Exploring Small Airway Disease in Idiopathic Pulmonary Fibrosis Patients: Insights from Oscillometry Analysis

doi:10.21203/rs.3.rs-5286900/v1

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease characterized by lung scarring. Recent evidence suggests that small airway dysfunction (SAD) contributes to IPF pathogenesis. This study assessed SAD in IPF patients using oscillometry and examined associations with disease severity and cardiovascular comorbidity.

Methods: Forty-eight IPF patients were included in this cross-sectional study. Lung function was assessed using forced oscillation technique (FOT) and impulse oscillometry (IOS), spirometry, plethysmography, and DLCO measurements. Key parameters, including AX, Fres, and R5-R20, were analyzed. Correlations between oscillometry parameters, disease severity indices (GAP index, MRC dyspnea score), and coronary artery disease (CAD) risk were evaluated.

Results: Elevated R5-R20 was observed in 65% of patients (FOT) and 60% (IOS). Increased Fres was found in 94% (FOT) and 90% (IOS), while AX was elevated in all participants. Significant correlations were found between AX and DLCO% (r = -0.502, p < 0.001), and between Fres and DLCO% (r = -0.705, p < 0.001). Higher AX was associated with increased CAD score (r = 0.283, p = 0.045), while improved lung compliance (less negative X5) correlated with a lower CAD score (r = -0.314, p = 0.037). No significant correlations were found between oscillometry parameters and GAP or MRC scores.

Conclusion: SAD is prevalent in IPF and is associated with impaired gas exchange and increased cardiovascular risk. Oscillometry provides valuable insights into lung stiffness and small airway resistance, particularly in cases where spirometric abnormalities may not be evident, supporting its use in monitoring disease progression in IPF.

Biological sciences/Physiology

Health sciences/Diseases

Health sciences/Medical research

CAD score

Forced Oscillation Technique

GAP index

Idiopathic Pulmonary Fibrosis

Impulse Oscillometry

Small Airway Disease

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease characterized by the gradual scarring of lung tissue, leading to worsening dyspnea and respiratory failure [1–9]. Traditionally, IPF was thought to predominantly affect the alveolar spaces and lung interstitium, where excessive collagen deposition and fibrosis severely impair gas exchange [1–9]. However, recent advances in diagnostic techniques and histopathological studies have revealed that small airway involvement may also play a critical role in the pathogenesis and progression of IPF [1–9].

Historically, the involvement of small airways in lung diseases has been well-recognized in conditions such as chronic obstructive pulmonary disease (COPD) and asthma, where small airway dysfunction (SAD) is central to airflow limitation and disease progression. For decades, the focus of IPF research and treatment has been on fibrotic processes occurring within the alveolar and interstitial compartments. However, over time, clinical observations and post-mortem studies began to reveal that airway changes—particularly in the small airways—are also prevalent in IPF patients [1–9].

Early studies on the role of small airways in IPF date back several decades. In the 1980s, Myre et al. (1988) were among the first to suggest that small airway obstruction could be present in early-stage IPF [9]. Their findings were limited by the diagnostic tools available at the time, but they laid the groundwork for future research into airway involvement in fibrotic lung diseases. Later, Parra et al. (2008) used immunophenotyping and histological analysis to show that small airways in idiopathic interstitial pneumonias, including IPF, undergo significant remodeling, suggesting that these changes could be both functional and prognostic [8].

More recently, advancements in oscillometry and imaging technologies have allowed for more precise measurement of small airway function in IPF patients. Ikezoe et al. (2021) showed that small airway reduction and fibrosis are early pathologic features of IPF, preceding more widespread interstitial involvement using stereology and multiresolution computed tomography (CT) imaging [5]. This finding was supported by subsequent studies using forced oscillation technique (FOT) and impulse oscillometry (IOS) to assess small airway function [3,10]. These studies confirmed that small airway dysfunction is not just an incidental finding in IPF but may significantly contribute to the disease’s progression and clinical presentation [1–10].

The growing body of evidence supporting small airway involvement in IPF has prompted renewed interest in exploring SAD as a potential therapeutic target. Ziora (2024) and others have suggested that therapies traditionally used for obstructive airway diseases, such as bronchodilators, may have a role in managing SAD in IPF, though further research is needed [1]. Understanding the extent and impact of small airway dysfunction in IPF is crucial, as it opens up new avenues for treatment and may improve the accuracy of prognostic models.

Although the management of IPF has improved with the introduction of antifibrotic therapies such as pirfenidone and nintedanib, these treatments primarily target the fibrotic process in the interstitium rather than small airways. Moreover, the impact of factors such as smoking status and age on small airway function in IPF patients remains unclear. Thus, investigating SAD in IPF is vital for understanding the full spectrum of the disease and improving therapeutic strategies.

The primary aim of this study was to investigate the presence and implications of SAD in IPF patients using FOT and IOS oscillometry. Specifically, this study seeks to determine the relationship between SAD and IPF severity, prognosis, IPF severity, prognosis, patient demographics (age, smoking status), and use of antifibrotic treatment.

2.1. Study Design

This study employed a cross-sectional design to investigate the presence and implications of SAD in patients with IPF. IPF was diagnosed based on clinical, radiological, and pathological criteria [11]. Demographics, smoking history, antifibrotic treatment, and time since diagnosis were recorded for each patient. The study followed the ethical standards set forth by the Helsinki Declaration, and all participants provided informed consent.

2.2. Study Population

Patients were recruited from the interstitial lung diseases (ILDs) External Unit of the Respiratory Medicine Department at the University General Hospital of Larissa in Greece, a tertiary care center specializing in ILDs. The unit serves the entire Thessaly region, home to 687,527 people, making it Greece's third most populous area (https://en.wikipedia.org/wiki/Thessaly), with more than 230,000 people residing in the metropolitan area of Larissa. Diagnoses were established based on clinical, radiological, and histopathological criteria in line with the American Thoracic Society/European Respiratory Society (ATS/ERS) guidelines [11].

The inclusion criteria were: (1) a confirmed diagnosis of IPF, (2) stable disease for at least 30 days prior to the study, and (3) the ability to perform all required pulmonary function tests. Patients with concurrent obstructive airway diseases such as COPD or asthma were excluded, as were those with recent respiratory infections or exacerbations.

Data on smoking history, antifibrotic medication use (pirfenidone or nintedanib), and disease duration were collected through patient interviews and medical record reviews. Smoking history was categorized as current, former, or never smoker. Patients were also stratified based on their treatment status (antifibrotic use versus no antifibrotic treatment).

2.3. Ethics

The study was conducted in accordance with the ethical standards of the Declaration of Helsinki and was approved by the Human Research Ethics Committee of the University General Hospital of Larissa (approval number: 48841/25.10.2019). Informed consent was obtained from all participants prior to their enrollment in the study. Ethical guidelines were strictly adhered to, ensuring participants' privacy, confidentiality, and rights throughout the study. Additionally, the experimental protocol was reviewed and approved by the Institutional Review Board to ensure the safety and well-being of all subjects involved.

2.4. Pulmonary Function Assessment

Lung function was measured using the following techniques:

2.4.1. Oscillometry

The primary tools used to assess small airway function were the TremoFlo® c-100 (THORASYS Thoracic Medical Systems Inc.) for FOT and the MasterScreen IOS system (Jaeger, Germany) [12–17]. FOT and IOS measure respiratory impedance by applying small pressure oscillations to the airway during tidal breathing. The key parameters analyzed were R5: Resistance at 5 Hz, reflecting total airway resistance, R20: Resistance at 20 Hz, reflecting large airway resistance, R5-R20: The difference between R5 and R20, representing peripheral/small airway resistance, X5: Reactance at 5 Hz, indicating lung compliance and stiffness, AX: Area of reactance, representing the extent of lung stiffness and small airway dysfunction, Fres: Resonant frequency, at which reactance is zero, reflecting the balance between inertive and elastic forces in the lung [12–17].

These parameters were chosen for their sensitivity in detecting small airway obstruction and lung stiffness, which are often undetectable by conventional spirometry. Each test was conducted in accordance with standardized protocols, with patients performing three technically acceptable trials, and the best effort was recorded [12–17].

2.4.2. Spirometry and Plethysmography

Standard spirometry was performed using the Spirolab (MIR, Rome, Italy) to assess Forced Vital Capacity (FVC, ml), FVC%, Forced Expiratory Volume in One Second (FEV1, ml), FEV1%, FEV1/FVC ratio, Peak Expiratory Flow (PEF, ml), PEF%, Forced Expiratory Flow at 25–75% (FEF25-75, ml), and FEF25-75% [18].

Lung volumes were assessed via plethysmography (Cardinal Health, Yorba Linda, CA, USA), measuring Total lung capacity (TLC), Residual volume (RV), RV/TLC ratio [19]:

2.4.3. Gas Exchange Measurement

DLCO (Diffusing capacity of the lungs for carbon monoxide) was measured using the single-breath method to assess gas exchange efficiency [20].

2.5. Data Collection

Demographic and clinical data, including age, gender, smoking history, disease duration, medication use, and comorbidities, were collected from patient records and interviews. In this study, several validated tools were used to assess disease severity, dyspnea, and the presence of coronary artery disease (CAD) risk in patients IPF.

2.5.1. Gender, Age, Physiology (GAP) Index

The GAP index is a clinical prediction model used to estimate mortality risk in patients with IPF [21]. It takes into account three key variables, gender as males have a higher mortality risk in IPF, age as older patients tend to have worse outcomes and physiology that includes lung function parameters such as FVC and DLCO% [21]. Higher scores are associated with increased mortality risk. The index has been validated in multiple studies and is widely used in clinical practice to help stratify IPF patients by risk [21].

2.5.2. Medical Research Council (MRC) Dyspnea Scale

The Medical Research Council (MRC) dyspnea scale is a widely used and validated tool designed to quantify the level of breathlessness experienced by patients during their daily activities [22]. It is a simple scale ranging from 1 to 5, with higher scores indicating more severe dyspnea. A patient with Grade 1 dyspnea is not troubled by breathlessness except during strenuous exertion, while a Grade 2 patient experiences shortness of breath when hurrying or walking up a slight hill. At Grade 3, patients walk slower than others of the same age due to breathlessness or need to stop for breath when walking at their own pace. Grade 4 patients need to stop for breath after walking approximately 100 meters or a few minutes, and those at Grade 5 are too breathless to leave the house or become breathless even when dressing [22].

2.5.3. Coronary Artery Disease (CAD) score

The CAD score is a tool used to assess the risk of coronary artery disease in individuals based on several clinical parameters, including age, gender, and cardiovascular risk factors such as hypertension, smoking status, and cholesterol levels [23]. In patients with IPF, cardiovascular comorbidities are common and can significantly impact prognosis. The CAD score is important in identifying patients who may require further cardiac evaluation or management [23].

2.6. Statistical Analysis

All data were analyzed using SPSS 26.0 (IBM Corp, Armonk, NY). Continuous variables were expressed as means with standard deviations, while categorical variables were presented as frequencies and percentages. To assess relationships between oscillometry parameters and clinical variables such as disease duration, GAP score, and DLCO%, Pearson’s correlation was used for normally distributed data. For non-normally distributed data, Spearman’s rank correlation was applied. To compare differences between groups, Student’s t-test was used for continuous variables that followed a normal distribution, while the Mann-Whitney U test was used for non-parametric data. For categorical variables, the Chi-square test was applied to assess differences in frequencies. A p-value of < 0.05 was considered statistically significant for all analyses.

3.1. Demographic and clinical characteristics of the study population

The study involved a group of 48 IPF individuals, with an average age of 72±2 years. Of the participants, 33 out of 48 (69%) were male. The average age at which IPF was diagnosed was 70±7 years. Among the group, 63% were ex-smokers, 33% were non-smokers, and only 6% were current smokers, with an average of 47±38 pack years. Interestingly, a higher proportion of males were ex-smokers compared to females. About 54% of the participants were under antifibrotic treatment for IPF, with 48% receiving pirfenidone, and the remainder receiving nintedanib. The main characteristics of the sample and according to gender are presented in Table 1.

Males were significantly more likely to have a history of smoking compared to females, indicating a higher prevalence of smoking in the male population. Additionally, disease severity, as measured by the GAP index, was greater in males, suggesting that male patients may have a worse prognosis. CAD score, was also significantly higher in males than in females, highlighting an increased risk of cardiovascular comorbidities in male IPF patients. These findings emphasize the influence of gender on smoking history, disease progression, and comorbidities in IPF.

Table 1. The main characteristics of the study population (N=48), and comparisons between genders

Parameter	Sum (N=48)	Males (n=33)	Females (n=15)	p-value
Age, years	72±2	73±8	71±5	0.541
Age of IPF diagnosis, years	70±7	70±8	70±6	0.953
Current smokers, n (%)	3 (6)	2 (6)	1 (7)	0.780
Ever smokers, n (%)	30 (63)	25 (76)	5 (33)	0.019
Nonsmokers, n (%)	15 (31)	6 (18)	9 (6)	0.890
Pys	47±38	43±22	66±77	0.180
Under antifibrotic treatment, n (%)	26 (54)	20 (61)	6 (40)	0.155
Pirfenidone, n (%)	13 (27)	10 (30)	3 (20)	0.862
Nintedanib, n (%)	13 (27)	10 (30)	3 (20)	0.862
MRC score	2±1	2±1	2±1	0.636
GAP index	4±1	4±1	3±1	0.016
GAP stage I	26 (54)	15 (45)	11 (73)	0.106
GAP stage II	16 (33)	12(37)	4 (27)	0.106
GAP stage III	6 (13)	6 (18)	0	0.106
CAD score	18±9	21±8	10±4	0.001

Note: The data are presented as mean value ± SD or frequencies (percentages)

Abbreviations: CAD score, Coronary Artery Disease score; IPF, idiopathic pulmonary fibrosis; GAP index, Gender, Age, Physiology index; MRC score, Medical Research Council score; Pys, pack per years

3.2. Lung function parameters of the study population

The lung function parameters of the study population are presented in Table 2. 52% (25/48) of the patients had restrictive pattern (TLC<70%). 6% (3/48) of the participants had obstructive pattern (FEV1/FVC<0.70). The mean DLCO/VA% for the study population was 51±16% predicted. Pulmonary hyperinflation was detected in IPF patients as the mean RV/TLC was above 40%.

Abnormal FOT and IOS values of AX, R5-R20 and Fres were found, indicating the presence of SAD in the study population.

Table 2. Lung function parameters of the study population and comparisons between genders

Parameters	Sum (N=48)	Males (n=33)	Females (n=15)	p-value
FVC, ml	2.7±0.8	2.9±0.8	2.1±0.6	<0.001
FVC%	83±22	80±22	89±20	0.171
FEV1, ml	2.2±0.7	2.5±0.7	1.8±0.4	<0.001
FEV1%	88±22	86±23	92±18	0.382
FEV1/FVC	82±7	81±7	84±6	0.147
FEV1/FVC<70%	3 (6)	3 (0.9)	0	0.315
PEF, ml	6.4±2.3	7±2.5	6.1±1.6	0.006
PEF%	90±28	89±29	92±25	0.756
FEF25-75, ml	2.3±1.2	2.7±1.3	2.2±0.8	0.161
FEF25-75%	94±44	94±44	94±47	0.948
FEF25-75<70%	15 (31)	9 (27)	6 (40)	0.289
TLC, ml	4.1±1.1	4.4±1.2	3.7±0.6	0.024
TLC%	70±17	67±17	77±14	0.067
TLC<70%	25 (52)	19 (58)	6 (40)	0.177
RV, ml	1.7±0.6	1.7±0.6	1.7±0.8	0.754
RV%	70±28	64±23	83±34	0.031
RV/TLC%	66±28	64±30	73±26	0.359
RV/TLC>40%	35 (72)	23 (69)	12 (80)	0.484
DLCO, ml/min/mmHg/L	4.1±1.4	4.4±1.4	3.5±1.2	0.072
DLCO/VA	51±16	50±17	50±16	0.966
DLCO<80%	48 (100)	33 (100)	15 (100)	0.978
FOT parameters
R5, kPa s L−1	0.3±0.1	0.28±0.09	0.34±0.09	0.038
R5 z-score	-0.2±1.2	-0.1±1.2	-0.6±1.2	0.132
R5%	100.1±34.6	105±35	89±30	0.146
R20 kPa, s L−1	0.26±0.06	0.24±0.05	0.29±0.06	0.100
R5-20, kPa s L−1	0.05±0.04	0.05±0.03	0.05±0.04	0.611
R5-20 >0.03 kPa s L−1	31 (65)	20 (60)	11 (73)	0.302
R5-20 z-score	0.4±1.6	0.4±1.6	0.4±1.7	0.950
X5, cmH₂O/L/s	-0.19±0.08	-0.16±0.07	-0.23±0.08	0.004
AX, KpA/L	1.2±0.8	1.2±0.8	1.5±0.7	0.133
AX>0.5 KpA/L	48 (100)	33 (100)	15 (100)	0.978
AX z-score	1.5±0.9	1.6±1	1.3±0.8	0.279
AX%	380±285	426±326	284±138	0.116
Fres, Hz	20±4.3	20±5	20±4	0.641
Fres >12 Hz	45 (94)	31 (94)	14 (93)	0.685
IOS parameters
R5, kPa s L−1	0.3±0.05	0.3±0.05	0.3±0.06	0.523
R5%	95±19	98±18	79±17	0.112
R20, kPa s L−1	0.25±0.05	0.2±0.04	0.3±0.08	0.141
R20%	90±18	91±18	83±25	0.516
R5-20, kPa s L−1	0.1±0.02	0.1±0.02	0.03±0.02	0.183
R5-R20 ≥0. 1 kPa s L−1	29 (60)	23 (70)	6 (40)	0.048
X5, kPa s L−1	-0.07±0.05	-0.06±0.03	-0.09±0.01	0.122
X5%	271±204	303±205	89±34	0.096
AX, kPa s L−1	0.38±0.22	0.39±0.23	0.32±0.12	0.661
AX>0.1 kPa s L−1	48 (100)	33 (100)	15 (100)	0.999
Fres, Hz	16±3	17±3	14±4	0.219
Fres> 12 Hz	43 (90)	29 (87)	14 (93	0.284

Note: The data are presented as mean value ± SD or frequencies (percentages)

Abbreviations: AX, area under the curve; between X5 Hz and cross-point of the curve to horizontal line; DLCO/VA, diffusing capacity for carbon monoxide per liter of lung volume corrected for alveolar transfer; DLCO/VA%, predicted diffusing capacity for carbon monoxide per liter of lung volume corrected for alveolar transfer; FEF25-75, forced mid-expiratory flow; FEF25-75%, predicted forced mid-expiratory flow; FEV1%, forced expiratory volume in 1 s of FVC test; FVC%, forced vital capacity; PEF, peak expiratory flow, PEF, predicted peak expiratory flow; R5, actual measure of resistance at frequency 5 Hz; R5%, predicted resistance at frequency 5 Hz; R20, actual measure of resistance at frequency 20 Hz; R20%, predicted resistance at frequency 20 Hz; R5–20, actual lung resistance when subtraction resistance at frequency 5 Hz minus resistance at frequency 20 Hz; RV, residual volume; RV%, predicted residual volume; TLC, total lung capacity; TLC%, predicted total lung capacity; X5%, predicted lung reactance at frequency 5 Hz; X5, actual measure of lung reactance at frequency 5 Hz.

Abnormal IOS values: R5 ≥ 0.5 kPa s L−1, R5% >150%, R20 ≥ 0.3 kPa s L−1, R20%>150%, Di5-20 ≥ 0.1 kPa s L−1, X5≤-0.1 kPa s L−1, Fres>12 Hz, AX ≥ 0.1 kPa s L−1. [14,15]

Normal Tremoflo values: R5: 0.2 – 0.4 kPA/L/s; R20: 0.2 – 0.4 KpA /L/s; R5-R20 ≤ 0.3 cmHO2.s/L; X5: -0.1 to -0.4 KpA/L/s; AX:< 0.5 KpA/L; AX z-score: <1.64; R5-R20 z-score: <1.64, R5 z-score <1.64 [16,17]

52% of patients with IPF exhibited reduced lung volumes, indicative of restrictive patterns, while a smaller subset (6%) presented with airflow limitation (FEV1/FVC<70%), suggesting obstructive conditions. Impaired gas exchange was noted across all individuals. Many participants also showed signs of small airway obstruction, evidenced by reduced mid-expiratory flow rates. Additionally, more than half of the participants demonstrated air trapping, reflected in elevated RV/TLC ratios.

The findings from both FOT and IOS further support these observations, with many participants exhibiting increased small airway resistance, reduced lung compliance, and heightened lung stiffness.

The R5-R20 parameter (in both FOT and IOS) measures resistance in the peripheral or small airways, and an elevated R5-R20 value is a direct marker of small airway dysfunction. In this study, a substantial 65% of participants had an abnormal R5-R20 value (greater than 0.03 kPa·s/L) in FOT, indicating increased small airway resistance. Similarly, in IOS, 60% of participants had R5-R20 values exceeding the abnormal threshold (0.1 kPa·s/L), underscoring widespread small airway dysfunction in this population.

Additionally, Fres proved to be highly sensitive in detecting lung stiffness and small airway obstruction, with 94% of participants in FOT and 90% in IOS showing values above 12 Hz. This further highlights the prevalence of small airway dysfunction and increased lung stiffness. While R5-R20 specifically assesses small airway resistance, Fres is more sensitive to broader changes in lung mechanics, encompassing both lung stiffness and small airway obstruction.

The AX parameter, which reflects reduced lung compliance and increased small airway resistance, was elevated in all participants, with 100% of participants in both FOT and IOS showing AX values above the abnormal threshold. In FOT, AX values exceeded 0.5 kPa·s/L in all participants, while in IOS, AX exceeded 0.1 kPa·s/L in all cases. This universal abnormality in AX highlights the severity and prevalence of small airway dysfunction and reduced lung compliance across the study group, reinforcing its role as a critical marker in detecting restrictive and obstructive lung pathologies.

The FEF25-75% parameter indicated SAD in 31% of participants, with values below 70%, making it a less sensitive marker for small airway dysfunction compared to AX, R5-R20 and Fres in FOT and IOS.

Lastly, the RV/TLC ratio proved particularly effective in identifying air trapping, with 72% of participants displaying ratios above 40%, a hallmark of obstructive lung diseases. However, it was slightly less sensitive than AX, Fres in detecting overall lung dysfunction.

Collectively, these findings point to a combination of restrictive and obstructive lung pathologies, with SAD being particularly prevalent in this cohort.

3.3. Correlations between CAD Score and lung function parameters: The role of reactance and lung stiffness in cardiovascular risk

There was a positive correlation (r =0.283, p = 0.045) between CAD score and AX (Figure 1). This suggests that higher AX (indicating increased lung stiffness or small airway obstruction) was associated with a higher CAD score, suggesting that patients with more severe lung stiffness or small airway obstruction were more likely to have coronary artery disease. There was a negative correlation (r =-0.314, p = 0.037) of CAD with X5, which relates to lung compliance (Figure 2). This suggests that as X5 becomes less negative (indicating better lung compliance or less stiffness), the CAD score decreases, meaning patients with less lung stiffness were less likely to have coronary artery disease.

3.4. Correlations Between GAP Score and MRC and Lung Function Parameters: The Role of Reactance and Lung Stiffness in IPF Severity

No significant correlations were observed between the MRC or GAP Index and the IOS or FOT parameters in this dataset, indicating that while SAD is prevalent in IPF patients, these oscillometry parameters may not correlate strongly with disease severity as measured by the MRC score and GAP index in this cohort.

3.5. Other correlations between lung function, dyspnea, severity and cardiovascular risk in IPF patients

Correlation analyses showed that there was a significant negative correlation (r = -0.522, p = 0.001) between DLCO% and MRC score, indicating that as DLCO% decreases, MRC score increase, which reflects worsening dyspnea as gas exchange becomes impaired. There was an even stronger negative correlation (r = -0.711, p < 0.001) between DLCO% and GAP score, reinforcing that as DLCO% decreases, the GAP index increases, reflecting worsening lung function and prognosis. There was a positive correlation (r = 0.474, p = 0.001) between GAP score and MRC, suggesting that as GAP index points increase, which indicates a worse prognosis in IPF, the MRC score also increases, indicating higher levels of dyspnea. There was a positive correlation (r = 0.255, p =0.046), suggesting that a higher GAP index (worse prognosis in IPF) is associated with a higher CAD score, indicating that coronary artery disease risk may increase as IPF severity worsens. There was a positive correlation (r = 0.428, p = 0.002) between RV/TLC and CAD score, indicating that as RV/TLC increases (reflecting more air trapping), the CAD score increases, which could indicate more coronary artery disease risk in patients with more severe lung dysfunction.

There was a positive correlation between age and GAP index (r = 0.381, p = 0.008), suggesting that older age at diagnosis is associated with a higher GAP index, indicating a worse prognosis. There was a positive correlation (r = 0.506, p < 0.001), indicating that older age at diagnosis is associated with a higher CAD score, reflecting that older individuals are more likely to have coronary artery disease.

There was no significant association between IOS/FOT parameters and IPF patients’ age, medication or smoking status.

3.6. Correlation Between DLCO% and FOT or IOS Parameters

There was a significant positive correlation between DLCO% and X5 (r = 0.377, p = 0.014), indicating that as DLCO% improves (reflecting better gas exchange), X5 becomes less negative. This suggests that better lung function was associated with improved lung compliance and less lung stiffness. Conversely, DLCO% showed a strong negative correlation with Fres (r = -0.705, p < 0.001), meaning that as gas exchange worsens, Fres increases, which is indicative of increased lung stiffness and small airway obstruction (Figure 3). Additionally, there was a significant negative correlation between DLCO% and AX (r = -0.502, p < 0.001), demonstrating that decreased gas exchange was associated with greater airway obstruction and worse lung compliance (Figure 4). Together, these findings showed that as lung function declines, stiffness and resistance in the airways increase.

3.7. Correlation Between Disease Duration and Lung Function Decline in FOT Parameters

The only statistically significant correlation found was between disease duration and X5, suggesting that as the disease progresses, lung compliance decreases, leading to increased stiffness. Other trends, such as increasing small airway obstruction (AX) and lung stiffness (Fres) with disease duration, were observed but did not reach statistical significance.

This study highlights the significant involvement of SAD in IPF, as evidenced by abnormal oscillometry findings in a large proportion of the study cohort. The present study emphasizes the intricate relationship between SAD, disease severity, and clinical outcomes in IPF patients, underscoring the potential under-recognized role of small airway involvement in IPF pathophysiology. Our findings align with recent literature that increasingly recognizes the role of small airways in the pathogenesis of IPF [1-10].

One of the most important observations in our cohort is the widespread presence of SAD, evidenced by elevated R5-R20 values in over 60% of patients and abnormal AX and Fres values in nearly all participants, as measured by FOT and IOS. These findings suggest that small airway resistance and lung stiffness are key features of IPF, even in patients with relatively normal spirometry. While traditional spirometry and plethysmography identified restrictive patterns and air trapping in more than half of the study population, oscillometry was notably more sensitive in detecting peripheral airway obstruction and lung stiffness [3-5,7]. This aligns with research by Ikezoe et al. (2021) [5] and Zhang et al. (2022) [4], who highlighted the role of oscillometry in revealing SAD in fibrotic lung diseases. Further supporting our results, Berger and Podolanczuk (2024) [2] emphasized the importance of assessing small airway involvement in IPF progression, reinforcing the value of FOT and IOS in evaluating peripheral airway resistance when traditional methods fall short. Yin et al. (2022) reported that 72.7% of IPF patients exhibited SAD, based on oscillometry findings such as R5-R20 and AX [3]. Zhang et al. (2022) found that 69.1% of IPF patients had SAD, with significant small airway obstruction identified through similar oscillometry markers [4].

Ikezoe et al. (2021) identified that small airway reduction and fibrosis are early pathologic features of IPF, potentially preceding clinical manifestations [5]. In line with this, our study's findings of elevated AX values in all patients reinforce the critical role of monitoring small airway changes as IPF progresses. The universal presence of increased AX highlights that SAD is a common feature in IPF and may precede more overt signs of alveolar and interstitial fibrosis. AX emerged as the most sensitive marker for detecting SAD in our IPF cohort, strongly correlating with disease severity, gas exchange impairment (DLCO%), and detecting abnormalities even in patients with relatively preserved spirometry results. While R5-R20 and Fres values were also elevated, AX consistently captured SAD and lung stiffness across the cohort, making it a more reliable indicator of disease progression and severity. This sensitivity underscores the value of using oscillometry, particularly AX, in assessing small airway involvement in IPF, offering crucial insights that traditional spirometry often misses.

The strong correlation between increased AX as well as Fres and impaired gas exchange (DLCO%) further underscores the critical role of SAD in exacerbating the respiratory impairment seen in IPF. As DLCO% declines, indicating worsening gas exchange, both AX and Fres values increase, reflecting growing airway resistance and lung stiffness. These findings suggest that SAD is not merely an incidental feature of IPF, but a key contributor to its progression, aligning with earlier work by Hu et al. (2020) who reported similar relationships between SAD and gas exchange abnormalities [6].

Moreover, Hu et al. (2020) proposed that functional parameters of small airways could guide bronchodilator use in IPF [6]. Although bronchodilators are not typically part of standard IPF management, our findings—particularly the correlation between AX, Fres, and impaired gas exchange (DLCO%)—suggest that targeting SAD could offer a novel therapeutic approach. Interestingly, while Parra et al. (2008) focused on the immunophenotyping of small airways in idiopathic interstitial pneumonias, they concluded that the remodeling processes in small airways have functional and prognostic significance [8]. This perspective has not yet been widely adopted but warrants further exploration, especially given the lack of significant improvement in small airway parameters despite antifibrotic treatments.

Another interesting finding in our study is that the higher lung stiffness (higher AX) was associated with a higher CAD score, while improved lung compliance (less negative X5) correlated with a lower CAD score. This aligns with previous studies, such as those by Ziora et al. (2024), have suggested that SAD could serve as an independent prognostic marker in IPF [1], and our data support this hypothesis.

The association between RV/TLC ratios and CAD scores further highlights the relationship between respiratory and cardiovascular health in IPF patients. Elevated RV/TLC ratios indicate more pronounced air trapping, and their positive correlation with CAD score suggests that individuals with more advanced lung disease might experience higher cardiovascular risk. This finding mirrors earlier observations by Berend (2014), who suggested that the interplay between IPF and cardiovascular comorbidities is multifaceted and may vary depending on disease stage [2].

Interestingly, antifibrotic treatment with pirfenidone and nintedanib was not associated with lung function parameters. This suggests that while these therapies may slow the progression of fibrosis, they do not appear to improve SAD. This finding aligns with the current understanding that antifibrotic treatments primarily target the interstitial fibrosis rather than the small airways, underscoring the need for further research into therapies specifically aimed at preserving small airway function in IPF patients.

Contrary to expectations, there was no significant correlation between smoking status and SAD in this cohort. This finding is consistent with previous studies that suggest factors such as genetic predisposition or disease progression may play a more significant role in SAD in IPF than smoking alone. Additionally, while older age was associated with a trend toward increased small airway resistance and lung stiffness, these correlations were not statistically significant, suggesting that small airway dysfunction is prevalent across all age groups in IPF.

IOS and FOT are both valuable for assessing small airway function, but they offer different insights. Both measure respiratory impedance, including resistance (R) and reactance (X). In our study, FOT was more sensitive in detecting abnormalities, particularly through R5-R20 and AX, which reflect small airway resistance and lung stiffness. While IOS also detected dysfunction, FOT offered a more detailed analysis of lung compliance. Both methods showed abnormal Fres, indicating lung stiffness, but FOT provided a more comprehensive assessment of lung mechanics in this cohort.

A few limitations should be noted in this study. First, the relatively small sample size may limit the generalizability of the findings to the broader population of IPF patients. Larger studies are needed to confirm these results and better understand the prevalence and impact of small airway dysfunction in IPF. Additionally, the cross-sectional design prevents us from assessing longitudinal changes in small airway function and their relation to disease progression. Future studies should incorporate longitudinal data to evaluate how small airway dysfunction evolves over time. Another limitation is the exclusion of patients with concomitant obstructive airway diseases, such as COPD, which may have provided further insight into the interplay between IPF and SAD in a more diverse patient cohort. Finally, while oscillometry is a sensitive tool for detecting small airway dysfunction, it remains a relatively specialized technique not yet widely available in clinical settings, which could affect the broader applicability of these findings.

On the other hand, this study significantly advances previous research on SAD in IPF by offering a comprehensive and multifaceted analysis. Unlike earlier studies that typically utilized a single diagnostic method, this research employs both the FOT and the IOS to assess small airway function, enhancing the sensitivity and specificity of detection. The inclusion of extensive pulmonary function tests—including oscillometry, spirometry, plethysmography, and diffusing capacity measurements—captures a full spectrum of lung function parameters. By correlating oscillometry findings with clinical severity indices like the GAP index and MRC dyspnea score, the study provides valuable insights into how SAD relates to overall disease severity and patient symptoms. Furthermore, the study uniquely explores the relationship between lung function parameters and cardiovascular comorbidity, uncovering significant correlations that were not addressed in previous research. By analyzing a larger and more diverse patient cohort and considering factors such as smoking status, gender differences, and the impact of antifibrotic treatments, the research offers a more holistic view of IPF and its complexities. These comprehensive and novel approaches differentiate this study from prior work, ultimately contributing to a better understanding of SAD in IPF and highlighting the need for new therapeutic strategies aimed at preserving small airway function to improve patient outcomes.

This study demonstrates the presence of SAD in a significant proportion of IPF patients, as evidenced by abnormal oscillometry values and is linked to impaired gas exchange and increased cardiovascular risk. This study highlights the potential utility of oscillometry in detecting SAD and monitoring disease progression in IPF, particularly in cases where spirometric abnormalities may not be evident. Further research into therapeutic strategies targeting SAD is warranted to improve patient outcomes. Oscillometry may serve as a valuable tool for monitoring disease progression and response to treatment in this population.

Author Contribution

Ourania S. Kotsiou: Conceptualization, methodology, data analysis, manuscript writing, supervision.Paraskevi Kirgou: Data collection, investigation, drafting of the manuscript.Ilias E. Dimeas: Formal analysis, data interpretation, manuscript editing.Konstantinos I. Gourgoulianis: Study supervision, funding acquisition.Zoe Daniil: Study conception, review and editing, final approval of the manuscript.

Data Availability

Data is provided within the manuscript.

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No competing interests reported.

Exploring Small Airway Disease in Idiopathic Pulmonary Fibrosis Patients: Insights from Oscillometry Analysis

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methods