Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 Aug 13;49(9):2013–2024. doi: 10.1111/acer.70133

Validation of blood phosphatidylethanol as an alcohol consumption biomarker in patients with alcohol use disorder and liver disease at a liver transplant center

J Watt 1,2, K C Morley 1,2, P S Haber 1,2, D Seth 1,2,3,✉, A Volovets 4
PMCID: PMC12463739  PMID: 40801443

Abstract

Background

Phosphatidylethanol (PEth), ethyl glucuronide (EtG), and ethyl sulphate (EtS) are highly sensitive and specific biomarkers of alcohol intake. This study investigated their application and relationship to traditional self‐report measures in a mixed cohort of liver disease patients to guide decision making in liver transplant populations.

Methods

We recruited 183 participants (mean age 49.2 years, 62% male), with N = 99 liver disease (88% alcohol‐associated liver disease [ALD]), N = 35 alcohol use disorder (AUD), and N = 49 healthy volunteers. Patient‐reported alcohol intake and AUDIT score served as references and were compared to traditional biomarkers, PEth and serum EtG/EtS. Receiver operating characteristic (ROC) analysis and a range of biomarker cutoffs were examined to determine optimal test characteristics. A subset of blood samples modified to a standardized hematocrit analyzed the relationship between hematocrit and PEth.

Results

Compared to traditional biomarkers, both PEth and EtG were sensitive and specific for alcohol intake. At the limit of detection (LOD), PEth was 95% sensitive at detecting any drinking. PEth cutoff of 300 μg/L was 86% sensitive and 92% specific for “heavy drinking,” and 600 μg/L was 88% sensitive and specific for “very heavy drinking.” PEth displayed superior test characteristics (sensitivity, specificity, PPV, NPV, and AUC) to all measured traditional biomarkers over two‐day and one‐month time frames. A subset of participants suspected of drinking but reporting abstinence had positive PEth tests (35%), suggestive of unreported drinking. PEth was positively correlated with hematocrit (r 2 = 0.83, p < 0.01) and correction to a standardized median resulted in increases in PEth concentration in most cases.

Conclusions

PEth is clinically useful as an alcohol biomarker in patients with liver disease and is superior to traditional biomarkers, providing good test characteristics for “heavy” and “very heavy” drinking using stepwise cutoffs. PEth detected a subset of patients underreporting their alcohol use, with implications for the management of patients in liver transplant clinics.

Keywords: alcohol use disorder, alcohol‐associated liver disease, biomarker study, liver transplant, phosphatidylethanol

Phosphatidylethanol (PEth) outperformed traditional markers and accurately detected abstinence and heavy drinking in liver disease and AUD populations. Stepwise cutoffs were identified for drinking stratification, while hematocrit correction altered PEth levels but had minimal impact on detection thresholds. PEth also revealed likely underreporting of alcohol use. These findings support the integration of PEth into liver transplant and addiction services to complement clinical decision making.

graphic file with name ACER-49-2013-g001.jpg

INTRODUCTION

Alcohol use disorder (AUD) is a chronic condition characterized by loss of control over alcohol intake, often resulting in harm to the individual and others (Carvalho et al., 2019), and is recognized as a key public health concern (Haber et al., 2021). Alcohol is attributed to around three million deaths globally each year and is a major contributor to disability‐adjusted life years (Venkateswaran et al., 2018). Heavy alcohol intake has been linked to a variety of physical and mental health complications including cardiovascular disease, cancers, depression, anxiety, and dementia (Haber et al., 2021) and is a leading cause of liver disease requiring transplantation worldwide (Corrao et al., 2004; Mathurin & Lucey, 2020).

Detection of alcohol use and abstinence in clinical settings is useful to improve treatment outcomes. The most widely used method for assessment of alcohol consumption is self‐report, including the Alcohol Use Disorder Identification Test (AUDIT) (Saunders et al., 1993) and TimeLine Follow Back (TLFB) (Sobell & Sobell, 1992). While they are inexpensive and easy to administer, self‐report methods are limited by their potential for inaccuracy from memory impairment or underreporting, particularly where there are perceived adverse consequences to truthfulness (Schieber et al., 2015).

Indirect biomarkers of heavy alcohol use such as abnormal liver enzymes or elevated mean cell volume (MCV) are not sufficiently sensitive or specific enough to have found a routine place in practice. Carbohydrate‐deficient transferrin (CDT) is specific but has poor sensitivity, particularly in patients with severe liver disease (Fagan et al., 2014; Piano et al., 2014). Given these deficits, there is increasing interest in the utilization of direct alcohol metabolites (ethyl glucuronide [EtG], ethyl sulfate [EtS] in hair and urine, and serum phosphatidylethanol [PEth]) as more accurate biomarkers of alcohol consumption. PEth has gained recognition in this respect, especially with the utilization of ultra‐high performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS) as a more precise technique for its analysis. PEth has been validated in important clinical populations, including critically ill patients (Afshar et al., 2017) and those with chronic liver disease (Arnts et al., 2021).

A particular group of patients who require very careful assessment of alcohol intake are people with alcohol‐associated liver disease (ALD) in a peritransplant setting. The rate of return to harmful drinking following liver transplant is estimated at 12%–33% and is associated with significant morbidity, including recurrence of alcohol‐associated liver disease and graft failure (Faure et al., 2012; Lim et al., 2017). As briefer periods of pretransplant abstinence have been associated with an increased probability of return to heavy drinking (Gedaly et al., 2008; Lim et al., 2017), many transplant programs stipulate a period of abstinence prior to transplantation, usually of 3–6 months (McCaughan & Munn, 2016; Varma et al., 2010), though this may be waived in certain clinical scenarios (Testino et al., 2014). Reliable estimates of alcohol consumption can be challenging, and a strong body of literature suggests this cohort is at risk of underreporting or concealing alcohol intake from clinicians (Allen et al., 2013; Schieber et al., 2015; Torruellas et al., 2014). Direct biomarkers, particularly PEth, are increasingly relied upon to confirm or refute such abstinence to inform decision making around transplant candidacy and engage patients in alcohol treatment (Andresen‐Streichert et al., 2017; Arnts et al., 2021; Fleming et al., 2017).

PEth forms in the blood only in the presence of ethanol under the actions of phospholipase D (PLD) (Viel et al., 2012). PEth is considered highly specific and sensitive for alcohol intake in the preceding 3–5 weeks, though substantial interindividual variability exists in formation and elimination rates (Hahn et al., 2016) and several factors including hematocrit, Body Mass Index (BMI), and the presence of liver disease have been proposed to impact sensitivity (Hahn et al., 2021; Nguyen, Haber, & Seth, 2018a). The relationship between PEth and liver disease is complex. Reduced alcohol clearance in cirrhosis may increase alcohol exposure and therefore increase PEth formation (Bartel et al., 2023). Another speculative contribution could be due to increased phospholipase D activity in hepatic fibrosis (Nguyen, Haber, & Seth, 2018a). Opposing effects in liver disease may include increased red blood cell turnover, which may enhance PEth elimination (Bartel et al., 2023), and the presence of anemia, which is common in liver disease and has been associated with reduced PEth sensitivity (Hahn et al., 2021). In addition to detection of any alcohol intake, attempts have been made to use PEth to quantitate drinking more precisely. Proposed cutoffs of PEth for “heavy drinking” (defined variably as >40–60 g ethanol/day) range from lower than 80 μg/L (Stewart et al., 2014) to above 700 μg/L (Schröck et al., 2016). The majority of PEth cutoffs fall between 200 and 359 μg/L; for example, 200 μg/L (Ulwelling & Smith, 2018), 210 μg/L (Helander & Hansson, 2013), 221 μg/L (Kummer et al., 2015), 250 μg/L (Majid Afshar et al., 2017), 253 μg/L (Kechagias et al., 2015), and 359 μg/L (Kechagias et al., 2015). Emerging consensus suggests PEth levels >200 μg/L are strongly suggestive of chronic heavy drinking (Luginbühl et al., 2022). No published evidence exists for an optimal cutoff to determine very heavy drinking, defined here as >80 g ethanol/day, a level of intake that confers a substantially increased risk of alcohol‐related harm (Rehm et al., 2018).

Building on these findings and our previous validation of a novel method of direct biomarker analysis (Nguyen, Paull, et al., 2018b), in this study, we examined PEth in a relatively large mixed cohort of patients with alcohol use disorder, liver disease (predominantly ALD), and healthy controls. We aimed to define the specificity and sensitivity of PEth and its ability to distinguish different drinking amounts, as well as the influence of hematocrit on PEth quantification. Finally, we compared PEth to EtS/EtG and traditional biomarkers such as CDT and liver enzymes to test PEth utility in mixed cohort settings.

METHODS

Participants

A total of N = 183 participants were recruited into the study, consisting of N = 99 patients with mixed liver disease, predominantly ALD (N = 87), with the remainder comprising liver disease due to hepatitis C, primary biliary cholangitis, nonalcoholic steatohepatitis, or autoimmune hepatitis; N = 35 patients with alcohol use disorder (AUD) and N = 49 healthy volunteers without a history of AUD or liver disease.

Patients were recruited from the Department of Gastroenterology and Department of Drug Health Services at Royal Prince Alfred Hospital (RPAH), Sydney, Australia. Healthy volunteers aged over 18 were recruited from RPAH and the University of Sydney, Australia. Consenting participants underwent clinical interview, a self‐report questionnaire, and venepuncture to obtain blood for the analysis of direct (PEth, EtG, EtS) and indirect alcohol markers. Multiple PEth samples were drawn at different clinical time points for participants in the liver disease and AUD groups to give a total of N = 231 values. The study was conducted in compliance with the National Health and Medical Research Council National Statement on Ethical Conduct in Human Research and the Australian Code for the Responsible Conduct of Research and approved by the Sydney University Human Research Ethics Committee (HREC) and the Sydney Local Health Districts (SLHD) Ethics Review Committee (ERC) under protocols X11‐0154, X17‐029 (HREC/17/RPAH/331) and X16‐0231 (HREC/16/RPAH/283). The study population was recruited between October 2017 and July 2019.

Eligibility included: (i) clinically diagnosed ALD or other cause of end‐stage liver disease with/without AUD; (ii) clinically diagnosed AUD without significant liver disease; (iii) adequate cognition and English‐language skills to give valid consent and complete research interviews; (iv) willingness to give written informed consent; and (v) willingness to provide a blood sample.

Exclusion criteria for healthy volunteers: (i) aged <18; (ii) prior history of liver disease; (iii) current diagnosis of alcohol use disorder; and (iv) other significant health problems.

Procedure

Following informed consent, patients were asked questions about their alcohol consumption using the assessments outlined below. Blood samples were collected and processed for serum and whole blood as previously described (Nguyen, Paull, et al., 2018b). Definitions on various levels of drinking amount per day (low, moderate, heavy, and very heavy) and AUDIT‐based scores (WHO Zones I, II, III, IV, any or severe alcohol misuse) are provided in (Table S1).

A structured diagnostic interview was used to gather information regarding alcohol dependence, liver disease status, and demographic variables. Alcohol consumption in the previous 30 days was determined using the TLFB alcohol consumption form (Sobell & Sobell, 1992) and a daily monitoring diary utilized in previous alcohol treatment studies (Morley et al., 2006, 2018). Alcohol consumption was also assessed using the AUDIT questionnaire (Saunders et al., 1993). One standard drink was defined as 10 g ethanol. Referring clinicians were asked to flag patients they suspected of underreporting their alcohol consumption.

Biomarker quantification

Levels of PEth in erythrocytes and EtG and EtS in serum were assayed using ultra‐high performance liquid chromatography‐tandem‐mass spectrometry (UHPLC–MS/MS, LCMS‐8050—Shimadzu). PEth and serum EtG/EtS assays and method validation were performed in RPAH Pathology Department through a previously reported technique (Nguyen, Paull, et al., 2018b). The Limit of Quantitation (LOQ) used as abstinence cutoff was 20 μg/L. The sensitivity of these tests for their windows of detection (3 days for EtG/EtS and 3–4 weeks for PEth) was determined by comparing drinking status within this timeframe and their biomarker levels.

Phosphatidylethanol and hematocrit

Given the high prevalence of anemia in chronic liver disease patients, we assessed the impact of hematocrit on PEth quantification. PEth‐positive samples (N = 30) were tested using the original whole blood sample. These samples were reassayed after modifying samples to have matching percentages of hematocrit representing the median values of reference interval (normal reference ranges for men 40%–54% and women 36%–48%, respectively; Billett, 1990). The chosen median was 45%. The original sample was separated into plasma and red cell component and adjusted to create 45% “modified” samples as per our previous report (Nguyen & Seth, 2018). Differences in PEth values between the two sets of samples were recorded. Both sets of PEth values (original and modified) for each sample were corrected to g/L hemoglobin, and discrepancies analyzed. Absolute PEth values obtained from these two datasets were then compared to published cutoffs for drinking levels.

Statistical analysis

Data acquisition and method validation were performed using the LabSolutions software (version 5.6, Shimadzu), R software (version X64 3.3.2), and Microsoft Excel (version 14.0.7015.1000). Self‐reported alcohol consumption from the TLFB was used to categorize the drinking status of all participants into either an abstinent or positive drinking group. Participants were additionally classified into WHO AUDIT Zones for alcohol consumption risk. The sensitivity, specificity, PPV, and NPV of biomarkers above the abstinence cutoff were determined by comparing the biomarker levels to the patient's drinking status within discreet time frames. A regression analysis was performed to examine the relationship between self‐reported alcohol consumption and biomarker concentrations among each group. ROC curve analysis was performed at different amounts of alcohol consumed (abstinence = 0 g ethanol/day, low level = <10 g ethanol/day), moderate drinking (10‐40 g ethanol/day), heavy drinking (>40–80 g ethanol/day), and very heavy drinking (>80 g ethanol/day) over the past month, as well as for AUDIT Zones 1–4 and AUDIT categories. All analyses were two‐tailed, with a significance level at p < 0.05.

RESULTS

Patient characteristics

The overall mean age of the cohort was 48.2 years, with 66% male participants. The mean age of the healthy volunteers was 40.2 years, which was significantly lower (p = 0.001) and the mean age of the liver disease group was 53.6 years, which was significantly higher (p = 0.005) than the total cohort. Of the patients with liver disease, 73% were men, 87% had a diagnosis of ALD, and the remaining 13% comprised liver disease due to hepatitis C, primary biliary cholangitis, nonalcoholic steatohepatitis, or autoimmune hepatitis (Table 1).

TABLE 1.

Participant characteristics.

Group Total mixed cohort (n = 183), PEth samples (n = 231) Liver disease (n = 99), PEth samples (n = 123) AUD (n = 35), PEth samples (n = 59) Healthy volunteers, (n = 49), PEth samples (n = 49)
Mean age 48.2 years (22–71 years) 53.6 years (27–71 years) 44.3 years (29–63 years) 40.2 years (22–80 years)
Gender (% male) 66% 73% 57% 59%
Ethnicity (% Caucasian) 73% 73% 100% a 55%
% post liver transplant N/A 32.1% N/A N/A
% Smokers 23% 26% 31% a 12%
Liver disease etiology (% ALD) N/A 87% N/A N/A
Open in a new tab
a

Imputed % average.

The distribution of patients for alcohol consumption is shown by AUDIT score, and equivalent WHO risk classification is depicted in Figure 1A, and by self‐report for daily average alcohol consumption over the past month (grams) is depicted in Figure 1B. PEth biomarker to distinguish levels of alcohol use in patients is shown in Figure 1C.

FIGURE 1.

FIGURE 1

Open in a new tab

(A) Distribution of patients by AUDIT score. Data available for N = 113 participants: N = 56 liver disease, N = 13 AUD, N = 44 Healthy volunteers. Classification of AUDIT Score and WHO risk: 0 = “no‐risk,” 1‐7 = “low risk,” 8‐15 = “risky,” 16‐19 = “hazardous,” 20+ “dependent.” (B) Distribution of patients by self‐report for daily average alcohol consumption (grams) over past month. Classification: 0 = nondrinker, 1–40 = nonheavy drinker, 41–80 = heavy drinker, >80 g = very heavy drinker. N = 200. (C) Distributions of patients by PEth as measure of alcohol use. Patients were distributed using a range of PEth concentrations from <20 to >2000 μg/L. N = 231.

ROC curve analysis for PEth was performed at a range of self‐reported drinking levels for the previous month. The highest AUC was observed with very heavy drinking (>80 g/day) followed by heavy drinking (41–80 g/day). The lowest AUC was seen for moderate alcohol (10–40 g/day) consumption (Table 2, Figure 2A–D).

TABLE 2.

AUC values for ROC.

Low (<10 g of ethanol/day) Moderate (10–40 g of ethanol/day) Heavy (>40–80 g of ethanol/day) Very heavy (>80 g of ethanol/day)
PEth 0.67 (0.59, 0.75) 0.58 (0.49, 0.67) 0.85 (0.79, 0.91) 0.93 (0.88, 0.97)
Open in a new tab

FIGURE 2.

FIGURE 2

Open in a new tab

(A) AUC‐ROC for PEth vs one‐month “very heavy” alcohol intake (>80 g ethanol/day). (B) AUC‐ROC for PEth vs one‐month “heavy” alcohol intake (>40–80 g ethanol/day). (C) AUC‐ROC for PEth vs one‐month “moderate” alcohol intake (>10–40 g ethanol/day). (D) AUC‐ROC for PEth vs one‐month “low” alcohol intake (<10 g ethanol/day).

A range of PEth cutoffs to determine drinking levels over a one‐month period were investigated for optimal test characteristics. Using the LOD 20 μg/L for “any alcohol consumption” PEth displayed a sensitivity of 95%, specificity of 75%, PPV of 81% and NPV of 83%. For “heavy drinking,” a cutoff of 300 μg/L yielded a sensitivity of 86%, specificity of 92%, PPV of 83% and NPV of 94%. For “very heavy drinking,” a cutoff of 600 μg/L yielded a sensitivity of 88%, specificity of 88%, PPV of 45%, and NPV of 98%.

Comparison of PEth to TLFB

PEth was relatively weakly associated (r 2 = 0.33, p < 0.001) with self‐reported alcohol consumption by TLFB over 1 month for the entire mixed cohort. This relationship remained statistically significant and positive (r 2 = 0.34, p < 0.05) when adjusted for age, gender, ethnicity, smoking status, and BMI, with covariates producing no confounding effects. Subgroup analysis revealed that this relationship was strongest (r 2 = 0.79) for healthy volunteers. In AUD and liver disease patients, the relationship between PEth and one‐month average drinking was relatively weak (r 2 = 0.32 and 0.18, respectively) (Figure S1).

PEth positivity in self‐reported abstinent subcohort

PEth was tested in a subcohort of patients (N = 46) reporting alcohol abstinence suspected by clinicians to be underreporting alcohol consumption. PEth confirmed abstinence in n = 30 (65%) and tested positive in n = 16 (35%). Of individuals with a positive PEth, N = 7 (43.7%) had a PEth concentration of 20–100 μg/L and N = 8 (50%) had a concentration ranging from 300 to >2000 μg/L (Figure 3). Subanalysis of PEth versus self‐reported alcohol consumption was performed after values for patients suspected of denying alcohol use were removed, with the coefficient of determination increasing to r 2 = 0.69 from the unadjusted r 2 = 0.33.

FIGURE 3.

FIGURE 3

Open in a new tab

Detectable PEth in patients self‐reporting abstinence.

PEth vs. AUDIT classifications

AUDIT scores were regressed against PEth (N = 113) with r 2 = 0.35, p < 0.001. ROC curves were used to investigate performance classifying Audit Zones 1–4 for PEth, EtG, and EtS. Best performance for PEth was achieved for Zone 4 (AUC = 0.85, 95% CI: 0.69, 0.96) and Zone 1 (AUC = 0.85, 95% CI: 0.77, 0.92), with reduced performance for Zone 2 (AUC 0.69, 95% CI: 0.59, 0.77) and Zone 3 (AUC 0.73, 95% CI: 0.50, 0.93) (Figure S2A–D).

PEth performed reasonably well at distinguishing other AUDIT‐based classifications such as “alcohol related harm” (AUC = 0.85, 95% CI: 0.77, 0.92), “alcohol misuse” (AUC = 0.84, 95% CI: 0.76, 0.90), and “severe alcohol misuse” (AUC = 0.84, 95% CI: 0.72, 0.93) (Figures S3A–C).

At a cutoff of 75 μg/L to identify “alcohol related harm,” PEth had a sensitivity of 83%, specificity of 79%, positive predictive value of 72%, and negative predictive value of 88% (Table 3).

TABLE 3.

Sensitivity, specificity, PPV, and NPV for PEth at a range of concentrations (μg/L) to identify alcohol‐related harm (n = 118).

Cutoff (μg/L) Sensitivity (%) Specificity (%) Positive predictive value (%) Negative predictive value (%)
50 85 68 63 87
75 83 79 72 88
100 70 80 70 80
150 64 85 73 78
200 60 89 78 77
250 55 93 84 76
300 49 94 85 74
400 40 97 90 71
500 34 99 94 69
Open in a new tab

PEth and hematocrit

There was a significant positive (r 2  = 0.83, p < 0.01) relationship between hematocrit and PEth (Figure 4A). After original samples were modified to a target median of 45%, original and modified samples remained strongly positively correlated (r 2  = 0.98) with an approximate +15% bias (Figure 4B). Changes in PEth raw concentration following correction remained positive and were <100 μg/L in most cases (Figure 4C). Modified samples were compared to literature‐based “heavy drinking” cutoffs ranging between 112 and 700 μg/L. Correction resulted in reclassification from “low–moderate drinker” to “heavy drinker” in only <10% of all cases (Table S2).

FIGURE 4.

FIGURE 4

Open in a new tab

(A) Percentage change in PEth concentrations with changes in hematocrit (r 2 = 0.83), the dashed line indicates a perfect relationship. N = 30. (B) Concentrations of PEth in original and modified samples with matched hematocrits at 45% set target median. Dashed line indicates a perfect fit. N = 30. (C) Actual difference in PEth concentrations between sample sets with changes in hematocrit. N = 30.

EtG/EtS as a biomarker to distinguish levels of alcohol use

Both EtG and EtS were positively correlated with alcohol consumption in the 2 days prior, r 2 = 0.51, p < 0.001 and r 2 = 0.56, p < 0.001, respectively. After adjustment for age, gender, ethnicity, smoking status, and BMI, statistically significant and positive associations were observed for both EtG and EtS (r 2 = 0.52 and 0.57, respectively) with no confounding effects (p > 0.05). Both displayed high sensitivity/specificity for the detection of heavy drinking. The optimal EtG cutoff was 50 μg/L, which achieved 87% sensitivity, 96% specificity, 76% PPV, and 98% NPV. For EtS, the optimal cutoff was 25 μg/L, with 80% sensitivity, 95% specificity, 71% PPV, and 97% NPV. Favorable AUC values were obtained for alcohol consumption >40 g/day, with no significant differences between EtG and EtS (Table S3).

Self‐report and indirect biomarkers

All indirect biomarkers displayed relatively poor performance in distinguishing drinking levels over both two‐day and one‐month periods in comparison with PEth (Table 4). The best results were obtained for GGT in detecting low (0.66, 95% CI 0.35, 0.95) and very heavy drinking (0.68, 95% CI 0.43, 0.88) over two days with peak sensitivity of 64%. %CDT differentiated low (0.74, 95% CI 0.48, 0.97) and very heavy drinking (0.76, 95% CI, 0.36, 1.00) over a one‐month period. %CDT was found to be highly specific for heavy (95%) and any (91%) drinking over a one‐month period but displayed poor sensitivity for heavy (35%) and any (11%) drinking.

TABLE 4.

(a) Sensitivity, specificity, PPV, and NPV of indirect and direct biomarkers in identifying heavy alcohol consumption over a one‐month period. (b) Sensitivity, specificity, PPV, and NPV of indirect and direct biomarkers in identifying any alcohol consumption over a one‐month period.

Biomarker (cutoff) Sensitivity (%) Specificity (%) Positive predictive value (%) Negative predictive value (%)
(a)
ALT Elevation outside reference interval 35 72 42 66
AST 47 62 41 67
GGT 63 51 42 71
MCV 10 83 23 63
%CDT 35 95 60 80
PEth 300 (μg/L) 86 92 83 94
(b)
ALT Elevation outside reference interval 26 63 53 35
AST 34 48 51 31
GGT 55 48 63 40
MCV 15 76 69 32
%CDT 11 91 60 45
PEth 20 μg/L 95 75 81 93
Open in a new tab

DISCUSSION

This study evaluated the use of PEth as a sensitive and specific direct biomarker to both exclude and classify alcohol intake in a large mixed cohort, including in populations with AUD, liver disease, and in liver transplant settings. We also confirmed serum EtG and EtS are highly sensitive and specific biomarkers for drinking over the past 2 days, particularly for higher levels of intake, without performance differences between them. Except for %CDT, which was highly specific for heavy alcohol intake, traditional indirect markers performed poorly by comparison to direct biomarkers, achieving little discrimination for drinking levels over all time frames and AUDIT classifications.

To our knowledge, this is the first study to identify a PEth cutoff for very high levels of alcohol consumption (>600 μg/L), showing high sensitivity and specificity for intake of >80 g/day averaged over the previous month. Identification of this subgroup is clinically important, as very heavy alcohol intake confers a significantly elevated risk of harm (Rehm et al., 2018) and is particularly relevant to the development of ALD. Our results suggest, similar to most biomarkers, that PEth is best suited to classify extremes of alcohol consumption, confirming abstinence with high negative predictive value and any heavy drinking with high sensitivity. PEth was less discriminatory for moderate drinking (10–40 g ethanol/day). A similar pattern was observed in classifying patients by AUDIT Zone, as PEth and other direct biomarkers performed well in distinguishing extreme Zones 1 and 4, but less well in distinguishing intermediate Zones 2 and 3. While still modestly discriminative, this was reflected in the poorer performance of the classifier, as shown by the ROC curve for moderate drinking. We suspect this may be due to variability in PEth kinetics (Helander et al., 2019), interindividual differences in alcohol metabolism, and inaccuracies in self‐reported intake, which may be more relevant in the low–moderate drinking range where PEth values may overlap with adjacent categories.

While all direct biomarkers were positively correlated with AUDIT score, the association was weaker with EtG/EtS. PEth also performed much better than EtG/EtS in classifying patients into AUDIT categories, likely reflective of its capacity to detect harmful drinking behavior over a longer time frame, which was correlated best with a pure measure of intake (i.e., TLFB).

Interestingly, while PEth correlated well with reported alcohol intake in healthy volunteers, the relationship was less robust in those with AUD/liver disease. A likely explanation for the weaker correlation we observed in AUD/liver disease populations is underreporting of alcohol use. This phenomenon is well‐documented in patients awaiting liver transplant as alcohol intake can result in removal from transplant waitlists (Davis et al., 2009; Schieber et al., 2015). Other studies have observed underreporting in liver disease populations with an incidence of up to 24% (Fleming et al., 2017; Stewart et al., 2014). In support of this conclusion, we observed a significant increase in the coefficient of determination when a subgroup of liver disease and AUD patients suspected by addiction clinicians of underreporting alcohol intake was excluded from analysis. Within the liver disease subgroup reporting abstinence, the test positivity rate was markedly elevated (~35%), and nearly half of these values were in the “heavy drinking” range (>300 μg/L). The reliance on clinical acumen to identify patients who may be underreporting alcohol intake highlights the benefit of integrating addiction medicine expertise within liver transplant units and the value of PEth in identifying clinically significant drinking otherwise not detectable by self‐report (Daniel et al., 2023).

PEth is a valuable tool for detecting alcohol use, but its interpretation in liver transplant has some limitations. Liver disease may increase PEth sensitivity due to altered ethanol handling, while anemia and increased red cell turnover may reduce levels, leading to variability that may complicate interpretation. Routine PEth testing has been linked to higher exclusion rates from transplant wait lists (Selim et al., 2022), despite evidence that pretransplant abstinence confirmed by PEth may not consistently predict posttransplant relapse (Torosian et al., 2024), and early transplantation may improve survival in acute alcoholic hepatitis without sustained abstinence (Weinberg et al., 2022). Classifying low–moderate drinking and distinguishing isolated drinking from low‐level chronic drinking with PEth is particularly challenging (Perilli et al., 2023), and our study found PEth performed less well in this range, yet this is precisely where accurate detection could help identify lower risk patients who may benefit from transplantation (Testino et al., 2014). Notably, we found PEth correlated substantially better with intake when participants suspected of underreporting were excluded, suggesting increased value when combined with clinical context and improving confidence in the use of PEth in liver disease. The use of serial PEth testing to identify trends in concentration, and the adjunctive use of high accuracy biomarkers with a shorter window of detection (e.g., EtG) to detect daily/near daily drinking (Staufer & Yegles, 2016), may further improve confidence in clinical decision making and could support more nuanced treatment targets such as sustained reductions in WHO drinking levels, which are associated with positive outcomes in liver disease (Meyerhoff & Durazzo, 2020; Perilli et al., 2023; Testino et al., 2014; Witkiewitz et al., 2018).

A final consideration for interpreting PEth is the presence of anemia, which is common in chronic liver disease and observed in up to 75% of patients with advanced cirrhosis (Manrai et al., 2022). We first reported reduced PEth concentrations for patients with lower hematocrit (Nguyen & Seth, 2018), confirmed by other findings (Hahn et al., 2021). Absolute PEth concentrations following correction varied in nearly all cases within ±100 μg/L. This resulted in only a small minority of participants crossing thresholds for drinking levels across a range of different cutoffs suggested in the literature. Importantly, no patient moved below detection thresholds, suggesting the influence of hematocrit may be of lesser consequence where detection of any alcohol use is sufficient, such as in abstinence only settings. However, our results imply hematocrit should be controlled for where greater precision is required, for example, anemic patients, or medico‐legal contexts, where crossing drinking thresholds may have forensic or professional consequences (Ulwelling & Smith, 2018).

There are several strengths and limitations of the current study. A key strength is the validation of PEth in a large, mixed cohort (AUD, ALD, ALD + AUD) in an active liver transplant center and alcohol treatment clinic with a wide range of reported alcohol intake. The robustness of our methodology using sophisticated UHPLC–MS/MS for evaluating PEth and EtG/EtS provides high confidence in our data. Limitations include the possibility for false‐positive PEth results due to several influences (e.g., blood transfusion, foods, ethanol‐containing products), which were not included in data collection or analysis. Inaccuracies in self‐reported alcohol consumption are a significant limitation, though the existence of underreporting may imply that the performance metrics of PEth as a biomarker are underestimated by the study methodology. Further controlled drinking experiments, or the use of transdermal alcohol monitoring devices may improve confidence in reported alcohol intake as corroborated by PEth, particularly in classifying the low–moderate drinking range.

In conclusion, our results further validate the use of PEth as a gold standard alcohol biomarker across a range of ALD and AUD populations, particularly for the confirmation of abstinence and the detection of heavy drinking. The capacity of PEth to quantitate, and in some cases detect unreported drinking, highlights the importance of specialist drug and alcohol services in this clinical setting. Our findings should increase confidence in the integration of PEth into routine clinical practice, including liver transplant services.

CONFLICT OF INTEREST STATEMENT

PH is on the Board of Field Editors. The authors have no other conflict of interest.

Supporting information

Data S1

ACER-49-2013-s001.docx (135.3KB, docx)

ACKNOWLEDGMENTS

We acknowledge the contribution of Professor David Sullivan, Chemical Pathology, Royal Prince Alfred Hospital in Sydney, Australia, to provide support for PETH testing. This work was performed by Dr. Van Long Nguyen (VLN) as part of his PhD dissertation, who has provided his permission to publish the work. The support of Research Training Program Stipend for VLN from the University of Sydney is duly acknowledged. Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians. We acknowledge the support of Liver Transplant Unit, AW Morrow Gastroenterology and Liver Centre and Australian National Liver Transplant Unit, Royal Prince Alfred Hospital, Sydney, as a recruitment site. We are grateful for the contribution of all participants in the study.

Watt, J. , Morley, K.C. , Haber, P.S. & Seth, D. (2025) Validation of blood phosphatidylethanol as an alcohol consumption biomarker in patients with alcohol use disorder and liver disease at a liver transplant center. Alcohol: Clinical and Experimental Research, 49, 2013–2024. Available from: 10.1111/acer.70133

P. S. Haber and D. Seth shared equal senior author.

[Correction added on 31 August 2025 after first online publication. The authorship and Acknowledgments have been updated.]

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

REFERENCES

  1. Afshar, M. , Burnham, E.L. , Joyce, C. , Clark, B.J. , Yong, M. , Gaydos, J. et al. (2017) Cut‐point levels of phosphatidylethanol to identify alcohol misuse in a mixed cohort including critically ill patients. Alcoholism, Clinical and Experimental Research, 41(10), 1745–1753. Available from: 10.1111/acer.13471 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Allen, J.P. , Wurst, F.M. , Thon, N. & Litten, R.Z. (2013) Assessing the drinking status of liver transplant patients with alcoholic liver disease. Liver Transplantation, 19(4), 369–376. Available from: 10.1002/lt.23596 [DOI] [PubMed] [Google Scholar]
  3. Andresen‐Streichert, H. , Beres, Y. , Weinmann, W. , Schröck, A. , Müller, A. , Skopp, G. et al. (2017) Improved detection of alcohol consumption using the novel marker phosphatidylethanol in the transplant setting: results of a prospective study. Transplant International, 30(6), 611–620. Available from: 10.1111/tri.12949 [DOI] [PubMed] [Google Scholar]
  4. Arnts, J. , Vanlerberghe, B.T.K. , Roozen, S. , Crunelle, C.L. , Masclee, A.A.M. , Olde‐Damink, S.W.M. et al. (2021) Diagnostic accuracy of biomarkers of alcohol use in patients with liver disease: a systematic review. Alcoholism, Clinical and Experimental Research, 45(1), 25–37. Available from: 10.1111/acer.14512 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bartel, M. , Hofmann, V. , Wang, S. , Mueller, J. , Sundermann, T.R. & Mueller, S. (2023) Confounders of serum phosphatidylethanol: role of red blood cell turnover and cirrhosis. Hepatology Medicine, 15, 195–208. Available from: 10.2147/hmer.S420732 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Billett, H. (1990) Hemoglobin and hematocrit. In: Walker, H. , Hall, W. & Hurst, J. (Eds.) Clinical methods: the history, physical, and laboratory examinations, 3rd edition. Boston, MA: Butterworths, pp. 718–719. [PubMed] [Google Scholar]
  7. Carvalho, A.F. , Heilig, M. , Perez, A. , Probst, C. & Rehm, J. (2019) Alcohol use disorders. The Lancet, 394(10200), 781–792. Available from: 10.1016/S0140-6736(19)31775-1 [DOI] [PubMed] [Google Scholar]
  8. Corrao, G. , Bagnardi, V. , Zambon, A. & La Vecchia, C. (2004) A meta‐analysis of alcohol consumption and the risk of 15 diseases. Preventive Medicine, 38(5), 613–619. Available from: 10.1016/j.ypmed.2003.11.027 [DOI] [PubMed] [Google Scholar]
  9. Daniel, J. , Dumortier, J. , Del Bello, A. , Gamon, L. , Molinari, N. , Faure, S. et al. (2023) Integrating an addiction team into the management of patients transplanted for alcohol‐associated liver disease reduces the risk of severe relapse. JHEP Reports, 5(10), 100832. Available from: 10.1016/j.jhepr.2023.100832 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Davis, C.G. , Thake, J. & Vilhena, N. (2009) Social desirability biases in self‐reported alcohol consumption and harms. Addictive Behaviors, 35(4), 302–311. Available from: 10.1016/j.addbeh.2009.11.001 [DOI] [PubMed] [Google Scholar]
  11. Fagan, K.J. , Irvine, K.M. , McWhinney, B.C. , Fletcher, L.M. , Horsfall, L.U. , Johnson, L. et al. (2014) Diagnostic sensitivity of carbohydrate deficient transferrin in heavy drinkers. BMC Gastroenterology, 14(1), 97. Available from: 10.1186/1471-230X-14-97 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Faure, S. , Herrero, A. , Jung, B. , Duny, Y. , Daures, J.‐P. , Mura, T. et al. (2012) Excessive alcohol consumption after liver transplantation impacts on long‐term survival, whatever the primary indication. Journal of Hepatology, 57(2), 306–312. Available from: 10.1016/j.jhep.2012.03.014 [DOI] [PubMed] [Google Scholar]
  13. Fleming, M.F. , Smith, M.J. , Oslakovic, E. , Lucey, M.R. , Vue, J.X. , Al‐Saden, P. et al. (2017) Phosphatidylethanol detects moderate‐to‐heavy alcohol use in liver transplant recipients. Alcoholism, Clinical and Experimental Research, 41(4), 857–862. Available from: 10.1111/acer.13353 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gedaly, R. , McHugh, P.P. , Johnston, T.D. , Jeon, H. , Koch, A. , Clifford, T.M. et al. (2008) Predictors of relapse to alcohol and illicit drugs after liver transplantation for alcoholic liver disease. Transplantation, 86(8), 1090–1095. Available from: 10.1097/TP.0b013e3181872710 [DOI] [PubMed] [Google Scholar]
  15. Haber, P.S. , Riordan, B.C. & Morley, K.C. (2021) Treatment of alcohol problems: current status and future directions. The Medical Journal of Australia, 215(7), 315–316. Available from: 10.5694/mja2.51265 [DOI] [PubMed] [Google Scholar]
  16. Hahn, J.A. , Anton, R.F. & Javors, M.A. (2016) The formation, elimination, interpretation, and future research needs of phosphatidylethanol for research studies and clinical practice. Alcoholism, Clinical and Experimental Research, 40(11), 2292–2295. Available from: 10.1111/acer.13213 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hahn, J.A. , Murnane, P.M. , Vittinghoff, E. , Muyindike, W.R. , Emenyonu, N.I. , Fatch, R. et al. (2021) Factors associated with phosphatidylethanol (PEth) sensitivity for detecting unhealthy alcohol use: an individual patient data meta‐analysis. Alcoholism, Clinical and Experimental Research, 45(6), 1166–1187. Available from: 10.1111/acer.14611 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Helander, A. , Böttcher, M. , Dahmen, N. & Beck, O. (2019) Elimination characteristics of the alcohol biomarker phosphatidylethanol (PEth) in blood during alcohol detoxification. Alcohol and Alcoholism, 54(3), 251–257. Available from: 10.1093/alcalc/agz027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Helander, A. & Hansson, T. (2013) National harmonization of the alcohol biomarker PEth. Läkartidningen, 110(39–40), 1747–1748. (Nationell harmonisering av alkoholmarkören PEth.). [PubMed] [Google Scholar]
  20. Kechagias, S. , Dernroth, D.N. , Blomgren, A. , Hansson, T. , Isaksson, A. , Walther, L. et al. (2015) Phosphatidylethanol compared with other blood tests as a biomarker of moderate alcohol consumption in healthy volunteers: a prospective randomized study. Alcohol and Alcoholism, 50(4), 399–406. Available from: 10.1093/alcalc/agv038 [DOI] [PubMed] [Google Scholar]
  21. Kummer, N. , Ingels, A.‐S. , Wille, S.M.R. , Hanak, C. , Verbanck, P. , Lambert, W.E.E. et al. (2015) Quantification of phosphatidylethanol 16:0/18:1, 18:1/18:1, and 16:0/16:0 in venous blood and venous and capillary dried blood spots from patients in alcohol withdrawal and control volunteers. Analytical and Bioanalytical Chemistry, 408(3), 825–838. Available from: 10.1007/s00216-015-9169-1 [DOI] [PubMed] [Google Scholar]
  22. Lim, J. , Curry, M.P. & Sundaram, V. (2017) Risk factors and outcomes associated with alcohol relapse after liver transplantation. World Journal of Hepatology, 9(17), 771–780. Available from: 10.4254/wjh.v9.i17.771 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Luginbühl, M. , Wurst, F.M. , Stöth, F. , Weinmann, W. , Stove, C.P. & Van Uytfanghe, K. (2022) Consensus for the use of the alcohol biomarker phosphatidylethanol (PEth) for the assessment of abstinence and alcohol consumption in clinical and forensic practice (2022 consensus of Basel). Drug Testing and Analysis, 14(10), 1800–1802. Available from: 10.1002/dta.3340 [DOI] [PubMed] [Google Scholar]
  24. Manrai, M. , Dawra, S. , Kapoor, R. , Srivastava, S. & Singh, A. (2022) Anemia in cirrhosis: an underestimated entity. World Journal of Clinical Cases, 10(3), 777–789. Available from: 10.12998/wjcc.v10.i3.777 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Mathurin, P. & Lucey, M.R. (2020) Liver transplantation in patients with alcohol‐related liver disease: current status and future directions. The Lancet Gastroenterology & Hepatology, 5(5), 507–514. Available from: 10.1016/s2468-1253(19)30451-0 [DOI] [PubMed] [Google Scholar]
  26. McCaughan, G.W. & Munn, S.R. (2016) Liver transplantation in Australia and New Zealand. Liver Transplantation, 22(6), 830–838. Available from: 10.1002/lt.24446 [DOI] [PubMed] [Google Scholar]
  27. Meyerhoff, D.J. & Durazzo, T.C. (2020) Not all is lost for relapsers: relapsers with low WHO risk drinking levels and complete abstainers have comparable regional gray matter volumes. Alcoholism, Clinical and Experimental Research, 44(7), 1479–1487. Available from: 10.1111/acer.14377 [DOI] [Google Scholar]
  28. Morley, K.C. , Baillie, A. , Fraser, I. , Furneaux‐Bate, A. , Dore, G. , Roberts, M. et al. (2018) Baclofen in the treatment of alcohol dependence with or without liver disease: multisite, randomised, double‐blind, placebo‐controlled trial. British Journal of Psychiatry, 212(6), 362–369. Available from: 10.1192/bjp.2018.13 [DOI] [PubMed] [Google Scholar]
  29. Morley, K.C. , Teesson, M. , Reid, S.C. , Sannibale, C. , Thomson, C. , Phung, N. et al. (2006) Naltrexone versus acamprosate in the treatment of alcohol dependence: a multi‐centre, randomized, double‐blind, placebo‐controlled trial. Addiction (Abingdon, England), 101(10), 1451–1462. Available from: 10.1111/j.1360-0443.2006.01555.x [DOI] [PubMed] [Google Scholar]
  30. Nguyen, V.L. , Haber, P.S. & Seth, D. (2018a) Applications and challenges for the use of phosphatidylethanol testing in liver disease patients (Mini Review). Alcoholism, Clinical and Experimental Research, 42(2), 238–243. Available from: 10.1111/acer.13558 [DOI] [PubMed] [Google Scholar]
  31. Nguyen, V.L. , Paull, P. , Haber, P.S. , Chitty, K. & Seth, D. (2018b) Evaluation of a novel method for the analysis of alcohol biomarkers: ethyl glucuronide, ethyl sulfate and phosphatidylethanol. Alcohol (Fayetteville, NY), 67, 7–13. Available from: 10.1016/j.alcohol.2017.08.009 [DOI] [PubMed] [Google Scholar]
  32. Nguyen, V.L. & Seth, D. (2018) Letter to the Editor Regarding Afshar et al. (2017): cut‐point levels of phosphatidylethanol to identify alcohol misuse in a mixed cohort including critically Ill patients. Alcoholism, Clinical and Experimental Research, 42(10), 2061–2063. Available from: 10.1111/acer.13851 [DOI] [PubMed] [Google Scholar]
  33. Perilli, M. , Toselli, F. , Franceschetto, L. , Cinquetti, A. , Ceretta, A. , Cecchetto, G. et al. (2023) Phosphatidylethanol (PEth) in blood as a marker of unhealthy alcohol use: a systematic review with novel molecular insights. International Journal of Molecular Sciences, 24(15), 12175. Available from: 10.3390/ijms241512175 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Piano, S. , Marchioro, L. , Gola, E. , Rosi, S. , Morando, F. , Cavallin, M. et al. (2014) Assessment of alcohol consumption in liver transplant candidates and recipients: the best combination of the tools available. Liver Transplantation, 20(7), 815–822. Available from: 10.1002/lt.23881 [DOI] [PubMed] [Google Scholar]
  35. Rehm, J. , Guiraud, J. , Poulnais, R. & Shield, K.D. (2018) Alcohol dependence and very high risk level of alcohol consumption: a life‐threatening and debilitating disease: alcohol dependence and very high risk level of alcohol consumption. Addiction Biology, 23(4), 961–968. Available from: 10.1111/adb.12646 [DOI] [PubMed] [Google Scholar]
  36. Saunders, J.B. , Aasland, O.G. , Babor, T.F. , De La Fuente, J.R. & Grant, M. (1993) Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption‐II. Addiction, 88(6), 791–804. Available from: 10.1111/j.1360-0443.1993.tb02093.x [DOI] [PubMed] [Google Scholar]
  37. Schieber, K. , Lindner, M. , Sowa, J.‐P. , Gerken, G. , Scherbaum, N. , Kahraman, A. et al. (2015) Self‐reports on symptoms of alcohol abuse: liver transplant patients versus rehabilitation therapy patients. Progress in Transplantation, 25(3), 203–209. Available from: 10.7182/pit2015618 [DOI] [PubMed] [Google Scholar]
  38. Schröck, A. , Thierauf‐Emberger, A. , Schürch, S. & Weinmann, W. (2016) Phosphatidylethanol (PEth) detected in blood for 3 to 12 days after single consumption of alcohol—a drinking study with 16 volunteers. International Journal of Legal Medicine, 131(1), 153–160. Available from: 10.1007/s00414-016-1445-x [DOI] [PubMed] [Google Scholar]
  39. Selim, R. , Zhou, Y. , Rupp, L.B. , Trudeau, S. , Naffouj, S. , Shamaa, O. et al. (2022) Availability of PEth testing is associated with reduced eligibility for liver transplant among patients with alcohol‐related liver disease. Clinical Transplantation, 36, e14595. Available from: 10.1111/ctr.14595 [DOI] [PubMed] [Google Scholar]
  40. Sobell, L. & Sobell, M. (1992) Timeline follow‐back: a technique for assessing self‐reported alcohol consumption. In: Measuring alcohol consumption: psychosocial and biochemical methods. Totowa, NJ: Humana Press, pp. 41–72. [Google Scholar]
  41. Staufer, K. & Yegles, M. (2016) Biomarkers for detection of alcohol consumption in liver transplantation. World Journal of Gastroenterology, 22(14), 3725–3734. Available from: 10.3748/wjg.v22.i14.3725 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Stewart, S.H. , Koch, D.G. , Willner, I.R. , Anton, R.F. & Reuben, A. (2014) Validation of blood phosphatidylethanol as an alcohol consumption biomarker in patients with chronic liver disease. Alcoholism, Clinical and Experimental Research, 38(6), 1706–1711. Available from: 10.1111/acer.12442 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Testino, G. , Burra, P. , Bonino, F. , Piani, F. , Sumberaz, A. , Peressutti, R. et al. (2014) Acute alcoholic hepatitis, end stage alcoholic liver disease and liver transplantation: an Italian position statement. World Journal of Gastroenterology: WJG, 20(40), 14642–14651. Available from: 10.3748/wjg.v20.i40.14642 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Torosian, K. , Shahrvini, B. , Johnson, W.M., Jr. , Vodkin, I. , Tincopa, M. , Lim, N. et al. (2024) Psychosocial predictors of return to alcohol use after liver transplant: a multicenter cohort study. Alcohol, Clinical & Experimental Research (Hoboken, N.J.), 48(11), 2137–2144. Available from: 10.1111/acer.15438 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Torruellas, C. , French, S.W. & Medici, V. (2014) Diagnosis of alcoholic liver disease. World Journal of Gastroenterology: WJG, 20(33), 11684–11699. Available from: 10.3748/wjg.v20.i33.11684 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Ulwelling, W. & Smith, K. (2018) The PEth blood test in the security environment: what it is; why it is important; and interpretative guidelines. Journal of Forensic Sciences, 63(6), 1634–1640. Available from: 10.1111/1556-4029.13874 [DOI] [PubMed] [Google Scholar]
  47. Varma, V. , Webb, K. & Mirza, D.F. (2010) Liver transplantation for alcoholic liver disease. World Journal of Gastroenterology: WJG, 16(35), 4377–4393. Available from: 10.3748/wjg.v16.i35.4377 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Venkateswaran, V. , Salama, J.S. , Abate, K.H. , Abrar, M.M. , Afarideh, M. , Ahmed, M.B. et al. (2018) Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the global burden of disease study 2016. The Lancet, 392(10152), 1015–1035. Available from: 10.1016/S0140-6736(18)31310-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Viel, G. , Boscolo‐Berto, R. , Cecchetto, G. , Fais, P. , Nalesso, A. & Ferrara, S.D. (2012) Phosphatidylethanol in blood as a marker of chronic alcohol use: a systematic review and meta‐analysis. International Journal of Molecular Sciences, 13(11), 14788–14812. Available from: 10.3390/ijms131114788 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Weinberg, E.M. , Dukewich, M. , Jakhete, N. , Stonesifer, E. , Im, G.Y. , Lucey, M.R. et al. (2022) Early liver transplantation for severe alcohol‐associated hepatitis and a history of prior liver decompensation. The American Journal of Gastroenterology, 117, 1990–1998. Available from: 10.14309/ajg.0000000000001901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Witkiewitz, K. , Kranzler, H.R. , Hallgren, K.A. , O'Malley, S.S. , Falk, D.E. , Litten, R.Z. et al. (2018) Drinking risk level reductions associated with improvements in physical health and quality of life among individuals with alcohol use disorder. Alcoholism, Clinical and Experimental Research, 42(12), 2453–2465. Available from: 10.1111/acer.13897 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1

ACER-49-2013-s001.docx (135.3KB, docx)

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Articles from Alcohol, Clinical & Experimental Research are provided here courtesy of Wiley

RESOURCES