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Background: While smokeless tobacco (ST) causes oral cancer and is associated with cardiovascular diseases, less is known about how its effects differ from other tobacco use. Biomarkers of potential harm (BOPH) can measure short-term health effects such as inflammation and oxidative stress. Methods: We compared BOPH concentrations [IL6, high-sensitivity C-reactive protein, fibrinogen, soluble intercellular adhesion molecule-1 (sICAM-1), and F2-isoprostane] across 3,460 adults in wave 1 of the Population Assessment of Tobacco and Health study (2013–2014) by tobacco use groups: primary ST users (current exclusive ST use among never smokers), secondary ST users (current exclusive ST use among former smokers), exclusive cigarette smokers, dual users of ST and cigarettes, former smokers, and never tobacco users. We estimated geometric mean ratios using never tobacco users, cigarette smokers, and former smokers as referents, adjusting for demographic and health conditions, creatinine (for F2-isoprostane), and pack-years in smoker referent models. Results: BOPH levels among primary ST users were similar to both never tobacco users and former smokers. Most BOPH levels were lower among ST users compared with current smokers. Compared with never tobacco users, dual users had significantly higher sICAM-1, IL6, and F2-isoprostane. However, compared with smokers, dual users had similar biomarker levels. Former smokers and secondary ST users had similar levels of all five biomarkers. Conclusions: ST users have lower levels of inflammatory and oxidative stress biomarkers than smokers. Impact: ST use alone and in combination with smoking may result in different levels of inflammatory and oxidative stress levels.

ObjectiveTo report on longitudinal tobacco product cessation rates, by product type, among adults (ages 18+ years) in the USA between 2013 and 2019.MethodsThe Population Assessment of Tobacco and Health Study, a nationally representative, longitudinal cohort study was used to report on annual and biennial rates of the following three cessation behaviours across 2013–2019: (1) discontinuing tobacco product use (ie, transition from past 30-day use to no past 30-day use), (2) attempting to quit tobacco product use and (3) quitting tobacco product use among those who attempted to quit. Each cessation behaviour was evaluated separately for cigarettes, electronic nicotine delivery systems (ENDS), cigars, hookah and smokeless tobacco. Generalised estimating equations were used to evaluate linear and nonlinear trends in cessation rates across the study period.ResultsBetween 2013 and 2019, rates of discontinuing cigarette smoking among adults in the USA statistically increased from 16% to 18%, though these were consistently lower than rates of discontinuing use of other tobacco products. Similarly, quit attempt rates and rates of quitting among attempters increased for cigarette smokers. However, rates of discontinuing ENDS use sharply declined across the study period, from 62% to 44%.ConclusionsFindings show that tobacco product cessation rates have been changing in recent years in the USA alongside the changing tobacco product marketplace and regulatory environment, though rates of discontinuing cigarette smoking remain relatively low. Findings can serve as a benchmark against which future cessation rates can be compared to evaluate the impacts of future tobacco regulatory policies.

Objectives To examine factors important to cigar smoking and subsequent nicotine exposure, we evaluated the impact of cigar type, cigarette smoking history, and inhalation behaviors on nicotine dependence, smoking topography, and biomarkers of exposure in current exclusive cigar smokers. Methods Adult, exclusive cigar smokers (N = 77) were recruited based on cigar type, cigarette smoking history, and self-reported inhalation behaviors. Participants smoked their own brand product ad libitum for up to one hour; dependence symptoms, smoking topography, and biomarkers of exposure were assessed. Results Cigar smokers showed low levels of dependence. Cigar smoking alleviated withdrawal and craving symptoms, increased plasma nicotine concentration, and increased exhaled CO. Multiple regression analyses indicate that inhalation behaviors were associated with increased dependence and greater reductions in withdrawal symptoms upon cigar smoking. Large cigar smokers smoked longer and smoked more tobacco than small cigar and cigarillo smokers. Furthermore, large cigar smokers and self-reported inhalers were exposed to more nicotine than small cigar smokers and non-inhalers. Conclusions Our study suggests that cigar type and smoking behaviors affect dependence and nicotine exposure upon cigar smoking. These findings provide additional insight into the substantial risks associated with cigar smoking.

BackgroundAt the population level, obesity has been reported to be positively associated with low-level chronic inflammation, and negatively associated with several indices of health-related quality of life (HRQOL). It is however not clear if obesity-associated inflammation is partly responsible for the observed negative associations between obesity and HRQOL. The present study investigates this question by testing the hypothesis that systemic inflammation is a mediator of the observed association between obesity and a specific HRQOL index called “healthy days”, as measured via a subset of the CDC HRQOL-4 questionnaire.MethodsDemographic, body mass index (BMI), C-reactive protein (CRP), inflammatory disease status, medication use, smoking, and HRQOL data were obtained from NHANES (2005-2008) and analyzed using sampling-weighted generalized linear models. Both main effects and interaction effects were analyzed to evaluate possible mediator-outcome confounding. Model robustness was tested via sensitivity analysis. Prior to model development, data was subjected to multiple imputation in order to mitigate information loss from survey non-response. Averaged results from the imputed datasets were reported in the form of odds ratios (OR) and confidence intervals (CI).ResultsObesity (BMI >30kg/m2) was positively associated with poor physical healthy days (OR: 1.59, 95% CI: 1.15-2.21) in unadjusted models. ‘Elevated’ and ‘clinically raised’ levels of the inflammation marker CRP were also positively associated with poor physical healthy days (OR= 1.61, 95% CI: 1.23-2.12, and OR= 2.45, 95% CI: 1.84-3.26, respectively); additionally ‘clinically raised’ CRP was positively associated with mental unhealthy days (OR= 1.66, 95% CI: 1.26-2.19). The association between obesity and physical HRQOL was rendered non-significant in models including CRP. Association between ‘elevated’ and ‘clinically raised’ CRP and physical unhealthy days remained significant even after adjustment for obesity or inflammation-modulating covariates (OR= 1.36, 95% CI :1.02-1.82, and OR= 1.75, 95% CI: 1.21-2.54, respectively).ConclusionsSystemic inflammation is a significant mediator of the association between obesity and physical unhealthy days. and is also an independent determinant of physical and mental unhealthy days. Importantly, elevated inflammation below the clinical threshold is also negatively associated with physical healthy days and may warrant more attention from a population health perspective than currently appreciated.

Background Obesity is positively associated with low-level chronic inflammation, and negatively associated with several indices of health-related quality of life (HRQOL). It is however not clear if obesity-associated inflammation is partly responsible for the observed negative associations between obesity and HRQOL, and also whether systemic inflammation independently affects HRQOL. We conducted an exploratory analysis to investigate the relationships between obesity, systemic inflammation and indices of HRQOL, using NHANES survey data. Methods Data for the variables of interest were available for 6325 adults (aged 20–75 years, BMI > 18.5 kg/m2). Demographic, body mass index (BMI), C-reactive protein (CRP), inflammatory disease status, medication use, smoking, and HRQOL data were obtained from NHANES (2005–2008) and analyzed using sampling-weighted generalized linear models. Data was subjected to multiple imputation in order to mitigate information loss from survey non-response. Both main effects and interaction effects were analyzed to evaluate possible mediation or moderation effects. Model robustness was ascertained via sensitivity analysis. Averaged results from the imputed datasets were reported in as odds ratios (OR) and confidence intervals (CI). Results Obesity was positively associated with poor physical healthy days (OR: 1.59, 95% CI: 1.15–2.21) in unadjusted models. ‘Elevated’ and ‘clinically raised’ levels of the inflammation marker CRP were also positively associated with poor physical healthy days (OR = 1.61, 95% CI: 1.23–2.12, and OR = 2.45, 95% CI: 1.84–3.26, respectively); additionally, ‘clinically raised’ CRP was positively associated with mental unhealthy days (OR = 1.66, 95% CI: 1.26–2.19). The association between obesity and physical HRQOL was rendered non-significant in models including CRP. Association between ‘elevated’ and ‘clinically raised’ CRP and physical unhealthy days remained significant even after adjustment for obesity or inflammation-modulating covariates (OR = 1.36, 95% CI: 1.02–1.82, and OR = 1.75, 95% CI: 1.21–2.54, respectively). Conclusions Systemic inflammation appears to mediate the association between obesity and physical unhealthy days. Clinically raised inflammation is an independent determinant of physical and mental unhealthy days. Importantly, elevated (but sub-clinical) inflammation is also negatively associated with physical healthy days, and may warrant more attention from a population health perspective than currently appreciated. Electronic supplementary material The online version of this article (10.1186/s40608-018-0196-2) contains supplementary material, which is available to authorized users.

This article is an incomplete review of the rapidly growing research on network models. It focuses chiefly upon contributions to network science that have been made by sociologists, physicists, and statisticians. It addresses several topics in the area of dynamic network modeling, which is perhaps the most challenging area of current active research. Several issues in dynamic network modeling are discussed in the context of two applications: baboon grooming networks and growth in the Wikipedia. WIREs Comp Stat 2012, 4:13–27. doi: 10.1002/wics.184 For further resources related to this article, please visit the WIREs website.

We introduce a class of spatiotemporal models for Gaussian areal data. These models assume a latent random field process that evolves through time with random field convolutions; the convolving fields follow proper Gaussian Markov random field (PGMRF) processes. At each time, the latent random field process is linearly related to observations through an observational equation with errors that also follow a PGMRF. The use of PGMRF errors brings modeling and computational advantages. With respect to modeling, it allows more flexible model structures such as different but interacting temporal trends for each region, as well as distinct temporal gradients for each region. Computationally, building upon the fact that PGMRF errors have proper density functions, we have developed an efficient Bayesian estimation procedure based on Markov chain Monte Carlo with an embedded forward information filter backward sampler (FIFBS) algorithm. We show that, when compared with the traditional one-at-a-time Gibbs sampler, o...

Genome-wide association (GWA) studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, for most of these variants the causal biological mechanisms remain unclear. By integrating GWA data with prior biological knowledge about pathways and functional networks, we sought to obtain novel insights into the causal processes of CAD. Using the iGSEA4GWAS analysis tool and the Reactome pathway database, we carried out a two-stage gene set enrichment analysis strategy. From a discovery cohort of seven large GWAS data sets for CAD (n=9,889 cases, 11,089 controls), nominally significant gene-sets were tested for replication in a meta-analysis of nine additional studies (n=15,502 cases, 55,730 controls) that were part of the CARDIoGRAM Consortium. A total of 32 of the 639 pathways tested representing 22 distinct biological processes showed convincing association with CAD (replication p< 0.05). After adjusting for redundancies by removing pathways with 50% or greater identity with other pathways, 19 pathways representing 9 biological processes remained including those relevant to Notch signaling, extracellular matrix integrity, innate immunity, and lipid metabolism. Network analysis of 751 unique genes within replicated pathways further revealed several interconnected functional modules representing novel associations (semaphorin regulated axonal guidance pathway) as well as confirmatory known processes (lipid metabolism). Our analyses point to potentially novel causal associations between CAD and several biological processes, many of which were not previously linked to CAD. These findings improve our understanding of the biological basis of CAD and highlight potential novel therapeutic targets.

We previously reported the findings from a genome-wide association study of the response of maximal oxygen uptake (V̇o2max) to an exercise program. Here we follow up on these results to generate hypotheses on genes, pathways, and systems involved in the ability to respond to exercise training. A systems biology approach can help us better establish a comprehensive physiological description of what underlies V̇o2maxtrainability. The primary material for this exploration was the individual single-nucleotide polymorphism (SNP), SNP-gene mapping, and statistical significance levels. We aimed to generate novel hypotheses through analyses that go beyond statistical association of single-locus markers. This was accomplished through three complementary approaches: 1) building de novo evidence of gene candidacy through informatics-driven literature mining; 2) aggregating evidence from statistical associations to link variant enrichment in biological pathways to V̇o2maxtrainability; and 3) predicting possible consequences of variants residing in the pathways of interest. We started with candidate gene prioritization followed by pathway analysis focused on overrepresentation analysis and gene set enrichment analysis. Subsequently, leads were followed using in silico analysis of predicted SNP functions. Pathways related to cellular energetics (pantothenate and CoA biosynthesis; PPAR signaling) and immune functions (complement and coagulation cascades) had the highest levels of SNP burden. In particular, long-chain fatty acid transport and fatty acid oxidation genes and sequence variants were found to influence differences in V̇o2maxtrainability. Together, these methods allow for the hypothesis-driven ranking and prioritization of genes and pathways for future experimental testing and validation.

Unparalleled technological advances have fueled an explosive growth in the scope and scale of biological data and have propelled life sciences into the realm of "Big Data" that cannot be managed or analyzed by conventional approaches. Big Data in the life sciences are driven primarily via a diverse collection of 'omics'-based technologies, including genomics, proteomics, metabolomics, transcriptomics, metagenomics, and lipidomics. Gene-set enrichment analysis is a powerful approach for interrogating large 'omics' datasets, leading to the identification of biological mechanisms associated with observed outcomes. While several factors influence the results from such analysis, the impact from the contents of pathway databases is often under-appreciated. Pathway databases often contain variously named pathways that overlap with one another to varying degrees. Ignoring such redundancies during pathway analysis can lead to the designation of several pathways as being significant due to high content-similarity, rather than truly independent biological mechanisms. Statistically, such dependencies also result in correlated p values and overdispersion, leading to biased results. We investigated the level of redundancies in multiple pathway databases and observed large discrepancies in the nature and extent of pathway overlap. This prompted us to develop the application, ReCiPa (Redundancy Control in Pathway Databases), to control redundancies in pathway databases based on user-defined thresholds. Analysis of genomic and genetic datasets, using ReCiPa-generated overlap-controlled versions of KEGG and Reactome pathways, led to a reduction in redundancy among the top-scoring gene-sets and allowed for the inclusion of additional gene-sets representing possibly novel biological mechanisms. Using obesity as an example, bioinformatic analysis further demonstrated that gene-sets identified from overlap-controlled pathway databases show stronger evidence of prior association to obesity compared to pathways identified from the original databases.