research paper about drinking alcohol

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.14(10); 2022 Oct

Effects of Alcohol Consumption on Various Systems of the Human Body: A Systematic Review

Jerin varghese.

1 Medical School, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Sarika Dakhode

2 Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Prolonged alcohol intake for many years has been known to cause serious ailments in human beings since time memorial. Even after knowing that this dangerous addiction paves the way to one’s own grave, there isn’t much difference in the way the community sees this deadly habit. Time and again history has proven that this fatal addiction could make the life of those who consume it terrible. Also, the lives of the dear ones of alcoholic people are affected as alcohol not only affects those who consume them but also kin and friends. Various research studies conducted over many years clearly show the association of prolonged alcohol intake in the causation, aggravation, worsening, and deterioration of the health of its consumers. Moreover, chronic alcohol intake single-handedly is one of the major etiological factors in various serious diseases.

Introduction and background

Through the ages, alcoholism has been undisputedly maintaining its position in the list of risk factors for preventable diseases in the world. According to a WHO report, 5.3% of all deaths that occurred worldwide in the year 2016 were because of harmful alcohol use [ 1 ]. It is the main culprit behind the advancing nature of many chronic diseases. It drastically increases the severity of diseases and also makes the treatments less effective. Alcohol not only affects the person physiologically, but it has many adverse effects psychologically and socially too. Also, the habit of alcoholism leads to huge expenses [ 2 ]. Apart from systemic involvement, which causes various clinical manifestations, there are certain signs and symptoms that are most of the times non-specific and that as such don’t point out or say lead to a particular diagnosis, such as nausea, agitation, vomiting, anxiety, diaphoresis, tremors, headache, visual hallucinations, tachycardia, seizures, delirium, temperature elevation, etc. It is not always necessary that these mentioned signs and symptoms are compulsorily linked with disease conditions.

Alcohol clearly plays a very important role in making many other diseases progress to their advanced stages. It has been also noted that alcohol intake and its related disorders are often associated with many other manifestations; for example, patients with alcoholic neuropathy often have associated nutritional deficiencies. Recent studies have clearly proved that alcoholism is associated with many types of cancers too and this understanding of alcoholism has spurred research minds all over the globe to find out the exact pathophysiology behind the same. Alcohol is a very easily available source of addiction, which is one of the main reasons why it remains a serious threat to the community. There is a huge variety that is available as far as alcoholic drinks are concerned. Alcohol is also one of the cheaply accessible means of addiction; this explains why alcoholism is so prevalent. A person may initially start consuming alcohol in very low amounts most probably with just a desire to try it, but once he or she gets addicted, then getting rid of the habit becomes extremely difficult. Even if a person is mentally resolute enough to quit alcoholism, his or her body, which has been modified because of the chronic use of alcohol, won’t be up to the challenge anytime soon; he or she has to overcome many hurdles put forward by the body, which could in an umbrella term be referred to as alcohol withdrawal syndrome.

There are many social stigmas associated with alcohol intake. Most people get into this addiction by getting inspired by the people whom they admire, like actors, celebrities, role models, etc. Also, exposure to the sight of family members, relatives and friends drinking alcohol has a huge impact on one’s mindset as he or she may take it to be something that is normal. In the long run, most of the time, even without their realization, people get pathetically trapped in this dangerous fatal habit of alcoholism, which eventually makes their lives pitiful in almost all aspects. Studies have shown that alcohol is also a key player in many other domains too like accidents, suicide, depression, hallucinations, violence, memory disturbances, etc.

The main purpose of this review article is to enable any person reading this article to get a comprehensive insight into the effects of alcohol on the various systems of the human body, and for the same, many recognized research articles published in numerous well-acknowledged journals across the globe are reviewed. The article is written using very basic and simple terminologies so that even a layperson who reads it would be able to understand it. For the easy acceptability and understanding of the reader, the discussion is written in such a way that almost every major system is reviewed one by one and the effect of alcohol on these systems put forward in very simple language. The strategies used for the establishment of this review article are summarised in Figure ​ Figure1; 1 ; these include considering research articles that have been published in journals with are indexed in reputed platforms, segregating articles according to the different systems, framing the review like a discussion section of an article where details are explained in simple and straight forward sentences, etc.

Impact of alcohol on the central nervous system (CNS)

Alcohol exerts various effects on our CNS in various ways, the common ones being depression of the CNS, destruction of the brain cells, contraction of the tissues of the brain, suppression of the excitatory nerve pathway activity, neuronal injury, etc [ 3 ]. Alcohol’s impact on the functioning of the brain ranges from mild and anxiolytic disinhibitory effects, motor incoordination, sedation, emesis, amnesia, hypnosis and ultimately unconsciousness [ 4 ]. The synaptic transmission is heavily disturbed and altered by ethanol, and the intrinsic excitability in various areas of the brain is also compromised. The effects of ethanol may be pre-synaptic, post-synaptic, and at times, non-synaptic too. Alcohol being a psychotropic depressant of the CNS exerts a deeply profound impact on the neurons, which alters the biological and behavioural well-being of the one who consumes it by the promotion of interference in various neuronal pathways [ 5 ]. The treatments of many disorders of the CNS are shown to be affected by the consumption of alcohol, and thus, it is generally advised to keep oneself away from alcohol if one is undergoing treatment for any CNS manifestations, like anxiety or mood disorders [ 6 ].

Alcohol use disorder (AUD) is chronic in nature and is characterized by uncontrolled drinking and also a preoccupation with alcohol. The severity of AUD is a crucial factor in how it is going to affect the human body. AUD can be mild, moderate, or severe according to the symptoms a person experiences. The clinical manifestations of AUD include signs and symptoms such as inability to control the amount of alcohol intake, spending a lot of time drinking, feeling an uncontrollable craving for alcohol, loss of interest in social activities, failure to fulfil tasks within the time provided, etc. Most of the time, along with the person who consumes alcohol, several other factors are also to be taken care of in order to effectively manage alcohol-related health conditions. These factors can be social, environmental, genetic, psychological, etc, which make a considerable impact on how alcohol affects the behaviour and body of those consuming it. Binge drinking, i.e., drinking to such an extent on a single occasion that the blood alcohol concentration level becomes 0.08% or more, is a very relevant aspect of alcohol intake, which has to be dealt with, with utmost urgency. Certain research studies suggest that mild to moderate alcohol intake provides a certain sort of protection against a few CNS disorders like dementia, ischemia of neurons, etc, but this in no way should encourage the community in promoting alcohol intake as in reality, it is very difficult to remain within the limits of mild to moderate alcohol intake, and thus, eventually, people do end up as full-time severe alcohol abusers. Epilepsy, a seizure disorder caused by disturbed nerve cell activity in the brain, aggravates on excessive alcohol intake as alcohol increases the frequency of seizures in patients of epilepsy [ 7 ]. The issue becomes more severe in those epileptic patients who have refractory forms of epilepsy. As far as comorbidities are concerned, a valid history of abuse of substances or alcohol dependence is believed to be strongly associated with a high risk of sudden unexpected death in epilepsy (SUDEP) [ 8 ]. Heavy alcohol drinking over a long period of time has been found to have an intensely negative undesirable effect on the autonomic nervous system too.

Impact of alcohol on the cardiovascular system (CVS)

Chronic alcohol intake is undoubtedly a very important risk factor as far as cardiovascular diseases are concerned and several clinical trials do point out this fact. The results of several research studies conducted in various settings clearly indicate that increased intake of alcohol has increased adverse effects on our heart and its vasculature. Alcohol exerts its action on the cardiovascular system both directly and indirectly. Blood pressure, a very vital player in the domain of cardiovascular diseases, is in turn itself affected by increased alcohol consumption. Blood pressure gets increased on regular consumption of alcohol in a manner which is dose-dependent, which in turn increases the risk of hypertension and eventually leads to various cardiovascular complications. How exactly alcohol causes hypertension is still unclear with many pathophysiological theories out there. Atrial fibrillation, one of the most common causes of arrhythmia, is associated with the high-volume chronic intake of alcohol and above 14 g alcohol/day, the relative risk dramatically increases by 10% for each extra standard drink (14 g ethanol) [ 9 ].

Cerebrovascular accidents are increased to a great extent at almost all levels of alcohol intake [ 10 ]. Alcohol intake leads to both acute (depresses the cardiac function and also alters the blood flow of the involved region) and chronic cardiovascular manifestations [ 11 ]. Alcohol abuse along with other associated factors is one of the leading causes of secondary cardiomyopathy [ 12 ]. Cardiac arrhythmias get precipitated by alcohol consumption, be it acute or chronic. Heavy alcohol drinking is shown to impact the cardiovascular system in many ways, one of the most important among them being rebound hypertension [ 13 ]. Apart from congenital disorders of the cardiovascular system, it indeed is a very well-evident fact, which could be understood from the history of most of the patients diagnosed with cardiovascular disorders, that they used to consume a lot of alcohol for many years.

Impact of alcohol on the digestive system

Chronic alcoholism is found to have a very strong relationship with both acute pancreatitis and chronic pancreatitis. Chronic alcohol intake impairs the repair ability of the structures of the exocrine pancreas, thereby leading to pancreatic dysfunctioning [ 14 ]. Most of the patients diagnosed with pancreatitis have a strong history of chronic intake of alcohol. Liver diseases related to alcohol intake are known to humankind from the very beginning and probably are one of the oldest known forms of injury to the liver [ 15 ]. In liver diseases linked with alcohol, liver cirrhosis is a major concern. Statistics show that liver cirrhosis is one of the top 10 causes of death worldwide and this in itself indicates the severity of the same [ 16 ]. The changing lifestyle and also many people turning to prolonged alcohol intake for many years are contributing to the increased number of liver cirrhosis patients in the modern world. In liver cirrhosis patients, there occurs an increased severity of fibrosis due to the loss of parenchyma and fibrous scar proliferation [ 17 ]. Alcoholic liver disease (ALD) is an umbrella term which incorporates a wide range of injuries of the liver, spanning from simple steatosis to cirrhosis, and this also includes alcohol-related fatty liver disease (AFLD) and also alcoholic hepatitis [ 18 ]. Advancements in the diagnostic modalities have helped to diagnose ALD at an early phase and there is no doubt that newer and better investigations that have helped to detect more cases have led to a surge in the number of ALD patients on whole. Alcohol intake has a prominently bigger impact on the mortality of liver cirrhosis when compared with the morbidity [ 19 ]. A systemic review and meta-analysis suggests that women might be at a higher risk as far as developing liver cirrhosis is concerned even with little consumption of alcohol, as compared to men [ 20 ].

Impact of alcohol on the causation of cancer

Alcohol has much to do with cancers too and continuous research studies are conducted in order to find out the relationship between the two in detail. In a meta-analysis, it was found that women consuming alcohol had a later menopause onset, which is found to be associated with reduced cardiovascular disease risk and also all-cause mortality, but unfortunately, the happiness of this advantage gets compromised by the ironic fact that it has an increased risk of cancer (including ovarian and breast cancers) [ 21 , 22 ]. Large cohort studies, many meta-analyses, experimental research studies, etc are suggestive of the fact that the chronic intake of alcohol clearly increases colon and gastric cancer risk [ 23 ]. A causal association is also found between alcohol intake and cancers of the rectum, colon, liver, oesophagus, larynx, pharynx and oral cavity [ 24 ]. There are various theories put forward so as to understand the role of the consumption of alcohol in the development of cancer; there is suspicion that the rise in the number of alcohol users worldwide may be one of the reasons why the number of cancer patients is increasing at a global level. Chronic intake of alcohol may promote the genesis of cancer in many ways, some of the most notable ones being acetaldehyde (weak mutagen and carcinogen) production, cytochrome P450 2E1 induction associated oxidative stress, S-adenosylmethionine depletion/ which leads to global DNA hypomethylation induction, iron induction associated oxidative stress, retinoic acid metabolism impairment, etc [ 25 ].

Impact of alcohol on other systems

Apart from the systemic manifestations which do affect a particular system of the body, there are various disorders in which alcohol indirectly provides its crucial contribution. It is a common finding that one could perceive that alcohol is most of the time in the list of risk factors for various diseases. Alcohol has been found to adversely affect our immune system and the matter of concern as far as this issue is concerned is that immune responses are influenced by even moderate amounts of alcohol intake [ 26 ]. Alcohol affects innate immunity and also interferes with almost all the various aspects of the adaptive immune response. Alcohol is a key player in impairing anti-inflammatory cytokines and also promotes proinflammatory immune responses. The gastrointestinal biome is severely manipulated by the use of alcohol over a long period of time, which in turn is found to have a link with the establishment of various complications [ 27 ]. Alcohol and its metabolites are found to promote inflammation in the intestines and they do so through varied pathways [ 28 ]. Alcohol being a teratogen is documented to cause abnormalities of the brain, limbs, etc [ 29 ]. Multiple studies have been conducted across the globe to understand the effect of alcohol on humans; implications from certain such studies are put forth in Table ​ Table1 1 . 

Conclusions

Alcohol seldom leaves any system untouched as far as leaving its impression is concerned, spanning from single tissue involvement to complex organ system manifestations. Almost all the major organs that make up a human’s physiological being are dramatically affected by the overconsumption of alcohol. There is an enormous overall economic cost that is paid for alcohol abuse all over the world.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Systematic Review
• Open access
• Published: 25 August 2022

Age-related differences in the effect of chronic alcohol on cognition and the brain: a systematic review

• Lauren Kuhns   ORCID: orcid.org/0000-0002-3156-8905 1 , 2 ,
• Emese Kroon   ORCID: orcid.org/0000-0003-1803-9336 1 , 2 ,
• Heidi Lesscher 3 ,
• Gabry Mies 1 &
• Janna Cousijn 1 , 2 , 4  

Translational Psychiatry volume  12 , Article number:  345 ( 2022 ) Cite this article

4305 Accesses

3 Citations

3 Altmetric

Metrics details

• Human behaviour

Adolescence is an important developmental period associated with increased risk for excessive alcohol use, but also high rates of recovery from alcohol use-related problems, suggesting potential resilience to long-term effects compared to adults. The aim of this systematic review is to evaluate the current evidence for a moderating role of age on the impact of chronic alcohol exposure on the brain and cognition. We searched Medline, PsycInfo, and Cochrane Library databases up to February 3, 2021. All human and animal studies that directly tested whether the relationship between chronic alcohol exposure and neurocognitive outcomes differs between adolescents and adults were included. Study characteristics and results of age-related analyses were extracted into reference tables and results were separately narratively synthesized for each cognitive and brain-related outcome. The evidence strength for age-related differences varies across outcomes. Human evidence is largely missing, but animal research provides limited but consistent evidence of heightened adolescent sensitivity to chronic alcohol’s effects on several outcomes, including conditioned aversion, dopaminergic transmission in reward-related regions, neurodegeneration, and neurogenesis. At the same time, there is limited evidence for adolescent resilience to chronic alcohol-induced impairments in the domain of cognitive flexibility, warranting future studies investigating the potential mechanisms underlying adolescent risk and resilience to the effects of alcohol. The available evidence from mostly animal studies indicates adolescents are both more vulnerable and potentially more resilient to chronic alcohol effects on specific brain and cognitive outcomes. More human research directly comparing adolescents and adults is needed despite the methodological constraints. Parallel translational animal models can aid in the causal interpretation of observed effects. To improve their translational value, future animal studies should aim to use voluntary self-administration paradigms and incorporate individual differences and environmental context to better model human drinking behavior.

Similar content being viewed by others

Yohimbine as a pharmacological probe for alcohol research: a systematic review of rodent and human studies

Dallece E. Curley, Talia R. Vasaturo-Kolodner, … Carolina L. Haass-Koffler

Consequences of adolescent drug use

Michael R. Steinfeld & Mary M. Torregrossa

Chronic voluntary alcohol consumption causes persistent cognitive deficits and cortical cell loss in a rodent model

Annai J. Charlton, Carlos May, … Christina J. Perry

Introduction

Alcohol use disorder (AUD) is the most prevalent substance use disorder worldwide [ 1 ]. Most AUDs remain untreated [ 2 ] and for those seeking treatment, relapse rates are high [ 3 ]. Adolescence marks a rapid increase in AUD and an earlier onset of AUD is associated with worse long-term outcomes, including greater problem severity and more relapses [ 4 , 5 ]. Loss of control over alcohol use is a core aspect of AUD [ 6 ] and the developmentally normative difficulty to control motivational urges in tempting and arousing situations is thought to put adolescents at risk for developing addictive behaviors [ 7 ]. Moreover, neurotoxic consequences of alcohol use may be more severe for a developing brain [ 8 ]. Paradoxically, adolescence is also a period of remarkable behavioral flexibility and neural plasticity [ 9 , 10 , 11 ], allowing adolescents to adapt their goals and behavior to changing situations [ 12 ] and to recover from brain trauma more easily than adults [ 10 ]. In line with this, the transition from adolescence to adulthood is associated with high rates of AUD recovery without formal intervention [ 13 ]. While the adolescent brain may be a vulnerability for the development of addiction, it may also be more resilient to long-term effects compared to adults. Increased neural plasticity during this period could help protect adolescents from longer-term alcohol use-related cognitive impairments across multiple domains, from learning and memory to decision-making and cognitive flexibility. Therefore, the goal of this systematic review was to examine the evidence of age-related differences in the effect of alcohol on the brain and cognitive outcomes, evaluating evidence from both human and animal studies.

In humans, the salience and reinforcement learning network as well as the central executive network are involved in the development and maintenance of AUD [ 7 , 14 ]. The central executive network encompasses fronto-parietal regions and is the main network involved in cognitive control [ 15 ]. The salience network encompasses fronto-limbic regions crucial for emotion regulation, salience attribution, and integration of affective information into decision-making [ 15 , 16 ], which overlaps with fronto-limbic areas of the reinforcement learning network (Fig. 1 ). Relatively early maturation of salience and reinforcement learning networks compared to the central executive network is believed to put adolescents at heightened risk for escalation of alcohol use compared to adults [ 7 ]. Rodent models are regularly used for AUD research and allow in-depth neurobehavioral analyses of the effects of ethanol exposure during different developmental periods while controlling for experimental conditions such as cumulative ethanol exposure in a way that is not possible using human subjects because exposure is inherently confounded with age. For example, animal models allow for detailed neurobiological investigation of the effects of alcohol exposure in a specific age range on neural activation, protein expression, gene expression, epigenetic changes, and neurotransmission in brain regions that are homologous to those that have been implicated in AUD in humans.

A visual representation of the translational model of the executive control and salience networks in humans and rodents. The executive control and salience are key networks believed to play a part in adolescent vulnerability to alcohol-related problems.

While most of our knowledge on the effects of alcohol on the brain and cognitive outcomes is based on research in adults, several recent reviews have examined the effects of alcohol on the brain and cognition in adolescents and young adults specifically [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Heavy or binge drinking has been associated with reduced gray and white matter. Also, altered task-related brain activity [ 20 ], structural abnormalities [ 25 ], and overlapping behavioral impairment in executive functioning have been identified in adolescent and young adult alcohol users [ 19 ]. While some of the observed neurocognitive differences between drinkers and non-drinkers may be predisposing factors, they may be further exacerbated by heavy and binge drinking [ 21 , 23 ]. Furthermore, reviews of longitudinal studies concluded that adolescent alcohol use is associated with neural and cognitive alterations in a dose-dependent manner [ 17 , 22 ].

Although previous reviews underscore the potential negative consequences of heavy alcohol use on the brain and cognition in adolescence, they do not typically address the question of whether adolescents are differentially vulnerable compared to adults to the effects of alcohol on these outcomes. Explicit comparisons between adolescents and adults are crucial to identify potential risk and resilience factors. In the current review, we aimed to extend previous work by systematically examining this critical question: does the relationship between chronic alcohol use and neurocognitive outcomes differ between adolescents and adults? To address this question, we systematically reviewed human and animal studies that included both age groups and used a factorial design that would allow for the comparison of the effects of chronic alcohol use on cognitive and brain-related outcomes across age groups. We specifically highlight outcomes from voluntary self-administration paradigms when available and discuss the translational quality of the animal evidence base. We conclude with a discussion of prominent knowledge gaps, future research directions, and clinical implications.

Study inclusion criteria and search strategy

We followed the PRISMA guidelines for the current systematic review (The PRIMSA Group, 2009). An initial MedLine, Cochrane Library, and PsycInfo search was conducted during September of 2018 with terms related to alcohol, cognition, adolescence/adulthood, and study type (see Appendix for full search strategy and syntax). Two search updates using the same search strategy were conducted on 31 March 2020 and 3 February 2021. For all searches, the identified citations were split into batches and at least two of the following assessors (GM, LK, JC, or CG) conducted a blinded review to determine whether articles met the inclusion criteria. In the first phase of screening, only titles and abstracts were screened and articles that clearly did not meet the inclusion criteria were excluded. In the second phase, the remaining articles received a full-text review and those that did not meet all inclusion criteria were excluded. The first inclusion criterion that was not adhered to was recorded as the reason for excluding. If there was a discrepancy between authors after initial and full-text screening process, the reviewing authors discussed the article and a consensus was reached.

The inclusion criteria were: (1) Human samples including both adolescents younger than 18 and adults older than 18 and animal samples including adolescent (Post Natal Day (PND) 25–42 for rodents) and adult [ 8 ] animals (greater than PND 65 for rodents); (2) Exploration of alcohol as the independent variable and cognitive, reward-related, or brain outcomes as the dependent variables; (3) Alcohol and cognitive outcomes must meet our operationalization defined below; (4) Study design comparing adults and adolescents on outcome measures; (5) Administering or measuring alcohol use during adolescence or adulthood, not retrospectively (e.g., no age of onset work in humans using retrospective self-reports of alcohol consumption); (6) Primary quantitative data collection (no case studies, or review papers); (7) Solely looking at alcohol-related factors as the independent variables (e.g., cannot explore alcohol-related factors in individuals with psychosis); (8) Written in English; (9) Published in a peer-reviewed journal before February 3, 2021 (see Fig. 2 for a detailed screening process).

The definitions for adolescence are variable, hampering the direct comparison of human and rodent research. In rodents, the end of early-mid adolescence is considered to be approximately PND 42 when rats reach sexual puberty. By contrast, the boundaries for the onset of early adolescence are less clear. Based on the notion that most age-typical physiological changes that are characteristic of adolescence emerge from PND 28 [ 26 ], the conservative boundary for adolescence has been set at PND 28 (e.g., seminal review on adolescence [ 27 ]). The preceding week (PND 21-PND 28) has been described as the juvenile period (e.g., [ 28 , 29 ]) but these same reports consider PND 21-PND 23 as the lower boundary for early adolescence [ 28 , 29 ], further emphasizing that the boundary of PND28 may be too conservative. Indeed, multiple studies (e.g., [ 30 , 31 ]), have chosen to take PND25 as the boundary for early adolescence. Hence, we have decided to also follow this less conservative approach and include all studies where alcohol was administered between PND 25 and PND 42.

The exact boundaries of human adolescence are similarly nebulous. From a neurodevelopmental perspective, adolescence is now often thought of as continuing until approximately age 25 because of the continuing maturation of the brain [ 32 ]. However, the delineation of adolescence and adulthood is also dependent on societal norms, and is commonly defined as the transitional period between puberty and legal adulthood and independence which typically begins around age eighteen. In light of this, we chose a relatively liberal inclusion criteria for the human studies; studies needed to include at least some adolescents below eighteen, the age at which drinking typically begins, as well as ‘adult’ participants over the age of eighteen. We are careful to interpret the results of human studies within the neurodevelopmental framework of adolescence, such that 18–25-year-olds are considered late adolescents to young adults who are still undergoing cognitive and brain maturation.

Notably, we excluded studies that assessed alcohol exposure retrospectively (primarily early onset alcohol studies) because age of onset variables are often inaccurate, with reported age of alcohol onset increasing with both historical age [ 33 ] and current alcohol use patterns [ 34 ]. In addition, we excluded work that has not undergone peer-review to ensure high-quality papers.

In humans, we defined cognition as any construct that typically falls within the umbrella of neuropsychological testing, as well as brain-based studies. We also included more distal constructs of cognition, like craving and impulsivity, because they play a prominent role in addictive behaviors [ 35 , 36 ]. In rodents, we defined cognition as attention, learning, and memory in line with a seminal review paper [ 37 ]. Given the importance of social cognition in patterns of alcohol use particularly in adolescence [ 38 ] and its proposed role in adolescent risk and resilience to addiction [ 39 ], we included social behavior as an outcome. Furthermore, because many rodent studies assessed anxiety-related behaviors and the high degree of comorbidity between anxiety disorders and alcohol addiction [ 40 ], we also included anxiety as a secondary outcome. On the other hand, locomotor activity was excluded as an outcome because even though behavioral sensitization is considered to reflect neurobiological changes that may underlie certain aspects of addictive behavior [ 36 ], the translational relevance for addictive behavior and human addiction in particular remains unclear [ 41 , 42 ]. Across both rodents and humans, general alcohol metabolization and ethanol withdrawal studies were not included except if they included brain-related outcomes. The relevant reported findings (i.e., the results of an analysis of comparing age groups on the effect of alcohol on an included outcome) were extracted by a one reviewer and then confirmed by at least one other reviewer. In addition, the characteristics of the sample, details of alcohol exposure, and study design were extracted by a single reviewer and then confirmed by at least one other reviewer. No automation tools were used for extraction. Within the included studies, peripheral findings that did not relate to cognition were excluded from review and not extracted. The protocol for this systematic review was not registered and no review protocol can be accessed.

Study search

Our searches identified 7229 studies once duplicates were removed. A total of 6791 studies were excluded after initial review of abstracts. Then, 434 studies received a full-text review and 371 were excluded for failing to meet all inclusion criteria. See Fig. 2 for a flow diagram of the full screening process. At the end of the inclusion process, 59 rodent studies and 4 human studies were included. The characteristics and findings of the final studies are detailed in Table 1 (rodents) and Table 2 (humans). Due to the heterogeneity of outcomes, meta-regression was not suitable for synthesizing results. Results are narratively synthesized and grouped based on forced or voluntary ethanol exposure and by outcome within the tables and by outcome only in text. Two authors independently rated the quality of evidence for human studies (Table 2 ) based on criteria used in a similar systematic review [ 43 ]: (1) strong level of causality: longitudinal design comparing adolescent and adults while adjusting for relevant covariates; (2) moderate level of causality: longitudinal design comparing adolescents and adults without adjusting for relevant covariates or cross-sectional designs with matched groups that considered relevant covariates; (3) weak level of causality: cross-sectional design without matched adolescent and adult groups and/or did not adjust for relevant covariates. A methodological quality assessment was not conducted for the animal studies due to a lack of empirically validated risk of bias tools and lack of standardized reporting requirements in the animal literature.

PRIMSA flow diagram detailing the screening process.

Animal studies

Cognitive outcomes, learning and memory.

Human evidence clearly suggests that alcohol is related to learning and memory impairments, both during intoxication [ 44 ] and after sustained heavy use and dependence [ 45 , 46 ]. Paradigms that assess learning and memory provide insight into the negative consequences of alcohol consumption on brain functioning, as well as the processes underlying the development and maintenance of learned addictive behaviors.

Conditioned alcohol aversion or preference: Lower sensitivity to alcohol’s aversive effects (e.g., nausea, drowsiness, motor incoordination) but higher sensitivity to alcohol’s rewarding effects has been hypothesized to underlie the higher levels of alcohol use, especially binge-like behavior, in adolescents compared to adults [ 47 ]. Several conditioning paradigms have been developed to assess the aversive and motivational effects of alcohol exposure.

The conditioned taste aversion (CTA) paradigm is widely used to measure perceived aversiveness of alcohol in animals. Repeated high-dose ethanol injections are paired with a conditioned stimulus (CS, e.g., a saccharin or NaCL solution). The reduction in CS consumption after conditioning is used as an index of alcohol aversion. Two studies examined CTA in mice [ 48 , 49 ] and two in rats [ 50 , 51 ]. Three of the four studies found age-related differences. In all three studies using a standard CTA paradigm, adolescents required a higher ethanol dosage to develop aversion compared to adults [ 48 , 49 , 50 ]. Using a similar second-order conditioning (SOC) paradigm pairing high doses of ethanol (3.0 g/kg) with sucrose (CS), both adolescent and adult rats developed equal aversion to the testing compartment paired with ethanol [ 51 ].

Overall, three studies found support for lower sensitivity to alcohol’s aversive effects in adolescents, whereas one observed no differences. Future research should employ intragastric as opposed intraperitoneal exposure to better mimic human binge-like drinking in order to increase the translational value of the findings.

To measure differences in alcohol’s motivational value, conditioned place preference (CPP) paradigms have been used. This involves repeated pairings of ethanol injections with one compartment and saline injections with another compartment of the testing apparatus. On test days, CPP is assessed by measuring how long the animal stays in the compartment paired with ethanol relative to saline injections. Four studies examined CPP, with two studies observing age-related differences [ 52 , 53 , 54 , 55 ]. In the only mouse study, history of chronic ethanol exposure during adolescence (2.0 g/kg for 15 days) but not adulthood [ 52 ] led to increased CPP after brief abstinence (5 days) before the conditioning procedure (2.0 g/kg, four doses over 8 days). This suggests that early ethanol exposure increases alcohol’s rewarding properties later on. However, two rat studies did not observe either preference or aversion in either age when using lower ethanol doses and a shorter exposure period (0.5 and 1.0 g/kg for 8 days) [ 53 ], nor when using higher doses and intermittent exposure (3.0 g/kg, 2 days on, 2 days off schedule) [ 55 ]. Next to species and exposure-specific factors, environmental factors also play a role [ 54 ], with adolescents raised in environmentally enriched conditions demonstrating CPP (2 g/kg) while adolescents raised in standard conditions did not. In contrast, CPP was insensitive to rearing conditions in adults with both enriched and standard-housed rats showing similar levels of CPP.

Overall, there is inconsistent evidence for age-related differences in the motivational value of ethanol. One study found support for increased sensitivity to the rewarding effects of ethanol in adolescents, whereas one found support for adults being more sensitive and two observed no differences.

Fear conditioning and retention: Pavlovian fear conditioning paradigms are used to investigate associative learning and memory in animals. These paradigms are relevant for addiction because fear and drug-seeking behavior are considered conditioned responses with overlapping neural mechanisms [ 56 ]. Rodents are administered an unconditioned stimulus (US; e.g., foot shock) in the presence of a conditioned stimulus (CS; unique context or cue). Conditioned responses (CR; e.g., freezing behavior) are then measured in the presence of the CS without the US as a measure of fear retention. Contextual fear conditioning is linked to hippocampus and amygdala functioning and discrete cue-based (e.g., tone) fear is linked to amygdala functioning. [ 57 , 58 , 59 ], and fear extinction involves medial PFC functioning [ 60 ]. Five studies investigated fear conditioning, four in rats [ 61 , 62 , 63 , 64 ] and one in mice [ 65 ].

Only one of the four studies observed age-related differences in tone fear conditioning. Bergstrom et al. [ 61 ] found evidence for impaired tone fear conditioning in male and female alcohol-exposed (18d) adolescent compared to adult rats after extended abstinence (30d). However, adolescent rats consumed more ethanol during the one-hour access period than adults, which may explain the observed age differences in fear tone conditioning. Small but significant sex differences in consumption also emerged in the adolescent group, with males showing more persistent impairment across the test sessions compared to females, despite adolescent females consuming more ethanol than males. In contrast, three studies found no evidence of impaired tone fear conditioning in either age group after chronic alcohol exposure (4 g/kg, every other day for 20d) and extended abstinence [ 62 , 63 ] (22d), [ 64 ].

Two of the three studies observed age-related differences in contextual fear conditioning [ 62 , 63 , 64 ]. In two studies with similar exposure paradigms, only adolescents exposed to chronic high dosages of ethanol (4 g/kg) showed disrupted contextual fear conditioning after extended abstinence (22d) [ 62 , 63 ]. Importantly, differences disappeared when the context was also paired with a tone, which is suggestive of a potential disruption in hippocampal-linked contextual fear conditioning specifically [ 64 ]. Furthermore, there may be distinct vulnerability periods during adolescence as contextual fear retention was disrupted after chronic alcohol exposure (4 g/kg, every other day for 20d) during early-mid adolescence but not late adolescence [ 62 ]. In the only study to combine chronic exposure and acute ethanol challenges, contextual conditioning was impaired by the acute challenge (1 g/kg) but there was no effect of pre-exposure history in either age group (4 g/kg, every other day for 20d) [ 63 ].

Only one study examined fear extinction, and found no effect of ethanol exposure (4/kg, every other day for 20d) on extinction after tone conditioning. However, adults had higher levels of contextual fear extinction compared to mid-adolescents while late adolescents performed similar to adults [ 62 ]. Moreover, looking at binge-like exposure in mice (three binges, 3d abstinence), Lacaille et al. [ 65 ] showed comparable impairments in long-term fear memory in adolescents and adults during a passive avoidance task in which one compartment of the testing apparatus was paired with a foot shock once and avoidance of this chamber after a 24 h delay was measured.

In sum, there is limited but fairly consistent evidence for adolescent-specific impairments in hippocampal-linked contextual fear conditioning across two rat studies, while no age differences emerged in context-based fear retention in one study of mice. In contrast, only one of the four studies found evidence of impaired tone fear conditioning in adolescents (that also consumed more alcohol), with most finding no effect of alcohol on tone fear conditioning regardless of age. With only one study examining medial PFC-linked fear extinction, no strong conclusions can be drawn, but initial evidence suggests context-based fear extinction may be diminished in mid-adolescents compared to adults and late adolescents. Research on age-related differences on the effect of alcohol on longer-term fear memory is largely missing.

Spatial learning and memory: The Morris Water Maze (MWM) is commonly used to test spatial learning and memory in rodents. Across trials, time to find the hidden platform in a round swimming pool is used as a measure of spatial learning. Spatial memory can be tested by removing the platform and measuring the time the animal spends in the quadrant where the escape used to be. The sand box maze (SBM) is a similar paradigm in which animals need to locate a buried appetitive reinforcer.

Six rat studies examined spatial learning and memory using these paradigms. Three of the six studies observed age-related differences. Four examined the effects of repeated ethanol challenges 30 minutes prior to MWM training, showing mixed results [ 30 , 66 , 67 , 68 ]. While one found ethanol-induced spatial learning impairments in adolescents only (1.0 and 2.0 g/kg doses) [ 66 ], another found no age-related differences, with both age groups showing impairments after moderate doses (2.5 g/kg) and enhancements in learning after very low doses (0.5 g/kg) [ 67 ]. Sircar and Sircar [ 68 ] also found evidence of ethanol-induced spatial learning and memory impairments in both ages (2.0 g/kg). However, memory impairments recovered after extended abstinence (25d) in adults only. Importantly, MWM findings could be related to thigmotaxis, an anxiety-related tendency to stay close to the walls of the maze. Developmental differences in stress sensitivity may potentially confound ethanol-related age effects in these paradigms. Using the less stress-inducing SBM, adults showed greater impairments in spatial learning compared to adolescents after 1.5 g/kg ethanol doses 30 min prior to training [ 30 ].

Two studies examined the effects of chronic ethanol exposure prior to training with or without acute challenges [ 69 , 70 ]. Matthews et al. [ 70 ] looked at the effect of 20 days binge-like (every other day) pre-exposure and found no effect on spatial learning in either age following an extended abstinence period (i.e., 6–8 weeks). Swartzwelder et al. [ 69 ] examined effects of 5-day ethanol pre-exposure with and without ethanol challenges before MWM training. Ethanol challenges (2.0 g/kg) impaired learning in both age groups regardless of pre-exposure history. Thigmotaxis was also increased in both age groups after acute challenges while pre-exposure increased it in adults only.

In sum, evidence for impaired spatial learning and memory after acute challenges is mixed across six studies. Two studies found support for ethanol having a larger impact in adolescents compared to adults, whereas one study found the opposite and three studies did not observe any differences. Differences in ethanol doses stress responses may partially explain the discrepancies across studies. Importantly, given the sparsity of studies addressing the effects of long-term and voluntary ethanol exposure, no conclusion can be drawn about the impact of age on the relation between chronic alcohol exposure and spatial learning and memory.

Non-spatial learning and memory: Non-spatial learning can also be assessed in the MWM and SBM by marking the target location with a pole and moving it across trials, measuring time and distances traveled to locate the target. By assessing non-spatial learning as well, studies can determine whether learning is more generally impaired by ethanol or whether it is specific to hippocampal-dependent spatial learning processes. A total of six studies assessed facets of non-spatial learning and memory. Two of the six studies observed age-related differences.

In the four studies that examined non-spatial memory using the MWM or SBM in rats, none found an effect of alcohol regardless of dose, duration, or abstinence period in either age group [ 30 , 66 , 67 , 70 ]. Two other studies examined other facets of non-spatial memory in rats [ 65 , 71 ]. Galaj et al. [ 71 ] used an incentive learning paradigm to examine conditioned reward responses and approach behavior towards alcohol after chronic intermittent ethanol (CIE; 4 g/kg; 3d on, 2d off) exposure to mimic binge drinking. To examine reward-related learning and approach behavior, a CS (light) was paired with food pellets and approach behavior to CS only presentation and responses to a lever producing the CS were measured. In both adolescents and adults, the ethanol-exposed rats showed impaired reward-related learning after both short (2d) and extended (21d) abstinence. No effect of alcohol on conditioned approach behavior was observed in either age group during acute (2d) or extended (21d) abstinence. Using a novel object recognition test in mice, Lacaille et al. [ 65 ] assessed non-spatial recognition memory by replacing a familiar object with a novel object in the testing environment. Explorative behavior of the new object was used as an index of recognition. After chronic binge-like exposure (three injections daily at 2 h intervals) and limited abstinence (4d), only adolescents showed reduced object recognition.

Across facets of non-spatial memory, there is little evidence for age-related differences in the effect of chronic alcohol, with four of the six studies finding no age differences. For memory of visually cued target locations in the MWM and SBM paradigms, alcohol does not alter performance in either age. Also, both adolescents and adults appear similarly vulnerable to alcohol-induced impairments in reward-related learning based on the one study. Only in the domain of object memory did any age-related differences emerge, with adolescents and not adults showing reduced novel object recognition after binge-like alcohol exposure in one study. However, more research into object recognition memory and reward-related learning and memory is needed to draw strong conclusions in these domains.

Executive function and higher-order cognition

Executive functions are a domain of cognitive processes underlying higher-order cognitive functions such as goal-directed behavior. Executive functions can include but are not limited to working memory, attentional processes, cognitive flexibility, and impulse control or inhibition [ 72 ]. A core feature of AUD is the transition from goal-directed alcohol use to habitual, uncontrolled alcohol use. Impaired executive functioning, linked to PFC dysfunction [ 73 ], is assumed to be both a risk factor and consequence of chronic alcohol use. A meta-analysis of 62 studies highlighted widespread impairments in executive functioning in individuals with AUD that persisted even after 1-year of abstinence [ 46 ]. Thirteen studies examined facets of executive functioning and higher-order cognition, specifically in the domains of working memory, attentional processes, cognitive flexibility, impulsivity in decision-making, and goal-directed behavior [ 65 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 ].

Working memory: Working memory refers to the limited capacity system for temporarily storing and manipulating information, which is necessary for reasoning and decision-making [ 84 ]. In the Radial Arm Maze test (RAM) [ 85 ], some of the equally spaced arms (typically eight) around a circular platform contain a food reward for animals to find. Spatial working memory is measured by recording the number of revisits to previously visited arms (i.e., working memory error) and first entries into unbaited arms (i.e., reference memory). Alternatively, the hippocampus mediated [ 86 ] spontaneous tendency to alternate arms can be used as a measure of spatial working memory. In this case, revisiting an arm in back-to-back trials in close temporal succession is interpreted as a working memory error. Five studies examined the effects of chronic ethanol exposure on spatial working memory [ 65 , 75 , 79 , 80 , 83 ]. One of the five studies observed age-related differences.

Chronic binge-like alcohol exposure had no effects on spontaneous alterations after prolonged abstinence (2d on, 2d off; 3 weeks abstinence) [ 79 , 80 ] in rats or limited abstinence (three injections daily at 2 h intervals; 24 h abstinence) [ 65 ] in mice, nor on RAM performance in rats (2d on, 2d off) [ 75 , 83 ]. However, acute ethanol challenges (1.5 g/kg) after chronic binge-like exposure (2d on, 2d off) resulted in RAM test impairments in both age groups in rats [ 75 , 83 ], with some evidence for increased working memory errors in adolescents [ 83 ].

In sum, there is little evidence for impairments in working memory function in rats after chronic ethanol exposure, with four of the five studies observing no difference between age groups. While acute intoxication impairs working memory function in both ages, there is evidence from only one study that adolescents may make more working memory errors.

Attentional processes: Attentional processing refers to the selection of information that gains access to working memory [ 87 ]. PPI is a pre-attentional cognitive function which provides an index of sensorimotor gating and measures the ability of a lower intensity sensory stimulus to reduce the magnitude of response to a more intense stimulus presented closely afterward. Reduced sensorimotor gating (reduced PPI) can disrupt information processing and thereby impair cognitive function, while enhanced sensorimotor gating (enhanced PPI) may reflect behavioral inflexibility [ 88 ]. For example, lesions in the medial PFC produce both behavioral inflexibility and enhancements in PPI in rats. Two studies assessed attentional processes by measuring prepulse inhibition (PPI) in rats [ 82 , 89 ]. One study observed age-related differences and one did not.

Slawecki and Ehlers [ 82 ] observed age-related differences in sensorimotor gating following ethanol vapor exposure (2w) and brief abstinence (6d), with adolescents showing enhanced PPI at some decibels reflective of behavioral inflexibility, while adults did not exhibit PPI at any of the intensities tested. Slawecki et al. [ 89 ] did not observe any age-related differences in PPI during the acute phase of ethanol withdrawal (7–10 h abstinence) during a period of chronic ethanol exposure (14d).

In sum, there is limited and mixed evidence from two studies of age-related differences in the pre-attentional process of sensorimotor gating. Only one study found support for adolescent sensitivity to ethanol effects.

Cognitive flexibility: Cognitive flexibility refers to the ability to update information based on environmental factors r changing goals in order to adaptively guide decision-making and is linked to the inability to reduce or abstain from drinking [ 90 ]. Three studies examined facets of cognitive and behavioral flexibility [ 79 , 80 , 81 ]. Two of the three studies observed age-related differences.

In two rat studies, cognitive flexibility was assessed using reversal learning paradigms [ 79 , 80 ]. In the reversal learning paradigm, rats were trained on simple (e.g., visual cue) and more complex discriminations (e.g., visual + scent cue) between rewarded and non-rewarded bowls. After learning the discriminants, the rewards were reversed. Ethanol exposure reduced flexibility in both adolescents and adults for simple discriminations in both studies. Age-related differences emerged for the more complex discriminations in one study, with only adults showing reduced flexibility after prolonged abstinence (21d) following binge-like exposure (5 g/kg, 2d on, 2d off) [ 79 ]. In contrast, both age groups showed reduced flexibility for complex discrimination in the other study after prolonged abstinence (21d) despite adolescents consuming more ethanol orally than adults during the 28 week exposure [ 80 ].

In another study, Labots et al. [ 81 ] used a conditioned suppression of alcohol-seeking task after two months of voluntary ethanol consumption (2 months) in rats to examine flexibility around alcohol-seeking behavior. After stratifying the age groups based on levels of ethanol consumption, medium- and high-consuming, adolescents showed higher levels of conditioned suppression compared to similarly drinking adults, indicating greater behavioral flexibility and control over alcohol-seeking in adolescents after chronic voluntary exposure.

Overall, there is limited evidence for adolescent resilience to the effects of chronic alcohol on cognitive flexibility. Two studies found support for adolescent resilience to ethanol’s effect on behavioral flexibility, whereas another study found no differences between adolescents and adults.

Impulsivity: Impulsivity is a multi-faceted behavioral trait that encompasses impaired response inhibition, preference for an immediate reward over a larger but delayed reward, and premature expression of behaviors which may be maladaptive or in conflict with conscious goals. Impulsivity is a risk-factor for the development of addiction and may also be a consequence of sustained substance use [ 35 ]. Pharmacological evidence points towards overlapping neuronal mechanisms in impulsivity and addictive behavior, particularly within the mesolimbic dopamine system [ 91 ]. Two studies examined impulsive decision-making behavior in rats [ 74 , 78 ]. Both studies observed age-related differences.

One study examined impulsive behavior using a delay-discounting task in which choices are made between immediate small rewards and larger delayed rewards [ 78 ]. Regardless of age, chronic intermittent exposure (2d on, 2d off) had no effect on choice behavior in non-intoxicated rats. Following acute challenges, adolescents but not adults demonstrated a reduced preference for the large reward regardless of ethanol exposure history, reflecting a general adolescent-specific heightened impulsivity during intoxication. Another study examined decision-making under risk conditions using an instrumental training and probability-discounting task [ 74 ]. After prolonged abstinence (20d), rats were trained to press two levers for sucrose rewards and were concurrently trained to choose between two levers with different associated probabilities of reward and reward size, creating a choice between a certain, small reward and an uncertain, large reward (i.e., riskier choice). Ethanol consumption was voluntary and while adolescents initially consumed more ethanol than adults at the beginning of the exposure period, the total amount of consumption was similar by the end of the exposure period. Only adolescents showed increased risky and sub-optimal decision-making compared to age-matched controls, while adults performed similarly to controls.

In sum, both studies found support for ethanol having a larger impact on adolescent compared to adults on impulsive behavior.

Goal-directed behavior: Goal-directed behavior refers to when actions are sensitive to both the outcome value (goal) and contingency between the behavior and the outcome [ 92 ]. Two studies used a sign-tracking and omission contingency learning paradigm to examine goal-directed versus habitual behavior [ 76 , 77 ]. One study observed age-related differences and the other did not. Sign tracking refers to tasks where a cue predicts a reward, but no response is needed for the reward to be delivered. Despite this, after repeated pairings of the cue and reward, animals and humans may respond (e.g., via a lever) when the cue is presented anyway, and even when no reward is known to be available. Sign-directed behavior is considered habitual and has been proposed to underlie the lack of control of alcohol use in addiction [ 93 ]. In humans, sign-tracking behavior is difficult to differentiate from goal-directed behavior based on only the observable behavior, i.e., seeing a cue such as a favorite drink or bar and then having a drink [ 94 ]. In the context of alcohol use, reflexively having a drink when seeing an item that is often associated with the rewarding effects of alcohol (e.g., wine glass, bar, smell of alcohol) despite not consciously desiring the alcohol ‘reward’ is an example of how habitual behavior (possibly driven by sign-tracking) can initiate the behavior as opposed to an intentional goal [ 93 ]. Omission contingency refers to a 2nd phase after sign-tracking when the response is punished and the behavior must be inhibited to avoid punishment. After both forced and voluntary ethanol exposure (6w), no alterations to sign-tracking behavior were observed in adolescent and adult rats [ 76 , 77 ]. One study did observe an age-related difference in omission contingency learning, with adolescents performing better than adults after chronic voluntary ethanol exposure [ 77 ]. This preliminarily suggests that adolescents may be more capable of adapting their behavior to avoid punishment compared to adults after chronic use. However, before behavioral testing began, adolescent rats were abstinent for 17 days, while adults were only abstinence for 10 days which may have influenced the results.

In summary, one study found support for adolescents being less sensitive to ethanol effects on goal-directed behavior compared to adults, whereas one study found no effect of ethanol in either age group.

Across the domains of executive function, there is some evidence that adolescents may be more vulnerable to impairments in certain executive and higher-order cognitive functions following chronic alcohol exposure, with increased risky decision-making after prolonged abstinence [ 74 ], impulsivity during intoxication [ 78 ], and reduced working memory function during intoxication after chronic exposure. In contrast, animals exposed to alcohol during adolescence may better retain cognitive flexibility [ 77 , 79 ] and are better able to regain control over alcohol-seeking in adulthood [ 81 ].

Other behavioral outcomes

Anxiety : AUD is highly comorbid with anxiety disorders [ 95 ], especially in adolescence [ 96 ]. While anxiety is not strictly a cognitive outcome, it is related to altered cognitive functioning [ 97 , 98 ]. Many studies assessing the effects of ethanol on the rodent brain and cognition also include anxiety-related measures. Multiple paradigms have been developed to elicit behaviors thought to reflect anxiety in rodents (e.g., rearing, startle, avoidance, etc.). In the open field test (OFT), anxiety is indexed as the tendency to stay close to perimeter walls as animals have a natural aversion to brightly lit open spaces [ 99 ]. In the elevated plus maze paradigm, rodents are placed at the center of an elevated four-arm maze with two open arms two closed arms [ 100 ]. The open arms elicit unconditioned fear of heights/open spaces and the closed arms elicit the proclivity for enclosed, dark spaces. Anxiety is indexed as entries/duration of time in open vs. closed arms, as well as rearing, freezing, or other postural indices of anxiety. In startle paradigms, the startle response is a defensive mechanism reflecting anxiety which follows a sudden, unpredictable stimulus (e.g., tones, light) [ 101 ]. In light-dark box paradigms, anxiety is elicited using a testing apparatus with a light and dark compartment, relying on the conflict between natural aversions to well-lit spaces and the tendency to explore new areas. Percentage of time spent in the light compartment, latency to return to the dark compartment, movement between compartments (transitions), and rearing-behavior are measured as indices of anxiety [ 102 ]. Anxiety can also be assessed using a social interaction test with an unfamiliar partner, with approach and avoidance behaviors measured to index anxiety [ 103 ]. In the novel object test (NOT) [ 104 ], anxiety is elicited by the introduction of a new object in the rodent’s environment. The amount of contacts and time spent in contact with the object is used as an index of anxiety. Similarly, in the marble-burying test (MBT), novel marbles are placed in an environment and the amount of defensive burying of the objects is used as an index of anxiety [ 105 ].

Eleven studies examined anxiety-like behavior in rodents with mixed results across paradigms [ 70 , 78 , 82 , 83 , 89 , 106 , 107 , 108 , 109 , 110 , 111 ]. Overall, five of the eleven studies observed age-related differences.

Two studies used the OFT, finding no effects of voluntary (2w, 4 h/day access) or forced (12/day vapor) ethanol exposure on anxiety-like behavior in adolescents or adult rats during withdrawal (7–9 h) [ 110 ] or after a brief abstinence period (4 days) [ 107 ]. One study used both the MBT and NOT after voluntary ethanol consumption (2 h/d for 2 weeks; no abstinence) and observed higher anxiety in ethanol-exposed adults and reduced anxiety in ethanol-exposed adolescents compared to controls as indexed by marble burying [ 106 ]. However, no age effects were observed in response to a novel object, with reduced interaction with the novel object in both age groups after chronic exposure.

Four studies used the elevated maze paradigm with mixed results. Only one study observed age-related differences in mice after chronic exposure (8–10w vapor) [ 109 ]. Adolescents showed reduced anxiety compared to adults during the acute withdrawal period, but all mice were kept under chronic social isolation and unpredictable stress conditions, which may have affected the results. Two studies in rats found no effect of intermittent (1 g/kg) or binge-like (5 g/kg) exposure in either age group after short (24 h) [ 70 ] or sustained abstinence (20d) [ 83 ]. A third study observed heightened anxiety in both age groups after intermittent exposure (4 g/kg), with anxiety increasing with prolonged abstinence periods (24 h to 12d) [ 108 ].

Three rat studies used a startle paradigm to assess anxiety. Two observed reduced acoustic startle responses after ethanol exposure (12 h/d vapor) in both age groups during acute withdrawal periods (7–10 h) and following more sustained abstinence (6d) [ 82 , 89 ]. In the other study, light-potentiated startle was also reduced in both ages during days 1–10 of withdrawal after binge-like exposure (2d on, 2d off), but age-related differences emerged when the rats were re-exposed via a 4-day binge (1–4/kg). Then, only adults showed higher levels of light-potentiated startle compared to controls [ 78 ], suggesting that ethanol pre-exposure increases anxiety in adults but not adolescents when re-exposed to ethanol after withdrawal.

Two studies used the light-dark box paradigm with mixed results [ 89 , 111 ]. Only adult rats showed increased mild anxiety-like behaviors during early withdrawal (7–10 h) after chronic vapor exposure 12 h/d) [ 89 ]. In contrast, no age-related differences emerged after voluntary ethanol consumption (18 h/d access; 3d/w for 6 weeks), with male mice showing less anxiety-like behavior in both ages [ 111 ]. In contrast, the one study using the social interaction test observed reduced anxiety in adult mice compared to both adolescents and age-matched controls during early withdrawal (4–6 h) after chronic, unpredictable vapor exposure [ 109 ].

In summary, there is inconsistent evidence for age-related differences in the effect of chronic ethanol exposure on anxiety outcomes in rodents. The substantial differences across studies in how anxiety was elicited and measured make it challenging to draw strong conclusions. In the five studies that found age-related differences, adults tend to show higher levels of anxiety, particularly during early withdrawal; however, the opposite was found in the one study examining anxiety in social interactions. Six studies did not observe any age-related differences. Overall, adolescents may be less sensitive to the anxiety-inducing effects of chronic alcohol exposure.

Social behavior: Two studies were identified that examined the effects of chronic ethanol exposure on social behavior in rats [ 112 , 113 ], with both observing age-related differences. After chronic exposure (1 g/kg, 7d), followed by a brief abstinence period (24–48 h), one study found a decrease in social preference in adolescents only [ 112 ], while the other study found no ethanol-related effects on social behavior (2 g/kg, 10d) [ 113 ]. After acute challenges, age and treatment interactions emerged in both studies, but the directions of the results are inconsistent. In the first study, adolescents showed increased social preference, as indexed by the number of cross-overs between compartments toward and away from a peer, across multiple acute doses (0.5–1.0 g/kg) administered immediately before testing, while adults showed no changes in social preference [ 112 ]. In contrast, Morales et al. [ 113 ] found evidence for age-related temporal differences in social activity after acute challenge, with adults showing decreased social impairment five minutes post injection (1 g/kg) and adolescents (1.25 g/kg) after 25 min compared to age-matched controls.

The findings from these two studies paint a complicated and inconsistent picture of the effects of ethanol on social behavior in adults and adolescents warranting further research. One study found support for a larger effect of chronic ethanol on adolescent social behavior compared to adults, while the other did not observe effects of ethanol in either group. One study found support for a larger effect of chronic plus acute ethanol intoxication on social behavior, with the opposite observed in the other.

Brain outcomes

Neurotransmitter systems.

Glutamate is the brain’s main excitatory neurotransmitter and plays a crucial role in synaptic plasticity (i.e., experience-related strengthening or weakening of synaptic connections). Glutamatergic transmission plays an important role in the formation and maintenance of addictive behaviors and the nucleus accumbens (NAc) is considered an important hub in this, receiving glutamatergic input from cortical-limbic areas and dopaminergic input from the midbrain [ 114 ]. Seven studies investigated glutamate functioning in regions of the brain [ 106 , 107 , 108 , 109 , 115 , 116 , 117 , 118 ]. Four of the seven studies observed age-related differences.

Three studies investigated glutamate-related processes in the NAc [ 106 , 107 , 118 ]. Two weeks of voluntary binge drinking (4-h access, no abstinence) did not affect expression of calcium-dependent kinase II alpha (CaMKIIα) and the AMPA receptor GluA1 subunit in the NAc of mice [ 107 ]. In contrast, Lee et al. [ 106 ] showed that voluntary binge drinking (2-h access, no abstinence) increased mGlu1, mGlu5, and GluN2b expression in the shell of the NAc, as well as PKCε and CAMKII in the core of the NAc in adult mice only. In rats, Pascual et al. [ 118 ] showed reduced NR2B phosphorylation in the NAc of adolescents only after two weeks of chronic intermittent ethanol exposure; an effect that also lasted until 24 h after end of exposure. This indicates that adolescents might be less affected by the effects of ethanol on NAc-related glutamatergic neurotransmission than adults. This may in turn mediate decreased withdrawal symptoms and potentially facilitate increased drinking [ 106 ].

Two studies investigated glutamate-related processes in the (basolateral) amygdala [ 107 , 116 ]. In mice, Agoglia et al. [ 107 ] showed decreased CaMKIIα phosphorylation in adolescents, but increased GluA1 expression in adults after two weeks of voluntary binge drinking (4-h access, no abstinence). Also, drug-induced AMPAR activation resulted in increased binge drinking in adolescents but decreased binge drinking in adults, highlighting the potential importance of glutamatergic signaling in age-related differences in alcohol consumption. However, Falco et al. [ 116 ] reported no difference in NR2A mRNA levels in the basolateral amygdala for either age group after 60-day abstinence.

Alcohol’s effects on frontal cortex functioning is thought to be mediated by alterations in NMDA receptor subunit expression [ 119 , 120 ]. Two studies investigated glutamate-related processes in the frontal cortex of rats [ 115 , 118 ]. Pascual et al. [ 118 ] showed reduced NR2B phosphorylation after two weeks of forced intermittent ethanol exposure in adolescents only. Using a 2-week ethanol vapor paradigm, Pian et al. [ 115 ] found different patterns of NMDAR subunit expression. These patterns were highly dependent on abstinence duration (0 h, 24 h, 2w), however, they only statistically compared results within rather than between age groups. Ethanol exposure was associated with decreased NR1 receptor expression in both age groups, but only the adult group showed a decrease in NR2A and NR2B expression. The NR1 and NR2A expression returned to normal during withdrawal, but in adults NR2B expression increased after two weeks of abstinence.

Conrad and Winder [ 109 ] assessed long-term potentiation (LTP) in the bed nucleus stria terminalis (BNST), a major output pathway of the amygdala towards the hypothalamus and thalamus. Voluntary ethanol exposure resulted in blunted LTP responses in the dorsolateral BNST regardless of age. However, all mice were socially isolated during the experiments to induce anxiety, so it is unclear whether the effects were solely due to ethanol exposure.

Two studies looked at glutamate receptor subunit expression in the hippocampus [ 108 , 115 ]. Pian et al. [ 115 ] observed increased expression of NR1, NR2A, and NR2B in adults after 2 weeks of ethanol exposure. In adolescents, a reduction in NR2A expression was observed. After abstinence, adult levels returned to normal, while in adolescents, decreased NR1 and NR2A expression was seen after 24 h but an increased expression of these subunits was seen after 2 weeks of abstinence. These findings support regional specific effects of age group, with potentially increased sensitivity to the impact of alcohol on glutamatergic mediated hippocampal functioning in adolescents. Unlike expected, van Skike et al. [ 108 ] did not find effects of chronic intermittent ethanol exposure or withdrawal on NMDA receptor subunit expression in the hippocampus and cortex as a whole in adolescent and adult rats. The authors speculate that these null results might be associated with the exposure design (limited exposure and route of administration) and lack of withdrawal periods compared to Pian et al. [ 115 ].

In sum, there is limited and inconsistent evidence for age-related differences in glutamate function across seven studies. The direction of the observed age-related differences varies across regions, with evidence of both increased and decreased sensitivity to ethanol effects in adolescents compared to adults in the four studies that observed age-related differences.

GABA is the brain’s main inhibitory neurotransmitter. GABA A receptors are a primary mediator of alcohol’s pharmacological effects [ 121 ]. A total of four studies looked at GABAergic functioning [ 108 , 116 , 122 , 123 ]. Three of the four studies observed age-related differences.

One study investigated GABA-related processes in the (basolateral) amygdala, showing reduced GABA A α1 and GAD67 (enzyme that converts Glutamate to GABA) mRNA expression in adult rats only, 60 days after 18-days ethanol exposure [ 116 ].

Two studies looked at the rat cortex as a whole [ 108 , 122 ]. Van Skike et al. did not find effects of chronic intermittent ethanol exposure on GABA A receptor expression [ 108 ]. Grobin et al. [ 122 ] showed that, while basal GABA A receptor functioning was not affected by 1 month of chronic intermittent ethanol exposure, GABA A receptors were less sensitive to the neurosteroid THDOC in adolescents. This neuromodulatory effect was not found in adults and did not persist after 33 days of abstinence. However, these results indicate that neurosteroids may play an indirect role in age differences in the GABAA receptor’s response to alcohol.

Two studies focused on the rat hippocampus [ 108 , 124 ]. Fleming et al. [ 124 ] found age-specific effects of chronic intermittent ethanol exposure on hippocampal (dentate gyrus) GABA A receptor functioning. Adolescent rats showed decreased tonic inhibitory current amplitudes after ethanol exposure, which was not the case for young adult and adult rats. Also, only the adolescents showed greater sensitivity to (ex vivo) acute ethanol exposure induced enhanced GABAergic tonic currents. The specificity of these effects to adolescent exposure might indicate adolescent vulnerability to ethanol-induced effects on the hippocampus; however, Van Skike et al. [ 108 ] did not find any effects of chronic intermittent ethanol exposure on GABA A receptor expression in the hippocampus.

In sum, given the limited number of studies and lack of replicated effects, no clear conclusions can be drawn about the role of age on the effects of alcohol on GABAergic neurotransmission. Age-specific effects appear to be regionally distinct. The only available study found support for heightened adult sensitivity to ethanol in the amygdala. In contrast, one study found support for greater adolescent sensitivity in the hippocampus and whole cortex, whereas the other found no age-related differences.

The mesocorticolimbic dopamine system, with dopaminergic neurons in the ventral tegmental area (VTA) projecting to the NAc and prefrontal cortex, plays a key role in AUD, particularly through reward and motivational processes [ 14 ]. Only two studies investigated dopaminergic processes, focusing on the frontal cortex, NAc, and broader striatum [ 118 , 125 ]. Both studies observed age-related differences in certain dopamine outcomes.

Carrara-Nascimento et al. [ 125 ] investigated acute effects of ethanol in adolescent and adult mice 5 days after a 15-day treatment with either ethanol or saline. In the PFC, ethanol pretreated adolescents showed reduced dopamine levels (DA) and related metabolites (DOPAC and HVA) in response to an acute ethanol challenge compared to ethanol pretreated adults and adolescent saline controls. In the NAc, there were no differences between pretreated adolescents and adults, but analyses within each age group revealed that ethanol-pretreatment with an acute challenge decreased DOPAC within the adolescent group. Results from the dorsal striatum also showed no differences between adolescents and adults. However, within the adolescent group, ethanol pre-treatment increased DOPAC and, within the adult group, it increased HVA. Pascual et al. [ 118 ] found similar results looking at the expression of DRD1 and DRD2 dopamine receptors after two weeks of chronic intermittent ethanol exposure in rats. In the NAc and dorsal striatum, DRD2 expression was reduced in adolescent compared to adult exposed rats, while both DRD1 and DRD2 expression were reduced in the frontal cortex.

These results suggest reduced alcohol-induced dopamine reactivity in adolescents in the PFC and NAc based on the two available studies, but more studies are warranted for a more detailed understanding of the relationship between age and dopamine receptor expression following chronic ethanol exposure.

Acetylcholine

Acetylcholine is a known neuromodulator of reward and cognition-related processes [ 126 ]. The composition and expression of nicotinic and muscarinic acetylcholine receptors have been implicated in various alcohol use-related behaviors [ 127 , 128 ]. Only one study investigated cholinergic processes and observed age-related differences. Vetreno et al. [ 129 ] showed global reductions in choline acetyltransferase (ChAT; cholinergic cell marker) expression after adolescent onset, but not adult onset of forced intermittent binge-like exposure (20 days – every other day, 25 days abstinence).

Neuromodulatory processes

Neurodegeneration and neurodevelopment.

Chronic alcohol consumption is thought to lead to brain damage by influencing processes involved in neurodegeneration and neurogenesis. The formation of addictive behaviors is paralleled by the formation of new axons and dendrites, strengthening specific neuronal pathways [ 130 ]. While brain morphology is commonly investigated in humans, it is a proxy of the impact of alcohol on the brain and therefore rarely studied in rodents. Five studies investigated facets of neurodegeneration or development in rodents [ 55 , 65 , 131 , 132 , 133 ]. All five studies observed age-related differences.

Huang et al. [ 131 ] showed reduced cerebral cortex mass in adolescent mice, but shortening of the corpus collosum in adults after 45 days of ethanol injections, suggesting some age-specific regional effects. Using an amino cupric silver staining, significant brain damage was revealed for both adolescent and adult rats after 4 days of binge-like ethanol exposure [ 132 ]. However, adolescents showed more damage in the olfactory-frontal cortex, perirhinal cortex, and piriform cortex.

Looking at hippocampal neurogenesis, ethanol exposure has been shown to initially reduce hippocampal neurogenesis in adult rodents, recovering after 1-month abstinence [ 134 ]. Compared to adults, neurogenesis in the dentate gyrus of the hippocampus was found to be reduced in adolescent exposed mice (Bromodeoxyuridine levels) [ 65 ] and rats (doublecortin levels) [ 133 ]. Lacaille et al. [ 65 ] also measured the expression level of genes involved in oxidative mechanisms after binge-like alcohol exposure. In whole brain samples, they found increased expression of genes involved in brain protection (i.e., gpx3, srxn1) in adults, but increased expression of genes involved in cell death (i.e., casp3) combined with decreased expression of genes involved in brain protection (i.e., gpx7, nudt15) in adolescents. Casp3 protein levels were also higher in the whole brain of adolescent exposed mice [ 65 ] and the adolescent dentate gyrus [ 133 ], suggesting more neurodegeneration and less neurogenesis in adolescents versus adults following ethanol consumption.

Cyclin-dependent kinase 5 (CDK5) is involved in axon, dendrite, and synapse formation and regulation. CDK5 is overexpressed in the prefrontal cortex and the NAc following exposure to substances of abuse including alcohol [ 135 ]. Moreover, CDK5 inhibition has been shown to reduce operant self-administration of alcohol in alcohol-dependent rats [ 136 ]. One study reported higher H4 acetylation of the CDK5 promoter in the PFC of adult versus adolescent ethanol-exposed rats during acute withdrawal, however, CDK5 mRNA expression was control-like after 2 weeks of abstinence [ 55 ].

In sum, strong conclusions cannot be drawn due to the limited number of studies and lack of replicated effects. However, preliminary evidence points to adolescent vulnerability to damage in the cortex, reduced neurogenesis, and increased neurodegeneration in the hippocampus and the cortex as a whole based on four of the five studies. In contrast, one study found support for adult vulnerability to ethanol’s effects axon, dendrite, and synapse formation and regulation.

Growth factors

Brain-derived neurotrophic factor (BNDF) and nerve growth factor (NGF) are involved in brain homeostasis and neural recovery [ 137 , 138 ]. While ethanol exposure initially increases BDNF and NGF, chronic ethanol exposure seems to reduce BDNF and NGF levels and can thereby result in long-term brain damage and related cognitive problems [ 139 , 140 ]. Four studies investigated growth factor expression in the frontal cortex [ 54 , 55 , 79 , 80 ] and two studies also investigated the hippocampus [ 79 , 80 ]. All four studies of the frontal cortex observed age-related differences. Neither study of the hippocampus observed age-related differences.

In rats, 30 weeks of chronic ethanol exposure reduced prefrontal mBDNF and β-NGF regardless of age, despite adolescents consuming more ethanol [ 80 ]. Moreover, the reduction of mBDNF was correlated with higher blood alcohol levels and was persistent up to 6–8 weeks abstinence. Interestingly, during acute withdrawal (48 h) adolescents but not adults temporarily showed control-like mBDNF levels. This might indicate an attempt to counteract neurodegeneration as a result of ethanol exposure in adolescents. These results were partially replicated using a shorter intermittent exposure paradigm (13 doses, 2 days on/off) [ 79 ]. While intoxication after chronic ethanol exposure reduced prefrontal BDNF, levels recovered after 3-weeks abstinence regardless of age. However, during acute withdrawal (24 h), BDNF was still reduced in early-adolescent onset rats, increased in adult-onset rats, but control-like in mid-adolescent onset-rats, suggesting slower recovery in younger animals. Looking at BDNF gene regulation, a similar study (8 doses, 2 days on/off) reported higher H3 demethylation but lower H4 acetylation of the BDNF promoter in the PFC of adult versus adolescent ethanol-exposed rats during acute withdrawal [ 55 ]. However, prefrontal BDNF mRNA expression returned to control levels after 2 weeks of abstinence. Interestingly, social housing may be protective, as reduced prefrontal BDNF was no longer observed in alcohol-exposed adolescent mice housed in environmentally enriched relative to standard conditions [ 54 ]. Two studies investigated hippocampal BDNF expression but reported no significant interactions between alcohol exposure and age group [ 79 , 80 ].

In sum, the results of the four available studies suggest lower prefrontal BDNF during chronic alcohol use that recovers after abstinence regardless of age. However, the rate of recovery may be influenced by age with slower recovery in adolescents. In the two available studies, no age-related differences were observed in BDNF expression in the hippocampus.

Transcription factors

The transcription factors cFos and FosB are transiently upregulated in response to substance use, and ΔFosB accumulates after chronic exposure, particularly in striatal and other reward-related areas [ 141 ]. Two studies investigated cFos and FosB [ 55 , 142 ] and one study ΔFosB related processes [ 111 ]. All three studies observed age-related differences.

After chronic ethanol exposure (8 doses, 2 days on/off), adolescent compared to adult rats showed increased prefrontal H3 and H4 acetylation of the cFos promotor region and increased H4 acetylation and H3 dimethylation of FosB promotor regions after acute abstinence [ 55 ]. Moreover, mRNA expression of FosB was elevated in adolescents but not adults after 2-weeks abstinence. The upregulating effects of an acute ethanol challenge on prefrontal cFos appears to reduce after chronic pre-treatment to a larger extent in adolescent than adult exposed mice [ 142 ]. This pattern of results was similar in the NAc, but desensitization to ethanol’s acute effects on cFos in the hippocampus was more pronounced in adults. Faria et al. [ 142 ] also looked at Egr-1 (transcription factor, indirect marker of neuronal activity and involved in neuroplasticity), showing a stronger reduction in Egr-1 expression in the PFC, NAc, and hippocampus of adolescent versus adults after repeated ethanol exposure. Regarding ∆FosB, Wille-Bille et al. [ 111 ] found increased ∆FosB in adolescent compared to adult rats in the prelimbic PFC, dorsomedial striatum, NAc core and shell, central amygdala nucleus capsular, and basolateral amygdala after 3 days per week 18 h ethanol exposure sessions for 6 weeks. In sum, the three available studies provide preliminary evidence for increased adolescent vulnerability to ethanol-induced long-term genetic (mRNA expression) and epigenetic (methylation) changes in mesocorticolimbic areas.

Immune factors

Ethanol is known to trigger immune responses in the brain (e.g., increase production of hemokines and cytokines), causing inflammation and oxidative stress [ 143 , 144 , 145 ]. Three studies examined immune factors [ 146 , 147 , 148 ]. Two of the three studies observed age-related differences.

Microglia remove damaged brain tissue and infectious agents and are key to the brain’s immune defense. Only one study investigated microglia levels [ 146 ]. Although direct comparisons between age groups were missing, both adolescent and adult rats showed less microglia in the hippocampus (CA and DG) and peri-entorhinal cortex, and more dysmorphic microglia in the hippocampus after 2 and 4 days of binge-like ethanol exposure [ 146 ]. Notably, age groups were matched on intoxication scores, with adolescents needing more ethanol to reach the same level of intoxication. An in silico transcriptome analysis of brain samples from mice after 4 days of 4 h/day drinking in the dark, suggest overexpression of neuroimmune pathways related to microglia action (toll-like receptor signaling, MAPK signaling, Jak-STAT signaling, T-cell signaling, and chemokine signaling) in adults that was not observed in adolescents, while adolescents consumed more ethanol [ 147 ]. Similarly, ethanol-exposed adult mice showed higher chemokine expression (CCL2/MCP-1) in the hippocampus, cerebral cortex, and cerebellum and higher cytokine expression (IL-6, but not TNF-α) in the cerebellum, while no chemokine or cytokine changes were observed in ethanol exposed adolescent mice [ 148 ]. Both adolescents and adults showed increased astrocyte levels in the hippocampus (CA1) and the cerebellum after ethanol exposure, but changes in astrocyte morphology were only observed in the adult hippocampus.

In sum, two of the studies found support for increased immune responses after ethanol exposure in adults compared to adolescents, whereas the one other study found no difference between the age groups.

HPA-axis functionality

Chronic stress and HPA-axis functionality have been associated with the maintenance of AUD (e.g., reinstatement drug seeking, withdrawal) [ 149 ]. Two studies investigated corticotropin-release factor (CRF) expression in rats [ 116 , 150 ]. One study observed age-related differences and the other did not.

Falco et al. [ 116 ] found decreased CRF mRNA expression in the adult but not adolescent basolateral amygdala 2 months after 18-day restricted ethanol exposure. In contrast, Slawecki et al. did not find any interaction between age and treatment on CRF levels in the amygdala, as well as the frontal lobe, hippocampus, hypothalamus, and caudate 7 weeks after 10-days of ethanol vapor exposure.

No conclusions can be drawn. One study observed found support for reduced effects of ethanol on HPA-axis functionality compared to adults, whereas the other observed no difference between the age groups. Future studies using different (voluntary) exposure paradigms are needed to further investigate the effects of alcohol on HPA activity in relation to age of alcohol exposure.

Neuropeptides

Neuropeptides are a diverse class of proteins that have a modulatory function in many different processes, including but not limited to neurotransmission, stress, immune responses, homeostasis, and pain [ 151 , 152 , 153 ]. Only one study investigated neuropeptides in rats and observed age-related differences [ 150 ].

Slawecki et al. [ 150 ] specifically investigated neuropeptide-Y, substance-P, and interleukine expression in the frontal lobe, hippocampus, hypothalamus, dorsal striatum, and amygdala 7 weeks after 10-days of ethanol vapor exposure in rats [ 150 ]. Interactions between age and treatment were found for the hippocampus and caudate only. Ethanol-induced reductions in hippocampal neuropeptide-Y and increases in caudate neurokinine were more pronounced in adults compared to adolescents suggesting long-lasting effects of ethanol in adults but not adolescents.

Ethanol metabolism

The first metabolite of ethanol is acetaldehyde, which has been theorized to mediate the effects of ethanol on both brain and behavior [ 154 ]. Only one study investigated ethanol metabolism in the brain and did not observe age-related differences [ 155 ].

Rhoads et al. showed that despite the fact that adolescent rats consumed more alcohol brain catalase levels after 3-weeks of ethanol exposure (no abstinence) did not differ between adolescents and adults [ 155 ]. Although the general role of catalase in ethanol metabolism is small, catalase can oxidize ethanol to acetaldehyde in the brain, affecting elimination of ethanol after consumption [ 156 , 157 ]. These findings may therefore imply that ethanol metabolism may not differ between adolescent and adult animals, which should be studied in a more direct manner.

Full proteome analysis

While the previously described studies focused on specific factors involved in neurotransmission, brain health, and plasticity, proteomics allows for the study of the full proteome in a specific region or tissue type. One study investigated the impact of age on ethanol-induced changes in the hippocampal proteome, observing age-related differences [ 158 ]. In this study, rats intermittently and voluntarily consumed beer for 1 month and the hippocampal proteome was analyzed after 2 weeks of abstinence. The results point to the involvement of many of the factors described above and imply age-specific effects of alcohol. Adult beer exposure increased citrate synthase (part of the citric acid, or Krebs, cycle) and fatty acid binding proteins (involved in membrane transport) compared to controls. Adolescent beer exposure increased cytoskeletal protein T-complex protein 1 subunit epsilon (TCP-1), involved in ATP-dependent protein folding, and reduced expression of a variety of other proteins involved in glycolysis, glutamate expression, aldehyde detoxification, protein degradation, and synaptogenesis, as well as neurotransmitter release. These more extensive changes suggest that the adolescent hippocampus might be more vulnerable to the effects of ethanol exposure, but more studies are needed to clarify and replicate these findings and extend the focus to different brain areas.

Neuronal activity and functioning

Ethanol-induced molecular changes may eventually change neuronal activity. Three studies investigated neuronal activity and functioning [ 89 , 159 , 160 ] using electrophysiological methods. All three studies observed age-related differences.

Galaj et al. [ 159 ] assessed firing patterns and the structure of pyramidal neurons in the L2 and L5 layers of the prelimbic cortex of the rat brain using ex vivo electrophysiological recordings and morphological staining. Following chronic intermittent ethanol exposure and brief abstinence (2 days), adolescents, but not adults, showed reduced amplitudes of spontaneous excitatory post-synaptic currents (sEPSCs) in L5 neurons compared to controls, indicating reductions in intrinsic excitability. In line with this, Dil staining showed increased thin spine ratios in the L5 layer in adolescents only. Age differences were more pronounced after prolonged abstinence (21 days), with adolescents showing reduced amplitude and frequency of sEPSCs in L5 neurons while adult’s L5 neurons showed augmented firing patterns (i.e., amplitude and frequency). Furthermore, adolescent rats showed decreased total spine density and non-thin spines, indicating less excitatory postsynaptic receptors in the L5 layer. In contrast, adults showed increases in spine density and non-thin spines.

Li et al. [ 160 ] examined the functioning of CA1 interneurons, which are important for learning and memory processes [ 161 ], in the rat hippocampus using ex vivo whole-cell recordings. After prolonged abstinence (20 days), voltage-gated A-type potassium channel ( I A ) conductance was measured. Differences emerged between age groups (although no statistical interaction effect was directly assessed): EtOH-exposed adolescents and adults both showed lower I A mean peak amplitude compared to the respective control groups. However, adolescents also showed reduced I A density and increased mean decay time, which decreased in adults. Furthermore, only adolescents showed increased depolarization required for activation compared to controls, which can result in higher interneuron firing rates in the CA1 region that could affect learning processes. Additional research is needed to connect these findings to behavioral measures of learning and memory.

Slawecki et al. [ 89 ] was the only study to use in vivo electroencephalogram (EEG) recordings with rats to examine function in the frontal and parietal cortex at different times during a 14-day vapor exposure period. During acute withdrawal (7–10 h abstinence period), following daily exposure no effects emerged in frontal cortical regions throughout the exposure period. In parietal regions, only adolescents showed increased high frequency (16–32 Hz and 32–50 Hz) power on days 8 and 12 compared to controls. Adolescent hyperexcitability during withdrawal may indicate increased arousal in adolescents compared to adults during withdrawal, but more studies linking brain activity to behavioral indices of withdrawal will allow for clearer interpretations.

Overall, strong conclusions cannot be drawn given the disparate paradigms and outcomes utilized. While adolescents and adults appear to differ in the effect of ethanol on neuronal firing, the meaning of these differences is not clear given the lack of connection between these findings and behavioral outcomes.

Human studies

Four studies examined age-related differences of the effect of alcohol on brain or cognition in humans [ 162 , 163 , 164 , 165 ].

Müller-Oehring et al. [ 162 ] examined the moderating role of age on resting state functional connectivity and synchrony in the default mode, central executive, salience, emotion, and reward networks of the brain in a sample of no/low and heavier drinkers aged 12–21 years old. While the study did not compare discrete groups of adolescents and adults, analyses investigating the interaction between continuous age and alcohol exposure history were conducted which provide insight into the effect of alcohol use on functional brain networks from early adolescence to emerging adulthood. Regardless of age, no differences were observed between matched subgroups of no/low drinkers and moderate/heavy drinkers in the default mode, salience, or reward networks. However, in the central executive network, connectivity between the superior frontal gyrus (SFG) and insula increased with age in the no/low drinkers but not in heavier drinkers. Age-related strengthening of this fronto-limbic connection correlated with better performance on a delay discounting task in boys, suggesting that adolescent alcohol use may interfere with typical development of higher-level cognitive functions. In the emotion network, amygdala-medial parietal functional synchrony was reduced in the heavier drinkers compared to the no/low drinkers and exploratory analyses suggested that weaker amygdala-precuneus/posterior cingulate connectivity related to later stages of pubertal development in the no/low drinking group only. Interestingly, in the default mode (posterior cingulate-right hippocampus/amygdala) and emotional networks (amygdala, cerebellum), connectivity in regions that exhibited age-related desynchronization was negatively correlated with episodic memory performance in the heavy drinkers. These results give preliminary evidence that alcohol might have age-dependent effects on resting state connectivity and synchronization in the central executive, emotion, and default mode networks that could potentially interfere with normative maturation of these networks during adolescence.

Three studies examined age effects in alcohol-related implicit cognitions, specifically attentional bias [ 163 , 165 ], alcohol approach bias [ 165 ], and implicit memory associations and explicit outcome expectancies [ 164 ]. Attentional bias refers to the preferential automatic allocation or maintenance of attention to alcohol-related cues compared to neutral cues which is correlated with alcohol use severity and craving [ 166 ]. McAteer et al. [ 163 ] measured attentional bias with eye tracking during presentation of alcohol and neutral stimuli in heavy and light drinkers in early adolescents (12–13 yrs), late adolescents (16–17 yrs), and young adults (18–21 yrs). Regardless of age, heavy drinkers spent longer fixating on alcohol cues compared to light drinkers. Cousijn et al. [ 165 ] measured attentional bias with an Alcohol Stroop task [ 167 ], comparing the speed of naming the print color of alcohol-related and control words. Consistent with the findings of McAteer et al. [ 163 ], adults and adolescents matched on monthly alcohol consumption showed similar levels of alcohol attentional bias. In the same study, Cousijn et al. [ 165 ] did not find any evidence for an approach bias towards alcohol cues in any age group.

Rooke and Hine [ 164 ] found evidence for age-related differences in implicit and explicit alcohol cognitions and their relationship with binge drinking. Using a teen-parent dyad design, adolescents (13–19 yrs) showed stronger memory associations in an associative phrase completion task and more positive explicit alcohol expectancies than adults. Interestingly, both explicit positive alcohol expectancies and implicit memory associations were a stronger predictor of binge drinking in adolescents compared to adults. It is important to note that adolescents also had higher levels of binge drinking than adults in the study.

Cousijn et al. [ 165 ] also investigated impulsivity, drinking motives, risky decision-making, interference control, and working memory. No age differences emerged in the cognitive functioning measures including risky decision-making (Columbia Card Task – “hot” version), interference control (Classical Stroop Task), or working memory (Self-Ordered Pointing Task). However, adolescents were more impulsive (Barrett Impulsiveness Scale) than adults and reported more enhancement motives. Importantly, impulsivity as well as social, coping, and enhancement motives of alcohol use correlated with alcohol use in both ages. However, age only moderated the relationship between social drinking motives and alcohol use-related problems (as measured by the Alcohol Use Disorder Identification Test), with a stronger positive association in adolescents compared to adults. Importantly, the adolescent group had a different pattern of drinking, with less drinking days per month but more drinks per episode than the adult group.

In summary, human evidence is largely missing, with no studies comparing more severe and dependent levels of alcohol use between adolescents and adults. The preliminary evidence is too weak and heterogeneous to draw conclusions, warranting future studies investigating the impact of age.

The current systematic review assessed the evidence for the moderating role of age in the effects of chronic alcohol use on the brain and cognition. The identified 59 rodent studies (Table 1 ) and 4 human studies (Table 2 ) provide initial evidence for the presence of age-related differences. Rodents exposed to ethanol during adolescence show both increased risk and resilience to the effects of ethanol depending on the outcome parameter. However, due to the high variability in the outcomes studied and the limited number of studies per outcome, conclusions should be considered preliminary. Moreover, brain and behavioral outcomes were mostly studied separately, with studies focusing on either brain or behavioral outcomes. The behavioral consequences of changes in certain brain outcomes still need to be investigated. Table 3 provides a comprehensive overview of the strength of the evidence for age-related differences for all outcomes. Below, we will discuss the most consistent patterns of results, make connections between the behavioral and neurobiological findings when possible, highlight strengths and limitations of the evidence base, and identify the most prominent research gaps.

Patterns of results

Age-related differences in learning and memory-related processes appear to be highly domain specific. There is limited but fairly consistent evidence for adolescent-specific impairments in contextual fear conditioning, which could be related to hippocampal dysfunction. Results for other hippocampus-related memory processes such as spatial memory are mixed and largely based on forced exposure with acute challenge studies rather than voluntary long-term exposure to alcohol. The evidence base is currently insufficient to draw conclusions about the role of age in alcohol’s effects on non-spatial types of learning and memory. Alcohol generally did not impact performance in the non-spatial variants of the MWM and SBM paradigms or in reward-learning, but the results of the limited studies in the object-learning domain highlight potential impairments and the importance of age therein. For example, adolescents but not adults demonstrated impaired object memory in the only study using the novel object recognition task [ 65 ]. Acute challenges after chronic pre-exposure to alcohol also appear to impair performance in the working memory domain, with one study suggesting heightened adolescent sensitivity to working memory impairment [ 83 ]. Thus, although the domain-specific evidence is limited by the relative lack of research, overall patterns suggest that learning and memory functions that are primarily hippocampus-dependent may be differentially affected by adolescent compared to adult alcohol use. Studies focusing on neural hippocampal processes corroborate these findings, reporting more extensive changes in protein expression [ 158 ], less desensitization of cFos upregulation [ 142 ], larger changes in GABAa receptor subunit expression [ 124 ], longer lasting changes in NMDA receptor expression [ 115 ], and larger reductions in neurogenesis [ 65 , 133 ] in the hippocampus of adolescent compared to adult ethanol-exposed rodents. On the other hand, ethanol-induced changes in the hippocampus recovered more quickly in younger animals after abstinence [ 150 ] and adolescent mice showed less signs of ethanol-induced neuroinflammation compared to adults [ 148 ].

Higher rates of adolescent alcohol use, especially binge drinking, may be facilitated by a heightened sensitivity to the rewarding properties of alcohol in combination with a reduced sensitivity to the negative effects of high doses [ 47 ]. In line with this, there is limited but consistent evidence that adolescents show less CTA in response to chronic ethanol and consequently voluntarily consume more ethanol [ 50 ]. Importantly, distinct vulnerability periods within adolescence for altered CTA may exist [ 168 , 169 ], with early adolescents potentially being least sensitive to aversive effects. Future studies using chronic exposure paradigms comparing different stages of adolescence to adults are needed. In contrast to CTA, there is insufficient evidence of age-related differences in the motivational value of alcohol based on CPP paradigms, with only one of five studies reporting stronger CPP in adolescents than adults [ 52 ]. Adolescents may be more sensitive to the effects of environmental factors on the motivational value of alcohol than adults, as adolescents housed in enriched environments acquired CPP while those in standard housing did not, an effect that was not found in adults [ 54 ]. Evidence for environmentally enriched housing being protective against these changes in adolescents provides an important indication that environmental factors matter and are important factors to consider in future research on the motivational value of ethanol on both the behavioral and neural level. Complementary studies on the functioning of brain regions within the mesolimbic dopamine pathway and PFC, which play an important role in motivated behavior, indicate limited but consistent evidence for age-related differences. Adolescents showed less dopamine reactivity in the PFC and NAc compared to adults after chronic ethanol exposure. Furthermore, there is limited but consistent evidence that adolescents are more vulnerable to epigenetic changes in the frontal cortex and reward-related areas after chronic ethanol exposure. For instance, adolescents may be more sensitive to histone acetylation of transcription factors in motivational circuits underlying the rewarding effects of alcohol [ 55 ], which may contribute to addictive behaviors [ 170 , 171 ]. Chronic alcohol use is also associated with lower BDNF levels in the PFC and subsequent increases in alcohol consumption, implicating BDNF as an important regulator of alcohol intake [ 172 ]. While evidence is limited, chronic alcohol use consistently reduced prefrontal BDNF in both age groups. However, the rate of recovery of BDNF levels after abstinence appears to be slower in adolescents.

Regarding executive functioning, there is limited but fairly consistent evidence from animal studies that adolescents are more vulnerable to long-term effects of chronic exposure on decision-making and are more impulsive than adults during acute intoxication and after prolonged abstinence following chronic exposure. Impulsivity is associated with functional alterations of the limbic cortico-striatal systems [ 91 ], with involvement of both the dopaminergic and serotonergic neurotransmitter systems [ 173 ]. While no studies investigating serotonergic activity were identified, the consistent reduction in dopamine reactivity observed in the PFC and NAc in adolescents compared to adults parallel the behavioral findings. There is also limited but fairly consistent evidence that adolescents are more resilient to impairments in cognitive flexibility than adults following chronic exposure to alcohol, and that adolescents may more easily regain control over their alcohol-seeking behavior than adults. These behavioral findings provide preliminary support for the paradox of adolescent risk and resilience in which adolescents are at once more at risk to develop harmful patterns of drinking, but are also more resilient in that they may be more equipped to flexibly change behavior and with time regain control over alcohol consumption. However, studies assessing processes that might be related to brain recovery provide little conclusive evidence for potential underlying mechanisms of these behavioral findings. While adolescents appear more vulnerable to ethanol-induced brain damage [ 131 , 132 ], show reduced neurogenesis [ 65 , 133 ], and show less changes in gene expression associated with brain recovery [ 65 , 133 ], adults show relatively higher immune responses after repeated ethanol exposure [ 147 , 148 ]. The limited evidence for adolescent resilience to alcohol’s effects on cognitive flexibility diverge from the conclusions of recent reviews that focused mostly on adolescent-specific research. Spear et al. [ 18 ] concluded that adolescents are more sensitive to impairments in cognitive flexibility; however, this was based on adolescent-only animal studies. Similarly, the systematic review of Carbia et al. [ 19 ] on the neuropsychological effects of binge drinking in adolescents and young adults also revealed impairments in executive functions, particularly inhibitory control. However, as pointed out by the authors, the lack of consideration of confounding variables (e.g., other drug use, psychiatric comorbidities, etc.) in the individual studies and the lack of prospective longitudinal studies limit our ability to causally interpret these results. This further highlights the difficulty of conducting human studies which elucidate causal associations of the effects of alcohol, and the need for animal research that directly compares adolescents to adults to bolster interpretation of findings from human research.

Only a few studies have investigated age-related differences in cognitive functioning in humans. These studies focused on mostly non-dependent users and studied different outcomes, including cognitive biases and implicit and explicit alcohol-related cognitions. Overall, there was limited but consistent evidence that age does not affect alcohol attentional or approach biases, with heavy drinkers in both age groups allocating more attention to alcohol cues compared to controls [ 163 , 165 ]. In contrast, in line with a recent meta-analysis of the neurocognitive profile of binge-drinkers aged 10–24 [ 23 ], there is limited evidence that age affects alcohol associations. One study found age effects on implicit (memory associations) and explicit (expectancies) cognition in relation to alcohol use. Adolescents showed stronger memory associations and more positive expectancies than adults [ 164 ]. These expectancies were also predictive of higher binge drinking in adolescents but not adults, highlighting the importance of future research into age differences in alcohol-related cognitions and their consequences on alcohol consumption. However, the quality of the evidence was rated as weak based on the methodological design of the included studies.

Regarding anxiety-related outcomes, results are inconsistent across studies and paradigms. When age-differences are observed, adolescents often show reduced anxiety compared to adults during both acute withdrawal and sustained abstinence following chronic ethanol exposure. However, the direction of age-related effects of alcohol may also be anxiety-domain specific. In social settings, adults show reduced anxiety compared to adolescents. Research on the neurocircuitry of anxiety processes implicates the extended amygdala, especially the BNST, in anxiety behaviors with an emphasis on the role of GABAergic projections to the limbic, hindbrain, and cortical structures in rodents [ 174 ]. Despite adolescents showing less non-social anxiety than adults after ethanol exposure, no age-differences were observed for LTP in the BNST [ 109 ]. Also, GABA receptor expression in the hippocampus and whole cortex was not altered by ethanol exposure in either age group [ 108 ]. However, the anxiolytic effects of NMDA antagonists [ 175 ] also highlight the importance of glutamatergic activity in anxiety processes [ 176 ]. In line with behavioral findings, adolescents were less sensitive to changes in glutamate expression: adults showed heightened expression in the NAc, which has been suggested to underlie the higher levels of anxiety observed in adults compared to adolescents [ 106 ]. Importantly, across the various studies, different paradigms were used to assess anxiety, potentially contributing to the inconsistent results. Furthermore, most of the identified studies used a forced ethanol exposure paradigm. As alcohol-induced anxiety is likely also dependent on individual trait anxiety, voluntary consumption studies in high and low trait anxiety animals are important to further our understanding of the interaction between alcohol use and anxiety. Of note, the observed pattern suggestive of reduced anxiety in adolescents compared to adults diverges from conclusions of previous reviews such as Spear et al. [ 18 ] which concluded that adolescents are more likely to show augmented anxiety after alcohol exposure based on animal studies with adolescent animals only. Importantly, anxiety was included as a secondary outcome in this review because of the high comorbidity between anxiety disorders and alcohol addiction, warranting the inclusion of age-related differences in the relation between alcohol and anxiety. However, the search strategy was not specifically tailored to capturing all studies assessing age-related differences in the effect of alcohol on anxiety.

Translational considerations, limitations, and future directions

The reviewed studies revealed a high degree of variability in study designs and outcomes, hindering integration and evaluation of research findings. We were unable to differentiate our conclusions based on drinking patterns (i.e., comparing binge drinking, heavy prolonged use, AUD). The prevalence of binge-drinking in adolescence is very high and is associated with neurocognitive alterations [ 177 ]. Studies investigating the potential differential impact of binge-drinking compared to non-binge-like heavy alcohol use in adolescence and adulthood are critical for understanding the risks of chronic binge-like exposure in adolescence, even if it does not progress to AUD.

It is also important to acknowledge the limitations of the choice of adolescent and adult age ranges in our inclusion criteria. Rodent studies had to include an adolescent group exposed to alcohol between the ages of PND 25–42 and an adult group exposed after age PND 65. Ontogenetic changes may still be occurring between PND 42–55, and this period may more closely correspond to late adolescence and emerging adulthood in humans (e.g., 18–25 years). Studies that compared animals in this post-pubertal but pre-adulthood age range were not reviewed. Studies investigating age-related differences in the effects of ethanol on brain and cognitive outcomes in emerging adulthood are also translationally valuable given the high rates and risky patterns of drinking observed during this developmental period [ 178 ]. Indeed, an important future direction is to examine whether there are distinct vulnerability periods within adolescence itself for the effects of ethanol on brain and cognitive outcomes. Given that emerging adulthood is a period of continued neurocognitive maturation and heightened neural plasticity, studies comparing this age range to older adults (e.g., over 30) are also necessary for a more thorough understanding of periods of risk and resilience to the effects of alcohol.

Furthermore, we did not conduct a risk of bias assessment to examine the methodological quality of the animal studies. The applicability and validity of the risk of bias tools for general animal intervention studies, such as the SYRCLE risk of bias tool [ 179 ], remain in question at the moment. The lack of standardized reporting in the literature for many of the criteria (e.g., process of randomizing animals into intervention groups) would lead to many studies being labeled with an ‘unclear risk of bias’. Furthermore, there is still a lack of empirical evidence regarding the impact of the criteria in these tools on bias [ 179 , 180 ]. This is a significant limitation in evaluating the strength of the evidence for age-related differences based on the animal studies, which highlights the importance of more rigorous reporting standards in animal studies.

Moreover, most work is done in male rodents and is based on forced ethanol exposure regimes. In a recent opinion article, Field and Kersbergen [ 181 ] question the usefulness of these types of animal models to further our understanding of human substance use disorders (SUD). They argue that animal research has failed to deliver effective SUD treatment and that social, cultural, and other environmental factors crucial to human SUD are difficult, if not impossible, to model in animals. While it is clear that more sophisticated multi-symptom models incorporating social factors are needed to further our understanding of SUD and AUD specifically, a translational approach is still crucial in the context of investigating the more fundamental impact of alcohol use on brain and cognition. In humans, comparing the impact of alcohol use on brain and cognition between adolescents and adults is complicated by associations between age and cumulative exposure to alcohol; i.e., the older the individual, the longer and higher the overall exposure to alcohol. Although animal models may be limited in their ability to model every symptom of AUD, they can still provide critical insights into causal mechanisms underlying AUD by allowing direct control over alcohol exposure and in-depth investigation of brain mechanisms.

The intermittent voluntary access protocol resembles the patterns of alcohol use observed in humans, and also result in physiologically relevant levels of alcohol intake [ 182 , 183 , 184 ]. Only a minority of the studies included in this review employed a voluntary access protocol, with one study using beer instead of ethanol in water [ 158 ], which better accounts for the involvement of additional factors (e.g., sugar, taste) in the appeal of human alcohol consumption. Voluntary access protocols can also model behavioral aspects of addictive behavior such as loss of control over substance use and relapse [ 185 , 186 , 187 ], an important area in which little is known about the role of age. Ideally, one would also investigate choices between ethanol and alternative reinforcers, such as food or social interaction, that better mimic human decision-making processes [ 188 ]. However, studies on the effects of ethanol on social behavior are limited and show inconsistent results and studies assessing reward processes often lack a social reward component as an alternative reinforcer.

On a practical level, rodents mature quickly and choice-based exposure paradigms are more complex and time-consuming than most forced exposure paradigms. Consequently, by the time final behavioral measurements are recorded, both the adolescent and adult exposure groups have reached adulthood. To combat this, many of the included studies use forced ethanol exposure, such as ethanol vapor, to quickly expose rodents to very high doses of ethanol. Although the means and degrees of alcohol exposure may not directly translate to human patterns of alcohol use, such studies do allow for the assessment of the impact of high cumulative doses of ethanol within a relatively short period of time which allows for more time in the developmental window to test age-related differences in the outcomes. When considering the translational value of a study, it is therefore important to evaluate studies based on the goal, while not ignoring the practical constraints.

While human research is challenging due to the lack of experimental control and the inherent confounds in observational studies between age and alcohol exposure history, large-scale prospective longitudinal studies offer a gateway towards a better understanding. Comparisons of different trajectories of drinking from adolescence to adulthood (i.e., heavy drinking to light drinking, light drinking to heavy drinking, continuously heavy drinking, and continuously light drinking) could offer insight into the associated effects on cognitive and brain-related outcomes. Of course, different drinking trajectories are likely confounded with potentially relevant covariates which limits causal inference. Direct comparisons of low and heavy adolescent and adult drinkers, supported by a parallel animal model can help to bolster the causality of observed age-related differences in human studies. In addition, changes in legislation around the minimum age for alcohol consumption in some countries provide a unique opportunity to investigate how delaying alcohol use to later in adolescence or even young adulthood impacts cognitive functioning over time. Importantly, future studies investigating the moderating role of age in humans should carefully consider the impact of psychiatric comorbidities. While adolescence into young adulthood is the period in which mental health issues often emerge [ 189 , 190 ], there is some evidence that the prevalence of comorbidities is higher in adults with AUD [ 95 ]. This is an important to control for when considering age-related differences on cognition and the brain given the evidence of altered cognitive functioning in other common mental illnesses [ 191 , 192 ].

Concluding remarks

The aim of this systematic review was to extend our understanding of adolescent risk and resilience to the effects of alcohol on brain and cognitive outcomes compared to adults. In comparison to recent existing reviews on the impact of alcohol on the adolescent brain and cognition [ 17 , 18 , 19 , 22 , 23 ], a strength of the current review is the direct comparison of the effects of chronic alcohol exposure during adolescence versus adulthood. This approach allows us to uncover both similarities and differences in the processes underlying alcohol use and dependence between adolescents and adults. However, due to the large degree of heterogeneity in the studies included in sample, designs, and outcomes, we were unable to perform meta-analytic synthesis techniques.

In conclusion, while the identified studies used varying paradigms and outcomes, key patterns of results emerged indicating a complex role of age, with evidence pointing towards both adolescent vulnerability and resilience. The evidence suggests adolescents may be more vulnerable than adults in domains that may promote heavy and binge drinking, including reduced sensitivity to aversive effects of high alcohol dosages, reduced dopaminergic neurotransmission in the NAc and PFC, greater neurodegeneration and impaired neurogenesis, and other neuromodulatory processes. At the same time, adolescents may be more resilient than adults to alcohol-induced impairments in domains which may promote recovery from heavy drinking, such as cognitive flexibility. However, in most domains, the evidence was too limited or inconsistent to draw clear conclusions. Importantly, human studies directly comparing adolescents and adults are largely missing. Recent reviews of longitudinal human research in adolescents, however, revealed consistent evidence of alterations to gray matter, and to a lesser extent white matter, structure in drinkers [ 17 , 18 ], but also highlight the limited evidence available in the domains of neural and cognitive functioning in humans [ 17 ]. Future results from ongoing large-scale longitudinal neuroimaging studies like the ABCD study [ 193 ] will likely shed valuable light on the impact of alcohol use on the adolescent brain. However, our results also stress the need for direct comparisons with adult populations. Moreover, while the lack of experimental control and methodological constraints limit interpretations and causal attributions in human research, translational work aimed at connecting findings from animal models to humans is necessary to build upon the current knowledge base. Furthermore, the use of voluntary self-administration paradigms and incorporation of individual differences and environmental contexts are important steps forward in improving the validity of animal models of alcohol use and related problems. A more informed understanding of the effects of alcohol on adolescents compared to adults can further prevention efforts and better inform policy efforts aimed at minimizing harm during a crucial period for both social and cognitive development.

Degenhardt L, Charlson F, Ferrari A, Santomauro D, Erskine H, Mantilla-Herrara A, et al. The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry. 2018;5:987–1012.

Article   Google Scholar  

Kohn R, Saxena S, Levav I, Saraceno B. The treatment gap in mental health care. World Health Organization; 2004. https://doi.org/10.1590/S0042-96862004001100011 .

Fleury MJ, Djouini A, Huỳnh C, Tremblay J, Ferland F, Ménard JM, et al. Remission from substance use disorders: a systematic review and meta-analysis. Drug Alcohol Depend. 2016;168:293–306.

Article   PubMed   Google Scholar  

Hingson RW, Heeren T, Winter MR. Age of alcohol-dependence onset: associations with severity of dependence and seeking treatment. Pediatrics. 2006;118:e755–e763.

Hingson RW, Heeren T, Winter MR. Age at drinking onset and alcohol dependence: age at onset, duration, and severity. Arch Pediatr Adolesc Med. 2006;160:739–46.

American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th edn. Arlington, VA: American Psychiatric Association; 2013. https://doi.org/10.1176/appi.books.9780890425596.dsm04 .

Conrod P, Nikolaou K. Annual Research Review: On the developmental neuropsychology of substance use disorders. J Child Psychol Psychiatry Allied Discip. 2016;57:371–94.

Spear LP. Adolescent alcohol exposure: Are there separable vulnerable periods within adolescence? Physiol Behav. 2015;148:122–30.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Simon NW, Gregory TA, Wood J, Moghaddam B. Differences in response initiation and behavioral flexibility between adolescent and adult rats. Behav Neurosci. 2013;127:23–32.

Article   PubMed   PubMed Central   Google Scholar  

Carroll LJ, Cassidy JD, Peloso PM, Borg J, von Holst H, Holm L, et al. Prognosis for mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehabil Med 2004(Suppl. 43):84–105.

Mastwal S, Ye Y, Ren M, Jimenez DV, Martinowich K, Gerfen CR, et al. Phasic dopamine neuron activity elicits unique mesofrontal plasticity in adolescence. J Neurosci. 2014;34:9484–96.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Crone EA, Dahl RE. Understanding adolescence as a period of social-affective engagement and goal flexibility. Nat Rev Neurosci. 2012;13:636–50.

Article   CAS   PubMed   Google Scholar  

Vergés A, Haeny AM, Jackson KM, Bucholz KK, Grant JD, Trull TJ, et al. Refining the notion of maturing out: results from the national epidemiologic survey on alcohol and related conditions. Am J Public Health. 2013;103:e67–e73.

Koob GF, Volkow ND. Neurocircuitry of addiction. Neuropsychopharmacology 2010;35:217–38.

Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349–56.

Ochsner KN, Gross JJ. The cognitive control of emotion. Trends Cogn Sci. 2005;9:242–9.

de Goede J, van der Mark-Reeuwijk KG, Braun KP, le Cessie S, Durston S, Engels RCME, et al. Alcohol and brain development in adolescents and young adults: a systematic review of the literature and advisory report of the health council of the Netherlands. Adv Nutr. 2021. https://doi.org/10.1093/advances/nmaa170 .

Spear LP. Effects of adolescent alcohol consumption on the brain and behaviour. Nat Rev Neurosci. 2018;19:197–214.

Carbia C, López-Caneda E, Corral M, Cadaveira F. A systematic review of neuropsychological studies involving young binge drinkers. Neurosci Biobehav Rev. 2018;90:332–49.

Feldstein Ewing SW, Sakhardande A, Blakemore SJ. The effect of alcohol consumption on the adolescent brain: a systematic review of MRI and fMRI studies of alcohol-using youth. NeuroImage Clin. 2014;5:420–37.

Article   PubMed Central   Google Scholar  

Squeglia LM, Boissoneault J, Van Skike CE, Nixon SJ, Matthews DB. Age-related effects of alcohol from adolescent, adult, and aged populations using human and animal models. Alcohol Clin Exp Res. 2014;38:2509–16.

Lees B, Meredith LR, Kirkland AE, Bryant BE, Squeglia LM. Effect of alcohol use on the adolescent brain and behavior. Pharm Biochem Behav. 2020;192:172906.

Article   CAS   Google Scholar  

Lees B, Mewton L, Stapinski LA, Squeglia LM, Rae CD, Teesson M. Neurobiological and cognitive profile of young binge drinkers: a systematic review and meta-analysis. Neuropsychol Rev. 2019;29:357–85.

Cservenka A, Brumback T. The burden of binge and heavy drinking on the brain: effects on adolescent and young adult neural structure and function. Front Psychol. 2017;8:1111.

Welch KA, Carson A, Lawrie SM. Brain structure in adolescents and young adults with alcohol problems: systematic review of imaging studies. Alcohol Alcohol. 2013;48:433–44.

Maeda K-I, Satoshi O, Hiroko T. Physiology of reproduction. Academic Press; 2000.

Spear LP. The adolescent brain and age-related behavioral manifestations. Neurosci Biobehav Rev. 2000;24:417–63.

Burke AR, Miczek KA. Stress in adolescence and drugs of abuse in rodent models: role of dopamine, CRF, and HPA axis. Psychopharmacology. 2014;231:1557–80.

Doremus-Fitzwater TL, Spear LP. Reward-centricity and attenuated aversions: an adolescent phenotype emerging from studies in laboratory animals. Neurosci Biobehav Rev. 2016;70:121–34.

Rajendran P, Spear LP. The effects of ethanol on spatial and nonspatial memory in adolescent and adult rats studied using an appetitive paradigm. In: Annals of the New York Academy of Sciences. New York Academy of Sciences; 2004. p. 441–4.

Morales M, Schatz KC, Anderson RI, Spear LP, Varlinskaya EI. Conditioned taste aversion to ethanol in a social context: impact of age and sex. Behav Brain Res. 2014;261:323–7.

Dumontheil I. Adolescent brain development. Curr Opin Behav Sci. 2016;10:39–44.

Shillington AM, Woodruff SI, Clapp JD, Reed MB, Lemus H. Self-reported age of onset and telescoping for cigarettes, alcohol, and marijuana: across eight years of the national longitudinal survey of youth. J Child Adolesc Subst Abus. 2012;21:333–48.

Livingston MD, Xu X, Komro KA. Predictors of recall error in self-report of age at alcohol use onset. J Stud Alcohol Drugs. 2016;77:811–8.

De Wit H. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict Biol. 2009;14:22–31.

Robinson TE, Berridge KC. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Rev. 1993;18:247–91.

Rodriguiz RM, Wetsel WC. Assessments of cognitive deficits in mutant mice. In: Levin ED, Buccafusco JJ, editors. Animal models of cognitive impairment. CRC Press; 2006. p. 223–82.

Leung RK, Toumbourou JW, Hemphill SA. The effect of peer influence and selection processes on adolescent alcohol use: a systematic review of longitudinal studies. Health Psychol Rev. 2014;8:426–57.

Cousijn J, Luijten M, Feldstein Ewing SW. Adolescent resilience to addiction: a social plasticity hypothesis. Lancet Child Adolesc Heal. 2018;2:69–78.

Kushner MG, Abrams K, Borchardt C. The relationship between anxiety disorders and alcohol use disorders: a review of major perspectives and findings. Clin Psychol Rev. 2000;20:149–71.

Robinson TE, Berridge KC. Review. The incentive sensitization theory of addiction: some current issues. Philos Trans R Soc Lond B Biol Sci. 2008;363:3137–46.

Vanderschuren LJMJ, Pierce RC. Sensitization processes in drug addiction. Curr Top Behav Neurosci. 2010;3:179–95.

Gorey C, Kuhns L, Smaragdi E, Kroon E, Cousijn J. Age-related differences in the impact of cannabis use on the brain and cognition: a systematic review. Eur Arch Psychiatry Clin Neurosci. 2019;269:37–58.

Schweizer TA, Vogel-Sprott M, Danckert J, Roy EA, Skakum A, Broderick CE. Neuropsychological profile of acute alcohol intoxication during ascending and descending blood alcohol concentrations. Neuropsychopharmacology. 2006;31:1301–9.

Ambrose ML, Bowden SC, Whelan G. Working memory impairments in alcohol-dependent participants without clinical amnesia. Alcohol Clin Exp Res. 2001;25:185–91.

Stavro K, Pelletier J, Potvin S. Widespread and sustained cognitive deficits in alcoholism: a meta-analysis. Addict Biol. 2013;18:203–13.

Spear LP. Adolescent neurobehavioral characteristics, alcohol sensitivities, and intake: setting the stage for alcohol use disorders? Child Dev Perspect. 2011;5:231–8.

Holstein SE, Spanos M, Hodge CW. Adolescent C57BL/6J mice show elevated alcohol intake, but reduced taste aversion, as compared to adult mice: a potential behavioral mechanism for binge drinking. Alcohol Clin Exp Res. 2011;35:1842–51.

Moore EM, Forrest RD, Boehm SL. Genotype modulates age-related alterations in sensitivity to the aversive effects of ethanol: an eight inbred strain analysis of conditioned taste aversion. Genes, Brain Behav. 2013;12:70–77.

Schramm-Sapyta NL, DiFeliceantonio AG, Foscue E, Glowacz S, Haseeb N, Wang N, et al. Aversive effects of ethanol in adolescent versus adult rats: potential causes and implication for future drinking. Alcohol Clin Exp Res. 2010;34:2061–9.

Pautassi RM, Myers M, Spear LP, Molina JC, Spear NE. Ethanol induces second-order aversive conditioning in adolescent and adult rats. Alcohol. 2011;45:45–55.

Carrara-Nascimento PF, Olive MF, Camarini R. Ethanol pre-exposure during adolescence or adulthood increases ethanol intake but ethanol-induced conditioned place preference is enhanced only when pre-exposure occurs in adolescence. Dev Psychobiol. 2014;56:36–48.

Leichtweis KS, Carvalho M, Morais-Silva G, Marin MT, Amaral VCS. Short and prolonged maternal separation impacts on ethanol-related behaviors in rats: sex and age differences. Stress. 2020;23:162–73.

Pautassi RM, Suárez AB, Hoffmann LB, Rueda AV, Rae M, Marianno P, et al. Effects of environmental enrichment upon ethanol-induced conditioned place preference and pre-frontal BDNF levels in adolescent and adult mice. Sci Rep. 2017;7:1–12.

Pascual M, Do Couto BR, Alfonso-Loeches S, Aguilar MA, Rodriguez-Arias M, Guerri C. Changes in histone acetylation in the prefrontal cortex of ethanol-exposed adolescent rats are associated with ethanol-induced place conditioning. Neuropharmacology. 2012;62:2309–19.

Peters J, Kalivas PW, Quirk GJ. Extinction circuits for fear and addiction overlap in prefrontal cortex. Learn Mem. 2009;16:279–88.

Antoniadis EA, McDonald RJ. Amygdala, hippocampus and discriminative fear conditioning to context. Behav Brain Res. 2000;108:1–19.

Marschner A, Kalisch R, Vervliet B, Vansteenwegen D, Büchel C. Dissociable roles for the hippocampus and the amygdala in human cued versus context fear conditioning. J Neurosci. 2008;28:9030–6.

Orsini CA, Maren S. Neural and cellular mechanisms of fear and extinction memory formation. Neurosci Biobehav Rev. 2012;36:1773–802.

Quirk GJ, Garcia R, González-Lima F. Prefrontal mechanisms in extinction of conditioned fear. Biol Psychiatry. 2006;60:337–43.

Bergstrom HC, McDonald CG, Smith RF. Alcohol exposure during adolescence impairs auditory fear conditioning in adult Long-Evans rats. Physiol Behav. 2006;88:466–72.

Broadwater M, Spear LP. Consequences of ethanol exposure on cued and contextual fear conditioning and extinction differ depending on timing of exposure during adolescence or adulthood. Behav Brain Res. 2013;256:10–19.

Broadwater M, Spear LP. Consequences of adolescent or adult ethanol exposure on tone and context fear retention: effects of an acute ethanol challenge during conditioning. Alcohol Clin Exp Res. 2014;38:1454–60.

Broadwater M, Spear LP. Tone conditioning potentiates rather than overshadows context fear in adult animals following adolescent ethanol exposure. Dev Psychobiol. 2014;56:1150–5.

Lacaille H, Duterte-Boucher D, Liot D, Vaudry H, Naassila M, Vaudry D. Comparison of the deleterious effects of binge drinking-like alcohol exposure in adolescent and adult mice. J Neurochem. 2015;132:629–41.

Markwiese BJ, Acheson SK, Levin ED, Wilson WA, Swartzwelder HS. Differential effects of ethanol on memory in adolescent and adult rats. In: Chapple L, editors. Alcoholism: clinical and Experimental Research. Blackwell Publishing Ltd; 1998. p. 416–21.

Acheson SK, Ross EL, Swartzwelder HS. Age-independent and dose-response effects of ethanol on spatial memory in rats. Alcohol. 2001;23:167–75.

Sircar R, Sircar D. Adolescent rats exposed to repeated ethanol treatment show lingering behavioral impairments. Alcohol Clin Exp Res. 2005;29:1402–10.

Swartzwelder HS, Hogan A, Risher ML, Swartzwelder RA, Wilson WA, Acheson SK. Effect of sub-chronic intermittent ethanol exposure on spatial learning and ethanol sensitivity in adolescent and adult rats. Alcohol. 2014;48:353–60.

Matthews DB, Watson MR, James K, Kastner A, Schneider A, Mittleman G. The impact of low to moderate chronic intermittent ethanol exposure on behavioral endpoints in aged, adult, and adolescent rats. Alcohol. 2019;78:33–42.

Galaj E, Barrera E, Morris D, Ma YY, Ranaldi R. Aberrations in incentive learning and responding to heroin in male rats after adolescent or adult chronic binge-like alcohol exposure. Alcohol Clin Exp Res. 2020;44:1214–23.

Diamond A. Executive functions. 2013;64:135–68. https://doi.org/10.1146/annurev-psych-113011-143750 .

Funahashi S, Andreau JM. Prefrontal cortex and neural mechanisms of executive function. J Physiol Paris. 2013;107:471–82.

Schindler AG, Tsutsui KT, Clark JJ. Chronic alcohol intake during adolescence, but not adulthood, promotes persistent deficits in risk-based decision making. Alcohol Clin Exp Res. 2014;38:1622–9.

Risher ML, Fleming RL, Boutros N, Semenova S, Wilson WA, Levin ED, et al. Long-term effects of chronic intermittent ethanol exposure in adolescent and adult rats: radial-arm maze performance and operant food reinforced responding. PLoS ONE. 2013;8:e62940.

Pickens CL, Cook A, Gaeddert B. Dose-dependent effects of alcohol injections on omission-contingency learning have an inverted-U pattern. Behav Brain Res. 2020;392:112736.

Pickens CL, Kallenberger P, Pajser A, Fisher H. Voluntary alcohol access during adolescence/early adulthood, but not during adulthood, causes faster omission contingency learning. Behav Brain Res. 2019;370:111918.

Mejia-Toiber J, Boutros N, Markou A, Semenova S. Impulsive choice and anxiety-like behavior in adult rats exposed to chronic intermittent ethanol during adolescence and adulthood. Behav Brain Res. 2014;266:19–28.

Fernandez GM, Lew BJ, Vedder LC, Savage LM. Chronic intermittent ethanol exposure leads to alterations in brain-derived neurotrophic factor within the frontal cortex and impaired behavioral flexibility in both adolescent and adult rats. Neuroscience. 2017;348:324–34.

Fernandez GM, Stewart WN, Savage LM. Chronic drinking during adolescence predisposes the adult rat for continued heavy drinking: neurotrophin and behavioral adaptation after long-term, continuous ethanol exposure. PLoS ONE 2016;11. https://doi.org/10.1371/journal.pone.0149987 .

Labots M, Cousijn J, Jolink LA, Leon Kenemans J, Vanderschuren LJMJ, Lesscher HMB. Age-related differences in alcohol intake and control over alcohol seeking in rats. Front Psychiatry. 2018;9:419.

Slawecki CJ, Ehlers CL. Enhanced prepulse inhibition following adolescent ethanol exposure in Sprague-Dawley rats. Alcohol Clin Exp Res. 2005;29:1829–36.

White AM, Ghia AJ, Levin ED, Scott Swartzwelder H. Binge pattern ethanol exposure in adolescent and adult rats: differential impact on subsequent responsiveness to ethanol. Alcohol Clin Exp Res. 2000;24:1251–6.

Baddeley A. Working memory. Science. 1992;255:556–9.

Olton DS, Samuelson RJ. Remembrance of places passed: spatial memory in rats. J Exp Psychol Anim Behav Process. 1976;2:97–116.

Deacon RMJ, Rawlins JNP. T-maze alternation in the rodent. Nat Protoc. 2006;1:7–12.

Knudsen EI. Fundamental components of attention. Annu Rev Neurosci. 2007;30:57–78.

Koch M, Schnitzler HU. The acoustic startle response in rats—circuits mediating evocation, inhibition and potentiation. Behav Brain Res. 1997;89:35–49.

Slawecki CJ, Roth J, Gilder A. Neurobehavioral profiles during the acute phase of ethanol withdrawal in adolescent and adult Sprague-Dawley rats. Behav Brain Res. 2006;170:41–51.

Cunha PJ, Nicastri S, de Andrade AG, Bolla KI. The frontal assessment battery (FAB) reveals neurocognitive dysfunction in substance-dependent individuals in distinct executive domains: abstract reasoning, motor programming, and cognitive flexibility. Addict Behav. 2010;35:875–81.

Jupp B, Dalley JW. Convergent pharmacological mechanisms in impulsivity and addiction: insights from rodent models. Br J Pharm. 2014;171:4729–66.

Dickinson A, Balleine B. Motivational control of goal-directed action. Anim Learn Behav. 1994;22:1–18.

Tomie A, Sharma N. Pavlovian sign-tracking model of alcohol abuse. Curr Drug Abus Rev. 2013;6:201–19.

Tomie A, Jeffers P, Zito B. Sign-tracking model of the addiction blind spot. In: Tomie JMA, editors. Sign tracking and drug addiction. Maize Books; 2018. p. 8–34.

Castillo-Carniglia A, Keyes KM, Hasin DS, Cerdá M. Psychiatric comorbidities in alcohol use disorder. Lancet Psychiatry. 2019;6:1068–80.

Wolitzky-Taylor K, Bobova L, Zinbarg RE, Mineka S, Craske MG. Longitudinal investigation of the impact of anxiety and mood disorders in adolescence on subsequent substance use disorder onset and vice versa. Addict Behav. 2012;37:982–5.

Park J, Moghaddam B. Impact of anxiety on prefrontal cortex encoding of cognitive flexibility. Neuroscience 2017;345:193–202.

Vytal KE, Cornwell BR, Letkiewicz AM, Arkin NE, Grillon C. The complex interaction between anxiety and cognition: Insight from spatial and verbal working memory. Front Hum Neurosci. 2013;7:93.

Prut L, Belzung C. The open field as a paradigm to measure the effects of drugs on anxiety-like behaviors: a review. Eur J Pharmacol. 2003;463:3–33.

Pellow S, Chopin P, File SE, Briley M. Validation of open: closed arm entries in an elevated plus-maze as a measure of anxiety in the rat. J Neurosci Methods. 1985;14:149–67.

Elsey JWB, Kindt M. Startle reflex. In: Zeigler-Hill V, Shackelford TK, editors. Encyclopedia of personality and individual differences. Springer International Publishing; 2018. p. 1–5.

Crawley J, Goodwin FK. Preliminary report of a simple animal behavior model for the anxiolytic effects of benzodiazepines. Pharm Biochem Behav. 1980;13:167–70.

File SE, Seth P. A review of 25 years of the social interaction test. Eur J Pharm. 2003;463:35–53.

Misslin R, Ropartz P. Responses in mice to a novel object author. Behavior. 1981;78:169–77.

Njung’e K, Handley SL. Evaluation of marble-burying behavior as a model of anxiety. Pharm Biochem Behav. 1991;38:63–67.

Lee KM, Coelho MA, McGregor HA, Solton NR, Cohen M, Szumlinski KK. Adolescent mice are resilient to alcohol withdrawal-induced anxiety and changes in indices of glutamate function within the nucleus accumbens. Front Cell Neurosci. 2016;10:265.

Agoglia AE, Holstein SE, Reid G, Hodge CW. CaMKIIα-GluA1 activity underlies vulnerability to adolescent binge alcohol drinking. Alcohol Clin Exp Res. 2015;39:1680–90.

Van Skike CE, Diaz-Granados JL, Matthews DB. Chronic intermittent ethanol exposure produces persistent anxiety in adolescent and adult rats. Alcohol Clin Exp Res. 2015;39:262–71.

Conrad KL, Winder DG. Altered anxiety-like behavior and long-term potentiation in the bed nucleus of the stria terminalis in adult mice exposed to chronic social isolation, unpredictable stress, and ethanol beginning in adolescence. Alcohol. 2011;45:585–93.

Slawecki CJ, Roth J. Comparison of the onset of hypoactivity and anxiety-like behavior during alcohol withdrawal adolescent and adult rats. Alcohol Clin Exp Res. 2004;28:598–607.

Wille-Bille A, de Olmos S, Marengo L, Chiner F, Pautassi RM. Long-term ethanol self-administration induces ΔFosB in male and female adolescent, but not in adult, Wistar rats. Prog Neuro-Psychopharmacol Biol Psychiatry. 2017;74:15–30.

Varlinskaya EI, Spear LP. Chronic tolerance to the social consequences of ethanol in adolescent and adult Sprague-Dawley rats. Neurotoxicol Teratol. 2007;29:23–30.

Morales M, Varlinskaya EI, Spear LP. Age differences in the expression of acute and chronic tolerance to ethanol in male and female rats. Alcohol Clin Exp Res. 2011;35:1614–24.

PubMed   PubMed Central   Google Scholar  

Neuhofer D, Kalivas P. Metaplasticity at the addicted tetrapartite synapse: a common denominator of drug induced adaptations and potential treatment target for addiction. Neurobiol Learn Mem. 2018;154:97–111.

Pian JP, Criado JR, Milner R, Ehlers CL. N-methyl-d-aspartate receptor subunit expression in adult and adolescent brain following chronic ethanol exposure. Neuroscience. 2010;170:645–54.

Falco AM, Bergstrom HC, Bachus SE, Smith RF. Persisting changes in basolateral amygdala mRNAs after chronic ethanol consumption. Physiol Behav. 2009;96:169–73.

Chin VS, Van Skike CE, Berry RB, Kirk RE, Diaz-Granados J, Matthews DB. Effect of acute ethanol and acute allopregnanolone on spatial memory in adolescent and adult rats. Alcohol. 2011;45:473–83.

Pascual M, Boix J, Felipo V, Guerri C. Repeated alcohol administration during adolescence causes changes in the mesolimbic dopaminergic and glutamatergic systems and promotes alcohol intake in the adult rat. J Neurochem. 2009;108:920–31.

Akkus F, Mihov Y, Treyer V, Ametamey SM, Johayem A, Senn S, et al. Metabotropic glutamate receptor 5 binding in male patients with alcohol use disorder. Transl Psychiatry 2018;8. https://doi.org/10.1038/s41398-017-0066-6 .

Leurquin-Sterk G, Ceccarini J, Crunelle CL, De Laat B, Verbeek J, Deman S, et al. Lower limbic metabotropic glutamate receptor 5 availability in alcohol dependence. J Nucl Med. 2018;59:682–90.

Davies M. The role of GABAA receptors in mediating the effects of alcohol in the central nervous system. J Psychiatry Neurosci. 2003;28:263–74.

Grobin AC, Matthews DB, Montoya D, Wilson WA, Morrow AL, Swartzwelder HS. Age-related differences in neurosteroid potentiation of muscimol-stimulated 36Cl- flux following chronic ethanol treatment. Neuroscience. 2001;105:547–52.

Fleming RL, Acheson SK, Moore SD, Wilson WA, Swartzwelder HS. GABA transport modulates the ethanol sensitivity of tonic inhibition in the rat dentate gyrus. Alcohol. 2011;45:577–83.

Fleming RL, Li Q, Risher ML, Sexton HG, Moore SD, Wilson WA, et al. Binge-pattern ethanol exposure during adolescence, but not adulthood, causes persistent changes in GABAA receptor-mediated tonic inhibition in dentate granule cells. Alcohol Clin Exp Res. 2013;37:1154–60.

Carrara-Nascimento PF, Hoffmann LB, Flório JC, Planeta CS, Camarini R. Effects of ethanol exposure during adolescence or adulthood on locomotor sensitization and dopamine levels in the reward system. Front Behav Neurosci. 2020;14:31.

Picciotto MR, Higley MJ, Mineur YS. Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior. Neuron 2012;76:116–29.

Wu J, Gao M, Taylor DH. Neuronal nicotinic acetylcholine receptors are important targets for alcohol reward and dependence. Acta Pharmacol Sin. 2014;35:311–5.

Walker LC, Berizzi AE, Chen NA, Rueda P, Perreau VM, Huckstep K, et al. Acetylcholine muscarinic M4 receptors as a therapeutic target for alcohol use disorder: converging evidence from humans and rodents. Biol Psychiatry. 2020;88:898–909.

Vetreno RP, Broadwater M, Liu W, Spear LP, Crews FT. Adolescent, but not adult, binge ethanol exposure leads to persistent global reductions of choline acetyltransferase expressing neurons in brain. PLoS ONE. 2014;9:113421.

Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. 2016;3:760–73.

Huang C, Titus JA, Bell RL, Kapros T, Chen J, Huang R. A mouse model for adolescent alcohol abuse: stunted growth and effects in brain. Alcohol Clin Exp Res. 2012;36:1728–37.

Crews FT, Braun CJ, Hoplight B, Switzer RC, Knapp DJ. Binge ethanol consumption causes differential brain damage in young adolescent rats compared with adult rats. Alcohol Clin Exp Res. 2000;24:1712–23.

Broadwater MA, Liu W, Crews FT, Spear LP. Persistent loss of hippocampal neurogenesis and increased cell death following adolescent, but not adult, chronic ethanol exposure. Dev Neurosci. 2014;36:297–305.

Nixon K, Kim DH, Potts EN, He J, Crews FT. Distinct cell proliferation events during abstinence after alcohol dependence: microglia proliferation precedes neurogenesis. Neurobiol Dis. 2008;31:218–29.

Camp MC, Mayfield RD, McCracken M, McCracken L, Alcantara AA. Neuroadaptations of Cdk5 in cholinergic interneurons of the nucleus accumbens and prefrontal cortex of inbred alcohol-preferring rats following voluntary alcohol drinking. Alcohol Clin Exp Res. 2006;30:1322–35.

Goulding SP, de Guglielmo G, Carrette LLG, George O, Contet C. Systemic administration of the cyclin-dependent kinase inhibitor (S)-CR8 selectively reduces escalated ethanol intake in dependent rats. Alcohol Clin Exp Res. 2019;43:2079–89.

Joe KH, Kim YK, Kim TS, Roh SW, Choi SW, Kim YB, et al. Decreased plasma brain-derived neurotrophic factor levels in patients with alcohol dependence. Alcohol Clin Exp Res. 2007;31:1833–8.

Huang MC, Chen CH, Chen CH, Liu SC, Ho CJ, Shen WW, et al. Alterations of serum brain-derived neurotrophic factor levels in early alcohol withdrawal. Alcohol Alcohol. 2008;43:241–5.

Miller R, King MA, Heaton MB, Walker DW. The effects of chronic ethanol consumption on neurotrophins and their receptors in the rat hippocampus and basal forebrain. Brain Res. 2002;950:137–47.

Vetreno RP, Crews FT. Binge ethanol exposure during adolescence leads to a persistent loss of neurogenesis in the dorsal and ventral hippocampus that is associated with impaired adult cognitive functioning. Front Neurosci. 2015;9:35.

Robison AJ, Nestler EJ. Transcriptional and epigenetic mechanisms of addiction. Nat Rev Neurosci. 2011;12:623–37.

Faria RR, Lima Rueda AV, Sayuri C, Soares SL, Malta MB, Carrara-Nascimento PF, et al. Environmental modulation of ethanol-induced locomotor activity: Correlation with neuronal activity in distinct brain regions of adolescent and adult Swiss mice. Brain Res. 2008;1239:127–40.

Crews FT, Bechara R, Brown LA, Guidot DM, Mandrekar P, Oak S, et al. Cytokines and alcohol. In: Alcoholism: clinical and experimental research. John Wiley & Sons, Ltd; 2006. p. 720–30.

Davis RL, Syapin PJ. Chronic ethanol inhibits CXC chemokine ligand 10 production in human A172 astroglia and astroglial-mediated leukocyte chemotaxis. Neurosci Lett. 2004;362:220–5.

Knapp DJ, Crews FT. Induction of cyclooxygenase-2 in brain during acute and chronic ethanol treatment and ethanol withdrawal. Alcohol Clin Exp Res. 1999;23:633–43.

Marshall SA, McClain JA, Wooden JI, Nixon K. Microglia dystrophy following binge-like alcohol exposure in adolescent and adult male rats. Front Neuroanat. 2020;14:52.

Agrawal RG, Owen JA, Levin PS, Hewetson A, Berman AE, Franklin SR, et al. Bioinformatics analyses reveal age-specific neuroimmune modulation as a target for treatment of high ethanol drinking. Alcohol Clin Exp Res. 2014;38:428–37.

Kane CJM, Phelan KD, Douglas JC, Wagoner G, Johnson JW, Xu J, et al. Effects of ethanol on immune response in the brain: region-specific changes in adolescent versus adult mice. Alcohol Clin Exp Res. 2014;38:384–91.

Blaine SK, Sinha R. Alcohol, stress, and glucocorticoids: from risk to dependence and relapse in alcohol use disorders. Neuropharmacology 2017;122:136–47.

Slawecki CJ, Jiménez-Vasquez P, Mathé AA, Ehlers CL. Effect of ethanol on brain neuropeptides in adolescent and adult rats. J Stud Alcohol. 2005;66:46–52.

van den Pol AN. Neuropeptide transmission in brain circuits. Neuron. 2012;76:98–115.

Souza-Moreira L, Campos-Salinas J, Caro M, Gonzalez-Rey E. Neuropeptides as pleiotropic modulators of the immune response. Neuroendocrinology. 2011;94:89–100.

Carniglia L, Ramírez D, Durand D, Saba J, Turati J, Caruso C, et al. Neuropeptides and microglial activation in inflammation, pain, and neurodegenerative diseases. Mediators Inflamm. 2017;2017. https://doi.org/10.1155/2017/5048616 .

Hipolito L, Sanchez M, Polache A, Granero L. Brain metabolism of ethanol and alcoholism: an update. Curr Drug Metab. 2007;8:716–27.

Rhoads DE, Contreras C, Fathalla S. Brain levels of catalase remain constant through strain, developmental, and chronic alcohol challenges. Enzyme Res. 2012;2012. https://doi.org/10.1155/2012/572939 .

Vasiliou V, Ziegler TL, Bludeau P, Petersen DR, Gonzalez FJ, Deitrich RA. CYP2E1 and catalase influence ethanol sensitivity in the central nervous system. Pharmacogenet Genomics. 2006;16:51–58.

Zimatkin SM, Buben AI. Ethanol oxidation in the living brain. Alcohol Alcohol. 2007;42:529–32.

Hargreaves GA, Quinn H, Kashem MA, Matsumoto I, McGregor IS. Proteomic analysis demonstrates adolescent vulnerability to lasting hippocampal changes following chronic alcohol consumption. Alcohol Clin Exp Res. 2009;33:86–94.

Galaj E, Guo C, Huang D, Ranaldi R, Ma YY. Contrasting effects of adolescent and early-adult ethanol exposure on prelimbic cortical pyramidal neurons. Drug Alcohol Depend. 2020;216:108309.

Li Q, Fleming RL, Acheson SK, Madison RD, Moore SD, Risher ML, et al. Long-term modulation of A-type K+ conductances in hippocampal CA1 interneurons in rats after chronic intermittent ethanol exposure during adolescence or adulthood. Alcohol Clin Exp Res. 2013;37:2074–85.

Artinian J, Lacaille JC. Disinhibition in learning and memory circuits: new vistas for somatostatin interneurons and long-term synaptic plasticity. Brain Res Bull. 2018;141:20–26.

Müller-Oehring EM, Kwon D, Nagel BJ, Sullivan EV, Chu W, Rohlfing T, et al. Influences of age, sex, and moderate alcohol drinking on the intrinsic functional architecture of adolescent brains. Cereb Cortex. 2018;28:1049–63.

McAteer AM, Hanna D, Curran D. Age-related differences in alcohol attention bias: a cross-sectional study. Psychopharmacology. 2018;235:2387–93.

Rooke SE, Hine DW. A dual process account of adolescent and adult binge drinking. Addict Behav. 2011;36:341–6.

Cousijn J, Green KH, Labots M, Vanderschuren LJMJ, Kenemans JL, Lesscher HMB. Motivational and control mechanisms underlying adolescent versus adult alcohol use. NeuroSci. 2020;1:44–58.

Field M, Cox WM. Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug Alcohol Depend. 2008;97:1–20.

Cousijn J, van Benthem P, van der Schee E, Spijkerman R. Motivational and control mechanisms underlying adolescent cannabis use disorders: a prospective study. Dev Cogn Neurosci. 2015;16:36–45.

Saalfield J, Spear L. Consequences of repeated ethanol exposure during early or late adolescence on conditioned taste aversions in rats. Dev Cogn Neurosci. 2015;16:174–82.

Saalfield J, Spear L. The ontogeny of ethanol aversion. Physiol Behav. 2016;156:164–70.

McQuown SC, Wood MA. Epigenetic regulation in substance use disorders. Curr Psychiatry Rep. 2010;12:145–53.

Renthal W, Nestler EJ. Epigenetic mechanisms in drug addiction. Trends Mol Med. 2008;14:341–50.

Logrip ML, Barak S, Warnault V, Ron D. Corticostriatal BDNF and alcohol addiction. Brain Res. 2015;1628:60–67.

Dalley JW, Roiser JP. Dopamine, serotonin and impulsivity. Neuroscience 2012;215:42–58.

Robinson OJ, Pike AC, Cornwell B, Grillon C. The translational neural circuitry of anxiety. J Neurol Neurosurg Psychiatry. 2019;90:1353–60.

PubMed   Google Scholar  

Martínez G, Ropero C, Funes A, Flores E, Blotta C, Landa AI, et al. Effects of selective NMDA and non-NMDA blockade in the nucleus accumbens on the plus-maze test. Physiol Behav. 2002;76:219–24.

Bergink V, Van Megen HJGM, Westenberg HGM. Glutamate and anxiety. Eur Neuropsychopharmacol. 2004;14:175–83.

Scott AJ, Jordan M, Lueras BJN. Effects of binge drinking on the developing brain. Alcohol Res. 2018;39:87–96.

Google Scholar  

Krieger H, Young CM, Anthenien AM, Neighbors C. The epidemiology of binge drinking among college-age individuals in the United States. Alcohol Res. 2018;39:23–30.

Hooijmans CR, Rovers MM, De Vries RBM, Leenaars M, Ritskes-Hoitinga M, Langendam MW. SYRCLE’s risk of bias tool for animal studies. BMC Med Res Methodol. 2014;14:1–9.

Krauth D, Woodruff TJ, Bero L. Instruments for assessing risk of bias and other methodological criteria of published animal studies: a systematic review. Environ Health Perspect. 2013;121:985–92.

Field M, Kersbergen I. Are animal models of addiction useful? Addiction. 2020;115:6–12.

Simms JA, Steensland P, Medina B, Abernathy KE, Chandler LJ, Wise R, et al. Intermittent access to 20% ethanol induces high ethanol consumption in Long-Evans and Wistar rats. Alcohol Clin Exp Res. 2008;32:1816–23.

Rhodes JS, Best K, Belknap JK, Finn DA, Crabbe JC. Evaluation of a simple model of ethanol drinking to intoxication in C57BL/6J mice. Physiol Behav. 2005;84:53–63.

Spoelder M, Hesseling P, Baars AM, Lozeman-van’t Klooster JG, Rotte MD, Vanderschuren LJMJ, et al. Individual variation in alcohol intake predicts reinforcement, motivation, and compulsive alcohol use in rats. Alcohol Clin Exp Res. 2015;39:2427–37.

Fredriksson I, Venniro M, Reiner DJ, Chow JJ, Bossert JM, Shaham Y. Animal models of drug relapse and craving after voluntary abstinence: a review. Pharm Rev. 2021;73:1050–83.

Vanderschuren LJMJ, Ahmed SH. Animal models of the behavioral symptoms of substance use disorders. Cold Spring Harb Perspect Med. 2021;11:a040287.

Kuhn BN, Kalivas PW, Bobadilla AC. Understanding addiction using animal models. Front Behav Neurosci. 2019;13. https://doi.org/10.3389/fnbeh.2019.00262 .

Venniro M, Shaham Y. An operant social self-administration and choice model in rats. Nat Protoc. 2020;15:1542–59.

Thapar A, Collishaw S, Pine DS, Thapar AK. Depression in adolescence. Lancet 2012;379:1056–67.

Costello EJ, Egger H, Angold A. 10-Year research update review: the epidemiology of child and adolescent psychiatric disorders: I. Methods and public health burden. J Am Acad Child Adolesc Psychiatry. 2005;44:972–86.

Snyder HR, Kaiser RH, Whisman MA, Turner AEJ, Guild RM, Munakata Y. Opposite effects of anxiety and depressive symptoms on executive function: the case of selecting among competing options. 2014;28:893–902. https://doi.org/10.1080/026999312013859568 .

McDermott LM, Ebmeier KP. A meta-analysis of depression severity and cognitive function. J Affect Disord. 2009;119:1–8.

Volkow ND, Koob GF, Croyle RT, Bianchi DW, Gordon JA, Koroshetz WJ, et al. The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev Cogn Neurosci. 2018;32:4–7.

Download references

Acknowledgements

This work was supported by grant 1RO1 DA042490-01A1 awarded to Janna Cousijn and Francesca Filbey from the National Institute on Drug Abuse/National Institutes of Health. The grant supported the salaries of authors Lauren Kuhns, Emese Kroon, and Janna Cousijn. Thank you to Claire Gorey (CG) for running the initial search and aiding in the screening process.

Author information

Authors and affiliations.

Neuroscience of Addiction (NofA) Lab, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands

Lauren Kuhns, Emese Kroon, Gabry Mies & Janna Cousijn

The Amsterdam Brain and Cognition Center (ABC), University of Amsterdam, Amsterdam, The Netherlands

Lauren Kuhns, Emese Kroon & Janna Cousijn

Department of Animals in Science and Society, Division of Behavioural Neuroscience, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands

Heidi Lesscher

Department of Psychology, Education & Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands

Janna Cousijn

You can also search for this author in PubMed   Google Scholar

Contributions

LK conducted the systematic searches; LK, EK, GM, and JC screened the citations for exclusion and inclusion; LK, EK, HL, and JC wrote the review; LK, EK, HL, GM, and JC revised the manuscript.

Corresponding author

Correspondence to Lauren Kuhns .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Kuhns, L., Kroon, E., Lesscher, H. et al. Age-related differences in the effect of chronic alcohol on cognition and the brain: a systematic review. Transl Psychiatry 12 , 345 (2022). https://doi.org/10.1038/s41398-022-02100-y

Download citation

Received : 26 October 2021

Revised : 21 June 2022

Accepted : 28 July 2022

Published : 25 August 2022

DOI : https://doi.org/10.1038/s41398-022-02100-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Disclaimer » Advertising

• HealthyChildren.org

• Previous Article
• Next Article

Epidemiology

Alcohol use by adolescents, hazards of alcohol use, factors that contribute to harmful use, genetic, familial, and environmental factors, other factors, adolescent developmental and neurobiological factors, normal adolescent brain development, effect of substances on adolescent brain development, screening and brief interventions, conclusions, lead authors, committee on substance use and prevention, 2018–2019, former committee member, alcohol use by youth.

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• CME Quiz Close Quiz
• Open the PDF for in another window
• Get Permissions
• Cite Icon Cite
• Search Site

Sheryl A. Ryan , Patricia Kokotailo , COMMITTEE ON SUBSTANCE USE AND PREVENTION , Deepa R. Camenga , Stephen W. Patrick , Jennifer Plumb , Joanna Quigley , Leslie Walker-Harding; Alcohol Use by Youth. Pediatrics July 2019; 144 (1): e20191357. 10.1542/peds.2019-1357

Download citation file:

• Ris (Zotero)
• Reference Manager

Alcohol use continues to be a major concern from preadolescence through young adulthood in the United States. Results of recent neuroscience research have helped to elucidate neurobiological models of addiction, substantiated the deleterious effects of alcohol on adolescent brain development, and added additional evidence to support the call to prevent and reduce underage drinking. This technical report reviews the relevant literature and supports the accompanying policy statement in this issue of Pediatrics .

Alcohol is the substance most widely used by adolescents, often in large volumes, although the minimum legal drinking age across the United States is 21 years. 1 Some people may initiate harmful alcohol consumption in childhood. The prevalence of problematic alcohol use continues to escalate from adolescence into young adulthood. Heavy episodic drinking by students enrolled in college remains a major public health problem. In results of recent research, it has been indicated that brain development continues well into early adulthood 2 and that alcohol consumption can interfere with such development, underscoring concerns that alcohol use by youth is an even greater pediatric health concern than previously thought. 3 , 4 This technical report supports the accompanied policy statement that outlines recommendations from the American Academy of Pediatrics (AAP). 5  

Alcohol, tobacco, and marijuana remain the substances most widely used by youth in the United States. There is both heartening and less heartening news about the use of alcohol by US youth, however. The 2018 Monitoring the Future Study, supported by the National Institute of Drug Abuse and conducted by the University of Michigan, is now in its 44th year of tracking the prevalence of alcohol, tobacco, and other drug use and youth perceptions of such use. A sample of more than 45 000 young people in eighth, 10th, and 12th grade in approximately 380 private and public secondary schools in the United States provides these data. 1 The data include use by youth in all 3 grades in their lifetime, in the past year (annual use), and in the 30 days preceding the survey as well as “binge” drinking, defined as the consumption of 5 or more drinks in a row on at least 1 occasion in the past 2 weeks, and “extreme binge drinking,” defined as the consumption of 10 or more drinks in a row in the previous 2 weeks. The good news is that there has been a long, substantial decline in alcohol use in all of these categories from peaks in the 1990s. For example, in 1997, the highest number of youth reported using alcohol over the previous year (61%); by 2018, 36.1% of youth in the 3 grades surveyed reported use in the 12 months before the survey. Perhaps even more important, the percentage of young people in the 3 grades reporting binge drinking decreased by half or more from peaks in 1997. In 2017, rates of lifetime prevalence, annual prevalence, and 30-day prevalence of alcohol use in all 3 grades showed plateauing, which was interpreted as a sign that the trend of declining rates was at an end. In addition, in 2017, 4% of eighth-graders, 10% of 10th-graders, and 17% of 12th-graders still reported binge drinking in the past 2 weeks, all slightly increased from 2016. 1 However, in 2018, declines in rates of use continued: the 30-day prevalence rates for eighth, 10th, and 12th-graders was 8%, 19%, and 30%, respectively, and the prevalence of binge drinking in the previous 2 weeks in 10th- and 12th-graders declined to 9% and 14%, respectively, although it remained at 4% for eighth-graders. For the 3 grades combined, this survey documented the lowest levels of alcohol use and binge drinking that have been recorded to date. 1 The criterion used for binge drinking as 5 or more drinks in a row has been thought to be too high, especially for younger children and girls, with the literature suggesting that for 9- to 13-year-old children and 14- to 17-year-old girls, binge drinking should be defined as 3 or more drinks. For boys, binge drinking should be defined as 4 or more drinks for those 14 or 15 years old and 5 or more drinks for those 16 or 17 years old. 6  

To examine higher levels of consumption by 12th-graders, the Monitoring the Future study has more recently been tracking 2 levels of extreme binge drinking, defined as having 10 or more or 15 or more drinks in a row on at least 1 occasion in the preceding 2 weeks. These measures have also declined from 11% in 2005 (the first year of this category’s measurement) to 4.6% for the 10 drinks in a row category and from 6% to 2.5% for 15 drinks in a row in 2018. Each of these measures increased slightly from 2016 to 2017 but resumed the decline in 2018. 1 Declines in perceived availability as well as increased peer disapproval of binge drinking may be some of the factors that are contributing to these lower prevalence numbers. 1 These epidemiologic statistics are corroborated by data from 2 other large surveys of youth alcohol use in the United States: the Youth Risk Behavior Survey, conducted biannually by the Centers for Disease Control and Prevention, and the National Survey on Drug Use and Health, conducted annually by the Substance Abuse and Mental Health Services Administration. 7 , 8  

Use of alcohol at an early age is particularly problematic and is associated with future alcohol-related problems. 9 , – 11 Data from the National Longitudinal Alcohol Epidemiologic Study indicate that the prevalence of both lifetime alcohol dependence and alcohol abuse, as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria, show a striking decrease with increasing age at the onset of alcohol use. 9 According to the National Longitudinal Alcohol Epidemiologic Study, for people 12 years or younger at first use, the prevalence of lifetime alcohol dependence was 40.6%. In contrast, for people who initiated alcohol consumption at 18 years of age, the prevalence was 16.6%, and for those who initiated drinking at 21 years, the prevalence was 10.6%. Similarly, the prevalence of lifetime alcohol abuse was 8.3% for those who initiated use at 12 years or younger, 7.8% for those who initiated at 18 years, and 4.8% for those who initiated at 21 years. The contribution of age at alcohol use initiation to the odds of lifetime dependence and abuse varied little across sex and racial subgroups in the study. 9 In analyses of data from subsequent surveys, researchers have also illustrated this relationship between early initiation of drinking and subsequent alcohol use disorder (AUD). 12 , – 15  

Adolescent alcohol exposure covers a spectrum, from primary abstinence to alcohol dependence. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) 16 defines AUD as follows:

A problematic pattern of alcohol use leading to clinically significant impairment or distress as manifested by 2 or more of the following, occurring during a 12-month period: 1. Alcohol is often taken in larger amounts or over a longer period than was intended. 2. There is a persistent desire or unsuccessful efforts to cut down or control alcohol use. 3. A great deal of time is spent in activities necessary to obtain alcohol, use alcohol, or recover from its effects. 4. Craving, or a strong desire or urge to use alcohol. 5. Recurrent alcohol use results in a failure to fulfill major role obligations at work, school, or home. 6. Continued alcohol use despite having persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of alcohol. 7. Important social, occupational, or recreational activities are given up or reduced because of alcohol use. 8. Recurrent alcohol use in situations in which it is physically hazardous. 9. Alcohol use is continued despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to have been caused or exacerbated by alcohol. 10. Tolerance, as defined by either of the following: a. A need for markedly increased amounts of alcohol to achieve intoxication or desired effect. b. A markedly diminished effect with continued use of the same amount of alcohol. 11. Withdrawal, as manifested by either of the following: a. The characteristic withdrawal syndrome for alcohol. b. Alcohol is taken to relieve or avoid withdrawal symptoms. Reprinted with permission from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (Copyright 2013). American Psychiatric Association. All Rights Reserved.

The disorder is characterized as mild (2–3 symptoms), moderate (4–5 symptoms), or severe (6 or more symptoms). Because these diagnostic criteria were developed largely from research and clinical work with adults, there are limitations to applying these diagnostic criteria to classify alcohol use and associated risks to adolescents. 17 , – 19 As defined by the DSM-5, an adolescent, especially a younger one, may not have had time to develop an AUD, yet the adolescent may be engaging in very risky behavior. Despite being viewed as an improvement in specificity for adolescents, the applicability of these revised criteria may still be limited in that several of the criteria, such as withdrawal, are not typically experienced by adolescents, and other criteria, such as tolerance, have low sensitivity for adolescents. 20 Tolerance can be anticipated as a developmental process that will occur over time in most adolescents who drink. 17 Thus, an adolescent may present with a subsyndromal level of alcohol use that may not meet the formal threshold for addiction or an AUD but that may still be associated with significant impairments in social functioning and well-being. 21 These limitations to applying a diagnostic algorithm designed for adults to children and youth are often cited as a reason for advocating for the development of more age-appropriate criteria.

Alcohol misuse, although not a formal diagnosis, can be defined as “alcohol-related disturbances of behavior, disease, or other consequences that are likely to cause an individual, his/her family, or society harm now or in the future.” 22 Because the term “alcohol misuse” encompasses earlier stages of AUDs that do not meet diagnostic criteria, it may be a more useful concept clinically in pediatrics and when developing alcohol use primary prevention programs for youth.

Underage drinking is associated with wide range of negative consequences for adolescents, including adverse effects on normal brain development and cognitive functioning, risky sexual behavior, physical and sexual assaults, injuries, AUD, blackouts, alcohol overdose, and even death. When compared with use by adults, alcohol use by adolescents is much more likely to be episodic and in larger volumes (binge drinking), which makes alcohol use by those in this age group particularly dangerous. Rapid binge drinking puts the teenager at even higher risk of alcohol overdose or alcohol poisoning, in which suppression of the gag reflex and respiratory drive and hypoglycemia can be fatal. Binge drinking and its sequelae of elevated blood alcohol concentration (BAC) are especially dangerous for young people who, when compared with adults, may be less likely to be sedated and, therefore, more likely to engage in activities such as driving despite impairment in coordination and judgment. 23  

Alcohol use is a major contributor to the leading causes of adolescent death (ie, motor vehicle crashes, homicide, and suicide) in the United States. Motor vehicle crashes rank as the leading cause of death for US teenagers and young adults. Data from the 2017 Youth Risk Behavior Survey found that during the 30 days preceding the survey, 16.5% of high school students nationwide had ridden one or more times in a car or other vehicle driven by someone who had been drinking alcohol. Of the 62.6% of high school students reporting having driven in the 30 days preceding the survey, 5.5% of students had driven a car or other vehicle at least once when they had been drinking alcohol during this time. 7 These data represent a significant linear decline in reports of use while driving after alcohol use or riding with someone who had been drinking since 1991, when rates reported for riding with a drinking driver and driving oneself after drinking were 39.9% and 16.7%, respectively. 7 In further analysis of the Youth Risk Behavior Survey data, it was shown that in 2011, the prevalence of drinking and driving was more than 3 times higher among those youth who binge drank compared with those who reported current alcohol use but not binge drinking (32.1% vs 9.7%). 24  

The important relationship of alcohol use and motor vehicle crashes involving youth is also highlighted by the fact that after the legal drinking age was changed uniformly to 21 years across the United States, the number of motor vehicle fatalities in individuals younger than 21 years decreased significantly. 25 Since 1998, every state has enacted laws establishing a lower BAC for drivers younger than 21 years, referred to as “zero tolerance laws.” These laws are important because young people who drive after consuming any amount of alcohol pose risk to themselves and others. These laws are also estimated to have reduced alcohol-involved fatal crashes among inexperienced drivers by 9% to 24%. 26 Data show that for each 0.02 increase in BAC, the relative risk of a 16- to 20-year-old driver dying in a motor vehicle crash is estimated to be more than double. 27 Graduated driver licensing (GDL) systems have now been adopted in all 50 states and the District of Columbia. 28 These laws indirectly affect drinking and driving by restricting nighttime driving and the transportation of young passengers in the early months after licensure. In a recent national study, it was shown that GDL nighttime driving restrictions were associated with a 13% reduction in fatal drinking driver crashes among drivers 16 to 17 years old compared with drivers 19 to 20 years old who were not under these restrictions. 29 In a Cochrane review, the implementation of GDL was shown to be effective in reducing the crash rates of young drivers and specifically alcohol-related crashes in most studies in the United States and internationally. 30  

Adolescents who report binge drinking violate GDL laws more frequently and engage in more high-risk driving behaviors, such as speeding and using a cell phone while driving. They also received more traffic tickets and reported having more crashes and near crashes. 31 The importance of the additive effect of alcohol with other illicit substances, particularly marijuana, in contributing to motor vehicle crashes should also not be underestimated. Researchers have suggested that the combination of marijuana and alcohol significantly increases the likelihood of a motor vehicle crash, particularly at levels of alcohol that are below legal limits. For example, Dubois et al 32 found that the odds of a motor vehicle crash increased from 66% to 117% with BACs at 0.05 and 0.08, respectively, to 81% and 128% when detectable levels of tetrahydrocannabinol (THC) were present at these same BACs.

Although legislation has greatly improved transportation safety, young people still are involved in a high proportion of fatal motor vehicle accidents involving alcohol. In 2016, the National Highway Traffic Safety Administration reported a 5.6% increase in traffic fatalities from 2015. 33 Although many factors were reported as responsible for this increase, 10 497 people were killed as a direct result of alcohol-impaired driving crashes, accounting for 28% of the total motor vehicle traffic fatalities (37 461 people) in the United States. 33 In fatal crashes in 2016, the second highest percentage of drivers with BACs of 0.08 or higher was for drivers 21 to 24 years old at 26%; the rate for drivers 16 to 20 years old was 15%. 34  

Underage alcohol use and AUD in adolescents are also associated with other mental and physical disorders. AUD is a risk factor for suicide attempts. 35 Miller et al 36 estimated that 9.1% of suicide attempts resulting in hospitalization by people younger than 21 years involved alcohol and that 72% of these cases were attributable to alcohol. Of note, higher minimum legal drinking ages in the United States have been associated with lower youth suicide rates. 37 Psychiatric conditions most likely to co-occur with AUD include mood disorders, particularly depression; anxiety disorders; attention-deficit/hyperactivity disorder; conduct disorders; bulimia; posttraumatic stress disorder; and schizophrenia. 38 Associated physical health problems include trauma sequelae, 39 sleep disturbance, modestly elevated serum liver enzyme concentrations, and dental and other oral abnormalities, 40 despite relatively few abnormalities being evident on physical examination. 40 , 41  

Early alcohol initiation, in particular, has been associated with greater involvement in a number of high-risk behaviors, such as sexual risk-taking (unprotected sexual intercourse, multiple partners, being drunk or high during sexual intercourse, and pregnancy), academic problems, other substance use, and delinquent behavior in mid to later adolescence. 18 , 19 , 38 , 42 , – 45 By young adulthood, early alcohol use is associated with employment problems, other substance abuse, and criminal and violent behavior. 42  

Twin studies in adult populations have consistently demonstrated genetic influences on the use of alcohol, 46 , – 48 but less research has examined genetic influences in the adolescent age range. 49 , – 51 Through a sibling, twin, and adoption study of adolescents, Rhee et al 52 examined the relative contribution of genetics and environment on initiation, use, and problem use of substances. The results of this study demonstrated that for adolescents (compared with adult twin study findings), the magnitude of genetic influences was greater than the effect of shared environmental influences on problem alcohol or drug use. The reverse was true, however, for initiation of use, with shared environmental factors more important than genetic background. In a recent study, Chorlian et al 53 concluded that when alcohol is consumed regularly in the youngest age range, affecting a less-mature brain, the addiction-producing effects in those who have 2 copies of the genetic allele of the cholinergic M2 receptor gene are accelerated, which can lead to rapid transition from regular alcohol use to alcohol dependence. It has been suggested that gene and environmental effects may vary depending on developmental period of the individual and the stage of the problematic use or addiction. 54  

It has been suggested that the progression to heavy or compulsive alcohol or other drug use is strongly influenced by genetics. 54 Specific genetic studies have helped to elucidate the scientific basis for the relationship observed between early initiation of drinking and subsequent AUD. 12 , – 15 A longitudinal study of the genetic and neurophysiologic correlates of AUD in adolescents and young adults has identified neurophysiological endophenotype differences and variants of the cholinergic M2 receptor gene in adolescent brains that have an age-specific influence on the age of onset of such a disorder. 53 The authors reported that among people who became regular users of alcohol before the age of 16 years, a majority of those who became alcohol dependent within 2 years had the risk genotype, whereas the majority of people who became alcohol dependent 4 or more years after the onset of regular drinking did not have the risk genotype. 53 Another study also found an association between a polymorphism of the μ-opioid receptor encoding gene and adolescent alcohol use. 55  

In a number of studies, researchers have demonstrated the importance of family and social factors on the initiation and early use of alcohol and other drugs. Independent of genetic risk, families play an important role in the development of alcohol and other drug problems in youth, and exposure to alcohol or other drug use disorders of parents predicts substance use disorders in children. 56 Generational transmission has been widely hypothesized as a factor shaping the alcohol use patterns of youth. Whether through genetics, social learning, or cultural values and community norms, researchers have repeatedly found a correlation between youth drinking and a number of family factors, such as the drinking practices of parents. 57 , 58 Results of these studies suggest that policies primarily affecting adult drinkers, such as pricing and taxation, hours of sale, and on-premises drink promotions, may also affect underage drinking. Foley et al 59 found in a national sample ( n = 6245) of teenagers 16 to 20 years old whose parents provided alcohol to them and supervised their drinking were less likely to report being regular drinkers or binge drinkers than those who obtained alcohol through friends or nonparent relatives and participated in unsupervised drinking. They also found that teenagers who obtained alcohol from parents for parties that were unsupervised by those parents reported the highest rates of regular and binge drinking. Although the practice of parents buying alcohol for their teenagers and supervising their drinking cannot be recommended, this study highlights the role that parental behaviors toward alcohol can have on an adolescent’s subsequent drinking behaviors. Parental monitoring of children’s use, the convincing conveyance and consistent enforcement of household rules governing use, and perceived consequences of “getting caught” by parents after drinking all protected youth from drinking behaviors. 59 , – 62  

In the United States, approximately 7.5 million children younger than 18 years (10.5% of all children) are reported to live with at least 1 parent who had an AUD in the past year. 63 These children are at increased risk of many behavioral and medical problems, including depression, anxiety disorders, problems with cognitive and verbal skills, and parental abuse or neglect. 64 Children who have a parent with an AUD are also estimated to be 4 times more likely than other children to develop alcohol problems themselves. 65 See the AAP clinical report “ Families Affected by Parental Substance Use ” for further information. 66  

Having friends who use alcohol, tobacco, or other substances is one of the strongest predictors of substance use by youth. 67 Social and physical settings for underage drinking also affect patterns of alcohol consumption. In a special data-analytic study conducted in 2012, the Substance Abuse and Mental Health Services Administration and the Center for Behavioral Health Statistics and Quality, using data from the National Survey on Drug Use and Health, 68 , 69 found that the usual number of drinks consumed by young people is substantially higher when 2 or more other people are present than when drinking by oneself or with 1 other person. Drinking in the presence of others is by far the most common setting for youth, with more than 80% of youth who had consumed alcohol in the past month reporting doing so when at least 2 others were present. 68 , 69 Most young people drink in social contexts that appear to promote heavy consumption. Private residences are the most common setting for youth alcohol consumption, and the majority of underage drinkers report drinking in either someone else’s home or their own. The next most popular drinking locations reported are at a restaurant, bar, or club; at a park, on a beach, or in a parking lot; or in a car or other vehicle. Older youth in the 18- to 20-year-old age group are more likely than younger adolescents to report drinking in restaurants, bars, or clubs, although the absolute rates of such drinking are low compared with drinking in private residences. The data that demonstrate that underage drinking occurs primarily in social settings in groups at a private residence are consistent with previous research findings that underage drinking parties are high-risk settings for binge drinking and associated alcohol problems. 70 Similar findings exist for binge drinking by college students. 71  

Media influences on the use of alcohol by young people are substantial. 72 , 73 Exposure to alcohol marketing increases the likelihood to varying degrees that young people will initiate drinking and drink at higher levels. 74 , 75 Grenard et al 76 have recently demonstrated using prospective data that exposure to alcohol advertising and liking of those ads by adolescents in seventh grade has a significant influence on the severity of alcohol-related problems reported by 10th grade. In 2003, the US alcohol industry voluntarily agreed not to advertise products on television programs for which greater than 30% of the audience is reasonably expected to be younger than 21 years. The National Research Council of the Institute of Medicine (now the National Academy of Medicine) proposed in that same year that the industry standard should move toward a 15% threshold for alcohol advertising on television. A recent evaluation of adherence to these standards conducted in 25 of the largest US television markets revealed that the alcohol industry has not consistently met its self-regulatory standards, indicating the need for continued public health surveillance of youth exposure to alcohol advertising. 77 Young people can be influenced in their alcohol use by other media, including movies, the Internet, and social media. A 2014 study demonstrated that adolescents with exposure to friends’ risky online displays are more likely to use alcohol themselves. 78  

Over the past decade, great strides have been made in understanding the neurobiological basis of addiction. Studies investigating normal brain development have also yielded information that elucidates the effects of alcohol and other drugs on the developing adolescent brain. As summarized by Sowell et al, 79 results of postmortem studies have shown that myelination, a cellular maturational process of the lipid and protein sheath of nerve fibers, begins near the end of the second trimester of fetal development and extends well into the third decade of life and beyond. Autopsy results have revealed both a temporal and spatial systematic sequence of myelination, which progresses from inferior to superior and posterior to anterior regions of the brain. This sequencing results in initial brain myelination occurring in the brainstem and cerebellar regions and myelination of the cerebral hemispheres and frontal lobes occurring last. Converging evidence from electrophysiological and cerebral glucose metabolism studies shows that frontal lobe maturation is a relatively late process, and neuropsychological studies have shown that performance of tasks involving the frontal lobes continues to improve into adolescence and young adulthood.

Sowell et al 79 documented reduction in gray matter in the regions of the frontal cortex between adolescence and adulthood, which probably reflects increased myelination in the peripheral regions of the cortex. Gray matter loss, with pruning and elimination of neural connections during normative adolescent development, reflects a sculpting process that progresses in a caudal-to-rostral direction. The prefrontal cortex is the last area to reach adult maturation, and this may not be completed until young adulthood. 80 These changes are thought to improve cognitive processing in adulthood, such as cognitive control (ie, the ability to discount rewards) and executive functioning in risk-reward decision-making. 80 Results of neuropsychological studies have shown that the prefrontal cortex areas are essential for functions such as response inhibition, emotional regulation, planning, and organization, all of which may continue to develop between adolescence and young adulthood. Conversely, parietal, temporal, and occipital lobes show little change in maturation between adolescence and adulthood. Parietal association cortices are involved in spatial relationships and sensory functions, and the lateral temporal lobes are associated with auditory and language processing; these functions are largely mature by adolescence. Hence, the observed patterns of brain maturational changes are consistent with cognitive development. 79 Connections are being fine-tuned in adolescence with the pruning of overabundant synapses and the strengthening of relevant connections with development and experience. It is likely that the further development of the prefrontal cortex aids in the filtering of information and suppression of inappropriate actions. 80  

Our current understanding of the biology of brain development in the adolescent has lent support to several models that explain the vulnerability of the adolescent to AUDs. One of these models posits that because the subcortical systems that are important for incentive and reward mature earlier than the areas responsible for cognitive control, this results in an “imbalance.” Thus, activation and reinforcement of those incentive and reward pathways in response to the substance used may occur. This leaves youth uniquely vulnerable to the motivational aspects of alcohol and other drugs and the development of problematic substance use. 21 Without the modulating effect of cognitive control, an adolescent may be less able to resist the short-term result of using substances, compared with long-term, goal-oriented behaviors, such as abstaining. Given that these maturation imbalances in the development of different brain systems is greatest during adolescence, it is not surprising that teenagers may not be able to regulate the emotional or motivational states experienced with the use of substances as adults. 3 , 21 Researchers studying the role of several neurotransmitters in the development and maintenance of substance use and dependence have elucidated the underlying effects of these neurotransmitters in key areas of the brain involved in substance dependence and addiction.

Alcohol interacts with a number of neurotransmitter systems throughout the brain, including the inhibitory neurotransmitters γ-aminobutyric acid and glutamate, that are responsible for the euphoric as well as sedating effects of alcohol intoxication. In addition, neurons that release the neurotransmitter dopamine are activated by all addictive substances, including alcohol. The activation of dopamine release in the nucleus accumbens subregion of the basal ganglia, the area involved in both reward experiences and motivation, results in the “rewarding effect” experienced by users of alcohol and other drugs. In addition, the brain’s endogenous opioid system and the 3 opioid receptors (μ, κ, and δ) interact with the dopamine system and play a key role in the effect that substances such as alcohol have on “rewards” and incentives to continue use of a substance. Brain imaging studies have demonstrated that both the opioid and the dopamine neurotransmitter systems are activated during alcohol and other substance use. The reader is referred to the comprehensive discussion of this in the Surgeon General’s 2016 report: “Facing Addiction in America: The Surgeon General’s Report on Alcohol, Drugs, and Health.” 81  

Determining the specific effect of alcohol exposure or dependence on brain function and structure is challenging given potential biological differences that are normative versus those reflective of recent or past use of substances other than alcohol or of comorbid psychiatric disorders. In several studies, researchers using animal models have demonstrated the inhibition of the growth of adolescent neural progenitor cells with acute alcohol ingestions; similar results were observed with binge alcohol ingestion. 82 , 83 Chronic alcohol ingestion in animal models also disrupts neurogenesis primarily in the hippocampus, an area of the brain especially important for memory. 84  

In adolescents, varying levels of alcohol ingestion ranging from binge-pattern drinking to AUDs have been correlated with both structural and functional brain changes. 21 For example, hippocampal asymmetry was increased and hippocampal volumes were decreased in adolescents with alcohol abuse or dependence patterns compared with both controls who did not use substances and those reporting both alcohol and cannabis use. 85 In another study, adolescents with AUDs had smaller overall and white matter prefrontal cortex volumes compared with nondrinking controls, with girls with AUDs having larger decreases than boys with AUDs. 86 In studies in which researchers used diffusion tensor imaging techniques, which are used to assess white matter architecture, adolescent binge drinking or alcohol use was correlated with reduced factional anisotropy, which is an index that measures neural fiber tract integrity and organization. 87 , – 90 These changes in white matter tract integrity were seen in multiple brain pathways, including those in the corpus callosum as well as limbic, brainstem, and cortical projection fibers. 87 , – 89 It is important to note, however, that all of these studies are correlational and that a true causal relationship between alcohol use in youth and subsequent brain changes has not been demonstrated with this research.

Deficits in neurocognitive function have also been found in adolescents using both alcohol and marijuana compared with controls using no substances. These include deficits in attention, visuospatial processing in teenagers experiencing alcohol withdrawal, poorer performance with verbal and nonverbal retention tasks in adolescents reporting protracted alcohol use, and reduced speed of information processing and overall memory and executive functioning in those reporting alcohol dependence. 4 , 91 , – 93 These abnormalities are postulated to result, in part, from the morphologic and functional changes seen in specific brain areas involved in memory (hippocampus) and executive function and decision-making (prefrontal cortex). In addition, genetic predisposition, such as family history of alcoholism, may enhance the vulnerability of specific brain areas, such as the hippocampus, to the effects of alcohol use in adolescents. 94 These potential genetic factors and epigenetic contributors (the impact of environmental and social factors on gene expression) are areas of active study. 21 The Adolescent Brain and Cognitive Development study, supported by the National Institutes of Health and the National Institute on Drug Abuse, is a 10-year longitudinal study that started in 2015 designed to assess the environmental, social, genetic, and biological factors involved in adolescent brain and cognitive development. The initial year of recruitment and baseline assessment of 11 875 10-year-olds has been completed, and this study holds great promise in terms of informing scientists and clinicians of the effect of licit and illicit substances, among many factors being studied, on the trajectory of brain development and cognitive functioning over the course of adolescent and young adulthood. 95  

Several recent Cochrane reviews have examined the prevention of substance abuse in young people through family-based prevention programs, 96 universal school-based prevention programs, 97 brief school-based interventions, 98 universal multicomponent prevention programs, 99 and mentoring programs. 100 Although there were variations in programs in all of these reviews and generally few high-quality studies, all of these prevention strategies showed some success. Family-based prevention programs typically take the form of supporting the development of parenting skills, including parental support, nurturing behaviors, establishing clear boundaries or rules, and parental monitoring. The development of social and peer resistance skills and the development of positive peer affiliations can also be addressed in these programs. The Cochrane systematic review found that “the effects of family-based prevention are small but generally consistent and persistent into the medium- to longer-term” 96 and are consistent with an earlier systematic review supporting the effectiveness of family-focused prevention programs. 101  

Recognition of the pervasive use of alcohol among young people, the hazards that may be encountered with even low-level use, and the association between early initiation of alcohol use and future alcohol problems underscores the need to integrate our approaches to alcohol and other drug use by youth into pediatric primary care. The AAP recommends that pediatricians screen and discuss substance use as part of anticipatory guidance and preventive care. 102 , – 104 Screening, brief intervention, and referral to treatment (SBIRT) for youth is such an integrated approach that has grown in recent years to bridge the gap between universal prevention programs and specialty substance abuse treatment by pediatric primary care providers. 105 , 106 The reader is referred to the AAP clinical report on SBIRT for pediatricians. 104 The effectiveness of SBIRT is well supported for addressing hazardous use of alcohol by adults in medical settings, but there is less evidence for its effectiveness in adolescents. 107 , – 115  

Several screening strategies have been validated and used to identify youth at risk for or involved in the use of alcohol and other substances that can be incorporated into general psychosocial screening efforts, such as interviewing strategies like HEADSS (home, education, activities, drugs and alcohol, sex, suicidality) 116 and SSHADESS (strengths, school, home, activities, drugs and alcohol, substance use, emotions and depression, sexuality, safety). 117 The CRAFFT is a tool developed for screening adolescents for alcohol and other substance use with 3 introductory questions followed by 6 questions using the CRAFFT mnemonic. 118 It has been well validated and is brief enough for use in busy clinical settings. 119 In 2011, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) collaborated with the AAP to develop a brief screening tool to assist health care providers in identifying alcohol use, AUD, and risk for use in children and adolescents ages 9 to 18 years. 120 This tool includes brief 2-question screeners and support materials about brief intervention and referral to treatment and is designed to help surmount common obstacles to youth alcohol screening in primary care. The screen administration varies by age and grade and focuses on drinking frequency over the previous 12 months to determine level of risk. 121 This tool has been expanded to include tobacco and other substances and is sensitive and specific for identifying substance use disorders in a pediatric clinic population. 122 Although developed for use primarily in the primary care setting, Spirito et al 123 have demonstrated its usefulness in screening for AUDs in pediatric emergency settings.

In several studies, researchers have confirmed the validity of using a single question about the frequency of use of alcohol and other drugs over the previous 12 months to determine level of risk. 124 Studying a population of adolescents and young adults in rural Pennsylvania, Clark et al 124 compared a single question of past-year frequency of alcohol use versus comprehensive diagnostic interviews on the basis of DSM-5 criteria for AUD to determine the validity of this question in identifying problematic alcohol use. They found both high sensitivity and specificity for adolescents ages 12 to 17 years using 3 or more days with 1 or more drinks as a cutoff to identify AUDs. For young adults 18 to 20 years of age, using 12 or more days or 12 or more drinks over the previous year also had excellent ability to identify AUDs. 124 Levy et al 125 have also validated a single-question screen, referred to as the “S2BI”: “In the past year, how many times have you used alcohol?” They have found that responses that include never, once or twice, monthly, weekly, almost daily, or daily can differentiate between those with mild, moderate, and severe AUDs, per DSM-5 criteria, and can indicate those individuals who would benefit from education versus brief intervention or more-specific substance abuse treatment. 125 This screening question has also been shown to identify problematic use of illicit drugs, over-the-counter medications, and tobacco. These screening tools, as well as the NIAAA screening tool, continue to be validated, and the results reported here are promising.

Questions often remain about how to incorporate parents into this screening process and how and when to provide confidentiality for a youth’s report of underage alcohol use. The NIAAA 2-question screening tool recommends that screening begin as early as 9 to 11 years of age, and given that most preteens will be questioned in the presence of a parent or guardian, this offers an opportunity to discuss the parent’s philosophy regarding alcohol use by minors, situations in which they might deem it appropriate (such as at holidays), and their own practices regarding their own drinking and consequences for their child’s drinking. This screening can also be performed routinely for all adolescents during preventive care visits. For the older adolescent, whenever possible, it is preferable to include parents in any discussion with a youth who reports drinking; however, when this is seen by the youth as a major deterrent to his or her alliance with the provider and there are no “red flag” behaviors that are believed to be unsafe, such as the youth riding or driving after drinking, heavy binge drinking, or when an AUD is suspected, maintaining confidentiality and counseling the adolescent is often preferable because this maintains the alliance between the provider and the adolescent. There are no hard and fast rules as to when parents should be included in discussions about their adolescent’s alcohol use; this can be a delicate matter and is generally a judgment call by the primary medical provider, unless the safety of the youth is put in jeopardy by drinking behaviors. Studies have shown that parents tend to underestimate the extent of their teenagers’ drinking behaviors, and including parents in the discussions with their teenagers often serves to highlight a greater amount of use than what is anticipated by parents. Discussions about minimizing risk, such as contracting with the youth to call parents if they are concerned about friends drinking while driving, may also be helpful. Students Against Destructive Decisions is a youth-focused organization promoting healthy and safe decision-making, especially around driving behaviors. The Students Against Destructive Decisions Web site ( https://www.sadd.org/what-we-care-about/ ) provides educational information as well as the “Contract for Life,” which is a contract that teenagers sign along with their parents, promising to avoid alcohol and other substances when driving.

Once screening has been conducted and the level of risk has been determined, the provider can provide anticipatory guidance supporting abstinence, perform brief intervention strategies, or refer the adolescent for further evaluation or to a higher level of treatment. Brief intervention strategies are short, efficient, office-based techniques that health care providers who work with adolescents can use to detect alcohol use and intervene. On the basis of the principles of motivational interviewing, these procedures can be readily performed in the office setting, build on the individual’s readiness to change drinking behaviors, and support the adolescent’s need for involvement in one’s own health care choices and decisions. Harris et al 105 have provided an excellent review of counseling strategies at different levels of risk behaviors of young people, and the NIAAA Alcohol Screening Practitioner Guide provides strategies for brief intervention at different ages. 120 D’Onofrio and colleagues 126 have developed a brief (5- to 7-minute) scripted intervention approach, the Brief Negotiation Interview (BNI), for use with adults reporting harmful and hazardous alcohol use in the emergency setting, and Ryan et al 127 have adapted this BNI for use in a pediatric residency training setting for use with adolescents in a primary care clinic. Pediatrics residents trained in the BNI reported that this intervention was easily learned and highly applicable in clinical settings with teens reporting alcohol and other illicit substance use. 127  

The National Institute on Drug Abuse publication “Principles of Adolescent Substance Use Disorders Treatment: A Research Guide” is a comprehensive guide of evidence-based approaches to treating adolescent substance use disorders and emphasizes that treatment is not “one size fits all” but requires taking into consideration the needs of the individual, including his or her developmental stage; cognitive abilities; the influence of friends, family, and others; and mental and physical health conditions. 128 The AAP clinical report on SBIRT also includes a list of optimal standards for a substance use disorder treatment program. 66 Behavioral therapies are effective in treating alcohol and other substance use disorders as well as multiple substances and include individual therapy, such as cognitive-behavioral therapy and motivational enhancement therapy. Family-based approaches, including multidimensional family therapy and multisystemic therapy, have been proven to be effective. 129 Addiction medications for AUD include acamprosate, disulfiram, and naltrexone. Medication-assisted therapies are not commonly used to treat adolescent AUDs but may be used in specific circumstances. These medications are approved by the US Food and Drug Administration for treatment of people 18 years and older.

In most cases, the primary care pediatrician’s initial role is to identify, through screening, teenagers in need of intervention and referral for further treatment. However, continued involvement by the primary pediatric provider with the teenager and the family, through regular follow-up and care coordination, is essential in any treatment plan after referral.

Although it is heartening that alcohol use among adolescents and youth has decreased over the last several years, researchers have even more clearly elucidated links between alcohol use and deleterious effects on adolescents’ developing brains as well as other aspects of their physical and mental health. Pediatricians are in an excellent position to recognize risk factors for use and screen for hazardous use among youth. Pediatricians can also assess youth whose screening results are positive for alcohol use to determine the level of intervention needed. Brief intervention techniques used by pediatricians have been shown to be effective in a limited number of studies and may be especially helpful in aiding youth and their families to obtain appropriate treatment of AUDs. Pediatricians also have an important advocacy role in health systems’ changes as well as legislative efforts, such as increasing alcohol taxes, resisting efforts to weaken minimum drinking age laws, and supporting GDL programs. 130 , 131  

Drs Ryan and Kokotailo were directly involved in the planning, researching, and writing of this report; and both authors approved the final manuscript as submitted.

This document is copyrighted and is property of the American Academy of Pediatrics and its Board of Directors. All authors have filed conflict of interest statements with the American Academy of Pediatrics. Any conflicts have been resolved through a process approved by the Board of Directors. The American Academy of Pediatrics has neither solicited nor accepted any commercial involvement in the development of the content of this publication.

Technical reports from the American Academy of Pediatrics benefit from expertise and resources of liaisons and internal (AAP) and external reviewers. However, technical reports from the American Academy of Pediatrics may not reflect the views of the liaisons or the organizations or government agencies that they represent.

The guidance in this report does not indicate an exclusive course of treatment or serve as a standard of medical care. Variations, taking into account individual circumstances, may be appropriate.

All technical reports from the American Academy of Pediatrics automatically expire 5 years after publication unless reaffirmed, revised, or retired at or before that time.

FUNDING: No external funding.

American Academy of Pediatrics

alcohol use disorder

blood alcohol concentration

Brief Negotiation Interview

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

graduated driver licensing

National Institute on Alcohol Abuse and Alcoholism

screening, brief intervention, and referral to treatment

Sheryl A. Ryan, MD, FAAP

Patricia Kokotailo, MD, MPH, FAAP

Sheryl A. Ryan, MD, FAAP, Chairperson

Deepa R. Camenga, MD, MHS, FAAP

Stephen W. Patrick, MD, MPH, MS, FAAP

Jennifer Plumb, MD, MPH, FAAP

Joanna Quigley, MD, FAAP

Leslie Walker-Harding, MD, FAAP

Gregory Tau, MD, PhD – American Academy of Child and Adolescent Psychiatry

Renee Jarrett, MPH

Competing Interests

Advertising Disclaimer »

Citing articles via

Email alerts.

Affiliations

• Editorial Board
• Editorial Policies
• Journal Blogs
• Pediatrics On Call
• Online ISSN 1098-4275
• Print ISSN 0031-4005
• Pediatrics Open Science
• Hospital Pediatrics
• Pediatrics in Review
• AAP Grand Rounds
• Latest News
• Pediatric Care Online
• Red Book Online
• Pediatric Patient Education
• AAP Toolkits
• AAP Pediatric Coding Newsletter

First 1,000 Days Knowledge Center

Institutions/librarians, group practices, licensing/permissions, integrations, advertising.

• Privacy Statement | Accessibility Statement | Terms of Use | Support Center | Contact Us
• © Copyright American Academy of Pediatrics

This Feature Is Available To Subscribers Only

Sign In or Create an Account

Advertisement

Life Satisfaction and Alcohol Consumption Among Young Adults at Social Gatherings

• Research Paper
• Published: 26 July 2017
• Volume 19 , pages 2023–2034, ( 2018 )

Cite this article

• Stefano Tartaglia 1 ,
• Silvia Gattino 1 &
• Angela Fedi 1  

1630 Accesses

9 Citations

1 Altmetric

Explore all metrics

Although low life satisfaction is related to alcohol abuse among young adults, there is no clear evidence of a specific relationship between wellbeing indexes and alcohol consumption. Several studies have reported different nonlinear relationships. The role of other variables may explain the inconsistent relationships between life satisfaction and alcohol consumption. Concerning individual factors, people’s expectations regarding drinking alcohol (i.e., drinking motives) are considered the most proximal antecedents of alcohol use and may mediate the relationship between life satisfaction and drinking alcohol. Regarding relational factors, social relations are related to both wellbeing and alcohol consumption. The aim of the present study was to examine the relationships among life satisfaction, drinking motives, and alcohol consumption in a sample of young adults. The data were collected by means of a self-report questionnaire from a sample of 536 young adults (median age: 22 years). We tested a structural equation model, assuming the hypothesized relationships, simultaneously on males and females to investigate gender differences. The results showed the influence of social relations on life satisfaction, which in turn influenced participants’ expectations regarding drinking alcohol. Drinking motives were antecedents of alcohol use. Among women, low satisfaction increased coping expectation, which, in turn, increased alcohol consumption. The most dangerous expectation about drinking was that alcohol may enhance a person. Prevention campaigns should aim to deconstruct this idea.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Life Satisfaction and Alcohol Consumption: An Empirical Analysis of Self-Reported Life Satisfaction and Alcohol Consumption in OECD Countries

Correlates of alcohol consumption among Germans in the second half of life. Results of a population-based observational study

André Hajek, Jens-Oliver Bock, … Hans-Helmut König

Does Drinking in Moderation Lead to Higher Life Satisfaction?

Masanori Kuroki

The legal age for buy alcoholic beverage in Italy is 18 years old.

Because the impossibility to establish causality in cross-sectional studies, in order to provide a more solid evidence of the hypothesized relationships among variables some authors (e.g., Hayes 2013 ) suggest to establish a counter-argument to rule out the alternative possibility of a different relation path, by testing alternative models in which the pathways among variables are reversed. Following this suggestion, we tested an alternative model in which the pathways among variables are reversed. The model fit dramatically worsened, suggesting that the model presented in the paper is correct: χ 2 (252) = 849.37, p  < .001, CFI = .84, TLI = .81, RMSEA = .067 (90% CL = .062 .072).

Alati, R., Kinner, S., Najman, J. M., Fowler, G., Watt, K., & Green, D. (2004). Gender differences in the relationships between alcohol, tobacco and mental health in patients attending an emergency department. Alcohol and Alcoholism, 39 (5), 463–469.

Article   Google Scholar  

Atwell, K., Abraham, C., & Duka, T. (2011). A parsimonious, integrative model of key psychological correlates of UK university students’ alcohol consumption. Alcohol and Alcoholism, 46 (3), 253–260.

Bagozzi, R. P. (1993). Assessing construct validity in personality research: Application to measures of self-esteem. Journal of Research in Personality, 27, 49–87.

Bagozzi, R. P., & Edwards, J. R. (1998). A general approach for representing constructs in organizational research. Organizational Research Methods, 1, 45–87.

Barrett, A. E. (2000). Marital trajectories and mental health. Journal of Health and Social Behavior, 41, 451–464.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.

Browne, M. W. (1990). Mutmum PC: User’s guide . Columbus, OH: Ohio State University, Department of Psychology.

Google Scholar  

Caldwell, T. M., Rodgers, B., Jorm, A. F., Christensen, H., Jacomb, P. A., & Korten, A. E. (2002). Patterns of association between alcohol consumption and symptoms of depression and anxiety in young adults. Addiction, 97 (5), 583–594.

Capozza, D., Brown, R., Aharpour, S., & Falvo, R. (2006). A comparison of motivational theories of identification. In R. Brown & D. Capozza (Eds.), Social identities: Motivational, emotional and cultural influences (pp. 51–72). Hove: Psychology Press.

Chatterton, P., & Hollands, R. (2002). Theorising urban playscapes: Producing, regulating and consuming youthful nightlife city spaces. Urban Studies, 39 (1), 95–116.

Cox, W. M., & Klinger, E. (1988). A motivational model of alcohol use. Journal of Abnormal Psychology, 97 (2), 168–180.

Diener, E. D., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49 (1), 71–75.

Diulio, A. R., Cero, I., Witte, T. K., & Correia, C. J. (2014). Alcohol-related problems and life satisfaction predict motivation to change among mandated college students. Addictive Behaviors, 39 (4), 811–817.

Ennett, S. T., Foshee, V. A., Bauman, K. E., Hussong, A., Cai, L., Reyes, H. L. M., et al. (2008). The social ecology of adolescent alcohol misuse: An integration of social ecology, social learning, and social control theories. Child Development, 79, 1777–1791.

Fox, C. L., Towe, S. L., Stephens, R. S., Walker, D. D., & Roffman, R. A. (2011). Motives for cannabis use in high-risk adolescent users. Psychology of Addictive Behaviors, 25 (3), 492.

Ham, L. S., & Hope, D. A. (2003). College students and problematic drinking: A review of the literature. Clinical Psychology Review, 23 (5), 719–759.

Haslam, S. A., Jetten, J., Postmes, T., & Haslam, C. (2009). Social identity, health and well-being: An emerging agenda for applied psychology. Applied Psychology, 58 (1), 1–23.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach . New York: Guilford Press.

Holmila, M., & Raitasalo, K. (2005). Gender differences in drinking: Why do they still exist? Addiction, 100 (12), 1763–1769.

Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453.

Kroger, J. (2000). Ego identity status research in the new millennium. International Journal of Behavioral Development, 24 (2), 145–148.

Kuntsche, E., & Kuntsche, S. (2009). Development and validation of the drinking motive questionnaire revised short form (DMQ-R SF). Journal of Clinical Child & Adolescent Psychology, 38, 899–908.

Levy, A. B., Bell, R. A., & Lin, E. (1980). Life satisfaction: Aspirations and alcohol use: A preliminary report. Social Indicators Research, 8 (4), 441–452.

Lipton, R. I. (1994). The effect of moderate alcohol use on the relationship between stress and depression. American Journal of Public Health, 84 (12), 1913–1917.

Maggs, J. L., Frome, P. M., Eccles, J. S., & Barber, B. L. (1997). Psychosocial resources, adolescent risk behaviour and young adult adjustment: Is risk taking more dangerous for some than others? Journal of Adolescence, 20 (1), 103–119.

Massin, S., & Kopp, P. (2014). Is life satisfaction hump-shaped with alcohol consumption? Evidence from Russian panel data. Addictive Behaviors, 39 (4), 803–810.

Mazzardis, S., Vieno, A., Kuntsche, E., & Santinello, M. (2010). Italian validation of the drinking motives questionnaire revised short form (DMQ-R SF). Addictive Behaviors, 35, 905–908.

Meirer, A., & Allen, G. (2008). Intimate relationship development during the transition to adulthood: Differences by social class. New Directions for Child and Adolescent Development, 119, 25–39.

Montgomery, M. (2005). Psychosocial intimacy and identity: From early adolescence to emerging adulthood. Journal of Adolescent Research, 20, 346–374.

Murphy, J. G., McDevitt-Murphy, M. E., & Barnett, N. P. (2005). Drink and be merry? Gender, life satisfaction, and alcohol consumption among college students. Psychology of Addictive Behaviors, 19 (2), 184.

O’Malley, P. M., & Johnston, L. D. (2002). Epidemiology of alcohol and other drug use among American college students. Journal of Studies on Alcohol and Drugs, 14, 23–39.

Oberle, E., Schonert-Reichl, K. A., & Zumbo, B. D. (2011). Life satisfaction in early adolescence: Personal, neighborhood, school, family, and peer influences. Journal of Youth and Adolescence, 40 (7), 889–901.

Proctor, C. L., Linley, P. A., & Maltby, J. (2009). Youth life satisfaction: A review of the literature. Journal of Happiness Studies, 10 (5), 583–630.

Rodgers, B., Korten, A. E., Jorm, A. F., Jacomb, P. A., Christensen, H., & Henderson, A. S. (2000). Non-linear relationships in associations of depression and anxiety with alcohol use. Psychological Medicine, 30 (2), 421–432.

Schaap, M. M., Kunst, A. E., Leinsalu, M., Regidor, E., Espelt, A., Ekholm, O., et al. (2009). Female ever-smoking, education, emancipation and economic development in 19 European countries. Social Science and Medicine, 68 (7), 1271–1278.

Simon, R. W., & Barrett, A. E. (2010). Nonmarital romantic relationships and mental health in early adulthood does the association differ for women and men? Journal of Health and Social Behavior, 51 (2), 168–182.

Skogen, J. C., Harvey, S. B., Henderson, M., Stordal, E., & Mykletun, A. (2009). Anxiety and depression among abstainers and low-level alcohol consumers. The Nord-Trondelag Health Study. Addiction, 104 (9), 1519–1529.

Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25 (2), 173–180.

Swain, N. R., Gibb, S. J., Horwood, L., & Fergusson, D. M. (2012). Alcohol and cannabis abuse/dependence symptoms and life satisfaction in young adulthood. Drug and Alcohol Review, 31 (3), 327–333.

Tartaglia, S. (2013). Different predictors of quality of life in urban environment. Social Indicators Research, 113, 1045–1053.

Tartaglia, S. (2014). Alcohol consumption among young adults in Italy: The interplay of individual and social factors. Drugs: Education, Prevention and Policy, 21 (1), 65–71.

Tartaglia, S., Fedi, A., & Miglietta, A. (2017a). Family or friends: What counts more for drinking behaviour of young adults? Revista de Psicología Social, 32 (1), 1–22.

Tartaglia, S., Miglietta, A., & Gattino, S. (2017b). Life satisfaction and cannabis use: A study on young adults. Journal of Happiness Studies, 18 (3), 709–718.

Tartaglia, S., & Rollero, C. (2015). Gender stereotyping in newspaper advertisements: A cross-cultural study. Journal of Cross-Cultural Psychology, 46 (8), 1103–1109. doi: 10.1177/0022022115597068 .

Træen, B., & Nordlund, S. (1993). Visiting public drinking places in Oslo: An application of the theory of planned behaviour. Addiction, 88 (9), 1215–1224.

Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.

Ventegodt, S. (1995). Liskvalitet I Danmark [quality of life in Denmark] . Copenhagen: Forskningscentrets Forlag.

Zhan, W., Shaboltas, A. V., Skochilov, R. V., Kozlov, A. P., Krasnoselskikh, T. V., & Abdala, N. (2012). Gender differences in the relationship between alcohol use and depressive symptoms in St. Petersburg, Russia. Journal of Addiction Research & Therapy, 3 (2), 1–5.

Zullig, K. J., Valois, R. F., Huebner, E. S., Oeltmann, J. E., & Drane, J. W. (2001). Relationship between perceived life satisfaction and adolescents’ substance abuse. Journal of Adolescent Health, 29 (4), 279–288.

Download references

Author information

Authors and affiliations.

Dipartimento di Psicologia, Università di Torino, Via Verdi 10, 10124, Turin, Italy

Stefano Tartaglia, Silvia Gattino & Angela Fedi

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Silvia Gattino .

Rights and permissions

Reprints and permissions

About this article

Tartaglia, S., Gattino, S. & Fedi, A. Life Satisfaction and Alcohol Consumption Among Young Adults at Social Gatherings. J Happiness Stud 19 , 2023–2034 (2018). https://doi.org/10.1007/s10902-017-9907-5

Download citation

Published : 26 July 2017

Issue Date : October 2018

DOI : https://doi.org/10.1007/s10902-017-9907-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Life satisfaction
• Alcohol consumption
• Drinking motives
• Young adults
• Structural equation modelling
• Find a journal
• Publish with us
• Track your research

• Research article
• Open access
• Published: 09 October 2009

Teenage drinking, alcohol availability and pricing: a cross-sectional study of risk and protective factors for alcohol-related harms in school children

• Mark A Bellis 1 ,
• Penelope A Phillips-Howard 1 ,
• Karen Hughes 1 ,
• Sara Hughes 1 ,
• Penny A Cook 1 ,
• Michela Morleo 1 ,
• Kerin Hannon 1 ,
• Linda Smallthwaite 2 &
• Lisa Jones 1  

BMC Public Health volume  9 , Article number:  380 ( 2009 ) Cite this article

48k Accesses

61 Citations

10 Altmetric

Metrics details

There is a lack of empirical analyses examining how alcohol consumption patterns in children relate to harms. Such intelligence is required to inform parents, children and policy relating to the provision and use of alcohol during childhood. Here, we examine drinking habits and associated harms in 15-16 year olds and explore how this can inform public health advice on child drinking.

An opportunistic survey of 15-16 year olds (n = 9,833) in North West England was undertaken to determine alcohol consumption patterns, drink types consumed, drinking locations, methods of access and harms encountered. Cost per unit of alcohol was estimated based on a second survey of 29 retail outlets. Associations between demographics, drinking behaviours, alcohol pricing and negative outcomes (public drinking, forgetting things after drinking, violence when drunk and alcohol-related regretted sex) were examined.

Proportions of drinkers having experienced violence when drunk (28.8%), alcohol-related regretted sex (12.5%) and forgetting things (45.3%), or reporting drinking in public places (35.8%), increased with drinking frequency, binge frequency and units consumed per week. At similar levels of consumption, experiencing any negative alcohol-related outcome was lower in those whose parents provided alcohol. Drunken violence was disproportionately associated with being male and greater deprivation while regretted sex and forgetting things after drinking were associated with being female. Independent of drinking behaviours, consuming cheaper alcohol was related to experiencing violence when drunk, forgetting things after drinking and drinking in public places.

There is no safe level of alcohol consumption for 15-16 year olds. However, while abstinence removes risk of harms from personal alcohol consumption, its promotion may also push children into accessing drink outside family environments and contribute to higher risks of harm. Strategies to reduce alcohol-related harms in children should ensure bingeing is avoided entirely, address the excessively low cost of many alcohol products, and tackle the ease with which it can be accessed, especially outside of supervised environments.

Peer Review reports

In recent decades alcohol has emerged as one of the major international threats to public health [ 1 ], and is now the third largest risk factor for disability and death in Europe [ 2 ]. Alcohol alone is thought to be responsible for 4.0% of the global burden of disease [ 3 ] with Europe having higher levels of consumption per person than any other global region [ 4 , 5 ]. As a result Europe suffers 195,000 deaths relating to alcohol each year [ 5 ], amounting to 6.1% of all deaths and 12.3% of all years of life lost [ 6 ]. Despite much of the chronic burden of alcohol-related disease falling on adults [ 7 ], the foundations of such damage are often established in childhood. Early alcohol initiation (e.g. before age 15) [ 8 , 9 ] and drinking in larger quantities in childhood and adolescence [ 10 , 11 ] are associated with a wide range of negative outcomes including initiation of drug use, suicide ideation, delinquency, violence, injury, depression and school absenteeism. Such drinking also increases the risks of developing chronic health and other problems (e.g. alcohol dependency, illicit drug use, liver disease) in later life [ 12 – 14 ]. Those initiating alcohol use before the age of 13 are particularly vulnerable to adverse health outcomes [ 8 , 9 ].

Misuse of alcohol by children is an international problem. Pan-European studies report that between 35% (Isle of Man) and 2% (Armenia) of 15-16 year olds have been drunk at least once in the past 30 days [ 15 ]. Further, a substantial proportion have binged (five or more drinks in one session) three or more times over the same period (ranging from 34% in the Isle of Man to 8% in Iceland and Romania) [ 15 ]. By both survey measures, the UK shows high levels of alcohol misuse by youths (33% and 27% respectively). Moreover, recent trends suggest such problems have increased in the UK with the average weekly quantity of alcohol consumed by 11-15 years old drinkers having doubled (1990-2008) [ 16 ] and the number of children under 16 admitted to hospital (with diagnoses specific to alcohol) increasing by 29% (1995/96-2005/06) [ 17 ]. Such increases in alcohol-related ill health in children are not restricted to the UK (e.g. Germany [ 18 ], Australia [ 19 ]).

Despite considerable acute and chronic health and social consequences relating to child alcohol consumption, evidence based guidance on whether children should drink alcohol at all, and how to moderate potential harm, is still being sought [ 20 ]. In particular, the effects of moderate or occasional consumption are unclear. Thus, while drinking at early ages (under 15 years) is linked to experiencing a range of health and social problems, the effects of alcohol use at age 15 can depend on amounts consumed, frequency of consumption, types of alcohol consumed and the context in which consumption takes place [ 21 , 22 ]. Alcohol illicitly obtained by children is associated with misuse [ 23 ]. However, alcohol provided by parents has been associated with reduced involvement in binge drinking and drinking in public places [ 23 , 24 ] compared with other means of access, and strict alcohol-specific parenting rules have been associated with reduced consumption [ 25 – 27 ]. However, in those aged 12, easy access to alcohol from parents is associated with increased alcohol abuse [ 28 ] and parental provision for parties has been linked to increased drinking [ 24 ]. With no clear understanding of the relationships between drinking behaviours, environments where alcohol is accessed and consumed, and resultant harms, more research is urgently needed to examine how such factors interact and to inform appropriate interventions.

In this paper we examine the drinking behaviours of alcohol-consuming 15-16 year olds and their relationships with a range of adverse alcohol-related outcomes. Thus, based on previous associations between alcohol consumption and violence [ 29 ] we examine experience of violence when drunk and how it relates to current drinking behaviours. With greater alcohol consumption at early ages also being associated with sexual risk-taking [ 30 , 31 ], we explore relationships between drinking behaviours and having experienced regretted sex following alcohol consumption. As a proxy measure of potential damage to mental health we analyse associations between drinking patterns and reported tendency to forget things after drinking [ 32 ]. Finally, to measure effects on others through public nuisance and potentially anti-social behaviour, we examine which drinking patterns are associated with consumption in public places (here; outside in streets, around shops and in parks). Together, analyses are also used to examine potential thresholds for safer drinking and explore factors that may moderate relationships between consumption and immediate harms. Finally, by examining the types of alcohol products individuals consume we also explore which drinking behaviours are associated with consumption of particular products.

Questionnaire design

The North West Region (population, 6,840,000) [ 33 ] suffers some of the highest levels of alcohol-related harm in England [ 34 ]. Consequently, an anonymous school based survey was undertaken across this Region, led by Trading Standards North West, to examine the drinking behaviours of its residents. Building on a survey tool developed and utilised in 2005 [ 23 ], the questionnaire consisted of closed, self-completed questions including: demographics (age, sex and postcode of residence); usual frequency of alcohol consumption and bingeing (here, drinking five or more drinks in one session [ 15 ]); and how individuals accessed alcohol and types of alcohol products consumed in a typical week (e.g. cans of beer, bottles of wine). For alcohol types consumed, respondents were provided with short descriptions and small pictures of typical products to help with identification. The types of alcohol products listed were based on those in established national surveys [ 35 ]. Individuals were also asked to identify if they drank alcohol in public places and these were described to respondents as outside in streets, parks or shops. The questionnaire asked respondents to identify (by tick box) if they had ever been violent or in a fight whilst drunk; whether they had regretted having had sex with someone after drinking; and whether they tended to forget things when they had been drinking alcohol. For regretted sex after drinking, the questionnaire did not distinguish between those who were sexually active but had never had regretted sex after drinking and those who were sexually inactive. Both were considered positive outcomes compared with having had regretted sex related to alcohol consumption. To analyse the question 'I tend to forget things when I have been drinking alcohol', a four point ordinal Likert Scale (agree strongly, agree, disagree, disagree strongly) was dichotomised into those that agreed that they tended to forget things after drinking and those that did not. Income was calculated from three questions identifying monies obtained from parents, work and other sources. For access to alcohol, variables measured were: personal purchase from on- and off-licence settings; access through parents, friends and family; and proxy purchasing through other adults. Access through parents distinguished between deliberate provision of alcohol by parents and alcohol covertly taken by youths.

Questionnaire delivery

The questionnaire was made available to secondary schools across the North West for whom participation was voluntary. Students were informed that participation was voluntary and anonymous and data were collected solely for the purpose of aggregated analyses. All aspects of the research methodology complied fully with the Helsinki Declaration. The survey (run every two years) was established by Local Authority Trading Standards in the North West and was scrutinised and approved by the Trading Standards North West Executive committee and supported by the cross-departmental Alcohol Forum at Government Office North West. Formal ethical approval was not requested in 2007 as this survey is an ongoing biennial process established by Trading Standards in 2005 (in agreement with public sector partners) as an audit of their role in preventing alcohol sales to minors. Sampling was not intended to be representative of all students across the North West but was designed to encompass a wide range of community types. School staff delivered questionnaires to students within normal school hours in years 10 and 11 (including individuals aged 14 to 17 years) [ 23 ] with classrooms being surveyed on an opportunistic basis. Previous North West surveys of youth alcohol consumption provided appropriate sample sizes (target 10,000 respondents [ 23 ]) and sampling targeted an age range typically associated with the early stages of routine alcohol use [ 15 , 16 ]. Sampling was completed after a total of 140 schools across 19 local authorities in the North West had participated providing 11,724 questionnaires (between January and March 2007). For the purposes of analyses undertaken here, the sample was then restricted to those aged 15 or 16 (n = 9,833). Response rates were not recorded in each class as the sample was not intended to be representative but was opportunistic (for both students and classroom participation), with analyses focusing on relationships between variables recorded by individual participants. To study drinking behaviour the sample was further limited only to those who identified that they drank alcohol (n = 8,263; 84%). Individuals who did not drink were only excluded at this stage (cf. at the point of questionnaire distribution) so that those who drank would not have to reveal this in class.

Respondent deprivation classification

Using an ecological methodology, all individuals were allocated to a quintile of deprivation across the North West. Index of Multiple Deprivation (IMD) [ 36 ] has been calculated for all Lower Super Output Areas (LSOAs) in England. LSOAs are geographical areas with an average population size of approximately 1,500 individuals and are the smallest areas for which an index of deprivation have been calculated across England [ 37 ]. Individuals were allocated directly to a LSOA by full postcode when provided (n = 4,158) with postcodes being mapped directly to LSOA geographical boundaries. Those pupils providing partial postcodes (which spanned more than one LSOA) were allocated to a LSOA on the basis of which LSOA contained the majority of postcodes possible within the partial postcode provided (n = 1,744). A further 2,063 individuals provided no postcode and therefore school postcode was used as a proxy deprivation geography [ 23 ]; a method which has been successfully used elsewhere [ 38 ]. Furthermore, in our sample for those respondents providing a postcode of residence, deprivation scores by postcode of residence correlated with deprivation scores by postcode of school (P < 0.001). However, LSOA (and therefore deprivation) was calculated from individuals' specific postcodes of residence rather than the more general school postcodes when both were available. Once LSOA was established for each individual, they were categorised into deprivation quintiles according to where their LSOA fell in the list of all LSOAs in the North West ranked by deprivation. Questionnaires providing insufficient data for any method of geographical classification (n = 298) were excluded from geographic analyses.

Retail costs of alcohol types

The retail price of each alcohol product type described on the questionnaire was collected from 29 off-licence venues. Sampling included supermarkets, off-licences and other licensed shops within the residential boundaries of the school sample. Although not all underage drinkers may select the cheapest alcohol (e.g. product status may also affect choice), based on other studies we hypothesised that economic pressures may result in the heaviest drinkers being the most price sensitive in their drink selection [ 39 ]. Therefore, in each outlet mystery shoppers were asked to identify the cheapest (cost per unit of alcohol) example of each product type and record the volume, price and alcohol content. Items were priced based on individual or multi-pack costs (e.g. bottle of wine or four-pack of beers). Price reductions for larger bulk buys (e.g. 40 cans of beer or six bottles of wine) were excluded. In total, seven different product types were sampled (alcopops, regular bottles/cans of beer, regular bottles/cans of cider, bottles of wine, bottles of spirits and large multi-litre value bottles of cider and of beer). Cost per unit of alcohol for each product was calculated from its volume, alcohol concentration and retail value. For each product type, costs per unit of alcohol were then averaged across all retailers. However, large multi-litre bottles of beer were excluded from product analyses as few respondents reported drinking them and most retail outlets did not sell them.

Calculating weekly alcohol consumption

To estimate weekly consumption, the alcohol products listed on the questionnaire were converted into standard units (1 unit = 8 grams or 10 ml of pure alcohol) consumed using: an alcopop (bottle) = 1.5; bottle or can of beer = 2; bottle or can of cider = 2; glass of wine (or quarter of a bottle) = 2.5; shot of spirits = 1; large value cider (2 litres) = 10.5 and large value beer (2 litres) = 10.5 units (based on updated units per drink methodologies [ 35 ]). An open question allowed individuals to list other less commonly consumed products (e.g. a liqueur). These were also converted into units based on alcohol contents typical of each product. As questions only addressed numbers consumed during a typical week, those drinking less than once a week were excluded from analyses relating to units per week consumed. The lack of consumption data on those drinking less than weekly means this variable was excluded from logistic regression models. All data were entered into SPSS v14 by Ci Research and sent for cleaning and analysis at Liverpool John Moores University. Analyses utilised Chi square, Spearman's correlation, ANOVA and backward conditional Logistic Regression techniques.

All individuals answered questions on age and gender as well as those on sources of alcohol consumed (e.g. buy own, parents provide, from adults outside shop). For other variables utilised, completeness of data was: weekly income 88.1%; binge frequency 98.8% and drinking frequency 99.9%. Units consumed per week were only calculable for those drinking at least weekly and for such individuals estimates were possible for 81.2% of respondents. Data completeness for negative outcome dependent variables was: drink outside 100%; alcohol-related violence 95.7%; alcohol-related regretted sex 90.8% and; tend to forget things after drinking 96.6%.

Regretted sex after drinking (12.5%), having been involved in violence when drunk (28.8%), consuming alcohol in public places (e.g. streets, parks and shops; 35.8%) and forgetting things after drinking (45.3%) had all been experienced by relatively large proportions of respondents. Violence when drunk and alcohol-related regretted sex both increased with age (Table 1 ). While violence when drunk and drinking in public places were more common amongst boys, alcohol-related regretted sex and forgetting things after drinking were more commonly reported by girls. Proportions who drank in public places, experienced violence when drunk and regretted sex after drinking all increased with deprivation. However, forgetting things after drinking showed no such relationship. Having a higher weekly income was positively associated with all adverse outcomes as were respondents buying their own alcohol or asking adults outside retail venues to buy it for them (i.e. proxy purchasing; Table 1 ). Importantly, accessing alcohol through parents was associated with significantly lower levels of having experienced all negative outcomes (Table 1 ).

Negative drinking outcomes were also strongly associated with the types of alcohol products respondents consumed in a typical week. Thus, while only 34.1% of those drinking wine drank in public places, this increased to over 70% amongst those who drank large value cider bottles (Table 2 ). In fact, higher proportions of large value cider and spirits drinkers had suffered alcohol-related regretted sex, violence when drunk and forgetting things after drinking compared with drinkers of other products (e.g. alcopops; Table 2 ). Correlation was used to examine whether consumption of lower priced drinks was related to greater percentages of consumers experiencing negative alcohol-related outcomes. Results suggest a strong relationship between consumption of cheaper alcohol products and increased proportions of respondents reporting violence when drunk, alcohol-related regretted sex and drinking in public places (Table 2 ).

Table 3 presents the relationship between three reported drinking measures (units per week, frequency of drinking, and of bingeing) and proportions reporting each negative outcome overall and separately for those who do and do not have alcohol provided by parents. Overall, all negative outcomes increased in frequency significantly as drinking frequency, bingeing frequency and units of alcohol consumed per week increased. However, provision of alcohol by parents was associated with lower levels of harm at the same drinking and bingeing frequency, and at the same weekly quantities of consumption. Thus, while 19.9% of individuals whose parents provide alcohol and who drink once a week had been involved in violence when drunk, this rises to 35.9% in those whose parents do not provide alcohol (Table 3 ). Similarly for those without parental provision of alcohol, 15.2% of those who drink up to five units of alcohol per week reported some alcohol-related regretted sex, while for those with parental provision rates are only 11.7% even at >10-20 units per week (Table 3 ). However, such protective effects were not sustained across all adverse outcomes at higher levels of consumption (especially at high levels of binge drinking).

Finally, logistic regression analysis was used to examine factors relating to having experienced negative alcohol outcomes while controlling for confounding relationships between sources of alcohol, types consumed, drinking patterns and individuals' demographics. Here, frequency of binge drinking remained strongly related to having experienced all negative outcomes (Table 4 ). However, compared with drinking less than once a month, drinking at greater frequency was only related to having been involved in violence when drunk and drinking in public places. Independent of drinking and binge frequency, typically consuming multi-litre value cider bottles was associated with increased risks of all negative outcomes. Equally, spirits consumption was related to increases in all risks except regretted sex and drinking standard bottles and cans of beer to all except forgetting things after drinking (Table 4 ). Importantly, wine consumption was associated with less public drinking and alcopops with less violence when drunk. Source of alcohol was also an important factor, with accessing alcohol through proxy purchasing increasing risks of all negative outcomes and parental provision being associated with reduced risks. Respondents' personal income was positively related to risks of having experienced alcohol-related regretted sex and violence (Table 4 ). However, deprivation was only associated with violence when drunk. Thus, those in the poorest quintile were at highest risks even after adjustments for drinking and binge frequency (Table 4 ). Increasing age was related to a small but significant decrease in proportions drinking in public places and finally, females were more likely to report regretted sex and especially forgetting things as negative outcomes of drinking, while males were more likely to report violence (Table 4 ).

Consistent with studies in the USA [ 11 , 29 ], our results show that substantial proportions of even those that drink at relatively low frequencies (e.g. weekly) or never binge have experienced adverse effects. Thus, 10.6% of individuals who drink less than once a month have still experienced violence when drunk and nearly a third report forgetting things after drinking (Table 3 ). However, amongst children whose parents provide alcohol, violence when drunk and forgetfulness drop to 6.1% and 25.5% in such lower frequency drinkers. Previous studies suggest that both parental attitudes towards, and their supervision of youth drinking can affect young people's drinking behaviours [ 23 – 28 ]. However, results here suggest that similar drinking patterns are more likely to be related to adverse outcomes when alcohol is accessed outside of parental environments. Thus, as well as drinking frequency, parental provision also appears to have a mediating effect on risks associated with binge drinking and units consumed per week (Table 3 ). However, any protective effects are limited. Thus, 35.4% of those bingeing once a week, even with parental provision, have been involved in violence when drunk (Table 3 ) and amongst respondents reporting the highest frequency of binge drinking, protective effects of parental provision disappear (Table 3 ). However, as we were unable to differentiate types of parental provision (e.g. for unsupervised parties or consumption at family meals), here we cannot identify specifically how context relates to risks.

With 84.0% of 15 and 16 year olds surveyed already consuming alcohol we have analysed the data to quantify the relationship between increased consumption and changes in risk of adverse outcomes. After correcting for confounding factors, risks for drinking in public places increase as frequency of consumption increases. However, differences in risks of involvement in violence when drunk only approach significance when drinking frequencies exceed once a week (compared with drinking less than once a month). Our results identify that bingeing at any frequency (c.f. those that drink but never binge) is associated with significantly higher levels of violence when drunk, tendency to forget things after drinking and drinking in public places (Table 4 ). Alcohol-related regretted sex was also associated with bingeing but increased risks (compared with never bingeing) only escalated significantly at binge frequencies of one to three times a month or more.

Overall, results suggest any binge drinking by 15 and 16 year olds should be avoided. Such findings are supported by neurocognitive studies, which have found underage heavy episodic or binge drinking to be associated with brain damage as adolescent brains are more susceptible to neurochemical changes, neurodegeneration and long-lasting changes in functional activity [ 32 , 40 ]. However, a recent review of the evidence suggests that the precise risks that alcohol consumption represents to the adolescent brain are still unclear [ 41 ]. Our results, even after correcting for binge and drinking frequency, identify an independent association between tendency to forget things after drinking and being female (Table 4 ). Such damage may now be exacerbated by young females' consumption of alcohol in the UK approaching the same level as males [ 16 ].

While all adverse outcomes increased with weekly units consumed (Table 3 ) not all were significantly different between < = 5 and >5-10 units/week categories. Thus, proportions of respondents having experienced violence, regretted sex and drinking in public places did not differ significantly (P = 0.364; 0.734; 0.329 respectively) between < = 5 and >5-10 unit categories. However, forgetting things did show a significant increase (P < 0.05). At >10-20 units/week all negative outcomes were significantly higher than both < = 5 and >5-10 unit categories. Consequently, while teenage drinkers may experience similar behavioural risks while increasing consumption up to 10 units/week, effects on tendency to forget things appear to increase with consumption at all levels. However, our results suggest types of alcohol consumed may mitigate or aggravate alcohol-related harms. Consuming value multi-litre cider was strongly linked with increases in all risks, and consuming spirits with all except regretted sex (Table 4 ). Both value cider and spirits purchases often result in having large amounts of alcohol in a single bottle. Whilst our study did not examine how such products were consumed, a single bottle may encourage individuals to consume the contents more quickly or, where sharing occurs (e.g. passing around the bottle), rapidly consume greater quantities on their turn. Furthermore, drinking may finish only when the contents are exhausted. Importantly, both products were two of the cheapest ways of purchasing units of alcohol. Cider provided alcohol for as little as £0.11 per unit (Table 2 ) meaning that consuming five units (more than adult daily recommended levels in the UK) was comparable with the price of a can of a popular cola. By contrast alcopops provide a relatively expensive cost per unit of alcohol, having typically been sold in smaller volume containers. In our analyses alcopops were not positively associated with increased risk of any alcohol-related harms (Table 4 ).

With our results showing cheaper alcohol products linked most strongly to adverse drinking outcomes and other work identifying underage alcohol consumption being sensitive to price [ 42 ], governments should establish a minimum price for alcohol (per unit). Drinking bottles and cans of beer was also linked to violence, regretted sex and public drinking while alcopops and wine appeared protective against alcohol-related violence and public drinking respectively (Table 4 ). Although it is possible to speculate that such effects may relate to the image of each product (e.g. beer may be considered a drink for tougher youths than alcopops) or the location in which such drinks are consumed (e.g. wine may be more likely to be consumed in moderating environments such as at home with parents) understanding such factors requires further investigation [ 43 ].

As with any questionnaire based cross-sectional study this survey has a number of limitations. Both drinking behaviours and negative outcomes were self-reported and relied on the honesty and recollection of respondents [ 44 ]. Whilst guaranteed anonymity can encourage the former, our results establish that recollection of behaviours relating to alcohol consumption may be incomplete because of forgetting things after drinking, especially amongst those who binge (Table 4 ). Calculations of units of alcohol consumed per week could only be broad approximations as a wide variety of products are available and our calculations are based on individuals classifying their drinking according to only seven general product descriptions. In particular, estimates for alcopops assume a volume of 275 ml for each bottle consumed but 700 ml bottles are now stocked in a number of outlets. Moreover, while the survey specifically examined alcohol-related outcomes (e.g. violence when drunk), it did not provide information on the amount individuals had consumed precisely when such outcomes occurred but only measured their current typical drinking patterns. Consequently, we cannot rule out that some adverse drinking behaviours may have developed as a coping mechanism after, for instance, being a victim of alcohol-related violence or regretted sex [ 45 , 46 ]. Sampling did not include individuals who were excluded from or had otherwise left school-based education, and deprivation was assigned on an ecological basis rather than through individual circumstance. Analyses did not account for potential effects relating to variance at school level but did include deprivation as a measure of community level effects. Adverse effects of alcohol were limited to four measures and did not include correlates with prevalence of injury (e.g. hospital attendance) or other potential consequences (e.g. effects on education, relationship problems) [ 15 , 47 ]. However, chosen outcomes did include adverse measures previously associated with males (violence) [ 29 ], an adverse sexual outcome linked to alcohol (regretted sex) [ 30 , 31 ], a measure of potential damage to mental health and development (forgetting things after drinking) [ 32 ] and a proxy for involvement in public nuisance (drinking in public places). Finally, no quantitative measures of compliance were collected from schools and although response rates were high for most questions (>85%), for those drinking at least weekly responses only allowed calculation of units consumed per week in 81.2% of cases. Thus, some selection bias effects could not be ruled out and consequently we have not extrapolated results to population levels.

Our results support those of others that suggest even low levels of consumption can not be considered safe for children [ 11 ]. While studies suggest that levels of youth alcohol consumption may be high in England, and especially in the North West region [ 48 ], the reality in many countries is that by the ages of 15 and 16 a higher proportion of children drink alcohol than abstain [ 15 , 16 ]. Any efforts to move more children towards or into abstinence through parental rules and controls may be effective for some individuals [ 26 , 27 ], but may also result in alcohol consumption moving out of the family environment into parks, streets or other public spaces. Our results suggest that such a move, even if overall consumption did not increase, could exacerbate negative outcomes from alcohol consumption amongst teenagers. More studies and meta-analyses are needed to refine public information on alcohol consumption by children. Our results, nevertheless, do suggest that those parents who allow children aged 15-16 years to drink may limit harms by restricting consumption to lower frequencies (e.g. no more than once a week) and under no circumstances permitting binge drinking. However, parental efforts should be matched by genuine legislative and enforcement activity to reduce independent access to alcohol by children, and examination of costs per unit and bottle sizes to discourage large bottle purchases. While these measures are unlikely to eradicate the negative effects of alcohol on children, they may reduce them substantially while allowing children to prepare themselves for life in an adult environment dominated by this drug.

World Health Organization: WHO Expert Committee on Problems Related to Alcohol Consumption. Second report. WHO technical report series 944. 2007, Geneva: World Health Organization

Google Scholar  

Ezzati M, Lopez AD, Rodgers A, Murray CJ, eds: Comparative quantification of health risks. Global and regional burden of disease attributable to selected major risk factors. 2004, Geneva: World Health Organization

Room R, Babor T, Rehm J: Alcohol and public health. The Lancet. 2005, 365: 519-530.

Article   Google Scholar  

World Health Organization: Global status report on alcohol 2004. 2004, Geneva: World Health Organization

Anderson P, Baumberg B: Alcohol in Europe. 2006, London: Institute of Alcohol Studies

Rehm J, Taylor B, Patra J: Volume of alcohol consumption, patterns of drinking and burden of disease in the European region 2002. Addiction. 2006, 101: 1086-1095. 10.1111/j.1360-0443.2006.01491.x.

Article   PubMed   Google Scholar  

Alcohol-related deaths in the United Kingdom 1991-2006. [ http://www.statistics.gov.uk/statbase/Product.asp?vlnk=14496 ]

Gruber E, DiClemente RJ, Anderson MM, Lodico M: Early drinking onset and its association with alcohol use and problem behavior in late adolescence. Prev Med. 1996, 25: 293-300. 10.1006/pmed.1996.0059.

Article   CAS   PubMed   Google Scholar  

Swahn M, Bossart R: Gender, early alcohol use and suicide ideation and attempts: finding from the 2005 Youth Risk Factor Behaviour Survey. J Adolesc Health. 2007, 41: 175-181. 10.1016/j.jadohealth.2007.03.003.

Best D, Manning V, Gossop M, Gross S, Strang J: Excessive drinking and other problem behaviours among 14-16 year old children. Addict Behav. 2006, 31: 1424-1435. 10.1016/j.addbeh.2005.12.002.

Miller JW, Naimi TS, Brewer RD, Everett Jones S: Binge drinking and associated health risk behaviors among high school students. Pediatrics. 2007, 119: 76-85. 10.1542/peds.2006-1517.

Bonomo Y, Bowes G, Coffey C, Carlin JB, Patton GC: Teenage drinking and the onset of alcohol dependence: a cohort study over seven years. Addiction. 2004, 99: 1520-1528. 10.1111/j.1360-0443.2004.00846.x.

Viner RM, Taylor B: Adult outcomes of binge drinking in adolescence: findings from a UK national birth cohort. J Epidemiol Community Health. 2007, 61: 902-907. 10.1136/jech.2005.038117.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Faden VB, Goldman M: The effects of alcohol on physiological processes and biological development. Alcohol Res Health. 2005, 28 (3): 125-131.

Hibell B, Guttormsson U, Ahlström S, Balakireva O, Bjarnason T, Kokkevi A, Kraus L: The 2007 ESPAD Report: substance use among students in 35 European countries. 2009, Stockholm: Swedish Council for Information on Alcohol and Other Drugs

Fuller E, ed: Smoking, drinking, and drug use among young people in England in 2008. 2009, London: National Centre for Social Research and National Foundation for Educational Research

Information Centre: Statistics on alcohol: England 2007. 2007, Leeds: The Information Centre

Meyer S, Steiner M, Mueller H, Nunold H, Gottschling S, Gortner L: Recent trends in the burden of alcohol intoxication on pediatric in-patient services in Germany. Klin Padiatr. 2008, 220: 6-9. 10.1055/s-2007-972566.

Livingston M: Recent trends in risky alcohol consumption and related harm among young people in Victoria, Australia. Aust N Z J Public Health. 2008, 32: 266-271. 10.1111/j.1753-6405.2008.00227.x.

HM Government: Safe Sensible Social. The next steps in the National Alcohol Strategy. 2007, London: HM Government

Andersen A, Due P, Holstein BE, Iversen L: Tracking drinking behaviour from age 15-19 years. Addiction. 2003, 98: 1505-1511. 10.1046/j.1360-0443.2003.00496.x.

Kuntsche E, Knibbe R, Gmel G, Engels R: 'I drink spirits to get drunk and block out my problems...' Beverage preference, drinking motives and alcohol use in adolescence. Alcohol Alcohol. 2006, 41: 566-573.

Bellis MA, Hughes K, Morleo M, Tocque K, Hughes S, Allen T, Harrison D, Fe-Rodriguez E: Predictors of risky alcohol consumption in schoolchildren and their implications for preventing alcohol-related harm. Subst Abuse Treat Prev Policy. 2007, 2: 15-10.1186/1747-597X-2-15.

Article   PubMed   PubMed Central   Google Scholar  

Foley KL, Altman D, Durant RH, Wolfson M: Adults approval and adolescent alcohol use. J Adolesc Health. 2004, 34: 345e17-26.

O'Donnell L, Stueve A, Duran R, Myint-U A, Agronick G, San Doval A, Wilson-Simmons R: Parenting practices, parents' underestimation of daughters' risks, and alcohol and sexual behaviors of urban girls. J Adolesc Health. 2008, 42: 496-502. 10.1016/j.jadohealth.2007.10.008.

Vorst Van der H, Engels RCME, Meeus W, Dekovic M, Van Leeuwe J: The role of alcohol-specific socialization in adolescents' drinking behaviour. Addiction. 2005, 100: 1464-1476. 10.1111/j.1360-0443.2005.01193.x.

Van Zundert RM, Vorst Van Der H, Vermulst AA, Engels RC: Pathways to alcohol use among Dutch students in regular education and education for adolescents with behavioral problems: the role of parental alcohol use, general parenting practices, and alcohol-specific parenting practices. J Fam Psychol. 2006, 20: 456-467. 10.1037/0893-3200.20.3.456.

Komro KA, Maldonado-Molina MM, Tobler AL, Bonds JR, Muller KE: Effects of home access and availability of alcohol on young adolescents alcohol use. Addiction. 2007, 102: 1597-1608. 10.1111/j.1360-0443.2007.01941.x.

French M, MacLean J: Underage alcohol use, delinquency, and criminal activity. Health Econ. 2006, 15: 1261-1281. 10.1002/hec.1126.

Bonomo Y, Coffey C, Wolfe R, Lynskey M, Bowes G, Patton G: Adverse outcomes of alcohol use in adolescents. Addiction. 2001, 96 (10): 1485-1496. 10.1046/j.1360-0443.2001.9610148512.x.

Wells JE, Horwood LJ, Fergusson DM: Drinking patterns in mid-adolescence and psychosocial outcomes in late adolescence and early adulthood. Addiction. 2004, 99 (12): 1529-1541. 10.1111/j.1360-0443.2004.00918.x.

Zeigler DW, Wang CC, Yoast RA, Dickinson BD, McCaffree MA, Robinowitz CB, Sterling ML: The neurocognitive effects of alcohol on adolescents and college students. Prev Med. 2005, 40: 23-32. 10.1016/j.ypmed.2004.04.044.

Office for National Statistics: Mid-2006 to mid-2007 population estimates: components of population change for local authorities in the United Kingdom. 2008, London: Office for National Statistics

Deacon L, Hughes S, Tocque K, Bellis MA: Indications of public health in the English regions 8: alcohol. 2007, York: Association of Public Health Observatories

Goddard E: Estimating alcohol consumption from survey data: updated method of converting volumes to units. 2007, Cardiff: Office for National Statistics

Indices of Deprivation 2004 - summary (revised). [ http://www.communities.gov.uk/archived/publications/communities/indicesdeprivation ]

Office for National Statistics: Names and codes for super output area geography. 10.1093/her/cym096. [ http://www.ons.gov.uk/about-statistics/geography/products/geog-products-area/names-codes/soa/index.html ]

Levin KA, Currie C: Inequalities in toothbrushing among adolescents in Scotland 1998-2006. Health Educ Res. 2009, 24: 87-97. 10.1196/annals.1308.028.

Galloway J, Forsyth A, Shewan D: Young people's street drinking behaviour: unvestigating the influence of marketing & subculture. 2007, Glasgow: Glasgow Centre for the Study of Violence

Brown SA, Tapert SF: Adolescence and the trajectory of alcohol use: basic to clinical studies. Ann NY Acad Sci. 2004, 1021: 234-244. 10.1023/A:1023084927465.

Donaldson L, Jewell T, McBride M: Draft guidance on the consumption of alcohol by children and young people from the Chief Medical Officers of England, Wales and Northern Ireland. 2009, London: Department of Health

Booth A, Meier P, Stockwell T, Sutton A, Wilkinson A, Wong K: Independent review of the effects of alcohol pricing and promotion. Part A: systematic reviews. 2008, Sheffield: University of Sheffield

Lintonen TP, Konu AI: Adolescent alcohol beverage type choices reflect their substance use patterns and attitudes. J Youth Adolescence. 2003, 32: 279-289. 10.1111/j.1360-0443.2008.02197.x.

Greenfield TK, Kerr WC: Alcohol measurement methodology in epidemiology: recent advances and opportunities. Addiction. 2008, 103: 1082-1099. 10.1111/j.1360-0443.2008.02197.x.

Bellis MA, Hughes K, Hughes S, eds: World Health Organization. Interpersonal violence and alcohol: WHO Policy Briefing. 2006, Geneva: World Health Organization

Young R, Sweeting H, West P: A longitudinal study of alcohol use and antisocial behaviour in young people. Alcohol Alcohol. 2008, 43: 204-214. 10.1080/14659890802446202.

Lavikainen HM, Lintonen TP: Alcohol use in adolescence: identifying harms related to teenager's alcohol drinking. Journal of Substance Use. 2009, 14: 39-48. 10.1080/14659890802446202.

North West Public Health Observatory: Local alcohol profiles for England. [ http://www.nwph.net/alcohol/lape/ ]

Pre-publication history

The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2458/9/380/prepub

Download references

Acknowledgements

This work would not have been possible without the cooperation and dedicated work of the staff in all participating schools and Trading Standards Offices in the North West. We are also grateful to the North West Alcohol Forum who coordinated input from the participating agencies, to Ci Research who collected and inputted the data, and to staff at the Centre for Public Health, Liverpool John Moores University who were involved in the data collection from licensed premises. Finally, we would like to thank Anette Andersen, Tomi Lintonen, Alasdair Forsyth and Girdhar Agarwal for extensive comments that helped to improve an earlier draft of this manuscript and Karen Tocque for her help with geographical data.

Author information

Authors and affiliations.

Centre for Public Health, Faculty of Applied Health and Social Science, Liverpool John Moores University, Fifth Floor Kingsway House, Hatton Garden, Liverpool, L3 2AJ, UK

Mark A Bellis, Penelope A Phillips-Howard, Karen Hughes, Sara Hughes, Penny A Cook, Michela Morleo, Kerin Hannon & Lisa Jones

Halton Consumer Protection Service, Rutland House, Halton Lea, Runcorn, WA7 2GW, UK

Linda Smallthwaite

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Mark A Bellis .

Additional information

Competing interests.

The authors declare that they have no competing interests.

Authors' contributions

MAB contributed to study design, analysed the data, and wrote the manuscript. PAPH assisted MAB in developing concepts and writing the manuscript. KH and SH contributed to study design and co-ordination, and commented on the manuscript. MM coordinated data collection from licensed premises. PAC, MM, and LJ assisted in the production of the manuscript. KH contributed to data analysis and commented on the manuscript. LS conducted the survey. All authors read and approved the final version.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article.

Bellis, M.A., Phillips-Howard, P.A., Hughes, K. et al. Teenage drinking, alcohol availability and pricing: a cross-sectional study of risk and protective factors for alcohol-related harms in school children. BMC Public Health 9 , 380 (2009). https://doi.org/10.1186/1471-2458-9-380

Download citation

Received : 14 November 2008

Accepted : 09 October 2009

Published : 09 October 2009

DOI : https://doi.org/10.1186/1471-2458-9-380

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Alcohol Consumption
• Binge Drinking
• Public Place
• Drinking Behaviour
• Alcohol Product

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Advertisement

Supported by

Complications From Alcohol Use Are Rising Among Women

New research shows that alcohol-related liver disease and other health problems increased even more than expected among women ages 40 to 64 during the pandemic.

• Share full article

By Dani Blum

A new study adds to a mounting body of evidence showing that rising alcohol consumption among women is leading to higher rates of death and disease. The report, published Friday in the journal JAMA Health Forum , examined insurance claims data from 2017 to 2021 on more than 14 million Americans ages 15 and older. Researchers found that during the first year and a half of the coronavirus pandemic, women ages 40 to 64 were significantly more likely than expected to experience serious complications like alcohol-related cardiovascular and liver disease, as well as severe withdrawal.

The Background

Alcohol consumption in the United States has generally increased over the last 20 years , said Dr. Timothy Naimi, the director of the Canadian Institute for Substance Use Research at the University of Victoria. Dr. Naimi was a co-author on a recent paper that showed deaths from excessive alcohol use in the United States rose by nearly 30 percent between 2016 and 2021.

While men still die more often from drinking-related causes than women, deaths among women are climbing at a faster rate. “The gap is narrowing,” said Dr. Bryant Shuey, an assistant professor of medicine at the University of Pittsburgh and the lead author of the new study.

The Research

The study looked at serious health issues related to drinking, including alcohol-related liver and heart disease, inflammation of the stomach lining that led to bleeding, pancreatitis, alcohol-related mood disorders and withdrawal. Researchers compared insurance claims data for these complications with the rates they expected to see based on past prevalence of these conditions.

In nearly every month from April 2020 to September 2021, women ages 40 to 64 experienced complications from alcohol-related liver disease — a range of conditions that can develop when fat begins to accumulate in the liver — at higher rates than researchers predicted. If damage from drinking continues, scar tissue builds up in the liver and leads to a later stage of the disease, called cirrhosis. Some people with alcohol-related liver disease also develop severe liver inflammation, known as alcohol-associated hepatitis.

Rates of alcohol-related complications during the pandemic were also higher than predicted among men ages 40 to 64, but those increases were not statistically significant. But “men are not out of the woods” and still face health risks, Dr. Shuey said.

The Limitations

The study examined data only up until September 2021. Katherine Keyes, a professor of epidemiology at Columbia University who was not involved in the latest study, said she expected that alcohol use might keep rising among women — a pattern that could contribute to even more health issues.

And since the study relied on insurance claims, Dr. Shuey said it told an incomplete story. If someone is treated in the emergency room for an inflamed pancreas but doesn’t disclose a drinking history, for example, that instance may not be registered as an alcohol-related complication.

“The truth is, we’re probably underestimating this,” he said.

The Takeaways

These findings underscore how patterns of heavy drinking can translate into serious health consequences. Over the last 10 years, a growing number of American women — and particularly women in middle age — have reported binge-drinking, Dr. Keyes said.

“It used to be that 18- to 25-year-old males were the most likely to drink or the most likely to binge,” said Aaron White, a neuroscientist at the National Institute on Alcohol Abuse and Alcoholism. Now, binge drinking occurs more among people between the ages of 26 to 34, and is becoming more common among women. “Everything’s just getting pushed back later,” he said.

Demographic shifts can also help explain why women are drinking at higher rates, Dr. Keyes said. Women tend to marry and have children at later ages than in previous decades, so they spend more time in what Dr. Keyes calls a “high-risk period for heavy drinking.”

“People don’t realize the real health consequences these heavy drinking patterns can have,” she added.

These consequences take time to develop and often emerge between ages 40 and 60. Complications can occur after “years of heavy, persistent alcohol use,” Dr. Shuey said.

These longer-term increases in drinking predate the pandemic and might have increased the risk of health problems among women before Covid-19 hit. But higher levels of drinking during lockdowns may have exacerbated these issues or contributed to new complications, especially as women bore the brunt of family responsibilities, Dr. White said.

Even as research mounts on the harms of alcohol, many people might struggle to change their habits, Dr. White said.

“If you’ve been drinking wine with dinner every night for the last 20 years, just seeing a headline is not going to be enough to make you throw your wine away,” he said. “I think it’s going to be a slow cultural shift.”

Dani Blum is a health reporter for The Times. More about Dani Blum