Modeling Inter-individual Differences to Address Pain of Sickle cell disease (MIDAS) - Project Abstract: Sickle Cell Disease (SCD) is the most common human monogenic disorder, with 100,000 people with SCD in the US predominantly of African descent. On average, individuals with SCD have 3 hospitalizations for severe acute vaso-occlusive crisis (VOC) pain annually at costs of over 1 billion US dollars, but 10% of individuals with SCD account for 50% of hospitalizations. Increased need for hospitalization is associated with high opioid use and risk of early death. Burden of pain worsens, and chronic daily pain develops as adolescents with SCD age to become young adults (AYAs). Given these inter-individual or between-person differences in pain, our objective is the rigorous, longitudinal collection of whole-person health data to enable innovative computational modeling & risk stratification of Black AYAs with SCD for high-impact pain. We took the novel approach of clinically phenotyping AYAs with difficult-to-control pain & adverse drug effects (ADEs) for associations with pharmacogenetic variants. We found that 84% had CYP2D6 variants linked to altered metabolic phenotypes. We also found that the COMT genotype corresponds to pain severity & chronic pain risk in a higher proportion of Black Americans with acute low back pain. The prevalence of significant pain variants is 7-39%, but we were the first to report that both allele & genotype prevalence of 12 pain variants are significantly different in AYAs with SCD compared to the general African American population. Prevalence among the 10% of Black AYAs with SCD who are frequently hospitalized for pain is unknown. We were also among the first to conduct spatial analysis to find US counties with more racial minorities have higher severe pain prevalence, and rurality is associated with higher opioid prescribing. Given the US opioid epidemic and heightened concerns for fatal opioid effects, doctors are now resistant to prescribe opioids. This places Black AYAs with SCD at increased risk for severe pain, pain burden, ADEs, and both pain-related and racial bias. We will pursue 3 specific aims: 1. Collect pain variants and longitudinal NIH HEAL common data elements, social determinants of health, and other biopsychosocial data that Black AYAs with SCD and their caregivers identify as important to their lived experience and use of pain management therapies, including opioids. We will collect data from 1000 Black AYAs with SCD from 3 US regions. 2. Train & test a computational model of frequent hospitalization for acute VOC pain and evaluate pharmacologic treatment efficacy. 3. Train & test predictive models to determine the age of chronic daily pain onset for Black AYAs with SCD. Our focus on Black AYAs with SCD is responsive to their significant inter-individual differences in pain burden, high healthcare utilization, health disparities, and racial pain treatment bias. Our approach has great potential to help elucidate underlying biopsychosocial mechanisms of pain occurring with pain management therapies, including opioid use. Identifying modifiable variables will accelerate the development of evidence-based solutions toward precision pain management for AYAs with SCD and pain.
