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Original Research: COPD |

The Relationship of the Fibrinogen Cleavage Biomarker Aα-Val360 With Disease Severity and Activity in α1-Antitrypsin DeficiencyA03B1-Val360 in Alpha-1-Antitrypsin Deficiency FREE TO VIEW

Richard I. Carter, PhD; Michael J. Ungurs, PhD; Anilkumar Pillai, MBChB; Richard A. Mumford, PhD; Robert A. Stockley, DSc
Author and Funding Information

From The Royal Wolverhampton Hospitals NHS Trust (Dr Carter), West Midlands, England; Centre for Translational Inflammation Research (Drs Ungurs and Pillai and Prof Stockley), University of Birmingham Research Laboratories, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, England; Mumford Pharma Consulting, LLC (Dr Mumford), Red Bank, NJ; and The ADAPT Project (Prof Stockley), Lung Function and Sleep, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, England.

CORRESPONDENCE TO: Robert A. Stockley, DSc, Lung Function and Sleep Department, ADAPT Office (Office 4), Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, B15 2WB, England; e-mail: rob.stockley@uhb.nhs.uk


FUNDING/SUPPORT: The authors have reported to CHEST that no funding was received for this study.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2015;148(2):382-388. doi:10.1378/chest.14-0520
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BACKGROUND:  New markers of COPD and emphysema disease activity are urgently required since current measures of disease severity do not reflect the total disease burden nor predict disease progression. A recently described in vivo marker of neutrophil elastase activity (Aα-Val360) may be an effective marker of COPD and emphysema disease activity, and the current study explores its use in patients with α1-antitrypsin deficiency (AATD) across the disease severity spectrum with particular interest in whether it can be used as an early predictor of the need for intervention.

METHODS:  Cross-sectional and longitudinal relationships between Aα-Val360 and full lung-function tests, CT scan densitometry, and other biomarkers were explored in this study of a registry of untreated patients with PiZZ AATD.

RESULTS:  The Aα-Val360 related cross-sectionally to physiologic, radiologic, and symptomatic markers of disease severity though not disease progression. Similar cross-sectional relationships were observed in subjects with mild physiologic abnormalities; however, in this subgroup, baseline Aα-Val360 concentration did relate to subsequent disease progression.

CONCLUSIONS:  In cross-sectional studies, Aα-Val360 reflects disease severity in AATD and may be a useful marker of disease activity in patients with early disease in whom therapeutic intervention may be indicated.

Figures in this Article

COPD is a group of conditions that includes chronic bronchitis, bronchiectasis, airflow obstruction, and emphysema. Despite the heterogeneity of this syndrome, guidelines suggest a diagnosis is only made in the presence of airflow obstruction, which is usually defined by an FEV1/FVC below 0.7. However, spirometric criteria are not representative of the overall disease burden within an individual since symptomatic emphysema may occur in the absence of airflow obstruction.1 Markers of disease activity are, therefore, required to quantify the underlying pathophysiologic mechanisms leading to tissue damage prior to changes in markers of disease severity (such as FEV1), to identify subjects at risk for developing more severe disease, and to offer early evaluation of specific therapeutic interventions.2,3

Subjects with symptoms suggestive of COPD/emphysema with little or no airflow obstruction may have mild disease (without significant potential for disease progression) or early disease that progresses relatively rapidly to develop more significant symptoms or deranged physiology.4 At present, the distinction between mild and early disease can only be made retrospectively by studying disease progression. However, it would be more appropriate (if possible) to measure disease activity in patients at risk for developing COPD/emphysema prior to progression to irreversible lung damage.

Footprints of the underlying pathologic process in COPD/emphysema may provide ideal biomarkers of disease activity and, furthermore, neutrophil elastase (NE) is generally accepted as important in disease pathogenesis, especially in α1-antitrypsin deficiency (AATD). However, since NE is rapidly inhibited within the neutrophil microenvironment, further direct confirmation of its role is difficult, and we have, therefore, developed a specific surrogate marker of preinhibition NE activity (Aα-Val360) that can be measured in plasma.5 Aα-Val360 is a specific cleavage product generated by the action of NE on fibrinogen, which demonstrates a treatment response in individuals with AATD who receive augmentation therapy5 and relates to disease severity and partly to disease progression in subjects with usual COPD.6

In the current study, we wished to assess any longitudinal and cross-sectional relationships between this marker of disease activity (Aα-Val360) and indexes of COPD/emphysema severity in a large cohort of patients with PiZZ AATD, with subgroup analysis of those with initial spirometry within the normal range.

Patients with PiZZ AATD who had been assessed annually with at least four data points were identified from the UK AAT Registry (ADAPT [Antitrypsin Deficiency Assessment and Programme for Treatment]) which has been recruiting patients since 1997. This study is an analysis of prospectively acquired registry data and, therefore, excludes subjects who did not have complete follow-up, including those who had only recently joined the registry, had not attended further follow-up through choice, or had worsening health or death. All patients reported here had a baseline high-resolution CT scan of the thorax and at each visit underwent full lung-function testing (e-Appendix 1) and clinical assessment when in the clinically stable state (at least 6 weeks after any exacerbation). Any patient who had received α1-antitrypsin (AAT) augmentation therapy was excluded.

Objective Quality-of-Life Assessment

All patients completed the St. George’s Respiratory Questionnaire (SGRQ),7 and a subset (those who had attended more recently) were also assessed using the COPD assessment tool (CAT).8 Patient-reported exacerbation data were also recorded at each assessment.

Biochemical Assays

Aα-Val360 is a specific peptide formed when fibrinogen is degraded by NE and is, therefore, a surrogate marker of NE activity in vivo. Aα-Val360 was measured in plasma samples obtained at the baseline assessment visit using a highly specific enzyme-linked immunosorbent assay-based assay; the methodology, coefficient of variation, and initial validation of the Aα-Val360 assay have been described previously.5 The methodology for other assays is outlined in e-Appendix 1, together with the lung-function testing and CT scan protocols.

Ethical Approval

Ethical approval was provided by the University Hospitals Birmingham NHS Foundation Trust Research Ethics Committee (LREC ref 3359). Informed consent was provided by all.

Statistical Analysis

Statistical analyses were performed using IBM SPSS Statistics, version 21.0.0.0 for Windows (IBM Corp). Normality was tested for using the Kolmogorov-Smirnov test, and statistical significance was taken as P < .05. Aα-Val360 data were nonparametric and are, therefore, presented as median (interquartile range [IQR]) with correlations assessed using Spearman rho. Where appropriate, multivariate analysis was performed using (backward) stepwise linear regression with the following parameters: Aα-Val360, BMI, index status, smoking status (current, ex, never), age, sex, and inhaled therapy (combination therapy, long-acting muscarinic antagonist, or steroid), which are associated with differences in lung-function measures and may influence decline. In the subset of patients with FEV1 within the normal range, for the multivariate analysis, three outlying individuals were excluded to meet the assumptions required for this test. Mann-Whitney tests were used to compare values between two groups, while Kruskal-Wallis tests were used for comparisons between three or more groups. Comparisons were made between values obtained at baseline and follow-up using paired Wilcoxon rank tests. Regression analysis was used to assess change in lung-function measures over time across each point of assessment.

At the time of analysis, the study included 378 subjects with PiZZ AATD (Table 1) who were assessed over a median follow-up period of 5.64 years (IQR, 3.00-8.30). There was no significant difference (P = .490) in the plasma Aα-Val360 concentration between the 157 women (median = 16.43 nM; IQR, 11.89-21.57) and the 221 men (median = 16.20; IQR, 12.82-23.71). The Aα-Val360 was significantly higher (P = .008) in 21 current smokers (median = 21.67 nM; IQR, 15.77-32.11) compared with 259 ex-smokers (median = 16.31; 12.66-24.04) and 98 never smokers (median = 15.26; IQR, 10.66-20.06).

Table Graphic Jump Location
TABLE 1 ]  The Age and Lung-Function Tests at Baseline and at the Final Follow-up Visit Are Quoted for All 378 Subjects at Baseline and Follow-up

The statistical significance refers to the difference between baseline and follow-up values. IQR = interquartile range; Kco = transfer coefficient.

Inhaled Therapy

At baseline, there were 134 patients (35.4%) on inhaled steroids, 90 (23.8%) on a long-acting β agonist (LABA), 114 (30.2%) on a combined steroid and LABA, and 97 (25.7%) on a long-acting muscarinic antagonist. The proportion of subjects on combined steroid and LABA rose to 52.9% by the end of the study period. For the purposes of the current study, subjects were, therefore, considered to be on a particular therapy if it was received for at least 50% of the study period. The Aα-Val360 was not significantly different between the inhaled therapy groups.

Index and Nonindex Cases

Index cases are patients who present with symptoms of chronic lung disease, while nonindex cases were identified through family screening. The median Aα-Val360 was higher in index cases compared with nonindex cases and this difference persisted in never smokers (Fig 1).

Figure Jump LinkFigure 1 –  The median Aα-Val360 and interquartile range is shown for the index and nonindex patients as a group and for the subset of never smokers. The significance (P) of the difference between index and nonindex patients is shown.Grahic Jump Location
Cross-sectional Relationships With Markers of Disease Severity

The baseline Aα-Val360 related to both physiologic and radiologic measures of COPD disease severity as well the SGRQ7 (Fig 2, Table 2). The Aα-Val360 concentration also related to symptom severity measured by the CAT,8 in the subset (n = 175) who underwent this evaluation (r = 0.237, P = .001). Although the Aα-Val360 concentration did not relate to age (P = .378), height (P = .364), or weight (P = .097), a weak but statistically significant relationship was observed between Aα-Val360 and BMI (r = −0.09, P = .049).

Figure Jump LinkFigure 2 –  The relationship between the baseline KCO (% predicted) and Aα-Val360 in this group of patients with α1-antitrypsin deficiency. Each point is the data from a single patient and the correlation coefficient (r) and significance (P) is shown. KCO = transfer coefficient.Grahic Jump Location
Table Graphic Jump Location
TABLE 2 ]  The Correlation (R) Between Aα-Val360 and Physiologic, Radiologic, and Symptomatic Markers of Disease Severity in PiZZ Subjects With AATD Together With the Significance (P) of the Correlation

AATD = α1-antitrypsin deficiency. See Table 1 legend for expansion of abbreviation.

Complete exacerbation data were available for the 2-year period following the baseline assessment in 308 subjects (81.48%). There was an inverse relationship between the baseline FEV1 (% predicted) and the number of exacerbations each subject experienced (r = −0.290, P < .001); however, the exacerbation frequency did not relate to transfer coefficient (Kco) (% predicted) (r = −0.060, P = .145) or the Aα-Val360 (r = 0.041, P = .239).

Multivariate analysis accounting for age, sex, height, smoking history, and AAT concentration demonstrated significant independent relationships between Aα-Val360 and both the Kco (standardized B value, −0.113; P = .009) and FEV1 (standardized B value, −0.105; P = .016). However, the patient’s age remained the strongest predictor of both Kco (standardized B value, −0.541; P < .001) and FEV1 (standardized B value, −0.373; P < .001).

Aα-Val360 related to the AAT/NE complex concentration (r = 0.398, P = .002), which is a measure of total NE release; however, there was no relationship to systemic inflammation as reflected by the plasma C-reactive protein (r = −0.059, P = .238). Aα-Val360 also related weakly to the plasma AAT concentration (r = −0.101, P = .027) even within this group of subjects with severe (PiZZ) AATD. However, neither the absolute AAT concentration itself nor the AAT/NE complex related to physiologic or radiologic measures of disease severity, which suggests that it is the activity of NE prior to its inhibition rather than total NE release that is relevant to the disease severity.

Longitudinal Relationships

There was significant progression of all physiologic parameters over the study period (Table 1). The median annual decline in FEV1 (% predicted) was −0.73% (IQR, −0.26 to −1.78), which was equivalent to −40.05 mL/y (−12.03 to −74.15) while the annual decline in Kco (% predicted) was −1.23% (−0.48 to −2.02).

The Aα-Val360 concentration related cross-sectionally to physiologic parameters at both baseline and follow-up, however, there was no relationship between the baseline Aα-Val360 and rate of progression of either FEV1 (% predicted) or Kco (% predicted). Linear regression analysis identified inhaled steroid use, greater age, index case status, female sex, and higher baseline Kco as independent predictors of a more rapid decline in Kco (% predicted) (standardized B values of −0.135, −0.158, −0.105, 0.117, and −0.135; P values, .010, .003, .060, .024, and .005, respectively). Linear regression identified being a current smoker as the only independent predictor of a more rapid decline in FEV1 (% predicted) (standardized B values, −0.107; P = .037).

Cross-sectional and Longitudinal Relationships in Subjects With a “Normal” FEV1

There was a subgroup of 104 subjects with AATD (35 index and 69 nonindex) with an FEV1 (% predicted) within the normal range (> 80%), of whom 58 (55.8%) were never smokers, 43 (41.3%) were ex-smokers, and 3 (2.9%) were current smokers. Within this subgroup, there was no relationship between Aα-Val360 and age, weight, BMI, or pack-year smoking history, however, there were weak relationships between Aα-Val360 and measures of physiologic, radiologic, and particularly quality-of-life status (Table 3).

Table Graphic Jump Location
TABLE 3 ]  Relationships Between Aα-Val360 and Markers of Disease Severity in Subjects With an FEV1 (% Predicted) Within the Normal Range

See Table 1 legend for expansion of abbreviation.

These 104 subjects were also followed-up on an annual basis for a median of 6.04 years (IQR, 3.09-9.10), and the median annual change in FEV1 (% predicted) was −0.38 (IQR, +0.61 to −1.63) equivalent to 46.95 mL/y (−16.49 to −92.64) and −0.91 (−0.40 to −1.67) for Kco (% predicted). The annual change in FEV1 (% predicted) was greater (P = .033) in index patients with a median of −0.76 (IQR, +0.24 to −2.53) compared with nonindex patients with a median of −0.07 (+1.01 to −1.29). Importantly, the baseline Aα-Val360 concentration related to the annual change in Kco (% predicted) (r = −0.193, P = .025; Fig 3) in this subgroup. The standardized residuals of these 104 subjects were not normally distributed, and, therefore, it was not possible to perform multivariate analysis. However, if three outliers were excluded (the two fastest and one slowest decliners) then the assumptions of multivariate analysis were met, and there remained a relationship between decline in Kco (% predicted) and baseline Aα-Val360, age, and Kco with standardized B values of −0.255, −0.329, and −0.269 and P values of .011, .013, and .037, respectively.

Figure Jump LinkFigure 3 –  The relationship between Aα-Val360 measured at baseline and subsequent decline in KCO (% predicted) is shown for each subject with a baseline value for FEV1 > 80% predicted. The correlation coefficient and significance of the relationship is shown. See Figure 2 for expansion of abbreviation.Grahic Jump Location

The current study describes the cross-sectional and longitudinal relationships between a unique plasma marker of NE activity (Aα-Val360)5,6 and measures of disease severity and progression in a large cohort of patients with AATD. FEV1 is a widely used marker of disease severity and progression in usual COPD and in AATD and, although imperfect, it relates to overall and lung-specific mortality.9 Therefore, the relationship between FEV1 and Aα-Val360 in the current study is of primary importance as in the assessment of all potential biomarkers. Additionally, Aα-Val360 related weakly though highly significantly to other widely accepted markers of disease severity including gas transfer and CT densitometry, demonstrating that this marker is (at least partly) associated with current emphysema severity. This is of importance, since emphysema as assessed by CT densitometry is a better predictor of mortality than FEV1 in AATD.10 Furthermore, there were highly significant relationships (although again weak) with the subjective measures of disease severity quantified by the SGRQ7 and CAT score,8 which is also important since patients may have significant symptoms but mild physiologic changes.11

The number of exacerbations a patient experienced related to FEV1 but not Kco or Aα-Val360. This probably reflects the greater relationship between inflammation of airways and FEV1, in contrast to Kco and Aα-Val360 which may be better measures or markers of peripheral tissue destruction.

Nonindex subjects with AATD had a lower Aα-Val360 concentration than other subjects with AATD, as did never smokers compared with ex-smokers and current smokers. This suggests additional neutrophilic factors (genetic or environmental) are present in index patients reflected by the greater Aα-Val360 footprint. Such additional factors have yet to be identified, but this is consistent with the observation that nonindex, never smokers with AATD develop less lung damage12 and, hence, have a slower progression and a normal life expectancy.13

The current study also demonstrates that Aα-Val360 relates to BMI, which is of interest since a low BMI is associated with both a higher mortality in subjects with COPD14 and an emphysematous phenotype15 and is, therefore, consistent with the systemic nature of this disease process.

Longitudinal Relationships

The relevance of index case status in determining progression was shown by its relationship with subsequent decline in gas transfer, and indicates the presence of symptoms leading to diagnosis is (in general) a simple marker of future prognosis. The relationship of age with decline in percent predicted (which accounts for normal aging) is also understandable since it reflects duration of the patient’s exposure to their genetic or other risk factors. The relationship of a higher baseline Kco with more rapid decline is also of interest and may be explained by greater disease activity in subjects with earlier disease. However, the association with inhaled steroids is counterintuitive and should be interpreted with caution since it was weak and, therefore, may reflect a confounding factor rather than a true association.16,17

Linear regression analysis unsurprisingly identified current smoking as an independent risk factor for disease progression measured by spirometry. The Aα-Val360 was greater in subjects who continued to smoke, and these subjects, therefore, have both evidence of greater ongoing NE activity and greater spirometric decline.

Aα-Val360 as a Marker of Early Disease

Support for the concept that Aα-Val360 is a marker of activity in early disease was demonstrated by the relationships with physiologic, radiologic, and symptomatic markers of COPD disease severity even in subjects with an FEV1 within the normal range (> 80% predicted). Furthermore, within this subgroup, there was also a significant (though weak) association between Aα-Val360 and subsequent disease progression measured by decline in gas transfer. This suggests the pathophysiologic processes in AATD are complex and may be different in early and mild compared with moderate and severe disease (where Aα-Val360 does not relate to disease progression). Potentially, intervention with an antielastase therapy in patients with earlier disease and a higher Aα-Val360 could be of greatest benefit; however, large prospective studies are required to investigate this concept further6 together with more in-depth study of the pathophysiologic processes and clinical phenotypes seen in AATD. It remains possible that other serine proteinases such as proteinase 3 play an even greater role in the development of lung damage in AATD than has previously been considered.18

The Relationship Between Disease Severity, Disease Activity, and Progression

The relationship of Aα-Val360 to all markers of disease severity supports its use as a biomarker; however, in the current study, Aα-Val360 did not relate to disease progression except in early disease. In addition, cross-sectional relationships were relatively weak (with overlap of CIs). To put this in context, the highly significant relationships shown here are associated with r values of about 0.2, equivalent to r2 values of 0.04, indicating that the associations with Aα-Val360 only account for about 4% of the variability of all measures. This is important in determining the value of a biomarker, especially when considering its use as a predictor of progression. Although this is better than other markers that have been studied,6 it is somewhat disappointing as a specific marker of a disease process thought to be central to AATD. There are several ways of interpreting these data:

  • • Aα-Val360 as a footprint of NE activity is not a marker of disease activity in the clinically stable state;

  • • Disease activity itself is not stable but fluctuant (eg, associated with exacerbations), hence, the weak association between NE activity in the clinically stable state and disease severity;

  • • None of the physiologic or radiologic markers alone reflect the total active disease burden;

  • • The pathophysiology may become more complex with time, with the involvement of additional enzymes or mechanisms.

The current data provide some support for the latter two since Aα-Val360 relates to disease severity independently of age, predicts disease progression only early in the disease, and yet is reduced by augmentation therapy.5 Furthermore, while patients experience exacerbations and an associated increase in Aα-Val360, it returns relatively rapidly to baseline6 which is consistent with both the stability of this component of the underlying disease activity and the relatively small contribution of exacerbations to decline in FEV1.19 Finally, correlation of progression with activity requires that progression is linear with time, and longitudinal data confirm that this is not necessarily the case, particularly when measured by physiologic parameters20; and this further complicates studies of the relationship between disease activity and severity. These observations may be central to understanding why augmentation therapy seems to modify rather than eradicate established disease progression as defined by lung densitometry. In addition, the data provide important lessons in interpreting the value of biomarkers even when developed specifically and where the pathophysiologic process is generally understood. It may be that with the complexities of pathologic variations, fluctuating disease activity and the possibility that inflammation in itself becomes a self-propagating process, the combination of more than one biomarker or multiple measures will be necessary for clinical and regulatory purposes even in a disease process thought to be as well understood as AATD.

In the current study of patients with AATD, Aα-Val360 relates cross-sectionally to physiologic, radiologic, and symptomatic markers of disease severity but only to disease progression (as assessed by gas transfer) in subjects with spirometry within the normal range. Aα-Val360 may be of some use in identifying subjects with progressive disease at an early stage, especially as a measure of the emphysematous component. However, further work is required to understand the complex relationships between this marker and disease activity, severity, and progression even in subjects with AATD, potentially by exploring the activity of other important enzymes as well as the impact of acute events such as exacerbations and defining the physiologic and radiologic features in even more detail than that undertaken here.

Author contributions: R. A. S. is the guarantor; assisted in the development of the assay; is the director of the ADAPT Project; and provided overview for the study design, implementation, and analyses. R. I. C., M. J. U., A. P., R. A. M., and R. A. S. contributed to the development of the manuscript and approved its content prior to submission; R. I. C. was the first author, shared joint responsibility for study design, performed biochemical and data analyses, and assessed patients; M. J. U. contributed to biochemical analyses and manuscript development; A. P. performed data collection and assisted with analyses; and R. A. M. was a key member of the assay development team and provided advice on study design and analyses.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Pillai has received assistance from Boehringer Ingelheim GmbH, Almirall SA, and Otsuka America Pharmaceutical Inc for attending international conferences. Prof Stockley has lectured widely as part of pharmaceutical sponsored symposia, sat on numerous advisory boards for drug design and trial implementation, and has received noncommercial grant funding. Drs Carter, Ungurs, and Mumford have reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Other contributions: We thank all of the members and patients of the ADAPT Project (Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, England) for their contributions to this study. We also thank Grifols (Sant Cugat del Valles, Barcelona, Spain) who have provided unrestricted educational grants to the ADAPT Project.

Additional information: The e-Appendix can be found in the Supplemental Materials section of the online article.

AAT

α1-antitrypsin

AATD

α1-antitrypsin deficiency

CAT

COPD assessment tool

IQR

interquartile range

Kco

transfer coefficient

LABA

long-acting β agonist

NE

neutrophil elastase

SGRQ

St. George’s Respiratory Questionnaire

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Figures

Figure Jump LinkFigure 1 –  The median Aα-Val360 and interquartile range is shown for the index and nonindex patients as a group and for the subset of never smokers. The significance (P) of the difference between index and nonindex patients is shown.Grahic Jump Location
Figure Jump LinkFigure 2 –  The relationship between the baseline KCO (% predicted) and Aα-Val360 in this group of patients with α1-antitrypsin deficiency. Each point is the data from a single patient and the correlation coefficient (r) and significance (P) is shown. KCO = transfer coefficient.Grahic Jump Location
Figure Jump LinkFigure 3 –  The relationship between Aα-Val360 measured at baseline and subsequent decline in KCO (% predicted) is shown for each subject with a baseline value for FEV1 > 80% predicted. The correlation coefficient and significance of the relationship is shown. See Figure 2 for expansion of abbreviation.Grahic Jump Location

Tables

Table Graphic Jump Location
TABLE 1 ]  The Age and Lung-Function Tests at Baseline and at the Final Follow-up Visit Are Quoted for All 378 Subjects at Baseline and Follow-up

The statistical significance refers to the difference between baseline and follow-up values. IQR = interquartile range; Kco = transfer coefficient.

Table Graphic Jump Location
TABLE 2 ]  The Correlation (R) Between Aα-Val360 and Physiologic, Radiologic, and Symptomatic Markers of Disease Severity in PiZZ Subjects With AATD Together With the Significance (P) of the Correlation

AATD = α1-antitrypsin deficiency. See Table 1 legend for expansion of abbreviation.

Table Graphic Jump Location
TABLE 3 ]  Relationships Between Aα-Val360 and Markers of Disease Severity in Subjects With an FEV1 (% Predicted) Within the Normal Range

See Table 1 legend for expansion of abbreviation.

References

Klein JS, Gamsu G, Webb WR, Golden JA, Müller NL. High-resolution CT diagnosis of emphysema in symptomatic patients with normal chest radiographs and isolated low diffusing capacity. Radiology. 1992;182(3):817-821. [CrossRef] [PubMed]
 
Agusti A, MacNee W. The COPD control panel: towards personalised medicine in COPD. Thorax. 2013;68(7):687-690. [CrossRef] [PubMed]
 
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