UCSF

Aim

Chronic cigarette smoking appears to adversely affect several domains of neurocognition in those with alcohol use disorders (AUDs). The primary goal of this study was to identify which measures commonly used to assess neurocognition in AUDs accurately predict smoking status of individuals seeking treatment of alcohol dependence.

Methods

Treatment-seeking alcohol-dependent participants (ALC; n = 92) completed a comprehensive neuropsychological battery after 33 ± 9 days of abstinence. Measures significantly different between smoking and non-smoking ALC were entered as predictors in binary logistic regression and discriminant analysis models, with smoking status as the dependent variable.

Results

Smoking ALC performed significantly worse than non-smoking ALC on measures assessing processing speed, auditory-verbal and visuospatial learning and memory. Using these measures as predictors, a logistic regression model accurately classified 91% of smokers and non-smokers into their respective groups overall and accounted for 68% of the variance in smoking status. The discriminant analysis confirmed the findings from the logistic regression. In smoking ALC, smoking chronicity was inversely related to performance on multiple measures after controlling for lifetime alcohol consumption.

Conclusions

Measures of processing speed, learning and memory robustly predicted the smoking status of ALC with high sensitivity and specificity during early abstinence. The results identified specific measures within a comprehensive neurocognitive battery that discriminated smoking and non-smoking alcohol-dependent individuals with a high sensitivity and specificity. The association of greater smoking chronicity and poorer performance on multiple measures after control for alcohol consumption suggests that chronic smoking adds an additional burden to neurocognitive function in those with alcohol dependence.