Effect of oral alcohol administration on plasma cytokine concentrations in heavy drinking individuals

Abstract

Background:

Alcohol is known to modulate the immune system, including cytokines, under conditions of both acute consumption and chronic use. The specific pro- and anti-inflammatory effects and mechanisms whereby alcohol consumption modulates circulating cytokine concentrations are not well understood. Few studies in humans have investigated the effect of acute alcohol consumption on plasma cytokine concentrations in individuals who are heavy drinkers.

Methods:

Data were pooled from two studies involving a total of 25 non-treatment seeking, heavy drinking individuals who undertook an oral alcohol administration procedure. Plasma cytokine [Interleukin-10 (IL-10), Interleukin-6 (IL-6), Interleukin-18 (IL-18) and Tumor Necrosis Factor-alpha (TNF-α)] concentrations were measured at two baseline timepoints, then three hours after alcohol administration, and finally when breath alcohol concentrations returned to zero. Linear mixed models were conducted to determine whether there was a significant effect of time on cytokine concentrations.

Results:

There was a significant reduction in TNF-α concentration (F [3, 20.42] = 4.96, p = 0.01, η²_p = 0.42) post alcohol administration, compared to baseline concentrations, and a significant increase in IL-6 concentrations (F [3, 27.81] = 9.06, p < 0.001, η²_p = 0.49) post alcohol administration, compared to baseline. There were no significant changes in IL-18 or IL-10 concentrations.

Conclusions:

To our knowledge, this is the first study to examine the acute effect of oral alcohol consumption on peripheral inflammatory markers in individuals with alcohol use disorder. Results indicate a clinically relevant increase in proinflammatory cytokines approximately 3 hours after initial alcohol ingestion. Further research should be done to elucidate the complex interaction between alcohol and the immune system.

1. Introduction

Alcohol is known to modulate the immune system under conditions of both acute consumption and chronic use. One facet of this immune response is the release of cytokines, some of which exert pro- and others, anti-inflammatory effects (Crews et al., 2015). Whether the effect is pro or anti-inflammatory, or mixed, as well as the mechanism(s) whereby alcohol consumption modulates circulating cytokine concentrations is not well understood.

Binge alcohol drinking [defined by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) as a pattern of drinking that brings blood alcohol concentration levels to 0.08 g/dL, typically occurring with consumption of 4 or more drinks for females and 5 or more drinks for males within a 2-hour period (https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/alcohol-facts-and-statistics)], is linked to altered cytokine concentrations in healthy volunteers. More specifically, binge alcohol drinking has been associated with increased levels of circulating anti-inflammatory Interleukin 1 receptor antagonist (IL-1Ra), IL-10, and pro-inflammatory IL-8, as well as decreased levels of pro-inflammatory Monocyte Chemoattractant Protein-1 (MCP-1), IL-1β, and TNF-α (Afshar et al., 2015; Hillmer et al., 2020; Neupane et al., 2016). Further, Karoly et al. (Karoly et al., 2018) reported a positive association between serum concentrations of IL-6 and self-reported consumption of alcohol.

Long-term, heavy drinking [defined as binge drinking on 5 or more days per month (https://www.niaaa.nih.gov/publications/brochures-and-fact-sheets/alcohol-facts-and-statistics)], also altered cytokine concentrations. The most consistent finding in individuals with Alcohol Use Disorder (AUD) was an elevation in serum concentration of proinflammatory IL-6 (González-Reimers et al., 2012; Leclercq et al., 2012; Zago et al., 2016), as well as an increased concentration of cytokine-producing monocytes in peripheral blood (Laso et al., 2007). In addition, IL-6 expression was upregulated in peripheral blood neutrophils and monocytes from individuals with AUD, compared to controls (Laso et al., 2007; Li et al., 2017). Proinflammatory TNF-α and IL-18 were also elevated in serum in individuals diagnosed with AUD, compared to controls (Laso et al., 2007; Leclercq et al., 2012; Portelli et al., 2019). In one study, anti-inflammatory IL-10 significantly decreased during alcohol withdrawal, and was negatively correlated with several psychological measures, such as depression, anxiety and selective attention to alcohol cues, while TNF-α was positively correlated with alcohol craving (Leclercq et al., 2012).

Acute and chronic alcohol consumption modulate systemic and central cytokine signaling (Crews et al., 2015). Further, cytokines may modulate brain neurocircuitries that are involved in the development and maintenance of alcohol craving and drinking behavior. There is evidence that peripheral cytokine concentrations are correlated with psychological symptoms of AUD, such as craving (de Timary et al., 2017), leading to the hypothesis that peripheral cytokines may impact neuroimmune signaling. Several studies have shown upregulated IL-6 signaling pathways in select central nervous system regions in alcohol preferring rodents (Blednov et al., 2012; Kimpel et al., 2007). In contrast, central IL-10 administration (into basolateral amygdala) was associated with a reduction in drinking behavior, while binge alcohol consumption reduced amygdala IL-10 concentrations in mice (Marshall et al., 2017).

Taken together, there is evidence from preclinical and human studies that cytokines are modulated by acute and chronic alcohol drinking. To our knowledge, there are no human studies in chronic heavy drinkers in well-controlled settings investigating the acute effect (within several hours) of a single session of alcohol consumption, orally administered, on plasma pro- and anti-inflammatory cytokine concentrations. Accordingly, we analyzed samples and data from two clinical studies that involved a well-controlled laboratory experimental session with alcohol administration in patients who were chronic heavy drinkers (Farokhnia et al., 2017; Lee et al., 2020).

2. Materials and Methods

2.1. Subjects

Data from two alcohol administration experiments, conducted as part of two previously published placebo-controlled medication studies (Farokhnia et al., 2017; Lee et al., 2020), were pooled and analyzed. Both studies were conducted at the National Institutes of Health Clinical Center, Bethesda, MD. Subjects (N=25) were predominantly male (N=23) and just over half were current cigarette smokers (N=14). See Table 1 for a summary of their demographic characteristics. All participants were heavy alcohol drinkers (> 20 and > 14 standard drinking units per week for men and women, respectively) as determined from the 90-day alcohol TimeLine FollowBack (TLFB) (Sobell and Sobell, 1996); most had current Alcohol Dependence (Diagnostic and Statistical Manuel of Mental Disorders, fourth edition, text revision(DSM-IV-TR)) diagnosed with the Structural Clinical Interview for DSM-IV-TR Axis I Disorders (SCID) (First et al., 2002).

Table 1:

Baseline demographic characteristics of the sample analyzed.

Demographic Characteristics	Number
Subjects (males)	25 (23)
Current cigarette smokers (%)	14 (56%)
Race (AA/Caucasian/Mixed) All not Hispanic	21/3/1
SCID diagnosis of AUD (%)	24 (96%)
	Mean (SD)
Age, years	40.7 (11.8)
BMI, kg/m2	27.2 (4.3)
Education, years	12.6 (3.4)
Age at first drink, years	14.5 (3.6)
Alcohol drinking history1 -Average drinks per drinking days -Number of heavy drinking days	10.4 (6.0) 56.1 (20.0)
AUDIT score -Total score -Hazardous alcohol consumption -Alcohol dependence symptoms -Alcohol-related problems	23.0 (7.1) 9.6 (1.8) 6.4 (3.6) 7.0 (3.9)
Cigarette pack years	4.4 (6.5)
FTND score	2.1 (1.8)

¹Based on alcohol Timeline Followback (TLFB) 90 days prior to the in-person screening visit.

Abbreviations: AA: African American; AUDIT: Alcohol Use Disorders Identification Test; BMI: Body Mass Index; FTND: Fagerström Test for Nicotine Dependence; SCID: Structured Clinical Inventory for DSM-IV; SD: Standard Deviation.

Exclusion criteria included significant medical or comorbid psychiatric illness, pregnant nursing women or desire to seek treatment to reduce or stop drinking. Written informed consent was obtained and all procedures were approved by the NIH Addictions Institutional Review Board. For a full list of inclusion/exclusion criteria of both studies, see Supplemental Material, Appendices S1 and S2.

2.2. Studies

Study 1 (Farokhnia et al., 2017), tested the effects of a GABA-B receptor agonist (baclofen) in a randomized, double-blind, placebo-controlled, between-subject design; only subjects in the placebo arm of this study were included in this analysis. Study 2 (Lee et al., 2020) tested the effects of a ghrelin receptor inverse agonist (PF-5190457) in heavy drinkers (mostly with alcohol dependence) in a single-blind, placebo-controlled, within-subject, dose-escalating design; only data from the placebo session (first study session) were included in this analysis. Days of alcohol abstinence varied between these two studies of current drinkers: in Study 1, the patients were drinking in their usual pattern and the alcohol administration procedure took place on an outpatient basis (Farokhnia et al., 2017); in Study 2, patients were abstinent for 3 days prior to the alcohol challenge session (Lee et al., 2020).

2.3. Alcohol Challenge Procedure

In the alcohol challenge in Study 1 (Farokhnia et al., 2017) (Figure S1), each subject underwent an alcohol cue reactivity procedure followed by consumption of an alcohol containing drink (within 5 minutes) that was designed to raise the blood alcohol concentration to 0.03g/dL. The drink administered was the participant’s preferred drink (alcohol + mixer). Forty-five minutes after, a 120-minute alcohol self-administration session started during which the participant was presented with 8 mini-drinks (4 mini-drinks during each 60 minutes) which he/she could decide to consume at the expense of a token which had monetary value.

In the alcohol challenge in Study 2 (Lee et al., 2020) (Figure S2), each participant underwent an oral alcohol administration session (5 minutes) designed to raise the blood alcohol concentration to 0.06 g/dL. The drink administered in this study was Smirnoff vodka (40% alcohol by volume) with participant’s choice of mixer (see Appendix S3 for mixer options). In this study, there was no cue reactivity or alcohol self-administration session on the alcohol challenge day.

In both studies, the grams of alcohol contained in the drink was calculated based on total body water (Watson et al., 1980). For details about contents of alcohol challenge session in each study, see Supplemental Figures 1 and 2. Patients in both studies stayed overnight at the NIH Clinical Center after the alcohol challenge.

2.4. Blood Samples and Breath Alcohol Concentration

Both studies collected blood samples and breath alcohol concentration (BrAC) throughout the duration of the experimental day. Description of the specimen processing for Study 1 is detailed in (Farokhnia et al., 2018) and that for Study 2 is detailed in (Lee et al., 2020). For a full description of study timepoints refer to the parent studies (Farokhnia et al., 2017; Lee et al., 2020), while currently used timepoints are schematically represented in Figures S1 and S2. For the current secondary analyses, blood and BrAC data were used from the following timepoints on the day of the alcohol challenge: 8:30 AM, approximately 0–20 minutes before alcohol administration (Pre-AC), approximately 3 hours after the initial alcohol administration (Post-AC), and when BrAC returned to 0 (BrAC=0). These timepoints were chosen as they were common to Study 1 and Study 2.

2.5. Cytokine Analysis

Simple Plex Ella Immunoassay (ProteinSimple, Wallingford, CT) (Aldo et al., 2016) was used to quantify IL-10, IL-6, IL-18, and TNF-α concentrations. Briefly, plasma samples were thawed on wet ice, agitated using a vortex mixer, and then centrifuged to separate lipid content from the plasma. Samples were diluted by a factor of 2 using diluent supplied by the manufacturer. Fifty microliters of the diluted sample were loaded onto the Ella Simple Plex Immunoassay cartridge. IL-10, IL-6, IL-18, and TNF-α were assayed together on a multi-plex cartridge. Cartridges were processed using the provided ProteinSimple Ella software and equipment per manufacturer instructions. [Note: Only in Study 1, samples were centrifuged to separate lipid content; relative centrifugal force: 3000×g, temperature: 4 °C, centrifugation time: 10 minutes].

2.6. Statistical Analysis

For each cytokine, a linear mixed model was constructed with TIME as a within-subjects factor, STUDY and SMOKING as between-subjects factors, TIME x SMOKING and TIME x STUDY as interaction terms, and SUBJECT as a random factor. We included SMOKING as smoking has been shown to affect systemic markers of inflammation (Petrescu et al., 2010), there is high comorbidity with AUD (Grant et al., 2004), and smoking was not an exclusion criterion for either study. We entered STUDY in the model to account for the variance attributed to the differing methodologies in each study. The effect of TIME was considered significant with α<0.0125 (Bonferroni correction with m=4 cytokines). Partial eta squared (η²_p) values were also calculated to indicate effect sizes. For all significant post-hoc findings, Cohen’s D was also computed. For each analysis, residuals were examined for normality. Non-normal data were transformed, and residuals were confirmed normally distributed.

For cytokines in which the linear mixed model yielded a significant main effect of time that survived correction for multiple comparisons, 6 pairwise comparisons between the 4 timepoints were also corrected for multiple comparisons and were considered significant with α<0.008. Further, we conducted a linear mixed model analysis on the data from each of the two pooled studies separately to determine whether there was a significant main effect of TIME in each individual study. In the model for each study, we entered TIME as a within-subject factor, SMOKING as a between-subjects factor, and SUBJECT as a random factor.

For the cytokines that demonstrated a significant main effect of time that survived correction for multiple comparisons, we also conducted linear mixed model analyses to examine whether variables that potentially affect cytokine levels or variables that significantly differed between the two studies, themselves, had a significant effect on each cytokine concentration time-course. These variables were age, drinking behavior [Alcohol Use Disorders Identification Test (AUDIT) total score (Babor et al., 2001), TLFB (heavy drinking days and average drinks per drinking day) (Sobell and Sobell, 1996), BrAC, and measures of depression and anxiety [Comprehensive Psychopathological Rating Scale (CPRS)(Åsberg et al., 1978)], STAI-T (Spielberger, 1983) . Individual linear mixed models were constructed with TIME as a repeated measure and one of each of the variables listed above (e.g., TIME x AGE, TIME x STAI-T, etc.). All analyses were conducted with SPSS (Version 1.0.0 IBM, Armonk, NY).

3. Results:

3.1. Baseline Cytokine Concentrations:

Mean baseline concentration for each of the 4 cytokines is tabulated in Table 2. Full statistical findings are presented in Table 3. Figure 1 shows the time course of the plasma concentrations for the four cytokines measured for all subjects.

Table 2:

Mean baseline cytokine concentrations.

	Means, SDs, and ranges of Individuals with AUD from Current Study
	Mean (SD) (pg/ml)	Range (pg/ml)
IL-10	2.29 (.52)	1.464–3.08
IL-18	171.79(80.12)	63.496–358.097
IL-6	2.64 (1.64)	1.21–8.819
TNF-α	4.456 (1.10)	3.551–7.654

Abbreviations: AUD: Alcohol Use Disorder; IL-6: Interleukin-6; IL-10: Interleukin-10; IL-18: Interleukin-18; SD: standard deviation; TNF-α: Tumor Necrosis Factor-alpha

Table 3:

Time, study, time × study, smoking, and time × smoking effects on cytokine outcomes during the oral alcohol consumption experiment

Outcome	IL-10	IL-18	IL-6	TNF-α
Time Main Effect	F (3, 56.75) = 2.80, p = 0.05 η2p = 0.13	F (3, 51.07) = 2.96, p = 0.04 η2p = 0.15	F (3, 27.81) = 9.10, p <0.001 η2p = 0.49	F (3, 20.42) = 4.96, p = 0.01 η2p = 0.42
Study Main Effect	F (1, 20.67) = 2.83, p = 0.12 η2p = 0.12	F (1, 21.95) = 0.11, p = 0.75 η2p = 0.005	F (1, 23.74) = 0.04, p = 0.85 η2p = 0.002	F (1, 20.86) = 0.41, p = 0.53 η2p = 0.02
Time × Study Interaction Effect	F (3, 56.56) = 0.59, p = 0.62 η2p = 0.03	F (3, 51.10) = 1.39, p = 0.26 η2p = 0.08	F (3, 28.19) = 3.38, p = 0.032 η2p = 0.26	F (3, 19.88) = 1.18, p = 0.343 η2p = 0.15
Smoking Main Effect	F (1, 21.03) = 2.35, p = 0.14 η2p = 0.10	F (1, 21.98) = 0.46, p = 0.51 η2p = 0.02	F (1, 24.16) = 0.01, p = 0.92 η2p = 0.0004	F (1, 20.90) = 0.78, p = 0.39 η2p = 0.04
Time × Smoking Interaction Effect	F (3, 56.75) = 1.78, p = 0.16 η2p = 0.09	F (3, 50.55) = 0.60, p = 0.62 η2p = 0.03	F (3, 27.62) = 1.70, p = 0.19 η2p = 0.16	F (3, 20.15) = 0.26, p = 0.86 η2p = 0.04

Abbreviations: IL-10: interleukin 10; IL-18: interleukin 18, IL-6: interleukin 6; TNF-α: tumor necrosis factor-alpha

Figure 1:

Time course of cytokine concentrations in plasma for participants from Study 1 (purple) and Study 2 (green) at: 08:30, approximately 10 minutes before alcohol administration which occurred mid-day, approximately 3 hours after alcohol administration, and when BrAC returned to 0. Cytokines are Interleukin-10 (IL-10), Interleukin-18 (IL-18), Interleukin-6 (IL-6), and Tissue Necrosis Factor-alpha (TNFα). Significant post-hoc pairwise comparisons are noted where significant within subject differences in cytokine concentrations over time were found, p (corrected)<0.0125.

AC: alcohol challenge; BrAC: breath alcohol concentration; Conc: concentration

3.2. Linear Mixed Model: Effect of Alcohol on Plasma Cytokine Concentrations

IL-10: The main effect of TIME: [F (3, 56.75)=2.80, p=0.048], did not survive correction for multiple comparisons. There was no main effect of SMOKING or STUDY. There was no interaction between TIME and STUDY or TIME and SMOKING.

IL-18: Data were normalized using square root transformation. The main effect of TIME: [F (3, 51.07)=2.96, p=0.041], did not survive correction for multiple comparisons. There was no main effect of SMOKING or STUDY. There was no interaction between TIME and STUDY or TIME and SMOKING.

IL-6: Data were normalized using ln transformation. There was a significant main effect of TIME: [F (3, 27.81)=9.10, p<0.001]; post-hoc significant pairwise comparisons between 8:30 AM and Post-AC (p<0.001, d=0.623), between 8:30 AM and BrAC=0 (p<0.001, d=0.856), between Pre-AC and Post-AC (p<0.001, d=0.566), and between Pre-AC and BrAC=0 (p=0.003, d=0.711).

There was no significant main effect of STUDY or SMOKING. There was no significant TIME x SMOKING interaction. There was a significant TIME x STUDY interaction [F (3, 28.19)=3.38, p=0.032] where the Pre-AC concentration of IL-6 in Study 2 was lower than that in Study 1.

TNF-α: There was a significant main effect of TIME: [F (3, 20.42)=4.96, p=0.010]; post-hoc comparisons were significant between 8:30 AM and BrAC=0 (p=0.003, d=0.767). There was no main effect of SMOKING or STUDY. There was no interaction between TIME and STUDY or TIME and SMOKING.

For IL-6 (lnIL-6), analysis of each study separately, yielded similar results with a main effect of TIME for both Study 1 and 2: [F (3, 9.07)=5.66, p=0.018] and [F (3, 15.16)=8.27, p=0.002], respectively. There were no main effects of SMOKING and no SMOKING x TIME interaction for either study. For TNF-α, linear mixed model analysis of data from each study separately yielded no significant results. There was no effect of age, degree of problematic drinking (AUDIT score), levels of alcohol drinking (TLFB-based heavy drinking days and average drinks per drinking day), BrAC, depression levels (CPRS depression sub-score) or anxiety levels (CPRS anxiety sub-score and STAI-T score) on IL-6 or TNF-α concentrations.

4. Discussion

We report here that after an oral alcohol challenge, plasma concentrations of IL-6 significantly increased and TNF-α concentrations significantly decreased in heavy-drinking individuals. These results are consistent with our previous study (Farokhnia et al., 2020), which described similar findings after an intravenous alcohol challenge. We observed most notably for IL-6, a robust increase in its concentration with oral alcohol challenge. Of note, the increase in IL-6 seems to be independent from the magnitude of the blood alcohol concentration, given that there was no significant effect of BrAC on lnIL-6 concentration. Medium to large effect sizes were found for IL-6 changes between the pre-alcohol consumption and post-alcohol consumption timepoints. These findings indicate that alcohol induces a meaningful change in circulating IL-6 levels in heavy drinking individuals, an observation suggesting that our results are of potential clinical relevance. Case in point, we note that, for example, after the alcohol challenge, IL-6 plasma concentrations rose with a magnitude comparable to that increase measured in patients with acute viral infections, e.g. COVID-19 (Wu et al., 2020) (mean levels of 6.61 pg/ml and 6.98 pg/ml, respectively). Lastly, the pattern of cytokine alterations reported here in the plasma reflects that found in brain regions, such as hippocampus, paraventricular nucleus of the hypothalamus, and amygdala, three hours after acute alcohol exposure delivered systemically bypassing the gut (Gano et al., 2019). This consistent finding of these reproducible brain changes during acute intoxication are referred to as Rapid Alterations in Neuroimmune Gene Expression (RANGE) effects (Gano et al., 2019).

Chronic heavy alcohol use results in a pro-inflammatory state. One potential mechanism inducing production of pro-inflammatory cytokines is mediated by increased endotoxins, such as lipopolysaccharide translocated from the gut lumen, which is thought to be due to oral alcohol-induced impairment of the gut barrier function (Keshavarzian et al., 2009). This hypothesis is consistent with a previous study that compared oral versus intravenous (IV) alcohol administration on gut permeability and showed that gastroduodenal permeability measured via sucrose absorption was increased after oral, but not intravenous alcohol administration (Keshavarzian et al., 1994). Alcohol-induced activation of the innate immune system via endotoxemia occurs through Toll-like receptor 4 (TLR4) signaling, promoting secretion of proinflammatory cytokines from activated macrophages (Szabo, 2015). In the absence of chronic heavy alcohol use, binge drinking also resulted in similar effects: elevated plasma concentrations of IL-6 and TNF-α, as well as endotoxins, specifically lipopolysaccharide (Bala et al., 2014).

In moderate drinkers (Hillmer et al., 2020), an oral alcohol challenge intended to raise the blood alcohol level to 0.12 g/dl resulted in an increase in IL-8 (physiologically similar to IL-6, but with a longer half-life) and a decrease in TNF-α, the latter being an observation similar to the results reported here. The longer time between measurements in the study by Hillmer and colleagues may have precluded measuring differences over time in IL-6, while they were able to capture changes in IL-8, which has a longer half-life (IL-8 was not measured in the present study). Moreover, this same pattern of cytokine modulation in the plasma was reported by us previously when alcohol was administered by the intravenous route (Farokhnia et al., 2020). As above, Gano and colleagues reported brain gene expression changes in similar cytokines when alcohol was delivered intraperitoneally (Gano et al., 2019).

We also reported a significant reduction in TNF-α concentration after approximately 3 hours; the same effect was also reported previously after 6 hours (Hillmer et al., 2020), but another study did not find any significant decrease 2 and 5 hours post-alcohol consumption (Afshar et al., 2015). The reduction in TNF-α after alcohol administration may have been part of the general immunosuppressive effect of acute alcohol, although previous studies have indicated that alcohol alone cannot suppress proinflammatory cytokines (Gavala et al., 2015; Pruett et al., 2004).

Taken together, these studies raise several important questions regarding the mechanism(s) whereby alcohol consumption, including its route of administration, as well as its pattern of use, modulates the innate immune system. It appears an interaction between alcohol and the luminal surface of the gut is not required to entrain a pro-inflammatory state and this pro-inflammatory state may also occur during IV alcohol administration and in the absence of chronic heavy alcohol use. However, these conclusions are based on our results and cannot be necessarily generalized.

Since this was an analysis of pooled data, there were important differences in experimental procedures between the two studies, yielding some limitations. We added STUDY as a covariate in the linear mixed model and we did not observe a significant main effect or STUDY x TIME interaction that was meaningful. The only significant STUDY x TIME interaction was for IL-6. Importantly, this was driven by a significant pairwise difference in the pre-alcohol challenge IL-6 concentration, not in the time course of IL-6 concentrations between studies. Such an interaction driven by between study difference in the time course would suggest that our main outcomes could be influenced by differences between the two studies. Our analyses did not reveal this. Indeed, the findings for IL-6 were reproduced when analyzing data from each study separately, though, interpretation is limited by the small sample size in each one.

The source of the IL-6 elevation and TNF-α reduction in this study with an acute alcohol challenge remains unclear. Further, this effect did not vary with study, BrAC, or smoking. The increase in IL-6 appeared to be a binary response to alcohol, which may entrain protective effects on liver function. Hepatic neutrophils are one source of IL-6 that may be activated with acute or chronic alcohol consumption. In a group of individuals with AUD, neutrophil counts were positively correlated with transaminase concentrations only in the group of individuals with recent drinking (Li et al., 2017). Further, miR-223 is an important regulator, blocking neutrophil infiltration into hepatocytes in alcohol-associated liver disease (Li et al., 2017). Overall, the pattern of change of plasma cytokines with acute alcohol challenge is consistent with that described in the RANGE effect (Gano et al., 2019). However, it is difficult to infer mechanisms from preclinical literature given the differences between preclinical and clinical paradigms. This aspect points to an important issue in translational research in the alcohol research field. Apart from studying models for alcohol-associated liver disease, as well as binge drinking, it is also important to understand the physiologic response to alcohol administration in healthy populations. This understanding of the physiology of the immune response, as it relates to alcohol administration, may shed light on the complex interaction between alcohol and the immune system.

Highlights.

• We examined the acute effect of alcohol intake on cytokine levels in adults with AUD

• Alcohol increased IL-6 and decreased TNF-α about 3 hours after initial consumption

• These findings extend our knowledge of how alcohol interacts with the immune system