Dataset Information

Investigating gateway effects using the PATH study.

ABSTRACT: BACKGROUND:A recent meta-analysis of nine cohort studies in youths reported that baseline ever e-cigarette use strongly predicted cigarette smoking initiation in the next 6-18 months, with an adjusted odds ratio of 3.62 (95% confidence interval 2.42-5.41). A recent review of e-cigarettes agreed there was substantial evidence for this "gateway effect". However, the number of confounders considered in the studies was limited, so we investigated whether the effect might have resulted from inadequate adjustment, using Waves 1 and 2 of the Population Assessment of Tobacco and Health study. METHODS:Our main analyses considered Wave 1 never cigarette smokers who, at Wave 2, had information available on smoking initiation. We constructed a propensity score for ever e-cigarette use from Wave 1 variables, using this to predict ever cigarette smoking. Sensitivity analyses accounted for use of other tobacco products, linked current e-cigarette use to subsequent current smoking, or used propensity scores for ever smoking or ever tobacco product use as predictors. We also considered predictors using data from both waves to attempt to control for residual confounding from misclassified responses. RESULTS:Adjustment for propensity dramatically reduced the unadjusted odds ratio (OR) of 5.70 (4.33-7.50) to 2.48 (1.85-3.31), 2.47 (1.79-3.42) or 1.85 (1.35-2.53), whether adjustment was made as quintiles, as a continuous variable or for the individual variables. Additional adjustment for other tobacco products reduced this last OR to 1.59 (1.14-2.20). Sensitivity analyses confirmed adjustment removed most of the gateway effect. Control for residual confounding also reduced the association. CONCLUSIONS:We found that confounding is a major factor, explaining most of the observed gateway effect. However, our analyses are limited by small numbers of new smokers considered and the possibility of over-adjustment if taking up e-cigarettes affects some predictor variables. Further analyses are intended using Wave 3 data which should avoid these problems.

SUBMITTER: Lee P

PROVIDER: S-EPMC6950312 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Investigating gateway effects using the PATH study.

Lee Peter P Fry John J

F1000Research 20190307

<h4>Background</h4>A recent meta-analysis of nine cohort studies in youths reported that baseline ever e-cigarette use strongly predicted cigarette smoking initiation in the next 6-18 months, with an adjusted odds ratio of 3.62 (95% confidence interval 2.42-5.41). A recent review of e-cigarettes agreed there was substantial evidence for this "gateway effect". However, the number of confounders considered in the studies was limited, so we investigated whether the effect might have resulted from ...[more]

PMID: 31956397

Similar Datasets

Project description:High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of "gateway" nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes.By mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus.This research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network.

Dataset Information

Investigating gateway effects using the PATH study.

Publications

Investigating gateway effects using the PATH study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure