Home Current Issue Previous Issues Podcasts Online First ASA Practice Parameters CME For Authors Journal Info
Skip Navigation LinksHome > July 2012 - Volume 117 - Issue 1 > Aversive and Reinforcing Opioid Effects: A Pharmacogenomic T...
Anesthesiology:
doi: 10.1097/ALN.0b013e31825a2a4e
Perioperative Medicine

Aversive and Reinforcing Opioid Effects: A Pharmacogenomic Twin Study

Angst, Martin S. M.D.*; Lazzeroni, Laura C. Ph.D.; Phillips, Nicholas G. M.S.; Drover, David R. M.D.§; Tingle, Martha R.N.; Ray, Amrita Ph.D.#; Swan, Gary E. Ph.D.**; Clark, J. David M.D., Ph.D.††

Free Access
Press Release
Article Outline
Collapse Box

Author Information

Collapse Box

Abstract

Background: The clinical utility of opioids is limited by adverse drug effects including respiratory depression, sedation, nausea, and pruritus. In addition, abuse of prescription opioids is problematic. Gaining a better understanding of the genetic and environmental mechanisms contributing to an individual's susceptibility to adverse opioid effects is essential to identify patients at risk.
Methods: A classic twin study paradigm provided estimates for the genetic and familial (genetic and/or shared environment) contribution to acute adverse and affective opioid responses, all secondary outcomes of a larger dataset. One hundred twenty-one twin pairs were recruited in a single occasion, randomized, double-blind, and placebo-controlled study. The μ-opioid receptor agonist alfentanil and saline placebo were administered as target-controlled infusions under carefully monitored laboratory conditions. Measured outcomes included respiratory depression, sedation, nausea, pruritus, drug liking, and drug disliking. Demographic information was collected, and aspects of mood and sleep were evaluated.
Results: Significant heritability was detected for respiratory depression (30%), nausea (59%), and drug disliking (36%). Significant familial effects were detected for sedation (29%), pruritus (38%), dizziness (32%), and drug liking (26%). Significant covariates included age, sex, race, ethnicity, education, mood, and depression. Covariates affected sedation, pruritus, drug liking and disliking, and dizziness.
Conclusions: This study demonstrates that large-scale efforts to collect quantitative and well-defined opioid response data are not only feasible but also produce data that are suitable for genetic analysis. Genetic, environmental, and demographic factors work together to control adverse and reinforcing opioid responses, but contribute differently to specific responses.
Back to Top | Article Outline

What We Already Know about This Topic

* Interindividual variability in drug response can be influenced by genetic and environmental factors
* A classic twin study paradigm can be used to identify the contribution of genetic and environmental factors to interindividual variability
Back to Top | Article Outline

What This Article Tells Us That Is New

* The adverse effects of a steady-state alfentanil infusion were studied in 114 monozygotic and dizygotic twin pairs
* Genetic effects were detected for decreased respiratory rate, nausea, and drug disliking
* Genetic and/or shared environmental effects were detected for increased transcutaneous carbon dioxide, sedation, pruritis, dizziness, and drug liking
OPIOIDS are the cornerstone medication for the management of moderate to severe pain. They are a key component of balanced anesthetic techniques and remain pivotal for the management of pain after surgery or trauma. Unfortunately, the clinical utility of opioids is limited by several aversive drug effects including respiratory depression, sedation, nausea, pruritus, and addiction. Patients' susceptibility to any of these effects varies greatly.1,2 Gaining a better understanding of the mechanisms underlying such differences is essential to identify patients who are at risk.
Sedation and respiratory depression are among the most worrisome adverse opioid effects. For example, patient-controlled opioid analgesia in the postoperative period is associated with severe respiratory depression requiring administration of an opioid antagonist at a rate of approximately 0.5%.3,4 Co-occurrence of somnolence is typical. Although advanced age, obesity, and concomitant use of sedative medications are well-established covariates that increase the risk of respiratory depression, work examining genetic factors is quite limited.5 For example, an experimental study in homozygous carriers of the 118G allele of the OPRM1 (opioid receptor, μ 1) variant suggested that carriers of the G allele experienced less respiratory depression at equianalgesic opioid doses.6 However, another experimental study in heterozygous carriers of the G allele as well as a clinical study could not confirm such protective effects of the G allele.7,8
Approximately one-third of patients undergoing surgery suffer from postoperative nausea and vomiting, a condition strongly associated with the perioperative use of opioids.9 Postoperative nausea and vomiting appear to be more prevalent in homozygous carriers of the 118A allele of the OPRM1 variant.1012 Although variants of the ABCB1 (adenosine triphosphate binding cassette, subfamily B, member 1) gene have also been associated with the incidence of postoperative nausea and vomiting and opioid-mediated nausea during chronic opioid therapy, results of these studies are inconsistent.1315 Finally, a large gene-association study in cancer patients linked opioid-related nausea to variants of the HTR3B (5-hydroxytryptamine receptor 3B), COMT (catechol-O-methyltransferase), and CHRM3 (cholinergic receptor, muscarinic 3) genes.16 Remarkably, variants of the CHRM3 gene were also associated with the risk of postoperative nausea and vomiting in surgical patients.17 Very few data are available regarding the genetics of pruritus, another common opioid-related side effect.
In addition, prescription opioid abuse has reached alarming dimensions as accidental death from overdose has increased exponentially.18,19 Although many studies have focused on identifying relevant gene variants in addict populations, not much work has examined the genetics underlying acute reinforcing opioid effects.20 However, subjective responses such as drug liking or disliking may be promising index phenotypes. For example, drug liking on first exposure is predictive of opioid abuse and is a commonly assessed outcome for estimating the abuse potential of novel opioid formulations.21,22
Studies examining the relative importance of genetic and environmental influences on aversive and reinforcing opioid responses are lacking. The aim of this study was to provide estimates of heritability and familial aggregation by studying twins under well-controlled, laboratory-type conditions.2325 Demonstration of significant heritability is particularly important to clarify whether genetic factors are of clinical importance, which in turn would justify larger-scale and more detailed molecular studies.
Back to Top | Article Outline

Materials and Methods

Fig. 1
Fig. 1
Image Tools
This pharmacogenomic study was registered at ClinicalTrials.gov on May 2, 2008 (NCT00672438; PI: Angst MS) and was conducted during September 2008 and June 2010. The study produced large datasets covering four outcome domains. Here we present data on aversive and reinforcing opioid effects, whereas data on pain sensitivity and analgesic opioid effects are reported in a separate manuscript.‡‡ Pain sensitivity and analgesic opioid effects were primary outcomes, whereas aversive and reinforcing effects were secondary outcomes. Some portions of the methods section including the description of subjects, general study setting (including fig. 1), drug administration, and statistical analysis are analogous and are included in both manuscripts to ensure completeness. A detailed description of the methods and procedures required for the conduct of an interventional and laboratory-type pharmacogenomic study in a sizable number of twins has previously been published.25
Back to Top | Article Outline
Subjects
Twins were recruited by a joint effort of SRI International and Stanford University School of Medicine. Initial contact and primary enrollment were the responsibility of study staff of SRI International. Recruitment was mainly achieved through the Twin Research Registry and advertisements broadcasted by regional radio stations.26 The study was approved by the Institutional Review Boards of SRI International (Menlo Park, California) and Stanford University School of Medicine (Stanford, California). Two hundred forty-two monozygotic and dizygotic twins were enrolled after giving written informed consent. A medical history was taken and participants were screened for inclusion and exclusion criteria. Inclusion criteria were (1) age 18–70 yr, (2) fluency in the English language, and (3) negative urine pregnancy test on the study day (premenopausal women). Exclusion criteria were (1) clinically relevant systemic diseases such as psychiatric, neurologic, and dermatologic conditions interfering with the collection and interpretation of study data, (2) cardiorespiratory diseases causing at least moderate impairment in daily activities, (3) renal and hepatic diseases with functional impairment, (4) morbid obesity, (5) sleep apnea, (6) history of addiction, (7) allergy to study medication, (8) chronic intake of medications with recognized analgesic/antihyperalgesic activity, (9) intake of over-the-counter analgesics within 2 days before the study, (10) Raynaud disease, (11) pregnancy, and (12) other conditions compromising a participant's safety or the integrity of the study.
Back to Top | Article Outline
Study Setting
The study took place in the Human Pain Laboratory of the Department of Anesthesia at Stanford University School of Medicine. The laboratory offers a quiet environment and precise lighting and temperature control. Critical equipment for the successful and safe conduction of the study included ergonomic and adjustable treatment chairs (Cloud 9, Living Earth Crafts, Vista, CA), vital signs monitors (Propaq Model 244, Welch Allyn, Beaverton, OR), oxygen supply, and a resuscitation cart with a defibrillator, airway management equipment, and emergency drugs. Laboratory staff included (1) a research associate who was blinded to treatment, solely interacted with study participants, performed all testing procedures, and collected all subjective and behavioral data; (2) a registered nurse trained in critical care or emergency medicine who was not blinded to treatment, performed the phlebotomy, administered the study drug, monitored and recorded vital signs, and collected blood specimens; and (3) an anesthesiologist who was blinded and was physically present to oversee the drug infusion and ensure the safety of study participants. Vital signs including heart rate (electrocardiogram), blood pressure, and hemoglobin oxygen saturation were monitored throughout the study. Participants were required to fast overnight except for clear liquids, which were allowed up to 2 h before starting the drug infusion. Participants were also required to have at least 6 h of nighttime sleep before a study session. Twin pairs were not allowed to share their study experience before the completion of the experiments in both twins. During the drug infusion participants received supplemental oxygen (2 l/min) via nasal cannula. Resting periods during the study were standardized, the room lighting was dimmed, participants listened to relaxing music of their choice via headphones, and activities by study staff causing noise or possibly distracting participants were prohibited. At the end of the study session, participants were discharged when they met criteria used for patients undergoing noninvasive, ambulatory procedures requiring sedation (e.g., colonoscopy). Criteria included (1) blood pressure ± 20% of baseline, (2) hemoglobin oxygen saturation more than 95%, (3) wakefulness, (4) no or mild nausea, (5) no vomiting, (6) ability to urinate, and (7) prearranged transport home available.
Back to Top | Article Outline
General Study Design and Randomization
Twins underwent a single occasion, randomized, double-blind and placebo-controlled study protocol (fig. 1). During the preinfusion phase, cognitive speed, respiratory parameters, pain sensitivity, and covariates potentially affecting measured opioid effects were assessed. Blood was collected for genotyping. During the infusion phase, changes in cognitive speed, respiratory parameters, and pain sensitivity were measured to infer sedative, respiratory depressant, and analgesic opioid effects. Similarly, occurrence and magnitude of nausea, pruritus, and reinforcing opioid effects were assessed. Blood was collected to assay for drug plasma concentrations. Fifty percent of the twin pairs were randomized to receive an infusion of the μ-opioid agonist alfentanil followed by the infusion of saline placebo, whereas the other 50% of twin pairs were randomized to receive the infusions in reverse order. The randomization list allocating twin pairs to a particular infusion sequence was generated via Research Randomizer.§§ The list was generated by staff of SRI International who were not further involved in the conduct of the study.
The single-occasion study design had to control for placebo effects that could potentially confound some of the measured opioid effects. Traditional study designs test for drug and placebo effects on separate study occasions. However, asking twins to return for a second study occasion was not considered feasible, because such a requirement may have hampered our ability to recruit and retain a sufficient number of twins. For example, 31% of twins lived more than 60 miles away from the study location. Although a single-occasion design did not allow assessing placebo effects in all participants, such effects could be assessed in the 50% of twins randomized to receive saline placebo before alfentanil. Randomizing the other 50% of twins to receive alfentanil before saline placebo was necessary to maintain the blinding. However, placebo effects could not be assessed in these twins because residual alfentanil plasma concentrations were still present during the saline placebo infusion.
Back to Top | Article Outline
Opioid Administration and Assay
A computer-controlled infusion paradigm was used to quickly achieve and maintain steady-state drug plasma and effect site concentrations. An equilibration period of 20 min was observed between starting the infusion of alfentanil or saline placebo and the first assessment of drug effects, which allowed measuring all outcomes at similar drug concentrations.27 Although a computer-controlled infusion allows maintaining a stable plasma concentration in an individual participant, plasma concentrations vary among participants. Therefore, alfentanil plasma concentrations were measured to include interindividual differences in drug concentrations as a covariate in the final analysis. The μ-opioid agonist alfentanil (Janssen Pharmaceutica, Titusville, NJ) was chosen because of its quick onset and offset of action, and a previously validated computer-controlled infusion algorithm for its administration.27 Alfentanil was administered intravenously via a computer-controlled infusion pump (Harvard Pump 22, Harvard Apparatus, Inc., South Natick, MA) targeting a steady-state plasma concentration of 100 ng/ml. This target concentration produces clinically relevant opioid effects without causing harmful side effects.27,28 STANPUMP‖‖ using Scott's weight-adjusted pharmacokinetic parameters was the software driving the infusion pump.29
Alfentanil plasma concentrations were assayed at the Clinical Research and Development Unit of the Department of Anesthesia at the University of Colorado Health Sciences Center (Denver, Colorado). Six milliliters of venous blood were drawn into heparinized glass tubes, centrifuged, and the plasma frozen and stored at −70°C until assayed. Using liquid chromatography/liquid chromatography mass spectrometry/mass spectrometry, the lower limit of quantitation was 1.25 pg/ml with a 1,000-fold linear range (R = 0.99), and an intraassay and between-assay coefficient of variations ranging between 4–16% and 3–14%.
Back to Top | Article Outline
Respiratory Depression
Respiratory depression was quantified by measuring changes in partial pressure of transcutaneous carbon dioxide and respiratory rate. Transcutaneous carbon dioxide was quantified with aid of a partial pressure of oxygen/partial pressure of carbon dioxide (pO2/pCO2) electrode (Perimed Inc., North Royalton, OH) mounted on the anterior chest wall. Although measured tissue carbon dioxide is higher than the arterial carbon dioxide, the two measures correlate strongly and relative changes match closely.30 Respiratory rate was assessed by counting the number of breaths over a period of 1 min. Both measures were obtained after strictly observing a standardized resting period of at least 15 min. During this period, participants listened to soft music via headphones, the room lighting was dimmed, any interaction was avoided, and activities by study staff causing noise or possibly distracting participants were prohibited. The partial pressure of transcutaneous carbon dioxide was continuously monitored and measures were only recorded at the end of the resting period in undisturbed participants with readings that were stable over a period of 2–3 min.
The use of more invasive or complex techniques to assess respiratory depression was considered but not deemed feasible. Additional interventions such as the insertion of an arterial line for repeatedly measuring arterial carbon dioxide, or the use of time-consuming and stressful rebreathing techniques for determining the transcutaneous carbon dioxide response function, may have significantly hampered our ability to recruit a sufficient number of twins for this demanding study protocol.
Back to Top | Article Outline
Sedation
Subjective sedation scores were assessed immediately after the recording of the respiratory parameters. Participants indicated on a 100-mm visual analog scale (VAS) anchored by the words “not sedated at all” (VAS = 0) and “sedated as much as possible” (VAS = 100) how sedated they felt.
Cognitive performance was assessed with the trail-making test. The trail-making test measures cognitive speed and correlates significantly with tests quantifying intelligence.31 This paper-and-pencil test consists of four matrices featuring 90 numbers organized in 9 rows and 10 columns on a 23 × 21 cm sheet of paper. Subsequent numbers are located randomly in neighboring rows or columns. Starting at number 1, a participant has to connect numbers in ascending order as quickly as possible. The time to completion of the test is recorded. Mistakes are called out by an observer and have to be corrected by the participant before continuing with the test. The particular matrix that a participant had to complete during a test cycle was chosen randomly. All participants were trained in the trail-making test before first recordings were made.
Back to Top | Article Outline
Nausea, Pruritus, and Dizziness
At the end of an infusion stage participants were asked to rate the average and maximum severity of nausea, pruritus, and dizziness on a 100-mm VAS anchored by the words “not at all” and “as much as possible.”
Back to Top | Article Outline
Reinforcing Opioid Effects
At the end of an infusion stage participants were asked the following questions: (1) Did you like the drug at any moment (yes/no)? (2) Did you dislike the drug at any moment (yes/no)? (3) If you liked and disliked the drug, did you like or dislike it first? (4) How much did you like the drug on average (100-mm VAS, 0 = “not at all,” 100 = “as much as possible”)? (5) What was the maximum that you liked the drug at any moment (VAS)? (6) What was the maximum that you disliked the drug at any moment (VAS)?
Back to Top | Article Outline
Covariates
Several covariates potentially affecting opioid-mediated aversive and reinforcing effects were assessed.
Pharmacokinetics: Pharmacokinetic parameters included the measured alfentanil plasma concentrations.
Demographics: Demographic factors included race, ethnicity, sex, age, and educational status.
Anxiety: Anxiety was assessed with the Profile of Mood States, a self-reported questionnaire that evaluates six dimensions of mood (anxiety, depression, anger, vigor, inertia, and bewilderment).32 Participants rated 65 mood-related adjectives on a five-point scale (0 = not at all, 1 = a little, 2 = moderately, 3 = quite a bit, 4 = extremely). The Profile of Mood States yields a total score and subscores for each dimension of mood (anxiety subscore range: 0 to 36).
Depression: Depressive symptom severity was assessed with the Beck Depression Inventory, a self-reported questionnaire yielding a single score between 0 and 63 (0–13 = no depression, 14–19 = mild depression, 20–28 = moderate depression, >29 = severe depression).33
Sleep: Sleep quality during the month preceding the study was assessed with the Pittsburgh Sleep Quality Index, a self-reported questionnaire that assesses seven components of sleep (quality, latency, duration, efficiency, disturbance, medication, and daytime dysfunction). The total score ranges between 0 and 21, and a value more than 6 is indicative for sleep disturbance.
Back to Top | Article Outline
Zygosity Testing
Zygosity was assessed by genotyping 47 single nucleotide polymorphisms, a recently published high throughput method providing high accuracy.34 Genotyping was performed with a custom-designed Oligo Pool for Methylation Assay (Golden Gate Genotyping Assay, Illumina Inc, San Diego, CA) and BeadXpress (iGenix Inc., Bainbridge Island, WA).
Back to Top | Article Outline
Statistical Analysis
Data are presented as mean and SD (SD) or as median and interquartile range (IQR). Summary statistics, parametric or nonparametric hypothesis testing with paired or nonpaired test procedures, and correlation analysis on continuous and ranked data were performed in Systat Version 13 (Chicago, IL). An α level of P < 0.05 indicated statistical significance. All outcomes were of secondary nature, which did not require adjusting the P value to the number of outcomes.
The primary statistical analysis of heritability (genetic effects) and familial aggregation (genetic and/or shared environmental effects) was based on a classic twin model.35 In principle, this model takes advantage of the fact that monozygotic twins share 100% of their genes, whereas dizygotic twins share approximately 50% of their genes. On the other hand, monozygotic and dizygotic twins share the same familial environment. Comparing the degree of similarity in a phenotype by correlational analysis in monozygotic and dizygotic twin pairs allows estimating the relative contribution of genetic and environmental factors, which can be broken down further into shared (familial) and unique (random) environmental factors. Greater similarity in monozygotic than in dizygotic twin pairs suggests that genetic effects at least partially account for studied phenotype. Similarities that are equal or nearly equal in monozygotic and dizygotic twin pairs suggest that shared environmental effects at least partially account for studied phenotype. Finally, phenotypical dissimilarities in monozygotic twin pairs suggest that unique environmental factors at least partially account for studied phenotype, because monozygotic twins share all of their genes and all of the common environment.
The specific analysis was based on a generalized form of the Defries-Fulker (DF) regression model for twins (Stata Version 11, StataCorp LP, College Station, TX).36 In this model, phenotypic measurements of each twin are regressed on his or her cotwin's phenotypic measurement, while accounting for the joint distribution of the twin data. In contrast with alternative methods of twin analysis, the DF regression model produces unbiased estimates of twin pair intraclass correlations, which provide the basis for estimates of genetic and familial effects. The DF regression model was modified to allow for the simultaneous estimation of covariate effects, as well as genetic and familial effects. The method is equivocal to competing methods in terms of power but is more robust in the presence of nonnormality. The modified model was specifically developed to address the highly skewed distribution of the visual analog scale data collected in this study.
To identify the most significant covariate effects, a forward selection algorithm using the DF regression model with both genetic and familial effects was applied to each of the studied phenotypes. Covariates were added sequentially, starting with the covariate associated with the smallest P value (P < 0.05; Wald test for the corresponding Z-statistic). This procedure was repeated until remaining covariates no longer yielded a P value less than 0.05.
All subsequent tests of genetic and familial effects incorporated the significant covariates for the respective phenotypes. Z-statistics (Wald test) were used for significance testing. Reported significance levels and 95% CIs for selected covariates, and genetic and familial effects were estimated by generating 10,000 random bootstrap datasets. Datasets were created with clustered resampling techniques using twin pairs as the resampling unit. Bootstrap resampling was stratified by zygosity. A one-sided Wald test was used to test for a significant genetic effect. This test examined whether the intraclass correlation within monozygotic twin pairs was significantly larger than the intraclass correlation for dizygotic twin pairs, using the standard assumption that monozygotic and dizygotic twins shared a common environment. If the genetic test was not statistically significant, a secondary one-sided test procedure was used to test for a significant familial effect. This test examined whether the average twin-pair intraclass correlation, assumed to be equal for monozygotic and dizygotic pairs, was greater than zero. Familial aggregation was not assessed for phenotypes that showed a significant genetic effect. Heritability estimates were calculated under the DF ACE twin model that allows estimating the differential contributions of additive genetic (A), shared environmental (C), and unique environmental effects (E). Familial aggregation was estimated under a DF CE model. CIs for heritability and familial aggregation were truncated to lie between 0 and 1, which is consistent with the assumptions underlying the twin model.
Back to Top | Article Outline
Power Analysis
The power analysis was based on the enrollment of 85 monozygotic and 40 dizygotic twin pairs. Power was approximated based on Fisher log transformation for the Pearson correlation coefficient between measurements for twin A and twin B at an α level of P < 0.05. Eighty-five monozygotic pairs yielded a power of 0.8 to detect familial aggregation more than 30%. Eighty-five monozygotic and 40 dizygotic pairs yielded a power of 0.5 to detect heritability more than 50%, assuming that shared environmental effects account for a modest 20% of the variance. However, these calculations were best approximations because of uncertainty regarding the relative contribution of additive genetic, shared environmental, and unique environmental effects to the response variance.
Back to Top | Article Outline

Results

This manuscript reports results on aversive and reinforcing opioid effects, whereas a separate manuscript reports pain sensitivity and analgesic outcomes.‡‡ For completeness, demographic variables, drug plasma concentrations and safety parameters are reported in both manuscripts. During the study no adjustments to the protocol or outcome assessments were required.
Back to Top | Article Outline
Subjects
Table 1
Table 1
Image Tools
A total of one hundred twenty-one twin pairs were recruited. One hundred fourteen pairs completed the study. Seven pairs were excluded for the following reasons: (1) two pairs because of positive pregnancy test; (2) two pairs because of missed appointments; and (3) three pairs showed poor compliance with the study procedures. Detailed demographics of the final cohort are depicted in table 1. Most twins were women (62%), monozygotic (71%), Caucasian (78%), and of non-Hispanic origin (82%). The median age was 29 yr (22–47; IQR) and the median body mass index was 24.3kg/m2 (21.8–27.8; IQR). Educational levels were a high school degree for 19, some college education for 95, and a college degree for 114 subjects.
Back to Top | Article Outline
Alfentanil Plasma Concentrations
The average alfentanil plasma concentration was 72 ± 16 ng/ml (SD), which is 28% lower than predicted. The size of this prediction error is consistent with previous results, and actual plasma concentrations produced sizable drug effects.27 Although plasma concentrations covered a 3.7-fold range (lowest to highest), approximately 80% of the concentrations were within a twofold range. The plasma concentration was positively correlated with the body mass index (R = 0.53; P < 0.001) and was significantly higher in men than in women (76.6 ± 22.7 ng/ml vs. 62.6 ± 18.8 ng/ml (SD); P < 0.001). The body mass index was not different between men and women. The average within-pair difference in plasma concentration was 17 ± 14% in monozygotic twins and 21 ± 16% (SD) in dizygotic twins.
Back to Top | Article Outline
Respiratory Depression
Fig. 2
Fig. 2
Image Tools
Alfentanil-induced changes in transcutaneous carbon dioxide varied widely among studied twins, being absent in some and increasing by up to 18.6 mmHg in others (fig. 2). The median transcutaneous carbon dioxide before drug infusion was 40.7 mmHg (38.0 to 43.7, IQR) and significantly increased to 47.7 mmHg (43.9 to 51.8; IQR) during the infusion of alfentanil (P < 0.001). Due to an equipment malfunction involving the pO2/pCO2-electrode, reliable measurements were only obtained in 196 twins (missing values in 32 participants).
The effects of alfentanil on respiratory rate varied widely among studied twins, increasing in a few but decreasing by up to 12 breaths/min in most others (fig. 2). The median respiratory rate before drug infusion was 15 min−1 (13–17; IQR) and significantly decreased to 11 min−1 (9–13) during the infusion of alfentanil (P < 0.001).
Back to Top | Article Outline
Sedation
Fig. 3
Fig. 3
Image Tools
Alfentanil-induced changes in cognitive speed (trail-making test) varied widely among studied twins, being absent in some and increasing by more than 60 s in others (fig. 3). The median time to complete the trail-making test before drug infusion was 62 s (55–73; IQR) and significantly increased to 69 s (55–84; IQR) during the infusion of alfentanil (P < 0.001).
Table 2
Table 2
Image Tools
The effects of alfentanil on subjective sedation scores varied remarkably among studied twins, ranging from a 5- to a 100-point VAS score (fig. 3). The median sedation score was 75 (60–85; IQR) during the infusion of alfentanil. Given that sedation scores were only determined during the infusion of saline placebo and alfentanil but not at baseline, the statistical significance of drug-induced changes was evaluated in the subpopulation of twins receiving placebo before alfentanil (table 2; P < 0.001).
Back to Top | Article Outline
Nausea, Pruritus, and Dizziness
Fig. 4
Fig. 4
Image Tools
Subjects were asked to rate average as well as maximum drug-induced changes in nausea, pruritus, and dizziness. Average and maximum changes were tightly correlated (R = 0.89 to 0.92; P < 0.001). Effects of alfentanil varied widely among studied twins as shown for maximum drug effects in figure 4. The median VAS scores for average and maximum nausea during the infusion of alfentanil were 1 (0–32; IQR) and 8 (0–70; IQR) in all subjects, whereas they were 32 (17–60; IQR) and 70 (41–89; IQR) in the 50% of subjects reporting this side effect. The median VAS scores for average and maximum pruritus were 20 (0–44; IQR) and 30 (0–65; IQR), whereas they were 30 (15–50; IQR) and 50 (26–71; IQR) in the 58% of subjects reporting this side effect. The median VAS scores for average and maximum dizziness were 20 (0–50; IQR) and 40 (0–71; IQR), whereas they were 39 (20–60; IQR) and 60 (34–80; IQR) in the 68% of subjects reporting this side effect. Given that scores were only determined during the infusion of saline placebo and alfentanil but not at baseline, the statistical significance of drug-induced changes was evaluated in the subpopulation of twins receiving placebo before alfentanil (table 2; P < 0.001 for all comparisons).
Back to Top | Article Outline
Reinforcing Opioid Effects
Fig. 5
Fig. 5
Image Tools
During the infusion of alfentanil, 65 participants liked the drug, 31 disliked it, 14 neither liked nor disliked it, and 118 both liked and disliked it at different times. Of the 118 participants who liked and disliked the drug, 93 liked it initially and 25 disliked it initially. The VAS scores for average liking, maximum liking, and maximum disliking varied greatly among participants (fig. 5). Average and maximum liking were strongly correlated (R = 0.81; P < 0.001). Average liking was negatively correlated with maximum disliking (R = −0.35; P < 0.001), whereas maximum liking and maximum disliking were not correlated. The median VAS scores for average and maximum liking during the infusion of alfentanil were 50 (0–75; IQR) and 70 (30–90; IQR). The median VAS score for maximum disliking was 35 (0–80; IQR).
Table 3
Table 3
Image Tools
Liking and disliking correlated with some of the subjective aversive opioid effects (table 3). Most notably, average liking was negatively correlated with nausea and dizziness, which accounted for 3–8% of the observed variance. Maximum disliking was positively correlated with nausea and dizziness, which accounted for 19–37% of the observed variance. By contrast, maximum liking was neither positively nor negatively correlated with nausea or dizziness.
Back to Top | Article Outline
Effects during the Infusion of Saline Placebo
Possible placebo effects were evaluated in the 50% of twins who received the infusion of saline placebo before the infusion of alfentanil. Objective outcomes assessed at baseline and during the infusion of saline placebo and alfentanil included respiratory rate, transcutaneous carbon dioxide, and the trail-making test. For these outcomes, changes observed between baseline and saline placebo administration were compared with changes observed between baseline and alfentanil administration. However, subjective drug-related outcomes including sedation, dizziness, nausea, drug liking, and drug disliking were only assessed during saline placebo and alfentanil administration. For these outcomes the absolute values obtained during the administration of saline placebo and alfentanil were compared.
Overall, measures of respiratory depression (respiratory rate and transcutaneous carbon dioxide) and cognitive speed changed modestly if at all during the infusion of saline placebo compared with predrug assessments. Consequently, drug effects were inferred in all subjects by subtracting predrug measurements from measurements obtained during the infusion of alfentanil.
The median respiratory rate was 16 min−1 (13–18; IQR) before drug infusion and 15 min−1 (13–17; IQR) during the infusion of saline placebo (P = 0.06). The median net decrease in respiratory rate from predrug measurements was −1 min−1 (−2 to 1; IQR) during saline placebo and −4 min−1 (−7 to −2; IQR) during alfentanil administration.
The median transcutaneous carbon dioxide was 40.4 mmHg (38.2 to 43.4; IQR) before drug infusion and 39.1 mmHg (37.5 to 42.4; IQR) during the infusion of saline placebo (P < 0.001). The median net decrease of transcutaneous carbon dioxide from predrug measurements was −0.8 mmHg (−2.1 to 0.3; IQR) during saline placebo administration, whereas the median net increase was 6.2 mmHg (4.1 to 8.9; IQR) during alfentanil administration.
The median time required to complete the trail-making test was 65 s (56–74) before drug infusion and 64 s (55–74) during the infusion of saline placebo (P = 0.15). The median net decrease from predrug measurements was −1.5 s (−5 to 3; IQR) during saline placebo administration, whereas the median net increase was 4.5 s (−1 to 11; IQR) during alfentanil administration.
Subjective outcomes were differentially affected by the administration of saline placebo (table 2; also see figures, Supplemental Digital Content 1 and 2, http://links.lww.com/ALN/A854, which depict incidence and magnitude of aversive and reinforcing opioid effects during administration of saline placebo and alfentanil). The incidence of dizziness, nausea, and drug disliking during placebo administration was low (3–8%), and the median effect size was zero (0–0; IQR). The incidence of pruritus and drug liking was modest (12–18%), and the median effect size was zero (0–0; IQR). However, the incidence of sedation was remarkable (48%), whereas the median effect size was zero (0–15; IQR). These results suggest that changes in dizziness, nausea, and drug disliking during alfentanil administration were essentially drug-related. Changes in pruritus and drug liking were largely drug-related. However, changes in sedation were only partially drug-related. The heritability analysis assumed that effects measured during the administration of alfentanil were entirely drug-related. Although this assumption appears to be reasonable for most subjective outcomes, it may not be entirely valid for sedation.
Possible effects of drug sequence (saline placebo – alfentanil vs. alfentanil – placebo) on assessed outcomes were examined by comparing effects sizes measured during the infusion of alfentanil between the two groups of twins who received the infusions in reversed order. No significant sequential effects were detected (P = 0.12–0.92).
Back to Top | Article Outline
Covariates
Table 4
Table 4
Image Tools
Table 5
Table 5
Image Tools
Covariates significantly affected several of the measured phenotypes. Table 4 and table 5 summarize these covariate contributions. Age and sex were the covariates most commonly associated with the measured phenotypes.
Fig. 6
Fig. 6
Image Tools
Alfentanil plasma concentration was not a significant covariate for any of the measured phenotypes, implying that pharmacokinetic variability played a minor role for estimates of heritability and familial aggregation. This result is corroborated by two additional findings. First, differences in the magnitude of opioid-mediated aversive or reinforcing effects were not related to differences in plasma concentrations when considering all study participants. Second, differences in the magnitude of opioid-mediated effects within twin pairs were not related to differences in plasma concentration within twin pairs. Exemplary data for parameters of respiratory depression are shown in figure 6.
Back to Top | Article Outline
Heritability and Familial Aggregation
Table 6
Table 6
Image Tools
Strong heritability was detected for respiratory depression, nausea and drug disliking. With the exception of cognitive speed, all other studied phenotypes showed significant familial aggregation (genetic and/or shared environmental effects). Results are summarized in table 6.
Respiratory depression: Significant heritability was detected for opioid-mediated decreases in respiratory rate. Genetic effects accounted for 30% of observed response variance. Although no significant heritability was detected for increases in transcutaneous carbon dioxide, significant familial effects accounted for 31% of observed response variance.
Sedation: No heritability was detected for opioid-mediated increases in subjective sedation scores or alterations in cognitive speed. Although 29% of the response variance associated with subjective sedation scores was explained by significant familial effects, no such effects were detected for alterations in cognitive speed.
Nausea, pruritus, and dizziness: Significant heritability was detected for opioid-induced nausea. Genetic effects accounted for an impressive 56–59% of observed response variance. Although no significant heritability was detected for pruritus and dizziness, both phenotypes were significantly aggregated in families. For pruritus, familial effects accounted for 17–38% of observed response variance. For dizziness, familial effects accounted for 32–39% of observed response variance.
Reinforcing effects: Significant heritability was detected for the disliking of the drug. Genetic effects accounted for 36% of observed response variance. Although no significant heritability was detected for drug liking, significant familial effects accounted for 23–26% of observed response variance.
Back to Top | Article Outline
Safety Parameters
Systolic arterial blood pressure, diastolic arterial blood pressure, heart rate, and hemoglobin oxygen saturation remained stable during the infusion of alfentanil compared with predrug measurements. Respective values were 120 ± 18 versus 116 ± 13 mmHg for systolic arterial blood pressure, 63 ± 11 versus 64 ± 10 mmHg for diastolic arterial blood pressure, 61 ± 12 min−1 versus 61 ± 11 min−1 for heart rate, and 99 ± 2 versus 98 ± 2% for hemoglobin oxygen saturation. Respiratory rate decreased from 15 ± 3 to 11 ± 3 min−1 during the infusion of alfentanil (P < 0.001). There were no episodes of arterial hypotension (symptomatic or decrease of mean arterial pressure more than 25%) or hypoxia (hemoglobin oxygen saturation less than 90%) that required a medical intervention.
Back to Top | Article Outline

Discussion

The use of opioids has grown dramatically over the past decades due to the increased attention of health care providers to pain-related suffering. Similarly, our appreciation of factors limiting the utility of opioids has grown. Problems often acutely manifest after initiating opioid therapy include nausea, sedation, pruritus, and respiratory depression. When mild, these factors may simply be nuisances that can be addressed with adjustments in opioid dosing, the use of alternative opioid formulations, and the addition of other agents. On the opposite end of the spectrum, severe sedation and respiratory depression can result in patient injury and death. Moreover, opioid abuse and addiction have become very problematic. Lacking from our current knowledge is an understanding of the relative importance of genetic and environmental factors that underlie patients' susceptibility to experience aversive opioid effects and develop abusive behavior. We used a twin study paradigm and an experimental laboratory setting to provide quantitative estimates of the overall genetic and environmental contributions to aversive and affective opioid effects and quantify the influence of important covariates on these effects. Aversive and affective opioid effects were secondary outcomes of a larger dataset. Consequently, the statistical analysis did not require adjusting P values to the number of reported outcomes. However, exact P values are reported, which allows independent assessment for potential type I errors.
We observed a wide diversity of responses for most of the studied phenotypes. In particular, significant heritability was documented for opioid-mediated respiratory depression, nausea, and drug disliking. With the exception of the trail-making test quantifying cognitive speed, all other outcomes including sedation, pruritus, dizziness, and drug liking were significantly aggregated in families. Both genetic and shared environmental effects contribute to the finding of familial aggregation; thus, failure to detect heritability per se for these outcomes does not preclude relevant genetic effects. In particular, mild to moderate genetic effects may have gone undetected considering the size of our study.
Nausea related to the use of opioids is particularly problematic, both in the acute and more chronic setting. After surgery, nausea delays discharge from the recovery room and results in unanticipated hospital admissions.37,38 Furthermore, nausea in postoperative periods while using opioids is common and often necessitates intervention.39,40 Our results suggest that the interindividual variability in opioid-mediated nausea is highly heritable, with more than 50% of the response variance attributable to genetic factors.
A limited amount of existing genetic data demonstrate that specific gene variants may be associated with opioid-induced nausea. A small study in cancer patients found that variants of UGT2B7 (UDP glucuronosyltransferase 2 family, polypeptide B7) were associated with higher levels of opioid-induced nausea, whereas variants of ABCB1 were associated with the frequency of vomiting.14 In a much larger multicenter study also involving cancer patients on chronic opioid therapy, variants of HTR3B, COMT, and CHRM3 were significantly associated with nausea.16 The 5-HT3 association is particularly plausible as this receptor is the target for efficacious antiemetic drugs. In agreement with some of the results in cancer patients, a more recent genome-wide association study in surgical patients also reported a significant association between a variant of CHRM3 and nausea.17 Finally, a study in patients receiving morphine for postoperative pain found that an interaction between the A118G variant of OPRM1 and the G1947A variant of COMT was associated with reduced levels of nausea.12
The chief barriers to the aggressive use of opioids are concerns regarding their respiratory depressant effects. For example, most studies reviewing patient-controlled analgesia protocols indicate an incidence of approximately 0.5% for severe respiratory depression requiring administration of an opioid antagonist.3,4 Incidences tend to be higher if decreases in respiratory rate, hypoxia, or hypercapnia are used as measures of respiratory depression.3 Moreover, the rapidly expanding use of opioids for the control of chronic pain has been associated with an equally rapid increase in the number of emergency department visits related to opioid overdose and, more concurringly, deaths from accidental overdose.41 We found that approximately 30% of interindividual variance in opioid-induced respiratory depression as measured by changes in respiratory rate was heritable and therefore attributable to genetic factors. Similarly, approximately 30% of the response variance in opioid-induced elevations of transcutaneous carbon dioxide was aggregated in families. Few gene association studies have carefully quantified opioid-mediated respiratory depression. One study indicated that homozygous carriers of the G allele of the A118G variant of OPRM1 experienced less respiratory depression at equianalgesic plasma concentration of alfentanil compared with carriers of the A allele.6 However, a previous study administering the morphine metabolite morphine-6-glucuronide failed to identify an effect of the A118G variant of OPRM1 on respiratory depression. It is noteworthy that this small study of 16 subjects did not include any homozygous carriers of the G allele.8 More recently, a study administering an intravenous bolus of fentanyl in 189 patients after laparoscopic surgery also failed to detect a clinically relevant correlation between the A118G variant of OPRM1 and opioid-induced respiratory depression.7 Thus it appears that although genetics may contribute significantly to interindividual differences in opioid-mediated respiratory depression, we hardly understand the basis underlying these differences.
The abuse potential of opioids prescribed for pain control has come to the forefront of interest due to the rapidly escalating rate of abuse and accidental overdose.19,41 Addiction to opioids is heritable, and genetic studies have been designed to address the specific molecular underpinning.20,42 Although our study paradigm did not allow direct study of the complex clinical phenotype of opioid addiction, we were able to precisely measure acute reinforcing effects such as the liking and disliking of the drug. Liking in response to acute opioid administration is an established index phenotype to predict abuse potential, whereas disliking upon first exposure is associated with lack of abuse.21 Scales of liking and disliking are included in questionnaires such as the Drug Effect Questionnaire that are used to assess abuse potential.43,44 Drug liking has been assessed to quantify abuse potential of several opioids including heroin, morphine, buprenorphine, oxycodone, fentanyl, and remifentanil.21,22,45,46 Our results indicate that drug liking is significantly aggregated in families, although heritability could not be established. We observed that “maximum” liking was less influenced by other subjective opioid effects than “average” liking. Maximum liking may therefore be a less convoluted and perhaps preferable measure to assess positive reinforcing opioid effects. On the other hand, disliking was significantly heritable, suggesting that genetics may contribute to mechanisms protective against the abuse of opioids. Furthermore, disliking was correlated with nausea, which seems quite plausible. We suggest that opioid disliking may constitute a useful and easily measurable index phenotype to assess the abuse potential of opioids in future research.
Another dimension of data analysis made possible by our study design concerns the effect of major covariates on measured outcomes. This analysis also eliminated confounding influences of covariates such as age and sex on estimates of heritability and familial aggregation when assuming similar influences of genetic and common environmental effects on covariate strata. However, the number of twins enrolled in our study precluded formal analysis of this assumption. Age and sex were the covariates most commonly affecting aversive and reinforcing opioid effects. Age was associated with greater respiratory depression and drug-induced slowing of cognitive speed. Such observations are consistent with previous reports.47,48 Likewise, advanced age was associated with greater drug disliking, which is consistent with lower rates of opioid abuse in aging patients with chronic pain.49 Women reported more pruritus and dizziness during the infusion of alfentanil. However, there was no detectable effect of female sex on nausea, a well-established risk factor for nausea when using opioids in pain management.50 On the other hand, our analysis did reveal some novel findings. Asians displayed much higher VAS dizziness scores than did members of the other races. We also found significantly greater drug liking in Caucasians and non-Hispanics. Interestingly, studies focused on prescription opioid abuse have identified Caucasian race as a risk factor.51,52 Finally, the measured alfentanil plasma concentration was not a significant covariate for any of the studied phenotypes. This finding suggests that substantial pharmacodynamic variability concealed any pharmacokinetic variability within the range of studied plasma concentrations. Inspection of our data supports this conclusion as the range of measured plasma concentration was 3.7-fold, whereas the range of observed pharmacodynamic changes was substantially greater than 10-fold.
In summary, we provide estimates for the global genetic and environmental contributions to a range of common and clinically important aversive and reinforcing opioid effects. We also report on the frequency, variability, and magnitude of these effects as well as their modulation by a series of important covariates. To our knowledge this is the first study to amass a broad array of quantitative data characterizing acute opioid effects under carefully controlled and laboratory-type conditions. We also demonstrated that results were typically not affected in relevant ways by placebo responses or sequential effects. Laboratory-type procedures as described here may therefore provide an excellent paradigm for future studies examining the molecular genetics of individual opioid response profiles.
The authors thank Jill Rubin (Certified Medical Assistant and Research Analyst II), Mary McElroy, M.P.H. (Senior Projects Administrator), Lisa Jack, M.A. (Senior Projects Administrator), Ruth Krasnow, M.A. (Programmer and Coordinator for Twin Research Registry), Dina Basin, B.A. (Public Relations Specialist), and Lucia Panini, M.B.A., B.S. (Marketing Communications Specialist), all from the Center for Health Sciences, SRI International, Menlo Park, California.
§§ http://www.randomizer.org/. Accessed March 6, 2012. Cited Here...
‖‖ http://www.opentci.org/lib/exe/fetch.php?media=code:stanpump.zip. Accessed March 7, 2012. Cited Here...
‡‡ Angst MS, Phillips NG, Drover DR, Tingle M, Ray A, Swan GE, Lazzeroni LC, Clark JD: Pain sensitivity and opioid analgesia: A pharmacogenomic twin study. Pain 2012 Mar 21 [Epub ahead of print] Cited Here...
Back to Top | Article Outline

References

1. Angst MS, Chu LF, Tingle MS, Shafer SL, Clark JD, Drover DR: No evidence for the development of acute tolerance to analgesic, respiratory depressant and sedative opioid effects in humans. Pain 2009; 142:17–26

2. Dolin SJ, Cashman JN: Tolerability of acute postoperative pain managemen: Nausea, vomiting, sedation, pruritus, and urinary retention. Evidence from published data. Br J Anaesth 2005; 95:584–91

3. Cashman JN, Dolin SJ: Respiratory and haemodynamic effects of acute postoperative pain management: Evidence from published data. Br J Anaesth 2004; 93:212–23

4. Dahan A, Aarts L, Smith TW: Incidence, reversal, and prevention of opioid-induced respiratory depression. ANESTHESIOLOGY 2010; 112:226–38

5. Hagle ME, Lehr VT, Brubakken K, Shippee A: Respiratory depression in adult patients with intravenous patient-controlled analgesia. Orthop Nurs 2004; 23:18–27; quiz 28–9

6. Oertel BG, Schmidt R, Schneider A, Geisslinger G, Lötsch J: The mu-opioid receptor gene polymorphism 118A>G depletes alfentanil-induced analgesia and protects against respiratory depression in homozygous carriers. Pharmacogenet Genomics 2006; 16:625–36

7. Wu WD, Wang Y, Fang YM, Zhou HY: Polymorphism of the micro-opioid receptor gene (OPRM1 118A>G) affects fentanyl-induced analgesia during anesthesia and recovery. Mol Diagn Ther 2009; 13:331–7

8. Romberg RR, Olofsen E, Bijl H, Taschner PE, Teppema LJ, Sarton EY, van Kleef JW, Dahan A: Polymorphism of mu-opioid receptor gene (OPRM1:c.118A>G) does not protect against opioid-induced respiratory depression despite reduced analgesic response. ANESTHESIOLOGY 2005; 102:522–30

9. Apfel CC, Laara E, Koivuranta M, Greim CA, Roewer N: A simplified risk score for predicting postoperative nausea and vomiting: Conclusions from cross-validations between two centers. ANESTHESIOLOGY 1999; 91:693–700

10. Sia AT, Lim Y, Lim EC, Goh RW, Law HY, Landau R, Teo YY, Tan EC: A118G single nucleotide polymorphism of human mu-opioid receptor gene influences pain perception and patient-controlled intravenous morphine consumption after intrathecal morphine for postcesarean analgesia. ANESTHESIOLOGY 2008; 109:520–6

11. Zhang W, Yuan JJ, Kan QC, Zhang LR, Chang YZ, Wang ZY: Study of the OPRM1 A118G genetic polymorphism associated with postoperative nausea and vomiting induced by fentanyl intravenous analgesia. Minerva Anestesiol 2011; 77:33–9

12. Kolesnikov Y, Gabovits B, Levin A, Voiko E, Veske A: Combined catechol-O-methyltransferase and mu-opioid receptor gene polymorphisms affect morphine postoperative analgesia and central side effects. Anesth Analg 2011; 112:448–53

13. Coulbault L, Beaussier M, Verstuyft C, Weickmans H, Dubert L, Trgouet D, Descot C, Parc Y, Lienhart A, Jaillon P, Becquemont L: Environmental and genetic factors associated with morphine response in the postoperative period. Clin Pharmacol Ther 2006; 79:316–24

14. Fujita K, Ando Y, Yamamoto W, Miya T, Endo H, Sunakawa Y, Araki K, Kodama K, Nagashima F, Ichikawa W, Narabayashi M, Akiyama Y, Kawara K, Shiomi M, Ogata H, Iwasa H, Okazaki Y, Hirose T, Sasaki Y: Association of UGT2B7 and ABCB1 genotypes with morphine-induced adverse drug reactions in Japanese patients with cancer. Cancer Chemother Pharmacol 2010; 65:251–8

15. Campa D, Gioia A, Tomei A, Poli P, Barale R: Association of ABCB1/MDR1 and OPRM1 gene polymorphisms with morphine pain relief. Clin Pharmacol Ther 2008; 83:559–66

16. Laugsand EA, Fladvad T, Skorpen F, Maltoni M, Kaasa S, Fayers P, Klepstad P: Clinical and genetic factors associated with nausea and vomiting in cancer patients receiving opioids. Eur J Cancer 2011; 47:1682–91

17. Janicki PK, Vealey R, Liu J, Escajeda J, Postula M, Welker K: Genome-wide Association study using pooled DNA to identify candidate markers mediating susceptibility to postoperative nausea and vomiting. ANESTHESIOLOGY 2011; 115:54–64

18. Maxwell JC: The prescription drug epidemic in the United States: A perfect storm. Drug Alcohol Rev 2011; 30:264–70

19. Wunsch MJ, Nakamoto K, Behonick G, Massello W: Opioid deaths in rural Virginia: A description of the high prevalence of accidental fatalities involving prescribed medications. Am J Addict 2009; 18:5–14

20. Yuferov V, Levran O, Proudnikov D, Nielsen DA, Kreek MJ: Search for genetic markers and functional variants involved in the development of opiate and cocaine addiction and treatment. Ann N Y Acad Sci 2010; 1187:184–207

21. Haertzen CA, Kocher TR, Miyasato K: Reinforcements from the first drug experience can predict later drug habits and/or addiction: Results with coffee, cigarettes, alcohol, barbiturates, minor and major tranquilizers, stimulants, marijuana, hallucinogens, heroin, opiates and cocaine. Drug Alcohol Depend 1983; 11:147–65

22. Johnson F, Setnik B: Morphine sulfate and naltrexone hydrochloride extended-release capsules: Naltrexone release, pharmacodynamics, and tolerability. Pain Physician 2011; 14:391–406

23. Kaprio J, Silventoinen K: Advanced methods in twin studies. Methods Mol Biol 2011; 713:143–52

24. Rahmiolu N, Ahmadi KR: Classical twin design in modern pharmacogenomics studies. Pharmacogenomics 2010; 11:215–26

25. Angst MS, Phillips NG, Drover DR, Tingle M, Galinkin JL, Christians U, Swan GE, Lazzeroni LC, Clark JD: Opioid pharmacogenomics using a twin study paradigm: Methods and procedures for determining familial aggregation and heritability. Twin Res Hum Genet 2010; 13:412–25

26. Swan GE, Benowitz NL, Jacob P 3rd, Lessov CN, Tyndale RF, Wilhelmsen K, Krasnow RE, McElroy MR, Moore SE, Wambach M: Pharmacogenetics of nicotine metabolism in twins: Methods and procedures. Twin Res 2004; 7:435–48

27. Angst MS, Ramaswamy B, Davies MF, Maze M: Comparative analgesic and mental effects of increasing plasma concentrations of dexmedetomidine and alfentanil in humans. ANESTHESIOLOGY 2004; 101:744–52

28. van den Nieuwenhuyzen MC, Engbers FH, Burm AG, Lemmens HJ, Vletter AA, van Kleef JW, Bovill JG: Computer-controlled infusion of alfentanil for postoperative analgesia. A pharmacokinetic and pharmacodynamic evaluation. ANESTHESIOLOGY 1993; 79:481–92; discussion 27A

29. Scott JC, Stanski DR: Decreased fentanyl and alfentanil dose requirements with age. A simultaneous pharmacokinetic and pharmacodynamic evaluation. J Pharmacol Exp Ther 1987; 240:159–66

30. Tobias JD: Noninvasive carbon dioxide monitoring during one-lung ventilation: End-tidal versus transcutaneous techniques. J Cardiothorac Vasc Anesth 2003; 17:306–8

31. Oswald WD, Fleischmann UM: Psychometrics in aging and dementia: Advances in geropsychological assessments. Arch Gerontol Geriatr 1985; 4:299–309

32. McNair DM, Loor M, Droppelmann LF: Profile of mood states. San Diego, CA, Educational and Industrial Testing Service, 1992

33. Beck A, Steer R, Brown G: Beck Depression Inventory-Manual, 2nd edition. San Antonio, The Psychological Corporation, 1996

34. Hannelius U, Gherman L, VV, Lindstedt A, Zucchelli M, Lagerberg C, Tybring G, Kere J, Lindgren CM: Large-scale zygosity testing using single nucleotide polymorphisms. Twin Res Hum Genet 2007; 10:604–25

35. Maes HH: ACE Model, Encyclopedia of Statistics in Behavioral Science. Edited by Everitt BS, Howell DC. New York, John Wiley & Sons Ltd, 2005, pp 5–10

36. DeFries JC, Fulker DW: Multiple regression analysis of twin data. Behav Genet 1985; 15:467–73

37. Zahl K: The role of ondansetron and other antiemetics in ambulatory anesthesia. J Clin Anesth 1993; 5:52S–6S

38. Kratz CD, Seifart U, Geldner G, Eberhart LH: Pharmacoeconomic considerations of tropisetron for prophylaxis and treatment of CINV and PONV. Expert Rev Pharmacoecon Outcomes Res 2003; 3:575–85

39. Pueyo FJ, Lpez-Olaondo L, Sanchez-Ledesma MJ, Ortega A, Carrascosa F: Cost-effectiveness of three combinations of antiemetics in the prevention of postoperative nausea and vomiting. Br J Anaesth 2003; 91:589–92

40. Jellish WS, Leonetti JP, Sawicki K, Anderson D, Origitano TC: Morphine/ondansetron PCA for postoperative pain, nausea, and vomiting after skull base surgery. Otolaryngol Head Neck Surg 2006; 135:175–81

41. Dunn KM, Saunders KW, Rutter CM, Banta-Green CJ, Merrill JO, Sullivan MD, Weisner CM, Silverberg MJ, Campbell CI, Psaty BM, Von Korff M: Opioid prescriptions for chronic pain and overdose: A cohort study. Ann Intern Med 2010; 152:85–92

42. Ho MK, Goldman D, Heinz A, Kaprio J, Kreek MJ, Li MD, Munaf MR, Tyndale RF: Breaking barriers in the genomics and pharmacogenetics of drug addiction. Clin Pharmacol Ther 2010; 88:779–91

43. Fraser HF, Van Horn GD, Martin WR, Wolbach AB, Isbell H: Methods for evaluating addiction liability. (A) “Attitude” of opiate addicts toward opiate-like drugs. (B) a short-term “direct” addiction test. J Pharmacol Exp Ther 1961; 133:371–87

44. Wilkinson CJ: The abuse potential of zolpidem administered alone and with alcohol. Pharmacol Biochem Behav 1998; 60:193–202

45. Baylon GJ, Kaplan HL, Somer G, Busto UE, Sellers EM: Comparative abuse liability of intravenously administered remifentanil and fentanyl. J Clin Psychopharmacol 2000; 20:597–606

46. Singhal A, Tripathi BM, Pal HR, Jena R, Jain R: Subjective effects of additional doses of buprenorphine in patients on buprenorphine maintenance. Addict Behav 2007; 32:320–31

47. Mann C, Pouzeratte Y, Eledjam JJ: Postoperative patient-controlled analgesia in the elderly: Risks and benefits of epidural versus intravenous administration. Drugs Aging 2003; 20:337–45

48. Mann C, Pouzeratte Y, Boccara G, Peccoux C, Vergne C, Brunat G, Domergue J, Millat B, Colson P: Comparison of intravenous or epidural patient-controlled analgesia in the elderly after major abdominal surgery. ANESTHESIOLOGY 2000; 92:433–41

49. Edlund MJ, Martin BC, Fan MY, Devries A, Braden JB, Sullivan MD: Risks for opioid abuse and dependence among recipients of chronic opioid therapy: Results from the TROUP study. Drug Alcohol Depend 2010; 112:90–8

50. Lee YY, Kim KH, Yom YH: Predictive models for post-operative nausea and vomiting in patients using patient-controlled analgesia. J Int Med Res 2007; 35:497–507

51. Surratt HL, Inciardi JA, Kurtz SP: Prescription opioid abuse among drug-involved street-based sex workers. J Opioid Manag 2006; 2:283–9

52. McCabe SE, Teter CJ, Boyd CJ, Knight JR, Wechsler H: Nonmedical use of prescription opioids among U.S. college students: Prevalence and correlates from a national survey. Addict Behav 2005; 30:789–805

Cited By:

This article has been cited 1 time(s).

Anesthesiology
Genetic Contributions to Opioid Side Effects: Helix Me, Helix Me Not
Fillingim, RB
Anesthesiology, 117(1): 6-7.
10.1097/ALN.0b013e31825a2e3f
PDF (312) | CrossRef
Back to Top | Article Outline

Supplemental Digital Content

Back to Top | Article Outline

© 2012 American Society of Anesthesiologists, Inc.

Publication of an advertisement in Anesthesiology Online does not constitute endorsement by the American Society of Anesthesiologists, Inc. or Lippincott Williams & Wilkins, Inc. of the product or service being advertised.
Login

Article Tools

Images

Share