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Citation
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Baker EJ, Walter NA, Salo A, Rivas P, Moore S, Gonzales S, Grant KA. Alcohol Clin. Exp. Res. 2017; 41(3): 626-636. |
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Affiliation
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Division of Neuroscience at the Oregon National Primate Research Center, Oregon Health and Science University, Portland, OR, USA. |
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Abstract
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BACKGROUND: The Monkey Alcohol and Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well-documented Non-Human Primate (NHP) alcohol self-administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of Very Heavy Drinking (VHD), Heavy Drinking (HD), Binge Drinking (BD), and Low Drinking (LD) individuals. Here we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment.
METHODS: The classification strategy uses a machine learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across seven cohorts of five to eight monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self-administration.
RESULTS: Predictive outcome accuracy is approximately 78% when classes are aggregated into two groups, "LD and BD" and "HD and VHD". A subsequent two-step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average four-category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings.
CONCLUSION: We demonstrate that data derived from the induction phase of this ethanol self-administration protocol has significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through three stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having increasingly fewer bouts with more drinks. This analysis also identifies characteristic avoidance of intoxicating topographies in future low drinkers, such as increasing number of bouts and waiting longer before the first ethanol drink. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.
Language: en |