Skip to main content
Journal of Diabetes Investigation logoLink to Journal of Diabetes Investigation
. 2024 Sep 4;15(11):1669–1674. doi: 10.1111/jdi.14303

Clinical significance of coefficient of variation in continuous glucose monitoring for glycemic management in children and adolescents with type 1 diabetes

Tatsuhiko Urakami 1,2,, Hiroki Terada 1, Satomi Tanabe 1, Yusuke Mine 1, Masako Aoki 1, Ryoji Aoki 1, Junichi Suzuki 1, Ichiro Morioka 1
PMCID: PMC11527802  PMID: 39230367

Abstract

Aims/Introduction

Coefficient of variation (CV) is an indicator for glucose variability in continuous glucose monitoring (CGM), and the target threshold of %CV in type 1 diabetes is proposed to be ≤36%. This study aimed to evaluate the clinical significance of CV in children and adolescents with type 1 diabetes.

Materials and Methods

Participants included 66 children with type 1 diabetes. A total of 48 participants were treated with multiple daily injections of insulin, and 18 with continues subcutaneous insulin infusion, using intermittently scanned CGM. The frequencies of the CGM metrics and glycosylated hemoglobin values were examined, and the significance of a threshold %CV of 36% was evaluated.

Results

The mean frequencies in time in range (TIR), time below range, %CV and the mean glycosylated hemoglobin value were 59.3 ± 16.1, 4.0 ± 3.5, 39.3 ± 6.2 and 7.3 ± 0.8%, respectively. The frequencies of participants who achieved a TIR >70% and a %CV of ≤36% were 24.1 and 27.3%, respectively. A total of 18 participants with a %CV of ≤36% had significantly higher TIR, lower time below range and lower glycosylated hemoglobin than the 48 with a %CV of >36% (72.6 ± 12.6 vs 52.4 ± 13.6, 2.4 ± 1.9 vs 4.6 ± 3.6, 6.9 ± 0.8 vs 7.4 ± 0.7%, respectively).

Conclusions

Children and adolescents with type 1 diabetes using intermittently scanned CGM had difficulties in achieving the recommended targets of TIR and CV. However, the target %CV of ≤36% seems to be an appropriate indicator for assessing glycemic control and risk of hypoglycemia in pediatric patients with type 1 diabetes with any treatment.

Keywords: Children and adolescents, Coefficient of variation, Continuous glucose monitoring


Children and adolescents with type 1 diabetes using is continuous glucose monitoring had difficulties in achieving the recommended targets of time in range and coefficient of variation. The target coefficient of variation percentage of ≤36% seems to be an appropriate indicator for assessing glycemic control and risk of hypoglycemia in pediatric patients with type 1 diabetes with any treatment.

graphic file with name JDI-15-1669-g003.jpg

INTRODUCSTION

Currently, continuous glucose monitoring (CGM) is used worldwide, and its benefits include sustaining optimal glycemic control and preventing hypoglycemia, particularly in individuals with insulin‐requiring diabetes and/or those at risk of hypoglycemia 1 , 2 . Various studies have shown that CGM provides a more accurate assessment of glucose stability and hypoglycemia than self‐monitoring of blood glucose 1 , 2 , 3 , 4 , 5 , 6 , 7 . To analyze CGM data, CGM‐derived metrics are available for evaluating glycemic control. These include three glucose ranges: time in target range (TIR), 70–180 mg/dL; time above target range, >180 mg/dL; and time below target range (TBR), <70 mg/dL. These ranges are defined according to the consensus statement of the Advanced Technologies & Treatment for Diabetes (ATTD) 8 , 9 .

CGM‐derived metrics also include the glucose management indicator and the following glucose variability indices: standard deviation (SD) and coefficient of variation (CV). The CV is an indicator of hyper‐ and hypoglycemia that shows the amplitude of glucose excursion, and is more sensitive to hypoglycemia than SD 5 . The target threshold of %CV in type 1 diabetes is proposed to be ≤36% 9 because of increased frequency of hypoglycemia beyond this threshold 10 .

Children and adolescents with type 1 diabetes usually have great variations in glucose levels due to their unstable lifestyles and eating habits; hence, they have increased risk of developing severe hypoglycemia 11 , 12 . Severe hypoglycemia can cause permanent brain damage and mental deterioration, particularly in young children with type 1 diabetes 13 . Careful attention must be paid to avoid severe hypoglycemia in at‐risk patients who show significant glucose variability. Therefore, the CV, which shows glucose variability and is particularly sensitive to hypoglycemia, is an important indicator of glucose variability and risk of hypoglycemia 10 .

In the present study we examined the CGM metrics of TIR, TBR and CV, and glycosylated hemoglobin (HbA1c) values in children and adolescents with type 1 diabetes on intermittently scanned CGM (isCGM), who were mainly treated with multiple daily injections of insulin (MDI) without using an advanced insulin delivery method. In addition, we evaluated the clinical significance of the recommended CV threshold of ≤36% to achieve optimal glycemic control and prevent hypoglycemia for pediatric patients with type 1 diabetes.

MATERIALS AND METHODS

The present retrospective observational study was carried out in the Department of Pediatrics at Nihon University Hospital in Tokyo, Japan, from January to May 2023. All data were retrospectively collected from medical records and anonymized.

Participants included patients who used isCGM, FreeStyle® Libre, without hyper‐ and hypoglycemia alerts/alarms for at least 6 months. None of the participants used a sensor‐augmented pump, a predictive low‐glucose suspension function pump or a hybrid closed‐loop system. Before initiating the study, participants were instructed to scan the CGM‐sensors at least four times per day, and the frequencies of scans ranged from four to 18 times (mean 10.8 ± 3.5 times per day). Participants were also instructed to carry out self‐monitoring of blood glucose when they noticed that the FreeStyle® Libre showed a low (<70 mg/dL) or high glucose level (>250 mg/dL) to confirm hypo‐ and hyperglycemia, respectively. Bolus insulin doses were determined using the carbohydrate counting method, which assesses the consumption of carbohydrates in each meal.

Assessments

We examined the frequencies of CGM‐derived metrics, including TIR (70–180 mg/dL), TBR (<70 mg/dL), %CV and laboratory‐measured HbA1c values during the 3‐month study period, and evaluated the correlation between these four indices. The frequencies of TIR and TBR were evaluated at every month, and the mean frequencies for the 3‐month period were used for the analyses. Next, we divided the participants into two subgroups according to the target threshold of %CV proposed by the ATTD panel 9 , 10 : Group A had a %CV of ≤36%, and group B had a %CV of >36%. We compared the TIR, TBR and HbA1c values between the two groups.

The CV was calculated as: 100 × (SD / mean glucose), and reported as %CV. HbA1c values were measured using high‐performance liquid chromatography and expressed as the National Glycohemoglobin Standardization Program unit (%; reference value 4.6–6.1%).

Statistical analysis

Data are presented as means ± SDs. Comparisons between the two groups were carried out using the Mann–Whitney U‐test and Pearson's correlation coefficient. All statistical analyses were carried out using IBM SPSS Statistics for Windows version 25.0 (Released 2017; IBM Corp., Armonk, NY, USA). A P‐value of <0.05 was considered statistically significant.

RESULTS

Participant characteristics

A total of 66 patients (30 boys, 36 girls) with a mean age of 13.5 ± 4.7 years (4.2–17.2 years) were included in the study. The mean diabetes duration was 6.3 ± 4.6 years (2.0–12.8 years). Regarding insulin treatment, 48 patients were treated with MDI using rapid‐acting and long‐acting insulin analogs, and 18 with continuous subcutaneous insulin infusion using a rapid‐acting insulin analog. The mean dose of administered insulin was 0.8 ± 0.5 units/kg/day (0.4–1.2 units/kg/day; Table 1). The participants maintained nearly stable lifestyles without excessive physical activity, eating disorders or psychosocial problems during the study period. None of the patients experienced severe hypoglycemia, diabetic ketoacidosis or other health problems during the study period. Furthermore, none of the patients had macrovascular or microvascular complications.

Table 1.

Background characteristics of the participants and continuous glucose monitoring metrics

Participant characteristics
n 66
Male/female 30/36
Age (years) 13.5 ± 4.7 (4.2–17.2)
Diabetes duration (years) 6.3 ± 4.6 (2.0–12.8)
Insulin treatment: MDI/CSII 48/18
Insulin dose (units/kg/day) 0.8 ± 0.5 (0.4–1.2)
HbA1c (%) 7.3 ± 0.8 (5.7–9.5)
CGM metrics
TIR (%) 59.3 ± 16.1 (26–86)
TAR (%) 36.7 ± 15.5 (11–74)
TBR (%) 4.0 ± 3.5 (0–16)
Mean glucose (mg/dL) 158.5 ± 31.7 (102–241)
SD 62.3 ± 12.4 (25–128)
CV (%) 39.3 ± 6.2 (25–53)

CV, coefficient variation for glucose; SD, standard deviation; TAR, time above target range (>180 mg/dL); TIR, time in the target glucose range (70–180 mg/dL); TRR, time below target range (<70 mg/dL).

Participant TIR, TBR, %CV and HbA1c

The mean frequencies of TIR, TBR and %CV were 59.3 ± 16.1 (26–86), 4.0 ± 3.5 (0–16) and 39.3 ± 6.2 (25–53)%, respectively. Whereas the mean value of HbA1c was 7.3 ± 0.8% (5.7–9.5%; Table 1). There were no statistically significant differences in these results between the patients treated with MDI or continuous subcutaneous insulin infusion. The frequencies of glycemic targets of TIR >70% and TBR <4%, proposed by the ATTD panel 8 , 9 , were 24.1 and 53.0%, respectively. In contrast, 18 (27.3%) participants had a CV of ≤36% (group A), and 48 (72.7%) had a CV of >36% (group B).

Correlations among TIR, TBR and HbA1c, and between %CV and TIR, TBR, and HbA1c

The correlations among TIR, TBR and HbA1c were statistically significant (TIR, TBR: r = 0.248, P = 0.044; TIR, HbA1c: r = −0.848, P < 0.001; and TBR, HbA1c: r = −0.439, P = 0.002). As for the correlation of these indices with %CV, there were significant correlations between %CV and TIR (r = −0.664, P < 0.001), and %CV and HbA1c (r = 0.461, P < 0.001). However, %CV did not significantly correlate with TBR (r = 0.203, P = 0.102).

Comparison of frequencies of TIR, TBR and HbA1c levels between the two groups

Children and adolescents in group A had a significantly higher frequency of TIR (72.9 ± 12.6 vs 52.4 ± 13.6%, respectively; P < 0.001; Figure 1), lower frequency of TBR (2.4 ± 1.9 vs 4.6 ± 3.6%, respectively; P = 0.038; Figure 2) and lower value of HbA1c (6.9 ± 0.8 vs 7.4 ± 0.7%, respectively; P = 0.005; Figure 3) than those in group B.

Figure 1.

Figure 1

Comparison of the frequency of time in the target glucose range (70–180 mg/dL) between the two groups. Frequency of time in the target glucose range: 72.9 ± 12.6% in group A versus 52.4 ± 13.6% in group B, P < 0.001. Group A, children and adolescents with a coefficient variation for glucose percentage ≤36%; Group B, children and adolescents with a coefficient variation for glucose percentage >36%.

Figure 2.

Figure 2

Comparison of the frequency of time below the target glucose range (<70 mg/dL) between the two groups. Frequency of time below the target glucose range: 2.4 ± 1.9% in group A versus 4.6 ± 3.6% in group B, P = 0.038. Group A, children and adolescents with a coefficient variation for glucose percentage ≤36%; Group B, children and adolescents with a coefficient variation for glucose percentage >36%.

Figure 3.

Figure 3

Comparison of glycosylated hemoglobin levels between the two groups. glycosylated hemoglobin level: 6.9 ± 0.8% in group A versus 7.4 ± 0.7 in group B, P = 0.005. Group A, children and adolescents with a coefficient variation for glucose percentage ≤36%; Group B, children and adolescents with a coefficient variation for glucose percentage >36%.

DISCUSSION

The primary goal of optimal glycemic control in CGM is to increase TIR to >70% while simultaneously reducing TBR to <4%, as proposed by the ATTD panel 8 , 9 . Hypoglycemia is a concern for all children and adolescents with type 1 diabetes, as well as for their family members and caregivers, and is a barrier to achieving appropriate glycemia 13 . Therefore, reduction in TBR, rather than achieving the target TIR, should be the cardinal glycemic goal for effective management of pediatric patients with type 1 diabetes 14 . However, achieving a TIR of >70% and a TBR of <4% is quite difficult in pediatric patients who usually have considerable interindividual and day‐to‐day glucose variations due to their irregular lifestyles and eating habits 11 , 12 .

Several studies have shown that children and adolescents who do not use an advanced insulin delivery system, such as a sensor‐augmented insulin pump with a low‐glucose suspension system or a closed‐loop system, are unlikely to attain the recommended glycemic targets 15 , 16 , 17 , 18 . Participants in our previous 19 , 20 , 21 and present study consisted of children and adolescents with type 1 diabetes treated mainly with MDI and wearing isCGMs without hyperglycemic or hypoglycemic alerts/alarms. Our first report in 2020 showed that the frequency of TIR was 50.7 ± 12.2%, and that of TBR was 11.8 ± 5.8% 19 . Our other reports also showed that the frequencies of TIR were 52.7 ± 11.3 and 52.3 ± 12.3%, and those of TBR were 10.8 ± 5.4 and 10.2 ± 5.4%, respectively 20 , 21 .

Although, the present study showed the frequency of TIR as 59.3 ± 12.3%, and that of TBR as 4.3 ± 2.7%, even though the majority of participants continued to use the same isCGM equipment as the previous reports. It is particularly worth mentioning that there was a notable decrease in TBR compared with previous results, and more than half of the participants achieved a TBR target of <4%. This is possibly because children and their parents had achieved mastery of using isCGM with frequent scanning, and could prevent the development of hypoglycemia by adjusting insulin doses according to glucose variations and trends, even without a hypoglycemia alert/alarm. Change from generation 1 to generation 3 of the FreeStyle Libre® could be also related to the decrease in TBR. However, the frequency of a TIR of >70% was only achieved in 24.1% of participants; therefore, it seems difficult to attain this target in pediatric patients without using an advanced insulin delivery system, especially because they have good appetite and often show a considerable rise in glucose levels after consuming excessive food and carbohydrates 22 .

Monnier et al. 10 showed that the %CV for glucose was significantly higher in patients with type 1 diabetes than in those with type 2 diabetes. They proposed that a %CV of 36% is a suitable threshold to distinguish between stable and unstable glycemia in diabetes, because the frequency of hypoglycemia (<56 mg/dL) significantly increases beyond this threshold, particularly in type 1 diabetes (type 2 vs type 1 diabetes: 0.28/patient‐day vs 0.86/patient‐day, P < 0.0001).

The ATTD panel proposed the %CV target to evaluate glucose variability in an international consensus statement 9 . Therefore, we used this threshold to evaluate glucose variability in the present study. We found that children and adolescents with %CV of <36% showed a significantly higher frequency of TIR and lower HbA1c level than those with a CV of ≥36%. We also showed that those with a CV of <36% showed a significantly lower frequency of TBR than those with a CV of ≥36%. These results suggest that pediatric patients with lower variability in glucose levels could achieve better glycemic control with low risk of hypoglycemia, whereas those with higher glucose variability might have inappropriate glucose control with a high risk of hypoglycemia.

It is known that the CV might increase if accompanied by a decrease in mean glucose, even though the SD is the same, because CV is calculated as the SD divided by mean glucose 9 . In addition, it is possible that CV only indicates the amplitude of glucose variability and might not reflect the TIR. However, the present results suggest that pediatric patients with a lower CV might spend a greater amount of time in an appropriate glucose range with a shorter time in hypoglycemia. Furthermore, the recommended target of %CV of ≤36% appears to be suitable for evaluating glycemic stability and risk of hypoglycemia even in children and adolescents with type 1 diabetes. Alternatively, some patients with good glycemic control showed a CV of ≥36%, if they by chance ate excessively when eating outside or while attending a party, which can cause a steep rise in glucose levels, leading to an increase in SD, although usually an appropriate glucose trend. Although, some patients with poor glycemic control had a CV of <36%, and always showed hyperglycemia with small glucose variations. Nevertheless, we found that individuals with a CV of <36% generally showed optimal glycemic control with a TIR of >70% and a HbA1c level of <7.0%, with low risk of hypoglycemia with a TBR of <4.0%.

The present study had some limitations. First, the sample size of 66 participants might limit the generalizability of the findings. Larger, multicenter studies would be more robust and provide greater external validity.

Second, the retrospective observational design can introduce biases, such as selection bias and information bias. A prospective design would be preferable to mitigate these limitations.

Third, we used an older generation of CGM equipment: isCGM without hyper‐ and hypoglycemia alerts/alarms. The new generation of CGM equipment, real‐time CGM, with hyper‐ and hypoglycemic alerts/alarms, might show different results for glucose variability indices. However, Hásková et al. 23 reported that there was no significant difference of %CV between real‐time CGM and isCGM (36.1 ± 5.1 vs 38.4 ± 8.3%, P = 0.176), although real‐time CGM was superior to isCGM for the frequencies of TIR and TBR in adults with type 1 diabetes. Although, the newer CGM systems potentially limit the applicability of the findings to current clinical practice.

Fourth, there might be different results for the %CV using advanced insulin delivery systems, such as a hybrid closed‐loop system or an automated insulin delivery system 24 . Bergenstal et al. 25 demonstrated that a hybrid closed‐loop system showed a 44% reduction in the time spent with a sensor glucose level of <70 mg/dL, with a 40% decline in dangerous hypoglycemia (<50 mg/dL). Further advanced technologies, such as automated insulin delivery systems, might offer more glucose stability with less glucose variability 26 , 27 , 28 . Thrasher et al. 27 reported that users of the advanced hybrid closed‐loop system, MiniMed™ 780G, showed a %CV of 31.0 ± 5.0%, which was similar to that observed with MiniMed™ 770G.

Finally, Monnier et al. 10 investigated the relationship between %CV and hypoglycemia of <56 mg/dL, and concluded that a %CV of ≤36% was a suitable threshold for distinguishing between patients with and without an increased risk of hypoglycemia. However, in the present study, we defined hypoglycemia as a glucose level <70 mg/dL, and showed that children and adolescents with a %CV of ≤36% had a significantly lower frequency of time spent in hypoglycemia. We did not study the relationship between a %CV of ≤36% and a TBR of <56 mg/dL, because we considered hypoglycemia as glucose levels <70 mg/dL, which might be a problematic threshold for pediatric patients with type 1 diabetes.

The International Society for Pediatric and Adolescent Diabetes Consensus Guideline 2022 for hypoglycemia proposes that a glucose level of <70 mg/dL should be used as the clinical alert or threshold value for initiating treatment for hypoglycemia because of the potential for glucose to fall further and avoid the consequences of severe hypoglycemia. Therefore, children with type 1 diabetes should spend <4% of their time with glucose levels <70 mg/dL, which is the threshold for hypoglycemia 13 . Nevertheless, we achieved similar results showing that a CV of ≤36% appears to be a suitable indicator for distinguishing adequate glycemic control in pediatric patients with and without developing hypoglycemia.

The present study showed that children and adolescents with type 1 diabetes using isCGM without hyper/hypoglycemic alerts/alarms and not treated with a closed‐loop system had difficulties in achieving the recommended targets of TIR and CV. In contrast, a target %CV of ≤36% seems to be an appropriate indicator for assessment of glycemic control and risk of hypoglycemia in the management of pediatric patients with type 1 diabetes mainly treated with MDI. Large glucose excursions might increase oxidative stress and inflammation, which causes vascular endothelial cell damage, leading to increased cardiovascular mortality 29 .

Several studies have shown that glucose fluctuation might increase the risk of cardiovascular complications, decrease cognitive function and reduce quality of life in individuals with diabetes 30 , 31 , 32 . Therefore, the suppression of glucose variation is essential for the management of diabetes, even during childhood. Future multicenter studies confirming the importance of reducing glucose variability with a large number of pediatric patients with type 1 diabetes might be required to further validate our findings.

DISCLOSURE

Tatsuhiko Urakami received honoraria from Novo Nordisk Pharma Ltd., Terumo Corp., Abbott Japan LLC. and JCR Pharmaceuticals Co., Ltd. The other authors declare no conflict of interest.

Approval of the research protocol: This study was approved by the Human Ethics Review Committee of Nihon University Hospital (approval no. 20220306), and was conducted in accordance with the ethical standards set forth in the 1964 Declaration of Helsinki and its later amendments.

Informed consent: N/A.

Registry and the registration no. of the study/trial: N/A.

Animal studies: N/A.

ACKNOWLEDGMENTS

We thank Honyaku Center Inc. for English language editing.

REFERENCES

  • 1. American Diabetes Association . Diabetes technology: standards of care in diabetes—2024. Diabetes Care 2024; 47(Suppl. 1): S126–S144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Tauschmann M, Forlenza G, Hood K, et al. ISPAD clinical practice consensus guidelines 2022: diabetes technologies: glucose monitoring. Pediatr Diabetes 2022; 23: 1390–1405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group , Tamborlane WV, Beck RW, et al. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 2008; 359: 1464–1476. [DOI] [PubMed] [Google Scholar]
  • 4. Tumminia A, Crimi S, Sciacca L, et al. Efficacy of real‐time continuous glucose monitoring on glycaemic control and glucose variability in type 1 diabetic patients treated with either insulin pumps or multiple insulin injection therapy: a randomized controlled crossover trial. Diabetes Metab Res Rev 2015; 31: 61–68. [DOI] [PubMed] [Google Scholar]
  • 5. Danne T, Nimri R, Battelino T, et al. International consensus on use of continuous glucose monitoring. Diabetes Care 2017; 40: 1631–1640. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Tauschman M, Forlenza G, Hood K, et al. Diabetes technologies: glucose monitoring. Pediatr Diabetes 2022; 23: 1390–1405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Maiorino MI, Signoriello S, Mario A, et al. Effects of continuous glucose monitoring on metrics of glycemic control in diabetes: a systemic review with meta‐analysis of randomized controlled trials. Diabetes Care 2020; 43: 1146–1156. [DOI] [PubMed] [Google Scholar]
  • 8. Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care 2019; 42: 1593–1603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Battelino T, Alexander CM, Amiel SA, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes 2023; 11: 42–57. [DOI] [PubMed] [Google Scholar]
  • 10. Monnier L, Colatte C, Wojtusciszyn A, et al. Toward defining the threshold between low and high glucose variability in diabetes. Diabetes Care 2017; 40: 832–838. [DOI] [PubMed] [Google Scholar]
  • 11. American Diabetes Association . Children and adolescents: standards of care in diabetes—2024. Diabetes Care 2024; 47(Suppl. 1): S258–S281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. de Bock M, Codner E, Craig ME, et al. ISPAD clinical practice consensus guidelines 2022: glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes. Pediatr Diabetes 2022; 23: 1270–1276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Abraham MB, Karges B, Dovc K, et al. Clinical practice consensus guidelines 2022: assessment and management of hypoglycemia in children and adolescents with diabetes. Pediatr Diabetes 2022; 23: 1322–1340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Urakami T. Significance of time in range in children and adolescents with type 1 diabetes. Endocr J 2022; 69: 1035–1042. [DOI] [PubMed] [Google Scholar]
  • 15. Edge J, Acerini C, Campbell F, et al. An alternative sensor‐based method for glucose monitoring in children and young people with diabetes. Arch Dis Child 2017; 102: 543–549. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Campbell F, Kordonouri O, Murphy N, et al. FreeStyle libre use for self‐management of diabetes in children and adolescents. Program of 77th Scientific Sessions of American Diabetes Association 2017; 110‐LB (Abstract).
  • 17. Cherubini V, Bonfanti R, Casertano A, et al. Time in range in children with type 1 diabetes using treatment strategies based on nonautomated insulin delivery systems in the real world. Diabetes Technol Ther 2020; 22: 509–515. [DOI] [PubMed] [Google Scholar]
  • 18. Jiao X, Shen Y, Chen Y. Better TIR, HbA1c, and less hypoglycemia in closed‐loop insulin system in patients with type 1 diabetes: a meta‐analysis. BMJ Open Diabetes Res Care 2022; 10: e002633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Urakami T, Yoshida K, Kuwabara R, et al. Individualization of recommendations from the international consensus on continuous glucose monitoring‐derived metrics in Japanese children and adolescents with type 1 diabetes. Endocr J 2020; 67: 1055–1062. [DOI] [PubMed] [Google Scholar]
  • 20. Urakami T, Yoshida K, Kuwabara R, et al. Significance of “time below range” as a glycemic marker derived from continuous glucose monitoring in Japanese children and adolescents with type 1 diabetes. Horm Res Paediatr 2020; 93: 251–257. [DOI] [PubMed] [Google Scholar]
  • 21. Urakami T, Terada H, Yoshida K, et al. Comparison of the clinical effects of intermittently scanned and real‐time continuous glucose monitoring in children and adolescents with type 1 diabetes: a retrospective cohort study. J Diabetes Investig 2022; 13: 1745–1752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Annan S, Higgins LA, Jelleryd E, et al. ISPAD clinical practice consensus guidelines 2022: nutritional management in children and adolescents with diabetes. Pediatr Diabetes 2022; 23: 1297–1321. [DOI] [PubMed] [Google Scholar]
  • 23. Hásková A, Radovnická L, Petruźelková L, et al. Real‐time CGM is superior to flash glucose monitoring for glucose control in type 1 diabetes: the CORRIDA randomized controlled trial. Diabetes Care 2020; 43: 2744–2750. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Heile MH, Hollstegge B, Broxterman L, et al. Automated insulin delivery: easy enough to use in primary care? Clin Diabetes 2020; 38: 474–485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Bergenstal RM, Garg S, Weinzimer SA, et al. Safety of a hybrid closed‐loop insulin delivery system in patients with type 1 diabetes. JAMA 2016; 316: 1407–1408. [DOI] [PubMed] [Google Scholar]
  • 26. Lewis D, Leibrand S, #OpenAPS Community . Real‐world use of open source artificial pancreas. J Diabetes Sci Technol 2016; 10: 1411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Thrasher JR, Arrieta A, Niu F, et al. Early real‐world performance of the MiniMed™ 780G advanced hybrid closed‐loop system and recommended settings use in the United States. Diabetes Technol Ther 2024; 26(Suppl 3): S24–S31. [DOI] [PubMed] [Google Scholar]
  • 28. Choudhary P, Arrieta A, van den Heuvel T, et al. Celebrating the data from 100,000 real‐world users of MiniMed™ 780G system in Europe, Middle East, from data to clinical evidence. Diabetes Technol Ther 2024; 26(Suppl 3): S32–S37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Ceriello A, Monnier L, Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet Diabetes Endocrinol 2019; 7: 221–230. [DOI] [PubMed] [Google Scholar]
  • 30. Temelkova‐Kruktschiev TS, Koehler C, Henkel E, et al. Postchallenge plasma glucose and glycemic spikes are more strongly associated with atherosclerosis than fasting glucose or HbA1c level. Diabetes Care 2000; 23: 1830–1834. [DOI] [PubMed] [Google Scholar]
  • 31. Haffner S. The importance of postprandial hyperglycaemia in development of cardiovascular disease in people with diabetes: point. Int J Clin Pract Suppl 2001; 123: 24–26. [PubMed] [Google Scholar]
  • 32. Cox D, Gender‐Frederick L, McCall A, et al. The effect of glucose fluctuation on cognitive function and QOL: the function costs of hypoglycemia and hyperglycemia among adults with type 1 or type 2 diabetes. Int J Clin Pract Suppl 2002; 129: 20–26. [PubMed] [Google Scholar]

Articles from Journal of Diabetes Investigation are provided here courtesy of Wiley

RESOURCES