Cerhan, James R.

Abstract Background: The International Lymphoma Epidemiology Consortium (InterLymph) provides an opportunity to analyze the relationship between cigarette smoking and non-Hodgkin lymphoma with sufficient statistical power to consider non-Hodgkin lymphoma subtype. The results from previous studies of this relationship have been inconsistent, likely due to the small sample sizes that arose from stratification by disease subtype. To clarify the role of cigarette smoking in the etiology of non-Hodgkin lymphoma, we conducted a pooled analysis of original patient data from nine case-control studies of non-Hodgkin lymphoma conducted in the United States, Europe, and Australia. Methods: Original data were obtained from each study and uniformly coded. Risk estimates from fixed-effects and two-stage random-effects models were compared to determine the impact of interstudy heterogeneity. Odds ratios (OR) and 95% confidence intervals (95% CI) were derived from unconditional logistic regression models, controlling for study center, age, sex, and race. Results: In our pooled study population of 6,594 cases and 8,892 controls, smoking was associated with slightly increased risk estimates (OR, 1.07; 95% CI, 1.00-1.15). Stratification by non-Hodgkin lymphoma subtype revealed that the most consistent association between cigarette smoking and non-Hodgkin lymphoma was observed among follicular lymphomas (n = 1452). Compared with nonsmokers, current smokers had a higher OR for follicular lymphoma (1.31; 95% CI, 1.12-1.52) than former smokers (1.06; 95% CI, 0.93-1.22). Current heavy smoking (≥36 pack-years) was associated with a 45% increased OR for follicular lymphoma (1.45; 95% CI, 1.15-1.82) compared with nonsmokers. Conclusions: Cigarette smoking may increase the risk of developing follicular lymphoma but does not seem to affect risk of the other non-Hodgkin lymphoma subtypes we examined. Future research is needed to determine the biological mechanism responsible for our subtype-specific results.

Abstract Obesity and adult weight gain are well-established risk factors for postmenopausal breast cancer. Although there are a few studies demonstrating the contribution of adult weight gain to breast cancer risk, whether weight gain during a critical time period is specifically associated with risk, or whether subsequent weight loss among women who have gained weight will reduce the excess risk, is not firmly established. We investigated the association of changes in weight (loss or gain in excess of 5% of body weight) using two risk factor models: (a) age 18 to 30 years and age 30 years to menopause and (b) age 30 years to menopause and after the menopause to the baseline study in 1986 on risk of postmenopausal breast cancer in a prospective cohort of 33,660 postmenopausal women in Iowa. Over 15 years of follow-up, 1,987 cases of breast cancer occurred. Data were analyzed using proportional hazards regression models adjusted for established breast cancer risk factors. The most frequently observed pattern of body weight over time was a consistent increase; these women were observed to have the highest rates of breast cancer and served as the reference category for all comparisons. The lowest-risk groups were (a) women who maintained or lost weight from age 18 to 30 years and then lost weight from age 30 years to menopause [risk ratio (RR), 0.36; 95% confidence interval (95% CI), 0.22-0.60] and (b) women who maintained or lost weight from age 30 years to menopause and then lost weight after the menopause (RR, 0.48; 95% CI, 0.22-0.65). Women who gained weight from age 30 years to menopause but then lost weight after the menopause experienced risk reductions (RR, 0.77; 95% CI, 0.64-0.92) although perhaps slightly smaller in magnitude than women who maintained their weight in both time intervals (RR, 0.63; 95% CI, 0.55-0.73). Women who gained weight from age 18 to 30 years and then lost weight from age 30 years to menopause had comparable risk reductions (RR, 0.61; 95% CI, 0.46-0.8) with women who maintained their weight in both time intervals (RR, 0.73; 95% CI, 0.64-0.84). Women who gained weight during the period from age 30 years to menopause but who had stable weight after menopause had rates similar to the reference group. These data suggest prevention of weight gain between age 18 years and menopause or weight loss and maintenance during these years reduces risk of postmenopausal breast cancer.

Abstract A possible role for SV40, a macaque polyomavirus, in non-Hodgkin lymphoma (NHL) in humans was raised recently by the reported detection of SV40 DNA in tumor tissue. Animals with SV40-induced tumors frequently produce high-level antibodies against T antigen, the SV40 oncoprotein. In this study, we assessed whether SV40 T antibody measured in humans supported a relationship between SV40 and NHL. Subjects were sampled from a U.S. population-based case-control study of NHL, according to presence of antibodies against capsids of SV40 and BK, a related human polyomavirus (n = 85 cases, n = 95 controls). T antibody was measured by enzyme immunoassay. We also evaluated serum specimens from SV40-infected and SV40-uninfected macaques (n = 19 and n = 8, respectively), SV40-uninfected hamsters (n = 5), and hamsters with SV40-induced tumors (n = 10). Hamsters with SV40-induced tumors all produced robust SV40 T antibody [median absorbance, 0.99), whereas SV40-uninfected hamsters and macaques had much lower levels (median absorbance, 0.05 and 0.04, respectively). NHL cases, controls, and SV40-infected macaques resembled these latter two groups, generally showing only low-level T antibody (median absorbance, 0.03, 0.04, and 0.04, respectively). Overall, only five cases (6%) and five controls (5%) had T antibody responses classified as seropositive (odds ratio, 1.2; 95% confidence interval, 0.3-4.6). Interestingly, all 10 humans with T antibody responses also showed antibody responses to BK capsid. We found no association between the presence of T antibody and NHL, arguing against SV40 as a cause of NHL. Infrequent and low-level T antibody responses among humans could represent cross-reactivity to BK virus T antigen.

AbstractBecause fruits and vegetables are rich in bioactive compounds with potential cancer-preventive actions, increased consumption may reduce the risk of ovarian cancer. Evidence on the association between fruit and vegetable intake and ovarian cancer risk has not been consistent. We analyzed and pooled the primary data from 12 prospective studies in North America and Europe. Fruit and vegetable intake was measured at baseline in each study using a validated food-frequency questionnaire. To summarize the association between fruit and vegetable intake and ovarian cancer, study-specific relative risks (RR) were estimated using the Cox proportional hazards model, and then combined using a random-effects model. Among 560,441 women, 2,130 cases of invasive epithelial ovarian cancer occurred during a maximum follow-up of 7 to 22 years across studies. Total fruit intake was not associated with ovarian cancer risk—the pooled multivariate RR for the highest versus the lowest quartile of intake was 1.06 [95% confidence interval (95% CI), 0.92-1.21; P value, test for trend = 0.73; P value, test for between-studies heterogeneity = 0.74]. Similarly, results for total vegetable intake indicated no significant association (pooled multivariate RR, 0.90; 95% CI, 0.78-1.04, for the highest versus the lowest quartile; P value, test for trend = 0.06; P value, test for between-studies heterogeneity = 0.31). Intakes of botanically defined fruit and vegetable groups and individual fruits and vegetables were also not associated with ovarian cancer risk. Associations for total fruits and vegetables were similar for different histologic types. These results suggest that fruit and vegetable consumption in adulthood has no important association with the risk of ovarian cancer.

Abstract Quantitative measurements of environmental factors greatly improve the quality of epidemiologic studies but can pose challenges because of the presence of upper or lower detection limits or interfering compounds, which do not allow for precise measured values. We consider the regression of an environmental measurement (dependent variable) on several covariates (independent variables). Various strategies are commonly employed to impute values for interval-measured data, including assignment of one-half the detection limit to nondetected values or of “fill-in” values randomly selected from an appropriate distribution. On the basis of a limited simulation study, we found that the former approach can be biased unless the percentage of measurements below detection limits is small (5–10%). The fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits. Truncated data methods (e.g., Tobit regression) and multiple imputation offer two unbiased approaches for analyzing measurement data with detection limits. If interest resides solely on regression parameters, then Tobit regression can be used. If individualized values for measurements below detection limits are needed for additional analysis, such as relative risk regression or graphical display, then multiple imputation produces unbiased estimates and nominal confidence intervals unless the proportion of missing data is extreme. We illustrate various approaches using measurements of pesticide residues in carpet dust in control subjects from a case–control study of non-Hodgkin lymphoma.

Purpose: We conducted this study because the duration of excess lung cancer risk among former smokers has been inconsistently reported, doubt has been raised regarding the population impact of smoking cessation, and differential risk reduction by histologic cell type after smoking cessation needs to be confirmed. Methods: The Iowa Women’s Health Study is a prospective cohort study of 41,836 Iowa women aged 55 to 69 years. In 1986, mailed questionnaires were used to collect detailed smoking history. Age-adjusted lung cancer incidence through 1999 was analyzed according to years of smoking abstinence. Relative risks were estimated using Cox regression analysis. Results: There were 37,078 women in the analytic cohort. Compared with the never smokers, former smokers had an elevated lung cancer risk (relative risk, 6.6; 95% confidence interval, 5.0 to 8.7) up to 30 years after smoking cessation for all former smokers. However, a beneficial effect of smoking cessation was observed among recent and distant former smokers. The risk of adenocarcinoma remained elevated up to 30 years for both former heavier and former lighter smokers. Conclusion: The risk for lung cancer is increased for both current and former smokers compared with never smokers and declines for former smokers with increasing duration of abstinence. The decline in excess lung cancer risk among former smokers is prolonged compared with other studies, especially for adenocarcinoma and for heavy smokers, suggesting that more emphasis should be placed on smoking prevention and lung cancer chemoprevention.