what's hot in diabetes care 2018 -...

What's Hot in Diabetes Care 2018

Fred Toffel, MD, FACP, FACE

Objectives

• A Taste of Technology

– Updates on Continuous Glucose Monitoring

– Smart Insulin Pumps

– Connected Insulin Pens

• Getting to the Heart of Type 2 Algorithms

– Review of the 2018 AACE Algorithm

– Review of Cardiovascular Outcomes Trials and How these Impact Diabetes Treatment Recommendations

History of Glucose Monitoring

In 1500 BCDiabetes First Described In Writing

Hindu healers wrote that flies and ants were attracted to urine of people with a mysterious disease that caused intense

thirst, enormous urine output, and wasting away of the body

In 1941 Clinitest® effervescent

urine sugar testing tablets

launched by Bayer (formerly

Miles Laboratories)

Methodology: Urinalysis for glucose

Uses alkaline copper

sulphate and Na citrate –

forms colour in re-hydrated

state dependent on glucose

content – ‘non-specific’ test

subject to interference

0% 2%

Red

cuprous oxide

Monitoring Glycaemic Control : Early History

Methodology: Urinalysis for glucose

In 1954 Glucotest /Testape roll

licensed by Eli Lilly to Boehringer

Mannheim)

• In 1964 Combur-Test (BM) for glucose, protein and pH of urine.

• Later range extended to include ketones – Ketostix/Ketodiastix

During 196Os the ‘dipstix’ :

Diastix, Clinistix,

Chemstrip uG available

Monitoring Glycaemic Control : Early History

Early History : Self-Monitoring Blood Glucose (SMBG)

Visual Reading: ‘semi-quantitative’ estimations

Glucose + O2 gluconic acid + H2O2

H2O2 + dye* reduced oxidised dye + H2O

colourless coloured

*chromogen

Glucose oxidase

Peroxidase

In 1964 Earnest C Adams

developed Dextrostix

(Ames – Miles Laboratories)

Patent No 3,092,465

issued 4th April 1963)

Limitations :• Large drop of blood required to cover test zone (30μl)• Reaction time 60 seconds, • Remove blood - wash/blot or wipe, • Read result within 1-2 seconds after washing • Reliance on color matching

1st dry-reagent blood sugar test-

strip using immobilized glucose

oxidase with horse-

radish peroxidase and a

color indicator.

Modifications: covering over

enzymes and dye with water

resistant material on a plastic

support

1968 Haemo-Glukotest

developed (improved

1979). Remains the gold

standard of accuracy for

purely visual blood

glucose determination.

Automated Evaluation : Reflectance Meters (ARM)

(i) photometric (colourimetric) – ‘Desk-Top’

1974 Reflomat1979 Dextrometer

Reflectance

Meters

Bulky

Heavy

Expensive

1st film-based colorimetric test-strip


1983 Reflolux / Accu-chek : from Roche diagnostix

1986 Reflolux II / Accu-chek II

1987 Reflolux II M / Accu-chek II M , memory & PC interface

1990 Reflolux S / Accu-chek III

Automated Evaluation : Glucose meters - digital read-out

(1) photometric test strips –Roche (ex Boehringer M)


1987 One Touch meter : introduced by LifeScan

Automated digital read-out meter using

photometric test strips

Second Generation Meters - requirements1. Recognise blood sample application and

time the reaction,

2. Eliminate need for blood removal step by

separation of plasma from RBCs, or

correct for blood color in colorimetric

devices or use electrochemical reactions

4. Incorporate checks to identify defects and

user error in procedure

One Touch II


Simplify: No timing, wiping, blotting or washing of blood

Today’s Many Choices

The Transformation from Intermittent Self Blood Glucose

Monitoring (SMBG) to Continuous Glucose Monitoring (CGM)

Cygnus Glucowatch

MiniMed Guardian Real-TimeMARD=19.7%

(2006)Dexcom STS

MARD=26.0%(2006)

Abbott NavigatorMARD=13%

(2006)

2006 The Year EVERYTHING Changed

The Second Generation

2008 Medtronic Paradigm522/722 System

MARD 19.7%

2008 Dexcom Seven 2009 Dexcom Seven PlusMARD 15.9%

CGMs 2018

Eversense Implanted CGM

Dexcom G6 CGM→(No Calibration)

Guardian Connect CGM →

Freestyle Libre CGM(No Calibration)

Trials Comparing CGM to SMBG

Study Population Design Key Outcome(s)

GOLDLind M, et al. JAMA

2017; 317(4) :379-387

T1D on MDI

A1C ≥ 7.5%

Randomized

crossover 1:1 to 26

weeks of CGM

before (n=82) or

after (n=79) 26

weeks of usual care

Between-group

difference of 0.43

percentage points in

favor of CGM,

p<0.001 AND less

hypoglycemia

COMISAIRSoupal J, et al.

Diabetes TechTher.

2016 ; 18(9):532-8.

T1D / MDI or CSII

A1C 7.0% to 10%

1 year

Nonrandomized,

controlled: CGM

(n=15 SAP, 12 MDI)

or SMBG (n= 20

using CSII, 18 MDI)

Comparable

reductions in A1C

and hypoglycemia in

CGM/MDI and

CGM/CSII groups

HypoDEHeinemann L, et al.

Lancet 2018; 391:1367-

1377

T1D on MDI

History of

impaired hypo

awareness or

recent severe

hypo

Randomized 1:1 to

CGM (n=75) or

usual care (n=74)

for 26 weeks

Incidence of

hypoglycemic events

fell by 72% for CGM

group, p<0.0001

Studycenter picture on

slidePopulation Design Key Outcome(s)

ImpactBolinder J, et al.

Lancet 2016; 388:

2254–2263,

T1D on MDI or

CSII

A1C <7.5%

Excluded IAH

Randomized 1:1

to Flash (n=119)

or usual care

(n=120) for 24

weeks

Reduced

hypoglycemia. No

between-group

difference in A1C

change, p=0.9556

ReplaceHaak T, et al. Diabetes

Ther. 2017; 8(1): 55–

73.

T2D on MDI or

CSII

A1C 7.5% to

12.0%

Randomized 2:1

to CGM (n=149)

or usual care

(n=75) for 6

months

No between-group

difference in A1C

change,

p=0.82222.

Reduced

hypoglycemia

I HART

CGMReddy M, et al. Diabet

Med. 2018; 35(4): 483–

490

T1D on MDI

Gold score ≥4 or

recent severe

hypo

Randomized 1:1

to CGM (n=20) or

flash glucose

monitoring

(n=20) for 8

weeks

CGM reduces

hypoglycemia

more effectively

than flash glucose

monitoring

CMS Will Cover Home CGM for Patients Insured by Medicare

• Has a diagnosis of diabetesand

• Currently monitors fingerstick glucose four (4) times dailyand

• Injects insulin three (3) times daily

If the patient:

Currently only Dexcom G5 and Freestyle Libre are Medicare approved

Adding the Other DimensionFollow the Arrow

+ Active Insulin

Grams of CHO ÷ (CHO to Insulin Ratio)

+ (Fingerstick Glucose - Target Glucose) ÷

Correction Factor

Freestyle Report

Dexcom Report

Combining CGM with CSII

Bionic Pancreas 1.0

History of BP

• Under Development for more than 50 years

• Described in 1974

– As a “computerized control system …closely simulating the endocrine function of the pancreas.”1

– Intravascular delivery of dextrose and insulin based on instantaneous glucose readings

• Only worked on supervised inpatient conditions

Peyser et al., 2014, Annals of New York Academy of Science, 1311, 102-1231. Albisser et al., 1974, Diabetes, 23, 389-396.

Biostator: The Artificial Pancreas 1977

Clemens, AH. The development of Biostator, a glucose controlled insulin infusion system (GCIIS), Horm Metab Res 1977.

Current Outpatient Conceptual Models

• Closed Loop

– Patient is removed from interaction once the system has been initiated

– Algorithm has total control

• Open Loop (Hybrid Closed Loop)

– Varying Degrees of Interaction

• Meal / Exercise announcement

Hormonal Models

• Uni-Hormonal – Only insulin is used

– Algorithms to reduce risk of hypoglycemia

• Bi-Hormonal – Insulin to lower the blood glucose

– Glucagon to raise the glucose

– Algorithms balance the effects of the two hormones

– Generally doses of glucagon are quite small

Peyser et al., 2014, Annals of New York Academy of Science, 1311, 102-123

Basal Suspend

Medtronic 630G Tandem T:Slim X2 with Basal IQ Software

Currently Available Hybrid Closed Loop PumpMedtronic 670G

670G Data Download

Dual Hormone Bionic Pancreas at Camp

A Camper’s Thoughts

• I was never hypoglycemic• I never felt hypoglycemic• I was never worrying about

hypoglycemia• I was never recovering from

hypoglycemia• That’s already enough of a game

changer for me. But there was more…

• If I started veering low, my bionic pancreas figured it out and gave me the perfect amount of glucagon to make sure that hypoglycemia didn’t occur

• I always felt safe during the week – at no time did I feel threatened or scared

• My glucoses were being watched and stayed perfectly in range overnight, every night. Wow.

• I counted zero carbs• I never “corrected”• I never thought about insulin

sensitivity and how I couldn’t figure that out

• I never thought about insulin to carb ratios

• I never bolused• I was a nicer and kinder person the

entire week with the bionic pancreas

Results:There were 328 patients, caregivers, and care partners who generated 3347 tweets. One overarching theme, OpenAPS changes lives, and five subthemes emerged from the data: (1) OpenAPS use suggests self-reported A1C and glucose variability improvement, (2) OpenAPS improves sense of diabetes burden and quality of life, (3) OpenAPS is perceived as safe, (4) patient/caregiver–provider interaction related to OpenAPS, and (5) technology adaptation for user needs.

Conclusions:As users of a patient-driven technology, OpenAPS users are self-reporting improved A1C, day-to-day glucose levels, and quality of life. Safety features important to individuals with diabetes are perceived to be embedded into OpenAPS technology. Twitter analysis provides insight on a patient population driving an innovative solution to improve their quality of diabetes care.

Twitter Analysis of #OpenAPS DIY Artificial Pancreas Technology Use Suggests Improved A1C and Quality of Life

Article first published online: September 10, 2018 https://doi.org/10.1177/1932296818795705

Background:Patient-driven innovation in diabetes management has resulted in a group of people with type 1 diabetes who choose to build and share knowledge around a do-it-yourself (DIY) open source artificial pancreas systems (OpenAPS). The purpose of this study was to examine Twitter data to understand how patients, caregivers, and care partners perceive OpenAPS, the personal and emotional ramifications of using OpenAPS, and the influence of OpenAPS on daily life.

Methods:Qualitative netnography was used to analyze #OpenAPS on Twitter over a two-year period.

https://doi.org/10.1177/1932296818795705

Not Interested in Wearing a Pump?

Connected Insulin Pens

Pen Based Decision Support Merging Insulin Pen, CGM and Activity data

Can provide dosing decision support, hypoglycemia predictions, proactive alerts, exercise advice

Cloud Based Computing

What Does the Future Hold?

1. Bi-Hormonal systems in a single unit.2. Apps for smart phones to combine CGM, Smart Pen, Smart Watch

exercise data and maybe even to estimate nutrient content from food picture to suggest bolus dose.

3. Implantable systems with glucose sensing and insulin delivery to the portal system.

4. In hospital CGM and automated insulin delivery.5. ????

However we need to remember……

The Dumbest Beta Cells are Still Smarter than Endocrinologists and

their Technologies

Type 2 Diabetes Treatment Algorithm Changes from Results of CVOTs

ADA Glycemic Control Algorithm

Timings represent estimated completion dates as per ClinicalTrials.gov

1. Johansen OE. 20152. White WB et al. 20133. Scirica BM et al. 2013 4. Green JB et al. 20155. Pfeffer MA et al. 2015

6. ORIGIN. 20127. Zinman B et al. 20158. Marso SP et al. 20169. Marso SP et al. 201610. NCT01455896

11. Marso SP et al. 201712. Neal B et al. 201713. NCT0114433814. NCT0189753215. NCT02465515

16. NCT0206579117. Gerstein HC et al. 201718. NCT02692716 19. NCT0247939920. NCT00700856

21. NCT0124342422. NCT0173053423. NCT01394952 24. NCT01986881

Overview of CVOTs of Glucose-lowering Drugs1 (1 of 2)

Copyright © 2017 Eli Lilly and Company

PIONEER 618

(n=~3176)3P-MACE

TOSCA IT20

(n=~3371)4P-MACE

ORIGIN6

(n=12,537)3P-MACE

2013 2015 2016 2017 2018 2019 2020

SAVOR-TIMI 533

(n=16,492)1222 3P-MACE

EXAMINE2

(n=5380)621 3P-MACE

TECOS4

(n=14,671)≥1300 4P-MACE

LEADER8

(n=9341)≥611 3P-MACE

DECLARE-TIMI 5822

(n=~17,150)≥1390 3P-MACE

EMPA-REG OUTCOME7

(n=7028)≥691 3P-MACE

CREDENCE23

(n=~4200)Renal + 5P-MACE

CAROLINA21

(n=~6041)≥631 4P-MACE

FREEDOM CVO10

(n=4156)4P-MACE

VERTIS CV Study24

(n=~8000)3P-MACE

CARMELINA14

(n=~7053)4P-MACE + renal

REWIND16,17

(n=9901)≥1200 3P-MACE

ELIXA5

(n=6068)≥805 4P-MACE

STELLA-LONG TERM19

(n=~11,412)3P-MACE + Tumors

HARMONY Outcomes15

(n=~9400) 3P-MACE

DPP-4i

SGLT-2i

GLP1 RA

Insulin

TZD

Oral GLP1 RA

DEVOTE11

(n=7637)3P-MACE

CANVAS-R12

(n=5812)Albuminuria

CANVAS12

(n=4330)≥420 3P-MACE

EXSCEL13

(n=>14,000)≥1591 3P-MACE

SUSTAIN-69

(n=3297)3P-MACE

Timings represent estimated completion dates as per ClinicalTrials.gov

1. Johansen OE. 20152. White WB et al. 20133. Scirica BM et al. 2013 4. Green JB et al. 20155. Pfeffer MA et al. 2015

6. ORIGIN. 20127. Zinman B et al. 20158. Marso SP et al. 20169. Marso SP et al. 201610. NCT01455896

11. Marso SP et al. 201712. Neal B et al. 201713. NCT0114433814. NCT0189753215. NCT02465515

16. Gerstein HC et al. 201717. NCT01394952 18. NCT02692716 19. NCT0247939920. NCT00700856

21. NCT0124342422. NCT0173053423. NCT0206579124. NCT01986881

Overview of CVOTs of Glucose-lowering Drugs1 (2 of 2)


VERTIS CV Study24

(n=~8000)3P-MACE

REWIND16,17

(n=9901)≥1200 3P-MACE

STELLA-LONG TERM19

(n=~11,412)3P-MACE + Tumors

DPP-4i

SGLT-2i

GLP1 RA

Study not yet completed

Insulin

PIONEER 618

(n=~3176)3P-MACE

TOSCA IT20

(n=~3371)4P-MACE

ORIGIN6

(n=12,537)3P-MACE

2013 2015 2016 2017 2018 2019 2020

SAVOR-TIMI 533

(n=16,492)1222 3P-MACE

EXAMINE2

(n=5380)621 3P-MACE

TECOS4

(n=14,671)≥1300 4P-MACE

LEADER8

(n=9341)≥611 3P-MACE

DECLARE-TIMI 5822

(n=~17,150)≥1390 3P-MACE

EMPA-REG OUTCOME7

(n=7028)≥691 3P-MACE

CREDENCE23

(n=~4200)Renal + 5P-MACE

CAROLINA21

(n=~6041)≥631 4P-MACE

FREEDOM CVO10

(n=4156)4P-MACE

CARMELINA14

(n=~8300)4P-MACE + renal

ELIXA5

(n=6068)≥805 4P-MACE

HARMONY Outcomes15

(n=~9400) 3P-MACE

DEVOTE11

(n=7637)3P-MACE

CANVAS-R12

(n=5812)Albuminuria

CANVAS12

(n=4330)≥420 3P-MACE

SUSTAIN-69

(n=3297)3P-MACE

EXSCEL13

(n=>14,000)≥1591 3P-MACE

aTwo-sided tests for superiority were conducted (statistical significance was indicated if p≤.0498); bIntegrated analysis of CANVAS and CANVAS-R studiescEstimated study completion per clinicaltrials.govClick on the study title to view additional details regarding each study

Study Identifier No. of Patients Study DesignPrimary

EndpointResults

HR (95% CI)

EMPA-REG OUTCOME1

CVD; HbA1c ≥7.0-10.0%7028

EmpagliflozinPlacebo

3P-MACE0.86 (0.74, 0.99)

p=.04a (superiority)

CANVAS2

High risk/history of CV eventHbA1c 7.0-10.5%

4330Canagliflozin 100 mgCanagliflozin 300 mgPlacebo

3P-MACE

0.86 (0.75-0.97)b

p=.02 (superiority)CANVAS-R2

High risk/history of CV eventHbA1c ≥7.0-≤10.5%

5812Canagliflozin (100 or 300 mg)Placebo

Progression of albuminuria

STELLA LONGTERM3

First ipragliflozin use (July 2014-July 2015)

~11,412Ipragliflozin(Observational study)

CV AEs and malignant tumors

2018c

DECLARE-TIMI 584

CVD~17,150

DapagliflozinPlacebo

3P-MACE 2019c

CREDENCE5

CKD; HbA1c 6.5-12.0%~4200

CanagliflozinPlacebo

ESRD, doubling of sCR,or renal/CV death

2019c

VERTIS CV Study6

Vascular diseaseHbA1c 7.0-10.5%

~8000Ertugliflozin 5 mgErtugliflozin 15 mgPlacebo

3P-MACE 2019c

1. Zinman B et al. N Engl J Med 2015;373:2117-282. Neal B et al. N Engl J Med 2017;Ahead of print3. https://clinicaltrials.gov/ct2/show/NCT02479399

4. https://clinicaltrials.gov/ct2/show/NCT017305345. https://clinicaltrials.gov/ct2/show/NCT020657916. https://clinicaltrials.gov/ct2/show/NCT01986881

Cardiovascular Outcome Trials for SGLT-2 Inhibitors


Study Identifier No. of Patients Study DesignPrimary

EndpointResults

HR (95% CI)

ELIXA1

ACS; FPG >7.0 mmol/L6068

LixisenatidePlacebo

4P-MACE1.02 (0.89-1.17)

p=.81 (superiority)

LEADER2

CV risk/CVD; HbA1c ≥7.0%9340

LiraglutidePlacebo

3P-MACE0.87 (0.78-0.97)

p=.01 (superiority)

SUSTAIN 63

CVD; HbA1c ≥7.0%3297

SemaglutidePlacebo

3P-MACE0.74 (0.58-0.95)

p=.02 (superiority)

FREEDOM CVO4

CV risk/vascular diseaseHbA1c 6.5-10%

4156ITCA 650Placebo

4P-MACETrial met its primary

endpointa

EXSCEL5

CV risk/CVDHbA1c 6.5-10.0%

~14,000ExenatidePlacebo

3P-MACETrial met its primary

endpointb

REWIND6

High CV riskHbA1c ≤9.5%

~9622DulaglutidePlacebo

3P-MACE 2018c

HARMONY Outcomes7

CVD, vascular diseaseHbA1c >7.0%

~9400Albiglutide 30 mgAlbiglutide 50 mgPlacebo

3P-MACE 2019c

aThe primary endpoint was a composite of CV death, nonfatal MI, nonfatal stroke, or hospitalization for unstable angina (4P-MACE);bThe primary endpoint was a composite of CV death, nonfatal MI, or nonfatal stroke (3P-MACE); cEstimated study completion per clinicaltrials.gov; Click on the study title to view additional details regarding each study1. Pfeffer MA et al. N Engl J Med 2015;373:2247-22572. Marso SP et al. N Engl J Med 2016;375(4):311-223. Marso SP et al. N Engl J Med 2016;375(19):1834-444. https://clinicaltrials.gov/ct2/show/NCT01455896

5. https://clinicaltrials.gov/ct2/show/NCT011443386. https://clinicaltrials.gov/ct2/show/NCT013949527. https://clinicaltrials.gov/ct2/show/NCT02465515

Cardiovascular Outcome Trials for GLP-1 RA (Parenteral)


ORLANDO — The treatment approach to type 2 diabetes should begin with an assessment of cardiovascular disease (CVD) status, other comorbidities, and patient preferences, according to a draft of the upcoming 2018 joint consensus statement from the American Diabetes Association (ADA) and European Association for the Study of Diabetes (EASD).

The final version of the 2018 update to the current 2015 ADA/EASD Management of Hyperglycemia in Type 2 Diabetes statement (Diabetes Care. 2015;38:140-149) will be presented on October 5, 2018 at the EASD annual meeting in Berlin and will be published in Diabetes Care and Diabetologia.

The statement will aim to help clinicians navigate the increasingly complex options for management of hyperglycemia in type 2 diabetes, with particular emphasis on data published since 2014, including those suggesting cardiovascular benefit for the sodium-glucose cotransport-2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists.

ADA/EASD Consensus Statement

ADA Updated Glycemic Control Algorithm

andEmpower Our Patients to Control Their

Diabetes and Not Let Diabetes Control Them

Thank You!

Questions?

Remember as Physicians we need to……

Combine High Tech with High Touch!

what's hot in diabetes care 2018 -...

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