COPYRIGHT. Treatment of Type 2 Diabetes: What To Do When Treatment with Metformin is Inadequate? Can We Achieve Therapeutic Goals More Safely?

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1 Treatment of Type 2 Diabetes: What To Do When Treatment with Metformin is Inadequate? Can We Achieve Therapeutic Goals More Safely? Martin J. Abrahamson, MD FACP Associate Professor of Medicine, Harvard Medical School

2 What I will cover Magnitude of the problem Pathophysiology of type 2 diabetes The guidelines Factors to consider when choosing a medication/s to add to metformin Cardiovascular outcomes studies Which combinations might be appropriate?

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4 Diabetes in the USA Today: An Epidemic More than 29 million people just over 9% of the population 25% undiagnosed The number is growing by > 1 million per year A major cause of mortality and morbidity Cost (direct and indirect) $ 245 billion per year 1 in 10 US health care dollars spent on diabetes Almost 86 million people at risk for diabetes Good news the rate of new cases is declining (only 1.4 million new cases per year vs 1.8 million) Diabetes Care 2010

5 Pathogenesis of Type 2 DM: From the Triumvirate From DeFronzo Diabetes 2009; 58:

6 To The Ominous Octet Islet α-cells Increased glucagon secretion Pancreas Impaired insulin secretion (β-cell decline) Kidneys Increased glucose reabsorption Hyperglycemia Energy homeostasis GI tract Decreased incretin effect Brain Neurotransmitter dysfunction Skeletal muscle Decreased glucose uptake Liver Insulin resistance Increased HGP Adipose tissue Increased lipolysis GI, gastrointestinal; HGP, hepatic glucose production; SU, sulphonylurea; T2D, type 2 diabetes DeFronzo RA. Diabetes 2009;58:

7 Addressing the Ominous Octet Islet α-cells Increased glucagon secretion Pancreas Impaired insulin secretion (β-cell decline) Insulin Kidneys Increased glucose reabsorption GLP-1 DPP-4 DPP-4 SUs GLP-1 SGLT-2 GI tract Decreased incretin effect GLP-1 AGI Colesevelam Hyperglycemia Energy homeostasis GLP-1 Insulin GLP-1 Insulin Brain Cycloset Neurotransmitter dysfunction GLP-1 Metformin DPP-4 GLP-1 Insulin Skeletal muscle Decreased glucose uptake Liver Insulin resistance Insulin Increased HGP Adipose tissue Increased lipolysis DPP-4, dipeptidyl peptidase 4; GI, gastrointestinal; GLP-1, glucagon-like peptide-1; HGP, hepatic glucose production; SGLT-2, sodium-glucose co-transporter-2; SU, sulphonylurea; T2D, type 2 diabetes; DeFronzo RA. Diabetes 2009;58:

8 Type 2 Diabetes Management 2016 Lowering A1c to around 7% especially early after diagnosis can reduce the risk for the development or progression of the long term complications of diabetes There are many medications available today to treat type 2 diabetes if used appropriately this could translate to improved control and less risk for complications The challenge for the practicing physician is to know which medications to use and when best to use them

9 Type 2 Diabetes Management 2016 There IS consenus that metformin should be first line therapy There is NO clear consensus what to add to metformin when A1c goals are not met Few head to head comparator trials Even fewer long term studies evaluating durability of medications on glycemic control, especially when added to metformin

10 Figure'1.'Modula$on'of'the' intensiveness'of'glucose' lowering'therapy'in't2dm' PATIENT / DISEASE FEATURES Risks potentially associated with hypoglycemia and other drug adverse effects Disease duration Life expectancy Important comorbidities more stringent low newly diagnosed long absent Approach to the management of hyperglycemia HbA1c' 7%(' few / mild less stringent high long-standing short severe Established vascular complications absent few / mild severe Usually not modifiable Patient attitude and expected treatment efforts highly motivated, adherent, excellent self-care capacities less motivated, non-adherent, poor self-care capacities Potentially modifiable Resources and support system Readily available limited Diabetes Care 2015;38: ; Diabetologia 2015; /s

11 ADA/EASD Position Statement Guidelines for Type 2 Diabetes Dual Therapy SU Triple Therapy TZD Lifestyle Modification, Education Metformin HbA1c >7.0% DPP-4 inhibitor SGLT2 inhibitor To Above Dual Therapy add 3 rd Drug from these Categories GLP-1 RA Basal Insulin Combination Injectable Rx Basal Insulin + Prandial Insulin or GLP-1 RA ADA = American Diabetes Association; EASD = European Association for the Study of Diabetes. Inzucchi SE, et al. Diabetes Care. 2016

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13 Therapeutic and behavioural barriers to glycemic control/treatment intensification Weight gain Improved glycemic control Fear of hypoglycemia Inflexible/rigid and complex treatment regimens Depression? Weight worry Reduced adherence to avoid hypoglycemia Resistance to initiation and poor adherence

14 How should we choose which drug/s to add to metformin? Safety Efficacy Tolerability/acceptability Cost Phenotypic and genotypic approaches to determine most effective therapy are lacking

15 Safety Hypoglycemia Cardiac safety

16 Hypoglycemia Insulin Sulfonylureas (SUs) NOT (when used alone/without insulin or SUs) Metformin DPP-IV Inhibitors GLP-1 agonists TZD SGLT-2 inhbitors Dopamine receptor agonist Colesevalam Alpha glucosidase inhibitors

17 Glibenclamide is Associated with Considerably More Hypoglycemia Than Glimepiride Prospective 4 year study in Germany Plasma glucose < 50 mg/dl and ER Treatment with IV glucose or glucagon People with hypoglycemia - mean age 79, Mean HbA1c 5.4%, 62% had creat clear <60 ml/min Incidents/1000 personyears Glimepiride Glibenclamide Prescription frequency/ 1000 person-years Holstein A et al, Diabetes Metab Res Rev 2001; 17:

18 Cardiovascular safety

19 Cumulative Incidence (95% CIs) of Cardiovascular Disease or Death Comparing Sulfonylureas to Metformin 21% increase Roumie, C.L. et al, Ann Intern Med. 2012;157:

20 DPP 4 Inhibitors vs SU as Add-On to Metformin: Comparison of Outcomes DPP 4 inhibitor* SU* Hazard ratio P value All cause death < MACE < MI Ischemic stroke < Hospitalization for heart failure Hypoglycemia < * Incidence rate per 1000 person years Ou S-M et al. Annals of Internal Medicine 2015; published online 10/13/15

21 Many recent trials of newer glucose-lowering agents have been neutral on the primary CV outcome HR: 1.0 (95% CI: 0.89, 1.12) HR: 0.96 (95% CI: UL 1.16) DPP-4 inhibitors* SAVOR-TIMI 53 EXAMINE HR: 0.98 (95% CI: 0.88, 1.09) HR: 1.02 (95% CI: 0.89, 1.17) ELIXA TECOS Lixisenatide Empagliflozin EMPA-REG OUTCOME CV, cardiovascular; HR, hazard ratio; DPP-4, dipeptidyl peptidase-4 *Saxagliptin, alogliptin, sitagliptin Adapted from Johansen OE. World J Diabetes 2015;6:

22 N Engl J Med 2015; 373:

23 EMPA-REG OUTCOME Randomised, double-blind, placebo-controlled CV outcomes trial Objective To examine the long-term effects of empagliflozin versus placebo, in addition to standard of care, on CV morbidity and mortality in patients with type 2 diabetes and high risk of CV events CV, cardiovascular

24 Pre-specified primary and key secondary outcomes Primary outcome 3-point MACE: Time to first occurrence of CV death, non-fatal MI or non-fatal stroke Key secondary outcome 4-point MACE: Time to first occurrence of CV death, non-fatal MI, non-fatal stroke or hospitalisation for unstable angina CV, cardiovascular; MI, myocardial infarction; MACE, Major Adverse Cardiovascular Event 24

25 Baseline Characteristics Mean age Mean A1c 8% CV disease % on insulin 48 % on statin 77 % on ACEI or ARB 80 % on aspirin 82 Mean BP Mean BMI years (72% male participants) 100 % (about half had previous MI; and 10% had CHF) 135/77 mm Hg Mean LDL 85 mg/dl

26 HbA1c Adjusted mean (SE) HbA1c (%) Empagliflozin 10 mg Empagliflozin 25 mg Week Placebo Placebo Empagliflozin 10 mg Empagliflozin 25 mg All patients (including those who discontinued study drug or initiated new therapies) were included in this mixed model repeated measures analysis (intent-to-treat) X-axis: timepoints with reasonable amount of data available for pre-scheduled measurements

27 Systolic blood pressure Adjusted mean (SE) systolic blood pressure (mmhg) Week Placebo Empagliflozin 25 mg Empagliflozin 10 mg Placebo 2322 Empagliflozin 10 mg 2322 Empagliflozin 25 mg All patients (including those who discontinued study drug or initiated new therapies) were included in this mixed model repeated measures analysis (intent-to-treat) X-axis: timepoints with reasonable amount of data available for pre-scheduled measurements 27

28 Primary outcome: 3-point MACE HR 0.86 (95.02% CI 0.74, 0.99) p=0.0382* Cumulative incidence function. MACE, Major Adverse Cardiovascular Event; HR, hazard ratio. * Two-sided tests for superiority were conducted (statistical significance was indicated if p )

29 CV death HR 0.62 (95% CI 0.49, 0.77) p< Cumulative incidence function. HR, hazard ratio

30 Hospitalisation for heart failure HR 0.65 (95% CI 0.50, 0.85) p= Cumulative incidence function. HR, hazard ratio

31 All-cause mortality HR 0.68 (95% CI 0.57, 0.82) p< Kaplan-Meier estimate. HR, hazard ratio

32 CV death, MI and stroke Patients with event/analysed Empagliflozin Placebo HR (95% CI) p-value 3-point MACE 490/ / (0.74, 0.99)* CV death 172/ / (0.49, 0.77) < Non-fatal MI 213/ / (0.70, 1.09) Non-fatal stroke 150/ / (0.92, 1.67) Favours empagliflozin Favours placebo Cox regression analysis. MACE, Major Adverse Cardiovascular Event; HR, hazard ratio; CV, cardiovascular; MI, myocardial infarction *95.02% CI

33 Number needed to treat (NNT) to prevent one death across landmark trials in patients with high CV risk Simvastatin 1 for 5.4 years High CV risk 5% diabetes, 26% hypertension Pre-statin era Ramipril 2 for 5 years High CV risk 38% diabetes, 46% hypertension Pre-ACEi/ARB era <29% statin Empagliflozin for 3 years T2DM with high CV risk 92% hypertension >80% ACEi/ARB >75% statin S investigator. Lancet 1994; 344: , 2. HOPE investigator N Engl J Med 2000;342:145-53,

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35 LEADER 13% reduction in MACE (primary outcome) Number needed to treat to prevent 1 event in 3 years = 66

36 LEADER death from cardiovascular causes

37 LEADER death from any cause

38 LEADER no difference in non fatal stroke

39 N Engl J Med 2016; 375:

40 Empaglaflozin slows progression of renal disease

41 Empaglaflozin slows rate of decline of GFR

42 Efficacy of Monotherapy in Type 2 Diabetes Agent A1C reduction (%) Insulin secretagogues Metformin TZDs Alpha glucosidase inhibitors Colesevelam Bromocriptine DPP-IV Inhibitors GLP-1 receptor analogues SGLT2 Inhibitors 1.0

43 AntihyperglycemicMedications: Effect on Weight Weight gain Weight neutral Weight loss Sulfonylureas Metformin GLP 1 RA Insulin DPP4 inhibitors SGLT2 I Alpha glucosidase inhibitors Bromocriptine Colesevelam

44 SO LET S LOOK AT SOME TREATMENT OPTIONS TRYING TO MINIMIZE WEIGHT GAIN AND HYPOGLYCEMIA

45 Dapagliflozin in Combination with Metformin: Change in HbA1c Over 24 Weeks Adjusted mean change from baseline in HbA1c (%) Week 24 (LOCF)* change from baseline, adjusted mean (95% CI) DAPA 10 mg + MET (n=202) DAPA 10 mg (n=216) MET (n=203) P value < ; DAPA 10 mg non-inferior to MET (-1.59, -1.29) (-1.59, -1.31) (-2.13, -1.83) Study week (-1.53, -1.18) (-1.36, -1.02) (-2.23, -1.88) DAPA 5 mg + MET (n=185) DAPA 5 mg (n=196) MET (n=195) Study week Henry R, et al. Int J Clin Pract. 2012;66(5): LOCF, last observation carried forward.

46 Dapagliflozin Versus Sulfonylurea as Add-on to Metformin: Change in HbA1c Over 208 Weeks HbA1c durability was better with dapagliflozin than glipizide The rise from weeks was less compared with glipizide, giving a significant difference between treatments at 208 weeks *Data are adjusted mean change from baseline ±95% CI derived from a longitudinal repeated-measures mixed model. Del Prato S, et al. ADA 2013; poster 62-LB.

47 Canagliflozin vs Sitagliptin add on to MTF and SU: Change in A1c G Scherthaner et al. Diabetes Care 2013; epub April 5

48 Canagliflozin vs. Sitagliptin: Change in Weight G Scherthaner et al. Diabetes Care 2013; epub April 5

49 Once Weekly Exenatide Plus Dapaglaflozin Daily Alone or in Combination as Add on to Metformin Mean HbA1c at baseline 9.3% Frias JP et al. Lancet Diabetes Endocrinol published online September

50 Weight Change GLP1 RA or SGLT2 I or Both Dapaglaflozin Exenatide weekly Both drugs Frias JP et al. Lancet Diabetes Endocrinol published online September

51 Basal Insulin Analogues are they any safer? Glargine U-100 Biosimilar glargine Levemir Glargine U-300 (Toujeo) Degludec (Tresiba)

52 U300 Glargine (Toujeo) vs U100 Glargine: Equally effective but with higher dose of U300 Glargine Yki Jarvinen H et al. Diabetes, Obesity and Metabolism 17: , 2015.

53 U300 Glargine is associated with less hypoglycemia than U100 glargine Yki Jarvinen H et al. Diabetes, Obesity and Metabolism 17: , 2015.

54 SWITCH 2 Reduced risk of hypoglycaemia with insulin degludec vs. insulin glargine U100 in a T2D population on basal insulin: A randomised double-blind crossover trial Clinical trial.gov identifier: NCT Wysham et al. Presented at the American Diabetes Association, 76th Annual Scientific Sessions, June 2016, New Orleans, LA, USA

55 Objective Primary objective: To confirm superiority of IDeg OD compared with IGlar U100 OD in the rates of severe or BG-confirmed symptomatic hypoglycaemia during the maintenance period (after 16 weeks of treatment) Secondary objectives: To confirm superiority of IDeg OD compared with IGlar U100 OD in the rates of severe or BG-confirmed symptomatic nocturnal hypoglycaemia and the proportion of patients with severe hypoglycaemia during the maintenance period BG, blood glucose; IDeg, insulin degludec; IGlar U100, insulin glargine U100; OD, once daily Wysham et al. Presented at the American Diabetes Association, 76th Annual Scientific Sessions, June 2016, New Orleans, LA, USA

56 Hypoglycaemia risk: inclusion criteria Eligible patients had at least one of the following hypoglycaemia risk factors: 1 severe hypoglycaemic episodes within the last year Moderate chronic renal failure (egfr ml/min/1.73 m 2 ) Hypoglycaemic symptoms unawareness Exposure to insulin >5 years Episode of hypoglycaemia episode within the last 12 weeks (according to ADA definition: 70 mg/dl [ 3.9 mmol/l]) ADA, American Diabetes Association; egfr, estimated glomerular filtration rate Wysham et al. Presented at the American Diabetes Association, 76th Annual Scientific Sessions, June 2016, New Orleans, LA, USA

57 HbA 1c over time HbA1c (%) End of treatment period 1 Non-inferiority is confirmed CROSSOVER End of treatment period 2 Non-inferiority is confirmed 7.06% 7.08% 6.98% Titration period 1 Maintenance period 1 Titration period 2 Maintenance period 2 Treatment period 1 Treatment period 2 IDeg IGlar U % HbA1c (%) Mean SEM SEM, standard error of the mean Wysham et al. Presented at the American Diabetes Association, 76th Annual Scientific Sessions, June 2016, New Orleans, LA, USA

58 Severe or BG-confirmed symptomatic hypoglycaemia: maintenance period Severe or BG-confirmed symptomatic hypoglycaemia (cumulative events per patient) Time since start of treatment period (weeks) 30% lower rate with IDeg (p<0.0001) Proportion (% patients) IDeg Rate (episodes/ 100 PYE) IDeg IGlar U100 Proportion (% patients) IGlar U100 Rate (episodes/ 100 PYE) 22.5% % SAS Comparisons: Estimates adjusted for multiple covariates PYE, patient-year of exposure; SAS, safety analysis set Wysham et al. Presented at the American Diabetes Association, 76th Annual Scientific Sessions, June 2016, New Orleans, LA, USA

59 Severe or BG-confirmed symptomatic nocturnal hypoglycaemia: maintenance period Severe or BG-confirmed symptomatic nocturnal hypoglycaemia (cumulative events per patient) % lower rate with IDeg (p<0.0001) Proportion (% patients) IDeg IGlar Rate (episodes/ 100 PYE) IDeg U100 Proportion (% patients) IGlar U100 Rate (episodes/ 100 PYE) 9.7% % 93.6 Time since start of treatment period (weeks) SAS Comparisons: Estimates adjusted for multiple covariates Wysham et al. Presented at the American Diabetes Association, 76th Annual Scientific Sessions, June 2016, New Orleans, LA, USA

60 Uptitrating basal insulin vs basal insulin and GLP1 RA

61 L DUAL V Study design Subjects with T2D uncontrolled on basal insulin (N=557) Inclusion criteria T2D Metformin + IGlarU100 (20 50 units) HbA 1c7 10% Age 18 years BMI 40 kg/m 2 Screening IDegLira + metformin (n=278) IGlar U100 + metformin (n=279) Randomisation 1:1 (open label) End of trial Follow-up 2 0 Week Titration algorithm: IDegLira and IGlar U100 Mean fasting PG mmol/l mg/dl <4.0 < >5.0 >90 Dose change dose steps or U IDegLira Starting dose: 16 dose steps Maximum dose: 50 dose steps IGlar U100 Starting dose: Pre-trial dose Maximum dose: None BMI, body mass index; HbA 1c, glycated haemoglobin; IDegLira, insulin degludec/liraglutide; IGlar U100, insulin glargine U100; PG, plasma glucose; T2D, type 2 diabetes; U, units Lingvay I et al. JAMA 2016;315:

62 HbA1c (%) DUAL V HbA 1c over time Time (weeks) 7.1% 6.6% IDegLira (n=278) IGlar U100 (n=279) Mean observed values with error bars (standard error mean) based on FAS and LOCF imputed data. Treatment difference is estimated from an ANCOVA analysis while values are observed LOCF. ADA/EASD HbA 1c target <7.0%; AACE HbA 1c target 6.5% AACE, American Association of Clinical Endocrinologists; ADA, American Diabetes Association; ANCOVA, analysis of covariance; EASD, European Association for the Study of Diabetes; EOT, end of trial; FAS, full analysis set; HbA 1c, glycated haemoglobin; IDegLira, insulin degludec/liraglutide; IGlar U100, insulin glargine U100; LOCF, last observation carried forward Lingvay I et al. JAMA 2016;315: EOT HbA 1c 1.13% 1.81% Treatment difference 0.59% p<0.001 L

63 Number of episodes per subject DUAL V Confirmed hypoglycaemia IDegLira IGlar U100 HbA 1c at week % 7.1% IDegLira (n=278) IGlar U100 (n=279) Treatment ratio: 0.43 p<0.001 L Time (weeks) Mean cumulative function based on SAS Treatment ratio is estimated from a negative binomial model based on FAS FAS, full analysis set; HbA 1c, glycated haemoglobin; IDegLira, insulin degludec/liraglutide; IGlar U100, insulin glargine U100; SAS, safety analysis set Lingvay I et al. JAMA 2016;315:

64 Change in body weight (kg) DUAL V Change in body weight over time Time (weeks) IDegLira (n=278) IGlar U100 (n=279) +1.8 kg 1.4 kg L Treatment difference: 3.2 kg; p<0.001 Mean observed values with error bars (standard error mean) based on FAS and LOCF imputed data Treatment difference is estimated from an ANCOVA analysis while values are observed LOCF ANCOVA, analysis of covariance; FAS, full analysis set; IDegLira, insulin degludec/liraglutide; IGlar U100, insulin glargine U100; LOCF, last observation carried forward Lingvay I et al. JAMA 2016;315:

65 Dose (units) DUAL V Daily insulin dose U 41 U IDegLira (n=278) IGlar U100 (n=279) Treatment difference: U p<0.001 L Time (weeks) There was no maximum dose for IGlar U100 Mean observed values with error bars (standard error mean) based on SAS and LOCF imputed data Treatment difference is estimated from an ANCOVA analysis ANCOVA, analysis of covariance; IDegLira, insulin degludec/liraglutide; IGlar U100, insulin glargine U100; LOCF, last observation carried forward; SAS, safety analysis set; U, units Lingvay I et al. JAMA 2016;315:

66 L Percentage of subjects achieving targets (%) DUAL V Subjects achieving treatment targets 100 OR: 3.45 [2.36;5.05]; p<0.001 OR: 3.24 [2.24;4.70]; p<0.001 OR: 5.53 [3.49;8.77]; p< Values are based on FAS and LOCF imputed data Odds ratios (IDegLira/IGlar U100) are from a logistic regression model FAS, full analysis set; HbA 1c, glycated haemoglobin; IDegLira, insulin degludec/liraglutide; IGlar U100, insulin glargine U100; LOCF, last observation carried forward; OR, odds ratio Lingvay I et al. JAMA 2016;315: HbA 1c <7% HbA 1c <7% without hypoglycaemia HbA 1c <7% without hypoglycaemia and weight gain IDegLira (n=278) IGlar U100 (n=279)

67 Adding GLP1 RA to basal insulin: impact on glucose control, weight and hypoglycemia

68 Change in HbA1c level (%) Adding exenatide to insulin glargine HbA 1c Insulin glargine + placebo Insulin glargine + exenatide * * Week Change in body weight (kg) Weight Hypoglycaemia rate (events/patient-year) Week Minor hypoglycaemia p=0.49 There was one episode of major hypoglycaemia in the placebo group *p<0.001 Minor hypoglycaemic episodes (plasma glucose, 3.1 mmol/l) were self-treated Major episodes required third-party assistance, irrespective of plasma glucose levels Comparisons: Estimates based on ANCOVA with multiple covariates Buse et al. Ann Intern Med 2011;154: Exenatide + insulin glargine Placebo + insulin glargine

69 GLP1 RA as effective at Lispro with meals when added to basal insulin Diamant M et al. Diabetes Care 2014; 37:

70 GLP1 RA vs Prandial Insulin added to Basal Insulin Weight loss vs Weight Gain Diamant M et al. Diabetes Care 2014; 37:

71 Comparison of Medications that Could be Added to Metformin SU TZD DPP-IV GLP-1 Efficacy High High Moderate High Tolerability High Moderate High Moderate Side effects Hypoglycemia Weight gain Edema/CHF Fractures Weight gain Rare pancreatitis Risk of hypoglycemia Moderate Low Low Low GI Rare pancreatitis CV Safety Neutral Neutral Neutral Lowers MACE (Leader) Cost Low Low - Mod High High Adapted from Abrahamson, MJ. Diabetes Care 2015; 38:

72 Comparison of Medications that Could be Added to Metformin SGLT 2 Inhibitor Cycloset Colesevalam AGI Insulin Efficacy High Moderate Moderate Moderate Highest Tolerability High Moderate High Low - Mod High Side effects Risk of hypoglycemia CV Safety UTI Vag yeast infn Orthostasis Bone fractures? Nausea Vomiting Nil GI: Flatulence Diarrhea Low Low Low Low High Empa lowers MACE Hypoglycemia Weight gain Lower MACE Neutral Lower MACE Neutral Cost High Mod Mod Low - Mod Variable Adapted from Abrahamson, MJ. Diabetes Care 2015; 38:

73 Cost notwithstanding, is there an alternate approach to treating type 2 diabetes? Lifestyle Lifestyle + Metformin Lifestyle + Metformin + GLP-1 analogue or DPP-IV inhibitor or SGLT2 inhibitor Lifestyle + Metformin + GLP-1 analogue/ DPP-IV inhibitor/ SGLT2 inhibitor + Insulin Bariatric surgery?

74 Summary Multiple pathophysiological abnormalities exist in the person with type 2 DM There are medications that can address most of these pathophysiologic derangements Newer data is emerging on the CV safety of drugs used to treat type 2 DM More drugs are now available that have weight benefit and are not associated with hypoglycemia Use of newer basal insulin analogues, and combinations of basal analogues with GLP1 receptor agonists reduces rates of hypoglycemia GLP1 RA can be used as effectively as prandial insulin with fewer adverse events