Personalized medicine is a concept in which medical care is individualized to a patient based on their unique characteristics, including comorbidities, demographics, genetics, and microbiome. After major surgery, some patients are at increased risk of opioid dependence. By identifying unique genetic and microbiome markers, clinicians may potentially identify individual risk factors for opioid dependence. By identifying these high risk patients early-on, personalized interventions may be applied to these patients in order to reduce the incidence of opioid-dependence.
Using Genomics and Gut Microbiome Data to Predict Postoperative Opioid Use in Patients Undergoing Lower Extremity Joint Replacement
The primary objective of this study is to identify associations with genetic variants, gut microbiome, and metabolomics (i.e. exosome profiling) with postoperative opioid use in surgical patients. Patients will be recruited preoperatively who underwent lower extremity joint replacement. The following tests will be performed: 1) genome-wide single nucleotide polymorphisms and structural variation, with a particular focus on the following genes: COMT, BDNF, SCN11a, OPRM1, ACBC1, CYPD26, CYP34A, ANKK1, OPRD1, OPRK1,NGFB, UGT2B7, FFAR2, FFAR3, GABRG2, SLCO1B1, DRD4; 2) longitudinal gut microbiome sampling; and 3) exosome profiling - blood will be collecting for RNAseq and plasma for metabolomics and extracellular vesicle characterization with ultimate impact on in vitro cell function. These genes were selected because they have been shown to be associated with opioid use, opioid metabolism, and pain. Furthermore, subjects will fill out surveys preoperatively, including: pain catastrophizing scale, brief pain inventory, PROMIS-29, and fibromyalgia survey criteria. Other data collected will include body mass index, age, sex, comorbidities, lifestyle habits, and medication use.
The hypothesis is that there will be clinically significant associations with patient genetics, microbiome, exosome profiles with their postoperative opioid use. Such findings will help personalize pain interventions for high-risk patients undergoing knee or hip arthroplasty in order to help improve postoperative pain control and reduce incidence of chronic opioid use.
Specific Aim #1. To validate and identify pharmacogenomic associations with acute postoperative opioid use (during the first 48 postoperative hours) and chronic opioid use (at >3-4 months after surgery) in patients who underwent lower extremity joint replacement.
Specific Aim #2. To identify gut microbiome and metabolomics associations with acute postoperative opioid use (during the first 48 postoperative hours) and chronic opioid use (at >3-4 months after surgery) in patients underwent lower extremity joint replacement.
Specific Aim #3. To identify blood RNAseq patterns, plasma metabolic markers, extracellular vesicles, and impact of plasma on in vitro cell metabolism associated with acute postoperative opioid use (during the first 48 postoperative hours) and chronic opioid use (at >3-4 months after surgery) in patients underwent lower extremity joint replacement.
Machine learning approaches will be used to combine all data to improve prediction of the primary outcomes.
