Obese patients have lower rates of alcohol use than found in the general population of women. As BMI increases, lower rates of alcohol consumption are found. Overeating may compete with alcohol for brain reward sites, making alcohol ingestion less reinforcing.

These results contribute to the understanding of the relationship between tobacco smoking and potential biological effects.

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Though marijuana has been reported to stimulate appetite, we searched for a correlation between obesity and decreased marijuana use. We examined charts of all females referred for morbid obesity/weight management in a 12-month period. BMI and substance use data were collected from 297 charts. While 29% of the sample with BMI < 30 (n = 7) used marijuana in the past year, only 21% of those with BMI 30-39 (n = 84), 16% of those with BMI 40-49 (n = 110) and 14% (n = 96) of those with BMI > 50 used marijuana in the past year. Linear regression revealed a negative correlation between BMI group and percent marijuana use (R-squared = 0.96; P = 0.0173). These findings provide support for overeating as competition for drugs and alcohol in brain reward sites.

Cigarette smoke is a complex aerosol that includes a gas vapor phase and a particulate phase. Inclusion of activated carbon in the cigarette filter can reduce some of the gas-phase smoke constituents implicated as toxicologically relevant. The present study evaluated exposure to selected gas-phase constituents when adult smokers switched to prototype cigarettes with a highly activated carbon filter. Smokers (N = 160) in two separate studies were randomized to continue to smoke conventional cigarettes (either a 6-mg or 11-mg FTC tar product), to smoke test cigarettes containing carbon filters (comparable tar levels), or to stop smoking. After completing 8 days in controlled smoking conditions (short-term studies), smokers had the option to continue in 24-week long-term ambulatory studies with unrestricted smoking. Urinary excretion of mercapturic acid metabolites of 1,3-butadiene, acrolein, and benzene; nicotine and five of its metabolites, total NNAL, and 1-hydroxypyrene were measured at baseline in the conventional cigarette group, in all groups in the short-term studies, and every 4 weeks in the long-term studies. In the short-term studies, statistically significant reductions (>70%, p<.001) in gas-phase biomarker levels were observed in the test cigarette group for both tar level products compared with the conventional cigarette group. These reductions were similar to those observed in the stop-smoking groups. The reductions continued consistently (p<.001) throughout the long-term studies. Switching to test cigarettes minimally affected the particulate-phase biomarkers. Statistically significant and consistent reductions in selected gas vapor phase biomarkers were observed when smokers switched to activated carbon filter cigarettes.

Background: Cigarette smoking is a risk factor for several diseases, including cardiovascular disease, chronic obstructive pulmonary disease, and lung cancer, but the role of specific smoke constituents in these diseases has not been clearly established.Methods: The relationships between biomarkers of potential harm (BOPH), associated with inflammation [white blood cell (WBC), high sensitivity C-reactive protein (hs-CRP), fibrinogen, and von Willebrand factor (vWF)], oxidative stress [8-epi-prostaglandin F 2a (8-epiPGF 2a )] and platelet activation [11-dehydro-thromboxin B 2 (11-dehTxB 2 )], and machine-measured tar yields (grouped into four categories), biomarkers of exposure (BOE) to cigarette smoke: nicotine and its five metabolites (nicotine equivalents), 4-methylnitrosamino-1-(3-pyridyl)-1-butanol (total NNAL), carboxyhemoglobin, 1-hydroxypyrene, 3-hydroxypropylmercapturic acid, and monohydroxybutenyl-mercapturic acid, were investigated in 3,585 adult smokers and 1,077 nonsmokers.Results: Overall, adult smokers had higher levels of BOPHs than nonsmokers. Body mass index (BMI), smoking duration, tar category, and some of the BOEs were significant factors in the multiple regression models. Based on the F value, BMI was the highest ranking factor in the models for WBC, hs-CRP, fibrinogen, and 8-epiPGF 2a , respectively, and gender and smoking duration for 11-dehTxB 2 and vWF, respectively.Conclusions: Although several demographic factors and some BOEs were statistically significant in the model, the R 2 values indicate that only up to 22% of the variability can be explained by these factors, reflecting

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