Influenza infection is associated with myocardial infarction (MI), suggesting that respiratory viral infection may induce biologic pathways that contribute to MI. We tested the hypotheses that 1) a ...validated blood gene expression signature of respiratory viral infection (viral GES) was associated with MI and 2) respiratory viral exposure changes levels of a validated platelet gene expression signature (platelet GES) of platelet function in response to aspirin that is associated with MI.
A previously defined viral GES was projected into blood RNA data from 594 patients undergoing elective cardiac catheterization and used to classify patients as having evidence of viral infection or not and tested for association with acute MI using logistic regression. A previously defined platelet GES was projected into blood RNA data from 81 healthy subjects before and after exposure to four respiratory viruses: Respiratory Syncytial Virus (RSV) (n=20), Human Rhinovirus (HRV) (n=20), Influenza A virus subtype H1N1 (H1N1) (n=24), Influenza A Virus subtype H3N2 (H3N2) (n=17). We tested for the change in platelet GES with viral exposure using linear mixed-effects regression and by symptom status.
In the catheterization cohort, 32 patients had evidence of viral infection based upon the viral GES, of which 25% (8/32) had MI versus 12.2% (69/567) among those without evidence of viral infection (OR 2.3; CI 1.03-5.5, p=0.04). In the infection cohorts, only H1N1 exposure increased platelet GES over time (time course p-value = 1e-04).
A viral GES of non-specific, respiratory viral infection was associated with acute MI; 18% of the top 49 genes in the viral GES are involved with hemostasis and/or platelet aggregation. Separately, H1N1 exposure, but not exposure to other respiratory viruses, increased a platelet GES previously shown to be associated with MI. Together, these results highlight specific genes and pathways that link viral infection, platelet activation, and MI especially in the case of H1N1 influenza infection.
Introduction: Emergency departments (ED) use many medications with a range of therapeutic efficacy and potential significant side effects, and many medications have dosage adjustment recommendations ...based on the patient’s specific genotype. How frequently medications with such pharmaco-genetic recommendations are used in United States (US) EDs has not been studied.
Methods: We conducted a cross-sectional analysis of the 2010–2015 National Hospital Ambulatory Medical Care Survey (NHAMCS). We reported the proportion of ED visits in which at least one medication with Clinical Pharmacogenetics Implementation Consortium (CPIC) recommendation of Level A or B evidence was ordered. Secondary comparisons included distributions and 95% confidence intervals of age, gender, race/ethnicity, ED disposition, geographical region, immediacy, and insurance status between all ED visits and those involving a CPIC medication.
Results: From 165,155 entries representing 805,726,000 US ED visits in the 2010–2015 NHAMCS, 148,243,000 ED visits (18.4%) led to orders of CPIC medications. The most common CPIC medication was tramadol (6.3%). Visits involving CPIC medications had higher proportions of patients who were female, had private insurance and self-pay, and were discharged from the ED. They also involved lower proportions of patients with Medicare and Medicaid.
Conclusion: Almost one fifth of US ED visits involve a medication with a pharmacogenetic recommendation that may impact the efficacy and toxicity for individual patients. While direct application of genotyping is still in development, it is important for emergency care providers to understand and support this technology given its potential to improve individualized, patient- centered care.
We applied implementation science frameworks to identify barriers and facilitators to veterans’ acceptance of pharmacogenomic testing (PGx), which was made available as a part of clinical care at 25 ...VA medical centers. We conducted 30 min interviews with veterans who accepted (n = 14), declined (n = 9), or were contemplating (n = 8) PGx testing. Six team members coded one transcript from each participant group to develop the codebook and finalize definitions. Three team members coded the remaining 28 transcripts and met regularly with the larger team to reach a consensus. The coders generated a matrix of implementation constructs by testing status to identify the similarities and differences between accepters, decliners, and contemplators. All groups understood the PGx testing procedures and possible benefits. In the decision-making, accepters prioritized the potential health benefits of PGx testing, such as reducing side effects or the number of medications. In contrast, decliners prioritized the possibilities of data breach or the negative impact on healthcare insurance or Veterans Affairs benefits. Contemplators desired to speak to a provider to learn more before making a decision. Efforts to improve the clarity of data security and the impact on benefits may improve veterans’ abilities to make more informed decisions about whether to undergo PGx testing.
Module-based analysis (MBA) aims to evaluate the effect of a group of biological elements sharing common features, such as SNPs in the same gene or metabolites in the same pathways, and has become an ...attractive alternative to traditional single bio-element approaches. Because bio-elements regulate and interact with each other as part of network, incorporating network structure information can more precisely model the biological effects, enhance the ability to detect true associations, and facilitate our understanding of the underlying biological mechanisms. However, most MBA methods ignore the network structure information, which depicts the interaction and regulation relationship among basic functional units in biology system. We construct the connectivity kernel and the topology kernel to capture the relationship among bio-elements in a module, and use a kernel machine framework to evaluate the joint effect of bio-elements. Our proposed kernel machine approach directly incorporates network structure so to enhance the study efficiency; it can assess interactions among modules, account covariates, and is computational efficient. Through simulation studies and real data application, we demonstrate that the proposed network-based methods can have markedly better power than the approaches ignoring network information under a range of scenarios.
Cardiovascular disease and its sequelae are major causes of global mortality, and better methods are needed to identify patients at risk for future cardiovascular events. Gene expression analysis can ...inform on the molecular underpinnings of risk factors for cardiovascular events. Smoking and aspirin have known opposing effects on platelet reactivity and MACE, however their effects on each other and on MACE are not well described.
We measured peripheral blood gene expression levels of ITGA2B, which is upregulated by aspirin and correlates with platelet reactivity on aspirin, and a 5 gene validated smoking gene expression score (sGES) where higher expression correlates with smoking status, in participants from the previously reported PREDICT trial (NCT 00500617). The primary outcome was a composite of death, myocardial infarction, and stroke/TIA (MACE). We tested whether selected genes were associated with MACE risk using logistic regression.
Gene expression levels were determined in 1581 subjects (50.5% female, mean age 60.66 +/-11.46, 18% self-reported smokers); 3.5% of subjects experienced MACE over 12 months follow-up. Elevated sGES and ITGA2B expression were each associated with MACE (odds ratios OR =1.16 95% CI 1.10-1.31 and 1.42 95% CI 1.00-1.97, respectively; p < 0.05). ITGA2B expression was inversely associated with self-reported smoking status and the sGES (p < 0.001). A logistic regression model combining sGES and ITGA2B showed better performance (AIC = 474.9) in classifying MACE subjects than either alone (AIC = 479.1, 478.2 respectively).
Gene expression levels associated with smoking and aspirin are independently predictive of an increased risk of cardiovascular events.
Specific selective serotonin reuptake inhibitors (SSRIs) metabolism is strongly influenced by two pharmacogenes, CYP2D6 and CYP2C19. However, the effectiveness of prospectively using pharmacogenetic ...variants to select or dose SSRIs for depression is uncertain in routine clinical practice. The objective of this prospective, multicenter, pragmatic randomized controlled trial is to determine the effectiveness of genotype‐guided selection and dosing of antidepressants on control of depression in participants who are 8 years or older with ≥3 months of depressive symptoms who require new or revised therapy. Those randomized to the intervention arm undergo pharmacogenetic testing at baseline and receive a pharmacy consult and/or automated clinical decision support intervention based on an actionable phenotype, while those randomized to the control arm have pharmacogenetic testing at the end of 6‐months. In both groups, depression and drug tolerability outcomes are assessed at baseline, 1 month, 3 months (primary), and 6 months. The primary end point is defined by change in Patient‐Reported Outcomes Measurement Information System (PROMIS) Depression score assessed at 3 months versus baseline. Secondary end points include change inpatient health questionnaire (PHQ‐8) measure of depression severity, remission rates defined by PROMIS score < 16, medication adherence, and medication side effects. The primary analysis will compare the PROMIS score difference between trial arms among those with an actionable CYP2D6 or CYP2C19 genetic result or a CYP2D6 drug–drug interaction. The trial has completed accrual of 1461 participants, of which 562 were found to have an actionable phenotype to date, and follow‐up will be complete in April of 2024.
Opioid prescribing for postoperative pain management is challenging because of inter‐patient variability in opioid response and concern about opioid addiction. Tramadol, hydrocodone, and codeine ...depend on the cytochrome P450 2D6 (CYP2D6) enzyme for formation of highly potent metabolites. Individuals with reduced or absent CYP2D6 activity (i.e., intermediate metabolizers IMs or poor metabolizers PMs, respectively) have lower concentrations of potent opioid metabolites and potentially inadequate pain control. The primary objective of this prospective, multicenter, randomized pragmatic trial is to determine the effect of postoperative CYP2D6‐guided opioid prescribing on pain control and opioid usage. Up to 2020 participants, age ≥8 years, scheduled to undergo a surgical procedure will be enrolled and randomized to immediate pharmacogenetic testing with clinical decision support (CDS) for CYP2D6 phenotype‐guided postoperative pain management (intervention arm) or delayed testing without CDS (control arm). CDS is provided through medical record alerts and/or a pharmacist consult note. For IMs and PM in the intervention arm, CDS includes recommendations to avoid hydrocodone, tramadol, and codeine. Patient‐reported pain‐related outcomes are collected 10 days and 1, 3, and 6 months after surgery. The primary outcome, a composite of pain intensity and opioid usage at 10 days postsurgery, will be compared in the subgroup of IMs and PMs in the intervention (n = 152) versus the control (n = 152) arm. Secondary end points include prescription pain medication misuse scores and opioid persistence at 6 months. This trial will provide data on the clinical utility of CYP2D6 phenotype‐guided opioid selection for improving postoperative pain control and reducing opioid‐related risks.
Objective
The study objective was to identify differences in gene expression between treatment responders (TRs) and treatment non‐responders (TNRs) diagnosed with juvenile dermatomyositis (JDM).
...Methods
Gene expression analyses were performed using whole blood messenger RNA sequencing in patients with JDM (n = 17) and healthy controls (HCs; n = 10). Four analyses were performed (A1‐4) comparing differential gene expression and pathways analysis exploiting the timing of sample acquisition and the treatments received to perform these comparative analyses. Analyses were done at diagnosis and follow‐up, which averaged 7 months later in the cohort.
Results
At diagnosis, the expression of 10 genes differed between TRs and TNRs. Hallmark and canonical pathway analysis revealed 11 and 60 pathways enriched in TRs and 3 and 21 pathways enriched in TNRs, respectively. Pathway enrichment at diagnosis in TRs was strongest in pathways involved in metabolism, complement activation, and cell signaling as mediated by IL‐8, p38/microtubule associated protein kinases (MAPK)/extracellular signal‐regulated kinases (ERK), Phosphatidylinositol 3 Kinase Gamma (PI3Kγ), and the B cell receptor. Follow‐up hallmark and canonical pathway analysis showed that 2 and 14 pathways were enriched in TRs, whereas 24 and 123 pathways were enriched in treatment TNRs, respectively. Prior treatment with glucocorticoids significantly altered expression of 13 genes in the analysis of subjects at diagnosis with JDM as compared with HCs.
Conclusion
Numerous genes and pathways differ between TRs and TNRs at diagnosis and follow‐up. Prior treatment with glucocorticoids prior to specimen acquisition had a small effect on the performed analyses.
Summary
Cytochrome P-450 2C9 (CYP2C9) polymorphisms (CYP2C9*2 and CYP2C9*3) reduce the clearance of warfarin, increase the risk of bleeding, and prolong the time to stable dosing. Whether prospective ...use of a retrospectively developed algorithm that incorporates CYP2C9 genotype and nongenetic factors can ameliorate the propensity to bleeding and delay in achieving a stable warfarin dose is unknown. We initiated warfarin therapy in 48 orthopedic patients tailored to the following variables: CYP2C9 genotype, age, weight, height, gender, race, and use of simvastatin or amiodarone. By using pharmacogenetics-based dosing, patients with a CYP2C9 variant achieved a stable, therapeutic warfarin dose without excessive delay. However compared to those without a CYP2C9 variant, patients with a variant continued to be at increased risk (hazard ratio 3.6, 95% confidence interval 1.4–9.5, p = 0.01) for an adverse outcome (principally INR > 4), despite pharmacogenetics-based dosing. There was a linear relationship (R
2
= 0.42, p < 0.001) between the pharmacogenetics-predicted warfarin doses and the warfarin maintenance doses, prospectively validating the dosing algorithm. Prospective, perioperative pharmacogenetics-based dosing of warfarin is feasible; however, further evaluation in a randomized, controlled study is recommended.
•The nr1 cause of morbidity and mortality in hepatic steatosis (HS) is CV disease.•Clinically, HS is associated with dyslipidemia and coronary artery disease (CAD).•Lipoprotein particle number/size ...are associated with CAD and CV events.•We analyzed the association lipoprotein particle size/number and HS on CT/biopsy.•Large TRL, mean sizes of TRL-, and HDL were associated with HS on CT/biopsy.•The use of lipoprotein subclasses may improve CV risk assessment in patients with HS.
To determine the relationship between lipoprotein particle size/number with hepatic steatosis (HS), given its association with traditional lipoproteins and coronary atherosclerosis.
Individuals with available CT data and blood samples enrolled in the PROMISE trial were studied. HS was defined based on CT attenuation. Lipoprotein particle size/number were measured by nuclear magnetic resonance spectroscopy. Principal components analysis (PCA) was used for dimensionality reduction. The association of PCA factors and individual lipoprotein particle size/number with HS were assessed in multivariable regression models. Associations were validated in an independent cohort of 59 individuals with histopathology defined HS.
Individuals with HS (n=410/1,509) vs those without (n=1,099/1,509), were younger (59±8 vs 61±8 years) and less often females (47.6 % vs 55.9 %). All PCA factors were associated with HS: factor 1 (OR:1.36, 95 %CI:1.21–1.53), factor 3 (OR:1.75, 95 %CI:1.53–2.02) and factor 4 (OR:1.49; 95 %CI:1.32–1.68) were weighted heavily with small low density lipoprotein (LDL) and triglyceride-rich (TRL) particles, while factor 2 (OR:0.86, 95 %CI:0.77–0.97) and factor 5 (OR:0.74, 95 %CI:0.65–0.84) were heavily loaded with high density lipoprotein (HDL) and larger LDL particles. These observations were confirmed with the analysis of individual lipoprotein particles in PROMISE. In the validation cohort, association between HS and large TRL (OR: 8.16, 95 %CI:1.82–61.98), and mean sizes of TRL- (OR: 2.82, 95 %CI:1.14–9.29) and HDL (OR:0.35, 95 %CI:0.13–0.72) were confirmed.
Large TRL, mean sizes of TRL-, and HDL were associated with radiographic and histopathologic HS. The use of lipoprotein particle size/number could improve cardiovascular risk assessment in HS.
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