Liraglutide Use in A Real-World Setting: Patient Characteristics at Antidiabetic Treatment Initiation Modulate Cardiovascular Safety Outcomes

Liraglutide Use in A Real-World Setting: Patient Characteristics at Antidiabetic Treatment Initiation Modulate Cardiovascular Safety Outcomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Liraglutide Use in A Real-World Setting: Patient Characteristics at Antidiabetic Treatment Initiation Modulate Cardiovascular Safety Outcomes Arlene Gallagher, Helge Gydesen, Thomas Jon Jensen, Atheline Major-Pedersen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-66177/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Generation of real-world cardiovascular drug safety evidence in patients with type 2 diabetes (T2D) warrants robust methodology. Regulatory authorities are increasingly seeking to support their decision making through real-world evidence. At the time of marketing authorization, this study was required by regulatory authorities to further characterize the liraglutide safety profile in routine real-world clinical practice (external validity). Safety outcomes were compared to those of other non-insulin antidiabetic (NIAD) treatments in patients with T2D initiating NIADs in a UK real-word setting. The design was endorsed by health regulatory authorities. This paper analyzes the methodology and study results, and postulates that it was the differences in patient characteristics at NIAD initiation, not the treatment itself, that modulated the observed differences in cardiovascular risk between NIAD cohorts. Methods: Data were obtained from linked UK electronic repositories: the Clinical Practice Research Datalink primary care database (CPRD GOLD) and Hospital Episode Statistics (HES). Risks of selected outcomes with current liraglutide use were compared to current use of other NIADs. Rates of each outcome and corresponding crude and adjusted incidence rate ratios were examined using Poisson regression analysis. Results : Overall, 149 788 patients met the study inclusion criteria; of these, 3432 initiated liraglutide. Components of the metabolic syndrome were more common among liraglutide initiators than those initiating other NIADs. The risk of some macrovascular conditions was increased in liraglutide initiators versus other NIAD initiators; following stepwise adjustment, this was only seen in liraglutide initiators when compared to biguanide initiators (incidence rate ratio: 1.33 [99% confidence interval (CI): 1.11;1.59]). Conclusions : The observed increase of macrovascular outcomes in liraglutide initiators compared with other NIAD initiators emphasizes the importance of countering potential selection bias when designing studies comparing cardiovascular outcomes across treatment initiator cohorts in heterogenous populations, such as patients with T2D. Baseline differences in the NIAD cohort may have modulated cardiovascular outcomes and likely explain the observed increased cardiovascular risk in liraglutide initiators. Selection bias, channeling bias and residual confounding, inherent in such observational studies, must be considered early when designing the study if the real-world data are to support decision-making. Health Economics & Outcomes Research diabetes selection bias real world liraglutide CPRD post-marketing safety surveillance Background Type 2 diabetes (T2D) is a highly heterogeneous condition, encompassing several subgroups with differing disease progression and comorbidities/complications [ 1 ]. T2D is primarily characterized by abnormal carbohydrate, lipid and protein metabolism, involving multiple pathophysiological disturbances within multiple organs. Insulin resistance, beta-cell dysfunction and impairment of incretin hormone action contribute to the development and progression of hyperglycemia [ 2 ]. Cardiovascular (CV) disease accounts for approximately half of all deaths in patients with T2D [ 3 ]. The subgroup of patients with T2D who present the metabolic syndrome (MetS) is at higher risk of developing CV outcomes than the rest of the population [ 4 ]. MetS is a clustering of cardiometabolic risk factors including hypertension, dyslipidemia and visceral obesity [ 4 , 5 ], and is closely associated with the pre-existence of hypersinsulinemia and insulin resistance [ 6 ]. Persons with MetS have elevated rates and severity of CV outcomes, including microvascular dysfunction, coronary atherosclerosis and calcification, cardiac dysfunction, myocardial infarction and heart failure [ 7 ]. International guidelines recommend an individualized approach to guide the choice of subsequent pharmacotherapies, when treatment intensification is required in patients with T2D [ 5 ]. The choice of antidiabetic pharmacotherapy is based on the comorbiditites presented by the patient (e.g. components of the MetS) and the stage of disease progression, thus leading to differential therapy choices among patients with T2D. Optimal glycemic control is one of the cornerstones in the multifactorial treatment approach in patients with T2D to prevent long-term complications associated with chronic hyperglycemia [ 8 – 10 ]. Diet, exercise and education are the established first-line treatment of T2D, together with initiation of metformin in patients who are unlikely to achieve their individualized glycated hemoglobin (HbA1c) target without pharmacotherapy. At the time of this study, diabetes treatment guidelines recommended glucagon-like peptide-1 receptor agonists (GLP-1RAs) as second-line after metformin, on equal terms with other oral glucose-lowering agents and insulin, as part of a multifactorial risk-reduction strategy in the management of T2D [ 11 ]. Liraglutide is a human GLP-1RA analog approved for treatment of T2D in the European Union on 30 June 2009. The safety and efficacy of liraglutide were established through a rigorous and comprehensive phase 3 clinical trial program, demonstrating improvement of glycemic control, reduction in body weight and a low risk of hypoglycemic events [ 12 ]. Due to these drug characteristics, the UK National Institute for Health and Care Excellence (NICE) guidelines recommended limiting the use of liraglutide to patients with a body mass index (BMI) ≥ 35 kg/m 2 who require optimization of their glycemic control [ 13 ]. Furthermore, liraglutide could be taken concomitantly with other glucose-lowering drugs, including insulin. Thus, liraglutide use in the UK real-world setting differed from its use in the pre-authorization randomized clinical trials (RCTs), where patients were not necessarily obese and were not taking insulin. Generally, RCTs are often short in duration, include restricted populations, are conducted in specialized centers, and treatment is mandated by study protocol. Consequently, real-world data are needed to ascertain external validity and complement RCT data, further supporting regulatory decision-making. Accordingly, regulatory authorities required a liraglutide post-authorization safety study (PASS) at the time of marketing authorization (2009). The study was to further characterize the safety profile of liraglutide after market launch, as well as to explore events identified as of special interest during the marketing authorization application review in the real-world setting. Hence, in order to fulfill this regulatory requirement, a database PASS was set up comparing safety outcomes (including CV outcomes) in initiators of the newly marketed liraglutide with outcomes in initiators of the most commonly used non-insulin antidiabetics (NIADs) in the UK population. The study design was endorsed by health regulatory authorities at a time before the establishment of the Pharmacovigilance Risk Assessment Committee (PRAC). This paper evaluates the methodology used to collect and perform comparative drug CV safety assessments in a real-world UK population initiating liraglutide and other NIADs. Methods Data sources In this real-world, observational, safety surveillance study of adult patients prescribed NIADs, data were obtained using individually linked UK electronic repositories: the Clinical Practice Research Datalink (CPRD) primary care database (GOLD) and Hospital Episode Statistics (HES). Clinical Practice Research Datalink (CPRD) GOLD The CPRD collects anonymized medical records from general practitioners (GPs). CPRD GOLD is based on GP records using the Vision software system within the UK [ 14 ]. Patients are semi-permanently affiliated to a practice, which centralizes the medical information from the GPs, specialist referrals and hospitalizations. The data recorded in CPRD GOLD include demographic information, prescription details, clinical events, preventive care provided, specialist referrals, hospital admissions and their major outcomes. All information is recorded by staff at the GP practice and not necessarily validated externally, although the database has generally been shown to be of high quality [ 4 , 15 ]. The database currently includes more than 13 million patients, from 1987 onwards [ 16 ]. Hospital Episode Statistics (HES) HES contains details of all admissions to UK National Health Service (NHS) hospitals in England. All NHS trusts in England, including acute hospitals, primary care trusts and mental health trusts, are included. HES data are collected during a patient's stay at a hospital to allow hospitals to be reimbursed for the care they deliver. As the data are extracted from patients’ notes, they may also reflect the quality of clinical record-keeping. HES data have been collected for admitted patients since 1989 and outpatient attendances since 2003. At the time of this study, linked CPRD GOLD and HES data were available from 1 April 1997 to 31 March 2014. Study design and patient population Patients in CPRD GOLD who were considered ‘new NIAD users’ were eligible for inclusion. New users for each class of drug were identified by the first time each drug was prescribed, with no prescription record of that drug in the previous 12 months. Excluded patients were those aged < 18 years with a history of cancer or human immunodeficiency virus/acquired immunodeficiency syndrome, or with a history of liraglutide use, and any patient in any comparator group who had initiated liraglutide during the study. Patients were only included in each outcome analysis if they had no record of that outcome in the previous 12 months. The study period was between 1 January 2005 and the end of follow-up time for a patient. End of follow-up was defined as the earliest of: the date of the last data collection from the GP practice; transfer out of the practice; death; or the end of data collection from HES (31 March 2014). Patients without any follow-up were excluded. All patients initiating NIADs after the end of follow-up were excluded from the analyses, as were any outcomes recorded in CPRD GOLD after this time. NIAD treatment groups NIAD treatment groups included patients who initiated (new users) the following: liraglutide, exenatide, dipeptidyl peptidase-4 (DPP-4) inhibitors (linagliptin, saxagliptin, sitagliptin, vildagliptin), sulfonylureas, biguanides, acarbose, glinides (nateglinide, repaglinide) or glitazones (rosiglitazone, pioglitazone). Outcomes assessed As part of post-marketing safety surveillance activity, outcomes captured included macrovascular conditions overall, consisting of acute myocardial infarction, ischemic heart disease (IHD), percutaneous transluminal coronary angioplasty, coronary artery bypass graft, lower limb amputation, stroke, transient ischemic attack and heart failure. These sub-outcomes were also studied separately. Other captured outcomes included malignant neoplasms (overall) and separately thyroid and pancreatic cancer and acute pancreatitis. This manuscript focuses on the CV outcomes in the study population fulfilling the inclusion/exclusion criteria in CPRD GOLD and eligible for linkage with HES. Statistical analysis Rates of each outcome and corresponding crude- and adjusted-incidence rate ratios (IRRs) were examined using Poisson regression analysis and expressed for liraglutide versus each of the comparators. The Poisson model was considered adequate for analyzing the number of occurred outcomes during the study period for each exposure in a database. Only variables contributing to the model were included using a forward stepwise procedure. Baseline characteristic measurements and confounding factor adjustments were as follows: baseline characteristics for groups of patients initiating liraglutide and other NIADS were examined at the index date; duration of diabetes was calculated based on the patient’s date of the first diabetes record; laboratory test data were used to assess HbA1c; insulin prescription (as an indicator of the burden/progression of diabetes and its comorbidities/complications) was assessed at various time points. The number of prior antidiabetes medication types was assessed by looking at the number of antidiabetes medications prescribed in the previous year. Patients who initiated various NIADs may have continued to use these medications after index date. Where possible, the following confounding factors were selected for adjustment: age, gender, duration of diabetes, HbA1c, insulin prescription, smoking status, alcohol consumption status and BMI. For the crude analyses, where the number of outcome events in the liraglutide cohort was < 5, the model was not run due to the CPRD small cell data governance regulations. Where the number of outcome events in patients initiating liraglutide was < 10, further adjusted analyses could not be made, as there was not enough information to make a meaningful comparison. Adjustment for age and gender, and further adjusted analyses, could be made for outcomes with at least 10 patient events among those initating liraglutide, with convergence achieved. This included macrovascular events, IHD and heart failure. Results Patient population There were 149 788 patients fulfilling the inclusion criteria in CPRD GOLD and eligible for linkage with HES. Table 1 shows the definition of the study population. Of these, 3432 patients initiated liraglutide. Table 1 Definition of the study population. Number of patients Acceptable male or female patients with at least 1 day of follow-up time ¶ in CPRD GOLD Patients with at least one prescription for a NIAD drug during the study period Aged at least 18 years Eligible for linkage with HES data, with at least 1 day of follow-up, ¶ excluding those who start treatment after the end of follow-up With no history of malignant neoplasm † With no history of HIV/AIDs recorded in HES ‡ Total in the study population 11 673 099 313 544 313 281 174 883 149 910 149 788 149 788 ¶ The start of follow-up was defined as the latest date of the start of HES collection (1 April 1997), or the start of UTS follow-up. The end of follow-up was defined as the earliest date of the end of HES collection (31 March 2014), the date of the last collection from the GP practice, transfer out of the practice or death. † Previous cancer was assessed using the CPRD GOLD clinical and referral files and HES records. ‡ HIV/AIDS was identified from a prescription of HIV medication in the therapy file, a positive record of HIV/AIDS in the CPRD GOLD clinical, referral or test files or a HES record. AIDS, acquired immunodeficiency syndrome; CPRD, Clinical Practice Research Datalink; GP, general practitioner; HES, Hospital Episode Statistics database; HIV, human immunodeficiency virus; NIAD, non-insulin antidiabetic; UTS, up-to-standard. NIAD treatment groups Characteristics of the patients initiating each of the NIADs are shown in Table 2 . Compared with patients initiating other NIADs, other than exenatide, patients initiating liraglutide were younger, had a longer mean diabetes duration and higher mean HbA1c level, were more overweight or obese, more had been prescribed insulin > 1 year prior to index date, more had a greater frequency of any microvascular complications, more had a history of hypertension at index date, and had a greater number of concomitant antidiabetic medications. Table 2 Baseline characteristics of patients initiating treatment with each NIAD. Characteristic Liraglutide Exenatide DPP-4 inhibitors Acarbose Biguanides Glinides Glitazones SUs Total 3 432 3 252 17 279 600 74 067 964 16 749 33 142 Follow-up, years, mean (SD) 2.6 (1.3) 3.6 (1.8) 2.8 (1.6) 5.2 (2.8) 4.1 (2.6) 4.6 (2.6) 5.3 (2.7) 4.1 (2.5) Female sex 1 569 (45.7%) 1 481 (45.5%) 7 325 (42.4%) 259 (43.2%) 34 604 (46.7%) 437 (45.3%) 6 931 (41.4%) 13 854 (41.8%) Age, years, mean (SD) 56.2 (10.6) 55.8 (10.6) 61.8 (12.2) 63.0 (12.9) 58.2 (15.3) 62.1 (13.5) 61.2 (12.2) 61.6 (13.4) BMI, kg/m 2 , mean (SD) 38.1 (7.1) 38.5 (7.1) 32.7 (6.6) 31.5 (7.2) 32.0 (6.7) 30.7 (6.4) 31.7 (6.4) 31.4 (6.6) Obese (BMI ≥ 30 kg/m 2 ) 3 127 (91.1%) 2 989 (91.9%) 10 776 (62.4%) 305 (50.8%) 41 825 (56.5%) 475 (49.3%) 9 348 (55.8%) 17 674 (53.3%) Duration of diabetes, years, mean (SD) 9.7 (6.1) 9.1 (5.7) 8.4 (6.2) 8.5 (6.7) 2.8 (4.9) 7.6 (6.7) 6.7 (5.8) 4.4 (4.6) Smoking status Non-smoker 1 203 (35.1%) 1 107 (34.0%) 6 215 (36.0%) 193 (32.2%) 28 574 (38.6%) 361 (37.4%) 5 972 (35.7%) 11 806 (35.6%) Ex-smoker 1 659 (48.3%) 1 593 (49.0%) 8 250 (47.7%) 297 (49.5%) 30 553 (41.3%) 438 (45.4%) 7 762 (46.3%) 14 963 (45.1%) Smoker 570 (16.6%) 551 (16.9%) 2 808 (16.3%) 105 (17.5%) 14 490 (19.6%) 164 (17.0%) 2 959 (17.7%) 6 225 (18.8%) Unknown smoking status 0 (0.0%) 1 (< 0.1%) 6 (< 0.1%) 5 (0.8%) 450 (0.6%) 1 (0.1%) 56 (0.3%) 148 (0.4%) Alcohol Non-drinker 362 (10.5%) 405 (12.5%) 2 043 (11.8%) 90 (15.0%) 9 322 (12.6%) 136 (14.1%) 2 226 (13.3%) 4 112 (12.4%) Ex-drinker 664 (19.3%) 565 (17.4%) 3 069 (17.8%) 98 (16.3%) 8 358 (11.3%) 176 (18.3%) 2 425 (14.5%) 4 593 (13.9%) Drinker 2 304 (67.1%) 2 143 (65.9%) 11 622 (67.3%) 378 (63.0%) 50 417 (68.1%) 571 (59.2%) 11 213 (66.9%) 22 314 (67.3%) Unknown drinking status 102 (3.0%) 139 (4.3%) 545 (3.2%) 34 (5.7%) 5 970 (8.1%) 81 (8.4%) 885 (5.3%) 2 123 (6.4%) Diabetes complications in the 12 months prior to index date Any microvascular complication Ophthalmic complications Neurological complications Kidney complications Circulatory complications Diabetic foot or arthropathy complications 1 947 (56.7%) 387 (11.3%) 61 (1.8%) 16 (0.5%) 17 (0.5%) 1 730 (50.4%) 1 152 (35.4%) 385 (11.8%) 35 (1.1%) 30 (0.9%) 12 (0.4%) 843 (25.9%) 9 437 (54.6%) 1 748 (101.1%) 196 (1.1%) 126 (0.7%) 68 (0.4%) 8 426 (48.8%) 112 (18.7%) 58 (9.7%) 9 (1.5%) 5 (0.8%) 3 (0.5%) 54 (9.0%) 11 508 (15.5%) 2 039 (2.8%) 262 (0.4%) 198 (0.3%) 73 (< 0.1%) 9 760 (13.2%) 201 (20.9%) 71 (7.4%) 16 (1.7%) 6 (0.6%) 0 (0.0%) 128 (13.3%) 2 944 (17.6%) 1 086 (6.5%) 111 (0.7%) 161 (1.0%) 17 (0.1%) 1 819 (10.9%) 8 548 (25.8%) 1 737 (5.2%) 225 (0.7%) 196 (0.6%) 79 (0.2%) 7 102 (21.4%) Other comorbidities History of hypertension History of congestive heart failure 2 747 (80.0%) 136 (4.0%) 2 615 (80.4%) 136 (4.2%) 12 980 (75.1%) 801 (4.6%) 456 (76.0%) 38 (6.3%) 43 236 (58.4%) 2 244 (3.0%) 696 (72.2%) 66 (6.8%) 12 408 (74.1%) 438 (2.6%) 22 803 (68.8%) 1 599 (4.8%) HbA1c (%), mean (SD) 9.4 (1.7) 9.3 (1.7) 8.9 (1.5) 8.8 (1.7) 8.6 (1.9) 8.7 (1.7) 8.9 (1.5) 9.0 (1.8) HbA1c (mmol/mol) † , mean (SD) 79.2 (18.6) 78.1 (18.6) 73.8 (16.4) 72.7 (18.6) 70.5 (20.8) 71.6 (18.6) 73.8 (16.4) 74.9 (19.7) Insulin prescribed > 1 year before index 974 (28.4%) 945 (29.1%) 1 495 (8.7%) 58 (9.7%) 2 526 (3.4%) 110 (11.4%) 833 (5.0%) 822 (2.5%) No insulin prescribed before or on index date 2 354 (68.6%) 2 193 (67.4%) 15 617 (90.4%) 533 (88.8%) 70 724 (95.5%) 799 (82.9%) 15 809 (94.4%) 32 029 (96.6%) Number of diabetes medications prescribed in the year prior to index date, mean (SD) 2.3 (1.0) 2.1 (0.9) 1.7 (0.7) 1.8 (1.0) 0.1 (0.3) 1.7 (0.9) 1.5 (0.6) 0.9 (0.6) Data are n (%), unless stated otherwise stated. † Calculated, not measured. BMI, body mass index; DPP-4, dipeptidyl peptidase-4; HbA1c, glycated hemoglobin; NIAD, non-insulin antidiabetic; SD, standard deviation; SU, sulfonylurea. Baseline patient characteristics for exenatide were similar to those initiating liraglutide. Clinical outcomes For macrovascular outcomes overall, adjustments could be made for all eight confounders (age, gender, duration of diabetes, HbA1c, insulin prescribing, smoking status, alcohol consumption status and BMI). For IHD and heart failure, the stepwise regression resulted in differing predictive variables. For IHD, HbA1c did not meet the threshold in the comparison with glitazones, nor BMI for sulfonylureas. Patients with a previous history of a given CV outcome were excluded, and important baseline characteristics and confounders can be seen in Table 2 . An increased risk of IHD and heart failure was observed with liraglutide use compared to NIADs (Table 3 ). For macrovascular conditions overall, the estimated unadjusted IRRs were significantly higher for liraglutide versus biguanides or glitazone initiators (Table 3 ). After age and gender adjustment, the IRRs for liraglutide initiators were also significantly higher versus DPP-4 inhibitor or sulfonylurea initiators. However, after stepwise adjustment of the IRR, adjusting for age, smoking, alcohol, gender, insulin prescription, BMI, HbA1c and diabetes duration, liraglutide initiators only had an increased risk of macrovascular conditions versus biguanide initiators (IRR: 1.33 [99% confidence interval (CI): 1.11;1.59]). Table 3 Risk of macrovascular conditions for patients initiating liraglutide compared with other NIADs. Risk of macrovascular conditions overall Treatment Number of patients Number of outcome events Person-years Incidence (99% CI) † Unadjusted IRR (99% CI) Age- and gender-adjusted IRR (99% CI) Stepwise adjusted IRR (99% CI) Liraglutide 3 323 263 4 009 6.56 (5.60;7.69) N/A N/A N/A Exenatide 3 153 242 3 762 6.43 (5.45;7.59) 1.02 (0.81;1.28) 1.02 (0.81;1.29) 1.04 (0.82;1.30) a,b,d,e DPP-4is 16 656 1 540 2 3973 6.42 (6.02;6.86) 1.02 (0.86;1.21) 1.38 (1.16;1.65)* 1.07 (0.88;1.29) a,b,c,d,e,f,g,h Acarbose 563 53 730 7.26 (5.10;10.34) 0.90 (0.61;1.33) 1.25 (0.83;1.88) 0.99 (0.65;1.52) a,d,e,f Biguanides 71 445 7 549 17 0581 4.43 (4.30;4.56) 1.48 (1.26;1.74)* 1.92 (1.63;2.26)* 1.33 (1.11;1.59)* ,a,b,c,d,e,f,g,h Glinides 903 114 1 307 8.72 (6.85;11.10) 0.75 (0.56;1.00) 1.00 (0.74;1.35) 0.80 (0.56;1.12) a,b,d,e Glitazones 16 290 2 040 40 845 4.99 (4.72;5.29) 1.31 (1.11;1.55)* 1.73 (1.45;2.05)* 1.19 (0.98;1.45) a,b,c,d,e,f,g,h Sulfonylureas 31 379 4 037 67 929 5.94 (5.71;6.19) 1.10 (0.94;1.30) 1.55 (1.31;1.83)* 1.06 (0.88;1.29) a,b,c,d,e,f,g,h Risk of ischemic heart disease Liraglutide 3369 230 4 078 5.64 (4.76;6.68) N/A N/A N/A Exenatide 3182 206 3823 5.39 (4.50;6.45) 1.05 (0.82;1.34) 1.04 (0.82;1.34) 1.06 (0.83;1.36) a,b,d,e DPP-4is 16 959 1254 24 633 5.09 (4.73;5.47) 1.11 (0.92;1.33) 1.50 (1.24;1.81)* 1.15 (0.94;1.41) a,b,c,d,e,f,g,h Acarbose 574 42 762 5.51 (3.70;8.20) 1.02 (0.66;1.58) 1.38 (0.88;2.17) 1.18 (0.75;1.87) a,d,e Biguanides 72 537 5777 17 6250 3.28 (3.17;3.39) 1.72 (1.45;2.04)* 2.19 (1.84;2.60)* 1.45 (1.20;1.76)* ,a,b,c,d,e,f,g,h Glinides 930 97 1363 7.12 (5.48;9.24) 0.79 (0.58;1.08) 1.04 (0.75;1.44) 0.75 (0.52;1.09) a,d,e,f,g Glitazones 16 453 1524 41 853 3.64 (3.41;3.89) 1.55 (1.29;1.86)* 1.99 (1.66;2.40)* 1.40 (1.13;1.73)* ,a,b,c,d,e,f,h Sulfonylureas 32 198 3145 71 068 4.43 (4.23;4.63) 1.27 (1.07;1.52)* 1.77 (1.48;2.12)* 1.13 (0.92;1.40) a,b,c,d,e,g,h Risk of heart failure Liraglutide 3405 66 4290 1.54 (1.12;2.11) N/A N/A N/A Exenatide 3229 52 4062 1.28 (0.90;1.83) 1.20 (0.75;1.94) 1.22 (0.76;1.97) 1.26 (0.78;2.03) a,c,d,e,f,h DPP-4is 17087 436 25 944 1.68 (1.49;1.90) 0.92 (0.65;1.29) 1.47 (1.03 ;2.08)* 0.97 (0.67;1.41) a,b,d,e,f,g,h Acarbose 591 17 805 2.11 (1.13;3.95) 0.73 (0.36;1.47) 1.14 (0.54;2.41) 0.67 (0.30;1.48) a,d,e,f,h Biguanides 73 613 2187 18 7903 1.16 (1.10;1.23) 1.32 (0.96;1.82) 2.00 (1.45;2.77)* 1.28 (0.90;1.81) a,b,c,d,e,f,g,h Glinides 951 43 1494 2.88 (1.94;4.26) 0.53 (0.32;0.88)* 0.83 (0.49;1.43) 0.63 (0.36;1.11) a,d,e,f,h Glitazones 16 682 664 44 246 1.50 (1.36;1.66) 1.02 (0.73;1.43) 1.59 (1.13;2.22)* 0.99 (0.68;1.45) a,b,c,e,f,g Sulfonylureas 32 660 1487 75 832 1.96 (1.83;2.10) 0.78 (0.57;1.08) 1.33 (0.96;1.85) 0.84 (0.58;1.21) a,b,c,d,e,f,g All IRRs are for the comparator vs liraglutide. † Per 100 person-years.* P < 0.01. Bonferroni correction applied. Adjusted for a age, b smoking, c alcohol use, d gender, e insulin prescribing, f BMI, g HbA1c, h diabetes duration. BMI, body mass index; CI, confidence interval; DPP-4is, dipeptidyl peptidase-4 inhibitors; HES, Hospital Episode Statistics database; IRR, incidence rate ratio; NIAD, non-insulin antidiabetic; N/A, not applicable. A total of 230 IHD events were observed, leading to an estimated incidence per 100 person years of 5.64 (99% CI: 4.76;6.68). The estimated unadjusted and age- and gender-adjusted IRRs were significantly higher for patients initiating liraglutide versus biguanide, glitazone or sulfonylurea initiators (Table 3 ). After adjusting for age and gender, the IRR was signficantly higher for liraglutide initiators versus DPP-4 inhibitor, biguanide, glitazone or sulfonylurea initiators. The comparison to biguanide and gliatazone initiators remained significant for the stepwise-adjusted IRR after adjusting for age, smoking, alcohol, gender, insulin prescription, BMI, HbA1c and diabetes duration (IRR: 1.45 [99% CI: 1.20;1.76] and IRR: 1.40 [99% CI:1.13;1.73], respectively). For heart failure, the estimated age- and gender-adjusted IRRs were significantly higher for liraglutide initiators versus DPP-4 inhibitor, biguanide and glitazone initiators (Table 3 ). However, none of the stepwise-adjusted IRRs were statistically significant (see Table 3 for specific adjustments). Discussion An increased risk of some macrovascular conditions was observed in liraglutide initiators, compared with those initiating most other NIADs. However, after performing a stepwise adjustment, increased risk was only seen in liraglutide initiators compared with biguanide initators. These findings are likely explained by the differential drug choice applied to the different subpopulations of T2D. Compared with other NIAD initiators at baseline, liraglutide initiators had more advanced and longer duration of T2D, reflected by having more microvascular complications, more concomitant NIAD and insulin use and higher HbA1c levels. In addition, more components of the MetS were captured for liraglutide initiators. These findings suggest that UK physicians prescribed liraglutide to a selected T2D subpopulation who presented with MetS (selection bias) and/or had a more advanced stage of T2D, poorly controlled with the marketed antidiabetics available at the time liraglutide entered the market (chanelling bias). The observed increase of macrovascular outcomes in the liraglutide initiator cohort may rather be due to the population’s baseline metabolic risk profile and not to liraglutide exposure itself (confounding). In addition, liraglutide initiators may have had other inherent residual baseline characteristics which have not been measured but increase CV risk (residual confounding), e.g. higher levels of hyperinsulinemia/insulin resistance (which are associated with MetS and independently associated with increased risk of CV outcomes [ 17 ]) or diabetic dyslipidemia (increased triglyceride levels, decreased high-density lipoprotein-cholesterol levels and increased small dense low-density lipoprotein particle levels), which is a component of the MetS and independently associated with increased risk of CV outcomes [ 2 , 5 , 18 ]. This study´s real-world findings on pre-liraglutide patient characteristics are in line with those reported by the UK Association of British Clinical Diabetologists’ audits, which examined data on 6238 liraglutide-treated patients from 2009 to 2013 [ 19 ]. Liraglutide users from diabetes centers across the UK were found to be heavier and with poorer glycemic control than patients from liraglutide phase 3 RCTs. The conclusion was that the baseline characteristics of liraglutide-treated patients were influenced by the UK NICE guidelines, which recommend liraglutide for patients with high BMI (≥ 35 kg/m 2 ) [ 19 ]. Patients with higher BMIs inherently have a higher probability of having more cardiometabolic risk factors [ 12 ]. In addition, UK reimbursement conditions add to further confounding by indication, reinforcing that patients initiating liraglutide have higher CV risk [ 20 ]. In this study, liraglutide initiation itself appears to be an indicator for the presence of a higher clustering of cardiometabolic risk and more advanced disease stage within the continuum of T2D. The same appears to apply to exenatide, which can be expected, as both belong to the same drug class (GLP-1RAs). Non-interventional database studies lack randomization, with resulting bias and confounding. This is a specific problem in T2D, where increasing diabetes severity (comorbidities/complications and stage of progression) is followed by treatment steps, and lack of response leads to treatment intensification. Thus, separating disease severity from treatment exposure as the causal agent for increased incidences of CV outcomes in a heterogeneous T2D population, as in the presented study, can be challenging. Baseline treatment characteristics must be balanced between cohorts to achieve homogeneity across cohorts. In order to finalize a study design with homogeneity across treatment cohorts, thorough background knowledge is required, encompassing the disease and its natural history, complications and comorbidities, the likely real-world target population of the drug and current local pharmacotherapy guidelines, and the feasibility of obtaining required data from the chosen source. The presented study highlights that comparative analysis of treatment initiator cohorts, without thoroughly balancing baseline characteristics, is not suited to addressing new drug safety. In this study, patient baseline characteristics had not been matched among cohorts. In addition, it was not possible to adjust for all of the a priori planned covariates, owing in part to the rarity of the outcomes under investigation. Risk estimates for liraglutide initiators are likely to have been increased when adjusting for age, since liraglutide users were younger. Even with a relatively large database such as the CPRD, the comparison of a new drug to existing therapies was challenging due to the heterogeneity of the populations treated with different drugs, and the relatively low numbers of new drug initiators. Of note, real-world liraglutide observational data emerging from comparative studies based on cohorts which had been matched (baseline characteristics were equally weighted across cohorts) have shown that liraglutide use was associated with significantly lower major adverse CV event (MACE) risk [ 21 ]. Additionally, CV outcomes trials with GLP-1RAs have not reported increased CV risk [ 13 ]. The blinded, randomized, placebo-controlled LEADER trial demonstrated that MACE risk was signficantly reduced in liraglutide- versus placebo-treated patients [ 22 ]. These findings support our interpretation that selection bias, confounding and possibly channelling bias drive the observed increased CV risk in liraglutide initiators in the UK real-world setting in the presented study. Observational studies provide external validity of a drug’s safety and effectiveness in everyday clinical practice, thus supporting decision-makers and key stakeholders regarding treatment options to be carried out in the real world [ 23 ]. Observational PASS are key in gathering this type of real-world evidence. However, as patients are not randomized, the study design for investigating the safety of newly marketed products should, to the extent possible, account for confounding and bias. This requires the feasibility of aquiring sufficient data quantity and quality to obtain true homogeneity among cohorts. These considerations are in line with the Guidelines for Good Pharmacoepidemiology Practice [ 24 ] and should be taken into account also when designing and interpreting studies that seek to support regulatory decisions. This regulatory health authorityrequired PASS sought to investigate the safety of liraglutide in the UK realworld setting. CPRD GOLD reflects the UK real-world clinical practice setting [ 16 ]. Patients prescribed liraglutide had higher components of the MetS and markedly more advanced T2D than NIAD initiators. It was not always possible to adjust for all confounders and, in some cases, this may have modulated safety outcomes, although in others, significant results persisted despite adjustment for all confounders (e.g. comparison with biguanides). The study design allows for a descriptive analysis of the cohort populations initiating different antidiabetic medications. An improved study design to assess CV safety with liraglutide would require equally weighting the baseline characteristics. This study was endorsed by the health regulatory authorities at a time before the establishment of the Pharmacovigilance Risk Assessment Committee (PRAC). PRAC is responsible for reviewing the design and for the evaluation of post-authorisation safety studies. In addition, this observational real world study was thought to complement the the liraglutide Cardiovascular Outcome Trial (CVOT), LEADER, which was also a regulatory authority required PASS at the time of marketing authorization (2009). Conclusions The methodology for this study was insufficient to generate reliable real-world CV safety data to compare liraglutide initiators with other NIAD initiators. An increased risk of some macrovascular conditions was observed in patients initiating liraglutide compared to those initiating other NIADs. However, the observed difference in CV risk is likely due to differences in baseline characteristics between the NIAD cohorts, and not to the specific NIAD initiated. These results emphasize the importance of countering selection bias and confounding when designing a study comparing CV outcomes across treatment initiator cohorts in heterogenous populations, in order to generate robust real-world evidence to support regulatory and health providers’ decisions, enhancing patient safety. Abbreviations BMI, body mass index; CI, Confidence Interval ; CPRD, Clinical Practice Research Datalink; CV, cardiovascular; DPP4, dipeptidyl peptidase-4; GLP-1RA, glucagon-like peptide-1 receptor agonist; GP, general practitioner; HES, Hospital Episode Statistics; IHD, ischemic heart disease; IRR, incidence rate ratios; MACE, major adverse cardiovascular events; MetS, metabolic syndrome; NHS, National Health Service; NIAD, non-insulin antidiabetic; PASS, post authorization safety study; RCT, randomized clinical trial; T2D, type 2 diabetes; SU, sulphonylurea Declarations Ethics approval and consent to participate: This study was approved by the Independent Scientific Advisory Committee (ISAC) of the Medicines and Healthcare Products Regulatory Agency, under reference number 10_044. This work uses data provided by patients and collected by the NHS as part of their care and support. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: HG, TJ and AMP are employees of and stockholders in Novo Nordisk. AG has no potential conflicts of interest to declare. Funding: This analysis was funded by Novo Nordisk, which, in collaboration with the CPRD, was involved in the design of the study, analysis and interpretation of data, and in writing the manuscript. Authors' contributions : AG, HG, TJ and AMP analyzed the data and wrote the manuscript. Acknowledgements: Medical writing and submission support were provided by Nathan Ley, Aneela Majid and Helen Marshall, of Watermeadow Medical, an Ashfield company, part of UDG Healthcare plc, funded by Novo Nordisk. We also thank Hans Askelund Sævereid (Novo Nordisk) for his review and input to the manuscript. 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Management of Hyperglycemia in Type 2 Diabetes, 2018. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2018;41(12):2669–701. Despres JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444(7121):881–7. Tune JD, Goodwill AG, Sassoon DJ, Mather KJ. Cardiovascular consequences of metabolic syndrome. Transl Res. 2017;183:57–70. Turner RC. The UK. Prospective Diabetes Study. A review. Diabetes Care. 1998;21(Suppl 3):C35-8. Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, Peters AL, Tsapas A, Wender R, Matthews DR, et al. Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2012;35(6):1364–79. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group. Lancet. 1998;352(9131):837–53. American Diabetes Association. Standards of medical care in diabetes–2011. Diabetes Care. 2011;34(Suppl 1):11–61. Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circulation Res. 2015;116(6):991–1006. Cefalu WT, Kaul S, Gerstein HC, Holman RR, Zinman B, Skyler JS, Green JB, Buse JB, Inzucchi SE, Leiter LA, et al. Cardiovascular Outcomes Trials in Type 2 Diabetes: Where Do We Go From Here? Reflections From a Diabetes Care Editors' Expert Forum. Diabetes Care. 2018;41(1):14–31. Medicines and Healthcare products Regulatory Agency and NIHI. Clinical Practice Research Datalink. https://www.cprd.com/home/ . Accessed 21 Nov 2019. Herrett E, Thomas SL, Schoonen WM, Smeeth L, Hall AJ. Validation and validity of diagnoses in the General Practice Research Database: a systematic review. Br J Clin Pharmacol. 2010;69(1):4–14. Herrett E, Gallagher AM, Bhaskaran K, Forbes H, Mathur R, van Staa T, Smeeth L. Data Resource Profile: Clinical Practice Research Datalink (CPRD). Int J Epidemiol. 2015;44(3):827–36. Srinivasan M, Kamath P, Bhat N, Pai N, Bhat R, Shah T, Manjrekar P, Mahabala C. Basal hyperinsulinemia beyond a threshold predicts major adverse cardiac events at 1 year after coronary angiogram in type 2 diabetes mellitus: a retrospective cohort study. Diabetol Metab Syndr. 2017;9:38. Warraich HJ, Rana JS. Diabetic Dyslipidemia: Epidemiology and Prevention of Cardiovascular Disease and Implications of Newer Therapies. Curr Cardiol Rep. 2018;20(12):125. Thong Ky, Gupta PS, Cull ML, Adamson KA, Dove DS, Rowles SV, Tarpey S, Duncan C, Chalmers J, Harper R. GLP-1 receptor agonists in type 2 diabetes-NICE guidelines versus clinical practice. Br J Diabetes. 2014;14(2):52–9. NICE. Type 2 diabetes in adults: management. https://www.nice.org.uk/guidance/ng28 . Accessed 2018. Svanstrom H, Ueda P, Melbye M, Eliasson B, Svensson AM, Franzen S, Gudbjornsdottir S, Hveem K, Jonasson C, Pasternak B. Use of liraglutide and risk of major cardiovascular events: a register-based cohort study in Denmark and Sweden. Lancet Diabetes Endocrinol. 2019;7(2):106–14. Marso SP, Daniels GH, Brown-Frandsen K, Kristensen P, Mann JF, Nauck MA, Nissen SE, Pocock S, Poulter NR, Ravn LS, et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2016;375(4):311–22. Loudon K, Treweek S, Sullivan F, Donnan P, Thorpe KE, Zwarenstein M. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ. 2015;350:h2147. International Society for Pharmacoepidemiology. Guidelines for Good Pharmacoepidemiology Practices (GPP). https://www.pharmacoepi.org/resources/policies/guidelines-08027/#1 . Accessed 21 Nov 2019. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-66177","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":1995292,"identity":"f346a438-7a70-4312-a035-84255c76061b","order_by":0,"name":"Arlene Gallagher","email":"","orcid":"https://orcid.org/0000-0002-7507-4923","institution":"Medicines and Healthcare Products Regulatory Agency","correspondingAuthor":false,"prefix":"","firstName":"Arlene","middleName":"","lastName":"Gallagher","suffix":""},{"id":1995293,"identity":"f02b1589-eee4-4e9f-89c1-69b3cd0471b2","order_by":1,"name":"Helge Gydesen","email":"","orcid":"","institution":"Novo Nordisk AS","correspondingAuthor":false,"prefix":"","firstName":"Helge","middleName":"","lastName":"Gydesen","suffix":""},{"id":1995294,"identity":"f973fea9-734b-4451-a61d-b6d7ff2e6b60","order_by":2,"name":"Thomas Jon Jensen","email":"","orcid":"","institution":"Novo Nordisk AS","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"Jon","lastName":"Jensen","suffix":""},{"id":1995295,"identity":"861998af-2d90-4563-8b50-0473b57c4e85","order_by":3,"name":"Atheline Major-Pedersen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYLCCDzAGjwGDAWHlbAwMjDNI1sLMA9fCQIQW/vndaY9td9gk9ks3H/vwpqDOmIH98NEN+LRIHOPdbpx7Ji1x5pxjyTPnGBw2Y+BJS7uB15pjvNukc9sOGxvcyDFm5jE4YMMgwWOGV4s8SIslQksdYS0GIC2MbYfloFqYzQhqMTyWu92wty1NTnJGWjIj0C/GbIT8Inf47LYHP9tsePglkg8zvPlTZ9jPfvgYfu+DYwYPlxgto2AUjIJRMArQAQDaLUKDSBN5uQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1755-630X","institution":"Novo Nordisk AS","correspondingAuthor":true,"prefix":"","firstName":"Atheline","middleName":"","lastName":"Major-Pedersen","suffix":""}],"badges":[],"createdAt":"2020-08-26 12:33:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-66177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-66177/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13588053,"identity":"b439cc85-de37-44ca-9fe9-c89cf56fc7d4","added_by":"auto","created_at":"2021-09-17 04:53:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":439779,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-66177/v1/f099f420-7758-4683-b6bf-6b8f76c59e1d.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eLiraglutide Use in A Real-World Setting: Patient Characteristics at Antidiabetic Treatment Initiation Modulate Cardiovascular Safety Outcomes\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eType 2 diabetes (T2D) is a highly heterogeneous condition, encompassing several subgroups with differing disease progression and comorbidities/complications [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. T2D is primarily characterized by abnormal carbohydrate, lipid and protein metabolism, involving multiple pathophysiological disturbances within multiple organs. Insulin resistance, beta-cell dysfunction and impairment of incretin hormone action contribute to the development and progression of hyperglycemia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCardiovascular (CV) disease accounts for approximately half of all deaths in patients with T2D [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The subgroup of patients with T2D who present the metabolic syndrome (MetS) is at higher risk of developing CV outcomes than the rest of the population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. MetS is a clustering of cardiometabolic risk factors including hypertension, dyslipidemia and visceral obesity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and is closely associated with the pre-existence of hypersinsulinemia and insulin resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Persons with MetS have elevated rates and severity of CV outcomes, including microvascular dysfunction, coronary atherosclerosis and calcification, cardiac dysfunction, myocardial infarction and heart failure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInternational guidelines recommend an individualized approach to guide the choice of subsequent pharmacotherapies, when treatment intensification is required in patients with T2D [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The choice of antidiabetic pharmacotherapy is based on the comorbiditites presented by the patient (e.g. components of the MetS) and the stage of disease progression, thus leading to differential therapy choices among patients with T2D.\u003c/p\u003e \u003cp\u003eOptimal glycemic control is one of the cornerstones in the multifactorial treatment approach in patients with T2D to prevent long-term complications associated with chronic hyperglycemia [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Diet, exercise and education are the established first-line treatment of T2D, together with initiation of metformin in patients who are unlikely to achieve their individualized glycated hemoglobin (HbA1c) target without pharmacotherapy. At the time of this study, diabetes treatment guidelines recommended glucagon-like peptide-1 receptor agonists (GLP-1RAs) as second-line after metformin, on equal terms with other oral glucose-lowering agents and insulin, as part of a multifactorial risk-reduction strategy in the management of T2D [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLiraglutide is a human GLP-1RA analog approved for treatment of T2D in the European Union on 30 June 2009. The safety and efficacy of liraglutide were established through a rigorous and comprehensive phase 3 clinical trial program, demonstrating improvement of glycemic control, reduction in body weight and a low risk of hypoglycemic events [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to these drug characteristics, the UK National Institute for Health and Care Excellence (NICE) guidelines recommended limiting the use of liraglutide to patients with a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;35\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e who require optimization of their glycemic control [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, liraglutide could be taken concomitantly with other glucose-lowering drugs, including insulin. Thus, liraglutide use in the UK real-world setting differed from its use in the pre-authorization randomized clinical trials (RCTs), where patients were not necessarily obese and were not taking insulin.\u003c/p\u003e \u003cp\u003eGenerally, RCTs are often short in duration, include restricted populations, are conducted in specialized centers, and treatment is mandated by study protocol. Consequently, real-world data are needed to ascertain external validity and complement RCT data, further supporting regulatory decision-making.\u003c/p\u003e \u003cp\u003eAccordingly, regulatory authorities required a liraglutide post-authorization safety study (PASS) at the time of marketing authorization (2009). The study was to further characterize the safety profile of liraglutide after market launch, as well as to explore events identified as of special interest during the marketing authorization application review in the real-world setting. Hence, in order to fulfill this regulatory requirement, a database PASS was set up comparing safety outcomes (including CV outcomes) in initiators of the newly marketed liraglutide with outcomes in initiators of the most commonly used non-insulin antidiabetics (NIADs) in the UK population. The study design was endorsed by health regulatory authorities at a time before the establishment of the Pharmacovigilance Risk Assessment Committee (PRAC).\u003c/p\u003e \u003cp\u003eThis paper evaluates the methodology used to collect and perform comparative drug CV safety assessments in a real-world UK population initiating liraglutide and other NIADs.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eIn this real-world, observational, safety surveillance study of adult patients prescribed NIADs, data were obtained using individually linked UK electronic repositories: the Clinical Practice Research Datalink (CPRD) primary care database (GOLD) and Hospital Episode Statistics (HES).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical Practice Research Datalink (CPRD) GOLD\u003c/h2\u003e \u003cp\u003eThe CPRD collects anonymized medical records from general practitioners (GPs). CPRD GOLD is based on GP records using the Vision software system within the UK [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Patients are semi-permanently affiliated to a practice, which centralizes the medical information from the GPs, specialist referrals and hospitalizations.\u003c/p\u003e \u003cp\u003eThe data recorded in CPRD GOLD include demographic information, prescription details, clinical events, preventive care provided, specialist referrals, hospital admissions and their major outcomes. All information is recorded by staff at the GP practice and not necessarily validated externally, although the database has generally been shown to be of high quality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The database currently includes more than 13\u0026nbsp;million patients, from 1987 onwards [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eHospital Episode Statistics (HES)\u003c/h2\u003e \u003cp\u003eHES contains details of all admissions to UK National Health Service (NHS) hospitals in England. All NHS trusts in England, including acute hospitals, primary care trusts and mental health trusts, are included. HES data are collected during a patient's stay at a hospital to allow hospitals to be reimbursed for the care they deliver. As the data are extracted from patients\u0026rsquo; notes, they may also reflect the quality of clinical record-keeping. HES data have been collected for admitted patients since 1989 and outpatient attendances since 2003. At the time of this study, linked CPRD GOLD and HES data were available from 1 April 1997 to 31 March 2014.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patient population\u003c/h2\u003e \u003cp\u003ePatients in CPRD GOLD who were considered \u0026lsquo;new NIAD users\u0026rsquo; were eligible for inclusion. New users for each class of drug were identified by the first time each drug was prescribed, with no prescription record of that drug in the previous 12\u0026nbsp;months. Excluded patients were those aged\u0026thinsp;\u0026lt;\u0026thinsp;18\u0026nbsp;years with a history of cancer or human immunodeficiency virus/acquired immunodeficiency syndrome, or with a history of liraglutide use, and any patient in any comparator group who had initiated liraglutide during the study. Patients were only included in each outcome analysis if they had no record of that outcome in the previous 12\u0026nbsp;months.\u003c/p\u003e \u003cp\u003eThe study period was between 1 January 2005 and the end of follow-up time for a patient. End of follow-up was defined as the earliest of: the date of the last data collection from the GP practice; transfer out of the practice; death; or the end of data collection from HES (31 March 2014). Patients without any follow-up were excluded. All patients initiating NIADs after the end of follow-up were excluded from the analyses, as were any outcomes recorded in CPRD GOLD after this time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNIAD treatment groups\u003c/h2\u003e \u003cp\u003eNIAD treatment groups included patients who initiated (new users) the following: liraglutide, exenatide, dipeptidyl peptidase-4 (DPP-4) inhibitors (linagliptin, saxagliptin, sitagliptin, vildagliptin), sulfonylureas, biguanides, acarbose, glinides (nateglinide, repaglinide) or glitazones (rosiglitazone, pioglitazone).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes assessed\u003c/h2\u003e \u003cp\u003eAs part of post-marketing safety surveillance activity, outcomes captured included macrovascular conditions overall, consisting of acute myocardial infarction, ischemic heart disease (IHD), percutaneous transluminal coronary angioplasty, coronary artery bypass graft, lower limb amputation, stroke, transient ischemic attack and heart failure. These sub-outcomes were also studied separately. Other captured outcomes included malignant neoplasms (overall) and separately thyroid and pancreatic cancer and acute pancreatitis.\u003c/p\u003e \u003cp\u003eThis manuscript focuses on the CV outcomes in the study population fulfilling the inclusion/exclusion criteria in CPRD GOLD and eligible for linkage with HES.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eRates of each outcome and corresponding crude- and adjusted-incidence rate ratios (IRRs) were examined using Poisson regression analysis and expressed for liraglutide versus each of the comparators. The Poisson model was considered adequate for analyzing the number of occurred outcomes during the study period for each exposure in a database. Only variables contributing to the model were included using a forward stepwise procedure. Baseline characteristic measurements and confounding factor adjustments were as follows: baseline characteristics for groups of patients initiating liraglutide and other NIADS were examined at the index date; duration of diabetes was calculated based on the patient\u0026rsquo;s date of the first diabetes record; laboratory test data were used to assess HbA1c; insulin prescription (as an indicator of the burden/progression of diabetes and its comorbidities/complications) was assessed at various time points.\u003c/p\u003e \u003cp\u003eThe number of prior antidiabetes medication types was assessed by looking at the number of antidiabetes medications prescribed in the previous year. Patients who initiated various NIADs may have continued to use these medications after index date.\u003c/p\u003e \u003cp\u003eWhere possible, the following confounding factors were selected for adjustment: age, gender, duration of diabetes, HbA1c, insulin prescription, smoking status, alcohol consumption status and BMI.\u003c/p\u003e \u003cp\u003eFor the crude analyses, where the number of outcome events in the liraglutide cohort was \u0026lt;\u0026thinsp;5, the model was not run due to the CPRD small cell data governance regulations. Where the number of outcome events in patients initiating liraglutide was \u0026lt;\u0026thinsp;10, further adjusted analyses could not be made, as there was not enough information to make a meaningful comparison. Adjustment for age and gender, and further adjusted analyses, could be made for outcomes with at least 10 patient events among those initating liraglutide, with convergence achieved. This included macrovascular events, IHD and heart failure.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient population\u003c/h2\u003e \u003cp\u003eThere were 149 788 patients fulfilling the inclusion criteria in CPRD GOLD and eligible for linkage with HES. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the definition of the study population. Of these, 3432 patients initiated liraglutide.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinition of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptable male or female patients with at least 1\u0026nbsp;day of follow-up time\u003csup\u003e\u0026para;\u003c/sup\u003e in CPRD GOLD\u003c/p\u003e \u003cp\u003ePatients with at least one prescription for a NIAD drug during the study period\u003c/p\u003e \u003cp\u003eAged at least 18\u0026nbsp;years\u003c/p\u003e \u003cp\u003eEligible for linkage with HES data, with at least 1\u0026nbsp;day of follow-up,\u003csup\u003e\u0026para;\u003c/sup\u003e excluding those who start treatment after the end of follow-up\u003c/p\u003e \u003cp\u003eWith no history of malignant neoplasm\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWith no history of HIV/AIDs recorded in HES\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTotal in the study population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 673 099\u003c/p\u003e \u003cp\u003e313 544\u003c/p\u003e \u003cp\u003e313 281\u003c/p\u003e \u003cp\u003e174 883\u003c/p\u003e \u003cp\u003e149 910\u003c/p\u003e \u003cp\u003e149 788\u003c/p\u003e \u003cp\u003e149 788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003e\u0026para;\u003c/sup\u003eThe start of follow-up was defined as the latest date of the start of HES collection (1 April 1997), or the start of UTS follow-up. The end of follow-up was defined as the earliest date of the end of HES collection (31 March 2014), the date of the last collection from the GP practice, transfer out of the practice or death. \u003csup\u003e\u0026dagger;\u003c/sup\u003ePrevious cancer was assessed using the CPRD GOLD clinical and referral files and HES records. \u003csup\u003e\u0026Dagger;\u003c/sup\u003eHIV/AIDS was identified from a prescription of HIV medication in the therapy file, a positive record of HIV/AIDS in the CPRD GOLD clinical, referral or test files or a HES record.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAIDS, acquired immunodeficiency syndrome; CPRD, Clinical Practice Research Datalink; GP, general practitioner; HES, Hospital Episode Statistics database; HIV, human immunodeficiency virus; NIAD, non-insulin antidiabetic; UTS, up-to-standard.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNIAD treatment groups\u003c/h2\u003e \u003cp\u003eCharacteristics of the patients initiating each of the NIADs are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared with patients initiating other NIADs, other than exenatide, patients initiating liraglutide were younger, had a longer mean diabetes duration and higher mean HbA1c level, were more overweight or obese, more had been prescribed insulin\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year prior to index date, more had a greater frequency of any microvascular complications, more had a history of hypertension at index date, and had a greater number of concomitant antidiabetic medications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients initiating treatment with each NIAD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiraglutide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExenatide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDPP-4 inhibitors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAcarbose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBiguanides\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGlinides\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGlitazones\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSUs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74 067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16 749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33 142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up, years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.6 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.3 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 569 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 481 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 325 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e259 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 604 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e437 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 931 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13 854 (41.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.2 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.8 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.0 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.2 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.1 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61.2 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.6 (13.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.1 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.5 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.7 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.5 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.0 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.7 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.7 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.4 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 127 (91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 989 (91.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 776 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41 825 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e475 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9 348 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17 674 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of diabetes, years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.6 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.7 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.4 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 203 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 107 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 215 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 574 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e361 (37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 972 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11 806 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 659 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 593 (49.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 250 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e297 (49.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 553 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e438 (45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 762 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14 963 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e570 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e551 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 808 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 490 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e164 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 959 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6 225 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown smoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e450 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e148 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 043 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 322 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e136 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 226 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 112 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e664 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 069 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 358 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e176 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 425 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 593 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 304 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 143 (65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 622 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e378 (63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 417 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e571 (59.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11 213 (66.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22 314 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown drinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 970 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e885 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 123 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes complications in the 12\u0026nbsp;months prior to index date\u003c/p\u003e \u003cp\u003eAny microvascular complication\u003c/p\u003e \u003cp\u003eOphthalmic complications\u003c/p\u003e \u003cp\u003eNeurological complications\u003c/p\u003e \u003cp\u003eKidney complications\u003c/p\u003e \u003cp\u003eCirculatory complications\u003c/p\u003e \u003cp\u003eDiabetic foot or arthropathy complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 947 (56.7%)\u003c/p\u003e \u003cp\u003e387 (11.3%)\u003c/p\u003e \u003cp\u003e61 (1.8%)\u003c/p\u003e \u003cp\u003e16 (0.5%)\u003c/p\u003e \u003cp\u003e17 (0.5%)\u003c/p\u003e \u003cp\u003e1 730 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 152 (35.4%)\u003c/p\u003e \u003cp\u003e385 (11.8%)\u003c/p\u003e \u003cp\u003e35 (1.1%)\u003c/p\u003e \u003cp\u003e30 (0.9%)\u003c/p\u003e \u003cp\u003e12 (0.4%)\u003c/p\u003e \u003cp\u003e843 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 437 (54.6%)\u003c/p\u003e \u003cp\u003e1 748 (101.1%)\u003c/p\u003e \u003cp\u003e196 (1.1%)\u003c/p\u003e \u003cp\u003e126 (0.7%)\u003c/p\u003e \u003cp\u003e68 (0.4%)\u003c/p\u003e \u003cp\u003e8 426 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (18.7%)\u003c/p\u003e \u003cp\u003e58 (9.7%)\u003c/p\u003e \u003cp\u003e9 (1.5%)\u003c/p\u003e \u003cp\u003e5 (0.8%)\u003c/p\u003e \u003cp\u003e3 (0.5%)\u003c/p\u003e \u003cp\u003e54 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 508 (15.5%)\u003c/p\u003e \u003cp\u003e2 039 (2.8%)\u003c/p\u003e \u003cp\u003e262 (0.4%)\u003c/p\u003e \u003cp\u003e198 (0.3%)\u003c/p\u003e \u003cp\u003e73 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003cp\u003e9 760 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e201 (20.9%)\u003c/p\u003e \u003cp\u003e71 (7.4%)\u003c/p\u003e \u003cp\u003e16 (1.7%)\u003c/p\u003e \u003cp\u003e6 (0.6%)\u003c/p\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003cp\u003e128 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 944 (17.6%)\u003c/p\u003e \u003cp\u003e1 086 (6.5%)\u003c/p\u003e \u003cp\u003e111 (0.7%)\u003c/p\u003e \u003cp\u003e161 (1.0%)\u003c/p\u003e \u003cp\u003e17 (0.1%)\u003c/p\u003e \u003cp\u003e1 819 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8 548 (25.8%)\u003c/p\u003e \u003cp\u003e1 737 (5.2%)\u003c/p\u003e \u003cp\u003e225 (0.7%)\u003c/p\u003e \u003cp\u003e196 (0.6%)\u003c/p\u003e \u003cp\u003e79 (0.2%)\u003c/p\u003e \u003cp\u003e7 102 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther comorbidities\u003c/p\u003e \u003cp\u003eHistory of hypertension\u003c/p\u003e \u003cp\u003eHistory of congestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 747 (80.0%)\u003c/p\u003e \u003cp\u003e136 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 615 (80.4%)\u003c/p\u003e \u003cp\u003e136 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 980 (75.1%)\u003c/p\u003e \u003cp\u003e801 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e456 (76.0%)\u003c/p\u003e \u003cp\u003e38 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43 236 (58.4%)\u003c/p\u003e \u003cp\u003e2 244 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e696 (72.2%)\u003c/p\u003e \u003cp\u003e66 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12 408 (74.1%)\u003c/p\u003e \u003cp\u003e438 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22 803 (68.8%)\u003c/p\u003e \u003cp\u003e1 599 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.6 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.7 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.9 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.0 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (mmol/mol)\u003csup\u003e\u0026dagger;\u003c/sup\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.2 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.1 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.8 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.7 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.5 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.6 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.8 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74.9 (19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin prescribed\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year before index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e974 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e945 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 495 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 526 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e833 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e822 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo insulin prescribed before or on index date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 354 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 193 (67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 617 (90.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e533 (88.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70 724 (95.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e799 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15 809 (94.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32 029 (96.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of diabetes medications prescribed in the year prior to index date, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.9 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eData are n (%), unless stated otherwise stated. \u003csup\u003e\u0026dagger;\u003c/sup\u003eCalculated, not measured. BMI, body mass index; DPP-4, dipeptidyl peptidase-4; HbA1c, glycated hemoglobin; NIAD, non-insulin antidiabetic; SD, standard deviation; SU, sulfonylurea.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBaseline patient characteristics for exenatide were similar to those initiating liraglutide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical outcomes\u003c/h2\u003e \u003cp\u003eFor macrovascular outcomes overall, adjustments could be made for all eight confounders (age, gender, duration of diabetes, HbA1c, insulin prescribing, smoking status, alcohol consumption status and BMI). For IHD and heart failure, the stepwise regression resulted in differing predictive variables. For IHD, HbA1c did not meet the threshold in the comparison with glitazones, nor BMI for sulfonylureas. Patients with a previous history of a given CV outcome were excluded, and important baseline characteristics and confounders can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAn increased risk of IHD and heart failure was observed with liraglutide use compared to NIADs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For macrovascular conditions overall, the estimated unadjusted IRRs were significantly higher for liraglutide versus biguanides or glitazone initiators (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After age and gender adjustment, the IRRs for liraglutide initiators were also significantly higher versus DPP-4 inhibitor or sulfonylurea initiators. However, after stepwise adjustment of the IRR, adjusting for age, smoking, alcohol, gender, insulin prescription, BMI, HbA1c and diabetes duration, liraglutide initiators only had an increased risk of macrovascular conditions versus biguanide initiators (IRR: 1.33 [99% confidence interval (CI): 1.11;1.59]).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk of macrovascular conditions for patients initiating liraglutide compared with other NIADs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eRisk of macrovascular conditions overall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNumber of patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNumber of outcome events\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePerson-years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eIncidence (99% CI)\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUnadjusted IRR (99%\u0026nbsp;CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAge- and gender-adjusted IRR (99%\u0026nbsp;CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eStepwise adjusted IRR (99%\u0026nbsp;CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiraglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.56 (5.60;7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExenatide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.43 (5.45;7.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.81;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02 (0.81;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04 (0.82;1.30)\u003csup\u003ea,b,d,e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP-4is\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 3973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.42 (6.02;6.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.86;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.38 (1.16;1.65)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07 (0.88;1.29)\u003csup\u003ea,b,c,d,e,f,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcarbose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.26 (5.10;10.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (0.61;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25 (0.83;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.65;1.52)\u003csup\u003ea,d,e,f\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiguanides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 0581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.43 (4.30;4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48 (1.26;1.74)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.92 (1.63;2.26)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.33 (1.11;1.59)*\u003csup\u003e,a,b,c,d,e,f,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlinides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.72 (6.85;11.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75 (0.56;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (0.74;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80 (0.56;1.12)\u003csup\u003ea,b,d,e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlitazones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.99 (4.72;5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.31 (1.11;1.55)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.73 (1.45;2.05)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19 (0.98;1.45)\u003csup\u003ea,b,c,d,e,f,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.94 (5.71;6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.94;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.55 (1.31;1.83)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (0.88;1.29)\u003csup\u003ea,b,c,d,e,f,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk of ischemic heart disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiraglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.64 (4.76;6.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExenatide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.39 (4.50;6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (0.82;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04 (0.82;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (0.83;1.36)\u003csup\u003ea,b,d,e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP-4is\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.09 (4.73;5.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11 (0.92;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.50 (1.24;1.81)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.15 (0.94;1.41)\u003csup\u003ea,b,c,d,e,f,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcarbose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.51 (3.70;8.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.66;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.38 (0.88;2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.18 (0.75;1.87)\u003csup\u003ea,d,e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiguanides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 6250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.28 (3.17;3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72 (1.45;2.04)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.19 (1.84;2.60)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.45 (1.20;1.76)*\u003csup\u003e,a,b,c,d,e,f,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlinides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.12 (5.48;9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79 (0.58;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04 (0.75;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75 (0.52;1.09)\u003csup\u003ea,d,e,f,g\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlitazones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.64 (3.41;3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55 (1.29;1.86)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.99 (1.66;2.40)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.40 (1.13;1.73)*\u003csup\u003e,a,b,c,d,e,f,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.43 (4.23;4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27 (1.07;1.52)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.77 (1.48;2.12)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.13 (0.92;1.40)\u003csup\u003ea,b,c,d,e,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk of heart failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiraglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54 (1.12;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExenatide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28 (0.90;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20 (0.75;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.22 (0.76;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.26 (0.78;2.03)\u003csup\u003ea,c,d,e,f,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP-4is\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68 (1.49;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92 (0.65;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47 (1.03 ;2.08)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97 (0.67;1.41)\u003csup\u003ea,b,d,e,f,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcarbose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11 (1.13;3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73 (0.36;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14 (0.54;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67 (0.30;1.48)\u003csup\u003ea,d,e,f,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiguanides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 7903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (1.10;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32 (0.96;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00 (1.45;2.77)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28 (0.90;1.81)\u003csup\u003ea,b,c,d,e,f,g,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlinides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.88 (1.94;4.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53 (0.32;0.88)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83 (0.49;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63 (0.36;1.11)\u003csup\u003ea,d,e,f,h\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlitazones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50 (1.36;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.73;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.59 (1.13;2.22)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99 (0.68;1.45)\u003csup\u003ea,b,c,e,f,g\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96 (1.83;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78 (0.57;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.33 (0.96;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.84 (0.58;1.21)\u003csup\u003ea,b,c,d,e,f,g\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAll IRRs are for the comparator vs liraglutide. \u003csup\u003e\u0026dagger;\u003c/sup\u003ePer 100 person-years.*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Bonferroni correction applied. Adjusted for \u003csup\u003ea\u003c/sup\u003eage, \u003csup\u003eb\u003c/sup\u003esmoking, \u003csup\u003ec\u003c/sup\u003ealcohol use, \u003csup\u003ed\u003c/sup\u003egender, \u003csup\u003ee\u003c/sup\u003einsulin prescribing, \u003csup\u003ef\u003c/sup\u003eBMI, \u003csup\u003eg\u003c/sup\u003eHbA1c, \u003csup\u003eh\u003c/sup\u003ediabetes duration. BMI, body mass index; CI, confidence interval; DPP-4is, dipeptidyl peptidase-4 inhibitors; HES, Hospital Episode Statistics database; IRR, incidence rate ratio; NIAD, non-insulin antidiabetic; N/A, not applicable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 230 IHD events were observed, leading to an estimated incidence per 100 person years of 5.64 (99% CI: 4.76;6.68). The estimated unadjusted and age- and gender-adjusted IRRs were significantly higher for patients initiating liraglutide versus biguanide, glitazone or sulfonylurea initiators (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting for age and gender, the IRR was signficantly higher for liraglutide initiators versus DPP-4 inhibitor, biguanide, glitazone or sulfonylurea initiators. The comparison to biguanide and gliatazone initiators remained significant for the stepwise-adjusted IRR after adjusting for age, smoking, alcohol, gender, insulin prescription, BMI, HbA1c and diabetes duration (IRR: 1.45 [99% CI: 1.20;1.76] and IRR: 1.40 [99% CI:1.13;1.73], respectively).\u003c/p\u003e \u003cp\u003eFor heart failure, the estimated age- and gender-adjusted IRRs were significantly higher for liraglutide initiators versus DPP-4 inhibitor, biguanide and glitazone initiators (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, none of the stepwise-adjusted IRRs were statistically significant (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for specific adjustments).\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eAn increased risk of some macrovascular conditions was observed in liraglutide initiators, compared with those initiating most other NIADs. However, after performing a stepwise adjustment, increased risk was only seen in liraglutide initiators compared with biguanide initators. These findings are likely explained by the differential drug choice applied to the different subpopulations of T2D. Compared with other NIAD initiators at baseline, liraglutide initiators had more advanced and longer duration of T2D, reflected by having more microvascular complications, more concomitant NIAD and insulin use and higher HbA1c levels. In addition, more components of the MetS were captured for liraglutide initiators. These findings suggest that UK physicians prescribed liraglutide to a selected T2D subpopulation who presented with MetS (selection bias) and/or had a more advanced stage of T2D, poorly controlled with the marketed antidiabetics available at the time liraglutide entered the market (chanelling bias).\u003c/p\u003e \u003cp\u003eThe observed increase of macrovascular outcomes in the liraglutide initiator cohort may rather be due to the population\u0026rsquo;s baseline metabolic risk profile and not to liraglutide exposure itself (confounding). In addition, liraglutide initiators may have had other inherent residual baseline characteristics which have not been measured but increase CV risk (residual confounding), e.g. higher levels of hyperinsulinemia/insulin resistance (which are associated with MetS and independently associated with increased risk of CV outcomes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]) or diabetic dyslipidemia (increased triglyceride levels, decreased high-density lipoprotein-cholesterol levels and increased small dense low-density lipoprotein particle levels), which is a component of the MetS and independently associated with increased risk of CV outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study\u0026acute;s real-world findings on pre-liraglutide patient characteristics are in line with those reported by the UK Association of British Clinical Diabetologists\u0026rsquo; audits, which examined data on 6238 liraglutide-treated patients from 2009 to 2013 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Liraglutide users from diabetes centers across the UK were found to be heavier and with poorer glycemic control than patients from liraglutide phase 3 RCTs. The conclusion was that the baseline characteristics of liraglutide-treated patients were influenced by the UK NICE guidelines, which recommend liraglutide for patients with high BMI (\u0026ge;\u0026thinsp;35\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Patients with higher BMIs inherently have a higher probability of having more cardiometabolic risk factors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, UK reimbursement conditions add to further confounding by indication, reinforcing that patients initiating liraglutide have higher CV risk [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, liraglutide initiation itself appears to be an indicator for the presence of a higher clustering of cardiometabolic risk and more advanced disease stage within the continuum of T2D. The same appears to apply to exenatide, which can be expected, as both belong to the same drug class (GLP-1RAs).\u003c/p\u003e \u003cp\u003eNon-interventional database studies lack randomization, with resulting bias and confounding. This is a specific problem in T2D, where increasing diabetes severity (comorbidities/complications and stage of progression) is followed by treatment steps, and lack of response leads to treatment intensification. Thus, separating disease severity from treatment exposure as the causal agent for increased incidences of CV outcomes in a heterogeneous T2D population, as in the presented study, can be challenging. Baseline treatment characteristics must be balanced between cohorts to achieve homogeneity across cohorts. In order to finalize a study design with homogeneity across treatment cohorts, thorough background knowledge is required, encompassing the disease and its natural history, complications and comorbidities, the likely real-world target population of the drug and current local pharmacotherapy guidelines, and the feasibility of obtaining required data from the chosen source.\u003c/p\u003e \u003cp\u003eThe presented study highlights that comparative analysis of treatment initiator cohorts, without thoroughly balancing baseline characteristics, is not suited to addressing new drug safety. In this study, patient baseline characteristics had not been matched among cohorts. In addition, it was not possible to adjust for all of the a priori planned covariates, owing in part to the rarity of the outcomes under investigation. Risk estimates for liraglutide initiators are likely to have been increased when adjusting for age, since liraglutide users were younger. Even with a relatively large database such as the CPRD, the comparison of a new drug to existing therapies was challenging due to the heterogeneity of the populations treated with different drugs, and the relatively low numbers of new drug initiators.\u003c/p\u003e \u003cp\u003eOf note, real-world liraglutide observational data emerging from comparative studies based on cohorts which had been matched (baseline characteristics were equally weighted across cohorts) have shown that liraglutide use was associated with significantly lower major adverse CV event (MACE) risk [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, CV outcomes trials with GLP-1RAs have not reported increased CV risk [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The blinded, randomized, placebo-controlled LEADER trial demonstrated that MACE risk was signficantly reduced in liraglutide- versus placebo-treated patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings support our interpretation that selection bias, confounding and possibly channelling bias drive the observed increased CV risk in liraglutide initiators in the UK real-world setting in the presented study.\u003c/p\u003e \u003cp\u003eObservational studies provide external validity of a drug\u0026rsquo;s safety and effectiveness in everyday clinical practice, thus supporting decision-makers and key stakeholders regarding treatment options to be carried out in the real world [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Observational PASS are key in gathering this type of real-world evidence. However, as patients are not randomized, the study design for investigating the safety of newly marketed products should, to the extent possible, account for confounding and bias. This requires the feasibility of aquiring sufficient data quantity and quality to obtain true homogeneity among cohorts. These considerations are in line with the Guidelines for Good Pharmacoepidemiology Practice [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and should be taken into account also when designing and interpreting studies that seek to support regulatory decisions.\u003c/p\u003e \u003cp\u003eThis regulatory health authorityrequired PASS sought to investigate the safety of liraglutide in the UK realworld setting. CPRD GOLD reflects the UK real-world clinical practice setting [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Patients prescribed liraglutide had higher components of the MetS and markedly more advanced T2D than NIAD initiators. It was not always possible to adjust for all confounders and, in some cases, this may have modulated safety outcomes, although in others, significant results persisted despite adjustment for all confounders (e.g. comparison with biguanides). The study design allows for a descriptive analysis of the cohort populations initiating different antidiabetic medications. An improved study design to assess CV safety with liraglutide would require equally weighting the baseline characteristics. This study was endorsed by the health regulatory authorities at a time before the establishment of the Pharmacovigilance Risk Assessment Committee (PRAC). PRAC is responsible for reviewing the design and for the evaluation of post-authorisation safety studies. In addition, this observational real world study was thought to complement the the liraglutide Cardiovascular Outcome Trial (CVOT), LEADER, which was also a regulatory authority required PASS at the time of marketing authorization (2009).\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThe methodology for this study was insufficient to generate reliable real-world CV safety data to compare liraglutide initiators with other NIAD initiators. An increased risk of some macrovascular conditions was observed in patients initiating liraglutide compared to those initiating other NIADs. However, the observed difference in CV risk is likely due to differences in baseline characteristics between the NIAD cohorts, and not to the specific NIAD initiated. These results emphasize the importance of countering selection bias and confounding when designing a study comparing CV outcomes across treatment initiator cohorts in heterogenous populations, in order to generate robust real-world evidence to support regulatory and health providers\u0026rsquo; decisions, enhancing patient safety.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eBMI, body mass index; CI, Confidence Interval ; CPRD, Clinical Practice Research Datalink; CV, cardiovascular; DPP4, dipeptidyl peptidase-4; GLP-1RA, glucagon-like peptide-1 receptor agonist; GP, general practitioner; HES, Hospital Episode Statistics; IHD, ischemic heart disease; IRR, incidence rate ratios; MACE, major adverse cardiovascular events; MetS, metabolic syndrome; NHS, National Health Service; NIAD, non-insulin antidiabetic; PASS, post authorization safety study; RCT, randomized clinical trial; T2D, type 2 diabetes; SU, sulphonylurea\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate: \u003c/em\u003eThis study was approved by the Independent Scientific Advisory Committee (ISAC) of the Medicines and Healthcare Products Regulatory Agency, under reference number 10_044. This work uses data provided by patients and collected by the NHS as part of their care and support.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003e The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e HG, TJ and AMP are employees of and stockholders in Novo Nordisk. AG has no potential conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e This analysis was funded by Novo Nordisk, which, in collaboration with the CPRD, was involved in the design of the study, analysis and interpretation of data, and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e: AG, HG, TJ and AMP analyzed the data and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements:\u003c/em\u003e Medical writing and submission support were provided by Nathan Ley, Aneela Majid and Helen Marshall, of Watermeadow Medical, an Ashfield company, part of UDG Healthcare plc, funded by Novo Nordisk. We also thank\u0026nbsp;Hans Askelund S\u0026aelig;vereid (Novo Nordisk) for his review and input to the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eAhlqvist E, Storm P, K\u0026auml;r\u0026auml;j\u0026auml;m\u0026auml;ki A, Martinell M, Dorkhan M, Carlsson A, Vikman P, Prasad RB, Aly DM, Almgren P. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eAbdul-Ghani M, DeFronzo RA, Del Prato S, Chilton R, Singh R, Ryder REJ. Cardiovascular Disease and Type 2 Diabetes: Has the Dawn of a New Era Arrived? Diabetes Care. 2017;40(7):813\u0026ndash;20.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eEinarson TR, Acs A, Ludwig C, Panton UH. 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Reflections From a Diabetes Care Editors' Expert Forum. Diabetes Care. 2018;41(1):14\u0026ndash;31.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMedicines and Healthcare products Regulatory Agency and NIHI. Clinical Practice Research Datalink. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cprd.com/home/\u003c/span\u003e\u003c/span\u003e. Accessed 21 Nov 2019.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eHerrett E, Thomas SL, Schoonen WM, Smeeth L, Hall AJ. Validation and validity of diagnoses in the General Practice Research Database: a systematic review. Br J Clin Pharmacol. 2010;69(1):4\u0026ndash;14.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eHerrett E, Gallagher AM, Bhaskaran K, Forbes H, Mathur R, van Staa T, Smeeth L. Data Resource Profile: Clinical Practice Research Datalink (CPRD). Int J Epidemiol. 2015;44(3):827\u0026ndash;36.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSrinivasan M, Kamath P, Bhat N, Pai N, Bhat R, Shah T, Manjrekar P, Mahabala C. Basal hyperinsulinemia beyond a threshold predicts major adverse cardiac events at 1 year after coronary angiogram in type 2 diabetes mellitus: a retrospective cohort study. Diabetol Metab Syndr. 2017;9:38.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWarraich HJ, Rana JS. Diabetic Dyslipidemia: Epidemiology and Prevention of Cardiovascular Disease and Implications of Newer Therapies. Curr Cardiol Rep. 2018;20(12):125.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eThong Ky, Gupta PS, Cull ML, Adamson KA, Dove DS, Rowles SV, Tarpey S, Duncan C, Chalmers J, Harper R. GLP-1 receptor agonists in type 2 diabetes-NICE guidelines versus clinical practice. Br J Diabetes. 2014;14(2):52\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eNICE. Type 2 diabetes in adults: management. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nice.org.uk/guidance/ng28\u003c/span\u003e\u003c/span\u003e. Accessed 2018.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSvanstrom H, Ueda P, Melbye M, Eliasson B, Svensson AM, Franzen S, Gudbjornsdottir S, Hveem K, Jonasson C, Pasternak B. Use of liraglutide and risk of major cardiovascular events: a register-based cohort study in Denmark and Sweden. Lancet Diabetes Endocrinol. 2019;7(2):106\u0026ndash;14.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMarso SP, Daniels GH, Brown-Frandsen K, Kristensen P, Mann JF, Nauck MA, Nissen SE, Pocock S, Poulter NR, Ravn LS, et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2016;375(4):311\u0026ndash;22.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLoudon K, Treweek S, Sullivan F, Donnan P, Thorpe KE, Zwarenstein M. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ. 2015;350:h2147.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eInternational Society for Pharmacoepidemiology. Guidelines for Good Pharmacoepidemiology Practices (GPP). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pharmacoepi.org/resources/policies/guidelines-08027/#1\u003c/span\u003e\u003c/span\u003e. Accessed 21 Nov 2019.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"diabetes, selection bias, real world, liraglutide, CPRD, post-marketing safety surveillance ","lastPublishedDoi":"10.21203/rs.3.rs-66177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-66177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGeneration of real-world cardiovascular drug safety evidence in patients with type 2 diabetes (T2D) warrants robust methodology.\u003cstrong\u003e \u003c/strong\u003eRegulatory\u003cstrong\u003e \u003c/strong\u003eauthorities are increasingly seeking to support their decision making through real-world evidence. At the time of marketing authorization, this study was required by regulatory authorities to further characterize the liraglutide safety profile in routine real-world clinical practice (external validity). Safety outcomes were compared to those of other non-insulin antidiabetic (NIAD) treatments in patients with T2D initiating NIADs in a UK real-word setting. The design was endorsed by health regulatory authorities. This paper analyzes the methodology and study results, and postulates that it was the differences in patient characteristics at NIAD initiation, not the treatment itself, that modulated the observed differences in cardiovascular risk between NIAD cohorts.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData were obtained from linked UK electronic repositories: the Clinical Practice Research Datalink primary care database (CPRD GOLD) and Hospital Episode Statistics (HES). Risks of selected outcomes with current liraglutide use were compared to current use of other NIADs. Rates of each outcome and corresponding crude and adjusted incidence rate ratios were examined using Poisson regression analysis. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Overall, 149 788 patients met the study inclusion criteria; of these, 3432 initiated liraglutide. Components of the metabolic syndrome were more common among liraglutide initiators than those initiating other NIADs. The risk of some macrovascular conditions was increased in liraglutide initiators versus other NIAD initiators; following stepwise adjustment, this was only seen in liraglutide initiators when compared to biguanide initiators (incidence rate ratio: 1.33 [99% confidence interval (CI): 1.11;1.59]).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The observed increase of macrovascular outcomes in liraglutide initiators compared with other NIAD initiators emphasizes the importance of countering potential selection bias when designing studies comparing cardiovascular outcomes across treatment initiator cohorts in heterogenous populations, such as patients with T2D. Baseline differences in the NIAD cohort may have modulated cardiovascular outcomes and likely explain the observed increased cardiovascular risk in liraglutide initiators. Selection bias, channeling bias and residual confounding, inherent in such observational studies, must be considered early when designing the study if the real-world data are to support decision-making.\u003c/p\u003e","manuscriptTitle":"Liraglutide Use in A Real-World Setting: Patient Characteristics at Antidiabetic Treatment Initiation Modulate Cardiovascular Safety Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-04 16:29:56","doi":"10.21203/rs.3.rs-66177/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"caeb5d21-d280-4d9a-b897-07591e87fba3","owner":[],"postedDate":"September 4th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":434088,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2020-09-23T18:47:41+00:00","versionOfRecord":[],"versionCreatedAt":"2020-09-04 16:29:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-66177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-66177","identity":"rs-66177","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}