Subjective Responses to Alcohol in the Development and Maintenance of Alcohol Use Disorder
Publication: American Journal of Psychiatry
Abstract
Objective:
Alcohol use disorder (AUD) remains an urgent public health problem. Longitudinal data are needed to clarify the role of acute subjective responses to alcohol in the development and maintenance of excessive drinking and AUD. The authors report on 10 years of repeated examination of acute alcohol responses in the Chicago Social Drinking Project.
Methods:
Young adult drinkers (N=190) participated in an initial alcohol challenge (0.8 g/kg of alcohol compared with placebo) that was repeated 5 and 10 years later. They were also assessed on drinking behavior and AUD symptoms at numerous intervals across the decade. Retention was high, as 184 of the 185 (99%) nondeceased active participants completed the 10-year follow-up, and 91% (163 of 179) of those eligible for alcohol consumption engaged in repeated laboratory testing during this interval.
Results:
At the end of the decade, 21% of participants met criteria for past-year AUD. Individuals who reported the greatest alcohol stimulation, liking, and wanting at the initial alcohol challenge were most likely to have developed AUD 10 years later. Further, alcohol-induced stimulation and wanting increased in reexamination testing among those with the highest AUD symptoms as the decade progressed.
Conclusions:
Initial stimulant and rewarding effects of alcohol predicted heavy alcohol use, and the magnitude of these positive subjective effects increased over a 10-year period in those who developed AUD compared with those who did not develop the disorder. The findings demonstrate systematic changes in subjective responses to alcohol over time, providing an empirical basis for prevention, early intervention, and treatment strategies.
Heavy drinking is increasing among U.S. adults (1–3) and remains a major preventable contributor to disability and mortality worldwide (4). It is also a strong predictor of subsequent alcohol use disorder (AUD) (5, 6), which carries additional serious consequences for health and functioning (7). Given the global burden of alcohol misuse, identifying factors that increase the susceptibility to the development and maintenance of AUD is a critical public health need (8).
One way to examine vulnerability to AUD is to characterize acute subjective responses to alcohol at different stages of development of the disorder (9). Two large studies have found that greater initial stimulation and reward responses to alcohol (10, 11), as well as lesser intoxicating and sedating responses (12), predict future drinking problems through young adulthood. These acute responses may change with chronic heavy drinking as a result of neuroadaptations that produce either tolerance or sensitization. Tolerance, or a diminished response to the same dose of a drug after repeated use, is a diagnostic criterion for AUD. Conversely, sensitization, or an increased drug effect with repeated exposure, has also been linked to addiction (13, 14). Although both tolerance and sensitization are integral components of neurobiological theories of the development and progression of addiction (14–16), most of the empirical evidence for these theories has been based on studies using animal models.
Examination of adaptive responses to alcohol in humans requires rigorous longitudinal investigations in persons who develop AUD or progress in severity of the disorder. Such prospective, repeated-measurement studies of acute alcohol responses are challenging, as they are time and labor intensive and require high retention rates. Yet the knowledge from such investigations is necessary to adequately test the translational significance of animal models of addiction to humans (17) and thus to inform empirically based strategies for prevention, early intervention, and treatment. Whether the excitatory, euphoric, or sedating effects of alcohol increase, decrease, or remain constant over time in problem drinkers represents an unresolved issue in clinical psychiatry.
In the Chicago Social Drinking Project, we undertook such an analysis in our first cohort and documented that, compared with lighter drinkers, young adult heavy drinkers were more sensitive to the stimulating, motivating (“wanting”), and rewarding (“liking”) effects of alcohol and were less sensitive to its sedative effects (10). Although stimulation and sedation were inversely correlated (18), we found that higher alcohol stimulation, wanting, and liking, and not lower sedation, predicted progression of AUD symptoms 5–6 years after the initial alcohol challenge in heavy drinkers (11). Further, when participants were retested with alcohol 5–6 years later, heavy drinkers who developed more AUD symptoms reported persistently greater stimulation, wanting, and liking after consuming alcohol (at both the initial test and 5 years later), whereas light drinkers did not report experiencing these euphoric and motivating responses (19). However, the testing was limited to a 5-year reexamination period when participants were just entering their fourth decade of life, which may not have been sufficient to fully examine AUD development. In addition, the previous study focused mainly on heavy drinkers and did not allow for an integrated examination of the trajectory of both light and heavy drinkers.
We now report on a more comprehensive and extended 10-year follow-up and reexamination of acute responses to alcohol in the Chicago Social Drinking Project. We shift the analytical focus to examine differences in the acute responses to alcohol, based on who, among the entire sample, did or did not manifest AUD after a decade of natural drinking. The approach comprises a three-phase assessment: initial, 5-year, and 10-year examinations of alcohol responses from young adulthood through middle age. We evaluated the existence of adaptive changes in subjective responses to alcohol consumption in the development and progression of addiction. Our goal was to determine whether AUD is marked by increases, decreases, or no change in sensitivity to alcohol’s stimulating, motivating, rewarding, or sedating effects.
Methods
The Chicago Social Drinking Project is a multicohort, double-blind study with repeated, within-subject laboratory assessments of acute responses to alcohol compared with placebo, combined with long-term follow-up of drinking behaviors and AUD symptoms. The study was approved by the University of Chicago institutional review board, and participants provided written informed consent. Testing sessions were conducted at the Clinical Addictions Research Laboratory at the University of Chicago. Participants attended initial laboratory sessions from March 2004 to July 2006. They underwent regular follow-up interviews and were invited to participate in identical double-blind alcohol and placebo reexaminations 5 and 10 years later (see the CONSORT diagram in Figure S1 in the online supplement ).
Initial Screening and Enrollment
Recruitment was conducted through local media and Internet advertisements and word-of-mouth referrals. Initial inclusion criteria were age between 21 and 35 years, weight between 110 and 210 pounds, good general health, not pregnant or lactating, no current or past major medical or psychiatric disorders, including DSM-IV alcohol and substance dependence (other than nicotine), and no current use of any psychotropic medications. Participants were included if they met criteria for being either a high- or low-risk drinker. This distinction was defined by a predominant adult pattern of drinking (and minimally for the past 2 years). High-risk, heavy drinkers reported weekly binge drinking of ≥5 drinks for men, ≥4 drinks for women, and regular consumption of 10–40 drinks per week. Low-risk, light drinkers reported rare or no binges and regular consumption of 1–5 drinks per week. These criteria were based on established high- and low-risk drinking guidelines (20, 21) and were consistent with previous studies (22–25). Candidates underwent medical and psychiatric screening to determine eligibility for an alcohol challenge (for details, see the supplemental methods section of the online supplement ).
Laboratory Procedures
At each of the three testing phases, participants attended two individual 4- to 5-hour laboratory sessions, one with alcohol, and the other with placebo, in randomized order and under double-blind conditions. Sessions were conducted in a comfortable living room–like environment, separated by at least 24 hours. The sessions were identical except for the alcohol content of the beverage, 0.8 g/kg of alcohol (adjusted for sex), or placebo with a 1% alcohol taste mask. Upon arrival, objective breath tests confirmed recent alcohol abstinence, and a urine sample was collected for women to verify nonpregnancy. Subjective, objective, and alcohol breath tests were obtained throughout the session (for more details, see the supplemental methods section in the online supplement ).
Starting at experimental time 0, the participant received the beverages in lidded, clear plastic cups in two equal portions and, in the presence of the research assistant, consumed each portion over two 5-minute periods, separated by a 5-minute rest (22, 23, 26). The alternative substance paradigm (27) was employed to reduce expectancy effects by informing each participant that the beverage may contain a stimulant, sedative, alcohol, placebo, or a combination of these substances. At the end of each approximate 4-hour session, when breath alcohol concentration was <0.04 g/dL (28), the participant was transported home by a car service.
Identical procedures and instructions were followed for the 5- and 10-year laboratory testing reexamination phases. For ethical reasons, participants who had major medical or psychiatric contraindications (N=3), who were pregnant or nursing (N=3), or who were abstinent from alcohol (N=3) were not eligible for the final 10-year reexamination sessions. As nearly half (49%) of the sample no longer resided in the area, transportation to Chicago, lodging accommodations, and other per diem expenses were provided as warranted. Reexamination sessions were scheduled for the same month as the initial testing; the mean interval from participants’ initial session was 61 months (SD=3.0) to the year-5 session and 122 months (SD=3.7) to the year-10 session.
Outcome and Measures
In the testing sessions, measures were obtained before beverage consumption and repeated 30, 60, 120, and 180 minutes after consumption. To maintain blinding, the alcohol breath tests (Alco-Sensor IV, Intoximeter; St. Louis) displayed 0.000 g/dL during real-time assessments, and the actual values were downloaded later. The primary dependent measures were scores on the stimulation and sedation subscale of the 14-item Biphasic Alcohol Effects Scale (29) and responses to two items from the Drug Effects Questionnaire (100-mm visual analogue scale) for hedonic reward (“do you LIKE the effect you are feeling now?”) and motivational salience (“would you like MORE of what you consumed, right now?”) (30, 31). The surveys instructed participants to focus on their current mood state and did not reveal the beverage content (32). Stimulation, sedation, liking, and wanting were examined across the breath alcohol concentration curve by subtracting the placebo from the alcohol response at each time point. Secondary measures were general drug (the “feel-drug” item of the Drug Effects Questionnaire) and physiological responses, including heart rate and salivary cortisol measures.
Follow-Up Assessments
After each participant’s initial testing, follow-up assessments were conducted at 1, 2, 4, 5, 6, 8, and 10 years. No subject was lost to follow-up (i.e., unreachable throughout this decade of participation), reflecting our protocol to attain strong retention (33). Continuous participation was strong (185 of 189 living participants), with four study dropouts. The follow-up assessments included Internet or mailed surveys and a telephone or in-person administration of the Structured Clinical Interview for DSM-IV to determine AUD symptoms. The main outcome from these assessments was the presence (AUD+) or absence (AUD−) of the disorder in the last year of follow-up. Twenty-one percent of participants (N=39) met DSM-5 criteria for AUD at year 10, forming the AUD+ group. Of these, 20 met criteria for mild AUD, 10 for moderate AUD, and nine for severe AUD. The remaining 79% did not meet AUD criteria and formed the AUD− group. Of these, 124 reported no AUD symptoms, and 21 reported one symptom. A review of the preceding 10 years revealed that the AUD+ group showed more AUD symptoms than the AUD− group and reported heavier drinking (Figure 1A and Table 1).
FIGURE 1. Mean alcohol use disorder (AUD) symptom count and heavy drinking occasions per month across 10 years of follow-up in a study of the subjective responses to alcohol in the development and maintenance of AUDa
a Panel A depicts results for the presence of AUD (AUD+ group) or the absence of AUD (AUD− group) as determined at year 10, and panel B depicts results for AUD symptom subgroups. Data shown are mean values, and error bars indicate standard error. The left panel depicts AUD symptoms at each follow-up interval for each group, and the right panel depicts the frequency of binge drinking during follow-up. As shown in panel A, the AUD+ and AUD− groups were identified based on AUD symptom counts per DSM-5 at year 10 of follow-up. As shown in panel B, three trajectory subgroups were identified based on symptom counts from DSM-IV criteria for alcohol abuse and alcohol dependence (0–11 symptoms possible) over 10 years of follow-up, and symptom counts were ascertained during initial testing and at years 1, 2, 4, 5, 6, 8, and 10 (dotted lines represent model fits). Because the study began before DSM-5 was released, symptom counts for years 1–5 were based on the 11 criteria for DSM-IV alcohol abuse and dependence (as reflected in the Structured Clinical Interview for DSM-IV). For unity, these DSM-IV criteria also were used for the follow-up in years 6–10 for trajectory analysis. Notably, only one symptom is different between the criteria for DSM-IV alcohol abuse and dependence compared with those for DSM-5 AUD.
| AUD+ at Year 10 (N=39) | AUD– at Year 10 (N=145) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Characteristic | Initial Testing | Year 10 Testing | Initial Testing | Year 10 Testing | Significance Testing | ||||
| N | % | N | % | N | % | N | % | ||
| Male | 25 | 64 | – | – | 75 | 52 | – | – | ns |
| Whitea | 32 | 82 | – | – | 108 | 74 | – | – | ns |
| Family history of AUDb | 7 | 18 | – | – | 35 | 24 | – | – | ns |
| Married or living with partner | 3 | 8 | 17 | 44 | 20 | 14 | 98 | 68 | ns |
| Nicotine dependencec | 6 | 15 | 10 | 26 | 3 | 2 | 6 | 4 | Group, p<0.01 |
| Substance dependencec | 3 | 8 | 1 | 3 | 1 | 1 | 0 | 0 | ns |
| Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
| Age (years) | 24.6 | 3.0 | 34.6 | 3.0 | 25.9 | 3.2 | 35.9 | 3.2 | ns |
| Education (years) | 15.5 | 1.7 | 16.4 | 2.1 | 16.2 | 1.8 | 17.7 | 3.0 | ns |
| Beck Depression Inventory score | 4.1 | 3.3 | 6.5 | 5.7 | 2.2 | 2.6 | 3.2 | 3.8 | ns |
| Spielberger State Anxiety t score | 49.2 | 8.9 | 51.8 | 7.8 | 43.6 | 6.8 | 46.6 | 5.9 | ns |
| AST (U/L) | 21.7 | 6.7 | 21.0 | 5.3 | 22.6 | 9.5 | 20.5 | 10.4 | ns |
| ALT (U/L) | 20.1 | 10.6 | 22.1 | 11.3 | 22.1 | 17.2 | 21.5 | 16.8 | ns |
| Drinking days in past monthd | 14.3 | 6.9 | 20.6 | 6.3 | 9.7 | 5.1 | 15.0 | 6.9 | Group, p<0.05 |
| Heavy drinking days in past monthd | 7.5 | 4.5 | 13.7 | 6.6 | 3.5 | 4.3 | 4.9 | 4.9 | Group-by-phase interaction, p<0.01 |
| Alcohol problemse | 11.3 | 5.0 | 18.7 | 6.2 | 6.8 | 4.6 | 8.6 | 5.4 | Group-by-phase interaction, p<0.01 |
| AUD symptoms during follow-upe | – | – | 4.5 | 1.8 | – | – | 1.2 | 1.5 | Group, p<0.001 |
a
Race was provided by participants from a list of options consistent with National Institutes of Health classifications.
b
Family history of AUD was defined as having one biological primary or two or more biological secondary relatives with AUD.
c
Nicotine dependence and substance dependence were determined using the Structured Clinical Interview for DSM-IV (SCID), indicating the number of participants who met past-year criteria at year 1 and during any follow-up time point across 10 years.
d
From the timeline follow-back calendar for the month preceding initial testing and maxima across follow-up time points for days that any alcohol was consumed and days with heavy drinking, defined as ≥5 drinks per occasion for men and ≥4 drinks for women, based on the standard definition of one drink (i.e., 12 oz. of beer, 5 oz. of wine, or 1.5 oz. of liquor).
e
From the Alcohol Use Disorders Identification Test and the SCID, indicating the past year at initial testing and mean scores or the number of 11 DSM-IV symptoms met across follow-up time points for 10 years.
In addition to the AUD group classifications based on the 10-year follow-up, we employed a complementary approach to determine trajectory subgroups based on the growth and progression (or regression) in AUD symptom severity throughout all follow-up time points (10, 11, 19). The subgroup analysis was based on DSM-IV symptom counts because DSM-5 was not yet developed when the follow-up assessments were initiated in 2005. This trajectory analysis (34) included a zero-inflated Poisson mixture model with a cubic trajectory for each subgroup. The number of trajectory groups was determined by the Bayesian information criterion using 2ln(B10) ≥ 10 as strong evidence for rejecting the null model. This analysis showed that a three-trajectory group model best fit the data: a low AUD symptom subgroup (N=103), an intermediate AUD symptom subgroup (N=62), and a high AUD symptom group (N=19). The AUD symptoms across these subgroups corresponded to the frequency of heavy drinking across follow-up (Figure 1B). Of note, the classifications of individuals in the AUD symptom trajectory subgroup were fairly stable, relative to the classifications of initial heavy drinkers in the 5-year analysis (11). Eighty-one percent of the participants remained in their respective low, intermediate, and high AUD symptom subgroup, while 15% moved by one category to the next higher symptom group and 4% changed to the next lower group. Demographic and drinking characteristics for the AUD symptom subgroups are shown in Table 2.
| Low AUD Symptom Group (N=103) | Intermediate AUD Symptom Group (N=62) | High AUD Symptom Group (N=19) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Characteristic | Initial Testing | Year 10 Testing | Initial Testing | Year 10 Testing | Initial Testing | Year 10 Testing | Significance Testing | ||||||
| N | % | N | % | N | % | N | % | N | % | N | % | ||
| Male | 48 | 47 | – | – | 40 | 65 | – | – | 12 | 63 | – | – | ns |
| Whitea | 75 | 73 | – | – | 50 | 81 | – | – | 15 | 79 | – | – | ns |
| Family history of AUDb | 28 | 27 | – | – | 10 | 16 | – | – | 4 | 21 | – | – | ns |
| Married or living with partner | 15 | 15 | 71 | 69 | 6 | 10 | 37 | 60 | 2 | 10 | 7 | 37 | ns |
| Nicotine dependencec | 2 | 2 | 3 | 3 | 4 | 6 | 6 | 10 | 3 | 16 | 7 | 37 | Group, p<0.05 |
| Substance dependencec | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 3 | 16 | 1 | 5 | ns |
| Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
| Age (years) | 26.2 | 3.3 | 36.2 | 3.3 | 24.8 | 3.0 | 34.7 | 3.0 | 25.3 | 2.9 | 35.3 | 2.9 | ns |
| Education (years) | 16.4 | 1.8 | 18.0 | 3.2 | 15.9 | 1.5 | 17.0 | 1.8 | 15.1 | 2.2 | 15.6 | 2.2 | Group-by-phase interaction, p<0.05 |
| Beck Depression Inventory score | 1.8 | 2.3 | 2.9 | 3.2 | 3.2 | 3.3 | 4.0 | 3.6 | 5.1 | 3.8 | 9.1 | 6.2 | Group-by-phase interaction, p<0.001 |
| Spielberger State Anxiety t score | 50.1 | 12.7 | 46.5 | 4.6 | 49.6 | 10.1 | 48.2 | 6.9 | 49.2 | 11.4 | 53.4 | 8.2 | Group-by-phase interaction, p<0.001 |
| AST (U/L) | 22.2 | 4.8 | 19.3 | 7.4 | 22.5 | 6.8 | 22.8 | 12.9 | 23.0 | 7.7 | 20.8 | 6.5 | ns |
| ALT (U/L) | 21.9 | 11.9 | 19.6 | 14.6 | 21.2 | 9.5 | 24.6 | 17.8 | 22.1 | 14.0 | 22.9 | 14.6 | ns |
| Drinking days in past monthd | 8.0 | 4.3 | 13.3 | 6.8 | 13.6 | 5.5 | 18.8 | 5.7 | 15.4 | 7.1 | 22.8 | 5.5 | Group-by-phase interaction, p<0.01 |
| Heavy drinking days in past monthd | 2.0 | 3.4 | 3.0 | 3.8 | 6.9 | 4.0 | 10.0 | 4.5 | 9.0 | 4.6 | 16.7 | 6.9 | Group-by-phase interaction, p<0.01 |
| Alcohol problemse | 5.0 | 3.3 | 6.4 | 3.8 | 11.0 | 4.3 | 14.4 | 4.8 | 12.4 | 5.4 | 21.9 | 6.2 | Group-by-phase interaction, p<0.01 |
| AUD symptoms during follow-upe | – | – | 0.5 | 0.7 | – | – | 2.9 | 1.4 | – | – | 6.1 | 1.3 | Group, p<0.001 |
a
Race was provided by participants from a list of options consistent with National Institutes of Health classifications.
b
Family history of AUD was defined as having one biological primary or two or more biological secondary relatives with AUD.
c
Nicotine dependence and substance dependence were determined using the Structured Clinical Interview for DSM-IV (SCID), indicating the number of participants who met past-year criteria at year 1 and during any follow-up time point across 10 years.
d
From the timeline follow-back calendar for the month preceding initial testing and maxima across follow-up time points for days that any alcohol was consumed and days with heavy drinking, defined as ≥5 drinks per occasion for men and ≥4 drinks for women, based on the standard definition of one drink (i.e., 12 oz. of beer, 5 oz. of wine, or 1.5 oz. of liquor).
e
From the Alcohol Use Disorders Identification Test and the SCID, indicating the past year at initial testing and mean scores or the number of 11 DSM-IV symptoms met across follow-up time points for 10 years.
Statistical Analysis
Demographic and drinking characteristics were compared between the AUD+ and AUD− groups by t tests and chi-square tests, as appropriate. Acute alcohol responses were analyzed by generalized estimating equation models (35) that included group, time, and phase, with the latter two variables treated as continuous variables. To assess potential nonlinearity over time, models for each alcohol response included linear, square, and cubic terms of time. Results revealed that stimulation only (and not sedation, liking, or wanting), as measured by the Biphasic Alcohol Effects Scale, required the second- and third-order polynomial terms. This is consistent with previous work showing rapid increases in stimulation during the ascending breath alcohol concentration limb, followed by an inflection point and sharp declines during the descending breath alcohol concentration limb (10). Generalized estimating equation models were also used for secondary measures, including general subjective response (the “feel-drug” item of the Drug Effects Questionnaire) and physiological effects (heart rate and cortisol secretion), which are sensitive to alcohol (36–38). The AUD symptom subgroups were assessed on alcohol responses in generalized estimating equation models similar to those conducted in the main analysis to examine subgroup, time, and phase effects and their interactions. All generalized estimating equation models adjusted for age, race, education, family history of alcoholism (39), breath alcohol concentration (as a time-varying covariate), and the number of baseline AUD symptoms.
Results
Participant retention in the Chicago Social Drinking Project was high, with 99% (184 of 185) of the nondeceased active participants completing follow-up at 10 years (i.e., 184 out of 190 participants from the original sample, or 97%; see Figure S1 in the online supplement). The 5-year and 10-year laboratory reexamination sessions were conducted in 156 and 163 participants, respectively. These rates represent 88% and 91% of the 178 and 179 participants eligible for reexamination at each phase, respectively.
The AUD+ and AUD− groups at 10 years did not differ on most demographic characteristics; however, as expected, the AUD+ group reported heavier alcohol consumption and more drinking problems than the AUD− group at the 10-year reexamination and at the initial testing phase (Table 1). Across the test phases, the groups did not differ significantly on average breath alcohol concentration (see Figure S2A in the online supplement), with both showing a rapid rising breath alcohol concentration limb that reached peak level 60 minutes after the initiation of the beverage consumption, followed by a slow breath alcohol concentration declining limb (group-by-time-by-phase interaction: χ2=5.78, df=8, p=0.672). Thus, while reflecting individual variability in alcohol pharmacokinetics as they relate to oral administration (40), the mean values of breath alcohol concentration of the AUD groups did not substantially differ across the testing phases.
In terms of subjective response, alcohol produced markedly different effects in the 10-year AUD+ and AUD− groups. These differences were evident starting at the initial testing phase and increased further over the course of the reexamination phases (Figure 2). Overall, the AUD+ group exhibited increasing sensitivity for pleasurable alcohol responses during the decade of participation, while the AUD− group showed low initial levels on these measures with little to no change through the testing phases. Specifically, alcohol produced initially higher stimulation in the AUD+ group relative to the AUD− group during the early portion of the breath alcohol concentration curve, and stimulation level increased in intensity through the reexamination phases (group-by-time-by-phase interaction, p=0.016; Table 3). Alcohol also initially increased ratings of motivational salience (wanting) in the AUD+ group relative to the AUD− group across the entire breath alcohol concentration curve, and wanting also increased in intensity through the reexamination phases (group-by-phase interaction, p=0.001). Initial hedonic reward (liking) from alcohol was also higher in the AUD+ group (p=0.017), and this effect increased through reexamination, but the escalation was not statistically significant (group-by-phase interaction, p=0.104). Finally, both groups showed increases in alcohol-induced sedation, but this effect did not differ by group or test phase (group-by-phase interaction, p>0.385). Reanalyzing the data based on DSM-IV criteria for alcohol dependence yielded similar results.
FIGURE 2. Subjective alcohol responses during initial testing and at 5-year and 10-year reexaminations in alcohol use disorder (AUD) groups as determined at year 10 in a study of the development and maintenance of AUDa
a Data shown are mean values, with error bars indicating standard error, of the change in scores (alcohol session scores minus placebo session scores) for the four primary subjective response measures at each time point and at each testing phase for the presence of AUD (AUD+ group) at year 10 (N=39) or the absence of AUD (AUD− group) at year 10 (N=145). The AUD+ and AUD− groups were identified based on AUD symptom counts per DSM-5 at year 10 of follow-up. The y-axis ranges for liking and wanting differ slightly given the range of values and because liking is based on a scale with the midpoint as neutral. Details on the statistical testing are provided in Table 3.
| Primary Outcome | β | SE | p | β | SE | p | β | SE | p | β | SE | p | β | SE | p | β | SE | p |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AUD group | Time | Phase | AUD group-by-phase interaction | AUD group-by-time interaction | AUD group-by-time-by-phase interaction | |||||||||||||
| Stimulation | 1.126 | 2.192 | 0.607 | –4.089 | 4.383 | 0.351 | –0.037 | 0.137 | 0.788 | –0.305 | 0.289 | 0.292 | 6.027 | 6.300 | 0.339 | 2.541 | 0.987 | 0.010 |
| Sedation | –1.980 | 1.958 | 0.312 | 1.885 | 0.373 | <0.001 | –0.030 | 0.099 | 0.760 | –0.182 | 0.210 | 0.385 | –1.250 | 0.789 | 0.113 | 0.159 | 0.124 | 0.198 |
| Wanting | 5.534 | 6.093 | 0.364 | –2.819 | 1.172 | 0.016 | –0.830 | 0.332 | 0.012 | 2.203 | 0.702 | 0.002 | –1.367 | 2.368 | 0.564 | 0.268 | 0.371 | 0.470 |
| Liking | 11.454 | 4.691 | 0.015 | –8.699 | 0.996 | <0.001 | –0.574 | 0.282 | 0.042 | 1.091 | 0.598 | 0.068 | –1.463 | 2.016 | 0.468 | –0.148 | 0.316 | 0.639 |
| AUD symptom trajectory subgroups | Time | Phase | AUD symptom group-by-phase interaction | AUD symptom group-by-time interaction | AUD symptom group-by-time-by-phase interaction | |||||||||||||
| Stimulation | 0.759 | 1.380 | 0.582 | –9.354 | 7.061 | 0.185 | 0.326 | 0.297 | 0.273 | –0.281 | 0.176 | 0.111 | 5.104 | 3.820 | 0.181 | 1.818 | 0.600 | 0.002 |
| Sedation | –0.927 | 1.270 | 0.465 | 1.822 | 0.812 | 0.025 | 0.123 | 0.216 | 0.569 | –0.125 | 0.128 | 0.328 | –0.133 | 0.479 | 0.781 | –0.001 | 0.075 | 0.991 |
| Wanting | 10.741 | 3.792 | 0.005 | –4.727 | 2.475 | 0.056 | –1.959 | 0.733 | 0.008 | 1.054 | 0.433 | 0.015 | 1.033 | 1.456 | 0.478 | –0.090 | 0.229 | 0.693 |
| Liking | 8.712 | 2.972 | 0.003 | –8.215 | 2.079 | <0.001 | –1.194 | 0.616 | 0.052 | 0.567 | 0.364 | 0.119 | –0.553 | 1.223 | 0.651 | –0.075 | 0.192 | 0.696 |
a
Data are results from a generalized estimating equation analysis and include the cubic term for stimulation and linear terms for sedation, wanting, and liking as the best fit for these four variables. For stimulation, the three-way interaction of AUD group by time by phase and of AUD group by time-squared by phase were also significant. All generalized estimating equations analyses controlled for baseline AUD count, breath alcohol concentration, sex, race, and family history of alcoholism. Boldface indicates statistical significance.
As with the AUD groups, for the AUD symptom groups, breath alcohol concentration curves did not differ across subgroups (see Figure S2B in the online supplement). For alcohol responses, a similar pattern of results emerged for the AUD symptom subgroups as for the AUD+ and AUD− groups (Figure 3). Relative to the low AUD symptom subgroup, the intermediate and high AUD symptom subgroups exhibited initially heightened alcohol stimulation, wanting, and liking. For alcohol stimulation and wanting, these responses were potentiated through the reexamination phases (i.e., augmented across the decade of participation), with liking remaining elevated but not increasing further as the decade progressed (Figure 3). Further, initial subjective responses to alcohol significantly predicted the frequency of heavy drinking through follow-up; this behavioral outcome was used in our previous work (10), and associations were evident over the decade for liking and wanting (all r values, 0.19; all p values, <0.01), stimulation (r=0.14, p=0.06), and sedation (r=−0.17, p<0.05).
FIGURE 3. Subjective alcohol responses during initial testing and at 5-year and 10-year reexaminations in alcohol use disorder (AUD) symptom trajectory subgroups in a study of the development and maintenance of AUDa
a Data are shown as mean values, with error bars indicating standard error, for low AUD symptom count (N=103), intermediate AUD symptom count (N=62), and high AUD symptom count (N=19) trajectory groups at the initial and reexamination phases. Subjective response data are change scores (alcohol session scores minus placebo session scores) at each time point. The y-axis ranges for liking and wanting differ slightly given the range of values and because liking is based on a scale with the midpoint as neutral. Details on the statistical testing are provided in Table 3.
On secondary measures, alcohol increased feel-drug ratings during the rising breath alcohol concentration limb (time, p=0.026; see Table S1 in the online supplement ); this response became more intense through the reexamination phase for the AUD+ group (group-by-phase interaction, p=0.004). Alcohol-induced heart rate and cortisol increases did not differ by group or across test phases (see Table S1 and Figure S3 in the online supplement). The same results were observed when based on an analysis of AUD symptom subgroups (see Table S1 and Figure S4 in the online supplement). For all analyses, there were no sex differences.
Discussion
AUD is phenotypically complex (41), and a better understanding of factors that increase vulnerability to sustained excessive drinking is crucial for effective prevention, early intervention, and treatment development (42). The present study provides the first repeated assessment of acute alcohol responses in the same people across a substantial 10-year period of adulthood. The results comprise two main findings: first, heightened sensitivity to the pleasurable effects of alcohol (stimulation, liking, and wanting) in young adulthood preceded the development of AUD through middle age; and second, sensitivity to alcohol stimulation and wanting increased across repeated testing over a decade in persons who developed AUD. Trajectory analyses of AUD symptom progression over follow-up confirmed these findings by demonstrating amplification of alcohol stimulation and wanting as a function of AUD severity across a decade. Notably, physiological responses such as heart rate and cortisol did not predict AUD development. Taken together, these findings support the idea that the positive stimulating and motivating effects of alcohol increase during the development and maintenance of AUD. The findings do not support the idea that low sensitivity to alcohol increases the risk for AUD (12) or that a reward deficit stage emerges in the progression to addiction (43).
The study results are consistent with the central tenet of the incentive-sensitization theory, namely, that motivational (“wanting”) but not hedonic (“liking”) processes become sensitized in the progression to addiction (14). In our study, the individuals who developed AUD over the 10-year period reported increases in wanting alcohol over time, while liking of alcohol remained high and stable over time. The incentive-sensitization theory is based on behavioral and pharmacological studies in rodents demonstrating wanting and liking as distinct components of reward and mediated by separate neural systems. In the animal studies, repeated exposure to the drug produced incremental neuroadaptations in the circuits mediating motivational salience, rendering them hypersensitive to the drug and its associated stimuli (14, 44). Until now, however, this idea had not been examined in a longitudinal manner in humans. Although cross-sectional studies of the continuum of alcohol use severity support this idea (45–49), to our knowledge, our study, which includes repeated alcohol challenge testing in the same participants over an extensive period, provides the first longitudinal support for the central tenet of this theory.
There has been substantial disagreement among clinicians and researchers as to whether AUD is progressive, what behavioral or subjective changes occur in response to alcohol over extended periods of use, and what processes underlie neuroadaptive changes that result from chronic drinking. It is known that chronic exposure to alcohol leads to pharmacodynamic tolerance such that users can withstand a higher breath alcohol concentration than inexperienced drinkers before experiencing stupor, coma, and eventual death (50). Although tolerance to the subjective effects of alcohol is a hallmark symptom of AUD, it has been challenging to track the development of such tolerance in humans. In a similar vein, it has been difficult to document the progressive stages of allostasis, another construct believed to contribute to the disorder.
Several issues are relevant when reconciling the results of the present study with existing theory. First, retrospective patient reports suggesting the need for more alcohol to get the same effects as compared with when one first started drinking regularly are not sufficient support for chronic tolerance. The well-controlled findings from the present study are not consistent with the notion of robust chronic tolerance of alcohol’s subjective effects, so anecdotal patient reports of lessened effects of alcohol over time may be influenced by factors other than acute alcohol response. Second, studies of chronic exposure to alcohol derived from animal models of addiction, while providing critical information about the behavioral and physiological correlates of consumption (51), are not able to shed light on changes in the euphoric effects of drinking in humans and how these change over time. Third, some theories of addiction, such as allostasis, indicate development of a deficit in reward or a change in aversive states and stress responses. Although our data do not provide support for this idea, it is possible that this putative stage requires more than a decade to develop or that it exists only in excessive drinkers prone to negative affect or extremely aversive withdrawal. Alternatively, while certain tenets of the allostasis theory may relate to animal models, they may not translate to manifestations of AUD as observed within the demographic characteristics of our study sample. Importantly, our within-subject, longitudinal findings demonstrated that the perceived pleasurable effects of alcohol are not diminished as AUD severity progressed during a 10-year period. However, cross-sectional neuroimaging studies suggest that heavy drinkers relative to lighter social drinkers exhibit reduced activation of the nucleus accumbens after alcohol infusion (52–54). Although it is difficult to reconcile the contrasting findings, given the differences in samples and methodologies, we may speculate that other neurobiological substrates (i.e., the basal ganglia) or circuits underlie the persistent positive subjective response to alcohol. Nevertheless, this possibility highlights the need for studies employing both neurobiological techniques and validated subjective measures.
The Chicago Social Drinking Project has several strengths, including a prospective design with alcohol and placebo testing at three phases, outstanding retention, and determination of drinking and AUD symptoms from early to middle adulthood. Most notably, we were able to attain near-perfect retention of participants across the decade. The inclusion of dual follow-up outcomes, that is, categorical AUD diagnosis and trajectory analyses of symptom growth over time, provides a powerful picture of the development of alcohol problems (55). Relatedly, as this longitudinal study began in 2004, nearly a decade before the 2013 release of DSM-5, the trajectory analysis was based on DSM-IV symptom counts for alcohol abuse and dependence. Thus, symptom counts may have been “undercounts,” and the actual symptom count severity may well have been higher if DSM-5 were available because DSM-IV included the item for legal problems, which was determined to have infrequent endorsement (56), and did not include the item for craving, which is more often endorsed (57).
The study also has limitations. First, the standardized body weight–adjusted and sex-adjusted fixed dose of alcohol was chosen to produce a sharp rise in breath alcohol concentration to minimize variability. However, this procedure did not allow for other clinically relevant aspects of drinking, including both self-paced drinking and choice to drink in the presence of both alcohol and other reinforcers or consequences. Second, we were not able to test participants younger than 21 years of age, and it is possible that alcohol-related changes had already occurred before the participants enrolled in the Chicago Social Drinking Project. Relevant to this issue is recent collaborative work by the first author (A.K.) and international colleagues showing that older adolescent heavy drinkers exhibit sensitivity to alcohol stimulation with heightened tonic wanting (58), so prospective studies with younger participants, perhaps in locations where adolescent drinking is permitted, would be valuable in determining the earliest precipitants of adaptive responses to alcohol. Third, although our sample developed AUD at a higher rate than that of 12.7% in the general population (1), the final sample size of the AUD+ group was modest. However, using two similar but not overlapping outcome approaches may ameliorate concerns about sample size. Comparing the AUD+ and AUD− groups at year 10 provided a practical clinical endpoint used by clinicians, and notably, 64% of AUD+ subjects met DSM-IV alcohol dependence at some point through follow-up, compared with 7% in the AUD− group. The AUD symptom trajectory construct provided a data-driven approach, affording consistency with our previous work (11, 19). All of the participants in the high AUD symptom subgroup met alcohol dependence through follow-up, compared with 26% and 0% in the intermediate and low AUD symptom groups, respectively. Although an examination of consequences and distress related to excessive drinking was beyond the scope of this article, persons in the AUD+ group had a higher likelihood of seeking advice or help with drinking or of seriously considering getting help relative to the AUD− group (49% compared with 8%).
In sum, this study is the most extensive repeated human testing study of its kind, examining the adaptive processes underlying the development and maintenance of AUD. Alcohol-induced subjective stimulation increased over time, and euphoric processes did not wane as AUD symptoms emerged. Instead, these effects either stayed the same or increased, challenging the notion that conventional chronic tolerance to alcohol’s subjective effects plays a key role in escalation of excessive drinking. The findings are consistent with the incentive-sensitization theory, as alcohol wanting increased across a decade in persons developing AUD. However, the study results were not consistent with the allostasis model of a progressive deficit in reward. In terms of prevention of alcohol problems, rather than a sole focus on tolerance, young adults might be informed that marked stimulant-like, pleasurable, and appetitive effects after consuming alcohol are risk factors for the development and maintenance of addiction. Finally, pharmacological and behavioral interventions focused on reducing positive rewarding effects and motivational salience during acute alcohol consumption may be crucial in future medication development and treatment of AUD.
Acknowledgments
The authors thank Patrick McNamara for project coordination, data collection, and database management and Royce Lee, M.D., and Jon Grant, M.D., for medical supervision and oversight.
Footnotes
Presented in part at the 41st annual meeting of the Research Society on Alcoholism, San Diego, June 16–20, 2018.
ClinicalTrials.gov identifier: NCT00961792.
Supplementary Material
File (appi.ajp.2020.20030247.ds001.pdf)
- View/Download
- 726.97 KB
References
1.
Grant BF, Chou SP, Saha TD, et al : Prevalence of 12-month alcohol use, high-risk drinking, and DSM-IV alcohol use disorder in the United States, 2001–2002 to 2012–2013: results from the National Epidemiologic Survey on Alcohol and Related Conditions. JAMA Psychiatry 2017 ; 74 : 911 – 923
2.
McKetta S, Keyes KM : Heavy and binge alcohol drinking and parenting status in the United States from 2006 to 2018: an analysis of nationally representative cross-sectional surveys. PLoS Med 2019 ; 16 : e1002954
3.
Hasin DS, Shmulewitz D, Keyes K : Alcohol use and binge drinking among U.S. men, pregnant and non-pregnant women ages 18–44: 2002–2017. Drug Alcohol Depend 2019 ; 205 : 107590
4.
Griswold MG, Fullman N, Hawley C, et al : Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2018 ; 392 : 1015 – 1035
5.
Chassin L, Pitts SC, Prost 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 : 67 – 78
6.
Zucker RA, Wong MM, Clark DB, et al : Predicting risky drinking outcomes longitudinally: what kind of advance notice can we get? Alcohol Clin Exp Res 2006 ; 30 : 243 – 252
7.
Parry CD, Patra J, Rehm J : Alcohol consumption and non-communicable diseases: epidemiology and policy implications. Addiction 2011 ; 106 : 1718 – 1724
8.
Rehm J : The risks associated with alcohol use and alcoholism. Alcohol Res Health 2011 ; 34 : 135 – 143
9.
Ray LA, Bujarski S, Roche DJO : Subjective response to alcohol as a Research Domain criterion. Alcohol Clin Exp Res 2016 ; 40 : 6 – 17
10.
King AC, de Wit H, McNamara PJ, et al : Rewarding, stimulant, and sedative alcohol responses and relationship to future binge drinking. Arch Gen Psychiatry 2011 ; 68 : 389 – 399
11.
King AC, McNamara PJ, Hasin DS, et al : Alcohol challenge responses predict future alcohol use disorder symptoms: a 6-year prospective study. Biol Psychiatry 2014 ; 75 : 798 – 806
12.
Schuckit MA : Low level of response to alcohol as a predictor of future alcoholism. Am J Psychiatry 1994 ; 151 : 184 – 189
13.
Wise RA, Bozarth MA : A psychomotor stimulant theory of addiction. Psychol Rev 1987 ; 94 : 469 – 492
14.
Robinson TE, Berridge KC : The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev 1993 ; 18 : 247 – 291
15.
Koob GF, Le Moal M : Drug abuse: hedonic homeostatic dysregulation. Science 1997 ; 278 : 52 – 58
16.
Roberts AJ, Heyser CJ, Cole M, et al : Excessive ethanol drinking following a history of dependence: animal model of allostasis. Neuropsychopharmacology 2000 ; 22 : 581 – 594
17.
Spanagel R : Animal models of addiction. Dialogues Clin Neurosci 2017 ; 19 : 247 – 258
18.
King AC, Cao D, deWit H, et al : The role of alcohol response phenotypes in the risk for alcohol use disorder. BJPsych Open 2019 ; 5 : e38
19.
King AC, Hasin D, O’Connor SJ, et al : A prospective 5-year re-examination of alcohol response in heavy drinkers progressing in alcohol use disorder. Biol Psychiatry 2016 ; 79 : 489 – 498
20.
Dawson DA : US low-risk drinking guidelines: an examination of four alternatives. Alcohol Clin Exp Res 2000 ; 24 : 1820 – 1829
21.
National Institute on Alcohol Abuse and Alcoholism : Rethinking Drinking (https://www.rethinkingdrinking.niaaa.nih.gov)
22.
King AC, Houle T, de Wit H, et al : Biphasic alcohol response differs in heavy versus light drinkers. Alcohol Clin Exp Res 2002 ; 26 : 827 – 835
23.
King AC, Epstein AM : Alcohol dose-dependent increases in smoking urge in light smokers. Alcohol Clin Exp Res 2005 ; 29 : 547 – 552
24.
McCaul ME, Wand GS, Eissenberg T, et al : Naltrexone alters subjective and psychomotor responses to alcohol in heavy drinking subjects. Neuropsychopharmacology 2000 ; 22 : 480 – 492
25.
Wiers RW, van de Luitgaarden J, van den Wildenberg E, et al : Challenging implicit and explicit alcohol-related cognitions in young heavy drinkers. Addiction 2005 ; 100 : 806 – 819
26.
Brumback T, Cao D, King A : Effects of alcohol on psychomotor performance and perceived impairment in heavy binge social drinkers. Drug Alcohol Depend 2007 ; 91 : 10 – 17
27.
Conrad M, McNamara P, King A : Alternative substance paradigm: effectiveness of beverage blinding and effects on acute alcohol responses. Exp Clin Psychopharmacol 2012 ; 20 : 382 – 389
28.
National Advisory Council on Alcohol Abuse and Alcoholism : Recommended Council Guidelines on Ethyl Alcohol Administration in Human Experimentation. 2005 ( https://www.niaaa.nih.gov/Resources/ResearchResources/job22.htm#levels)
29.
Martin CS, Earleywine M, Musty RE, et al : Development and validation of the Biphasic Alcohol Effects Scale. Alcohol Clin Exp Res 1993 ; 17 : 140 – 146
30.
Johanson CE, Uhlenhuth EH : Drug preference and mood in humans: diazepam. Psychopharmacology (Berl) 1980 ; 71 : 269 – 273
31.
Morean ME, de Wit H, King AC, et al : The drug effects questionnaire: psychometric support across three drug types. Psychopharmacology (Berl) 2013 ; 227 : 177 – 192
32.
Rueger SY, McNamara PJ, King 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 : 916 – 924
33.
Smith LJ, McNamara PJ, King AC : Optimizing follow-up and study retention in the 21st century: advances from the front line in alcohol and tobacco research. Drug Alcohol Depend 2017 ; 175 : 171 – 178
34.
Jones BL, Nagin DS : A note on a Stata plugin for estimating group-based trajectory models. Sociol Methods Res 2013 ; 42 : 608 – 613
35.
Liang K-Y, Zeger SL : Longitudinal data analysis using generalized linear models. Biometrika 1986 ; 73 : 13 – 22
36.
Conrod PJ, Peterson JB, Pihl 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 : 20 – 30
37.
Newlin DB, Byrne EA, Porges SW : Vagal mediation of the effect of alcohol on heart rate. Alcohol Clin Exp Res 1990 ; 14 : 421 – 424
38.
King A, Munisamy G, de Wit H, et al : Attenuated cortisol response to alcohol in heavy social drinkers. Int J Psychophysiol 2006 ; 59 : 203 – 209
39.
Goodwin DW, Schulsinger F, Hermansen L, et al : Alcohol problems in adoptees raised apart from alcoholic biological parents. Arch Gen Psychiatry 1973 ; 28 : 238 – 243
40.
Ramchandani VA, Plawecki M, Li TK, et al : Intravenous ethanol infusions can mimic the time course of breath alcohol concentrations following oral alcohol administration in healthy volunteers. Alcohol Clin Exp Res 2009 ; 33 : 938 – 944
41.
Reilly MT, Noronha A, Goldman D, et al : Genetic studies of alcohol dependence in the context of the addiction cycle. Neuropharmacology 2017 ; 122 : 3 – 21
42.
Kranzler HR, Soyka M : Diagnosis and pharmacotherapy of alcohol use disorder: a review. JAMA 2018 ; 320 : 815 – 824
43.
Koob GF, Le Moal M : Addiction and the brain antireward system. Annu Rev Psychol 2008 ; 59 : 29 – 53
44.
Berridge KC, Robinson TE : What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res Brain Res Rev 1998 ; 28 : 309 – 369
45.
Hobbs M, Remington B, Glautier S : Dissociation of wanting and liking for alcohol in humans: a test of the incentive-sensitisation theory. Psychopharmacology (Berl) 2005 ; 178 : 493 – 499
46.
Ostafin BD, Marlatt GA, Troop-Gordon W : Testing the incentive-sensitization theory with at-risk drinkers: wanting, liking, and alcohol consumption. Psychol Addict Behav 2010 ; 24 : 157 – 162
47.
Cofresí RU, Bartholow BD, Piasecki TM : Evidence for incentive salience sensitization as a pathway to alcohol use disorder. Neurosci Biobehav Rev 2019 ; 107 : 897 – 926
48.
Bujarski S, Ray LA : Subjective response to alcohol and associated craving in heavy drinkers vs. alcohol dependents: an examination of Koob’s allostatic model in humans. Drug Alcohol Depend 2014 ; 140 : 161 – 167
49.
Bujarski S, Jentsch JD, Roche DJO, et al : Differences in the subjective and motivational properties of alcohol across alcohol use severity: application of a novel translational human laboratory paradigm. Neuropsychopharmacology 2018 ; 43 : 1891 – 1899
50.
Le AD, Mayer JM : Aspects of alcohol tolerance in humans and experimental animals, in Pharmacological Effects of Ethanol on the Nervous System. Edited by Deitrich RA, Erwin VG. Boca Raton, Fla, CRC Press, 1995, pp 251 – 268
51.
Tabakoff B, Hoffman PL : Animal models in alcohol research. Alcohol Res Health 2000 ; 24 : 77 – 84
52.
Gilman JM, Ramchandani VA, Davis MB, et al : Why we like to drink: a functional magnetic resonance imaging study of the rewarding and anxiolytic effects of alcohol. J Neurosci 2008 ; 28 : 4583 – 4591
53.
Gilman JM, Ramchandani VA, Crouss T, et al : Subjective and neural responses to intravenous alcohol in young adults with light and heavy drinking patterns. Neuropsychopharmacology 2012 ; 37 : 467 – 477
54.
Spagnolo PA, Ramchandani VA, Schwandt ML, et al : Effects of naltrexone on neural and subjective response to alcohol in treatment-seeking alcohol-dependent patients. Alcohol Clin Exp Res 2014 ; 38 : 3024 – 3032
55.
Schuckit MA : The answer you get depends on the question you ask. Biol Psychiatry 2014 ; 75 : 754 – 755
56.
Saha TD, Chou SP, Grant BF : Toward an alcohol use disorder continuum using item response theory: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychol Med 2006 ; 36 : 931 – 941
57.
Murphy CM, Stojek MK, Few LR, et al : Craving as an alcohol use disorder symptom in DSM-5: an empirical examination in a treatment-seeking sample. Exp Clin Psychopharmacol 2014 ; 22 : 43 – 49
58.
Chavarria J, Fridberg DJ, Obst E, et al : Subjective alcohol responses in high- and low-risk adolescents: results from the Dresden Longitudinal Study on Alcohol Use in Young Adults. Addiction (in press)
Information & Authors
Information
Published In
History
Received: 3 March 2020
Revision received: 13 July 2020
Accepted: 10 August 2020
Published online: 5 January 2021
Published in print: June 2021
Keywords
Authors
Competing Interests
Dr. King has served on an advisory board for Pfizer and as a consultant to the Respiratory Health Association. The other authors report no financial relationships with commercial interests.
Funding Information
Supported by National Institute on Alcohol Abuse and Alcoholism grants AA-013746 to Dr. King, AA-023839 to Dr. Cao, AA-07611 to Dr. O’Connor, and AA-025309 to Dr. Hasin, and by NIDA grants DA-043469 to Drs. Vena and deWit and DA-018652 to Dr. Hasin.
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBLogin options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).