Does Creating Person-specific Precision Thresholds Enhance the Ability of a Single Bone Density Measure to Predict Fractures in Minority Women? - Project Summary/Abstract Conventional thresholds of physiological parameters for clinical diagnosis have become a root cause of racial/ethnic disparities in healthcare. These thresholds were established decades ago using limited samples of Caucasian subjects and simplistic statistics without considering genetic effects. Consequently, these thresholds have no capacity to account for the normal variability intrinsic in race/ethnicity, genes, and environments, which can lead to errors in disease diagnosis and risk assessment when applied to racial/ethnic minorities. As the minority population grows and becomes even more diverse, continuing to use Caucasian-based, “one-size-fits- all” thresholds creates the risk of exacerbating racial/ethnic disparities in healthcare. Therefore, an effective alternative must be developed to address this issue. Hence, we propose creating Person-specific Precision Thresholds (PPTs), which are designed to account for normal variability in genetic and biological makeup, variation in race/ethnicity, sex, age, and other characteristics. PPTs will eliminate the root cause of healthcare disparities caused by conventional thresholds for diagnosis and risk assessment. The innovative precision of PPTs will fundamentally shift current research paradigms regarding clinical thresholds from a single cutoff point for everyone to precise, person-specific thresholds matched to individual genetics and phenotypic characteristics. PPTs will effectively improve disease diagnosis and risk assessment while reducing healthcare disparities. The first PPT iteration will focus on women's bone mineral density (BMD), as osteoporosis primarily affects women the most. The existing T-score threshold of BMD has become controversial because many women who sustain fragility fractures have a “normal” BMD value, as defined by the commonly employed T-score method. Because genetic factors contribute to 50-85% of BMD variation, genetic variants must be considered when creating precision thresholds. With feasibility tested by pilot studies, we hypothesize that PPTs will enhance the ability of a single BMD measure to predict fractures in minority women compared to conventional T-score and prior model-based methods, which lack consideration of genetics. We will pursue the following three specific aims: (1) Develop a modeling framework for PPTs of BMD using novel machine-learning techniques; (2) Calibrate PPTs of BMD using clinical data; (3) Validate the PPTs of BMD using genomic cohort data. In addition, we will create a publicly available, cloud-enabled PPT application for community investigators. This proposed approach is innovative because it integrates individual genetics into clinical precision thresholds and uses novel machine- learning techniques to create PPTs. By replacing the traditional one-size-fits-all threshold, the innovative PPTs will shift existing research paradigms regarding how clinical thresholds have been developed and implemented. The knowledge gained from this research can be utilized to create many other PPTs to improve the diagnosis and prevention of various diseases and illnesses. These next-generation PPTs will help significantly improve patient outcomes, leading to better health, a higher quality of life, and improved health equity.
