Molecular mechanisms of environmental exposures and human disease.

A substantial proportion of disease risk for common complex disorders is attributable to environmental exposures and pollutants. An appreciation of how environmental pollutants act on our cells to produce deleterious health effects has led to advances in our understanding of the molecular mechanisms underlying the pathogenesis of chronic diseases, including cancer and cardiovascular, neurodegenerative and respiratory diseases. Here, we discuss emerging research on the interplay of environmental pollutants with the human genome and epigenome. We review evidence showing the environmental impact on gene expression through epigenetic modifications, including DNA methylation, histone modification and non-coding RNAs. We also highlight recent studies that evaluate recently discovered molecular processes through which the environment can exert its effects, including extracellular vesicles, the epitranscriptome and the mitochondrial genome. Finally, we discuss current challenges when studying the exposome - the cumulative measure of environmental influences over the lifespan - and its integration into future environmental health research.

Multivariable regression: understanding one of medicine's most fundamental statistical tools.

Multivariable regression is a fundamental tool that drives observational research in orthopaedic surgery. However, regression analyses are not always implemented correctly. This study presents a basic overview of regression analyses and reviews frequent points of confusion. Topics include linear, logistic, and time-to-event regressions, causal inference, confounders, overfitting, missing data, multicollinearity, interactions, and key differences between multivariable versus multivariate regression. The goal is to provide clarity regarding the use and interpretation of multivariable analyses for those attempting to increase their statistical literacy in orthopaedic research.

Unsupervised machine learning methods and emerging applications in healthcare.

Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.

Extracellular Vesicle-Encapsulated microRNAs as Novel Biomarkers of Lung Health.

Rationale: Early detection of respiratory diseases is critical to facilitate delivery of disease-modifying interventions. Extracellular vesicle-enriched microRNAs (EV-miRNAs) may represent reliable markers of early lung injury. Objectives: Evaluate associations of plasma EV-miRNAs with lung function. Methods: The prospective NAS (Normative Aging Study) collected plasma EV-miRNA measurements from 1996-2015 and spirometry every 3-5 years through 2019. Associations of EV-miRNAs with baseline lung function were modeled using linear regression. To complement the individual miRNA approach, unsupervised machine learning was used to identify clusters of participants with distinct EV-miRNA profiles. Associations of EV-miRNA profiles with multivariate latent longitudinal lung function trajectories were modeled using log binomial regression. Biological functions of significant EV-miRNAs were explored using pathway analyses. Results were replicated in an independent sample of NAS participants and in the HEALS (Health Effects of Arsenic Longitudinal Study). Measurements and Main Results: In the main cohort of 656 participants, 51 plasma EV-miRNAs were associated with baseline lung function (false discovery rate-adjusted P value < 0.05), 28 of which were replicated in the independent NAS sample and/or in the HEALS cohort. A subset of participants with distinct EV-miRNA expression patterns had increased risk of declining lung function over time, which was replicated in the independent NAS sample. Significant EV-miRNAs were shown in pathway analyses to target biological pathways that regulate respiratory cellular immunity, the lung inflammatory response, and airway structural integrity. Conclusions: Plasma EV-miRNAs may represent a robust biomarker of subclinical lung injury and may facilitate early identification and treatment of patients at risk of developing overt lung disease.

Fragility Part I: a guide to understanding statistical power.

The aim of this paper is to close the knowledge-to-practice gap around statistical power. We demonstrate how four factors affect power: p value, effect size, sample size, and variance. This article further delves into the advantages and disadvantages of a priori versus post hoc power analyses, though we believe only understanding of the former is essential to addressing the present-day issue of reproducibility in research. Upon reading this paper, physician-scientists should have expanded their arsenal of statistical tools and have the necessary context to understand statistical fragility.

Predicting risk of lung function impairment and all-cause mortality using a DNA methylation-based classifier of tobacco smoke exposure.

BACKGROUND: The Epigenetic Smoking Status Estimator (EpiSmokEr) predicts smoking phenotypes based on DNA methylation at 121 CpG sites. OBJECTIVE: Evaluate associations of EpiSmokEr-predicted versus self-reported smoking phenotypes with lung function and all-cause mortality in a cohort of older adults. METHODS: The prospective Normative Aging Study collected DNA methylation measurements from 1999 to 2012 with follow-up through 2016. The R package EpiSmokEr derived predicted smoking phenotypes based on DNA methylation levels assayed by the Illumina HumanMethylation450 Beadchip. Spirometry was collected every 3-5 years. Airflow limitation was defined as forced expiratory volume in 1 s/forced vital capacity <0.7. Vital status was monitored through periodic mailings. RESULTS: Among 784 participants contributing 5414 person-years of follow-up, the EpiSmokEr-predicted smoking phenotypes matched the self-reported phenotypes for 228 (97%) never smokers and 22 (71%) current smokers. In contrast, EpiSmokEr classified 407 (79%) self-reported former smokers as never smokers. Nonetheless, the EpiSmokEr-predicted former smoking phenotype was more strongly associated with incident airflow limitation (hazard ratio [HR] = 3.15, 95% confidence interval [CI] = 1.50-6.59) and mortality (HR = 2.11, 95% CI = 1.56-2.85) compared to the self-reported former smoking phenotype (airflow limitation: HR = 2.21, 95% CI = 1.13-4.33; mortality: HR = 1.08, 95% CI = 0.86-1.36). Risk of airflow limitation and death did not differ among self-reported never smokers and former smokers who were classified as never smokers. The discriminative accuracy of EpiSmokEr-predicted phenotypes for incident airflow limitation and mortality was improved compared to self-reported phenotypes. CONCLUSIONS: The DNA methylation-based EpiSmokEr classifier may be a useful surrogate of smoking-induced lung damage and may identify former smokers most at risk of adverse smoking-related health effects.

Lung function impairment and risk of incident heart failure: the NHLBI Pooled Cohorts Study.

AIMS: The aim is to evaluate associations of lung function impairment with risk of incident heart failure (HF). METHODS AND RESULTS: Data were pooled across eight US population-based cohorts that enrolled participants from 1987 to 2004. Participants with self-reported baseline cardiovascular disease were excluded. Spirometry was used to define obstructive [forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) <0.70] or restrictive (FEV1/FVC ≥0.70, FVC <80%) lung physiology. The incident HF was defined as hospitalization or death caused by HF. In a sub-set, HF events were sub-classified as HF with reduced ejection fraction (HFrEF; EF <50%) or preserved EF (HFpEF; EF ≥50%). The Fine-Gray proportional sub-distribution hazards models were adjusted for sociodemographic factors, smoking, and cardiovascular risk factors. In models of incident HF sub-types, HFrEF, HFpEF, and non-HF mortality were treated as competing risks. Among 31 677 adults, there were 3344 incident HF events over a median follow-up of 21.0 years. Of 2066 classifiable HF events, 1030 were classified as HFrEF and 1036 as HFpEF. Obstructive [adjusted hazard ratio (HR) 1.17, 95% confidence interval (CI) 1.07-1.27] and restrictive physiology (adjusted HR 1.43, 95% CI 1.27-1.62) were associated with incident HF. Obstructive and restrictive ventilatory defects were associated with HFpEF but not HFrEF. The magnitude of the association between restrictive physiology and HFpEF was similar to associations with hypertension, diabetes, and smoking. CONCLUSION: Lung function impairment was associated with increased risk of incident HF, and particularly incident HFpEF, independent of and to a similar extent as major known cardiovascular risk factors.

Environmental Exposures and Extracellular Vesicles: Indicators of Systemic Effects and Human Disease.

PURPOSE OF REVIEW: Environmental pollutants contribute to the pathogenesis of numerous diseases including chronic cardiovascular, respiratory, and neurodegenerative diseases, among others. Emerging evidence suggests that extracellular vesicles (EVs) may mediate the association of environmental exposures with chronic diseases. The purpose of this review is to describe the impact of common environmental exposures on EVs and their role in linking environmental pollutants to the pathogenesis of chronic systemic diseases. RECENT FINDINGS: Common environmental pollutants including particulate matter, tobacco smoke, and chemical pollutants trigger the release of EVs from multiple systems in the body. Existing research has focused primarily on air pollutants, which alter EV production and release in the lungs and systemic circulation. Air pollutants also impact the selective loading of EV cargo including microRNA and proteins, which modify the cellular function in recipient cells. As a result, pollutant-induced EVs often contribute to a pro-inflammatory and pro-thrombotic milieu, which increases the risk of pollutant-related diseases including obstructive lung diseases, cardiovascular disease, neurodegenerative diseases, and lung cancer. Common environmental exposures are associated with multifaceted changes in EVs that lead to functional alterations in recipient cells and contribute to the pathogenesis of chronic systemic diseases. EVs may represent emerging targets for the prevention and treatment of diseases that stem from environmental exposures. However, novel research is required to expand our knowledge of the biological action of EV cargo, elucidate determinants of EV release, and fully understand the impact of environmental pollutants on human health.

Environmental Exposures and Lung Aging: Molecular Mechanisms and Implications for Improving Respiratory Health.

PURPOSE OF REVIEW: Inhaled environmental exposures cause over 12 million deaths per year worldwide. Despite localized efforts to reduce environmental exposures, tobacco smoking and air pollution remain the urgent public health challenges that are contributing to the growing prevalence of respiratory diseases. The purpose of this review is to describe the mechanisms through which inhaled environmental exposures accelerate lung aging and cause overt lung disease. RECENT FINDINGS: Environmental exposures related to fossil fuel and tobacco combustion and occupational exposures related to silica and coal mining generate oxidative stress and inflammation in the lungs. Sustained oxidative stress causes DNA damage, epigenetic instability, mitochondrial dysfunction, and cell cycle arrest in key progenitor cells in the lung. As a result, critical repair mechanisms are impaired, leading to premature destruction of the lung parenchyma. Inhaled environmental exposures accelerate lung aging by injuring the lungs and damaging the cells responsible for wound healing. Interventions that minimize exposure to noxious antigens are critical to improve lung health, and novel research is required to expand our knowledge of therapies that may slow or prevent premature lung aging.