Dataset Information

Validating Self-Reported Unhealthy Alcohol Use With Phosphatidylethanol (PEth) Among Patients With HIV.

ABSTRACT:

Background

We sought to compare self-reported alcohol consumption using Timeline Followback (TLFB) to biomarker-based evidence of significant alcohol use (phosphatidylethanol [PEth] > 20 ng/ml). Using data from patients with HIV (PWH) entering a clinical trial, we asked whether TLFB could predict PEth > 20 ng/ml and assessed the magnitude of association between TLFB and PEth level.

Methods

We defined unhealthy alcohol use as any alcohol use in the presence of liver disease, at-risk drinking, or alcohol use disorder. Self-reported alcohol use obtained from TLFB interview was assessed as mean number of drinks/day and number of heavy drinking days over the past 21 days. Dried blood spot samples for PEth were collected at the interview. We used logistic regression to predict PEth > 20 ng/ml and Spearman correlation to quantify the association with PEth, both as a function of TLFB.

Results

Among 282 individuals (99% men) in the analytic sample, approximately two-thirds (69%) of individuals had PEth > 20 ng/ml. The proportion with PEth > 20 ng/ml increased with increasing levels of self-reported alcohol use; of the 190 patients with either at-risk drinking or alcohol use disorder based on self-report, 82% had PEth > 20 ng/ml. Discrimination was better with number of drinks per day than heavy drinking days (AUC: 0.80 [95% CI: 0.74 to 0.85] vs. 0.74 [95% CI: 0.68 to 0.80]). The number of drinks per day and PEth were significantly and positively correlated across all levels of alcohol use (Spearman's R ranged from 0.29 to 0.56, all p values < 0.01).

Conclusions

In this sample of PWH entering a clinical trial, mean numbers of drinks per day discriminated individuals with evidence of significant alcohol use by PEth. PEth complements self-report to improve identification of self-reported unhealthy alcohol use among PWH.

SUBMITTER: Eyawo O

PROVIDER: S-EPMC8856627 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Validating Self-Reported Unhealthy Alcohol Use With Phosphatidylethanol (PEth) Among Patients With HIV.

Eyawo Oghenowede O Deng Yanhong Y Dziura James J Justice Amy C AC McGinnis Kathleen K Tate Janet P JP Rodriguez-Barradas Maria C MC Hansen Nathan B NB Maisto Stephen A SA Marconi Vincent C VC O'Connor Patrick G PG Bryant Kendall K Fiellin David A DA Edelman E Jennifer EJ

Alcoholism, clinical and experimental research 20200919 10

<h4>Background</h4>We sought to compare self-reported alcohol consumption using Timeline Followback (TLFB) to biomarker-based evidence of significant alcohol use (phosphatidylethanol [PEth] > 20 ng/ml). Using data from patients with HIV (PWH) entering a clinical trial, we asked whether TLFB could predict PEth > 20 ng/ml and assessed the magnitude of association between TLFB and PEth level.<h4>Methods</h4>We defined unhealthy alcohol use as any alcohol use in the presence of liver disease, at-ris ...[more]

PMID: 33460225

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Project description:BackgroundDirect biomarkers such as phosphatidylethanol (PEth) have the capability to detect heavy alcohol use, but it is unclear how strongly self-reported reduction in alcohol use correlates with reduction in PEth. We sought to explore the strength of correlation between reductions in self-reported alcohol use and change in PEth among a sample of women living with HIV (WLWH) who participated in a clinical trial to reduce heavy alcohol use. We also sought to determine whether this correlation was stronger in women with lower body mass index (BMI) and women without an alcohol use disorder (AUD).Methods81 WLWH (mean age = 48.7, 80% Black) engaging in a randomized trial of naltrexone versus placebo with a positive baseline PEth (≥8 ng/ml), and alcohol use data at baseline, 2, and 7 months were included in this analysis. Spearman correlation coefficients were compared to measure the correlation between baseline PEth and number of drinks per week by demographic, biological, and alcohol use factors. Mini-International Neuropsychiatric Interview was used to screen for AUD. Further analyses were stratified by BMI and AUD. Spearman correlation coefficients were calculated for the change in PEth and the change in number of drinks per week over 7 months, including 3 time-points: baseline, 2, and 7 months.ResultsAt baseline, the correlation between baseline PEth and the number of drinks per week was significantly stronger for those with a BMI ≤25 compared to those with a BMI > 25 (r = 0.66; r = 0.26, respectively). Similarly, the correlation between baseline PEth and number of drinks was stronger for those who did not screen positive for AUD compared with those who did (r = 0.66; r = 0.25, respectively). When stratifying by BMI, a low-to-moderate correlation (r = 0.32, p = 0.02) was present for persons with a BMI > 25; when stratifying by AUD, a moderate correlation (r = 0.50, p < 0.01) was present for persons without an AUD between 0 and 2 months only.ConclusionsIn this sample of WLWH, BMI and AUD affected the strength of correlation between PEth and drinks per week. Future work examining changes in PEth over time in broader populations is needed, particularly to understand the sex differences in PEth levels.

Project description:Although alcohol risk is heritable, few genetic risk variants have been identified. Longitudinal electronic health record (EHR) data offer a largely untapped source of phenotypic information for genetic studies, but EHR-derived phenotypes for harmful alcohol exposure have yet to be validated. Using a variant of known effect, we used EHR data to develop and validate a phenotype for harmful alcohol exposure that can be used to identify unknown genetic variants in large samples. Herein, we consider the validity of 3 approaches using the 3-item Alcohol Use Disorders Identification Test consumption measure (AUDIT-C) as a phenotype for harmful alcohol exposure.First, using longitudinal AUDIT-C data from the Veterans Aging Cohort Biomarker Study Cohort (VACS-BC), we compared 3 metrics of AUDIT-C using correlation coefficients: (i) AUDIT-C closest to blood sampling (closest AUDIT-C), (ii) the highest value (highest AUDIT-C), (iii) and longitudinal trajectories generated using joint trajectory modeling (AUDIT-C trajectory). Second, we compared the associations of the 3 AUDIT-C metrics with phosphatidylethanol (PEth), a direct, quantitative biomarker for alcohol in the overall sample using chi-square tests for trend. Last, in the subsample of African Americans (AAs; n = 1,503), we compared the associations of the 3 AUDIT-C metrics with rs2066702 a common missense (Arg369Cys) polymorphism of the ADH1B gene, which encodes an alcohol dehydrogenase isozyme.The sample (n = 1,851, 94.5% male, 65% HIV+, mean age 52 years) had a median of 7 AUDIT-C scores over a median of 6.1 years. Highest AUDIT-C and AUDIT-C trajectory were correlated r = 0.86. The closest AUDIT-C was obtained a median of 2.26 years after the VACS-BC blood draw. Overall and among AAs, all 3 AUDIT-C metrics were associated with PEth (all p < 0.05), but the gradient was steepest with AUDIT-C trajectory. Among AAs (36% with the protective ADH1B allele), the association of rs2066702 with AUDIT-C trajectory and highest AUDIT-C was statistically significant (p < 0.05), and the gradient was steeper for the AUDIT-C trajectory than for the highest AUDIT-C. The closest AUDIT-C was not statistically significantly associated with rs2066702.EHR data can be used to identify complex phenotypes such as harmful alcohol use. The validity of the phenotype may be enhanced through the use of longitudinal trajectories.

Project description:BACKGROUND:Unhealthy alcohol use (UAU) is one of the major causes of preventable morbidity, mortality, and associated behavioral risks worldwide. Although mobile health (mHealth) interventions can provide consumers with an effective means for self-control of UAU in a timely, ubiquitous, and cost-effective manner, to date, there is a lack of understanding about different health outcomes brought by such interventions. The core components of these interventions are also unclear. OBJECTIVE:This study aimed to systematically review and synthesize the research evidence about the efficacy of mHealth interventions on various health outcomes for consumer self-control of UAU and to identify the core components to achieve these outcomes. METHODS:We systematically searched 7 electronic interdisciplinary databases: Scopus, PubMed, PubMed Central, CINAHL Plus with full text, MEDLINE with full text, PsycINFO, and PsycARTICLES. Search terms and Medical Subject Headings "mHealth," "text message," "SMS," "App," "IVR," "self-control," "self-regulation," "alcohol*," and "intervention" were used individually or in combination to identify peer-reviewed publications in English from 2008 to 2017. We screened titles and abstracts and assessed full-text papers as per inclusion and exclusion criteria. Data were extracted from the included papers according to the Consolidated Standards of Reporting Trials-EHEALTH checklist (V 1.6.1) by 2 authors independently. Data quality was assessed by the Mixed Methods Appraisal Tool. Data synthesis and analyses were conducted following the procedures for qualitative content analysis. Statistical testing was also conducted to test differences among groups of studies. RESULTS:In total, 19 studies were included in the review. Of these 19 studies, 12 (63%) mHealth interventions brought significant positive outcomes in improving participants' health as measured by behavioral (n=11), physiological (n=1), and cognitive indicators (n=1). No significant health outcome was reported in 6 studies (6/19, 32%). Surprisingly, a significant negative outcome was reported for the male participants in the intervention arm in 1 study (1/19, 5%), but no change was found for the female participants. In total, 5 core components reported in the mHealth interventions for consumer self-control of UAU were context, theoretical base, delivery mode, content, and implementation procedure. However, sound evidence is yet to be generated about the role of each component for mHealth success. The health outcomes were similar regardless of types of UAU, deployment setting, with or without nonmobile cointervention, and with or without theory. CONCLUSIONS:Most studies reported mHealth interventions for self-control of UAU appeared to be improving behavior, especially the ones delivered by short message service and interactive voice response systems. Further studies are needed to gather sound evidence about the effects of mHealth interventions on improving physiological and cognitive outcomes as well as the optimal design of these interventions, their implementation, and effects in supporting self-control of UAU.

Dataset Information

Validating Self-Reported Unhealthy Alcohol Use With Phosphatidylethanol (PEth) Among Patients With HIV.

Background

Methods

Results

Conclusions

Publications

Validating Self-Reported Unhealthy Alcohol Use With Phosphatidylethanol (PEth) Among Patients With HIV.

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