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Page 1: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Redefining the Treatment Algorithm for Type 2

Diabetes – 2009

Robert J. Rushakoff, MDProfessor of Medicine

University of California, San Francisco

[email protected]

Page 2: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Welcome to Today’s Medical Education Program!

• I am pleased to be here with you on behalf of Merck & Co., Inc. who is sponsoring this medical education program.

• The program you are participating in is not an accredited Continuing Medical Education program.

• The information presented throughout the program will be consistent with FDA guidelines.

Page 3: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

What do you think of when told:

2-2.5 fold increased risk of CHF

2-3 fold increase in risk for initial MI

3 fold increase in risk for pancreatitis

Decreased leukocyte function

Risk for lactic acidosis

Increased risk for renal failure, retinopathy, neuropathy

Type 2 Diabetes

Page 4: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hyperglycemia

β

hepatic glucose output

Insulin secretion

Glucose uptake glucose utilization

Page 5: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hyperglycemia

β α Postprandial glucagonsecretion

Page 6: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hyperglycemia

β α

FFA Lipotoxicity

Page 7: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hyperglycemia

β α

Incretins

Page 8: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hyperglycemia

β α Altered glucose reabsorption

Page 9: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hyperglycemia

β α

Altered Hypothalamic

Appetite Control

Page 10: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hyperglycemia

β α

Ominous Octet

Page 11: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Fundamental Questions Just because a drug may work at

one or more of the sites of defect in Type 2 DM - what about:

Efficacy Side effects Actually improve outcomes or

make them worse Decrease mortality or kill people

Page 12: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Fundamental Questions Is there anything wrong with the

current group of medications? Do the newer medications fix what is

wrong with the older medications? Does it really matter what medication

is used first, second, third? Does it really matter what medication

is used?

Page 13: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

New Drug Truthiness Often no clinically relevant literature

published before medication is released

Studies performed to obtain FDA approval are useful for FDA approval

Clinically useful studies may lag release to market by 5 years, or are never done

Page 14: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Today What are the goals? What differentiates the

medications? Does it really matter what

medication is used? Put it all together – ADA way, AACE

way and of course, MY WAY

Page 15: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Diabetes Care 31:1473–1478, 2008

Relationship Between Plasma Glucose and HgA1c

Page 16: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hemoglobin A1c

50% of level determined in previous month

25% by month before 12.5% by month before 12.5% by month before

Page 17: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Hemoglobin A1c

False High Levels Thalassemia (Hgb F) Lead poisoning Large amount of

ASA High alcohol, Tg

bilirubin levels Hemoglobinopathies

J,K,I,H, Bart’s, Raleigh, Long Island and South Florida

False Low Levels Hemoglobinopathi

es S,D,C,E,G, Lepore and O-Arab.

Hemolytic anemia, bleeding

Large ingestions of Vitamin C and E

Page 18: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

The relationship between baseline A1C group and observed reduction from baseline in A1C

and in FPG

Baseline A1C (%)

n enrolled in clinical trials

Change in A1C (%)

Change in FPG (mmol/l)

6.0–6.9 410 –0.2 –0.5

7.0–7.9 1,620 –0.1 –0.8

8.0–8.9 5,269 –0.6 –1.6

9.0–0.9.9 1,228 –1.0 –2.3

10.0–11.8 266 –1.2 –3.4

Diabetes Care 29:2137-2139, 2006

Page 19: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

The ADVANCE Collaborative Group. N Engl J Med 2008;358:2560-2572

ADVANCE: Relative Effects of Glucose-Control Strategy on All Prespecified Primary and Secondary Outcomes

Page 20: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ACCORD: Hazard Ratios for the Primary Outcome and Death from Any Cause in Prespecified Subgroups

The Action to Control Cardiovascular Risk in Diabetes Study Group. N Engl J Med 2008;358:2545-2559

Page 21: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

VADT - Veterans Administration Diabetes Trial

•1742 Enrollees•97% male•Mean age 60.4

•BMI 31.3•Majority had multiple CV risk factors

•72% HTN•40% macrovascular dx•62% retinopathy•43% neuropathy

Page 22: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

VADT - Veterans Administration Diabetes Trial

Primary Endpoint: NO DIFFERENCE IN CARDIOVASCULAR DISEASE OUTCOMES Standard: 29.3% (predicted –

40%) Intensive: 27.4% (predicted –

31.6%)

Page 23: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

VADT - Veterans Administration Diabetes Trial Baseline Predictor of CVD:

Age and prior CVD event On-trial hypoglycemia – low

glucose and altered consciousness in the three months prior to an event was predictive of CVD outcome

Page 24: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

VADT - Veterans Administration Diabetes Trial When duration of DM factored

in: Intensive glycemic control

showed benefit Benefit declines until about 12-15

years of disease

Page 25: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

UKPDS: 10 year follow-up Glucose Control

Between-group differences in HgA1c gone after 1 year

In the sulfonylurea–insulin group, relative reductions in risk persisted at 10 years for:

any diabetes-related end point (9%, P=0.04) microvascular disease (24%, P=0.001) risk reductions for myocardial infarction (15%, P=0.01) death from any cause (13%, P=0.007)

In the metformin group: any diabetes-related end point (21%, P=0.01) myocardial infarction (33%, P=0.005) and death from any cause (27%, P=0.002).

Published at www.nejm.org September 10, 2008 Published at www.nejm.org September 10, 2008

Page 26: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Effect of Metformin-Containing Antidiabetic Regimens on All-cause Mortality in Veterans With Type 2 Diabetes Mellitus

Decreased Hazard Ratio for all cause mortality for patients on metformin

vs no metformin – 0.77 (p<0.01)

Increased Hazard Ratio for all cause mortality for patients on insulin:

1.62 (p<0.001)

Decreased Hazard Ratio for all cause mortality for patients on metformin and insulin vs insulin

0.62 (p<0.04)

Am J Med Sci 2008; 336:241-247Am J Med Sci 2008; 336:241-247

Page 27: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ADA Targets for Glycemic Control

Preprandial plasma glucose Preprandial plasma glucose 80–130 mg/dl (5-7.2 mmol/l)80–130 mg/dl (5-7.2 mmol/l)

Peak postprandial plasma glucosePeak postprandial plasma glucose <180 mg/dl (<10 mmol/l)<180 mg/dl (<10 mmol/l)

Hemoglobin AHemoglobin A1c1c <7 (%) <7 (%)

Biochemical IndexBiochemical Index GoalGoal

Page 28: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ADA Targets for Glycemic Control

Key concepts in setting glycemic goals: A1C is the primary target for glycemic control.

Goals should be individualized based on: duration of diabetesage/life expectancycomorbid conditionsknown CVD or advanced microvascular complicationshypoglycemia unawarenessindividual patient considerations

More or less stringent glycemic goals may be appropriate for individual patients.

Postprandial glucose may be targeted if A1C goals are not met despite reaching preprandial glucose goals.

Page 29: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ClassGeneric Name(Brand Name)

Mechanism of Action

Dosage Relative

Effective-ness

Major Side Effects / Interactions / Uses

Weight Effects

(average)

Cost

Sulfonylureas

Glyburide(Micronase)

Glipizide(Glucotrol)

glimepiride(Amaryl)

Stimulate insulin release from beta cells of the pancreas

2.5-10 mg bid

5-20 mg bid

0.5-4 mg qd

1

1

1

HypoglycemiaGain 2 lbs $

Page 30: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ClassGeneric Name(Brand Name)

Mechanism of Action

Dosage Relative

Effective-ness

Major Side Effects / Interactions / Uses

Weight Effects

(average)

Cost

Sulfonylureas Stimulate insulin release

1 Hypoglycemia Gain 2 lbs

$

Meglitinides

repaglinide(Prandin)

nateglinide(Starlix)

Stimulate insulin release from beta cells of the pancreas

0.5-2 mg tid(before meals)

60-360 mg tid(before meals)

1

.8

HypoglycemiaUseful in pts on glucocorticoids and in pts with renal failure who often have good FBS and high BS over the course of the dayPrandin is short-acting. Starlix is very short-acting

Gain 1 lb

$$$

Page 31: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ClassGeneric Name(Brand Name)

Mechanism of Action

Dosage Relative

Effective-ness

Major Side Effects / Interactions / Uses

Weight Effects

(average)

Cost

Sulfonylureas Stimulate insulin release

1 Hypoglycemia Gain 2 lbs

$

Meglitinides Stimulate insulin

.8-1 Hypoglycemiashort-acting Gain 1 lb $$$

Biguanidemetformin(Glucophage)

Primarily inhibits hepatic gluconeogen-esis.

500-2000 mg daily with meals

1Diarrhea, nausea, vomitingIncreased risk of lactic acidosis if impaired renal or hepatic function or heavy EtOH use

Loss2-3 lbs $

Page 32: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Metformin and Lactic Acidosis

• “Metformin may provoke lactic Acidosis which is most likely to occur in patients with renal impairment. It should not be used with even mild renal impairment” 1

• Metformin probably not as unsafe as previously thought. – 25% users have relative contraindication 2

– Patient’s with lactic acidosis usually have acute renal failure 3

1. Joint Formulary Committee British National Formulary. 2006:3532. Diabet Med 2001; 18:483-4883. Diabet Med 2007; 24:494-497

Page 33: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Metformin and eGFR

• 186 x (Creat / 88.4)-1.154 x (Age)-0.203 x (0.742 if female) x (1.210 if black)

• Current Guidelines call for discontinuation of Metformin serum creatinine >150 umol/l (1.7 mg/dl).

• Estimated GFR (eGFR) being introduced as possible better measure of renal function than serum creatinine alone

• eGFR of 36 ml/min per 1.73m2 would be somewhat neutral to current use

Page 34: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ClassGeneric Name(Brand Name)

Mechanism of Action

Dosage Relative

Effective-ness

Major Side Effects / Interactions / Uses

Weight Effects

(average)

Cost

Sulfonylureas Stimulate insulin release

1 Hypoglycemia Gain 2 lbs

$

Meglitinides Stimulate insulin

.8-1 Hypoglycemiashort-acting Gain 1 lb $$$

Biguanide inhibits hepatic gluconeogenesis.

1 Diarrhealactic acidosis

Loss2-3 lbs

$

Alpha-glucosidaseInhibitoracarbose(Precose)

Inhibits enzymes needed to break down complex CHO in the small intestine

50 mg with 1st bite of each meal (start at 12.5 mg and titrate up over weeks)

0.7 Gas/ GI upset Loss1-2 lbs

$$$

Page 35: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Treatment of Hypoglycemia in Patients Treated with Acarbose

• In case of hypoglycemia(due to sulfonylurea or insulin treatment)– Glucose (dextrose) must be administered– Sucrose and complex carbohydrates should

not be administered

Page 36: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Bile Acid Sequestrants• Bile acid sequestrants lower LDL cholesterol

• Colesevelam (Welchol) a bile acid sequestrant, lowers glucose levels and AIC levels in T2D patients

Page 37: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

–The bad

–The good

–The very ugly

Thiazolidenediones

Page 38: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

TZDs and Liver Disease

• From troglitazone – contraindicated in patients with liver disease

• Diabetes patients frequently have fatty liver (NASH---Non- Alcoholic Steatorrhoeic Hepatosis) with elevated LFT

• TZDs decrease liver fat and improve NASH• TZDs may be best treatment for NASH and

preventing cirrhosis

Rushakoff RJ: Normalization of abnormal liver function tests in Type 2 diabetic patients after administration of Troglitazone. Diabetes 48 supplement 1999

Page 39: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Current TZD Side Effects

• Weight Gain: 5-12 lbs in 1 year– Blunted with metformin– Worse with insulin

• Edema: 4-30%– Unresponsive to diuretics

• BUT:– Increased Cardiac Index– Increased Stroke volume– Decreased systemic resistance– Decreased Blood Pressure

Page 40: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Thiazolidinediones and Risk of Repeat Target Vessel Revascularization Following Percutaneous

Coronary Intervention

Diabetes Care 30:384-388, 2007

Page 41: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Positive Side to TZDs

• Reduction in glucose• Reduces BP• Reduces albuminuria• Reduces CRP• Possible DM

prevention• Reduces NASH• Reduces LFT

• Reduces IMT• Reduces stent failure• Reduces death after

CHF

• Increases adiponectin• Increases HDL

Page 42: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

The N E W E N G L A N DJ O U R N A L of M E D I C I N E

ESTABLISHED IN 1812 JUNE 14, 2007 VOL. 356 NO. 24

Effect of Rosiglitazone on the Risk of Myocardial InfarctionAnd Death from Cardiovascular Causes

Steven E. Nissen, M.D., and Kathy Wolski, M.P.H.

Rosiglitazone was associated with a significant increase in the risk of myocardial infarction and with an increase in the risk of death…that had borderline significance.

CONCLUSIONS

Page 43: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Meta-analysis of MI and Death risk with rosiglitazone

Nissen SE, Wolski K. N Engl J Med. 2007;356.

n = 15,560 on rosiglitazone; n = 12,283 on comparator drug or placebo

Rosiglitazone group

Control group

Study No. of events/Total no. (%)Odds ratio

(95% CI) P

Myocardial infarction

Small trials combined

DREAM

ADOPT

Overall

44/10,280 (0.43)

15/2635 (0.57)

27/1456 (1.85)

86

22/6105 (0.36)

9/2634 (0.34)

41/2895 (1.44)

72

1.45 (0.88–2.39)

1.65 (0.74–3.68)

1.33 (0.80–2.21)

1.43 (1.03–1.98)

0.15

0.22

0.27

0.03

86/14371 (.60%) 72/11634 (0.62%)Relative Risk = 86/72 = 1.19

Absolute Risk = -.02%

Page 44: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Comparison of RSG to SU or MET MI/CV Death/Stroke

Meta-analysis database (ICT), ADOPT and RECORD

Page 45: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Myocardial Infarction

Overall pooled data (N=26011)

ADOPT (N=4351)

DREAM (N=5269)

Small trials combined(N=16391)

0 1 2 3 4Odds ratio

Uncorrected (Peto)

1.45 (0.88-2.39)

1.43 (1.03-1.98)

0 1 2 3 4Odds ratio

Corrected (MH/CC)

1.16 (0.76-1.78)

1.28 (0.95-1.72)

Rosiglitazone and Cardiovascular Events

Meta-Analytic Subgroups

Rosiglitazone and Cardiovascular Events

Meta-Analytic Subgroups

Page 46: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Center for Drug Evaluation and ResearchCenter for Drug Evaluation and ResearchJoint Meeting of the Endocrinologic and Metabolic Drugs Advisory Committee and the Joint Meeting of the Endocrinologic and Metabolic Drugs Advisory Committee and the Drug Safety and Risk Management Advisory CommitteeDrug Safety and Risk Management Advisory CommitteeJuly 30, 2007July 30, 2007

David Graham, MD MPH

Page 47: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Center for Drug Evaluation and ResearchCenter for Drug Evaluation and ResearchJoint Meeting of the Endocrinologic and Metabolic Drugs Advisory Committee and the Joint Meeting of the Endocrinologic and Metabolic Drugs Advisory Committee and the Drug Safety and Risk Management Advisory CommitteeDrug Safety and Risk Management Advisory CommitteeJuly 30, 2007July 30, 2007

David Graham, MD MPH

Page 48: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

PANIC

Page 49: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Rosiglitazone Associated Fractures in Type 2 Diabetes: An Analysis From

ADOPT

Diabetes Care Publish Ahead of Print, published online on February 5, 2008

Page 50: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Changes in BMD during pioglitazone or placebo treatment in patients with PCOS

J Clin Endocrinol Metab. 2008 Feb 19 [Epub ahead of print]

Page 51: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ClassGeneric Name(Brand Name)

Mechanism of Action

Dosage Relative

Effective-ness

Major Side Effects / Interactions / Uses

Weight Effects

(average)

Cost

Sulfonylureas Stimulate insulin release

1 Hypoglycemia Gain 2 lbs

$

Meglitinides Stimulate insulin

1 Hypoglycemiashort-acting Gain 1 lb $$$

Biguanide inhibits hepatic gluconeogenesis.

1 Diarrhealactic acidosis

Loss2-3 lbs

$

Alpha-glucosidaseInhibitor

Decreased CHO absorption

0.7Gas/ GI upset Loss

1-2 lbs$$$

Thiazolidine-diones

rosiglitazone(Avandia)

pioglitazone(Actos)

Insulin sensitizers—Activate receptor molecules inside cell nuclei to decrease insulin resistance

4-8 mg daily

15-45 mg daily

1

1

Weight gain, edema which is resistant to diuretic therapy, CHF.

Associated with bone loss and fractures.

Gain 12 lbs $$

$

Page 52: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Comparison of metabolic effects of pioglitazone, metformin, and glimepiride over 1 year in Japanese patients with newly diagnosed Type 2 diabetes

114 drug naïve patients Initial HgA1c Duration DM about 3 years Initial Hga1c 10% Body mass index 25

Diabetes Medicine 2005; 22:980-985

Page 53: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Time course of reduction in glycated haemoglobin (HbA1c) in patients receiving pioglitazone (O), metformin (●), or glimepiride (). Data are mean ±sd. *P < 0.05; **P < 0.01; ***P < 0.005 vs. baseline.

Diabetes Medicine 2005; 22:980-985

Page 54: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

-80-60

-40-20

0

2040

0 7 14 21 28Weeks

Me

an

Ch

an

ge

(m

g/d

l)

Continuedglyburide(n=209)

Switched tometformin(n=210)

Metformin +glyburide(n+213)

Fasting Plasma glucose: Mean Change From Baseline

Diabetes 452:146, 1993

Page 55: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Generic Oral Hypoglycemic Slide

HgA1c

Time

Change from Drug A to B, C, or D

Add Drug A to B, or B to A

Add Drug C

Add Drug D

Page 56: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

3.94

-5.29

7.32

10.84

-6

-4

-2

0

2

4

6

8

10

12

Change in Weight

SulfonylureaMetforminInsulinTZD

Weight Changes Associated with Anti-Hyperglycemic Therapies for Type 2

Diabetes

ADA Scientific Meeting 2005 ABS 13-or

Page 57: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Postprandial Glucose Control

Page 58: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Shin J et al. Abstract 424-P. ADA; 2004: New Orleans, La.

Postprandial Glucose Excursions in Subjects With or Without Diabetes

350

300

250

200

150

100

50

0–1 0 1 2 3 4 5 6 7 8

Time (hours)

Se

rum

Glu

cos

e V

alu

e (

mg

/mL

) Without diabetesType 1 diabetesType 2 diabetes

Meal Event

Subjects

Page 59: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Relative Contribution of FPG and PPG to Overall Hyperglycemia Depending on A1C Quintiles

n=58 n=58 n=58 n=58n=58

Monnier L et al. Diabetes Care. 2003;26:881–885.

0

20

40

60

80

100

<7.3 7.3–8.4 8.5–9.2 9.3–10.2 >10.2

Postprandial glucose Fasting glucose

A1C

Co

ntr

ibu

tio

n,

%

Page 60: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

INCRETINS

• Gut factors that promote insulin secretion in response to nutrients

•Major incretins: GLP-1, CCK, GIP

Page 61: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Oral Glucose Promotes More Insulin Release than IV Glucose - Indicating a

Role for Incretins

Page 62: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Non-Diabetic Diabetic

Plasma Insulin Responses to Oral and Intravenous Glucose

J Clin Invest 1967; 46:1954-1962

OralIntravenous

OralIntravenous

60

Insu

lin

(U

/mL

)

30

0

0 60 120 18030 90 150 0 60 120 18030 90 150

90

Insu

lin

(U

/mL

)

60

30

0

90

MinutesMinutes

Page 63: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Glucose-Dependent Effects of GLP-1 on Insulin and Glucagon Levels in Patients With Type 2 Diabetes

Glucose

Glucagon When glucose levels approach normal values, glucagon levels rebound.

When glucose levels approach normal values,insulin levels decreases.

*P <0.05Patients with type 2 diabetes (N=10)

mm

ol/

L

15.012.510.0

7.55.0

25020015010050

mg

/dL*

* * * * * *p

mo

l/L 250

200150100

50

40

30

20

10

0

mU

/L

* ** ** * * *

Infusion

Minutes

pm

ol/

L 20

15

10

5

0 60 120 180 240

* * * *

pm

ol/L

20

15

10

5

Placebo

GLP-1

Insulin

2.50

0

0 0

0

Adapted with permission from Nauck MA et al. Diabetologia. 1993;36:741–744. Copyright © 1993 Springer-Verlag.

–30

Page 64: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

GLP-1 and GIP Are Degraded by the DPP-4 Enzyme

Meal

Intestinal GIP and GLP-1 release

GIP and GLP-1 Actions

DPP-4 (Dipeptidyl Peptidase IV)Enzyme

GIP-(1–42)GLP-1(7–36)

Intact

GIP-(3–42)GLP-1(9–36)Metabolites

Rapid Inactivation

Half-life*GLP-1 ~ 2 minutes

GIP ~ 5 minutes

Deacon CF et al. Diabetes. 1995;44:1126–1131.*Meier JJ et al. Diabetes. 2004;53:654–662.

Page 65: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Incretin Drugs GLP Agonists

Exenatide Liraglutide CJC-1131 AVE-0010 Albugon Glp-1-transferin Exenatide Lar

DPP IV Inhibitors Vildagliptin Sitagliptin Saxagliptin PSN-931 Takeda-Syrrx

Page 66: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu
Page 67: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

N

ONH2

NN

CF3

F

F

F

N

Sitagliptin

Page 68: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Improvements in HbA1C With Initial Co-administration of Sitagliptin and Metformin

Placebo

Sita 100 mg QD

Sita 50 mg BID + Met 500 mg BID

Sita 50 mg BID + Met 1000 mg BID

Met 1000 mg BID

Met 500 mg BID

* Placebo-subtracted LS mean change form baseline at Week 24. Sita=sitagliptin; Met=metformin.

-2.5

-2.0

-1.5

-1.0

-0.5

Hb

A1C

(%

)* -0.8-1.0

-1.3

-1.6

-2.1

Mean Baseline HbA1C = 8.8%N=1091

Aschner P, et al. Oral presentation at the EASD 42nd Annual Meeting; 14-17 September 2006; Copenhagen.

Page 69: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Proportion of Patients Achieving HbA1C Goals

0

10

20

30

40

50

60

70

Placebo

Sita 100 mg QD

Sita 50 mg BID + Met 500 mg BID

Sita 50 mg BID + Met 1000 mg BID

Met 1000 mg BID

Met 500 mg BID

HbA1C <7.0%HbA1C <6.5%

To

Go

al (

%)

Sita=sitagliptin; Met=metformin.Aschner P, et al. Oral presentation at the EASD 42nd Annual Meeting; 14-17 September 2006; Copenhagen.

Page 70: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

79

bid=twice a day; LSM=least-squares mean; qd=once a day. aResults include only randomized patients who agreed to enter the extension study, had not received glycemic rescue therapy through week 54, took at least 1 dose of study medication after week 54, and had at least 1 post-54-week A1C measurement.bValues represented are rounded, actual values 1.15 for Sitagliptin 100 mg qd and 1.06 for Metformin 500 mg bid.Data available on request from Merck & Co., Inc. Please specify 20852883(1)-JAN.

LS

M A

1C C

ha

ng

e F

rom

Bas

elin

e,

%

–2.0

–1.5

–1.0

–0.5

0.0

–1.1 –1.1

–1.3

Initial Combination Therapy With Sitagliptin Plus Metformin Study: A1C Results at 104 Weeks

(Extension Study)a

Sitagliptin 100 mg qd

Metformin 1,000 mg bid

Metformin 500 mg bid

Sitagliptin 50 mg bid + metformin 1,000 mg bid

Sitagliptin 50 mg bid + metformin 500 mg bid

n=50 n=64 n=87

104-week resultsMean baseline A1C = 8.5%–8.7%

–1.4

–1.7

n=96 n=105

b b

Page 71: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

DPP-4 Study Summary

Sitagliptin vs glipizide added to metformin

52 weeks, 100 mg/d vs 20 mg/d

Baseline HgA1c 7.5

Both 0.67% reduction in HgA1c

Both about 60% reached HgA1c <7

Hypoglycemia –

• glipizide: 32%

• sitagliptin: 4.9%

Page 72: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ClassGeneric Name(Brand Name)

Mechanism of Action

Dosage Relative

Effective-ness

Major Side Effects / Interactions / Uses

Weight Effects

(average)

Cost

Sulfonylureas Stimulate insulin release

1 Hypoglycemia Gain 2 lbs

$

Meglitinides Stimulate insulin

1 Hypoglycemiashort-acting Gain 1 lb $$$

Biguanide inhibits hepatic gluconeogenesis.

1 Diarrhealactic acidosis

Loss2-3 lbs

$

Alpha-glucosidaseInhibitor

Decreased CHO absorption

0.7Gas/ GI upset Loss

1-2 lbs$$$

Thiazolidinediones

Insulin 1 Weight gain, edema, fractures Gain 12 lbs

$$$

Incretins

exenatide(Byetta)

sitagliptin(Januvia)

Mimics GLP-1 (gut hormone which affects insulin, glucagon, gastric emptying and satiety)DPP-4 inhibitor (enzyme that breaks down GLP-1)

5-10 mcg bid SQ

100, 50, or 25 mg daily (dose by Cr Cl)

1

1

Nausea, Vomiting, constipation, pancreatitis (?)Weight loss achieved through appetite suppression

Side effects are rare. Occ GI side effects. Pancreatitis (?), vasculitis (?)

Loss 8 lbs

Neutral

$$$

$$$

Page 73: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Conventional Therapies Do Not Influence -Cell Failure: UKPDS

UKPDS 34. Lancet 1998; 352: 854-865UKPDS 16: Diabetes 1995; 44: 1249-1258

cohort, median values

06

7

8

9

10

-1 0 2 4 6 8 10Years from randomization

ChlorpropamideConventionalGlibenclamideInsulin

Metformin

Hb

A1

c(%

)

0

20

40

60

80

100

0 1 2 3 4 5 6 7

ß c

ell

fu

ncti

on

(%

)0

20

40

60

80

100

0 1 2 3 4 5 6 7

ß c

ell

fu

ncti

on

(%

)

Years from randomizationConventional Sulphonylurea Metformin

OverweightNon-OverweightOverweight

Page 74: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

DIGAMI2 (European Heart J. Prepublication Feb

2005)

Group 1 – IV insulin then long term SQ insulinGroup 2 – IV insulin then standard treatmentGroup 3 – Standard treatment

Mortality

Page 75: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Effect of different updated glucose lowering treatments on mortality and morbidity

Mellbin, L. G. et al. Eur Heart J 2008 29:166-176

Page 76: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

ClassGeneric Name(Brand Name)

Mechanism of Action

Dosage Relative

Effective-ness

Major Side Effects / Interactions / Uses

Weight Effects

(average)

Cost

Sulfonylureas Stimulate insulin release

1 Hypoglycemia Gain 2 lbs

$

Meglitinides Stimulate insulin

.8-1 Hypoglycemiashort-acting Gain 1 lb $$$

Biguanide inhibits hepatic gluconeogenesis.

1 Diarrhealactic acidosis

Loss2-3 lbs

$

Alpha-glucosidaseInhibitor

Decreased CHO absorption

0.7Gas/ GI upset Loss

1-2 lbs$$$

Thiazolidinediones

Insulin 1 Weight gain, edema, fractures Gain 12 lbs

$$$

Incretins exenatide

sitagliptin

Increase insulin, decrease glucagon

11

NauseaWeight loss

Side effects are rare.

Loss 8 lbs

Neutral

$$$

Insulin Titrated to need

1+ Hypoglycemia Gain 8 lbs

$$

Page 77: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Drug Cost Comparison

Drug and Dose Cost/month

Glucose Strips (2 per day) $60

Sulfonylurea Generic $4-14Brand $50

Rapaglinide 2 mg tid $175Acarbose 100 mg tid $88Metformin 1000 bid Generic $ 4-32

Brand $132Rosiglitazone 8 mg qd $223Pioglitazone 45 mg/d $222Sitagliptin $181Exenatide 5mcg $230

10mcg $255Colesevelam 3750 mg/d $212Glargine, 45 U/d $150

24 hour fitness center $44YMCA $60

Page 78: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

No Meds

1 Drug

2 Drugs

3 Drugs

Insulin

added

7

9

10

8

HgA1c

Page 79: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Diabetes Care. Published online Oct 22, 2008

2009 ADA Type 2 Consensus Statement Diabetes Treatment Algorithm

An American Diabetes Association consensus statement represents the authors’ collective analysis, evaluation, and opinion at the time of publication and does not represent official association opinion.

Page 80: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Road Maps to Achieve Glycemic Control

In Type 2 Diabetes Mellitus

ACE/AACE Diabetes Road Map Task Force

ChairpersonsPaul S. Jellinger, MD, MACE, Co-Chair

Jaime A. Davidson, MD, FACE, Co-Chair

Task Force MembersLawrence Blonde, MD, FACP, FACE

Daniel Einhorn, MD, FACP, FACE

George Grunberger, MD, FACP, FACE

Yehuda Handelsman, MD, FACP, FACE

Richard Hellman, MD, FACP, FACE

Harold Lebovitz, MD, FACE

Philip Levy, MD, FACE

Victor L. Roberts, MD, MBA, FACP, FACE

© 2007 AACE. All rights reserved. No portion of the Roadmap may be altered, reproducedor distributed in any form without the express permission of AACE.

Page 81: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Road Map to Achieve Glycemic Goals: Naïve to Therapy (Type 2)

Initial

A1C%

Achieve ACEGlycemic Goals†

( FPG, PPG, and A1C ) Intervention

ContinuousTitration of Rx( 2 - 3 months )

If ≤ 6.5% A1C GoalNot Achieved

Assess FPG

and PPG

Initial Therapy

Monitor / adjust Rx to

maximal effective dose to meet ACE

Glycemic Goals

Intensify Lifestyle Modification

Intensify or combine Rx

including incretin mimetic*1

Target: PPG

and FPG

Monitor / adjust Rx to

maximal effective dose to meet ACE

Glycemic Goals

Combine Therapies 6,7Intensify Lifestyle

Modification

Intensify or combine Rx, including incretin mimetic

with SU, TZD, and/or metformin

6 - 7

7 - 8

Lifestyle

Mo

dificatio

n

Lifestyle

Mo

dificatio

n

If ≤ 6.5% A1C GoalNot Achieved

Alternatives• Glinides• SU (low dose)• Prandial insulin5,8

Preferred:• Metformin4

• TZD10,11

• AGI• DPP-4 Inhibitor

Alternatives• Prandial insulin5,8

• Premixed insulinpreparations8

• Basal insulinanalog9

• Metformin• Glinides• AGI• TZD• SU• DPP-4 Inhibitor

© 2007 AACE. All rights reserved. No portion of the Roadmap may be altered, reproduced or distributed in any form without the express permission of AACE.

ACE/AACE Diabetes Road Map Task Force

Paul S. Jellinger, MD, MACE, Co-ChairJaime A. Davidson, MD, FACE, Co-ChairLawrence Blonde, MD, FACP, FACEDaniel Einhorn, MD, FACP, FACE George Grunberger, MD, FACP, FACEYehuda Handelsman, MD, FACP, FACERichard Hellman, MD, FACP, FACEHarold Lebovitz, MD, FACEPhilip Levy, MD, FACEVictor L. Roberts, MD, MBA, FACP, FACE

Endocr Pract. 2007;13:260-268

†ACE Glycemic Goals ≤ 6.5% A1C< 110 mg/dL FPG < 110 mg/dL Preprandial< 140 mg/dL 2-hr PPG

Access Roadmap at: www.aace.com/pub

* Available as exenatide 1 Indicated for patients not at goal despite SU and/or

metformin or TZD therapy; incretin mimetic is notindicated for insulin-using patients

4 Preferred first agent in most patients 5 Rapid-acting insulin analog (available as lispro, aspart and

glulisine), inhaled insulin, or regular insulin 6 Appropriate for most patients 7 2 or more agents may be required 8 Analog preparations preferred 9 Available as glargine and detemir10 A recent report (NEJM; 6/14/07) suggests a possible link of rosiglitazone to cardiovascular events that requires further evaluation.11 Cannot be used in NYHA CHF Class 3 or 4

Page 82: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

Start onsulfonylurea or insulin

TYPE 2 DIABETES

SYMPTOMATICAnd very highNO YES

Referral for:•Diet•HGM•Exercise•Foot Care

Goal Met

Start Metformin

Referral for:•Diet•HGM•Sick Day Rules•Exercise (+/- EST)•Foot Care

NO

Continue Current TreatmentAdd

Medication

YESConsidertransitionto metformin

Page 83: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu

TYPE 2 DIABETES

Metformin

OBESE THIN

Exenatide Sulfonylurea

Add Sulfonylurea(consider TZD)

•Start insulin – use pens•Add detemir, glargine or PM NPH (isolated fasting hyperglycemia or insurance)

•? of which existing meds to continue, generally all •Change to bid premixed insulin

•? of which existing meds to continue, generally just metformin•Change to basal and with premeal insulin

•? of which existing meds to continue, generally just metformin

Goal Not Met

Sitagliptin

Thin or no injection

Sitagliptin(consider TZD)

Goal Not Met

Add Sulfonylurea(consider TZD)

Goal Not Met

Goal Not Met

Page 84: Redefining the Treatment Algorithm for Type 2 Diabetes – 2009 Robert J. Rushakoff, MD Professor of Medicine University of California, San Francisco robert.rushakoff@ucsf.edu