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Respiratory abnormalities in sarcoidosis: physiopathology and early diagnosis using oscillometry combined with respiratory modeling

BMC Pulmonary Medicine volume 25, Article number: 68 (2025) Cite this article

Abstract

Background

Sarcoidosis is a multisystemic syndrome of uncertain etiology with abnormal respiratory findings in approximately 90% of cases. Spirometry is the most common lung function test used for assessing lung function in diagnosis and monitoring pulmonary health. Respiratory oscillometry allows a simple alternative for the analysis of respiratory abnormalities. Integer-order and fractional-order modeling have increasingly been used to interpret measurements obtained from oscillometry, offering a detailed description of the respiratory system. In this study, we aimed to enhance our understanding of the pathophysiological changes in sarcoidosis and assess the diagnostic accuracy of these models.

Methods

This observational study includes 25 controls and 50 individuals with sarcoidosis divided into normal to spirometry (SNS) and abnormal spirometry (SAS). The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC).

Results

The integer-order model showed significant airway and total resistance increases in the SNS and SAS groups. There was a reduction in compliance and an increase in peripheral resistance in the SAS group (p < 0.001). The fractional-order model showed increased energy dissipation and hysteresivity in the SNS and SAS groups. Correlation analysis revealed significant associations among model and spirometric parameters, where the strongest associations were between total resistance and FEV1 (r: -0.600, p = 0.0001). The diagnostic accuracy analysis showed that total resistance and hysteresivity were the best parameters, reaching an AUC = 0.986 and 0.938 in the SNS and SAS groups, respectively.

Conclusion

The studied models provided a deeper understanding of pulmonary mechanical changes in sarcoidosis. The results suggest that parameters obtained through the studied models enhance evaluation and enable better management of these patients. Specifically, total resistance and hysteresivity parameters demonstrated diagnostic potential, which may be beneficial for the early identification of individuals with sarcoidosis, even when spirometry results are within normal ranges.

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Background

Sarcoidosis represents a multisystem syndrome of uncertain etiology, manifesting abnormal respiratory findings in approximately 90% of patients. The primary pathology involves the accumulation of non-infectious granulomas, leading to alterations in lung structure that impact compliance and induce airway obstruction. Predominant clinical manifestations include cough, fatigue, and exertional dyspnea, while around one-third of patients remain asymptomatic. The clinical course exhibits considerable variability, with spontaneous remissions observed in approximately 60% of cases. Acute sarcoidosis typically exhibits a highly inflammatory nature with a favorable prognosis, often resolving without steroid therapy. Conversely, the chronic phase may lead to progressive loss of lung function characterized by fibrotic changes [1,2,3].

Managing sarcoidotic patients poses challenges due to the diverse array of nonspecific manifestations and symptoms. Guidelines from the American Thoracic Society (ATS), European Respiratory Society (ERS), and the World Association of Sarcoidosis and Other Granulomatous Disorders (WASOG) recommend routine chest radiography and pulmonary function tests [4].

In this context, the forced oscillation technique (FOT), also known as oscillometry, offers clinically relevant advantages. Notably, its minimal requirement for patient cooperation renders it suitable for individuals unable to undergo traditional tests [5, 6]. Additionally, FOT enables a detailed assessment of respiratory biomechanics by analyzing respiratory impedance across various frequencies.

In a previous study conducted in our laboratory, Faria et al. [1] explored the clinical utility of FOT in detecting changes among sarcoidosis patients. The study revealed FOT parameters consistent with the underlying pathophysiology of sarcoidosis, demonstrating their efficacy in detecting alterations in respiratory mechanics as described by spirometric tests. However, this investigation did not employ modeling techniques.

The extended resistance-inertance-compliance (eRIC) model enables a comprehensive assessment of both central and peripheral airways and respiratory inertia and compliance. This modeling approach has enhanced our understanding of biomechanical alterations in individuals with mild asthma [7] and asbestos exposure [8]. Previous studies have demonstrated clear associations between eRIC parameters and anatomical changes observed via computed tomography pulmonary densitometry in silicosis [9]. Earlier research showed that fractional-order (FrOr) models offer significant advantages in accurately representing the complex dynamics of the respiratory system, providing better insights into physiological processes, and facilitating the development of improved diagnostic and therapeutic strategies for respiratory-related disorders [7, 10,11,12]. However, no studies utilize these models in individuals with sarcoidosis.

In this context, the primary objectives of this study were to (1) utilize respiratory oscillometry in conjunction with the eRIC and FrOr models to enhance our comprehension of the pathophysiology of sarcoidosis and (2) assess the utility of this method in diagnosing early respiratory changes in these patients.

Methods

Study design and ethics

This research protocol follows the guidelines of the declaration of Helsinki and is a cross-sectional, observational study. The present research is focused on understanding fundamental physiological principles using existing pulmonary function methods rather than testing novel interventions. Therefore, clinical trial registration is not applicable. This study was approved by the Ethics Committee at Pedro Ernesto University Hospital (456/1997-CEP/HUPE). Prior to the tests, participants provided written informed consent. The FOT and spirometry evaluations were conducted at the Biomedical Instrumentation Laboratory and at Pedro Ernesto University Hospital of the State University of Rio de Janeiro.

Subjects

We investigated 25 healthy individuals, devoid of smoking history or lung diseases, aged 18 years and above, who were clinically stable and exhibiting spirometric test results within the normal range, constituting the control group (CG, n = 25). Additionally, we studied individuals with sarcoidosis presenting normal spirometric test results (SNS, n = 25) and those with abnormal spirometric findings (SAS, n = 25). Exclusion criteria included individuals with a history of cardiovascular, COVID-19, or orthopedic diseases, recent respiratory infections in the last thirty days, and smokers.

Measurements

The FOT assessment utilized an instrument developed in our laboratory, which generates pressure oscillations ranging from 4 to 32 Hz with an amplitude of 2 cmH2O, delivered to the respiratory system via a mouthpiece using a loudspeaker [13]. Flow and pressure signals from this process were captured near the mouth utilizing a pneumotachometer and a pressure transducer, respectively. Subsequently, these signals underwent amplification and were processed using Fourier transform (F) to estimate respiratory impedance (Zrs) through the ratio of pressure signals (P) to respiratory flow (V´) [Zrs = F(P)/F(V´)] [6].

Spirometric measurements allowed the evaluation of forced expiratory volume in one second (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, and forced expiratory flow (FEF). These assessments were conducted with individuals in a seated position using a computerized pneumotachometer system (nSpire Health, Inc., 1830 Lefthand Circle, Longmont, CO 80501), following the acceptance and reproducibility criteria outlined by the Brazilian Society of Pulmonology and Tisiology (SBPT) [14] and ATS/ERS guidelines [15]. Pereira’s spirometry reference equations were applied for data interpretation [16].

Respiratory modelling

The extended Respiratory-Inertance-Compliance (eRIC) model (depicted in Fig. 1) has been proposed as an enhancement over the basic Respiratory-Inertance-Compliance (RIC) model. Within these models, R represents central airway resistance, Rp describes peripheral resistance, and I and C are linked to respiratory inertia and compliance. Additionally, the eRIC model enables the assessment of total resistance (Rt = R + Rp) [17].

Fig. 1
figure 1

Two-compartment model employed for respiratory impedance analysis. In this model, resistance (R), inductance (I), and capacitance (C) correspond to respiratory resistance, inertia, and compliance, respectively. Specifically, R is analogous to central airway resistance, Rp describes peripheral resistance, I is associated with pulmonary inertia, and C is linked to respiratory compliance

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The FrOr model (Fig. 2) is delineated by Eq. 1.

Fig. 2
figure 2

Fractional-order model analysed in this study, including a constant phase inertance (CPL) and a constant phase compliance (CPC) composed by a frequency-dependent fractional inertia (FrL) and a frequency-dependent fractional compliance (FrC) associated with their respective fractional exponents α and β. The ability of the fractional terms to describe the resistive and reactive respiratory properties, depending of α and β values is also described

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This model is composed of an element describing central airways encompassing frequency-dependent inertia (FrL), which considers the capacity of fractional terms to approximate resistive properties (0 ≤ α ≤ 1), along with a more peripheral component characterized as a constant-phase impedance represented by a fractional compliance (FrC) linked with a fractional coefficient (0 ≤ β ≤ 1).

$$\:{Z}_{FrOr}\left(j\omega\:\right)={L\left(j\omega\:\right)}^{\alpha\:}+\frac{1}{C{\left(j\omega\:\right)}^{\beta\:}}$$
(1)

These results were physiologically interpreted using the damping factor (G, Eq. 2), elastance (H, Eq. 3), and the hysteresis coefficient (η, Eq. 4), as elaborated below:

$$\:G=\:\frac{1}{C}cos\left(\frac{\pi\:}{2}\beta\:\right)$$
(2)
$$\:H=\:\frac{1}{C}sin\left(\frac{\pi\:}{2}\beta\:\right)$$
(3)
$$\:\eta\:=\:\frac{G}{H}$$
(4)

The damping factor is linked with energy dissipation in the respiratory system, while H reflects potential energy storage (elastance). Hysteresivity describes the heterogeneity of pulmonary ventilation [12, 17].

Statistical analysis

MedCalc® 14 software (MedCalc Software, Mariakerke, Belgium) was utilized to determine the required sample size for this study. Values utilized for calculations were derived from preliminary findings from the current study [18], with assumed type I and type II errors of 5%. These analyses yielded a minimum of 25 volunteers in each studied group.

Comparative analysis was conducted using the OriginPro 8 software (Microcal Software Inc, Northampton, USA). Significance was established at p < 0.05. Results are expressed as mean ± standard deviation (SD). The Shapiro-Wilk normality test was initially applied. For normally distributed data, one-way ANOVA corrected by Tukey’s test was employed. In cases of non-normal distribution, a nonparametric approach combining the Kruskal-Wallis test with the Mann-Whitney U test was utilized.

The relationships among the studied models and spirometric parameters were evaluated using Spearman correlation coefficients. These analyses were conducted utilizing GraphPad Prism 7, and the interpretation of the results was guided by the classification proposed by Dawson and colleagues [19].

Receiver Operating Characteristic (ROC) analyses were conducted to discern potential diagnostic use of the parameters derived from respiratory models. The software employed for these calculations was MedCalc® version 14.12.0 (MedCalc Software, Mariakerke, Belgium). ROC curves with an Area Under the Curve (AUC) ≥ 0.80 were deemed appropriate for diagnostic purposes, while AUC values between 0.90 and 1.00 were indicative of high diagnostic accuracy [20]. The method suggested by Delong et al. [21] was used to compare [21] AUCs and identify the highest accuracy.

Results

The anthropometric and spirometric characteristics of the studied groups are described in Table 1. We evaluated 25 volunteers in the control group, 25 patients with sarcoidosis but normal spirometric examination (SNS), and 25 patients with sarcoidosis and altered spirometric examination (SAS). The SAS group included 13 obstructive patients (12 mild and 1 severe), 11 restrictive (7 mild, 2 moderate, and 2 severe), and 1 patient with an unspecific disorder. Sixteen patients in the SNS group (64%) and 20 patients in the SAS group (80%) were under steroid therapy.

Table 1 Anthropometric and spirometric data of the studied groups
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Table 2 describes the comparative analysis of the traditional oscillometric parameters among the groups classified according to spirometry. Significant R0, Rm, R4, and R20 increases were observed (ANOVA, p < 0.05). S and R4-R20 were also significantly influenced by changes in the spirometric classification (p < 0.001). Table 2 also shows that Xm became more negative, while Cdyn reduced and Fr, Ax, and Z4 increased significantly with changes in spirometry (p < 0.001).

Table 2 Respiratory oscillometry data
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Considering the eRIC model, R (Fig. 3A), Rp (Fig. 3B), and Rt (Fig. 3C) were significantly influenced by obstruction, as assessed by spirometry (ANOVA, p < 0.004). Rp presented greater resistance only in more advanced stages of the disease, while R and Rt presented increased values even in the absence of airway obstruction.

Fig. 3
figure 3

Behaviour of eRIC parameters; p < 0.05 significant in comparison CG – control group and NE – normal spirometry; and SALT – sarcoidosis with changes

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An important observation in the present study was the reduction in C with airway obstruction (Fig. 3D, ANOVA, p < 0.002) and the presence of significant changes in more advanced stages. Respiratory inertance (Fig. 3D) did not show changes with respiratory obstruction (ANOVA, p = ns), and no changes were observed in relation to the control group. A reduction was observed in the SAS compared to the SNS (p < 0.05).

About fractional order modeling, the damping factor illustrated in Fig. 4A shows a significant increase (ANOVA, p < 0.001) with obstruction. When compared to the CG, the NE and SALT groups were significantly higher. Elastance, conversely, does not present significant changes either in relation to obstruction or in comparison between groups (Fig. 4B). Regarding lung heterogeneity (hysteresivity, Fig. 4C), we observed a significant dependence on obstruction (ANOVA, p < 0.0001). Higher values were observed with significant differences (p < 0.0001) in the early and advanced stages of the disease.

Fig. 4
figure 4

Behaviour of the FrOr parameters; p < 0.0001 significant in comparison CG – control group and NE – normal spirometry; and SALT – sarcoidosis with changes

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Table 3 shows the correlation analysis between the spirometry parameters and the eRIC and FrOr models in the sarcoidosis groups, which were normal on examination and with changes.

Table 3 Correlation analysis between spirometry and parameters of the eRIC and FrOr model in the sarcoidosis groups normal to the exam and with spirometric alterations. Significant associations are described in bold
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Table 4 shows the diagnostic accuracy in patients with NE sarcoidosis. The highest AUC values were shown in bold. The AUC obtained for hysteresivity was significantly higher than that obtained for Rt (p = 0.0109).

Table 4 Diagnostic accuracy, sensitivity, specificity and cut-off point of traditional parameters in patients with sarcoidosis NE. The highest AUC values are shown in bold
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Table 5 shows the diagnostic accuracy in patients with sarcoidosis and altered spirometry. The highest AUC values are shown in bold. The AUC obtained for Rt and hysteresivity were not statistically different (p = 0.4320).

Table 5 Diagnostic accuracy, sensitivity, specificity and cut-off point of traditional parameters in patients with Sarcoidosis with alteration. The highest AUC values are shown in bold
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Figure 5 shows the ROC curves of the two most accurate parameters in the SNS and SAS groups.

Fig. 5
figure 5

Receiver operator characteristic curves of the two most accurate parameters in patients with normal spirometry (A) and altered spirometry (B)

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Discussion

This study investigated the utility of the eRIC and FrOr models in patients with sarcoidosis, revealing three key findings: (1) These models provided a comprehensive understanding of sarcoidosis pathophysiology. Initial stages, while spirometry is still normal, exhibited alterations in ventilation homogeneity linked with airway changes, while in patients with mild spirometric abnormalities, peripheral airway changes, and restrictive characteristics are also observed; (2) Alterations in model parameters correlated with oscillometric parameters and (3) The eRIC and FrOr models effectively identified early abnormal changes in sarcoidosis.

Previous studies have shown that age and weight almost did not influence respiratory resistance and reactance [22, 23]. Height is the most influential parameter, and resistance and reactance slightly decrease with height [22, 23]. Although a significant increase in height was observed in the patient group (Table 1), the associated resistance and reactance differences [23] are minor (near 0.05 cmH2O/L/s). In light of the differences observed between controls and patients (Table 2; Figs. 3 and 4), this value may be considered not relevant.

Several of the studied patients were under steroid therapy. Previous systematic reviews concluded that, although this therapy improved results on the chest radiograph [24, 25], produced only a small improvement in vital capacity [25], and that there is little evidence of an improvement in lung function [24]. This indicates that the use of this medication may have a small influence on the results of the present study.

Oscillometric analysis showed that sarcoidosis introduces an increase in central (Fig. 3A), peripheral (Fig. 3B), and total (Fig. 3C) resistances. The abnormalities in central and total resistances are relevant even in patients with normal spirometry. It is essential to observe that these resistance values align with what is expected in patients with mild airflow obstruction [26].

The observed changes in central airways (Fig. 3A) are consistent with the pathophysiology since sarcoidosis is a chronic inflammatory condition characterized by pulmonary architecture alterations and airway narrowing. The correlation analysis (Table 3) showed that the most substantial relationship was with FEV1, confirming that the reduction in central airway radius was the predominant factor described by this parameter. A significant inverse correlation between R and FVC also indicated that higher central resistance is associated with lower volumes mobilized during ventilation. More minor but significant inverse correlations were also observed with FEF25-75, suggesting a weaker influence of the smaller airways.

Rp, which is associated with peripheral airways, displayed an inverse and significant correlation with descriptive spirometric parameters of airway obstruction (Table 3). The observed increase in Rp in SAS (Fig. 3B) aligns with the hypothesis that the effects of sarcoidosis primarily target most peripheral airways, leading to inflammation and fibrosis, thereby increasing resistance in this region. Recently, there has been renewed interest in studying small airways from both a pathophysiological perspective and clinical management [27, 28]. The results presented in Fig. 3B suggest that this methodology could be valuable in identifying changes in the “silent zone” in patients with sarcoidosis.

The total resistance (Fig. 3C) reflects the sum of the chest wall, airways, and lung tissue resistances. Hence, the increase in this parameter, observed even in the SNS group, may reflect an elevation in airway resistance alongside the impact of sarcoidosis on pulmonary and chest wall tissue. The significant inverse correlations observed with spirometric parameters describing central and peripheral obstructions and volume reductions (Table 4) are consistent with this interpretation. The strongest correlation with FEV1 may suggest a more significant association of Rt with the decrease in the caliber of the more central airways [29].

A decrease in C is observed in more advanced stages of sarcoidosis, as depicted in Fig. 3D. These findings are consistent with previous studies, such as that of Lopes and Jansen [30], which observed the presence of interstitial fibrosis with inflammatory interalveolar exudate, whose progression resulted in loss of alveolar units. It compromises the elastic properties of the lungs, resulting in reduced lung volume and parenchymal distensibility, leading to increased pulmonary elastic pressure [31]. Table 4 exhibited a direct association of C with spirometric indices associated with volume reductions (FVC), indicative of reduced static compliance [31]. Thus, the decrease in C is, at least in part, attributable to reduced static compliance. Direct correlations with spirometric indices related to central (FEV1) and peripheral (FEF25-75) airways were also observed. These associations may be explained by the reduction in dynamic compliance caused by increased airway obstruction [31]. In these analyses, the strongest association (R = 0.593) was observed with FEV1 (L), indicating a more significant influence of reduced dynamic compliance associated with a more minor but relevant influence of reduced static compliance.

Respiratory inertance primarily reflects the mass of gas moved during spontaneous ventilation. This parameter was not influenced by airway obstruction when the control group was considered (ANOVA p = ns, Fig. 3E). In line with the observed non-significant alterations (Fig. 6E), no significant correlations were found between respiratory inertance and spirometric parameters (Table 4). Interestingly, reduced I values were observed in patients with airflow obstruction compared to those without obstruction. This finding may be attributed to the reduced ability of these patients to keep gas movement in and out of the lungs, which may be confirmed by the decreasing FVC (%) values in Table 1.

The fractional-order model can provide insights into resistance and hysteresivity characteristics, which are indicators of increased heterogeneity and alterations in pulmonary architecture. This approach showed a strong potential for early detection of smoking-related effects in smokers and in the early stages of chronic obstructive pulmonary disease (COPD) [32]. In the present study, the significant increase in G (Fig. 4A) can be attributed to worsening irregularities in lung tissue. It suggests that changes in lung structure, possibly due to disease progression, contributed to greater energy dissipation during ventilation. These findings are consistent with those of Ribeiro et al. in their analysis of COPD patients [32] and with the observations made by Faria et al. studying asthmatic patients [7]. In accordance with this interpretation, Table 3 shows that G presented significant correlations with spirometric parameters, ranging from those describing peripheral airways to those more indicative of central airways, which exhibited the most substantial relationship. It indicates a more significant influence of reduced diameter of the more central airways with increased heterogeneity in pulmonary ventilation [29].

No significant influence of respiratory alterations on elastance was observed (Fig. 7B, ANOVA p = ns). In sarcoidosis, this parameter may help evaluate pulmonary fibrosis, representing the irreversible stage of the disease. In line with this interpretation, H showed an inverse and significant correlation with FVC (Table 4), descriptive of lung volumes, with a reduction indicating elevated static elastance. It suggests that H describes the progression of fibrosis in these patients. An inverse association with obstruction-related spirometric parameters (FEV1 and FEF25-75) was also observed. This association can be explained by the elevation of dynamic elastance with obstruction. In sarcoidosis, the increasing presence of interstitial fibrosis, accompanied by alveolar exudate accumulation, results in alveolar loss and increased peripheral airway obstruction [30], contributing to elevated dynamic elastance. It is worth noting that moderate associations were observed with parameters describing restriction and obstruction (Table 4), likely stemming from the heterogeneous nature of the sample, with 44% restrictive, 52% obstructive, and 4% with unspecified disorder.

A small but significant reduction in elastance was observed in the SSN group compared to the control group (Fig. 7B). It indicates that in the early stages of sarcoidosis, where fibrotic processes are not yet predominant, alveolar exudate accumulation and resulting alveolar loss may be predominant [30]. This loss of lung tissue could explain the observed decrease in elastance.

The increase in hysteresivity (Fig. 4C) indicates an escalation in heterogeneity and alterations in pulmonary structure during the gradual progression of the disease. It can be explained by the fact that, as the disease progresses, inflammation and granulomas may spread to other pulmonary areas, resulting in pulmonary fibrosis and chronic inflammation that can cause stiffness and loss of elasticity in the lungs. These changes in pulmonary architecture can lead to improper air distribution during respiration, resulting in alterations in ventilatory time constants [1, 33,34,35]. In close agreement with this hypothesis, there was a stronger inverse and significant correlation between hysteresivity and FEF25-75, which is associated with alterations in smaller airways (Table 4). The irregular distribution of granulomatous tissue and fibrosis, with or without hyperinflation, may result in reduced airway conductance and loss of elasticity [1, 30]. Additionally, smaller inverse and significant correlations were observed with FEV1 and FVC, which are associated with central airway obstruction and decreased lung volumes [29].

The alterations noted in all parameters obtained by the studied models align with an augmented total mechanical load on the respiratory system. Consequently, this may be linked with fatigue and breathlessness, which are among the most significant symptoms for predicting the quality of life in sarcoidotic patients [36].

An increasing body of evidence indicates that oscillometry could serve as a crucial functional test in diagnosing and managing respiratory diseases [5, 6]. There is also evidence that this method may contribute to identifying early abnormal changes when clinical interventions may be more effective. Promising results were recently obtained in assessing early respiratory abnormalities in cystic fibrosis [37] and low-intensity air pollution exposure [38]. Given that oscillometry is conducted during tidal volume breathing and necessitates minimal cooperation, it is highly applicable for patients with sarcoidosis, particularly in the geriatric stage, who may encounter challenges in performing traditional lung function tests.

In this practical context, the findings of the present study could enhance clinical practice by demonstrating that Rt may achieve adequate diagnostic accuracy while η obtained a high accuracy in detecting respiratory abnormalities in sarcoidosis (Table 4). It suggests that oscillometry, in conjunction with the eRIC and FrOr models, may aid in identifying early abnormalities in these patients. These findings closely align with those obtained in mild COPD [26], smokers [17], asbestos exposition [8], and patients with sickle cell anemia [39].

Table 5 shows the evaluation of diagnostic accuracy in patients with abnormal spirometry. Similar to the SNS (Table 4), adequate values were obtained for Rt, while the highest AUC was obtained for η. This finding aligns with previous studies demonstrating that η was the more sensitive parameter in identifying an initial decline in lung function among adult patients with sickle cell anemia [39]. This parameter also has proven helpful in diagnosing patients with COPD and asthma [10, 12, 40].

It was interesting to note that η was the most accurate parameter in patients with normal spirometry and those with mild abnormalities (Tables 4 and 5; Fig. 5). This finding likely illustrates the capacity of this parameter to encapsulate the primary physiological changes observed in these two classes of patients. The higher accuracy of the FrOr model in comparison with the eRIC model is associated with the increased ability of this model to describe the underlying physiopathology [7, 10,11,12]. The versatility of these models has enabled advancements in diagnostic accuracy across various medical disciplines. Their applications extend to identifying breast lesions [41], diagnosing Parkinson’s Disease dysgraphia [42], detecting brain tumors [43], and recognizing peripheral arterial disease [44]. This widespread applicability highlights the potential of these models to significantly impact medical diagnostics of respiratory illnesses.

Recognizing and sharing this research’s limitations may help push research forward and contextualize the findings. The subjects were sourced from a Brazilian population at a single practice site, thereby potentially limiting the generalizability of the study’s findings. Therefore, multicentre studies are imperative in the future to broaden the applicability and generalizability of these findings.

One could argue that biometrical differences are comparing the control group with the groups of patients (Table 1). Although these differences are statistically significant, they were tiny, resulting in negligible changes in oscillometric parameters.

The parameters of the eRIC and FrOr models were not compared with functional tests. Conducting these tests could elucidate the association of the studied parameters with the daily life limitations of patients. We plan to do these analyses in the next steps of this research.

Plethysmography and diffusion capacity analysis may help further clarify the association of the eRIC and FrOr models with respiratory changes in sarcoidosis. Correlations with radiological information may also help to clarify these associations. We believe these analyses deserve further studies.

The present study examined a relatively small sample size. While this limitation is linked to the low prevalence of the studied disease, it remains a constraint, and further investigations involving a more significant number of subjects are warranted.

Conclusions

The studied models provided a deeper understanding of the pulmonary changes in sarcoidosis. The results suggest that these models help clinicians make diagnosis and treatment decisions. Specifically, total resistance and hysteresivity parameters demonstrated diagnostic potential, which may be beneficial for the early identification of individuals with sarcoidosis, even when spirometry results are within normal ranges.

Data availability

The dataset supporting the conclusions of this article will be available in the Open Science Framework repository at the following link: https://osf.io/.

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Acknowledgements

This study was supported by the Brazilian Council for Scientific and Technological Development (CNPq), the Rio de Janeiro State Research Supporting Foundation (FAPERJ), and in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001.

Funding

This study was supported by the Brazilian Council for Scientific and Technological Development (CNPq), the Rio de Janeiro State Research Supporting Foundation (FAPERJ), and in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001.

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Authors and Affiliations

  1. Department of Physiology, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Bruno Falcão Oliveira, Caroline Oliveira Ribeiro, Cíntia Moraes de Sá Sousa & Pedro Lopes de Melo

  2. Faculty of Medical Sciences, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Mariana Carneiro Lopes

  3. Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil

    Mariana Carneiro Lopes

  4. Pulmonary Function Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Agnaldo José Lopes

Authors
  1. Bruno Falcão Oliveira
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  2. Caroline Oliveira Ribeiro
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  3. Cíntia Moraes de Sá Sousa
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  4. Mariana Carneiro Lopes
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  5. Agnaldo José Lopes
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  6. Pedro Lopes de Melo
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Contributions

Literature search: B.F.O.; Data collection: B.F.O., C.O.R., C.M.S.S., M.C.L., A.J.L.; Study design: B.F.O., M.C.L., A.J.L., P.L.M.; Analysis of data: B.F.O., C.O.R., C.M.S.S.; Manuscript preparation: B.F.O., C.O.R.; Review of manuscript: C.M.S.S., M. C.L., A.J. L., P. L. M.

Corresponding author

Correspondence to Pedro Lopes de Melo.

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Ethics approval and consent to participate

The study protocol was approved by the Research Ethics Committee of the Pedro Ernesto University Hospital of the State University of Rio de Janeiro (456/1997-CEP/HUPE). All participants signed an informed consent to participate and for publication, and the study was conducted following the Declaration of Helsinki and Resolution 466/12 of the National Health Council – Brazil.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Oliveira, B.F., Ribeiro, C.O., de Sá Sousa, C.M. et al. Respiratory abnormalities in sarcoidosis: physiopathology and early diagnosis using oscillometry combined with respiratory modeling. BMC Pulm Med 25, 68 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-025-03510-6

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Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

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