The Brief Addiction Science Information Source (BASIS)

The DRAM Vol. 6(7) – Different reasons why men and women drink: Examining the underlying association between maltreatment during childhood and later alcohol problems

Previous studies have demonstrated a positive association between maltreatment during childhood and drinking-related problems later in life (Bensley, Spieker, Van Eenywk, & Schoder, 1999). However, the motivational mechanisms underlying this association have not been studied. This week DRAM reviews a paper that examines why men and women who report experiencing childhood maltreatment abuse alcohol (Goldstein, Flett, & Wekerle, 2010).

Method

• Eighty-six male and 132 female college students answered questions regarding
  • Childhood maltreatment (i.e., physical, sexual and emotional abuse; physical and emotional neglect), assessed with the 28-item Childhood Trauma Questionnaire (CTQ; Bernstein and Fink, 1998).
  • Drinking motives, assessed with the 28-item Modified Drinking Motives Questionnaire-Revised (M-DMQ-R; Grant et al., 2007)o Alcohol consumption and consequences, assessed with the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, De La Fuente, & Grant, 2006).
• The researchers built models separately for men and women to examine which drinking motives mediate the effect of childhood maltreatment on alcohol problems.

Results

• Approximately half (51.6%) of the male students and 23.4% of the female students were binge drinking at least monthly.
• Overall, 31% of students reported experiencing any type of abuse and 30% reported any type of neglect
• There was a positive association between being abused (r = .32, p < .01) or neglected (r = .28, p <.05) and having alcohol problems.
• For men, the association between being maltreated (e.g., neglected or abused) and having alcohol problems was mediated by the motive “to feel good and get happy;” this model accounted for 42.12% of the variance in alcohol problems, adjusted R² =0.39.
• For women, this association was mediated by the desire to cope with depression and anxiety (this model accounted for 22.54% of the variance in alcohol problems (adjusted R² = .21)

Table 1 Mediation effects (Z values) of drinking motives on childhood maltreatment to alcohol problems association for both genders.

	Coping-anxiety motive	Coping-depression motive	Enhancement motive	Total model
Men	1.15	.31	2.01*	2.48*
Women	-.95	2.56**	2.02*	3.15**

* p < .05; ** p < .01

Limitations

• Retrospective reports might reflect some memory bias. For example, individuals who have drinking problems might be inclined to recall more childhood maltreatment.
• Correlational design prevents causality interpretation.
• Convenience sample (i.e., students) decreases generalizability of the results to other populations.

Conclusion

Men who report experiencing maltreatment during their childhood tend to report drinking to feel good and to get happy. In other words, the enhancement motive mediates the association between childhood maltreatment and alcohol problems among men. For women who were maltreated, problematic drinking is associated with a desire to cope with depression and anxiety. In other words, coping with depression and anxiety mediates the association between problematic drinking and childhood maltreatment among women. Further studies will need to conduct longitudinal studies to establish causality (i.e. to examine if childhood maltreatment indeed leads to future alcohol-related problems). 

-Julia Braverman

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References

Bensley, S. L., Spieker, S. J., Van Eenwyk, J., & Schoder, J. (1999). Self-reported abuse history and adolescent problem behaviours. II. Alcohol and drug use. Journal of Adolescent Health, 24, 173-180.

Bernstein, D. P., & Fink, L. (1998). Childhood Trauma Questionnaire: A retrospective self-report manual. San Antonio, TX: The Psychological Corporation.

Goldstein, A. L., Flett, G. L., & Wekerle, C. (2010). Child maltreatment, alcohol use and drinking consequences among male and female college students: An examination of drinking motives as mediators. Addictive Behaviors, 35(6), 636-639.

Grant, V. V., Stewart, S. H., O'Connor, R. M., Blackwell, E., & Conrod, P. J. (2007).Psychometric evaluation of the five-factor Modified Drinking Motives Questionnaire—Revised in undergraduates. Addictive Behaviors, 32, 2611-2632.

Saunders, J. B., Aasland, O. G., Babor, T. F., De La Fuente, J. R., & Grant, M. (2006).Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction, 88, 791-804.

The DRAM, Vol. 6(6) – Do as I say, not as they do: The effects of parenting style on adolescent alcohol use.

According to data from the national Youth Risk Behavior Survey (YRBS), during the 30 days prior to survey, 42% of high school students had at least 1 drink and 24% participated in heavy drinking (Center for Disease Control and Prevention, 2009). Parenting style likely has an effect on adolescent alcohol use. However, researchers have reported conflicting results about the effects of parenting practices, specifically control (i.e., monitoring, discipline) and support (i.e., warmth, nurturance), on adolescent alcohol use (e.g., Dubow, Boxer, & Huesmann, 2008; King & Chassin, 2004). This week’s DRAM reviews a recent article that examined the effect of parenting style (i.e., authoritative, authoritarian, indulgent, and neglectful) on adolescent alcohol use in a large representative sample (Bahr & Hoffmann, 2010).

Methods

• Bahr & Hoffman used a multistage probability sample to select 4,983 students in grades 6-12 to complete the survey.
• The survey included the following variables:
• Dependent variable: Past 30 day alcohol use: no use; light use (i.e., less than five drinks in one sitting); or heavy use (i.e., five or more drinks in one sitting)
• Independent variables: Parenting style: authoritative (i.e., high support, high control), authoritarian (i.e., low support, high control), indulgent (i.e., high support, low control), or neglectful (i.e., low support, low control); Religiosity: 2 items measuring frequency and importance of religion; Peer alcohol use: among four best friends the number who drink.  
• The researchers used structural equation modeling (SEM) to estimate the effect of parenting style, religiosity, and peer alcohol use on past 30 day alcohol use.

Table 1. Parenting Style and Adolescent Alcohol Use (adapted from Bahr & Hoffman, 2010).
Note. Results are from SEM that included adolescent religiousness and the following covariates: age; gender; race; family structure; and parental education.
Results

• As Table 1 shows, adolescents whose parents were authoritative were less likely to drink heavily than adolescents from the other three parenting styles, and they were less likely to have close friends who used alcohol.
• Contrary to expectations, authoritative parenting had no significant effect on light alcohol use compared to authoritarian and indulgent parenting styles, but did have a significant effect compared to neglectful parenting.

Limitations

• Barr and Hoffman employed a cross-sectional study design making it impossible to determine cause and effect.
• The authors relied on student self-report, and the results are subject to the biases associated with self-report.

Conclusion
These results support the hypothesis that parental style has a significant but small¹ association with the drinking behavior of their adolescent children. These results illustrate how strong parenting styles that include monitoring adolescent behavior and/or strong social support might help deter drinking behavior and insulate adolescents from the peer pressure of heavy drinking. Future research should include longitudinal studies to measure the influence of parenting style on adolescent drinking behavior across time and further refine our understanding of the moderators and mechanisms of that influence, such as parental drinking behavior and the promotion/tolerance of underage drinking.

- John H Kleschinsky
 
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References

Bahr, S. J., & Hoffmann, J. P. (2010). Parenting style, religiosity, peers, and adolescent heavy drinking. J Stud Alcohol Drugs, 71(4), 539-543.

Center for Disease Control and Prevention. (2009). Trends in the Prevalence of Alcohol Use. Youth Risk Behavior Survey   Retrieved July 09, 2010, from http://www.cdc.gov/healthyyouth/yrbs/pdf/us_alcohol_trend_yrbs.pdf

Dubow, E. F., Boxer, P., & Huesmann, L. R. (2008). Childhood and adolescent predictors of early and middle adulthood alcohol use and problem drinking: the Columbia County Longitudinal Study. Addiction, 103 Suppl 1, 36-47.

King, K. M., & Chassin, L. (2004). Mediating and moderated effects of adolescent behavioral undercontrol and parenting in the prediction of drug use disorders in emerging adulthood. Psychology of Addictive Behaviors, 18(3), 239-249.

¹The effect size for parenting style was smaller (0.08-0.14) compared to peer alcohol use (0.37-0.56).

The DRAM, Vol. 6(5) – Don’t Stand Too Close! Alcohol Use Might be Contagious within Social Networks

Our peers – best friends, enemies, acquaintances, coworkers, etc. – have considerable influence on our behaviors, both good and bad. This influence is observable as early as preschool, and appears to peak during adolescence, when peer influence begins to trump parental influence (Dishion & Owen, 2002; Hawkins, Catalano, & Miller, 1992). This week, the DRAM reviews a study by Rosenquist and colleagues (2010) that uses innovative new techniques to measure alcohol use patterns within social networks.  

Methods

• Participants (n=5,124) were members of the offspring cohort (i.e., children of the original participants) of the Framingham Heart Study (FHS), assessed across 7 waves of data collection from 1972 to 2000.
• Participants identified family members, other contacts, and friends at each wave; researchers also identified participants’ neighbors and coworkers through address and employment information collected at each wave. Only contacts who also were involved in the FHS were included in this study.
• Participants self-reported alcohol use (i.e., average drinks per day) at each wave.
• Researchers categorized participants as heavy drinkers (>1 / 2 drinks per day for women / men), moderate drinkers (any drinking less than heavy), or abstainers.

Results

• Participants’ drinking behavior varied according to the behavior of members of their social network, for up to three degrees of separation (i.e., friends’, friends’ of friends, and friends’ of friends of friends drinking behavior all relate to participants’ drinking behavior); geographical distance between contacts did not influence this effect.
• As Table 1 shows, having a social network with a concentration of heavy drinkers at one time point predicted an increase in participant drinking by the next time point; similarly, concentrations of abstainers predicted decreases in participant drinking.
• Friends and spouses, in particular female friends and wives, had the strongest effect on future drinking behavior; neighbors and coworkers had no discernable effect.

Table 1. Effect of Social Network Drinking Behavior on Participants’ Future Alcohol Consumption (adapted from Rosenquist et al., 2010)

Note. Regression models controlled for age, sex, education, and wave. ***p<.001; **p<.01; *p<.05

• The following link, provided by the authors as an Appendix to their article, provides an animation of the clustering of heavy drinking and abstaining participants and contacts across time in a social network: http://www.annals.org/content/suppl/2010/04/07/152.7.426.DC3/fhssn_drink_v1.mov

 Limitations

• Only contacts within the FHS were used; it is possible that participants’ friends and acquaintances not involved in the FHS had unmeasured influence.
• Alcohol use was self-reported and might be subject to the biases associated with self-report.
• Rosenquist et al. examined social network influence on future drinking behavior, but did not examine the influence of drinking behavior on the future development or maintenance of social networks.

Discussion
Similar to other studies investigating the spread of phenomena across social networks (e.g., Christakis & Fowler, 2007, 2008), the current study demonstrated that the alcohol using patterns of those around you tend to influence your own use. This, in itself, is not surprising, but the reach of that influence is: our drinking patterns reflect those of people separated from us by three degrees in our social networks. These findings highlight the importance of social network research capable of analyzing social influences beyond direct peer or aggregate group effects.   

-Sarah Nelson

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References

Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357, 370-379.

Christakis, N. A., & Fowler, J. H. (2008). The collective dynamics of smoking in a large social network. New England Journal of Medicine, 358(2249-2258).

Dishion, T. J., & Owen, L. D. (2002). A longitudinal analysis of friendships and substance use: Bidirectional influence from adolescence to adulthood. Developmental Psychology, 38(4), 480-491.

Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112(1), 64-105.

Rosenquist, J. N., Murabito, J., Fowler, J. H., & Christakis, N. A. (2010). The spread of alcohol consumption behavior in a large social network. Annals of Internal Medicine, 152, 426-433.

The DRAM, Vol. 6(4) - Alcohol2O: The Effect of Oxygen-enriched Drinks on Blood Alcohol Concentration

It takes three oxygen molecules for humans to metabolize one molecule of alcohol (i.e., ethanol). This week the DRAM reviews a study that compares the blood alcohol concentration (BAC) levels of participants after drinking oxygen-enriched drinks to their BAC level after drinking normal drinks (Baek, Lee, & Kwon, 2010). 

Methods

• Participants (n=49) were healthy adults who volunteered for up to three experiments that comprised this research. Investigators used thirty participants for each experiment; ten participants participated in one experiment, nineteen participated in two, and eleven participants participated in all three.
• In each experiment, participants completed two phases in which they consumed 40 ml every 5 minutes of a drink that was 19.5% ethanol by volume and enriched with varying amounts of dissolved oxygen concentration. In one phase, the drink was “normal” (oxygen concentration of 8 parts per million [ppm]); in the other, it was oxygen-enriched. Drink volume and oxygen concentration of the enriched drink varied as follows:
• Experiment 1 – 240 ml drink, 20 ppm (25 minutes to drink) 
• Experiment 2 – 360 ml drink, 20 ppm (40 minutes to drink) 
• Experiment 3 – 360 ml drink, 25 ppm (40 minutes to drink) 
• The researchers measured BAC levels by collecting breath samples at regularly timed intervals.

Results

• Participants’ maximum BAC levels and time to reach those levels did not differ significantly between phases for any of the experiments. Average maximum BACs ranged from .040% to .041% for 240ml drinks, and from .056% to .066% for 360ml drinks.
• As Table 1 shows, in all experiments, it took less time for participants to reach 0.000% BAC (i.e., completely metabolize the ingested alcohol) after drinking an oxygen-enriched drink than a normal drink.
• The researchers did not observe BAC level differences between the drinks until approximately 150 minutes after consumption.

Table 1. Time to Reach 0.000% BAC in Each Experiment after Consuming Normal and Oxygen-Enriched Drinks (adapted from Baek et al., 2010)

 

Limitations

• The researchers did not measure the subjective effects of each drink (e.g., how intoxicated did participants feel).
• The study used a small sample and several participants were used in multiple experiments.
• The maximum BAC levels measured in this study are all below the US legal limit of 0.08 BAC to drive (Insurance Institute for Highway Safety & Highway Loss Data Institute, 2010).

Discussion

The results indicate that oxygen-enriched drinks reduced BAC significantly faster than normal drinks. Based on these results, one could argue that oxygen-enriched drinks reduce BAC levels rapidly and might have the potential to lead to fewer alcohol-related negative consequences, such as traffic accidents. However, maximum BAC levels were similar for both types of drinks and did not diverge until more than 2 hours after consumption. There might also be unintended consequences of oxygen-enriched drinks; for example, people who enjoy feeling intoxicated might consume more alcohol than they otherwise would in an attempt to maintain their BAC levels. Future research should examine the subjective effects of oxygen-enriched drinks as well as their effects at higher alcohol concentrations.

-Tasha Chandler

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References

Baek, I. H., Lee, B. Y., & Kwon, K. I. (2010). Influence of dissolved oxygen concentration on the pharmacokinetics of alcohol in humans. Alcoholism: Clinical and Experimental Research, 34(5), 834-839.

Insurance Institute for Highway Safety, & Highway Loss Data Institute. (2010). DUI/DWI laws. Retrieved May 13, 2010, from http://www.iihs.org/laws/dui.aspx

The DRAM, Vol. 6(3) – Pass or fail? College students, binge drinking and academic performance.

In a national study, Wechsler et al. (2002) found that more than 40% of college students reported binge drinking (i.e., drinking at least 5 drinks in one sitting for males and at least 4 drinks for females) during the previous two weeks. The scientific literature suggests negative associations between binge drinking and college academic performance. However, experimental data about the influence of binge drinking on academic performance is limited (Engs, Hanson, & Diebold, 1996; Singleton, 2007; Wolaver, 2002). This week the DRAM reviews the first experimental study examining the effects of previous-night binge drinking on college students’ next-day test-taking scores (Howland et al., 2010).

Methods

• Participants acted as their own controls (i.e., they received both the experimental and placebo conditions) in a double-blind design.
• The 193 participants that completed the study were current and recently graduated college students aged 21 years and older from the Boston, Massachusetts area.
• Participants engaged in two overnight experimental sessions during successive weeks. During one session, they consumed alcohol to a target breath alcohol level (BrAC) of 0.12g% while during the other session they consumed a non-alcoholic placebo. The order of experimental and placebo sessions was randomized.
• Prior to beverage administration, participants spent 1.5 hours viewing and studying one of two educational videos on a public health topic. After 8 hours of sleep time in the controlled study environment, and recorded BrAC levels of 0.00g%, participants completed a variety of assessments.
• The investigators measured academic performance using a quiz based on the previous nights’ video along with verbal and quantitative questions from the Graduate Record Examination (GRE).  
• The Profile of Mood State Brief Form (POMS) assessed mood disturbance. 

Results

• Researchers found no significant differences between the alcohol and placebo consumption conditions for both lecture-based quizzes and sample GRE questions.
• Mood disturbance scores were significantly worse after alcohol consumption compared to the placebo consumption condition. Table 1 summarizes these results.

Table 1.  Assessment outcomes by beverage condition (Adapted from Howland et al.)

Note. All analyses controlled for the influence of gender and session number. NS = not significant
*Profile Of Mood States (POMS) - higher scores indicate a more negative mood state.
** 40 participants did not complete and return afternoon POMS assessments.

Limitations

• The study participants engaged in tests about an extra-curricular topic. It is possible that this resulted in a general tendency to underperform.
• Participants were not tested after a night when no drinking occurred. Consequently, the study had no measure of performance without drinking to compare to performance under the experimental conditions.

Discussion
This study showed that binge drinking, compared to drinking non-alcoholic placebo did not influence test-taking performance the next day. Nevertheless, drinking alcohol did negatively influence mood. Mood might influence other aspects of life (e.g., interpersonal relationships) that indirectly impact academic performance. In addition, Howland et al. measured performance after a single alcohol session, yet there are many occasions for college students to binge drink (Wechsler, 2002). Consequently, because the impact of alcohol consumption on academic performance might be short and/or long-term and the consequences of such drinking uncertain, we need more research to determine the impact and the mechanisms through which chronic alcohol use might affect academic performance.

-Erica Marshall

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References
Engs, R., Hanson, D., & Diebold, B. (1996). The drinking patterns and problems of a national sample of college students. Journal of Alcohol and Drug Education, 41, 13-33.

Howland, J., Rohsenow, D., Greece, J., Littlefield, C., Almeida, A., Heeren, T., et al. (2010). The effects of binge drinking on college students' next-day academic test-taking performance and mood state. Addiction, 105, 655-665.

National Institute of Alcohol Abuse and Alcoholism. (2004). NIAAA council approves definition of binge drinking. NIAAA Newsletter (3), 3.

Singleton, R. A. (2007). Collegiate alcohol consumption and academic performance. Journal of Studies on Alcohol and Drugs, 68(4), 548-555.

Wechsler, H., Lee, J. E., Kuo, M., Seibring, M., Nelson, T. F., & Lee, H. (2002). 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. Journal of American College Health, 50(5), 203-217.

Wolaver, A. (2002). Effects of heavy drinking on study effort, grade point average, and major choice. Contemporary Economic Policy, 20, 415-428.

The DRAM Vol. 6(2) - Drinking Gone Wild? Freshman Spring Break Drinking and Negative Consequences

Research indicates that special events (e.g., 21st birthdays, holidays, etc.) are times of increased alcohol consumption and alcohol-related negative consequences for college students (Lewis, Lindgren, Fossos, Neighbors, & Oster-Aaland, 2009; Perkins, 2002). This week’s DRAM reviews a study that examined the association between alcohol intake and negative consequences among freshman college students during Spring Break (SB) (Lee, Lewis, & Neighbors, 2009). 

Methods

• Participants (n=726) were freshman college students who participated in a longitudinal web-based survey regarding alcohol intake; these students qualified as heavy drinkers (i.e., one or more incidents of heavy drinking in the past month; 4+ drinks for women, 5+ drinks for men) at baseline (i.e., first quarter of freshman year).
• Measures, assessed one week after SB, included:
• Typical weekly alcohol intake – average number drinks/week during the past three months using the Daily Drinking Questionnaire (Collins, Parks, & Marlatt, 1985).  
• SB alcohol intake – total number of SB drinks using a retrospective Timeline Feedback report (Sobell & Sobell, 2002).  
• SB alcohol-related negative consequences – adapted from the Rutger’s Alcohol Problem Index (White, 1989) and the Young Adult Alcohol Problems Screening Test (Hurlbut & Sher, 1992).  

Results

• 70% of participants drank during SB; participants consumed an average of 15.26 drinks across the 10 days of SB.
• 36% of participants reported experiencing at least one SB alcohol-related negative consequence; drinking during SB was positively associated with negative consequences.
• As Figure 1 shows, participants who were typically light drinkers at the time of SB were more likely to report negative consequences if they drank during SB than typically heavy drinkers.

Figure 1: Odds Ratios for Reporting Zero Negative Consequences during SB by Typical Drinking Groups (from Lee et al., 2009) 

 
Note: The Y axis of the figure is the odds-ratio of experiencing no negative consequences due to drinking during SB. The plot shows the decreasing likelihood of no negative consequences as the number of SB drinks increases (X axis) for the three groups defined by recent typical amount of drinking.) Consequences included - 1. Had a hangover the morning after you had been drinking; 2. Felt sick to your stomach or threw up after drinking; 3. Passed out or fainted suddenly; 4. Had a fight, argument, or bad feelings with a friend; 5. Received an injury as a result of drinking; 6. Got into fights, acted bad, or did mean things; 7. Got into sexual situations that you later regretted; and 8. Had a fight, argument or bad feelings with a family member.

Limitations

• All alcohol consumption information is retrospective self report.
• Inclusion criteria included heavy drinking at baseline. Thus, even “light” drinkers at the six month follow-up had qualified as heavy drinkers in the first quarter assessment, and results should be interpreted accordingly.
• The study analyzed number of consequences, not type of consequence, despite the diversity and differing prevalence of consequences (e.g., hangover vs. injury).

Discussion

The results suggest that drinking during SB is associated with negative consequences for all levels of drinking, but is particularly problematic for students who have been light drinkers in the months surrounding SB. Since all of the participants in the study were initially classified as heavy drinkers in the first quarter of their freshman year, it is unclear whether these results generalize to all light drinkers or specifically to students who have recently limited their drinking. More research is needed to test these findings in a more diverse range of students. If confirmed, the results might inform intervention efforts for specific events associated with higher alcohol intake, suggesting a focus on students who typically drink infrequently.

-Tasha Chandler

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References

Collins, R. L., Parks, G. A., & Marlatt, G. A. (1985). Social determinants of alcohol consumption: The effects of social interaction and model status on the self-administration of alcohol. Journal of Consulting Clinical Psychology, 53(2), 189-200.

Hurlbut, S. C., & Sher, K. J. (1992). Assessing alcohol problems in college students. Journal of American College Health, 41(2), 49-58.

Lee, C. M., Lewis, M. A., & Neighbors, C. (2009). Preliminary examination of spring break alcohol use and related consequences. Psychology of Addictive Behaviors, 23(4), 689-694.

Lewis, M. A., Lindgren, K. P., Fossos, N., Neighbors, C., & Oster-Aaland, L. (2009). Examining the relationship between typical drinking behavior and 21st birthday drinking behavior among college students: Implications for event-specific prevention. Addiction, 104(5), 760-767.

Perkins, H. W. (2002). Surveying the damage: A review of research on consequences of alcohol misuse in college populations. Journal of Studies on Alcohol - Supplement(14), 91-100.

Sobell, L. C., & Sobell, M. B. (2002). Alcohol Timeline Followback (TLFB). In A. P. Association (Ed.), Handbook of psychiatric measures (pp. 477-479). Washington, DC: American Psychiatric Association.

White, H. R., & Labouvie, E.W. (1989). Towards the assessment of adolescent problem drinking. Journal of Studies on Alcohol, 50(1), 30-37.

The DRAM Vol. 6(1) – Drosophila study shows you have to be handsome to stay sober

Animal models help scientists to understand human behavior and develop new drugs to treat pathologies. This week’s DRAM reviews a study that examines an animal model of drinking behavior. Devineni and Hebertein (2009) sought to determine if the drinking behavior of drosophila flies resembles human drinking behavior and if genetic mutations modify this behavior.

Method

• Investigators gave flies the opportunity to choose between ethanol-containing and nonethanol-containing food. Researchers calculated a preference index (PI) as (ethanol consumption – nonethanol consumption) / total consumption. The index ranged from -1 to 1, with positive numbers indicating preference for ethanol.
• Researchers measured the following:
• • the basic preference of flies
  • the pattern of preference change across time and ethanol concentration shifts
  • the preference change after periods of starvation or ethanol deprivation
  • ethanol preference of different mutations.

Results

• At baseline, the flies consistently showed a statistically significant preference for the ethanol-containing food, even when the caloric ratio between ethanol and nonethanol food was balanced.
• The preference for ethanol-containing food increased for the first few days but leveled off after 4 – 5 days.
• The level of preference increased with larger ethanol dose concentration.
• Starved flies were willing to consume ethanol at levels sufficient to produce behavioral intoxication.
• After being deprived of ethanol for several days, flies quickly resumed ethanol consumption when given an opportunity.
• Previous testing of 27 different types of mutations by the researchers had found one mutant -- krasavietz – or handsome in Russian – that exhibited a significantly lower preference for alcohol compared to the non-mutated controls. The “handsome” flies were studied in this experiment (see Figure 1).During the first two days the “handsome” flies showed no preference for alcohol, a behavior that was significantly different from controls who took to alcohol immediately.

Figure 1. Five day ethanol preference for control group and mutant fly krasavietz (handsome). N = 25.

Limitations

• No animal model provides a perfect analog to human alcohol consumption or alcoholism. For example, social and cultural factors influence human alcoholism, and these influences are difficult to create within animal studies.

Conclusions

This study indicates that drosophila’s drinking behavior resembles human drinking patterns in many facets. Similar to humans, drosophila’s initial preference to ethanol containing food is low and apt to vary,however gradually it increases and becomes consistent. Drosophila gradually are attracted to ethanol despite its aversive taste and intoxicating effect. One characteristic of human alcohol dependence is relapse – a return to previous levels of alcohol consumption after a period of abstinence (Hunt, Barnett, & Branch, 1971). Flies also exhibit a relapse-like behavior after ethanol deprivation. Devineni and Hebertein (2009) showed that a mutation, specific to a single gene, is related to low preference for ethanol. Identifying this and potentially other mutations help scientists to understand the potential molecular mechanisms underlying alcohol preference. No animal model can explain all facets of alcohol dependence among humans. However, a better scientific understanding of ethanol preference in both animals and humans may lead to the discovery of features common to a general reward pathway involved with addiction.

 -Julia Braverman

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References

Devineni, A. V., & Hebertein, U. (2009). Preferential Ethanol Consumption in Drosophila Models Features of Addiction. Current Biology, 19, 2126-2132.

Hunt, W. A., Barnett, L. W., & Branch, L. G. (1971). Relapse rates in addictions programs. . Journal of Clinical Psychology 27, 455 - 456.

 

DRAM Vol. 5(11) – Which comes first – the drink or the distress?

Previous studies have demonstrated a higher prevalence of psychiatric disorders among individuals with alcohol use disorder (AUD) than among the general population (Grant et al., 2004; Kessler et al., 1996). However, there is limited literature that examines both the co-occurence and the temporal ordering (i.e., which disorder occurs first) of AUD and other psychiatric disorders. This week’s DRAM reviews a study that aims to fill this gap in the literature (Flensborg-Madsen et al., 2009).

Method

• Investigators analyzed 25 years of follow up data for 18,146 individuals from Copenhagen, Denmark.
• The data reflected all admissions to Danish hospitals since 1976 and the data from the Danish Psychiatric Central Register since 1969.
• The investigators classified individuals by diagnostic record for mood disorders, psychotic disorders, anxiety disorders, personality disorders, drug use disorder, and alcohol use disorder (AUD). 
• The researchers analyzed the data by means of Cox regression analysis, which is a type of survival analysis.

Results

o          Among individuals with AUD (N = 1,756), 50% had a lifetime comorbid psychiatric disorder.

• Personality disorders were the most common comorbid disorders (24%, N = 421), followed by mood disorders (16.8%, N = 295) and drug abuse (16.6%, N = 291).
• As Table 1 shows, the risk of developing a psychiatric disorder among AUD individuals (N = 1,200) was larger than the risk of developing an AUD among individuals who were already registered with another psychiatric disorder (N = 2812).

 

Table 1. Risk of AUD for individuals with psychiatric disorders and risk of psychiatric disorders for individuals with AUD adjusted for sex, smoking, cohabitation status and educational level.

Initial                  disorder     Future   disorder	AUD	Mood disorder	Psychotic disorder	Anxiety disorder	Personality disorder	Drug abuse
AUD		4.5	3.2	3.4	4.3	5.1
Mood disorder	4.8
Psychotic disorder	4.5
Anxiety disorder	6.4
Personality disorder	8.1
Drug abuse	18.3

Note: Data are presented in the form of odd-ratios.

Limitations

• The time of disorder onset is based on a diagnostic record and not a report of onset; consequently, the timing of AUD and other psychiatric disorders might reflect the order of treatment seeking or clinician recognition of a problem, not the actual onset.
• The sample contains only individuals who were diagnosed with a psychiatric disorder.
• Even if alcohol orders tend to precede other psychiatric disorders, this study does not provide evidence for a causal link between the two.

 

Conclusion

Comorbidity of AUD with other psychiatric disorders is the rule rather than the exception. These findings show that, for individuals who have been diagnosed with AUD, the risk of subsequently receiving a diagnosis for other psychiatric disorders is higher than the risk for individuals who have been diagnosed with other psychiatric disorders to receive a subsequent diagnosis of AUD. However, previous findings have shown that among people with both a psychiatric and an addictive disorder, psychiatric disorders typically occur first (Kessler et al., 2008; Kessler et al., 1996). This contradiction might be explained by the fact that the researchers in the current study used the onset of treatment as the definition of disorder onset. It might be that individuals with both AUD and psychiatric disorder register to the hospital to receive treatment for the AUD first. It also might be that individuals with comorbid AUD register at the hospital more frequently than individuals without comorbid AUD. Overall, these findings imply the need to develop improved and integrated treatment for individuals meeting criteria of AUD and other psychiatric disorders.

 

-Julia Braverman

 

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References

Flensborg-Madsen, T., Mortensen, E. L., Knop, J., Becker, U., Sher, L., & Grønbæk, M. (2009). Comorbidity and temporal ordering of alcohol use disorders and other psychiatric disorders: Results from a Danish register-based study. Comprehensive Psychiatry, 50(4), 307-314.

Grant, B. F., Stinson, F. S., Dawson, D. A., Chou, S. P., Dufour, M. C., Compton, W., et al. (2004). Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders. Archives of General Psychiatry, 61, 807-816.

Kessler, R. C., Hwang, I., LaBrie, R. A., Petukhova, M., Sampson, N. A., Winters, K. C., et al. (2008). DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychological Medicine 38(9), 1351-1360.

Kessler, R. C., Nelson, C. B., McGonagle, K. A., Edlund, M. J., Frank, R. G., & Leaf, P. J. (1996). The epidemiology of co-occurring addictive and mental disorders: Implications for prevention and service utilization. American Journal of Orthopsychiatry, 66(1), 17-31.

 

The DRAM, Vol. 5(10) – New data for an old debate: Does moderate alcohol intake really lower the risk of adverse health outcomes?

The harmful effects of heavy alcohol drinking are well documented (e.g., World Health Organization, 2004). Recent research, however, suggests that there are potential health benefits associated with moderate alcohol intake (e.g., physical functionality, reduced disability and mortality: Cawthon et al., 2007; Karlamangla et al., 2009; Lang, Guralnik, Wallace, & Melzer, 2007). However, these studies have not fully controlled for variables that might account for the apparent relationship between alcohol and health. This week, the DRAM reviews a longitudinal study that explores the possibility of a causal relationship between alcohol intake and physical mobility among older adults, controlling for multiple lifestyle factors (Maraldi et al., 2009).

Methods

• The current study included 3,061 participants enrolled in the Health Aging and Body Composition (Health ABC) study.
  • Participants were well-functioning adults, aged 70-79 at intake, recruited from the metropolitan areas of Pittsburgh, PA and Memphis, TN 
• Self-reported measures included:
  • At baseline, alcohol intake (i.e., number of drinks consumed in a typical week during the previous 12 months), as well as demographic, lifestyle, health, and cognitive variables.  
  • Mobility assessments every 6 months for 78 months coded as: No limitation; Mobility limitation (two consecutive reports of difficulty walking ¼ of a mile or climbing stairs without rest); or Mobility disability (two consecutive reports of severe difficulty or inability to walk ¼ mile or climb stairs).  
• Researchers performed Cox proportional hazard regression analyses (a form of survival analysis) on the data to determine the relationship between alcohol intake at baseline and mobility status while controlling for the baseline variables.

Results

• Table 1 shows the results of two models.
  • Model 1 predicted mobility status from baseline alcohol intake, controlling for demographics (i.e., age, sex, race and site location).
  • Model 2 replicated Model 1, but also controlled for lifestyle characteristics (i.e., education, family income, smoking status, physical activity and body mass index).
  • In Model 1, moderate alcohol intake was associated with a lower risk of mobility limitation and mobility disability compared to never or occasional alcohol intake, as shown in blue in Table 1.
  • As the green section of Table 1 shows, however, Model 2 found that moderate alcohol intake was no longer significantly associated with a lower risk of mobility limitation or mobility disability compared to never or occasional alcohol intake.  

Table 1: Risk of Mobility Problems According to Alcohol Intake

Click image to enlarge, or adjust your browser's zoom setting.
*Number of drinks in a typical week during the 12 months previous to baseline.
Note: Two participants are excluded from this analysis because they did not have follow-up data. Follow-up was from baseline to the time of the first of two consecutive reports of mobility measures. For participants who died and did not experience mobility disability prior to death, data were censored at time of death.

Limitations

• This study used self-report for the alcohol intake variables.
• Researchers assessed alcohol intake only once at the beginning of the study. Changes in alcohol intake over the study period could have affected the results.
• Researchers controlled for physical activity in Model 2 of the analysis. This control variable is so similar to the mobility measure that controlling for it might distort the results of the analysis.
• The age range and geographic locations might reduce the generalizability of the results.

Discussion

This study indicates that, among a cohort of well-functioning older adults, moderate and light alcohol intake was associated with a lower rate of reduced mobility. However, a causal relationship between alcohol intake and physical functionality could not be determined. After adjusting the results for lifestyle characteristics, the association between alcohol use and mobility was largely diminished. These results suggest that lifestyle choices, rather than alcohol intake, might be more important influences on physical functionality among older adults.

-Tasha Chandler

What do you think?  Please use the comment link below to provide feedback on this article.

References

Cawthon, P. M., Fink, H. A., Barrett-Connor, E., Cauley, J. A., Dam, T. T., Lewis, C. E., et al. (2007). Alcohol use, physical performance, and functional limitations in older men. Journal of the American Geriatrics Society, 55(2), 212-220.

Maraldi, C., Harris, T. B., Newman, A. B., Kritchevsky, S. B., Pahor, M., Koster, A., et al. (2009). Moderate alcohol intake and risk of functional decline: The health, aging, and body composition study. Journal of the American Geriatrics Society, 57(10), 1767-1775.

World Health Organization. (2004). Global status report on alcohol 2004: World Health Organization.

Cawthon, P. M., Fink, H. A., Barrett-Connor, E., Cauley, J. A., Dam, T. T., Lewis, C. E., et al. (2007). Alcohol use, physical performance, and functional limitations in older men. Journal of the American Geriatrics Society, 55(2), 212-220.

Karlamangla, A. S., Sarkisian, C. A., Kado, D. M., Dedes, H., Liao, D. H., Kim, S., et al. (2009). Light to moderate alcohol consumption and disability: Variable benefits by health status. American Journal of Epidemiology, 169(1), 96-104.

Lang, I., Guralnik, J., Wallace, R. B., & Melzer, D. (2007). What level of alcohol consumption is hazardous for older people? Functioning and mortality in U.S. and English national cohorts. Journal of the American Geriatrics Society, 55(1), 49-57.

The DRAM, Vol. 5 (9) - Binging Then and Now – 30 Years of Data on Youth Drinking

Public policy initiatives, such as zero tolerance laws and raising the minimum legal drinking age (MLDA) to 21 years old (circa 1985), have likely contributed to the reduction in alcohol-related traffic fatalities among youth and young adult populations seen in the past two decades.  However, it is not clear that the reduction in fatalities corresponds to change in binge drinking among young people. In this DRAM issue, we review Grucza and colleagues’ evaluation of 30 years of data from the National Survey on Drug Use and Health (NSDUH) reflecting patterns of binge drinking (Grucza, Norberg, & Bierut, 2009).

Methodology

• Grucza et al. analyzed data specific to binge drinking behavior (i.e., consuming five or more drinks on one occasion during the past 30 days) from 20 administrations of the NSDUH survey conducted between 1979 and 2006 with more than 500,000 individuals.
• Researchers grouped participant data into four youth categories, 12-14, 15-17, 18-20, and 21-23 years of age; they also created an adult control group 24-34 years old.
• This study combined information from all survey years—except 1982, for which there was no data about binge drinking—into a regression analysis to estimate the influence of gender, age, survey year and their interactions on binge drinking.

Results

• As Figure 1 demonstrates, binge drinking prevalence decreased over time for males under 21, but increased for males over 21 and all female age groups.
• Results of the regression indicated that:
• • All male age groups experienced a significant decrease in relative risk (compared to the 24-34 male age group) for binge drinking across time, ORs range from 0.96 (95% CI: 0.96-0.97; p < .001) to 0.99 (95% CI: 0.98-1.00; p < .05).
  • Females age 21-23 experienced a significant increase in relative risk (compared to the 24-34 female age group) for binge drinking across time, OR = 1.02 (95% CI: 1.01-1.03; p < .001).

Figure 1 Binge Drinking Prevalence (Percentages) by Age & Gender (adapted from Grucza et al., 2009).


Limitations

• Data were derived from self-report during in-person interviews
• There were multiple survey design changes during the 30 years of this project, potentially making it difficult to compare data over time.

Conclusions
Though the increase of the MLDA to 21 might partially be responsible for the overall reduction in binge drinking behavior among males under 21, this analysis of the NSDUH data suggests that female participants have not responded similarly and that binge drinking among both genders has increased among older youth. It is possible that young women, attempting to close the gender gap, have ignored the messages in these initiatives, a point that the authors highlight. Future research needs to investigate the reasons why certain groups (e.g., women and older youth) fail to benefit from the initiatives already in place, and explore new ways to influence binge drinking behavior within these groups.

References
Grucza, R. A., Norberg, K. E., & Bierut, L. J. (2009). Binge drinking among youths and young adults in the United States: 1979-2006. Journal of the American Academy of Child Adolescent Psychiatry, 48(7), 692-702.