Literature DB >> 27001945

Development and validation of a composite score for excessive alcohol use screening.

Wanzhu Tu1, Chenghao Chu1, Shanshan Li1, Suthat Liangpunsakul2.   

Abstract

This study was undertaken to develop a composite measure that combines the discriminant values of individual laboratory markers routinely used for excessive alcohol use (EAU) for an improved screening performance. The training sample consisted of 272 individuals with known history of EAU and 210 non-alcoholic individuals. The validation sample included 100 EAU and 75 controls. We used the estimated regression coefficients and the observed marker values to calculate the individual's composite screening score; this score was converted to a probability measure for excessive drinking in the given individual. A threshold value for the screening score based on an examination of the estimated sensitivity and specificity associated with different threshold values was proposed. Using regression coefficients estimated from the training sample, a composite score based on the levels of aspartate aminotransferase, alanine aminotransferase, per cent carbohydrate-deficient transferrin and mean corpuscular volume was calculated. The areas under the receiver operating characteristic curve (AUC) value of the selected model was 0.87, indicating a strong discriminating power and the AUC was better than that of each individual test. The score >0.23 corresponded to a sensitivity of 90% and a specificity of nearly 60%. The AUC value remained at a respectable level of 0.83 with the sensitivity and specificity at 91% and 49%, respectively, in the validation sample. We developed a novel composite score by using a combination of commonly used biomakers. However, the development of the mechanism-based biomarkers of EAU is needed to improve the screening and diagnosis of EAU in clinical practice.
Copyright © 2016 American Federation for Medical Research.

Entities:  

Keywords:  Alcohol Drinking; Biological Markers

Mesh:

Year:  2016        PMID: 27001945      PMCID: PMC5495183          DOI: 10.1136/jim-2015-000033

Source DB:  PubMed          Journal:  J Investig Med        ISSN: 1081-5589            Impact factor:   2.895


  20 in total

1.  Detecting alcoholism. The CAGE questionnaire.

Authors:  J A Ewing
Journal:  JAMA       Date:  1984-10-12       Impact factor: 56.272

2.  Advancing alcohol biomarkers research.

Authors:  Cynthia F Bearer; Shannon M Bailey; Jan B Hoek
Journal:  Alcohol Clin Exp Res       Date:  2010-04-05       Impact factor: 3.455

3.  Mortality associated with alcohol-related liver disease.

Authors:  G Trimble; L Zheng; A Mishra; S Kalwaney; H M Mir; Z M Younossi
Journal:  Aliment Pharmacol Ther       Date:  2013-07-29       Impact factor: 8.171

Review 4.  Carbohydrate-deficient transferrin (CDT)--a biomarker for long-term alcohol consumption.

Authors:  Klaus Golka; Andreas Wiese
Journal:  J Toxicol Environ Health B Crit Rev       Date:  2004 Jul-Aug       Impact factor: 6.393

5.  Effectiveness of the AUDIT-C as a screening test for alcohol misuse in three race/ethnic groups.

Authors:  Danielle Frank; Anna F DeBenedetti; Robert J Volk; Emily C Williams; Daniel R Kivlahan; Katharine A Bradley
Journal:  J Gen Intern Med       Date:  2008-04-18       Impact factor: 5.128

6.  Carbohydrate-deficient transferrin (CDT) as a biomarker in persons suspected of alcohol abuse.

Authors:  Klaus Golka; Rolf Sondermann; Susanne E Reich; Andreas Wiese
Journal:  Toxicol Lett       Date:  2004-06-15       Impact factor: 4.372

7.  Validity of brief alcohol screening tests among adolescents: a comparison of the AUDIT, POSIT, CAGE, and CRAFFT.

Authors:  John R Knight; Lon Sherritt; Sion Kim Harris; Elizabeth C Gates; Grace Chang
Journal:  Alcohol Clin Exp Res       Date:  2003-01       Impact factor: 3.455

Review 8.  Use of contemporary biomarkers in the detection of chronic alcohol use.

Authors:  Norman J Montalto; Pamela Bean
Journal:  Med Sci Monit       Date:  2003-12
View more
  5 in total

Review 1.  DNA methylation-based predictors of health: applications and statistical considerations.

Authors:  Paul D Yousefi; Matthew Suderman; Ryan Langdon; Oliver Whitehurst; George Davey Smith; Caroline L Relton
Journal:  Nat Rev Genet       Date:  2022-03-18       Impact factor: 53.242

2.  Quantity of alcohol drinking positively correlates with serum levels of endotoxin and markers of monocyte activation.

Authors:  Suthat Liangpunsakul; Evelyn Toh; Ruth A Ross; Laura E Heathers; Kristina Chandler; AdePeju Oshodi; Breann McGee; Elizabeth Modlik; Tobyn Linton; Darrin Mangiacarne; Claudie Jimenez; X Charlie Dong; Li Wang; Wanzhu Tu; David E Nelson
Journal:  Sci Rep       Date:  2017-06-30       Impact factor: 4.379

3.  Gradient Relationship between Increased Mean Corpuscular Volume and Mortality Associated with Cerebral Ischemic Stroke and Ischemic Heart Disease: A Longitudinal Study on 66,294 Taiwanese.

Authors:  Tzy-Haw Wu; Jean Ching-Yuan Fann; Sam Li-Sheng Chen; Amy Ming-Fang Yen; Chiung-Jung Wen; Yun-Ru Lu; Hsiu-Hsi Chen; Sherry Yueh-Hsia Chiu; Horng-Huei Liou
Journal:  Sci Rep       Date:  2018-11-08       Impact factor: 4.379

4.  Validation and characterisation of a DNA methylation alcohol biomarker across the life course.

Authors:  Paul Darius Yousefi; Rebecca Richmond; Ryan Langdon; Andrew Ness; Chunyu Liu; Daniel Levy; Caroline Relton; Matthew Suderman; Luisa Zuccolo
Journal:  Clin Epigenetics       Date:  2019-11-27       Impact factor: 6.551

5.  Association Between Aldehyde Dehydrogenase 2 Glu504Lys Polymorphism and Alcoholic Liver Disease.

Authors:  Binxia Chang; Shuli Hao; Longyu Zhang; Miaomiao Gao; Ying Sun; Ang Huang; Guangju Teng; Baosen Li; David W Crabb; Praveen Kusumanchi; Li Wang; Suthat Liangpunsakul; Zhengsheng Zou
Journal:  Am J Med Sci       Date:  2018-03-20       Impact factor: 3.462

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.