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• 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

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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.

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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.

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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.

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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

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The epidemiology of alcohol use disorders cross-nationally: Findings from the World Mental Health Surveys

Associated data.

Prevalences of Alcohol Use Disorders (AUDs) and Mental Health Disorders (MHDs) in many individual countries have been reported but there are few cross-national studies. The WHO World Mental Health (WMH) Survey Initiative standardizes methodological factors facilitating comparison of the prevalences and associated factors of AUDs in a large number of countries to identify differences and commonalities.

Lifetime and 12-month prevalence estimates of DSM-IV AUDs, MHDs, and associations were assessed in the 29 WMH surveys using the WHO CIDI 3.0.

Prevalence estimates of alcohol use and AUD across countries and WHO regions varied widely. Mean lifetime prevalence of alcohol use in all countries combined was 80%, ranging from 3.8% to 97.1%. Combined average population lifetime and 12-month prevalence of AUDs were 8.6% and 2.2% respectively and 10.7% and 4.4% among non-abstainers. Of individuals with a lifetime AUD, 43.9% had at least one lifetime MHD and 17.9% of respondents with a lifetime MHD had a lifetime AUD. For most comorbidity combinations, the MHD preceded the onset of the AUD. AUD prevalence was much higher for men than women. 15% of all lifetime AUD cases developed before age 18. Higher household income and being older at time of interview, married, and more educated, were associated with a lower risk for lifetime AUD and AUD persistence.

Conclusions

Prevalence of alcohol use and AUD is high overall, with large variation worldwide. The WMH surveys corroborate the wide geographic consistency of a number of well-documented clinical and epidemiological findings and patterns.

Introduction

Alcohol Use Disorders (AUDs) are serious psychiatric conditions often leading to major adverse consequences. The 2018 World Health Organization’s (WHO) Global Status Report on Alcohol and Health determined that in 2016 harmful use of alcohol caused approximately 3 million deaths (or 5.3% of all deaths), more than hypertension and diabetes combined. The WHO report estimated that 5.1% of the global burden of disease and injury, equivalent to 132.6 million Disability-adjusted Life Years (DALYs), was caused by alcohol use 1 . In 2016, an estimated 2.3 billion people were current drinkers and 283 million people aged 15+ years had an AUD (5.1% of adults). The economic burden of alcohol use has been estimated to be more than 1% of the gross national product in middle and high income countries 2 . Alcohol use was the 7 th leading risk for early death and disability 3 . Despite some decrease in per capita alcohol use in some WHO regions, worldwide per capita alcohol consumption is predicted to increase over the next 10 years with a possible increase in disease burden 1 . Multi-country epidemiologic data on AUDs can enable better understanding of patterns and characteristics of AUDs providing necessary information for prevention and treatment implementation and policy. There is little cross-national standardized data available 4 , 5 . It is difficult to compare findings from different studies as they generally have not used equivalent assessments, administrations, diagnostic systems, sampling and analysis approaches. A few limited multi-country or regional studies using a standardized assessment have been conducted. One early notable effort used the Diagnostic Interview Schedule 6 which includes an assessment of DSM-III 7 alcohol abuse and dependence to assess samples in coordinated studies in 10 different cultural regions 8 . There was wide variation in the lifetime prevalence rates of DSM-III alcohol abuse and/or dependence ranging from .45% in Shanghai to 22% in Korea and 23% in United States native Mexican Americans although there was similarity in a number of associated variables.

The WHO World Mental Health (WMH) Survey Initiative 9 standardizes survey design and implementation procedures that facilitate the comparison of estimates of prevalence and correlates of AUDs in a large number of participating countries 9 – 11 , making the study especially useful for investigating cross-national characteristics of disorders 12 . This paper reports findings on the prevalences and correlates of AUDs in the WMH countries.

Data for this paper come from 29 WMH surveys carried out in 27 countries or country regions between 2001 and 2015. The list of participating countries, their World Bank income classification 13 , and the sample characteristics for each country including the sample sizes are shown in Table 1 .

WMH sample characteristics by World Bank income categories a

Mental and substance use disorders were assessed using the WHO Composite International Diagnostic Interview (CIDI) Version 3.0, a validated fully-structured lay-administered interview 14 generating lifetime and 12-month prevalence estimates of DSM-IV-TR 15 mood, anxiety, behavioral, and substance use disorders 9 . The CIDI assesses AUDs by asking a series of questions that operationalize the DSM-IV symptom criteria for Alcohol Abuse (ALA) and Alcohol Dependence (ALD). Respondents who met criteria for either ALA or ALD were considered to have an AUD. Consistent with DSM-IV, any respondents who met criteria for both AUD disorders were diagnosed with ALD.

In an effort to reduce respondent burden a two-part sampling design was used in which all respondents were administered a Part I interview that contained questions about disorders of primary interest to the WMH investigators. A Part II sample, consisting of 100% of the Part I respondents who met lifetime criteria for any of the disorders assessed in Part I plus a probability sample (typically in the range between 20% and 33% depending on the country) of other Part I respondents were administered Part II of the survey. The Part II sample included questions about disorders of secondary interest along with questions about risk factors and consequences of disorders. The non-certainty respondents in the Part II sample were weighted by the inverse of their probability of selection so that weighted prevalence estimates of Part I disorders in the Part II sample are identical to unweighted estimates of these disorders in the Part I sample. Weights were also used to match the samples to population socio-demographic distributions. As discussed in detail elsewhere, 16 the sequence of steps in calculating analysis weights was the same across WMH surveys but differed in exact procedures depending on the sample frame and access to population data for post-survey adjustments. A total of 123,237 respondents across the 29 surveys were assessed for AUDs. The Part II sample includes 79,343 respondents. Further details on the WMH surveys are summarized in the Appendix Methods .

Data Analysis

All analyses were based on weighted data, accounting for stratification and clustering. Standard errors were estimated using Taylor series linearization as implemented in Statistical Analysis System ® (SAS) Version 9.4 17 . SAS PROC LIFETEST was used to produce life-table estimates of the age-of-onset (AOO) distributions of AUD and are reported as weighted prevalences. The associations of sociodemographic variables (see Appendix Methods for full list) with lifetime AUD prevalence as of given ages were assessed using discrete-time logistic regression analyses with person-year the unit of analysis. Similar analyses using standard logistic regression were used to investigate correlates of past year AUD among non-abstainers defined as those who report at least some use of alcohol in their lifetimes. Results are presented as odds ratios (OR) and 95% confidence intervals (CI). Tests of significance were evaluated using Wald F tests based on design-corrected coefficient variance-covariance matrices with statistical significance defined at the 2-tailed 0.05 level.

Lifetime Prevalence

Table 2 shows the lifetime prevalence of alcohol use, ALA, ALD and AUDs for each of the surveys, the countries combined, the countries grouped by World Bank income levels, and the WHO regions. There are significant differences in base rate prevalences of lifetime alcohol use and all DSM-IV diagnoses across countries, income levels and regions, as well as significant differences in prevalences when considered only among non-abstainers.

Prevalence of lifetime alcohol use, alcohol abuse and alcohol dependence in the World Mental Health Surveys

African region (Nigeria, South Africa);

Western Pacific region (Australia, Japan, New Zealand, PRC China (Beijing and Shanghai));

Eastern Mediterranean region (Iraq, Israel, Lebanon);

Western European region (Belgium, France, Germany, Italy, The Netherlands, Northern Ireland, Portugal, Spain, Spain(Murcia));

Eastern European region (Bulgaria, Poland, Romania, Ukraine).

The mean lifetime prevalence of alcohol use in all countries combined was 80%, ranging from 3.8% in Iraq to 97.1% in Peru. The average lifetime prevalence of ALA for all countries was 6.3%, ranging from 0.5% in Iraq to 18.7% in Australia. The average lifetime prevalence of ALD for all countries was 2.3%, ranging from 0.2% in Iraq to 6.0% in the United States. As expected, the lifetime prevalence of ALD was lower than ALA cross-nationally and for all within-country comparisons. The lifetime prevalence of AUDs for all countries combined was 8.6% and ranged from 0.7% in Iraq to 22.7% in Australia.

The lifetime prevalence of AUDs among non-abstainers for all countries combined was 10.7%. Once conditioned upon lifetime alcohol use, there was a noticeable shift in the ordering of prevalence across surveys. When excluding lifetime alcohol abstainers, the highest prevalence of lifetime AUD was found in South Africa (28.3%) exceeding that of Australia (24.1%) which had the highest unconditioned AUD prevalence. The lowest conditional prevalences were found for Italy with estimates of 1.2% for ALA, 0.5% for ALD, and 1.7% for AUD. When all survey participants were considered, Iraq had the lowest prevalence of AUDs. However, once conditioning on lifetime use, Iraq fell in the top three of all surveys for AUD prevalence indicating a low level of overall use but a high risk of AUD among users.

Unconditional lifetime prevalence of AUDs shows a clear positive trend with country income level, increasing from 5.9% for AUDs in low/lower-middle income countries to 7.2% in upper-middle income countries and 10.3% in high-income countries. Comparisons of AUD diagnoses by income group remain significant when conditioning upon lifetime use of alcohol but the trend was less consistent. Comparing between WHO regions, prevalence rates of AUDs were lowest among the Eastern Mediterranean surveys and highest among the Western Pacific surveys, regardless of whether conditioning on alcohol use or not.

Past-year Prevalence

Table 3 shows the prevalences of past-year alcohol use, ALA, ALD and AUDs for all countries, income levels and WHO regions, as well as past-year diagnoses conditional on past-year use. There were significant differences in unconditional and conditional past-year alcohol use and diagnoses across countries. The average 12-month prevalence of alcohol use was 52.3%, ranging from 1.7% in Iraq to 76.9% in Australia. The average 12-month prevalence of ALA for all countries was 1.3% and ranges from 0.1% in Iraq and Japan to 3.7% in the Ukraine. Similar to observations among lifetime prevalences, within-survey comparisons show past-year rates of ALA most often exceed past-year ALD. The average past-year prevalence of AUDs for all countries combined was 2.2% and ranges from 0.1% in Iraq to 5.9% in the Ukraine.

Prevalence of past-year alcohol use, alcohol abuse and alcohol dependence in the World Mental Health Surveys

Conditioning on past year alcohol use, the average past year prevalence for all countries combined was 2.6% for ALA, 1.8% for ALD, and 4.4% for AUD. Iraq had the highest past year prevalence rate (joint with New Zealand) of any survey for AUDs among past-year users (7.3%) which sharply contrasts with its unconditional past-year AUD estimate in which it reported the lowest of any survey (0.1%). The lowest conditional past year prevalence of AUDs was from Japan (1.1%).

There were significant differences in past-year prevalence estimates across WHO regions. The Eastern Mediterranean region had the lowest prevalence estimates among WHO regions for alcohol use (28.9%), ALA (0.5%), ALD (0.1%), and AUD (0.6%) and this pattern was consistent when conditioning upon past-year use ALA (1.8%), ALD (0.4%), and AUD (2.2%). The highest unconditional past year prevalence estimates of alcohol use were from the Western European Region (65.6%), the highest prevalence of ALA was from the Eastern Europe region (1.9%) the highest prevalence of ALD was from the Western Pacific and Region of the Americas (1.2%), and the highest prevalence of AUD was from the Eastern Europe region (3.0%). When excluding alcohol abstainers, the Western Pacific surveys had the highest prevalence estimates for past year ALA (3.3%), ALD (2.5%) and AUD (5.8%).

There were significant differences between income groups for ALA and AUDs but not ALD when alcohol abstainers were excluded, though there were no significant differences when they were included. Unconditional AUD prevalence ranged from 2.0% in low/lower-middle income surveys to 2.3% in upper-middle income surveys while conditional AUD prevalences ranged from 3.4% in lower income surveys to 4.8% in high income surveys.

Remission among people with lifetime AUD was defined at time of interview as having had more than 12 months, or at least two birthdays, since the last disorder related problem. Table 4 shows prevalence of remission at the time of interview among lifetime ALA, ALD and all AUD cases as well as among AUD cases that had at least one non-substance use mental disorders (MHD). For all conditional groups, there were significant differences in the prevalence of AUD remission across countries, income levels and regions. The average prevalence of ALA remission for all countries was 79.5% and 59.7% for ALD. The average prevalence of AUD remission was 75.2%, ranging from 58.2% in Ukraine to 89.3% in Japan. When conditioning on specific disorders, the average remission prevalence was higher among ALA (79.5%) compared to ALD (59.7%) cases, with this same trend consistent within most survey-specific comparisons. Among those with at least one MHD, the average prevalence of AUD remission was 72.5%, ranging from 47.9% in Lebanon to 94.5% in Spain.

Prevalence of remission from alcohol use disorders among those with specific disorders a

Table 5 shows 12-month and lifetime prevalences for AUD by gender. The average past year prevalence of AUD for all countries was 0.9% for women, ranging from no past year cases in Iraq and Nigeria to 2.6% in Australia. The average past year prevalence for all countries was 3.6% for men ranging from 0.2% in Iraq to 11.6% in the Ukraine. Among women, the average lifetime prevalence of AUD for all countries was 3.4% ranging from no lifetime cases in Iraq to 12.1% in Australia. The average lifetime prevalence of AUDs for men was 14.1% for all countries and ranges from 1.4% in Iraq to 33.4% in Australia. In all surveys, the prevalence estimates of both lifetime and 12-month AUDs were higher for men than women.

Prevalence of alcohol use disorders among women and men by country

A dash indicates a zero cell count.

Disorder Persistence

Using the retrospective data on determinations of past AUD it is possible to compute an indirect indicator of disorder persistence as the proportion of lifetimes cases of ALA, ALD and AUDs among subjects who also met criteria for the same diagnosis in the 12 months before interview ( Table 6 ). Significant differences can be seen across countries, survey income groups and WHO regions. Overall, a quarter of respondents who had ever had an AUD continued to have at least some symptoms of the disorder in the past year. For all countries combined, the rates of past-year persistence were 21.4% for ALA, 36.7% for ALD and 25.5% for AUD. There was significant variation for all diagnoses across countries with AUD persistence ranging from 10% in Japan to 43.3% in the Ukraine. Significant variation was also observed between income groups, with persistence highest for ALA among upper-middle income countries at 29.6% and for ALD and AUD among low/lower-middle income countries at 53.8% and 34.1%, respectively. Although Tables 2 and ​ and3 3 show ALA as being a more prevalent disorder than ALD, the estimates presented in Table 6 show ALD to be a more persistent disorder.

Prevalence of past-year alcohol use disorders among those with lifetime alcohol use disorders in the World Mental Health Surveys

Age of Onset

Figure 1 displays AUD AOO curves among those with a lifetime AUD by survey income group. The earliest 15% of lifetime AUD cases across all survey income groups combined had onset before 18 years of age, which means that of those individuals who will develop an AUD at some time in their life, 15% will do so before age 18. For higher percentages of AUDs, AOO generally decreased as survey income level increased. The median AOO for AUDs was 21 years in high-income survey countries, 23 years in upper-middle income countries and 24 years in low/lower-middle income countries.

Age of DSM-IV alcohol use disorder onset distributions among those with a diagnosis by survey income groups

Demographics

Consistent with previous studies, men were significantly more likely than women to have a lifetime or past year persistent AUD ( Table 7 ). The odds for men having a lifetime AUD were 4.6 (95% CI 4.3-4.9) times the odds for women and, among those with a lifetime AUD, the odds for a past year AUD among men were 1.2 (95% CI 1.0-1.4) times the odds for women.

Bivariate associations between sociodemographic correlates and DSM-IV alcohol use disorder

Based on age at interview, younger cohorts were more likely to have a lifetime AUD and past year AUD persistence, with respondents aged 18-29 years having the highest odds for both lifetime (OR 10.4, 95% CI 9.2-11.7) and persistent past-year (OR 3.1, 95% CI 2.2-4.3) AUD compared to those aged 60 or over at time of interview. Employment status was significantly associated with development and persistence of AUD, with students less likely to develop a lifetime AUD (OR 0.8, 95% CI 0.7-1.0) but significantly more likely to persist once developing the disorder (OR 1.5, 95% CI 1.1-2.1) compared to those in employment. Marital status was significantly associated with lifetime and past year persistence of AUD, with divorced, separated or widowed respondents associated with the greatest odds of lifetime AUD (OR 1.6, 95% CI 1.5-1.7) and the never-married at elevated odds of both lifetime (OR 1.3, 95% CI 1.2-1.4) and persistent past-year AUD (OR 1.9, 95% CI 1.7-2.2) compared to those married at time of interview. Education level was significantly associated with lifetime AUD and past year AUD among lifetime cases, with a general trend of an inverse association between educational attainment and likelihood of an AUD. Household income was a significant predictor of lifetime AUD, with those from low (OR 1.2, 95% CI 1.2-1.3) and low-average (OR 1.1, 95% CI 1.1-1.2) income households significantly more likely to develop the disorder compared to persons living in high income households.

Onset relative to other mental disorders

AUDs are highly comorbid with MHD ( Table 8 ). Among respondents with a lifetime AUD, 43.9% had at least one other lifetime MHD, and among those who had ever experienced any MHD, 17.9% had a lifetime AUD. Of those with a past year AUD, 58.6% had at least one other lifetime MHD and 42.9% had at least one other MHD in the same 12-month period ( Appendix Table 1 ). Regarding the ordering of onset of these disorders, the MHD most often preceded the onset of the AUD, as was the case for dysthymia, agoraphobia, social phobia, specific phobias, separation anxiety, ADHD, conduct disorder, intermittent explosive disorder (IED) and oppositional defiant disorder (ODD). The exceptions were bipolar disorder, panic disorder, and generalized anxiety disorder (GAD) where no significant trend in temporal order of onset was observed, and major depressive disorder (MDD), where AUD onset most often occurred prior to disorder onset.

Prevalence of other mental disorders onset before and after the onset of AUDs in the subset of respondents with lifetime alcohol use disorders

SE - standard error. N is the unweighted sample size. All prevalence estimates are weighted based on Part II weights. Each row includes respondents who met lifetime criteria for AUD and the specified DSM-IV disorder.

The mean lifetime prevalence of AUDs in the 29 WMH surveys combined was 8.6%, ranging from 0.7% in Iraq to 22.7% in Australia. The combined mean 12-month AUD prevalence was 2.2%, ranging from 0.1% in Iraq to 5.9% in the Ukraine. This is a high level of disorder prevalence given the substantial health and economic burdens associated with AUDs 18 – 20 . Although the AUD prevalence estimates range widely across the WMH countries, these estimates and the variations are consistent with findings from other national and regional surveys 21 – 31 . There was also a relatively wide cross-national range of AUD prevalence estimates among lifetime non-abstainers. The combined lifetime prevalence of AUDs among non-abstainers was 10.7%, ranging from 1.7% in Italy to 28.3% in South Africa and the combined 12-month prevalence of AUDs among non-abstainers was 4.4% ranging from 1.1% in Japan to 7.3% in Iraq and New Zealand. Both AUD remission and persistence were common. The average prevalence of remission for at least the last year was 79.5% for ALA, 59.7% for ALD, and 75.2% for AUD while the average prevalence of past-year persistence was 21.4% for ALA, 36.7% for ALD and 25.5% for AUD.

The rank ordering across countries of lifetime prevalence estimates in the total population and non-abstainers was the same or similar for most countries but notably dissimilar in a few countries. For example, among all WMH surveys, Iraq had the lowest population prevalence of both past year and lifetime AUDs but for the subgroup of non-lifetime abstainers, Iraq had the highest 12-month prevalence and the third highest prevalence of lifetime AUDs. Iraq’s general population had a relatively low risk for AUDs which may suggest that AUDs are not a significant concern in Iraq. However, among those who had ever used alcohol, the risk for AUDs was comparatively high (19.2%). A large difference was also found in South Africa comparing the AUD prevalence in the general population (11.5%) and among users (28.3%).

There were significant differences in AUD prevalence across the WHO Regions. The Eastern Mediterranean region which had the lowest per capita alcohol consumption and the lowest proportion of drinkers also had the lowest past year and lifetime AUD prevalences. Presumably low consumption and high abstention are limiting factors on the development of AUDs in the general population of a country or region. Supporting this, the range of AUD prevalences across the WHO Regions narrows when considering only lifetime alcohol non-abstainers. However, the limiting effect of low consumption and high abstention may exert more influence at lower levels of alcohol consumption. While the annual per capita alcohol consumption of both France and Australia is relatively high at 12.2 liters of pure alcohol 32 , the lifetime prevalence of AUDs was 7.1% for France and 22.7% for Australia. There are probably a number of contributors to this difference, but one possible factor might be a difference in the prevalence of heavy episodic or binge drinking which has been found to have relatively more severe consequences and association with AUDs than temperate patterns 33 . Studies have found that France has the lowest level of binge drinking in Europe 34 while Australia has comparatively high rates 35 , 36 . Some view heavy episodic drinking as a primary indicator of severe alcohol use problems and have recommended that AUDs, particularly dependence, be measured via heavy drinking over time with diagnosis thresholds set in average per day consumption of alcohol 37 – 39 . The WMH findings suggest that alcohol consumption measures may enhance understanding of psychiatric epidemiological findings.

Other potentially limiting contextual factors may account for and possibly reflect some of the cross-national variation in prevalence of AUDs. Some of these may include social-cultural influences 40 , 41 such as parental involvement, drinking culture, and stigma while other factors may be more policy related 42 , 43 such as legal sanctions, enforcement of prohibitions and alcohol availability, all of which may be subject to change over time. The possible influence of these factors is important to interpretation of the country AUD prevalences and to inferences about explanations for the varying AUD prevalences. However, interpretations should not assume simple causality and speculations must be cautious regarding how prevalences might change if different environmental and social conditions were instituted.

There is evidence that in Europe, North America, and Australia adolescent levels of drinking have declined in recent years, perhaps in relation to changing socio-environmental factors 44 – 48 . However, alcohol consumption is stable or increasing in other regions (1) and there are reports that abstaining or moderate alcohol using adolescents often considerably increase consumption to heavy drinking as young adults 1 , 49 , 50 and that middle-age and older adults may be increasingly engaging in binge drinking 51 .

Despite changes in alcohol use in different ages and regions, varying sociocultural environments, and the range of AUD prevalences, the multinational WMH results confirm a number of well-documented clinical and epidemiological findings from more local studies. For example, AUD prevalences are much higher for men than women 52 . Risk for onset of AUD begins in adolescence with half of all lifetime cases beginning by age 23 although new onsets continue through later life 53 . Being married 54 and more educated 55 is associated with lower risk for AUD. Comorbidity with other mental disorders is common 56 and mental health disorders often precede AUDs 57 . Despite the harms and impairments associated with AUDs, there is a low prevalence of treatment 58 . The consistency of the patterns of AUDs and correlates across the surveyed countries provides an informative perspective on the relatively consistent nature of AUDs. AUD prevalences were much higher for men than women with the odds for men having a lifetime AUD being 4.6 times the odds for women. Risk for onset of AUD began in adolescence with 15% of all lifetime cases developing before age 18 although new onsets continued through later life. Being older at time of interview, being married, being more educated, and having a higher household income were all associated with a lower risk for AUD and for AUD persistence.

The WMH findings also corroborate and extend previous research on AUD – MHD comorbidities 56 , 58 – 61 . Comorbidity with MHDs is common with 43.9% of individuals with a lifetime AUD having had at least one lifetime MHD. Among respondents with a lifetime MHD, 17.9% had a lifetime AUD. For most of the MHD categories considered (dysthymia, all phobias, separation anxiety, ADHD, conduct disorder, IED, and ODD) when there were MHD – AUD comorbidities, the onset of the MHD most often preceded the onset of the AUD. No significant trends in order of onset were observed for bipolar disorder, panic disorder, and GAD. The onset of AUD most often preceded the onset of only one disorder, MDD.

Onsets of MHD and AUD disorders are often not discrete events occurring at a single point in time and developing symptoms of comorbid disorders may reciprocally influence each other 62 , 63 . This complicates assessment of the exact temporal order of the onset of emerging disorders. In addition, research suggests that AUDs and mood, anxiety and other impulse-control related mental disorders may share common underlying influences or pathways 24 , 64 . There is growing interest in characterizations of mental illness that rely on dimensional rather than categorical frameworks 65 – 67 . Proposals such as these, most notably the Hierarchical Taxonomy of Psychopathology - HiTOP, are in the early stages of formulation and empirical validation but might facilitate understanding of comorbidities, underlying influences, and common transdiagnostic factors. The cross-national findings of prevalent comorbidities in the WMH surveys lend support for further explorations of dimensional approaches. In addition, the WMH findings corroborate local findings and affirm the potential value of further understanding of MHD – SUD comorbidities which is planned for future WMH research.

Limitations of the WMH surveys must be considered. As data come from 27 countries there is not a full representation of all regions, income levels and other country characteristics. Response rates and the year in which the surveys were administered varied across surveys, and cross-national differences in willingness to disclose personal information about alcohol use and associated problems are possible. The respondent information is subject to the limitations of retrospective reporting. The WMH surveys rely solely on household surveys. Data from subgroups that may differ from the larger national populations in terms of AUD prevalences and correlates were not included such as the homeless, people in jails, prisons, hospitals, halfway houses, SUD inpatient treatment facilities, or living on military bases. Although the WMH sampling does not include individuals who were residing in inpatient treatment facilities at the time of the interview, the sampling does not exclude individuals who were either in outpatient treatment at the time of the interview or who received either outpatient or inpatient treatment at any time prior to the interview. Nevertheless, if the WMH had specifically included current clinical AUD samples it is likely that more severe cases would have been identified 68 .

Supplementary Material

Supplementary material, funding acknowledgements.

The World Health Organization World Mental Health (WMH) Survey Initiative is supported by the United States National Institute of Mental Health (NIMH; R01 MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the United States Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical Inc., GlaxoSmithKline, and Bristol-Myers Squibb. This study was supported by an Australian National Health and Medical Research Council (NHMRC) project grant (APP1081984). We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on data analysis. None of the funders had any role in the design, analysis, interpretation of results, or preparation of this paper. The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of the World Health Organization, other sponsoring organizations, agencies, or governments.

The 2007 Australian National Survey of Mental Health and Wellbeing is funded by the Australian Government Department of Health and Ageing. The Argentina survey -- Estudio Argentino de Epidemiología en Salud Mental (EASM) -- was supported by a grant from the Argentinian Ministry of Health (Ministerio de Salud de la Nación). The São Paulo Megacity Mental Health Survey is supported by the State of São Paulo Research Foundation (FAPESP) Thematic Project Grant 03/00204-3. The Bulgarian Epidemiological Study of common mental disorders EPIBUL is supported by the Ministry of Health and the National Center for Public Health Protection. The Chinese World Mental Health Survey Initiative is supported by the Pfizer Foundation. The Colombian National Study of Mental Health (NSMH) is supported by the Ministry of Social Protection. The Mental Health Study Medellín – Colombia was carried out and supported jointly by the Center for Excellence on Research in Mental Health (CES University) and the Secretary of Health of Medellín. The ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042; SANCO 2004123, and EAHC 20081308), (the Piedmont Region (Italy)), Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spain (FIS 00/0028), Ministerio de Ciencia y Tecnología, Spain (SAF 2000-158-CE), Departament de Salut, Generalitat de Catalunya, Spain, DIUE de la Generalitat de Catalunya (2017 SGR 452; 2014 SGR 748), Instituto de Salud Carlos III (CIBER CB06/02/0046, RETICS RD06/0011 REM-TAP), and other local agencies and by an unrestricted educational grant from GlaxoSmithKline. Implementation of the Iraq Mental Health Survey (IMHS) and data entry were carried out by the staff of the Iraqi MOH and MOP with direct support from the Iraqi IMHS team with funding from both the Japanese and European Funds through United Nations Development Group Iraq Trust Fund (UNDG ITF). The Israel National Health Survey is funded by the Ministry of Health with support from the Israel National Institute for Health Policy and Health Services Research and the National Insurance Institute of Israel. The World Mental Health Japan (WMHJ) Survey is supported by the Grant for Research on Psychiatric and Neurological Diseases and Mental Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013, H25-SEISHIN-IPPAN-006) from the Japan Ministry of Health, Labour and Welfare. The Lebanese Evaluation of the Burden of Ailments and Needs Of the Nation (L.E.B.A.N.O.N.) is supported by the Lebanese Ministry of Public Health, the WHO (Lebanon), National Institute of Health / Fogarty International Center (R03 TW006481-01), anonymous private donations to IDRAAC, Lebanon, and unrestricted grants from, Algorithm, AstraZeneca, Benta, Bella Pharma, Eli Lilly, Glaxo Smith Kline, Lundbeck, Novartis, OmniPharma, Pfizer, Phenicia, Servier, UPO. The Mexican National Comorbidity Survey (MNCS) is supported by The National Institute of Psychiatry Ramon de la Fuente (INPRFMDIES 4280) and by the National Council on Science and Technology (CONACyT-G30544- H), with supplemental support from the Pan American Health Organization (PAHO). Te Rau Hinengaro: The New Zealand Mental Health Survey (NZMHS) is supported by the New Zealand Ministry of Health, Alcohol Advisory Council, and the Health Research Council. The Nigerian Survey of Mental Health and Wellbeing (NSMHW) is supported by the WHO (Geneva), the WHO (Nigeria), and the Federal Ministry of Health, Abuja, Nigeria. The Northern Ireland Study of Mental Health was funded by the Health & Social Care Research & Development Division of the Public Health Agency. The Peruvian World Mental Health Study was funded by the National Institute of Health of the Ministry of Health of Peru. The Polish project Epidemiology of Mental Health and Access to Care –EZOP Project (PL 0256) was supported by Iceland, Liechtenstein and Norway through funding from the EEA Financial Mechanism and the Norwegian Financial Mechanism. EZOP project was co-financed by the Polish Ministry of Health. The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health. The Romania WMH study projects “Policies in Mental Health Area” and “National Study regarding Mental Health and Services Use” were carried out by National School of Public Health & Health Services Management (former National Institute for Research & Development in Health), with technical support of Metro Media Transilvania, the National Institute of Statistics-National Centre for Training in Statistics, SC, Cheyenne Services SRL, Statistics Netherlands and were funded by Ministry of Public Health (former Ministry of Health) with supplemental support of Eli Lilly Romania SRL. The South Africa Stress and Health Study (SASH) is supported by the US National Institute of Mental Health (R01-MH059575) and National Institute of Drug Abuse with supplemental funding from the South African Department of Health and the University of Michigan. The Psychiatric Enquiry to General Population in Southeast Spain – Murcia (PEGASUS-Murcia) Project has been financed by the Regional Health Authorities of Murcia (Servicio Murciano de Salud and Consejería de Sanidad y Política Social) and Fundación para la Formación e Investigación Sanitarias (FFIS) of Murcia. The Ukraine Comorbid Mental Disorders during Periods of Social Disruption (CMDPSD) study is funded by the US National Institute of Mental Health (RO1-MH61905). The US National Comorbidity Survey Replication (NCS-R) is supported by the National Institute of Mental Health (NIMH; U01-MH60220) with supplemental support from the National Institute of Drug Abuse (NIDA), the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044708), and the John W. Alden Trust. Louisa Degenhardt is supported by a NHMRC Senior Principal Research Fellowship (no. 1135991) and NIDA NIH grant R01 DA044170-02. John McGrath is supported by the Danish National Research Foundation (Niels Bohr Professorship), and the John Cade Fellowship (APP1056929) from National Health and Medical Research Council. A complete list of all within-country and cross-national WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/ .

Disclosure/Conflict of Interest:

The views and opinions expressed in this publication are those of the authors only and do not necessarily represent the views, official policy, or position of the US. Department of Health and Human Services or any of its affiliated institutions or agencies. Dr. Glantz’s role on this study is through his involvement as a Science Officer on U01-MH60220. He had no involvement in the other cited grants. In the past 3 years, Dr. Kessler received support for his epidemiological studies from Sanofi Aventis; was a consultant for Johnson & Johnson Wellness and Prevention, Sage Pharmaceuticals, Shire, Takeda; and served on an advisory board for the Johnson & Johnson Services Inc. Lake Nona Life Project. Kessler is a co-owner of DataStat, Inc., a market research firm that carries out healthcare research. In the past 3 years, L.D. has received investigator-initiated untied educational grants for studies of opioid medications in Australia from Indivior, Mundipharma and Seqirus.

Binge drinking is a growing public health crisis − a neurobiologist explains how research on alcohol use disorder has shifted

Assistant Professor of Biology, Biomedical Engineering and Pharmacology, Penn State

Disclosure statement

Nikki Crowley receives funding from The National Institutes of Health, The Brain and Behavior Research Foundation, and the Penn State Huck Institutes of the Life Sciences endowment funds.

Penn State provides funding as a founding partner of The Conversation US.

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With the new Amy Winehouse biopic “Back to Black ” in U.S. theaters as of May 17, 2024, the late singer’s relationship with alcohol and drugs is under scrutiny again. In July 2011, Winehouse was found dead in her flat in north London from “death by misadventure” at the age of 27. That’s the official British term used for accidental death caused by a voluntary risk.

Her blood alcohol concentration was 0.416%, more than five times the legal intoxication limit in the U.S. – leading her cause of death to be later adjusted to include “alcohol toxicity” following a second coroner’s inquest.

Nearly 13 years later, alcohol consumption and binge drinking remain a major public health crisis , not just in the U.K. but also in the U.S.

Roughly 1 in 5 U.S. adults report binge drinking at least once a week, with an average of seven drinks per binge episode . This is well over the amount of alcohol thought to produce legal intoxication, commonly defined as a blood alcohol concentration over 0.08% – on average, four drinks in two hours for women, five drinks in two hours for men.

Among women, days of “heavy drinking” increased 41% during the COVID-19 pandemic compared with pre-pandemic levels , and adult women in their 30s and 40s are rapidly increasing their rates of binge drinking , with no evidence of these trends slowing down. Despite efforts to comprehend the overall biology of substance use disorders, scientists’ and physicians’ understanding of the relationship between women’s health and binge drinking has lagged behind.

I am a neurobiologist focused on understanding the chemicals and brain regions that underlie addiction to alcohol . I study how neuropeptides – unique signaling molecules in the prefrontal cortex , one of the key brain regions in decision-making, risk-taking and reward – are altered by repeated exposure to binge alcohol consumption in animal models.

My lab focuses on understanding how things like alcohol alter these brain systems before diagnosable addiction, so that we can better inform efforts toward both prevention and treatment.

The biology of addiction

While problematic alcohol consumption has likely occurred as long as alcohol has existed, it wasn’t until 2011 that the American Society of Addiction Medicine recognized substance addiction as a brain disorder – the same year as Winehouse’s death. A diagnosis of an alcohol use disorder is now used over outdated terms such as labeling an individual as an alcoholic or having alcoholism.

Researchers and clinicians have made great strides in understanding how and why drugs – including alcohol, a drug – alter the brain. Often, people consume a drug like alcohol because of the rewarding and positive feelings it creates, such as enjoying drinks with friends or celebrating a milestone with a loved one. But what starts off as manageable consumption of alcohol can quickly devolve into cycles of excessive alcohol consumption followed by drug withdrawal.

While all forms of alcohol consumption come with health risks, binge drinking appears to be particularly dangerous due to how repeated cycling between a high state and a withdrawal state affect the brain. For example, for some people, alcohol use can lead to “ hangxiety ,” the feeling of anxiety that can accompany a hangover.

Repeated episodes of drinking and drunkenness, coupled with withdrawal, can spiral, leading to relapse and reuse of alcohol. In other words, alcohol use shifts from being rewarding to just trying to prevent feeling bad.

It makes sense. With repeated alcohol use over time, the areas of the brain engaged by alcohol can shift away from those traditionally associated with drug use and reward or pleasure to brain regions more typically engaged during stress and anxiety .

All of these stages of drinking, from the enjoyment of alcohol to withdrawal to the cycles of craving, continuously alter the brain and its communication pathways . Alcohol can affect several dozen neurotransmitters and receptors , making understanding its mechanism of action in the brain complicated.

Work in my lab focuses on understanding how alcohol consumption changes the way neurons within the prefrontal cortex communicate with each other. Neurons are the brain’s key communicator, sending both electrical and chemical signals within the brain and to the rest of your body.

What we’ve found in animal models of binge drinking is that certain subtypes of neurons lose the ability to talk to each other appropriately. In some cases, binge drinking can permanently remodel the brain. Even after a prolonged period of abstinence, conversations between the neurons don’t return to normal .

These changes in the brain can appear even before there are noticeable changes in behavior . This could mean that the neurobiological underpinnings of addiction may take root well before an individual or their loved ones suspect a problem with alcohol.

Researchers like us don’t yet fully understand why some people may be more susceptible to this shift, but it likely has to do with genetic and biological factors, as well as the patterns and circumstances under which alcohol is consumed.

Women are forgotten

While researchers are increasingly understanding the medley of biological factors that underlie addiction, there’s one population that’s been largely overlooked until now: women.

Women may be more likely than men to have some of the most catastrophic health effects caused by alcohol use, such as liver issues, cardiovascular disease and cancer . Middle-aged women are now at the highest risk for binge drinking compared with other populations.

When women consume even moderate levels of alcohol, their risk for various cancers goes up, including digestive, breast and pancreatic cancer , among other health problems – and even death. So the worsening rates of alcohol use disorder in women prompt the need for a greater focus on women in the research and the search for treatments.

Yet, women have long been underrepresented in biomedical research.

It wasn’t until 1993 that clinical research funded by the National Institutes of Health was required to include women as research subjects. In fact, the NIH did not even require sex as a biological variable to be considered by federally funded researchers until 2016. When women are excluded from biomedical research, it leaves doctors and researchers with an incomplete understanding of health and disease, including alcohol addiction.

There is also increasing evidence that addictive substances can interact with cycling sex hormones such as estrogen and progesterone . For instance, research has shown that when estrogen levels are high, like before ovulation, alcohol might feel more rewarding , which could drive higher levels of binge drinking. Currently, researchers don’t know the full extent of the interaction between these natural biological rhythms or other unique biological factors involved in women’s health and propensity for alcohol addiction.

Looking ahead

Researchers and lawmakers are recognizing the vital need for increased research on women’s health. Major federal investments into women’s health research are a vital step toward developing better prevention and treatment options for women.

While women like Amy Winehouse may have been forced to struggle both privately and publicly with substance use disorders and alcohol, the increasing focus of research on addiction to alcohol and other substances as a brain disorder will open new treatment avenues for those suffering from the consequences.

For more information on alcohol use disorder, causes, prevention and treatments, visit the National Institute on Alcohol Abuse and Alcoholism .

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How Binge Drinking Shifted Research On Alcohol Use Disorders

"roughly 1 in 5 u.s. adults report binge drinking at least once a week.".

With the new Amy Winehouse biopic “Back to Black ” in U.S. theaters as of May 17, 2024, the late singer’s relationship with alcohol and drugs is under scrutiny again. In July 2011, Winehouse was found dead in her flat in north London from “death by misadventure” at the age of 27. That’s the official British term used for accidental death caused by a voluntary risk.

Her blood alcohol concentration was 0.416%, more than five times the legal intoxication limit in the U.S. – leading her cause of death to be later adjusted to include “alcohol toxicity” following a second coroner’s inquest.

Nearly 13 years later, alcohol consumption and binge drinking remain a major public health crisis , not just in the U.K. but also in the U.S.

Roughly 1 in 5 U.S. adults report binge drinking at least once a week, with an average of seven drinks per binge episode . This is well over the amount of alcohol thought to produce legal intoxication, commonly defined as a blood alcohol concentration over 0.08% – on average, four drinks in two hours for women, five drinks in two hours for men.

Among women, days of “heavy drinking” increased 41% during the COVID-19 pandemic compared with pre-pandemic levels , and adult women in their 30s and 40s are rapidly increasing their rates of binge drinking , with no evidence of these trends slowing down. Despite efforts to comprehend the overall biology of substance use disorders, scientists’ and physicians’ understanding of the relationship between women’s health and binge drinking has lagged behind.

I am a neurobiologist focused on understanding the chemicals and brain regions that underlie addiction to alcohol . I study how neuropeptides – unique signaling molecules in the prefrontal cortex , one of the key brain regions in decision-making, risk-taking and reward – are altered by repeated exposure to binge alcohol consumption in animal models.

My lab focuses on understanding how things like alcohol alter these brain systems before diagnosable addiction, so that we can better inform efforts toward both prevention and treatment.

The Biology Of Addiction

While problematic alcohol consumption has likely occurred as long as alcohol has existed, it wasn’t until 2011 that the American Society of Addiction Medicine recognized substance addiction as a brain disorder – the same year as Winehouse’s death. A diagnosis of an alcohol use disorder is now used over outdated terms such as labeling an individual as an alcoholic or having alcoholism.

Researchers and clinicians have made great strides in understanding how and why drugs – including alcohol, a drug – alter the brain. Often, people consume a drug like alcohol because of the rewarding and positive feelings it creates, such as enjoying drinks with friends or celebrating a milestone with a loved one. But what starts off as manageable consumption of alcohol can quickly devolve into cycles of excessive alcohol consumption followed by drug withdrawal.

While all forms of alcohol consumption come with health risks, binge drinking appears to be particularly dangerous due to how repeated cycling between a high state and a withdrawal state affect the brain. For example, for some people, alcohol use can lead to “ hangxiety ,” the feeling of anxiety that can accompany a hangover.

Repeated episodes of drinking and drunkenness, coupled with withdrawal, can spiral, leading to relapse and reuse of alcohol. In other words, alcohol use shifts from being rewarding to just trying to prevent feeling bad.

It makes sense. With repeated alcohol use over time, the areas of the brain engaged by alcohol can shift away from those traditionally associated with drug use and reward or pleasure to brain regions more typically engaged during stress and anxiety .

All of these stages of drinking, from the enjoyment of alcohol to withdrawal to the cycles of craving, continuously alter the brain and its communication pathways . Alcohol can affect several dozen neurotransmitters and receptors , making understanding its mechanism of action in the brain complicated.

Work in my lab focuses on understanding how alcohol consumption changes the way neurons within the prefrontal cortex communicate with each other. Neurons are the brain’s key communicator, sending both electrical and chemical signals within the brain and to the rest of your body.

What we’ve found in animal models of binge drinking is that certain subtypes of neurons lose the ability to talk to each other appropriately. In some cases, binge drinking can permanently remodel the brain. Even after a prolonged period of abstinence, conversations between the neurons don’t return to normal .

These changes in the brain can appear even before there are noticeable changes in behavior . This could mean that the neurobiological underpinnings of addiction may take root well before an individual or their loved ones suspect a problem with alcohol.

Researchers like us don’t yet fully understand why some people may be more susceptible to this shift, but it likely has to do with genetic and biological factors, as well as the patterns and circumstances under which alcohol is consumed.

Women are Forgotten

While researchers are increasingly understanding the medley of biological factors that underlie addiction, there’s one population that’s been largely overlooked until now: women.

Women may be more likely than men to have some of the most catastrophic health effects caused by alcohol use, such as liver issues, cardiovascular disease and cancer . Middle-aged women are now at the highest risk for binge drinking compared with other populations.

When women consume even moderate levels of alcohol, their risk for various cancers goes up, including digestive, breast and pancreatic cancer , among other health problems – and even death. So the worsening rates of alcohol use disorder in women prompt the need for a greater focus on women in the research and the search for treatments.

Yet, women have long been underrepresented in biomedical research.

It wasn’t until 1993 that clinical research funded by the National Institutes of Health was required to include women as research subjects. In fact, the NIH did not even require sex as a biological variable to be considered by federally funded researchers until 2016. When women are excluded from biomedical research, it leaves doctors and researchers with an incomplete understanding of health and disease, including alcohol addiction.

There is also increasing evidence that addictive substances can interact with cycling sex hormones such as estrogen and progesterone . For instance, research has shown that when estrogen levels are high, like before ovulation, alcohol might feel more rewarding , which could drive higher levels of binge drinking. Currently, researchers don’t know the full extent of the interaction between these natural biological rhythms or other unique biological factors involved in women’s health and propensity for alcohol addiction.

Looking Ahead

Researchers and lawmakers are recognizing the vital need for increased research on women’s health. Major federal investments into women’s health research are a vital step toward developing better prevention and treatment options for women.

While women like Amy Winehouse may have been forced to struggle both privately and publicly with substance use disorders and alcohol, the increasing focus of research on addiction to alcohol and other substances as a brain disorder will open new treatment avenues for those suffering from the consequences.

For more information on alcohol use disorder, causes, prevention and treatments, visit the National Institute on Alcohol Abuse and Alcoholism .

Nikki Crowley is an Assistant Professor of Biology, Biomedical Engineering and Pharmacology at Penn State. This article is republished from The Conversation under a Creative Commons license . Read the original article .

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Alcohol and Your Brain: The Latest Scientific Insights

Want to protect your brain here's what you need to know about alcohol consumption..

Posted March 18, 2024 | Reviewed by Devon Frye

• What Is Alcoholism?
• Find a therapist to overcome addiction
• Transient memory loss, “blackouts,” and hangovers related to alcohol consumption are brain health risks.
• Alcohol use disorder (alcoholism) is a risk factor for developing dementia.
• Heavy or excessive alcohol consumption is dangerous to the brain for a number of reasons.
• The impact of mild to moderate alcohol consumption (1-3 drinks a day) on brain function is less clear.

Depending on who you ask, you might be told to drink a few glasses of red wine a day or to avoid alcohol altogether. The reasons for such recommendations are many, but, by and large, they tend to stem from a study someone read about or saw reported in the news.

So why is it so hard to know whether alcohol is good or bad for us—especially for our brains? In this post, we’ll explore the current science and some practical ideas on how to approach the topic.

What Is Alcohol Anyway?

When people talk about drinking “alcohol,” they’re almost always referring to the consumption of ethanol. Ethanol is a natural product that is formed from the fermentation of grains, fruits, and other sources of sugar. It’s found in a wide range of alcoholic beverages including beer, wine, and spirits like vodka, whiskey, rum, and gin.

Evidence for human consumption of alcohol dates back over 10,000 years. Consumption of alcohol has and continues to serve major roles in religious and cultural ceremonies around the world. But unlike most food products, in the last century, alcohol has been wrapped up in nearly perpetual controversy over its moral effects and health implications.

How Does Alcohol Impact the Brain?

As anyone who’s consumed alcohol knows, ethanol can directly influence brain function. Ethanol is classified as a “depressant” because it has a generally slowing effect on brain activity through activation of γ-aminobutyric acid (GABA) pathways.

In an acute sense, consumption of alcohol can lead to uninhibited behavior, sedation, lapses in judgment, and impairments in motor function. At higher levels, the effects can progress to coma and even death.

The Known Brain-Damaging Effects of Excess Alcohol

There is no debate here: Excessively high levels of alcohol consumption over short periods of time are toxic and potentially deadly, specifically because of its effects on the brain.

One critical fact to understand about the overall and brain-specific effects of alcohol is that the entirety of the debate around the risk/benefit ratio concerns mild to moderate alcohol consumption. As it relates to the effects of high amounts of alcohol on the body and brain, the research is consistent: It’s a very bad choice.

High amounts of alcohol use are causal risk factors in the development of disease in the heart, liver, pancreas, and brain (including the brains of children in utero). In fact, 1 in 8 deaths in Americans aged 20-64 is attributable to alcohol use. When it comes to adults, excessive alcohol use can cause multiple well-defined brain issues ranging from short-term confusion to dementia .

What Is “Excessive” or “High” Alcohol Use?

Key to the nuance in the conversation about alcohol use are definitions. Across the board, “excessive” or “high” alcohol use is linked to worse overall and brain health outcomes. So what does that mean?

While definitions can be variable, one way to look at this is the consumption of 4 or more drinks on an occasion (for women) and 5 or more for men. Additionally, excess alcohol is defined as drinking more than 8 drinks a week (women) and 15 a week (men), or consuming alcohol if you are pregnant or younger than age 21.

Beyond this, by definition, consuming enough alcohol to cause a “brownout,” “blackout,” hangover, or other overt brain symptomatology is evidence that the alcohol you’ve consumed is creating problems in your brain. Alcohol use disorder (or alcoholism ) is also a clear issue for the brain. It has been linked to a higher risk for dementia, especially early-onset dementia in a study of 262,000 adults, as well as to smaller brain size .

Is There a “Safe” Amount of Alcohol for the Brain?

In a highly publicized article from Nature Communications , researchers looked at brain imaging data from nearly 37,000 middle-aged to older adults and cross-referenced their brain scans with their reported alcohol consumption. The findings were profound: People who drank more alcohol had smaller brains, even in people drinking only one or two alcoholic beverages a day.

Conversely, other recent data suggest a lower risk for dementia in people consuming a few alcoholic beverages a day. This includes a 2022 study showing that in around 27,000 people, consuming up to 40 grams of alcohol (around 2.5 drinks) a day was linked to a lower risk for dementia versus abstinence in adults over age 60. A much larger study of almost 4 million people in Korea noted that mild to moderate alcohol consumption was linked to a lower risk for dementia compared to non-drinking.

How Do We Make Sense of This Data?

When it comes to the bottom line as it relates to alcohol consumption and brain health, the data are rather solid on some fronts, and a bit less so on others. There’s also the potential for confounding variables, including the fact that many people like to drink alcohol to enjoy and enhance social bonds (which we know are beneficial for the brain). Here’s a summary of what the most recent research is telling us.

• Experiencing transient memory loss, “blackouts,” or hangovers related to alcohol consumption is overt evidence of threats to brain health.
• The impact of mild to moderate alcohol consumption (1-3 drinks a day) on brain function is less clear, but it seems unreasonable to start alcohol use for brain health.

Austin Perlmutter, M.D. , is a board-certified internal medicine physician and the co-author of Brain Wash .

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Does This Patient Have Alcohol Use Disorder? The Rational Clinical Examination Systematic Review

• 1 British Columbia Centre on Substance Use, Vancouver, Canada
• 2 Faculty of Medicine, University of British Columbia, Vancouver, Canada
• The Rational Clinical Examination Will This Hospitalized Patient Develop Severe Alcohol Withdrawal Syndrome? Evan Wood, MD, PhD, FRCPC; Loai Albarqouni, MD, MSc; Stacey Tkachuk, BSc, Pharm; Carolyn J. Green, PhD; Keith Ahamad, MD; Seonaid Nolan, MD, FRCPC; Mark McLean, MD, FRCPC; Jan Klimas, PhD, MSc JAMA
• JAMA Patient Page Patient Information: Treatment of Alcohol Use Disorder Avani K. Patel, MD, MHA; Alëna A. Balasanova, MD JAMA
• JAMA Patient Page Patient Information: Unhealthy Alcohol Use Avani K. Patel, MD, MHA; Alëna A. Balasanova, MD JAMA
• Research Letter Alcohol-Related Deaths During the COVID-19 Pandemic Aaron M. White, PhD; I-Jen P. Castle, PhD; Patricia A. Powell, PhD; Ralph W. Hingson, ScD; George F. Koob, PhD JAMA
• Original Investigation Pharmacotherapy for Alcohol Use Disorder Melissa McPheeters, PhD, MPH; Elizabeth A. O’Connor, PhD; Sean Riley, MSc, MA; Sara M. Kennedy, MPH; Christiane Voisin, MSLS; Kaitlin Kuznacic, PharmD; Cory P. Coffey, PharmD; Mark D. Edlund, MD, PhD; Georgiy Bobashev, PhD; Daniel E. Jonas, MD, MPH JAMA

Importance   The accuracy of screening tests for alcohol use disorder (defined as a problematic pattern of alcohol use leading to clinically significant impairment or distress) requires reassessment to align with the latest definition in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) ( DSM-5 ).

Objective   To assess the diagnostic accuracy of screening tools in identifying individuals with alcohol use disorder as defined in the DSM-5 .

Data Sources and Study Selection   The databases of MEDLINE and Embase were searched (January 2013-February 2023) for original studies on the diagnostic accuracy of brief screening tools to identify alcohol use disorder according to the DSM-5 definition. Because diagnosis of alcohol use disorder does not include excessive alcohol use as a criterion, studies of screening tools that identify excessive or high-risk drinking among younger (aged 9-18 years), older (aged ≥65 years), and pregnant persons also were retained.

Data Extraction and Synthesis   Sensitivity, specificity, and likelihood ratios (LRs) were calculated. When appropriate, a meta-analysis was performed to calculate a summary LR.

Results   Of 4303 identified studies, 35 were retained (N = 79 633). There were 11 691 individuals with alcohol use disorder or a history of excessive drinking. Across all age categories, a score of 8 or greater on the Alcohol Use Disorders Identification Test (AUDIT) increased the likelihood of alcohol use disorder (LR, 6.5 [95% CI, 3.9-11]). A positive screening result using AUDIT identified alcohol use disorder better among females (LR, 6.9 [95% CI, 3.9-12]) than among males (LR, 3.8 [95% CI, 2.6-5.5]) ( P  = .003). An AUDIT score of less than 8 reduced the likelihood of alcohol use disorder similarly for both males and females (LR, 0.33 [95% CI, 0.20-0.52]). The abbreviated AUDIT-Consumption (AUDIT-C) has sex-specific cutoff scores of 4 or greater for males and 3 or greater for females, but was less useful for identifying alcohol use disorder (males: LR, 1.8 [95% CI, 1.5-2.2]; females: LR, 2.0 [95% CI, 1.8-2.3]). The AUDIT-C appeared useful for identifying measures of excessive alcohol use in younger people (aged 9-18 years) and in those older than 60 years of age. For those younger than 18 years of age, the National Institute on Alcohol Abuse and Alcoholism age-specific drinking thresholds were helpful for assessing the likelihood of alcohol use disorder at the lowest risk threshold (LR, 0.15 [95% CI, 0.11-0.21]), at the moderate risk threshold (LR, 3.4 [95% CI, 2.8-4.1]), and at the highest risk threshold (LR, 15 [95% CI, 12-19]). Among persons who were pregnant and screened within 48 hours after delivery, an AUDIT score of 4 or greater identified those more likely to have alcohol use disorder (LR, 6.4 [95% CI, 5.1-8.0]), whereas scores of less than 2 for the Tolerance, Worried, Eye-Opener, Amnesia and Cut-Down screening tool and the Tolerance, Annoyed, Cut-Down and Eye-Opener screening tool identified alcohol use disorder similarly (LR, 0.05 [95% CI, 0.01-0.20]).

Conclusions and Relevance   The AUDIT screening tool is useful to identify alcohol use disorder in adults and in individuals within 48 hours postpartum. The National Institute on Alcohol Abuse and Alcoholism youth screening tool is helpful to identify children and adolescents with alcohol use disorder. The AUDIT-C appears useful for identifying various measures of excessive alcohol use in young people and in older adults.

Read More About

Wood E , Pan J , Cui Z, et al. Does This Patient Have Alcohol Use Disorder? The Rational Clinical Examination Systematic Review . JAMA. 2024;331(14):1215–1224. doi:10.1001/jama.2024.3101

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PRACTICE POINTERS BY ALYSSA BRUEHLMAN, MD, University of Pennsylvania, Philadelphia, Pennsylvania

PRACTICE POINTERS BY ELIZABETH SALISBURY-AFSHAR, MD, MPH, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin

Am Fam Physician. 2024;109(5):459-462

Related article:   Medications for Alcohol Use Disorder

Author disclosure: No relevant financial relationships.

Key Clinical Issue

What are the potential benefits and adverse effects of medications used to treat alcohol use disorder (AUD) in adults in outpatient settings?

Evidence-Based Answer

Oral naltrexone (Revia), 50 mg daily, reduces alcohol consumption across multiple outcomes, and once-daily dosing offers relative ease of use. (Strength of Recommendation [SOR]: C, disease-oriented evidence.) Acamprosate and topiramate also have evidence of benefit for reducing alcohol consumption; however, acamprosate has a higher pill burden, and topiramate has a less desirable adverse effect profile. (SOR: C, disease-oriented evidence.) There is lower strength of evidence for reducing alcohol consumption when using injectable naltrexone (Vivitrol), baclofen, or gabapentin. (SOR: C, disease-oriented evidence.) Evidence remains inadequate for use of disulfiram compared with placebo for reducing alcohol consumption. 1 Current data are insufficient to understand the effect of any of the pharmacotherapies for AUD on health outcomes (e.g., mortality, quality of life, function, accidents, injury).

Practice Pointers

Excessive alcohol use is a leading cause of preventable death, contributing to more than 178,000 deaths in the United States annually per the latest data from 2020–2021. 2 Screening and brief intervention for unhealthy alcohol use are recommended by the U.S. Preventive Services Task Force for adults, including pregnant people. 3 National surveillance data from 2022 suggest that 29.5 million people in the United States 12 years or older (10.5%) met criteria for AUD in the past year, but only 2.1% of those received medication to treat it. 4 In 2023, the Agency for Healthcare Research and Quality published an update to their 2014 systematic review of outpatient pharmacologic treatment for AUD. 1

Medications for AUD are generally studied with psychosocial cointervention; thus, this summary reflects effects attributed to medications beyond nonpharmacologic interventions and placebo. Few studies have directly compared any of these medications. The data are limited surrounding medication effectiveness in special populations, specific to primary care, or relative to longer-term health outcomes (e.g., quality of life, accidents, injury, mortality).

Acamprosate, disulfiram, oral naltrexone, and the long-acting injectable formulation of naltrexone are approved by the U.S. Food and Drug Administration for treatment of AUD. Baclofen, gabapentin, ondansetron, prazosin, topiramate, and varenicline (Chantix) have also been studied for off-label use. Of these medications, oral naltrexone and acamprosate have the strongest and most consistent evidence and should be considered first-line treatments for AUD in primary care.

Oral naltrexone, an opioid antagonist, reduces return to heavy drinking, percentage of drinking days, and percentage of heavy-drinking days. Studies of oral naltrexone also show a nonsignificant trend toward reduction in return to any drinking. Acamprosate, an N -methyl-d-aspartate (NMDA) receptor modulator, reduces return to any drinking and percentage of total drinking days. Studies comparing naltrexone with acamprosate have not established the superiority of one medication over the other (prevent return to any drinking: oral naltrexone, number needed to treat [NNT] = 18 vs. acamprosate, NNT = 11; prevent return to heavy drinking: oral naltrexone, NNT = 11 [data not available for acamprosate]). 5 No new comparative studies were identified since the 2014 Agency for Healthcare Research and Quality review.

Considerations when selecting acamprosate or naltrexone include dosing regimen, contraindications, and potential adverse effects ( Table 1 ) . Note that oral naltrexone is metabolized through the liver and acamprosate is metabolized by the kidneys.

Previous evidence on injectable naltrex -one's effectiveness was limited 6 ; however, a 2021 randomized controlled trial in a population of individuals experiencing homelessness showed reduction in total drinking days and heavy-drinking days. 7 Injectable naltrexone could be a good option for patients who have difficulty with daily medication adherence. Disulfiram causes accumulation of acetaldehyde during alcohol consumption, creating adverse effects of flushing, nausea, vomiting, and heart rate and blood pressure changes. Importantly, available evidence suggests that disulfiram does not reduce alcohol consumption outcomes and therefore is not recommended as treatment for AUD, particularly given the risk for negative events if alcohol use occurs while taking the medication. 1

Medications not approved by the U.S. Food and Drug Administration that have some effectiveness in AUD management include topiramate, baclofen, and gabapentin. Topiramate demonstrated reduction in total drinking days, heavy-drinking days, and number of drinks per drinking day; however, common adverse effects include anorexia, concentration changes, dizziness, paresthesia, and psycho-motor slowing. Studies with lower strength of evidence show reduction in return to any drinking when taking baclofen as well as reduction in return to heavy drinking when taking gabapentin.

A guide for physicians about medications for AUD is available from the Substance Abuse and Mental Health Services Administration. Additionally, a resource guide for AUD from the American Society of Addiction Medicine is available.

Editor's Note:  American Family Physician SOR ratings are different from the AHRQ Strength-of-Evidence ratings.

McPheeters M, O’Connor EA, Riley S, et al. Pharmacotherapy for adults with alcohol use disorder in outpatient settings: systematic review. Comparative effectiveness review no. 262. (Prepared by the RTI International–University of North Carolina at Chapel Hill Evidence-based Practice Center under contract no. 75Q80120D00007.) AHRQ publication no. 23(24)-EHC011. Agency for Healthcare Research and Quality; November 2023. https://doi.org/10.23970/AHRQEPCCER262

Centers for Disease Control and Prevention. Deaths from excessive alcohol use in the United States. Updated February 29, 2024. Accessed March 26, 2024. https://www.cdc.gov/alcohol/features/excessive-alcohol-deaths.html

Curry SJ, Krist AH, Owens DK, et al. Screening and behavioral counseling interventions to reduce unhealthy alcohol use in adolescents and adults: US Preventive Services Task Force recommendation statement. JAMA. 2018;320(18):1899-1909.

Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: results from the 2022 National Survey on Drug Use and Health. November 2023. Accessed March 26, 2024. https://www.samhsa.gov/data/sites/default/files/reports/rpt42731/2022-nsduh-nnr.pdf

Pettinati HM, Anton RF, Willenbring ML. The COMBINE Study—an overview of the largest pharmacotherapy study to date for treating alcohol dependence. Psychiatry (Edgmont). 2006;3(10):36-39.

Jonas DE, Amick HR, Feltner C, et al.; Agency for Healthcare Research and Quality. Pharmacotherapy for adults with alcohol-use disorders in outpatient settings. Comparative effectiveness reviews, No. 134. Report no.: 14-EHC029-EF. May 2014. Accessed March 26, 2024. https://www.ncbi.nlm.nih.gov/books/NBK208590

Collins SE, Duncan MH, Saxon AJ, et al. Combining behavioral harm-reduction treatment and extended-release naltrexone for people experiencing homelessness and alcohol use disorder in the USA: a randomised clinical trial. Lancet Psychiatry. 2021;8(4):287-300.

The Agency for Healthcare Research and Quality (AHRQ) conducts the Effective Health Care Program as part of its mission to produce evidence to improve health care and to make sure the evidence is understood and used. A key clinical question based on the AHRQ Effective Health Care Program systematic review of the literature is presented, followed by an evidence-based answer based on the review. AHRQ’s summary is accompanied by an interpretation by an AFP author that will help guide clinicians in making treatment decisions.

This series is coordinated by Joanna Drowos, DO, MPH, MBA, contributing editor. A collection of Implementing AHRQ Effective Health Care Reviews published in AFP is available at https://www.aafp.org/afp/ahrq .

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Copyright © 2024 American Academy of Family Physicians. All Rights Reserved.

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May 17, 2024

• Binge drinking is a growing public health crisis − here's how research on alcohol use disorder has shifted

The act of consuming several drinks within a short time frame can permanently remodel the brain.

All forms of alcohol consumption come with health risks, but binge drinking appears to be particularly dangerous due to how repeated cycling between a high state and a withdrawal state affect the brain.

With the new Amy Winehouse biopic "Back to Black " in U.S. theaters as of May 17, 2024, the late singer's relationship with alcohol and drugs is under scrutiny again. In July 2011, Winehouse was found dead in her flat in north London from "death by misadventure" at the age of 27. That's the official British term used for accidental death caused by a voluntary risk.

Her blood alcohol concentration was 0.416%, more than five times the legal intoxication limit in the U.S. – leading her cause of death to be later adjusted to include "alcohol toxicity" following a second coroner's inquest.

Nearly 13 years later, alcohol consumption and binge drinking remain a major public health crisis , not just in the U.K. but also in the U.S.

Roughly 1 in 5 U.S. adults report binge drinking at least once a week, with an average of seven drinks per binge episode . This is well over the amount of alcohol thought to produce legal intoxication, commonly defined as a blood alcohol concentration over 0.08% – on average, four drinks in two hours for women, five drinks in two hours for men.

Among women, days of "heavy drinking" increased 41% during the COVID-19 pandemic compared with pre-pandemic levels , and adult women in their 30s and 40s are rapidly increasing their rates of binge drinking , with no evidence of these trends slowing down. Despite efforts to comprehend the overall biology of substance use disorders, scientists' and physicians' understanding of the relationship between women's health and binge drinking has lagged behind.

I am a neurobiologist focused on understanding the chemicals and brain regions that underlie addiction to alcohol . I study how neuropeptides – unique signaling molecules in the prefrontal cortex , one of the key brain regions in decision-making, risk-taking and reward – are altered by repeated exposure to binge alcohol consumption in animal models.

My lab focuses on understanding how things like alcohol alter these brain systems before diagnosable addiction, so that we can better inform efforts toward both prevention and treatment.

The biology of addiction

While problematic alcohol consumption has likely occurred as long as alcohol has existed, it wasn't until 2011 that the American Society of Addiction Medicine recognized substance addiction as a brain disorder – the same year as Winehouse's death. A diagnosis of an alcohol use disorder is now used over outdated terms such as labeling an individual as an alcoholic or having alcoholism.

Researchers and clinicians have made great strides in understanding how and why drugs – including alcohol, a drug – alter the brain. Often, people consume a drug like alcohol because of the rewarding and positive feelings it creates, such as enjoying drinks with friends or celebrating a milestone with a loved one. But what starts off as manageable consumption of alcohol can quickly devolve into cycles of excessive alcohol consumption followed by drug withdrawal.

While all forms of alcohol consumption come with health risks, binge drinking appears to be particularly dangerous due to how repeated cycling between a high state and a withdrawal state affect the brain. For example, for some people, alcohol use can lead to " hangxiety ," the feeling of anxiety that can accompany a hangover.

Repeated episodes of drinking and drunkenness, coupled with withdrawal, can spiral, leading to relapse and reuse of alcohol. In other words, alcohol use shifts from being rewarding to just trying to prevent feeling bad.

It makes sense. With repeated alcohol use over time, the areas of the brain engaged by alcohol can shift away from those traditionally associated with drug use and reward or pleasure to brain regions more typically engaged during stress and anxiety .

All of these stages of drinking, from the enjoyment of alcohol to withdrawal to the cycles of craving, continuously alter the brain and its communication pathways . Alcohol can affect several dozen neurotransmitters and receptors , making understanding its mechanism of action in the brain complicated.

Work in my lab focuses on understanding how alcohol consumption changes the way neurons within the prefrontal cortex communicate with each other. Neurons are the brain's key communicator, sending both electrical and chemical signals within the brain and to the rest of your body.

What we've found in animal models of binge drinking is that certain subtypes of neurons lose the ability to talk to each other appropriately. In some cases, binge drinking can permanently remodel the brain. Even after a prolonged period of abstinence, conversations between the neurons don't return to normal .

These changes in the brain can appear even before there are noticeable changes in behavior . This could mean that the neurobiological underpinnings of addiction may take root well before an individual or their loved ones suspect a problem with alcohol.

Researchers like us don't yet fully understand why some people may be more susceptible to this shift, but it likely has to do with genetic and biological factors, as well as the patterns and circumstances under which alcohol is consumed.

Women are forgotten

While researchers are increasingly understanding the medley of biological factors that underlie addiction, there's one population that's been largely overlooked until now: women.

Women may be more likely than men to have some of the most catastrophic health effects caused by alcohol use, such as liver issues, cardiovascular disease and cancer . Middle-aged women are now at the highest risk for binge drinking compared with other populations.

When women consume even moderate levels of alcohol, their risk for various cancers goes up, including digestive, breast and pancreatic cancer , among other health problems – and even death. So the worsening rates of alcohol use disorder in women prompt the need for a greater focus on women in the research and the search for treatments.

Yet, women have long been underrepresented in biomedical research.

It wasn't until 1993 that clinical research funded by the National Institutes of Health was required to include women as research subjects. In fact, the NIH did not even require sex as a biological variable to be considered by federally funded researchers until 2016. When women are excluded from biomedical research, it leaves doctors and researchers with an incomplete understanding of health and disease, including alcohol addiction.

There is also increasing evidence that addictive substances can interact with cycling sex hormones such as estrogen and progesterone . For instance, research has shown that when estrogen levels are high, like before ovulation, alcohol might feel more rewarding , which could drive higher levels of binge drinking. Currently, researchers don't know the full extent of the interaction between these natural biological rhythms or other unique biological factors involved in women's health and propensity for alcohol addiction.

Looking ahead

Researchers and lawmakers are recognizing the vital need for increased research on women's health. Major federal investments into women's health research are a vital step toward developing better prevention and treatment options for women.

While women like Amy Winehouse may have been forced to struggle both privately and publicly with substance use disorders and alcohol, the increasing focus of research on addiction to alcohol and other substances as a brain disorder will open new treatment avenues for those suffering from the consequences.

Nikki Crowley , Assistant Professor of Biology, Biomedical Engineering and Pharmacology, Penn State

This article is republished from The Conversation under a Creative Commons license. Read the original article .

Nikki Crowley, Penn State

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Binge drinking is a growing public health crisis − a neurobiologist explains how research on alcohol use disorder has shifted

• Oops! Something went wrong. Please try again later. More content below

With the new Amy Winehouse biopic “Back to Black ” in U.S. theaters as of May 17, 2024, the late singer’s relationship with alcohol and drugs is under scrutiny again. In July 2011, Winehouse was found dead in her flat in north London from “death by misadventure” at the age of 27. That’s the official British term used for accidental death caused by a voluntary risk.

Her blood alcohol concentration was 0.416%, more than five times the legal intoxication limit in the U.S. – leading her cause of death to be later adjusted to include “alcohol toxicity” following a second coroner’s inquest.

Nearly 13 years later, alcohol consumption and binge drinking remain a major public health crisis , not just in the U.K. but also in the U.S.

Roughly 1 in 5 U.S. adults report binge drinking at least once a week, with an average of seven drinks per binge episode . This is well over the amount of alcohol thought to produce legal intoxication, commonly defined as a blood alcohol concentration over 0.08% – on average, four drinks in two hours for women, five drinks in two hours for men.

Among women, days of “heavy drinking” increased 41% during the COVID-19 pandemic compared with pre-pandemic levels , and adult women in their 30s and 40s are rapidly increasing their rates of binge drinking , with no evidence of these trends slowing down. Despite efforts to comprehend the overall biology of substance use disorders, scientists’ and physicians’ understanding of the relationship between women’s health and binge drinking has lagged behind.

I am a neurobiologist focused on understanding the chemicals and brain regions that underlie addiction to alcohol . I study how neuropeptides – unique signaling molecules in the prefrontal cortex , one of the key brain regions in decision-making, risk-taking and reward – are altered by repeated exposure to binge alcohol consumption in animal models.

My lab focuses on understanding how things like alcohol alter these brain systems before diagnosable addiction, so that we can better inform efforts toward both prevention and treatment.

The biology of addiction

While problematic alcohol consumption has likely occurred as long as alcohol has existed, it wasn’t until 2011 that the American Society of Addiction Medicine recognized substance addiction as a brain disorder – the same year as Winehouse’s death. A diagnosis of an alcohol use disorder is now used over outdated terms such as labeling an individual as an alcoholic or having alcoholism.

Researchers and clinicians have made great strides in understanding how and why drugs – including alcohol, a drug – alter the brain. Often, people consume a drug like alcohol because of the rewarding and positive feelings it creates, such as enjoying drinks with friends or celebrating a milestone with a loved one. But what starts off as manageable consumption of alcohol can quickly devolve into cycles of excessive alcohol consumption followed by drug withdrawal.

While all forms of alcohol consumption come with health risks, binge drinking appears to be particularly dangerous due to how repeated cycling between a high state and a withdrawal state affect the brain. For example, for some people, alcohol use can lead to “ hangxiety ,” the feeling of anxiety that can accompany a hangover.

Repeated episodes of drinking and drunkenness, coupled with withdrawal, can spiral, leading to relapse and reuse of alcohol. In other words, alcohol use shifts from being rewarding to just trying to prevent feeling bad.

It makes sense. With repeated alcohol use over time, the areas of the brain engaged by alcohol can shift away from those traditionally associated with drug use and reward or pleasure to brain regions more typically engaged during stress and anxiety .

All of these stages of drinking, from the enjoyment of alcohol to withdrawal to the cycles of craving, continuously alter the brain and its communication pathways . Alcohol can affect several dozen neurotransmitters and receptors , making understanding its mechanism of action in the brain complicated.

Work in my lab focuses on understanding how alcohol consumption changes the way neurons within the prefrontal cortex communicate with each other. Neurons are the brain’s key communicator, sending both electrical and chemical signals within the brain and to the rest of your body.

What we’ve found in animal models of binge drinking is that certain subtypes of neurons lose the ability to talk to each other appropriately. In some cases, binge drinking can permanently remodel the brain. Even after a prolonged period of abstinence, conversations between the neurons don’t return to normal .

These changes in the brain can appear even before there are noticeable changes in behavior . This could mean that the neurobiological underpinnings of addiction may take root well before an individual or their loved ones suspect a problem with alcohol.

Researchers like us don’t yet fully understand why some people may be more susceptible to this shift, but it likely has to do with genetic and biological factors, as well as the patterns and circumstances under which alcohol is consumed.

Women are forgotten

While researchers are increasingly understanding the medley of biological factors that underlie addiction, there’s one population that’s been largely overlooked until now: women.

Women may be more likely than men to have some of the most catastrophic health effects caused by alcohol use, such as liver issues, cardiovascular disease and cancer . Middle-aged women are now at the highest risk for binge drinking compared with other populations.

When women consume even moderate levels of alcohol, their risk for various cancers goes up, including digestive, breast and pancreatic cancer , among other health problems – and even death. So the worsening rates of alcohol use disorder in women prompt the need for a greater focus on women in the research and the search for treatments.

Yet, women have long been underrepresented in biomedical research.

It wasn’t until 1993 that clinical research funded by the National Institutes of Health was required to include women as research subjects. In fact, the NIH did not even require sex as a biological variable to be considered by federally funded researchers until 2016. When women are excluded from biomedical research, it leaves doctors and researchers with an incomplete understanding of health and disease, including alcohol addiction.

There is also increasing evidence that addictive substances can interact with cycling sex hormones such as estrogen and progesterone . For instance, research has shown that when estrogen levels are high, like before ovulation, alcohol might feel more rewarding , which could drive higher levels of binge drinking. Currently, researchers don’t know the full extent of the interaction between these natural biological rhythms or other unique biological factors involved in women’s health and propensity for alcohol addiction.

Looking ahead

Researchers and lawmakers are recognizing the vital need for increased research on women’s health. Major federal investments into women’s health research are a vital step toward developing better prevention and treatment options for women.

While women like Amy Winehouse may have been forced to struggle both privately and publicly with substance use disorders and alcohol, the increasing focus of research on addiction to alcohol and other substances as a brain disorder will open new treatment avenues for those suffering from the consequences.

For more information on alcohol use disorder, causes, prevention and treatments, visit the National Institute on Alcohol Abuse and Alcoholism .

This article is republished from The Conversation , a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: Nikki Crowley , Penn State

For decades, mothers have borne the brunt of scrutiny for alcohol use during pregnancy − new research points to dad’s drinking as a significant factor in fetal alcohol syndrome

Alcohol use is widely accepted in the US, but even moderate consumption is associated with many harmful effects

Drinking during holidays and special occasions could affect how you parent your kids

Nikki Crowley receives funding from The National Institutes of Health, The Brain and Behavior Research Foundation, and the Penn State Huck Institutes of the Life Sciences endowment funds.

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Intervening to reduce alcohol use is associated with a lower likelihood that an individual will receive a new opioid prescription or develop an opioid use disorder, according to a study led by researchers at Duke University and the Durham Veterans Affairs (VA) Medical Center.

The research, which appears in the May 6 issue of the American Journal of Psychiatry, suggests that delivering a brief alcohol-related intervention coincided with less opioid use, fewer opioid use disorder diagnoses, and potentially fewer opioid-related emergency department visits and hospitalizations.

“People are still dying. People are still struggling with addiction. People are still struggling to taper down opioid use. We can’t rest on our laurels. Instead, we need to look at different avenues where we can make improvements.” — Dan V. Blalock, PhD

“The opioid epidemic is something we’ve made a lot of progress on in a lot of ways, but there’s still much work to be done,” said lead author Dan V. Blalock, PhD , associate consulting professor in the Department of Psychiatry & Behavioral Sciences and a clinical research psychologist at the Durham VA’s Center of Innovation to Advance Discovery and Practice Transformation. “People are still dying. People are still struggling with addiction. People are still struggling to taper down opioid use. We can’t rest on our laurels. Instead, we need to look at different avenues where we can make improvements.”

The Link between Alcohol and Opioids

Simultaneous overuse of alcohol and opioid misuse is common. According to research from the National Institutes of Health, roughly 26 percent of people with an opioid use disorder consume high levels of alcohol. These individuals are more than two and a half times as likely to also have an alcohol use disorder than those without an opioid use disorder.* Additionally, people who binge drink (consuming four or five drinks in a single setting) are almost twice as likely to misuse prescription opioids, even after accounting for other relevant factors.

Alcohol use also undermines pain relief—the reason why many people take opioids. Initially, alcohol consumption can reduce pain sensations, much like opioids. However, high use of both substances over time can trigger an increase in pain sensitivity. As a result, a person may boost their alcohol and opioid use to achieve the same level of pain relief, increasing the risk of overdose and death.

With these factors in mind, Blalock and his team examined how reducing alcohol use could be a first-line defense to curbing opioid misuse. 

“Many people use alcohol to self-medicate for some pain. But this creates a spiral because excessive alcohol use is associated with higher levels of chronic pain,” he said. “Reducing alcohol use may help with some of the pain issues that lead people to seek out opioids in the first place and prompt them to examine other lifestyle factors.”

Examining an Alcohol-Based Intervention

To determine whether addressing alcohol use could reduce opioid use, including new prescriptions, opioid use disorder, and hospitalizations, Blalock’s team studied the impact of an alcohol-based intervention.

“If you’re using opioids and alcohol, that really increases your risk of harm because they’re both depressants on the system. You’re more likely to have an overdose, some other medical event, or death.” — Dan V. Blalock, PhD

“We know that alcohol use disorder and opioid use disorder co-occur at high rates. If you have one, you’re more likely to have the other,” Blalock said. “If you’re using opioids and alcohol, that really increases your risk of harm because they’re both depressants on the system. You’re more likely to have an overdose, some other medical event, or death.”

In a retrospective review study of medical records from almost 500,000 VA patients, the team determined that about 63,800 patients had elevated alcohol use. They also examined how many of those patients received a brief five- to 15-minute intervention in which providers explained the normative trends of drinking and the health effects of excessive drinking. Referrals for any necessary additional treatment may have been placed.

Among all study participants with elevated alcohol screenings, 72 percent were documented to have received the intervention. Within one year, 8.5 percent had a new opioid prescription, 1.1 percent received a new opioid use disorder diagnosis, and 0.8 percent experienced an opioid-related hospitalization. Patients who didn’t receive the intervention had higher rates of new opioid prescriptions and disorder diagnoses, and although not quite statistically significant, a strong trend toward more opioid-related hospitalizations as well. 

Based on these results, Blalock and his team want to dive deeper into the relationship between reduced alcohol consumption and opioid use.

“I want to spend the next several years looking further into the impact of pulling back on alcohol. Even if you’re using opioids to the same extent, it makes sense that there would be less risk involved,” he said. “You’re taking away a risk factor, so there’s a chance that reducing alcohol could also reduce opioid-related harms.”

An Overlooked Approach

While the alcohol-based intervention examined in this study isn’t new, Blalock’s team is the first to assess its impact on opioid use.

“We were trying for some outside-the-box thinking. There’s nothing different happening here with this questionnaire and intervention,” he said. “Instead, we’re looking at an existing tool to see if it may have some effects that we’re missing on other problems that are also happening.”

The hope, he says, is that providers will recognize the value of conducting the annual alcohol screening and intervention and consider it to be part of their opioid risk mitigation strategy. 

“We’re hoping that providers’ ears might perk up and that they will be sure to not only check these boxes because they have to, but actively look to administer this intervention anytime it might be indicated,” he said. “Doing so can contribute to building a strong alcohol-opioid surveillance system. And more concretely and directly, it might improve patients’ lives in more ways than we previously thought.”

“We’re hoping that providers’ ears might perk up and that they will be sure to not only check these boxes because they have to, but actively look to administer this intervention anytime it might be indicated. Doing so can contribute to building a strong alcohol-opioid surveillance system. And more concretely and directly, it might improve patients’ lives in more ways than we previously thought.” — Dan V. Blalock, PhD

*Statistic derived from two sources: NIAA and “ Co-Occurring Substance Use And Mental Disorders Among Adults With Opioid Use Disorder ”

Funding for the study was provided by a U.S. Department of Veterans Affairs Health Services Research and Development Career Development Award 19-035 (IK2HXOO3085-01A2, K2HX003087), The Duke Endowment (grant 6754-SP SUB #21 P3630024), and the Center of Innovation to Accelerate Discovery and Practice Transformation at the Durham VA Health Care System (CIN 13-410).

CITATION: “Associations Between a Primary-Care Delivered Alcohol-Related Brief Intervention and Subsequent Opioid-Related Outcomes,” David Blalock, Sophia Berlin, Theodore Berkowitz, Valerie Smith, Charlie Wright, Rachel Bachrach, Janet Grubber. American Journal of Psychiatry, May 1, 2024. DOI: 10.1176/appi.ajp.20230683  

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U.s. alcohol-related deaths jumped 5-fold in 20 years.

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The toll of alcohol abuse is rising dramatically.

The leading preventable cause of death in the United States is tobacco and second is poor diet and physical inactivity. Care to guess what comes in third? You can’t be faulted if you guessed opioids such as illicit fentanyl, given all the media attention it gets. But no, it’s something much more accessible, advertised directly to the consumer and having a negative impact across all socioeconomic groups: Alcohol, and specifically the problem of alcohol abuse, which has been dramatically worsening recently.

From 1999 to 2017, the number of alcohol-related deaths in the U.S. doubled, to more than 70,000 a year. These numbers got much worse at the height of the Covid-19 pandemic. According to the National Institute on Alcohol Abuse and Alcoholism, alcohol-related deaths soared , reaching 178,000 in 2020 and 2021. Comprehensive federal datasets have yet to be released for 2022 and 2023.

In a study published in 2020 in the Journal of the American Medical Association, researchers showed that significant increases in mortality started emerging in the mid 2010s across all racial and ethnic groups. But the steepest rate of acceleration of alcohol-induced deaths occurred among younger, white individuals, especially women. Authors noted that the large increases among younger age groups presaged “substantial future increases in alcohol-related disease.” In light of more recent figures which suggest an intensifying problem, it appears that the researchers’ warning provided more than four years ago was prescient.

What could be compounding the problem of youth drinking are the ways in which advertisers depict alcohol consumption. They emphasize its social acceptability—even its supposed link to social success—and this especially applies when commercials direct their messages at a comparatively young demographic.

The data demonstrate that the marketing works. Researchers publishing in the Journal of Public Health Research found a strong association between the youth-appeal of marketing content of televised alcohol advertisements and the brand-specific consumption of both underage youth and adults.

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Critics of certain commercials that are aimed at a younger demographic, like a beer ad which aired in 2019 promoting “Coors Light. The Official Beer of Saturday Morning,” suggest the companies that sponsor the advertising are going too far.

The negative health effects of alcohol are usually because of excessive drinking over long periods of time. Here, the leading causes of alcohol-attributable deaths are liver and cardiovascular diseases, seven types of cancer—including liver, throat, mouth, esophagus and stomach—as well as alcohol use disorder. NIAAA defines the latter as a “medical condition characterized by an impaired ability to stop or control alcohol use despite adverse social, occupational, or health consequences.” This can encompass alcohol abuse, dependence, addiction and the colloquial term, alcoholism.

But consuming a large amount of alcohol in a short period of time can also be deadly, as it may lead to alcohol poisoning or other dangers like motor vehicle accidents. The Centers for Disease Control and Prevention estimates that 17% of U.S. adults binge drink. Moreover, in 2021, alcohol-impaired driving fatalities accounted for 13,384 (roughly a third) of all motor vehicular deaths. And 40% of violent crimes such as assault, homicide and domestic abuse, were committed by people who had high blood alcohol content at the time of their arrests.

The rise in alcohol abuse certainly isn’t limited to the U.S. In the United Kingdom, for instance, The Guardian reported last month that heavier drinking during the Covid-19 pandemic led to 2,500 more deaths from alcohol in 2022 than in 2019, a 33% jump.

While alcohol can be a toxic, carcinogenic drug, it’s also enjoyed by many people in moderation and often as an accompaniment—a lubricant of sorts—in a variety of social settings. Research psychologists have found that drinking moderate amounts of alcohol in a group setting “boosts people’s emotions and enhances social bonding.”

In addition, there may be physical gains associated with consumption of small amounts of alcohol. The Harvard T.H. Chan School of Public Health and others, like WebMD, still tout certain cardiovascular health benefits related to moderate intake of alcohol.

Nevertheless, other health entities, such as the Mayo Clinic, appear to be taking a different stance lately. The hospital group now says that “drinking alcohol in any amount carries a health risk,” though it qualifies the statement by suggesting that “while the risk is low for moderate intake, the risk increases as the amount you drink goes up.”

And a STAT News article published this month indicates that “alcohol isn’t healthy after all.” The publication asks whether the new dietary guidelines, set by the U.S. Departments of Agriculture and Health and Human Services and scheduled to be released in 2025, will be shaped more by health or (alcohol) industry interests? Some suggest that changes in guidance are likely if the experts who draft the recommendations take into account the evidence of alcohol-related harms, including “heightened risks of certain cancers, chronic diseases, and injuries.”

While prevention is important, treatment is equally vital. Research published by The Lancet shows that early, preventive strategies in primary care can be effective, and a variety of interventions are available to treat alcohol dependence.

However, many lack access to quality care for alcohol misuse and alcohol-associated diseases. Additionally, there may be a stigma attached to seeking help for something as socially acceptable and easily accessible as alcohol.

Public health specialists have therefore asserted that it’s time for a national dialogue about substance misuse of all drugs, legal and illegal, and that this discussion should include alcohol. In this context, experts suggest that efforts need to be centered around research on alcoholism, addiction and abuse, as well as ways to improve access to therapy for alcohol use disorders, possible curbs on advertising and targeted awareness and education campaigns.

However, alcohol abuse and misuse is not (yet) considered a public health emergency. Without declaring it as such, sufficient funding for a concerted nationwide policy is not forthcoming, which means the federal government hasn’t prioritized an alcohol policy in the same way it has done for illicit drugs, or prescription opioids for that matter. Perhaps the latest alarming figures on the rising toll of alcohol abuse will help trigger more urgency.

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