Family risk factors Teens are more likely to use alcohol or drugs if: A parent uses alcohol or other substances. A parent or teen has depression, anxiety, or ADHD.
- 0.1 What percentage of someone’s genetic background may contribute to alcohol dependency?
- 1 What social factors contribute to alcohol?
- 2 How is family influence a risk factor for addiction?
- 3 What are 3 social problems associated with alcohol use?
- 4 What is a genetic predisposition to certain traits?
- 5 What are 7 factors that can influence the effect alcohol has on a person?
What are the factors contributing to alcoholism among the youth?
Having a biological family member with alcoholism or drug addiction. Having a mental health condition such as bipolar disorder, depression, or anxiety. Experiencing peer pressure to drink, especially as a young adult. Having low self-esteem or self-worth.
What percentage of someone’s genetic background may contribute to alcohol dependency?
Studies show that alcoholism is approximately 50% attributable to genetics.
Social Factors – Social factors can contribute to a person’s views of drinking. Your culture, religion, family and work influence many of your behaviors, including drinking. Family plays the biggest role in a person’s likelihood of developing alcoholism.
- Children who are exposed to alcohol abuse from an early age are more at risk of falling into a dangerous drinking pattern.
- Starting college or a new job can also make you more susceptible to alcoholism.
- During these times, you’re looking to make new friends and develop relationships with peers.
- The desire to fit in and be well-liked may cause you to participate in activities that you normally wouldn’t partake in.
Before you know it, you’re heading to every company happy hour, drinking more frequently and even craving alcohol after a long workday – all warning signs of AUD.
Are there genetic factors for alcohol dependence?
How do genes influence alcohol use disorder? Alcohol use disorder (AUD) often seems to run in families, and we may hear about scientific studies of an “alcoholism gene.” Genetics certainly influence our likelihood of developing AUD, but the story isn’t so simple.
Research shows that genes are responsible for about half of the risk for AUD. Therefore, genes alone do not determine whether someone will develop AUD. Environmental factors, as well as gene and environment interactions account for the remainder of the risk. Multiple genes play a role in a person’s risk for developing AUD.
There are genes that increase a person’s risk, as well as those that may decrease that risk, directly or indirectly. For instance, some people of Asian descent carry a gene variant that alters their rate of alcohol metabolism, causing them to have symptoms like flushing, nausea, and rapid heartbeat when they drink.
- Many people who experience these effects avoid alcohol, which helps protect them from developing AUD.** As we have learned more about the role genes play in our health, researchers have discovered that different factors can alter the expression of our genes.
- This field is called epigenetics.
- Scientists are learning more and more about how epigenetics can affect our risk for developing AUD.
Can our genes affect alcohol treatment? Scientists are also exploring how genes may influence the effectiveness of treatments for AUD. For instance, the drug naltrexone has been shown to help some, but not all, patients with AUD to reduce their drinking.
Research has shown that patients with AUD who also have variations in a specific gene respond positively to treatment with the drug, while those without the specific gene do not. A fuller understanding of how genes influence treatment outcomes will help doctors prescribe the treatment that is most likely to help each patient.*** What is NIAAA doing to learn more? NIAAA has funded the Collaborative Studies on Genetics of Alcoholism (COGA) since 1989, with the goal of identifying the specific genes that influence alcohol use disorder.
In addition, NIAAA funds investigators’ research in this important field, and also has an in-house research emphasis on the interaction of genes and the environment. NIAAA is committed to learning more about how genes affect AUD so that treatment—and prevention efforts—can continue to be developed and improved.
What are the genetic factors of alcohol?
Researchers believe that hundreds of different genes play a role in alcoholism. Experts have pinpointed two specific genes as having an effect on risk of alcohol use disorder. They are the ALDH2 and ADH1B genes. These genes affect how the body metabolizes alcohol, or breaks it down and processes it.
How is family influence a risk factor for addiction?
One of the biggest problems facing the healthcare system today involves drug and alcohol addiction, It is always better to prevent this problem from happening than to fix it after it has already taken place. Therefore, it is critical to take a look at the biggest addiction risk factors of addiction: family history.
According to the National Institute on Alcohol Abuse and Alcoholism, family history is one of addiction’s biggest risk factors. This means that anyone with family members who have developed substance abuse issues is at a higher risk of developing substance abuse issues of their own. By remaining aware of this major factor, it may be possible to engage in risk reduction strategies.
Learn more about family history, addiction risk, and risk reduction below.
Rationale for Early Screening – Preventive efforts on the part of family physicians are important because: (1) alcohol-related problems are prevalent in patients who visit family practices; (2) heavy alcohol use contributes to many serious health and social problems; and (3) physicians can successfully influence drinking behaviors.
- In the United States, the one-year prevalence of alcohol-use disorders, including alcohol abuse and alcohol dependence, is about 7.4 percent in the adult population.
- In patients who visit family practices, the prevalence is higher.
- One study of 17 primary care practices found a 16.5 percent prevalence of “problem drinkers,” and another study found a 19.9 percent prevalence of alcohol-use disorders among male patients.
Heavy alcohol use can affect nearly every organ system and every aspect of a patient’s life. lists many direct and indirect effects of alcohol-related problems. Alcohol causes diseases such as cirrhosis of the liver and exacerbates symptoms in existing conditions such as diabetes.
,, In addition, alcohol is implicated in many social and psychologic problems, including family conflict, arrests, job instability, injuries related to violence or accidents, and psychologic symptoms related to depression and anxiety., These problems take an enormous emotional toll on individuals and families, and are a great financial expense to health care systems and society.
Many of these problems may be avoided by early screening and intervention by family physicians. Several studies of early and brief physician interventions have demonstrated a reduction in alcohol consumption and improvement in alcohol-related problems among patients with drinking problems.
- A 40 percent reduction in alcohol consumption in nondependent problem drinkers has been demonstrated following physician advice to reduce drinking.
- And list diagnostic criteria for alcohol abuse and dependence specified by the Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV).
Alcohol abuse is manifested by recurrent alcohol use despite significant adverse consequences of drinking, such as problems with work, law, health or family life. The rightsholder did not grant rights to reproduce this item in electronic media. For the missing item, see the original print version of this publication.
The rightsholder did not grant rights to reproduce this item in electronic media. For the missing item, see the original print version of this publication. The diagnosis of alcohol dependence is based on the compulsion to drink. The dependent drinker devotes substantial time to obtaining alcohol, drinking and recovering, and continues to drink despite adverse social, psychologic or medical consequences.
A physiologic dependence on alcohol, marked by tolerance or withdrawal symptoms, may or may not be present. Note that quantity and frequency of drinking are not specified in the criteria for either diagnosis; instead, the key elements of these diagnoses include the compulsion to drink and drinking despite adverse consequences.
Alcohol-use disorders are easy to recognize in patients with longstanding problems, because these persons present to the family physician with diseases such as cirrhosis or pancreatitis (), Patients in the earlier stages of alcohol-related problems may have few or subtle clinical findings, and the physician may not suspect a high consumption of alcohol.
Certain medical complaints, such as headache, depression, chronic abdominal or epigastric pain, fatigue and memory loss, should alert the family physician to consider the possibility of alcohol-related problems (), The first signs of heavy drinking may be social problems.
- The compulsion to drink causes persons to neglect social responsibilities and relationships in favor of drinking.
- Intoxication may lead to accidents, occasional arrest or job loss.
- Recovering from drinking can decrease job performance or family involvement.
- Social problems that indicate alcohol-use disorders include family conflict, separation or divorce, employment difficulties or job loss, arrests and motor vehicle accidents.
The most effective tool for diagnosing alcohol-related problems is a thorough history of the drinking behavior and its consequences. The National Institute on Alcohol Abuse and Alcoholism (NIAAA) has published The Physician’s Guide to Helping Patients with Alcohol Problems, which presents a brief model for screening and assessing problems with alcohol.
- NIAAA recommends screening for alcohol-related problems during routine health examinations, before prescribing a medication that interacts with alcohol and in response to the discovery of medical problems that may be related to alcohol use (),
- Screening questions are listed in,
- The first four questions are related to alcohol consumption.
One drink is defined as 12 g of pure alcohol, which is equal to one 12-oz can of beer, one 5-oz glass of wine or 1.5 oz (one jigger) of hard liquor., NIAAA also recommends using the CAGE questionnaire to screen patients for alcohol use (), The CAGE questions are widely used in primary care settings and have high sensitivity and specificity for identifying alcohol problems.
- Among patients who screen positive for alcohol-related problems, additional questions should include the family history of alcohol abuse as well as family, legal, employment and health problems related to drinking.
- The rightsholder did not grant rights to reproduce this item in electronic media.
- For the missing item, see the original print version of this publication.
Other screening questionnaires are available and may perform better than the CAGE questionnaire. A recent study demonstrated the superiority of the AUDIT instrument in a Veterans Administration population (), The TWEAK and AUDIT questionnaires performed better than the CAGE questionnaire in women (),
- In the early stages of alcohol-related problems, the physical examination provides little evidence to suggest excessive drinking.
- Patients who abuse alcohol may have mildly elevated blood pressure but few other abnormal physical findings.
- Later, patients may develop significant and obvious signs of alcohol overuse, including gastrointestinal findings such as an enlarged and sometimes tender liver; cutaneous findings such as spider angiomata, varicosities and jaundice; neurologic signs such as tremor, ataxia or neuropathies; and cardiac arrhythmias.
When patients arrive at the doctor’s office inebriated, one should suspect a longstanding drinking problem. Certain chemical markers are indicative but not diagnostic of alcohol-use disorders.,, Among liver function tests, the γ-glutamyl transferase (GGT) level is usually the first to become elevated, followed by the aspartate aminotransferase (AST) level, which is often twice the level of alanine aminotransferase (ALT).
The complete blood cell count may display a number of abnormalities. In cases of end-stage disease, all cell lines are reduced as a direct toxic effect of alcohol on the bone marrow. The prothrombin time (PT) is elevated because of decreased production of clotting factors by the liver. However, in early disease mean corpuscular volume (MCV) may be slightly elevated as a result of folate deficiency and the direct effects of alcohol on red blood cells.
Patients with alcoholic gastritis may lose blood through the gastrointestinal tract, causing anemia and the production of smaller red blood cells, resulting in a low MCV. If both processes occur, the MCV will be normal, but the red cell distribution width will be elevated (around 20).
Social cognitive determinants of drinking in young adults: Beyond the alcohol expectancies paradigm , September–October 2001, Pages 689-706 In prior research into the psychological determinants of alcohol consumption, positive expectancies have been studied extensively (for reviews see Goldman et al., 1991, Leigh, 1989, Leigh & Stacy, 1991). Positive expectancies refer to the drinker’s perceptions of the positive outcomes of drinking, and have been shown to be causally related to alcohol consumption in both adults and adolescents Christiansen et al., 1989, Darkes & Goldman, 1993, Dunn & Goldman, 1998, Smith et al., 1995.
- Although this line of research has yielded an impressive amount of data on the mediating role of positive expectancies in drinking behavior, some recent studies have attempted to apply more comprehensive psychological models to alcohol consumption.
- For example, Ajzen’s (1988) theory of planned behavior has been used in some studies to identify additional psychological determinants of drinking behavior Marcoux & Shope, 1997, Schlegel et al., 1992.
Unfortunately, the predictors operationalized in these studies (viz., attitude, subjective norm, and perceived behavioral control) do not provide a basis for valid comparisons with existing findings in the alcohol expectancy literature. In the present study, Bandura, 1986, Bandura, 1997 social cognitive theory was used to map the psychology of drinking.
- This theory distinguishes several sources of behavior in general that appear to offer a useful basis for understanding drinking behavior.
- Moreover, in view of the nature of the constructs identified in the model, it provides a basis for assessing whether additional social cognitive constructs would enhance the predictive power of the traditional positive expectancies approach to drinking behavior.
One of the key sources of behavior distinguished in Bandura’s (1986) social cognitive theory is expectancies of outcome. Besides positive expectancies with regard to the desired effects of drinking, which form the focus of attention in the alcohol expectancy literature, expectancies with regard to negative effects may be important determinants of drinking behavior (e.g., Shafer & Leigh, 1996, Wiers et al., 1997).
- Whereas positive expectancies refer to motives to drink, negative expectancies refer to motives not to drink, or not to drink more.
- Lee, Greely, and Oei (1999) found that drinking was related not only to positive expectancies, but also to negative expectancies regarding the effects of drinking (e.g., negative affective changes and loss of control; see also Grube, Ames, & Delaney, 1994).
On the other hand, Earleywine (1995) found that positive but not negative expectancies were related to intentions to drink and drinking behavior. These inconsistent findings might be attributable to differences in the conceptualization of negative expectancies.
- For example, Leigh (1989) distinguished between expectancies regarding short-term, direct effects (e.g., decreased psychomotor coordination) and longer-term negative effects of drinking (e.g., liver cirrhosis).
- A second important factor contributing to behavior in general (Bandura, 1986), and drinking behavior in particular, is social influence (Christiansen et al., 1989).
Several operationalizations of social influence have been linked to alcohol consumption in prior research. Expectations concerning the positive social effects of drinking, which are typically taken into account in the alcohol expectancies framework (e.g., Smith et al., 1995), form one way of conceptualizing social influence.
A second social influence construct has been used in research on the modeling influences of the social environment on alcohol consumption (Curran, Stice, & Chassin, 1997). In this context, modeling refers to a fundamental process of learning about the effects and the controllability of alcohol consumption by observing and communicating with others.
A third type of social influence is emphasized in the theory of planned behavior (Ajzen, 1988), where subjective norms regarding the behavior in question form a key construct. Subjective norms refer to the perceived opinion of others about one’s drinking behavior.
- Ilty (1987), Schlegel et al.
- 1992), and Trafimov (1996) found that subjective norms predicted respondents’ intentions to drink alcohol.
- A third factor governing behavior in general Bandura, 1986, Bandura, 1997, and drinking behavior in particular (Marlatt & Gordon, 1985), is self-efficacy expectations.
Self-efficacy refers to judgments of one’s personal ability to accomplish a certain task Bandura, 1986, Bandura, 1997. People high in self-efficacy persist even in the face of obstacles. With regard to drinking behavior, self-efficacy expectations refer to self-perceptions of one’s ability not to give in to urges or social pressures to drink (Marlatt & Gordon, 1985).
Aas, Klepp, Laberg, and Aaro (1995) and Skutle (1999) did indeed find that self-efficacy expectations were negatively related to drinking behavior. Moreover, there appear to be distinct domains of self-efficacy expectations. For example, self-efficacy with regard to not giving in to social pressures to drink can be distinguished from self-efficacy expectations with regard to not giving in to the urge to drink away negative emotions (Marlatt & Gordon, 1985).
In sum, Bandura, 1986, Bandura, 1997 social cognitive theory identifies four types of social cognitive factors that appear to be important determinants of drinking behavior: positive outcome expectancies, negative outcome expectancies, social influence, and self-efficacy expectations.
As already noted, positive outcome expectancies have received considerable attention in prior research on drinking behavior. On the other hand, considerably less research attention has been given to the remaining three social cognitive factors. The present study was undertaken to test a comprehensive psychological model of drinking behavior incorporating all four types of social cognitive factors.
Besides examining the social cognitive determinants of drinking, the present study also aimed to shed light on the processes that give rise to changes in drinking behavior over time. The stages of change model (Prochaska, DiClemente, & Norcross, 1992) define five stages that individuals pass through in the process of intentionally changing their behavior.
- First, in the precontemplation stage people do not plan to change their present behavior.
- Second, in the contemplation stage people plan to change their behavior within the next 6 months.
- Third, in the preparation stage people plan to change their behavior within 1 month.
- Fourth, in the action stage people have already changed their behavior, but the change spans a period of less than 6 months.
Fifth, in the maintenance stage people have changed their behavior, and have maintained this change for 6 months or longer. Recently, Prochaska et al.’s (1992) model has been expanded to include both stages of acquisition and stages of cessation of a target behavior (Migneault, Pallonen, & Velicer, 1997).
- In an investigation of stages with regard to “usually drinking three or more drinks on a day you drink” in a sample of adolescents (aged 15 to 18), Migneault et al.
- 1997) distinguished five acquisition stages and five cessation stages.
- The five acquisition stages described the process of transition from not consuming three or more drinks per occasion and not planning to do so within the next 6 months (acquisition precontemplation), to consuming three or more drinks per occasion already for more than 6 months (acquisition maintenance).
Conversely, cessation stages described the process of transition from consuming three or more drinks per occasion and not planning to change this behavior within the next 6 months (cessation precontemplation), to having quit consuming three or more drinks per occasion for a period of 6 months or longer (cessation maintenance).
- Migneault et al.’s cross-sectional data indicated that adolescents who were consuming three or more drinks per occasion perceived more benefits and fewer disadvantages of drinking alcohol than those who did not drink that amount.
- Furthermore, adolescents who used to drink three or more beverages per occasion in the past, but had since stopped doing so, perceived fewer benefits and more disadvantages of drinking than those who presently drank that amount.
No data are available, however, on patterns of change in self-efficacy and social influence through the stages of acquisition and cessation of drinking behavior. The present study assessed drinking behavior and its determinants by means of a questionnaire distributed to a sample of university students.
The first goal of the study was to examine whether expectations with regard to the negative outcomes of drinking, social influence, and self-efficacy expectations would contribute substantially to the explained variance in alcohol consumption, over and above the variance explained by positive alcohol expectancies.
The second goal was to assess the relative strength of these four social cognitive factors as predictors of alcohol consumption. The study’s third goal was to investigate the stages of change model, that is, to assess to what extent acquisition and cessation stages provided a meaningful description of drinking behavior in the student sample, and to compare students in different stages of change in terms of the four social cognitive factors.
- Both the social cognitive factors and the stages of acquisition and cessation were operationalized in terms of a single criterion behavior, namely regularly consuming four or more drinks per occasion.
- This criterion was selected in view of a recognized definition of binge drinking in young adults, which specifies distinct criteria for male drinkers (five or more glasses in a row) and female drinkers (four or more glasses in a row; Wechsler, Dowdall, Davenport, & Rimm, 1995).
Because application of this sex-specific criterion would have unduly complicated the data collection process, the more conservative criterion of four drinks per occasion was used for all respondents. Also, epidemiological and other research has linked this consumption of four or more alcoholic drinks per occasion to negative health outcomes (e.g., Garretsen, Bongers, Oers, & Van de Goor, 1999).
Some of the university students in the sample were living on their own, while others lived with their parents. We expected that the relative influence of the social cognitive factors on drinking behavior might be sensitive to this difference in living environment. Therefore, the social cognitive determinants of drinking for students in these two living environments were examined separately and compared.
Furthermore, because males and females are exposed to different socialization influences, we expected that the relative influence of the social cognitive factors on drinking behavior might also be sensitive to gender. For example, Brown, Goldman, Inn, and Anderson (1980) found that women expected more positive social experiences as a result of alcohol consumption, whereas men were more apt to expect arousal and potentially aggressive behavior.
- Thus, the fourth goal of this study was to assess and compare the social cognitive determinants of drinking in subgroups defined on the basis of gender and living environment.
- A sample of psychology students at Leiden University was recruited during seminar-type course meetings in groups of 10 to 12 students.
Because all first-year psychology students are randomly assigned to such a seminar group, and attendance is mandatory, the sample may be viewed as representative of first-year psychology students at this university. At the end of the seminar meeting, self-report questionnaires were distributed to all seminar participants.
- By means of standardized verbal The correlations displayed in Table 1 indicate that seven of the eight social cognitive measures were significantly related to drinking behavior.
- All of these relations were in the expected direction: Participants reported drinking more to the extent that they expected more positive outcomes of consuming four or more drinks in a row, expected fewer negative self-evaluative outcomes, perceived more people in their environment as drinking this much, perceived those people to be more tolerant The present study was aimed at mapping the social cognitive factors underlying alcohol consumption.
More specifically, this study examined the predictive strength of a comprehensive psychological model of drinking behavior. In line with Bandura, 1986, Bandura, 1997 social cognitive theory, this model incorporated four clusters of cognitive factors: positive and negative outcome expectancies, social influence, and self-efficacy expectations.
A Skutle J.P Migneault et al. N.K Lee et al. A.J Hill et al. H Aas et al. I Ajzen A Bandura A Bandura S.A Brown et al. B.A Christiansen et al.
M Conner et al. P.J Curran et al. J Darkes et al. H De Vries et al. A Dijkstra et al. M.E Dunn et al. M Earleywine Engels, R. (1998). Forbidden fruits. Social dynamics in smoking and drinking behavior of adolescents. Doctoral.
Se analizó, en una muestra de adolescentes argentinos, el modelo de predisposición adquirida. El modelo propone que el rasgo desinhibición influye de manera indirecta, a través de variables cognitivas, sobre el consumo de alcohol. La exposición a modelos de consumo del grupo de pares influye directa e indirectamente (mediante las expectativas hacia el alcohol) sobre el uso de alcohol. Participaron 343 adolescentes asistentes a colegios públicos de educación media de la ciudad de Córdoba (Argentina). Se midió impulsividad, expectativas hacia el alcohol, motivos de consumo de alcohol, normas sociales de consumo y consumo de alcohol de los adolescentes. Para determinar el efecto de las variables señaladas como antecedentes del consumo se aplicó un análisis de senderos. Se propusieron dos modelos teóricos que diferían en la inclusión, o no, de las normas sociales del consumo de alcohol. Los resultados apoyan el modelo de predisposición adquirida e indican que el efecto de impulsividad sobre el consumo de alcohol es indirecto mediado por las expectativas hacia el alcohol. Aunque ambos modelos presentan adecuado ajuste a los datos, el modelo que incorpora el efecto de las normas sociales de consumo presenta un ajuste excelente. Los resultados de este trabajo, el primero de nuestro medio en evaluar el modelo de predisposición adquirida sobre el consumo de alcohol, destacan la utilidad de diagramar esfuerzos preventivos focalizados en el control de los impulsos, en las expectativas positivas hacia el alcohol y en la reducción de la percepción del consumo de los pares. The acquired preparedness model was examined in a sample of Argentinian adolescents. This model suggests that disinhibition has an indirect effect, through cognitive variables, on alcohol consumption. A sample of 343 adolescents from the city of Cordoba (Argentina) from public secondary education schools took part in the study. Standardised measurements were used to assess impulsivity, alcohol expectancies, social norms of alcohol drinking, and drinking patterns. A path analysis was conducted to determine the effect of these variables on quantity of alcohol consumption. Two theoretical models, that differed in the inclusion — or not — of social norms of alcohol drinking, were evaluated. The results, according to the acquired preparedness model, indicate that impulsivity influences alcohol consumption through alcohol expectancies. The model that incorporates the effect of social norms of alcohol drinking showed an excellent fit to the data. This study — the first in Argentina that evaluates this model — emphasises the usefulness of addressing impulse control and alcohol expectancies when developing interventions aimed at reducing alcohol consumption. Hazardous drinking rates among college students are exceedingly high. Despite the link between worry and alcohol use problems, there has been a dearth of empirical work examining worry-related risk factors in terms of motivations for alcohol use. Therefore, the aim of the present investigation was to examine the unique predictive ability of intolerance of uncertainty in terms of alcohol use motives. Participants were 389 college students (72.2% female, M age = 19.92, SD = 3.87, Range = 18–58 years) who completed self-report measures for course credit. As hypothesized, after controlling for the effects of gender, smoking status, marijuana use status, alcohol consumption, negative affect, and anxiety sensitivity, greater levels of intolerance of uncertainty were significantly predictive of greater coping (1.5% unique variance) and conformity (4.7% unique variance) drinking motives, but not social or enhancement drinking motives. These results suggest that intolerance of uncertainty is associated with drinking to manage or avoid negative emotions, and interventions aimed at reducing intolerance of uncertainty may be helpful in reducing problematic alcohol consumption among college students. The aim of this study was to describe the prevalence and predictors of alcohol drinking behavior in children. Data were obtained from 367 children, aged 8–12 years ( M = 10.44 years, SD = 1.21 years; 61.9% female) from the city of Córdoba, Argentina. Several scales were used to assess risk factors, including personality traits, alcohol expectancy (i.e., beliefs about the consequences of using alcohol), and perceived peer alcohol use, for alcohol drinking and alcohol drinking experiences. Hierarchical regression analysis was used to determine the contribution of multiple risk factors to the quantity of alcohol consumed. The results showed that 58% of the children had tasted alcohol, and approximately one-third drank alcohol again after the first drinking experience. Twelve-year-old children had a significantly higher prevalence of tasting and drinking alcohol and a significantly greater frequency and quantity of alcohol consumed than younger children. Eighty percent of the children who liked alcohol during their first drinking experience reported that they drank alcohol again. Among the children who did not like alcohol during their first drinking experience, only 31% drank alcohol again. Underage drinking usually occurred under adult supervision in family settings when parents or other relatives allowed them to drink or were aware of their children’s drinking. The hierarchical regression analysis showed that being older and male, having more peers that drink alcohol, having higher levels of extroversion, and having alcohol expectancy for social facilitation increased the risk for greater alcohol use. The final model explained 33% of the total variance. Un instrumento de medición desarrollado para un grupo cultural determinado no puede ser simplemente empleado en un grupo diferente, y es necesario realizar una serie de tareas que garanticen adecuadas propiedades psicométricas para la nueva aplicación del instrumento. En la literatura se proponen tres diferentes niveles de adaptación de un test: aplicación, adaptación y ensamble. La Comisión internacional de los tests estableció una serie de recomendaciones con el objetivo de minimizar las vías de error en los procesos de adaptación de instrumentos. En este marco, este estudio tiene como objetivo ofrecer una descripción de los diferentes procesos de adaptación seguidos para llegar a instrumentos que midan las expectativas hacia el alcohol, ofreciendo cada una de las experiencias como estudios independientes. A partir de los resultados obtenidos, se puede observar cómo un mayor nivel de complejidad en el tipo de adaptación realizada, y por lo tanto en el control de sesgos, se refleja en mejoras en cuanto a la adecuación psicométrica de los instrumentos. Todos los ejemplos presentados permiten destacar la importancia de considerar los aspectos sociales y culturales particulares del grupo donde se pretende aplicar un instrumento de medición. A test developed to measure a psychological variable cannot be directly used in a different group. Indeed, it is important to perform a series of tasks to ensure adequate psychometric properties for the new application of the test. The literature suggests three different level of adaptation of a test: application, adaptation and assembled. The International Tests Commission established a number of recommendations designed to minimize the error in the process of adaptation of instruments. In this framework, this study aims to provide a description of the different adaptation processes followed to obtain instruments to measure alcohol expectancies. To pursue this goal, each of the experiences is presented as an independent study. Results showed how a higher level of complexity in the type of adaptation performed, and therefore in control bias, is reflected by improvements in psychometric adequacy of the instruments. All examples presented allow highlighting the importance of considering the particular social and cultural aspects of the target. This study tests the acquired preparedness model (APM) to explain associations among trait impulsivity, social learning principles, and marijuana use outcomes in a community sample of female marijuana users. The APM states that individuals with high-risk dispositions are more likely to acquire certain types of learning that, in turn, instigate problematic substance use behaviors. In this study, three domains of psychosocial learning were tested: positive and negative marijuana use expectancies, and marijuana refusal self-efficacy. Participants were 332 community-recruited women aged 18–24 enrolled in a study of motivational interviewing for marijuana use reduction. The present analysis is based on participant self-reports of their impulsivity, marijuana use expectancies, marijuana refusal self-efficacy, marijuana use frequency, marijuana use-related problems, and marijuana dependence. In this sample, impulsivity was significantly associated with marijuana use frequency, marijuana-related problems, and marijuana dependence. Results also indicate that the effect of impulsivity on all three marijuana outcomes was fully mediated by the three principles of psychosocial learning tested in the model, namely, positive and negative marijuana expectancies, and marijuana refusal self-efficacy. These findings lend support to the APM as it relates to marijuana use. In particular, they extend the applicability of the theory to include marijuana refusal self-efficacy, suggesting that, among high-impulsives, those who lack appropriate strategies to resist the temptation to use marijuana are more likely to exhibit more frequent marijuana use and use-related negative consequences. Cognitive models of alcohol abuse posit that the context typically associated with alcohol use, such as negative affect, implicitly activates alcohol use cognitions, which in turn leads to alcohol consumption. We selected 40 undergraduate women based upon their alcohol use and reported anxiety sensitivity, and proposed that drinking for the purpose of negative reinforcement would predict increased semantic priming between anxiety and alcohol concepts. A lexical decision task compared the response latencies of alcohol targets preceded by anxiety words to those same targets preceded by neutral words (anxiety–alcohol priming). Level of anxiety sensitivity did not relate to anxiety–alcohol priming, but drinking following social conflict was associated with increased anxiety–alcohol priming. This study specifically suggests that the contextual antecedents to drinking behavior relate to the organization of semantic information about alcohol, and more generally supports cognitive models of substance abuse.
There is consistent evidence that impulsivity-like traits relate to problematic alcohol involvement; however, identifying mechanisms that account for this relation remains an important area of research. Drinking refusal self-efficacy (or a person’s ability to resist alcohol; DRSE) has been shown to predict alcohol use among college students and may be a relevant mediator of the impulsivity–alcohol relation. The current study examined the indirect effect of various constructs related to impulsivity (i.e., urgency, sensation seeking, and deficits in conscientiousness) via several facets of DRSE (i.e., social pressure, opportunistic, and emotional relief) on alcohol-related problems among a large sample of college students ( N = 891). Overall, results indicated that certain DRSE facets were significant mediators of the relation between impulsivity-related constructs and alcohol problems. More specifically, emotional-relief DRSE was a mediator for the respective relations between urgency and deficits in conscientiousness and alcohol problems, whereas social-DRSE was a significant mediator of the respective relations between urgency and sensation seeking with alcohol problems. Results from this study suggest particular types of DRSE are important mediators of the relations between specific impulsivity constructs and alcohol-related problems. These findings support prevention and intervention efforts that seek to enhance drinking refusal self-efficacy skills of college students, particularly those high in certain personality features, in order to reduce alcohol-related problems among this population. This study examined the relationship between self-reported drinking identity (SRDI), defined as how closely individuals believe drinking is a crucial aspect of their identity (Conner, Warren, Close, & Sparks, 1999), and alcohol use by considering drink-refusal self-efficacy (DRSE) as a potential mediator. Based on previous findings, we expected that SRDI would be negatively associated with DRSE and positively associated with drinking, and that DRSE would be negatively linked with drinking. Further, we expected that DRSE would mediate the association between SRDI and drinking. Participants included 1069 undergraduate students ( M age = 22.93 years, SD = 6.29, 76.25% female) from a large southern university who completed computer-based study materials. Gender was associated with SRDI, each of the DRSE subscales, and drinking, indicating that males report greater SRDI, lower DRSE, and increased alcohol consumption. Consistent with expectations, SRDI was negatively linked with DRSE and positively linked with drinking. DRSE subscales were negatively associated with drinking. Further, four measurement models for latent variables were tested for SRDI and each of the three DRSE subscales. Results showed that the emotional relief and social subscales of DRSE mediated the association between SRDI and drinking, however this mediating relationship did not emerge for the opportunistic subscale. Implications of these results are discussed. Alcohol demand, typically assessed at the trait-level, via single administration, reflects individualized alcohol value. We examined correspondence between baseline trait-level and daily brief measures of alcohol demand, and whether demand changes day-to-day in response to recent drinking-related consequences. Understanding whether consequences influence demand fluctuations may provide insight into when demand can be reduced in the context of intervention. Heavy drinking college students ( n = 95, age 18–20, 52% female) completed a baseline 14-item alcohol purchase task (APT). Observed demand indices were: intensity (consumption at zero cost), O max (maximum expenditure), and breakpoint (cost whereby consumption is suppressed to zero). Participants subsequently completed 28 daily reports including a 3-item APT (one item corresponding to each baseline index) and prior day drinking and consequences. Intraclass correlations revealed within-person variability (i.e., day-to-day change) across daily demand indices. In hierarchical linear models (HLM), each daily demand index was significantly predicted by its corresponding baseline full APT index, when all three baseline indices were entered, suggesting convergent validity of the daily measure. Lower day-level intensity was predicted by more prior day negative consequences, controlling for several day- and person-level variables in HLM. Recent positive consequences did not impact intensity, and daily O max and breakpoint were not predicted by any tested day- or person-level variables. APT indices collected daily map on well to traditional single-administration APT metrics and change in response to recent consequences. Intensity demonstrated the greatest within-person variability, the strongest association with its corresponding full APT index, and theoretically-consistent prediction by negative consequences of drinking. Increasing rates of cannabis use among emerging adults is a growing public health problem. Intensive longitudinal data can provide information on proximal motives for cannabis use, which can inform interventions to reduce use among emerging adults. As part of a larger longitudinal study, patients aged 18–25 years ( N = 95) recruited from an urban Emergency Department completed daily text message assessments of risk behaviors for 28 days, including daily cannabis quantity and motives. Using a mixed effects linear regression model, we examined the relationships between daily quantity of cannabis consumed and motives (i.e., enhancement, social, conformity, coping, and expansion). Participants were, on average, 22.0 years old (SD = 2.2); 48.4% were male, 45.3% were African American, and 56.8% received public assistance. Results from the multi-level analysis (clustering day within individual), controlling for gender, race, and receipt of public assistance, indicated daily use of cannabis use for enhancement (β = 0.27), coping (β = 0.15), and/or social motives (β = 0.34) was significantly associated with higher quantities of daily cannabis use; whereas expansion and conformity motives were not. Daily data show that emerging adults who use cannabis for enhancement, social, and coping motives reported using greater quantities of cannabis. Future research should examine more comprehensive cannabis motives (e.g., boredom, social anxiety, sleep) and test tailored interventions focusing on alternative cognitive/behavioral strategies to address cannabis motives. Although solitary drinking is less common than social drinking, it may be uniquely associated with heavy drinking and alcohol-related problems. There is also evidence that drinking contexts impact both expected and experienced alcohol effects. In particular, solitary drinking may be associated with an increased likelihood of drinking for negative reinforcement (e.g. to relieve stress). The current study examined how drinking context influences tension reduction expectancies and drinking motives, and the extent to which expectancies and motives mediate the link between solitary drinking and alcohol-related problems. We hypothesized that solitary drinking would be associated with greater tension reduction expectancies and coping motives which, in turn, would be associated with more alcohol related problems. Data were from 157 young adult moderate to heavy drinkers (21–30 years of age, 57% male) who completed baseline assessments in an alcohol administration study. A path model in Mplus tested the hypothesized mediated effects. Findings largely supported study hypotheses with significant indirect effects of solitary drinking (but not social drinking) on alcohol problems through stronger tension reduction expectancies and coping motives, though an indirect path through coping motives (but not expectancies) was also identified. Multi-group models by gender and race/ethnicity found that models operated similarly for men and women and for Non-Hispanic Caucasian and Racial/Ethnic Minority participants. The results provide important information about potential mechanisms through which solitary drinking may contribute to alcohol problems. These mechanisms represent potential targets of intervention (e.g. tension reduction expectancies, drinking to cope) for solitary drinkers. Relatively few cannabis dependent individuals seek treatment and little is known about the determinants of treatment seeking. Social Cognitive Theory (SCT) provides a useful framework for examining human behaviour and motivation which may be helpful in explaining treatment seeking. This study examined the differences in cannabis outcome expectancies and cannabis refusal self-efficacy between treatment seekers and non-treatment seekers with cannabis dependence. Non-treatment seekers were referred to an illicit drug diversion program. Treatment seekers commenced an outpatient cannabis treatment program and completed a comprehensive assessment that included measures of cannabis outcome expectancies and refusal self-efficacy.
A public hospital alcohol and drug outpatient clinic.269 non-treatment seekers and 195 individuals commencing cannabis dependence treatment.The Cannabis Expectancy Questionnaire (CEQ), Cannabis Refusal Self-Efficacy Questionnaire (CRSEQ), Severity of Dependence Scale – Cannabis (SDS-C), General Health Questionnaire (GHQ-28) and Readiness to Change Questionnaire (RTC) were completed.
Treatment seekers had significantly higher levels of negative cannabis outcome expectancies and significantly lower levels of emotional relief refusal self-efficacy (belief in ability to resist using cannabis when experiencing negative affect) ( p s < 0.001). Treatment seekers had significantly higher levels of psychological distress and self-perceived cannabis dependence compared to non-treatment seekers ( p s < 0.001). High negative cannabis outcome expectancies and low emotional relief refusal self-efficacy may play a key role in motivation to seek treatment.
: Social cognitive determinants of drinking in young adults: Beyond the alcohol expectancies paradigm
Can genetic factors play a part in developing alcohol and or drug addiction?
Genetics vs. Heredity: What’s the Difference? – The terms genetics and heredity are sometimes used interchangeably, but there are differences that are important to understand.
- Genetics refers to the study of genes or heredity.1,2 Genes are units of DNA that are passed down from parents and specify certain traits.1 Genes are housed on chromosomes. Humans have about 20,000 genes arranged on their chromosomes.3 People have 46 chromosomes in 23 pairs. Individuals inherit one of each pair from their mother and one of each pair from their father.4 This happens at random, which explains the differences between siblings. Individuals share about 50% of their genes with first-degree relatives, like parents, siblings, and children.5
- Heredity refers to the way that different characteristics and traits are passed down to from parents to children through changes in genes and the DNA sequence.6 An inherited trait is genetically determined.7 Not all traits are strictly genetic. In addition, the expression of many genes is also influenced by the environment.7 However, heredity accounts for certain traits, such as height and eye color.1
Since parents pass on certain genes to their children, certain diseases that are linked to genetics might be said to “run” in the family.6 Changes in DNA and genes can affect the risk for certain diseases, but it’s a complex story, and DNA can even be “remodeled” by exposure to different environmental factors.1 Some diseases, like cystic fibrosis, occur due to mutations in a single gene.1 However, many conditions, including addiction and other health complications, are thought to develop as a result of several potential genetic and environmental factors, as well as the interplay between these various influences.1 What this means is that even if you have a family history of addiction, you’re not necessarily guaranteed to develop an addiction.
Are children of alcoholics more likely to become alcoholics?
If you are among the millions of people in this country who have a parent, grandparent or other close relative with alcoholism, you may have wondered what your family’s history of alcoholism means for you. Are problems with alcohol a part of your future? Is your risk for becoming an alcoholic greater than for people who do not have a family history of alcoholism? If so, what can you do to lower your risk? Many scientific studies, including research conducted among twins and children of alcoholics, have shown that genetic factors influence alcoholism.
- These findings show that children of alcoholics are about four times more likely than the general population to develop alcohol problems.
- Children of alcoholics also have a higher risk for many other behavioral and emotional problems.
- But alcoholism is not determined only by the genes you inherit from your parents.
In fact, more than one-half of all children of alcoholics do not become alcoholic. Research shows that many factors influence your risk of developing alcoholism. Some factors raise the risk while others lower it. What Is Alcoholism? Alcoholism, or alcohol dependence, is a disease that includes four symptoms:
Craving — A strong need, or urge, to drink. Loss of control — Not being able to stop drinking once drinking has begun. Physical dependence — Withdrawal symptoms, such as upset stomach, sweating, shakiness and anxiety after stopping drinking. Tolerance — The need to drink greater amounts of alcohol to get “high.”
Genes are not the only things children inherit from their parents. How parents act and how they treat each other and their children has an influence on children growing up in the family. These aspects of family life also affect the risk for alcoholism. Researchers believe a person’s risk increases if he or she is in a family with the following difficulties:
An alcoholic parent is depressed or has other psychological problems Both parents abuse alcohol and other drugs The parents’ alcohol abuse is severe Conflicts lead to aggression and violence in the family
The good news is that many children of alcoholics from even the most troubled families do not develop drinking problems. Just as a family history of alcoholism does not guarantee that you will become an alcoholic, neither does growing up in a very troubled household with alcoholic parents.
Avoid underage drinking — First, underage drinking is illegal. Second, research shows that the risk for alcoholism is higher among people who begin to drink at an early age, perhaps as a result of both environmental and genetic factors. Drink moderately as an adult — Even if they do not have a family history of alcoholism, adults who choose to drink alcohol should do so in moderation — no more than one drink a day for most women and no more than two drinks a day for most men, according to guidelines from the U.S. Department of Agriculture and the U.S. Department of Health and Human Services. Some people should not drink at all, including women who are pregnant or who are trying to become pregnant, recovering alcoholics, people who plan to drive or engage in other activities that require attention or skill, people taking certain medications, and people with certain medical conditions. People with a family history of alcoholism, who have a higher risk for becoming dependent on alcohol, should approach moderate drinking carefully. Maintaining moderate drinking habits may be harder for them than for people without a family history of drinking problems. Once a person moves from moderate to heavier drinking, the risks of social problems (for example, drinking and driving, violence, and trauma) and medical problems (for example, liver disease, brain damage and cancer) increase greatly. Talk to a health care professional — Discuss your concerns with a doctor, nurse, nurse practitioner or other health care provider. They can recommend groups or organizations that could help you avoid alcohol problems. If you are an adult who already has begun to drink, a health care professional can assess your drinking habits to see if you need to cut back on your drinking and advise you about how to do that.
Additional Resources Al-Anon Family Group Headquarters 1-888-4AL-ANON (1-888-425–2666) or (757) 563-1600 Internet: www.alanon.alateen.org Alcoholics Anonymous (AA) World Services (212) 870-3400 Internet: www.aa.org National Association for Children of Alcoholics (NACoA) 1-888-55–4COAS or (301) 468–0985 Internet: www.nacoa.net E-mail: [email protected] National Council on Alcoholism and Drug Dependence (NCADD) 1-800-622–2255 Internet: https://ncadd.org/ National Institute on Alcohol Abuse and Alcoholism (NIAAA) Internet: www.niaaa.nih.gov (301) 443–3860 Source: National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health Updated: August 2005
What is a genetic predisposition to certain traits?
What does it mean to have a genetic predisposition to a disease? A genetic predisposition (sometimes also called genetic susceptibility) is an increased likelihood of developing a particular disease based on a person’s genetic makeup. A genetic predisposition results from specific genetic variations that are often inherited from a parent.
- These genetic changes contribute to the development of a disease but do not directly cause it.
- Some people with a predisposing genetic variation will never get the disease while others will, even within the same family.
- Genetic variations can have large or small effects on the likelihood of developing a particular disease.
For example, certain variants (also called mutations) in the or genes greatly increase a person’s risk of developing and, Particular variations in other genes, such as BARD1 and BRIP1, also increase breast cancer risk, but the contribution of these genetic changes to a person’s overall risk appears to be much smaller.
- Current research is focused on identifying genetic changes that have a small effect on disease risk but are common in the general population.
- Although each of these variations only slightly increases a person’s risk, having changes in several different genes may combine to increase disease risk significantly.
Changes in many genes, each with a small effect, may underlie susceptibility to many common diseases, including cancer, obesity, diabetes, heart disease, and mental illness. Researchers are working to calculate an individual’s estimated risk for developing a common disease based on the combination of variants in many genes across their genome.
- This measure, known as the polygenic risk score, is expected to help guide healthcare decisions in the future.
- In people with a genetic predisposition, the risk of disease can depend on multiple factors in addition to an identified genetic change.
- These include other genetic factors (sometimes called modifiers) as well as lifestyle and environmental factors.
Diseases that are caused by a combination of factors are described as, Although a person’s genetic makeup cannot be altered, some lifestyle and environmental modifications (such as having more frequent disease screenings and maintaining a healthy weight) may be able to reduce disease risk in people with a genetic predisposition.
Is alcohol tolerance in the genes?
Conclusion – This study has shown an association between the region containing the CYP2E1 gene and alcohol tolerance. These findings will need to be confirmed in other samples before any firm conclusions can be drawn. Importantly, the researchers could not identify any variations within the CYP2E1 gene that could potentially account for differences in alcohol tolerance.
- In addition, this region appears to account for only a small amount of the variation in people’s alcohol tolerance.
- This suggests that the majority of a person’s tolerance is explained by other factors (possibly genetic and environmental).
- It is also important to note that although the researchers suggest that alcohol tolerance may affect risk of alcoholism, this study did not directly look at people who were alcohol dependent.
Therefore they cannot say whether the CYP2E1 gene is also linked to alcoholism. Without further research, the current findings do not provide ways to predict or treat alcoholism. Contrary to what might be suggested by the newspapers, genes were already known to play a role in how a person deals with alcohol.
Does alcohol damage genes?
Genetic study provides new evidence that alcohol accelerates biological ageing — Nuffield Department of Population Health Results of a new analysis indicate that alcohol directly damages DNA, by shortening protective telomeres. Telomeres are repetitive DNA sequences that cap the end of chromosomes, protecting them from damage.
Telomere length is considered a potential biological marker of ageing, since 50-100 base pairs are lost each time a cell replicates. Critically short telomeres prevent cell division, and can even trigger cell death. Based on studies using leucocytes (immune system cells), shorter telomere lengths have been associated with several ageing-related diseases including Alzheimer’s disease, cancer, and coronary artery disease.
Telomere length is a partly heritable trait, but has also been found to be influenced by environmental and lifestyle factors, including exercise and smoking. However, to date the evidence from observational studies on how alcohol consumption impacts telomere length has been conflicting.
One issue is that these studies used various different methods to measure telomere length and categorise alcohol intake. Furthermore, observational studies can be affected by confounding variables (factors other than the one being studied that are associated both with the disease and with the factor being studied) and reverse causation effects (where the outcome precedes and causes the exposure, instead of the other way around).
To provide a more rigorous assessment, Oxford Population Health researchers led the first genetic study into the association between alcohol intake and telomere length, based on over 245,000 participants in the. The results have been published today in,
- The research team used Mendelian Randomisation (MR), a method which estimates the association between genetically-predicted levels of an exposure and an outcome of interest.
- Leucocyte telomere length (LTL) measurements were quantified using DNA samples collected when participants were recruited to the UK Biobank.
To estimate alcohol intake, the DNA samples were screened for 93 genetic variants that have previously been associated with weekly alcohol consumption, besides 24 variants that have previously been linked to a diagnosis of an alcohol use disorder. Because these genetic variants are randomly allocated and fixed before birth, MR studies are less likely to be affected by confounding factors or reverse causation than observational studies.
Most of the participants were current drinkers, with only 3% being never drinkers and 4% being previous drinkers. In the observational analysis, there was a significant association between high alcohol intake and shorter LTL. Compared with drinking less than 6 units of alcohol a week (about two large 250ml glasses of wine), drinking more than 29 units weekly (about ten glasses) was associated with between one and two years of age-related change on telomere length. Individuals who had been diagnosed with an alcohol-use disorder had significantly shorter LTLs compared with controls, equivalent to between three and six years of age-related change. Similarly, in the MR analysis, higher genetically-predicted alcohol consumption was associated with shorter telomere length. Each standard deviation increase in the genetically-predicted log-transformed weekly alcohol intake was associated with three years of ageing (for instance, an increase from 10 units of alcohol per week to 32.2 units). However, the association between genetically-predicted alcohol consumption and telomere length was only significant for those drinking more than 17 units per week. This suggests that a minimum threshold of alcohol consumption may be required to damage telomeres. The MR analysis also found a significant association between genetically-predicted alcohol-use disorder and telomere length, equivalent to around three years of ageing.
Although these results do not conclusively prove that alcohol directly affects telomere length, two findings from the study support this being the case.1) Effects were only found in current drinkers, and not previous or never-drinkers; 2) The most influential genetic variant in the MR analysis was AD1HB, an alcohol metabolism gene.
- A potential biological mechanism to explain alcohol’s influence on telomere length is increased oxidative stress and inflammation.
- Ethanol metabolism can both and,
- Study lead said: ‘These findings support the suggestion that alcohol, particularly at excessive levels, directly affects telomere length.
Shortened telomeres have been proposed as risk factors which may cause a number of severe age-related diseases, such as Alzheimer’s disease. Our results provide another piece of information for clinicians and patients seeking to reduce the harmful effects of excess alcohol.
What are 7 factors that can influence the effect alcohol has on a person?
What happens when you drink an alcoholic beverage? Although alcohol affects different people in different ways, in general, it is quickly absorbed from your digestive system into your blood. The amount of alcohol in your blood reaches its maximum within 30 to 45 minutes, according to the National Institute on Alcohol Abuse and Alcoholism (NIAAA).
Alcohol is metabolized — that is, broken down chemically so it can be eliminated from your body — more slowly than it is absorbed. You can become more intoxicated as you drink more alcohol than is eliminated, which will result in an increase in your blood alcohol level. A standard drink is considered to be 12 ounces of beer, 5 ounces of wine, or 1.5 ounces of 80-proof distilled spirits — all of these contain the same amount (approximately 15 grams or 1/2 ounce) of alcohol.
Genetics, body weight, gender, age, what type of beverage, food in your stomach, medications in your system, and your state of health, influence how people respond to alcohol.