Time in range centered diabetes care Dovc, Klemen; Battelino, Tadej
Clinical Pediatric Endocrinology,
01/2021, Volume:
30, Issue:
1
Journal Article
Open access
Optimal glycemic control remains challenging and elusive for many people with diabetes. With the comprehensive clinical evidence on safety and efficiency in large populations, and with broader ...reimbursement, the adoption of continuous glucose monitoring (CGM) is rapidly increasing. Standardized visual reporting and interpretation of CGM data and clear and understandable clinical targets will help professionals and individuals with diabetes use diabetes technology more efficiently, and finally improve long-term outcomes with less everyday disease burden. For the majority of people with type 1 or type 2 diabetes, time in range (between 70 and 180 mg/dL, or 3.9 and 10 mmol/L) target of more than 70% is recommended, with each incremental increase of 5% towards this target being clinically meaningful. At the same time, the goal is to minimize glycemic excursions: a recommended target for a time below range (< 70 mg/dL or < 3.9 mmol/L) is less than 4%, and time above range (> 180 mg/dL or 10 mmol/L) less than 25%, with less stringent goals for older individuals or those at increased risk. These targets should be individualized: the personal use of CGM with the standardized data presentation provides all necessary means to accurately tailor diabetes management to the needs of each individual with diabetes.
Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization ...of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.
The MiniMed™ 780G system includes an advanced hybrid closed loop (AHCL) algorithm that provides both automated basal and correction bolus insulin delivery. The preliminary performance of the system ...in real-world settings was evaluated.
Data uploaded from August 2020 to March 2021 by individuals living in Belgium, Finland, Italy, the Netherlands, Qatar, South Africa, Sweden, Switzerland, and the United Kingdom were aggregated and retrospectively analyzed to determine the mean glucose management indicator (GMI), percentage of time spent within (TIR), below (TBR), and above (TAR) glycemic ranges, system use, and insulin consumption in users having ≥10 days of sensor glucose (SG) data after initiating AHCL. The impact of initiating AHCL was evaluated in a subgroup of users also having ≥10 days of SG data, before AHCL initiation.
Users (
= 4120) were observed for a mean of 54 ± 32 days. During this time, they spent a mean of 94.1% ± 11.4% of the time in AHCL and achieved a mean GMI of 6.8% ± 0.3%, TIR of 76.2% ± 9.1%, TBR <70 of 2.5% ± 2.1%, and TAR >180 of 21.3% ± 9.4%, after initiating AHCL. There were 77.3% and 79.0% of users who achieved a TIR >70% and a GMI of <7.0%, respectively. Users for whom comparison with pre-AHCL was possible (
= 812) reduced their GMI by 0.4% ± 0.4% (
= 0.005) and increased their TIR by 12.1% ± 10.5% (
< 0.0001), post-AHCL initiation. More users achieved the glycemic treatment goals of GMI <7.0% (37.6% vs. 75.2%,
< 0.0001) and TIR >70% (34.6% vs. 74.9%,
< 0.0001) when compared with pre-AHCL initiation.
Most MiniMed 780G system users achieved TIR >70% and GMI <7%, while minimizing hypoglycemia, in a real-world condition.
Measurement of glycated hemoglobin (HbA
) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute ...events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA
testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA
measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.
OBJECTIVE: To assess the impact of continuous glucose monitoring on hypoglycemia in people with type 1 diabetes. RESEARCH DESIGN AND METHODS: In this randomized, controlled, multicenter study, 120 ...children and adults on intensive therapy for type 1 diabetes and a screening level of glycated hemoglobin A₁c (HbA₁c) <7.5% were randomly assigned to a control group performing conventional home monitoring with a blood glucose meter and wearing a masked continuous glucose monitor every second week for five days or to a group with real-time continuous glucose monitoring. The primary outcome was the time spent in hypoglycemia (interstitial glucose concentration <63 mg/dL) over a period of 26 weeks. Analysis was by intention to treat for all randomized patients. RESULTS: The time per day spent in hypoglycemia was significantly shorter in the continuous monitoring group than in the control group (mean ± SD 0.48 ± 0.57 and 0.97 ± 1.55 h/day, respectively; ratio of means 0.49; 95% CI 0.26-0.76; P = 0.03). HbA₁c at 26 weeks was lower in the continuous monitoring group than in the control group (difference -0.27%; 95% CI -0.47 to -0.07; P = 0.008). Time spent in 70 to 180 mg/dL normoglycemia was significantly longer in the continuous glucose monitoring group compared with the control group (mean hours per day, 17.6 vs. 16.0, P = 0.009). CONCLUSIONS: Continuous glucose monitoring was associated with reduced time spent in hypoglycemia and a concomitant decrease in HbA₁c in children and adults with type 1 diabetes.
Abstract Background Individuals with familial hypercholesterolemia (FH) who are untreated have up to 100-fold elevated risk for cardiovascular complications compared with those who are unaffected. ...Data for identification of FH with a universal screening for hypercholesterolemia in children are lacking. Objectives This study sought genetic identification of FH from a cohort of children with elevated serum total cholesterol (TC) concentration, detected in a national universal screening for hypercholesterolemia. Methods Slovenian children born between 1989 and 2009 (n = 272) with TC >6 mmol/l (231.7 mg/dl) or >5 mmol/l (193.1 mg/dl) plus a family history positive for premature cardiovascular complications, identified in a national universal screening for hypercholesterolemia at 5 years of age were genotyped for variants in LDLR , PCSK9 , APOB , and APOE. Results Of the referred children, 57.0% carried disease-causing variants for FH: 38.6% in LDLR , 18.4% in APOB , and none in PCSK9 . Nine novel disease-causing variants were identified, 8 in LDLR , and 1 in APOB . Of the remaining participants, 43.6% carried the APOE E4 isoform. Estimated detection rate of FH in the universal screening program from 2009 to 2013 was 53.6% (95% confidence interval CI: 34.5% to 72.8%), peaking in 2013 with an upper estimated detection rate of 96.3%. Variants in LDLR , APOB , or the APOE E4 isoform occurred in 48.6%, 60.0%, and 76.5%, respectively, of patients with a family history negative for cardiovascular complications. Conclusions Most participants who were referred from a national database of universal screening results for hypercholesterolemia had genetically confirmed FH. Data for family history may not suffice for reliable identification of patients through selective and cascade screening.
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