Technology in DiabetesContinuous Glucose Monitors

(CGM) and Insulin Pumps

Francine R. Kaufman, M.D.

Chief Medical Officer and VP of Global Clinical, Regulatory and Medical Affairs, Medtronic Diabetes

Distinguished Professor Emerita of Pediatrics at the Keck School of Medicine of the University of Southern California and Children’s Hospital Los Angeles

Type 2 Diabetes Status – 2007-2010 NHANES• Significant improvements from 1988 to 1994 (all P < 0.05)

• Statin use significantly increased between 1988–1994 (4.2%) and 2007–2010 (51.4%, P < 0.01)

• Younger individuals less likely to meet A1C and LDL goals

• Compared with non-Hispanic whites,

• Mexican Americans less likely to meet A1C and LDL goals (P < 0.03)

• Non-Hispanic blacks less likely to meet BP and LDL goals (P < 0.02)

• Compared with non-Hispanic blacks,

• Mexican Americans less likely to meet A1C goals (P < 0.01)

Casagrande SS, et al,Diabetes Care 2013 Aug; 36(8): 2271-2279.https://doi.org/10.2337/dc12-2258

A1C <7.0 % 52.5%

BP <130/80 mmHg 51.1%

LDL <100 mg/dL 56.2%

All 3 18.8%

A1C Outcome in the T1D Exchange –2016-2018 (n=22,697) in Blue vs. 2010-2012 (n=25,529) in Orange

Foster NC, et al, Diabetes Technology & Therapeutics Vol 21, Published online 2019, https://doi.org/10.1089/dia.2018.0384

The Use of Technology in the T1D Exchange

Foster NC, et al, Diabetes Technology & Therapeutics Vol 21, Published online 2019, https://doi.org/10.1089/dia.2018.0384

Adverse Outcomes in T1D Exchange - 2016-2018 Self Report from prior 3 months

Hypoglycemia • 6% had seizure or loss of

consciousness • 5% in <18 yrs; 10% in >50 yrs• Insulin pump use associated with

lower frequency (5% vs. 9%; p<0.001) • CGM use trended toward lower

frequency (5% vs. 7%; p=0.06) • CGM may have been prescribed or used

because offrequent severe hypoglycemia • Frequency of severe hypoglycemia not

associated with A1c level - high blood sugar not protective

DKA• 3% had DKA• Highest frequency (4%) in <26 yrs• Insulin pump use associated with less

DKA event than MDI (2% vs. 4%; p=0.002)

• CGM use fewer events than non-CGM users (1% vs. 3%; p=0.04)

• Higher A1c more likely to have DKA

Foster, et al, Diabetes Technology & Treatment Published online 2019, https://doi.org/10.1089/dia.2018.0384

Incidence Trends of Type 1 and Type 2Diabetes among Youths, 2002–2012

Mayer-Davis B, et al, N Engl J Med 376;15 NEJM.org April 13, 2017

1Danne T. et al. Diabetes Care 2017;40:1631-1640. 2Agiostratidou G. et al. Diabetes Care 2017;40(12):1622-1630. 3Beyond A1c Group. Diabetes Care 2018; 41(6):e92-e94. doi: 10.2337/dci18-0010.

Beyond HbA1cMetric ATTD1 US2 Beyond A1c Group3

Mean Sensor Glucose Yes Yes

Severe Hypoglycemia Clinical diagnosis: event requiring assistance (Level 3)

A severe event characterized by altered mental and/or physical status requiring assistance (Level 3)

Clinical diagnosis: a severe event characterized by altered mental and/or physical status requiring assistance

Time in Hypoglycemiamg/dL (mmol/L)

• Level 1: <70 (3.9)• Level 2: <54 (3.0)

• Level 1: <70 (3.9)• Level 2: <54 (3.0)

• < 70 (3.9)• < 54 (3.0)

Time In Rangemg/dL (mmol/L)

• Default: 70-180 (3.9-10.0)• Secondary: 70-140 (3.9-7.8)

• 70-180 (3.9-10.0) • 70-180 (3.9-10.0)

Time in Hyperglycemiamg/dL (mmol/L)

• Level 1: >180 (10.0)• Level 2: >250 (13.9)

• Level 1: >180 (10.0)• Level 2: >250 (13.9)

• Level 1: >180 (10.0)• Level 2: >250 (13.9)

Diabetic Ketoacidosis Clinical diagnosis: ketones, acidosis, and usually hyperglycemia (Level 3)

Elevated serum or urine ketones and serum bicarbonate <15 mmol/L or blood pH <7.3

Clinical diagnosis: presence of ketosis and acidosis

Glycemic Variability • Primary : Coefficient of variation (CV)

• Secondary: Standard Deviation (SD)

• Coefficient of variation (CV)

Lu J, et al. Diabetes Care Vol 41, in press.

TIME IN RANGEASSOCIATION WITH RETINOPATHY AND MICROALBUMINURIA

Beck RW, et al, Diabetes Care https://doi.org/10.2337/dc18-1444

3,409 Patients with Type 2 Diabetes with Profession CGM1440 Patients with Type 1 Diabetes from DCCT 7-point BG Measurements

Lu J, et al, Diabetes Care 2018 Nov; 41(11): 2370-2376. https://doi.org/10.2337/dc18-1131

Predictive Low Glucose

Management

The Iterative Path to the

Closed Loop

Intensive insulin therapy, multiple

insulin combinations.

Glucose sensing improves outcomes.

MDI

Hybrid Closed Loop with

automation of basal insulin

delivery.The next big advance will

include automatic bolusing.

Predictive Low Glucose

Management. Not only

mitigation of hypoglycemia

but also prevention –

60-80% do not hit low

threshold.First step in automation

Evidence of 35% reduction

in hypoglycemiawithout increase

in A1C.

Low Glucose Suspend

Closed Loop or the Artificial

Pancreas, no meal

announcement.

THE JOURNEY HAS BEEN LIKE CLIMBING A MOUNTAIN

Sensors and Pumps

Sensors communicateswith pump or

another deviceto give alerts and alarms,

more information to

improve glycemic control.

Garg S, et al. Diabetes Technol Ther. 2012;14:205-209. Bergenstal RM, et al. N Engl J Med. 2013;369(3):224-32.

Danne T, et al. Diabetes TechnolTher. 2014; 16(6):338:347. Battelino T, et al. Pending Publication.Zhong A, et al. Diabetes Technol Ther. 2016;18:657-663.

Continuous Glucose Monitoring

Present Sensors

• Improved Accuracy

• iCGM Criteria – Nonadjunctive

• No Calibration

• Longer Functional Time

• Transmit to Secondary Screens – Phones

Future Trends

• Integrate into EHR

• Replace Fingersticks Completely

• Long Term Implantation

• Micro-circuitry to Reduce On-Body Burden

• Alternative Sites – Contact Lens Discontinued

10

Data to the Cloud

• Cloud

Medtronic Confidential June 2015 11

TheCLOUD

1. Care Givers and Patients able to accessinformation on secondary devices to

improve safety and to be discrete2. Information can be stored in the electronic

medical record or in paper files3. Can be used to understand outcomes and

effectiveness4. Is part of closed loop systems that will

automate insulin delivery

The Clinical Studies Comparing CGM to SMBG

-0.4

0.02 0.04 0.02

-1.0

-0.5

-0.23

-0.5

-1.2

-0.8

-0.4

0

0.4

Mea

n ch

ange

from

bas

elin

e in

H

bA1c

SMBG CGM

1. Deiss 2006 2. Tamborlane 2008 3. Battelino 2011 4. Riveline 2012

P=0.003

P<0.001

P=0.008

P=0.006

1. Deiss, D. et al., 2006. Diabetes Care, 29(12), pp.2730–2732. 2. Tamborlane, W.V. et al., 2008. The New England journal of medicine, 359, pp.1464–1476. 3. Battelino, T. et al., 2011. Diabetes Care, 34(4), pp.795–800. 4. Riveline, J. et al., 2012. Diabetes Care, 35,

pp.965–971.

CGM Significantly Improves HbA1c Compared to SMBG

Guardian link 3 and Guardian sensor 3IMPROVED PERFORMANCE THROUGH DIAGNOSTICS

CGM SYSTEM

Improved accuracy and duration of wear

7 days of wear

Diagnostics chip automatically monitors sensor health with electrochemical impedance spectroscopy (EIS)

If EIS detects sensor is failing or partially dislodged the user will be alerted

SENSOR PERFORMANCE 9.64% MARD vs YSI (3-4 cals/day)

10.32% for HCL pivotal study with iSTAT reference – REAL LIFE USE after prolonged time period NON ADJUNCTIVE

Predictive Alerts, working with advanced analytics through IBM

Intelligent diagnostics chip

Christiansen M P, et al., Diabetes Technology & Therapeutics 19(8) · July 2017 DOI: 10.1089/dia.2017.0087

DexCom G6Accuracy of a Factory-Calibrated, Real-Time Continuous Glucose Monitoring System During 10 Days of Use in Youth and Adults with DiabetesR. Paul Wadwa, et al 2018 https://doi.org/10.1089/dia.2018.0150

• 262 study participants (21,569 matched CGM reference pairs)

• Overall %15/15, %20/20, and MARD were 82.4%, 92.3%, and 10.0%, respectively

• Matched pairs from 134 adults and 128 youth of ages 6–17 years were similar with respect to %20/20 (92.4% and 91.9%) and MARD (9.9% and 10.1%)

• Overall %20/20 values on days 1 and 10 of sensor wear were 88.6% and 90.6%, respectively

• “Urgent Low Soon” (predictive of hypoglycemia within 20 min) hypoglycemia alert was correctly provided 84% of the time within 30 min

• The 10-day sensor survival rate was 87%

Beck RW, et al, Ann Intern Med. 2017 Sep 19;167(6):365-374. doi: 10.7326/M16-2855. Epub 2017 Aug 22

Beck RW, et al, Ann Intern Med. 2017 Sep 19;167(6):365-374. doi: 10.7326/M16-2855. Epub 2017 Aug 22

Beck RW, et al, Ann Intern Med. 2017 Sep 19;167(6):365-374. doi: 10.7326/M16-2855. Epub 2017 Aug 22

Implanted SensorA Prospective Multicenter Evaluationof the Accuracy of a Novel ImplantedContinuous Glucose Sensor: PRECISE IIMark Christensen, et al.

DIABETES TECHNOLOGY & THERAPEUTICSVolume 20, Number 3, 2018DOI: 10.1089/dia.2017.0142

Implanted Eversense Sensor – Precise II Trial

• 90 participants – 90 day study

• Overall MARD - 8.8% (95% confidence interval: 8.1%–9.3%)

• Lower than the prespecified 20% performance goal for accuracy (P < 0.0001)

• 93% of values within 20/20% of reference values over the total glucose range of 40–400 mg/dL

• Clarke Error Grid - 99.3% of samples in zones A (92.8%) and B (6.5%)

• 91% of sensors functional through day 90 DIABETES TECHNOLOGY & THERAPEUTICSVolume 20, Number 3, 2018DOI: 10.1089/dia.2017.0142

Flash Glucose Monitoring

4-17 year old subjects:• Accuracy of a single sensor for 14 days

• 5493 paired glucose readings were available for analysis

• (MARD) of 13.9% with 83.8% of readings falling in zone A and 99.4% in zone A+ B of the Consensus Error Grid

Impact Clinical Trial• Key findings from the trial include (FreeStyle Libre

users versus traditional SMBG):• 38 percent reduction in time spent in

hypoglycemia (<70 mg/dl)• 40 percent reduction in time spent in nocturnal

hypoglycemia at night (11 p.m. to 6 a.m.)• 50 percent reduction in serious hypoglycemia (<55

mg/dl)• No increase in HbA1c at six months• Glucose monitoring increased to an average of 15

scans per day• Routine finger sticks were nearly eliminated—

reduced by 91 percent

ASH CONTINUOUS GLUCOSE MONITORING AND ITS IMPACT TO REPLACE BLOOD GLUCOSE MONITORING IN THE MANAGEMENT OF TYPE 1 AND TYPE 2 DIABETESStephen M Twigg, Mahmood R Kazemi, Maria E CraigUS Endocrinology, 2017;13(2):57–62 DOI: https://doi.org/10.17925/USE.2017.13.02.57

AGP Data Display – Beyond A1C Metrics

Type 2 diabetes mellitus is characterised by insulin resistance and progressive β-cell failure, resulting in increasing hyperglycemia.1

Type 2 Diabetes

Treatment Challenges:

• Patients with advanced type 2 diabetes often require insulin • A1C targets often are not achieved when basal insulin is added 2,3

• Multiple Daily Injections (MDI), long-acting + rapid-acting insulin, are typically added when target are not achieved

• 30% of type 2 patients on MDI still do not achieve A1C targets – These patients have increased risk of hypoglycemia and weight gain.4

Limitations of MDI highlight the need for new treatment strategies in this subgroup of patients

1.Ferrannini E, et al. Beta-cell function in subjects spanning the range from normal glucose tolerance to overt diabetes: a new analysis. J Clin Endocrinol Metab 2005;90:493-500. 2.Inzucchi SE, et al. Management of hyperglycemia in type 2 diabetes: a patient-centered approach. Diabetes Care 2012; 35:1364-1379.3.Riddle MC, et al. The treat-to-target trial: randomized addition of glargine or human NPH insulin to oral therapy of type 2 diabetic patients. Diabetes Care 2003;26:3080-3086.4.Riddle M, et al. Contributions of basal and postprandial hyperglycaemia over a wide range of A1C levels before and after treatment intensification in type 2 diabetes. Diabetes Care 2011;34:2508-14.

• 1.2% mean A1C reduction for all patients

• 88% only needed 1 to 2 basal rates to achieve glycemic control

• 45% achieved A1C ≤ 7.0%

• 28% achieved A1C ≤ 6.5%

Pump therapy, using simple dosing, improved glycemic control

Improved Glycemic Control in Type 2 Diabetes Using Simple Dosing

Mean reduction in A1C from baseline to week 16

Adapted from Edelman, et al.

6.5%

7.0%

7.5%

8.0%

8.5%

Baseline Wk 4 Wk 8 Wk 12 Wk 16

n = 56

Time

Perc

enta

ge o

f A1C

Lev

els

Published 2010

Edelman SV, et al. Insulin pump therapy in patients with type 2 diabetes safely improved glycemic control using a simple insulin dosing regimen. Diabetes Technol Ther. 2010;12:627-633.

Change in A1C - 6 months Reduction of A1C:

Pump group: 1.1%(A1C drop from 9.0% to 7.9%)

MDI group: 0.4%(A1C drop from 9.0% to 8.6%)

• Between group difference 0.7% in favor of pump group (p<0.001)

• 308 patients completed

7.9%

8.6%

Baseline: 9%

Insulin pump therapy significantly improved glycemic control compared to MDI

Significant A1C Reduction in Pump Group

7

8

9

10

-10 0 10 20 30

Mea

nA1

C an

d 95

% C

.I. (

%)

Run-in(8 weeks)

Study Phase(6 months + 3 weeks)

- 8 0 3 15 27Week

Reznik Y, et al. Insulin pump treatment compared with multiple daily injections for treatment of type 2 diabetes. Lancet. 2014;384:1265-72.

Treatment Effect on A1C ReductionDifference in A1C according to baseline levels

• Pump therapy group subjects with highest baseline A1C (9.3 to 11.5%) achievedlargest improvement in glycemic control compared to MDI patients with samebaseline characteristics (-1.1±1.4%,p<0.001)

Insulin pump therapy consistently superior to MDI in A1C reduction, especially in patients with highest baseline

-1.2

-1

-0.8

-0.6

-0.4

-0.2

08 to 8.5% 8.6 to 9.2% 9.3 to 11.5%

ΔA1

C (M

DI)

-ΔA1

C (P

ump)

, %

-0.3±0·85% (P=0.10)

-0.5±0·92% (P<0.01)

-1.1±1·4% (P<0.001)

Reznik Y, et al. Insulin pump treatment compared with multiple daily injections for treatment of type 2 diabetes. Lancet. 2014;384:1265-72.

Treatment Effect on Insulin Utilization

75

100

125

-10 -5 0 5 10 15 20 25 30 35

Tota

l Dai

ly D

ose

and

95%

C.I.

(U/d

ay)

Run-in(8 weeks)

Study Phase(6 months + 3 weeks)

- 4 0 15 27

Week

-20%(p<0.001)

Subjects using insulin pump therapy utilized less insulin vs. MDI

Insulin Utilization

Run-in(8 weeks)

Study Phase(6 months + 3 weeks)

Pump

MDI

Reznik Y, et al. Insulin pump treatment compared with multiple daily injections for treatment of type 2 diabetes. Lancet. 2014;384:1265-72.

Insulin pump therapy, multiple daily injections, andcardiovascular mortality in 18 168 people with type 1 diabetes: observational study

Steineck, I, et. al., for the Swedish National Diabetes RegisterBMJ 2015;350:h3234, doi: 10.1136/bmj.h3234

Objective: To investigate the long term effects of insulin pump therapy (CSII) on cardiovascular diseases (CVD) and mortality in people with type 1 diabetes

Design: Observational study from the Swedish National Diabetes Register, 2005-12

Study subjects: 18,168 type 1 diabetes subjects 2441 using CSII 15,727 using multiple daily insulin injections (MDI)

Main outcome measures: Hazard ratios estimated with Cox regression analysis

Follow-up: mean of 6.8 years; 114 135 person years.

Adjusted hazard ratios significantly lower for CSII 0.55 (95% confidence interval 0.36 to 0.83) for fatal

coronary heart disease 0.58 (0.40 to 0.85) for fatal CVD (coronary heart

disease or stroke) 0.73 (0.58 to 0.92) for all cause mortality

Hazard ratios lower, but not significant for CSII fatal or non-fatal coronary heart disease fatal or non-fatal CVD

Kaplan-Meier Survival Curves

0 30 60 90 120 150

200

180

140

100

60

0

Sens

or G

luco

se V

alue

minutes

Low Limit

0 30 60 90 120 150

200

180

140

100

60

0

Sens

or G

luco

se V

alue

minutes

Low Limit

SUSPEND THRESHOLD AND PREDICTIVE SUSPENDINSULIN PUMP AND CGM – FIRST STEPS IN AUTOMATION TO REDUCE AND PREVENT HYPOGLYCEMIA

Garg S, et al. Diabetes Technol Ther. 2012;14:205-209

Bergenstal R, et al. NEJM 2013 369:224-232.

0

100

200

300

6 12 18 24 6 120

4

8

12

0

5

10

15

20

Time (h)

PID

(U/h

)

Insulin (U)

Glu

cose

(mg/

dL)Yale University School of Medicine

6

Auto-pilot

Co-pilot BolusCLINICAL

Meal

Automating Insulin DeliveryComparing Pre-Bolusing for Meals vs. Full Automation

Automated Feedback-Controlled Insulin Delivery in Children with T1 Diabetes

Weinzimer SA, et al. Diabetes Care. 2008;31:934-939.

Auto basal adjusts as SG rises and fallsADJUSTS FOR VARIABILITY

SG rises SG falls

Auto basal increases Auto basal decreases

SG rises

Auto basal increases

SG falls

Auto basal decreases

SG falls

Auto basal decreases

WARNING: Medtronic performed an evaluation of the MiniMed 670G closed loop system and determined that it may not be safe for use in children under the age of 7 because of the way that the system is designed and the daily insulin requirements. Therefore, this device should not be used in anyone under the age of 7 years old. This device should also not be used in patients who require less than a total daily insulin does of 8 units per day because the device requires a minimum of 8 units per day to operate safely.

CHANGE IN A1C DISTRIBUTION FROM RUN-IN TO STUDY ENDNO DKA / NO SEVERE HYPOGLYCEMIA DURING STUDY PHASES WITH OVER 22,000 PATIENT DAYS

Study~62% A1C≤7%

Baseline~38% A1C≤7% A1C Decreased 0.5%

Run-in = 7.4±0.9%Study End = 6.9±0.6%

1. Bergenstal RM, et al. JAMA. 2016;316(13):1407-1408.2. Garg SK, et al. Diabetes Technol Ther. 2017;19(3):155-163.* Data on file with Medtronic, presented at ADA, EASD, ISPAD, 2018

Study~51% A1C≤7.5

Baseline~36% A1C≤7.5 A1C Decreased 0.4%

Run-in = 7.9±0.8%Study End = 7.5±0.6%

Adults and Adolescents (14-75 yrs) 1,2

N= 124 Children (7-13 yrs)*

N= 105

87.2% Auto Mode Use 79.4% Auto Mode UseForlenza G, et al, DIABETES TECHNOLOGY & THERAPEUTICSVolume 21, Number 1, 2019 DOI: 10.1089/dia.2018.0264

MOVING BEYOND A1C: REDUCED GLYCEMIC VARIABILITYPIVOTAL HCL TRIAL

HCL Utilization (% of time)

Adults = 88.0% Adolescents = 75.8% Children = 79.4%

All Patients14-75

AdultsMedian and Interquartile SG Values / Day & Night

Run-in Phase Study Phase

Adolescents

Due to inherent study limitations, caution is advised when attempting to extrapolate these results to new patients. There could be significant differences.

*Data on file1Bergenstal RM, et al. JAMA. 2016;316(13):1407-1408. 2Garg SK, et al. Diabetes Technol Ther. 2017;19(3):155-163.

7 to 13 Year Olds*

Forlenza G, et al, DIABETES TECHNOLOGY & THERAPEUTICSVolume 21, Number 1, 2019 DOI: 10.1089/dia.2018.0264

C

Shah SH, et al. Diab Technol Ther. 2014;16:477-490.

Closed-loop system in the management of diabetes:Past, Present, and Future

Shah SH, et al. Diab Technol Ther. 2014;16:477-490.

Closed-loop system in the Management of diabetes:Past, Present, and Future

Do It Yourself - Improvements in A1C and Time-in-Range in DIY Closed-Loop (OpenAPS) UsersDANA M. LEWIS, RICHARD S. SWAIN andTHOMAS W. DONNER

• The most up-to-date list can be found here: https://loopkit.github.io/loopdocs/setup/requirements/pump/

• The Riley Link is the hardware that communicates between the pump, iPhone, and continuous glucose monitor. It is about the size of a tic-tac box and needs to be carried on you at all times. This seems like a fair trade for the ability to have an automated insulin delivery system.

• An Apple Developer Account is required to install the “Loop” application on your phone. It is not available in the current Apple store.

• As referenced above, a continuous glucose monitor is required. Loop can read CGM data directly from the pump meaning that Medtronic and Dexcom systems are compatible.

• Any specific questions on requirements can be found online: www.loopdocs.com

Diabetes 2018 Jul; 67(Supplement 1): -.https://doi.org/10.2337/db18-352-OR

Effect of Artificial Pancreas Systems on Glycemic Control in Patients with Type 1 Diabetes: A Systematic Review and Meta-Analysis of Outpatient Randomized Controlled TrialsWeisman A, et al,Lancet Diabetes Endocrinol, May 19, 2017 http://dx.doi.org/10/1016/S2213-8587(17)30167-5

• Searched Medline, Embase, and the Cochrane Central Register of Controlled Trials for studies published from 1946, to Jan 1, 2017

• Excluded: non-English, pregnant women, in hospital, and using adjunct medications other than glucagon

• Primary outcome: mean difference % in target range (3·9–10 mmol/L or 3·9–8 mmol/L)

• 984 reports; after exclusions, 27 comparisons from 24 studies (23 crossover and one parallel design); 585 participants (219 in adult studies, 265 in paediatric studies, and 101 in combined studies)

• 5 dual-hormone (insulin and glucagon), 2 dual-hormone and single-hormone (insulin only), and 20 single- hormone

• Time in target -12·59% higher with artificial pancreas (95% CI 9·02–16·16; p<0·0001)

Effect of Artificial Pancreas Systems on Glycemic Control in Patients with Type 1 Diabetes: A Systematic Review and Meta-Analysis of Outpatient Randomized Controlled TrialsWeisman A, et al,Lancet Diabetes Endocrinol, May 19, 2017 http://dx.doi.org/10/1016/S2213-8587(17)30167-5

• Searched Medline, Embase, and the Cochrane Central Register of Controlled Trials for studies published from 1946, to Jan 1, 2017

• Excluded: non-English, pregnant women, in hospital, and using adjunct medications other than glucagon

• Primary outcome: mean difference % in target range (3·9–10 mmol/L or 3·9–8 mmol/L)

• 984 reports; after exclusions, 27 comparisons from 24 studies (23 crossover and one parallel design); 585 participants (219 in adult studies, 265 in paediatric studies, and 101 in combined studies)

• 5 dual-hormone (insulin and glucagon), 2 dual-hormone and single-hormone (insulin only), and 20 single- hormone

• Time in target -12·59% higher with artificial pancreas (95% CI 9·02–16·16; p<0·0001)

Interpretation: Artificial pancreas systems uniformly improved

glucose control in the outpatient settings, despite heterogeneous

clinical and technical factors.

The Next Part of the Journey

• Role in type 2• Single hormone; dual hormone• Improved hardware• Advanced software – AI and machine learning• Additional inputs• Ultra-rapid analogues or hepatic targeted insulin• Other adjunctive therapies• Interoperability• Automatic bolusing – no meal announcement• Hospital based system

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

SMBG1 CGM + MDI/Arrows1,2

CGM + Pump/Arrows3

CGM + Pump/Arrows2

SmartGuardSuspend On

Low4

SmartGuardSuspend Before

Low5

SmartGuardSuspend Before

Low6

SmartGuardAuto Mode(Pivotal)7

SmartGuardAuto Mode

(Real-World)8

BionicPancreas9

Technological effect on Time In Range

TECHNOLOGY EFFECT ON TIME IN RANGE

1 Beck R.W. et al. JAMA 2017;317(4):371-378. 2. Beck R.W. et al. Lancet Diabetes Endocrinol. 2017;5(9):700-708. 3. Bergenstal R.M. et al. N Eng J Med. 2010;363(4):311-320. 4. Bergenstal R.M., et al. N Engl J Med. 2013;369(3):224-232. 5. Abraham M.B. et al. Diabetes Care 2018;41(2):303-310. 6. Scaramuzza A.E. et al. Acta Diabetol. 2017;54(3):317-319. 7. Bergenstal R.M., et al. JAMA. 2016;316(13):1407-1408 . 8. Data on file. 9. El-Khatib F.H. et al. Lancet 2017;389, 369-380.

*Pending CE mark

Conclusions

• Diabetes Outcomes Still Suboptimal• New Metrics to Assess Performance – Beyond A1C• Technology – CGM and Insulin Pumps – Appear to Improve Outcomes

• Evidence to show efficacious and safe

• Penetration of Technology Still Very Low – Particularly in Type 2• The Promise of Insulin Automation

• The beginning of the journey

Insulin for Life – Brings supplies to children and during disasters

Sponsorship Across the GlobeThe IDF Life for a Child Program is currently helping over 12,000 CHILDREN AND YOUTH WITH DIABETES IN 43 COUNTRIES.

Life for a Child – the international program for

children

Life for a Child Program in Ethiopia –Teaching Standards of Care