Comparing the use of SMBG vs. CGM data to Optimize Glucose Control in T2DM
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1 Comparing the use of SMBG vs. CGM data to Optimize Glucose Control in T2DM For the first time using CGM to assess glucose control achieved in both groups Richard M. Bergenstal, MD International Diabetes Center, Minneapolis, MN
2 Disclosure Statement Richard M. Bergenstal, MD RMB has participated in clinical research, a scientific advisory board or served as a consultant for: Eli Lilly Novo Nordisk Sanofi T1D Exchange (Helmsley Charitable Trust) Roche Diabetes Care J&J Abbott Diabetes Care Bayer Diabetes Medtronic Diabetes Care BMS Merck ResMed Takeda Hygieia RMB inherited Merck stock and is a volunteer of the ADA & JDRF RMB s employer, non-profit Park Nicollet Institute, contracts for his services and he receives no personal income from these contracts.
3 T2D: Needs to improve glucose control 59 yo women with T2D for 12 years and has a BMI of 34 and A1c of 8.2% on Met and Basal Insulin. Would SMBG or CGM be the most effective approach to glucose monitoring and medication or lifestyle adjustment in this patient?
4 SMBG vs RT-CGM: Patients with T2D A1c Δ 0.5 N=50 Δ N= Vigersky R, et al. Diabetes Care 2012; 35(1): 32-38
5 Background Few studies compare utilizing SMBG data to CGM data Vigersky R, et al. Diabetes Care 2012 At 3 months: rtcgm A1c % Δ 1.0% n=50 SMBG A1c % Δ 0.5% n=50 No study to date has used CGM to compare the degree of glucose control achieved with using SMBG or CGM to guide management We define glucose control as Δ A1c + Δ GCM: time in range (TIR) & % of reading in hypoglycemic range(<70, 60 & 50 mg/dl)
6 Study Design Uncontrolled T2D (A1c >7%) using a variety of treatments Randomized and followed for 4 months. Structured SMBG testing (st SMBG) Real time CGM (rt CMG) stsmbg = SMBG 4x/day & 3 (7 point BG profiles (360 view) before visits rtcgm = CGM daily & CGM ambulatory glucose profile (AGP) report at visits
7
8 Ambulatory Glucose Profile (AGP)
9 Study Design Therapy adjusted every 2 4 weeks (360 view or AGP) Primary outcomes (baseline to 4 months) Change in A1c Change in CGM (2wk): Time in Range ( mg/dl) % readings in hypoglycemic range (<70, <60 <50 mg/dl)
10 N = 136 Subjects SMBG group = 68 CGM group = 68 Lead in Baseline Intervention Screen/V0 Screen/ V1 V2 V3 V4 CGM V5 V6 CGM V7 Week 4 Week 2 Wk 0 Wk 1 Wk 4 Wk 6 Wk 8 Wk 12 Wk 14 Wk 16 MNT ± Metformin SU ± Metformin Incretin ± Metformin Insulin ± Metformin n=34/medication Start Incretin (if Niave) CGM Group Learning Week SMBG Group CGM (14 days blinded) CGM (unblinded/ open)
11 Demographics CGM Group SMBG Group Baseline End 16 Wk Baseline End 16 Wk Number of Patients Number of Females Age (Years) 59.3 (8.9) 58.8 (10) Age Onset (Years) 11.8 (6.5) 12.7 (7) Height (in) 67.6 (3.3) 67.2 (3.9) Weight (lbs) (39.9) (42.1) (57.3) (57.4) BMI 34.5 (5.7) 34.2 (6.1) 37.1 (7.1) 36.7 (7.1) SBP (mmhg) (16) (16.3) (15.7) 126 (14.5) DBP (mmhg) 74.9 (12) 72.6 (11.9) 72.2 (9.3) 71.6 (9.9) Shown with mean (standard deviation)
12 Delta Baseline to Study End within CGM or SMBG group ^ p < 0.05 ^^ p < 0.01 ^^^ p < Between SMBG and CGM Group at Baseline or Study End * p < 0.05 ** p < 0.01 *** p < Shown with standard error
13 Delta Baseline to Study End within CGM or SMBG group ^ p < 0.05 ^^ p < 0.01 ^^^ p < Between SMBG and CGM Group at Baseline or Study End * p < 0.05 ** p < 0.01 *** p < Shown with standard error
14 Delta Baseline to Study End within CGM or SMBG group ^ p < 0.05 ^^ p < 0.01 ^^^ p < Between SMBG and CGM Group at Baseline or Study End * p < 0.05 ** p < 0.01 *** p < Shown with standard error
15 Delta Baseline to Study End within CGM or SMBG group ^ p < 0.05 ^^ p < 0.01 ^^^ p < Between SMBG and CGM Group at Baseline or Study End * p < 0.05 ** p < 0.01 *** p < Shown with standard error
16 Delta Baseline to Study End within CGM or SMBG group ^ p < 0.05 ^^ p < 0.01 ^^^ p < Between SMBG and CGM Group at Baseline or Study End * p < 0.05 ** p < 0.01 *** p < Shown with standard error
17 Delta Baseline to Study End within CGM or SMBG group ^ p < 0.05 ^^ p < 0.01 ^^^ p < Between SMBG and CGM Group at Baseline or Study End * p < 0.05 ** p < 0.01 *** p < Shown with standard error
18 Delta Baseline to Study End within CGM or SMBG group ^ p < 0.05 ^^ p < 0.01 ^^^ p < Between SMBG and CGM Group at Baseline or Study End * p < 0.05 ** p < 0.01 *** p < Shown with standard error
19 Delta Baseline to Study End within CGM or SMBG group ^ p < 0.05 ^^ p < 0.01 ^^^ p < Between SMBG and CGM Group at Baseline or Study End * p < 0.05 ** p < 0.01 *** p < Shown with standard error
20 Difference in Delta Between CGM & SMBG Groups (Baseline to Study 16 weeks) p value Final baseline HbA1c AUC Waking AUC Sleeping AUC IQR Stability % < 50 mg/dl % < 60 mg/dl % < 70 mg/dl % mg/dl % > 140 mg/dl % mg/dl % > 180 mg/dl
21 SMBG or CGM in T2D? Effectively utilizing Glucose Data can improve A1c SMBG (collected and analyzed in a structured way 360 view) CGM (AGP analysis) In T2D CGM may be more effective than SMBG in minimizing hypoglycemia while achieving target A1c control. Particularly in insulin treated patients.
22 CGM SMBG
23 SMBG or CGM in T2D? Effectively utilizing Glucose Data can improve A1c SMBG (collected and analyzed in a structured way 360 view) CGM (AGP analysis) In T2D CGM may be more effective than SMBG in minimizing hypoglycemia while achieving target A1c control. Particularly in insulin treated patients. Having a consistent snapshot view of CGM data (like an AGP) may help patients and health care professionals effectively use CGM data
24 Glucose Patterns & Trends are critical SMBG Meters not accurate enough Carb counting too hard CGM Sensors not accurate enough Lag time, false alarms, discomfort information overload Patterns / Trends still reliable & helpful Patterns / Trends still reliable & helpful
25 Identifying Glycemic Trouble Spots Frequent Hyperglycemia 50th 90th 75th 25th 10th Frequent Hypoglycemia High Variability Ambulatory Glucose Profile (AGP) Recommendations for Standardizing Glucose Reporting and Analysis to Optimize Clinical Decision Making in Diabetes: The Ambulatory Glucose Profile (AGP) March 1, 2013 online DTT and JDST Bergenstal et al.
26 Thank you. Questions?
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