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      Patient Characteristics are not Associated with Clinically Important Differential Response to Dapagliflozin: a Staged Analysis of Phase 3 Data

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          Abstract

          Introduction

          This study aimed to determine if data mining methodologies could identify reproducible predictors of dapagliflozin-specific treatment response in the phase 3 clinical program dataset.

          Methods

          Baseline and early treatment response variables were selected and data mining used to identify/rank all variables associated with reduction in glycated hemoglobin (HbA 1c) at week 26. Generalized linear modeling was then employed using an independent dataset to identify which (if any) variables were predictive of dapagliflozin-specific treatment response as compared with treatment response in the study’s control arm. The most parsimonious (i.e., simplest) model was validated by meta-analysis of nine other trials. This staged approach was used to minimize risk of type I errors.

          Results

          From the large dataset, 22 variables were selected for model generation as potentially predictive for dapagliflozin-specific reduction in HbA 1c. Although baseline HbA 1c was the variable most strongly associated with reduction in HbA 1c at study end (i.e., the best prognostic variable), baseline fasting plasma glucose (FPG) was the only predictive dapagliflozin-specific variable in the model. Placebo-adjusted treatment effect of dapagliflozin plus metformin vs. metformin alone for change in HbA 1c from baseline was −0.65% at the average baseline FPG of 192.3 mg/dL (10.7 mmol/L). This response changed by −0.32% for every SD [57.2 mg/dL (3.2 mmol/L)] increase in baseline FPG. Effect of baseline FPG was confirmed in the meta-analysis of nine studies, but the magnitude was smaller. No other variable was independently predictive of a dapagliflozin-specific reduction in HbA 1c.

          Conclusions

          This methodology successfully identified a reproducible baseline predictor of differential response to dapagliflozin. Although baseline FPG was shown to be a predictor, the effect size was not of sufficient magnitude to suggest clinical usefulness in identifying patients who would uniquely benefit from dapagliflozin treatment. The findings do support potential benefit for dapagliflozin treatment that is consistent with current recommended use.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s13300-014-0090-y) contains supplementary material, which is available to authorized users.

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          Most cited references13

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          Effects of dapagliflozin on body weight, total fat mass, and regional adipose tissue distribution in patients with type 2 diabetes mellitus with inadequate glycemic control on metformin.

          Dapagliflozin, a selective sodium-glucose cotransporter 2 (SGLT2) inhibitor, reduces hyperglycemia in patients with type 2 diabetes mellitus (T2DM) by increasing urinary glucose excretion, and weight loss is a consistent associated finding. Our objectives were to confirm weight loss with dapagliflozin and establish through body composition measurements whether weight loss is accounted for by changes in fat or fluid components. This was a 24-wk, international, multicenter, randomized, parallel-group, double-blind, placebo-controlled study with ongoing 78-wk site- and patient-blinded extension period at 40 sites in five countries. Included were 182 patients with T2DM (mean values: women 63.3 and men 58.6 yr of age; hemoglobin A1c 7.17%, body mass index 31.9 kg/m2, and body weight 91.5 kg) inadequately controlled on metformin. Dapagliflozin 10 mg/d or placebo was added to open-label metformin for 24 wk. Primary endpoint was total body weight (TBW) change from baseline at wk 24. Key secondary endpoints were waist circumference and dual-energy x-ray absorptiometry total-body fat mass (FM) changes from baseline at wk 24, and patient proportion achieving body weight reduction of at least 5% at wk 24. In a subset of patients, magnetic resonance assessment of visceral adipose tissue (VAT) and sc adipose tissue (SAT) volume and hepatic lipid content were also evaluated. At wk 24, placebo-corrected changes with dapagliflozin were as follows: TBW, -2.08 kg [95% confidence interval (CI)=-2.84 to -1.31; P<0.0001]; waist circumference, -1.52 cm (95% CI=-2.74 to -0.31; P=0.0143); FM, -1.48 kg (95% CI=-2.22 to -0.74; P=0.0001); proportion of patients achieving weight reduction of at least 5%, +26.2% (95% CI=15.5 to 36.7; P<0.0001); VAT, -258.4 cm3 (95% CI=-448.1 to -68.6; nominal P=0.0084); SAT, -184.9 cm3 (95% CI=-359.7 to -10.1; nominal P=0.0385). In the dapagliflozin vs. placebo groups, respectively, serious adverse events were reported in 6.6 vs. 1.1%; events suggestive of vulvovaginitis, balanitis, and related genital infection in 3.3 vs. 0%; and lower urinary tract infections in 6.6 vs. 2.2%. Dapagliflozin reduces TBW, predominantly by reducing FM, VAT and SAT in T2DM inadequately controlled with metformin.
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            Sodium-Glucose Cotransport Inhibition With Dapagliflozin in Type 2 Diabetes

            OBJECTIVE Dapagliflozin, a novel inhibitor of renal sodium-glucose cotransporter 2, allows an insulin-independent approach to improve type 2 diabetes hyperglycemia. In this multiple-dose study we evaluated the safety and efficacy of dapagliflozin in type 2 diabetic patients. RESEARCH DESIGN AND METHODS Type 2 diabetic patients were randomly assigned to one of five dapagliflozin doses, metformin XR, or placebo for 12 weeks. The primary objective was to compare mean change from baseline in A1C. Other objectives included comparison of changes in fasting plasma glucose (FPG), weight, adverse events, and laboratory measurements. RESULTS After 12 weeks, dapagliflozin induced moderate glucosuria (52–85 g urinary glucose/day) and demonstrated significant glycemic improvements versus placebo (ΔA1C −0.55 to −0.90% and ΔFPG −16 to −31 mg/dl). Weight loss change versus placebo was −1.3 to −2.0 kg. There was no change in renal function. Serum uric acid decreased, serum magnesium increased, serum phosphate increased at higher doses, and dose-related 24-h urine volume and hematocrit increased, all of small magnitude. Treatment-emergent adverse events were similar across all groups. CONCLUSIONS Dapagliflozin improved hyperglycemia and facilitates weight loss in type 2 diabetic patients by inducing controlled glucosuria with urinary loss of ∼200–300 kcal/day. Dapagliflozin treatment demonstrated no persistent, clinically significant osmolarity, volume, or renal status changes.
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              Effects of Dapagliflozin, an SGLT2 Inhibitor, on HbA1c, Body Weight, and Hypoglycemia Risk in Patients With Type 2 Diabetes Inadequately Controlled on Pioglitazone Monotherapy

              OBJECTIVE To examine the safety and efficacy of dapagliflozin, a sodium-glucose cotransporter-2 inhibitor, added on to pioglitazone in type 2 diabetes inadequately controlled on pioglitazone. RESEARCH DESIGN AND METHODS Treatment-naive patients or those receiving metformin, sulfonylurea, or thiazolidinedione entered a 10-week pioglitazone dose-optimization period with only pioglitazone. They were then randomized, along with patients previously receiving pioglitazone ≥30 mg, to 48 weeks of double-blind dapagliflozin 5 (n = 141) or 10 mg (n = 140) or placebo (n = 139) every day plus open-label pioglitazone. The primary objective compared HbA1c change from baseline with dapagliflozin plus pioglitazone versus placebo plus pioglitazone at week 24. Primary analysis was based on ANCOVA model using last observation carried forward; all remaining analyses used repeated-measures analysis. RESULTS At week 24, the mean reduction from baseline in HbA1c was −0.42% for placebo versus −0.82 and −0.97% for dapagliflozin 5 and 10 mg groups, respectively (P = 0.0007 and P < 0.0001 versus placebo). Patients receiving pioglitazone alone had greater weight gain (3 kg) than those receiving dapagliflozin plus pioglitazone (0.7–1.4 kg) at week 48. Through 48 weeks: hypoglycemia was rare; more events suggestive of genital infection were reported with dapagliflozin (8.6–9.2%) than placebo (2.9%); events suggestive of urinary tract infection showed no clear drug effect (5.0–8.5% for dapagliflozin and 7.9% for placebo); dapagliflozin plus pioglitazone groups had less edema (2.1–4.3%) compared with placebo plus pioglitazone (6.5%); and congestive heart failure and fractures were rare. CONCLUSIONS In patients with type 2 diabetes inadequately controlled on pioglitazone, the addition of dapagliflozin further reduced HbA1c levels and mitigated the pioglitazone-related weight gain without increasing hypoglycemia risk.
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                Author and article information

                Contributors
                Anne-marie.boothman@astrazeneca.com
                Journal
                Diabetes Ther
                Diabetes Ther
                Diabetes Therapy
                Springer Healthcare (Heidelberg )
                1869-6953
                1869-6961
                12 December 2014
                12 December 2014
                December 2014
                : 5
                : 2
                : 471-482
                Affiliations
                [ ]AstraZeneca, Alderley Park, Macclesfield, Cheshire, SK10 4TF UK
                [ ]AstraZeneca, Wilmington, DE USA
                [ ]AstraZeneca, Gaithersburg, MD USA
                [ ]AstraZeneca, Mölndal, Sweden
                [ ]Christie NHS Foundation Trust, Clinical Outcomes Unit, Manchester, UK
                [ ]UCB SA, Slough, UK
                Article
                90
                10.1007/s13300-014-0090-y
                4269640
                25502227
                1bbd3e99-62b2-4753-ba01-b6431fca9a74
                © The Author(s) 2014
                History
                : 7 September 2014
                Categories
                Original Research
                Custom metadata
                © Springer Healthcare 2014

                Endocrinology & Diabetes
                dapagliflozin,data mining,fasting plasma glucose,machine learning,meta-analysis,predictor,prognostic factors,response profiling,type 2 diabetes mellitus

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