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OBJECTIVE To evaluate the impact of once-weekly exenatide (EQW) on microvascular and cardiovascular (CV) outcomes by baseline renal function in the Exenatide Study of Cardiovascular Event Lowering (EXSCEL). RESEARCH DESIGN AND METHODS Least squares mean difference (LSMD) in estimated glomerular filtration rate (eGFR) from baseline between the EQW and placebo groups was calculated for 13,844 participants. Cox regression models were used to estimate effects by group on incident macroalbuminuria, retinopathy, and major adverse CV events (MACE). Interval-censored time-to-event models estimated effects on renal composite 1 (40% eGFR decline, renal replacement, or renal death) and renal composite 2 (composite 1 variables plus macroalbuminuria). RESULTS EQW did not change eGFR significantly (LSMD 0.21 mL/min/1.73 m2 [95% CI 20.27 to 0.70]). Macroalbuminuria occurred in 2.2% of patients in the EQW group and in 2.5% of those in the placebo group (hazard ratio [HR] 0.87 [95% CI 0.70-1.07]). Neither renal composite was reduced with EQW in unadjusted analyses, but renal composite 2 was reduced after adjustment (HR 0.85 [95% CI 0.74-0.98]). Retinopathy rates did not differ by treatment group or in the HbA1c-lowering or prior retinopathy subgroups. CV outcomes in those with eGFR

Although over 60 loci for type 2 diabetes (T2D) have been identified, there still remains a large genetic component to be clarified. To explore unidentified loci for T2D, we performed a genome-wide association study (GWAS) of 6 209 637 single-nucleotide polymorphisms (SNPs), which were directly genotyped or imputed using East Asian references from the 1000 Genomes Project (June 2011 release) in 5976 Japanese patients with T2D and 20 829 nondiabetic individuals. Nineteen unreported loci were selected and taken forward to follow-up analyses. Combined discovery and follow-up analyses (30 392 cases and 34 814 controls) identified three new loci with genome-wide significance, which were MIR129-LEP [rs791595; risk allele = A; risk allele frequency (RAF) = 0.080; P = 2.55 × 10(-13); odds ratio (OR) = 1.17], GPSM1 [rs11787792; risk allele = A; RAF = 0.874; P = 1.74 × 10(-10); OR = 1.15] and SLC16A13 (rs312457; risk allele = G; RAF = 0.078; P = 7.69 × 10(-13); OR = 1.20). This study demonstrates that GWASs based on the imputation of genotypes using modern reference haplotypes such as that from the 1000 Genomes Project data can assist in identification of new loci for common diseases.