Rheumatoid arthritis and changes on spirometry by smoking status in two prospective longitudinal cohorts.

OBJECTIVE: To compare longitudinal changes in spirometric measures between patients with rheumatoid arthritis (RA) and non-RA comparators. METHODS: We analysed longitudinal data from two prospective cohorts: the UK Biobank and COPDGene. Spirometry was conducted at baseline and a second visit after 5-7 years. RA was identified based on self-report and disease-modifying antirheumatic drug use; non-RA comparators reported neither. The primary outcomes were annual changes in the per cent-predicted forced expiratory volume in 1 s (FEV1%) and per cent predicted forced vital capacity (FVC%). Statistical comparisons were performed using multivariable linear regression. The analysis was stratified based on baseline smoking status and the presence of obstructive pattern (FEV1/FVC <0.7). RESULTS: Among participants who underwent baseline and follow-up spirometry, we identified 233 patients with RA and 37 735 non-RA comparators. Among never-smoking participants without an obstructive pattern, RA was significantly associated with more FEV1% decline (ß=-0.49, p=0.04). However, in ever smokers with ≥10 pack-years, those with RA exhibited significantly less FEV1% decline than non-RA comparators (ß=0.50, p=0.02). This difference was more pronounced among those with an obstructive pattern at baseline (ß=1.12, p=0.01). Results were similar for FEV1/FVC decline. No difference was observed in the annual FVC% change in RA versus non-RA. CONCLUSIONS: Smokers with RA, especially those with baseline obstructive spirometric patterns, experienced lower FEV1% and FEV1/FVC decline than non-RA comparators. Conversely, never smokers with RA had more FEV1% decline than non-RA comparators. Future studies should investigate potential treatments and the pathogenesis of obstructive lung diseases in smokers with RA.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes.

Rationale: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. Objectives: Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics. Methods: We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups. Measurements and Main Results: We examined two high-risk omics-defined groups in non-overlapping test sets (n=1,133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "High activity" (low PRS/high TRS) and "severe risk" (high PRS/high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signaling processes compared to a low-risk (low PRS, low TRS) reference subgroup. "High activity" but not "severe risk" participants had greater prospective FEV 1 decline (COPDGene: -51 mL/year; ECLIPSE: - 40 mL/year) and their proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Conclusions: Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Clinical factors associated with racial differences in the prevalence of occult hypoxemia: a retrospective case-control study.

Background: Recent studies showed that Black patients more often have falsely normal oxygen saturation on pulse oximetry compared to White patients. However, whether the racial differences in occult hypoxemia are mediated by other clinical differences is unknown. Methods: We conducted a retrospective case-control study utilizing two large ICU databases (eICU and MIMIC-IV). We defined occult hypoxemia as oxygen saturation on pulse oximetry within 92-98% despite oxygen saturation on arterial blood gas below 90%. We assessed associations of commonly measured clinical factors with occult hypoxemia using multivariable logistic regression and conducted mediation analysis of the racial effect. Results: Among 24,641 patients, there were 1,855 occult hypoxemia cases and 23,786 controls. In both datasets, Black patients were more likely to have occult hypoxemia (unadjusted odds ratio 1.66 [95%-CI: 1.41-1.95] in eICU and 2.00 [95%-CI: 1.22-3.14] in MIMIC-IV). In multivariable models, higher respiratory rate, PaCO2 and creatinine as well as lower hemoglobin were associated with increased odds of occult hypoxemia. Differences in the commonly measured clinical markers accounted for 9.2% and 44.4% of the racial effect on occult hypoxemia in eICU and MIMIC-IV, respectively. Conclusion: Clinical differences, in addition to skin tone, might mediate some of the racial differences in occult hypoxemia.

Genetically Predicted Body Mass Index and Mortality in COPD.

RATIONALE: BMI is associated with COPD mortality, but the underlying mechanisms are unclear. The effect of genetic variants aggregated into a polygenic score may elucidate causal mechanisms and predict risk. OBJECTIVES: To examine the associations of genetically predicted BMI with all-cause and cause-specific mortality in COPD. METHODS: We developed a polygenic score for BMI (PGSBMI) and tested for associations of the PGSBMI with all-cause, respiratory, and cardiovascular mortality in participants with COPD from the COPDGene, ECLIPSE, and Framingham Heart studies. We calculated the difference between measured BMI and PGS-predicted BMI (BMIdiff) and categorized participants into groups of discordantly low (BMIdiff < 20th percentile), concordant (BMIdiff between 20th - 80th percentile), and discordantly high (BMIdiff > 80th percentile) BMI. We applied Cox models, examined potential non-linear associations of the PGSBMI and BMIdiff with mortality, and summarized results with meta-analysis. MEASUREMENTS AND MAIN RESULTS: We observed significant non-linear associations of measured BMI and BMIdiff, but not PGSBMI, with all-cause mortality. In meta-analyses, a one standard deviation increase in the PGSBMI was associated with an increased hazard for cardiovascular mortality (HR=1.29, 95% CI=1.12-1.49), but not with respiratory or all-cause mortality. Compared to participants with concordant measured and genetically predicted BMI, those with discordantly low BMI had higher mortality risk for all-cause (HR=1.57, CI=1.41-1.74) and respiratory death (HR=2.01, CI=1.61-2.51). CONCLUSIONS: In people with COPD, higher genetically predicted BMI is associated with higher cardiovascular mortality but not respiratory mortality. Individuals with discordantly low BMI have higher all-cause and respiratory mortality compared to those with concordant BMI.

Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema.

Rationale: Emphysema is a chronic obstructive pulmonary disease phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. Objectives: To discover blood omics biomarkers for chest computed tomography-quantified emphysema and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training sample of 2,370 COPDGene participants with available blood RNA sequencing, plasma proteomics, and clinical data. Internal validation was conducted in a COPDGene testing sample (n = 1,016), and external validation was done in the ECLIPSE study (n = 526). Because low body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models with bootstrapping were also developed in the training sample sequentially using clinical, blood cell proportions, RNA-sequencing, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed by the area under the receiver operating characteristic curves for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: Totals of 3,829 genes, 942 isoforms, 260 exons, and 714 proteins were significantly associated with emphysema (false discovery rate, 5%) and yielded 11 biological pathways. Seventy-four percent of these genes and 62% of these proteins showed mediation by BMI. Our prediction models demonstrated reasonable predictive performance in both COPDGene and ECLIPSE. The highest-performing model used clinical, blood cell, and protein data (area under the receiver operating characteristic curve in COPDGene testing, 0.90; 95% confidence interval, 0.85-0.90). Conclusions: Blood transcriptome and proteome-wide analyses revealed key biological pathways of emphysema and enhanced the prediction of emphysema.

Precision Approaches to Chronic Obstructive Pulmonary Disease Management.

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD heterogeneity has hampered progress in developing pharmacotherapies that affect disease progression. This issue can be addressed by precision medicine approaches, which focus on understanding an individual's disease risk, and tailoring management based on pathobiology, environmental exposures, and psychosocial issues. There is an urgent need to identify COPD patients at high risk for poor outcomes and to understand at a mechanistic level why certain individuals are at high risk. Genetics, omics, and network analytic techniques have started to dissect COPD heterogeneity and identify patients with specific pathobiology. Drug repurposing approaches based on biomarkers of specific inflammatory processes (i.e., type 2 inflammation) are promising. As larger data sets, additional omics, and new analytical approaches become available, there will be enormous opportunities to identify high-risk individuals and treat COPD patients based on their specific pathophysiological derangements. These approaches show great promise for risk stratification, early intervention, drug repurposing, and developing novel therapeutic approaches for COPD.

Lower myostatin and higher MUC1 levels are associated with better response to mepolizumab and omalizumab in asthma: a protein-protein interaction analyses.

INTRODUCTION: Biomarkers are needed to inform the choice of biologic therapy in patients with asthma given the increasing number of biologics. We aimed to identify proteins associated with response to omalizumab and mepolizumab. METHODS: Aptamer-based proteomic profiling (SomaScan) was used to assess 1437 proteins from 51 patients with moderate to severe asthma who received omalizumab (n = 29) or mepolizumab (n = 22). Response was defined as the change in asthma-related exacerbations in the 12 months following therapy initiation. All models were adjusted for age, sex, and pre-treatment exacerbation rate. Additionally, body mass index was included in the omalizumab model and eosinophil count in the mepolizumab model. We evaluated the association between molecular signatures and response using negative binomial regression correcting for the false discovery rate (FDR) and gene set enrichment analyses (GSEA) to identify associated pathways. RESULTS: Over two-thirds of patients were female. The average age for omalizumab patients was 42 years and 57 years for mepolizumab. At baseline, the average exacerbation rate was 1.5/year for omalizumab and 2.4/year for mepolizumab. Lower levels of LOXL2 (unadjusted p: 1.93 × 10E-05, FDR-corrected: 0.028) and myostatin (unadjusted: 3.87 × 10E-05, FDR-corrected: 0.028) were associated with better response to mepolizumab. Higher levels of CD9 antigen (unadjusted: 5.30 × 10E-07, FDR-corrected: 0.0006) and MUC1 (unadjusted: 1.15 × 10E-06, FDR-corrected: 0.0006) were associated with better response to omalizumab, and LTB4R (unadjusted: 1.12 × 10E-06, FDR-corrected: 0.0006) with worse response. Protein-protein interaction network modeling showed an enrichment of the TNF- and NF-kB signaling pathways for patients treated with mepolizumab and multiple pathways involving MAPK, including the FcER1 pathway, for patients treated with omalizumab. CONCLUSIONS: This study provides novel fundamental data on proteins associated with response to mepolizumab or omalizumab in severe asthma and warrants further validation as potential biomarkers for therapy selection.

Rheumatoid arthritis, quantitative parenchymal lung features, and mortality among smokers.

OBJECTIVES: There have been limited investigations of the prevalence and mortality impact of quantitative computed tomography (QCT) parenchymal lung features in rheumatoid arthritis (RA). We examined the cross-sectional prevalence and mortality associations of QCT features, comparing RA and non-RA participants. METHODS: We identified participants with and without RA in COPDGene, a multicentre cohort study of current or former smokers. Using a k-nearest neighbor quantifier, high resolution CT chest scans were scored for percentage of normal lung, interstitial changes, and emphysema. We examined associations between QCT features and RA using multivariable linear regression. After dichotomizing participants at the 75th percentile for each QCT feature among non-RA participants, we investigated mortality associations by RA/non-RA status and quartile 4 vs quartiles 1-3 of QCT features using Cox regression. We assessed for statistical interactions between RA and QCT features. RESULTS: We identified 82 RA cases and 8820 non-RA comparators. In multivariable linear regression, RA was associated with higher percentage of interstitial changes (ß = 1.7 ± 0.5, p= 0.0008) but not emphysema (ß = 1.3 ± 1.7, p= 0.44). Participants with RA and >75th percentile of emphysema had significantly higher mortality than non-RA participants (HR 5.86, 95%CI 3.75-9.13) as well as RA participants (HR 5.56, 95%CI 2.71-11.38) with ≤75th percentile of emphysema. There were statistical interactions between RA and emphysema for mortality (multiplicative p= 0.014; attributable proportion 0.53, 95%CI 0.30-0.70). CONCLUSIONS: Using machine learning-derived QCT data in a cohort of smokers, RA was associated with higher percentage of interstitial changes. The combination of RA and emphysema conferred >5-fold higher mortality.

Prevalence and mortality associations of interstitial lung abnormalities in rheumatoid arthritis within a multicentre prospective cohort of smokers.

OBJECTIVE: To investigate the prevalence and mortality impact of interstitial lung abnormalities (ILAs) in RA and non-RA comparators. METHODS: We analysed associations between ILAs, RA, and mortality in COPDGene, a multicentre prospective cohort study of current and past smokers, excluding known interstitial lung disease (ILD) or bronchiectasis. All participants had research chest high-resolution CT (HRCT) reviewed by a sequential reading method to classify ILA as present, indeterminate or absent. RA cases were identified by self-report RA and DMARD use; non-RA comparators had neither an RA diagnosis nor used DMARDs. We examined the association and mortality risk of RA and ILA using multivariable logistic regression and Cox regression. RESULTS: We identified 83 RA cases and 8725 non-RA comparators with HRCT performed for research purposes. ILA prevalence was 16.9% in RA cases and 5.0% in non-RA comparators. After adjusting for potential confounders, including genetics, current/past smoking and other lifestyle factors, ILAs were more common among those with RA compared with non-RA [odds ratio 4.76 (95% CI 2.54, 8.92)]. RA with ILAs or indeterminate for ILAs was associated with higher all-cause mortality compared with non-RA without ILAs [hazard ratio (HR) 3.16 (95% CI 2.11, 4.74)] and RA cases without ILA [HR 3.02 (95% CI 1.36, 6.75)]. CONCLUSIONS: In this cohort of smokers, RA was associated with ILAs and this persisted after adjustment for current/past smoking and genetic/lifestyle risk factors. RA with ILAs in smokers had a 3-fold increased all-cause mortality, emphasizing the importance of further screening and treatment strategies for preclinical ILD in RA.

Polygenic risk scores identify heterogeneity in asthma and chronic obstructive pulmonary disease.

BACKGROUND: Asthma and chronic obstructive pulmonary disease (COPD) have distinct and overlapping genetic and clinical features. OBJECTIVE: We sought to test the hypothesis that polygenic risk scores (PRSs) for asthma (PRSAsthma) and spirometry (FEV1 and FEV1/forced vital capacity; PRSspiro) would demonstrate differential associations with asthma, COPD, and asthma-COPD overlap (ACO). METHODS: We developed and tested 2 asthma PRSs and applied the higher performing PRSAsthma and a previously published PRSspiro to research (Genetic Epidemiology of COPD study and Childhood Asthma Management Program, with spirometry) and electronic health record-based (Mass General Brigham Biobank and Genetic Epidemiology Research on Adult Health and Aging [GERA]) studies. We assessed the association of PRSs with COPD and asthma using modified random-effects and binary-effects meta-analyses, and ACO and asthma exacerbations in specific cohorts. Models were adjusted for confounders and genetic ancestry. RESULTS: In meta-analyses of 102,477 participants, the PRSAsthma (odds ratio [OR] per SD, 1.16 [95% CI, 1.14-1.19]) and PRSspiro (OR per SD, 1.19 [95% CI, 1.17-1.22]) both predicted asthma, whereas the PRSspiro predicted COPD (OR per SD, 1.25 [95% CI, 1.21-1.30]). However, results differed by cohort. The PRSspiro was not associated with COPD in GERA and Mass General Brigham Biobank. In the Genetic Epidemiology of COPD study, the PRSAsthma (OR per SD: Whites, 1.3; African Americans, 1.2) and PRSspiro (OR per SD: Whites, 2.2; African Americans, 1.6) were both associated with ACO. In GERA, the PRSAsthma was associated with asthma exacerbations (OR, 1.18) in Whites; the PRSspiro was associated with asthma exacerbations in White, LatinX, and East Asian participants. CONCLUSIONS: PRSs for asthma and spirometry are both associated with ACO and asthma exacerbations. Genetic prediction performance differs in research versus electronic health record-based cohorts.

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.

Identifying COPD subtypes using multi-trait genetics.

Chronic Obstructive Pulmonary Disease (COPD) has a simple physiological diagnostic criterion but a wide range of clinical characteristics. The mechanisms underlying this variability in COPD phenotypes are unclear. To investigate the potential contribution of genetic variants to phenotypic heterogeneity, we examined the association of genome-wide associated lung function, COPD, and asthma variants with other phenotypes using phenome-wide association results derived in the UK Biobank. Our clustering analysis of the variants-phenotypes association matrix identified three clusters of genetic variants with different effects on white blood cell counts, height, and body mass index (BMI). To assess the potential clinical and molecular effects of these groups of variants, we investigated the association between cluster-specific genetic risk scores and phenotypes in the COPDGene cohort. We observed differences in steroid use, BMI, lymphocyte counts, chronic bronchitis, and differential gene and protein expression across the three genetic risk scores. Our results suggest that multi-phenotype analysis of obstructive lung disease-related risk variants may identify genetically driven phenotypic patterns in COPD.

Clinical Features of Genetic Resilience in Chronic Obstructive Pulmonary Disease.

Introduction: In the personalized risk quantification of chronic obstructive pulmonary disease (COPD), genome-wide association studies and polygenic risk scores (PRS) complement traditional risk factors, such as age and cigarette smoking. However, despite being at considerable levels of risk, some individuals do not develop COPD. Research on COPD resilience remains largely unexplored. Methods: We applied the previously published COPD PRS to whole genome sequencing data from non-Hispanic white and African American individuals in the COPDGene study. We defined genetic resilience as individuals unaffected by COPD with a polygenic risk score above the 90 th percentile. We defined risk-matched case individuals as those with COPD (i.e., FEV 1 /FVC < 0.70) and a PRS above the 90 th percentile. We defined low risk individuals without COPD (i.e., FEV 1 /FVC > 0.70) as a polygenic risk score below the 10 th percentile. We compared genetically resilient individuals to risk-matched individuals with COPD and low risk individuals by demographics, lung function, respiratory symptoms, co-morbidities, and chest CT scan measurements. We also performed survival analyses, differential expression analysis, and matching for sensitivity analyses. Results: We identified 211 resilient individuals without COPD, 605 genetic risk-matched individuals with COPD, and 527 low-risk individuals without COPD. Resilient individuals had higher FEV 1 % predicted and lower percent emphysema. In contrast, resilient individuals had higher airway wall thickness compared to low-risk unaffected individuals. While there was no difference in survival between low-risk and resilient individuals, resilient individuals had higher survival compared to risk matched cases. We also identified two genes that were differentially expressed between low-risk unaffected individuals and resilient individuals. Conclusion: Genetically resilient individuals had a reduced burden of COPD disease-related measures compared to risk-matched cases but had subtly increased measures compared to low-risk unaffected individuals. Further genetic studies will be needed to illuminate the underlying pathobiology of our observations.

Cluster analysis of COVID-19 recovery center patients at a clinic in Boston, MA 2021-2022: impact on strategies for access and personalized care.

BACKGROUND: There are known disparities in COVID-19 resource utilization that may persist during the recovery period for some patients. We sought to define subpopulations of patients seeking COVID-19 recovery care in terms of symptom reporting and care utilization to better personalize their care and to identify ways to improve access to subspecialty care. METHODS: Prospective study of adult patients with prior COVID-19 infection seen in an ambulatory COVID-19 recovery center (CRC) in Boston, Massachusetts from April 2021 to April 2022. Hierarchical clustering with complete linkage to differentiate subpopulations was done with four sociodemographic variables: sex, race, language, and insurance status. Outcomes included ICU admission, utilization of supplementary care, self-report of symptoms. RESULTS: We included 1285 COVID-19 patients referred to the CRC with a mean age of 47 years, of whom 71% were female and 78% White. We identified 3 unique clusters of patients. Cluster 1 and 3 patients were more likely to have had intensive care unit (ICU) admissions; Cluster 2 were more likely to be White with commercial insurance and a low percentage of ICU admission; Cluster 3 were more likely to be Black/African American or Latino/a and have commercial insurance. Compared to Cluster 2, Cluster 1 patients were more likely to report symptoms (ORs ranging 2.4-3.75) but less likely to use support groups, psychoeducation, or care coordination (all p < 0.05). Cluster 3 patients reported greater symptoms with similar levels of community resource utilization. CONCLUSIONS: Within a COVID-19 recovery center, there are distinct groups of patients with different clinical and socio-demographic profiles, which translates to differential resource utilization. These insights from different subpopulations of patients can inform targeted strategies which are tailored to specific patient needs.

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk.

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies.

Quantitative Interstitial Abnormality Progression and Outcomes in the Genetic Epidemiology of COPD and Pittsburgh Lung Screening Study Cohorts.

BACKGROUND: The risk factors and clinical outcomes of quantitative interstitial abnormality progression over time have not been characterized. RESEARCH QUESTIONS: What are the associations of quantitative interstitial abnormality progression with lung function, exercise capacity, and mortality? What are the demographic and genetic risk factors for quantitative interstitial abnormality progression? STUDY DESIGN AND METHODS: Quantitative interstitial abnormality progression between visits 1 and 2 was assessed from 4,635 participants in the Genetic Epidemiology of COPD (COPDGene) cohort and 1,307 participants in the Pittsburgh Lung Screening Study (PLuSS) cohort. We used multivariable linear regression to determine the risk factors for progression and the longitudinal associations between progression and FVC and 6-min walk distance, and Cox regression models for the association with mortality. RESULTS: Age at enrollment, female sex, current smoking status, and the MUC5B minor allele were associated with quantitative interstitial abnormality progression. Each percent annual increase in quantitative interstitial abnormalities was associated with annual declines in FVC (COPDGene: 8.5 mL/y; 95% CI, 4.7-12.4 mL/y; P < .001; PLuSS: 9.5 mL/y; 95% CI, 3.7-15.4 mL/y; P = .001) and 6-min walk distance, and increased mortality (COPDGene: hazard ratio, 1.69; 95% CI, 1.34-2.12; P < .001; PLuSS: hazard ratio, 1.28; 95% CI, 1.10-1.49; P = .001). INTERPRETATION: The objective, longitudinal measurement of quantitative interstitial abnormalities may help identify people at greatest risk for adverse events and most likely to benefit from early intervention.