Preserved Ratio Impaired Spirometry in Relationship to Cardiovascular Outcomes

BackgroundPreserved ratio impaired spirometry (PRISm) findings are a heterogeneous condition characterized by a normal FEV1 to FVC ratio with underlying impairment of pulmonary function. Data relating to the association of baseline and trajectories of PRISm findings with diverse cardiovascular outcomes are sparse.Research QuestionHow do baseline and trajectories of PRISm findings impact subsequent cardiovascular events?Study Design and MethodsIn the UK Biobank cohort study, we included participants free of cardiovascular disease (CVD) with spirometry (FEV1 and FVC values) at baseline (2006-2010). Participants with baseline spirometry and follow-up spirometry (2014-2020) were included in the lung function trajectory analysis. Cox proportional hazards multivariate regression was performed to evaluate the outcomes of major adverse cardiovascular events (MACEs), incident myocardial infarction (MI), stroke, heart failure (HF), and CVD mortality in association with lung function.ResultsFor baseline analysis (329,954 participants), the multivariate adjusted hazard ratios (HRs) for participants had PRISm findings (vs normal spirometry findings) were 1.26 (95% CI, 1.17-1.35) for MACE, 1.12 (95% CI, 1.01-1.25) for MI, 1.88 (95% CI, 1.72-2.05) for HF, 1.26 (95% CI, 1.13-1.40) for stroke, and 1.55 (95% CI, 1.37-1.76) for CVD mortality, respectively. A total of 22,781 participants underwent follow-up spirometry after an average of 8.9 years. Trajectory analysis showed that persistent PRISm findings (HR, 1.96; 95% CI, 1.24-3.09) and airflow obstruction (HR, 1.43; 95% CI, 1.00-2.04) was associated with a higher incidence of MACE vs consistently normal lung function. Compared with persistent PRISm findings, changing from PRISm to normal spirometry findings was associated with a lower incidence of MACE (HR, 0.42; 95% CI, 0.19-0.99).InterpretationIndividuals with baseline or persistent PRISm findings were at a higher risk of diverse cardiovascular outcomes even after adjusting for a wide range of confounding factors. However, individuals who transitioned from PRISm to normal findings showed a similar cardiovascular risk as those with normal lung function. Preserved ratio impaired spirometry (PRISm) findings are a heterogeneous condition characterized by a normal FEV1 to FVC ratio with underlying impairment of pulmonary function. Data relating to the association of baseline and trajectories of PRISm findings with diverse cardiovascular outcomes are sparse. How do baseline and trajectories of PRISm findings impact subsequent cardiovascular events? In the UK Biobank cohort study, we included participants free of cardiovascular disease (CVD) with spirometry (FEV1 and FVC values) at baseline (2006-2010). Participants with baseline spirometry and follow-up spirometry (2014-2020) were included in the lung function trajectory analysis. Cox proportional hazards multivariate regression was performed to evaluate the outcomes of major adverse cardiovascular events (MACEs), incident myocardial infarction (MI), stroke, heart failure (HF), and CVD mortality in association with lung function. For baseline analysis (329,954 participants), the multivariate adjusted hazard ratios (HRs) for participants had PRISm findings (vs normal spirometry findings) were 1.26 (95% CI, 1.17-1.35) for MACE, 1.12 (95% CI, 1.01-1.25) for MI, 1.88 (95% CI, 1.72-2.05) for HF, 1.26 (95% CI, 1.13-1.40) for stroke, and 1.55 (95% CI, 1.37-1.76) for CVD mortality, respectively. A total of 22,781 participants underwent follow-up spirometry after an average of 8.9 years. Trajectory analysis showed that persistent PRISm findings (HR, 1.96; 95% CI, 1.24-3.09) and airflow obstruction (HR, 1.43; 95% CI, 1.00-2.04) was associated with a higher incidence of MACE vs consistently normal lung function. Compared with persistent PRISm findings, changing from PRISm to normal spirometry findings was associated with a lower incidence of MACE (HR, 0.42; 95% CI, 0.19-0.99). Individuals with baseline or persistent PRISm findings were at a higher risk of diverse cardiovascular outcomes even after adjusting for a wide range of confounding factors. However, individuals who transitioned from PRISm to normal findings showed a similar cardiovascular risk as those with normal lung function. Take-home PointsStudy Question: What are the associations between baseline and trajectories of preserved ratio impaired spirometry (PRISm) findings and subsequent cardiovascular events? Do individuals who transition from PRISm to normal findings show similar prognosis as those with persistent normal lung function?Results: In the general population, individuals with baseline PRISm and persistent PRISm findings and those who demonstrate PRISm findings after normal lung function experience an increased risk of cardiovascular events, but individuals who transition from PRISm to normal findings show similar risk as those with normal lung function.Interpretation: These results suggest that PRISm findings are an important subtype of spirometry categories related to the risks of diverse cardiovascular events and that individuals who transition from PRISm to normal findings show similar cardiovascular risk as those with normal lung function. Study Question: What are the associations between baseline and trajectories of preserved ratio impaired spirometry (PRISm) findings and subsequent cardiovascular events? Do individuals who transition from PRISm to normal findings show similar prognosis as those with persistent normal lung function? Results: In the general population, individuals with baseline PRISm and persistent PRISm findings and those who demonstrate PRISm findings after normal lung function experience an increased risk of cardiovascular events, but individuals who transition from PRISm to normal findings show similar risk as those with normal lung function. Interpretation: These results suggest that PRISm findings are an important subtype of spirometry categories related to the risks of diverse cardiovascular events and that individuals who transition from PRISm to normal findings show similar cardiovascular risk as those with normal lung function. Despite underlying pulmonary function impairment, proportional declines in FEV1 and FVC lead to a normal FEV1 to FVC ratio. Although they do not fit the criteria for COPD, individuals with preserved ratio impaired spirometry (PRISm) findings, previously called restricted pulmonary function,1Kalhan R. Dransfield M.T. Colangelo L.A. et al.Respiratory symptoms in young adults and future lung disease. The CARDIA Lung Study.Am J Respir Crit Care Med. 2018; 197: 1616-1624Crossref PubMed Scopus (55) Google Scholar,2Guerra S. Sherrill D.L. Venker C. Ceccato C.M. Halonen M. Martinez F.D. Morbidity and mortality associated with the restrictive spirometric pattern: a longitudinal study.Thorax. 2010; 65: 499-504Crossref PubMed Scopus (138) Google Scholar Global Initiative for Chronic Obstructive Lung Disease unclassified criteria,3Sood A. Petersen H. Qualls C. et al.Spirometric variability in smokers: transitions in COPD diagnosis in a five-year longitudinal study.Respir Res. 2016; 17: 1-10Crossref PubMed Scopus (30) Google Scholar or a nonspecific pattern of findings,4Kalhan R. Tran B.T. 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Miele C.H. et al.Prevalence and risk factors of restrictive spirometry in a cohort of Peruvian adults.Int J Tuberc Lung Dis. 2017; 21: 1062-1068Crossref PubMed Scopus (8) Google Scholar Few longitudinal cohort investigations with repeat spirometry have assessed the association between spirometry trajectories and cardiovascular outcomes, and most of these studies did not distinguish different cardiovascular events.7Jankowich M. Elston B. Liu Q. et al.Restrictive spirometry pattern, cardiac structure and function, and incident heart failure in African Americans. The Jackson Heart Study.Ann Am Thorac Soc. 2018; 15: 1186-1196Crossref PubMed Scopus (31) Google Scholar,20Wan E.S. Balte P. Schwartz J.E. et al.Association between preserved ratio impaired spirometry and clinical outcomes in US adults.JAMA. 2021; 326: 2287-2298Crossref PubMed Scopus (30) Google Scholar,21Wijnant S.R.A. De Roos E. Kavousi M. et al.Trajectory and mortality of preserved ratio impaired spirometry: the Rotterdam Study.Eur Respir J. 2020; 55: 1901217Crossref PubMed Scopus (65) Google Scholar A recent Danish study based on the Copenhagen City Heart Study,22Marott J.L. Ingebrigtsen T.S. Çolak Y. Vestbo J. Lange P. Trajectory of preserved ratio impaired spirometry: natural history and long-term prognosis.Am J Respir Crit Care Med. 2021; 204: 910-920Crossref PubMed Scopus (24) Google Scholar which included 1,160 individuals 20 to 40 years of age, found that incident PRISm findings could increase the risk of admission for ischemic heart disease or heart failure (HF). However, no association between cardiovascular events and lung function has been found in patients with persistent PRISm findings and those who transitioned from PRISm to normal spirometry findings. The study was restricted to young individuals and lacked adjustment for smoking status, which not only might have resulted in insufficient generalization for middle-aged people, but also limited the ability to measure the actual relationship of PRISm findings lung function trajectory to cardiovascular risk. In view of the uncertainty, we aimed to use data from the UK Biobank study to investigate the associations of baseline PRISm findings with the risk of certain outcomes (myocardial infarction [MI], HF, stroke, and CVD mortality) and analyzed major adverse cardiovascular events (MACEs) as a distinct outcome. We also assessed longitudinal patterns of trajectories and quantified the subsequent risks of cardiovascular outcomes using the follow-up data. The UK Biobank study is a nationwide cohort study of approximately 500,000 residents of the United Kingdom (40-69 years of age). The UK Biobank study has been described previously.23UK BiobankUK Biobank: protocol for a large-scale prospective epidemiological resource. UK Biobank website. Accessed August 20, 2022.https://www.ukbiobank.ac.uk/media/gnkeyh2q/study-rationale.pdfGoogle Scholar,24Sudlow C. Gallacher J. Allen N. et al.UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.PLoS Med. 2015; 12e1001779Crossref PubMed Scopus (4059) Google Scholar Participants were recruited between December 19, 2006, and October 10, 2010, and were identified from the National Health Service register. Baseline data on sociodemographic variables, lifestyle, and health-related conditions were obtained from self-completed questionnaires, brief interviews, functional and physical measurements, and blood sampling. Participants with missing smoking status, height, or weight data, as well as those having CVD at baseline, were excluded from the study (Fig 1). The North West Multicentre Research Ethics Committee approved the UK Biobank study. All participants signed informed consent at the initial visit to the assessment center. This research was conducted using the UK Biobank resource (application no.: 55794). At the time of recruitment, all participants were requested to undergo spirometry before bronchodilator administration. Participants were instructed to use a spirometer (under supervision of a trained technician) to record two to three blows (Pneumotrac 6800; Vitalograph). A computer was used to compare the initial two blows’ repeatability, and if acceptable (defined as a 5% difference in FVC and FEV1), the third blow was skipped.25Higbee D.H. Granell R. Smith G.D. Dodd J.W. Prevalence, risk factors, and clinical implications of preserved ratio impaired spirometry: a UK Biobank cohort analysis.Lancet Respir Med. 2022; 10: 149-157Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar The spirometry results were evaluated by the investigators. The highest FEV1 and FVC values from acceptable blows (best measure) were used. Patients whose baseline height, weight, or smoking status were unknown also were excluded. An FEV1 of less than 80% predicted and an FEV1 to FVC ratio of ≥ 0.70 were considered PRISm findings.25Higbee D.H. Granell R. Smith G.D. Dodd J.W. Prevalence, risk factors, and clinical implications of preserved ratio impaired spirometry: a UK Biobank cohort analysis.Lancet Respir Med. 2022; 10: 149-157Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar We used an FEV1 to FVC ratio of < 0.70, the Global Initiative for Chronic Obstructive Lung Disease criteria for stage I-IV obstruction,26Global Initiative for Chronic Obstructive Pulmonary Lung DiseasePocket guide to COPD diagnosis, management, and prevention. Global Initiative for Chronic Obstructive Pulmonary Lung Disease website.https://goldcopd.org/wp-content/uploads/2020/03/GOLD-2020-POCKET-GUIDE-ver1.0_FINAL-WMV.pdfDate accessed: May 15, 2022Google Scholar to determine airflow obstruction (AO). Control participants were those with an FEV1 of ≥ 80% predicted and an FEV1 to FVC ratio of ≥ 0.70. We also used Global Lung Initiative definitions for FEV1 and FVC with lower limit of normal thresholds to determine these statuses in the sensitivity analyses. Participants were invited back to take a repeat survey with repeat spirometry if they resided close to an assessment center. The follow-up study included only individuals who had been included at baseline. We included individuals who were re-examined for spirometry between April 30, 2014, and March 13, 2020 (e-Fig 1). In our analysis, the highest FEV1 and FVC values obtained from acceptable spirometry were used. Individuals without data on height, weight, or smoking status at the follow-up were not included (Fig 1). We evaluated several potential confounding variables using the baseline questionnaire, including sociodemographic characteristics (age, sex, ethnicity, and household income), social demographic parameters (Townsend Deprivation Index), and lifestyle behaviors (smoking status and alcohol consumption). Height, BMI, and waist circumference were obtained by physical examination. Prevalent CVD, hyperlipidemia, diabetes, renal impairment, asthma, and COPD were determined from self-report and diagnosis codes. Comorbidity was defined as having any of the above diseases. The UK Biobank directly provides the Townsend Deprivation Index, which is used as a measurement of social background and is generated from the home postcode.27Celis-Morales C.A. Lyall D.M. Welsh P. et al.Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study.BMJ. 2017; 357: j1456Crossref PubMed Scopus (348) Google Scholar,28Tyrrell J. Jones S.E. Beaumont R. et al.Height, body mass index, and socioeconomic status: mendelian randomisation study in UK Biobank.BMJ. 2016; 352: i582Crossref PubMed Scopus (191) Google Scholar BMI was determined by dividing weight in kilograms by the square of height in meters. High BP was defined as a self-reported history of hypertension, the use of antihypertensive medications, a systolic BP of ≥ 140 mm Hg, or a diastolic BP of ≥ 90 mm Hg. On the UK Biobank website (https://biobank.ndph.ox.ac.uk/ukb/search.cgi), details of these evaluations are available. The outcomes of the study included incidence of MACE, MI, HF, stroke, and mortality resulting from CVD. MACEs comprise nonfatal MI, nonfatal ischemic stroke, and CVD deaths.29Yeap B.B. Marriott R.J. Antonio L. et al.Associations of serum testosterone and sex hormone-binding globulin with incident cardiovascular events in middle-aged to older men.Ann Intern Med. 2022; 175: 159-170Crossref PubMed Scopus (10) Google Scholar CVD events were identified from the baseline (March 2006-October 2010) until the latest date (May 21, 2020) to which connected hospitalizations and mortality information from all UK Biobank sources were complete and available.30UK BiobankData providers and dates of data availability. UK Biobank website.http://biobank.ndph.ox.ac.uk/showcase/exinfo.cgi?src=Data_providers_and_dates on 30 May 2022Date accessed: May 30, 2022Google Scholar Time and cause of death for participants from England and Wales were determined by linking to the National Health Service Information Centre’s death registries; for participants from Scotland, their information was derived from the National Health Service Central Register.24Sudlow C. Gallacher J. Allen N. et al.UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.PLoS Med. 2015; 12e1001779Crossref PubMed Scopus (4059) Google Scholar In addition, the date and reason for hospital admissions of Scottish participants were determined by linking to the Scottish Morbidity Records; health episode statistics were used for participants from England and Wales.24Sudlow C. Gallacher J. Allen N. et al.UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.PLoS Med. 2015; 12e1001779Crossref PubMed Scopus (4059) Google Scholar,31UK Biobank, Primary care linked data. Version 1.0. UK Biobank website. Accessed August 20, 2022. https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/primary_care_data.pdfGoogle Scholar Diagnostic codes from the International Classification of Diseases, Ninth and Tenth Revisions, were used (e-Table 1). Participants were censored at the earliest of the date of death or corresponding end of follow-up (May 21, 2020), if they did not record an incident event. We used the RSpiro package32R Foundation for Statistical Computingrspiro: implementation of spirometry equations. R Foundation for Statistical Computing website. Accessed August 20, 2022.https://cran.r-project.org/web/packages/rspiro/index.htmlGoogle Scholar in R version 3.6.1 software (R Foundation for Statistical Computing) to determine FEV1 % predicted (according to Global Lung Initiative 2012 values). We evaluated demographic differences between participants with PRISm and those with normal spirometry findings, as well as between participants with normal spirometry findings and those with AO. For continuous outcomes, we used z scores, and for categorical outcomes, Pearson’s χ 2Guerra S. Sherrill D.L. Venker C. Ceccato C.M. Halonen M. Martinez F.D. Morbidity and mortality associated with the restrictive spirometric pattern: a longitudinal study.Thorax. 2010; 65: 499-504Crossref PubMed Scopus (138) Google Scholar test was used to calculate P values. For normally distributed variables, data were expressed as mean ± SD. For nonnormally distributed variables, data were expressed as median (interquartile range). Categorical variables are shown as count (percentage). After the exclusion phase, we applied multiple imputation using chained equations to assign any missing covariate values to reduce the potential for inferential bias.33van Buuren S, Groothuis-Oudshoorn K, Robitzsch A, et al. Mice: multivariate imputation by chained equations. R package version 2.22. 2015; 2019.Google Scholar e-Table 2 provides detailed information on the number of missing covariates. We defined two baselines: one was the time when participants underwent the first spirometry examination (2006-2010), the other was the time when individuals underwent the follow-up visit (for individuals with two spirometry results). We used Cox proportional hazards models in the study to calculate the hazard ratios (HRs) and corresponding 95% CIs for outcomes (CVD mortality; incidence of MACE, MI, HF, or stroke) in relationship to spirometry at baseline and lung function change patterns. We categorized lung function change patterns into nine groups—normal to normal, normal to PRISm, normal to AO, PRISm to PRISm, PRISm to AO, PRISm to normal, AO to AO, AO to PRISm, and AO to normal—and used normal to normal as the reference group. We used tests based on Schoenfeld residuals34Grambsch P.M. Therneau T.M. Proportional hazards tests and diagnostics based on weighted residuals.Biometrika. 1994; 81: 515-526Crossref Scopus (3955) Google Scholar to assess the proportional hazard assumption. We did not found any violation of this assumption in our study. We used two sets of models. We adjusted baseline age (years) and sex (male or female) in the basic model (model 1). The multivariate model (model 2) additionally included Townsend Deprivation Index, ethnicity, household income (< £31,000 or ≥ £31,000), height, BMI, smoking status (never, former, or current), alcohol intake (< 3 times/wk or ≥ 3 times/wk), diabetes (yes or no), hyperlipidemia (yes or no), high BP (yes or no), and renal impairment (yes or no). For spirometry trajectory analysis, we adjusted for the variables collected at the time when individuals underwent the follow-up spirometry test. To provide additional information on the MACE risk among different pulmonary function transition states, we further divided the samples into three groups, setting PRISm to PRISm, AO to AO, and normal to normal as the reference. This led to a decrease of sample size in each group. To maintain the reliability of the statistical tests, we adjusted only for age, sex, smoking status, and baseline FEV1 (phase 1) in this model. To examine the robustness of the main findings, we conducted a sensitivity analysis by (1) conducting a competing risks analysis with all-cause mortality as a competing event for MACE, MI, HF, and stroke and (2) using Global Lung Initiative definitions for FEV1 and FVC with lower limit of normal thresholds. For the baseline analysis, we additionally excluded patients with interstitial lung diseases, patients with missing values for covariates, or excluded participants with comorbidity. To modulate variations in smoking status amid the follow-up, time-varying measures of smoking status were fitted by Cox regression with time-dependent covariates. All analyses were carried out with R version 3.6.1 software. A P value of < .05 (two sided) was considered statistically significant. When enrolled in the UK Biobank study, 353,308 of the 457,479 participants (77.2%) who had spirometry measurements had the best measure FEV1 and FVC. Individuals who had prevalent CVD or missing data for smoking status, height, or weight were excluded (n = 23,354); thus, 329,954 participants were included for analysis at the baseline time point. At the baseline, 37,897 participants (11.5% of 329,954 participants) had PRISm results, 49,504 participants (15.0%) had stage I-IV AO, and 242,553 participants (73.5%) had normal spirometry results (Table 1).Table 1Baseline Demographics of Study Participants at Phase 1 (2006-2010)VariableNormal Spirometry FindingsPRISm FindingsAONo. of participants242,55337,89749,504Age, y55.4 ± 8.155.6 ± 8.258.54 ± 7.7aP < .05 compared with patients with normal spirometry findings. P values were calculated using z score for continuous outcomes and Pearson’s χ2 test fo