Validation and proposal for cut-off values of an abbreviated version of the Alcohol Use Disorder Identification Test using the Korean National Health and Nutrition Examination Survey

Lee, Jae Hee; Kong, Kyoung Ae; Lee, Dong Hoon; Choi, Yoon Hee; Jung, Koo Young

doi:10.15441/ceem.17.228

Cited by 15 publications

(13 citation statements)

References 30 publications

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“…HAD was defined as AUDIT ≥8 for men and ≥7 for women based on previous studies. Non-HAD was defined as AUDIT <8 for men and <7 for women [ 50 ]. There were 148 HADs and 354 non-HADs.…”

Section: Methodsmentioning

confidence: 99%

DNA methylation signature on phosphatidylethanol, not on self-reported alcohol consumption, predicts hazardous alcohol consumption in two distinct populations

et al. 2020

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The process of diagnosing hazardous alcohol drinking (HAD) is based on self-reported data and is thereby vulnerable to bias. There has been an interest in developing epigenetic biomarkers for HAD that might complement clinical assessment. Because alcohol consumption has been previously linked to DNA methylation (DNAm), we aimed to select DNAm signatures in blood to predict HAD from two demographically and clinically distinct populations (N total = 1,549). We first separately conducted an epigenome-wide association study (EWAS) for phosphatidylethanol (PEth), an objective measure of alcohol consumption, and for self-reported alcohol consumption in Cohort 1. We identified 83 PEth-associated CpGs, including 23 CpGs previously associated with alcohol consumption or alcohol use disorder. In contrast, no CpG reached epigenome-wide significance on selfreported alcohol consumption. Using a machine learning approach, two CpG subsets from EWAS on PEth and on self-reported alcohol consumption from Cohort 1 were separately tested for the prediction of HAD in Cohort 2. We found that a subset of 143 CpGs selected from the EWAS on PEth showed an excellent prediction of HAD with the area under the receiver operating characteristic curve (AUC) of 89.4% in training set and 73.9% in validation set of Cohort 2. However, CpGs preselected from the EWAS on self-reported alcohol consumption showed a poor prediction of HAD with AUC 75.2% in training set and 57.6% in validation set. Our results demonstrate that an objective measure for alcohol consumption is a more informative phenotype than self-reported data for revealing epigenetic mechanisms. The PEth-associated DNAm signature in blood could serve as a robust biomarker for alcohol consumption.

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“…HAD was defined as AUDIT ≥8 for men and ≥7 for women based on previous studies. Non-HAD was defined as AUDIT <8 for men and <7 for women [ 50 ]. There were 148 HADs and 354 non-HADs.…”

Section: Methodsmentioning

confidence: 99%

DNA methylation signature on phosphatidylethanol, not on self-reported alcohol consumption, predicts hazardous alcohol consumption in two distinct populations

et al. 2020

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“…AD patients were consecutively admit- Besides, they must score less than five points in AUDIT-C test. 28 To minimize the impact of unknown confounders, AD subjects and controls were excluded from the study if they met one or more of the following criteria: (1) had drug dependence other than nicotine, (2) had a history of psychiatric disorders such as schizophrenia and bipolar disorder, except for mild anxiety or depression, (3) had a history of neurodegeneration disease such as Parkinson's disease and Alzheimer disease, (4) had significant physical illness such as severe diabetes, renal dysfunction, infarction, liver cirrhosis, severe infection, and cancers, (5) had HIV infection, (6) were pregnant, and (7) refused to provide blood sample and/or written informed consent. This study was approved by the ethics committee of Sun Yat-sen Memorial Hospital at Sun Yat-sen University (number: SYSEC-KY-KS-2019007), and all participants provided written informed consent.…”

Section: Participantsmentioning

confidence: 99%

Circular RNA expression alteration identifies a novel circulating biomarker in serum exosomal for detection of alcohol dependence

Liu

et al. 2021

Addiction Biology

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Alcohol dependence (AD) is one of the most common and detrimental neuropsychological disorders. Recently, more and more studies have focused on circular RNA as markers for central nervous system (CNS) diseases. The present study was conducted to evaluate the circular RNA expression alteration in serum exosomal and to identify a novel circulating biomarker for the detection of AD. We first isolated exosomes from serum and then investigated the circRNA expression alterations by high throughput whole transcriptome sequencing. The data were then analyzed using bioinformatics methods. Moreover, we verified the circRNA‐seq by qRT‐PCR. Furthermore, we analyzed the correlations between the levels of hsa_circ_0004771 and both Severity of Alcohol Dependence Questionnaire (SADQ) and Alcohol Dependence Scale (ADS). The diagnostic value of hsa_circ_0004771 in AD patients was evaluated by receiver operating characteristic (ROC). In this study, 254 differentially expressed circRNAs were identified, with 149 upregulated and 105 downregulated. GO analysis showed that these differentially expressed circRNAs from exosomes might be associated with the regulation of neuron projection and axon regeneration. KEGG analysis revealed that T cell receptor signaling and antigen processing and presentation pathway had a regulating effect on upstream levels. We found that hsa_circ_0004771 was related to the severity of AD. The AUC for the diagnostic value of hsa_circ_0004771 in AD patients was 0.874. These findings indicated that circRNA in serum exosomes provide novel targets for further research on molecular mechanisms of AD. Among these, hsa_circ_0004771 may be a sensitive biomarker that was related to the severity of AD.

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“…Zone IV is the most serious condition. Details such as the cut off value of each test are described in Supplementary Methods (28,29).…”

Section: Evaluation Of Alcohol Usementioning

confidence: 99%

A Deep Learning Algorithm to Predict Hazardous Drinkers and the Severity of Alcohol-Related Problems Using K-NHANES

et al. 2021

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Purpose: The number of patients with alcohol-related problems is steadily increasing. A large-scale survey of alcohol-related problems has been conducted. However, studies that predict hazardous drinkers and identify which factors contribute to the prediction are limited. Thus, the purpose of this study was to predict hazardous drinkers and the severity of alcohol-related problems of patients using a deep learning algorithm based on a large-scale survey data.Materials and Methods: Datasets of National Health and Nutrition Examination Survey of South Korea (K-NHANES), a nationally representative survey for the entire South Korean population, were used to train deep learning and conventional machine learning algorithms. Datasets from 69,187 and 45,672 participants were used to predict hazardous drinkers and the severity of alcohol-related problems, respectively. Based on the degree of contribution of each variable to deep learning, it was possible to determine which variable contributed significantly to the prediction of hazardous drinkers.Results: Deep learning showed the higher performance than conventional machine learning algorithms. It predicted hazardous drinkers with an AUC (Area under the receiver operating characteristic curve) of 0.870 (Logistic regression: 0.858, Linear SVM: 0.849, Random forest classifier: 0.810, K-nearest neighbors: 0.740). Among 325 variables for predicting hazardous drinkers, energy intake was a factor showing the greatest contribution to the prediction, followed by carbohydrate intake. Participants were classified into Zone I, Zone II, Zone III, and Zone IV based on the degree of alcohol-related problems, showing AUCs of 0.881, 0.774, 0.853, and 0.879, respectively.Conclusion: Hazardous drinking groups could be effectively predicted and individuals could be classified according to the degree of alcohol-related problems using a deep learning algorithm. This algorithm could be used to screen people who need treatment for alcohol-related problems among the general population or hospital visitors.

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Validation and proposal for cut-off values of an abbreviated version of the Alcohol Use Disorder Identification Test using the Korean National Health and Nutrition Examination Survey

Cited by 15 publications

References 30 publications

DNA methylation signature on phosphatidylethanol, not on self-reported alcohol consumption, predicts hazardous alcohol consumption in two distinct populations

DNA methylation signature on phosphatidylethanol, not on self-reported alcohol consumption, predicts hazardous alcohol consumption in two distinct populations

Circular RNA expression alteration identifies a novel circulating biomarker in serum exosomal for detection of alcohol dependence

A Deep Learning Algorithm to Predict Hazardous Drinkers and the Severity of Alcohol-Related Problems Using K-NHANES

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