Using Ethyl Glucuronide in Urine to Detect Light and Heavy ...

Using Ethyl Glucuronide in Urine to Detect Light and Heavy Drinking

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Abstract

Aims

This study investigated which ethyl glucuronide immunoassay (EtG-I) cutoff best detects heavy versus light drinking over five days in alcohol-dependent outpatients.

Methods

A total of 121 adults with alcohol use disorders and co-occurring psychiatric disorders participated in an alcohol treatment study. They provided self-reported drinking data and urine samples three times a week for 16 weeks (total samples = 2761). Agreement between low (100 ng/mL, 200 ng/mL) and moderate (500 ng/mL) EtG-I cutoffs and light (women ≤3 standard drinks, men ≤4 standard drinks) and heavy drinking (women >3, men >4 standard drinks) were calculated from one to five days.

Results

The 100 ng/mL cutoff detected >76% of light drinking for two days and 66% at five days. It detected 84% (1 day) to 79% (5 days) of heavy drinking. The 200 ng/mL cutoff detected >55% of light drinking and >66% of heavy drinking across five days. The 500 ng/mL cutoff identified 68% of light drinking and 78% of heavy drinking for one day, with detection decreasing thereafter (light drinking: 2–5 days <58%, heavy drinking: 2–5 days <71%). Compared to the 100 ng/mL cutoff, the 200 ng/mL and 500 ng/mL cutoffs were less likely to yield false positives.

Conclusions

A 100 ng/mL EtG-I cutoff is most effective for detecting heavy drinking for up to five days and any drinking within the past two days. Cutoffs of ≥500 ng/mL are likely to only detect heavy drinking from the previous day.

Keywords:

ethyl glucuronide in urine, alcohol biomarkers, assessment of cut-off, heavy drinking

2. MATERIALS AND METHOD

Participants were 121 adults with DSM-IV diagnoses of alcohol dependence and co-occurring mood (72%, n = 87) or psychotic (28%, n = 34) disorders. The average age was 46.9 years (SD = 10.3), and 65% (n = 79) were male. Ethnicities included 52% (n = 63) Caucasian, 28% (n = 34) African-American, 10% (n = 12) Hispanic, 3% (n = 4) American Indian/Alaskan Native, 2% (n = 2) Asian/Pacific, and 5% (n = 6) multiracial or other. Participants reported drinking on an average of 15.8 days (SD = 8.5) in the past 30 days.

Study procedures were approved by the University’s Institutional Review Board. Participants were enrolled in a randomized controlled trial of a contingency management intervention for alcohol dependence (clinicaltrials.gov identifier: NCT01567943). They engaged in a four-week pre-randomization period with reimbursement for submitting urine samples and providing self-reported alcohol use data. Successful participants were randomized to 12 weeks of contingency management, receiving prizes for submitting alcohol-negative urine samples using EtG-I, or a control group receiving prizes regardless of EtG-I results. Participants received intensive outpatient addiction treatment and submitted up to 52 urine samples (M = 21.2, SD = 16.3) for EtG-I testing, totaling 2761 samples.

EtG immunoassays were conducted using spectrophotometry on a ThermoFisher Indiko analyzer. Calibration was performed weekly, and analysis was done on the collection day. Data on self-reported hours since last alcohol use (up to 120 hours/5 days) and standard drinks consumed were collected and recoded into categories of no drinking, light drinking (women ≤3 standard drinks, men ≤4), or heavy drinking (women >3, men >4) over the past one to five days.

Measured metrics like the Alcohol Timeline FollowBack assessed the accuracy, while frequencies and percentages reported the rates of light and heavy drinking. SPSS version 19.0 was used for data analysis.

3. RESULTS

Heavy drinking was reported in 29% (810/2761) of visits, while light drinking was reported in 17% (478/2761) of visits. EtG-I results were positive in 48% (1312/2761) of samples at 100 ng/mL, 37% (1022/2761) at 200 ng/mL, and 31% (842/2761) at 500 ng/mL. The mean EtG-I value was 596.7 ng/mL (SD = 824.7); the median was 89.2 ng/mL.

The 100 ng/mL cutoff detected the highest rates of light and heavy drinking over five days, identifying 85% of light drinking for one day, declining to 66% over five days, and detecting 84% to 79% of heavy drinking from day 1 to day 5. The 500 ng/mL cutoff showed the lowest detection rates for multiple days. False positives were more frequent at the 100 ng/mL level (16%-29%) compared to higher cutoffs.

4. DISCUSSION

This study emphasizes that relatively low EtG cutoff levels are necessary to detect alcohol use for more than two days. A 100 ng/mL cutoff can identify 79% of heavy drinking in the last five days. However, other methodologies like controlled drinking paradigms also assess biomarker accuracy.

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The 500 ng/mL cutoff commonly used by commercial labs significantly lowers detection rates of both light and heavy drinking across all time periods compared to the 100 ng/mL cutoff. The main advantage of using 200 ng/mL and 500 ng/mL cutoffs is the reduced frequency of false positives compared to the 100 ng/mL cutoff.

Other methodologies, such as controlled drinking paradigms, provide standardized methods for assessing biomarkers like EtG-I. However, using self-reported drinking data offers a way to validate the accuracy of EtG-I in scenarios where controlled drinking experiments are impractical.

Despite limitations, the study suggests that a 100 ng/mL EtG-I cutoff is effective in detecting heavy drinking over five days. In contrast, higher cutoffs like 200 ng/mL and above are recommended where minimizing false positives is crucial. These results support EtG-I as a measure of recent heavy drinking.

• Multiple cutoffs for ethyl glucuronide immunoassay (EtG-I) were compared with drinking self-report.

• The 100 ng/mL cutoff is most likely to detect heavy drinking up to five days.

• The 500 ng/mL cutoff is likely to only detect heavy drinking during the previous day.

Acknowledgments

Role of Funding Source

Funding for this study was provided by the National Institue on Alcohol Abuse and Alcoholism (R01AA02024801A1; PI McDonell). The NIAAA had no further role in study design, data collection, analysis, or interpretation, or in the writing and submission of this paper.

Footnotes

Contributors

Michael G. McDonell designed the study, assisted in conceptualization, conducted data analyses, and wrote the manuscript. Jordan Skalisky conducted study procedures, data analyses, and assisted in writing the manuscript. Emily Leickly conducted study procedures and assisted in writing the manuscript. Sterling McPherson provided input into study design, data analyses, and manuscript review. Samuel Battalio conducted study procedures and reviewed the manuscript. Jenny R. Nepom provided consultation in writing the manuscript. Debra Srebnik, John Roll, and Richard K. Ries provided input into study design and manuscript review.

Conflict of Interest

Michael G. McDonell, Jordan Skalisky, Emily Leickly, Samuel Battalio, Jenny R. Nepom, and Debra Srebnik have no disclosures to report. Sterling McPherson and John Roll have received research funding from the Bristol-Myers Squibb Foundation, unrelated to this investigation. Rick Ries has been on the speaker bureaus of Janssen, Alkermes, and Reckitt Benckiser in the past three years but has no disclosures to report.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. During the production process errors may be discovered that could affect the content, and all legal disclaimers applicable to the journal pertain.

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