Translating Lp(a) biology to clinical applications

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This proposal intends to generate novel, widely available reagents and methods to Improve the measurement of Lp(a) levels in order to improve patient care. Elevated Lp(a) levels are highly prevalent and generally accepted as an independent, genetic and likely causal risk factor for CVD. Although Lp(a) levels are measured in clinical laboratories, it is one of the most difficult laboratory analytes to measure accurately because of its unique structure of multiple, identical kringle repeats. Significant technological and methodological gaps exist that limit the accuracy of Lp(a) measurements at the both the clinical laboratory and clinical level. The major limitation is the lack of widely available, globally standardized, diagnostic methods, and specifically monoclonal antibodies that bind only once to Lp(a) that can be used to accurately quantitate Lp(a). This lack of standardization may have adverse clinical sequalae by mis-assigning Lp(a) risk thresholds or targets for therapy. Due to the limitations noted above, the FDA has yet to approve any Lp(a) assay in molar concentration. The NHLBI Working Group on Lp(a) organized 2 workshops in 2017 and 2019 and recommended constructive collaboration among all stakeholders to ensure standardization and harmonization of Lp(a) assays and to develop assays that are isoform independent using monoclonal antibodies that are specific to one site on apo(a). To address these gaps in the care of patients with elevated Lp(a), we propose the following specific aims: 1- to develop and validate an isoform independent Lp(a) assay with a recently generated isoform-independent, monoclonal antibody; 2- to generate a second, isoform- independent, monoclonal antibody to facilitate the development of a first, isoform-independent non-ELISA methodology adaptable to hospitals and commercial laboratories. We will collaborate with the CDC/IFCC to validate this new ELISA at the clinical laboratory interface; and 3- to apply these novel assays to clinical datasets for translatability to human disease, including studies of racial/ethnic differences, antisense Lp(a)- lowering therapy and in CVD outcome studies.