PROJECT SUMMARY / ABSTRACT Cardiovascular disease (CVD) kills 1 in 3 individuals and affects >2 in 3 individuals diagnosed with type 2 diabetes (T2D). Despite optimization of available therapies, CVD remains the leading cause of mortality in T2D, highlighting the considerable burden of residual risk. Achieving further reduction in CVD morbidity and mortality in people with T2D requires advancing promising candidate mediators of residual risk. The metabolite α-aminoadipic acid (2-AAA) predicts the development of both T2D and atherosclerosis, independent of other known risk factors. This may represent a novel independent risk mechanism for the development of CVD, particularly among individuals with T2D. Our overarching hypothesis is that 2-AAA is an independent mediator of CVD risk among individuals with T2D. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, both intensive glucose-lowering therapy, and intensive lipid management failed to attenuate CVD risk in individuals with T2D, and indeed showed evidence of increased risk. We hypothesize this was, in part, due to residual risk factors, including 2-AAA. We propose an analysis of 2-AAA in existing plasma samples from N=1,757 participants of the ACCORD study lipid treatment arms, with the following aims: 1) Define the effects of lipid- and glucose-lowering therapies on plasma 2-AAA, and address whether plasma 2-AAA changes in response to lipid-targeted therapy or intensive glycemic management. 2) Address the hypothesis that plasma 2-AAA is a CVD risk mechanism among individuals who experienced events despite optimal therapy. Successful completion of the aims will determine whether 2-AAA levels are impacted by lipid and glycemic management in T2D and establish whether elevated 2-AAA associates with CVD risk. This will provide important information on the utility of 2-AAA as a biomarker of risk and plausibility as a novel therapeutic target, allowing us to refine specific hypotheses to be probed in future studies. These aims represent novel and important questions and use existing NHLBI-supported sample and data resources to add considerable scientific value and address a key knowledge gap.