871-P: Baseline Time-in-Range Association with Glycemic Improvement in Adults with Type 1 Diabetes (T1D)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 871-P
Author(s):  
PETER CALHOUN ◽  
DAVID A. PRICE ◽  
ROY BECK
Keyword(s):  
Type 1 Diabetes ◽  
Time In Range

Increased Time-in-Range with Sotagliflozin as Adjunct Therapy to Insulin in Adults with Type 1 Diabetes as Demonstrated by 24-Week Continuous Glucose Monitoring (inTandem1, inTandem2)

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1179-P ◽  
Author(s):  
THOMAS DANNE ◽  
BERTRAND CARIOU ◽  
JOHN B. BUSE ◽  
SATISH K. GARG ◽  
JULIO ROSENSTOCK ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Adjunct Therapy ◽  
Time In Range

1066-P: Improvement in Time-in-Range (TIR) with Real-Life Use of Hybrid Closed-Loop System in Patients with Type 1 Diabetes (T1D)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1066-P
Author(s):  
HALIS K. AKTURK ◽  
DOMINIQUE A. GIORDANO ◽  
HAL JOSEPH ◽  
SATISH K. GARG ◽  
JANET K. SNELL-BERGEON
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Real Life ◽  
Loop System ◽  
Closed Loop System ◽  
Time In Range

Time in range for multiple technologies in type 1 diabetes: a systematic review and network meta-analysis

2020 ◽  
Author(s):  
Anthony Pease ◽  
Clement Lo ◽  
Arul Earnest ◽  
Velislava Kiriakova ◽  
Danny Liew ◽  
...  
Keyword(s):  
Systematic Review ◽  
Type 1 Diabetes ◽  
Closed Loop ◽  
Meta Analysis ◽  
Management Strategies ◽  
Glucose Testing ◽  
Closed Loop Systems ◽  
Percent Time ◽  
Time In Range

<b>Background: </b>Time-in-range is a key glycaemic metric, and comparisons of management technologies for this outcome are critical to guide device selection. <p><b> </b></p> <p><b>Purpose: </b>We conducted a systematic review and network meta-analysis to compare and rank technologies for time in glycaemic ranges.</p> <p> </p> <p><b>Data sources: </b>We searched All Evidenced Based Medicine Reviews, CINAHL, EMBASE, MEDLINE, MEDLINE In-Process and other non-indexed citations, PROSPERO, PsycINFO, PubMed, and Web of Science until 24 April, 2019.</p> <p> </p> <p><b>Study selection: </b>We included randomised controlled trials <u>></u>2 weeks duration comparing technologies for management of type 1 diabetes in adults (<u>></u>18 years of age), excluding pregnant women. </p> <p> </p> <p><b>Data extraction: </b>Data were extracted using a predefined template. Outcomes were percent time with sensor glucose levels 3.9–10.0mmol/l (70–180mg/dL), >10.0mmol/L (180mg/dL), and <3.9mmol/L (70mg/dL). </p> <p><b> </b></p> <p><b>Data synthesis: </b>We identified 16,772 publications, of which 14 eligible studies compared eight technologies comprising 1,043 participants. Closed loop systems lead to greater percent time-in-range than any other management strategy and was 17.85 (95% predictive interval [PrI] 7.56–28.14) higher than usual care of multiple daily injections with capillary glucose testing. Closed loop systems ranked best for percent time-in-range or above range utilising surface under the cumulative ranking curve (SUCRA–98.5 and 93.5 respectively). Closed loop systems also ranked highly for time below range (SUCRA–62.2). </p> <p><b> </b></p> <p><b>Limitations: </b>Overall risk of bias ratings were moderate for all outcomes. Certainty of evidence was very low.</p> <p><b> </b></p> <p><b>Conclusions: </b>In the first integrated comparison of multiple management strategies considering time-in-range, we found that the efficacy of closed loop systems appeared better than all other approaches. </p>

scholarly journals Improved Time-in-Range over 1 year is Associated with Reduced Albuminuria in Individuals with Sensor-Augmented Insulin Pump-Treated Type 1 Diabetes

2020 ◽  
Author(s):  
Ajenthen G. Ranjan ◽  
Signe V. Rosenlund ◽  
Tine W. Hansen ◽  
Peter Rossing ◽  
Steen Andersen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Pump ◽  
Controlled Trial ◽  
Daily Injection ◽  
Albumin Creatinine Ratio ◽  
Multiple Daily Injection ◽  
Blood And Urine Samples ◽  
Time In Range ◽  
One Year

<b>Aim:</b> To investigate the association between treatment-induced change in continuous glucose monitored (CGM) time-in-range (TIR) and albuminuria in persons with type 1 diabetes (T1D) treated with sensor-augmented-pumps (SAP). <p><b>Methods: </b><a></a><a>Twenty-six of fifty-five participants with albuminuria and multiple daily injection-therapy (25% females, 51 (46-63) years, HbA<sub>1c</sub> 75 (68-88) mmol/mol [9.0 (8.4-10.4)%], UACR 89 (37-250) mg/g) were in a randomized-controlled trial assigned to SAP-therapy for one year</a>. Anthropometrics, CGM-data, blood and urine samples were collected every three months.</p> <p><b>Results: </b>Mean change (95%-CI) in %TIR was +13.2 (6.2;20.2)%, HbA<sub>1C</sub> was -14.4 (-17.4;-10.5) mmol/mol [-1.3 (-1.6;-1.0)%] and urinary albumin-creatinine-ratio (UACR) was -15 (-38;17)%, all p<0.05. UACR decreased with 19 (10;28)% per 10% increase in %TIR (p=0.04), 18 (1;30)% per 10 mmol/mol decrease in HbA<sub>1C</sub> (p=0.07), and 31% per 10 mmHg decrease in mean arterial pressure (p<0.001).<b></b></p> <b>Conclusion: </b>In this longitudinal study, treatment-induced increase in %TIR was significantly associated with decrease in albuminuria in T1D.

Open-source automated insulin delivery systems for the management of type 1 diabetes during pregnancy

2021 ◽  
Vol 14 (9) ◽  
pp. e243522
Author(s):  
Khulood Bukhari ◽  
Rana Malek
Keyword(s):  
Type 1 Diabetes ◽  
Open Source ◽  
Closed Loop ◽  
Glycated Hemoglobin ◽  
Insulin Delivery ◽  
Third Trimester ◽  
Time In Range ◽  
Insulin Delivery System ◽  
Iphone Application

A 40-year-old woman used an open-source automated insulin delivery system to manage her type 1 diabetes (T1D) prior to conception. The code for building the iPhone application called ‘Loop’ that carried the software for the hybrid closed-loop controller was available online. Her glycated hemoglobin before conception was 6.4%. Between 6 and 12 weeks gestation, she spent 66% time-in-range (TIR), 28% time-above-range (TAR) and 6% time-below-range (TBR). Between 18 and 24 weeks gestation, she spent 68% TIR, 27% TAR and 5% TBR. During her third trimester, she spent 72% TIR, 21% TAR and 7% TBR. She delivered a healthy infant with no neonatal complications. Clinicians should be aware of this technology as it gains traction in the T1D community and seeks Food and Drug Administration approval.

Diabeloop DBLG1 Closed-Loop System Enables Patients With Type 1 Diabetes to Significantly Improve Their Glycemic Control in Real-Life Situations Without Serious Adverse Events: 6-Month Follow-up

2021 ◽  
Author(s):  
Coralie Amadou ◽  
Sylvia Franc ◽  
Pierre-Yves Benhamou ◽  
Sandrine Lablanche ◽  
Erik Huneker ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Adverse Events ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Real Life ◽  
Serious Adverse Events ◽  
Life Conditions ◽  
Time In Range

<b>OBJECTIVE </b> <p>To analyze safety and efficacy of the DBLG1 hybrid closed-loop artificial pancreas system in patients with Type 1 Diabetes in real life conditions. </p> <p> </p> <p><b>METHODS</b></p> <p>Following a one-week run-in period with usual pump, 25 patients were provided with the commercial DBLG1 system. We present the results of Time-in-Range and HbA1c over a 6-month period.</p> <p><b> </b></p> <p><b>RESULTS</b></p> <p>The mean (SD;range) age of patients was 43 years (13.8; 25-72). At baseline, mean HbA1c and TIR 70-180mg/dL were respectively 7.9% (0.93; 5.6- 8.5) [63mmol/mol (10; 38-69)] and 53% (16.4;21-85). One patient stopped using the system after 2 months. At 6-month, mean HbA1c decreased to 7.1% [54mmol/mol] (p<0.001) and TIR 70-180mg/dL increased to 69.7% (p<0.0001). TIR<70mg/dL decreased from 2.4 to 1.3% (p=0.03). TIR<54mg/dL decreased from 0.32 to 0.24% (p=0.42). No serious adverse event was reported during the study. </p> <p> </p> <p><b>CONCLUSION</b></p> <p>The DBLG1 System confirms its ability to significantly improve glycemic control in real life conditions, without serious adverse events. </p>

Frequency of flash glucose monitoring readings, hemoglobin A1c and time in range: a real life study in adults with type 1 diabetes

2020 ◽  
Vol 57 (11) ◽  
pp. 1395-1397 ◽  
Author(s):  
Andrea Laurenzi ◽  
Amelia Caretto ◽  
Mariluce Barrasso ◽  
Andrea Mario Bolla ◽  
Nicoletta Dozio ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Hemoglobin A1c ◽  
Real Life ◽  
Glucose Monitoring ◽  
Flash Glucose Monitoring ◽  
Life Study ◽  
Time In Range ◽  
Real Life Study

The added and interpretative value of CGM-derived parameters in type 1 diabetes depends on the level of glycaemic control.

2020 ◽  
Author(s):  
Helleputte Simon ◽  
De Backer Tine ◽  
Calders Patrick ◽  
Pauwels Bart ◽  
Shadid Samyah ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glycaemic Control ◽  
Coefficient Of Variation ◽  
Subgroup Analyses ◽  
Time In Range ◽  
Mean Blood Glucose ◽  
Level 2

OBJECTIVE: In type 1 diabetes mellitus (T1DM) management, CGM-derived parameters can provide additional insights, with the concept of time in range (TIR) and other parameters reflecting glycaemic control and variability (GV) being put forward. This study aimed to examine the added and interpretative value of the CGM-derived indices TIR and coefficient of variation (CV%) in T1DM patients stratified according to their level of glycaemic control by means of HbA1c. METHODS: T1DM patients with a minimum disease duration of 10 years and without known macrovascular disease were enrolled. Patients were equipped with a blinded CGM device (Dexcom G4) for seven days. TIR (70–180 mg/dl), time in hypoglycaemia (total: <70 mg/dl; level 2: <54 mg/dl) and hyperglycaemia (total: >180 mg/dl; level 2: >250 mg/dl) were determined, and CV% (=standard deviation(SD)/mean blood glucose(MBG)) was used as parameter for GV. Pearson and Spearman correlations, and regression analysis was used to examine associations. RESULTS: 95 patients (age: 45±10 years; HbAc1: 7.7±0.8%) were included (MBG: 159±31 mg/dl; TIR 55.8±14.9%; CV%: 43.5±7.8%) and labeled as having good (HbA1c ≤7%; n=20), moderate (7–8%; n=44) or poor (>8%; n=31) glycaemic control. HbA1c was significantly associated with MBG (rs=0.48, p<0.001) and time spent in hyperglycaemia (total: rs=0.52; level 2: r=0.46; p<0.001), but not with time in hypoglycaemia and CV%, even after analysis in HbA1c subgroups. Similarly, TIR was negatively associated with HbA1c (r=−0.53; p<0.001), MBG (rs=−0.81; p<0.001) and time in hyperglycaemia (total: rs=−0.90; level 2: rs=−0.84; p<0.001), but not with time in hypoglycaemia. Subgroup analyses, however, showed that TIR did associate with shorter time in level 2 hypoglycaemia in those patients with good (rs=−0.60; p=0.007) and moderate (rs=−0.25; p=0.047) glycaemic control. In contrast, CV% was strongly positively associated with time in hypoglycaemia (total: rs=0.78; level 2: rs=0.76; p<0.001), but not with TIR or time in hyperglycaemia in the entire cohort, although subgroup analyses showed that TIR was negatively associated with CV% in patients with good glycaemic control (r=−0.81, p<0.001) and positively in patients with poor glycaemic control (r=0.47; p<0.01). CONCLUSION: This study demonstrates that CGM-derived metrics TIR and CV% relate with clinically important situations, TIR being strongly dependent on hyperglycaemia and CV% being reflective of hypoglycaemic risk. However, the interpretation and applicability of TIR and CV%, and their relationship, depends on the level of glycaemic control of the individual patient, with CV% generally adding less clinically relevant information in those with poor control. This illustrates the need for further research and evaluation of composite measures of glycaemic control in T1DM. Abbreviations: T1DM = Type 1 diabetes mellitus; CGM = Continuous glucose monitoring; TIR = Time in range; TAR = Time above range; TBR = Time below range; GV = Glycaemic variability; CV% = Coefficient of variation; MBG = Mean blood glucose.

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