Empagliflozin and Cardiovascular and Kidney Outcomes across KDIGO Risk Categories: Post Hoc Analysis of a Randomized, Double-Blind, Placebo-Controlled, Multinational Trial

BACKGROUND AND OBJECTIVES: In the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG Outcome), empagliflozin, in addition to standard of care, significantly reduced risk of cardiovascular death by 38%, hospitalization for heart failure by 35%, and incident or worsening nephropathy by 39% compared with placebo in patients with type 2 diabetes and established cardiovascular disease. Using EMPA-REG Outcome data, we assessed whether the Kidney Disease Improving Global Outcomes (KDIGO) CKD classification had an influence on the treatment effect of empagliflozin. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Patients with type 2 diabetes, established atherosclerotic cardiovascular disease, and eGFR≥30 ml/min per 1.73 m2 at screening were randomized to receive empagliflozin 10 mg, empagliflozin 25 mg, or placebo once daily in addition to standard of care. Post hoc, we analyzed cardiovascular and kidney outcomes, and safety, using the two-dimensional KDIGO classification framework. RESULTS: Of 6952 patients with baseline eGFR and urinary albumin-creatinine ratio values, 47%, 29%, 15%, and 8% were classified into low, moderately increased, high, and very high KDIGO risk categories, respectively. Empagliflozin showed consistent risk reductions across KDIGO categories for cardiovascular outcomes (P values for treatment by subgroup interactions ranged from 0.26 to 0.85) and kidney outcomes (P values for treatment by subgroup interactions ranged from 0.16 to 0.60). In all KDIGO risk categories, placebo and empagliflozin had similar adverse event rates, the notable exception being genital infection events, which were more common with empagliflozin for each category. CONCLUSIONS: The observed effects of empagliflozin versus placebo on cardiovascular and kidney outcomes were consistent across the KDIGO risk categories, indicating that the effect of treatment benefit of empagliflozin was unaffected by baseline CKD status. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: EMPA-REG OUTCOME, NCT01131676

Increased Urinary Angiotensin-Converting Enzyme 2 in Renal Transplant Patients with Diabetes

Angiotensin-converting enzyme 2 (ACE2) is expressed in the kidney and may be a renoprotective enzyme, since it converts angiotensin (Ang) II to Ang-(1-7). ACE2 has been detected in urine from patients with chronic kidney disease. We measured urinary ACE2 activity and protein levels in renal transplant patients (age 54 yrs, 65% male, 38% diabetes, n = 100) and healthy controls (age 45 yrs, 26% male, n = 50), and determined factors associated with elevated urinary ACE2 in the patients. Urine from transplant subjects was also assayed for ACE mRNA and protein. No subjects were taking inhibitors of the renin-angiotensin system. Urinary ACE2 levels were significantly higher in transplant patients compared to controls (p = 0.003 for ACE2 activity, and p≤0.001 for ACE2 protein by ELISA or western analysis). Transplant patients with diabetes mellitus had significantly increased urinary ACE2 activity and protein levels compared to non-diabetics (p<0.001), while ACE2 mRNA levels did not differ. Urinary ACE activity and protein were significantly increased in diabetic transplant subjects, while ACE mRNA levels did not differ from non-diabetic subjects. After adjusting for confounding variables, diabetes was significantly associated with urinary ACE2 activity (p = 0.003) and protein levels (p<0.001), while female gender was associated with urinary mRNA levels for both ACE2 and ACE. These data indicate that urinary ACE2 is increased in renal transplant recipients with diabetes, possibly due to increased shedding from tubular cells. Urinary ACE2 could be a marker of renal renin-angiotensin system activation in these patients

Genetic Variants of the Renin Angiotensin System: Effects on Atherosclerosis in Experimental Models and Humans

The renin angiotensin system (RAS) has profound effects on atherosclerosis development in animal models, which is partially complimented by evidence in the human disease. Although angiotensin II was considered to be the principal effector of the RAS, a broader array of bioactive angiotensin peptides have been identified that have increased the scope of enzymes and receptors in the RAS. Genetic interruption of the synthesis of these peptides has not been extensively performed in experimental or human studies. A few studies demonstrate that interruption of a component of the angiotensin peptide synthesis pathway reduces experimental lesion formation. The evidence in human studies has not been consistent. Conversely, genetic manipulation of the RAS receptors has demonstrated that AT1a receptors are profoundly involved in experimental atherosclerosis. Few studies have reported links of genetic variants of angiotensin II receptors to human atherosclerotic diseases. Further genetic studies are needed to define the role of RAS in atherosclerosis

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation