Grilli, Christopher

Abstract Rationale Idiopathic Pulmonary Fibrosis (IPF) is characterized by chronic progressive pulmonary fibrosis and high mortality. Genetic markers, summarized into a polygenic risk score (PRS), associate with IPF in well-phenotyped research cohorts. Objectives To evaluate the performance of the PRS using real-world data from routinely captured electronic healthcare records. Methods We conducted an observational study evaluating the association of a PRS for IPF with electronic healthcare record diagnosis of IPF as well as lung transplant-free survival in four independent cohorts; the Mass General Brigham Biobank (MGBB), Mayo Clinic Biobank (MCBB), Mayo Clinic Tapestry Cohort (Tapestry), and U.K. Biobank (UKBB). We used multivariable logistic regression and multivariable Cox proportional hazards models adjusting for age, gender, and principal components of ancestry. The cohorts then underwent fixed and random effects meta-analysis. Measurements and Main Results Of 37,709; 44,195; 43,202; and 447,422 participants from MGBB, MCBB, Tapestry and UKBB respectively, 1,015 (2.7%) 2,879 (6.5%), 1,310 (3.0%), and 2,742 (0.6%) participants had an IPF diagnosis. Meta-analysis demonstrated a high-risk PRS associated with IPF diagnosis, OR 2.88 (95%CI 2.41-3.44) compared to all other individuals. A high-risk PRS also associated with the composite endpoint of mortality or lung transplant among those with an IPF diagnosis, HR 1.23(95%CI 1.11-1.35) compared to all other individuals. Conclusions A PRS can identify those at risk for an IPF diagnosis and mortality in biobank-scale data, which may have implications for clinical decisions. Further work is necessary to evaluate the utility of adding genetics in clinical settings.

Abstract INTRODUCTION Anti—amyloid‐β (Aβ) therapies are reshaping Alzheimer's disease (AD) management. Understanding changes in real‐world patterns of diagnostic testing and infusion chair usage is essential for optimizing access to care. METHODS Retrospective analysis of Mayo Clinic enterprise electronic health records (Jan 2019–Mar 2025) assessed trends in AD‐relevant brain imaging, fluid biomarkers, apolipoprotein E (APOE) testing, and lecanemab infusions. Rates of amyloid‐beta (Aβ) positivity by sex and age, APOE genotype frequencies, and lecanemab treatment initiation and discontinuation were evaluated. RESULTS Following national insurance coverage changes, lecanemab infusions grew by 110 infusions per quarter to 605 in Q1 2025. Aβ positron emission tomography scans increased (+22/quarter), cerebrospinal fluid biomarker orders declined (−25/quarter), and plasma p‐tau 217 orders rapidly increased (+238/quarter). Females were more likely to be Aβ positive ( p < 0.006). APOE ‐ε4 homozygotes were less likely to initiate lecanemab (HR = 0.11, p < 0.001). DISCUSSION The adoption of anti‐Aβ therapies coincided with a rapid shift in diagnostic workflows.

Abstract Background Pharmacogenomics is an emerging and affordable tool that may improve postoperative pain control. One challenge to successful pain control is the large interindividual variability among analgesics in their efficacy and adverse drug events. Whether preoperative pharmacogenomic testing is worthwhile for patients undergoing TKA is unclear. Questions/purposes (1) Are the results of preoperative pharmacogenetic testing associated with lower postoperative pain scores as measured by the Overall Benefit of Analgesic Score (OBAS)? (2) Do the results of preoperative pharmacogenomic testing lead to less total opioids given? (3) Do the results of preoperative pharmacogenomic testing lead to changes in opioid prescribing patterns? Methods Participants of this randomized trial were enrolled from September 2018 through December 2021 if they were aged 18 to 80 years and were undergoing primary TKA under general anesthesia. Patients were excluded if they had chronic kidney disease, a history of chronic pain or narcotic use before surgery, or if they were undergoing robotic surgery. Preoperatively, patients completed pharmacogenomic testing (RightMed, OneOME) and a questionnaire and were randomly assigned to the experimental group or control group. Of 99 patients screened, 23 were excluded, one before randomization; 11 allocated patients in each group did not receive their allocated interventions for reasons such as surgery canceled, patients ultimately undergoing spinal anesthesia, and change in surgery plan. Another four patients in each group were excluded from the analysis because they were missing an OBAS report. This left 30 patients for analysis in the control group and 38 patients in the experimental group. The control and experimental groups were similar in age, gender, and race. Pharmacogenomic test results for patients in the experimental group were reviewed before surgery by a pharmacist, who recommended perioperative medications to the clinical team. A pharmacist also assessed for clinically relevant drug-gene interactions and recommended drug and dose selection according to guidelines from the Clinical Pharmacogenomics Implementation Consortium for each patient enrolled in the study. Patients were unaware of their pharmacogenomic results. Pharmacogenomic test results for patients in the control group were not reviewed before surgery; instead, standard perioperative medications were administered in adherence to our institutional care pathways. The OBAS (maximum 28 points) was the primary outcome measure, recorded 24 hours postoperatively. A two-sample t-test was used to compare the mean OBAS between groups. Secondary measures were the mean 24-hour pain score, total morphine milligram equivalent, and frequency of opioid use. Postoperatively, patients were assessed for pain with a VAS (range 0 to 10). Opioid use was recorded preoperatively, intraoperatively, in the postanesthesia care unit, and 24 hours after discharge from the postanesthesia care unit. Changes in perioperative opioid use based on pharmacogenomic testing were recorded, as were changes in prescription patterns for postoperative pain control. Preoperative characteristics were also compared between patients with and without various phenotypes ascertained from pharmacogenomic test results. Results The mean OBAS did not differ between groups (mean ± SD 4.7 ± 3.7 in the control group versus 4.2 ± 2.8 in the experimental group, mean difference 0.5 [95% CI -1.1 to 2.1]; p = 0.55). Total opioids given did not differ between groups or at any single perioperative timepoint (preoperative, intraoperative, or postoperative). We found no difference in opioid prescribing pattern. After adjusting for multiple comparisons, no difference was observed between the treatment and control groups in tramadol use (41% versus 71%, proportion difference 0.29 [95% CI 0.05 to 0.53]; nominal p = 0.02; adjusted p > 0.99). Conclusion Routine use of pharmacogenomic testing for patients undergoing TKA did not lead to better pain control or decreased opioid consumption. Future studies might focus on at-risk populations, such as patients with chronic pain or those undergoing complex, painful surgical procedures, to test whether pharmacogenomic results might be beneficial in certain circumstances. Level of Evidence Level I, therapeutic study.

AbstractIntroductionAdverse reactions to pain medication and pain can delay discharge after outpatient knee arthroplasty (TKA). Pharmacogenomics is an emerging tool that might help reduce adverse events by tailoring medication use based on known genetic variations in the CYP genes determining drug metabolism. This study was undertaken to evaluate whether pre-operative pharmacogenomic testing could optimize peri-operative pain management in patients undergoing total knee arthroplasty (TKA).MethodsThis prospective, randomized study was performed in adults undergoing primary TKA. Patients in the experimental group underwent pre-operative pharmacogenomic evaluation and medication adjustments. Medications were not optimized for control patients. The Overall Benefit of Analgesic Score (OBAS) at 24 hours post-op was the primary outcome. Postoperative pain scores (VAS scale), total opioid use, time in recovery, and time to discharge were also compared.Results76 patients enrolled. 93% of patients had a poor-intermediate phenotype for at least one of four CYP genes. OBAS did not differ significantly between the two groups (4.2 control vs. 4.7 experimental; 95% CI −1.1 to 2.1) and was below the minimum clinically important difference of 2.0 points. Furthermore, there were no differences in any OBAS subscale including pain, satisfaction, or nausea. There were no differences in time in the recovery area or to discharge from hospital, or narcotic use.ConclusionsDespite many patients having a poor-intermediate phenotype for a CYP gene, pharmacogenomic testing prior to TKA and medication adjustment did not improve anesthesia or pain management outcomes, time to discharge or mean total opioid use after surgery.

e24114 Background: Pharmacogenomics (PGx) testing is used increasingly in cancer supportive care for drug and dose selection, and side effects prediction with antidepressants, antiemetics, and pain medications. Genetic variants in genes that encode for drug metabolizing enzymes (CYPs), transporters (SLC6A4) and drug receptors (HTR2A, OPRM1, etc) are well known for these medications. They contribute to inter-individual differences in medication safety and efficacy. We report on their frequency and effect on supportive care drugs in patients with solid tumors. Methods: Between 06/06/19 and 12/18/19, solid tumor cancer patients at Mayo Clinic Arizona were enrolled in a PGx prospective study. They were genotyped using a multi-gene panel, OneOme RightMed. The panel assessed 27 genes, including CYP2C9, CYP2C19, CYP2D6, CYP3A4, COMT, OPRM1, GRIK4, HTR2A, SLC6A4, associated with pain medications, antidepressants, and antiemetics. PGx pharmacist provided medication recommendations and dose adjustments based on PGx test results and concurrent medications. Results: 200 patients were swabbed and 196 patients were genotyped. The results were available within 5 days. All patients were also assessed for pheno-conversion (drug-drug-gene interactions) by a pharmacist. Recommendations were made based on predicted phenotype. Various solid tumor types were represented, including prostate (19.9%), colorectal (17.9%), melanoma (14.8%), and others (47.4%). Median age was 65 with 59.2% patients being male and 40.85% female. All had at least one actionable polymorphism related to these medications. The most significant findings were related to CYP2C19 and CYP2D6 genes. Of 196 patients, a total of 132 (67.3%) had other than normal CYP2D6 metabolizer phenotype and 112 (57.1%) had other than normal CYP2C19 metabolizer phenotype. Based on these genotypes, alternatives to pain medications were recommended for 37 patients, to ondansetron in 9 patients, and to anti-depressants in 51 patients. Conclusions: Genetic variants were commonly found that affect supportive care medications used in oncology practice and contribute to inter-individual differences associated with increased risk of adverse drug effects and reduced efficacy. PGx guided recommendation may help physicians in individualizing medication treatment outcomes for patients with different cancer types and with polypharmacy. PGx trained pharmacist can serve as a valuable asset in optimizing personalized medication management.