Comparative Effectiveness of Oral Antihyperglycemic Agents in Patients with Diabetes and Chronic Kidney Disease by

Reid H. Whitlock

A Thesis submitted to the Faculty of Graduate Studies of

The University of Manitoba

in partial fulfillment of the requirements of the degree of

MASTER OF SCIENCE

Department of Community Health Sciences

University of Manitoba

Winnipeg, Manitoba, Canada

Copyright © 2018 by Reid H. Whitlock

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Abstract

We sought to compare the safety of sulfonylureas to other oral antihyperglycemic agents (OHAs) in patients with type 2 diabetes (T2DM) and examine whether chronic kidney disease (CKD) is an effect modifier. Using Manitoba Center for Health Policy data, we identified adults with an incident OHA prescription between 2006 and 2016 (monotherapy; add-on to metformin

[combotherapy]), and a serum creatinine test. We conducted comparisons with Cox models in propensity score matched cohorts. In 1,777 matched monotherapy pairs, sulfonylureas were associated with all-cause mortality (HR 1.44; 95% CI 1.05 – 1.97) vs. metformin where CKD was an effect modifier (p<0.001) as sulfonylureas performed worse in patients without CKD. In

1,266 matched combotherapy pairs, sulfonylureas were associated with all-cause mortality (HR

2.40; 95% CI 1.15 – 5.02) vs. other OHAs. CKD was not an effect modifier for other comparisons. Our evidence supports metformin as monotherapy, and discourages sulfonylureas as add-on therapy for glycemic control.

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Acknowledgements

I would like to extend gratitude to everyone who assisted me with my studies and who contributed to the completion of this thesis project: Dr. Navdeep Tangri, Dr. Kristin Clemens,

Dr. Paul Komenda, Dr. Nathan Nickel, Dr. Marshall Pitz, Farzana Quddus, Heather Prior,

Manitoba Centre for Health Policy, Nicole Askin, Health Information Privacy Committee at

Manitoba Health, Winnipeg Regional Health Authority, Vital Statistics Manitoba, Diagnostic

Services of Manitoba, and the Research Team at the Chronic Disease Innovation Centre at Seven

Oaks Hospital.

Furthermore, I would like to acknowledge the financial assistance that I have received throughout completion of my graduate studies in Community Health Sciences, which include the

Canadian Frailty Network through the Interdisciplinary Fellowship Program, and the support of the Chronic Disease Innovation Centre at Seven Oaks Hospital. The results and conclusions are those of the authors and no official endorsement by Manitoba Health and Healthy Living was intended or should be inferred.

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Table of Contents

Abstract ...... ii

Acknowledgements ...... iii

Table of Contents ...... iv

List of Tables and Figures ...... vii

Glossary of Acronyms...... ix

1. Chapter 1: Introduction ...... 1

1.1 Pathophysiology of Type 2 Diabetes Mellitus ...... 1

1.2 Diagnosis and Treatment of Type 2 Diabetes Mellitus ...... 1

1.3 Epidemiology and Economic Impact of Type 2 Diabetes Mellitus ...... 2

1.4 Pathophysiology of Chronic Kidney Disease ...... 3

1.5 Diagnosis and Treatment of Chronic Kidney Disease ...... 4

1.6 Epidemiology and Economic Impact of Chronic Kidney Disease ...... 4

1.7 Concomitant Type 2 Diabetes Mellitus and Chronic Kidney Disease ...... 5

1.8 Study Objectives and Thesis Statement ...... 6

2. Chapter 2: Literature Review ...... 8

2.1 Metformin ...... 8

2.2 Sulfonylureas ...... 10

2.3 Thiazolidinediones ...... 12

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2.4 Dipeptidyl Peptidase-4 Inhibitors ...... 14

2.5 Meglitinides ...... 16

2.6 Alpha-glucosidase Inhibitors ...... 17

2.7 Glucagon-like Peptide-1 Receptor Agonists ...... 18

2.8 Sodium Glucose Cotransporter-2 Inhibitors ...... 20

2.9 Secular Trends and Prescribing Patterns for Oral Antihyperglycemic Agents ...... 22

3. Chapter 3: Materials and Methods ...... 24

3.1 Data Sources and Study Design ...... 24

3.2 Study Population ...... 25

3.3 Cohort Design ...... 26

3.4 Exposures ...... 27

3.5 Outcomes ...... 29

3.6 Sample Size Considerations ...... 30

3.7 Covariates ...... 31

3.8 Statistical Analysis ...... 32

4. Chapter 4: Results...... 36

4.1 Baseline Characteristics ...... 36

4.2 Propensity Matching ...... 40

4.3 Outcomes ...... 42

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4.4 Chronic Kidney Disease as an Effect Modifier ...... 46

5. Chapter 5: Discussion ...... 48

5.1 Summary of Findings ...... 48

5.2 Comparison with Previous Studies ...... 48

5.3 Implications ...... 53

5.4 Strengths and Limitations ...... 57

5.5 Conclusions ...... 59

Tables and Figures ...... 61

References ...... 91

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List of Tables and Figures

Figure 1. Classification of chronic kidney disease ...... 61

Table 1. Databases ...... 62

Table 2. Exposures ...... 63

Table 3. Safety outcomes ...... 64

Table 4. Baseline comorbidities ...... 65

Table 5. Baseline medications ...... 66

Figure 2. Study flow diagram for monotherapy cohort ...... 67

Figure 3. Study flow diagram for combotherapy cohort...... 68

Table 6. Pre-match counts of OHAs in ‘Other’ group ...... 69

Table 7. Pre-match baseline characteristics by monotherapy group ...... 70

Table 8. Pre-match baseline characteristics by combotherapy group ...... 72

Table 9. Post-match counts of OHAs in ‘other’ group ...... 74

Table 10. Post-match baseline characteristics by monotherapy group (sulfonylurea versus metformin) ...... 75

Table 11. Post-match baseline characteristics by monotherapy group (sulfonylurea versus

‘other’) ...... 77

Table 12. Post-match baseline characteristics by combotherapy group ...... 79

Table 13. Safety events, time at risk, crude rates and hazard ratios in monotherapy

(sulfonylurea versus metformin) ...... 81

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Table 14. Safety events, time at risk, crude rates and hazard ratios in monotherapy

(sulfonylurea versus ‘other’) ...... 83

Table 15. Safety events, time at risk, crude rates and hazard ratios in combotherapy ...... 84

Figure 4. Post-match subgroup analysis and interactions by chronic kidney disease in monotherapy (sulfonylurea versus metformin)...... 85

Figure 5. Post-match subgroup analysis and interactions by chronic kidney disease in monotherapy (sulfonylurea versus ‘other’) ...... 86

Figure 6. Post-match subgroup analysis and interactions by chronic kidney disease in combotherapy (sulfonylurea versus ‘other’) ...... 87

Figure 7. Post-match subgroup analysis and interactions by cardiovascular disease in monotherapy (sulfonylurea versus metformin)...... 88

Figure 8. Post-match subgroup analysis and interactions by cardiovascular disease in monotherapy (sulfonylurea versus ‘other’) ...... 89

Figure 9. Post-match subgroup analysis and interactions by cardiovascular disease in combotherapy (sulfonylurea versus ‘other’) ...... 90

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Glossary of Acronyms

A1C – Glycosylated Hemoglobin

ACE – Angiotensin-Converting Enzyme

ACR – Albumin to Creatinine Ratio

ADT – Emergency Department Admission, Discharge and Transfer

AKI – Acute Kidney Injury

ARB – Angiotensin II Receptor Blocker

ATC – Anatomical Therapeutic Chemical

CCB – Calcium Channel Blocker

CI – Confidence Interval

CIHI-DAD – Canadian Institute for Health Information Discharge Abstract Database

CKD – Chronic Kidney Disease

COPD – Chronic Obstructive Pulmonary Disease

CVD – Cardiovascular Disease

DERCA – Diabetes Education Resource for Children and Adolescents

DM – Diabetes Mellitus

DPP-4 – Dipeptidyl Peptidase-4

DSM – Diagnostic Services of Manitoba

EDIS – Emergency Department Information System eGFR – Estimated Glomerular Filtration Rate

ESKD – End-Stage Kidney Disease

GDM – Gestational Diabetes Mellitus

GFR – Glomerular Filtration Rate

GI – Gastrointestinal

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GLP-1 – Glucagon-like Peptide-1

HIV/AIDS – Human Immunodeficiency Virus Infection and Acquired Immune Deficiency Syndrome

HR – Hazard Ratio

ICD – International Classification of Diseases

KDIGO – Kidney Disease Improving Global Outcomes

KDOQI – Kidney Disease Outcomes Quality Initiative

KFRE – Kidney Failure Risk Equation

MALA – Metformin Associated Lactic Acidosis

MB – Manitoba

MHSC – Manitoba Health Services Commission

MI – Myocardial Infarction

NKF – National Kidney Foundation

OHA – Oral Antihyperglycemic Agent

PHIN – Personal Health Identification Number

PVD – Peripheral Vascular Disease

PY – Person-years

RCT – Randomized Controlled Trial

SGLT2 – Sodium-glucose Linked Transporter-2

SMD – Standardized Mean Difference

T1DM – Type 1 Diabetes Mellitus

T2DM – Type 2 Diabetes Mellitus

TZD – Thiazolidinedione

UK – United Kingdom

US – United States x

1. Chapter 1: Introduction

1.1 Pathophysiology of Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is a disease characterized by the combination of cellular resistance to insulin and dysregulated insulin release caused by pancreatic beta-cell dysfunction.1

By contrast, type 1 diabetes (T1DM) is characterized by autoimmune beta-cell destruction leading to absolute insulin deficiency.2 A result of T2DM is that the body’s ability to regulate glucose is impaired, leading to high blood glucose (hyperglycemia). Chronic hyperglycemia caused by T2DM can lead to major macrovascular complications like coronary artery disease and microvascular complications such as nephropathy, neuropathy and retinopathy.3

Compared with people who do not have diabetes, patients with T2DM have a 15% increased risk of all-cause mortality, which is twice as high in those who are younger than 55 years of age.4 The increased risk of coronary heart disease and ischemic stroke is more than 2-fold higher in individuals with T2DM.5 Longer duration of T2DM is associated with an increased risk of major osteoporotic fracture.6 In their lifetime, more than 60% of individuals with T2DM develop some form of retinopathy which can lead to blindness, more than 25% develop painful neuropathy and more than 20% develop foot ulcers which can lead to amputation.7-9 T2DM is insidious, often remaining undiagnosed for many years and thus patients often present with diabetic complications evident at the time of diagnosis.10

1.2 Diagnosis and Treatment of Type 2 Diabetes Mellitus

To diagnose whether an individual has T2DM, the current recommendation is to measure glycosylated hemoglobin (A1C) as an indicator of plasma glucose concentration. A threshold of

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≥ 6.5% is considered diagnostic for diabetes, and 5.7%-6.4% is categorized as pre-diabetes.11

Another option to detect T2DM is to directly measure plasma glucose concentration where a fasting glucose ≥ 7 mmol/L confirms the diagnosis.11 However, A1C testing is the preferred option in clinical practice because it best reflects chronic blood glucose values, whereas plasma glucose testing is more unreliable and the requirement of fasting is inconvenient for patients.12

Differentiating T2DM from T1DM at diagnosis is based on the clinical history, and results of biochemical tests, including C-peptide concentrations and absence of autoantibodies.1

Some weight-loss interventions such as a diet low in calories and carbohydrates, an exercise program or bariatric surgery can reverse or delay development of T2DM.13-15 However, the diet and exercise programs required to put T2DM into remission have limited long-term adherence, whereas bariatric surgery is an invasive procedure with risks of complication.16, 17 Therefore, individuals with T2DM typically treat their hyperglycemia pharmacologically with oral antihyperglycemic agents (OHAs). These agents work by stimulating insulin production, decreasing insulin resistance, or reducing the absorption of glucose. If OHAs fail to properly control A1C some T2DM patients add insulin to their medication regimen by injecting it directly. Insulin is required treatment for patients with T1DM.

1.3 Epidemiology and Economic Impact of Type 2 Diabetes Mellitus

Generally, the causal mechanisms for T2DM are both genetic and environmental. Studies of the human genome have identified common genetic traits associated with T2DM and obesity.18, 19

Many of the lifestyle risk factors for T2DM include dyslipidemia, excessive sugar and fat consumption, hypertension, and increased sedentary time.20 The increased prevalence of these

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lifestyle factors combined with an ageing population have contributed to a quadrupling of the global T2DM population between 1980 and 2004.21

Nearly 400 million people worldwide are living with T2DM and in 20 years the number is projected to rise to 600 million.22 Lost productivity and the burden of T2DM treatment and its related conditions have cost the global economy $825 billion annually.23 In Canada alone, 2.6 million adults currently live with T2DM and by 2035 it is estimated that 3.5 million adults will be impacted by the disorder.22 The projected increase in prevalence cannot be solely attributed to an ageing population, as there is evidence that the incidence of youth-onset T2DM is increasing in Canada, particularly in Manitoba which has by far the highest incidence compared to all other provinces.24, 25 In Manitoba, the direct cost of treating diabetes is estimated to total $258 million per year.26

1.4 Pathophysiology of Chronic Kidney Disease

A major function of the kidneys is to filter and excrete in the urine, soluble toxins and waste products of metabolism that would otherwise accumulate and harm the organism.27 Chronic kidney disease (CKD) refers to a chronic reduction in filtration, or other evidence of chronic kidney damage such as protein leakage in the urine.28 Patients with CKD are at much higher risk for cardiovascular disease (CVD) and acute kidney injury (AKI) compared to the general population and are more likely to reach an untimely death (mostly cardiovascular-related) before progressing to end-stage kidney disease (ESKD).29 The risk of such adverse events heightens as the severity of CKD increases. Having moderate kidney disease increases your risk of all-cause mortality by 20% and cardiovascular events by 40% whereas ESKD is associated with a 6-fold higher risk of all-cause mortality and a 3-fold higher risk of a cardiovascular event.30

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1.5 Diagnosis and Treatment of Chronic Kidney Disease

CKD is operationally defined as an estimated glomerular filtration rate (eGFR) lower than 60 mL/min per 1.73 m2 or a urine albumin to creatinine ratio (ACR) of 3 mg/mmol for 3 months or longer (Figure 1).31 The gold standard for calculation of glomerular filtration rate (GFR) involves injection and timed measurement in blood and urine of an exogenous filtration marker such as the sugar inulin. This procedure is cumbersome and impractical; therefore, values of GFR are in the clinical setting are typically calculated with estimating equations based on serum creatinine.32

There is no cure for CKD. However, treatment with angiotensin-converting enzyme (ACE) inhibitors and/or angiotensin II receptor blockers (ARBs) can slow both the progression of albuminuria and the decline of kidney function.33 Optimal management of CKD involves glycemic control, low blood-pressure and low-density lipoprotein cholesterol targets and a diet low in sodium and potassium. 33

1.6 Epidemiology and Economic Impact of Chronic Kidney Disease

It is estimated that the current global prevalence of CKD is 11-13%.34 Similar numbers are seen in Canada where a recent cross-sectional study approximates that 12.5% of Canadians have

CKD, including 3.1% (0.73 million adults) who have a moderate to severe decline in kidney function.35 Population aging and global increase in CKD risk factors including T2DM, hypertension and obesity suggest that the potential number of cases of CKD and kidney failure will continue to increase.32

CKD is also a very costly disease both to individuals and the healthcare system, especially at the end-stage level where life sustaining dialysis is required.36, 37 The burden of illness caused by

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CKD often leads to loss of income and treatment may require high out-of-pocket expenses for treatment.32 This is potentially devastating to the finances of people of low socioeconomic status where the disease is growing fastest.38

Because T2DM and hypertension are the leading causes of CKD and treatment of CKD is very expensive over the lifetime of a patient, screening patients with T2DM or hypertension for CKD, even if they are asymptomatic, is considered cost-effective. 39, 40 Furthermore, early identification of CKD is important to prevent disease progression and reduce the risk of cardiovascular morbidity and mortality.32 Screening can also improve the poor awareness of CKD which is a global phenomenon that transcends all demographic groups (including physicians) leading to delayed diagnosis and suboptimal intervention.41

1.7 Concomitant Type 2 Diabetes and Chronic Kidney Disease

More than one quarter of Canadian patients with Stage 3-5 CKD have concomitant T2DM and diabetic nephropathy is the primary cause of kidney failure in nearly half of all people who start dialysis.35 Because T2DM often goes undiagnosed, estimates of CKD prevalence in the T2DM population may be as high as 40%.42 Patients with kidney failure from diabetic nephropathy are more likely to be frail, and have a poor survival and quality of life.43

People with CKD and T2DM are a complex population and the two conditions interact with each other negatively. The progression of CKD accelerates when T2DM is poorly controlled and insulin resistance increases when kidney function declines.44 Reduced kidney function often exacerbates the adverse cardiovascular outcomes (chest pain, ischemia, myocardial infraction, serious arrhythmia etc.) associated with T2DM and patients with concomitant T2DM and CKD

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have a higher risk of mortality and CVD when compared to individuals with T2DM who do not have CKD.45, 46 Furthermore, patients with CKD and T2DM also experience a higher incidence of hypoglycemia than individuals with either CKD or T2DM alone.47, 48 For these reasons, glycemic control is even more important in this population.

It is well known that improvement in glycemic control (even from poor to moderate) is associated with substantial health benefits and reduced costs but the benefit of intensive glycemic control, defined as an A1C < 6.5%, remains controversial.49, 50 Although intensive glycemic control reduces the risk of microvascular complications, it may not reduce all-cause mortality and is associated with undesirable events such as severe hypoglycemia (low blood sugar) and weight gain.51-56 For most patients, achievement of optimal glycemic control is possible, but may require treatment with two or three OHAs, with or without insulin.49

Unfortunately, patients with T2DM and CKD have fewer therapeutic options as many medications are either contraindicated in the setting of reduced kidney function or require careful dose reductions.57-61 With dose reductions, patients with CKD may not achieve the glycemic targets that would otherwise be possible with standard dosage.62, 63

1.8 Study Objectives and Thesis Statement

Because patients with T2DM and CKD are at a higher risk of morbidity and mortality, physicians must carefully balance the safety and effectiveness tradeoffs of each OHA they prescribe. For example, sulfonylureas are among the most efficacious (for glycemic control) and commonly used drugs for the treatment of patients with T2DM but may be associated with safety concerns.64-67 However; rigorous comparative data on the long-term safety and efficacy of OHAs in real world settings are sparse, particularly in individuals with bothT2DM and CKD. The US

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Food and Drug administration accepts A1C as a primary outcome for new drug approval and as such, most new OHAs come to market without long term event driven trials. For newer OHAs, where trials are available, comparisons with sulfonylureas are rarely performed and the ability to detect rare adverse effects is limited.

In this thesis, data from the Manitoba Centre for Health Policy (MCHP) at the University of

Manitoba was analyzed to compare the safety and effectiveness of sulfonylureas to other OHAs.

We hypothesized that: 1) sulfonlyureas will be associated with a higher risk of all-cause mortality, cardiovascular events, severe hypoglycemia and fractures in patients with T2DM and concomitant CKD when compared to other OHAs; 2) sulfonylureas will be no more effective at lowering A1C when compared to other OHAs.

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2. Chapter 2: Literature Review

In this chapter we will review relevant literature related to the OHAs currently on the market in

Canada. Specifically, we will review pharmacokinetics, benefits, side effects, efficacy and safety profiles and guidelines in patients with CKD for: 1) metformin; 2) sulfonylureas; 3) thiazolidinediones (TZDs); 4) dipeptidyl peptidase-4 (DPP-4) inhibitors; 5) meglitinides; 6) alpha-glucosidase inhibitors; 7) glucagon-like peptide-1 (GLP-1) receptor agonists and 8) sodium-glucose linked transporter-2 (SGLT2) inhibitors. We will also briefly describe the current trends and patterns in prescribing OHAs for individuals with CKD in Canada.

2.1 Metformin

Metformin has been on the market in Canada since 1972. It works mainly by suppressing excessive hepatic glucose production and is excreted unchanged in the urine.68 Metformin is effective at lowering A1C by more than 1.0% and it rarely causes hypoglycemia.52 Its low cost, neutral effects on weight, and possible cardiovascular benefits has resulted in the widespread recommendation that this therapy be considered the first line treatment of T2DM.69-72

Metformin has been associated with several beneficial effects. Data available from subgroup analyses suggest benefits with metformin for mortality and CVD when compared to all other

OHAs in both the general population and in individuals with Stage 3a CKD.73, 74 Additionally, metformin has been associated with a reduced risk of fracture compared to a group of matched control subjects with T2DM who used different OHAs and/or insulin.75, 76

Metformin is not well tolerated due to negative gastrointestinal (GI) side effects77 and has also been associated with serious adverse events, the most concerning being the occurrence of

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metformin associated lactic acidosis (MALA), an accumulation of lactate in the body fluids, a rare but often fatal condition.78, 79 As with other biguanides, metformin inhibits mitochondrial respiration (i.e. oxidative metabolism), increasing anaerobic metabolism and thus lactate production.80 This effect is concentration dependent, and, because the drug is renally cleared, is most pronounced in the setting of severe AKI.81 Indeed, MALA typically occurs in patients with severe AKI from sepsis or shock, where, in addition to elevated metformin levels, impaired tissue perfusion further disrupts clearance of lactate.82 The mortality associated with MALA in this setting is >50%.82

MALA is fortunately rare. In Europe, where metformin has been on the market for over 50 years, the risk of lactic acidosis was 3 cases per 100,000 patient years.79 Since renal impairment is the most frequent underlying complication in lactic acidosis cases it is thought that the combination of metformin and low kidney function may exacerbate the risk of lactic acidosis83 but the degree to which CKD (as opposed to severe AKI) increases the risk of MALA is unclear. At least one large observational trial in patients with an eGFR < 60 ml/min/1.73 m2 found that the crude incidence rate of lactic acidosis was more than 3 times greater in metformin users than in non- metformin users (adjusted HR 6.37 [95% CI 1.48–27.5]) and that the risk increased with higher doses.84 However, other studies have not found an association between metformin and lactic acidosis in patients with CKD.85, 86

Current guidelines from Diabetes Canada recommend caution when prescribing metformin to patients with an eGFR between 30 and 60 ml/min/1.73 m2 and to halt the use of metformin altogether when eGFR is less than 30 ml/min/1.73 m2.87 Similar guidelines have been issued by the Kidney Disease Improving Global Outcomes (KDIGO) working group, the main differences

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are that KDIGO recommends metformin at full dose in patients with an eGFR > 45 ml/min/1.73 m2 and a review of guidelines for eGFR between 30 and 45 ml/min/1.73 m2.88 The labeling of metformin is even more cautious than the guidelines.89 However, previous studies have shown that metformin is often prescribed outside clinical guidelines.90-94 Furthermore, new data suggest that metformin plus a DPP4-inhibitor should be safe in eGFR < 30 ml/min/1.73 m2 because of the absence of pharmacokinetic interactions between the two drugs.95 Because metformin has not been well studied in patients with CKD in a real world setting, a large observational study may be useful to determine the long-term safety and effectiveness of metformin in patients with higher stages of CKD.

2.2 Sulfonylureas

Sulfonylureas are among the oldest and most commonly prescribed T2DM medication, having been on the market in Canada since the 1960s.71 Sulfonylureas are insulin secretagogues, a class of drugs that bind to sulfonylurea receptors on pancreatic beta-cells and stimulate insulin release.77 They continue to be used because they are inexpensive and effectively lower glucose, however, sulfonylureas are associated with weight gain which can adversely affect cardiometabolic risk, eroding the benefit of improved glycemic control.96, 97

First generation sulfonylureas (i.e. chloropropamide and tolbutamide) have longer half-lives than second generation sulfonylureas (i.e. gliclazide, glimepiride and glyburide) which are further prolonged in the setting of reduced GFR.98 Therefore, these first generation OHAs no longer recommended in patients with CKD. 98 Second generation sulfonylureas, for which this effect is less pronounced, continue to be widely prescribed in the CKD population. 99 Sulfonylureas are primarily excreted in the urine (50%-70%) but are also extensively metabolized in the liver.100

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The excretion in the urine is mainly metabolites and the drug does not appear to accumulate in the kidney.101, 102 The shorter half-life second generation sulfonylureas (e.g. gliclazide) can still be prescribed in the presence of mild or moderate renal impairment, but may require careful dose adjustments.45, 89, 103, 104

The major adverse risk with all sulfonylureas is the potential for severe hypoglycemia, which can be worse in patients with CKD.105 This is because a reduction in GFR prolongs their pharmacodynamic action.106 However, sulfonylureas differ in their hypoglycemia risk.107

Patients on hemodialysis are at higher risk of severe hypoglycemia with the use of some sulfonylureas such as glyburide.45, 89, 103, 104 By contrast, there is evidence to suggest that gliclazide can be used in hemodialysis patients if given at small doses titrated up every 1–4 weeks.108 Furthermore, one study found no excess hypoglycemia with gliclazide compared to placebo in patients with mild to moderate impaired renal function but no significant reduction in

A1C.109 Even so, the consensus is that all sulfonylureas can increase the risk of and some guidelines suggest that sulfonylureas be avoided altogether in patients with CKD.45, 89, 103, 104

Additional potential adverse effects include fracture risk and cardiovascular morbidity. It is speculated that sulfonylureas may be associated with fractures due to falls resulting from hypoglycemia.76 However, when compared against a group of matched controls who do not use sulfonylureas, the evidence for the relationship between sulfonylurea use and fracture is mixed110, 111 and no trials have analyzed this in the CKD population. Sulfonylureas may increase the risk of cardiovascular events since they may interfere with ischemic preconditioning and if present at the time of a myocardial infarction, may impair adequate coronary vasodilatation, thus resulting in a larger area of myocardial damage.112 However, a systematic review of placebo

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controlled trials conducted by Selvin et al found no association between sulfonylureas and cardiovascular events.113 Few direct controlled trials overall have been conducted with sulfonylureas in patients with reduced kidney function despite the frequency of prescriptions for these medications.89, 112

Current clinical guidelines from Diabetes Canada recommend caution in prescribing glyburide

(known as glibenclamide outside North America) at an eGFR between 30 and 60 ml/min/1.73 m2 and stopping the drug once eGFR is below 30 ml/min/1.73 m2. Diabetes Canada also recommends caution in prescribing glimepiride and gliclazide when eGFR is between 15 and 30 ml/min/1.73 m2 and stopping the drug once eGFR is below 15 ml/min/1.73 m2.87 In contrast,

KDIGO guidelines recommend avoiding glyburide in all patients with CKD and stopping glimepiride and gliclazide when eGFR is below 30 ml/min/1.73 m2.88

2.3 Thiazolidinediones

TZDs act as insulin sensitizers by reducing insulin resistance, increasing glucose uptake in muscle and adipose tissue, and decreasing hepatic glucose utilization.114 TZDs are effective at lowering A1C, are long lasting, less likely to cause hypoglycemia than sulfonylureas and insulin, and less likely than metformin to cause GI side effects but are also typically more costly than more commonly prescribed OHAs.71 In Canada, both rosiglitazone and pioglitazone are available.

TZDs were originally seen as a promising alternative to the riskier, more commonly prescribed

OHAs in patients with T2DM and CKD. Through peroxisome proliferator-activated receptors activation, TZDs have been shown to have favorable hemodynamic and anti-inflammatory

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effects on the kidneys.115 Furthermore, TZDs are extensively metabolized in the liver, barely excreted through the urine, and have similar pharmacokinetics and tolerability for those with and without renal impairment.115-118 Therefore, during initial testing no dose adjustments were deemed necessary for TZDs in the CKD population, although they may be warranted for patients on dialysis.103, 119-121 In hemodialysis patients, TZDs are associated with significantly lower all- cause mortality compared to all other agents not including insulin.122

However, TZDs have been associated with a number of adverse events such as bone fractures, osteoporosis and weight gain.123, 124 The most worrisome side effect from a nephrology point of view is fluid retention, which most often manifests as edema and congestive heart failure.125-127

Rosiglitazone in particular has been controversially associated with myocardial infarction in both the general T2DM population and among diabetic hemodialysis patients, resulting in its withdrawal from several markets.77, 128, 129 Not all studies agree, however, and other analyses showed that most hemodialysis patients on rosiglitazone met glycemic control targets and experienced no major safety issues.130 In a 6 month study, rosiglitazone added to a sulfonylurea was well tolerated and resulted in an overall 0.7% decrease in A1C in a population of patients with an eGFR 30-80 ml/min/1.73 m2, a better performance than a sulfonylurea plus placebo, with no difference in adverse events.131

In contrast to the data on rosiglitazone, pioglitazone may have beneficial effects on cardiovascular outcomes compared to placebo in T2DM patients with eGFR < 60 ml/min/1.73 m2.60, 132 In patients on hemodialysis, pioglitazone outperformed placebo in reducing A1C over

24 weeks, and was not associated with more hypoglycemia or other adverse events during a 12

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week period.133, 134 Nevertheless, because of the concerns with rosiglitazone, pioglitazone is not recommended in New York Heart Association (NYHA) Class III and IV heart failure.135

These contrasting data on TZDs with respect to cardiovascular outcomes and safety in CKD result in therapeutic uncertainty, a fact reflected in conflicting guideline recommendations.

Diabetes Canada, for example, does not recommend the use of TZDs in patients with eGFR < 30 ml/min/1.73 m2, whereas, the National Kidney Foundation (NKF) do not recommend any dose adjustments regardless of eGFR.87, 136 There is broad consensus that further study of TZD use in patients with CKD and kidney failure is needed.137

2.4 Dipeptidyl Peptidase-4 Inhibitors

DPP-4 inhibitors are newer OHAs that reduce fasting plasma glucose, postprandial glucose, and

A1C levels by inhibiting the breakdown of incretins including glucagon-like peptide 1 and glucose-dependent insulinotropic polypeptide.138, 139 This stimulates the release of insulin in a glucose-dependent manner. DPP-4 inhibitors are typically used as add-on therapy but they can be used for first-line therapy. They have neutral effects on body weight, and are associated with a low risk of hypoglycemia.138-140 However, DPP-4 inhibitors are also more costly than the more commonly prescribed older OHAs and have been linked with nasopharyngeal symptoms, headaches and rarely angioedema.71, 141 DPP-4 inhibitors have also been linked with pancreatitis, but this association remains controversial.142

There are 3 types of DPP-4 inhibitors currently on the Canadian market: linagliptin, saxagliptin and sitagliptin. Most DPP-4 inhibitors are primarily metabolized in the kidneys. Upwards of 60% of saxagliptin and sitagliptin are eliminated through the urine.143 However, linagliptin is

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primarily metabolized in the liver and only 5% of its elimination is through the urine.143 For this reason, linagliptin can be used in people with CKD as its pharmacokinetic profile is similar in patients with and without renal impairment.144

Various small trials, with follow-up ranging from 12-52 weeks have shown that DPP-4 inhibitors decreased A1C by an average of 0.5-1.0% in patients with CKD 143, 145-151 The majority of these studies showed a statistically significant decrease in A1C compared to placebo, and there was no difference in efficacy in individuals with CKD vs. normal kidney function. DPP-4 inhibitors have also been associated with few hypoglycemia events in CKD patients.143, 146, 148-151

Cheng et al conducted a meta-analysis of randomized controlled trials (RCTs) comparing DPP-4 inhibitors vs. placebo in subjects with CKD and also detected no difference in all-cause mortality.152 There is mixed evidence for the cardiovascular benefit or harm of DPP-4 inhibitors in the general T2DM population. Many studies have shown no connection between DPP-4 inhibitors and an increase in cardiovascular events such as cardiovascular related hospital admissions.153-155 On the other hand, DPP-4 inhibitors have been associated with an increase in hospitalizations for heart failure but have also been linked to CVD risk reduction.156, 157

Cardiovascular outcomes associated with DPP-4 inhibitors have been understudied in the CKD population. Studies examining the association of DPP-4 with fracture risk have yielded mixed results in the general population, and are non-existent in CKD populations, since renal impairment is often part of the exclusion criteria in such research.158, 159

Current guidelines from Diabetes Canada and NKF recommend a dose reduction for saxagliptin and sitagliptin in patients with an eGFR < 45 ml/min/1.73 m2 and suggest caution with linagliptin in patients with an eGFR < 15 ml/min/1.73 m2.87, 136 Once patients initiate dialysis, a

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dose supplement after dialysis might be necessary because a 4 hour dialysis session removes approximately 23% of the dose.95, 160 There are currently no KDIGO guidelines for the usage of

DPP-4 inhibitors. In the setting of small, short DPP-4 inhibitor trials, further evidence is needed to assess the long-term safety and effectiveness of DPP-4 inhibitors in the CKD population.

2.5 Meglitinides

Meglitinides are metabolized in the liver, excreted mainly in the bile, and therefore do not accumulate in the kidney.135, 161, 162 Although structurally different, meglitinides are mechanistically similar to sulfonylureas as they are also insulin secretagogues. Meglitinides however, are shorter acting, have a lower risk of hypoglycemia, require frequent dosing, and are more effective for postprandial glycemic control than sulfonylureas.71 They lower glucose by regulating potassium channels in pancreatic beta-cells and stimulate endogenous insulin secretion within a few minutes.163 Since meglitinides are rapidly absorbed, they should be taken right before meals.137

There are two classes of meglitinides currently available for dispensary in Canada: nateglinide and repaglinide. Nateglinide is generally well tolerated and has a low risk of hypoglycemia in the general T2DM population.164 Because there is no change in its pharmacokinetics in patients with

T2DM and CKD compared to healthy controls and no relation between time to elimination and renal function, nateglinide is in theory safe for patients with CKD without dose adjustment.165, 166

However, nateglinide has active metabolites that can accumulate in patients with reduced kidney function, causing major hypoglycemia.162 Therefore, nateglinide may be unsafe for individuals with advanced CKD and in patients on dialysis.167 However, the risk in hemodialysis may be slightly lower as there is significant dialytic clearance of these active metabolites.166

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Unlike nateglinide, repaglinide does not produce active metabolites and less than 8% of repaglinide is excreted unchanged in the urine.167 Therefore, no dose adjustments are thought to be necessary for this OHA in patients with mild-to-moderate impairment of renal function.103, 162

However, there are changes in pharmacokinetics of repaglinide in patients with eGFR < 30 ml/min/1.73 m2. As a result, repaglinide should be initiated conservatively in such patients.162

Overall, repaglinide is generally well tolerated, hypoglycemic episodes are rare, and there are no differences in glycemic control or adverse events between patients with normal kidney function or any stage of CKD.168, 169

Current guidelines are variable but suggest that meglitinides can be prescribed with caution for patients with moderate CKD and may cause less hypoglycemia than other OHAs.87, 136,169, 170

However, no large scale studies assessing the efficacy and safety of meglitinides in patients with

T2DM and CKD, exist to support these recommendations. Canadian labeling suggests no dose adjustments in renal impairment and labeling in the United States (US) recommend initiating repaglinide at a dose of 0.5 mg/day and to subsequently, carefully titrate doses.89

2.6 Alpha-glucosidase Inhibitors

Rather than stimulating insulin production to regulate glucose, alpha-glucosidase inhibitors directly decrease and slow the absorption of carbohydrates into the body. As a result, fewer carbohydrates are degraded into glucose and absorbed. A consequence of this is that undegraded, unabsorbed carbohydrates remain in the colon leading to abdominal discomfort, diarrhea and flatulence.77 The side effects are more commonly reported in the first 3 months of treatment and may be dose and diet dependent.171, 172 Alpha-glucosidase inhibitors are more popular and well- tolerated in Asia than in North America, which may relate to differences in diet.173

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Currently, acarbose is the only alpha-glucosidase inhibitor available in Canada. Acarbose is not excreted by the kidney and therefore no dose adjustment is necessary at any stage of kidney disease or on dialysis.77 However, some guidelines suggest that acarbose not be used in patients with elevated serum creatinine (> 2 mg/dL) because of a risk of accumulation that may lead to liver failure.71 In the general T2DM population, acarbose has been associated with a modest decrease in A1C and a low risk of hypoglycemia.174 Acarbose has also been shown to decrease coronary heart disease, cardiovascular death, congestive heart failure, cerebrovascular events, peripheral vascular disease and hypertension when compared to placebo.175

However, efficacy and safety data for the use of acarbose in CKD and ESRD patients is limited.

Because of this lack of data, Diabetes Canada does not recommend acarbose as treatment for

T2DM in individuals with an eGFR < 25 ml/min/1.73 m2 and the NKF cautions to avoid acarbose when eGFR is less than 30 ml/min/1.73 m2.87, 136

2.7 Glucagon-like Peptide-1 Receptor Agonists

Like DPP-4 inhibitors, GLP-1 receptor agonists are incretin-based OHAs that stimulate the release of insulin from the pancreas in a glucose dependent fashion. They have similar safety profiles compared to DPP-4 inhibitors in patients with CKD and are typically used as add-on therapy to metformin or sulfonylureas.176 However, GLP-1 receptor agonists are administered as a subcutaneous injection as opposed to orally and their dosing regimen can range from twice daily to once weekly. Through a process of gastric emptying and appetite suppression, GLP-1 receptor agonists help patients lose weight.46 Although not designed to treat hypertension, GLP-1 receptor agonists have also demonstrated efficacy in lowering systolic blood pressure, including for patients with lower eGFR, a beneficial effect perhaps associated with weight loss.177, 178

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The first GLP-1 agonist was prescribed in Canada in 2010 and there are currently 3 available: dulaglutide, exenatide and liraglutide. Exenatide is primarily eliminated by the kidney. As renal function decreases, the half-life of exenatide increases.179 As such, there have been documented cases where exenatide has led to ischemic renal failure in some patients.180 Therefore, caution is warranted when prescribing exenatide in patients with renal impairment.181 By contrast, both dulaglutide and liraglutide are not metabolized solely by the kidney and their pharmacokinetics are unaffected by renal impairment.105, 182 Hence, no dose adjustment of either is thought to be necessary in theory for those with reduced kidney function, including those on dialysis.77, 181, 183

In short term trials (12-26 weeks), GLP-1 receptor agonists been effective at lowering A1C by approximately 1%, though the decrease has not been statistically significant compared to placebo in some cases.178, 184-186 For liraglutide, efficacy has been consistent across all strata of renal function.177 Recently, results of a long-term RCT comparing liraglutide as add-on therapy vs. a placebo has shown a difference in efficacy of 0.4% in lowering A1C over 36 months.187

Because of their physiological action inhibiting gastric emptying, the most common side effects of GLP-1 receptor agonists have been nausea, vomiting.178, 184, 185, 188 Users of GLP-1 agonists have also reported experiencing diarrhea in higher proportions than non-users. 184, 185, 188 A meta- analysis of 9 trials showed no difference between dulaglutide and placebo in adverse cardiovascular events.189 In a pooled analysis of 19 trials, there was no statistical difference in cardiovascular events or hypoglycemia in users of exenatide compared to a comparator group of placebo or insulin.188 In a 5 year follow-up of patients at risk for cardiovascular events, liraglutide had lower rates than placebo for severe hypoglycemia and for the primary composite outcome of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal

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stroke.189 The association was strongest in the subgroup of patients with an eGFR between 30 and 60 ml/min/1.73 m2 who made up 23% of entire the cohort.

GLP-1 receptor agonists have also been associated with acute kidney injury and acute pancreatitis.190, 191 However, other larger observational studies have disputed the association with pancreatitis.192, 193 Data on GLP-1 receptor agonists and fracture risk is scarce and yet to be studied in patients with CKD. A large observational study in the general T2DM population has shown no association between GLP-1 receptor agonists and bone fracture when compared to a group other OHAs.194

Current guidelines from Diabetes Canada recommend a dose reduction for exenatide in patients with an eGFR < 50 ml/min/1.73 m2 and consistent with the NKF, Diabetes Canada indicates exenatide is contraindicated in individuals with an eGFR < 30 ml/min/1.73 m2.87, 136 Even though the labeling of dulaglutide and liraglutide describes it as safe for patients with renal impairment, there have not yet been enough studies in these patients.89 Therefore, Diabetes Canada recommends against their use in patients with an eGFR < 50 ml/min/1.73 m2 and the NKF recommends against their use in patients with an eGFR < 60 ml/min/1.73 m2.87, 136

2.8 Sodium Glucose Cotransporter-2 Inhibitors

SGLT2 inhibitors are the latest class of OHA. They lower blood sugar by inhibiting glucose reabsorption in the kidney and inducing excretion of glucose in the urine.195 Unlike insulin secratagogues, SGLT2 inhibitors do not stimulate insulin through beta-cell function. As such, their mechanism of action is complementary to the mechanisms of other OHAs and therefore they are suitable as an add-on for any type of combination therapy.196

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SGLT2 inhibitors are known for their tolerability, efficacy (reduce A1C from 0.5 - 1.5%), ability to lower blood pressure and weight, and low hypoglycemia risk .197-199 Additionally, SGLT2 inhibitors slow progression to macro- or microalbuminuria and reduce urine ACR in placebo controlled trials.145, 200-202 However, in addition to these renoprotective benefits, SGLT2 inhibitors have paradoxically been shown to reduce eGFR, although this effect has been shown to be attenuated in the long-term.200, 202-204

SGLT2 inhibitors were approved in Canada in 2014 and there are currently 3 of them on the market: canagliflozin, dapagliflozin and empagliflozin. Because of how they function, a significant portion of SGLT2 inhibitors are excreted renally but there are differences. Over 75% of dapagliflozin is excreted renally whereas only 60% of canagliflozin and empagliflozin are excreted in the urine.195 In early phase trials, no difference in mild or serious adverse events were noted at different levels of kidney function.195, 205 Yet, as renal function decreased, less drug was excreted in the urine, rendering the drug less effective in patients with renal impairment.195

In RCTs comparing SGLT2 inhibitors to placebos as add-on therapy in patients with an eGFR between 30 and 60 ml/min/1.73 m2 both canagliflozin and empagliflozin demonstrated a modest reduction in A1C compared to placebo (0.4%), whereas dapagliflozin was not associated with a significant comparative reduction.145, 200, 202 Canagliflozin and dapagliflozin were also associated with a decrease in body weight and blood pressure in the same population, mirroring benefits seen in the general population.145, 200, 202 There are currently limited safety results in the moderate

CKD population. Dapagliflozin, but not canagliflozin or empagliflozin, appears to increase risk of bone fracture in patients with an eGFR between 30 and 60 ml/min/1.73 m2 200 202, 204

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However, empagliflozin does reduce the risk of a composite outcome of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke or hospitalization for unstable angina for patients with and without CKD. 204 This finding held true in a subgroup analysis of individuals with high baseline risk of cardiovascular events.206 Empagliglozin has also been associated with a higher risk of hypoglycemia in patients with Stage 4 CKD.207

Ongoing RCT’s will assess these cardiovascular outcomes with canagliflozin, but results from phase 2 and 3 trials appear positive .208, 209 Overall, higher risk of urinary tract infections is a consistently reported adverse effect of all SGLT2 inhibitors.202, 205

Because SGLT2 inhibitors have only been recently approved, the latest nephrology and diabetes guidelines do not provide any specific safety recommendations for individuals with T2DM and

CKD as there is no consensus on which level of kidney function to stop SGLT2 inhibitors or reduce their dosage. Current labeling in Canada lists dapagliflozin as contraindicated in patients with an eGFR < 60 ml/min/1.73 m2 and canagliflozin and empagliflozin as contraindicated in patients with an eGFR < 45 ml/min/1.73 m2.89

2.9 Secular Trends and Prescribing Patterns for Oral Antihyperglycemic Agents

A population based study of 144,252 older adults with diabetes and chronic kidney disease in

Ontario found that from 2004 to 2013 metformin was the most commonly prescribed OHA among patients with an eGFR between 30 and 60 ml/min/1.73 m2.91 Insulin was the dominant treatment for T2DM patients with an eGFR < 30 ml/min/1.73 m2 and prescriptions for sulfonylureas became more common than metformin at lower levels of eGFR.91 Over the 10 year study period, prescriptions for TZDs decreased for this population while prescriptions for DPP-4 inhibitors increased, possibly due to cardiovascular concerns with the former.91 Meanwhile, at

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the beginning of the study glyburide was the sulfonylurea most commonly prescribed (45.5% of patients) whereas gliclazide was rarely prescribed (0.6% of patients).91 However, at the end of the study period glyburide was only prescribed for 9.5% of patients whereas gliclazide was prescribed for 26.4% of patients.91 The reversal is likely due to recognition of the increased risk of hypoglycemia associated with glyburide.

Recent national cross-sectional studies comparing the treatment of T2DM in the First Nations and the general Canadian population show similarities and differences in how these two groups manage their T2DM.4, 210 While both groups are equally likely to manage their T2DM without insulin or an OHA (12%), the general Canadian population was more likely to use insulin either as monotherapy or in combination with one or more OHA (52% vs. 27%), whereas the First

Nations population were more likely to use only OHAs either as monotherapy and combination therapy (61% vs. 36%).4, 210 This may be due to differences in drug coverage benefits: the First

Nations and Inuit Non-Insured Health Benefits (NIHB) program offers prescription drug coverage of all types of OHAs, thereby encouraging the use of more novel OHAs211 , whereas most provincial pharmacare plans, such as the one in Manitoba, typically cover only metformin and sulfonylureas, which are cheaper.212, 213

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Chapter 3. Materials and Methods

3.1 Data Sources and Study Design

We conducted a retrospective cohort study using various administrative health databases housed at the Repository at the MCHP at the University of Manitoba which holds population-wide, anonymized health information for Manitoba residents.214, 215 Our project was approved by the

Manitoba Health Information Privacy Committee (HIPC 2016/2017 - 03). This study also received ethics approval from the University of Manitoba Health Research Ethics Board (ethics file number HS19580). Our study period was from April 1, 2006 until March 31, 2016. We analysed 9 specific databases. Where applicable, each database contains a unique health record for a Manitoba resident.

We included the Diabetes Education Resource for Children and Adolescents (DERCA) database which contains demographic, clinical and laboratory data for children diagnosed with T1DM and

T2DM since 1986. We incorporated data from Diagnostic Services of Manitoba (DSM) which includes blood and urine tests conducted at various rural laboratories and at the hospitals in

Winnipeg and the Westman region. We used information from the Drug Program Information

Network (DPIN) which includes all prescription drugs dispensed in an outpatient setting. We included the Emergency Admission, Discharge, and Transfer (ADT) database as well as the

Emergency Department Information System (EDIS) database which both document the date and the reason for an emergency room visit. We incorporated the Canadian Institute for Health

Information Hospital Discharge Abstracts Database (CIHI-DAD) which records all inpatient hospital admissions with patient diagnoses and procedures (if applicable). We used the Manitoba

Health Insurance Registry which tracks health insurance coverage dates, date of birth and sex for

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all individuals who have been eligible for coverage with Manitoba Health at some point since

1970. We included the Medical Claims/Services database from the Manitoba Health Services

Commission (MHSC) which chronicles the billing information of every physician visit.

Finally, we incorporated the Vital Statistics database which includes all Manitoba residents who have died since 1970 with details on the date and cause of death. Each database and the time period we used to extract relevant data are listed in Table 1. All databases are de-identified but all the information in each database can be linked to a unique individual through a scrambled personal health identification number (PHIN). Only Manitoba Health possesses the algorithm that scrambles the PHINs thereby preventing any breach of patient privacy.

3.2 Study Population

Subjects were potentially eligible for our study if they met one the following criteria: at least one hospitalization with a diabetes diagnosis or at least 2 physician claims with a diabetes diagnosis or at least one prescription for an OHA or insulin. This case definition for diabetes was shown to have 92.7% sensitivity and 98.6% specificity when validated against self-reported survey data and is used in many MCHP deliverables.216 Diagnoses were determined via the International

Classification of Diseases (ICD). Records in CIHI-DAD prior to 2005 and all records in MHSC use the ICD ninth revision (ICD-9) for diagnosis and procedure coding. Where ICD-9 was used, a prefix of 250 was considered a diagnosis of diabetes. The vast majority of records in CIHI-

DAD beginning in 2005 use the ICD tenth revision (ICD-10) for coding. Where ICD-10 was used, a prefix in the range of E10-E14 was considered a diagnosis of diabetes. All records in

DPIN use Anatomical Therapeutic Chemical (ATC) codes to classify different types of drugs.

Any prescription with an ATC prefix of A10 was considered a diagnosis of diabetes.

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Individuals were automatically excluded from the study if they had gestational diabetes (GDM),

T1DM, or if they only ever used insulin to treat their hyperglycemia. We identified cases of

GDM through the CIHI-DAD database as a diagnosis with a prefix of 648.8 (ICD-9) or O24.4

(ICD-10) in the year prior to the index date. We determined someone had T1DM if at any point they met one of the following criteria: 1) had a T1DM diagnosis recorded in the CIHI-DAD database, i.e. a prefix of 250.x1, 250.x3 (ICD-9) or E10 (ICD-10); 2) they ever had a prescription for an infusion pump, i.e. a Drug Identification Number of 00905739, 000908300, 00992968,

00992976, 00992984, or 00992991; 3) were ever entered into DERCA with a diagnosis of

T1DM.

3.3 Cohort Design

Once we determined who was potentially eligible to be part of the study, all of their relevant health records were made available for analysis in each database. Individuals were eligible to enter up to 2 different cohorts: a monotherapy cohort and a combotherapy cohort. The monotherapy cohort was reserved for patients who treated their T2DM using only one OHA. The combotherapy cohort was reserved for patients who were prescribed a second OHA while already on metformin or concurrently prescribed metformin with another class of OHA either separately or combined into a single pill.

To qualify for the monotherapy cohort an individual needed to be an incident user of an OHA during the study period. An incident user was required to have a wash-in period of 365 days without using any antidiabetic medication (OHA or insulin) before they filled their prescription for an OHA. After such a prescription was identified, we only included individuals who were 18 years of age or older and had active insurance coverage through Manitoba Health during the

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entire previous year. Individuals excluded on that basis could potentially be included in the cohort at a further date once they met the age, health insurance, and incident prescription requirements.

An incident user in the combotherapy cohort was required to have a wash-in period of 365 days without using any antidiabetic medication, with the exception of metformin, before they filled their prescription for an OHA other than metformin during the study. An individual needed to be in possession of metformin at the time of this prescription. After such a prescription was identified, the same exclusion criteria used for the creation of the monotherapy cohort was applied. The date an individual filled an incident prescription for a monotherapy or combotherapy OHA was defined as the index date for each cohort.

After the cohorts were created, we isolated a subgroup of individuals with a baseline serum creatinine test. We used the test result closest to the index date within a maximum of 1 year prior or 30 days after. We obtained serum creatinine values from the DSM database and used them to calculate eGFR with the CKD-Epidemiology Collaboration study equation.217 Subjects were classified into 3 groups: eGFR ≥ 60 ml/min/1.73 m2 (normal kidney function), eGFR 45-60 ml/min/1.73 m2 (Stage 3a), and eGFR < 45 ml/min/1.73 m2 (Stage 3b-5). Patients undergoing chronic dialysis treatment, identified through the MHSC database218, at the time of the index date were excluded from this subgroup.

3.4 Exposures

Incident exposures were identified from the DPIN database through their ATC code and date of dispense. A list of all the OHAs prescribed in Manitoba during the study period is provided in

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Table 2. Subjects in the monotherapy cohort were divided into 3 exposure groups: metformin users, sulfonylurea users, and ‘other’ OHA users. A metformin user is defined as someone whose first eligible entry into the cohort was the date they were dispensed metformin without any concomitant antidiabetic medication. A sulfonylurea user is defined as someone whose first eligible entry into the cohort was the date they were dispensed chloropropamide, gliclazide, glimepiride, glyburide, or tolbutamide without any concomitant antidiabetic medication. An

‘other’ OHA user is defined as someone whose first eligible entry into the cohort was the date they were dispensed a TZD, a DPP-4 inhibitor, a meglitinide, acarbose, a GLP-1 receptor agonist, or an SGLT2 inhibitor without any concomitant antidiabetic medication.

Subjects in the combotherapy cohort were divided into 2 exposure groups: metformin plus sulfonylurea users, and metformin plus ‘other’ OHA users. The metformin plus sulfonylurea group consisted of individuals who were dispensed chloropropamide, gliclazide, glimepiride, glyburide, or tolbutamide without any concomitant antidiabetic medication other than metformin.

The metformin plus ‘other’ group consisted of individuals who were dispensed a TZD, a DPP-4 inhibitor, a meglitinide, acarbose, a GLP-1 receptor agonist, or an SGLT2 inhibitor without any concomitant antidiabetic medication other than metformin.

Patients were grouped by their incident OHA class irrespective of dosing regimen. Through the

DPIN database we identified the days of supply for each prescription and counted them as the number of days with the drug in-hand while accounting for early refills. Follow-up began on the index date and continued until the date of an outcome or the earliest censoring event.

In the monotherapy cohort a censoring event was defined as one of the following: 1) a switch to or addition of another antidiabetic drug; 2) discontinuation of pharmacological therapy plus a

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washout period of 90 days; 3) termination of insurance coverage through Manitoba Health either due to death or migration from the province; 4) the end of the study period (March 31, 2016).

In the combotherapy cohort a censoring event was defined as one of the following: 1) a switch to or addition of insulin; 2) discontinuation of either metformin or the index OHA plus a washout period of 90 days; 3) termination of insurance coverage through Manitoba Health either due to death or migration from the province; 4) the end of the study period. Subjects in the metformin plus ‘other’ group also reached a censoring event following a switch to or addition of a sulfonylurea.

3.5 Outcomes

There were 5 primary outcomes related to safety: 1) all-cause mortality; 2) cardiovascular-related mortality; 3) cardiovascular events; 4) major hypoglycemic episodes and 5) major osteoporotic fractures. The specific case definitions for each of the safety outcomes are outlined in Table 3.

We determined all-cause mortality using the date of death in the Vital Statistics database. We also supplemented this with the date of death found in the Manitoba Health Insurance Registry.

A subject was considered to have passed away if they were coded as dead in 1 of the 2 databases.

Where disagreement between the two databases existed we chose the date of death listed in Vital

Statistics. There was 99% agreement in our cohorts between the 2 databases on date of death.

We established cardiovascular-related mortality solely through the Vital Statistics database. If either the primary diagnosis code or any of the underlying diagnosis codes for death had an ICD code that indicated it was related to cardvioscular causes then we considered the cause of death to be cardiovascular-related.

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We defined a cardiovascular event as the composite outcome of acute myocardial infarction, heart failure, stroke (ischemic and/or hemorrhagic), or unstable angina. We attained this outcome from the CIHI-DAD database. We only included hospitalizations for the composite outcome that was a primary discharge diagnosis and excluded ICD codes that were explicitly associated with recurrent events.219-224

We obtained major hypoglycemic episodes from the ADT, CIHI-DAD, DSM and EDIS databases. Presentations to the emergency room with hypoglycemia and admissions to the hospital with a primary or underlying diagnosis of hypoglycemia were considered major hypoglycemic episodes.225 A blood glucose level of 3.5 mmol/L or lower was also deemed a major hypoglycemic episode.226

The presence of hip, clinical vertebral, forearm, and humerus fracture codes (collectively designated as major osteoporotic fractures) was determined through a combination of hospital discharge abstracts and physician billing claims.227, 228 A fracture was only considered incident if a previous fracture of the same type did not occur in the past 6 months. Hip and forearm fractures were required to be accompanied by a specific procedural or tariff code. Fractures associated with high trauma codes were excluded to ensure the fracture was the result of a regular fall. Our secondary outcome was effectiveness at lowering A1C. The DSM database was used to obtain A1C values at baseline and at 26 from the index date.

3.6 Sample Size Considerations

For each safety outcome, a risk reduction of 20% for the control group (OHA) compared to the reference group (sulfonylureas) will be considered clinically meaningful. Assuming 80% power,

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a two-sided alpha level of 0.05, 1.5 year median event free survival and a 1:1 treatment allocation from propensity matching, we will require 1,077 subjects in each treatment arm. For the effectiveness outcome, a mean difference of 0.5% in A1C will be considered clinically meaningful, Assuming 80% power, a two-sided alpha level of 0.05, a standard deviation of 2.0% and a 1:1 treatment allocation from propensity matching, we will require 253 subjects in each treatment arm.

3.7 Covariates

We abstracted a wide set of covariates chosen a priori that we suspected may confound the relationship between treatment and outcome. The covariates were identical for both cohorts. Date of birth (age) and sex were from the Manitoba Health Insurance Registry. We acquired baseline laboratory data from the DSM database. We included A1C, eGFR, hemoglobin, serum albumin, total cholesterol and urine ACR test results in our datasets. We used the test result closest to the index date within a maximum of 1 year prior or 30 days after. We did not conduct multiple imputations in the case of missing laboratory values.

We also abstracted comorbidities (yes [=present] or no [=absent]) from the CIHI-DAD and

MHSC databases. We acquired the baseline status of alcohol abuse, lower extremity amputation, asthma, chronic obstructive pulmonary disease (COPD), CVD, dementia, depression, human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS), hypertension, hyperlipidemia, liver disease, malignancy (excluding non-melanoma skin cancer), neuropathy, obesity, osteoporosis, Parkinson’s disease and prior fracture. Our minimum period for looking back was 3 years up to a maximum of 14 years. Only one diagnosis for a hospitalization or physician claim related to each of the comorbidities was required prior to the

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index date. The ICD codes we used to define our comorbidities are described in Table 4. There were no missing data.

Additionally, baseline exposure to selected concomitant medications indicative of the comorbidities of interest was also obtained from DPIN. These medications included ACE inhibitors, alpha blockers, anticoagulants, anticonvulsants, anti-Parkinson’s medications, anti- platelets, ARBs, beta blockers, bisphosphonates, calcium channel blockers (CCBs), denosumab, digoxin, direct renin inhibitors, direct vasodilators, lipid lowering medications, loop diuretics, opioids, proton pump inhibitors, potassium sparing diuretics and thiazide diurectics. We checked for prescriptions up to 1 year prior to the index date. Only one dispensed prescription was required during the lookback period for an individual to be designated as a user of the medication. The ATC codes we used for our medications are outlined in Table 5. For the combotherapy cohort, we also derived a nominal variable (< 1 year; 1-3 years; ≥ 3 years) based on the number of years prior an individual was adherent on metformin before they added a second OHA.

3.8 Statistical Analysis

All analyses were performed using SAS 9.4 (SAS Institute, Inc., Cary, NC, USA). Descriptive statistics were reported for both cohorts and stratified by OHA exposure group. Categorical variables were reported as frequency and percentage. Continuous variables were reported as mean plus standard deviation with the exception of urine ACR which was reported as median and interquartile range since it appeared to follow a right-skewed distribution. Categorical variables were compared with the Chi-Squared test. Continuous variables were compared with

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the t-test, or in the case of urine ACR, the Mann-Whitney U test. Incidence rates for each of the safety outcomes of interest were reported by number of events per 1,000 person years.

We calculated a predicted probability of being assigned a sulfonylurea (propensity score) based on baseline covariates for each cohort. We conducted two pairwise probability comparisons

(sulfonylureas vs. metformin; sulfonylureas vs. ‘other’) in the monotherapy cohort and one pairwise probability comparison in the comobotherapy cohort (sulfonylureas vs. ‘other’). The propensity score for each comparison was derived from binary logistic regression models. Both models contained the same covariates with the exception of the combotherapy cohort which also included the nominal variable of time spent on metformin. In the case of sulfonylurea and

‘other’ comparisons for both monotherapy and combotherapy, the propensity score used was the probability of being prescribed an OHA other than sulfonylurea. We did not use a multinomial logistic regression model for generating propensity as is usually recommended when there are more than two treatment arms229 as in our case it would have resulted in unbalanced matches for the sulfonylurea and ‘other’ groups in monotherapy.

We used a non-parsimonious approach in our propensity score models. Age, sex, eGFR, index fiscal year along with all concomitant medications and comorbidities were included as covariates in each model regardless of the statistical significance of their association with being assigned an exposure or experiencing an outcome. If a comborid condition or use of a medication was not observed for any individual in one of the exposure groups it was not included in the model. Due to missing data, laboratory test results were not included in either model. However, we created a binary variable based on whether data was missing for each variable and included that variable in both models.

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We evaluated the performance of the propensity score models with C statistics and the rescaled maximum R2. We assessed collinearity among covariates by creating a linear regression model where the propensity score was the dependent variable and considered variance inflation > 10 as grounds to remove a covariate from the final propensity score model. We log-transformed the propensity score for each model and executed a 1:1 match using a matching algorithm that matched sulfonylurea users to their nearest neighbor within a caliper distance 0.2 standard deviations of the logit of the propensity score.230, 231 We created 3 distinct propensity matched cohorts: sulfonylurea and metformin (monotherapy), sulfonylurea and ‘other’ (monotherapy), and sulfonylurea and ‘other’ (combotherapy).

We diagnosed balance post-match by calculating the standardized mean difference (SMD) to compare the distribution of baseline covariates between treatment groups. An SMD with a magnitude of 0.10 or less was considered to be balanced.232 In each propensity matched cohort, we constructed a series of Cox proportional hazards regression models to analyze time to event for each of the proposed safety outcomes comparing sulfonylureas to their corresponding comparator group. The proportional hazards assumption was assessed by the Kolmogorov-type supremum test.233 The assumption was met for all Cox proportional hazards regression models.

We accounted for matching by stratifying by matched pairs. We reported hazard ratios (HR) with 95% confidence intervals (CI) and p-values for each of the outcomes of interest.

We assessed real-world comparative effectiveness in a subgroup of patients who had both a baseline A1C recorded and an additional A1C measured at 26 ± 6 weeks from the index date. We created additional propensity score matched cohorts using the same approach as previously described but included baseline A1C as a covariate to influence the propensity score. At 26

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weeks, differences in A1C reduction between OHAs were analyzed in a linear regression model where the difference between baseline and post-index date A1C was the outcome and the exposure group was the explanatory variable. The beta coefficient (β) associated with the explanatory variable was considered the number of percentage points a sulfonylurea would lower

A1C relative to its comparator OHA and was reported at both time periods with a 95% confidence interval and p-value.

We examined CKD as an effect modifier of the outcome for each of our cox proportional hazards and linear regression models. We accomplished this by adding an interaction term of kidney function and our exposure variable to determine if the association of an OHA with safety and effectiveness differs based on kidney function. We used both a continuous measure (eGFR) and a categorical measure (eGFR ≥ 60 ml/min/1.73 m2 vs. eGFR < 60 ml/min/1.73 m2) for the kidney function portion of our interaction term. We also explored CVD as a possible effect modifier where the interaction of previous CVD (yes or no) and our exposure variable was added as an interaction term for each of our models.

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Chapter 4. Results

4.1 Baseline Characteristics

Between April 1, 2006 and March 31, 2016, we identified 60,509 residents of Manitoba with an incident monotherapy OHA prescription which met our inclusion/exclusion criteria. Of those, a subgroup of 20,979 subjects not on dialysis had a serum creatinine test within 1 year prior to, or

30 days after the index date and were eligible to enter the monotherapy cohort (Figure 2).

Metformin was the most commonly prescribed OHA for monotherapy with 18,880 (90.0%) individuals whereas 1,793 (8.5%) individuals were prescribed a sulfonylurea and 306 (1.5%) were prescribed an OHA from the ‘other’ group.

We also identified 29,736 residents of Manitoba with an incident combotherapy OHA prescription which met our inclusion/exclusion criteria. Of those, a subgroup of 11,615 subjects not on dialysis had a serum creatinine test within 1 year prior to, or 30 days after the index date and were eligible to enter the combotherapy cohort (Figure 3). Sulfonylureas were the most likely OHA to be added on to metformin and represented 10,349 (89.1%) of combotherapy prescriptions whereas 1,266 (10.9%) individuals were prescribed an OHA from the ‘other’ group as add-on therapy to metformin.

A breakdown of the specific OHA categories that were prescribed as part of the ‘other’ group for both the monotherapy and combotherapy cohorts is listed in Table 6. With 86 (28.1%) unique prescriptions, SGLT2 inhibitors represented a plurality in the ‘other’ group for the monotherapy cohort and DPP-4 inhibitors were a plurality of prescriptions in the ‘other’ group for the combotherapy cohort with a count of 520 (41.1%). Of the patients who were prescribed

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sulfonylureas for monotherapy, 965 (53.8%) were prescribed gliclazide, 814 (45.4%) were prescribed glyburide and 14 (0.8%) were prescribed a different sulfonylurea. Of the patients who were prescribed sulfonylureas for combotherapy, 7,085 (68.5%) were prescribed gliclazide,

3,222 (31.1%) were prescribed glyburide and 42 (0.4%) were prescribed a different sulfonylurea.

The baseline characteristics of the entire monotherapy cohort are described in Table 7.

Demographically, the entire cohort had a mean age of 56.0 ± 16.2 years and was 49.2% female.

The sulfonylurea group was the oldest group with a mean age of 62.7 ± 16.8 years, and had the lowest proportion of females (44.8%). In comparison, the metformin group was the youngest group (55.4 ± 16.0 years) and was 49.5% female and the ‘other’ group had a mean age of 58.6 ±

15.8 years and had the highest proportion of females (54.9%).

A total of 18,790 (89.6%) individuals had an eGFR calculated to be at or above 60 ml/min/1.73 m2, 1,354 (6.5%) had an eGFR between 45 and 60 ml/min/1.73 m2 (CKD Stage 3a) and 835

(4.0%) had an eGFR lower than 45 ml/min/1.73 m2 (CKD Stage 3b-5). The mean eGFR of the cohort was 92 ± 24 ml/min/1.73 m2. The sulfonylurea group (mean eGFR of 78 ± 31 ml/min/1.73 m2) had the highest proportion of patients with CKD where 223 (12.4%) patients were Stage 3a and 329 (18.3%) patients were Stage 3b-5. The ‘other’ group (mean eGFR of 78 ±

31 ml/min/1.73 m2) was also disproportionately comprised of patients with CKD where 223

(12.4%) patients were Stage 3a and 329 (18.3%) patients were Stage 3b-5. Lastly, the metformin group contained 1,105 (5.9%) CKD Stage 3a patients and 463 (2.5%) CKD Stage 3b-5 patients.

Compared to the metformin and ‘other’ groups, the sulfonylurea group was more likely to have baseline laboratory values that were indicative of anemia, kidney disease, and uncontrolled blood glucose – they had a lower mean hemoglobin (134 ± 21 g/L), higher median urine ACR (2.7

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mg/mmol [0.7, 14.8]), and higher mean A1C (8.7% ± 2.3) compared to the metformin group that had an average hemoglobin of 140 ± 18 g/L, a median urine ACR of 1.2 mg/mmol [0.4, 4.2], an average A1C of 8.2% ± 2.2. The ‘other’ group had a mean hemoglobin of 135 ± 18 g/L, a median urine ACR 1.2 mg/mmol (0.4, 4.0) and a mean A1C of 7.7% ± 2.3. Availability of laboratory test results varied widely and ranged from being available for 73.6% of the cohort

(hemoglobin) to 7.6% of the cohort (serum albumin).

At baseline, the sulfonylurea group generally had more comorbidities related to the circulatory system – in particular, higher rates of CVD (45.5%), compared to rates of 30.9% and 37.6% in the metformin and ‘other’ groups, respectively. They also had higher rates of hypertension

(70.9% vs. 63.3% for metformin and 68.6% for ‘other’), and peripheral vascular disease (PVD)

(4.5%), vs. 2.0% and 2.3%. Furthermore, the sulfonylurea group also had higher usage of certain medications for antihypertensive therapy and cardiovascular risk reduction such as ACE inhibitors (31.7%) vs. 27.9% and 22.9% in the metformin and ‘other’ groups, anticoagulants

(11.2%) vs. 6.3% and 10.5%, anti-platelets (23.9%) vs. 17.2% and 14.7%, beta blockers (34.1%) vs. 23.1% and 26.5%, CCBs (23.5%) vs. 15.6% and 22.5%, and loop diuretics (25.9%) vs.

10.2% and 17.6%.

The baseline characteristics of the entire combotherapy cohort are described in Table 8. Overall, differences between users of sulfonylureas and their counterparts were less marked in the combotherapy group compared to the monotherapy group. Demographically, the entire cohort had a mean age of 56.9 ± 14.9 years and was 45.6% female. The groups were closer demographically than the combotherapy cohort. The sulfonylurea group was the youngest group with a mean age of 56.8 ± 15.1 years, and had the lowest proportion of females (45.5%). In

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comparison, the ‘other’ group was the oldest group (57.6 ± 13.0 years) and had the highest proportion of females (46.1%).

A total of 10,277 (88.5%) individuals in the combotherapy cohort had an eGFR calculated to be at or above 60 ml/min/1.73 m2, 761 (6.6%) had an eGFR between 45 and 60 ml/min/1.73 m2

(CKD Stage 3a) and 577 (5.0%) had an eGFR lower than 45 ml/min/1.73 m2 (CKD Stage 3b-5).

The mean eGFR of the cohort was 93 ± 25ml/min/1.73 m2. The sulfonylurea group had a higher mean eGFR (93 ± 25 ml/min/1.73 m2) compared to the ‘other’ group (90 ± 22 ml/min/1.73 m2) had similar proportions in each CKD Stage strata (eGFR ≥ 60 ml/min/1.73 m2 (88.4% vs.

89.0%), CKD Stage 3a (6.6% vs. 6.2%, CKD Stage 3b-5 (5.0% vs. 4.7%)).

Compared to the ‘other’ combotherapy group, the sulfonylurea group was more likely to have baseline laboratory values that were indicative of kidney disease, higher cholesterol and uncontrolled blood glucose – they had a higher median urine ACR (1.8 mg/mmol (0.6, 6.6)), higher average total cholesterol (4.8 ± 1.3 mmol/L), and higher mean A1C (9.2% ± 2.3) compared to the ‘other’ group that had a median urine ACR of 1.2 mg/mmol (0.4, 4.9), an average total cholesterol of 4.6 ± 1.3 g/L, and an average A1C of 8.3% ± 1.8. Availability of laboratory test results varied widely and ranged from being available for 71.3% of the cohort

(hemoglobin) to 7.6% of the cohort (serum albumin).

Compared to the ‘other’ combotherapy group, the sulfonylurea group generally had a statistically higher proportion of patients with alcohol abuse (6.4% vs. 2.1%), dementia (5.1% vs. 3.5%) and prior fracture (16.6% vs. 12.6%); and a statistically lower proportion of patients with hypertension (67.8% vs. 74.2%), hyperlipidemia (37.0% vs. 42.4%) and obesity (11.9% vs.

9.9%). In terms of other medication use, the sulfonylurea group had higher usage of ACE

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inhibitors (40.0% vs. 36.5%), anti-platelets (24.2% vs. 19.4%) and loop diuretics (12.0% vs.

10.0%) compared the ‘other’ group but had lower usage of ARBs (16.4% vs. 12.4%) and lipid lowering medications (58.8% vs. 51.4%).

4.2 Propensity Matching

The 1:1 propensity matching algorithm matched 1,777 sulfonylurea users to 1,777 metformin users who were the most identical in their baseline covariates. It also matched 265 sulfonylurea users and ‘other’ OHA users in the monotherapy cohort and 1,266 sulfonylurea users and ‘other’

OHA users in the combotherapy cohort. No covariates were removed from any propensity score model due to multicolinearity. The logistic regression model comparing sulfonylurea and metformin users in the monotherapy cohort achieved an R2 of 15.4% whereas the model comparing sulfonylurea and ‘other’ OHA users in the monotherapy cohort achieved an R2 of

23.8% and the model used to derive the combotherapy propensity scores achieved an R2 of 7.4%.

The pairwise C statistics generated from binary logistic regression models were 0.75 for sulfonylurea and metformin (monotherapy), 0.76 for sulfonylurea and ‘other’ monotherapy and

0.68 for sulfonylurea and ‘other’ combotherapy.

A post-match breakdown of the specific OHA categories that were prescribed as part of the

‘other’ group is listed in Table 9. With 73 (27.6%) unique prescriptions, SGLT2 inhibitors represented a plurality in the ‘other’ group for the monotherapy cohort and DPP-4 inhibitors were a plurality of prescriptions in the ‘other’ group for the combotherapy cohort with a count of

520 (41.1%). Compared to before propensity matching, proportions of gliclazide and glyburide users in the sulfonylurea group were similar in each propensity matched cohort.

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After propensity matching, the sulfonylurea and metformin monotherapy groups were similar in profile (Table 10); the sulfonylurea group had a mean age of 62.5 ± 16.8 years, 44.8% of the group was female, 223 (12.5%) were classified as CKD Stage 3a and 313 (17.6%) were classified as CKD Stage 3b-5. In the metformin group, the mean age was 62.5 ± 16.6 years,

45.2% of the group was female, 229 (12.9%) were classified as CKD Stage 3a and 289 (16.3%) were classified as CKD Stage 3b-5. Overall, the 2 groups were balanced in their demographics,

CKD strata, comorbidities, baseline medication use, and index year of prescription as each comparison had an SMD < 0.10. However, for the subjects who had a documented laboratory measurement, the differences in A1C, urine ACR, hemoglobin and serum albumin were statistically significant (SMD ≥ 0.10) and were worse for the sulfonylurea group; although the differences were smaller than in the full cohort.

For the other monotherapy comparison, the sulfonylurea and ‘other’ monotherapy groups were similar in profile (Table 11); the sulfonylurea group had a mean age of 59.4 ± 17.2 years, 50.9% of the group was female, 22 (8.3%) were classified as CKD Stage 3a and 48 (18.1%) were classified as CKD Stage 3b-5. In the ‘other’ group, the mean age was 60.1 ± 15.8 years, 50.6% of the group was female, 26 (9.8%) were classified as CKD Stage 3a and 43 (16.2%) were classified as CKD Stage 3b-5. Overall, the 2 groups were balanced in their demographics, CKD strata, comorbidities, baseline medication use, index year of prescription and most laboratory measurements (SMD < 0.10). An exception to this was A1C, which was statistically higher in the sulfonylurea group (8.6 ± 2.6% vs. 7.8 ± 2.3%; SMD ≥ 0.10) but the difference was smaller than in the full cohort.

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After propensity matching in the combotherapy cohort, the sulfonylurea and ‘other’ groups were similar in profile (Table 12); the sulfonylurea group had a mean age of 58.2 ± 13.8 years, and

44.9% of the group was female, 94 (7.4%) were classified as CKD Stage 3a and 68 (5.4%) were classified as CKD Stage 3b-5. In the metformin group, the mean age was 57.6 ± 13.0 years,

46.1% of the group was female, 79 (6.2%) were classified as CKD Stage 3a and 60 (4.7%) were classified as CKD Stage 3b-5. Overall, the 2 groups were balanced in their demographics, CKD strata, comorbidities, baseline medication use, and index year of prescription as each comparison had an SMD < 0.10. However, for the subjects who had a documented laboratory measurement, the differences in A1C, total cholesterol and serum albumin were statistically significant (SMD ≥

0.10) but did not fully skew in favor of one particular group and were smaller than in the full cohort.

4.3 Outcomes

For the entire monotherapy cohort, the total follow-up time was 30,358 person-years (27,958 person-years for the metformin group; 2,170 person-years for the sulfonylurea group and 230 person years for the ‘other’ group). There were 897 deaths (690 metformin; 194 sulfonylurea; 13

‘other’), 488 cardiovascular-related deaths (368 metformin; 108 sulfonylurea; 12 ‘other’), 673 cardiovascular events (542 metformin; 121 sulfonylurea; 10 ‘other’), 126 major hypoglycemic episodes (64 metformin; 62 sulfonylurea) and 194 major osteoporotic fractures (173 metformin;

21 sulfonylurea). We did not report counts of major hypoglycemic episodes or major osteoporotic fractures in the ‘other’ OHA group since we could not conduct any meaningful statistical analysis for those outcomes.

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For the entire combotherapy cohort; the total follow-up time was 17,283 person-years (15,958 for the sulfonylurea group and 1,325 for the ‘other’ group). There were 482 deaths (463 sulfonylurea; 19 ‘other’), 267 cardiovascular-related deaths (254 sulfonylurea; 13 ‘other’), 425 cardiovascular events (398 sulfonylurea; 27 ‘other’) and 202 major hypoglycemic episodes (195 sulfonylurea; 7 ‘other’). We did not report counts of major osteoporotic fractures in the ‘other’

OHA group since we could not conduct any meaningful statistical analysis for that outcome.

Table 13 displays the safety events, time at risk, crude rates (expressed per 1,000 person-years) and hazard ratios for the sulfonylurea and metformin monotherapy groups, for both pre-match and post-match. Before propensity matching, higher crude rates of all-cause mortality (89.4 vs.

24.7), cardiovascular-related mortality (49.8 vs. 13.2), cardiovascular events (58.9 vs. 19.9), major hypoglycemic episodes (29.2 vs. 2.3) and major osteoporotic fractures (9.7 vs. 6.2) were observed in the sulfonylurea group compared to the metformin group. These unadjusted relationships were also statistically significant as sulfonylureas were associated with greater risks of all-cause mortality (HR 2.04; 95% CI 1.73 – 2.41), cardiovascular-related mortality (HR 3.76;

95% CI 3.04 – 4.67), cardiovascular events (HR 2.88; 95% CI 2.37 – 3.51), major hypoglycemic episodes (HR 12.20; 95% CI 8.62 – 17.24) and major osteoporotic fractures (HR 1.57; 95% CI

1.01 – 2.48) compared with metformin.

After adjusting for covariates through propensity matching the differences were less marked.

Compared with metformin, sulfonylureas were still associated with higher crude rates of all- cause mortality (86.9 vs. 49.6), cardiovascular-related mortality (49.8 vs. 31.3), cardiovascular events (56.4 vs. 33.8) and major hypoglycemic episodes (28.0 vs. 3.2) but were also associated with a lower crude rate of major osteoporotic fractures (9.8 vs. 10.1). In the propensity matched

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cohort, sulfonylureas were once again significantly associated with greater risks of all-cause mortality (HR 1.44; 95% CI 1.05 – 1.97), cardiovascular events (HR 1.58; 95% CI 1.08 – 2.32) and major hypoglycemic episodes (HR 5.57; 95% CI 2.49 – 12.46) when compared with metformin but were not significantly associated with greater risks of cardiovascular-related mortality (HR 1.13; 95% CI 0.74 – 1.72) or major osteoporotic fractures (HR 1.00; 95% CI 0.45

– 2.23).

Table 14 displays the safety events, time at risk, crude rates (expressed per 1,000 person-years) and hazard ratios for the sulfonylurea and ‘other’ monotherapy groups, for both pre-match and post-match. Before propensity matching, higher crude rates of all-cause mortality (89.4 vs. 56.5), and cardiovascular events (58.9 vs. 44.2) and a lower crude rate of cardiovascular-related mortality (49.8 vs. 52.2) were observed in the sulfonylurea group compared to the ‘other’ group.

None of these relationships were statistically significant as sulfonylureas were not associated with greater risks of all-cause mortality (HR 1.32; 95% CI 0.75 – 2.32), cardiovascular-related mortality (HR 0.99; 95% CI 0.55 – 1.81) or cardiovascular events (HR 1.46; 95% CI 0.77 – 2.79) compared with ‘other’ OHAs.

The relationship between sulfonylureas and ‘other’ OHA in the monotherapy cohort did not change after adjusting for covariates through propensity matching. Compared with ‘other’

OHAs, sulfonylureas were still associated with higher crude rates of all-cause mortality (80.9 vs.

60.7), cardiovascular events related mortality (48.7 vs. 47.6) and a lower crude rate of cardiovascular- related mortality (46.8 vs. 56.1). None of these relationships were statistically significant as sulfonylureas were not associated with greater risks of all-cause mortality (HR

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1.67; 95% CI 0.61 – 4.59), cardiovascular-related mortality (HR 0.67; 95% CI 0.19 – 2.36) or cardiovascular events (HR 2.67; 95% CI 0.71 – 10.05) compared with ‘other’ OHAs.

Table 15 displays the safety events, time at risk, crude rates (expressed per 1,000 person-years) and hazard ratios for the sulfonylurea and ‘other’ combotherapy groups, for both pre-match and post-match. Before propensity matching, higher crude rates of all-cause mortality (29.0 vs. 14.3), cardiovascular-related mortality (15.9 vs. 9.8), cardiovascular events (25.7 vs. 20.7) and major hypoglycemic episodes (12.3 vs. 5.3) were observed in the sulfonylurea group compared to the

‘other’ group. Most of these unadjusted relationships were also statistically significant as sulfonylureas as add-on therapy were associated with greater risks of all-cause mortality (HR

2.20; 95% CI 1.39 – 3.48), major hypoglycemic episodes (HR 2.60; 95% CI 1.22 – 5.52) and trended toward an association with cardiovascular-related mortality (HR 1.72; 95% CI 0.98 –

2.99) compared with ‘other’ OHAs as add-on therapy.

After adjusting for covariates through propensity matching the differences were similar in the combotherapy cohort. Compared with ‘other’ OHAs, sulfonylureas as add-on therapy were still associated with higher crude rates of all-cause mortality (29.1 vs. 14.3), cardiovascular-related mortality (13.4 vs. 9.8), cardiovascular events (29.0 vs. 20.7) and major hypoglycemic episodes

(6.2 vs. 5.3). In the propensity matched combotherapy cohort, sulfonylureas were significantly associated with greater risks of all-cause mortality (HR 2.40; 95% CI 1.15 – 5.02) but were not associated with greater risk of cardiovascular-related mortality (HR 1.14; 95% CI 0.41 – 3.15), cardiovascular events (HR 1.41; 95% CI 0.76 – 2.63) and major hypoglycemic episodes (HR

1.67; 95% CI 0.40 – 6.97) when compared with ‘other’ OHAs.

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For our secondary outcome, we were able to match 78 sulfonylurea and metformin users in our monotherapy cohort and 51 sulfonylurea and ‘other’ OHA users in our combotherapy cohort who had a documented A1C at baseline and at 26 ± 6 weeks. We could not conduct any meaningful analysis for a comparison of effectiveness between sulfonylureas and ‘other’ OHAs for monotherapy. Index A1C, eGFR and other baseline characteristics were statistically similar for each comparison.

For monotherapy, the mean baseline A1C was 8.5 ± 2.4% for the sulfonylurea group and 8.3 ±

2.2% for the metformin group. In this group of T2DM patients, sulfonylureas lowered A1C by an average of 1.0 ± 1.9% whereas metformin lowered A1C by an average of 1.3 ± 2.5%. The difference in A1C reduction between the two OHAs was not significant (β = -0.3; 95% CI: -1.0 –

0.4, p = 0.42). For combotherapy, the mean baseline A1C was 8.0 ± 1.1% for the sulfonylurea group and 7.9 ± 1.3% for the ‘other’ group. In this group of T2DM patients, sulfonylureas lowered A1C by an average of 0.7 ± 1.4%whereas the lowered A1C by an average of 0.4 ±

1.0%. The difference in A1C reduction between the two OHAs was not significant (β = 0.3; 95%

CI: -0.1 – 0.8, p = 0.160).

4.4 Chronic Kidney Disease as an Effect Modifier

The direction and significance of interaction terms between CKD and OHA group and their association with safety outcomes is presented in Figures 4-6 for each propensity matched pairwise comparison. CKD is a significant effect modifier for all-cause mortality when using a sulfonylurea vs. metformin as monotherapy. The interaction term between eGFR and OHA group was significant for this comparison (p < 0.001). As eGFR decreases the risk of all-cause mortality associated with a sulfonylurea relative to metformin also decreases. Therefore in

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individuals with normal kidney function (eGFR ≥ 60 ml/min/1.73 m2) sulfonylureas exacerbate the risk of all-cause mortality even further when compared with metformin.

When explored by CKD strata, sulfonylureas were associated with a nearly 3-fold higher hazard of all-cause mortality in individuals with an eGFR ≥ 60 ml/min/1.73 m2 (HR 2.72; 95% CI 1.60

– 4.64) when compared with metformin. This association was not present in individuals with an eGFR < 60 ml/min/1.73 m2 (HR 0.87; 95% CI 0.56 – 1.35) or in individuals with an eGFR < 45 ml/min/1.73 m2 (HR 0.92; 95% CI 0.52 – 1.61). The interaction term for the categorical CKD variable was also significant (p = 0.002). There were 146 deaths in the eGFR ≥ 60 ml/min/1.73 m2 group and 196 deaths in the eGFR < 60 ml/min/1.73 m2 group. For all other outcomes (both safety and effectiveness) and all other OHA comparisons the interaction term between eGFR and

OHA group was not significant (p > 0.05).

Similar results were found when we explored the how the interaction between previous CVD and

OHA group affected our outcomes of interest (Figures 7-9). CVD is a significant effect modifier for all-cause mortality when using a sulfonylurea vs. metformin as monotherapy (p = 0.021). The effect of the interaction is such that sulfonylureas were associated with a nearly 3-fold higher hazard of all-cause mortality in individuals without previous CVD (HR 2.72; 95% CI 1.60 –

4.64) when compared with metformin. This association was not present in individuals with prior

CVD (HR 0.93; 95% CI 0.57 – 1.51). For all other outcomes (both safety and effectiveness) and all other OHA comparisons the interaction term between CVD and OHA group was not significant (p > 0.05).

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Chapter 5. Discussion

5.1 Summary of Findings

In a retrospective cohort study of 20,979 patients with T2DM who used a single OHA to treat their hyperglycemia our findings suggest that sulfonylureas are inferior to metformin for the outcomes of all-cause mortality, cardiovascular events and major hypoglycemic episodes. Our results from the monotherapy cohort also suggest that CKD does modify the effect of sulfonylureas on all-cause mortality relative to metformin where the impact was that individuals with normal kidney function experienced the highest risk of all-cause mortality when using sulfonylureas rather than metformin. There is no evidence to suggest any difference between sulfonylureas and ‘other’ OHAs in the monotherapy cohort nor is there evidence to suggest that

CKD was an effect modifier for any other safety event that was analyzed in the monotherapy cohort. In our combotherapy cohort of 11,615 patients with T2DM who used a second OHA added on to metformin to treat their hyperglycemia our findings suggest that sulfonylureas are inferior to ‘other’ OHAs for the outcome of all-cause mortality. Our results from the combotherapy cohort also suggest that CKD does not modify the relationship between the add- on OHA chosen for glycemic control and any of the safety events we analyzed.

5.2 Comparison with Previous Studies

Our findings in the monotherapy cohort confirm and extend observations from previous observational studies. A retrospective cohort study with 76,811 patients from a primary care database in the United Kingdom (UK) found that sulfonylureas as monotherapy were associated with a significantly higher risk of all-cause mortality (HR 1.90; 95% CI 1.73 – 2.09) and

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cardiovascular events (HR 1.20; 95% CI 1.00 – 1.44) compared with metformin after adjusting by matching on propensity score.234 However, our study differed in some respects. First, our propensity matched cohort included a significantly higher proportion of individuals with an eGFR < 60 ml/min/1.73 m2 (30% vs. 11%). Second, our definition of cardiovascular events was more comprehensive and included heart failure, a key adverse cardiovascular outcome in diabetics. Finally, crude all-cause mortality in our study was approximately 2-fold higher, indicating our cohort was at higher adverse health risk than the UK monotherapy cohort.

Because a major objective of our study was to explore whether CKD status was an effect modifier, we only included patients with at least one serum creatinine test, whereas the UK study included all patients irrespective of creatinine testing. It is likely that patients who require serum creatinine testing for a clinical indication are less healthy than patients who do not. Thus, our cohort is plausibly at higher risk than the UK cohort as a result of this additional input filter bias.

However, other observational studies examining all-cause mortality in sulfonylurea users have also calculated higher than expected mortality rates.235, 236 Variability in event rates are likely due to differences in inclusion criteria.

Marcum et al also explored whether CKD is an effect modifier for the association of choosing sulfonylureas or metformin monotherapy and all-cause mortality in a population derived from the Veterans Health Administration in the US.237 This cohort differed from ours in being predominantly male (97%) and in excluding severe CKD (eGFR < 30 ml/min/1.73 m2). Both studies showed significant effect modification by CKD status. Both studies also showed that the hazard ratio for death associated with sulfonylureas was highest among individuals with normal

GFR, and diminished for those at lower GFRs. However, there were contrasts. Unlike our results, in the US study the HR for mortality remained significant and favored metformin in the 49

eGFR 45-60 ml/min/1.73 m2 (HR 0.80; 95% CI 0.71 – 0.91). They also tested an interaction term between the OHA and each eGFR category. In their research, the interaction term at eGFR 30-

45 ml/min/1.73 m2 was not significant (p = 0.20), a result that differed from our interaction term at eGFR < 45 ml/min/1.73 m2. Overall, it is reassuring that these observations hold in very different cohorts, and increase confidence in the generalizability of these results. Our results may also better inform practice for women and for patients with Stage 4 CKD and higher.

Another nationwide retrospective database study from the UK matched 14,012 initiators of sulfonylureas to 14,012 initiators of metformin and compared the hazard risk of severe hypoglycemia between the two OHAs.238 In this case, 11% of the propensity matched cohort had

CKD. The results were consistent with our findings as they found sulfonylureas to be associated with a higher risk of severe hypoglycemia (HR 4.53; 95% CI, 2.76 – 7.45). However, our study included a broader range of ICD-10 codes for hospitalizations along with emergency room presentations and laboratory results and therefore may have captured major hypoglycemic episodes that would have been missed in the other analysis.

Van Dalem et al also compared severe hypoglycemia among monotherapy users of metformin and sulfonylureas in the UK but reported additional results by eGFR strata.239 Their results also demonstrated that sulfonylureas were associated with a higher risk of a major hypoglycemic episode (HR 2.50; 95% CI, 2.23 – 2.82) compared to metformin. The magnitude of the HR also increased in later stages of CKD. However, it is not clear whether CKD was an effect modifier as this study did not explore kidney function as an interaction term. Furthermore, their cutoff for blood glucose level was lower (3.0 mmol/L vs. 3.5 mmol/L) therefore they likely did not capture

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cases of clinically significant hypoglycemic episodes where blood glucose is between 3.0 and 3.5 mmol/L.

Additionally, our neutral results for sulfonylureas relative to metformin on the outcome of major osteoporotic fractures are consistent with results from a large observational study (n = 20,964) from the US (A Diabetes Outcome Progression Trial (ADOPT)).240 The ADOPT trial and a prospective cohort study in British Columbia also compared incident monotherapy users of sulfonylureas to TZDs and both found that sulfonylureas were associated with a lower risk of fracture.240, 241 We could not assess whether this finding was consistent with our results.

Our findings in the combotherapy cohort, where a nearly four fifths of sulfonylurea comparators were either prescribed a DPP-4 inhibitor, an SGLT2 inhibitor or a TZD have a mixed consistency with previous observational studies exploring comparisons of dual therapy for

T2DM where the reference group is sulfonylureas added on to metformin. Most of these observational studies chose to investigate DPP-4 inhibitors as the comparator add-on therapy which is relevant to our comparisons of sulfonylureas to other OHAs. Although, we could not isolate a subgroup of DPP-4 inhibitors for meaningful analysis, a clear plurality of patients in the

‘other’ group of our combotherapy cohort was prescribed a DPP-4 inhibitor as add-on therapy.

A Taiwanese retrospective cohort study compared 10,089 propensity score matched pairs of

DPP-4 inhibitor users and sulfonylurea users where 100% of the cohort was prescribed these drugs as add-on therapy to metformin.242 The proportion of patients with eGFR < 60 ml/min/1.73 m2 at baseline was 11%, similar to the 12% we observed in our propensity matched combotherapy cohort. However, our study differed in terms of a broader definition of hypoglycemia, collecting more comorbidities and baseline medications to add to the propensity

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score, and was likely more racially diverse. Despite these differences, sulfonylureas were also associated with a higher risk of all-cause mortality (HR 1.56; 95% CI 1.41 – 1.75) and were neutral for myocardial infarction and heart failure in this Taiwanese cohort. However, in contrast to our findings, sulfonylureas were also associated with hypoglycemia (HR 2.27; 95% CI 1.82 –

2.86), and stroke (HR 1.61; 95% CI 1.33 – 1.96) which matches our findings in the unadjusted results but not the propensity matched results. This may be due to our small number of outcomes in the ‘other’ combotherapy group or due to the different definition used for a major hypoglycemic episode.

A UK database study where 7% of the cohort had CKD at baseline demonstrated that adding a sulfonylurea to metformin was worse for developing a cardiovascular event (myocardial infarction, stroke, acute coronary syndrome, unstable angina, or coronary revascularization) or experiencing cardiovascular-related mortality (HR 1.47; 95% CI 1.18 – 1.85) when compared to adding a TZD to metformin.243 This result differed from our findings and the differences may be due to important differences in inclusion criteria, as the UK combotherapy study excluded patients under 40 or who initiated their add-on drug to metformin before 90 days. In our combotherapy cohort patients who initiated their second-line therapy earlier than 1 year after they started metformin were much more likely to be prescribed a sulfonylurea (56.9% vs.

44.8%). Therefore, the UK combotherapy study would be excluding many patients with a longer duration of T2DM which is a predictor of cardiovascular events and these patients may disproportionately be on a TZD. Our analysis also differed from this study due to our inclusion of heart failure in the composite of cardiovascular outcomes. Since heart failure was not significantly associated with sulfonylureas in the Taiwan study242, and TZDs have been associated with heart failure in the past due to fluid retention127, this may have contributed to the 52

difference in findings. The UK combotherapy study also did not match the findings of an RCT in

Italy which compared sulfonylureas to TZDs in patients aged 50-75 who began treatment with metformin and assessed the very same cardiovascular outcomes and found no difference.244

In another similarity to our research, data from a nationwide registry in Sweden where 23,846 metformin plus sulfonylurea users were propensity matched to 11,923 metformin plus DPP-4 inhibitor users found that sulfonylureas were associated with an increased risk of all-cause mortality (HR 2.27; 95% CI 1.82 – 2.86), and not associated with an increased risk of hypoglycemia (only determined by hospitalization) (HR 1.88; 95% CI 0.98 – 3.62).245 One difference in the study was that DPP-4 inhibitors outperformed sulfonylureas for the outcome of

CVD (HR 1.23; 95% CI 1.05 – 1.44). This may be the case since their definition of CVD differed slightly from our definition of cardiovascular events as we also included hemorrhagic stroke and heart failure. Other differences were that the Sweden study used a washout period of

180 days rather than 90 days and only 1% of their cohort had kidney disease as defined by administrative codes.

5.3 Implications

Our findings support current guidelines recommending metformin as the first-line OHA for pharmacological therapy for T2DM patients unable to meet their A1C targets through lifestyle management alone. 87, 246 Current guidelines favor sulfonylureas as a second line OHA on the basis of glycemic efficacy and cost, except in the setting of pre-existing CVD, in which case

SGLT2 inhibitors or GLP-1 agonists are recommended based on a demonstrated benefit for cardiovascular outcomes in RCTs.87, 246 Our results, confirming a concerning mortality signal when sulfonylurea is used with metformin, suggest modifying these guidelines to discourage

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sulfonylurea prescription in addition to metformin. Additionally, given that there was no significant interaction between pre-existing CVD and the choice of add-on OHA for dual therapy this discouragement can apply regardless of whether a patient has pre-existing CVD.

In certain patients metformin may be contraindicated or may not be well tolerated due to GI side effects. For these patients, we are unable to make a data driven recommendation for a preferred alternative OHA, as we found no significant differences between monotherapy with sulfonylureas vs. ‘other’ OHAs for any of the safety outcomes after adjusting for covariates.

Because eGFR was shown to be an effect modifier when comparing sulfonylureas to metformin for all-cause mortality, our findings cannot be interpreted as a recommendation to change current guidelines which suggest that metformin should only be used with caution in patients with Stage

3a CKD and possibly not prescribed at all for patients with later stages of CKD. However, despite the significant interaction term, there was no association with metformin and all-cause mortality patients with eGFR < 60 ml/min/1.73 m2 compared to sulfonylureas. Furthermore, metformin was superior to sulfonylureas for outcomes where eGFR was not shown to be an effect modifier (i.e. cardiovascular events and major hypoglycemic episodes). As such, the decision not to treat a patient with metformin due to low kidney function should therefore be balanced against other risks and benefits.234

Our results show that real-world monotherapy prescriptions are mirroring recommendations as

90% of initial OHA monotherapy prescriptions were for metformin. In the rare cases where a sulfonylurea was prescribed as monotherapy rather than metformin, it was more likely to be for a patient who was older, male and had higher baseline cardiovascular risk. Unless metformin was specifically contraindicated in such patients this would be counterintuitive as these patients may

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be at even higher relative risk of harm from a sulfonylurea.247 Contraindications may have been a consideration in some of these cases as sulfonylureas were also disproportionately prescribed to patients with an eGFR < 60 ml/min/1.73 m2. Encouragingly, in our cohort, prescriptions of sulfonylureas as monotherapy declined in real numbers in almost every fiscal year since the beginning of the study. Despite the decline, sulfonylureas are still more commonly prescribed as monotherapy than all other non-metformin options combined. Further real-world studies are needed to analyze whether this should be the case.

Our findings demonstrate that sulfonylureas continue to be the main OHA of choice to add on to metformin as dual therapy. Unlike monotherapy, prescriptions of sulfonylureas as combotherapy increased in real numbers every fiscal year and represented 82% of all add-on OHA prescriptions in the most recent fiscal year. It may be that many physicians continue to consult outdated guidelines that elevate sulfonylureas above all other non-metformin OHAs in the top tier of validated add-on therapies.248 It is also the case that sulfonylureas continue to cost less than all other non-metformin OHAs and are also listed in all Canadian provincial drug formularies for coverage in public drug plans which is not the case for any other OHA other than metformin.212

Previous research in the US has shown that primary care physician was one of the main predictors of whether a patient received a sulfonylurea to treat their hyperglycemia.249 It is clear that clinical equipoise also continues to exist regarding the continuing need for sulfonylureas particularly when other OHAs with more favorable safety profiles and similar glycemic control are available.250, 251 A survey of primary care physicians on their prescribing practices of OHAs may be worthwhile to explore the reasoning behind their decisions.

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A discussion of changing prescribing practices vis-à-vis sulfonylureas would be incomplete without acknowledging differences within the drug class. Many252, but not all253, studies have shown gliclazide to be less harmful than glyburide for mortality and cardiovascular events.

Gliclazide is currently listed in the World Health Organization Model List of Essential

Medicines, a list that many in Canada would like to see covered permanently in a national pharmacare plan.254, 255 It is also specifically advised as the preferred second-line OHA in the

Netherlands.256 However, our results show sulfonylureas are more harmful for all-cause mortality than other OHAs when added on to metformin without an added benefit of superior glycemic control even when ~70% of people on a sulfonylurea were prescribed gliclazide.

Therefore, we believe a formal cost-utility study comparing sulfonylureas to other OHAs may be needed in order to force a policy change in drug coverage that will disincentivize prescription of sulfonylureas and be more congruent with more recent T2DM guidelines. Such studies have already been undertaken in Europe and Asia257-261 but no such economic evaluations have taken place in a Canadian setting. It is encouraging that sulfonylureas have been targeted for deprescription262 and we believe our findings support this initiative.

Throughout the course of our research we have also discovered gaps in the literature that our data may help solve with additional years of outcomes. To our knowledge combotherapy comparisons with sulfonylureas to other OHAs for major osteoporotic fractures are limited as none were included in a recent review of sulfonylureas and falls and fractures.263 Furthermore, although observational studies of SGLT2 inhibitors are becoming more common264direct comparisons between sulfonylureas and SGLT2 inhibitors as add-on to metformin are lacking and will be of interest if the updated guidelines and current prescribing practices continue to diverge.

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5.4 Strengths and Limitations

Our research, which explored a large amount of safety outcomes which are important to patients and explored both monotherapy and combotherapy regimens has several strengths. First, we included the use of large, robust regional databases from a population based cohort with universal health coverage. Second, we captured all possible diagnoses of T2DM by using a wide- ranging definition which included hospitalizations, physician claims and prescriptions for OHAs.

Another one of our strengths was that less than 1% of patients migrated before the end of the study period, resulting in minimal loss to follow up.

Additionally, unlike our study, many retrospective observational studies that compare sulfonylureas to metformin for monotherapy apply serum creatinine cutoffs or serious medical illness as exclusion criteria or do not report CKD status at all.265-267 Studies with combotherapy have also used renal insufficiency and previous CVD as exclusion criteria.268, 269 Because metformin and other OHAs are contraindicated in patients with CKD these restrictions are common and prevent metformin from being studied in this population in both a monotherapy and combotherapy setting. On the rare occasions that metformin is studied in populations with CKD direct comparisons with sulfonylureas are rarely performed.270 Our study included a relatively high percentage of patients with CKD (including individuals with an eGFR < 30 ml/min/1.73 m2) and was the first to analyze CKD as an effect modifier in a T2DM population which uses combotherapy to manage their hyperglycemia and is therefore unique.

Our study also has a few limitations. First, many individuals in our study did not have a laboratory test measured at baseline. As there are systematic differences in who receives a laboratory test, these data were determined to be missing not at random and therefore estimations

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with multiple imputations may be biased.271 As a result, we could not adequately control for laboratory results due to missing data. However, in our propensity score, we did control for whether a laboratory result was missing which may be an indicator of morbidity as most laboratory tests are not typically missing at random. Furthermore, many laboratory results were balanced in our propensity matched cohorts.

Second, due to small sample sizes we were underpowered in some of our analyses. We fell short of the estimated sample needed to detect differences in effectiveness; we were not able to compare specific OHAs in the ‘other’ group to sulfonylureas and could not analyze the sulfonylurea vs. ‘other’ comparison for every outcome. This grouping was also to our benefit as it allowed us enough power to make a combotherapy comparison of sulfonylurea users and non- sulfonylurea users for most of the outcomes. Third, our stratification of individuals into eGFR categories was based upon a single serum creatinine measurement rather than 2 measurements at least 90 days apart (the accepted definition for CKD) which may have been unrepresentative of true baseline kidney function. However, this would have resulted in overcounting patients with

CKD which would bias results toward the null hypothesis and would strengthen our findings when significant interaction terms are found due to the larger probability of a type 2 error.

Fourth, we could not fully account for duration of T2DM diagnosis in our dataset. Although

T2DM duration was not collected as a covariate, we feel that most individuals in the monotherapy cohort would have a short duration of T2DM since guidelines recommend initiating an OHA if lifestyle management alone is insufficient for optimal glycemic control after

2 or 3 months.87 Additionally, the duration of time on metformin in the combotherapy cohort is likely a close proxy for T2DM duration as our monotherapy data shows that approximately 90%

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of T2DM patients start their monotherapy on metformin. Fifth, our collection of visits to the emergency department is part of our definition for a major hypoglycemic event may be incomplete as the ADT & EDIS datasets only include data for emergency rooms in the Winnipeg

Regional Health Authority and not the entire province of Manitoba. However, we do not believe this affected the integrity of our findings as Winnipeg emergency rooms account for the majority of traffic in Manitoba and our definition of major hypoglycemic events also included all hospitalizations and lab test results in the province.

Sixth, we were unable to collect certain clinically relevant variables such as body mass index, systolic blood pressure and bone mineral density. However, we did collect administrative codes for obesity, hypertension and osteoporosis. Seventh, it was not possible to collect data on individual socioeconomic status. As a result we may have missed possible confounding as many persons with lower income may lack prescription drug coverage and may therefore be more likely to be prescribed a sulfonylurea compared to a newer agent as it is a lower cost option.

Since poverty is a social determinant of health, the potential imbalance of prescriptions between

OHA groups may have influenced our results to an unknown degree. Future studies should explore how socioecomonic status and insurance coverage affect OHA prescribing practices and outcomes. Finally, in an observational study design there is always the chance of other types of residual confounding that are unaccounted for in the analysis and we did not account for time- varying covariates.

5.5 Conclusions

To summarize, our evidence supports the use of metformin as first-line therapy for glycemic control in patients with normal kidney function in order to prevent higher rates of mortality,

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cardiovascular events and major hypoglycemic episodes in patients with T2DM. Our findings also discourage the use of sulfonylureas as add-on therapy as they are associated with a higher risk of all-cause mortality compared to other OHAs. CKD is an effect modifier for all-cause mortality in the choice of sulfonylureas vs. metformin but does not appear to be an effect modifier for any other OHA choice in relation to other safety outcomes. More real-world comparative effectiveness studies are needed to determine the optimal alternative to sulfonylureas as add-on therapy. Future directions that are recommended include a formal economic evaluation to explore the direct and indirect health care costs of treatment with sulfonylureas relative to other OHAs in a Canadian setting.

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Figure 1. Classification of chronic kidney disease

Green = low risk, yellow = moderate risk, orange = high rish, red = very high risk. Abbreviations: CKD - chronic kidney disease; GFR - glomerular filtration rate. Source: Levin, Adeera, et al. "Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease." Kidney International Supplements 3.1 (2013): 1-150.

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Table 1: Databases

Database Years Diabetes Education Resource for Children and Adolescents 1986 to 20151 (DERCA) Diagnostic Services of Manitoba (DSM) 2006 to 20151

Drug Program Information Network (DPIN) 2002/03 to 2015/162

Emergency Department - Admission, Discharge, and 2003 to 20151 Transfer (ADT) & Information System (EDIS) Hospital Discharge Abstracts (CIHI-DAD) 2002/03 to 2015/162

Manitoba Health Insurance Registry 2002/03 to 2015/162

Medical Claims/Services (MHSC) 2002/03 to 2015/162

1 Vital Statistics 2003 to 2016 1- Calendar Year: January 1 - December 31. 2 - Fiscal year: April 1 - March 31.

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Table 2: Exposures

OHA Class OHA Name ATC Code Biguanides Metformin A10BA (Metformin)

Sulfonylureas Chloropropamide, Gliclazide, Glimepiride, A10BB Glyburide, Tolbutamide

Metformin + TZD Metformin + Rosiglitazone A10BD03

Metformin + DPP-4 Metformin + Linagliptin, Metformin + A10BD07, inhibitors Saxagliptin, Metformin + Sitagliptin A10BD10, A10BD11

Metformin + SGLT2 Metformin + Dapagliflozin A10BD15 inhibitors

Alpha-glucosidase Acarbose A10BF inhibitors

TZDs Pioglitazone, Rosiglitazone A10BG

DPP-4 inhibitors Linagliptin, Saxagliptin, Sitagliptin A10BH

GLP-1 receptor Dulaglutide, Exenatide, Liraglutide A10BJ agonists

SGLT2 inhibitors Canagliflozin, Dapagliflozin, Empagliflozin A10BK

Meglitinides Nateglinide, Repaglinide A10BX02, A10BX03 Abbreviations: ATC - anatomical therapeutic chemical; DPP-4 - dipeptidyl peptidase-4; GLP-1 - glucagon like peptide-1; OHA - oral antihyperglycemic agent; SGLT2 - sodium-glucose linked transporter-2; TZD - thiazolidinedione.

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Table 3: Safety outcomes

Outcome Case definition All-cause mortality  Date of death as recorded in MB Vital Statistics or the MB Health Insurance Registry. Cardiovascular-related  Date of death where the reason code for primary or underlying cause of mortality death has a prefix of “I” as recorded in MB Vital Statistics. Cardiovascular events

Acute MI  Date of hospitalization where the reason code for the main diagnosis has a prefix of 410 (ICD-9) or I21 (ICD-10).

Heart Failure  Date of hospitalization where the reason code for the main diagnosis has a prefix of 428 (ICD-9) or I50 (ICD-10).

Stroke  Date of hospitalization where the reason code for the main diagnosis has a prefix of 430, 431, 432, 433, 434, 436 (ICD-9) or I60, I61, I62, I63 or I64 (ICD-10).

Unstable Angina  Date of hospitalization where the reason code for the main diagnosis has a prefix of 411.1 (ICD-9) or I20.0 (ICD-10).

Major hypoglycemic episodes  Date of hospitalization where the reason code for any diagnosis has a prefix of 251.0, 251.1, 251.2 (ICD-9) or E11.63, E13.63, E14.63, E15, E16.0, E16.1 or E16.2 (ICD-10) or date of Emergency Department visit where the reason for the visit is coded as “Hypoglycemia” or date of laboratory test where blood glucose is < 3.5 mmol/L. Major osteoporotic fractures

Forearm  Date of hospitalization where the reason code for the main diagnosis has a prefix of 813 (ICD-9) or S52 (ICD-10) and the accompanying procedure code has a prefix of (ICD-9), 1TV.73 or 1TV.74 (ICD-10) or date of first physician claim with a diagnosis code of 813 and an accompanying tariff code of 0807, 0810, 0811, 0821, 0823, 1851, 1854, 1856 or1860 if 2 such claims exist within 90 days.

Hip  Date of hospitalization where the reason code for the main diagnosis has a prefix of 820 (ICD-9), S72.0, S72.1 or S72.2 (ICD-10) and the accompanying procedure code has a prefix of (ICD-9), 1VA.53, 1VA.73, 1VA.74, 1VA.80 or 1VC.74 (ICD-10).

Humerus  Date of hospitalization where the reason code for the main diagnosis has a prefix of 812 (ICD-9) or S42.2 (ICD-10) or date of first physician claim with a diagnosis code of 812 without an accompanying laboratory or x-ray tariff code if 2 such claims exist within 90 days.

Vertebra  Date of hospitalization where the reason code for the main diagnosis has a prefix of 805 (ICD-9), S22.0, S22.1, S32.0, S32.7 or S32.8 (ICD-10) or date of first physician claim with a diagnosis code of 805 without an accompanying laboratory or x-ray tariff code. Abbreviations: ICD - international classification of diseases; MB - Manitoba; MI - myocardial infarction.

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Table 4: Baseline comorbidities

Hospital/Physician ICD-9 Codes ICD-10 Codes Claims Alcohol abuse 291, 303, 305.0, 357.5, 425.5, 535.3, 571.0, 571.1, F10, K70, X45, X65, Y15, Y90-Y91 571.2, 571.3, 780.0, 977.3, V11.3 Amputation 84.1 1VC.91, 1VC.93, 1VG.93, 1VQ93, 1WA.93, 1WE.93, 1WJ.93, 1WL.93 Asthma 493 J45 CVD 402.01, 402.11, 402.91, 410, 411, 420-434, 436 G45, I20.0, I20.9, I21-I23, I25.1, 00.61, 00.63, 00.66, 17.55, 36.06, 36.07, 36.1, 38.11, I25.2, I50-I51, I60-I64, R00.1, Z95 38.12, 39.28 1IJ.50, 1IJ.57, 1IJ.76, 1JE.50, 1JE.51, 1JE.57, 1JE.76

COPD 491, 492, 496 J41-J44 Dementia 290.0 , 290.1, 290.2, 290.3, 290.4, 290.8, 290.9, F00-F03, F05.1, G30, G31 291.2, 292.82, 294.0, 294.1, 294.2, 294.8, 331.0, 331.1, 331.2, 331.7, 331.8, 331.9, 797 Depression 296.2, 296.3, 300.4, 311 F20.4, F31.3–F31.5, F32, F33, F34.1, F41.2, F43.2 Fracture 733.1, 733.93-733.98, 805-807.4, 808-825, 827, M48.4, M84.3, Sx2 V54.13, V54.23 79.01,79.02, 79.05, 79.06, 79.11, 79.12, 79.15, 1TA.74, 1TK.73, 1TV.73, 1TV.74, 79.16, 79.21, 79.22, 79.25, 79.26, 79.31, 79.32, 1VA.73, 1VA.74, 1VA.80, 1VC.73, 79.35, 79.36, 79.61, 79.62, 79.65, 79.66, 81.65, 1VC.74, 1VQ.73, 1VQ.74 81.66 HIV/AIDS 042, 043, 044, 795.71, V08, 079.53 B20, B97.35, R75, Z21

Hyperlipidemia 272.0-272.2, 272.4 E78.0-E78.2, E78.4-E78.5 Hypertension 401, 402.00, 402.1, 402.10, 402.90, 403-405 I10-I13, I15 Liver disease 570-573 K70-K77 Malignancy 140-172, 174-209.3, 209.7 C00-C99 Neuropathy 337.1, 354, 355, 357.2 G56-G58, G99.0 Obesity 278.0, 793.91, V85.3, V85.4, 783.1 E66.01, E66.2, E66.3, E66.9 Osteoporosis 733.0 M81.0, M81.8 Parkinson’s Disease 332 F02.3, G20 PVD 440, 441, 444 I70-79 Abbreviations: COPD - chronic obstructive pulmonary disease; HIV/AIDS - human immunodeficiency virus infection and acquired immune deficiency syndrome; ICD - international classification of diseases; PVD - peripheral vascular disease

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Table 5: Baseline medications

Medication Class ATC Code

ACE inhibitors C09A

Alpha-blockers C02CA

Anticoagulants B01AA01, B01AA03, B01AE07, B01AF

Anticonvulsants N03

Anti-platelets B01AC04-B01AC07, B01AC22, B01AC24, B01AC26, L01XX35

Anti-Parkinson drugs N04

ARBs C09C

Beta-blockers C07

Bisphosphonates M05BA

CCBs C08

Denosumab M05BX04

Digoxin (cardiac glycoside) C01AA05

Direct Renin Inhibitors C09XA

Direct Vasodilators C02DB, C02DC

Lipid lowering medications C10

Loop diuretics C03C

Opioids (with or without non-opioid analgesics) N02A

Oral corticosteroids R01AD

Osteoporosis medications M05BA-M05BB, M05BX04

Potassium sparing diuretics C03D

Proton pump inhibitors A02BC

Thiazide diuretics C03A

Abbreviations: ACE - Angiotensin-converting enzyme; ARB - Angiotensin II receptor blocker; ATC - anatomical therapeutic chemical; CCB - calcium channel blocker.

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Figure 2: Study flow diagram for monotherapy cohort

Abbreviations: DM - diabetes mellitus; GDM - gestational diabetes; T1DM - type 1 diabetes mellitus.

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Figure 3: Study flow diagram for combotherapy cohort

Abbreviations: DM - diabetes mellitus; GDM - gestational diabetes; T1DM - type 1 diabetes mellitus.

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Table 6: Pre-match counts of OHAs in ‘Other’ group

Monotherapy Combotherapy OHA (n = 306) (n = 1,266) Acarbose 19 (6.2%) 40 (3.2%) DPP-4 inhibitors 39 (12.7%) 520 (41.1%) GLP-1 receptor agonists 39 (12.7%) 55 (4.3%) Meglitinides 75 (24.5%) 162 (12.8%) SGLT2 inhibitors 86 (28.1%) 250 (19.7%) TZDs 48 (15.7%) 239 (18.9%) Abbreviations: DPP-4 - dipeptidyl peptidase-4; GLP-1 - glucagon like peptide-1; SGLT2 - sodium-glucose linked transporter-2; TZD - thiazolidinedione.

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Table 7: Pre-match baseline characteristics by monotherapy group

Overall Sulfonylurea Metformin Other Characteristic p-value (n = 20,979) (n = 1,793) (n = 18,880) (n = 306) Demographics Age (years) 56.0 ± 16.2 62.7 ± 16.8 55.4 ± 16.0 58.6 ± 15.8 <0.001 Sex (% female) 10,320 (49.2%) 803 (44.8%) 9,349 (49.5%) 168 (54.9%) <0.001 CKD Stage <0.001 eGFR ≥ 60 ml/min/1.73 m2 18,790 (89.6%) 1,241 (69.2%) 17,312 (91.7%) 237 (77.5%) eGFR 45-60 ml/min/1.73 m2 1,354 (6.5%) 223 (12.4%) 1,105 (5.9%) 26 (8.5%) (Stage 3a) eGFR < 45 ml/min/1.73 m2 835 (4.0%) 329 (18.3%) 463 (2.5%) 43 (14.1%) (Stage 3b - 5) Baseline Labs eGFR (ml/min/1.73 m2) 92 ± 24 78 ± 31 93 ± 22 81 ± 28 <0.001 A1C (%) 8.2 ± 2.2 8.7 ± 2.3 8.2 ± 2.2 7.7 ± 2.3 <0.001 Available 8,198 (39.1%) 709 (39.5%) 7,385 (39.1%) 104 (34.0%) 0.173 Total cholesterol (mmol/L) 4.9 ± 1.2 4.7 ± 1.3 5.0 ± 1.2 4.8 ± 1.3 <0.001 Available 6,798 (32.4%) 532 (29.7%) 6,192 (32.8%) 74 (24.2%) <0.001 Urine ACR (mg/mmol) 1.3 (0.5, 4.9) 2.7 (0.7, 14.8) 1.2 (0.4, 4.2) 1.8 (0.5, 8.7) <0.001 Available 6,513 (31.0%) 605 (33.7%) 5,810 (30.8%) 98 (32.0%) 0.032 Hemoglobin (g/L) 139 ± 18 134 ± 21 140 ± 18 135 ± 18 <0.001 Available 15,446 (73.6%) 1,467 (81.8%) 13,750 (72.8%) 229 (74.8%) <0.001 Serum albumin (g/L) 37 ± 6 34 ± 6 37 ± 5 34 ± 8 <0.001 Available 1,584 (7.6%) 212 (11.8%) 1,338 (7.1%) 34 (11.1%) <0.001 Baseline Comorbidities Alcohol abuse 1,045 (5.5%) 92 (5.1%) 1,045 (5.5%) 14 (4.6%) 0.60 Asthma 3,825 (18.2%) 309 (17.2%) 3,453 (18.3%) 63 (20.6%) 0.30 COPD 2,995 (14.3%) 329 (18.3%) 2,615 (13.9%) 51 (16.7%) <0.001 CVD 6,766 (32.3%) 815 (45.5%) 5,836 (30.9%) 115 (37.6%) <0.001 Dementia 1,012 (4.8%) 142 (7.9%) 850 (4.5%) 20 (6.5%) <0.001 Depression 5,901 (28.1%) 386 (21.5%) 5,388 (28.5%) 127 (41.5%) <0.001 Fracture 3,249 (15.5%) 262 (14.6%) 2,929 (15.5%) 58 (19.0%) 0.145 Hypertension 13,431 (64.0%) 1,272 (70.9%) 11,949 (63.3%) 210 (68.6%) <0.001 Hyperlipidemia 7,840 (37.4%) 587 (32.7%) 7,124 (37.7%) 129 (42.2%) <0.001 Liver disease 1,749 (8.3%) 153 (8.5%) 1,565 (8.3%) 31 (10.1%) 0.49 Malignancy 3,538 (16.9%) 404 (22.5%) 3,070 (16.3%) 64 (20.9%) <0.001 Neuropathy 2,508 (12.0%) 188 (10.5%) 2,260 (12.0%) 60 (19.6%) <0.001 Obesity 2,045 (9.7%) 117 (6.5%) 1,887 (10.0%) 41 (13.4%) <0.001 Osteoporosis 1,416 (6.7%) 141 (7.9%) 1,249 (6.6%) 26 (8.5%) 0.062 PVD 460 (2.2%) 81 (4.5%) 372 (2.0%) 7 (2.3%) <0.001

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Baseline Medication Use ACE inhibitors 5,902 (28.1%) 569 (31.7%) 5,263 (27.9%) 70 (22.9%) <0.001 Anticoagulants 1,418 (6.8%) 200 (11.2%) 1,186 (6.3%) 32 (10.5%) <0.001 Anticonvulsants 2,245 (10.7%) 167 (9.3%) 2,024 (10.7%) 54 (17.6%) <0.001 Anti-Parkinson medications 374 (1.8%) 33 (1.8%) 330 (1.7%) 11 (3.6%) 0.052 Anti-platelets 3,728 (17.8%) 428 (23.9%) 3,255 (17.2%) 45 (14.7%) <0.001 ARBs 2,278 (10.9%) 240 (13.4%) 1,994 (10.6%) 44 (14.4%) <0.001 Beta blockers 5,050 (24.1%) 611 (34.1%) 4,358 (23.1%) 81 (26.5%) <0.001 Bisphosphonates 466 (2.1%) 77 (3.3%) 379 (1.9%) 10 (2.4%) <0.001 CCBs 3,443 (16.4%) 421 (23.5%) 2,953 (15.6%) 69 (22.5%) <0.001 Corticosteroids 1,553 (7.4%) 113 (6.3%) 1,404 (7.4%) 36 (11.8%) 0.003 Digoxin 687 (3.3%) 138 (7.7%) 533 (2.8%) 16 (5.2%) <0.001 Direct vasodilators 59 (0.3%) 23 (1.3%) 36 (0.2%) 0 (0.0%) <0.001 Lipid lowering medications 8,319 (39.7%) 699 (39.0%) 7,508 (39.8%) 112 (36.6%) 0.44 Loop diuretics 2,439 (11.6%) 464 (25.9%) 1,921 (10.2%) 54 (17.6%) <0.001 Opioids 6,619 (31.6%) 592 (33.0%) 5,923 (31.4%) 104 (34.0%) 0.23 Proton pump inhibitors 4,826 (23.0%) 477 (26.6%) 4,265 (22.6%) 84 (27.5%) <0.001 Potassium sparing diuretics 506 (2.4%) 87 (4.9%) 404 (2.1%) 15 (4.9%) <0.001 Thiazide diuretics 2,958 (14.1%) 256 (14.3%) 2,664 (14.1%) 38 (12.4%) 0.68 Index Fiscal Year <0.001 2006/07 1,010 (4.8%) 183 (10.2%) 807 (4.3%) 20 (6.5%) 2007/08 1,528 (7.3%) 223 (12.4%) 1,274 (6.7%) 31 (10.1%) 2008/09 1,615 (7.7%) 214 (11.9%) 1,377 (7.3%) 24 (7.8%) 2009/10 1,751 (8.3%) 198 (11.0%) 1,540 (8.2%) 13 (4.2%) 2010/11 1,760 (8.4%) 170 (9.5%) 1,579 (8.4%) 11 (3.6%) 2011/12 2,128 (10.1%) 203 (11.3%) 1,910 (10.1%) 15 (4.9%) 2012/13 2,423 (11.5%) 175 (9.8%) 2,228 (11.8%) 20 (6.5%) 2013/14 2,550 (12.2%) 148 (8.3%) 2,388 (12.6%) 14 (4.6%) 2014/15 2,941 (14.0%) 148 (8.3%) 2,750 (14.6%) 43 (14.1%) 2015/16 3,273 (15.6%) 131 (7.3%) 3,027 (16.0%) 115 (37.6%) Abbreviations: A1C - glycosylated hemoglobin; ACE - Angiotensin-converting enzyme; ACR - albumin to creatinine ratio; ARB - Angiotensin II receptor blocker; CCB - calcium channel blocker; COPD - chronic obstructive pulmonary disease; CVD - cardiovascular disease; eGFR - estimated glomerular filtration rate; PVD - peripheral vascular disease. Data was available for all indicators unless otherwise specified.

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Table 8: Pre-match baseline characteristics by combotherapy group

Overall Sulfonylurea Other Characteristic p-value (n = 11,615) (n = 10,349) (n = 1,266) Demographics Age (years) 56.9 ± 14.9 56.8 ± 15.1 57.6 ± 13.0 0.061 Sex (% female) 5,292 (45.6%) 4,709 (45.5%) 583 (46.1%) 0.019 CKD Stage 0.82 eGFR ≥ 60 ml/min/1.73 m2 10,277 (88.5%) 9,150 (88.4%) 1,127 (89.0%) eGFR 45-60 ml/min/1.73 m2 (Stage 3a) 761 (6.6%) 682 (6.6%) 79 (6.2%) eGFR < 45 ml/min/1.73 m2 (Stage 3b - 5) 577 (5.0%) 517 (5.0%) 60 (4.7%) Baseline Labs eGFR (ml/min/1.73 m2) 93 ± 25 93 ± 25 90 ± 22 <0.001 A1C (%) 9.1 ± 2.3 9.2 ± 2.3 8.3 ± 1.8 <0.001 Available 5,196 (44.7%) 4,721 (45.6%) 475 (37.5%) <0.001 Total Cholesterol (mmol/L) 4.8 ± 1.3 4.8 ± 1.3 4.6 ± 1.3 0.004 Available 4,063 (35.0%) 3,664 (35.4%) 399 (31.5%) 0.006 Urine ACR (mg/mmol) 1.7 (0.6, 6.4) 1.8 (0.6, 6.6) 1.2 (0.4, 4.9) <0.001 Available 5,249 (45.2%) 4,644 (44.9%) 605 (47.8%) 0.049 Hemoglobin (g/L) 138 ± 19 138 ± 19 138 ± 18 0.44 Available 8,278 (71.3%) 7,454 (72.0%) 824 (65.1%) <0.001 Albumin (g/L) 37 ± 5 37 ± 5 38 ± 5 0.52 Available 881 (7.6%) 798 (7.7%) 83 (6.6%) 0.143 Baseline Comorbidities Alcohol abuse 689 (5.9%) 663 (6.4%) 26 (2.1%) <0.001 Amputation 80 (0.7%) 74 (0.7%) 6 (0.5%) 0.33 Asthma 2,062 (17.8%) 1,818 (17.6%) 244 (19.3%) 0.134 COPD 1,592 (13.7%) 1,432 (13.8%) 160 (12.6%) 0.24 CVD 3,842 (33.1%) 3,448 (33.3%) 394 (31.1%) 0.117 Dementia 573 (4.1%) 529 (5.1%) 44 (3.5%) 0.011 Depression 3,055 (26.3%) 2,718 (26.3%) 337 (26.6%) 0.79 Fracture 1,876 (16.2%) 1,716 (16.6%) 160 (12.6%) <0.001 Hypertension 7,959 (68.5%) 7,020 (67.8%) 939 (74.2%) <0.001 Hyperlipidemia 4,362 (37.6%) 3,825 (37.0%) 537 (42.4%) <0.001 Liver disease 999 (8.6%) 879 (8.5%) 120 (9.5%) 0.24 Malignancy 1,901 (16.4%) 1,681 (16.2%) 220 (17.4%) 0.30 Neuropathy 1,373 (11.8%) 1,188 (11.5%) 185 (14.6%) 0.001 Obesity 1,178 (10.1%) 1,027 (9.9%) 151 (11.9%) 0.026 Osteoporosis 733 (6.3%) 656 (6.3%) 77 (6.1%) 0.72 Parkinson’s disease 88 (0.8%) 75 (0.7%) 13 (1.0%) 0.24 PVD 298 (2.6%) 269 (2.6%) 29 (2.3%) 0.30

72

Baseline Medication Use ACE inhibitors 4,599 (39.6%) 4,137 (40.0%) 462 (36.5%) 0.017 Alpha blockers 30 (0.3%) 23 (0.2%) 7 (0.6%) 0.029 Anticoagulants 774 (6.7%) 701 (6.8%) 73 (5.8%) 0.175 Anticonvulsants 1,316 (11.3%) 1,185 (11.5%) 131 (10.3%) 0.24 Anti-Parkinson medications 222 (1.9%) 204 (2.0%) 18 (1.4%) 0.178 Anti-platelets 2,753 (23.7%) 2,507 (24.2%) 246 (19.4%) <0.001 ARBs 1,486 (12.8%) 1,279 (12.4%) 207 (16.4%) <0.001 Beta blockers 3,001 (25.8%) 2,674 (25.8%) 327 (25.8%) 0.99 Bisphosphonates 223 (1.9%) 203 (2.0%) 20 (1.6%) 0.35 CCBs 2,135 (18.4%) 1,911 (18.5%) 224 (17.7%) 0.50 Corticosteroids 784 (6.7%) 670 (6.5%) 114 (9.0%) 0.073 Digoxin 371 (3.2%) 335 (3.2%) 36 (2.8%) 0.45 Lipid lowering medications 6,063 (52.2%) 5,319 (51.4%) 744 (58.8%) <0.001 Loop diuretics 1,367 (11.8%) 1,241 (12.0%) 126 (10.0%) 0.034 Opioids 3,782 (32.6%) 3,414 (33.0%) 368 (29.1%) 0.005 Proton pump inhibitors 2,772 (23.9%) 2,457 (23.7%) 315 (24.9%) 0.61 Potassium sparing diuretics 290 (2.5%) 263 (2.5%) 27 (2.1%) 0.38 Thiazide diuretics 1,705 (14.7%) 1,526 (14.7%) 179 (14.1%) 0.57 Index Fiscal Year <0.001 2006/07 535 (4.6%) 449 (4.3%) 86 (6.8%) 2007/08 760 (6.5%) 683 (6.6%) 77 (6.1%) 2008/09 840 (7.2%) 773 (7.5%) 67 (5.3%) 2009/10 926 (8.0%) 831 (8.0%) 95 (7.5%) 2010/11 982 (8.5%) 903 (8.7%) 79 (6.2%) 2011/12 1,235 (10.6%) 1,148 (11.1%) 87 (6.9%) 2012/13 1,353 (11.6%) 1,232 (11.9%) 121 (9.6%) 2013/14 1,437 (12.4%) 1,322 (12.8%) 115 (9.1%) 2014/15 1,698 (14.6%) 1,495 (14.4%) 203 (16.0%) 2015/16 1,849 (15.9%) 1,513 (14.6%) 336 (26.5%) Time on metformin before add-on <0.001 therapy ≥ 3 years 2,779 (23.9%) 2,400 (23.2%) 379 (29.9%) 1 - 3 years 2,378 (20.5%) 2,058 (19.9%) 320 (25.3%) < 1 year 6,458 (55.6%) 5,891 (56.9%) 567 (44.8%) Abbreviations: A1C - glycosylated hemoglobin; ACE - Angiotensin-converting enzyme; ACR - albumin to creatinine ratio; ARB - Angiotensin II receptor blocker; CCB - calcium channel blocker; COPD - chronic obstructive pulmonary disease; CVD - cardiovascular disease; eGFR - estimated glomerular filtration rate; PVD - peripheral vascular disease. Data was available for all indicators unless otherwise specified. 73

Table 9: Post-match counts of OHAs in ‘other’ group

Monotherapy Combotherapy OHA (n = 265) (n = 1,266) Acarbose 16 (6.0%) 40 (3.2%) DPP-4 inhibitors 38 (14.3%) 520 (41.1%) GLP-1 receptor agonists 24 (9.1%) 55 (4.3%) Meglitinides 73 (27.6%) 162 (12.8%) SGLT2 inhibitors 66 (24.9%) 250 (19.7%) TZDs 48 (18.1%) 239 (18.9%) Abbreviations: DPP-4 - dipeptidyl peptidase-4; GLP-1 - glucagon like peptide-1; SGLT2 - sodium-glucose linked transporter-2; TZD - thiazolidinedione.

74

Table 10: Post-match baseline characteristics by monotherapy group (sulfonylurea versus metformin)

Overall Sulfonylurea Metformin Characteristic p-value SMD (n = 3,554) (n = 1,777) (n = 1,777) Demographics Age (years) 62.5 ± 16.7 62.5 ± 16.8 62.5 ± 16.6 0.93 0.002 Sex (% female) 1,599 (45.0%) 796 (44.8%) 803 (45.2%) 0.81 -0.006 CKD Stage 0.56 eGFR ≥ 60 ml/min/1.73 m2 2,500 (70.3%) 1,241 (69.8%) 1,259 (70.8%) -0.018 eGFR 45-60 ml/min/1.73 m2 452 (12.7%) 223 (12.5%) 229 (12.9%) -0.008 (Stage 3a) eGFR < 45 ml/min/1.73 m2 602 (16.9%) 313 (17.6%) 289 (16.3%) 0.029 (Stage 3b - 5) Baseline Labs eGFR (ml/min/1.73 m2) 79 ± 30 78 ± 31 80 ± 29 0.24 -0.032 A1C (%) 8.5 ± 2.3 8.7 ± 2.3 8.3 ± 2.2 <0.001 0.144 Available 1,407 (39.6%) 700 (39.4%) 707 (39.8%) 0.81 -0.007 Total cholesterol (mmol/L) 4.8 ± 1.3 4.7 ± 1.3 4.9 ± 1.3 0.162 -0.070 Available 1,075 (30.2%) 529 (29.8%) 546 (30.7%) 0.53 -0.017 Urine ACR (mg/mmol) 2.2 (0.6, 10.0) 2.7 (0.7, 14.8) 1.7 (0.5, 7.0) <0.001 0.135 Available 1,193 (33.6%) 601 (33.8%) 592 (33.3%) 0.75 0.009 Hemoglobin (g/L) 136 ± 20 135 ± 21 138 ± 19 <0.001 -0.120 Available 2,905 (81.7%) 1,451 (81.7%) 1,454 (81.8%) 0.90 -0.004 Serum albumin (g/L) 35 ± 6 34 ± 6 36 ± 6 0.006 -0.23 Available 384 (10.8%) 204 (11.5%) 180 (10.1%) 0.195 0.035 Baseline Comorbidities Alcohol abuse 187 (5.3%) 92 (5.2%) 95 (5.3%) 0.82 -0.006 Amputation 29 (0.8%) 15 (0.8%) 14 (0.8%) 0.85 0.005 Asthma 631 (17.8%) 304 (17.1%) 327 (18.4%) 0.31 -0.028 COPD 651 (18.3%) 328 (18.5%) 323 (18.2%) 0.83 0.006 CVD 1,593 (44.8%) 802 (45.1%) 791 (44.5%) 0.71 0.010 Dementia 291 (8.2%) 139 (7.8%) 152 (8.6%) 0.43 -0.022 Depression 765 (21.5%) 385 (21.7%) 380 (21.4%) 0.84 0.006 Fracture 514 (14.5%) 260 (14.6%) 254 (14.3%) 0.77 0.008 Hypertension 2,510 (70.6%) 1,258 (70.8%) 1,252 (70.5%) 0.83 0.006 Hyperlipidemia 1,194 (33.6%) 585 (32.9%) 609 (34.3%) 0.39 -0.023 Liver disease 310 (8.7%) 152 (8.6%) 158 (8.9%) 0.72 -0.010 Malignancy 800 (22.5%) 398 (22.4%) 402 (22.6%) 0.87 -0.004 Neuropathy 368 (10.4%) 188 (10.6%) 180 (10.1%) 0.66 0.012 Obesity 217 (6.1%) 117 (6.6%) 100 (5.6%) 0.23 0.032 Osteoporosis 278 (7.8%) 140 (7.9%) 138 (7.8%) 0.90 0.003 Parkinson’s disease 48 (1.4%) 21 (1.2%) 27 (1.5%) 0.38 -0.024 PVD 171 (4.8%) 80 (4.5%) 91 (5.1%) 0.39 -0.024 75

Baseline Medication Use ACE inhibitors 1,127 (31.7%) 560 (31.5%) 567 (31.9%) 0.80 -0.007 Anticoagulants 389 (10.9%) 193 (10.9%) 196 (11.0%) 0.87 -0.004 Anticonvulsants 332 (9.3%) 165 (9.3%) 167 (9.4%) 0.91 -0.003 Anti-Parkinson medications 66 (1.9%) 32 (1.8%) 34 (1.9%) 0.80 -0.007 Anti-platelets 844 (23.7%) 419 (23.6%) 425 (23.9%) 0.81 -0.006 ARBs 485 (13.6%) 237 (13.3%) 248 (14.0%) 0.59 -0.015 Beta blockers 1,192 (33.5%) 600 (33.8%) 592 (33.3%) 0.78 0.008 Bisphosphonates 110 (3.1%) 58 (3.3%) 52 (2.9%) 0.56 0.016 CCBs 829 (23.3%) 416 (23.4%) 413 (23.2%) 0.91 0.003 Corticosteroids 204 (5.7%) 112 (6.3%) 92 (5.2%) 0.149 0.039 Digoxin 258 (7.3%) 133 (7.5%) 125 (7.0%) 0.61 0.014 Direct vasodilators 35 (1.0%) 18 (1.0%) 17 (1.0%) 0.87 0.005 Lipid lowering medications 1,396 (39.3%) 695 (39.1%) 701 (39.4%) 0.84 -0.006 Loop diuretics 874 (24.6%) 449 (25.3%) 425 (23.9%) 0.35 0.026 Opioids 1,179 (33.2%) 587 (33.0%) 592 (33.3%) 0.86 -0.005 Proton pump inhibitors 962 (27.1%) 470 (26.4%) 492 (27.7%) 0.41 -0.023 Potassium sparing diuretics 174 (4.9%) 84 (4.7%) 90 (5.1%) 0.64 -0.013 Thiazide diuretics 516 (14.5%) 254 (14.3%) 262 (14.7%) 0.70 -0.010 Index Fiscal Year 0.65 2006/07 370 (10.4%) 179 (10.1%) 191 (10.7%) -0.018 2007/08 447 (12.6%) 222 (12.5%) 225 (12.7%) -0.004 2008/09 443 (12.5%) 210 (11.8%) 233 (13.1%) -0.032 2009/10 412 (11.6%) 195 (11.0%) 217 (12.2%) -0.032 2010/11 330 (9.3%) 169 (9.5%) 161 (9.1%) 0.013 2011/12 410 (11.5%) 202 (11.4%) 208 (11.7%) -0.009 2012/13 340 (9.6%) 175 (9.8%) 165 (9.3%) 0.016 2013/14 286 (8.0%) 147 (8.3%) 139 (7.8%) 0.013 2014/15 268 (7.5%) 147 (8.3%) 121 (6.8%) 0.045 2015/16 248 (7.0%) 131 (7.4%) 117 (6.6%) 0.025 Abbreviations: A1C - glycosylated hemoglobin; ACE - Angiotensin-converting enzyme; ACR - albumin to creatinine ratio; ARB - Angiotensin II receptor blocker; CCB - calcium channel blocker; COPD - chronic obstructive pulmonary disease; CVD - cardiovascular disease; eGFR - estimated glomerular filtration rate; PVD - peripheral vascular disease; SMD - standardized mean difference. Data was available for all indicators unless otherwise specified.

76

Table 11: Post-match baseline characteristics by monotherapy group (sulfonylurea versus ‘other’) Overall Sulfonylurea Other Characteristic p-value SMD (n = 530) (n = 265) (n = 265) Demographics Age (years) 59.8 ± 16.5 59.4 ± 17.2 60.1 ± 15.8 0.66 -0.031 Sex (% female) 269 (50.8%) 135 (50.9%) 134 (50.6%) 0.93 0.006 CKD Stage 0.74 eGFR ≥ 60 ml/min/1.73 m2 391 (73.8%) 195 (73.6%) 196 (74.0%) -0.007 eGFR 45-60 ml/min/1.73 m2 (Stage 3a) 48 (9.1%) 22 (8.3%) 26 (9.8%) -0.044 eGFR < 45 ml/min/1.73 m2 91 (17.2%) 48 (18.1%) 43 (16.2%) 0.041 (Stage 3b - 5) Baseline Labs eGFR (ml/min/1.73 m2) 80 ± 31 81 ± 32 79 ± 29 0.46 0.052 A1C (%) 8.2 ± 2.5 8.6 ± 2.6 7.8 ± 2.3 0.022 0.26 Available 189 (35.7%) 97 (36.6%) 92 (34.7%) 0.65 0.032 Total cholesterol (mmol/L) 4.8 ± 1.3 4.8 ± 1.4 4.7 ± 1.3 0.69 0.055 Available 138 (26.0%) 72 (27.2%) 66 (24.9%) 0.55 0.042 Urine ACR (mg/mmol) 2.9 (0.7, 11.6) 2.9 (0.9, 14.5) 2.6 (0.6, 10.0) 0.54 0.090 Available 173 (32.6%) 87 (32.8%) 86 (32.5%) 0.93 0.007 Hemoglobin (g/L) 134 ± 20 133 ± 22 135 ± 18 0.52 -0.051 Available 410 (77.4%) 208 (78.5%) 202 (76.2%) 0.53 0.044 Serum albumin (g/L) 33 ± 7 33 ± 7 33 ± 7 0.91 0.025 Available 55 (10.4%) 25 (9.4%) 30 (11.3%) 0.48 -0.051 Baseline Comorbidities Alcohol abuse 32 (6.0%) 19 (7.2%) 13 (4.9%) 0.27 0.076 Asthma 109 (20.6%) 57 (21.5%) 52 (19.6%) 0.59 0.038 COPD 91 (17.2%) 46 (17.4%) 45 (17.0%) 0.91 0.008 CVD 215 (40.6%) 107 (40.4%) 108 (40.8%) 0.93 -0.006 Dementia 45 (8.5%) 26 (9.8%) 19 (7.2%) 0.28 0.076 Depression 184 (34.7%) 93 (35.1%) 91 (34.3%) 0.86 0.013 Fracture 98 (18.5%) 48 (18.1%) 50 (18.9%) 0.82 -0.016 Hypertension 367 (69.2%) 178 (67.2%) 189 (71.3%) 0.30 -0.073 Hyperlipidemia 200 (37.7%) 95 (35.8%) 105 (39.6%) 0.37 -0.064 Liver disease 53 (10.0%) 27 (10.2%) 26 (9.8%) 0.88 0.010 Malignancy 114 (21.5%) 58 (21.9%) 56 (21.1%) 0.83 0.015 Neuropathy 82 (15.5%) 42 (15.8%) 40 (15.1%) 0.81 0.017 Obesity 63 (11.9%) 34 (12.8%) 29 (10.9%) 0.50 0.047 Osteoporosis 48 (9.1%) 26 (9.8%) 22 (8.3%) 0.54 0.042 PVD 13 (2.5%) 6 (2.3%) 7 (2.6%) 0.78 -0.020

77

Baseline Medication Use ACE inhibitors 133 (25.1%) 68 (25.7%) 65 (24.5%) 0.76 0.021 Anticoagulants 63 (11.9%) 33 (12.5%) 30 (11.3%) 0.69 0.028 Anticonvulsants 77 (14.5%) 38 (14.3%) 39 (14.7%) 0.90 -0.009 Anti-Parkinson medications 13 (2.5%) 7 (2.6%) 6 (2.3%) 0.78 0.020 Anti-platelets 94 (17.7%) 50 (18.9%) 44 (16.6%) 0.50 0.048 ARBs 79 (14.9%) 40 (15.1%) 39 (14.7%) 0.90 0.009 Beta blockers 159 (30.0%) 81 (30.6%) 78 (29.4%) 0.78 0.020 CCBs 125 (23.6%) 62 (23.4%) 63 (23.8%) 0.92 -0.007 Corticosteroids 51 (9.6%) 23 (8.7%) 28 (10.6%) 0.46 -0.053 Digoxin 30 (5.7%) 15 (5.7%) 15 (5.7%) 1.00 0.000 Lipid lowering medications 205 (38.7%) 100 (37.7%) 105 (39.6%) 0.66 -0.032 Loop diuretics 105 (19.8%) 54 (20.4%) 51 (19.2%) 0.74 0.023 Opioids 189 (35.7%) 99 (37.4%) 90 (34.0%) 0.41 0.058 Proton pump inhibitors 160 (30.2%) 83 (31.3%) 77 (29.1%) 0.57 0.040 Potassium sparing diuretics 28 (5.3%) 15 (5.7%) 13 (4.9%) 0.70 0.027 Thiazide diuretics 56 (10.6%) 23 (8.7%) 33 (12.4%) 0.158 -0.103 Index Fiscal Year 0.78 2006/07 47 (8.9%) 27 (10.2%) 20 (7.5%) 0.074 2007/08 58 (10.9%) 27 (10.2%) 31 (11.7%) -0.040 2008/09 41 (7.7%) 17 (6.4%) 24 (9.1%) -0.083 2009/10 27 (5.1%) 14 (5.3%) 13 (4.9%) 0.014 2010/11 21 (4.0%) 10 (3.8%) 11 (4.2%) -0.016 2011/12 31 (5.8%) 16 (6.0%) 15 (5.7%) 0.013 2012/13 42 (7.9%) 22 (8.3%) 20 (7.5%) 0.023 2013/14 31 (5.8%) 17 (6.4%) 14 (5.3%) 0.039 2014/15 74 (14.0%) 31 (11.7%) 43 (16.2%) -0.109 2015/16 158 (29.8%) 84 (31.7%) 74 (27.9%) 0.067 Abbreviations: A1C - glycosylated hemoglobin; ACE - Angiotensin-converting enzyme; ACR - albumin to creatinine ratio; ARB - Angiotensin II receptor blocker; CCB - calcium channel blocker; COPD - chronic obstructive pulmonary disease; CVD - cardiovascular disease; eGFR - estimated glomerular filtration rate; PVD - peripheral vascular disease; SMD - standardized mean difference. Data was available for all indicators unless otherwise specified.

78

Table 12: Post-match baseline characteristics by combotherapy group

Characteristic Overall Sulfonylurea Other p-value SMD (n = 2,532) (n = 1,266) (n = 1,266) Demographics Age (years) 57.9 ± 13.4 58.2 ± 13.8 57.6 ± 13.0 0.24 0.036 Sex (% female) 1,152 (45.5%) 569 (44.9%) 583 (46.1%) 0.58 -0.018 CKD Stage 0.36 eGFR ≥ 60 ml/min/1.73 m2 2,231 (88.1%) 1,104 (87.2%) 1,127 (89.0%) -0.045 2 eGFR 45-60 ml/min/1.73 m 173 (6.8%) 94 (7.4%) 79 (6.2%) 0.038 (Stage 3a) eGFR < 45 ml/min/1.73 m2 128 (5.1%) 68 (5.4%) 60 (4.7%) 0.023 (Stage 3b - 5) Baseline Labs eGFR (ml/min/1.73 m2) 90 ± 23 89 ± 25 90 ± 22 0.52 -0.034 A1C (%) 8.6 ± 2.1 9.0 ± 2.3 8.3 ± 1.8 <0.001 0.27 Available 949 (37.5%) 474 (37.4%) 475 (37.5%) 0.97 -0.001 Total Cholesterol (mmol/L) 4.7 ± 1.3 4.8 ± 1.3 4.6 ± 1.3 0.054 0.126 Available 785 (31.0%) 386 (30.5%) 399 (31.5%) 0.58 -0.018 Urine ACR (mg/mmol) 1.5 (0.5, 5.8) 1.9 (0.7, 6.9) 1.2 (0.4, 4.9) <0.001 0.005 Available 1,223 (48.3%) 618 (48.8%) 605 (47.8%) 0.61 0.017 Hemoglobin (g/L) 138 ± 18 137 ± 20 138 ± 18 0.28 -0.042 Available 1,662 (65.6%) 838 (66.2%) 824 (65.1%) 0.56 0.019 Albumin (g/L) 38 ± 4 39 ± 4 38 ± 5 0.196 0.187 Available 162 (6.4%) 79 (6.2%) 83 (6.6%) 0.75 -0.011 Baseline Comorbidities Alcohol abuse 54 (2.1%) 28 (2.2%) 26 (2.1%) 0.78 0.009 Amputation 12 (0.5%) 6 (0.5%) 6 (0.5%) 1.00 0.000 Asthma 460 (18.2%) 216 (17.1%) 244 (19.3%) 0.149 -0.047 COPD 320 (12.6%) 160 (12.6%) 160 (12.6%) 1.00 0.000 CVD 785 (31.0%) 391 (30.9%) 394 (31.1%) 0.90 -0.004 Dementia 87 (3.4%) 43 (3.4%) 44 (3.5%) 0.91 -0.004 Depression 680 (26.9%) 343 (37.1%) 337 (26.6%) 0.79 0.009 Fracture 310 (12.2%) 150 (11.8%) 160 (12.6%) 0.54 -0.020 Hypertension 1,873 (74.0%) 934 (73.8%) 939 (74.2%) 0.82 -0.007 Hyperlipidemia 1,091 (43.1%) 554 (43.8%) 537 (42.4%) 0.50 0.022 Liver disease 222 (8.8%) 102 (8.1%) 120 (9.5%) 0.21 -0.042 Malignancy 431 (17.0%) 211 (16.7%) 220 (17.4%) 0.63 -0.015 Neuropathy 367 (14.5%) 182 (14.4%) 185 (14.6%) 0.87 -0.006 Obesity 295 (11.7%) 144 (11.4%) 151 (11.9%) 0.66 -0.014 Osteoporosis 150 (5.9%) 73 (5.8%) 77 (6.1%) 0.74 -0.011 Parkinson’s disease 26 (1.0%) 13 (1.0%) 13 (1.0%) 1.00 0.000 PVD 60 (2.4%) 31 (2.4%) 29 (2.3%) 0.79 0.008 79

Baseline Medication Use ACE inhibitors 918 (36.3%) 456 (36.0%) 462 (36.5%) 0.80 -0.008 Anticoagulants 151 (6.0%) 78 (6.2%) 73 (5.8%) 0.67 0.014 Anticonvulsants 228 (9.0%) 97 (7.7%) 131 (10.3%) 0.018 -0.078 Anti-Parkinson medications 36 (1.4%) 18 (1.4%) 18 (1.4%) 1.00 0.000 Anti-platelets 458 (18.1%) 212 (16.7%) 246 (19.4%) 0.079 -0.058 ARBs 427 (16.9%) 220 (17.4%) 207 (16.4%) 0.49 0.022 Beta blockers 660 (26.1%) 333 (26.3%) 327 (25.8%) 0.79 0.009 Bisphosphonates 43 (1.7%) 23 (1.8%) 20 (1.6%) 0.64 0.015 CCBs 448 (17.7%) 224 (17.7%) 224 (17.7%) 1.00 0.000 Corticosteroids 216 (8.5%) 102 (8.1%) 114 (9.0%) 0.39 -0.028 Digoxin 75 (3.0%) 39 (3.1%) 36 (2.8%) 0.73 0.011 Lipid lowering medications 1,489 (58.8%) 745 (58.8%) 744 (58.8%) 0.97 0.001 Loop diuretics 251 (9.9%) 125 (9.9%) 126 (10.0%) 0.95 -0.002 Opioids 716 (28.3%) 348 (27.5%) 368 (29.1%) 0.38 -0.029 Proton pump inhibitors 641 (25.3%) 326 (25.8%) 315 (24.9%) 0.62 -0.016 Potassium sparing diuretics 51 (2.0%) 24 (1.9%) 27 (2.1%) 0.67 -0.014 Thiazide diuretics 361 (14.3%) 182 (14.4%) 179 (14.1%) 0.86 0.006 Index Fiscal Year 0.89 2006/07 170 (6.7%) 84 (6.6%) 86 (6.8%) -0.005 2007/08 167 (6.6%) 90 (7.1%) 77 (6.1%) 0.033 2008/09 131 (5.2%) 64 (5.1%) 67 (5.3%) -0.009 2009/10 189 (7.5%) 94 (7.4%) 95 (7.5%) -0.002 2010/11 156 (6.2%) 77 (6.1%) 79 (6.2%) -0.005 2011/12 171 (6.8%) 84 (6.6%) 87 (6.9%) -0.008 2012/13 250 (9.9%) 129 (10.2%) 121 (9.6%) 0.017 2013/14 218 (8.6%) 103 (8.1%) 115 (9.1%) -0.028 2014/15 431 (17.0%) 228 (18.0%) 203 (16.0%) 0.043 2015/16 649 (25.6%) 313 (24.7%) 336 (26.5%) -0.034 Time on metformin before add-on therapy 0.32 ≥ 3 years 790 (31.2%) 411 (32.5%) 379 (29.9%) 0.044 1 - 3 years 616 (24.3%) 296 (23.4%) 320 (25.3%) -0.036 < 1 year 1,126 (44.5%) 559 (44.2%) 567 (44.8%) -0.010 Abbreviations: A1C - glycosylated hemoglobin; ACE - Angiotensin-converting enzyme; ACR - albumin to creatinine ratio; ARB - Angiotensin II receptor blocker; CCB - calcium channel blocker; COPD - chronic obstructive pulmonary disease; CVD - cardiovascular disease; eGFR - estimated glomerular filtration rate; PVD - peripheral vascular disease; SMD - standardized mean difference. Data was available for all indicators unless otherwise specified.

80

Table 13: Safety events, time at risk, crude rates and hazard ratios in monotherapy (sulfonylurea versus metformin)

All-cause mortality Time at Crude rate Hazard Ratio Cohort OHA group Events p-value risk (PY) (per 1,000 PY) (HR, 95% CI) Sulfonylurea 2.04 194 2,170 89.4 <0.001 Monotherapy (n = 1,793) (1.73 - 2.41) pre-match Metformin 690 27,958 24.7 reference - (n = 18,880) Sulfonylurea 1.44 187 2,152 86.9 0.023 Monotherapy (n = 1,777) (1.05 - 1.97) post-match Metformin 155 3,128 49.6 reference - (n = 1,777) Cardiovascular-related mortality Time at Crude rate Hazard Ratio Cohort OHA group Events p-value risk (PY) (per 1,000 PY) (HR, 95% CI) Sulfonylurea 3.76 108 2,170 49.8 <0.001 Monotherapy (n = 1,793) (3.04 - 4.67) pre-match Metformin 368 27,958 13.2 reference - (n = 18,880) Sulfonylurea 1.13 101 2,152 46.9 0.59 Monotherapy (n = 1,777) (0.74 - 1.72) post-match Metformin 98 3,128 31.3 reference - (n = 1,777) Cardiovascular events Time at Crude rate Hazard Ratio Cohort OHA group Events p-value risk (PY) (per 1,000 PY) (HR, 95% CI) Sulfonylurea 2.88 121 2,053 58.9 <0.001 Monotherapy (n = 1,793) (2.37 - 3.51) pre-match Metformin 542 27,233 19.9 reference - (n = 18,880) Sulfonylurea 1.58 115 2,039 56.4 0.019 Monotherapy (n = 1,777) (1.08 - 2.32) post-match Metformin 102 3,015 33.8 reference - (n = 1,777) Major hypoglycemic episodes Time at Crude rate Hazard Ratio Cohort OHA group Events p-value risk (PY) (per 1,000 PY) (HR, 95% CI) Sulfonylurea 12.20 62 2,124 29.2 <0.001 Monotherapy (n = 1,793) (8.62 - 17.24) pre-match Metformin 64 27,905 2.3 reference - (n = 18,880) Sulfonylurea 5.57 59 2,108 28.0 <0.001 Monotherapy (n = 1,777) (2.49 - 12.46) post-match Metformin 10 3,125 3.2 reference - (n = 1,777)

81

Major osteoporotic fractures Time at Crude rate Hazard Ratio Cohort OHA group Events p-value risk (PY) (per 1,000 PY) (HR, 95% CI) Sulfonylurea 1.57 21 2,159 9.7 0.049 Monotherapy (n = 1,793) (1.01 - 2.48) pre-match Metformin 173 27,722 6.2 reference - (n = 18,880) Sulfonylurea 1.00 21 2,141 9.8 1.00 Monotherapy (n = 1,777) (0.45 - 2.23) post-match Metformin 31 3,077 10.1 reference - (n = 1,777) Abbreviations: CI - confidence interval; HR - hazard ratio; OHA - oral antihyperglycemic agent; PY - person-years.

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Table 14: Safety events, time at risk, crude rates and hazard ratios in monotherapy (sulfonylurea versus ‘other’)

All-cause mortality Time at Crude rate Hazard Ratio Cohort OHA group Events risk p-value (per 1,000 PY) (HR, 95% CI) (PY) Sulfonylurea 1.32 194 2,170 89.4 0.33 Monotherapy (n = 1,793) (0.75 - 2.32) pre-match Other (n = 306) 13 230 56.5 reference - Sulfonylurea 1.67 19 235 80.9 0.32 Monotherapy (n = 265) (0.61 - 4.59) post-match Other 13 214 60.7 reference - (n = 265) Cardiovascular-related mortality Time at Crude rate Hazard Ratio Cohort OHA group Events risk p-value (per 1,000 PY) (HR, 95% CI) (PY) Sulfonylurea 0.99 108 2,170 49.8 0.98 Monotherapy (n = 1,793) (0.55 - 1.81) pre-match Other 12 230 52.2 reference - (n = 306) Sulfonylurea 0.67 11 235 46.8 0.53 Monotherapy (n = 265) (0.19 - 2.36) post-match Other 12 214 56.1 reference - (n = 265) Cardiovascular events Time at Crude rate Hazard Ratio Cohort OHA group Events risk p-value (per 1,000 PY) (HR, 95% CI) (PY) Sulfonylurea 1.46 121 2,053 58.9 0.25 Monotherapy (n = 1,793) (0.77 - 2.79) pre-match Other 10 226 44.2 reference - (n = 306) Sulfonylurea 2.67 11 226 48.7 0.147 Monotherapy (n = 265) (0.71 - 10.05) post-match Other 10 210 47.6 reference - (n = 265) Abbreviations: CI - confidence interval; HR - hazard ratio; OHA - oral antihyperglycemic agent; PY - person-years.

83

Table 15: Safety events, time at risk, crude rates and hazard ratios in combotherapy

All-cause mortality Time at Crude rate Hazard Ratio Cohort OHA group Events risk p-value (per 1,000 PY) (HR, 95% CI) (PY) Sulfonylurea 2.20 463 15,958 29.0 <0.001 Combotherapy (n = 10,349) (1.39 - 3.48) pre-match Other (n = 1,266) 19 1,325 14.3 reference - Sulfonylurea 2.40 52 1,790 29.1 0.020 Combotherapy (n = 1,266) (1.15 - 5.02) post-match Other 19 1,325 14.3 reference - (n = 1,266) Cardiovascular-related mortality Time at Crude rate Hazard Ratio Cohort OHA group Events risk p-value (per 1,000 PY) (HR, 95% CI) (PY) Sulfonylurea 1.72 254 15,958 15.9 0.059 Combotherapy (n = 10,349) (0.98 - 2.99) pre-match Other (n = 1,266) 13 1,325 9.8 reference - Sulfonylurea 1.14 24 1,790 13.4 0.80 Combotherapy (n = 1,266) (0.41 - 3.15) post-match Other 13 1,325 9.8 reference - (n = 1,266) Cardiovascular events Time at Crude rate Hazard Ratio Cohort OHA group Events risk p-value (per 1,000 PY) (HR, 95% CI) (PY) Sulfonylurea 1.35 398 15,486 25.7 0.134 Combotherapy (n = 10,349) (0.91 - 1.99) pre-match Other (n = 1,266) 27 1,307 20.7 reference - Sulfonylurea 1.41 40 1,377 29.0 0.28 Combotherapy (n = 1,266) (0.76 - 2.63) post-match Other 27 1,307 20.7 reference - (n = 1,266) Major hypoglycemic episodes Time at Crude rate Hazard Ratio Cohort OHA group Events risk p-value (per 1,000 PY) (HR, 95% CI) (PY) Sulfonylurea 2.60 195 15,806 12.3 0.013 Combotherapy (n = 10,349) (1.22 - 5.52) pre-match Other (n = 1,266) 7 1,319 5.3 reference - Sulfonylurea 1.67 11 1,783 6.2 0.48 Combotherapy (n = 1,266) (0.40 - 6.97) post-match Other 7 1,319 5.3 reference - (n = 1,266) Abbreviations: CI - confidence interval; HR - hazard ratio; OHA - oral antihyperglycemic agent; PY - person-years. 84

Figure 4: Post-match subgroup analysis and interactions by chronic kidney disease in monotherapy (sulfonylurea versus metformin)

Abbreviations: CI - confidence interval; eGFR - estimated glomerular filtration rate; MET - metformin; SUL - sulfonylurea.

85

Figure 5: Post-match subgroup analysis and interactions by chronic kidney disease in monotherapy (sulfonylurea versus ‘other’)

Abbreviations: CI - confidence interval; eGFR - estimated glomerular filtration rate; OTH - other; SUL - sulfonylurea.

86

Figure 6: Post-match subgroup analysis and interactions by chronic kidney disease in combotherapy (sulfonylurea versus ‘other’)

Abbreviations: CI - confidence interval; eGFR - estimated glomerular filtration rate; OTH - other; SUL - sulfonylurea.

87

Figure 7: Post-match subgroup analysis and interactions by cardiovascular disease in monotherapy (sulfonylurea versus metformin)

Abbreviations: CI - confidence interval; CVD cardiovascular disease; MET - metformin; SUL - sulfonylurea.

88

Figure 8: Post-match subgroup analysis and interactions by cardiovascular disease in monotherapy (sulfonylurea versus ‘other’)

Abbreviations: CI - confidence interval; CVD cardiovascular disease; OTH - other; SUL - sulfonylurea.

89

Figure 9: Post-match subgroup analysis and interactions by cardiovascular disease in combotherapy (sulfonylurea versus ‘other’)

Abbreviations: CI - confidence interval; CVD cardiovascular disease; OTH - other; SUL - sulfonylurea.

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