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Genetic Variants on 15q25.1, Smoking, and Lung Cancer: An Assessment of Mediation and Interaction

Sat, 04 Feb 2012 01:10:31 +0000

Genome-wide association studies have identified variants on chromosome 15q25.1 that increase the risks of both lung cancer and nicotine dependence and associated smoking behavior. However, there remains debate as to whether the association with lung cancer is direct or is mediated by pathways related to smoking behavior. Here, the authors apply a novel method for mediation analysis, allowing for gene-environment interaction, to a lung cancer case-control study (1992–2004) conducted at Massachusetts General Hospital using 2 single nucleotide polymorphisms, rs8034191 and rs1051730, on 15q25.1. The results are validated using data from 3 other lung cancer studies. Tests for additive interaction (P = 2 x 10^–10 and P = 1 x 10^–9) and multiplicative interaction (P = 0.01 and P = 0.01) were significant. Pooled analyses yielded a direct-effect odds ratio of 1.26 (95% confidence interval (CI): 1.19, 1.33; P = 2 x 10^–15) for rs8034191 and an indirect-effect odds ratio of 1.01 (95% CI: 1.00, 1.01; P = 0.09); the proportion of increased risk mediated by smoking was 3.2%. For rs1051730, direct- and indirect-effect odds ratios were 1.26 (95% CI: 1.19, 1.33; P = 1 x 10^–15) and 1.00 (95% CI: 0.99, 1.01; P = 0.22), respectively, with a proportion mediated of 2.3%. Adjustment for measurement error in smoking behavior allowing up to 75% measurement error increased the proportions mediated to 12.5% and 9.2%, respectively. These analyses indicate that the association of the variants with lung cancer operates primarily through other pathways.

Smoking Reduction at Midlife and Lifetime Mortality Risk in Men: A Prospective Cohort Study

Gerber, Y., Myers, V., Goldbourt, U. — Sat, 04 Feb 2012 01:10:31 +0000

Previous studies have not shown a survival advantage for smoking reduction. The authors assessed survival and life expectancy according to changes in smoking intensity in a cohort of Israeli working men. Baseline smokers recruited in 1963 were reassessed in 1965 (n = 4,633; mean age, 51 years) and followed up prospectively for mortality through 2005. Smoking intensity at both time points was self-reported and categorized as none, 1–10, 11–20, and ≥21 cigarettes per day. Change between smoking categories was noted, and participants were classified as increased (8%), maintained (65%), reduced (17%), or quit (10%) smoking. During a median follow-up of 26 (quartiles 1–3: 16–35) years, 87% of participants died. Changes in intensity were associated with survival. In multivariable-adjusted models, the hazard ratios for mortality were 1.14 (95% confidence interval (CI): 0.99, 1.32) among increasers, 0.85 (95% CI: 0.77, 0.95) among reducers, and 0.78 (95% CI: 0.69, 0.89) among quitters, compared with maintainers. Inversely, the adjusted odds ratios of surviving to age 80 years were 0.77 (95% CI: 0.60, 0.98), 1.22 (95% CI: 1.01, 1.47), and 1.33 (95% CI: 1.07, 1.66), respectively. The survival benefit associated with smoking reduction was mostly evident among heavy smokers and for cardiovascular disease mortality. These results suggest that decreasing smoking intensity should be considered as a risk-reduction strategy for heavy smokers who cannot quit abruptly.

Bayesian Posterior Distributions Without Markov Chains

Cole, S. R., Chu, H., Greenland, S., Hamra, G., Richardson, D. B. — Sat, 04 Feb 2012 01:10:31 +0000

Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984–1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.

Interaction and Exposure Modification: Are We Asking the Right Questions?

Weinberg, C. R. — Sat, 04 Feb 2012 01:10:30 +0000

Most diseases arise not purely through genetic abnormalities nor purely through environmental causes, but as "complex" conditions brought about by the combined effects of genetic susceptibility factors, nongenetic experiences and exposures, and bad luck. Finding simple models capable of both characterizing such joint effects and providing new insight into pathogenesis remains an ongoing challenge in etiologic epidemiology. Additive null models can capture certain pure forms of independent etiologic effects in studies of rare conditions and can be useful for predicting possible effects of interventions. The concept of exposure modification is here proposed as useful, particularly in thinking about biologic interactions between exposures and genetic variants. Openness to parsimonious joint models and the insights they can provide is key to advancing our understanding of etiology.

Endogenous Reproductive Hormones and C-reactive Protein Across the Menstrual Cycle: The BioCycle Study

Sat, 04 Feb 2012 01:10:30 +0000

C-reactive protein (CRP) is one of the most commonly used markers of acute phase reaction in clinical settings and predictors of cardiovascular risk in healthy women; however, data on its physiologic regulation in premenopausal women are sparse. The objective of this study was to evaluate the association between endogenous reproductive hormones and CRP in the BioCycle Study (2005–2007). Women aged 18–44 years from western New York were followed prospectively for up to 2 menstrual cycles (n = 259). Serum levels of CRP, estradiol, progesterone, luteinizing hormone, and follicle-stimulating hormone were measured up to 8 times per cycle, timed by fertility monitors. CRP levels varied significantly across the cycle (P < 0.001). More women were classified as being at elevated risk of cardiovascular disease (CRP, >3 mg/L) during menses compared with other phases (12.3% vs. 7.4%; P < 0.001). A 10-fold increase in estradiol was associated with a 24.3% decrease in CRP (95% confidence interval: 19.3, 29.0). A 10-fold increase in luteal progesterone was associated with a 19.4% increase in CRP (95% confidence interval: 8.4, 31.5). These results support the hypothesis that endogenous estradiol might have antiinflammatory effects and highlight the need for standardization of CRP measurement to menstrual cycle phase in reproductive-aged women.

Invited Commentary: Lost in Estimation–Searching for Alternatives to Markov Chains to Fit Complex Bayesian Models

Molitor, J. — Sat, 04 Feb 2012 01:10:29 +0000

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;00(0):000–000) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

A Causal Framework for Understanding the Effect of Losses to Follow-up on Epidemiologic Analyses in Clinic-based Cohorts: The Case of HIV-infected Patients on Antiretroviral Therapy in Africa

Sat, 04 Feb 2012 01:10:29 +0000

Although clinic-based cohorts are most representative of the "real world," they are susceptible to loss to follow-up. Strategies for managing the impact of loss to follow-up are therefore needed to maximize the value of studies conducted in these cohorts. The authors evaluated adult patients starting antiretroviral therapy at an HIV/AIDS clinic in Uganda, where 29% of patients were lost to follow-up after 2 years (January 1, 2004–September 30, 2007). Unweighted, inverse probability of censoring weighted (IPCW), and sampling-based approaches (using supplemental data from a sample of lost patients subsequently tracked in the community) were used to identify the predictive value of sex on mortality. Directed acyclic graphs (DAGs) were used to explore the structural basis for bias in each approach. Among 3,628 patients, unweighted and IPCW analyses found men to have higher mortality than women, whereas the sampling-based approach did not. DAGs encoding knowledge about the data-generating process, including the fact that death is a cause of being classified as lost to follow-up in this setting, revealed "collider" bias in the unweighted and IPCW approaches. In a clinic-based cohort in Africa, unweighted and IPCW approaches—which rely on the "missing at random" assumption—yielded biased estimates. A sampling-based approach can in general strengthen epidemiologic analyses conducted in many clinic-based cohorts, including those examining other diseases.

Re: "Longitudinal Health Study of us 1991 Gulf War Veterans: Changes in Health Status at 10-Year Follow-Up"

Delcher, C., Wang, Y. — Sat, 04 Feb 2012 01:10:29 +0000

Invited Commentary: The Action in the Interaction and Exposure Modification

Christiani, D. C. — Sat, 04 Feb 2012 01:10:29 +0000

The study of disease variability in populations is a goal of modern epidemiology. Because most common diseases arise out of a combination of factors and events (exposures, heritability, comorbidities, and chance), developing simple models of characterizing joint events is a daunting task. Dr. Weinberg argues successfully in this issue of the Journal (Am J Epidemiol. 2012;000(00):000–000) that additive null models can capture pure forms of independent causal effects in studies of rare conditions. Moreover, the concept of exposure modification, which characterizes most gene-environment interactions reported to date, is introduced. More cross-talk between biologists and epidemiologists is needed to tackle key issues in chronic disease etiology, and the argument for the use of parsimonious joint models in epidemiology is convincing.

The Authors Respond to "Lost in Estimation–Fitting Complex Bayesian Models"

Cole, S. R., Chu, H., Greenland, S., Hamra, G., Richardson, D. B. — Sat, 04 Feb 2012 01:10:29 +0000