Updates from the ADA Annual Meeting. The LEADER and DEVOTE Trials

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1 18/09/ Updates from the ADA Annual Meeting The LEADER and DEVOTE Trials Richard Pratley, M.D. Samuel Crockett Chair in Diabetes Research Director, Florida Hospital Diabetes Institute Senior Scientist and Diabetes Program Head, Translational Research Institute for Metabolism and Diabetes Adjunct Professor, Sanford Burnham Medical Disovery Institute Orlando, Florida Disclosures Grant support received by Florida Hospital from: Lexicon Pharmaceuticals Ligand Pharmaceuticals, Inc. Lilly Merck Sanofi-Aventis US LLC Takeda Speaker fees received by Florida Hospital from: AstraZeneca Novo Nordisk Takeda Consultancy fees received by Florida Hospital from: Boehringer-Ingelheim GlaxoSmithKline Hanmi Pharmaceutical Co. Ltd AstraZeneca Janssen Scientific Affairs LLC Ligand Pharmaceuticals, Inc. Lilly Merck Pfizer Eisai, Inc. Takeda All honoraria directed toward a non-profit which supports education and research DEVOTE: trial design 7637 patients randomised Insulin degludec once daily (blinded vial) + Standard of care IGlar U100 once daily (blinded vial) + Standard of care Follow-up period Follow-up period Randomisation Interim analysis (150 MACE accrued) End of treatment (633 MACE accrued) 30 days Inclusion criteria: Type 2 diabetes Current treatment with 1 oral or injectable antidiabetic agent(s) HbA 1c 7.0% OR HbA 1c <7.0% and basal insulin treatment 20 U/day High CV risk profile: CV or chronic kidney disease and aged 50 OR risk factors for CV disease and aged 60 Trial characteristics Primary endpoint Secondary endpoints Randomised, double blinded, active controlled, treat-to-target, event driven Time from randomisation to first occurrence of a 3-point MACE: cardiovascular death*, non-fatal myocardial infarction* or non-fatal stroke* Rate of severe hypoglycaemic episodes* Incidence of severe hypoglycaemic episodes* *Confirmed by the EAC; cardiovascular death includes undetermined cause of death; severe defined as an episode requiring the assistance of another person to actively administer carbohydrate, glucagon, or take other corrective actions. BG concentrations may not be available during an event, but neurological recovery following the return of BG to normal is considered sufficient evidence that the event was induced by a low BG concentration BG, blood glucose; CV, cardiovascular; EAC, Event Adjudication Committee; MACE, major adverse cardiovascular event; U, unit Presented at ADA Annual Scientific Meeting, 2017

2 Patients with an event (%) 18/09/ Study drugs Type of insulin Insulin degludec New generation long-acting basal insulin analogue IGlar U100 First generation basal insulin analogue Mode of protraction Forms soluble multihexamers Precipitates as microcrystals Half life ~25 hours ~12 hours Day-to-day variability (AUC GIR,0 24h) Coefficient of variation 20% Coefficient of variation 80% AUC GIR, area under the curve for glucose infusion rate; IGlar U100, insulin glargine U100 Insulin glargine image data on file; Jonassen et al. Pharm Res. 2012;29: ; Heise et al. Expert Opin Drug Metab Toxicol 2015;11: ; Heise et al. Diabetes Obes Metab 2012;14: Baseline characteristics Parameter Insulin degludec IGlar U100 Total number of patients, n Age, years* Sex, Male, % Duration of diabetes, years* CV risk profile Established CV or CKD and age 50 years, % With CV risk factors and age 60 years, % BMI, kg/m 2 * HbA 1c, %* FPG, mmol/l* [mg/dl]* 9.4 [169.8] 9.6 [173.5] *Mean value. HbA 1c and FPG measured at randomisation. All other parameters measured at the screening visit BMI, body mass index; CKD, chronic kidney disease; FPG, fasting plasma glucose Presented at ADA Annual Scientific Meeting, 2017 Time to first 3-point MACE HR: 0.91 [0.78; 1.06] 95% CI Non-inferiority confirmed p< Time to first EAC-confirmed event (months) Insulin degludec (N) IGlar U100 (N) IGlar U100 Insulin degludec 356 patients 325 patients Full analysis set; Cox regression analysis accounting for treatment. Analysis includes events between randomization date and follow-up date. Patients without an event are censored at the time of last contact (phone or visit) CI, confidence interval; N, number of patients at risk; PYO, patient-years of observation Presented at ADA Annual Scientific Meeting, 2017

3 Mean number of events/100 PYO HbA 1c (%) % 18/09/ point MACE, 4-point MACE and allcause death Hazard ratio [95% CI] 3-point MACE 0.91 [0.78; 1.06] CV death* 0.96 [0.76; 1.21] Non-fatal myocardial infarction 0.85 [0.68; 1.06] Non-fatal stroke 0.90 [0.65; 1.23] point MACE Hazard ratio [95% CI] 0.92 [0.80; Favours insulin degludec Favours IGlar U ] Unstable angina requiring hospitalisation 0.95 [0.68; 1.31] All-cause death 0.91 [0.76; 1.11] Insulin IGlar U100 degludec N % N % *CV death includes undetermined cause of death; 4-point MACE defined as cardiovascular death*, non-fatal myocardial infarction, non-fatal stroke or unstable angina requiring hospitalisation Presented at ADA Annual Scientific Meeting, 2017 Similar mean HbA 1c 9.0 Insulin 8.5 degludec IGlar U % % ET Months since randomisation Insulin degludec (N) IGlar U100 (N) HbA 1c (mmol/mol) Observed mean change from baseline at month 24 Insulin degludec IGlar U Post hoc ETD: 0.01% [-0.05; 0.07] 95% CI Full analysis set ET, end treatment visit; ETD, estimated treatment difference Pratley R. Presented at ADA Annual Scientific Meeting, 2017 Rates of severe hypoglycaemia Rate ratio: 0.60 [0.48; 0.76] 95% CI p<0.001 IGlar U100 Insulin degludec Time from randomisation (months) Insulin degludec (N=3818) IGlar U100 (N=3819) E R E R EAC-confirmed episodes Full analysis set; Mean number of confirmed severe hypoglycaemic episodes. The number of events is analysed using a negative binomial regression model using a log link and the logarithm of the observation time (100 years) as offset E, number of events; R, events per 100 patient-years of observation Pratley R. Presented at ADA Annual Scientific Meeting, 2017

4 Mean number of events/100 PYO 18/09/ Rates of nocturnal severe hypoglycaemia IGlar U100 5 Insulin degludec Rate ratio: [0.31; 0.73] 95% CI p< Time from randomisation (months) Insulin degludec (N=3818) IGlar U100 (N=3819) N % E R N % E R EAC-confirmed episodes Full analysis set; Nocturnal hypoglycaemia: EAC-confirmed severe hypoglycaemic episode with an investigator-reported onset between 00:01 and 05:59. Mean number of nocturnal EAC-confirmed severe hypoglycaemic episodes. The number of events is analysed using a negative binomial regression model using a log link and the logarithm of the observation time (100 years) as offset Presented at ADA Annual Scientific Meeting, 2017 DEVOTE Trial summary DEVOTE confirms that insulin degludec does not increase the risk of adverse cardiovascular outcomes compared with IGlar U100 Glycaemic control (insulin degludec vs. IGlar U100): End of treatment mean HbA 1c values 7.55% vs. 7.50% Change in FPG levels -2.2 mmol/l vs mmol/l The rate of severe hypoglycaemia was significantly reduced with insulin degludec versus IGlar U100 in DEVOTE: 40% rate reduction of severe hypoglycaemia 53% rate reduction of nocturnal severe hypoglycaemia No safety issues were identified with insulin degludec compared with IGlar U100 LEADER: Study design Placebo run-in Liraglutide mg OD + standard of care Placebo + standard of care Safety follow-up Safety follow-up 2 weeks Screening Randomization (1:1) Double-blind Minimum duration 3.5 years Maximum 5 years Minimum 611 primary events End of treatment 30 days Key inclusion criteria T2DM, HbA1c 7.0% Antidiabetic drug naïve; OADs and/or basal/premix insulin Age 50 years and established CV disease or chronic renal failure or Age 60 years and risk factors for CV disease CV: cardiovascular; DPP-4i, dipeptidyl peptidase-4 inhibitor; GLP-1RA: glucagon-like peptide-1 receptor agonist; HbA 1c: glycated hemoglobin; OAD: oral antidiabetic drug; OD: once daily; T2DM: type 2 diabetes mellitus. Marso SP et al. N Engl J Med 2016;375:

5 18/09/ Baseline characteristics (mean ± SD unless stated) Liraglutide (N=4668) Placebo (N=4672) Male sex, N (%) 3011 (64.5) 2992 (64.0) Age, years 64.2 ± ± 7.2 Diabetes duration, years 12.8 ± ± 8.1 HbA 1c, % 8.7 ± ± 1.5 BMI, kg/m ± ± 6.3 Body weight, kg 91.9 ± ± 20.8 Systolic blood pressure, mmhg ± ± 17.7 Diastolic blood pressure, mmhg 77.2 ± ± 10.1 Heart failure*, N (%) 835 (17.9) 832 (17.8) *Heart failure includes New York Heart Association class I, II, and III. BMI: body mass index; HbA 1c: glycated haemoglobin; SD: standard deviation Marso SP et al. N Engl J Med 2016;375: Primary outcome CV death, non-fatal myocardial infarction, or non-fatal stroke The primary composite outcome in the time-to-event analysis was the first occurrence of death from cardiovascular causes, non-fatal myocardial infarction, or non-fatal stroke. The cumulative incidences were estimated with the use of the Kaplan Meier method, and the hazard ratios with the use of the Cox proportional-hazard regression model. The data analyses are truncated at 54 months, because less than 10% of the patients had an observation time beyond 54 months. CI: confidence interval; CV: cardiovascular; HR: hazard ratio. Marso SP et al. N Engl J Med 2016;375: LEADER: Individual components of the primary endpoint Number of patients Hazard ratio (95% CI) p-value Liraglutide Placebo N % R N % R Primary endpoint 0.87 (0.78 ; 0.97) CV death 0.78 (0.66 ; 0.93) Non-fatal MI 0.88 (0.75 ; 1.03) Non-fatal stroke 0.89 (0.72 ; 1.11) Hazard ratio (95% CI) Favours liraglutide Favours placebo Hazard ratios and p-values were estimated with the use of a Cox proportional-hazards model with treatment as a covariate %, percentage of group; CI, confidence interval; CV, cardiovascular; MI, myocardial infarction; N, number of patients; R, incidence rate per 100 patient years of observation Marso SP et al. N Engl J Med 2016;375:

6 HbA 1c (%) HbA 1c (mmol/mol) Proportion of patients (%) 18/09/ Antihyperglycemic medication at baseline Liraglutide Placebo Metformin Sulfonylureas Alphaglucosidase inhibitors TZDs Glinides Insulin Marso et al. N Engl J Med 2016;375: MACE by insulin use at baseline Hazard Ratio Liraglutide Placebo (95% CI) N % N % FAS MACE primary analysis 0.87 (0.78; 0.97) Post-hoc analysis MACE - Insulin use at baseline (Y/N) Yes 0.88 (0.75; 1.03) No 0.86 (0.74; 1.01) Favours Liraglutide Favours Placebo FAS: full analysis set; N: number of patients; % proportion of patients Pratley R. Presented at ADA Annual Scientific Meeting, 2017 LEADER: HbA 1c changes over time Placebo Liraglutide ETD at month 36: -0.40% 95% CI ( ) p< Time from randomization (months) Number of patients at each visit Liraglutide Placebo Data are estimated mean values from randomization to month 48. CI: confidence interval; ETD: estimated treatment difference; HbA 1c: glycated hemoglobin. Marso et al. N Engl J Med 2016;375:311 22

7 Mean number of episodes per 1000 subjects 18/09/ Severe hypoglycemia over time Placebo Liraglutide Rate ratio: % CI: (0.51; 0.93) p= Time since randomization (months) Liraglutide Placebo Number of subjects with severe hypoglycemia (%) 114 (2.4) 153 (3.4) Full analysis set. Mean number of severe hypoglycemic episodes. Number of events analyzed using a negative binomial regression model using a log link and the logarithm of the observation time (100 years) as offset. Treatment, sex, region and antidiabetic therapy at baseline included as fixed effects and age at baseline included as covariates CI: confidence interval Pratley R. Presented at ADA Annual Scientific Meeting, 2017 MACE by occurrence of severe hypoglycemia Hazard ratio (95% CI) Liraglutide N % Placebo N % First MACE Without severe hypoglycemia 0.88 (0.78 ; 0.98) With severe hypoglycemia 0.85 (0.52 ; 1.39) Hazard ratio (95% CI) Favors liraglutide Favors placebo Full analysis set. Post-hoc analysis. With hypoglycemia is subjects with one/more severe hypoglycemic episodes (irrespective of the timing between the severe hypoglycemia and the event of interest); without hypoglycemia is subjects without severe hypoglycemic episodes. The hazard ratios are estimated in Cox regression for each of the events of interest with an interaction between hypoglycemic episode (with, without) and treatment %: proportion of subjects with events; CI: confidence interval; MACE: major adverse cardiovascular event; N: number of subjects with events Pratley R. Presented at ADA Annual Scientific Meeting, 2017 LEADER Summary Liraglutide reduced the risk of major CV events in patients with T2DM at high CV risk Both risk of first event and recurrent events The reduction in CV events with liraglutide appeared independent of: Baseline insulin or CV medication use Initiation of insulin or SU/TZD during the trial Experiencing an episode of severe hypoglycemia It appears unlikely that the CV risk reduction with liraglutide can be fully explained by the observed differences in HbA 1c, body weight, SBP and lipids CV: cardiovascular; HbA 1c: glycated hemoglobin; SBP: systolic blood pressure; SU: sulfonylurea; T2DM: type 2 diabetes mellitus

8 9/18/2017 The CANVAS Trial (CANigloflozin cardiovascular Assessment Study) NEJM 2017; 377: Cres P Miranda MD FACC FACP FSCAI Interventional and Preventive Cardiology Assistant Clinical Professor of Medicine University of Nevada School of Medicine Las Vegas, Nevada Disclosures Dr. Miranda has received honoraria from: Amarin Amgen Astra-Zeneca Boehringer-Ingelheim Janssen Kowa Lilly Pfizer Regeneron Sanofi Baseline Characteristics NEJM 2017; 377:

9 9/18/2017 CV Risk Factor Changes NEJM 2017; 377: Cardiovascular Endpoints NEJM 2017; 377: Adverse Events NEJM 2017; 377:

10 9/18/2017 Subgroup Analysis NEJM 2017; 377: Cardiorenal Endpoints NEJM 2017; 377: Adverse Events NEJM 2017; 377:

11 9/18/2017 Why it's Important to Automate Insulin Delivery and a Focus on the MiniMed 670G Jennifer Sherr, MD, PhD Associate Professor, Pediatrics Monday, September 18 th, 2017 Presenter Disclosures Advisory Board Consultant: Bigfoot Biomedical Advisory Board Consultant: Insulet Corporation Advisory Board Consultant: Eli Lilly Consultant: Medtronic Diabetes 2 Objectives 1. Recognize the rationale for closed-loop insulin delivery for optimal care of persons with T1D 2. Define hybrid closed loop insulin delivery 3. Discuss the findings from the pivotal trial of the Minimed 670G system 4. Describe real-world use of the system 1

12 9/18/2017 Why do we need a CL system? 1. Present methods of diabetes treatments are largely unsuccessful in helping patients meet glycemic targets 2. Intensive management schemes are very burdensome and negatively impact quality of life for those living with diabetes, and their loved ones Current State of T1D n = 16,057 ADA Target Miller Diabetes Care 2015 What is Life with Diabetes? The Burdens of 6 2

13 9/18/2017 Anatomy of a Closed-Loop System 7 The spectrum of CL technologies Usual Care: Full OL SAP Insulin Suspension: Threshold Predictive Insulin Delivery: Overnight Hybrid CL The goal: Full CL Bihormonal CL Automation System Complexity Hybrid Closed Loop Therapy Usual Care: Full OL SAP Insulin Suspension: Threshold Predictive Insulin Delivery: Overnight Hybrid CL Automation System Complexity 3

14 Glucose (mg/dl) 9/18/2017 Hybrid CL: Benefits of Pre-meal Bolus setpoint meals Closed Loop (N=8) Hybrid CL (N=9) A Noon 6P MidN 6A Noon 6P Mean Daytime Peak Post Meals Full CL Hybrid Weinzimer, Diabetes Care Medtronic HCL System Guardian 3 Sensor MARD 9.64% 3-4 calibrations/day 10.55% 2 calibrations/day 11 Study Overview Study Design Multicenter: 9 sites in US & 1 site in Israel Single-arm (no control group) Non-randomized Study Protocol RUN-IN PERIOD: Pump + CGM 2 weeks Participants N=124 Type 1 2 years A1C < 10% Ages years Pump therapy 6 months, with or without CGM STUDY PERIOD: Auto Mode 3 months Day 1: HCL Training Day 7: Auto Mode turned ON 4

15 9/18/2017 Auto mode basics Basal insulin delivers every 5 minutes Algorithm and current SG determine 5-minute basal dose Targets SG of 120 mg/dl Temp target of 150 mg/dl may be used for up to 12 hours Correction bolus initiated when fingerstick BG > 150 mg/dl Algorithm determines sensitivity factor Uses fingerstick value and targets 150 mg/dl Considers active insulin Meal bolus initiated by patient entering carbs Carb ratio and number of carbs determine amount Baseline Characteristics Adolescents (n=30) Adults (n=94) Sex 16F / 14M 53F / 41M Age (years) 16.5 ± ± 12.8 Weight (kg) BMI (kg/m 2 ) Duration of diabetes (years) 67.4 ± ± ± ± ± ± 12.4 Total daily dose of insulin (units/kg/day) 0.8 ± ± 0.2 A1C at screening (%) 7.7 ± ± 0.9 Reduced Glycemic Variability Median and Interquartile Range of SG Values / Day & Night All Patients Adults Adolescents Run-in Phase Study Phase Hybrid closed loop resulted in: Increased time in range Reduced time spent low and high Reduced variability Less post-prandial excursion Bergenstal JAMA

16 Auto Basal Auto Basal Auto Basal Auto Basal 9/18/ G Pivotal Trial Run-In Adolescents Study Phase Run-In Adults Study Phase Average Sensor Glucose 163±19 159±12 146±22 148±14 Time in Target (71-180) 61±11 67±8 69±12 74±8 Time <70mg/dL 4.3± ± ± ±2.1 HbA1c 7.7± ± ± ±0.6 HCL Utilization n/a 76% n/a 88% 16 Garg DTT 2017 Decreased A1c and reduced hypos Line endpoint shows A1C in each cohort Red line shows mean A1C Bubble size proportional to percent of nocturnal SG values 50 mg/dl 4 Nights in a Single week on Automode Data on file 6

17 Auto Basal Auto Basal Auto Basal Auto Basal 9/18/2017 Daily insulin requirements are unique each day Data on file Insulin requirements also vary throughout the day MANUAL MODE AUTO MODE Target Range: mg/dl Auto basal Max Deiiver y Who is an ideal candidate? Willing to wear a pump and sensor Testing BG 3+ times/day (minimum of 2 BG/day) Willing to learn how to calibrate the sensor Giving most meal boluses Willing/able to give control over to a system Not a micromanager or type A. If they are, understand that they will need to develop trust and let go. Willing to participate in close follow-up with required frequent downloads Willing to give the system time to optimize 7

18 9/18/2017 Case Study using Commercial Product 14 year old girl Living with type 1 diabetes> 10 years On insulin pump therapy x 10 years Sometimes missed her meal boluses On Dexcom CGM prior to MiniMed 670G system start Plays sports all 3 seasons and in theater productions Week 1: manual mode Week 2: Automode 8

19 9/18/2017 Glycemia improves quickly MANUAL MODE AUTO MODE Average SG already improved by 20 % Estimated A1C significantly reduced (8.5% to 7.2%) Patient wearing Guardian Sensor 3 well & spending 93% of time in Auto Model Week 2: Automode Improvement in the time in Range 44% 73% PATIENT SPENDING 66% MORE TIME IN RANGE IN AUTO MODE MANUAL MODE AUTO MODE 9

20 9/18/2017 Real World Use of the System Pivotal Trial Data Time in target mg/dl Manual Mode Auto Mode 66.7% 72.2% Time in Auto Mode(%)* N/A 87.2% Sensor Wear* % Time Below 50 mg/dl 1.0% 0.6% Time Below 70 mg/dl 5.9% 3.3% Time Above 180 mg/dl 27.4% 24.5% Time Above 300 mg/dl 2.3% 1.7% A1C 7.4% 6.9% Mean SG ± SD 150 ± ± 14 12,389 patient days of data, 123 patients (three-month study) Time in target mg/dl Real World Data* Manual Mode Auto Mode 63.09% 75.58% Time in Auto Mode (%) N/A 92.9% Sensor Wear % Time Below 50 mg/dl 0.57% 0.31% Time Below 70 mg/dl 3.32% 2.23% Time Above 180mg/dL 33.59% 22.19% Time Above 300 mg/dl 2.86% 0.84% Est. A1C 7.22% 6.84% Mean SG ± SD 161 ± ± 47 >24, 000 patient days of data, 730 patients (on-going weekly surveillance) Bergenstal RM, et al. JAMA. 2016;316(13): *Data on file, unpublished from Medtronic Take Home Messages The vast majority of those living with T1D do not meet prescribed glycemic targets despite increased use of both pumps and sensors in clinical practice. Closed loop insulin delivery has been shown to increase time in target and improve control, especially in the overnight period. The first iterations of closed loop technology will be a hybrid approach, requiring the user to bolus for meals. The ability to integrate hybrid closed loop technology into our daily practice is here. We are in the midst of a technological revolution. 29 Thank you 30 10

21 8/24/2017 Renal Effects of Diabetes GEORGE L. BAKRIS, MD, F.A.S.N, F.A.S.H. PROFESSOR OF MEDICINE DIRECTOR, ASH COMPREHENSIVE HYPERTENSION CENTER THE UNIVERSITY OF CHICAGO MEDICINE CHICAGO, IL Key Facts about Diabetes Effect on the Kidney Most common cause of renal failure in the Western World About 30% of people with diabetes will develop renal failure and has a genetic predisposition To assess kidney function decline must perform check of serum creatinine annually along with spot albumin:creatinine ratio Presence of microalbuminuria is NOT indicative of diabetic nephropathy but rather inflammation. Levels > 300 mg/d equal nephropathy. Average rate of kidney function decline is <1 ml/min/yr. In diabetes it ranges from 3-8 ml/min/yr. Composite Ranking for Relative Risks by glomerular filtration rate (GFR) and Albuminuria (Kidney Disease: Improving Global Outcomes (KDIGO) Levey AS et.al. Kidney Int 2010; doi: /ki

22 Number of patients (millions) 8/24/2017 Projected Growth in CKD Stage 5 Prevalence million (60% diabetic) million 0.7 million Year Gilbertson D et al. Presented at the 2003 ASN annual meeting. Available from: Studies With Primary Renal Endpoints That Show Differences in Outcome: min=2.5 year F/U Diabetes Baseline GFR Captopril Trial, N Engl J Med, Hannadouche et.al B Med J, Bakris et.al Kidney Int., Bakris et.al Hypertension, IDNT, N Engl J Med, RENAAL, N Engl J Med, ALTITUDE, N Engl J Med, VA NEPHRON D, N Engl J Med, * Signifies GFR measured using iothalamate or iohexol Relationship Between Achieved BP and Decline in Kidney Function from Primary Renal Endpoint Trials Nondiabetes MDRD. N Engl J Med AIPRI. N Engl J Med REIN. Lancet AASK. JAMA Hou FF, et al. N Engl J Med Parsa A et.al. NEJM 2013 Diabetes Captopril Trial. NEJM Hannadouche T, et al. BMJ Bakris G, et al. Kidney Int Bakris G, et al. Hypertension IDNT. NEJM RENAAL. NEJM ALTITUDE NEJM Normal decline in GFR Update from Kalaitzidis R and Bakris GL In: Handbook of Chronic Kidney Disease Daugirdas J (Ed.)

23 mg/day 8/24/2017 New Concepts Microalbuminuria (MAU) is NOT synonymous with presence of kidney disease Treatment with an ACE inhibitor or ARB is not indicated in normotensive diabetics even if MAU is present. If >300 mg/day albuminuria ACEi or ARB must be part of BP lowering plan and goal BP should be <130/80 mmhg De Boer I et.al. Diabetes Care 2017; 40: Indicates continuous variable for CV/CKD risk Higher CV Risk and Presence of CKD and Vascular Dysfunction Inflammation; CV Risk and Vascular Dysfunction Normal Microalbuminuria (High Albuminuria) Molitch M and Bakris GL Diabetes Care 2014 Macroalbuminuria (Very High Albuminuria) Goal BP and Initial Therapy in Diabetic Kidney Disease or to Reduce CV /Renal Progression Risk? Goal BP Group Initial Therapy (mmhg) ADA (2017) <140/90 &<130/80-if tolerated ACE Inhibitor/ARB* KDIGO/KDOQI (NKF) (2012) <140/90 ACE Inhibitor/ARB 2014 Expert Panel <140/90 ACE Inhibitor/ARB* KDOQI (NKF) (2004) <130/80 ACE Inhibitor/ARB* JNC 7 (2003) <130/80 ACE Inhibitor/ARB* Am. Diabetes Assoc (2003) <130/80 ACE Inhibitor/ARB* Canadian HTN Soc. (2002) <130/80 ACE Inhibitor/ARB* Am. Diabetes Assoc (2002) <130/80 ACE Inhibitor/ARB* Natl. Kidney Foundation (2000) <130/80 ACE Inhibitor* British HTN Soc. (1999) <140/80 ACE Inhibitor WHO/ISH (1999) <130/85 ACE Inhibitor * Indicates JNC use VI with (1997) diuretic <130/85 ACE Inhibitor 9 3

24 8/24/2017 Renal Outcomes From Empa-Reg Wanner C et al. N Engl J Med DOI: /NEJMoa A egfr (according to CKD-EPI formula) over 192 weeks in all treated patients Rate of GFR decline in subgroup egfr < ml/min/year Recent analyses in those with egfr <60 ml/min/1.73m 2 showed Decline slowed to 1.83 ml/min/1.73m 2 Wanner C et al. N Engl J Med DOI: /NEJMoa Effects of canagliflozin on CV and renal outcome-cont. Neal B et.al. N Eng J Med

25 8/24/2017 Ongoing Phase 3 CV/Renal Outcome Trials and New Approved Agents to Help in Management TRIALS FIDELIO-DN(Finerenone-MRA inhibitor) SONAR-(Atratsantan-ETA1 receptor blocker) CREDENCE-( Canagliflozin-SGLT2 antagonist Summary CKD Progression has slowed from an average of 5-7 ml/min/year in the 1990 s to an average of 2-3 ml/min/year in 2015 clinical trials. Three ongoing trials with different agents added to standard of care will determine if the rate of decline in kidney function can be normalized. No data to support advantage on CKD outcome in elderly without proteinuria. 5