A novel application of data-consistent inversion to overcome spurious inference in genome-wide association studies.

The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants may result in spurious conclusions about the associations between a variant and a trait. We propose an assumption-free model built upon data-consistent inversion (DCI), which is a recently developed measure-theoretic framework utilized for uncertainty quantification. This proposed DCI-derived model builds a nonparametric distribution on model inputs that propagates to the distribution of observed data without the required normality assumption of residuals in the regression model. This characteristic enables the proposed DCI-derived model to cover all genetic variants without emphasizing on additivity of the classic-GWAS model. Simulations and a replication GWAS with data from the COPDGene demonstrate the ability of this model to control the Type-I error rate at least as well as the classic-GWAS (additive linear model) approach while having similar or greater power to discover variants in different genetic modes of transmission.

Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is common and partially heritable and has no effective treatments. We carried out a genome-wide association study (GWAS) meta-analysis of imaging (n = 66,814) and diagnostic code (3,584 cases versus 621,081 controls) measured NAFLD across diverse ancestries. We identified NAFLD-associated variants at torsin family 1 member B (TOR1B), fat mass and obesity associated (FTO), cordon-bleu WH2 repeat protein like 1 (COBLL1)/growth factor receptor-bound protein 14 (GRB14), insulin receptor (INSR), sterol regulatory element-binding transcription factor 1 (SREBF1) and patatin-like phospholipase domain-containing protein 2 (PNPLA2), as well as validated NAFLD-associated variants at patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily 2 (TM6SF2), apolipoprotein E (APOE), glucokinase regulator (GCKR), tribbles homolog 1 (TRIB1), glycerol-3-phosphate acyltransferase (GPAM), mitochondrial amidoxime-reducing component 1 (MARC1), microsomal triglyceride transfer protein large subunit (MTTP), alcohol dehydrogenase 1B (ADH1B), transmembrane channel like 4 (TMC4)/membrane-bound O-acyltransferase domain containing 7 (MBOAT7) and receptor-type tyrosine-protein phosphatase δ (PTPRD). Implicated genes highlight mitochondrial, cholesterol and de novo lipogenesis as causally contributing to NAFLD predisposition. Phenome-wide association study (PheWAS) analyses suggest at least seven subtypes of NAFLD. Individuals in the top 10% and 1% of genetic risk have a 2.5-fold to 6-fold increased risk of NAFLD, cirrhosis and hepatocellular carcinoma. These genetic variants identify subtypes of NAFLD, improve estimates of disease risk and can guide the development of targeted therapeutics.

Deep Learning Integration of Chest Computed Tomography Imaging and Gene Expression Identifies Novel Aspects of COPD.

Rationale: Chronic obstructive pulmonary disease (COPD) is characterized by pathologic changes in the airways, lung parenchyma, and persistent inflammation, but the links between lung structural changes and blood transcriptome patterns have not been fully described. Objections: The objective of this study was to identify novel relationships between lung structural changes measured by chest computed tomography (CT) and blood transcriptome patterns measured by blood RNA sequencing (RNA-seq). Methods: CT scan images and blood RNA-seq gene expression from 1223 participants in the COPD Genetic Epidemiology (COPDGene®) study were jointly analyzed using deep learning to identify shared aspects of inflammation and lung structural changes that we labeled image-expression axes (IEAs). We related IEAs to COPD-related measurements and prospective health outcomes through regression and Cox proportional hazards models and tested them for biological pathway enrichment. Results: We identified 2 distinct IEAs: IEAemph which captures an emphysema-predominant process with a strong positive correlation to CT emphysema and a negative correlation to forced expiratory volume in 1 second and body mass index (BMI); and IEAairway which captures an airway-predominant process with a positive correlation to BMI and airway wall thickness and a negative correlation to emphysema. Pathway enrichment analysis identified 29 and 13 pathways significantly associated with IEAemph and IEAairway, respectively (adjusted p<0.001). Conclusions: Integration of CT scans and blood RNA-seq data identified 2 IEAs that capture distinct inflammatory processes associated with emphysema and airway-predominant COPD.

Mendelian randomization study of diabetes and dementia in the Million Veteran Program.

INTRODUCTION: Diabetes and dementia are diseases of high health-care burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases. METHODS: We conducted a one-sample Mendelian randomization (MR) analysis in the US Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data. RESULTS: For each standard deviation increase in genetically predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: odds ratio [OR] = 1.07 [1.05-1.08], P = 3.40E-18; vascular: OR = 1.11 [1.07-1.15], P = 3.63E-09, Alzheimer's disease [AD]: OR = 1.06 [1.02-1.09], P = 6.84E-04) and non-Hispanic Black participants (all-cause: OR = 1.06 [1.02-1.10], P = 3.66E-03, vascular: OR = 1.11 [1.04-1.19], P = 2.20E-03, AD: OR = 1.12 [1.02-1.23], P = 1.60E-02) but not in Hispanic participants (all P > 0.05). DISCUSSION: We found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies using two-sample MR techniques.

Causes of and Clinical Features Associated with Death in Tobacco Cigarette Users by Lung Function Impairment.

Rationale: Cigarette smoking contributes to the risk of death through different mechanisms. Objectives: To determine how causes of and clinical features associated with death vary in tobacco cigarette users by lung function impairment. Methods: We stratified current and former tobacco cigarette users enrolled in Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) into normal spirometry, PRISm (Preserved Ratio Impaired Spirometry), Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-2 COPD, and GOLD 3-4 COPD. Deaths were identified via longitudinal follow-up and Social Security Death Index search. Causes of death were adjudicated after a review of death certificates, medical records, and next-of-kin interviews. We tested associations between baseline clinical variables and all-cause mortality using multivariable Cox proportional hazards models. Measurements and Main Results: Over a 10.1-year median follow-up, 2,200 deaths occurred among 10,132 participants (age 59.5 ± 9.0 yr; 46.6% women). Death from cardiovascular disease was most frequent in PRISm (31% of deaths). Lung cancer deaths were most frequent in GOLD 1-2 (18% of deaths vs. 9-11% in other groups). Respiratory deaths outpaced competing causes of death in GOLD 3-4, particularly when BODE index ⩾7. St. George's Respiratory Questionnaire score ⩾25 was associated with higher mortality in all groups: Hazard ratio (HR), 1.48 (1.20-1.84) normal spirometry; HR, 1.40 (1.05-1.87) PRISm; HR, 1.80 (1.49-2.17) GOLD 1-2; HR, 1.65 (1.26-2.17) GOLD 3-4. History of respiratory exacerbations was associated with higher mortality in GOLD 1-2 and GOLD 3-4, quantitative emphysema in GOLD 1-2, and airway wall thickness in PRISm and GOLD 3-4. Conclusions: Leading causes of death vary by lung function impairment in tobacco cigarette users. Worse respiratory-related quality of life is associated with all-cause mortality regardless of lung function.

Heterogeneity and Progression of Chronic Obstructive Pulmonary Disease: Emphysema-Predominant and Non-Emphysema-Predominant Disease.

While variation in emphysema severity between patients with chronic obstructive pulmonary disease (COPD) is well-recognized, clinically applicable definitions of the emphysema-predominant disease (EPD) and non-emphysema-predominant disease (NEPD) subtypes have not been established. To study the clinical relevance of the EPD and NEPD subtypes, we tested the association of these subtypes with prospective decline in forced expiratory volume in 1 second (FEV1) and mortality among 3,427 subjects with Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric grade 2-4 COPD at baseline in the Genetic Epidemiology of COPD (COPDGene) Study, an ongoing national multicenter study that started in 2007. NEPD was defined as airflow obstruction with less than 5% computed tomography (CT) quantitative densitometric emphysema at -950 Hounsfield units, and EPD was defined as airflow obstruction with 10% or greater CT emphysema. Mixed-effects models for FEV1 demonstrated larger average annual FEV1 loss in EPD subjects than in NEPD subjects (-10.2 mL/year; P < 0.001), and subtype-specific associations with FEV1 decline were identified. Cox proportional hazards models showed higher risk of mortality among EPD patients versus NEPD patients (hazard ratio = 1.46, 95% confidence interval: 1.34, 1.60; P < 0.001). To determine whether the NEPD/EPD dichotomy is captured by previously described COPDGene subtypes, we used logistic regression and receiver operating characteristic (ROC) curve analysis to predict NEPD/EPD membership using these previous subtype definitions. The analysis generally showed excellent discrimination, with areas under the ROC curve greater than 0.9. The NEPD and EPD COPD subtypes capture important aspects of COPD heterogeneity and are associated with different rates of disease progression and mortality.

Changes in Lung Volumes with Spirometric Disease Progression in COPD.

Background: Abnormal lung volumes representing air trapping identify the subset of smokers with preserved spirometry who develop spirometric chronic obstructive pulmonary disease (COPD) and adverse outcomes. However, how lung volumes evolve in early COPD as airflow obstruction develops remains unclear. Methods: To establish how lung volumes change with the development of spirometric COPD, we examined lung volumes from the pulmonary function data (seated posture) available in the U.S. Department of Veterans Affairs electronic health records (n=71,356) and lung volumes measured by computed tomography (supine posture) available from the COPD Genetic Epidemiology (COPDGene®) study (n=7969) and the SubPopulations and InterMediate Outcome Measures In COPD Study (SPIROMICS) (n=2552) cohorts, and studied their cross-sectional distributions and longitudinal changes across the airflow obstruction spectrum. Patients with preserved ratio-impaired spirometry (PRISm) were excluded from this analysis. Results: Lung volumes from all 3 cohorts showed similar patterns of distributions and longitudinal changes with worsening airflow obstruction. The distributions for total lung capacity (TLC), vital capacity (VC), and inspiratory capacity (IC) and their patterns of change were nonlinear and included different phases. When stratified by airflow obstruction using Global initiative for chronic Obstructive Lung Disease (GOLD) stages, patients with GOLD 1 (mild) COPD had larger lung volumes (TLC, VC, IC) compared to patients with GOLD 0 (smokers with preserved spirometry) or GOLD 2 (moderate) disease. In longitudinal follow-up of baseline GOLD 0 patients who progressed to spirometric COPD, those with an initially higher TLC and VC developed mild obstruction (GOLD 1) while those with an initially lower TLC and VC developed moderate obstruction (GOLD 2). Conclusions: In COPD, TLC, and VC have biphasic distributions, change in nonlinear fashions as obstruction worsens, and could differentiate those GOLD 0 patients at risk for more rapid spirometric disease progression.

Longitudinal Assessment of Multimorbidity Medication Patterns among Smokers in the COPDGene Cohort.

Background and objectives: Chronic obstructive pulmonary disease (COPD) is usually comorbid with other chronic diseases. We aimed to assess the multimorbidity medication patterns and explore if the patterns are similar for phase 1 (P1) and 5-year follow-up phase 2 (P2) in the COPDGene cohort. Materials and Methods: A total of 5564 out of 10,198 smokers from the COPDGene cohort who completed 2 visits, P1 and P2 visits, with complete medication use history were included in the study. We conducted latent class analysis (LCA) among the 27 categories of chronic disease medications, excluding COPD treatments and cancer medications at P1 and P2 separately. The best number of LCA classes was determined through both statistical fit and interpretation of the patterns. Results: We found four classes of medication patterns at both phases. LCA showed that both phases shared similar characteristics in their medication patterns: LC0: low medication; LC1: hypertension (HTN) or cardiovascular disease (CVD)+high cholesterol (Hychol) medication predominant; LC2: HTN/CVD+type 2 diabetes (T2D) +Hychol medication predominant; LC3: Hychol medication predominant. Conclusions: We found similar multimorbidity medication patterns among smokers at P1 and P2 in the COPDGene cohort, which provides an understanding of how multimorbidity medication clustered and how different chronic diseases combine in smokers.

Mendelian randomization study of diabetes and dementia in the Million Veteran Program.

INTRODUCTION: Diabetes and dementia are diseases of high healthcare burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases. METHODS: We conducted a one-sample Mendelian randomization (MR) analysis in the U.S. Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data. RESULTS: For each standard deviation increase in genetically-predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: OR=1.07[1.05-1.08], P =3.40E-18; vascular: OR=1.11[1.07-1.15], P =3.63E-09, Alzheimer's: OR=1.06[1.02-1.09], P =6.84E-04) and non-Hispanic Black participants (all-cause: OR=1.06[1.02-1.10], P =3.66E-03, vascular: OR=1.11[1.04-1.19], P =2.20E-03, Alzheimer's: OR=1.12 [1.02-1.23], P =1.60E-02) but not in Hispanic participants (all P >.05). DISCUSSION: We found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies utilizing two-sample MR techniques.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Increased chest CT derived bone and muscle measures capture markers of improved morbidity and mortality in COPD.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a disease of accelerated aging and is associated with comorbid conditions including osteoporosis and sarcopenia. These extrapulmonary conditions are highly prevalent yet frequently underdiagnosed and overlooked by pulmonologists in COPD treatment and management. There is evidence supporting a role for bone-muscle crosstalk which may compound osteoporosis and sarcopenia risk in COPD. Chest CT is commonly utilized in COPD management, and we evaluated its utility to identify low bone mineral density (BMD) and reduced pectoralis muscle area (PMA) as surrogates for osteoporosis and sarcopenia. We then tested whether BMD and PMA were associated with morbidity and mortality in COPD. METHODS: BMD and PMA were analyzed from chest CT scans of 8468 COPDGene participants with COPD and controls (smoking and non-smoking). Multivariable regression models tested the relationship of BMD and PMA with measures of function (6-min walk distance (6MWD), handgrip strength) and disease severity (percent emphysema and lung function). Multivariable Cox proportional hazards models were used to evaluate the relationship between sex-specific quartiles of BMD and/or PMA derived from non-smoking controls with all-cause mortality. RESULTS: COPD subjects had significantly lower BMD and PMA compared with controls. Higher BMD and PMA were associated with increased physical function and less disease severity. Participants with the highest BMD and PMA quartiles had a significantly reduced mortality risk (36% and 46%) compared to the lowest quartiles. CONCLUSIONS: These findings highlight the potential for CT-derived BMD and PMA to characterize osteoporosis and sarcopenia using equipment available in the pulmonary setting.

GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing.

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries.

Rare genetic variants explain missing heritability in smoking.

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

A Diabetes Genetic Risk Score Is Associated With All-Cause Dementia and Clinically Diagnosed Vascular Dementia in the Million Veteran Program.

OBJECTIVE: Diabetes and dementia are diseases of high health care burden worldwide, and studies have shown that diabetes is associated with an increased relative risk of dementia. We set out to examine whether type 2 diabetes-associated genetic variants were associated with dementia and whether they differed by race/ethnicity or clinical dementia diagnosis. RESEARCH DESIGN AND METHODS: We evaluated associations of two type 2 diabetes genetic risk scores (GRS and GRS-nonAPOE: a score without rs429358, a variant associated with Alzheimer disease [AD]) with three classifications of clinical dementia diagnoses in the Million Veteran Program (MVP): all-cause dementia, vascular dementia (VaD), and AD. We conducted our analysis stratified by European (EUR), African (AFR), and Hispanic (HIS) races/ethnicities. RESULTS: In EUR, we found associations of the GRS with all-cause dementia (odds ratio [OR] 1.06, P = 1.60e-07) and clinically diagnosed VaD (OR 1.12, P = 5.2e-05) but not with clinically diagnosed AD (OR 1.02, P = 0.43). The GRS was not associated with any dementia outcome in AFR or HIS. When testing with GRS-nonAPOE, we found that effect size estimates in EUR increased and P values decreased for all-cause dementia (OR 1.08, P = 2.6e-12), for VaD (OR 1.14, P = 7.2e-07), and for AD (OR 1.06, P = 0.018). For AFR, the association of GRS-nonAPOE and clinically diagnosed VaD (OR 1.15, P = 0.016) was statistically significant. There were no significant findings for HIS. CONCLUSIONS: We found evidence suggesting shared genetic pathogenesis of diabetes with all-cause dementia and clinically diagnosed VaD.

Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models.

To increase power and minimize bias in statistical analyses, quantitative outcomes are often adjusted for precision and confounding variables using standard regression approaches. The outcome is modeled as a linear function of the precision variables and confounders; however, for many complex phenotypes, the assumptions of the linear regression models are not always met. As an alternative, we used neural networks for the modeling of complex phenotypes and covariate adjustments. We compared the prediction accuracy of the neural network models to that of classical approaches based on linear regression. Using data from the UK Biobank, COPDGene study, and Childhood Asthma Management Program (CAMP), we examined the features of neural networks in this context and compared them with traditional regression approaches for prediction of three outcomes: forced expiratory volume in one second (FEV1), age at smoking cessation, and log transformation of age at smoking cessation (due to age at smoking cessation being right-skewed). We used mean squared error to compare neural network and regression models, and found the models performed similarly unless the observed distribution of the phenotype was skewed, in which case the neural network had smaller mean squared error. Our results suggest neural network models have an advantage over standard regression approaches when the phenotypic distribution is skewed. However, when the distribution is not skewed, the approaches performed similarly. Our findings are relevant to studies that analyze phenotypes that are skewed by nature or where the phenotype of interest is skewed as a result of the ascertainment condition.

Weight gain trajectories and obesity rates in intensive and conventional treatments of type 1 diabetes from the DCCT compared with a control population without diabetes.

AIM: Obesity is a significant health issue for participants with type 1 diabetes undergoing intensive diabetes management. The temporal pattern and factors associated with weight gain after treatment initiation remain poorly understood including how weight gain in participants with and without type I diabetes compare. Our aim was to compare weight gain in those receiving intensive (INT) and conventional (CONV) type 1 diabetes treatment to a population without diabetes. METHODS: Participants included men and women of 18 years and older in the Diabetes Control and Complications Trial (DCCT) randomized to INT (n = 562) or CONV (n = 568) and a prospective, observational cohort without diabetes from the Coronary Artery Development in Young Adults (CARDIA, controls) study (n = 2446). Body mass index (BMI) trajectories and obesity prevalence were compared between groups and candidate metabolic and therapeutic moderators investigated. RESULTS: Annual weight gain with INT peaked 1.3 years after initiation and was greater than both CONV and controls before and after this peak. Obesity prevalence with INT was lower than controls at baseline, was similar to controls at 2 years and surpassed controls by 5 years. Obesity rates with CONV remained below controls at all time points. Greater annual weight gain in the DCCT was associated with lower haemoglobin A1c , higher insulin dose and family history of type 2 diabetes. CONCLUSIONS: Greater weight gain accompanying INT therapy occurs in two stages, leads to similar or greater obesity rates than controls after 2 years and is primarily modified by glucose control and family history, supportive of a therapeutic-genetic influence on weight trajectories.

Significant Spirometric Transitions and Preserved Ratio Impaired Spirometry Among Ever Smokers.

BACKGROUND: Emerging data from longitudinal studies suggest that preserved ratio impaired spirometry (PRISm), defined by proportionate reductions in FEV1 and FVC, is a heterogeneous population with frequent transitions to other lung function categories relative to individuals with normal and obstructive spirometry. Controversy regarding the clinical significance of these transitions exists (eg, whether transitions merely reflect measurement variability or noise). RESEARCH QUESTION: Are individuals with PRISm enriched for transitions associated with substantial changes in lung function? STUDY DESIGN AND METHODS: Current and former smokers enrolled in the Genetic Epidemiology of COPD (COPDGene) study with spirometry available in phases 1 through 3 (enrollment, 5-year follow-up, and 10-year follow-up) were analyzed. Postbronchodilator lung function categories were as follows: PRISm (FEV1 < 80% predicted with FEV1/FVC ratio ≥ 0.7), Global Initiative for Chronic Obstructive Lung Disease grade 0 (FEV1 ≥ 80% predicted and FEV1/FVC ≥ 0.7), and obstruction (FEV1/FVC < 0.7). Significant transition status was affirmative if a subject belonged to two or more spirometric categories and had > 10% change in FEV1 % predicted and/or FVC % predicted between consecutive visits. Ever-PRISm was present if a subject had PRISm at any visit. Logistic regression examined the association between significant transitions and ever-PRISm status, adjusted for age, sex, race, FEV1 % predicted, current smoking, pack-years, BMI, and ever-positive bronchodilator response. RESULTS: Among subjects with complete data (N = 1,775) over 10.1 ± 0.4 years of follow-up, the prevalence of PRISm remained consistent (10.4%-11.3%) between phases 1 through 3, but nearly one-half of subjects with PRISm transitioned into or out of PRISm at each visit. Among all subjects, 19.7% had a significant transition; ever-PRISm was a significant predictor of significant transitions (unadjusted OR, 10.3; 95% CI, 7.9-13.5; adjusted OR, 14.9; 95% CI, 10.9-20.7). Results were similar with additional adjustment for radiographic emphysema and gas trapping, when lower limit of normal criteria were used to define lung function categories, and when FEV1 alone (regardless of change in FVC % predicted) was used to define significant transitions. INTERPRETATION: PRISm is an unstable group, with frequent significant transitions to both obstruction and normal spirometry over time. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; No.: NCT000608764; URL: www. CLINICALTRIALS: gov.