0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Article |

Rewarding, Stimulant, and Sedative Alcohol Responses and Relationship to Future Binge Drinking FREE

Andrea C. King, PhD; Harriet de Wit, PhD; Patrick J. McNamara, BS; Dingcai Cao, PhD
[+] Author Affiliations

Author Affiliations: Departments of Psychiatry and Behavioral Neuroscience (Drs King and de Wit and Mr McNamara) and Surgery, Sections of Surgical Research and Ophthalmology and Visual Science (Dr Cao), The University of Chicago, Chicago, Illinois.


Arch Gen Psychiatry. 2011;68(4):389-399. doi:10.1001/archgenpsychiatry.2011.26.
Text Size: A A A
Published online

Context  Excessive consumption of alcohol is a major problem in the United States and abroad. Despite many years of study, it is unclear why some individuals drink alcohol excessively while others do not. It has been postulated that either lower or greater acute responses to alcohol, or both, depending on the limb of the breath alcohol concentration curve, contribute to propensity for alcohol misuse.

Objective  To prospectively assess the relationship of acute alcohol responses to future binge drinking.

Design  Within-subject, double-blind, placebo-controlled, multidose laboratory alcohol challenge study with intensive follow-up. Each participant completed 3 randomized sessions examining responses to a high (0.8 g/kg) and low (0.4 g/kg) alcohol dose and placebo, followed by quarterly assessments for 2 years examining drinking behaviors and alcohol diagnoses.

Setting  Participants recruited from the community.

Participants  High-risk heavy social drinkers aged 21 to 35 years who habitually engage in weekly binge drinking (n = 104) and light drinker controls (n = 86).

Intervention  We conducted 570 laboratory sessions with a subsequent 99.1% follow-up (1506 of 1520).

Main Outcome Measures  Biphasic Alcohol Effects Scale, Drug Effects Questionnaire, cortisol response, Timeline Follow-Back, Drinker Inventory of Consequences–Recent, and DSM-IV alcohol abuse and dependence.

Results  Alcohol produced greater stimulant and rewarding (liking and wanting) responses and lower sedative and cortisol responses in heavy vs light drinkers. Among the heavy drinkers, greater positive effects and lower sedative effects after alcohol consumption predicted increased binge drinking frequency during follow-up. In turn, greater frequency of binge drinking during follow-up was associated with greater likelihood of meeting diagnostic criteria for alcohol abuse and dependence.

Conclusions  The widely held low level response theory and differentiator model should be revised: in high-risk drinkers, stimulant and rewarding alcohol responses even at peak breath alcohol concentrations are important predictors of future alcohol problems.

Trial Registration:  clinicaltrials.gov Identifier: NCT00961792

Figures in this Article

Heavy alcohol drinking is a serious problem in young adults,13 and the personal, physical, familial, and financial consequences of this excessive use are enormous. Excessive drinking is the third leading preventable cause of death in the United States,4 and the cost of ethanol-related health care, loss of productivity, crime, and accidents totals more than $184 billion annually.5 However, it is unclear why some individuals escalate their consumption of alcohol over time to excessive levels while others do not. A greater understanding of the factors that contribute to the escalation and maintenance of heavy drinking, especially in young adults, is essential to guide prevention, public education, and early intervention strategies for alcohol use disorders.

One potential predictor of vulnerability to alcohol use problems is the quality and magnitude of one's acute response to alcohol.68 For example, a widely cited series of studies indicates that individuals with a positive biological family history (FH) of alcoholism, who are known to be at increased risk for developing alcohol dependence,9,10 have lower responses to alcohol on several measures, including subjective alcohol intoxication ratings, body sway, stress hormones, and evoked potential responses, relative to those with a negative FH.1116 In turn, even without regard to FH, lower level response to alcohol predicted the development of a future alcohol use disorder.8 These findings have been cited as support for the low-level response theory articulated by Schuckit,17 which posits that less intense alcohol responses may be associated with insensitivity to internal cues and warning signs to stop drinking, resulting in excessive consumption. This seminal work supports the idea that responses to an acute dose of alcohol may predict future drinking behaviors, above and beyond other personal risk factors such as FH.

However, there is also evidence inconsistent with the low-level response theory, suggesting instead that individuals with a FH of alcoholism or frequent heavy drinking experience greater alcohol-induced positive-like subjective and objective effects.7,1825 To resolve this apparent inconsistency, Newlin and Thomson6 proposed the differentiator model, which posits that high-risk persons have greater stimulant-like effects from alcohol during the early portion of a drinking episode, when breath alcohol concentrations (BrAC) are rising, and less sedative-like effects when BrAC are decreasing. Thus, the low-level response theory proposed by Schuckit17 may be based primarily on sedative or impairing effects during descending BrAC. Although there has been initial support for the differentiator model7 and although stimulant responses to alcohol may lead to greater acute alcohol consumption,2630 thus far studies have been limited by modest sample sizes and cross-sectional designs. In fact, the only longitudinal study examining the role of acute alcohol responses to future drinking problems was conducted more than 15 years ago by Schuckit and Smith,8 and the alcohol responses measured were primarily negative intoxicating effects.

Besides FH,9,31 there are other risk factors for the development of alcohol use disorders, including early age onset of drinking or first intoxication1,32,33 and frequent early adult “binge” drinking34—ie, consuming 5 or more drinks on 1 occasion for men, 4 or more for women.35 In terms of the latter, habitual binge drinking during adolescence and young adulthood is highly prevalent in the United States and represents a hazardous and harmful behavior in and of itself.3,3638 For reasons that are not understood, some young adult binge drinkers progress to become problem drinkers, whereas others reduce their consumption during middle and later adulthood.3946 Among a sample of primarily young, white, male alcohol abusers, Hasin and colleagues47 showed that several years after an initial assessment, 46% remitted and no longer met criteria for an alcohol use disorder, whereas 24% continued with alcohol abuse and 30% progressed to alcohol dependence. Preliminary cross-sectional studies1921 have shown that young adult binge drinkers exhibit quantitatively and qualitatively different alcohol responses than lighter drinkers. However, a key unanswered question is whether positively or negatively valenced alcohol responses predict subsequent drinking trajectories and alcohol problems over time.

The present study was a within-subject, double-blind, placebo-controlled, dose-ranging alcohol challenge procedure followed by a longitudinal follow-up of alcohol drinking behaviors in high-risk young adult heavy social drinkers compared with controls who were light social drinkers. The first goal was to determine whether the groups differed on subjective and objective responses to alcohol using measures sensitive to acute positive and sedative effects across the BrAC curve. The second goal was to determine whether acute alcohol responses predicted subsequent drinking patterns and alcohol-related diagnoses during a 2-year follow-up interval in each of the groups. This design provided a systematic evaluation of the low-level response model and differentiator model in the etiology of alcohol use disorders.

PARTICIPANTS

The Chicago Social Drinking Project was approved by The University of Chicago Institutional Review Board, and all participants provided written informed consent. Of the initial 196 participants who attended at least 1 laboratory session, 190 completed all 3 sessions and therefore comprised the sample for the study. Laboratory sessions were conducted from March 2004 to July 2006, with all follow-ups completed by July 2008. Inclusion criteria were age 21 to 35 years; a body mass index (calculated as weight in kilograms divided by height in meters squared) of 19 to 30; good general health with no current or past major medical or psychiatric disorders, including alcohol and substance dependence; and no current medications that would interact with study procedures. Participants were selected to belong to 1 of 2 distinct drinking groups on the basis of established guidelines for heavy/binge and light drinking3,35,48 and to be consistent with prior studies.21,4951 These groups were heavy drinkers (HD; n = 104 [44 women]) and light drinkers (LD; n = 86 [41 women]). Criteria for the HD group were (1) consuming at least 10 but no more than 40 standard alcoholic drinks per week and (2) engaging in regular binge drinking, defined as consuming 5 or more drinks on an occasion (≥4 for women) 1 to 5 times on average per week as their predominant adult pattern. Criteria for the LD group were (1) consuming at least 1 but no more than 5 standard alcoholic drinks per week and (2) engaging in infrequent binge drinking—ie, 5 or fewer times per year, but allowing for 1 past interval of a maximum of 6 months' duration of up to twice-weekly binge drinking (ie, typically during college-aged years). All attempts were made to match the groups on age as well as sex and race.

SCREENING AND EXPERIMENTAL PROCEDURES

Participants were recruited through advertisements in local media, on Internet Web sites, by flyers, and by word-of-mouth referrals. In-person screening included administration of the Alcohol Quantity-Frequency52 and Timeline Follow-Back53,54 interviews to assess alcohol drinking patterns during the past 3 to 6 months. In addition, the screening portion included self-report questionnaires, including the Alcohol Use Disorders Identification Test,55,56 the Beck Depression Inventory,57 and the Spielberger State-Trait Anxiety Inventory.58 Participants took part in a modified Structured Clinical Interview for DSM-IV Axis 1 Disorders–Patient Edition59 that included the screening module and specific modules for mood episodes and substance use disorders, as well as a brief physical examination and urine and blood testing. Exclusion criteria were history of major Axis I psychiatric conditions, including alcohol or substance dependence (excluding nicotine), abnormal blood chemistry or hepatic panel results (≥2 SD above the mean), positive breathalyzer or urine toxicology screen (cocaine, opiates, benzodiazepines, amphetamines, barbiturates, and phencyclidine), or positive pregnancy test. Recreational marijuana use was allowed, provided it was used no more than 3 times weekly. Participants also completed a 2-generational FH tree for alcohol use disorders and an interview of FH Research Diagnostic Criteria for drinking consequences.60 The term FH positive was defined as having at least 1 primary biological relative, or 2 or more secondary biological relatives, with an alcohol use disorder, and FH negative was defined as having no biological relatives in the past 2 generations with an alcohol use disorder. The 45 participants with either uncertain or intermediate FH were not included in analyses pertaining to FH. Participants also completed the Sensation Seeking Scale–Form V.61 As in other studies,62 we modified the disinhibition subscale to remove 3 of the 10 items pertaining to behavior associated with excessive alcohol drinking or partying.

PHASE 1: LABORATORY SESSIONS

The first phase of the study consisted of 3 randomized 5-hour laboratory sessions, each beginning between 3 and 5 PM. Subjects were tested individually, and sessions were conducted in a comfortable room and separated by at least 48 hours. Women were examined regardless of menstrual cycle phase because acute alcohol responses do not vary by menstrual phase.63 To reduce potential alcohol expectancies, participants were told that they might receive a beverage containing alcohol, a stimulant, a sedative, a placebo, or a combination of these substances. Participants were randomized to dose order and tested under double-blind conditions. In each session, they ingested a beverage containing placebo (0.0 g/kg; 1% volume of ethanol as taste mask), a low alcohol dose (0.4 g/kg), or a high alcohol dose (0.8 g/kg). The beverage was administered in clear plastic-lidded cups in 2 equal portions that were each consumed during a 5-minute interval and separated by a 5-minute interim rest. The beverages contained 190-proof ethanol prepared with water, flavored drink mix, and a sucralose-based sugar substitute. The mean total beverage volume was 471 mL (range, 327–668 mL), and doses for women were 85% of those of men to adjust for sex differences in total body water.64,65

Upon arrival, the participant completed a questionnaire to confirm compliance with 48-hour drug and alcohol abstinence instructions and 3-hour abstinence from food, caffeine, and smoking. Participants underwent several objective tests to verify compliance with pretesting instructions; these tests included carbon monoxide (<10 ppm) and alcohol breathalyzer tests and, before at least 1 session, a urine toxicology screen. Women also undertook a urinary human chorionic gonadotropin test to verify nonpregnancy before each session. After these tests, the participant consumed a low-fat snack at 20% of daily kilocalorie needs, based on a macronutrient distribution of 55% of kilocalories from carbohydrates, 10% from protein, and 35% from fat.66

Forty-five minutes after arrival, the participant completed baseline subjective and objective measures and then consumed his or her study beverage over 15 minutes (5 minutes for each portion separated by a 5-minute rest interval) in the presence of the research assistant.21,50,67 Subjective and objective measures were repeated during the ascending limb to the estimated peak BrAC (30 and 60 minutes, respectively, after beverage initiation) and the descending limb and alcohol elimination phase (120 and 180 minutes). The breathalyzer (Alco-Sensor IV; Intoximeter, St Louis, Missouri) was programmed to display readings of 0.000 mg/dL, with the actual levels later downloaded to a computer by a separate assistant. Between time points, to circumvent potential boredom, the participant was permitted to view movies or read magazines provided by the study in a comfortable, living room–like laboratory testing room. At the end of each session, when the BrAC was 0.04 mg/L (0.04%) or lower, the participant was transported home by a car service to ensure safety. At the end of the third session, the participant was debriefed and received instructions and schedule information for the follow-up phase. Participants received a $200 check for participation in the first phase ($50 per session and a $50 bonus for completing all 3 sessions).

SUBJECTIVE EFFECTS

Repeated assessments of subjective states by the Biphasic Alcohol Effects Scale68 and the Drug Effects Questionnaire69 comprised the main dependent subjective measures. The Biphasic Alcohol Effects Scale has shown good reliability and validity and yields 2 subscales, stimulation and sedation, shown to correspond to alcohol effects often observed on ascending and descending BrAC, respectively.68 As validated in a previous report,70 we modified the original Biphasic Alcohol Effects Scale instructions so as not to divulge beverage contents, and we administered the scale at predrink baseline. The Drug Effects Questionnaire consisted of 100-mm visual analog scales for the following postbeverage ratings: feel drug: “do you feel any drug effects?”; like: “do you like the effects you are feeling now?”; and want more: “would you like more of what you consumed, right now?”

OBJECTIVE EFFECTS

Saliva samples for cortisol determination were also collected at each time point using a cotton Salivette (Sarstedt AG & Company, Nümbrecht, Germany). Samples were stored in a –80°C freezer and assayed using a high-sensitivity enzyme immunoassay kit that was standardized and validated at The University of Chicago Clinical Research Center Core Laboratory. The inter- and intra-assay coefficients of variation were 6.88% and 7.12%, respectively. Other measures (eg, psychomotor performance and eye movements) were also obtained in the sessions, and these data are reported elsewhere.67,7174

PHASE 2: FOLLOW-UP

The second phase of the study was a 2-year follow-up interval. After completion of the laboratory phase, participants were scheduled for 8 quarterly follow-ups to assess alcohol use patterns. The main dependent measure was frequency of binge drinking, defined as the percentage of days in the past month when the participant consumed at least 5 alcoholic drinks (4 for women). Binge frequency and other drinking variables (typical quantity, frequency of any drinking, and maximum quantity) were based on the Timeline Follow-Back for the 4 previous Monday-to-Sunday weeks. At 12 and 24 months, the Alcohol Use Disorders Identification Test, the Drinker Inventory of Consequences–Recent,75 and Structured Clinical Interview for DSM-IV Axis 1 Disorders–Patient Edition modules for past-year alcohol abuse and dependence were also administered. Participants were compensated with $10 gift cards for each quarterly follow-up and $40 gift cards for the annual follow-ups. They also received $10 to $20 gift card incentives if they completed their follow-ups in a timely manner.

STATISTICAL ANALYSIS

Demographic and drinking characteristics for the groups were examined by t tests and χ2 tests, as appropriate. Experimental session data raw scores were analyzed by a series of 2 group × 3 dose × 5 time repeated-measures analyses of variance. Analyses were then repeated controlling for sociodemographic risk factors associated with alcohol misuse, including sex, race (white vs nonwhite), FH (positive or negative), educational level, and disinhibited personality.36,39,43,46,7678

To examine the relationship of subjective alcohol response (stimulation, like, want more, and sedation) to future drinking, change scores for these 4 responses were calculated as follows: (high dose [60-minute − baseline] − placebo [60-minute − baseline]). The main calculations were based on scores at 60 minutes (peak BrAC) and for the high dose because this time point and dose level produced consistent changes on study measures and represent clinically relevant BrAC levels, ie, the US legal limit for intoxicated driving. Change scores for the low dose at peak BrAC were also examined to determine whether there were threshold effects. In addition, change scores for ascending (15-minute) and descending (120-minute) BrAC limbs were used to examine the differentiator model in detail. For objective alcohol response (cortisol), the 180-minute time point was used in the change score calculation because this interval corresponded with the peak cortisol levels and the delay in detecting levels in saliva.79

Participants were classified into different drinking trajectory groups on the basis of their frequency of binge drinking during the follow-up interval, using a discrete mixture modeling approach, with the number of trajectory groups determined by the Bayesian Information Criterion for model selection.80 Linear trend analyses81 were conducted separately for the light drinking and heavy drinking groups to examine the relationship of alcohol response change scores to the drinking trajectory groups. These analyses controlled for the main sociodemographic risk factors associated with alcohol misuse by including them as covariates. Finally, the association of each participant's alcohol responses to quarterly frequency of subsequent binge drinking was examined using a generalized estimation equations modeling approach82 with alcohol response score, measurement time, and their interaction as covariates. The analysis used a logit link function because of the binomial distribution of binge drinking frequency. Composite generalized estimation equations models were then conducted to examine the effects of each response after controlling for the other significant responses. Given the low frequency of binge drinking among LD, generalized estimation equations analysis using any drinking frequency as the outcome was also conducted. Finally, mediational analysis83 was conducted to examine whether the binge drinking pattern during follow-up mediated the relationship between the alcohol responses and DSM-IV alcohol use disorder diagnoses (alcohol abuse and dependence).

OVERVIEW

The groups were similar on most demographic characteristics (Table 1). As expected, the HD were higher than the LD on alcohol drinking and related variables, but liver enzyme levels did not differ between groups. The HD also had fewer years of education and higher disinhibited personality trait scores than the LD, both of which were included as covariates in all analyses. In the laboratory sessions, the BrAC changes over time confirmed the expected pattern of ascending and descending limbs. Although the HD exhibited higher BrAC at the high dose than did the LD (group×dose×time: F5,940 = 3.43; P < .01; Figure 1A), the difference between groups was small (range, 0.001-0.008 mg/L at various time points), and covarying for BrAC did not alter the main findings.

Place holder to copy figure label and caption
Figure 1.

Heavy and light drinkers' breath alcohol concentrations (BrAC) (A) and Drug Effects Questionnaire (DEQ; range, 0.0-1.0) feel drug ratings (B) during the sessions. For feel drug, light drinkers had higher ratings during the high dose than did heavy drinkers at all time points; P < .001. The shaded bar indicates the alcohol drinking interval from time 0 to 15 minutes. Error bars indicate standard error.

Graphic Jump Location
Table Graphic Jump LocationTable 1. Participant Characteristics
ALCOHOL RESPONSE: HD VS LD

Alcohol dose-dependently increased self-report ratings of feel drug (Figure 1B), with the LD more sensitive than the HD at the high dose of alcohol (group×dose×time: F6,1128 = 4.57; P < . 001). However, the groups differed markedly in the nature of their alcohol responses. The HD reported greater positive and rewarding subjective effects of alcohol than the LD, particularly at the high dose (Figure 2A-C). High-dose alcohol significantly increased Biphasic Alcohol Effects Scale stimulation ratings in the HD vs the LD during the early, ascending limb of the BrAC (group×dose×time: F8,1504 = 3.21; P = .001) and both like and want more ratings throughout both limbs (group×dose: like, F2,376 = 15.68; P < .001; and want more, F2,376 = 12.04; P < .001). Furthermore, the low dose of alcohol also increased ratings for like and want more in the HD vs the LD (Figure 2).

Place holder to copy figure label and caption
Figure 2.

Heavy and light drinkers' subjective stimulant and rewarding responses, including the Biphasic Alcohol Effects Scale (BAES; range, 10-70) stimulation (A) and the Drug Effects Questionnaire (DEQ; range, 0.0-1.0) for like (B) and want more (C) during the sessions. Post hoc analysis of a significant group×dose×time interaction revealed that for stimulation at the high dose, heavy drinkers had higher ratings than light drinkers at 30 minutes (P< .05) and 60 minutes (P< .06). Post hoc analysis of a significant group×dose interaction for like at the high dose revealed that heavy drinkers had higher ratings than light drinkers at 30, 60, and 120 minutes (P< .01 for all), and a significant group×dose interaction for want more at the high dose and low dose revealed that heavy drinkers had higher ratings than light drinkers at all time points (P< .001 for all). The shaded bar indicates the alcohol drinking interval from time 0 to 15 minutes. Error bars indicate standard error.

Graphic Jump Location

Increases in Biphasic Alcohol Effects Scale sedation were noted in both groups after alcohol consumption, with the LD reporting higher sedation than the HD throughout both BrAC limbs after the high dose (group×dose×time: F8,1504 = 4.87; P < .001; Figure 3A). Sedation also increased after the low dose, albeit to a lesser extent than with the high dose, with higher ratings in the LD vs the HD at peak BrAC. Increases in salivary cortisol levels were evident in the LD during the descending limb after the high dose, but there were no cortisol increases in the HD (group×dose×time: F8,1504 = 6.03; P < .001; Figure 3B). Session order did not affect the main results (F ≥ 3.20 for all; P < .001 for all). The magnitude of the group differences on alcohol response change scores was moderate, with effect sizes computed by Cohen's d equaling 0.47 for stimulation, 0.46 for like, 0.53 for want more, 0.53 for sedation, and 0.36 for cortisol.

Place holder to copy figure label and caption
Figure 3.

Heavy and light drinkers' Biphasic Alcohol Effects Scale (BAES; range, 10-70) sedation (A) and salivary cortisol (B) responses during the sessions. Post hoc analysis of a significant group×dose×time interaction revealed that for sedation at the high alcohol dose, light drinkers had higher ratings than heavy drinkers at 30, 60, and 120 minutes (P <.001 for all) and at the low dose, light drinkers had higher ratings than heavy drinkers at 60 minutes (P < .05). Post hoc analysis of a significant group×dose×time interaction for cortisol at the high dose revealed that the light drinkers had higher levels than heavy drinkers at 120 and 180 minutes (P < .01 for all). The shaded bar indicates the alcohol drinking interval from time 0 to 15 minutes. Error bars indicate standard error. (To convert cortisol to nanomoles per liter, multiply by 27.588).

Graphic Jump Location

The aforementioned differences between the HD and the LD on stimulation, like, want more, sedation, and cortisol remained significant after controlling for sex, race, educational level, FH, and disinhibited personality. There were no significant effects of FH of alcoholism (F ≤ 1.27 for all; P≥ .27 for all) or interactions of FH and drinking group (F ≤ 1.25 for all; P≥ .26 for all) or FH and sex (F ≤ 0.93 for all; P≥ .47 for all) on alcohol response measures. Exploratory analyses examining a narrower criterion for FH—ie, men with alcoholic fathers (n = 14) vs men with negative FH (n = 44)—also were not significant for most alcohol response measures, including stimulation, sedation, like, and cortisol (F ≤ 1.06 for all; P> .39 for all). However, there was a trend for an interaction in these men in ratings for want more (FH×dose×time, =   2.03, = .06), which was driven by higher ratings during placebo for men with positive FH vs men with negative FH.

DRINKING BEHAVIORS DURING THE FOLLOW-UP INTERVAL

Retention rates were very high in this study, with 1506 of a possible 1520 quarterly follow-ups (99.1%) completed. One male LD withdrew from the study without specifying the reason during the second year of follow-up. No other participants withdrew or were lost to contact during the 2-year interval. We imputed missing data (0.9%) by taking the average of values from the 2 surrounding follow-ups or the last observation carried forward, as warranted. During the 2 years of follow-up, drinking rates remained significantly different between the groups: LD consumed alcohol on 23% (range, 2%-62%) of days, whereas the HD consumed alcohol on 46% (range, 9%-89%) of days (P < .001), and LD engaged in binge drinking on 1.4% (range, 0%-8%) of days compared with 26% (range, 3%-54%) of days for HD (P < .001).

Quarterly follow-ups revealed that 4 trajectory patterns best described the binge drinking patterns among HD and 2 patterns best described the LD (Table 2). Figure 4 depicts the 2 trajectory groups for LD: non-binge (81.4%) and infrequent binge (18.6%) and, consistent with other studies,46,8487 the 4 trajectory groups for HD: exacerbating binge (6.7%), high-frequency binge (25.0%), moderate-frequency binge (59.6%), and gradual maturing binge (8.7%). The distinction of these trajectory groups was supported by other alcohol-related outcomes during follow-up, including typical and maximal drinking quantity, alcohol-related consequences, and DSM-IV diagnoses of alcohol abuse and dependence (Table 3).

Place holder to copy figure label and caption
Figure 4.

Trajectory groups during the 2-year follow-up. Trajectory data were based on the self-reported frequency of past-month binge drinking days obtained at each quarterly interval. A discrete mixture modeling approach determined by the Bayesian Information Criterion for model selection revealed a 2-trajectory group (nonbinge and infrequent binge) model for the light drinkers and a 4-trajectory group (gradual maturing, moderate-frequency binge, high-frequency binge, and exacerbating) model for the heavy drinkers best described the data.

Graphic Jump Location
Table Graphic Jump LocationTable 2. Bayesian Information Criterion (BIC) and Model Selection
Table Graphic Jump LocationTable 3. Alcohol Use Disorder Diagnoses and Other Drinking Characteristics During the 2-Year Follow-up Among the Trajectory Groupsa
RELATIONSHIP OF ALCOHOL RESPONSE TO SUBSEQUENT DRINKING BEHAVIORS

In LD, none of the alcohol response measures (peak BrAC change scores at high dose or low dose) predicted trajectory group, binge, or any drinking outcome during the follow-up. For HD at the high dose, greater ratings of like (r = +0.37; P < .001) and want more (r = +0.38; P < .001) and lower ratings of sedation (r = −0.20; P < .05) were significantly linearly associated with subsequent drinking trajectory group, indicating an overall moderate effect (Figure 5). Ratings at the low alcohol dose for like (r = +0.24; P < .05) and want more (r = +0.23; P < .05) were also predictive of drinking trajectory group, but the association was weaker than for the high dose. Cortisol response for either high or low dose did not predict drinking behavior.

Analyses using the generalized estimation equations approach confirmed that in HD, greater alcohol stimulation, greater liking, greater wanting, and lower sedation at the high dose predicted greater binge drinking frequency during follow-up (P < .001 for all). Greater liking and wanting responses to the low dose of alcohol also related to future binge drinking, but again these were not as strong as those observed with the high dose. Composite generalized estimation equations models that included all the significant alcohol responses indicated that greater alcohol stimulation (P < .05), greater liking (P < .001), and lower sedation (P < .05) for high dose remained significant predictors of future drinking. These analyses were repeated for alcohol responses during the ascending (30-minute) and descending (120-minute) BrAC limbs. On the ascending limb, alcohol wanting (P < .001) was a significant predictor of future drinking, and on the descending limb, liking (P < .001), wanting (P < .01), and less sedation (P < .01) predicted future drinking. Although disinhibited personality and FH predicted future drinking in HD, controlling for these factors did not alter the relationships between alcohol responses and subsequent drinking behavior. For HD, increases in liking (odds ratio [OR] {SE}, 29.81 {29.42}; P = .001) and want more (OR [SE], 7.43 [5.32]; P = .005) were significantly associated with alcohol use disorder diagnosis during follow-up. When binge drinking pattern was included in the model, the magnitude of association for alcohol response to alcohol use disorder was reduced (like: OR [SE], 14.64 [15.04]; P < .01; and want more: OR [SE], 3.51 [2.64]; P = .10), suggesting that binge drinking partially (like) or fully (want more) mediated the relationship between alcohol response and subsequent alcohol use disorder.

We have replicated and extended our group's prior preliminary findings20,21 demonstrating that HD report greater acute subjective positive effects and lesser sedative and cortisol response to an intoxicating dose of alcohol than do LD. Multivariate models in the HD revealed that both the positive (more stimulation, like, and want more) and negative (less sedation) response factors were significant independent predictors of binge drinking during the subsequent 2 years. These findings were observed even after taking into account other risk factors for problem drinking, such as sex, race, FH, educational level, and disinhibited personality.2,31 Thus, young adult HD who experience heightened stimulant and rewarding and diminished sedative effects of alcohol are at risk for escalation of binge drinking over time. They also consume more alcohol overall (typical frequency, quantity, and maximum quantity) and experience clinically relevant outcomes, such as greater alcohol consequences and higher rates of DSM-IV alcohol use disorders (Table 2)

Although the finding that heavier drinkers enjoy the effects of alcohol more than lighter drinkers seems intuitive, there has been limited evidence thus far to support this notion. Indeed, the prevailing model, the low-level response theory, posits that persons who experience a lower level of response to alcohol will engage in heavier drinking over time because they do not feel the internal cues of intoxication.88 Our data partially support this theory because HD exhibited reduced subjective sedative and cortisol responses after drinking, compared with the responses of LD, and lack of sedative responses predicted future drinking. However, alcohol markedly increased positive-like effects during ascending to peak BrAC in this group, and these responses also predicted future drinking. In contrast, LD experienced significant increases in sedation during the ascending limb that was sustained for several hours, without concomitant stimulating or rewarding effects. Collectively, these responses may serve as a protective factor underlying these drinkers' ability to “put the brakes on” and limit their drinking. Taken together, the results indicate that the low-level response theory should be revised to include heightened sensitivity to rewarding and stimulating alcohol effects as equally important predictors as lack of sedative responses in the development and maintenance of problematic drinking among at-risk persons. Furthermore, we propose a modified differentiator model to focus on stimulant, rewarding, and sedative effects without connection to a specific BrAC limb or, conversely, to simplify the model and to focus on effects observed during peak BrAC.

The present study findings are also relevant to the incentive-sensitization theory of addiction.8991 This theory posits that sensitization and crucial neuroadaptations within mesolimbic dopamine systems92,93 may underlie the motivational reward properties (wanting) of a drug, but not the hedonic reward value (liking) of the drug, in persons with substance dependence. However, in this study, we observed that among nondependent HD, both motivational and hedonic aspects of reward were associated with increased frequency of binge drinking over time. Interestingly, in composite prediction models, wanting more alcohol during the ascending and descending limbs of the BrAC curve predicted subsequent binge drinking during follow-up. We may speculate that early in a binge drinking episode, alcohol may elicit heightened desire for alcohol, leading to impaired control and continued drinking. As modeled in this acute administration paradigm, abrupt cessation of drinking results in a sharp decrease in stimulation and liking, with sustained wanting that may underlie desire for further drinking to offset reduction of hedonic effects. Although acute responses to the high alcohol dose producing peak BrACs of 0.08% to 0.09% were more informative than responses to the lower dose, future studies of higher alcohol doses or sustained drinking for a longer interval may help discern whether positive effects decrease (ie, acute tolerance), remain elevated, or increase over time. Such studies would offer an ecologically valid model of more extreme binge episodes. Indeed, among the highly frequent and exacerbating HD binge drinkers, maximal drinking amounts were from 11 to 14 drinks on an occasion (Table 3), resulting in BrACs that would likely be 2 to 3 times the legal limit for driving.

There were several strengths of this study, including the use of a within-subjects, dose-ranging, and placebo-controlled design with intensive follow-up. Responses were obtained at baseline and repeated at several junctures during the course of the BrAC curve, expectancies were minimized by blinding beverage content, and ecological validity was maintained by using a trained research assistant to interact with the participant in a quasi-social context, albeit a paradigm that did not completely mimic all aspects of social drinking. Drinking outcomes were validated by assessment of alcohol consequences and DSM-IV diagnoses. Finally, the results were observed within a diverse sample of both men and women of various racial backgrounds, which served to increase generalizability, in contrast to prior studies focusing on white male participants.8,18,23 This latter issue may be relevant in terms of the lack of systematic FH effects observed for acute subjective and objective alcohol responses, as reported by Brumback et al67 and Roche and King.74 Given that participants were enrolled regardless of FH, the subgroup of sons of alcoholic fathers was relatively small, potentially reducing statistical power to detect FH effects. However, positive FH did predict increases in binge drinking over time, and similar to prior research findings, alcohol responses were predictive of future drinking regardless of FH classification.8 Further research using a priori stratifications of both drinking pattern and FH may provide a more thorough examination of the relationship and possible interactive effects of these risk factors to alcohol response and future drinking behaviors.

The study was limited by the legal requirement that participants be a minimum of 21 years old to be given alcohol, so it is unclear whether the findings can be generalized to even younger drinkers. This is particularly important because the ages of 18 to 20 years represent the highest prevalence of binge drinking and alcohol use disorders.36

Place holder to copy figure label and caption
Figure 5.

Heavy and light drinkers' trajectory groups’ initial alcohol responses, including the Drug Effects Questionnaire (DEQ; range, 0.0-1.0) for like (A) and want more (B) and the Biphasic Alcohol Effects Scale (BAES; range, 10-70) for stimulation (C) and sedation (D). Alcohol responses are indicated as the mean for each trajectory group based on participants' change score: high dose (60 minutes - baseline) − placebo (60 minutes - baseline). In light drinkers, there were no significant associations of alcohol response to trajectory group. In heavy drinkers, higher ratings for like (P< .001) and want more (P< .001) and lower ratings for sedation (P< .05) were significantly linearly associated with drinking trajectory group. Error bars indicate standard error.

Graphic Jump Location

Second, participants' average age was 25 years, which for many HD corresponds to 5 to 7 years of regular binge drinking. Therefore, it was not possible to determine whether alcohol-induced responses were constitutional or acquired with repeated exposure to alcohol. This question may be best addressed using animal models. Finally, although the quarterly follow-up assessments after the laboratory sessions were sufficiently frequent to show fluctuations in drinking for 2 years, the length of this interval may not have been adequate for the full range of drinking and related alcohol use disorders to emerge. To address this issue, we plan to continue to follow up these participants to examine their drinking patterns over a longer time.

In summary, we propose revisions to current theories of the role of alcohol response to subsequent drinking problems in at-risk individuals, and we set forth a modified differentiator model to focus on both positive (stimulant, as well as hedonic and motivational rewarding) and sedative alcohol effects either without ties to a specific BrAC limb, or simply at peak BrAC. Heavy drinkers experience markedly different responses to alcohol than do LD. For LD, an intoxicating dose of alcohol produces tired and sluggish feelings and activates release of the stress hormone cortisol. During a 2-year interval, few of them increased their drinking. In contrast, HD showed a variety of drinking trajectories during the 2 years of follow-up, and those experiencing less positive and more sedative acute alcohol effects gradually matured out of binge drinking over time. On the other hand, HD with heightened rewarding effects of alcohol perpetuated and increased binge drinking frequency over time, thereby increasing the likelihood of meeting DSM-IV diagnoses of alcohol abuse and dependence.

Correspondence: Andrea C. King, PhD, Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, 5841 S Maryland Ave (MC-3077), Chicago, IL 60637 (aking@bsd.uchicago.edu).

Submitted for Publication: May 4, 2010; final revision received September 14, 2010; accepted October 11, 2010.

Author Contributions: Drs King and Cao and Mr McNamara had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grant R01-AA013746 from the National Institute on Alcohol Abuse and Alcoholism; grant P30-CA14599 from The University of Chicago Comprehensive Cancer Center; and grant UL1 RR024999 from the National Center for Research Resources, an institute of the National Institutes of Health.

Additional Contributions: Appreciation is extended to Sean O’Connor, MD, Henry R. Kranzler, MD, and Stephanie O’Malley, PhD, for advice during this study and for their comments on an earlier version of the paper. Thanks also to Sandra Yu Rueger, PhD, for advice on technical writing and revisions; Constantine Trela, BA, for administrative and editorial assistance; and Royce Lee, MD, for medical supervision; the staff of the Clinical and Translational Service Awards at the General Clinical Research Center; and Irene Tobis, PhD, Curt Van Riper, BS, Alyssa Epstein, PhD, Adrienne Dellinger, BS, Lauren Kemp, BS, Ty Brumback, MS, and Megan Conrad, BS, for their role in data collection and database management.

Chou  SPPickering  RP Early onset of drinking as a risk factor for lifetime alcohol-related problems. Br J Addict 1992;87 (8) 1199- 1204
PubMed Link to Article
Naimi  TSBrewer  RDMokdad  ADenny  CSerdula  MKMarks  JS Binge drinking among US adults. JAMA 2003;289 (1) 70- 75
PubMed Link to Article
Wechsler  HLee  JEKuo  MSeibring  MNelson  TFLee  H Trends in college binge drinking during a period of increased prevention efforts: findings from 4 Harvard School of Public Health College Alcohol Study surveys: 1993-2001. J Am Coll Health 2002;50 (5) 203- 217
PubMed Link to Article
Mokdad  AHMarks  JSStroup  DFGerberding  JL Actual causes of death in the United States, 2000. JAMA 2004;291 (10) 1238- 1245
PubMed Link to Article
Harwood  H Updating Estimates of the Economic Costs of Alcohol Abuse in the United States: Estimates, Update Methods, and Data.  Bethesda, MD National Institutes of Health2000;
Newlin  DBThomson  JB Alcohol challenge with sons of alcoholics: a critical review and analysis. Psychol Bull 1990;108 (3) 383- 402
PubMed Link to Article
Newlin  DBRenton  RM High risk groups often have higher levels of alcohol response than low risk: the other side of the coin. Alcohol Clin Exp Res 2010;34 (2) 199- 202
PubMed Link to Article
Schuckit  MASmith  TL An 8-year follow-up of 450 sons of alcoholic and control subjects. Arch Gen Psychiatry 1996;53 (3) 202- 210
PubMed Link to Article
Cloninger  CRBohman  MSigvardsson  S Inheritance of alcohol abuse: cross-fostering analysis of adopted men. Arch Gen Psychiatry 1981;38 (8) 861- 868
PubMed Link to Article
Goodwin  DWSchulsinger  FHermansen  LGuze  SBWinokur  G Alcohol problems in adoptees raised apart from alcoholic biological parents. Arch Gen Psychiatry 1973;28 (2) 238- 243
PubMed Link to Article
Berman  SMWhipple  SCFitch  RJNoble  EP P3 in young boys as a predictor of adolescent substance use. Alcohol 1993;10 (1) 69- 76
PubMed Link to Article
Hill  SYSteinhauer  SLowers  LLocke  J Eight-year longitudinal follow-up of P300 and clinical outcome in children from high-risk for alcoholism families. Biol Psychiatry 1995;37 (11) 823- 827
PubMed Link to Article
Schuckit  MA Subjective responses to alcohol in sons of alcoholics and control subjects. Arch Gen Psychiatry 1984;41 (9) 879- 884
PubMed Link to Article
Schuckit  MA Ethanol-induced changes in body sway in men at high alcoholism risk. Arch Gen Psychiatry 1985;42 (4) 375- 379
PubMed Link to Article
Schuckit  MAGold  ERisch  C Plasma cortisol levels following ethanol in sons of alcoholics and controls. Arch Gen Psychiatry 1987;44 (11) 942- 945
PubMed Link to Article
Schuckit  MAGold  EOCroot  KFinn  PPolich  J P300 latency after ethanol ingestion in sons of alcoholics and in controls. Biol Psychiatry 1988;24 (3) 310- 315
PubMed Link to Article
Schuckit  MA Self-rating of alcohol intoxication by young men with and without family histories of alcoholism. J Stud Alcohol 1980;41 (3) 242- 249
PubMed
Conrod  PJPeterson  JBPihl  RO Reliability and validity of alcohol-induced heart rate increase as a measure of sensitivity to the stimulant properties of alcohol. Psychopharmacology (Berl) 2001;157 (1) 20- 30
PubMed Link to Article
Erblich  JEarleywine  M Behavioral undercontrol and subjective stimulant and sedative effects of alcohol intoxication: independent predictors of drinking habits? Alcohol Clin Exp Res 2003;27 (1) 44- 50
PubMed Link to Article
Holdstock  LKing  ACde Wit  H Subjective and objective responses to ethanol in moderate/heavy and light social drinkers. Alcohol Clin Exp Res 2000;24 (6) 789- 794
PubMed Link to Article
King  ACHoule  Tde Wit  HHoldstock  LSchuster  A Biphasic alcohol response differs in heavy versus light drinkers. Alcohol Clin Exp Res 2002;26 (6) 827- 835
PubMed Link to Article
Finn  PRPihl  RO Men at high risk for alcoholism: the effect of alcohol on cardiovascular response to unavoidable shock. J Abnorm Psychol 1987;96 (3) 230- 236
PubMed Link to Article
Gianoulakis  CBéliveau  DAngelogianni  PMeaney  MThavundayil  JTawar  VDumas  M Different pituitary beta-endorphin and adrenal cortisol response to ethanol in individuals with high and low risk for future development of alcoholism. Life Sci 1989;45 (12) 1097- 1109
PubMed Link to Article
Peterson  JBPihl  ROGianoulakis  CConrod  PFinn  PRStewart  SHLeMarquand  DGBruce  KR Ethanol-induced change in cardiac and endogenous opiate function and risk for alcoholism. Alcohol Clin Exp Res 1996;20 (9) 1542- 1552
PubMed Link to Article
Morean  MECorbin  WR Subjective response to alcohol: a critical review of the literature. Alcohol Clin Exp Res 2010;34 (3) 385- 395
PubMed Link to Article
Corbin  WRGearhardt  AFromme  K Stimulant alcohol effects prime within session drinking behavior. Psychopharmacology (Berl) 2008;197 (2) 327- 337
PubMed Link to Article
DeWit  HPierri  JJohanson  CE Assessing individual differences in ethanol preference using a cumulative dosing procedure. Psychopharmacology (Berl) 1989;98 (1) 113- 119
PubMed Link to Article
de Wit  HUhlenhuth  EHPierri  JJohanson  CE Individual differences in behavioral and subjective responses to alcohol. Alcohol Clin Exp Res 1987;11 (1) 52- 59
PubMed Link to Article
Ray  LAMiranda  R  JrTidey  JWMcGeary  JEMacKillop  JGwaltney  CJRohsenow  DJSwift  RMMonti  PM Polymorphisms of the μ-opioid receptor and dopamine D4 receptor genes and subjective responses to alcohol in the natural environment. J Abnorm Psychol 2010;119 (1) 115- 125
PubMed Link to Article
Wetherill  RRFromme  K Subjective responses to alcohol prime event-specific alcohol consumption and predict blackouts and hangover. J Stud Alcohol Drugs 2009;70 (4) 593- 600
PubMed
Kendler  KSDavis  CGKessler  RC The familial aggregation of common psychiatric and substance use disorders in the National Comorbidity Survey: a family history study. Br J Psychiatry 1997;170541- 548
PubMed Link to Article
Grant  BFDawson  DA Age at onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: results from the National Longitudinal Alcohol Epidemiologic Survey. J Subst Abuse 1997;9103- 110
PubMed Link to Article
Hingson  RHeeren  TZakocs  RWinter  MWechsler  H Age of first intoxication, heavy drinking, driving after drinking and risk of unintentional injury among US college students. J Stud Alcohol 2003;64 (1) 23- 31
PubMed
McCarty  CAEbel  BEGarrison  MMDiGiuseppe  DLChristakis  DARivara  FP Continuity of binge and harmful drinking from late adolescence to early adulthood. Pediatrics 2004;114 (3) 714- 719
PubMed Link to Article
SAMHSA, National Survey on Drug Use and Health.  Rockville, MD Office of Applied Studies2005;
Hasin  DSStinson  FSOgburn  EGrant  BF Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry 2007;64 (7) 830- 842
PubMed Link to Article
Oesterle  SHill  KGHawkins  JDGuo  JCatalano  RFAbbott  RD Adolescent heavy episodic drinking trajectories and health in young adulthood. J Stud Alcohol 2004;65 (2) 204- 212
PubMed
Slutske  WS Alcohol use disorders among US college students and their non-college-attending peers. Arch Gen Psychiatry 2005;62 (3) 321- 327
PubMed Link to Article
Bennett  MEMcCrady  BSJohnson  VPandina  RJ Problem drinking from young adulthood to adulthood: patterns, predictors and outcomes. J Stud Alcohol 1999;60 (5) 605- 614
PubMed
Chassin  LPitts  SCProst  J Binge drinking trajectories from adolescence to emerging adulthood in a high-risk sample: predictors and substance abuse outcomes. J Consult Clin Psychol 2002;70 (1) 67- 78
PubMed Link to Article
Dawson  DAGrant  BFStinson  FSChou  PS Toward the attainment of low-risk drinking goals: a 10-year progress report. Alcohol Clin Exp Res 2004;28 (9) 1371- 1378
PubMed Link to Article
Donovan  JEJessor  RJessor  L Problem drinking in adolescence and young adulthood: a follow-up study. J Stud Alcohol 1983;44 (1) 109- 137
PubMed
Gotham  HJSher  KJWood  PK Predicting stability and change in frequency of intoxication from the college years to beyond: individual-difference and role transition variables. J Abnorm Psychol 1997;106 (4) 619- 629
PubMed Link to Article
Grant  BFHarford  TCGrigson  MB Stability of alcohol consumption among youth: a National Longitudinal Survey. J Stud Alcohol 1988;49 (3) 253- 260
PubMed
Gruenewald  PJRussell  MLight  JLipton  RSearles  JJohnson  FTrevisan  MFreudenheim  JMuti  PCarosella  AMNochajski  TH One drink to a lifetime of drinking: temporal structures of drinking patterns. Alcohol Clin Exp Res 2002;26 (6) 916- 925
PubMed Link to Article
Schulenberg  JO’Malley  PMBachman  JGWadsworth  KNJohnston  LD Getting drunk and growing up: trajectories of frequent binge drinking during the transition to young adulthood. J Stud Alcohol 1996;57 (3) 289- 304
PubMed
Hasin  DSGrant  BEndicott  J The natural history of alcohol abuse: implications for definitions of alcohol use disorders. Am J Psychiatry 1990;147 (11) 1537- 1541
PubMed
Dawson  DA US low-risk drinking guidelines: an examination of four alternatives. Alcohol Clin Exp Res 2000;24 (12) 1820- 1829
PubMed Link to Article
Esterlis  ICosgrove  KPPetrakis  ILMcKee  SABois  FKrantzler  EStiklus  SMPerry  EBTamagnan  GDSeibyl  JPKrystal  JHStaley  JK SPECT imaging of nicotinic acetylcholine receptors in nonsmoking heavy alcohol drinking individuals. Drug Alcohol Depend 2010;108 (1-2) 146- 150
PubMed Link to Article
King  ACEpstein  AM Alcohol dose-dependent increases in smoking urge in light smokers. Alcohol Clin Exp Res 2005;29 (4) 547- 552
PubMed Link to Article
McKee  SAHarrison  ELShi  J Alcohol expectancy increases positive responses to cigarettes in young, escalating smokers. Psychopharmacology (Berl) 2010;210 (3) 355- 364
PubMed Link to Article
Cahalan  DCisin  IHCrossley  HM American Drinking Practices: A National Study of Drinking Behavior and Patterns.  New Brunswick, NJ Rutgers Center of Alcohol Studies1969;
Sobell  LCSobell  MB Alcohol Timeline Follow-Back Users' Manual.  Toronto, Canada Addiction Research Foundation1995;
Sobell  LCMaisto  SASobell  MBCooper  AM Reliability of alcohol abusers' self-reports of drinking behavior. Behav Res Ther 1979;17 (2) 157- 160
PubMed Link to Article
Babor  TFde la Fuente  JRSaunders  JGrant  M AUDIT: The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Health Care .  Geneva, Switzerland World Health Organization1989;Publication WHO/MNW/DAT/89.4
Saunders  JBAasland  OGBabor  TFde la Fuente  JRGrant  M Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption—II. Addiction 1993;88 (6) 791- 804
PubMed Link to Article
Beck  ATWard  CHMendelson  MMock  JErbaugh  J An inventory for measuring depression. Arch Gen Psychiatry 1961;4 (6) 561- 571
PubMed Link to Article
Spielberger  CDGorsuch  RLLuchene  RE Test Manual for the State-Trait Anxiety Inventory.  Palo Alto, CA Consulting Psychologist Press1970;
First  MBSpitzer  RLGibbon  MWilliams  JBW Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition (SCID-I/P).  New York, NY Biometrics Research, New York State Psychiatric Institute2002;
Andreasen  NCEndicott  JSpitzer  RLWinokur  G The family history method using diagnostic criteria: reliability and validity. Arch Gen Psychiatry 1977;34 (10) 1229- 1235
PubMed Link to Article
Zuckerman  MEysenck  SEysenck  HJ Sensation seeking in England and America: cross-cultural, age, and sex comparisons. J Consult Clin Psychol 1978;46 (1) 139- 149
PubMed Link to Article
King  ACByars  JA Alcohol-induced performance impairment in heavy episodic and light social drinkers. J Stud Alcohol 2004;65 (1) 27- 36
PubMed
Holdstock  Lde Wit  H Effects of ethanol at four phases of the menstrual cycle. Psychopharmacology (Berl) 2000;150 (4) 374- 382
PubMed Link to Article
Frezza  Mdi Padova  CPozzato  GTerpin  MBaraona  ELieber  CS High blood alcohol levels in women: the role of decreased gastric alcohol dehydrogenase activity and first-pass metabolism. N Engl J Med 1990;322 (2) 95- 99
PubMed Link to Article
Sutker  PBTabakoff  BGoist  KC  JrRandall  CL Acute alcohol intoxication, mood states and alcohol metabolism in women and men. Pharmacol Biochem Behav 1983;18(suppl 1)349- 354
PubMed Link to Article
Schofield  WN Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 1985;39(suppl 1)5- 41
PubMed
Brumback  TCao  DKing  A Effects of alcohol on psychomotor performance and perceived impairment in heavy social drinkers. Drug Alcohol Depend 2007;91 (1) 10- 17
PubMed Link to Article
Martin  CSEarleywine  MMusty  REPerrine  MWSwift  RM Development and validation of the Biphasic Alcohol Effects Scale. Alcohol Clin Exp Res 1993;17 (1) 140- 146
PubMed Link to Article
Johanson  CEUhlenhuth  EH Drug preference and mood in humans: diazepam. Psychopharmacology (Berl) 1980;71 (3) 269- 273
PubMed Link to Article
Rueger  SYMcNamara  PJKing  AC Expanding the utility of the Biphasic Alcohol Effects Scale (BAES) and initial psychometric support for the Brief-BAES (B-BAES). Alcohol Clin Exp Res 2009;33 (5) 916- 924
PubMed Link to Article
Epstein  AMSher  TGYoung  MAKing  AC Tobacco chippers show robust increases in smoking urge after alcohol consumption. Psychopharmacology (Berl) 2007;190 (3) 321- 329
PubMed Link to Article
King  AMunisamy  Gde Wit  HLin  S Attenuated cortisol response to alcohol in heavy social drinkers. Int J Psychophysiol 2006;59 (3) 203- 209
PubMed Link to Article
King  AEpstein  AConrad  MMcNamara  PCao  D Sex differences in the relationship between alcohol-associated smoking urge and behavior: a pilot study. Am J Addict 2008;17 (5) 347- 353
PubMed Link to Article
Roche  DJOKing  AC Alcohol impairment of saccadic and smooth pursuit eye movements: impact of risk factors for alcohol dependence. Psychopharmacology 2010;21233- 44
Link to Article
Miller  WRTonigan  JSLongabaugh  R The Drinker Inventory of Consequences (DrInC): An Instrument for Assessing Adverse Consequences of Alcohol Abuse. 4 Rockville, MD National Institute on Alcohol Abuse and Alcoholism1995;
Baer  JSKivlahan  DRMarlatt  GA High-risk drinking across the transition from high school to college. Alcohol Clin Exp Res 1995;19 (1) 54- 61
PubMed Link to Article
Dawson  DAGrant  BFStinson  FSChou  PS Maturing out of alcohol dependence: the impact of transitional life events. J Stud Alcohol 2006;67 (2) 195- 203
PubMed
Johnstone  BMLeino  EVAger  CRFerrer  HFillmore  KM Determinants of life-course variation in the frequency of alcohol consumption: meta-analysis of studies from the Collaborative Alcohol-Related Longitudinal Project. J Stud Alcohol 1996;57 (5) 494- 506
PubMed
Kirschbaum  CPirke  KMHellhammer  DH The ‘Trier Social Stress Test’—a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology 1993;28 (1-2) 76- 81
PubMed Link to Article
Jones  BLNagin  DSRoeder  KA A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res 2001;29 (3) 374- 393
Link to Article
Agresti  A An Introduction to Categorical Data Analysis.  New York, NY John Wiley & Sons, Inc1996;
Zeger  SLLiang  KYAlbert  PS Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988;44 (4) 1049- 1060
PubMed Link to Article
Baron  RMKenny  DA The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51 (6) 1173- 1182
PubMed Link to Article
Casswell  SPledger  MPratap  S Trajectories of drinking from 18 to 26 years: identification and prediction. Addiction 2002;97 (11) 1427- 1437
PubMed Link to Article
Greenbaum  PEDel Boca  FKDarkes  JWang  CPGoldman  MS Variation in the drinking trajectories of freshmen college students. J Consult Clin Psychol 2005;73 (2) 229- 238
PubMed Link to Article
Tucker  JSOrlando  MEllickson  PL Patterns and correlates of binge drinking trajectories from early adolescence to young adulthood. Health Psychol 2003;22 (1) 79- 87
PubMed Link to Article
Windle  MMun  EYWindle  RC Adolescent-to-young adulthood heavy drinking trajectories and their prospective predictors. J Stud Alcohol 2005;66 (3) 313- 322
PubMed
Schuckit  MA Low level of response to alcohol as a predictor of future alcoholism. Am J Psychiatry 1994;151 (2) 184- 189
PubMed
Robinson  TEBerridge  KC The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev 1993;18 (3) 247- 291
PubMed Link to Article
Robinson  TEBerridge  KC Incentive-sensitization and addiction. Addiction 2001;96 (1) 103- 114
PubMed Link to Article
Berridge  KC The debate over dopamine's role in reward: the case for incentive salience. Psychopharmacology (Berl) 2007;191 (3) 391- 431
PubMed Link to Article
Volkow  NDFowler  JSWang  GJSwanson  JMTelang  F Dopamine in drug abuse and addiction: results of imaging studies and treatment implications. Arch Neurol 2007;64 (11) 1575- 1579
PubMed Link to Article
Wise  RABozarth  MA A psychomotor stimulant theory of addiction. Psychol Rev 1987;94 (4) 469- 492
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.

Heavy and light drinkers' breath alcohol concentrations (BrAC) (A) and Drug Effects Questionnaire (DEQ; range, 0.0-1.0) feel drug ratings (B) during the sessions. For feel drug, light drinkers had higher ratings during the high dose than did heavy drinkers at all time points; P < .001. The shaded bar indicates the alcohol drinking interval from time 0 to 15 minutes. Error bars indicate standard error.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Heavy and light drinkers' subjective stimulant and rewarding responses, including the Biphasic Alcohol Effects Scale (BAES; range, 10-70) stimulation (A) and the Drug Effects Questionnaire (DEQ; range, 0.0-1.0) for like (B) and want more (C) during the sessions. Post hoc analysis of a significant group×dose×time interaction revealed that for stimulation at the high dose, heavy drinkers had higher ratings than light drinkers at 30 minutes (P< .05) and 60 minutes (P< .06). Post hoc analysis of a significant group×dose interaction for like at the high dose revealed that heavy drinkers had higher ratings than light drinkers at 30, 60, and 120 minutes (P< .01 for all), and a significant group×dose interaction for want more at the high dose and low dose revealed that heavy drinkers had higher ratings than light drinkers at all time points (P< .001 for all). The shaded bar indicates the alcohol drinking interval from time 0 to 15 minutes. Error bars indicate standard error.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.

Heavy and light drinkers' Biphasic Alcohol Effects Scale (BAES; range, 10-70) sedation (A) and salivary cortisol (B) responses during the sessions. Post hoc analysis of a significant group×dose×time interaction revealed that for sedation at the high alcohol dose, light drinkers had higher ratings than heavy drinkers at 30, 60, and 120 minutes (P <.001 for all) and at the low dose, light drinkers had higher ratings than heavy drinkers at 60 minutes (P < .05). Post hoc analysis of a significant group×dose×time interaction for cortisol at the high dose revealed that the light drinkers had higher levels than heavy drinkers at 120 and 180 minutes (P < .01 for all). The shaded bar indicates the alcohol drinking interval from time 0 to 15 minutes. Error bars indicate standard error. (To convert cortisol to nanomoles per liter, multiply by 27.588).

Graphic Jump Location
Place holder to copy figure label and caption
Figure 4.

Trajectory groups during the 2-year follow-up. Trajectory data were based on the self-reported frequency of past-month binge drinking days obtained at each quarterly interval. A discrete mixture modeling approach determined by the Bayesian Information Criterion for model selection revealed a 2-trajectory group (nonbinge and infrequent binge) model for the light drinkers and a 4-trajectory group (gradual maturing, moderate-frequency binge, high-frequency binge, and exacerbating) model for the heavy drinkers best described the data.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 5.

Heavy and light drinkers' trajectory groups’ initial alcohol responses, including the Drug Effects Questionnaire (DEQ; range, 0.0-1.0) for like (A) and want more (B) and the Biphasic Alcohol Effects Scale (BAES; range, 10-70) for stimulation (C) and sedation (D). Alcohol responses are indicated as the mean for each trajectory group based on participants' change score: high dose (60 minutes - baseline) − placebo (60 minutes - baseline). In light drinkers, there were no significant associations of alcohol response to trajectory group. In heavy drinkers, higher ratings for like (P< .001) and want more (P< .001) and lower ratings for sedation (P< .05) were significantly linearly associated with drinking trajectory group. Error bars indicate standard error.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Participant Characteristics
Table Graphic Jump LocationTable 2. Bayesian Information Criterion (BIC) and Model Selection
Table Graphic Jump LocationTable 3. Alcohol Use Disorder Diagnoses and Other Drinking Characteristics During the 2-Year Follow-up Among the Trajectory Groupsa

References

Chou  SPPickering  RP Early onset of drinking as a risk factor for lifetime alcohol-related problems. Br J Addict 1992;87 (8) 1199- 1204
PubMed Link to Article
Naimi  TSBrewer  RDMokdad  ADenny  CSerdula  MKMarks  JS Binge drinking among US adults. JAMA 2003;289 (1) 70- 75
PubMed Link to Article
Wechsler  HLee  JEKuo  MSeibring  MNelson  TFLee  H Trends in college binge drinking during a period of increased prevention efforts: findings from 4 Harvard School of Public Health College Alcohol Study surveys: 1993-2001. J Am Coll Health 2002;50 (5) 203- 217
PubMed Link to Article
Mokdad  AHMarks  JSStroup  DFGerberding  JL Actual causes of death in the United States, 2000. JAMA 2004;291 (10) 1238- 1245
PubMed Link to Article
Harwood  H Updating Estimates of the Economic Costs of Alcohol Abuse in the United States: Estimates, Update Methods, and Data.  Bethesda, MD National Institutes of Health2000;
Newlin  DBThomson  JB Alcohol challenge with sons of alcoholics: a critical review and analysis. Psychol Bull 1990;108 (3) 383- 402
PubMed Link to Article
Newlin  DBRenton  RM High risk groups often have higher levels of alcohol response than low risk: the other side of the coin. Alcohol Clin Exp Res 2010;34 (2) 199- 202
PubMed Link to Article
Schuckit  MASmith  TL An 8-year follow-up of 450 sons of alcoholic and control subjects. Arch Gen Psychiatry 1996;53 (3) 202- 210
PubMed Link to Article
Cloninger  CRBohman  MSigvardsson  S Inheritance of alcohol abuse: cross-fostering analysis of adopted men. Arch Gen Psychiatry 1981;38 (8) 861- 868
PubMed Link to Article
Goodwin  DWSchulsinger  FHermansen  LGuze  SBWinokur  G Alcohol problems in adoptees raised apart from alcoholic biological parents. Arch Gen Psychiatry 1973;28 (2) 238- 243
PubMed Link to Article
Berman  SMWhipple  SCFitch  RJNoble  EP P3 in young boys as a predictor of adolescent substance use. Alcohol 1993;10 (1) 69- 76
PubMed Link to Article
Hill  SYSteinhauer  SLowers  LLocke  J Eight-year longitudinal follow-up of P300 and clinical outcome in children from high-risk for alcoholism families. Biol Psychiatry 1995;37 (11) 823- 827
PubMed Link to Article
Schuckit  MA Subjective responses to alcohol in sons of alcoholics and control subjects. Arch Gen Psychiatry 1984;41 (9) 879- 884
PubMed Link to Article
Schuckit  MA Ethanol-induced changes in body sway in men at high alcoholism risk. Arch Gen Psychiatry 1985;42 (4) 375- 379
PubMed Link to Article
Schuckit  MAGold  ERisch  C Plasma cortisol levels following ethanol in sons of alcoholics and controls. Arch Gen Psychiatry 1987;44 (11) 942- 945
PubMed Link to Article
Schuckit  MAGold  EOCroot  KFinn  PPolich  J P300 latency after ethanol ingestion in sons of alcoholics and in controls. Biol Psychiatry 1988;24 (3) 310- 315
PubMed Link to Article
Schuckit  MA Self-rating of alcohol intoxication by young men with and without family histories of alcoholism. J Stud Alcohol 1980;41 (3) 242- 249
PubMed
Conrod  PJPeterson  JBPihl  RO Reliability and validity of alcohol-induced heart rate increase as a measure of sensitivity to the stimulant properties of alcohol. Psychopharmacology (Berl) 2001;157 (1) 20- 30
PubMed Link to Article
Erblich  JEarleywine  M Behavioral undercontrol and subjective stimulant and sedative effects of alcohol intoxication: independent predictors of drinking habits? Alcohol Clin Exp Res 2003;27 (1) 44- 50
PubMed Link to Article
Holdstock  LKing  ACde Wit  H Subjective and objective responses to ethanol in moderate/heavy and light social drinkers. Alcohol Clin Exp Res 2000;24 (6) 789- 794
PubMed Link to Article
King  ACHoule  Tde Wit  HHoldstock  LSchuster  A Biphasic alcohol response differs in heavy versus light drinkers. Alcohol Clin Exp Res 2002;26 (6) 827- 835
PubMed Link to Article
Finn  PRPihl  RO Men at high risk for alcoholism: the effect of alcohol on cardiovascular response to unavoidable shock. J Abnorm Psychol 1987;96 (3) 230- 236
PubMed Link to Article
Gianoulakis  CBéliveau  DAngelogianni  PMeaney  MThavundayil  JTawar  VDumas  M Different pituitary beta-endorphin and adrenal cortisol response to ethanol in individuals with high and low risk for future development of alcoholism. Life Sci 1989;45 (12) 1097- 1109
PubMed Link to Article
Peterson  JBPihl  ROGianoulakis  CConrod  PFinn  PRStewart  SHLeMarquand  DGBruce  KR Ethanol-induced change in cardiac and endogenous opiate function and risk for alcoholism. Alcohol Clin Exp Res 1996;20 (9) 1542- 1552
PubMed Link to Article
Morean  MECorbin  WR Subjective response to alcohol: a critical review of the literature. Alcohol Clin Exp Res 2010;34 (3) 385- 395
PubMed Link to Article
Corbin  WRGearhardt  AFromme  K Stimulant alcohol effects prime within session drinking behavior. Psychopharmacology (Berl) 2008;197 (2) 327- 337
PubMed Link to Article
DeWit  HPierri  JJohanson  CE Assessing individual differences in ethanol preference using a cumulative dosing procedure. Psychopharmacology (Berl) 1989;98 (1) 113- 119
PubMed Link to Article
de Wit  HUhlenhuth  EHPierri  JJohanson  CE Individual differences in behavioral and subjective responses to alcohol. Alcohol Clin Exp Res 1987;11 (1) 52- 59
PubMed Link to Article
Ray  LAMiranda  R  JrTidey  JWMcGeary  JEMacKillop  JGwaltney  CJRohsenow  DJSwift  RMMonti  PM Polymorphisms of the μ-opioid receptor and dopamine D4 receptor genes and subjective responses to alcohol in the natural environment. J Abnorm Psychol 2010;119 (1) 115- 125
PubMed Link to Article
Wetherill  RRFromme  K Subjective responses to alcohol prime event-specific alcohol consumption and predict blackouts and hangover. J Stud Alcohol Drugs 2009;70 (4) 593- 600
PubMed
Kendler  KSDavis  CGKessler  RC The familial aggregation of common psychiatric and substance use disorders in the National Comorbidity Survey: a family history study. Br J Psychiatry 1997;170541- 548
PubMed Link to Article
Grant  BFDawson  DA Age at onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: results from the National Longitudinal Alcohol Epidemiologic Survey. J Subst Abuse 1997;9103- 110
PubMed Link to Article
Hingson  RHeeren  TZakocs  RWinter  MWechsler  H Age of first intoxication, heavy drinking, driving after drinking and risk of unintentional injury among US college students. J Stud Alcohol 2003;64 (1) 23- 31
PubMed
McCarty  CAEbel  BEGarrison  MMDiGiuseppe  DLChristakis  DARivara  FP Continuity of binge and harmful drinking from late adolescence to early adulthood. Pediatrics 2004;114 (3) 714- 719
PubMed Link to Article
SAMHSA, National Survey on Drug Use and Health.  Rockville, MD Office of Applied Studies2005;
Hasin  DSStinson  FSOgburn  EGrant  BF Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry 2007;64 (7) 830- 842
PubMed Link to Article
Oesterle  SHill  KGHawkins  JDGuo  JCatalano  RFAbbott  RD Adolescent heavy episodic drinking trajectories and health in young adulthood. J Stud Alcohol 2004;65 (2) 204- 212
PubMed
Slutske  WS Alcohol use disorders among US college students and their non-college-attending peers. Arch Gen Psychiatry 2005;62 (3) 321- 327
PubMed Link to Article
Bennett  MEMcCrady  BSJohnson  VPandina  RJ Problem drinking from young adulthood to adulthood: patterns, predictors and outcomes. J Stud Alcohol 1999;60 (5) 605- 614
PubMed
Chassin  LPitts  SCProst  J Binge drinking trajectories from adolescence to emerging adulthood in a high-risk sample: predictors and substance abuse outcomes. J Consult Clin Psychol 2002;70 (1) 67- 78
PubMed Link to Article
Dawson  DAGrant  BFStinson  FSChou  PS Toward the attainment of low-risk drinking goals: a 10-year progress report. Alcohol Clin Exp Res 2004;28 (9) 1371- 1378
PubMed Link to Article
Donovan  JEJessor  RJessor  L Problem drinking in adolescence and young adulthood: a follow-up study. J Stud Alcohol 1983;44 (1) 109- 137
PubMed
Gotham  HJSher  KJWood  PK Predicting stability and change in frequency of intoxication from the college years to beyond: individual-difference and role transition variables. J Abnorm Psychol 1997;106 (4) 619- 629
PubMed Link to Article
Grant  BFHarford  TCGrigson  MB Stability of alcohol consumption among youth: a National Longitudinal Survey. J Stud Alcohol 1988;49 (3) 253- 260
PubMed
Gruenewald  PJRussell  MLight  JLipton  RSearles  JJohnson  FTrevisan  MFreudenheim  JMuti  PCarosella  AMNochajski  TH One drink to a lifetime of drinking: temporal structures of drinking patterns. Alcohol Clin Exp Res 2002;26 (6) 916- 925
PubMed Link to Article
Schulenberg  JO’Malley  PMBachman  JGWadsworth  KNJohnston  LD Getting drunk and growing up: trajectories of frequent binge drinking during the transition to young adulthood. J Stud Alcohol 1996;57 (3) 289- 304
PubMed
Hasin  DSGrant  BEndicott  J The natural history of alcohol abuse: implications for definitions of alcohol use disorders. Am J Psychiatry 1990;147 (11) 1537- 1541
PubMed
Dawson  DA US low-risk drinking guidelines: an examination of four alternatives. Alcohol Clin Exp Res 2000;24 (12) 1820- 1829
PubMed Link to Article
Esterlis  ICosgrove  KPPetrakis  ILMcKee  SABois  FKrantzler  EStiklus  SMPerry  EBTamagnan  GDSeibyl  JPKrystal  JHStaley  JK SPECT imaging of nicotinic acetylcholine receptors in nonsmoking heavy alcohol drinking individuals. Drug Alcohol Depend 2010;108 (1-2) 146- 150
PubMed Link to Article
King  ACEpstein  AM Alcohol dose-dependent increases in smoking urge in light smokers. Alcohol Clin Exp Res 2005;29 (4) 547- 552
PubMed Link to Article
McKee  SAHarrison  ELShi  J Alcohol expectancy increases positive responses to cigarettes in young, escalating smokers. Psychopharmacology (Berl) 2010;210 (3) 355- 364
PubMed Link to Article
Cahalan  DCisin  IHCrossley  HM American Drinking Practices: A National Study of Drinking Behavior and Patterns.  New Brunswick, NJ Rutgers Center of Alcohol Studies1969;
Sobell  LCSobell  MB Alcohol Timeline Follow-Back Users' Manual.  Toronto, Canada Addiction Research Foundation1995;
Sobell  LCMaisto  SASobell  MBCooper  AM Reliability of alcohol abusers' self-reports of drinking behavior. Behav Res Ther 1979;17 (2) 157- 160
PubMed Link to Article
Babor  TFde la Fuente  JRSaunders  JGrant  M AUDIT: The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Health Care .  Geneva, Switzerland World Health Organization1989;Publication WHO/MNW/DAT/89.4
Saunders  JBAasland  OGBabor  TFde la Fuente  JRGrant  M Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption—II. Addiction 1993;88 (6) 791- 804
PubMed Link to Article
Beck  ATWard  CHMendelson  MMock  JErbaugh  J An inventory for measuring depression. Arch Gen Psychiatry 1961;4 (6) 561- 571
PubMed Link to Article
Spielberger  CDGorsuch  RLLuchene  RE Test Manual for the State-Trait Anxiety Inventory.  Palo Alto, CA Consulting Psychologist Press1970;
First  MBSpitzer  RLGibbon  MWilliams  JBW Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition (SCID-I/P).  New York, NY Biometrics Research, New York State Psychiatric Institute2002;
Andreasen  NCEndicott  JSpitzer  RLWinokur  G The family history method using diagnostic criteria: reliability and validity. Arch Gen Psychiatry 1977;34 (10) 1229- 1235
PubMed Link to Article
Zuckerman  MEysenck  SEysenck  HJ Sensation seeking in England and America: cross-cultural, age, and sex comparisons. J Consult Clin Psychol 1978;46 (1) 139- 149
PubMed Link to Article
King  ACByars  JA Alcohol-induced performance impairment in heavy episodic and light social drinkers. J Stud Alcohol 2004;65 (1) 27- 36
PubMed
Holdstock  Lde Wit  H Effects of ethanol at four phases of the menstrual cycle. Psychopharmacology (Berl) 2000;150 (4) 374- 382
PubMed Link to Article
Frezza  Mdi Padova  CPozzato  GTerpin  MBaraona  ELieber  CS High blood alcohol levels in women: the role of decreased gastric alcohol dehydrogenase activity and first-pass metabolism. N Engl J Med 1990;322 (2) 95- 99
PubMed Link to Article
Sutker  PBTabakoff  BGoist  KC  JrRandall  CL Acute alcohol intoxication, mood states and alcohol metabolism in women and men. Pharmacol Biochem Behav 1983;18(suppl 1)349- 354
PubMed Link to Article
Schofield  WN Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr 1985;39(suppl 1)5- 41
PubMed
Brumback  TCao  DKing  A Effects of alcohol on psychomotor performance and perceived impairment in heavy social drinkers. Drug Alcohol Depend 2007;91 (1) 10- 17
PubMed Link to Article
Martin  CSEarleywine  MMusty  REPerrine  MWSwift  RM Development and validation of the Biphasic Alcohol Effects Scale. Alcohol Clin Exp Res 1993;17 (1) 140- 146
PubMed Link to Article
Johanson  CEUhlenhuth  EH Drug preference and mood in humans: diazepam. Psychopharmacology (Berl) 1980;71 (3) 269- 273
PubMed Link to Article
Rueger  SYMcNamara  PJKing  AC Expanding the utility of the Biphasic Alcohol Effects Scale (BAES) and initial psychometric support for the Brief-BAES (B-BAES). Alcohol Clin Exp Res 2009;33 (5) 916- 924
PubMed Link to Article
Epstein  AMSher  TGYoung  MAKing  AC Tobacco chippers show robust increases in smoking urge after alcohol consumption. Psychopharmacology (Berl) 2007;190 (3) 321- 329
PubMed Link to Article
King  AMunisamy  Gde Wit  HLin  S Attenuated cortisol response to alcohol in heavy social drinkers. Int J Psychophysiol 2006;59 (3) 203- 209
PubMed Link to Article
King  AEpstein  AConrad  MMcNamara  PCao  D Sex differences in the relationship between alcohol-associated smoking urge and behavior: a pilot study. Am J Addict 2008;17 (5) 347- 353
PubMed Link to Article
Roche  DJOKing  AC Alcohol impairment of saccadic and smooth pursuit eye movements: impact of risk factors for alcohol dependence. Psychopharmacology 2010;21233- 44
Link to Article
Miller  WRTonigan  JSLongabaugh  R The Drinker Inventory of Consequences (DrInC): An Instrument for Assessing Adverse Consequences of Alcohol Abuse. 4 Rockville, MD National Institute on Alcohol Abuse and Alcoholism1995;
Baer  JSKivlahan  DRMarlatt  GA High-risk drinking across the transition from high school to college. Alcohol Clin Exp Res 1995;19 (1) 54- 61
PubMed Link to Article
Dawson  DAGrant  BFStinson  FSChou  PS Maturing out of alcohol dependence: the impact of transitional life events. J Stud Alcohol 2006;67 (2) 195- 203
PubMed
Johnstone  BMLeino  EVAger  CRFerrer  HFillmore  KM Determinants of life-course variation in the frequency of alcohol consumption: meta-analysis of studies from the Collaborative Alcohol-Related Longitudinal Project. J Stud Alcohol 1996;57 (5) 494- 506
PubMed
Kirschbaum  CPirke  KMHellhammer  DH The ‘Trier Social Stress Test’—a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology 1993;28 (1-2) 76- 81
PubMed Link to Article
Jones  BLNagin  DSRoeder  KA A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res 2001;29 (3) 374- 393
Link to Article
Agresti  A An Introduction to Categorical Data Analysis.  New York, NY John Wiley & Sons, Inc1996;
Zeger  SLLiang  KYAlbert  PS Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988;44 (4) 1049- 1060
PubMed Link to Article
Baron  RMKenny  DA The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51 (6) 1173- 1182
PubMed Link to Article
Casswell  SPledger  MPratap  S Trajectories of drinking from 18 to 26 years: identification and prediction. Addiction 2002;97 (11) 1427- 1437
PubMed Link to Article
Greenbaum  PEDel Boca  FKDarkes  JWang  CPGoldman  MS Variation in the drinking trajectories of freshmen college students. J Consult Clin Psychol 2005;73 (2) 229- 238
PubMed Link to Article
Tucker  JSOrlando  MEllickson  PL Patterns and correlates of binge drinking trajectories from early adolescence to young adulthood. Health Psychol 2003;22 (1) 79- 87
PubMed Link to Article
Windle  MMun  EYWindle  RC Adolescent-to-young adulthood heavy drinking trajectories and their prospective predictors. J Stud Alcohol 2005;66 (3) 313- 322
PubMed
Schuckit  MA Low level of response to alcohol as a predictor of future alcoholism. Am J Psychiatry 1994;151 (2) 184- 189
PubMed
Robinson  TEBerridge  KC The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev 1993;18 (3) 247- 291
PubMed Link to Article
Robinson  TEBerridge  KC Incentive-sensitization and addiction. Addiction 2001;96 (1) 103- 114
PubMed Link to Article
Berridge  KC The debate over dopamine's role in reward: the case for incentive salience. Psychopharmacology (Berl) 2007;191 (3) 391- 431
PubMed Link to Article
Volkow  NDFowler  JSWang  GJSwanson  JMTelang  F Dopamine in drug abuse and addiction: results of imaging studies and treatment implications. Arch Neurol 2007;64 (11) 1575- 1579
PubMed Link to Article
Wise  RABozarth  MA A psychomotor stimulant theory of addiction. Psychol Rev 1987;94 (4) 469- 492
PubMed Link to Article

Correspondence

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
Submit a Comment

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 71

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Collections