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ImportanceCurrent Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) diagnoses of substance use disorders rely on criterion count-based approaches, disregarding severity grading indexed by individual criteria.ObjectiveTo examine correlates of alcohol use disorder (AUD) across count-based severity groups (ie, mild, moderate, mild-to-moderate, severe), identify specific diagnostic criteria indicative of greater severity, and evaluate whether specific criteria within mild-to-moderate AUD differentiate across relevant correlates and manifest in greater hazards of severe AUD development.Design, setting, and participantsThis cohort study involved 2 cohorts from the family-based Collaborative Study on the Genetics of Alcoholism (COGA) with 7 sites across the United States: cross-sectional (assessed 1991-2005) and longitudinal (assessed 2004-2019). Statistical analyses were conducted from December 2022 to June 2023.Main outcomes and measuresSociodemographic, alcohol-related, psychiatric comorbidity, brain electroencephalography (EEG), and AUD polygenic score measures as correlates of DSM-5 AUD levels (ie, mild, moderate, severe) and criterion severity-defined mild-to-moderate AUD diagnostic groups (ie, low-risk vs high-risk mild-to-moderate).ResultsA total of 13 110 individuals from the cross-sectional COGA cohort (mean [SD] age, 37.8 [14.2] years) and 2818 individuals from the longitudinal COGA cohort (mean baseline [SD] age, 16.1 [3.2] years) were included. Associations with alcohol-related, psychiatric, EEG, and AUD polygenic score measures reinforced the role of increasing criterion counts as indexing severity. Yet within mild-to-moderate AUD (2-5 criteria), the presence of specific high-risk criteria (eg, withdrawal) identified a group reporting heavier drinking and greater psychiatric comorbidity even after accounting for criterion count differences. In longitudinal analyses, prior mild-to-moderate AUD characterized by endorsement of at least 1 high-risk criterion was associated with more accelerated progression to severe AUD (adjusted hazard ratio [aHR], 11.62; 95% CI, 7.54-17.92) compared with prior mild-to-moderate AUD without endorsement of high-risk criteria (aHR, 5.64; 95% CI, 3.28-9.70), independent of criterion count.Conclusions and relevanceIn this cohort study of a combined 15 928 individuals, findings suggested that simple count-based AUD diagnostic approaches to estimating severe AUD vulnerability, which ignore heterogeneity among criteria, may be improved by emphasizing specific high-risk criteria. Such emphasis may allow better focus on individuals at the greatest risk and improve understanding of the development of AUD.

Physiologically based pharmacokinetic models have been used to describe the distribution and elimination of ethanol after intravenous administration. These models have been used to estimate the ethanol infusion profile that is sufficient for achieving a prescribed breath ethanol concentration time course in individuals, providing a useful platform for several pharmacokinetic and pharmacodynamic investigations. Mathematical foundations of these models are examined, including the derivation of an explicit set of governing equations in the form of a system of nonlinear ordinary differential equations. These equations can then be used to formulate and refine parameter identification and control strategies. Finally, a framework in which models related to this model can be constructed and analyzed is described. Index Terms--Alcohol, ethanol, model, pharmacokinetics, physiologically based pharmacokinetic (PBPK) models, physiology.

The understanding of drinking patterns leading to alcoholism has been hindered by an inability to unobtrusively measure ethanol consumption over periods of weeks to months in the community environment. An implantable ethanol sensor is under development using microelectromechanical systems technology. For safety and user acceptability issues, the sensor will be implanted subcutaneously and, therefore, measure peripheral-tissue ethanol concentration. Determining ethanol consumption and kinetics in other compartments from the time course of peripheral-tissue ethanol concentration requires sophisticated signal processing based on detailed descriptions of the relevant physiology. A statistical signal processing system based on detailed models of the physiology and using extended Kalman filtering and dynamic programming tools is described which can estimate the time series of ethanol concentration in blood, liver, and peripheral tissue and the time series of ethanol consumption based on peripheral-tissue ethanol concentration measurements. Index Terms--Bio-sensor, dynamic programming, ethanol consumption model, ethanol PBPK model, ethanol pharmacokinetics model, extended Kalman smoothing.

Human brain oscillations represent important features of information processing and are highly heritable. A common feature of beta oscillations (13-28 Hz) is the critical involvement of networks of inhibitory interneurons as pacemakers, gated by [gamma]-aminobutyric acid type A (GABAA) action. Advances in molecular and statistical genetics permit examination of quantitative traits such as the beta frequency of the human electroencephalogram in conjunction with DNA markers. We report a significant linkage and linkage disequilibrium between beta frequency and a set of GABAA receptor genes. Uncovering the genes influencing brain oscillations provides a better understanding of the neural function involved in information processing.

Background: The preference for immediate rewards and high sensation seeking are both potent risk factors for alcohol use disorder (AUD), but how they interact during intoxication is poorly understood. To model decision making linked to AUD risk, we tested heavy drinkers for impulsive choice (delay discounting with alcohol:money or money:money) and behavioral sensation seeking using a novel odor choice task. Laboratory tasks measured actual behavior with real contingencies. Our goals were to determine, in heavy drinkers, (i) alcohol's effects on delay discounting, and (ii) how AUD risk factors relate to delay discounting, and (iii) how delay discounting with alcohol choices compares with strictly monetary choices. Methods: Thirty‐five heavy drinkers (≥2 binges per month; age = 22.8 ± 2.2; 20 male; 5.8 ± 2.3 drinks/drinking day) performed cross‐commodity discounting (CCD) of immediate alcohol vs. delayed money, a monetary delay discounting (DD), and behavioral sensation‐seeking tasks. CCD and DD were performed while sober and during controlled alcohol infusion targeting 0.08 g/dl. The behavioral sensation‐seeking task presented binary choices of odorants varying in intensity and novelty, and the risk of exposure to a malodorant. Results: CCD and DD behaviors were highly correlated across conditions, mean r = 0.64. Alcohol increased delayed reward preference in DD, p = 0.001, but did not alter mean CCD, p > 0.16. However, alcohol‐induced changes in CCD correlated with behavioral sensation seeking, such that higher sensation seekers' immediate alcohol preference increased when intoxicated, p = 0.042; self‐reported sensation seeking was uncorrelated, ps > 0.08. Behavioral sensation seeking also correlated with "want" alcohol following a priming dose targeting 0.035 g/dl, p = 0.021. CCD and DD did not correlate with self‐reported drinking problems or other personality risk traits. Conclusions: Alcohol increased impulsive alcohol choice in high sensation seekers, suggesting an interaction that may underlie impaired control of drinking, at least in a subset of heavy drinkers—consistent with models highlighting high novelty/sensation‐seeking AUD subtypes. Discounting behavior overall appears to be a generalized process, and relatively stable across methods, repeated testing, and intoxication. These findings further support the utility of behavioral tasks in uncovering key behavioral phenotypes in AUD. [ABSTRACT FROM AUTHOR]