Do Stress Trajectories Predict Mortality in Older Men? Longitudinal Findings from the VA Normative Aging Study

We examined long-term patterns of stressful life events (SLE) and their impact on mortality contrasting two theoretical models: allostatic load (linear relationship) and hormesis (inverted U relationship) in 1443 NAS men (aged 41–87 in 1985; M = 60.30, SD = 7.3) with at least two reports of SLEs over 18 years (total observations = 7,634). Using a zero-inflated Poisson growth mixture model, we identified four patterns of SLE trajectories, three showing linear decreases over time with low, medium, and high intercepts, respectively, and one an inverted U, peaking at age 70. Repeating the analysis omitting two health-related SLEs yielded only the first three linear patterns. Compared to the low-stress group, both the moderate and the high-stress groups showed excess mortality, controlling for demographics and health behavior habits, HRs = 1.42 and 1.37, ps <.01 and <.05. The relationship between stress trajectories and mortality was complex and not easily explained by either theoretical model

Examining the Joint Effect of Multiple Risk Factors Using Exposure Risk Profiles: Lung Cancer in Nonsmokers

Bac k g r o u n d: Profile regression is a Bayesian statistical approach designed for investigating the joint effect of multiple risk factors. It reduces dimensionality by using as its main unit of inference the exposure profiles of the subjects that is, the sequence of covariate values that correspond to each subject. Objectives: We applied profile regression to a case–control study of lung cancer in nonsmokers, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, to estimate the combined effect of environmental carcinogens and to explore possible gene–environment interactions. Me t h o d s: We tailored and extended the profile regression approach to the analysis of case–control studies, allowing for the analysis of ordinal data and the computation of posterior odds ratios. We compared and contrasted our results with those obtained using standard logistic regression and classification tree methods, including multifactor dimensionality reduction. Res u l t s: Profile regression strengthened previous observations in other study populations on the role of air pollutants, particularly particulate matter ≤ 10 μm in aerodynamic diameter (PM 10), in lung cancer for nonsmokers. Covariates including living on a main road, exposure to PM 10 and nitrogen dioxide, and carrying out manual work characterized high-risk subject profiles. Such combinations of risk factors were consistent with a priori expectations. In contrast, other methods gave less interpretable results. Con c l u s i o n s: We conclude that profile regression is a powerful tool for identifying risk profiles that express the joint effect of etiologically relevant variables in multifactorial diseases. Key w o r d s: air pollutants, Bayesian inference, clustering, combined effect, gene–environment interactions. Environ Health Perspect 119:84–91 (2011). doi:10.1289/ehp.1002118 [Onlin

Do Stress Trajectories Predict Mortality in Older Men? Longitudinal Findings from the VA Normative Aging Study

We examined long-term patterns of stressful life events (SLE) and their impact on mortality contrasting two theoretical models: allostatic load (linear relationship) and hormesis (inverted U relationship) in 1443 NASmen (aged 41–87 in 1985; M = 60.30, SD = 7.3) with at least two reports of SLEs over 18 years (total observations = 7,634). Using a zero-inflated Poisson growth mixture model, we identified four patterns of SLE trajectories, three showing linear decreases over time with low, medium, and high intercepts, respectively, and one an inverted U, peaking at age 70. Repeating the analysis omitting two health-related SLEs yielded only the first three linear patterns. Compared to the low-stress group, both the moderate and the high-stress groups showed excess mortality, controlling for demographics and health behavior habits, HRs = 1.42 and 1.37, ps < .01 and < .05. The relationship between stress trajectories and mortality was complex and not easily explained by either theoretical model

Reference-free cell mixture adjustments in analysis of DNA methylation data

MOTIVATION: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known. RESULTS: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets. AVAILABILITY and IMPLEMENTATION: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Oxford University Press. The published article can be found at: http://bioinformatics.oxfordjournals.org/

Modeling spatially varying compliance effects of PM2.5 exposure reductions on gestational diabetes mellitus in southern California: Results from electronic health record data of a large pregnancy cohort.

Gestational diabetes mellitus (GDM) is a major pregnancy complication affecting approximately 14.0% of pregnancies around the world. Air pollution exposure, particularly exposure to PM2.5, has become a major environmental issue affecting health, especially for vulnerable pregnant women. Associations between PM2.5 exposure and adverse birth outcomes are generally assumed to be the same throughout a large geographical area. However, the effects of air pollution on health can very spatially in subpopulations. Such spatially varying effects are likely due to a wide range of contextual neighborhood and individual factors that are spatially correlated, including SES, demographics, exposure to housing characteristics and due to different composition of particulate matter from different emission sources. This combination of elevated environmental hazards in conjunction with socioeconomic-based disparities forms what has been described as a double jeopardy for marginalized sub-populations. In this manuscript our analysis combines both an examination of spatially varying effects of a) unit-changes in exposure and examines effects of b) changes from current exposure levels down to a fixed compliance level, where compliance levels correspond to the Air Quality Standards (AQS) set by the U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality guideline values. Results suggest that exposure reduction policies should target certain hotspot areas where size and effects of potential reductions will reap the greatest rewards in terms of health benefits, such as areas of southeast Los Angeles County which experiences high levels of PM2.5 exposures and consist of individuals who may be particularly vulnerable to the effects of air pollution on the risk of GDM

Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other.Health Effects Institute (HEI), an organization jointly funded by the United States Environmental Protection Agency (EPA) (Assistance Award No. R-82811201

Clinical Trial Design - Effect of Prone Positioning on Clinical Outcomes in Infants and Children With Acute Respiratory Distress Syndrome

Purpose This paper describes the methodology of a clinical trial of prone positioning in pediatric patients with acute lung injury (ALI). Nonrandomized studies suggest that prone positioning improves oxygenation in patients with ALI/acute respiratory distress syndrome without the risk of serious iatrogenic injury. It is not known if these improvements in oxygenation result in improvements in clinical outcomes. A clinical trial was needed to answer this question. Materials and Methods The pediatric prone study is a multicenter, randomized, noncrossover, controlled clinical trial. The trial is designed to test the hypothesis that at the end of 28 days, children with ALI treated with prone positioning will have more ventilator-free days than children treated with supine positioning. Secondary end points include the time to recovery of lung injury, organ failure–free days, functional outcome, adverse events, and mortality from all causes. Pediatric patients, 42 weeks postconceptual age to 18 years of age, are enrolled within 48 hours of meeting ALI criteria. Patients randomized to the prone group are positioned prone within 4 hours of randomization and remain prone for 20 hours each day during the acute phase of their illness for a maximum of 7 days. Both groups are managed according to ventilator protocol, extubation readiness testing, and sedation protocols and hemodynamic, nutrition, and skin care guidelines. Conclusions This paper describes the process, multidisciplinary input, and procedures used to support the design of the clinical trial, as well as the challenges faced by the clinical scientists during the conduct of the clinical trial