Dysanapsis Genetic Risk Predicts Lung Function Across the Lifespan.

Rationale Dysanapsis refers to a mismatch between airway tree caliber and lung size arising early in life. Dysanapsis assessed by computed tomography (CT) is evident by early adulthood and associated with chronic obstructive pulmonary disease (COPD) risk later in life. Objective By examining the genetic factors associated with CT-assessed dysanapsis, we aimed to elucidate its molecular underpinnings and physiological significance across the lifespan. Methods We performed a genome-wide association study (GWAS) of CT-assessed dysanapsis in 11,951 adults, including individuals from two population-based and two COPD-enriched studies. We applied colocalization analysis to integrate GWAS and gene expression data from whole blood and lung. Genetic variants associated with dysanapsis were combined into a genetic risk score that was applied to examine association with lung function in children from a population-based birth cohort (n=1,278) and adults from the UK Biobank (n=369,157). Measurements and Main Results CT-assessed dysanapsis was associated with genetic variants from 21 independent signals in 19 gene regions, implicating HHIP, DSP, and NPNT as potential molecular targets based on colocalization of their expression. Higher dysanapsis genetic risk score was associated with obstructive spirometry among 5 year old children and among adults in the 5th, 6th and 7th decades of life. Conclusions CT-assessed dysanapsis is associated with variation in genes previously implicated in lung development and dysanapsis genetic risk is associated with obstructive lung function from early life through older adulthood. Dysanapsis may represent an endo-phenotype link between the genetic variations associated with lung function and COPD.

A common polymorphism in the Intelectin-1 gene influences mucus plugging in severe asthma.

By incompletely understood mechanisms, type 2 (T2) inflammation present in the airways of severe asthmatics drives the formation of pathologic mucus which leads to airway mucus plugging. Here we investigate the molecular role and clinical significance of intelectin-1 (ITLN-1) in the development of pathologic airway mucus in asthma. Through analyses of human airway epithelial cells we find that ITLN1 gene expression is highly induced by interleukin-13 (IL-13) in a subset of metaplastic MUC5AC+ mucus secretory cells, and that ITLN-1 protein is a secreted component of IL-13-induced mucus. Additionally, we find ITLN-1 protein binds the C-terminus of the MUC5AC mucin and that its deletion in airway epithelial cells partially reverses IL-13-induced mucostasis. Through analysis of nasal airway epithelial brushings, we find that ITLN1 is highly expressed in T2-high asthmatics, when compared to T2-low children. Furthermore, we demonstrate that both ITLN-1 gene expression and protein levels are significantly reduced by a common genetic variant that is associated with protection from the formation of mucus plugs in T2-high asthma. This work identifies an important biomarker and targetable pathways for the treatment of mucus obstruction in asthma.

Asthma control in the United States: Relationships between short-acting ß2-agonist and systemic corticosteroid use.

BACKGROUND: Asthma control assessment is based on impairment (current symptoms) and risk (exacerbation history). OBJECTIVE: To understand the extent of uncontrolled asthma, we assessed relationships between prescription fills for systemic corticosteroids (SCS) and short-acting ß2-agonists (SABA) as risk and impairment markers, respectively. METHODS: Annual SCS and SABA fills among US patients with asthma were evaluated by a retrospective analysis of the IQVIA Longitudinal Access and Adjudication Data. Patients' disease severity was assigned based on the Global Initiative for Asthma step-therapy level. Exacerbations were evaluated by SCS fills within 12 months of a first asthma prescription fill. Uncontrolled asthma was defined as 2 or more SCS and/or 3 or more SABA fills annually. Individual patient relationships between SCS and SABA fills were assessed using Pearson's correlation coefficients. RESULTS: A total of 4,506,527 patients were included; 15.1% had 2 or more SCS fills, 29.1% had 3 or more SABA fills, and 37.4% fulfilled either or both criteria. If only SCS use was assessed, 21.4% of cases that were treated as mild to moderate and 27.6% that were treated as severe asthma would have been misclassified as controlled. If only SABA use was evaluated, 7.8% of cases treated as mild to moderate and 11.2% treated as severe asthma would have been misclassified. Overall, 80.9% of uncontrolled asthma occurred in patients treated for mild to moderate disease. Among patients with 2 or more SCS fills, the mean SABA fills were 2.9; the correlation between SCS and SABA fills per patient was significant but weak (r = 0.18; P < .001). CONCLUSION: High symptom burden and SCS exposures are not limited to severe asthma but are also characteristic of patients treated for mild to moderate disease. Both impairment and risk assessments are required to understand the full extent of uncontrolled asthma across disease severities.

Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes.

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Utility of eosinophil peroxidase as a biomarker of eosinophilic inflammation in asthma.

BACKGROUND: The relative utility of eosinophil peroxidase (EPX) and blood and sputum eosinophil counts as disease biomarkers in asthma is uncertain. OBJECTIVE: We sought to determine the utility of EPX as a biomarker of systemic and airway eosinophilic inflammation in asthma. METHODS: EPX protein was measured by immunoassay in serum and sputum in 110 healthy controls to establish a normal reference range and in repeated samples of serum and sputum collected during 3 years of observation in 480 participants in the Severe Asthma Research Program 3. RESULTS: Over 3 years, EPX levels in patients with asthma were higher than normal in 27% to 31% of serum samples and 36% to 53% of sputum samples. Eosinophils and EPX correlated better in blood than in sputum (rs values of 0.74 and 0.43, respectively), and high sputum EPX levels occurred in 27% of participants with blood eosinophil counts less than 150 cells/µL and 42% of participants with blood eosinophil counts between 150 and 299 cells/µL. Patients with persistently high sputum EPX values for 3 years were characterized by severe airflow obstruction, frequent exacerbations, and high mucus plug scores. In 59 patients with asthma who started mepolizumab during observation, serum EPX levels normalized in 96% but sputum EPX normalized in only 49%. Lung function remained abnormal even when sputum EPX normalized. CONCLUSIONS: Serum EPX is a valid protein biomarker of systemic eosinophilic inflammation in asthma, and sputum EPX levels are a more sensitive biomarker of airway eosinophilic inflammation than sputum eosinophil counts. Eosinophil measures in blood frequently miss airway eosinophilic inflammation, and mepolizumab frequently fails to normalize airway eosinophilic inflammation even though it invariably normalizes systemic eosinophilic inflammation.

A novel DNase assay reveals low DNase activity in severe asthma.

Secreted deoxyribonucleases (DNases), such as DNase-I and DNase-IL3, degrade extracellular DNA, and endogenous DNases have roles in resolving airway inflammation and guarding against autoimmune responses to nucleotides. Subsets of patients with asthma have high airway DNA levels, but information about DNase activity in health and in asthma is lacking. To characterize DNase activity in health and in asthma, we developed a novel kinetic assay using a Taqman probe sequence that is quickly cleaved by DNase-I to produce a large product signal. We used this kinetic assay to measure DNase activity in sputum from participants in the Severe Asthma Research Program (SARP)-3 (n = 439) and from healthy controls (n = 89). We found that DNase activity was lower than normal in asthma [78.7 relative fluorescence units (RFU)/min vs. 120.4 RFU/min, P < 0.0001]. Compared to patients with asthma with sputum DNase activity in the upper tertile activity levels, those in the lower tertile of sputum DNase activity were characterized clinically by more severe disease and pathologically by airway eosinophilia and airway mucus plugging. Carbamylation of DNase-I, a post-translational modification that can be mediated by eosinophil peroxidase, inactivated DNase-I. In summary, a Taqman probe-based DNase activity assay uncovers low DNase activity in the asthma airway that is associated with more severe disease and airway mucus plugging and may be caused, at least in part, by eosinophil-mediated carbamylation.NEW & NOTEWORTHY We developed a new DNase assay and used it to show that DNase activity is impaired in asthma airways.

Multivariate Cluster Analyses to Characterize Asthma Heterogeneity and Benralizumab Responsiveness.

BACKGROUND: An improved understanding of how severe asthma heterogeneity affects response could inform treatment decisions. OBJECTIVES: Characterize heterogeneity and benralizumab responsiveness in patients grouped by predefined Severe Asthma Research Program clusters using a multivariate approach. METHODS: In post-hoc analyses of the randomized, double-blind, placebo-controlled phase III SIROCCO (NCT01928771) and CALIMA (NCT01914757) studies, patients with severe asthma who received benralizumab or placebo were assigned to clusters using an established discriminant function to analyze 11 clinical characteristics simultaneously. The annualized asthma exacerbation rate, exacerbation incidence, and lung function were analyzed across clusters. RESULTS: Patients (n = 2,281) met criteria for four of five clusters: cluster 2 (early-onset moderate asthma, n = 393), cluster 4 (early-onset severe asthma, n = 386), cluster 3 (late-onset severe asthma, n = 641), and cluster 5 (late-onset severe, obstructed asthma, n = 861); no patients met cluster 1 criteria. Exacerbation rate reductions were significant in late-onset severe asthma (-48% [95% CI, -61% to -31%]; P < .0001) and late-onset severe, obstructed asthma (-50% [95% CI, -59% to -38%]; P < .0001), with nonsignificant reductions in early-onset clusters. These differences could not be fully explained by blood eosinophil count differences. Values for improvements in FEV1 were significant in late-onset severe asthma (+133 mL [95% CI, 66-200]; P = .0001) and late-onset severe, obstructed asthma (+160 mL [95% CI, 85-235]; P < .0001) while maintaining acute bronchodilator responsiveness. CONCLUSIONS: Benralizumab reduced exacerbations and improved lung function, primarily in late-onset asthma clusters. This multivariate approach to identify subphenotypes, potentially reflecting pathobiological mechanisms, can guide therapy beyond univariate approaches.

Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.

BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes. OBJECTIVES: To further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data. METHODS: We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We used k-prototype unsupervised ML to develop a clustering model where predictors were age, sex, race, body mass index, prebronchodilator and postbronchodilator spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated through LightGBM's supervised ML approach on their cross-validated F1 score to support their distinctiveness. RESULTS: Data from 13,498 patients with asthma with available postbronchodilator spirometry measurements were extracted to identify 5 stable clusters. Cluster 1 included a young nonsevere asthma population with normal lung function and higher frequency of acute exacerbation (0.8 /patient-year). Cluster 2 had the highest body mass index (mean ± SD, 44.44 ± 7.83 kg/m2), and the highest proportion of females (77.5%) and Blacks (28.9%). Cluster 3 comprised patients with normal lung function. Cluster 4 included patients with lower percent of predicted FEV1 of 77.03 (12.79) and poor response to bronchodilators. Cluster 5 had the lowest percent of predicted FEV1 of 68.08 (15.02), the highest postbronchodilator reversibility, and the highest proportion of severe asthma (44.9%) and blood eosinophilia (>300 cells/µL) (34.8%). CONCLUSIONS: Using real-world data and unsupervised ML, we classified asthma into 5 clinically important subphenotypes where group-specific asthma treatment and management strategies can be designed and deployed.

Protein-Protein interactive networks identified in bronchoalveolar lavage of severe compared to nonsevere asthma.

INTRODUCTION: Previous bronchoalveolar lavage fluid (BALF) proteomic analysis has evaluated limited numbers of subjects for only a few proteins of interest, which may differ between asthma and normal controls. Our objective was to examine a more comprehensive inflammatory biomarker panel in quantitative proteomic analysis for a large asthma cohort to identify molecular phenotypes distinguishing severe from nonsevere asthma. METHODS: Bronchoalveolar lavage fluid from 48 severe and 77 nonsevere adult asthma subjects were assessed for 75 inflammatory proteins, normalized to BALF total protein concentration. Validation of BALF differences was sought through equivalent protein analysis of autologous sputum. Subjects' data, stratified by asthma severity, were analysed by standard statistical tests, principal component analysis and 5 machine learning algorithms. RESULTS: The severe group had lower lung function and greater health care utilization. Significantly increased BALF proteins for severe asthma compared to nonsevere asthma were fibroblast growth factor 2 (FGF2), TGFα, IL1Ra, IL2, IL4, CCL8, CCL13 and CXCL7 and significantly decreased were platelet-derived growth factor a-a dimer (PDGFaa), vascular endothelial growth factor (VEGF), interleukin 5 (IL5), CCL17, CCL22, CXCL9 and CXCL10. Four protein differences were replicated in sputum. FGF2, PDGFaa and CXCL7 were independently identified by 5 machine learning algorithms as the most important variables for discriminating severe and nonsevere asthma. Increased and decreased proteins identified for the severe cluster showed significant protein-protein interactions for chemokine and cytokine signalling, growth factor activity, and eosinophil and neutrophil chemotaxis differing between subjects with severe and nonsevere asthma. CONCLUSION: These inflammatory protein results confirm altered airway remodelling and cytokine/chemokine activity recruiting leukocytes into the airways of severe compared to nonsevere asthma as important processes even in stable status.

Loss of Airway Phylogenetic Diversity Is Associated with Clinical and Pathobiological Markers of Disease Development in Chronic Obstructive Pulmonary Disease.

Rationale: The airway microbiome has the potential to shape chronic obstructive pulmonary disease (COPD) pathogenesis, but its relationship to outcomes in milder disease is unestablished. Objectives: To identify sputum microbiome characteristics associated with markers of COPD in participants of the Subpopulations and Intermediate Outcome Measures of COPD Study (SPIROMICS). Methods: Sputum DNA from 877 participants was analyzed using 16S ribosomal RNA gene sequencing. Relationships between baseline airway microbiota composition and clinical, radiographic, and mucoinflammatory markers, including longitudinal lung function trajectory, were examined. Measurements and Main Results: Participant data represented predominantly milder disease (Global Initiative for Chronic Obstructive Lung Disease stage 0-2 obstruction in 732 of 877 participants). Phylogenetic diversity (i.e., range of different species within a sample) correlated positively with baseline lung function, decreased with higher Global Initiative for Chronic Obstructive Lung Disease stage, and correlated negatively with symptom burden, radiographic markers of airway disease, and total mucin concentrations (P < 0.001). In covariate-adjusted regression models, organisms robustly associated with better lung function included Alloprevotella, Oribacterium, and Veillonella species. Conversely, lower lung function, greater symptoms, and radiographic measures of small airway disease were associated with enrichment in members of Streptococcus, Actinobacillus, Actinomyces, and other genera. Baseline sputum microbiota features were also associated with lung function trajectory during SPIROMICS follow-up (stable/improved, decline, or rapid decline groups). The stable/improved group (slope of FEV1 regression ⩾66th percentile) had greater bacterial diversity at baseline associated with enrichment in Prevotella, Leptotrichia, and Neisseria species. In contrast, the rapid decline group (FEV1 slope ⩽33rd percentile) had significantly lower baseline diversity associated with enrichment in Streptococcus species. Conclusions: In SPIROMICS, baseline airway microbiota features demonstrate divergent associations with better or worse COPD-related outcomes.

Real-world effectiveness of dupilumab in patients with asthma: Findings from the US ADVANTAGE study.

BACKGROUND: Dupilumab is approved as an add-on maintenance therapy for patients (≥6 years) with moderate-to-severe asthma. Better understanding of real-world effectiveness is needed. OBJECTIVE: To characterize the real-world effectiveness of dupilumab in asthma management. METHODS: This retrospective study included patients (≥12 years of age) diagnosed with asthma, initiating dupilumab between November 2018 and September 2020. The study used a US electronic medical record database (TriNetX Dataworks, Cambridge, Massachusetts). Asthma exacerbation rates before and after the initiation of dupilumab were analyzed using generalized estimating equations models with Poisson probabilistic link to estimate incidence rate ratios (IRRs). Sensitivity analyses were conducted based on previous exacerbation data, eosinophil levels, history of atopic dermatitis or chronic rhinosinusitis with nasal polyps, previous use of biologics, and presence of SARS-CoV-2 (COVID-19). RESULTS: A total of 2400 patients initiating dupilumab met all study criteria. After initiation of dupilumab, risk of asthma exacerbation was reduced by 44% (IRR, 0.56; 95% CI, 0.47-0.57; P = <0.0001) and systemic corticosteroid prescriptions by 48% (IRR, 0.52; 95% CI, 0.48, 0.56; P = <0.0001) compared with those before initiation of dupilumab. Adjustment for COVID-19 showed a greater reduction in asthma exacerbations (IRR, 0.50; 95% CI, 0.45-0.55; P = <0.0001). CONCLUSION: Current real-world efficacy evidence indicates that dupilumab reduces asthma exacerbations and total systemic corticosteroid prescriptions in clinical practice. The effectiveness of dupilumab was observed independent of exacerbation history, eosinophil levels, or COVID-19 impact.

Mitochondrial DNA Copy Number Variation in Asthma Risk, Severity, and Exacerbations.

Rationale: Although airway oxidative stress and inflammation are central to asthma pathogenesis, there is limited knowledge of the relationship of asthma risk, severity, or exacerbations to mitochondrial dysfunction, which is pivotal to oxidant generation and inflammation. Objectives: We investigated whether mitochondrial DNA copy number (mtDNA-CN) as a measure of mitochondrial function is associated with asthma diagnosis, severity, oxidative stress, and exacerbations. Methods: We measured mtDNA-CN in blood in two cohorts. In the UK Biobank (UKB), we compared mtDNA-CN in mild and moderate-severe asthmatics to non-asthmatics. In the Severe Asthma Research Program (SARP), we evaluated mtDNA-CN in relation to asthma severity, biomarkers of oxidative stress and inflammation, and exacerbations. Measures and Main Results: In UK Biobank, asthmatics (n = 29,768) have lower mtDNA-CN compared to non-asthmatics (n = 239,158) (beta, -0.026 [95% CI, -0.038 to -0.014], P = 2.46×10-5). While lower mtDNA-CN is associated with asthma, mtDNA-CN did not differ by asthma severity in either UKB or SARP. Biomarkers of inflammation show that asthmatics have higher white blood cells (WBC), neutrophils, eosinophils, fraction exhaled nitric oxide (FENO), and lower superoxide dismutase (SOD) than non-asthmatics, confirming greater oxidative stress in asthma. In one year follow-up in SARP, higher mtDNA-CN is associated with reduced risk of three or more exacerbations in the subsequent year (OR 0.352 [95% CI, 0.164 to 0.753], P = 0.007). Conclusions: Asthma is characterized by mitochondrial dysfunction. Higher mtDNA-CN identifies an exacerbation-resistant asthma phenotype, suggesting mitochondrial function is important in exacerbation risk.

Dupilumab Efficacy in Patients with Type 2 Asthma with and without Elevated Blood Neutrophils.

Introduction: Elevated neutrophil counts in blood, sputum, or lung have been associated with poor clinical outcomes and more severe disease in patients with type 2 asthma. In the phase 3 LIBERTY ASTHMA QUEST (NCT02414854), add-on dupilumab 200 and 300 mg every 2 weeks compared with matched placebo significantly reduced severe asthma exacerbations and improved forced expiratory volume in 1 s (FEV1) in patients with uncontrolled, moderate-to-severe asthma. This post hoc analysis explored the efficacy of dupilumab in patients with type 2 asthma enrolled in QUEST with or without elevated blood neutrophil counts. Methods: Annualized severe exacerbation rates during the 52-week treatment period and least-squares mean change from baseline in FEV1 over time were evaluated for patients with elevated type 2 biomarkers at baseline (blood eosinophils ≥ 150 cells/µL or fractional exhaled nitric oxide (FeNO) ≥ 20 ppb; and eosinophils ≥ 300 cells/µL or FeNO ≥ 50 ppb) and low (<4,000 cells/µL) or high (≥4,000 cells/µL) neutrophil counts. Results: Dupilumab significantly reduced annualized severe exacerbation rates compared with placebo during the 52-week treatment period in patients with elevated type 2 biomarkers, irrespective of baseline neutrophil count (P < 0.0001 for all comparisons). Significant improvements in FEV1 versus placebo were observed as early as Week 2 and over the 52-week treatment period, irrespective of baseline neutrophil count (P < 0.001 for all comparisons). Safety findings were similar across all subgroups, regardless of neutrophil counts at baseline. Conclusions: Dupilumab treatment significantly reduced annualized severe exacerbation rates and improved lung function in patients with uncontrolled, moderate-to-severe, type 2 asthma, irrespective of baseline blood neutrophil count. This trial is registered with NCT02414854.

Longitudinal Follow-Up of Participants With Tobacco Exposure and Preserved Spirometry.

Importance: People who smoked cigarettes may experience respiratory symptoms without spirometric airflow obstruction. These individuals are typically excluded from chronic obstructive pulmonary disease (COPD) trials and lack evidence-based therapies. Objective: To define the natural history of persons with tobacco exposure and preserved spirometry (TEPS) and symptoms (symptomatic TEPS). Design, Setting, and Participants: SPIROMICS II was an extension of SPIROMICS I, a multicenter study of persons aged 40 to 80 years who smoked cigarettes (>20 pack-years) with or without COPD and controls without tobacco exposure or airflow obstruction. Participants were enrolled in SPIROMICS I and II from November 10, 2010, through July 31, 2015, and followed up through July 31, 2021. Exposures: Participants in SPIROMICS I underwent spirometry, 6-minute walk distance testing, assessment of respiratory symptoms, and computed tomography of the chest at yearly visits for 3 to 4 years. Participants in SPIROMICS II had 1 additional in-person visit 5 to 7 years after enrollment in SPIROMICS I. Respiratory symptoms were assessed with the COPD Assessment Test (range, 0 to 40; higher scores indicate more severe symptoms). Participants with symptomatic TEPS had normal spirometry (postbronchodilator ratio of forced expiratory volume in the first second [FEV1] to forced vital capacity >0.70) and COPD Assessment Test scores of 10 or greater. Participants with asymptomatic TEPS had normal spirometry and COPD Assessment Test scores of less than 10. Patient-reported respiratory symptoms and exacerbations were assessed every 4 months via phone calls. Main Outcomes and Measures: The primary outcome was assessment for accelerated decline in lung function (FEV1) in participants with symptomatic TEPS vs asymptomatic TEPS. Secondary outcomes included development of COPD defined by spirometry, respiratory symptoms, rates of respiratory exacerbations, and progression of computed tomographic-defined airway wall thickening or emphysema. Results: Of 1397 study participants, 226 had symptomatic TEPS (mean age, 60.1 [SD, 9.8] years; 134 were women [59%]) and 269 had asymptomatic TEPS (mean age, 63.1 [SD, 9.1] years; 134 were women [50%]). At a median follow-up of 5.76 years, the decline in FEV1 was -31.3 mL/y for participants with symptomatic TEPS vs -38.8 mL/y for those with asymptomatic TEPS (between-group difference, -7.5 mL/y [95% CI, -16.6 to 1.6 mL/y]). The cumulative incidence of COPD was 33.0% among participants with symptomatic TEPS vs 31.6% among those with asymptomatic TEPS (hazard ratio, 1.05 [95% CI, 0.76 to 1.46]). Participants with symptomatic TEPS had significantly more respiratory exacerbations than those with asymptomatic TEPS (0.23 vs 0.08 exacerbations per person-year, respectively; rate ratio, 2.38 [95% CI, 1.71 to 3.31], P < .001). Conclusions and Relevance: Participants with symptomatic TEPS did not have accelerated rates of decline in FEV1 or increased incidence of COPD vs those with asymptomatic TEPS, but participants with symptomatic TEPS did experience significantly more respiratory exacerbations over a median follow-up of 5.8 years.

A Polygenic Risk Score for Idiopathic Pulmonary Fibrosis and Interstitial Lung Abnormalities.

Rationale: In addition to rare genetic variants and the MUC5B locus, common genetic variants contribute to idiopathic pulmonary fibrosis (IPF) risk. The predictive power of common variants outside the MUC5B locus for IPF and interstitial lung abnormalities (ILAs) is unknown. Objectives: We tested the predictive value of IPF polygenic risk scores (PRSs) with and without the MUC5B region on IPF, ILA, and ILA progression. Methods: We developed PRSs that included (PRS-M5B) and excluded (PRS-NO-M5B) the MUC5B region (500-kb window around rs35705950-T) using an IPF genome-wide association study. We assessed PRS associations with area under the receiver operating characteristic curve (AUC) metrics for IPF, ILA, and ILA progression. Measurements and Main Results: We included 14,650 participants (1,970 IPF; 1,068 ILA) from six multi-ancestry population-based and case-control cohorts. In cases excluded from genome-wide association study, the PRS-M5B (odds ratio [OR] per SD of the score, 3.1; P = 7.1 × 10-95) and PRS-NO-M5B (OR per SD, 2.8; P = 2.5 × 10-87) were associated with IPF. Participants in the top PRS-NO-M5B quintile had ∼sevenfold odds for IPF compared with those in the first quintile. A clinical model predicted IPF (AUC, 0.61); rs35705950-T and PRS-NO-M5B demonstrated higher AUCs (0.73 and 0.7, respectively), and adding both genetic predictors to a clinical model yielded the highest performance (AUC, 0.81). The PRS-NO-M5B was associated with ILA (OR, 1.25) and ILA progression (OR, 1.16) in European ancestry participants. Conclusions: A common genetic variant risk score complements the MUC5B variant to identify individuals at high risk of interstitial lung abnormalities and pulmonary fibrosis.

Large scale proteomic studies create novel privacy considerations.

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.