Objective Evaluate nicotinic acetycholine receptor (nAChR) single nucleotide polymorphism (SNP) association with seven day point prevalence abstinence (abstinence) in randomized clinical trials of smoking cessation therapies (RCTs) in individuals grouped by pharmacotherapy randomization to inform the development of personalized smoking cessation therapy. Methods We quantified association of four SNPs at three nAChRs with abstinence in eight RCTs. Participants were 2,633 outpatient treatment-seeking, self-identified European ancestry individuals smoking ≥10 cigarettes per day, recruited via advertisement, prescribed pharmacotherapy, and provided with behavioral therapy. Interventions included nicotine replacement therapy (NRT), bupropion, varenicline, placebo or combined NRT and bupropion, and five modes of group and individual behavioral therapy. Outcome measures tested in multivariate logistic regression were end of treatment (EOT) and six month (6MO) abstinence, with demographic, behavioral and genetic covariates. Results “Risk” alleles previously associated with smoking heaviness were significantly (P<0.05) associated with reduced abstinence in the placebo pharmacotherapy group (PG) at 6MO [for rs588765 OR (95%CI) 0.41 (0.17–0.99)], and at EOT and at 6MO [for rs1051730, 0.42 (0.19–0.93) and 0.31 (0.12–0.80)], and with increased abstinence in the NRT PG at 6MO [for rs588765 2.07 (1.11–3.87) and for rs1051730 2.54 (1.29–4.99)]. We observed significant heterogeneity in rs1051730 effects (F=2.48, P=0.021) between PGs. Conclusions chr15q25.1 nAChR SNP risk alleles for smoking heaviness significantly increase relapse with placebo treatment and significantly increase abstinence with NRT. These SNP-PG associations require replication in independent samples for validation, and testing in larger sample sizes to evaluate whether similar effects occur in other PGs.

The mu1 opioid receptor gene, OPRM1, has long been a high-priority candidate for human genetic studies of addiction. Because of its potential functional significance, the non-synonymous variant rs1799971 (A118G, Asn40Asp) in OPRM1 has been extensively studied, yet its role in addiction has remained unclear, with conflicting association findings. To resolve the question of what effect, if any, rs1799971 has on substance dependence risk, we conducted collaborative meta-analyses of 25 datasets with over 28,000 European-ancestry subjects. We investigated non-specific risk for “general” substance dependence, comparing cases dependent on any substance to controls who were non-dependent on all assessed substances. We also examined five specific substance dependence diagnoses: DSM-IV alcohol, opioid, cannabis, and cocaine dependence, and nicotine dependence defined by the proxy of heavy/light smoking (cigarettes-per-day > 20 versus ≤ 10). The G allele showed a modest protective effect on general substance dependence (OR = 0.90, 95% C.I. [0.83–0.97], p-value = 0.0095, N = 16,908). We observed similar effects for each individual substance, although these were not statistically significant, likely because of reduced sample sizes. We conclude that rs1799971 contributes to mechanisms of addiction liability that are shared across different addictive substances. This project highlights the benefits of examining addictive behaviors collectively and the power of collaborative data sharing and meta-analyses.

Rapid assessment of radiation signatures in non-invasive biofluids may aid in assigning proper medical treatments for acute radiation syndrome (ARS) and delegating limited resources after a nuclear disaster. Metabolomic platforms allow for rapid screening of biofluid signatures and show promise in differentiating radiation quality and time postexposure. Here, we use global metabolomics to differentiate temporal effects (1-60 d) found in nonhuman primate (NHP) urine and serum small molecule signatures after a 4 Gy total body irradiation. Random Forests analysis differentially classifies biofluid signatures according to days post 4 Gy exposure. Eight compounds involved in protein metabolism, fatty acid β oxidation, DNA base deamination, and general energy metabolism were identified in each urine and serum sample and validated through tandem MS. The greatest perturbations were seen at 1 d in urine and 1-21 d in serum. Furthermore, we developed a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) with multiple reaction monitoring (MRM) method to quantify a six compound panel (hypoxanthine, carnitine, acetylcarnitine, proline, taurine, and citrulline) identified in a previous training cohort at 7 d after a 4 Gy exposure. The highest sensitivity and specificity for classifying exposure at 7 d after a 4 Gy exposure included carnitine and acetylcarnitine in urine and taurine, carnitine, and hypoxanthine in serum. Receiver operator characteristic (ROC) curve analysis using *

BackgroundClinical trial and epidemiological studies need high quality biospecimens from a representative sample of participants to investigate genetic influences on treatment response and disease. Obtaining blood biospecimens presents logistical and financial challenges. As a result, saliva biospecimen collection is becoming more frequent because of the ease of collection and lower cost. This article describes an assessment of saliva biospecimen samples collected through the mail, trial participant demographic and behavioral characteristics, and their association with saliva and DNA quantity and quality.MethodsSaliva biospecimens were collected using the Oragene® DNA Self-Collection Kits from participants in a National Cancer Institute funded smoking cessation trial. Saliva biospecimens from 565 individuals were visually inspected for clarity prior to and after DNA extraction. DNA samples were then quantified by UV absorbance, PicoGreen®, and qPCR. Genotyping was performed on 11 SNPs using TaqMan® SNP assays and two VNTR assays. Univariate, correlation, and analysis of variance analyses were conducted to observe the relationship between saliva sample and participant characteristics.ResultsThe biospecimen kit return rate was 58.5% among those invited to participate (n = 967) and 47.1% among all possible COMPASS participants (n = 1202). Significant gender differences were observed with males providing larger saliva volume (4.7 vs. 4.5 ml, p = 0.019), samples that were more likely to be judged as cloudy (39.5% vs. 24.9%, p < 0.001), and samples with greater DNA yield as measured by UV (190.0 vs. 138.5, p = 0.002), but reduced % human DNA content (73.2 vs. 77.6 p = 0.005) than females. Other participant characteristics (age, self-identified ethnicity, baseline cigarettes per day) were associated with saliva clarity. Saliva volume and saliva and DNA clarity were positively correlated with total DNA yield by all three quantification measurements (all r > 0.21, P < 0.001), but negatively correlated with % human DNA content (saliva volume r = -0.148 and all P < 0.010). Genotyping completion rate was not influenced by saliva or DNA clarity.ConclusionFindings from this study show that demographic and behavioral characteristics of smoking cessation trial participants have significant associations with saliva and DNA metrics, but not with the performance of TaqMan® SNP or VNTR genotyping assays.Trial registrationCOMPASS; registered as NCT00301145 at clinicaltrials.gov.