GlyCulator2: an update on a web application for calculation of glycemic variability indices

Pagacz, Konrad; Stawiski, Konrad; Szadkowska, Agnieszka; Młynarski, Wojciech; Fendler, Wojciech

doi:10.1007/s00592-018-1140-0

Cited by 27 publications

(26 citation statements)

References 5 publications

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“…CGM data from iPro ™ 2 sensor was downloaded via Carelink iPro and was calculated using Glyculator 2.0 software, which was allowed for calculation of every index of CGM recommended by the International Consensus. 5,17…”

Section: Methodsmentioning

confidence: 99%

Impacts of glycemic variability on the relationship between glucose management indicator from iPro^™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

Liu

Yang

Deng

et al. 2020

Therapeutic Advances in Endocrinology

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Aims: Our aim was to investigate the impact of glycemic variability (GV) on the relationship between glucose management indicator (GMI) and laboratory glycated hemoglobin A1c (HbA1c). Methods: Adult patients with type 1 diabetes mellitus (T1D) were enrolled from five hospitals in China. All subjects wore the iPro™2 system for 14 days before HbA1c was measured at baseline, 3 months and 6 months. Data derived from iPro™2 sensor was used to calculate GMI and GV parameters [standard deviation (SD), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE)]. Differences between GMI and laboratory HbA1c were assessed by the absolute value of the hemoglobin glycation index (HGI). Results: A total of 91 sensor data and corresponding laboratory HbA1c, as well as demographic and clinical characteristics were analyzed. GMI and HbA1c were 7.20 ± 0.67% and 7.52 ± 0.73%, respectively. The percentage of subjects with absolute HGI 0 to lower than 0.1% was 21%. GMI was significantly associated with laboratory HbA1c after basic adjustment (standardized β = 0.83, p < 0.001). Further adjustment for SD or MAGE reduced the standardized β for laboratory HbA1c from 0.83 to 0.71 and 0.73, respectively (both p < 0.001). In contrast, the β remained relatively constant when further adjusting for CV. Spearman correlation analysis showed that GMI and laboratory HbA1c were correlated for each quartile of SD and MAGE (all p < 0.05), with the corresponding correlation coefficients decreased across ascending quartiles. Conclusions: This study validated the GMI formula using the iPro™2 sensor in adult patients with T1D. GV influenced the relationship between GMI and laboratory HbA1c.

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Section: Methodsmentioning

confidence: 99%

Impacts of glycemic variability on the relationship between glucose management indicator from iPro^™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

Liu

Yang

Deng

et al. 2020

Therapeutic Advances in Endocrinology

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show abstract

“…We improved on this design and based our approach on the consensus in terms of reported glucose derivatives calculated by an independent software . Other strengths of the study include paired design, adequate statistical power, high quality of analyzed data and homogeneity of the studied group.…”

Section: Discussionmentioning

confidence: 99%

“…In the next step, collected feeds were processed using Glyculator 2.0 software (Medical University of Lodz, Lodz, Poland), the specification of which was described previously . In brief, it allowed for calculation of every index of CGM data required by the International Consensus for the whole recording and separately day and night periods defined as 0:00 to 6:00 am and 6:00 am to 0:00 am…”

Section: Methodsmentioning

confidence: 99%

Discrepancies between methods of continuous glucose monitoring in key metrics of glucose control in children with type 1 diabetes

et al. 2019

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Objective: We aimed to compare glycemic control and variability parameters obtained from paired records of real-time continuous glucose monitoring (RT-CGM) and flash glucose monitoring (FGM). Methods: Ten Polish boys and 11 girls aged 15.3 ± 2.1 years with type 1 diabetes for 7.7 ± 4.5 years and glycated hemoglobin 7.35 ± 0.7% (57 ± 5 mmol/mol) were recruited between August 2017 and June 2018 and equipped with devices for RT-CGM (iPro2 system with Enlite electrodes) and FGM (FreeStyle Libre) for 1 week. Afterwards, Glyculator 2.0 software was used to calculate and compare key metrics of glycemic control listed in the International Consensus on Use of Continuous Glucose Monitoring, with distinction into all record/night-time/day-time blocks when appropriate. Results: Agreement between the two systems' measurements across patients ranged from poor (R 2 = .39) to nearly perfect (R 2 = .97). Significant differences between RT-CGM and FGM were observed in five important metrics: coefficient of variation (median difference: −4.12% [25%-75%: −7.50% to −2.96%], P = .0001), low blood glucose index (−0.88 [−1.88 to −0.18], P = .0004), % of time below 70 mg/dL (3.9 mmol/L) (−4.77 [−8.39 to −1.19], P = .0015) and 54 mg/dL (3 mmol/L) (−1.33 [−4.07 to 0.00], P = .0033) and primary time in range (TIR) 70-180 mg/dL (8.58 [1.31 to 12.66], P = .0006). Conclusions: RT-CGM and FGM differ in their estimates of clinically important indices of glycemic control. Therefore, such metrics cannot be directly compared between people using different systems. Our result necessitates system-specific guidelines and targets if TIR and glycemic variability are to be used as an endpoint in clinical trials.

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“…Gllycemic control parameters (daily mean glucose, standard deviation SD, coefficient of Nutrients 2020, 12, 454 5 of 11 variance CV, times above (TAR), in (TIR) and below (TBR) 3.88-10 mmol/L range) were calculated with Glyculator 2.0. [21]. The insulin dose was assessed as daily insulin dose per kg of body weight (IU/kg).…”

Section: The Control Of Blood Glucose Concentrationsmentioning

confidence: 99%

Acute Responses to Low and High Intensity Exercise in Type 1 Diabetic Adolescents in Relation to Their Level of Serum 25(OH)D

et al. 2020

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The main purpose of this study was to investigate the differences in glycaemic reaction in response to various physical activities in 20 young boys (14.4 ± 1.6 years) with type 1 diabetes mellitus (T1DM) and with either vitamin D deficiency or with suboptimal levels of vitamin D. Participants were divided into two groups (deficiency group-DG, n = 10; suboptimal group-SG, n = 10) according to their vitamin D levels. All patients performed aerobic and mixed (aerobic-anaerobic) physical efforts. During the exercise, the respiratory responses and glucose levels were monitored. Biochemical blood analyses were performed before each physical effort. The oxygen consumption was not significantly lower in SG during both aerobic and mixed effort (4.0% and 5.6%, respectively). The glycated haemoglobin (HbA1c) level was higher by 6.1% and the total daily dose of insulin (DDI) was higher by 18.4% in the DG. The differences were not statistically significant. Patients with lower vitamin D levels demonstrated an insignificantly higher glycaemic variability during days with both aerobic and mixed exercises. An appropriate vitamin D concentration in T1DM patients' blood may constitute a prophylactic factor for hyperglycaemia during anaerobic training and hypoglycaemia during aerobic training.

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GlyCulator2: an update on a web application for calculation of glycemic variability indices

Cited by 27 publications

References 5 publications

Impacts of glycemic variability on the relationship between glucose management indicator from iPro^™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

Impacts of glycemic variability on the relationship between glucose management indicator from iPro^™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

Discrepancies between methods of continuous glucose monitoring in key metrics of glucose control in children with type 1 diabetes

Acute Responses to Low and High Intensity Exercise in Type 1 Diabetic Adolescents in Relation to Their Level of Serum 25(OH)D

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GlyCulator2: an update on a web application for calculation of glycemic variability indices

Cited by 27 publications

References 5 publications

Impacts of glycemic variability on the relationship between glucose management indicator from iPro™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

Impacts of glycemic variability on the relationship between glucose management indicator from iPro™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

Discrepancies between methods of continuous glucose monitoring in key metrics of glucose control in children with type 1 diabetes

Acute Responses to Low and High Intensity Exercise in Type 1 Diabetic Adolescents in Relation to Their Level of Serum 25(OH)D

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Product

Resources

About

Impacts of glycemic variability on the relationship between glucose management indicator from iPro^™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

Impacts of glycemic variability on the relationship between glucose management indicator from iPro^™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus