Dynamic evaluation of blood marker changes induced by acute binge drinking in healthy individuals: a randomized controlled trial

Abstract

Acute alcohol consumption is known to exert widespread physiological effects, yet the immediate impacts on metabolic biomarkers remain incompletely understood. The present randomized controlled trial was conducted to investigate the acute effects of a single episode of alcohol ingestion on various biomarkers in healthy individuals. A total of 45 male participants were recruited and randomized into an alcohol group (n = 40) and a control group (n = 5) at an 8:1 ratio. Volunteers in the alcohol group ingested 40% Absolut vodka within 15 min. Blood pressure, heart rate, and blood oxygen saturation were measured at 0 h, 1 h, 3 h, 5 h, 12 h, and 24 h. Venous blood samples were drawn at 0 h, 1 h, 5 h, 12 h, and 24 h after alcohol intake. Our results showed that levels of liver function markers, including α-fucosidase (AFU), albumin (ALB), and alkaline phosphatase (ALP), were significantly increased in the alcohol group compared to the control group. The 24-h area under curve (AUC) of AFU, ALB, and ALP were significantly higher in the alcohol group. The liver fibrosis maker collagen type Ⅳ (Ⅳ-C) tended to be higher at 1 h and 12 h in the alcohol group compared to the control group. Lipid levels, including triglycerides (TG), apolipoprotein A1 (APOA1), and the APOA1/APOB, were significantly elevated after alcohol ingestion, particularly at 5 h and 12 h. The 24 h-AUC of TG, APOA1, and APOA1/APOB were higher in the alcohol group than in the control group. Additionally, cardiac function indicators, including heart rate, systolic blood pressure (SBP), and diastolic blood pressure (DBP), were significantly elevated in the alcohol group. SBP and DBP remained higher 24 h after alcohol ingestion compared to the control group. This study demonstrated that even a single episode of binge drinking could induce significant alterations of biomarkers related to liver function, cardiac function, and lipid profiles. These findings provided valuable insights into the short-term impact of alcohol on health and highlighted the importance of further research to explore the long-term implications of repeated acute alcohol exposure. Given the very small control group, these results should be interpreted as preliminary and confirmed in larger, more balanced randomized trials.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-40028-1.

Introduction

Alcohol consumption is a common social and cultural practice worldwide, contributing to over 2.6 million deaths annually, or about 4.7% of all global deaths^1,2. Epidemiological data reveal differences in drinking patterns between age groups³. Older adults may experience cumulative effects of long-term alcohol consumption, while young adults often engage in binge drinking, a pattern associated with acute health risks. Long-term alcohol ingestion has been shown to damage multiple physiological systems, including liver function, lipid metabolism, cardiovascular health, and cognitive performance^4–7. Despite extensive research on the chronic effects of alcohol, the acute impacts of a single episode of binge drinking remain less understood, particularly in healthy young individuals.

Binge drinking, defined as the consumption of at least five or four standard drinks within two hours for men or women, respectively⁸. It has been associated with an increased risk of liver disease, as the liver is the primary organ responsible for alcohol metabolism and is particularly vulnerable to alcohol-induced stress⁹. A previous study has shown that a binge drinking episode can alter the levels of circulating systemic and hepatic inflammatory markers, which were implicated in pathways involved in alcohol metabolism and hepatocellular damage¹⁰. Another study has shown that binge drinking could induce an acute burst of hepatic fibrosis marker (procollagen type III C-peptide) in people with early chronic liver disease¹¹. These changes may reflect early hepatic responses to alcohol and offer insights into its potential to initiate liver dysfunction.

In addition to liver function, acute alcohol consumption has also been shown to disrupt lipid metabolism, leading to transient alterations in blood lipid profiles, such as triglycerides, total cholesterol, and free fatty acids^12,13. These changes may result from alcohol-induced shifts in hepatic lipid processing, energy metabolism, and adipose tissue lipolysis¹⁴. These transient disruptions may contribute to the early stages of metabolic dysfunction, providing a potential link between episodic drinking and long-term risk of dyslipidemia.

Alcohol also exerts effects on cardiac function, with acute exposure influencing myocardial biomarkers such as brain natriuretic peptide and troponins, as well as hemodynamic parameters like heart rate and blood pressure¹⁵. These changes may signify myocardial stress and early cardiac responses to alcohol consumption. However, the extent of these effects may vary depending on several factors, including the dose and timing of alcohol ingestion, individual variability in alcohol metabolism, and the baseline health status of participants.

To explore these effects comprehensively, we conducted this acute alcohol ingestion study involving male university students without pre-existing metabolic dysfunction. In the present randomized clinical trial, we aimed to assess the acute effects of a single binge drinking on biomarkers of liver and cardiac function and lipids profiles in healthy adults. By systematically investigating the immediate physiological impacts of acute alcohol ingestion, we sought to clarify whether the long-term effects of alcohol could be triggered by a single binge drinking episode. Understanding these acute responses in healthy adults could provide important insights into alcohol’s role in the early pathogenesis of metabolic disorders.

Methods

Study design and ethical approval

This study was an investigator-initiated, randomized, placebo-controlled clinical trial conducted at Zhejiang Chinese Medical University in Hangzhou, China. The study protocol received approval from the Ethical Committee of Zhejiang Chinese Medical University (approval number: 20231117–7) and was registered on ClinicalTrials.gov registration number: NCT06298318 (07/03/2024). All procedures were performed in accordance with the relevant guidelines and regulations and complied with the Declaration of Helsinki.

Recruitment and participant eligibility

Participants were recruited through promotional posters and referrals from acquaintances. Inclusion criteria included: (1) male aged 18–30 years; (2) body mass index (BMI) ranging from 18.5 to 28 kg/m²; (3) having prior experience with binge drinking or hangovers, as assessed using the Personal Assessment of Maximum Drinking Capacity Survey Questionnaire¹⁶. Exclusion criteria for participants were: (1) with alcohol intolerance or alcohol dependence; (2) with serious health conditions, such as liver, kidney, cardiovascular, or gastrointestinal diseases; (3) vegetarians; (4) smokers; (5) with a history of drug use, including antihistamines, antihypertensives, antidiabetics, anxiolytics, and central nervous system depressants; (6) who had used antibiotics within two weeks prior to the trial; (7) who had consumed alcohol, alcoholic beverages or alcohol-containing foods within one week before the trial. Written informed consent was obtained from all participants.

Randomization

Volunteers were randomly assigned to either an alcohol group or a control group with an 8:1 ratio. Randomization was block-stratified by age and BMI to minimize potential differences between the two groups at baseline. Participants in the alcohol group were provided with 40% Absolut Vodka (Sweden), while those in the placebo group were provided with iso-volumetric water.

Since volunteers could easily distinguish whether they are drinking alcohol or not, it was challenging to implement blinding for the volunteers. To ensure the objectivity of the evaluation, our study employed a single-blind design. Both the alcohol and water were placed in identical packaging, only the individual responsible for distributing the intervention materials could differentiate between them. Other investigators, each participant’s nurse, and data analysts were all blinded to the group assignments.

Procedures

An overview of the study procedure is presented in Fig. 1. After a 10-h overnight fast, all the participants were provided with a breakfast, which included an egg, a cabbage bun, and a bottle of soy milk. They were instructed to finish the breakfast within 15 min. 30 min after the meal, the volunteers consumed either alcohol or water according to their group assignment. The alcohol dose was calculated according to their BMI: if 18.5 kg/m² ≤ BMI < 24.0 kg/m², individuals received 1.0 g/kg body weight of vodka; if 24.0 kg/m² ≤ BMI ≤ 28.0 kg/m², individuals received 0.8 g/kg body weight of vodka. The choice of BMI-based stratification for alcohol dosing was based on previous acute alcohol trials¹⁷ and by our pilot observations, which indicated that uniform dosing across BMI categories led to disproportionate intoxication symptoms in higher-BMI participants (Supplementary Table 1). The alcohol was divided into three equal portions, with one portion consumed every 5 min.

Fig. 1.

Overall design of the randomized controlled trial.

All volunteers were required to stay in a specified room in Zhejiang Chinese Medical University for at least 12 h. Each volunteer was matched with a dedicated nurse, who provided comprehensive care to ensure adherence to the study protocol. Venous blood was collected at five time points during the study, at 0 h, 1 h, 5 h, 12 h and 24 h after the start of alcohol consumption. In addition, breathalyzer tests were conducted every 15 min during the first 6 h following alcohol consumption. In addition, blood pressure was measured with a Kefu (KF-65D) device at 0 h, 1 h, 3 h, 5 h, 12 h and 24 h after alcohol ingestion. Heart rate and blood oxygen saturation were measured using a fingertip pulse oximeter at 0 h, 1 h, 3 h, 5 h, 12 h and 24 h after alcohol ingestion.

Biochemical analysis

Serum was separated from blood after centrifugation with 2000 rpm at 4 ℃ for 15 min and was kept at -80 ℃ until use. The levels of biomarkers related to liver function (alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholinesterase (CHE), α-fucosidase (AFU), total protein (TP), albumin (ALB), lactate dehydrogenase (LDH), γ-glutamyl transferase (GGT), alkaline phosphatase (ALP) and total bile acids (TBA)), lipids metabolism (total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein (Apo) A1; APOB, lipoprotein (a) and cardiac function (creatine kinase (CK), CK-MB, hydroxybutyrate dehydrogenase (HBDH)) were analyzed using the Beckman Coulter AU5800 automated biochemical analyzer. The levels of biomarkers related to liver fibrosis, including collagen type Ⅳ (Ⅳ-C), laminin (LN), cholyglycine acid (CG), hyaluronic acid (HA) and procollagen type Ⅲ N-terminal peptide (PⅢNP) were measured using a magnetic particle chemiluminescence immunoanalyzer.

Statistical analysis

The primary endpoint of this trial was the change in liver fibrosis-related biomarkers following acute alcohol consumption. To maximize the precision of biomarker estimation in the alcohol-exposed group, an unequal allocation scheme was adopted. Based on previous data, the control group was assumed to have a mean PRO-C3 level of 9.6 (SD 1.1), whereas the alcohol group was assumed to have a mean of 10.6 (SD 1.5)¹¹. Using PASS software, we calculated that a minimum of 5 participants in the control group and 35 in the alcohol group would provide 80% power to detect this difference at α = 0.05, allowing for a 20% anticipated dropout rate. This allocation also reflected feasibility considerations for this acute intervention, where ethical and logistical constraints limited the number of non-exposed volunteers undergoing repeated blood sampling. Although this sample size was considered feasible, we acknowledge that the very small control group reduces the robustness of between-group comparisons, and the resulting estimates should therefore be interpreted with caution.

Each variable was tested for normality. Data were presented as means ± standard deviation (SD) if normally distributed, and as median (interquartile range) if not. Student t-test (for continuous variables) or chi-square test (for categorical variables) was conducted to analyze the group difference at baseline. A linear mixed-effects model was used to analyze the difference of changes of each indicator between two groups. A structured covariance matrix was used for repeated measures. Time since started drinking was included in the model as a categorical variable (0, 1, 5, 12 and 24 h), and the group × time interaction was treated as the fixed effect in the model and was the primary effect of interest. Other potential confounders included in the model as fixed effects were age and baseline BMI. The mixed-effects model accounted for missing data¹⁸. Areas under the curve (AUC) were calculated using the trapezoidal method. The differences of 24 h-AUC values between the two groups were analyzed with unpaired t-test¹⁹. All these analyses were carried out with R software version 4.2.0.

Results

Population characteristics of the included participants

From March 1 and June 3, 2024, a total of 105 volunteers were recruited to participate in the study. Among them, 45 volunteers met the enrollment criteria. The mean age of the participants was 21.87 ± 2.19 years. At baseline, characteristics of the participants were similar between the two groups (Table 1). In the placebo group, one individual was heterozygous (ALDH2*1/2). In the alcohol group, two individuals were heterozygous. However, both of them withdrew before study completion. Therefore, the final intervention analyses included only homozygous participants. Totally, 5 participants in the alcohol group withdrew during the intervention due to vomiting and no withdrawals occurred in the control group, with retention rates of 87.5% and 100%, respectively (Fig. 2). Participants reached a maximum breath alcohol concentration of 91.97 ± 26.21 mg/100 mL at 45 min, with a breath alcohol concentration of 0 at 600 min (Supplementary Fig. 1).

Table 1.

Baseline characteristics of enrolled participants in the study.

	Control group (n = 5)	Alcohol group (n = 40)	P value
Age, year	20.80 ± 1.92	22.00 ± 2.21	0.253
BMI, kg/m2	21.76 ± 3.62	22.61 ± 2.60	0.513
SBP, mm Hg	118.40 ± 8.02	116.38 ± 12.36	0.724
DBP, mm Hg	76.00 ± 14.04	77.17 ± 12.45	0.845
Blood oxygen	98.00 ± 1.00	97.42 ± 1.02	0.235
ALT, U/L	17.20 ± 8.79	20.90 ± 15.84	0.613
AST, U/L	23.00 ± 7.45	21.23 ± 8.00	0.640
GGT, U/L	23.20 ± 12.60	18.88 ± 10.31	0.392
ALP, U/L	82.80 ± 19.10	77.08 ± 16.39	0.473
IV-C, ng/mL	61.30 ± 58.11	40.77 ± 19.50	0.099
LN, ng/mL	97.38 ± 15.27	86.02 ± 37.63	0.511
TC, mmol/L	4.36 ± 0.63	4.14 ± 0.59	0.428
TG, mmol/L	0.95 ± 0.37	0.96 ± 0.35	0.955
Genotype			0.304
ALDH2*1/*1	4 (75%)	38 (95%)
ALDH2*1/*2	1 (25%)	2 (5%)
WBC, 109 cells/L	6.12 ± 0.91	5.60 ± 1.91	0.554
MON, 109 cells/L	0.21 ± 0.06	0.32 ± 0.14	0.098
NEU, 109 cells/L	3.13 ± 0.53	3.17 ± 1.17	0.948

Student t test (for continuous variables) or chi-square test (for categorical variables) was performed to analyze the group difference at baseline. Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, γ-glutamyl transferase; ALP, alkaline phosphatase; IV-C, collagen type IV; LN, laminin; TC, total cholesterol; TG, triglycerides; ALDH2, aldehyde dehydrogenase 2; WBC, white blood cell count; MON, monocytes; NEU, neutrophils.

Fig. 2.

Flow diagram of the present study.

Acute effects of binge drinking on biomarkers of liver function

Compared to the control group, levels of AFU (p = 0.01, Fig. 3D), TP (p = 0.037, group, Fig. 3E), ALB (p = 0.018, Fig. 3F) and ALP (p = 0.035, Fig. 3I) were significantly increased in alcohol group. In the post-hoc analyses, AFU, TP and ALB levels showed significant increases at 5 h (p = 0.025 for AFU; p = 0.029 for TP; p = 0.050 for ALB) and 12 h (p = 0.003 for AFU; p = 0.041 for TP; p = 0.024 for ALB). ALP levels were significant higher in the alcohol group at 12 h (p = 0.013). Although the time by group interaction for LDH and GGT did not reach statistical significance, LDH (p = 0.031, Fig. 3G) and GGT (p = 0.057, Fig. 3H) levels were all markedly elevated 12 h after binge drinking. In addition, we observed significant increases in the 24 h-AUC of AFU, ALB, GGT, ALP, and CHE in the alcohol group compared to the control group. Compared to the control group, levels of TBA (p = 0.026) were significantly decreased in the alcohol group, particularly at 5 h (p = 0.038, Fig. 3J). There was no difference in ALT (Fig. 3A), AST (Fig. 3B), or CHE (Fig. 3C, Supplementary Table 2).

Fig. 3.

Acute effects of binge drinking on markers of liver function across multiple time points (0 h, 1 h, 5 h, 12 h, and 24 h). * indicated significant difference in change of the marker over time between groups. *P < 0.05; **P < 0.01; ***P < 0.001. ^# indicated significant difference in change of the marker at specific time between groups. ^#P < 0.05; ^##P < 0.01; ^###P < 0.001. The 24 h-AUC values for each marker were presented in the insets, and analyzed by two-tailed unpaired t-test for between-group significance. Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; CHE, cholinesterase; AFU, α-fucosidase; TP, total protein; ALB, albumin; LDH, lactate dehydrogenase; GGT, γ-glutamyl transferase; ALP, alkaline phosphatase; TBA, total bile acids; AUC, area under the curve.

Acute effects of binge drinking on liver fibrosis markers

We found significant time by group interaction for serum IV-C (p = 0.041, Fig. 4A) and PⅢNP (p = 0.011, Fig. 4E), but not for other parameters. Post-hoc analysis further revealed that IV-C showed significant differences between the groups at 1 h (p = 0.007) and 12 h (p = 0.052) post drinking. In addition, the AUC of IV-C was significantly higher in the alcohol group than that in the control group (p = 0.044). CG levels in the alcohol group were significantly higher than that in the control group 5 h after alcohol ingestion (p = 0.005, Fig. 4C). In contrast to previous observations, hyaluronic acid (HA) levels exhibited a statistically significant decrease at the 5-h time point in the alcohol-treated group (p = 0.045, Fig. 4D), while laminin (LN) concentrations remained statistically indistinguishable between experimental groups throughout the observation period (Fig. 4B, Supplementary Table 3).

Fig. 4.

Acute effects of binge drinking on markers of liver fibrosis across multiple time points (0 h, 1 h, 5 h, 12 h, and 24 h). * indicated significant difference in change of the marker over time between groups. *P < 0.05; **P < 0.01; ***P < 0.001. ^# indicated significant difference in change of the marker at specific time between groups. ^#P < 0.05; ^##P < 0.01; ^###P < 0.001. The 24 h-AUC values for each marker were presented in the insets, and analyzed by two-tailed unpaired t-test for between-group significance. Abbreviations: Ⅳ-C, collagen type Ⅳ; LN, laminin; CG, cholyglycine acid; HA, hyaluronic acid; PⅢNP, procollagen type Ⅲ N-terminal peptide; AUC, area under the curve.

Acute effects of binge drinking on serum glycolipid metabolism

There was no significant difference in blood glucose levels between the alcohol and control groups (Fig. 5A). Serum TG levels showed a significant elevation in the alcohol group compared to the control group (p = 0.047, Fig. 5C). Specifically, TG levels showed time-dependent increases, with significant elevations at 5 h (p = 0.004) and 12 h (p = 0.035) in the alcohol group compared to the control group. Also, the 24 h-AUC for TG was significantly higher in the alcohol group (p = 0.044). Similarly, APOA1 concentrations were significantly higher in the alcohol group (p = 0.012), with marked elevations observed at both 5 h (p = 0.026) and 12 h (p = 0.027) after drinking (Fig. 5F). The AUC for APOA1 showed a significant increase in the alcohol group compared to the control group (p = 0.009). APOA1/APOB ratio was higher in the alcohol group relative to the control group (p = 0.036), with significant difference persisting across multiple time points: 1 h (p = 0.023), 5 h (p = 0.038), 12 h (p = 0.035) after alcohol ingestion (Fig. 5H). The AUC for APOA1/APOB showed a significant increase in the alcohol group compared to the control group (p = 0.042). No differences were observed in TC (Fig. 5B), HDL (Fig. 5D), LDL (Fig. 5E), APOB (Fig. 5G), or LP(α) (Fig. 5I, Supplementary Table 4).

Fig. 5.

Acute effects of binge drinking on lipids profiles across multiple time points (0 h, 1 h, 5 h, 12 h, and 24 h). * indicated significant difference in change of the marker over time between groups. *P < 0.05; **P < 0.01; ***P < 0.001. ^# indicated significant difference in change of the marker at specific time between groups. ^#P < 0.05; ^##P < 0.01; ^###P < 0.001. The 24 h-AUC values for each marker were presented in the insets, and analyzed by two-tailed unpaired t-test for between-group significance. Abbreviations: TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; Lp(a); lipoprotein(a); AUC, area under the curve.

Acute effects of binge drinking on cardiac function indicators

Heart rate significantly increased after binge drinking (p = 0.01), with notable elevations observed at 3 h (p = 0.021) and 5 h (p = 0.051) post-drinking (Fig. 6A). Blood oxygen saturation exhibited a significant decline 1 h following binge drinking. (p = 0.041, Fig. 6B). The SBP (p = 0.002) and DBP (p = 0.007) in the alcohol group were both significantly higher than those in the control group 24 h after drinking, highlighting the lasting cardiovascular impact of acute alcohol intake (Fig. 6C-E). Serum levels of CK-MB, a biomarker of cardiac function, showed a significant increase 1 h after binge drinking (p = 0.027, Fig. 6F). No differences were observed in CK (Fig. 6G) or HBDH (Fig. 6H, Supplementary Table 5).

Fig. 6.

Acute effects of binge drinking on cardiac function indicators across multiple time points (0 h, 1 h, 5 h, 12 h, and 24 h). * indicated significant difference in change of the marker over time between groups. *P < 0.05; **P < 0.01; ***P < 0.001. ^# indicated significant difference in change of the marker at specific time between groups. ^#P < 0.05; ^##P < 0.01; ^###P < 0.001. The 24 h-AUC values for each marker were presented in the insets, and analyzed by two-tailed unpaired t-test for between-group significance. H). represented the changes of SBP and DBP in each individual from 0 to 24 h. Abbreviations: CK-MB, creatine kinase-MB; HBDH, hydroxybutyrate dehydrogenase; SBP, systolic blood pressure; DBP, diastolic blood pressure; AUC, area under the curve.

Discussion

This randomized controlled trial investigated the acute effects of binge alcohol consumption on multiple serum markers in healthy young males. To minimize variability from sex-related differences in alcohol metabolism, and because acute alcohol consumption is substantially more prevalent among men in China, the study was restricted to male participants. Our results demonstrated significant increases in liver function biomarkers (AFU, ALB, ALP), hepatic fibrosis markers (PIIINP, IV-C), lipid metabolism parameters (TG, APOA1), and cardiac function indicators (heart rate, SBP, DBP) within 24 h after alcohol compared to the control group. These findings provide new insights into the immediate physiological impacts of acute alcohol consumption on multiple metabolic systems.

The liver function indicators, including AFU, ALP and ALB, were significantly elevated in the alcohol group compared to the control group. AFU is a lysosomal enzyme involved in the degradation of glycoproteins and glycolipids and serves as a sensitive marker of hepatocyte stress and liver function²⁰. The observed elevation of AFU after binge drinking in this study may reflect early hepatocellular stress or damage induced by alcohol metabolism. Ethanol metabolism generates reactive oxygen species (ROS) and acetaldehyde, both of which are known to disrupt lysosomal stability and trigger the release of enzymes such as AFU into the bloodstream. Additionally, alcohol-induced hepatic inflammation may activate fibrogenic pathways, as AFU is also associated with early stages of liver fibrosis²¹. Our findings align with prior studies indicating elevated AFU levels in chronic liver conditions, such as alcoholic liver disease and cirrhosis²². However, research on acute alcohol exposure and AFU is limited, making our study one of the first to demonstrate this enzyme’s sensitivity to acute alcohol-induced hepatic stress. Notably, the observed rise in AFU contrasts with studies focusing on other liver function markers, such as ALT and AST, which may show less pronounced changes in response to short-term alcohol exposure²³. This suggests that AFU could serve as a more specific indicator of early lysosomal involvement and liver response during acute alcohol stress.

The increased ALB level may reflect compensatory protein metabolism shifts in response to acute alcohol exposure. As a key protein synthesized by the liver, ALB plays a critical role in maintaining oncotic pressure and transporting various substances, including hormones and drugs²⁴. The increase in ALB levels post-alcohol ingestion may indicate an acute hepatic response to metabolic stress, possibly as a compensatory mechanism to counteract alcohol-induced changes in plasma osmolarity or fluid balance²⁵. This finding aligns with some studies suggesting that acute alcohol exposure may transiently enhance hepatic protein synthesis as part of an adaptive metabolic response²⁶. However, alcohol’s diuretic effect may also lead to mild dehydration and hemoconcentration, which can cause an apparent increase in ALB and total protein. Because hematocrit and urine-specific gravity were not measured, this potential confounding effect cannot be excluded and should be considered a primary alternative explanation for the observed ALB increase.

ALP, an enzyme associated with bile ducts and liver function, is involved in dephosphorylation processes and is commonly used as a marker of biliary tract function and bone turnover²⁷. Elevated ALP levels post-alcohol consumption may indicate mild bile duct dysfunction or transient cholestasis caused by alcohol metabolism. This phenomenon may be linked to alcohol-induced oxidative stress and the hepatotoxic effects of acetaldehyde²⁸. Interestingly, while chronic alcohol use often leads to sustained ALP elevation due to liver or biliary damage, the transient increase observed in acute alcohol exposure could represent a milder, reversible hepatic response.

The significant increase in liver fibrosis markers, particularly collagen type Ⅳ (IV-C), may indicate activation of fibrogenic pathways, even after a single drinking episode. IV-C is a major component of the basement membrane and plays a critical role in maintaining the structural integrity of the extracellular matrix (ECM)²⁹. Elevated levels of IV-C are commonly associated with liver fibrosis, as it reflects ECM remodeling and increased collagen turnover, particularly during the early stages of hepatic fibrogenesis³⁰. The rise in IV-C levels post-alcohol ingestion in our study aligns with previous research suggesting that alcohol metabolism, particularly the production of acetaldehyde, promoted ECM remodeling through pathways involving oxidative stress and inflammatory cytokine release³¹. Notably, while IV-C levels increased, traditional liver enzymes such as ALT and AST did not differ significantly between groups. This discrepancy may reflect the fact that ALT and AST primarily indicate overt hepatocellular injury, whereas fibrosis markers are more sensitive to early and reversible changes in ECM metabolism. Previous studies have shown that serum levels of MMP-9 and TIMP-1 were also elevated in adolescents with acute alcohol intoxication, indicating heightened ECM turnover activity even in absence of overt liver injury³². In addition, earlier studies in chronic alcohol users and liver disease populations have consistently demonstrated that elevated IV-C levels were correlated with the severity of liver fibrosis^33,34. Interestingly, in contrast to the rise in IV-C, hyaluronic acid (HA) levels decreased after alcohol intake. Unlike IV-C, HA has a very short plasma half-life and is strongly influenced by hepatic clearance³⁵. The transient reduction in HA observed here may therefore be related to hemodynamic changes in hepatic sinusoidal clearance during acute alcohol exposure, rather than a true suppression of fibrogenic activity. Taken together, these observations implied that acute alcohol exposure can perturb ECM dynamics, but they do not yet establish that such changes forecast long-term fibrosis. Given that the acute kinetics and clearance of IV-C are less characterized, these findings should be interpreted with caution and tested further in longitudinal studies.

Acute alcohol consumption also disrupted lipid metabolism, as evidenced by elevated levels of TG, APOA1, and APOA1/APOB ratio. TG serves as a primary energy storage molecule and is transported in the bloodstream as part of lipoproteins³⁶. Elevated TG levels post-alcohol ingestion likely result from alcohol-induced alterations in hepatic lipid metabolism, including increased lipolysis in adipose tissue and enhanced hepatic triglyceride synthesis. This is consistent with previous studies demonstrating that alcohol metabolism promotes the accumulation of TG due to impaired β-oxidation and the inhibitory effects of alcohol on lipoprotein lipase activity, which reduces peripheral clearance of triglycerides³⁰. Surprisingly, although alcohol metabolism is biochemically linked to increased NADH production that can suppress β-oxidation and gluconeogenesis, overall blood glucose levels did not differ significantly between groups. A mild decline in glucose was observed in the alcohol group before the meal, consistent with transient suppression of hepatic gluconeogenesis during the early post-ingestion phase. However, subsequent carbohydrate intake and hormonal counter-regulation likely restored normoglycemia, maintaining overall glucose homeostasis within the 24-h observation period.

APOA1 and APOB is the main protein of HDL and LDL, respectively. APOA1 plays a crucial role in reverse cholesterol transport, facilitating the removal of excess cholesterol from peripheral tissues to the liver for excretion³⁷. In this study, APOA1 levels and the APOA1/APOB ratio increased significantly after alcohol consumption, suggesting a transient adaptive response to counteract alcohol-induced lipid perturbation. Notably, total cholesterol levels did not differ significantly between groups, which may reflect the slower kinetics of cholesterol turnover compared with the more dynamic regulation of apolipoproteins^38,39. Apolipoprotein synthesis and secretion can respond rapidly to acute metabolic stress, whereas measurable changes in total cholesterol require longer-term alterations in synthesis, absorption, or clearance. Therefore, the observed divergence between apolipoprotein and total cholesterol responses likely represents a short-term compensatory mechanism rather than a true improvement in lipid status. Previous studies have also reported similar transient elevations in APOA1 and TG levels following acute alcohol ingestion, particularly at moderate doses, though the clinical relevance of these short-term lipid responses remains uncertain⁴⁰. Over the long term, chronic alcohol consumption has been associated with dyslipidemia and increased cardiovascular risk⁴¹. Collectively, our findings highlight the complexity of alcohol’s acute effects on lipid metabolism and underscore the need to investigate how repeated episodes of acute exposure may cumulatively affect lipid homeostasis.

The significant increases of SBP, DBP and heart rate pointed to alcohol-induced stress on the cardiovascular system. Heart rate is a vital indicator of autonomic regulation and reflects the balance between sympathetic and parasympathetic nervous system activity⁴². Alcohol consumption is known to acutely stimulate the sympathetic nervous system while inhibiting vagal tone, resulting in tachycardia⁴³. This aligns with previous studies demonstrating that even moderate alcohol intake can transiently increase heart rate through heightened sympathetic activation and the direct myocardial effects of ethanol and its metabolite, acetaldehyde.

SBP and DBP, critical measures of arterial pressure, are influenced by vascular tone, blood volume, and cardiac output⁴⁴. The elevation in SBP and DBP following alcohol consumption can be attributed to alcohol-induced vasodilation and subsequent compensatory mechanisms. Alcohol initially acts as a vasodilator, reducing peripheral vascular resistance, which triggers baroreceptor-mediated reflexes to increase cardiac output and restore arterial pressure⁴⁵. This biphasic effect is consistent with earlier findings, where acute alcohol exposure temporarily raises blood pressure after the initial vasodilatory phase. Moreover, oxidative stress and the release of catecholamines during alcohol metabolism may further contribute to the observed increases in blood pressure⁴⁶.

This study had several strengths. First, it included a non-drinking control group, allowing for a clear understanding of the specific effects of alcohol independent of other confounding factors. Second, the study collected blood samples at multiple time points within 24 h, offering a detailed time-course analysis of biomarker changes. It allowed for a deeper understanding of the dynamic and transient effects of alcohol on metabolic markers, capturing both early and delayed responses that might be missed with fewer sampling points. Third, all volunteers were under continuous monitoring throughout the study period, ensuring high-quality data collection and minimizing potential biases. Additionally, all participants were provided with standardized meals, simulating real-world drinking scenarios while controlling for dietary variability. This rigorous control over environmental and dietary factors enhanced the reliability of the findings by eliminating confounding influences from inconsistent dietary intake. Fourth, a wide range of metabolic biomarkers was analyzed. This comprehensive evaluation provided a holistic view of how acute alcohol consumption impacted different physiological systems, making the findings more informative and clinically relevant.

Nevertheless, there were still several limitations to consider. The study had a relatively small sample size and included only healthy young males, which limit the generalizability to other populations such as females, older adults, or individuals with pre-existing health conditions. In addition, no heterozygous carriers were included in the final alcohol intervention analyses, and alcohol dehydrogenase 1B (ADH1B) polymorphisms, which also influence alcohol metabolism, were not assessed. These may further restrict our ability to fully account for interindividual variability. Furthermore, C-IV exhibited a high coefficient of variation (> 50% across time-points), indicating substantial inter-individual variability and reducing the stability of its temporal estimates. Furthermore, alcohol dosage was adjusted by BMI categories, an approach that does not fully capture body composition or total body water and may have introduced bias in actual alcohol exposure. Although mixed-effects models are relatively robust to unbalanced designs, the very small size of the control group may reduce the stability of parameter estimates and increase sensitivity to outliers, requiring cautious interpretation of between-group comparisons. Taken together, these issues indicate that the present findings should be regarded as preliminary and require confirmation in studies with larger and more balanced participant groups. Importantly, five participants in the alcohol group withdrew due to vomiting, whereas no attrition occurred in the control group. Although baseline characteristics did not differ between dropouts and completers (Supplementary Table 6), differential attrition may still introduce selection bias and should be considered when interpreting the findings. Moreover, this study focused on biomarkers within 24 h of alcohol consumption, longer-term follow-up could provide further insights into recovery patterns and delayed effects. Future research should investigate whether the observed acute changes persist with repeated binge drinking episodes and explore potential interventions to mitigate these effects.

Conclusions

This study demonstrated that even a single episode of binge drinking could significantly trigger detectable changes in multiple serum biomarkers, related to liver function, lipid metabolism, and cardiac function, within 24 h in healthy young males. These findings provided a deeper understanding of the immediate physiological consequences of binge drinking and highlighted the importance of educating young adults about the potential health risks associated with binge drinking. Further research is needed to determine whether these acute changes have implications for long-term health risk and to explore preventive strategies to address alcohol-related health risks. Given the very small control group, these findings should be interpreted as preliminary and will require confirmation in larger, more balanced studies.

Abbreviations

Apo A1

Apolipoprotein A1

Apo B

Apolipoprotein B

AUC

Areas under the curve

ALT

Alanine aminotransferase

AST

Aspartate aminotransferase

ALP

Alkaline phosphatase

ALB

Albumin

BMI

Body mass index

CHE

Cholinesterase

CG

Chologlycine acid

Ⅳ-C

Collagen type IV

CK

Creatine kinase

CK-MB

Creatine kinase-MB

ECM

Extracellular matrix

GGT

γ-Glutamyl transferase

HDL

High-density lipoprotein

HBDH

Hydroxybutyrate dehydrogenase

LDH

Lactate dehydrogenase

LN

Laminin

PⅢNP

Procollagen type III N-terminal peptide

PRO-C3

Procollagen type III C-peptide

TBA

Total bile acids

TC

Total cholesterol

TG

Triglycerides

LDL

Low-density lipoprotein

Lp (a)

Lipoprotein (a)

SBP

Systolic blood pressure

DBP

Diastolic blood pressure

Apo A1

Apolipoprotein A1

Apo B

Apolipoprotein B

AUC

Areas under the curve

Author contributions

Conceptualization, J.L., S.L. and K.P.; methodology, J.L., K.P.; software, Y.Z., Q.H.; validation K.P., Y.W., and Q.H.; formal analysis, Y.W., Z.L.; investigation, M.Y., Y.Z., D.Z.; data curation, K.P., C.S.; writing-original draft preparation, J.L., K.P., Y.Z.; writing-review and editing, J.L., K.P.; visualization, J.L., S.L.; supervision, K.P., Y.Z.; project administration, J.L., S.L.; All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC: 82273625), Natural Science Foundation of Zhejiang Province (ZCLY24H2602), Zhejiang Provincial Xinmiao Talents Program (2024R410B061), and National Project for Innovation and Entrepreneurship Training Program for College Students (202410344043; 202410344075X).

Data availability

The corresponding author can provide an anonymized, de-identified version of the dataset upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All procedures relative to this study were in accordance with the Helsinki Declaration and approved by the Ethical Committee of Zhejiang Chinese Medical University (20231117–7) and registered at clinicaltrials.gov (NCT05882214). We conducted the trial in compliance with guidelines of the International Conference on Harmonization Good Clinical Practice (ICH-GCP) and Medical Ethics Committee of Zhejiang Chinese Medical University.