Postmortem toxicology findings from the Camden Opioid Research Initiative

The United States continues to be impacted by decades of an opioid misuse epidemic, worsened by the COVID-19 pandemic and by the growing prevalence of highly potent synthetic opioids (HPSO) such as fentanyl. In instances of a toxicity event, first-response administration of reversal medications such as naloxone can be insufficient to fully counteract the effects of HPSO, particularly when there is co-occurring substance use. In an effort to characterize and study this multi-faceted problem, the Camden Opioid Research Initiative (CORI) has been formed. The CORI study has collected and analyzed post-mortem toxicology data from 42 cases of decedents who expired from opioid-related toxicity in the South New Jersey region to characterize substance use profiles. Co-occurring substance use, whether by intent or through possible contamination of the illicit opioid supply, is pervasive among deaths due to opioid toxicity, and evidence of medication-assisted treatment is scarce. Nearly all (98%) of the toxicology cases show the presence of the HPSO, fentanyl, and very few (7%) results detected evidence of medication-assisted treatment for opioid use disorder, such as buprenorphine or methadone, at the time of death. The opioid toxicity reversal drug, naloxone, was detected in 19% of cases, but 100% of cases expressed one or more stimulants, and sedatives including xylazine were detected in 48% of cases. These results showing complex substance use profiles indicate that efforts at mitigating the opioid misuse epidemic must address the complications presented by co-occurring stimulant and other substance use, and reduce barriers to and stigmas of seeking effective medication-assisted treatments.

Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants

Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.

rs11670527 Upstream of ZNF264 Associated with Body Mass Index in the Coriell Personalized Medicine Collaborative

INTRODUCTION

the effects of obesity on health are a concern for the military as they affect the fitness to serve of active service members, increase costs to the Military Health System, and reduce quality of life for veterans and beneficiaries. Although obesity can be influenced by behavioral and environmental factors, it has also been shown to be associated with genetic risk factors that are not fully understood.

MATERIALS AND METHODS

we performed a genome-wide association study of 5,251 participants in the Coriell Personalized Medicine Collaborative, which includes 2,111 Air Force participants. We applied a generalized linear model, using principal component analysis to account for population structure, and analyzed single-variant associations with body mass index (BMI) as a continuous variable, using a Bonferroni-corrected P-value threshold to account for multiplicity.

RESULTS

we identified one genome-wide significant locus, rs11670527, upstream of the ZNF264 gene on chromosome 19, associated with BMI.

CONCLUSIONS

the finding of an association between rs11670527 and BMI adds to the growing body of literature characterizing the complex genetics of obesity. These efforts may eventually inform personalized interventions aimed at achieving and maintaining healthy weight.