Abstract
Rationale: The preclinical natural history of progressive lung fibrosis is poorly understood.
Objectives: Our goals were to identify risk factors for interstitial lung abnormalities (ILA) on high-resolution computed tomography (HRCT) scans and to determine progression toward clinical interstitial lung disease (ILD) among subjects in a longitudinal cohort of self-reported unaffected first-degree relatives of patients with familial interstitial pneumonia.
Methods: Enrollment evaluation included a health history and exposure questionnaire and HRCT scans, which were categorized by visual assessment as no ILA, early/mild ILA, or extensive ILA. The study endpoint was met when ILA were extensive or when ILD was diagnosed clinically. Among subjects with adequate study time to complete 5-year follow-up HRCT, the proportion with ILD events (endpoint met or radiographic ILA progression) was calculated.
Measurements and Main Results: Among 336 subjects, the mean age was 53.1 (SD, 9.9) years. Those with ILA (early/mild [n = 74] or extensive [n = 3]) were older, were more likely to be ever smokers, had shorter peripheral blood mononuclear cell telomeres, and were more likely to carry the MUC5B risk allele. Self-reported occupational or environmental exposures, including aluminum smelting, lead, birds, and mold, were independently associated with ILA. Among 129 subjects with sufficient study time, 25 (19.4%) had an ILD event by 5 years after enrollment; of these, 12 met the study endpoint and another 13 had radiologic progression of ILA. ILD events were more common among those with early/mild ILA at enrollment (63.3% vs. 6.1%; P < 0.0001).
Conclusions: Rare and common environmental exposures are independent risk factors for radiologic abnormalities. In 5 years, progression of ILA occurred in most individuals with early ILA detected at enrollment.
Keywords: pulmonary fibrosis, lung diseases, interstitial, telomere, epidemiology
At a Glance Commentary
Scientific Knowledge on the Subject
Genetic and environmental factors that determine the natural history of idiopathic interstitial lung disease are incompletely understood. Accumulating evidence indicates that there is a presymptomatic interval in most patients during which radiologically detectable interstitial lung abnormalities (ILA) may be present, enabling investigations into critical factors that modulate disease onset and early progression.
What This Study Adds to the Field
In this ongoing, prospective, longitudinal cohort of asymptomatic first-degree relatives of patients with familial interstitial pneumonia in which 23% of study subjects had ILA detectable by enrollment high-resolution computed tomography (HRCT), increasing age, smoking history, and decreased peripheral blood mononuclear cell telomere length, as well as self-reported exposure to mold, birds, lead, and aluminum smelting, were significantly associated with ILA risk. Among study subjects followed for 5 years, the majority (63%) of individuals with early/mild ILA at the time of enrollment had evidence of progression, whereas less than 10% of individuals without ILA on enrollment HRCT progressed. Together, these data suggest that there are modifiable risk factors for familial interstitial pneumonia and support a role for HRCT-based screening of high-risk individuals in families with familial interstitial pneumonia.
Interstitial lung disease (ILD) is a collection of chronic respiratory disorders that result in substantial morbidity and mortality. Many patients with ILD have no definable underlying systemic disease (e.g., systemic sclerosis) or causative environmental exposure (e.g., asbestos) to explain disease development and thus are considered to have idiopathic disease. Studies have demonstrated that up to 20% of individuals diagnosed with idiopathic ILD have at least one affected relative; this syndrome is termed “familial interstitial pneumonia” (FIP) (1–3). Most FIP kindreds exhibit an autosomal dominant inheritance pattern with incomplete penetrance; therefore, first-degree relatives of affected individuals in these families may have up to a 50% lifetime risk for development of clinical ILD (3–6). Despite recent progress in the discovery of genetic risk factors for pulmonary fibrosis, a pathologic rare gene variant is currently identifiable in only 20–30% of FIP kindreds (7, 8). In addition to investigating genetic factors that impact development of ILD, studying members of families with FIP offers the possibility of better defining the preclinical natural history of ILD, which remains poorly understood.
Interstitial lung abnormalities (ILA) are patterns of increased density of the lung parenchyma identified on computed tomographic (CT) scans that have been hypothesized to represent subclinical disease in individuals without reported history of ILD (9–12). The prevalence of ILA varies across reported cohorts because of differences in ILA definitions and study population characteristics. Up to two-thirds of ILA reported in prior studies have patterns consistent with established fibrotic ILD, including architectural distortion of the lung parenchyma and possible or definite usual interstitial pneumonia patterns, suggesting that these individuals have established but undiagnosed ILD (13–17). The implications of less extensive, indeterminate patterns, including early interstitial abnormalities without fibrosis, are not yet well understood (10–12, 14, 18).
We have established an ongoing, longitudinal, prospective cohort study of first-degree relatives of patients affected by FIP to 1) determine genetic and environmental risk factors for development of clinical ILD, 2) identify early disease mechanisms, and 3) define the natural history before development of clinically evident disease (1). In this report focusing on risk factors for development and progression of early ILD, we identify several potentially modifiable risk factors for early radiologic interstitial abnormalities in the lungs of at-risk individuals, including several environmental exposures other than cigarette smoking. We also report on progression of early and nonspecific radiologic abnormalities and development of ILD during a 5-year follow-up period after enrollment in a subset of this cohort. Some subjects were included in previous interim reports (1, 19), and some of these results were published previously in abstracts (20, 21).
Methods
Subject Identification and Enrollment Evaluation
Through the Vanderbilt FIP registry, unaffected first-degree relatives of patients with FIP were invited to enroll in this longitudinal cohort study (1). Eligible individuals were siblings or offspring of individuals affected by ILD and were members of families in which at least two relatives had ILD, at least one of whom was diagnosed with idiopathic pulmonary fibrosis. Eligible individuals were older than 40 years of age or within 10 years of the age of diagnosis of the youngest affected family member. At enrollment, each subject completed a health questionnaire (see online supplement for excerpts and methods specific to processing exposure and symptom data), blood draw, high-resolution CT (HRCT), and pulmonary function testing (initiated routinely in 2017). Peripheral blood mononuclear cell telomere restriction fragment (TRF) length was determined via Southern blot (22). Measurement of the number of copies of the MUC5B (Mucin 5B) gene promoter polymorphism (rs35705950, with each copy denoted by a T allele) was performed using a competitive allele-specific PCR-based TaqMan SNP assay (1, 23).
Subjects included in this interim report were enrolled between October 28, 2008, and April 22, 2019, and had complete information available on important covariates (MUC5B genotype, TRF length, and smoking history). The Vanderbilt Institutional Review Board approved this study (IRB080780, IRB020343).
HRCT
Each HRCT scan was reviewed by an expert thoracic radiologist, with ILA categorized as none, early/mild, or extensive on the basis of the presence and extent of specific interstitial features, including ground-glass opacities, intralobular reticular opacities, irregular thickening of interlobular septa, traction bronchiectasis, traction bronchiolectasis, and honeycombing. ILA were categorized as extensive when there was >5% honeycombing in two or more zones or when >30% of total lung parenchyma had interstitial features.
In addition to assignment to one of the ILA categories described above, semiquantitative visual scores were determined for each of the six interstitial features, which were rated as 0 (absent), 1 (involving <5% of the lung zone), or 2 (>5% of the lung zone) in the upper, middle, lower, and lowest zones of the left and right lungs. These values were summed across all lung zones for a whole-lung visual ILA score (range, 0–96). Unless they opted out during informed consent, subjects were informed of their visual HRCT findings.
To further evaluate interstitial fibrosis on the basis of HRCT, HRCT scans with appropriate image quality were assigned a score using the data-driven textural analysis (DTA) method, hereinafter called the “DTA score.” Briefly, this approach classifies areas of lung parenchyma as either normal or fibrotic (on the basis of exemplar lung regions labeled on CT scans as honeycombing, reticular abnormality, or traction bronchiectasis) and has been applied in preclinical cohorts and cohorts with established pulmonary fibrosis (19, 24, 25). The DTA score represents the proportion of lung regions classified as fibrotic (25).
Longitudinal Follow-up and ILD Event Adjudication
The study endpoint was met when ILA were categorized as extensive on any HRCT scan or when ILD was diagnosed in the clinical setting. Clinical ILD diagnoses were confirmed by a physician investigator after reviewing medical records. Subjects not meeting the endpoint at enrollment (i.e., those with categorically early/mild or no ILA on the enrollment HRCT scan) underwent yearly health questionnaires (including the dyspnea scale and inquiry about new ILD diagnoses) and were offered a second round of HRCT approximately 5 years after enrollment. Subjects meeting the endpoint were referred for clinical care and did not undergo additional study procedures.
Statistical Analysis
Patient characteristics at enrollment were described using mean ± SD (continuous normally distributed variables), median and interquartile range (continuous nonnormally distributed), or number and percent (categorical variables). Odds ratios and 95% confidence intervals (CIs) were used to assess the risk of having ILA (composite of early/mild or extensive) on the enrollment HRCT scan based on patient characteristics and environmental exposures. Continuous covariates were tested for linearity on the basis of a restricted cubic spline with five knots. Among those with exposure questionnaire information available, the odds of having ILA based on each queried exposure were calculated, together with 95% CI, raw P values, and false discovery rate (FDR)-adjusted P values (linear step-up method of Benjamini and Hochberg across 48 exposures) (26). Exposures with an FDR-adjusted P < 0.1 were examined in multivariable models including one exposure per model and adjusted for age, smoking, MUC5B genotype, and TRF length and in a multivariable model including all significant exposures and adjustment covariates. All models accounted for within-family correlation using a random intercept.
Among the subgroup of subjects who were enrolled in this longitudinal cohort long enough to complete the second round of HRCT by the time of data lock (April 22, 2019) and who did not meet the study endpoint (categorically extensive ILA) at enrollment, the number and type of ILD events were summarized. ILD events were defined as meeting the study endpoint (extensive ILA on an HRCT scan or clinical ILD diagnosis) or progression of ILA on the second HRCT scan (increasing whole-lung visual ILA score between enrollment and 5-year HRCT). In a subset of 20 subjects with enrollment and 5-year HRCT scans, a second radiologist dichotomously documented 5-year ILA progression as present or absent. Patient characteristics at enrollment were summarized with subjects grouped by ILD event type to explore characteristics potentially associated with development of ILD. Among subjects completing two rounds of HRCT and/or two dyspnea questionnaires, average annual changes in whole-lung visual ILA score, DTA score, and dyspnea scale score were calculated for each subject as (score 2 − score 1)/(number of years between scores) and summarized as mean (SD) change per year. Patient characteristics among those subjects lost to follow-up (i.e., enrolled >5 yr without completing follow-up procedures) and those awaiting follow-up (i.e., enrolled within the 5 yr before data lock) were also summarized.
The relationship between the baseline DTA score, the whole-lung visual ILA score, and other patient characteristics at baseline and during follow-up was evaluated using Spearman correlation or the Mann-Whitney U test. In subjects with enrollment and 5-year HRCT scans reviewed by two radiologists, interobserver agreement on progression of visual ILA was calculated by Cohen’s κ, with κ < 0.4 defined as poor, 0.4 < κ < 0.6 as intermediate, 0.6 < κ < 0.75 as good, and κ > 0.75 as excellent (27). SAS version 7.13 software (SAS Institute) was used for analysis.
Results
Patient Characteristics at Enrollment
In this ongoing longitudinal study of self-reported unaffected first-degree relatives of patients with FIP, 336 individuals had completed the initial evaluation at the time of this analysis. This cohort represents 157 families with 1 to 11 at-risk individuals per family. The mean age of the cohort was 53.1 (9.9) years; 212 (63%) were female; 95 (28%) were ever smokers; and 142 (42%) were MUC5B T-allele carriers. On the basis of the enrollment HRCT scan, 77 of 336 (22.9%) subjects were categorized as having early/mild (n = 74) or extensive (n = 3) ILA. Table 1 displays complete enrollment characteristics by enrollment ILA status. Older age (adjusted odds ratio [aOR], 1.09 per 1-yr increase; 95% CI, 1.05–1.13; P < 0.0001), history of cigarette smoking (aOR, 2.31; 95% CI, 1.24–4.32; P = 0.009), and having shorter telomere length (aOR, 0.70 per 1-kb increase; 95% CI, 0.51–0.97; P = 0.03) were independently associated with ILA on the enrollment HRCT scan (Table E1 in the online supplement). MUC5B T-allele carriers were nominally (P = 0.055) more likely to have ILA, and sex (P = 0.98) was not associated with ILA status. Age and TRF length had a linear association with ILA (Figure E1).
Table 1.
Patient Characteristics at Enrollment
| No ILA (n = 259) | Early/Mild or Extensive ILA (n = 77) | |
|---|---|---|
| Age, yr, mean (SD) | 51.6 (9.2) | 58.8 (10.0) |
| Sex, F, n (%) | 165 (64) | 47 (61) |
| Ever smoker, n (%) | 61 (24) | 34 (44) |
| Self-reported medical history, n (%) | ||
| Asthma | 48 (19) | 17 (22) |
| Autoimmune disease | 2 (0.01) | 4 (5) |
| Emphysema | 0 (0) | 3 (4) |
| Gastroesophageal reflux disease | 8 (3) | 3 (4) |
| History of pneumonia | 62 (24) | 26 (34) |
| Hypertension | 78 (30) | 26 (34) |
| Liver disease | 11 (4) | 6 (8) |
| Obstructive sleep apnea | 5 (2) | 1 (1) |
| Venous thromboembolism | 0 (0) | 1 (1) |
| Dyspnea score (range, 0–5), median (Q1–Q3) | 0 (0–1) | 0 (0–1) |
| Enrollment HRCT visual ILA category, n (%) | ||
| Early/mild | — | 74 (96) |
| Extensive | — | 3 (4) |
| Specific enrollment HRCT findings, n (%) | ||
| Honeycombing | 0 (0) | 6 (8) |
| Ground-glass opacities | 1 (0.4) | 28 (36) |
| Intralobular reticular opacities | 5 (2) | 65 (84) |
| Irregular septal thickening | 1 (0.4) | 38 (49) |
| Traction bronchiectasis | 0 (0) | 5 (7) |
| Traction bronchiolectasis | 0 (0) | 9 (12) |
| Any bilateral findings | 1 (0.4) | 45 (58) |
| Visual ILA score (range, 0–96), median (Q1–Q3) | 0 (0–0) | 4 (2–8) |
| DTA score (range, 0–100), median (Q1–Q3) | 0.6 (0.4–1.2) (n = 240) | 2.2 (1.0–4.6) (n = 73) |
| Pulmonary function measures, % predicted, mean (SD) | n = 54 | n = 16 |
| FVC | 100.8 (13.5) | 98.8 (14.7) |
| FEV1 | 100.7 (14.4) | 104.1 (15.5) |
| TLC | 102.2 (13.2) | 99.2 (14.9) (n = 15) |
| DlCO | 92.8 (14.0) | 89.4 (12.7) (n = 15) |
| TRF length, kb, mean (SD) | 6.4 (0.98) | 5.95 (0.9) |
| MUC5B genotype, n (%)* | ||
| GG | 158 (61) | 36 (47) |
| GT | 93 (36) | 38 (49) |
| TT | 8 (3) | 3 (4) |
Definition of abbreviations: DTA = data-driven textural analysis; HRCT = high-resolution computed tomography; ILA = interstitial lung abnormalities; Q1–Q3 = interquartile range; TRF = telomere restriction fragment.
n = 336 subjects, except where noted.
Each allele with a copy of the MUC5B (Mucin 5B) gene promoter polymorphism (rs35705950) is denoted by a T.
Environmental Exposures and ILA on Enrollment HRCT Scan
Two hundred sixty-five subjects completed an exposure questionnaire before receiving any information about their screening HRCT. Note that an additional 68 subjects completed a shorter questionnaire whose results are not readily superimposable on the standard version and thus were not analyzed for this report. Three subjects did not complete either questionnaire. Among the 265 subjects with complete questionnaire data available, self-reported exposures were common, with 60.0% reporting at least one dust, 49.1% a chemical, and 44.1% an organic antigen exposure (Table E2). Several self-reported exposures were independently associated with the presence of ILA on the enrollment HRCT scan, including aluminum smelting, lead, birds, and mold, on the basis of an FDR-adjusted P < 0.10 and age-, smoking-, MUC5B-, and TRF-adjusted P < 0.05 (Table 2). Of note, subjects frequently reported multiple specific exposures. The median number of specific exposures reported was 3 (interquartile range, 1–6; range, 0–33). Among exposures with unadjusted P < 0.05, frequent coexposures included sulfur oxide with carbon monoxide, lead and welding, and aluminum smelting and welding (Table E3). Table E4 shows a multivariable model including the four statistically significant exposures and adjustment covariates, demonstrating an independent association of age (aOR, 2.79 per +1 yr; 95% CI, 1.24–6.27), ever-smoker status (aOR, 1.09; 95% CI, 1.04–1.13), aluminum smelting (aOR, 11.52; 95% CI, 1.50–88.33), and mold exposure (aOR, 3.22; 95% CI, 1.40–7.41) with ILA. These data suggest an important role for environmental exposures in determining ILA in individuals from FIP families.
Table 2.
Odds of Having Interstitial Lung Abnormalities on Enrollment High-Resolution Computed Tomography, Based on Self-reported Occupational and Environmental Exposures
| Exposure | n (%) of Subjects Exposed (n = 265) | Univariable |
Multivariable* |
|||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | FDR P Value | aOR (95% CI) | P Value | ||
| Dusts | ||||||
| Grain | 44 (16.6) | 1.34 (0.60–3.00) | 0.4717 | 0.6119 | — | — |
| Hay | 67 (25.3) | 1.70 (0.85–3.38) | 0.1329 | 0.3038 | — | — |
| Asbestos | 39 (14.7) | 1.44 (0.62–3.22) | 0.3903 | 0.5476 | — | — |
| Silica or sand | 38 (14.3) | 1.04 (0.44–2.46) | 0.9330 | 0.9330 | — | — |
| Mica feldspar | 5 (1.9) | 2.37 (0.33–16.84) | 0.3847 | 0.5476 | — | — |
| Coal | 22 (8.3) | 3.33 (1.24–8.99) | 0.0177 | 0.1340 | — | — |
| Rock | 34 (12.8) | 2.44 (1.06–5.64) | 0.0368 | 0.1472 | — | — |
| Clay or ceramics | 27 (10.2) | 1.83 (0.72–4.65) | 0.2022 | 0.3801 | — | — |
| Wood | 71 (26.8) | 2.00 (1.03–3.89) | 0.0409 | 0.1510 | — | — |
| Fiberglass | 37 (14.0) | 2.04 (0.91–4.61) | 0.0851 | 0.2150 | — | — |
| Cotton | 29 (10.9) | 1.96 (0.80–4.84) | 0.1418 | 0.3094 | — | — |
| Fumes | ||||||
| Welding | 42 (15.9) | 2.60 (1.22–5.53) | 0.0138 | 0.1325 | — | — |
| Metal fume | 35 (13.2) | 1.82 (0.77–4.29) | 0.1707 | 0.3562 | — | — |
| Ferrous sulfate | 9 (3.4) | 1.66 (0.35–7.77) | 0.5183 | 0.6547 | — | — |
| Aluminum smelting | 9 (3.4) | 13.95 (2.44–79.79) | 0.0033 | 0.0528 | 14.88 (2.67–97.73) | 0.005 |
| Plastic | 26 (9.8) | 2.86 (1.10–7.39) | 0.0307 | 0.1340 | — | — |
| Gases | ||||||
| Hydrogen sulfide | 4 (1.5) | 8.85 (0.74–105.7) | 0.0845 | 0.2150 | — | — |
| Sulfur oxide | 7 (2.6) | 8.52 (1.36–53.29) | 0.0223 | 0.1340 | — | — |
| Nitrogen oxide | 8 (3.0) | 3.40 (0.70–16.40) | 0.1270 | 0.3038 | — | — |
| Carbon monoxide | 33 (12.6) | 2.68 (1.13–6.35) | 0.0255 | 0.1340 | — | — |
| Ethylene oxide | 9 (3.4) | 5.88 (1.19–29.14) | 0.0305 | 0.1340 | — | — |
| Ozone | 5 (1.9) | 2.66 (0.21–13.33) | 0.6294 | 0.7102 | — | — |
| Elements and metals | ||||||
| Arsenic | 4 (1.5) | 4.07 (0.46–36.24) | 0.2059 | 0.3801 | — | — |
| Cadmium | 7 (2.6) | 1.32 (0.22–8.04) | 0.7594 | 0.8100 | — | — |
| Chromium | 10 (3.8) | 2.35 (0.56–9.82) | 0.2407 | 0.4126 | — | — |
| Copper | 23 (8.7) | 3.17 (1.18–8.49) | 0.0224 | 0.1340 | — | — |
| Lead | 27 (10.2) | 3.73 (1.50–9.25) | 0.0049 | 0.0588 | 2.91 (1.05–8.05) | 0.04 |
| Mercury | 14 (5.3) | 1.81 (0.52–6.29) | 0.3508 | 0.5432 | — | — |
| Beryllium | 4 (1.5) | 9.85 (0.84–116.17) | 0.0689 | 0.2149 | — | — |
| Hard metal | 14 (5.3) | 1.37 (0.37–5.02) | 0.6352 | 0.7102 | — | — |
| Zinc | 13 (4.9) | 2.19 (0.60–7.95) | 0.2305 | 0.4098 | — | — |
| Nickel | 10 (3.8) | 1.44 (0.31–6.63) | 0.6362 | 0.7102 | — | — |
| Chemicals | ||||||
| Acid | 22 (8.3) | 2.48 (0.91–6.77) | 0.0761 | 0.2149 | — | — |
| Alkali | 15 (5.7) | 2.93 (0.92–9.35) | 0.0699 | 0.2149 | — | — |
| Ammonia | 54 (20.4) | 1.23 (0.58–2.61) | 0.5812 | 0.6974 | — | — |
| Detergent | 87 (32.8) | 1.16 (0.61–2.21) | 0.6563 | 0.7160 | — | — |
| Dyes | 18 (6.8) | 1.39 (0.43–4.48) | 0.5807 | 0.6974 | — | — |
| Pesticides | 59 (22.3) | 1.44 (0.70–2.97) | 0.3197 | 0.5115 | — | — |
| Herbicides | 46 (17.4) | 1.65 (0.72–3.57) | 0.2024 | 0.3801 | — | — |
| Rodenticides | 13 (4.9) | 1.70 (0.45–6.37) | 0.4300 | 0.5733 | — | — |
| Resins | 21 (7.9) | 1.06 (0.34–3.37) | 0.9175 | 0.9330 | — | — |
| Formaldehyde | 16 (6.0) | 2.83 (0.90–8.89) | 0.0755 | 0.2149 | — | — |
| Organic antigens | ||||||
| Birds | 51 (19.3) | 3.40 (1.63–7.09) | 0.0012 | 0.0528 | 3.37 (1.53–7.41) | 0.003 |
| Mold | 63 (23.8) | 2.89 (1.45–5.77) | 0.0028 | 0.0528 | 3.83 (1.78–8.25) | 0.001 |
| Hot tubs | 42 (15.9) | 0.90 (0.38–2.12) | 0.8070 | 0.8421 | — | — |
| Flooding | 21 (7.9) | 1.79 (0.63–5.09) | 0.2698 | 0.4466 | — | — |
| Leaking pipes | 15 (5.7) | 1.69 (0.50–5.68) | 0.3926 | 0.5476 | — | — |
| Basement water | 38 (14.3) | 0.66 (0.24–1.76) | 0.3993 | 0.5476 | — | — |
Definition of abbreviations: aOR = adjusted odds ratio; CI = confidence interval; FDR = false discovery rate; OR = odds ratio.
Each exposure with an FDR-adjusted P < 0.1 is included in a multivariable model adjusted for age, smoking status, MUC5B genotype, and telomere restriction fragment length, with one exposure included per model.
ILD Events during 5 Years of Follow-up
At the time of this analysis, 129 enrolled subjects without extensive ILA on their initial HRCT scan had sufficient follow-up time to be eligible for a second HRCT 5 years after enrollment. A total of 86 subjects (66.7% of 129) completed a second round of HRCT, including 5 who underwent it as part of clinical care outside of the study but were available for visual ILA analysis; 32 (24.8%) did not complete a second round of HRCT but completed at least one follow-up questionnaire or contacted the study coordinator, and 11 (8.5%) were lost to follow-up. Enrollment characteristics in the groups lost to and awaiting longitudinal follow-up (i.e., not yet enrolled for 5 yr) were similar to those included in this interim analysis (Table E5).
Among all 129 individuals eligible for longitudinal follow-up analysis, 25 had ILD events as defined by 1) meeting the overall study endpoint or 2) an increase in the whole-lung visual ILA score. Twelve (9.3%) of these subjects met the study endpoint, and 13 (10.1%) had progression defined by an increase in the whole-lung visual ILA score (but were not categorized as extensive ILA). ILD events were significantly more common among subjects with early/mild ILA on the enrollment HRCT scan (19 of 30; 63.3%) than among those with no ILA at enrollment (6 of 99; 6.1%) (P < 0.0001). Table 3 shows patient characteristics at enrollment and during follow-up, with subjects stratified by ILD event type. Nominally, those with ILD events were older, more likely to have smoked, more likely to have a MUC5B risk allele, had shorter telomeres, and had a higher baseline DTA score than those without ILD events. Among those who completed at least two dyspnea questionnaires, those with events had a nominal increase in dyspnea, whereas those without events did not. Of note, three subjects considered to have early/mild ILA at study enrollment had small areas of honeycombing identified on their initial HRCT scan; in one subject, honeycombing occupied >5% of a lung zone. All three of these individuals attained clinical ILD diagnoses during follow-up (see Table 3).
Table 3.
Patient Characteristics at Enrollment and during Follow-up, by Interstitial Lung Disease Event Type
| ILD Events |
No Event (n = 104) | ||
|---|---|---|---|
| Developed Clinical ILD or Extensive ILA (n = 12) | ILA Progressed on HRCT 2 (n = 13) | ||
| Characteristics at enrollment | |||
| Age, yr, mean (SD) | 59.7 (12.2) | 53.4 (9.6) | 51.6 (7.3) |
| Sex, F, n (%) | 6 (50) | 4 (31) | 69 (66) |
| Ever smoker, n (%) | 5 (42) | 6 (46) | 26 (25) |
| MUC5B GT or TT, n (%) | 5 (42) | 5 (39) | 30 (29) |
| TRF length, kb, mean (SD) | 6.17 (1.07) | 6.26 (0.96) | 6.47 (1.04) |
| HRCT findings | |||
| ILA category, n (%) | |||
| None | 0 (0) | 6 (46.2) | 93 (89) |
| Early/mild | 12 (100) | 7 (53.9) | 11 (11) |
| Honeycombing, n (%) | 3 (25) | 0 (0) | 0 (0) |
| Ground-glass opacities, n (%) | 4 (33) | 1 (7.7) | 5 (5) |
| Intralobular reticular opacities, n (%) | 12 (100) | 5 (39) | 7 (7) |
| Irregular septal thickening, n (%) | 10 (83) | 4 (31) | 4 (4) |
| Traction bronchiectasis, n (%) | 0 (0) | 0 (0) | 0 (0) |
| Traction bronchiolectasis, n (%) | 1 (8) | 0 (0) | 1 (1) |
| Bilateral interstitial features, n (%) | 9 (75) | 3 (23) | 4 (4) |
| Visual ILA score, median (Q1–Q3) | 5.0 (3.5–12.0) | 0 (0–2) | 0 (0–0) |
| DTA score, median (Q1–Q3) | 3.8 (1.0–9.3) (n = 11) | 1.0 (0.7–1.5) (n = 13) | 0.8 (0.4–1.4) (n = 100) |
| Characteristics during longitudinal follow-up | |||
| Second HRCT findings | n = 6 | n = 13 | n = 67 |
| Inter-HRCT interval, yr, median (Q1–Q3) | 5.7 (3.8–6.2) | 5.3 (5.0–5.7) | 5.0 (3.6–6.2) |
| ILA category, n (%) | |||
| None | 0 (0) | 1 (7.7) | 65 (97.0) |
| Early/mild ILA | 2 (33.3) | 12 (92.3) | 2 (3.0) |
| Extensive ILA | 4 (66.7) | 0 (0) | 0 (0) |
| Honeycombing, n (%) | 2 (33) | 1 (7.7) | 0 (0) |
| Bilateral interstitial features, n (%) | 6 (100) | 8 (61.5) | 0 (0) |
| Visual ILA score, median (Q1–Q3) | 24.5 (15.0–27.0) | 6.0 (2.0–13.0) | 0 (0–0) |
| DTA score, median (Q1–Q3) | n/a (n = 1) | 2.2 (0.7–4.1) (n = 12) | 0.6 (0.4–1.3) (n = 62) |
| Yearly change in dyspnea score, mean (SD) | 0.2 (0.5) (n = 10) | 0.04 (0.1) | −0.04 (0.2) (n = 89) |
| Yearly change in visual ILA score, mean (SD) | 3.7 (1.7) (n = 6) | 0.8 (0.6) | −0.05 (0.3) (n = 67) |
| Yearly change in DTA score, mean (SD) | n/a (n = 1) | 0.36 (0.55) (n = 12) | −0.09 (0.62) (n = 62) |
Definition of abbreviations: DTA = data-driven textural analysis; HRCT = high-resolution computed tomography; ILA = interstitial lung abnormalities; ILD = interstitial lung disease; n/a = not applicable (mean yearly change not calculated for one subject with available data); Q1–Q3 = interquartile range; TRF = telomere restriction fragment.
n = 129 subjects, except where noted. Continuous values are given as mean (SD) or median (interquartile range).
Eighty-six of 129 eligible subjects had both enrollment and follow-up HRCT performed and analyzed (median inter-CT interval, 5.23 yr; interquartile range, 3.77–6.19). Of the 67 subjects in this group with no ILA on the enrollment HRCT scan, only 6 (9.0% of 67) showed radiologic progression by visual ILA score on a follow-up HRCT scan. In comparison, 13 (68.4%) of 19 subjects in the group with early/mild ILA on their enrollment HRCT scan showed radiologic progression (9 [47.4%] continued to have early/mild ILA but with an increased whole-lung visual ILA score, and 4 [21.1%] progressed to extensive ILA). In the 6 of 19 subjects with early/mild ILA on the enrollment HRCT scan that did not progress, 4 (21.1%) had regression (decreasing ILA score and no categorical ILA) and 2 (10.5%) continued to have early/mild ILA with unchanged whole-lung visual ILA score on the second HRCT scan. Interobserver agreement on radiologic progression was good (n = 20; κ = 0.69; 95% CI, 0.38–1.00). Figure 1 shows HRCT images from a subject with early/mild ILA at enrollment and progression to extensive ILA on the second HRCT scan; areas measured as fibrotic via the DTA score on each HRCT scan are overlaid in red, corresponding to visually identifiable ILA.
Figure 1.
Representative high-resolution computed tomography (HRCT) scans showing progressive interstitial lung abnormalities. Images are from a single subject at the time of enrollment and at 5-year follow-up. (A) The upper image is from the enrollment HRCT scan and shows a prone axial image of the lower lungs, demonstrating subtle nondependent reticulation (gray boxes) bilaterally, visually categorized as “early/mild” interstitial lung abnormalities. The lower image was obtained 5 years, 8 months later and shows a prone axial image of the lower lungs, demonstrating an increase in the reticulation in the same areas noted previously (gray boxes). (B) The same enrollment (upper) and follow-up (lower) HRCT scans shown in A, with data-driven textural analysis–measured fibrosis shown in red overlay, demonstrating progression of fibrosis.
Quantitative Assessment of ILA
To further quantify ILA in these subjects, we analyzed areas of lung fibrosis as measured by the DTA score and compared this measurement with visually assessed ILA and other variables. On the basis of enrollment HRCT scans, the DTA score correlated with the visual whole-lung ILA score (r = 0.47; P < 0.0001), and both the DTA and visual ILA scores correlated with dyspnea score, TRF length, and MUC5B risk allele (Figures 2 and E2). Among subjects with two HRCT scans appropriate for DTA scoring, the yearly change in DTA score correlated with the yearly change in visual ILA score (P = 0.0006), and those with ILD events had an increase in DTA score, whereas those without ILD events had a decrease in DTA score (P = 0.001) (Figure E3 and Table 3).
Figure 2.
Correlation of data-driven textural analysis (DTA) scores and patient characteristics at enrollment. On the three correlation plots, each dot represents an individual. In the boxplot, dots represent individuals falling outside the range of 1.5 times the interquartile range (also called “upper and lower fences”); the upper whisker represents the maximum observation below the upper fence; the upper line of the box represents the upper quartile (75th percentile); the line inside the box represents the median; the lower line of the box represents the first lower quartile (25th percentile); and the lower whisker represents the lowest observation before the lower fence. HRCT = high-resolution computed tomography; ILA = interstitial lung abnormalities; TRF = telomere restriction fragment.
Discussion
Overall, these data provide support for the concept that symptomatic ILD is preceded by an asymptomatic period of considerable length during which radiologically detectable ILA are present. Although the factors that predict development of ILA and progression to clinical ILD in high-risk populations remain incompletely understood, this longitudinal cohort of at-risk individuals without clinical disease at enrollment provides several important observations regarding the natural history of familial ILD. First, 23% of subjects in this cohort had detectable ILA at the time of enrollment, which is consistent with our prior report and other studies (1, 2). The mean age of individuals who had ILA on the enrollment HRCT scan was 58.8 years, which is approximately 7 years younger than the average age of clinical diagnosis in our FIP cohort (3, 5). ILA prevalence increased with older age and history of smoking and in the presence of shorter peripheral blood mononuclear cell telomeres. Second, these data provide evidence that there are modifiable environmental risk factors for FIP beyond cigarette smoking, which is an important finding because there are limited data characterizing environmental risk factors for ILA in presymptomatic populations. Third, although 6% of subjects without detectable ILA at enrollment developed new interstitial changes, the majority (63%) of subjects with early/mild ILA at the time of enrollment had evidence of progression through approximately 5 years of follow-up. Together, these data identify several modifiable risk factors, as well as other phenotypic characteristics, that are associated with ILA and ILA progression in presymptomatic individuals at high risk for development of clinical ILD.
A variety of environmental exposures (air pollution, farming, metal dust, stone cutting, bird raising, and others) are proposed as risk factors for pulmonary fibrosis; however, many published studies in this area are hampered by case–control design and risk of recall bias whereby subjects with disease recall exposures differently from subjects without disease (28–31). In this prospective, longitudinal cohort study, exposure history was ascertained via a standard questionnaire administered before subject knowledge of ILA status, mitigating the risk for this type of recall bias. Controlling for multiple hypothesis testing, we identified several environmental exposures, specifically aluminum smelting, lead, mold, and birds, that were independently associated with ILA on the enrollment HRCT scan after adjusting for potential confounders. Despite limitations of questionnaire-based research, our findings suggest that environmental exposures contribute to disease penetrance in individuals at risk for FIP.
Our definition of ILA warrants discussion because it differs from other published definitions (12–14, 16–18). We have applied our definition of ILA prospectively and in real time since the inception of this cohort (in 2008) to investigate the early natural history of ILD development in families. We aimed to capture early and mild radiologic findings, with a long-term goal of determining specific radiologic abnormalities that predict progression toward clinical ILD. Our “early/mild” ILA category captures interstitial findings that were present but sometimes minimal and focal (i.e., <5% in one or more lung zones); many of these findings have been considered “indeterminate” for ILA in other studies (10–14, 18). Our “extensive” ILA category is similar to the “established fibrosis” category used elsewhere (13, 14, 18). Importantly, we considered subjects with “extensive” ILA on their enrollment HRCT scan to have established fibrotic disease, recommended clinical evaluation, and did not continue to follow them in this study. Despite excluding subjects with established fibrosis (extensive ILA) from 5-year follow-up, we observed ILD events (including subclinical radiologic progression and development of clinical disease) in 19% of the cohort overall and in 63% of those with early ILA on their enrollment HRCT scan. Although the published rate of progression of ILA in the general population varies widely (8–72% of individuals with two chest CT scans spaced 2–6 yr apart) (14, 16–18), prior studies have included those with established fibrosis in the ILA definition and have not generally reported progression outcomes in the “indeterminate” for ILA group, making direct comparisons with our cohort difficult. Several important questions about classification and management of ILA remain unanswered and are areas of active research. Validated tools to distinguish benign or static abnormalities from those likely to progress are needed (14, 18, 19, 32). Although this cohort is the largest of its kind reported to date, to our knowledge, we do not yet have sufficient statistical power to analyze the role of genetic and environmental risk factors in disease progression or to develop robust prediction models for progression based on specific clinical or radiologic characteristics. As this ongoing cohort matures, we hope to clarify specific characteristics associated with development of clinically meaningful disease, the rate of disease progression, and the best application of software-based HRCT analysis in a preclinical cohort (19).
This study has several important limitations and caveats. First, reliance on self-reported exposure history for determining associations between ILA and environmental exposures could result in failure to capture information on remote or occult exposures that are relevant to disease development (33). In addition, questionnaire data do not allow us to discern which of several coexposures contributes to HRCT abnormalities or to document the presence of specific toxins (e.g., lead and aluminum) in the environment or the body. Although external validation of our provocative findings linking environmental exposures to ILA would be valuable, this is not feasible in our unique, single-center cohort. Second, the well-reported MUC5B promoter polymorphism approached but did not reach a statistically significantly association with ILA, potentially due to the relatively small sample size or the presence of other genetic risk factors in some subjects; however, we elected to include MUC5B genotype as an adjustment variable in inferential models because of the association with disease risk in many other cohorts (8, 11, 13, 19, 34, 35). Third, a single radiologist interpreted each HRCT scan and therefore assigned the ILA outcomes of interest. Although a subgroup analysis of individuals with follow-up HRCT scans reviewed by a second radiologist suggested good agreement on radiologic ILA progression in this cohort, agreement among radiologists on the presence and extent of HRCT features or patterns is well described in the literature as modest (36–38). Despite this issue, the correlation between DTA-measured fibrosis and visually measured ILA in our cohort further supports the validity of our outcome measure based on visual ILA assessment. Fourth, we were unable to apply the DTA fibrosis measure to every HRCT scan that was scored visually. The image quality and acquisition protocol must be carefully reviewed before application, with use of a similar acquisition protocol being particularly important when estimating within-subject change in fibrosis (19, 24, 25). Finally, our longitudinal analysis is limited by availability of a follow-up HRCT scan for only two-thirds of individuals eligible to complete the examination. Loss to follow-up could introduce bias into estimates of the rate of radiologic progression and the association between baseline characteristics and rate of disease onset. In this regard, use of interim questionnaires allowed capture of clinical ILD diagnoses in most subjects unable to complete the 5-year HRCT, and <10% of subjects failed to complete any form of follow-up.
In conclusion, we identified early ILA in 23% of individuals with multiple family members affected by pulmonary fibrosis and established an independent association of both rare and relatively common environmental exposures with ILA on the enrollment HRCT scan. In addition, we found that the majority of individuals with early/mild ILA on the initial HRCT scan progressed during a 5-year follow-up period. Future studies are required to define the optimal approach to screening, surveillance, and risk factor mitigation for individuals with a family history of pulmonary fibrosis.
Supplementary Material
Acknowledgments
Acknowledgment
The authors are most grateful to the patients and families who made this study possible.
Footnotes
Supported by NIH grants P01HL092870 (T.S.B.), K23HL141539 (M.L.S.), K08HL130595 (J.A.K.), and K24HL143281 (L.R.Y.) and by Boehringer Ingelheim Pharmaceuticals.
Author Contributions: M.L.S., J.A.P., J.D.C., L.R.Y., J.E.L., J.A.K., and T.S.B. contributed to conception and design of the study. J.C.H., G.D., C.R.M., K.D., W.M., A.G., J.A.P., J.D.C., S.R., D.B.M., L.R.Y., L.H.L., J.E.L., S.M.H., D.A.L., J.A.K., and T.S.B. contributed to data acquisition. M.L.S., J.C.H., G.D., P.W., J.A.K., and T.S.B. contributed to analysis and interpretation. M.L.S., J.C.H., L.R.Y., J.A.K., and T.S.B. drafted and critically revised the manuscript. All authors provided final approval of the manuscript version for submission.
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.
Originally Published in Press as DOI: 10.1164/rccm.201909-1834OC on February 3, 2020
Author disclosures are available with the text of this article at www.atsjournals.org.
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