Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT04974463 |
Other study ID # |
210132 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
July 19, 2021 |
Est. completion date |
December 31, 2023 |
Study information
Verified date |
July 2023 |
Source |
University of California, San Diego |
Contact |
Rodney A Gabriel, MD, MAS |
Phone |
858-663-7747 |
Email |
ragabriel[@]health.ucsd.edu |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
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.
Description:
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.