Copyright. Jenny Chun Ye Wong

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Copyright by

Jenny Chun Ye Wong 2016

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The Thesis Committee for Jenny Chun Ye Wong

Certifies that this is the approved version of the following thesis:

Evaluation of Glycemic Control and Medication Utilization Patterns in Patients with Type 2 Diabetes Mellitus on Sodium Glucose Cotransporter 2 Inhibitors

Compared to Dipeptidyl Peptidase IV Inhibitors

APPROVED BY

SUPERVISING COMMITTEE:

Co-Supervisor: Karen L. Rascati

Co-Supervisor: James P. Wilson

Paul J. Godley

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Evaluation of Glycemic Control and Medication Utilization Patterns in Patients with Type 2 Diabetes Mellitus on Sodium Glucose Cotransporter 2 Inhibitors

by

By Jenny Chun Ye Wong, B.S., PharmD

Thesis

Presented to the Faculty of the Graduate School of The University of Texas at Austin

in Partial Fulfillment of the Requirements

for the Degree of

Master of Science in Pharmaceutical Sciences

The University of Texas at Austin

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Dedication

I dedicate this thesis to, My Husband

My Parents My Brother

For their unconditional love, patience and support, For supporting me in my pursuit of academic achievement

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Acknowledgements

I would like to thank Dr. Paul Godley, Dr. Jim Wilson, Dr. Karen Rascati and Dr. Delaney Ivy for their guidance and support throughout my fellowship and for serving on my thesis committee.

I would also like to thank Dr. Laurel Copeland and Dr. I-Chia Liao for their efforts and assistance in providing laboratory data for analysis and statistical expertise.

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Abstract

Evaluation of Glycemic Control and Medication Utilization Patterns in Patients

with Type 2 Diabetes Mellitus on Sodium Glucose Cotransporter 2 Inhibitors

Jenny Chun Ye Wong, MSPS The University of Texas at Austin, 2016 Supervisors: James P. Wilson and Karen L. Rascati

Objectives: To compare patient demographics, glycemic control and medication

utilization patterns in type-2 diabetes patients initiating Sodium Glucose Cotransporter 2 Inhibitors (SGLT2-I) or Dipeptidyl Peptidase IV Inhibitors (DPP4-I).

Methods: Retrospective analysis of medical and pharmacy claims from the Scott and

White Health Plan. Patient > 18 and < 62 years old with a diagnosis of type 2 DM ([ICD-9-CM] of 250.X0 or 250.X2) with a six-month continuous enrollment criterion during the study period were included. Patients on either a SGLT2-I or DPP4-I and a glucagon like peptide-1 agents were excluded. Demographic, clinical, and economic characteristics were assessed at baseline. Adherence was measured using proportion of days covered greater than 80% and persistence was defined as number of days until treatment discontinuation with a gap in therapy of 45 days. Mann-Whitney U or

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chi-square tests were used to compare cohorts. Multivariable linear regression models assessed baseline factors associated with change in A1C.

Results: Analysis included a total of 99 SGLT2-I and 201 DPP4-I patients. The average

age (50.9 vs 52.2, p=0.1559), and Charlson comorbidity index (2.25 vs 2.11, p=0.1359) were comparable. The SGLT2-I cohort had a lower proportion of patients receiving sulfonylureas (45% vs. 65%, p=0.0011), a higher proportion receiving insulin (62% vs. 31%, p <0.001) and a higher baseline A1C (8.9 vs. 8.3, p=0.0324). The SGLT2-I cohort was found to be less persistent than the DPP4-I cohort with a mean time to treatment discontinuation of 181.5 days compared to 210.1 days (p = 0.0497). Though the change in A1C was not statistically significant, clinically the SGLT2-I had a larger decrease in A1C of -0.7 compared to -0.5 in the DPP4-I cohort.

Conclusion: In our sample, there was no statistical difference in the improvement of

glycemic control (A1C) between a SGLT2-I or a DPP4-I. Medication utilization patterns also did not differ between either cohorts, except that patients in the SGLT2-I cohort were found to be less persistent than the DPP4-I cohort. Limitations of the study include a small sample size and a limited study period of one year.

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List of Tables

Table 1. Criteria for diagnosis of DM ... 2

Table 2. Risk factors for Type 2 DM ... 4

Table 3. Glycemic goals of therapy by ADA and AACE ... 5

Table 4. ADA and AACE guidelines on therapeutic Lifestyle Changes ... 6

Table 5. Available insulin preparations ... 7

Table 6. Agents for the treatment of type 2 DM (excluding Insulin, see Table 5) ... 8

Table 7. Effects of Diabetes Drug Action ... 10

Table 8. FDA approval dates of DPP4-I and SGLT2-I ... 14

Table 9. ICD-9-CM Codes Used for Exclusion Criteria ... 21

Table 10. Study Hypotheses ... 21

Table 11. Variables analyzed in the study ... 22

Table 12. Baseline Demographics, Comorbidities, and Laboratory Values ... 27

Table 13. Change in patients still using insulin six months during, before or after the initiation of index drug ... 30

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List of Figures

Figure 1. ADA/EASD 2016 Anti-Hyperglycemic Therapy in Type 2 DM ... 11

Figure 2. AACE Glycemic Control Algorithm 2013 ... 12

Figure 3. AACE Glycemic Control Algorithm 2016 ... 15

Figure 4. Flow-chart of patient identification ... 25

Figure 5. Linear Regression Results (N = 1684) ... 31

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CHAPTER 1: INTRODUCTION

Epidemiology of Diabetes Mellitus

Diabetes Mellitus (DM) is a metabolic disorder characterized by persistent

hyperglycemia caused by the body’s resistance to use of or lack of secretion of insulin, or both. It is also associated with abnormalities in the metabolism of carbohydrate, fat and proteins. The Centers for Disease Control and Prevention (CDC) estimates that 9.3% (29.1 million) of the United States (US) population in 2012 have DM, with an additional 1.4 million US adults newly diagnosed every year. Eighty-six million US adults (aged 20 or older) have prediabetes, defined as hyperglycemia above normal but below diabetic diagnostic levels (hemoglobin A1C 5.7-<6.4) with only 11% that have been informed of the diagnosis.1,2

An economic report by the American Diabetes Association (ADA) in 2012 estimates the total economic burden of diagnosed DM at $245 billion, including $176 billion in direct medical costs and $69 billion in indirect costs (disability, work loss, premature mortality, absenteeism). Anti-diabetic agents and supplies contributed to 12.2% of total costs. Adults with diagnosed DM are also likely to incur 2.3 times more healthcare costs than those without DM.2,3

Pathophysiology of Diabetes Mellitus

The majority of DM cases are classified into two types: 1) Type 1 DM, defined by an absolute deficiency in the production of insulin, and 2) Type 2 DM, defined by insulin resistance

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Diagnosis of Diabetes Mellitus

DM is typically diagnosed based on glycemic cut points. Both the ADA and the American Association of Clinical Endocrinologist (AACE) have published criteria for the diagnosis of DM (shown in Table 1).

Table 1. Criteria for diagnosis of DM6,9,12

ADA AACE

1. A1C ≥6.5 percent. The test should be performed in a laboratory using a method that is NGSP certified and standardized to the DCCT assay.*

1. FPG concentration (after 8 or more hours of no caloric intake) ≥126 mg/dL

2. FPG ≥126 mg/dL (7.0 mmol/L). Fasting is defined as no caloric intake for at least eight hours.*

2. Plasma glucose concentration ≥200 mg/dL 2 hours after ingesting a 75-g oral glucose load in the morning after an overnight fast of at least 8 hours

3. Two-hour plasma glucose ≥200 mg/dL (11.1 mmol/L) during an OGTT. The test should be performed as described by the World Health Organization, using a glucose load containing the equivalent of 75-gram anhydrous glucose dissolved in water.*

3. Symptoms of hyperglycemia (e.g., polyuria, polydipsia, polyphagia) and a random

(casual, nonfasting) plasma glucose concentration ≥200 mg/dL

4. In a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, a random plasma glucose ≥200 mg/dL (11.1 mmol/L).

4. A1C level ≥6.5% (secondary)**

A1C: glycated hemoglobin; DCCT: diabetes control and complications trial; FPG: fasting plasma glucose; NGSP: national glycohemoglobin standardization program; OGTT: oral glucose tolerance test. * In the absence of unequivocal hyperglycemia, criteria 1 to 3 should be confirmed by repeat testing.; ** Glucose criteria are preferred for the diagnosis of DM. In all cases, the diagnosis should be confirmed on a separate day by repeating glucose or A1C testing. When A1C is used for diagnosis, follow-up glucose testing should be done when possible to help manage DM

Recommendations are either the plasma glucose or A1C measure be repeated on different days to confirm diagnosis. However, a glucose level ≥ 200 mg/dL while in the presence of DM

symptoms does not need to be confirmed. An International Expert Committee has also recommended that an A1C level ≥6.5% as a criterion for diagnosis of DM.12

Type 1 Diabetes Mellitus

Type 1 DM develops due to the lack of insulin caused by an autoimmune destruction of beta cells in the pancreas. It often manifests in children and adolescents but can occur at any age. Classic symptoms include polyuria, polyphagia, weight loss and fatigue, followed by hyperglycemia. About 20-40% will present with diabetic ketoacidosis (DKA). Screening is not recommended due to the acute onset of symptoms. 4-6,9-12

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Patients diagnosed with type 2 DM do not normally present with symptoms, though lethargy, polyuria, nocturia and polydipsia can often be found. They are usually characterized by defects in insulin secretion, insulin resistance involving muscle, liver and the adipocyte, excess glucagon secretion, and deficiency in the glucagon-like peptide 1 (GLP-1). Glucagon and insulin are closely linked in that when one increases, the other decreases to keep plasma glucose level within normal limits. The result of hyperinsulinemia suppresses the production of glucose in the liver, enable more glucose uptake in the peripheral tissues and suppress glucagon release.

4,5,6,11,12

Impaired insulin secretion thus results in excess glucose in the blood. Weight gain often leads to insulin resistance as well, hence most patients with type 2 DM are found to be

overweight or obese. The CDC estimated that in 2012, 85% of patients with type 2 DM were overweight or obese. Both the ADA and the AACE recommend screening for any individuals in the presence of risk factors for DM (shown in Table 2).1,2

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Table 2. Risk factors for Type 2 DM 6, 9,12

ADA AACE

 Overweight or obese  Physical inactivity

 First degree relative with diabetes or high-risk ethnicity/race

 Women who delivered a baby > 9 lbs (>4kg)  History of GDM

 Hypertension  High triglycerides  Low HDL-C

 Polycystic ovary syndrome (PCOS)  Previous diagnosis of prediabetes  Acanthosis nigricans

 History of cardiovascular disease

 Age ≥45 years without other risk factors  CVD or family history of Type 2 DM  Overweight or obese*

 Sedentary lifestyle

 Member of an at-risk racial or ethnic group: Asian, African American, Hispanic, Native American (Alaska

Natives and American Indians), or Pacific Islander  HDL-C <35 mg/dL (0.90 mmol/L) and/or a triglyceride

level >250 mg/dL (2.82 mmol/L)  IGT, IFG, and/or metabolic syndrome  PCOS, acanthosis nigricans, NAFLD

 Hypertension (BP >140/90 mm Hg or on therapy for hypertension)

 History of gestational diabetes or delivery of a baby weighing more than 4 kg (9 lb)

 Antipsychotic therapy for schizophrenia and/or severe bipolar disease

 Chronic glucocorticoid exposure

 Sleep disorders in the presence of glucose intolerance (A1C >5.7%, IGT, or IFG on previous testing),including OSA, chronic sleep deprivation, and night-shift occupation BP = blood pressure; CVD = cardiovascular disease; HDL-C = high-density lipoprotein cholesterol; IFG = impaired fasting glucose; IGT = impaired glucose tolerance; NAFLD = nonalcoholic fatty liver disease; OSA = obstructive sleep apnea; PCOS = polycystic ovary syndrome.*Testing should be considered in all adults who are obese (BMI ≥30 kg/m2), and those who are overweight (BMI 25 to <30 kg/m2) and have additional risk factors. At-risk BMI may be lower in some ethnic groups, in whom parameters such as waist circumference and other factors may be used

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Diabetes Mellitus Treatment Guidelines

Hemoglobin A1C goals

Reducing the risk of microvascular and macrovascular complications are the primary goals in managing DM. Measurements of hemoglobin A1C remain the gold standard for long-term glycemic control. Depending on the patient history and health status, the A1C may vary. Less stringent A1C goals may be appropriate for patients with a history of hypoglycemia, severe microvascular or macrovascular complications, the elderly or younger children. 4-6,9-12

Published guidelines from both the ADA and the AACE have varied A1C goals (see Table 3), however both organizations agree that having a controlled A1C of less than 7% can lead to reduced complications if implemented soon after a DM diagnosis. These guidelines are supported by studies have shown that glycemic control is vital to reducing such risk in both Type 1 and Type 2 DM patients. One study showed that patients with type 1 and type 2 DM patients and a mean A1C of 10% or more would incur twice the adjusted rate of diabetes-related

hospitalization costs compared to those with an A1C of less than 7% ($6,759 vs $2,792).7 Shetty et al reported that patients with type 2 DM who maintained an A1C of less 7% incurred lower diabetes-related costs compared to those above 7% ($1,171 vs. $1,540, p<0.001).8

Table 3. Glycemic goals of therapy by ADA and AACE 6,9,12

ADA AACE

Hemoglobin A1C < 7% < 6.5%

Preprandial plasma glucose 70-130 mg/dL < 110 mg/dL

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Approaches to Glycemic Treatment

Nonpharmacologic therapy

Therapeutic lifestyle changes have been shown to reduce the risk of or delay the onset of Type 2 DM. Randomized controlled trials have shown that after 3 years, programs involving lifestyle change and a hypoglycemic medication program may reduce the risk of DM by up to 58%. Follow-up studies such as the U.S. Diabetes Prevention Program Outcomes Study have shown sustained risk reduction of 34% at 10 years.6,10

Therapeutic lifestyle changes typically involve healthy eating habits, regular physical activity, limited alcohol consumption and stress reduction. Evidence has shown that obesity management can help delay the progression of pre-diabetes to type 2 DM, thus weight reduction is recommended for overweight and obese patients with pre-diabetes. Improved glycemic control coupled with sustained weight loss can also help reduce the need for pharmacologic therapy. Guidelines on therapeutic lifestyle changes from both the ADA and AACE can be found on Table 4.

Table 4. ADA and AACE guidelines on therapeutic Lifestyle Changes4-6,9-12

ADA AACE

Weight loss (for overweight and obese patients)

Reduce by 7% Reduce by 5% to 10%

Physical activity 150min/week minimum

High intensity (≥16 sessions in 6 months) and focus on diet, physical activity, and behavioral strategies to achieve a 500–750 kcal/day energy deficit

150 min/week of moderate-intensity exercise (eg, brisk walking) plus flexibility and strength training

Diet • Achieve 500–750 kcal/day energy deficit or provide approximately:

• 1,200–1,500 kcal/day for women and 1,500–1,800 kcal/day for men

• Eat regular meals and snacks; avoid fasting to lose weight

• Consume plant-based diet (high in fiber, low in calories/glycemic index, and high in phytochemicals/antioxidants)

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Pharmacologic therapy

Insulin remains the gold standard of treatment for Type 1 DM patients. Insulin, an anabolic and anticatabolic hormone, plays a major role in the metabolism of protein,

carbohydrate and fat. Type 1 DM patients should be treated with multiple injections of daily and basal insulin or with continuous subcutaneous insulin (CS2). Table 5 shows a comprehensive list of available insulin preparations, time to onset of their effects, timing of peak levels and duration of effects.

Table 5. Available insulin preparations 4,5

Type Agent Onset (h) Peak (h) Duration (h) Brand Names

Rapid acting Aspart 15-30 min 1-2 3-5 Novolog

Lispro 15-30 min 1-2 3-4 Humalog

Glulisine 15-30 min 1-2 3-4 Apidra

Short acting Regular 0.5 - 1 hr 2-3 4-6 Humulin R,

Novolin R

Intermediate NPH 2 – 4 hr 4-8 8-12 Humulin N,

Novolin N

Long Acting Determir 2 hr n/a 14-24 Levemir

Glargine 4-5 hr n/a 22-24 Lantus

Degludec 1 hr n/a 42 Tresiba

In addition to insulin, 11 additional classes of agents are approved to treat type 2 DM, nine of which are oral agents. Use of any approved agents will depend on the patients’ medical needs and treatment goals.4-6, 9-12

The CDC has shown that 14% of patients diagnosed with type 2 DM take insulin only,14.7% take both insulin and oral medications, 56.9% take oral medication only, and an additional 14.4% do not take either.1 A study by the ADA on the economic costs of diabetes in

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Table 6. Agents for the treatment of type 2 DM (excluding Insulin, see Table 5) 4-6,9-12

Drug Class Compound Name Mechanism of action Efficacy Advantages Disadvantages

Biguanides Metformin ↓ Hepatic glucose production ↓A1C 1.5-2% ↓ FPG 60-80mg/dL • Extensive experience • No hypoglycemia • ↓CVD (UKPDS) • Diarrhea, abdominal cramping • Vit B12 deficiency • Lactic acidosis (rare) Sulfonylureas Glyburide Glipizide Glimepiride

↑ Insulin secretion ↓ A1C 1.5-2% ↓ FPG 60-70mg/dL • Extensive experience • ↓Microvascular risk (UKPDS) • Hypoglycemia • ↑ weight Meglitinides Repaglinide Nateglinide

↑ Insulin secretion ↓ A1C 1.7% ↓ FPG 60-80mg/dL • ↓Post prandial glucose • Dosing flexibility • Hypoglycemia • ↑ weight • Frequent dosing schedule Thiazolidinediones Pioglitazone Rosiglitazone

↑ Insulin sensitivity ↓ A1C ~1-1.5% ↓ FPG 60-70mg/dL • No hypoglycemia • Durability • ↑ HDL-C • ↓ Triglycerides (pioglitazone) • ↑ weight • Edema/heart failure • Bone fractures • ↑ LDL-C (rosiglitazone) Alpha Glucosidase inhibitors Acarbose Miglitol Slows intestinal carbohydrate digestion/absorption ↓ A1C 0.3-1% FPG ~10% reduction • No hypoglycemia • ↓ Post prandial glucose excursions • Nonsystemic • Modest A1C efficacy • Flatulence, diarrhea • Frequent dosing schedule Alpha Glucosidase inhibitors Acarbose Miglitol Slows intestinal carbohydrate digestion/absorption ↓ A1C 0.3-1% FPG ~10% reduction • No hypoglycemia • ↓ Post prandial glucose excursions • Nonsystemic • Modest A1C efficacy • Flatulence, diarrhea • Frequent dosing schedule DPP-4 Inhibitors Sitagliptin Vildagliptin Saxagliptin Linagliptin Alogliptin ↑ Insulin secretion ↓ Glucagon secretion (both glucose dependent) ↓ A1C 0.7-1% • No hypoglycemia

• Well tolerated • Angioedema/ Urticarial and other immune mediated dermatological effects • ? Acute pancreatitis • ? ↑ heart failure hospitalizations Bile acid sequestrants Colesevelam ? ↑ Incretin levels

? ↓ Hepatic glucose production ↓ A1C 0.4% when added to stable metformin, sulfonylurea, or insulin ↓ FPG 5-10mg/dL • No hypoglycemia • ↓ LDL-C • Generally modest A1C efficacy • Constipation • ↑ Triglycerides • May ↓ absorption of other medications Dopamine-2 agonists Bromocriptine Modulates

hypothalamic regulation of metabolism ↑ insulin sensitivity ↓ A1C 0.3%-0.6% • No hypoglycemia • ? ↓ CVD events • Generally modest A1C efficacy • Dizziness/syncope • Nausea • Fatigue • Rhinitis

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Table 7. Agents for the treatment of type 2 DM (excluding Insulin, see Table 5) 4-6,9-12

Drug Class Compound Name Mechanism of action Efficacy Advantages Disadvantages

SGLT2 inhibitors Canagliflozin Dapagliflozin Empagliflozin Blocks glucose reabsorption by the kidney, increasing glucosuria ↓ A1C 0.5-1% • No hypoglycemia • ↓ weight • ↓ blood pressure • Effective at all stages of type 2 DM • Genitourinary infections • Polyuria • Volume depletion/ hypotension/ dizziness • ↑ LDL-C • ↑ Creatinine • Diabetic ketoacidosis • Urinary tract infections GLP-1 receptor agonists Exenatide Liraglutide Albiglutide Lixisenatide Dulaglutide ↑ Insulin secretion ↓ Glucagon secretion (both glucose dependent) Slows gastric emptying ↑ Satiety ↓ A1C 0.9-1.1% • No hypoglycemia • ↓ weight • ↓ post prandial glucose excursions • ↓ some cardiovascular risk factors • Nausea, vomiting, diarrhea • ↑ heart rate • Injectable

Amylin mimetics Pramlintide (typically used in combination with insulin) ↓ Glucagon secretion Slows gastric emptying ↑ Satiety ↓ A1C 0.6% • ↓ weight • ↓ post prandial glucose excursions • Generally modest A1C efficacy • Nausea, vomiting • Hypoglycemia

unless insulin dose is simultaneously reduced

• Injectable • Frequent dosing

schedule A1C = glycosylated hemoglobin A1C; FPG = fasting plasma glucose; DPP4-i=Dipeptidyl Peptidase-4 inhibitors; SGLT2-I =Sodium dependent Glucose Cotransporter-2 inhibitors; GLP-1 = Glucagon-like Peptide-1 agonists

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Table 8. Effects of Diabetes Drug Action 11,12

Met GLP-1

RA

SGLT2-I DPP4-I TZD AG-I Coles

BCR-QR SU/Glinide Insulin Pram

FPG

lowering Mod

Mild to

mod* Mod Mild Mod Neutral Mild Neutral

SU: mod Glinide: mild Mod to marked (basal insulin or premixed) Mild PPG lowering Mild Mod to

marked Mild Mod Mild Mod Mild Mild Mod

Mod to marked (short/ rapid-acting insulin or premixed) Mod to marked NAFLD

benefit Mild Mild Neutral Neutral Mod Neutral Neutral Neutral Neutral Neutral Neutral

Hypo-glycemia Neutral Neutral Neutral Neutral Neutral Neutral Neutral Neutral

SU: mod to severe Glinide: mild to mod Mod to severe* Neutral Weight Slight

loss Loss Loss Neutral Gain Neutral Neutral Neutral Gain Gain Loss

Renal impair-ment/ GU Contra-indicated in stage 3B, 4, 5 CKD Exenatide contra-indicated CrCl <30 mg/mL Not effective w eGFR < 45 / GU infection risk Dose adjust-ment (except lina-gliptin)

Neutral Neutral Neutral Neutral

Increased hypo-glycemia risk Increased risks of hypo-glycemia and fluid retention Neutral GI adverse effects

Mod Mod* Neutral Neutral* Neutral Mod Mild Mod Neutral Neutral Mod

CHF Neutral Neutral Possible

benefit Neutral †

Mod Neutral Neutral Neutral Neutral Neutral Neutral CVD Possible

benefit Neutral

Possible

benefit Neutral Neutral Neutral Neutral Safe ? Neutral Neutral Bone Neutral Neutral Neutral Neutral Mod

bone loss Neutral Neutral Neutral Neutral Neutral Neutral AGI = α-glucosidase inhibitors; BCR-QR = bromocriptine quick release; CHF = congestive heart failure; CKD = chronic kidney disease; Coles = colesevelam; CrCl = creatinine clearance; CV = cardiovascular; DPP4I = dipeptidyl peptidase 4 inhibitors; FPG = fasting plasma glucose; GI = gastrointestinal; GLP1RA = glucagon-like peptide 1 receptor agonists; GU = genitourinary; Met = metformin; NAFLD = nonalcoholic fatty liver disease; PI = prescribing information; PPG = postprandial glucose; SGLT2I = sodiumglucose cotransporter 2 inhibitors; SU = sulfonylureas; TZD = thiazolidinediones. Boldface type highlights a benefit or potential benefit; italic type highlights adverse effects.*Especially with short/ rapid-acting or premixed.

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an A1C below or approaching 7% may be treated with therapeutic lifestyle changes alone or may be treated with an agent that does not cause hypoglycemia such as metformin. Patients with A1C between 7% and 8.5% may be treated with one single agent or a combination of agents. Patients with a A1C > 9% will likely require more than 2 agents and may begin to involve the use of insulin.4-6,9-12

Due to the differing characteristics of available agents, a patient-centered approach including patient preferences, cost and potential side effects needs to be taken when selecting an agent. The ADA and the European Association for the Study of Diabetes (EASD) have

published a position statement together with a treatment algorithm for patients with type 2 DM (see Figure 1).

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of recommended agents that do not increase the risk of hypoglycemia and weight gain (see Figure 2).

Figure 2. AACE Glycemic Control Algorithm 201311

While both the ADA/EASD and the AACE guidelines may differ in recommendations of certain anti-diabetic agents, they do agree that in addition to therapeutic lifestyle modifications, metformin remains the agent of first choice to begin treatment for patients with type 2 DM. Metformin has a long history of evidence for efficacy and safety and is considerably less expensive compared to other agents and has shown a decrease in total mortality.1,2,26

Changes in glycemic treatment guidelines from 2012 to present 2016

The last class of anti-diabetic agents to be approved by the Food and Drug

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Precaution about the risk of joint pain that can be severe and disabling; this warning is required on the labels of all medicines in the DDP4-I class.14 The FDA had also reviewed heart failure risk with diabetes drug saxagliptin (marketed as Onglyza and Kombiglyze XR) in February 2014 and concluded that more patients receiving saxagliptin- or alogliptin-related agents were

hospitalized for heart failure compared to placebo. In the saxagliptin study, 3.5% of patients were hospitalized for heart failure compared to 2.8% of patients who received a placebo. In the alogliptin study, 3.9% of alogliptin-treated patients were hospitalized for heart failure compared to 3.3% in the placebo group. Results of both studies led the FDA to add a new Warnings and Precaution statement for saxagliptin or alogliptin about the potential increased risk of heart failure, but it was not added to other molecules in the same class.25

As such, the ADA/EASD guidelines suggest that this class be used cautiously in type 2 DM patients with heart failure.10

A major change from the ADA/EASD guidelines since its publication of a treatment algorithm in 2012 is the inclusion of sodium glucose co-transporter-2 inhibitors (SGLT2-I), an oral class of anti-diabetic agents approved in 2013 by the FDA. The SGLT2-I compounds, also known as “gliflozins”, include canagliflozin, empagliflozin, and dapagliflozin. Table 8 shows the FDA approval timeline of drugs in both DPP4-I and SGLT2-I classes.15 SGLT2-I works in the kidney by blocking the re-absorption of glucose. This allows more glucose to pass through the urine and thus lowers serum glucose and allows weight loss. The mechanism of action for SGLT2-I also enables it to be used at any stage of type 2 DM as SGLT2-I are independent of

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fractures and added new information about decreased bone mineral density to the product labeling of canagliflozin. Furthermore, in December 2015, the FDA revised the labels of SGLT2-I for diabetes to include warnings about excess acid in the blood and risk of serious urinary tract infections.13 The FDA had identified 19 cases of life-threatening blood infections (urosepsis) and kidney infections (pyelonephritis) that started as urinary tract infections with the SGLT2-I reported from March 2013 through October 2014. All 19 patients were hospitalized, however few required admission to an intensive care unit or dialysis in order to treat kidney failure.

Table 9. FDA approval dates of DPP4-I and SGLT2-I 15

DPP4 Inhibitor FDA approval Date SGLT2 Inhibitor FDA Approval Date

Sitagliptin (Januvia®) October 2006 Canagliflozin (Invokana®) March 2013 Sitagliptin and Metformin

(Janumet®)

March 2007 Dapagliflozin (Farxiga®) January 2014 Saxagliptin (Onglyza®) July 2009 Canagliflozin and Metformin

(Invokamet®₎

August 2014 Saxagliptin and Metformin

(Kombiglyze XR®)

November 2010 Empagliflozin (Jardiance®) August 2014 Linagliptin (Tradjenta®₎ _{May 2011} _{Dapagliflozin and Metformin}

extended release (Xigduo XR®)

October 2014

Linagliptin and Metformin (Jentadueto®)

January 2012 Empagliflozin and Metformin (Synjardy®)

August 2015 Sitagliptin and Metformin

extended release (Janumet XR®)

February 2012 Alogliptin (Nesina) January 2013 Alogliptin and Metformin

(Kazano)

January 2013 Alogliptin and Pioglitazone

(Oseni)

January 2013

Combination DDP4-I and SGLT2-I Empagliflozin and Linagliptin (Glyxambi)

January 2015

When the first SGLT2-I, canagliflozin (Invokana®) was approved in 2013, the AACE guidelines of 2013 (Figure 2) ranked SGLT2-I as a fifth line treatment along with “use with caution” based on early clinical trial data. Three years later, with more clinical evidence and more SGLT2-I approved, the AACE guidelines of 2015 ranked SGLT2-I as a third line treatment

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changes in 2015 for SGLT2-I, the 2016 AACE algorithm on glycemic control has not changed from its 2015 recommendations (see Figure 3).16

Figure 3. AACE Glycemic Control Algorithm 2016 16

DPP4 inhibitors vs. SGLT2 inhibitors

Head-to-head clinical comparisons of SGLT2-I to DPP4-I have also been shown to favor the SGLT2-I. Schernthaner et al. compared canagliflozin to sitagliptin (DPP4-I) in patients who have not achieved glycemic control while on metformin and sulfonylurea.18 Results showed that at 52 weeks, canagliflozin 300mg/day showed non-inferiority and subsequent superiority to

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(100mg up to 300mg once daily from baseline) reduced A1C by -0.70% to -0.95% after 12 weeks of treatment compared to sitagliptin 100mg, which reduced A1C by -0.74%.

Furthermore, clinical trial evidence has shown that a combination of a SGLT2-I and DPP4-I was better at reducing A1C compared to single agents of either class. Rosenthal et al. (2015) found that saxagliptin and dapagliflozin produced better A1C reductions compared to single agent saxagliptin and dapagliflozin (all three cohorts were also treated with metformin). 19 Results at 24 weeks showed differences in adjusted mean changes in A1C between

saxa+dapa+met and saxa+met -0.59 (p<0.001) and saxa+dapa+met and dapa+met, -0.27 (p = 0.016). While a statistical analysis was not performed to compared saxa+met to dapa+met, the reduction in A1C among those two cohorts was -0.32 in favor of the SGLT2-I cohort. Defronzo et al. (2015) found that the combination of empagliflozin and linagliptin was superior in

reduction of A1C compared to single-agent empagliflozin and linagliptin (along with metformin). 20 At 24 weeks, reductions in A1C were -0.58% for emp+lina compared to empagliflozin alone and -0.50% compared to linagliptin alone (both p<0.001).

Study Rationale

While clinical trials have shown favorable results towards SGLT2-I compared to DPP4-I, there is still limited evidence on the effectiveness of such agents in a “real-world setting”. A review of the literature have yielded only two studies, both focused on canagliflozin, despite the fact that the agent was approved in 2013 and five additional new SGLT2-I agents (though only 3 actual molecules) have been approved by the FDA since then. Buysman et al. was the first to study the use of canagliflozin in a real-world setting. It was a descriptive study aimed at

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a mean A1C that decreased from 8.54 to 7.76 (p<0.001) and a decreased usage of other anti-diabetic agents following initiation of canagliflozin. While the study was able to determine change in A1C, the study period was limited to a three-month follow-up period after the first canagliflozin claim. This limited timeframe is not enough to determine long-term effects of canagliflozin, as A1C is a measure of a person’s average level of blood glucose over the past three months. In essence, this study period would have enabled only one evaluation of A1C laboratory data with the study period and would have failed to capture the full response to canagliflozin.

Garbner et al. was the second study that compared baseline demographic, clinical and economic characteristics of patients with type 2 DM on canagliflozin compared to DPP4-I.22 The study showed that canagliflozin initiators were more likely to have higher A1C levels and more likely to incur higher pharmacy utilization and costs compared to DPP4-I initiators. Limitations of the study included the amount of available laboratory data, the study period and differences in baseline patient characteristics. Patients with A1C laboratory results accounted for only 30% of the overall patient population, and the study period of January 2011 to September 2013 would only allow, at most, a 3-6 month follow up for patients on canagliflozin - thus limiting the ability to identify trends in treatment patterns or a more comprehensive assessment of outcome post-initiation. Finally, the study did not control for differences in patient

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daily, however, it is possible (but cannot be assumed ) that adherence and persistence do not differ. Aside from clinical efficacy and safety profiles of anti-diabetic drugs, patient adherence continues to be a factor in determining patient glycemic control. Studies have shown that nonadherent patients have both statistically and clinically worsened outcomes compared to adherence patients.

Pladevall et al showed that an 10% increase in nonadherence can lead to a 0.14% increase in A1C, while Schectman et al showed that a 10% increase in adherence can lead to a A1C decrease of 0.16% (p< 0.0001).23,24 Rozenfeld et al found an inverse relationship between oral antidiabetic medication and A1C, demonstrating that each 10% increase in adherence led to a 0.1% decrease in A1C (p = 0.004).27 Al-Qazaz et al found significant correlations between A1C and adherence (p< 0.05), showing that higher scores on the Morisky Medication Adherence Scale were found in patients with lower A1C.28 Medication regimen can also play a factor in adherence as Odegard et al demonstrated that taking more than 2 doses of diabetes medication daily were significantly associated with higher A1C (p =0.2) and Donnan et al showed that there was a significant linear trend of poorer adherence with each increase in the daily number of tablets taken (p=0.001).29-30

It has been more than 3 years since the approval of the first SGLT2-I compound in 2013, with five additional approved SGLT2-I agents on the market. The ADA/EASD and the AACE guidelines have since incorporated that drug class into their treatment algorithms for glycemic control. With over nine oral drug classes available and the DPP4-I and SGLT2-I drug classes gaining more favor as treatment options for patients with type 2 DM, more studies are needed to determine the effectiveness of these medications in the real world. Patient adherence and

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study will contribute significantly to the literature by conducting a comparison of A1C reduction, and quantifying differences in adherence, persistence and costs of treatment for patients initiating SGLT2-I compared to DPP4-I. The study is feasible and timely, now that the SGLT2-I has had enough time to establish itself as a treatment option for patients with type 2 DM.

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CHAPTER 2: METHODOLOGY

Study Objectives and Hypotheses

This study evaluated the level of A1C reduction between SGLT2-Is compared to DPP4-Is and documented changes in medication utilization patterns among those two classes of oral hypoglycemic agents in patients with type 2 DM within the Scott & White Health Plan. The specific objectives of this study are listed below; specific hypotheses are listed in Table 10: 1. To determine if patient characteristics (age, gender, race, or comorbidities) differ between

patients taking SGLT2-Is versus DPP4-Is.

2. To determine if A1C reductions differ between patients taking SGLT2-Is versus DPP4-Is. 3. To determine if change in body mass index at initiation and after 6 months from initiation

differs between patients taking SGLT2-Is versus DPP4-Is.

4. To determine if patient adherence differs between patients taking SGLT2-Is versus DPP4-Is. 5. To determine if patient persistence differs between patients taking SGLT2-Is versus DPP4-Is. 6. To determine if the use of insulin at initiation and after 6 months from initiation differs

between patients that were on insulin while initiating SGLT2-Is versus DPP4-Is.

Study Design and Data Source

This retrospective cohort study took place from October 1, 2014 to September 30, 2015. Patients selected for this study were required to have one or more pharmacy claims for index drug of SGLT2-I or DPP4-I. Patients were required to have continuous enrollment in the health plan for at least 6 months during the study period. Patient-level data were extracted from

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lives, with over 60% as commercial lives. As part of an integrated health system, the SWHP has access to medical laboratory data through electronic medical records. This study was approved by Baylor Scott & White Health and the University of Texas at Austin institutional review boards (IRBs) following expedited review.

Inclusion/Exclusion Criteria

Patients were required to be between the age of 18 and 62 years old with a confirmed diagnosis of type 2 DM (International classification of diseases, ninth revision, clinical modification [ICD-9-CM] of 250.X0 or 250.X2) and continuous enrollment within the SWHP for at least six months for the duration of the study. Patients on either a SGLT2 or a DPP4-I and a GLP-1 were excluded. Patients with a medical claim for any of the ICD-9-CM listed in Table 9 were also excluded.22

Table 10. ICD-9-CM Codes Used for Exclusion Criteria

Condition ICD-9-CM Code

Type 1 diabetes 250.x1, 250.x3

Gestational diabetes 648.8x

Hyperglycemia not otherwise specified 790.6x

Neonatal diabetes mellitus 775.1x

Nonclinical diabetes 790.29

Diabetes with hyperosmolar coma 250.2x

Table 11. Study Hypotheses

H01: There is no statistical difference in patient characteristics between patients taking a SGLT2-I versus a DPP4-I. H02: There is no statistical difference in A1C between patients taking a SGLT2-I versus a DPP4-I.

H03: The is no statistical difference in BMI differences in patients taking a SGLT2-I versus a DPP4-I.

H04: There is no statistical difference in the patient adherence between patients taking a SGLT2-I versus a DPP4-I. H05: There is no statistical difference in patient persistence between patients taking a SGLT2-I versus a DPP4-I. H06: There is no statistical difference in the use of insulin between patients taking a SGLT2-I versus a DPP4-I.

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Outcome Measures

Table 12. Variables analyzed in the study

Category Type Measure Source

Independent Demographics Age in years ?

Independent Demographics Gender (M, F) EHR

Independent Demographics Race/ethnicity (white, black, other race, Hispanic)

EHR (Inclusion

criterion)

Clinical Diabetes Type 2 Claims

Independent Clinical Charlson Comorbidity Index Claims

Dependent Clinical Change in Body mass index EHR

Dependent Clinical Change in A1c EHR

Dependent Clinical Use of insulin (yes/no) Claims

Dependent Clinical Use of DPP4i (yes/no) Claims

Dependent Clinical Use of SGLT2i (yes/no) Claims

Dependent Clinical Adherence: PDC ≥80% (yes/no) Claims

Dependent Clinical Persistence in days Claims

Dependent Healthcare

utilization

Costs of DM drugs per 6-month period Claims EHR = electronic health records

1. Baseline patient characteristics (age, Charlson co-morbidity index, race, ethnicity, drug therapeutic class) were evaluated from historical data in EHR and pharmacy drug claims. 2. Change in A1C was calculated from historical data laboratory in EHR

3. Change in body mass index was calculated from historical data laboratory in EHR 4. Adherence

a. Adherence defined as a proportion of days covered (PDC):

Number of days covered by prescription fills during the denominator period Number of days between the first fill of the medication during the measurement period and the end of the measurement period),

• Limited to those patients with at least one prescription • If the PDC >= 80%, then adherent

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• Time from the initial prescription fill until the patient has a gap in therapy (time gap of > 45 days)

6. Change in insulin use was evaluated by selecting the subset of patients that were on insulin while on a SGLT2-I and a DPP4-I and determining whether the patient was still using insulin 6 months after initiation of the novel hypoglycemic agent (YES/NO)

Analysis Plan

• Descriptive statistics (mean and standard deviation or median and interquartile range) will characterize demographic data before and after matching.

o Continuous variables: mean, standard deviation, median, interquartile range, minimum, maximum

o Categorical variables: N (frequency), % (percent frequency)

• Tests for normality were conducted to determine appropriate statistical tests

o Chi-square analyses was conducted to analyze differences in baseline co-morbidities between the two treatment groups

o Mann-Whitney U tests were conducted for nonparametric continuous variable comparisons between groups (eg. Baseline A1C)

• Linear regression modeling was conducted to assess the adjusted effect of SGLT2-I versus DPP4-I on the interval-level outcomes, A1C, BMI, and costs, controlling for baseline values and clinical and demographic covariates. Baseline differences were assessed and handled as

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• Statistical analyses were computed using SAS 9.4 TS1M2 (SAS Institute Inc, Cary, North Carolina).

• A value of p<0.05 was used to determine statistical significance.

Feasibility Analysis

 Sample size calculations were performed using G*Power 3.1.9.2 software:

o Using a conventional estimation for small effect size (w = 0.55), a total sample size of 118 would be required to determine a difference in A1C between treatment groups, assuming an alpha of 0.05 and power of 0.80.

 Post hoc sample size calculations were performed using G*Power 3.1.9.2 software to compute the power achieved

o Using an estimation for small effect size (w = 0.134), a total sample size of 300 and assuming an alpha of 0.05, the calculated achieved power was low at 0.29.

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CHAPTER 3: RESULTS

Patient Selection

A total of 300 patients met the inclusion criteria for this study. Among these, 99 patients met criteria for inclusion in the SGLT2-I cohort, and 201 met criteria for inclusion in the DPP4-I cohort. (Figure 4).

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Demographic Characteristics

Baseline demographic information for the 300 patients are shown in table 12. The mean age (SD) was 51 (7.7) years for SGLT2-I and 52 (7.1) years for DPP4 initiators. Gender

distributions for both cohorts were almost equal. Among other antidiabetic therapies, a chi-square analysis showed that a greater proportion of patients in the DPP4-I cohort were prescribed sulfonylureas compared to the SGLT2-I cohort. (x2 = 10.6369; df=1; p = 0.0011). A Wilcoxon-Mann-Whitney test showed that the baseline A1C in the DPP4-I cohort was significantly lower than the SGLT2-I cohort (mean [SD], 8.9 [2.0] vs. 8.4 [1.8], p = 0.0324).

Chi-square analysis between insulin use (yes/no) at baseline and medication cohort showed that patients in the SGLT2-I cohort were prescribed significantly more insulin compared to the DPP4-I cohort. (x2 = 26.7067; df=1; p < 0.001). In addition, chi-square analysis also revealed that among those prescribed insulin in both cohorts, the SGLT2-I group was more likely to have been prescribed insulin prior to the initiation of SGLT2-I (X2 = 17.9455; df=1; p <

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Table 13. Baseline Demographics, Comorbidities, and Laboratory Values SGLT2-1

(n = 99)

DPP4-I

(n = 201) p-value

Age at index (years)

mean (SD) 51 (7.7) 52 (7.11) 0.1599

Charlson Comorbidity Index

mean (SD) 2.25 (1.5) 2.11 (1.88) 0.1395 Gender Male, n (%) Female, n (%) 51 (51.5) 48 (48.5) 94 (46.8) 107 (53.2) 0.4389 Race

(missing 545 [32.4% from sample])

White, n (%) Black, n (%) Other, n (%) 40 (67.8) 7 (11.9) 12 (20.3) 85 (64.4) 20 (15.1) 27 (20.5) 0.8253 Ethnicity

(missing 546 [32.4% from sample])

Non-Hispanic, n (%) hispanic, n (%) 48 (81.4) 11 (18.6) 107 (80.5) 26 (19.5) 0.8834 Medication Class AGI, n (%) Biguanides, n (%) Sulfonylurea, n (%) Meglinitides, n (%) TZD, n (%) Combination productions, n (%) 2 (2.02) 76 (76.8) 45 (45.5) 6 (6.06) 2 (2.02) 4 (4.04) 5 (2.49) 149 (74.1) 131 (65.2) 0 (0%) 23 (11.4) 7 (3.5) 1 0.6197 0.0011 0.138 0.1082 0.7559

Mean follow-up in days (SD) 168.74

(114.5) 209.5 (118.9) 0.0061 Baseline A1C mean (SD) 8.9 (2.0) 8.4 (1.8) 0.0324 Baseline BMI (kg/m2) mean (SD) 34.5 (7.1) 34.4 (7.2) 0.5223 Insulin use Categories, N (%)

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Change in A1C

The change in A1C was calculated based on difference between the initial A1C at baseline and the final A1C at the end of the study period. The difference (mean [sd]) in A1C for the SGLT2-I cohort was -0.7 (1.6) compared to -0.5 (1.7) for the DPP4-I cohort. A WilcoxonMannWhitney test showed that the difference in A1C was not statistically significant. (Z = -1.1927, p = 0.2330). Among the subset of patients that were not on insulin at baseline (N = 16 SGLT2-I and N = 40 DPP4-I), a Wilcoxon-Mann-Whitney test also showed no statistical significance (Z = 0.5282, p = 0.5974)

Change in Body Mass Index (BMI)

The change in BMI was calculated based on the difference between the initial BMI at baseline and the final BMI at the end of the study period. The difference (mean [sd]) in BMI for the SGLT2-I cohort was -0.26 (2.55) compared to -0.15 (2.21) for the DPP4-I cohort. A Wilcoxon-Mann-Whitney test showed that the difference in A1C was not statistically significant. (Z = -1.2583, p = 0.2083).

Adherence

Adherence was defined by a PDC ≥ 80%. PDC was calculated using the number of days covered by prescription fills during the denominator period divided by the number of days between the first fill of the medication during the measurement period and the end of the measurement period). Among the SGLT2-I cohort, 67 (67%) patients were adherent compared to 147 (73%) in the DPP4-I cohort. A chi square analysis showed that the difference in

adherence was not statistically significant (X2 = 0.9661; df=1; p = 0.3256)

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discontinuation was 181.5 days (sd= 115.2 days) for the SGLT2-I cohort compared to 210.2 days (sd = 120.6 days) for the DPP4-I cohort. A Wilcoxon-Mann-Whitney test showed that the days to treatment discontinuation was statistically significant. (Z = -1.9628, p = 0.0497).

Insulin use

Insulin use was determined by selecting the subset of patients that were on SGLT2-I or DPP4-I and concurrently with insulin during the course of the study period. Among the subset of patients on insulin, 63% (n=62) of patients from the SGLT2-I cohort was on insulin compared to 31% (n=63) patients from the DPP4-I cohort. A chi square analysis showed that the

difference in insulin use was statistically significant (X2 = 26.7067; df=1; p < 0.001). However, while the change in number of patients still on insulin decreased, the results were not statistically different between both cohorts (X2 = 0.3440; df=1; p = 0.5575).

Chi square analysis also showed that there was a significant difference between both cohorts regarding the order of insulin use. (X2 = 17.9455; df=1; p < 0.001). Results showed that 46.5% (n=46) of patients from the SGLT2-I cohort were on insulin prior to starting a SGLT2-I compared to 11.4% (n=23) of patients from the DPP4-I cohort who were on insulin prior to starting a DPP4-I. However, while the change in number of patients still on insulin decreased after the index drug was initiated, the results were not statistically different between both cohorts (Fisher’s exact test; p = 0.5351). A summary of results related to insulin is displayed in Table 13.

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Table 14. Change in patients still using insulin six months during, before or after the initiation of index drug

Insulin use SGLT2-I DPP4-I P value

1) Regardless of before or after index drug, N ₆₂ ₆₃ _<0.001

Number of patients still on insulin at 6 months

post-index; N,(%) 47 (75.8) 49 (77.8) 0.5575

2A) Before initiation of index drug, N ₄₆ ₂₃ _<0.001

post-index; N,(%) 39 (84.8) 19 (82.6) 0.5351

2B)After initiation of index drug, N ₁₆ ₄₀ _<0.001

post-index; N,(%) 10 (62.5) 28 (70.0) 0.8060

Linear regression analysis

A simple linear regression analysis was performed for all patients on any diabetic medications that met criteria (N=1684 –see Figure 4) to determine which variables would affect a change in A1C. The model was defined as “Y(A1C_DIFF) = e + INTERCEPT + BMI_1 (X1) + AGE(X2) +GENDER(X3) +RACE_BLACK_MISS(X4) + RACE_OTHER_MISS(X5) + CHARLSON COMORB(X6) + INSULIN(X7) + RX_CLASS_COUNT(X8) +

ADHERENCE(X9) + OVERALL_COPAY(X10)”. A fitted model was also performed to assess for any co-linearity.

Results of the fitted model showed that there were no co-linearities as variance inflation was less than 10. The overall model was significant (p<0.001). Linear regression analysis indicated that for every 10-year increase in age, there is an expected 0.2 increase in A1C

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Figure 5. Linear Regression Results (N = 1684) – fix table so more readable Need more complete title

Figure 6 displays results of the linear regression analysis specific to the SGLT2-I and the DPP4-I cohort (N=300). While the overall model was significant (p < 0.001), none of the variables were significant.

Figure 6. Linear Regression Results specific to SGLT2-I and DPP4-I Cohorts (N=300)

Parameter Standard Variance

Estimate Error Standard Inflation

Error Intercept Intercept 1 -1.19264 0.27738 -4.3 <.0001 0.30937 -3.86 0.0001 0 age_decade 1 0.20177 0.05116 3.94 <.0001 0.05561 3.63 0.0003 1.01879 female 1 0.19139 0.0797 2.4 0.0164 0.07999 2.39 0.0168 1.00329 Charlson 1 -0.08229 0.02422 -3.4 0.0007 0.02405 -3.42 0.0006 1.01892 log_overall_sum_copay 1 -0.05796 0.01965 -2.95 0.0032 0.01877 -3.09 0.002 1.08713 SGLT2 1 -0.11659 0.15212 -0.77 0.4435 0.15336 -0.76 0.4472 1.02708 DPP4 1 0.06338 0.11952 0.53 0.596 0.12433 0.51 0.6103 1.06459

Variable Label DF t Value Pr > |t| Heteroscedasticity Consistent

t Value Pr > |t| Parameter Estimates

Parameter Standard Variance

Estimate Error Standard Inflation

Error Intercept Intercept 1 -1.1833 0.74434 -1.59 0.113 0.74897 -1.58 0.1152 0 age_decade 1 0.21091 0.13521 1.56 0.1199 0.13629 1.55 0.1228 1.03022 female 1 0.35826 0.19625 1.83 0.0689 0.19446 1.84 0.0664 1.00465 Charlson 1 -0.10163 0.05554 -1.83 0.0683 0.0462 -2.2 0.0286 1.02579 log_overall_sum_copay 1 -0.0697 0.03771 -1.85 0.0656 0.02627 -2.65 0.0084 1.00299 SGLT2 1 -0.21854 0.20941 -1.04 0.2975 0.20484 -1.07 0.2869 1.01279 Heteroscedasticity Consistent t Value Pr > |t| Parameter Estimates

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Summary

A summary of the results for each hypothesis is provided in Table 14.

Table 15. Summary of hypotheses tested

Hypothesis

P-Value Conclusion

Outcome variable

type Statistical test

H01: There is no statistical difference in patient characteristics between patients taking SGLT2-I versus DPP4-I. Age 0.1599 Failed to reject Continuous Wilcoxon Mann Whitney U Charlson Comorbidity Index 0.1395 Failed to

reject Count

Wilcoxon Mann Whitney U Gender 0.4389 Failed to

reject Categorical nominal Chi square Race 0.8253 Failed to

reject Categorical nominal Chi square Ethnicity 0.8834 Failed to

reject Categorical nominal Chi square Alpha glucosidase inhibitor 1 Failed to

reject Categorical nominal Fischer’s Exact Biguanides 0.6197 Failed to

reject Categorical nominal Chi square Sulfonylurea 0.0011 Rejected Categorical nominal Chi square Thiazolidinones 0.138 Failed to

reject Categorical nominal Chi square Meglinitides 0.1082 Failed to

reject Categorical nominal Fischer’s Exact Combination products 0.7559 Failed to

reject Categorical nominal Fischer’s Exact Baseline A1C 0.0324 Rejected Continuous Wilcoxon Mann

Whitney U Baseline BMI 0.5223 Failed to

reject Continuous

Wilcoxon Mann Whitney U H02: There is no statistical difference in A1C change

between patients taking SGLT2-I versus DPP4-I 0.233

Failed to

reject Continuous

Wilcoxon Mann Whitney U H02a: There is no statistical difference in A1C change

between patients taking SGLT2-I versus DPP4-I that are NOT on insulin

0.5974 Failed to

reject Continuous

Wilcoxon Mann Whitney U H03: The is no statistical difference in BMI change in

patients taking SGLT2-I versus DPP4-I 0.2083

Failed to

reject Continuous

Wilcoxon Mann Whitney U H04: There is no statistical difference in the patient

adherence between patients taking SGLT2-I versus DPP4-I.

0.3256 Failed to

reject Categorical nominal Chi square H05: There is no statistical difference in patient

persistence between patients taking SGLT2-I versus DPP4-I.

0.0497 Rejected Count Wilcoxon Mann

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Table 16. Summary of hypotheses tested

Hypothesis

P-Value Conclusion

Outcome variable

type Statistical test

H06.0a: There is no statistical difference in the use of insulin, 6 months after the first use of insulin, between patients taking SGLT2-I versus DPP4-I.

0.5575 Failed to reject Categorical

nominal Chi square

H06.1: There is no statistical difference in patients on

insulin BEFORE the initiation of index drug <0.001 Rejected

Categorical

nominal Chi square H06.1a: There is no statistical difference in the use of

insulin 6 months after the first use of insulin, in patients on insulin BEFORE the initiation of index drug

0.5357 Failed to reject Categorical

nominal Fisher’s Exact

H06.2: There is no statistical difference in patients on

insulin AFTER the initiation of index drug <.001 Rejected

Categorical

nominal Chi square H06.2a: There is no statistical difference in the use of

insulin 6 months after the first use of insulin, in patients on insulin AFTER the initiation of index drug

0. 8060 Failed to reject Categorical

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CHAPTER 4: DISCUSSION

This study was conducted to compare the change in A1C in patients initiated on SGLT2-I and DPP4-I. It quantified the differences in baseline patient demographics, medication

adherence, persistence, patient cost burden (defined as patient copay) and impact on insulin use.

Demographic characteristics

Study results showed that patients on a SGLT2-I or a DPP4-I were comparable in age. The average age in this study was 51 years old, comparable to the average age (55 years old) in previous studies by Grabner et al and Buysman et al.21,22 Gender differences in this study showed that the SGLT2-I cohort group had an almost even split between males and females, with the DPP4-I cohort having 53% of its sample size as male. This differed slightly from previous studies, where males tended to comprise 60% of the sample size. The mean Charlson

Comorbidity Index (CCI) score was comparable in both cohorts at 2.2 but it differed from the Grabner et al, which determined a mean (SD) CCI score of 1.05 (1.7) for its canagliflozin cohort and 0.92 (1.6) for its DPP4-I cohort.

Baseline A1C for the SGLT2-I was 8.9 compared to 8.4 in the DPP4-I cohort, averaging 8.6 for both cohorts. This was consistent with baseline A1Cs of 8.6 from previous studies. 21,22 Study results also showed a significantly larger proportion of DPP4-I patients were receiving sulfonylureas compared to the SGLT2-I cohort, which was the opposite of the Grabner et al study, which showed more patients in the SGLT2-I cohort receiving sulfonylureas. Insulin use was consistent with the results from Grabner et al, where a significantly larger proportion of SGLT2-I patients were also receiving insulin compared to the DPP4-I cohort. The order of insulin use was interesting as results showed that significantly more patients in the SGLT2-I

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cohort that were using insulin had been receiving it prior to the initiation of the SGLT2-I agent compared to the DPP4-I cohort.

Change in A1C

Results of this study showed that a difference in A1C reduction of -0.7 and -0.5 for the SGLT2-I and DPP4-I cohort, respectively. While the results were not statistically significant, it does show a clinically meaningful decrease in A1C. The reduction in A1C is also similar to the results from both Grabner et al and Busyman et al. Grabner et al determined a reduction in A1C of 0.93 in its canagliflozin group, while Buysman et al determined a 0.78 reduction in A1C among patients treated with canagliflozin.21,22 Results are also within range in reductions of A1C from clinical studies, where change in A1C for sitagliptin ranged from -0.45 to -0.74 and change in A1C for SGLT2-I agents (canagliflozin and empaglifozin) ranged from -0.66 to -1.03.17-19

Change in BMI

Study results showed a reduction in BMI of -0.26 and -0.15 for the SGLT2-I and DPP4-I cohort, respectively. Though the results were not statistically different, clinically the SGLT2-I cohort has shown to have a larger reduction in BMI compared to the DPP4-I cohort. Reductions in body weight are consistent with clinical guidelines which recommend SGLT2-Is due their potential added benefit of weight loss.11,12,17

Adherence and persistence

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proportion of days covered.21 Persistence was found to be greater in the DPP4-I cohort. The average number of days until treatment discontinuation was 181 days vs. 210 days (p=0.0497) for SGLT2-I and DPP4-I, respectively. Given that the drug regimen was similar for both cohorts, further analysis on other factors such as side effects of urinary tract infections may be warranted to determine the reasons why the DPP4-I would be more persistent than the SGLT2-I cohort.

Insulin use

Among the subset of patients in both cohorts that were also on insulin, results of the study found that statistically more patients in the SGT2-I cohort were receiving insulin compared to the DPP4-I cohort. Furthermore, the SGLT2-I cohort was also found to have had a higher proportion of patients that were on insulin prior to the initiation of SGLT2-I agents. This was consistent with results of the Grabner et al study, which found that a significantly larger proportions of patients in the canagliflozin cohort compared to the DPP4-I cohort were treated with insulin (24.7% vs. 9.1%; P < 0.001). 21

Study Limitations

External validity of this study is limited due to the geographic location of the healthcare system. Study results and demographic characteristics may only be generalized to the central Texas region. The retrospective nature of the study relies on administrative claims data and available EHR data. Miscoding of ICD-9 codes could affect the sample size and the calculation of CCI. A larger study period and a longer follow up period may be required to detect a

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size of 118 would be required to determine a difference A1C between treatment groups,

assuming an alpha of 0.05 and power of 0.80. However, post hoc power analysis showed that the sample size did not have enough power to detect a difference between cohorts. This may have been affected the 1:2 subject ratio of SGLT2-I patient to DPP4-I patients, which may have lowered the power. Because of the small sample size of 300 and that the difference in A1C was found to be non-significant, definitive conclusions cannot be made. A priori sample size

calculations assumed larger HA1C differences in change than found.

Conclusions

The findings of this study demonstrate that patients in the SGLT2-I cohort started out with higher baseline A1C, co-morbidity index, a lower portion of patients on sulfonylureas and a higher portion that were on insulin and that also received it prior to initiating a SGLT2-I agent. While there was no statistical difference in change in A1C among the SGLT2-I and the DPP4-I agents, the SGLT2-I cohort demonstrated a clinically larger change in A1C.

This study was the third to assess the use of SGLT2-Is compared to DPP4-Is in a real world setting, however the study period was limited to a year, which may have minimized the amount of A1C laboratory data that could have been evaluated. Results of this study suggest that a need for future studies to include a longer study period and a larger sample size with both baseline and follow up A1C laboratory data.

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REFERENCES

1. Centers for Disease Control. National diabetes statistics report, 2014. Available at:

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Copyright. Jenny Chun Ye Wong

APPROVED BY

SUPERVISING COMMITTEE:

by

Thesis

Dedication

Acknowledgements

Abstract

Table of Contents

List of Tables

List of Figures

CHAPTER 1: INTRODUCTION

Epidemiology of Diabetes Mellitus

Pathophysiology of Diabetes Mellitus

Diabetes Mellitus Treatment Guidelines

Approaches to Glycemic Treatment

Changes in glycemic treatment guidelines from 2012 to present 2016

DPP4 inhibitors vs. SGLT2 inhibitors

Study Rationale

CHAPTER 2: METHODOLOGY

Study Objectives and Hypotheses

Study Design and Data Source

Outcome Measures

Analysis Plan

Feasibility Analysis

CHAPTER 3: RESULTS

CHAPTER 4: DISCUSSION

Study Limitations

Conclusions

REFERENCES