Rationale: The small conducting airways are the major site of airflow obstruction in chronic obstructive pulmonary disease and may precede emphysema development.Objectives: We hypothesized a novel computed tomography (CT) biomarker of small airway disease predicts FEV 1 decline.Methods: We analyzed 1,508 current and former smokers from COPDGene with linear regression to assess predictors of change in FEV 1 (ml/yr) over 5 years. Separate models for subjects without and with airflow obstruction were generated using baseline clinical and physiologic predictors in addition to two novel CT metrics created by parametric response mapping (PRM), a technique pairing inspiratory and expiratory CT images to define emphysema (PRM emph ) and functional small airways disease (PRM fSAD ), a measure of nonemphysematous air trapping.Measurements and Main Results: Mean (SD) rate of FEV 1 decline in ml/yr for GOLD (Global Initiative for Chronic Obstructive Lung Disease) 0-4 was as follows: 41.8 (47.7), 53.8 (57.1), 45.6 (61.1), 31.6 (43.6), and 5.1 (35.8), respectively (trend test for grades 1-4; P , 0.001). In multivariable linear regression, for participants without airflow obstruction, PRM fSAD but not PRM emph was associated with FEV 1 decline (P , 0.001). In GOLD 1-4 participants, both PRM fSAD and PRM emph were associated with FEV 1 decline (P , 0.001 and P = 0.001, respectively). Based on the model, the proportional contribution of the two CT metrics to FEV 1 decline, relative to each other, was 87% versus 13% and 68% versus 32% for PRM fSAD and PRM emph in GOLD 1/2 and 3/4, respectively.Conclusions: CT-assessed functional small airway disease and emphysema are associated with FEV 1 decline, but the association with functional small airway disease has greatest importance in mildto-moderate stage chronic obstructive pulmonary disease where the rate of FEV 1 decline is the greatest.Clinical trial registered with www.clinicaltrials.gov (NCT 00608764).

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Acute Respiratory Distress Syndrome (ARDS) and Acute Lung Injury (ALI) result in high permeability pulmonary edema causing hypoxic respiratory failure with high morbidity and mortality. As the population ages, the incidence of ALI is expected to rise. Over the last decade, several studies have identified biomarkers in plasma and bronchoalveolar lavage fluid providing important insights into the mechanisms involved in the pathophysiology of ALI. Several biomarkers have been validated in subjects from the large, multicenter ARDS clinical trials network. Despite these studies, no single or group of biomarkers has made it into routine clinical practice. New high throughput ‘omics’ techniques promise improved understanding of the biologic processes in the pathogenesis in ALI and possibly new biomarkers that predict disease and outcomes. In this article we review the current knowledge on biomarkers in ALI.

BackgroundOral taxa are often found in the chronic obstructive pulmonary disease (COPD) lung microbiota, but it is not clear if this is due to a physiologic process such as aspiration or experimental contamination at the time of specimen collection.MethodsMicrobiota samples were obtained from nine subjects with mild or moderate COPD by swabbing lung tissue and upper airway sites during lung lobectomy. Lung specimens were not contaminated with upper airway taxa since they were obtained surgically. The microbiota were analyzed with 16S rRNA gene qPCR and 16S rRNA gene hypervariable region 3 (V3) sequencing. Data analyses were performed using QIIME, SourceTracker, and R.ResultsStreptococcus was the most common genus in the oral, bronchial, and lung tissue samples, and multiple other taxa were present in both the upper and lower airways. Each subject’s own bronchial and lung tissue microbiota were more similar to each other than were the bronchial and lung tissue microbiota of two different subjects (permutation test, p = 0.0139), indicating more within-subject similarity than between-subject similarity at these two lung sites. Principal coordinate analysis of all subject samples revealed clustering by anatomic sampling site (PERMANOVA, p = 0.001), but not by subject. SourceTracker analysis found that the sources of the lung tissue microbiota were 21.1% (mean) oral microbiota, 8.7% nasal microbiota, and 70.1% unknown. An analysis using the neutral theory of community ecology revealed that the lung tissue microbiota closely reflects the bronchial, oral, and nasal microbiota (immigration parameter estimates 0.69, 0.62, and 0.74, respectively), with some evidence of ecologic drift occurring in the lung tissue.ConclusionThis is the first study to evaluate the mild-moderate COPD lung tissue microbiota without potential for upper airway contamination of the lung samples. In our small study of subjects with COPD, we found oral and nasal bacteria in the lung tissue microbiota, confirming that aspiration is a source of the COPD lung microbiota.