BACKGROUND: The combination of unhealthy alcohol use and depression is associated with adverse outcomes including higher rates of alcohol use disorder and poorer depression course. Therefore, addressing alcohol use among individuals with depression may have a substantial public health impact. We compared the effectiveness of a brief intervention (BI) for unhealthy alcohol use among patients with and without depression. METHOD: This observational study included 312,056 adult primary care patients at Kaiser Permanente Northern California who screened positive for unhealthy drinking between 2014 and 2017. Approximately half (48%) received a BI for alcohol use and 9% had depression. We examined 12-month changes in heavy drinking days in the previous three months, drinking days per week, drinks per drinking day, and drinks per week. Machine learning was used to estimate BI propensity, follow-up participation, and alcohol outcomes for an augmented inverse probability weighting (AIPW) estimator of the average treatment (BI) effect. This approach does not depend on the strong parametric assumptions of traditional logistic regression, making it more robust to model misspecification. RESULTS: BI had a significant effect on each alcohol use outcome in the non-depressed subgroup (-0.41 to -0.05, all ps < .003), but not in the depressed subgroup (-0.33 to -0.01, all ps > .28). However, differences between subgroups were nonsignificant (0.00 to 0.11, all ps > .44). CONCLUSION: On average, BI is an effective approach to reducing unhealthy drinking, but more research is necessary to understand its impact on patients with depression. AIPW with machine learning provides a robust method for comparing intervention effectiveness across subgroups.

Recently developed measures of genetic liability to suicide attempt may convey unique information regarding an individuals risk of suicidal behavior. We calculated a polygenic risk score for suicide attempt (SA-PRS) for soldiers of European ancestry who participated in the Army STARRS New Soldier Study (NSS; n = 6573) or Pre/Post Deployment Study (PPDS; n = 4900). Multivariable logistic regression models were fit within each sample to estimate the association of SA-PRS with lifetime suicide attempt (LSA), and to examine whether SA-PRS displayed additive or interactive effects with environmental and behavioral risk/protective factors (lifetime trauma burden, childhood maltreatment, negative urgency impulsivity, social network size, perceived mattering, and dispositional optimism). Age, sex, and within-ancestry variation were included as covariates. Observed prevalence of LSA was 6.3% and 4.2% in the NSS and PPDS samples, respectively. In the NSS model, SA-PRS and environmental/behavioral factors displayed strictly additive effects on odds of LSA. Results indicated an estimated 21% increase in odds of LSA per 1 SD increase in SA-PRS [adjusted odds ratio (AOR; 95% CI) = 1.21 (1.09-1.35)]. In PPDS, the effect of SA-PRS varied by reports of optimism [AOR = 0.85 (0.74-0.98) for SA-PRS x optimism effect]. Individuals reporting low and average optimism had 37% and 16% increased odds of LSA per 1 SD increase in SA-PRS, respectively, whereas SA-PRS was not associated with LSA in those reporting high optimism. Overall, results suggested the SA-PRS had predictive value over and above several environmental and behavioral risk factors for LSA. Moreover, elevated SA-PRS may be more concerning in the presence of environmental and behavioral risk factors (e.g., high trauma burden; low optimism). Given the relatively small effect magnitudes, the cost and incremental benefits of utilizing SA-PRS for risk targeting must also be considered in future work.

BACKGROUND: Identification of genetic risk factors may inform the prevention and treatment of posttraumatic stress disorder (PTSD). This study evaluates the associations of polygenic risk scores (PRS) with patterns of posttraumatic stress symptoms following combat deployment. METHOD: US Army soldiers of European ancestry (n = 4900) provided genomic data and ratings of posttraumatic stress symptoms before and after deployment to Afghanistan in 2012. Latent growth mixture modeling was used to model posttraumatic stress symptom trajectories among participants who provided post-deployment data (n = 4353). Multinomial logistic regression models tested independent associations between trajectory membership and PRS for PTSD, major depressive disorder (MDD), schizophrenia, neuroticism, alcohol use disorder, and suicide attempt, controlling for age, sex, ancestry, and exposure to potentially traumatic events, and weighted to account for uncertainty in trajectory classification and missing data. RESULTS: Participants were classified into low-severity (77.2%), increasing-severity (10.5%), decreasing-severity (8.0%), and high-severity (4.3%) posttraumatic stress symptom trajectories. Standardized PTSD-PRS and MDD-PRS were associated with greater odds of membership in the high-severity v. low-severity trajectory [adjusted odds ratios and 95% confidence intervals, 1.23 (1.06-1.43) and 1.18 (1.02-1.37), respectively] and the increasing-severity v. low-severity trajectory [1.12 (1.01-1.25) and 1.16 (1.04-1.28), respectively]. Additionally, MDD-PRS was associated with greater odds of membership in the decreasing-severity v. low-severity trajectory [1.16 (1.03-1.31)]. No other associations were statistically significant. CONCLUSIONS: Higher polygenic risk for PTSD or MDD is associated with more severe posttraumatic stress symptom trajectories following combat deployment. PRS may help stratify at-risk individuals, enabling more precise targeting of treatment and prevention programs.