Improved classification of alcohol intake groups in the Intermittent-Access Two-Bottle choice rat model using a latent class linear mixed model

Abstract

Alcohol use disorder (AUD) is a major public health problem in which preclinical models allow the study of AUD development, comorbidities and possible new treatments. The intermittent access two-bottle choice (IA2BC) model is a validated preclinical model for studying alcohol intake patterns similar to those present in AUD in human clinical studies. Typically, the mean/median of overall alcohol intake or the last drinking sessions is used as a threshold to divide groups of animals into high or low alcohol consumers. However, it would be more statistically valuable to stratify the groups using the full consumption data from all drinking sessions. In this study, we aimed to evaluate the effectiveness of using the time series data of all drinking sessions to stratify the population into high or low alcohol consumption groups, using a latent class linear mixed model (LCLMM). We compared LCLMM to traditional classification methods: percentiles, k-means clustering, and hierarchical clustering, and used simulations to compare accuracy between methods. Our results demonstrated that LCLMM outperforms other approaches, achieving superior accuracy (0.94) in identifying consumption patterns. By considering the entire trajectory of alcohol intake, LCLMM provides a more robust and nuanced characterization of high and low alcohol consumers. We advocate for the adoption of longitudinal statistical models in substance use disorder research, both in human studies and preclinical investigations, as they hold promise for enhancing population stratification and refining treatment strategies.