's primary outcome is PPGR, calculated as the incremental area under the curve 2 hours after each logged meal. Discussion: This study will provide the first large scale examination variability in blood sugar responses to food in India and will be among the first to estimate PPGR variability for individuals with DM2 in any jurisdiction. Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with DM2.

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“…The American Diabetes Association recommends that automated insulin delivery systems be offered to patients with type 1 and other forms of insulindefi cient diabetes, with the choice based on the patient's circumstances, preferences, and needs. 34 Clinical trials provide evidence of reduced A1c levels and improved time in goal range. 3 Time below the desired glucose range is often reduced as well through features that can decrease or suspend insulin delivery based on predicted low glucose levels.…”

Section: ■ Closing Thoughtsmentioning

confidence: 99%

“…3,15,33 Automated insulin delivery systems are now the standard of care for patients living with type 1 diabetes. 34 Automated insulin delivery-or sensor-augmented pumps may also be considered in other forms of insulin-defi cient diabetes, including type 2 diabetes.…”

Section: Patch Pumpsmentioning

confidence: 99%

Diabetes technology: A primer for clinicians

Ambalavanan,

Isaacs,

Lansang

2024

CCJM

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Diabetes technology is evolving rapidly and is changing the way both patients and clinicians approach the management of diabetes. With more devices gaining US Food and Drug Administration approval and insurance coverage expanding, these new technologies are being widely adopted by people living with diabetes. We provide a summary of the commonly available devices in the market today that clinicians will likely encounter. This includes continuous glucose monitors (CGMs); connected insulin pens, caps, and buttons; and insulin pumps. Clinicians' awareness of and familiarity with this technology will enhance its accessibility for patients with diabetes. KEY POINTSCGMs measure interstitial glucose and transmit data wirelessly to a receiver. Evidence supports their use in patients on insulin therapy or who are at a high risk of hypoglycemia.Connected insulin pens, caps, and buttons act as bridges between traditional insulin pens and insulin pumps. The technology allows patients to calculate how much insulin to take, accounting for carbohydrate intake, glucose level, and, in some models, previous insulin dose.Insulin pumps deliver rapid-acting insulin continuously via the subcutaneous route either independently (open loop) or in association with CGMs (automated insulin delivery).

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7. Diabetes Technology: Standards of Care in Diabetes—2024

Cited by 87 publications

References 281 publications

Association Between Adherence, A1C Improvement, and Type of Continuous Glucose Monitoring System in People with Type 1 Diabetes or Type 2 Diabetes Treated with Intensive Insulin Therapy

Association Between Adherence, A1C Improvement, and Type of Continuous Glucose Monitoring System in People with Type 1 Diabetes or Type 2 Diabetes Treated with Intensive Insulin Therapy

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India

Diabetes technology: A primer for clinicians

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