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.

Non-cigarette tobacco products, aryl-hydrocarbon receptor repressor gene methylation and smoking-related health outcomes.

INTRODUCTION: Cigarette smoking leads to altered DNA methylation at the aryl-hydrocarbon receptor repressor (AHRR) gene. However, it remains unknown whether pipe or cigar smoking is associated with AHRR methylation. We evaluated associations of non-cigarette tobacco use with AHRR methylation and determined if AHRR methylation was associated with smoking-related health outcomes. METHODS: Data were pooled across four population-based cohorts that enrolled participants from 1985 to 2002. Tobacco exposures were evaluated using smoking questionnaires. AHRR cg05575921 methylation was measured in peripheral blood leucocyte DNA. Spirometry and respiratory symptoms were evaluated at the time of methylation measurements and in subsequent visits. Vital status was monitored using the National Death Index. RESULTS: Among 8252 adults (mean age 56.7±10.3 years, 58.1% women, 40.6% black), 4857 (58.9%) participants used cigarettes and 634 (7.7%) used non-cigarette tobacco products. Exclusive use of non-cigarette tobacco products was independently associated with lower AHRR methylation (-2.44 units, 95% CI -4.42 to -0.45), though to a lesser extent than exclusive use of cigarettes (-6.01 units, 95% CI -6.01 to -4.10). Among participants who exclusively used non-cigarette tobacco products, reduced AHRR methylation was associated with increased respiratory symptom burden (OR 1.60, 95% CI 1.03 to 2.68) and higher all-cause mortality (log-rank p=0.02). CONCLUSION: Pipe and cigar smoking were independently associated with lower AHRR methylation in a multiethnic cohort of US adults. Among users of non-cigarette tobacco products, lower AHRR methylation was associated with poor respiratory health outcomes and increased mortality. AHRR methylation may identify non-cigarette tobacco users with an increased risk of adverse smoking-related health outcomes.

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.

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.

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.

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.

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.

Clinical Impact of Telomere Length Testing for Interstitial Lung Disease.

BACKGROUND: Shortened telomere length (TL) is a genomic risk factor for fibrotic interstitial lung disease (ILD), but its role in clinical management is unknown. RESEARCH QUESTION: What is the clinical impact of TL testing on the management of ILD? STUDY DESIGN AND METHODS: Patients were evaluated in the Columbia University ILD clinic and underwent Clinical Laboratory Improvement Amendments-certified TL testing by flow cytometry and fluorescence in situ hybridization (FlowFISH) as part of clinical treatment. Short TL was defined as below the 10th age-adjusted percentile for either granulocytes or lymphocytes by FlowFISH. Patients were offered genetic counseling and testing if they had short TL or a family history of ILD. FlowFISH TL was compared with research quantitative polymerase chain reaction (qPCR) TL measurement. RESULTS: A total of 108 patients underwent TL testing, including those with clinical features of short telomere syndrome such as familial pulmonary fibrosis (50%) or extrapulmonary manifestations in the patient (25%) or a relative (41%). The overall prevalence of short TL was 46% and was similar across clinical ILD diagnoses. The number of short telomere clinical features was independently associated with detecting short TL (OR, 2.00; 95% CI, 1.27-3.32). TL testing led to clinical treatment changes for 35 patients (32%), most commonly resulting in reduction or avoidance of immunosuppression. Of the patients who underwent genetic testing (n = 34), a positive or candidate diagnostic finding in telomere-related genes was identified in 10 patients (29%). Inclusion of TL testing below the 1st percentile helped reclassify eight of nine variants of uncertain significance into actionable findings. The quantitative polymerase chain reaction test correlated with FlowFISH, but age-adjusted percentile cutoffs may not be equivalent between the two assays. INTERPRETATION: Incorporating TL testing in ILD impacted clinical management and led to the discovery of new actionable genetic variants.

Extracellular Vesicle-Encapsulated microRNAs and Respiratory Health Among American Indians in the Strong Heart Study.

BACKGROUND: American Indian populations have experienced marked disparities in respiratory disease burden. Extracellular vesicle-encapsulated microRNAs (EV-miRNAs) are a novel class of biomarkers that may improve recognition of lung damage in indigenous populations. RESEARCH QUESTION: Are plasma EV-miRNAs viable biomarkers of respiratory health in American Indian populations? STUDY DESIGN AND METHODS: The Strong Heart Study (SHS) is a prospective cohort study that enrolled American Indians aged 45 to 74 years. EV-miRNA expression was measured in plasma (1993-1995). Respiratory health outcomes, including pre-bronchodilator FEV1, FVC, and respiratory symptom burden, were ascertained in the same study visit. Club cell secretory protein (CC-16), an antiinflammatory pneumoprotein implicated in COPD pathogenesis, was measured in serum. Linear and logistic regression were used for statistical analyses. Biological pathway analyses were used to elucidate gene targets of significant EV-miRNAs. RESULTS: Among 853 American Indian adults, three EV-miRNAs were associated with FEV1, four EV-miRNAs were associated with FVC, and one EV-miRNA was associated with FEV1/FVC (P < .05). Increased miR-1294 expression was associated with higher odds of airflow limitation (OR, 1.29; 95% CI, 1.07-1.55), whereas increased expression of miR-1294 (OR, 1.32; 95% CI, 1.07-1.63) and miR-532-5p (OR, 1.57; 95% CI, 1.02-2.40) was associated with higher odds of restriction. Increased miR-451a expression was associated with lower odds of exertional dyspnea (OR, 0.71; 95% CI, 0.59-0.85). Twenty-two EV-miRNAs were associated with serum CC-16 levels (q < 0.05), suggesting that EV-miRNAs may play a role in the pathway linking CC-16 to COPD pathogenesis. A pathway analysis showed key EV-miRNAs targeted biological pathways that modulate inflammation, immunity, and structural integrity in the lungs. INTERPRETATION: Circulating EV-miRNAs are novel mechanistic biomarkers of respiratory health and may facilitate the early detection and treatment of lung damage in American Indian populations that have been disproportionately affected by chronic lung diseases.

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.