Zone of Uncertainty in Pulmonary Function Interpretation: A Proof-of-Concept Study from Lung Diffusing Capacity

Zone of Uncertainty in Pulmonary Function Interpretation: A Proof-of-Concept Study from Lung Diffusing Capacity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Zone of Uncertainty in Pulmonary Function Interpretation: A Proof-of-Concept Study from Lung Diffusing Capacity Giovanni Barisione, Sanja Stanojevic, Giovanni Brusasco This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7496536/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The probabilistic interpretation of low lung diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO) has been recently standardized but the impact of different lower limits of normal (z-scores) on clinical assessment is not well established. Objective To assess the uncertainty zone of DLCO and DLNO z-score interpretation in computed tomography (CT)-determined interstitial pulmonary fibrosis (IPF) and pulmonary emphysema (PE). Design and methods In a combined retrospective (IPF) and prospective (PE), proof-of-concept study, standard lung function, including DLCO, and single-breath DLNO were measured. Z-scores derived from Global Lung Function Initiative (GLI) for DLCO and non-specific (ERS), sex- and device-specific (Munkholm et al.) or -corrected (Zavorsky & Cao) DLNO reference equations, analyzed. Results 120 adults subjects with available CT of the chest participated in the study, 66 of them with IPF and the other 54 with a CT pattern consistent with PE. 56 asymptomatic subjects served as a control group. DLCO from GLI and DLNO from any reference equations showed high sensitivity and specificity for both IPF and PE, with DLCO from GLI showing the highest diagnostic accuracy for PE and DLNO from Munkholm et al. for IPF. However, the best thresholds separating IPF and PE from asymptomatic control subjects were widely different and ranging from ~14th percentile (-1.095 z-score) for DLCO from GLI to ~3rd percentile (-1.900 z-score) for DLNO from ERS. The DLCO z-scores from GLI showed the strongest negative correlation with PE (r=-0.710, P<0.0001) and DLNO from ERS with IPF (r=-0.750, P<0.0001) but in females z-scores from ERS and Zavorsky & Cao were not significantly correlated with extent of IPF. Conclusion DLCO and DLNO thresholds separating subjects with IPF or PE from healthy controls may differ substantially from standard lower limits of normal of -1.645 and -1.960 z-scores. For DLCO it may be due to inhomogeneous impairment of blood-to-air barrier whereas for DLNO different devices and reference equations seem to play a major role. Clinical trial registered with https://register.clinicaltrials.gov/prs/beta/records (ClinicalTrials.gov Identifier: NCT07091838) Carbon monoxide Nitric oxide Pulmonary diffusion Z-score Figures Figure 1 Figure 2 Figure 3 Introduction The measurement of pulmonary diffusion is the key diagnostic and monitoring tool to examine the integrity of air-blood barrier [ 1 ]. Traditionally, the lung diffusing capacity for carbon monoxide (DL CO ) has been widely used in both interstitial pulmonary fibrosis (IPF) [ 2 , 3 ] and pulmonary emphysema (PE) [ 4 – 6 ] and is currently recommended as a standard lung function measure [ 7 , 8 ]. More recently, the measurement of lung diffusing capacity for nitric oxide (DL NO ) was introduced [ 9 , 10 ] and subsequently standardized [ 11 ] on the premise that it is a more specific marker of alveolar membrane diffusive conductance (DM) than DL CO [ 12 ]. For DL CO , several predicting equations were made available over the years [ 13 ], but the changes in equipment and measurement techniques made them outdated, and a new set of standardized reference equations was provided by the Global Lung Function Initiative (GLI) [ 14 ]. For the DL NO , reference equations were provided by four small studies [ 15 – 18 ] and the data of three of them [ 15 – 17 ] were eventually pooled by a European Respiratory Society (ERS) Task Force to derive predicting equations with suitable sample size [ 11 ]. Major limitations of this initiative were that no sex-specific separate equations were derived, and the pooled data had been obtained by different devices without robust technical standardization. Sex- and device-specific or corrected predicting equations for DL NO have been subsequently made available by two studies, using a single device [ 19 ] or including sex and device as covariates [ 20 ]. The choice of reference equations is a crucial issue in the assessment of respiratory function, as it may impact on both definition of normality and assessment of severity [ 21 ]. Although for long time the sex-, age- and size-biased percentages of predicted were used for these purposes, the use of z -score is currently recommended as an unbiased estimator of the probability for an individual to deviate from the range expected in healthy individuals [ 8 ]. The 5th percentile (-1.645 z -score) of the reference population is currently recommended as the lower limit of normal (LLN 5 ) for all standard lung function measurements including DL CO [ 8 ], but for DL NO either LLN 5 [ 20 ] or the 2.5th percentile (LLN 2.5 , -1.960 z -score) [ 11 , 19 , 20 ] have been also recommended. In any case, lung function measures are a continuum and a z -score below a probabilistic threshold such as the LLN 5 or LLN 2.5 does not necessarily indicate a clinically meaningful abnormality to diagnose a disease but gives the probability of a false positive diagnosis in a healthy subject [ 8 ]. By converse, z -scores above these thresholds do not allow to rule out disease with certainty due to the known overlap between healthy and diseased distributions, which may vary depending on various factors including nature and severity of disease, type and method of measures, and reference equations. These proposed limits of normality were based on the distribution in healthy population but not validated for the accuracy to separate diseased from healthy subjects [ 8 ]. We designed the present study with the primary aim to test whether the z -score thresholds of DL CO and DL NO separating IPF and PE from healthy subjects may differ from LLN 5 or LLN 2.5 . A secondary aim of the study was to estimate the impact of the choice of test and predicting equations on severity stratification in relation to the extent of lung parenchymal abnormalities typical of IPF and PE. For these purposes, we retrospectively utilized the DL CO and DL NO data obtained by a single device in sixty-six subjects with IPF included in two previous studies [ 22 , 23 ] and in fifty-four new subjects with PE, both documented by quantitative computed tomography (CT) of the chest. Materials and methods Study subjects This combined retrospective and prospective study included a total of 120 adults subjects of white European ancestry (Table 1). Sixty-six subjects from our previous studies [22, 23] had IPF, 43 due to usual or nonspecific interstitial pneumonia and 23 to systemic sclerosis. Fifty-four new subjects, with a smoking history ≥10 pack-years and a CT pattern consistent with mild-to-advanced PE, were consecutively enrolled between October 2022 and December 2024. Seven of them underwent preoperative pulmonary function tests for localized peripheral nodules and one for esophageal cancer; all subjects included in this group were free from history of any comorbidities potentially affecting lung diffusing capacity, i.e. , congestive heart failure, liver or kidney diseases, and morbid obesity. Forty-four (~67%) subjects of the IPF group had a restrictive impairment, as defined by plethysmographic total lung capacity (TLC) <LLN 5 [8]. Forty-five (~83%) subjects of PE group had an obstructive impairment, as defined by forced expiratory volume in 1-s-to-forced vital capacity ratio (FEV 1 /FVC) <LLN 5 [8] and were under treatment with long- or short-acting inhaled bronchodilators only ( n =13) or in association with inhaled corticosteroids ( n =23). Fifty-six asymptomatic healthy subjects, selected among health professionals and their relatives, were included as a control group. Standard lung function measurements Smokers were asked to refrain from smoking for 24 h prior to measurements. All subjects were tested at 160 m above sea level, following the scheduled administration of ongoing inhalation therapy if any. Lung volumes [24],spirometry [25],and standard DL CO [7] with 11.4±0.48 s (10.3 to 13.0 s) actual breath-hold time (BHT) and adjusted for effective hemoglobin concentration [26]were sequentially measured with subjects sitting in a whole-body plethysmograph (Vyntus™ BODY Plethysmograph, Vyaire Medical, Höchberg, Germany). The predicting equations used for lung volumes [27], spirometry [28], and DL CO [14] were from the GLI. DL NO measurement Single-breath DL NO with 5.52±0.49 s (4.60 to 6.90 s) actual BHT, was measured (MasterScreen-PFT System; Jaeger, Vyaire Medical, Höchberg, Germany) at least twice at 5-min interval with subjects in a sitting posture and wearing a nose clip, as previously reported [23]. The values retained for analysis were the average of two repeatable measurements, i.e. , within 17.0 mL·min −1 ·mmHg −1 [11]. The predicting equations used for DL NO were the sex- and device-non-specific ones from Zavorsky et al. [11], hereinafter referred to as ERS, sex-and device-specific from Munkholm et al. [19] and sex- and device-corrected from Zavorsky & Cao [20]. Quantitative CT analysis Volumetric CT scans of the chest (1.25-mm slice thickness, 200 mAs, 120 kVp) were obtained within 4 months before or after pulmonary function tests, during breath-hold at full inspiration, by a multi-detector row-spiral scanner (SOMATOM Emotion 6; Siemens AG Medical, Forchheim, Germany). Only scans with CT lung volume-to-TLC ratio ≥0.8 were retained for automatic quantitative three-dimensional analysis [29].For the IPF group, the overall structural abnormality was the sum of percentage areas with reticular opacities, honeycombing, and traction bronchiectases (RO+HC+TB), as in our original studies [22, 23].For the PE group, the CT images were reconstructed in contiguous submillimeter-thickness axial sections using a medium-sharp resolution reconstruction (3D Slicer 5.6.1 software; https://www.slicer.org/) [30]. The overall structural abnormality was percent voxel with low-attenuation areas <-950 Hounsfield Units (LAA -600 HU (HAA >-600 HU ), if any. Statistical analysis For each lung function measure, absolute value and z -score were calculated. Categorical variables were compared by chi-square test with Yates correction. ANOVA, with the Holm-Sidak method for pairwise comparison testing, was used for significance testing of within group DL NO z -scores by ERS, Munkholm et al ., and Zavorsky & Cao (SigmaPlot 11; 2008 Systat Software, Germany). The agreement between z -scores derived from different reference equations was assessed by Bland-Altman plot (GraphPad Prism 8.4.2; GraphPad Software, San Diego, CA, USA). The accuracy of each reference equation to separate healthy from diseased subjects was determined by analysis of the area under the receiver operating characteristic curve (AUROC), Youden index, and Matthews correlation coefficient, using the presence of CT abnormalities ≥1% of total lung volume for IPF and both ≥1% and >6% [31] for PE as gold standard. The relationships between z -scores and CT abnormalities were determined by Pearson’s correlation coefficient r. In all analyses, the acceptable Type I error was set at P value <0.05. Results Standard lung function None of healthy controls showed standard lung function z -scores from GLI below the LLN 5 . By contrast, TLC was <LLN 5 in forty-four (~67%) of IPF subjects and FEV 1 /FVC <LLN 5 in forty-five (~83%) of PE subjects. As expected, the average absolute values and z -scores of TLC were reduced in the IPF group ( P <0.001 vs . controls) and FEV 1 /FVC in the PE group ( P <0.001 vs . controls) whereas FVC, FEV 1 , DL CO and K CO were decreased in both IPF and PE groups ( P <0.001 for all vs. controls). DL NO Within the control group, the DL NO mean z -scores from Munkholm et al. and Zavorsky & Cao did not differ significantly between each other (mean difference 0.025; 95% confidence interval [CI], -0.407 to 0.457; P =0.867), while those from ERS were significantly lower than either Munkholm et al. (mean difference -0.717; 95% CI, -1.486 to 0.052; P <0.001) or Zavorsky & Cao (mean difference -0.691; 95% CI, -1.132 to -0.251; P <0.001). Within the IPF group, the DL NO mean z -score was significantly lower from Munkholm et al . than Zavorsky & Cao (mean difference -0.597; 95% CI, -1.119 to -0.075; P =0.008). No other significant differences were observed between mean z -scores from different predicting equations within IPF and PE groups. However, Bland-Altman plots with pooled control, IPF, and PE subjects showed poor agreement between DL NO z -scores from ERS and either Munkholm et al . or Zavorsky & Cao, due to large differences in females over the low range of values (Fig. 1). By contrast, there was a good agreement between DL NO z -scores from Munkholm et al . and Zavorsky & Cao for both males and females. AUROC analysis Collectively, the DL CO z -scores from GLI and the DL NO z -scores from any reference equations showed excellent classification performance for both IPF and PE, with DL CO showing the highest accuracy for PE of ≥1% and >6% extent and DL NO from Munkholm et al. for IPF (Table 2). However, the best z -score thresholds separating diseased from control subjects were widely different for both DL CO (-1.095 or 13.7 th percentile in IPF and -1.180 or 11.9 th percentile in PE of any extent) and DL NO (-1.900 or 2.9 th percentile in both IPF and PE of any extent with ERS; -1.560 or 5.9 th percentile in IPF and -1.650 or 4.9 th percentile and -1.665 or 4.8 th percentile in PE of ≥1% and >6% extent, respectively, with Munkholm et al. ; -1.370 or 8.5 th percentile in IPF and -1.330 or 9.2 nd percentile in PE of any extent with Zavorsky & Cao). Structure-to-function relationships In the IPF group, the extent of RO+HC+TB was on average 29±16% of CT lung volume; in the PE group, the extent of LAA -600 HU . The DL CO z -scores from GLI were negatively correlated with both RO+HC+TB (r=-0.667; 95% CI, -0.783 to -0.507; P <0.0001) in IPF and with LAA -600 HU (r=-0.710, 95% CI, -0.822 to -0.546; P <0.0001) in PE (Fig. 2). Notably, among subjects with moderate or severe DL CO impairments, i.e ., z -scores <-2.50 or <-4.00, females had less RO+HC+TB abnormalities than males. The DL NO z -scores from ERS were also negatively correlated with RO+HC+TB (r=-0.754; 95% CI, -0.843 to -0.627; P <0.0001) in IPF but moderately with LAA -600 HU (r=-0.549, 95% CI, -0.712 to -0.330; P <0.0001) in PE (Fig. 3). The DL NO z -scores from Munkholm et al. were negatively correlated with RO+HC+TB (r =-0.497; 95% CI, -0.659 to -0.289; P <0.0001) in IPF and even more with LAA -600 HU (r=-0.638; 95% CI, -0.774 to -0.466; p <0.0001) in PE. The DL NO z -scores from Zavorsky & Cao were negatively correlated with RO+HC+TB (r=-0.568; 95% CI, -0.712 to -0.378; P <0.0001) in IPF and with LAA -600 HU (r=-0.662, 95% CI, -0.790 to -0.479; P <0.0001) in PE. In females, however, DL NO z -score from either ERS (r=-0.184; 95% CI, -0.472 to 0.140; P =0.262) or Zavorsky & Cao (r=-0.241; 95% CI, -0.517 to 0.081; P =0.140) z -scores were not correlated with RO+HC+TB. This was apparently due to excess of females with moderate or severe DL NO impairment despite moderate RO+HC+TB abnormalities. Discussion The main findings of the present study are that 1) thresholds derived using DL CO from GLI and DL NO from different reference equations were able to differentiate between CT-documented IPF or PE; 2) the optimal thresholds separating diseased from healthy subjects differed variably from the recommended LLN 5 or LLN 2.5 depending mainly on type of measure, i.e. , DL CO or DL NO , and choice of reference equations for the latter; and 3 ) the negative correlation between extent of CT abnormalities and DL NO z -scores also varied with predicting equations, thus potentially influencing severity classification. Comments on results The present study was designed to test the proof-of-concept that the lung function thresholds separating healthy from diseased subjects may differ from the generally assumed limits of normality, i.e. , the 5 th or 2.5 th percentiles of normal population. For this purpose, we examined measures of gas transfer that are considered strictly linked to the lung parenchymal abnormalities typical of IPF or PE objectively demonstrable by CT of the chest. We chose standard DL CO , which is the most widely recommended and used test of gas transfer in clinical practice, and DL NO because its relative insensitivity to intrapulmonary hematocrit in vivo [32] makes it a measurement likely close to a “true” alveolar membrane diffusion (DM) [33].Although previous studies have suggested that DL NO may be more sensitive than standard DL CO to lung parenchymal abnormalities [22, 23, 34, 35], some uncertainties still exist due to differences in reference equations obtained with different devices. For the purposes of the present study, this offered the opportunity to investigate the possible impact of reference equations and device. Collectively, the results support the hypothesis that using z -scores thresholds of normality uniquely based on the distribution of healthy population may result in misclassification of individual subjects, possibly due to either physiological or technical reasons. For standard DL CO , the z -scores thresholds best separating PE and particularly IPF from healthy subjects were surprisingly well above the recommended LLN 5 [8]. This was apparently due to a number of subjects with preserved DL CO despite the presence of CT abnormalities typical of IPF or PE. A physiological reason for this may be the high variability of the pulmonary capillary blood volume (V C ) [36-38],which is the major determinant of DL CO [12]. Although we had DL CO also measured simultaneously with DL NO , method that allows a single-step partition of DM and V C , we did not include subcomponents for the purposes of the present study because the too many assumptions about blood NO and CO kinetic properties (θ) in vivo make uncertain their calculation [12].Whatever the reason, the possible existence of a grey area of DL CO z -scores <~14 th percentile for IPF and <~12 th percentile for PE should be carefully considered before excluding such diseases. For DL NO , the z -score thresholds separating subjects with IPF or PE from healthy subjects were substantially different depending on reference equations, though similar between IPF and PE. In detail, the best threshold for DL NO from device-specific Munkholm et al. were very close to the LLN 5 but well above the recommended LLN 2.5 [19], from ERS close to the recommended LLN 2.5 [11], and from Zavorsky & Cao well above the recommended LLN 5 and LLN 2.5 [17]. Therefore, while the LLN 2.5 seems an appropriate trade-off between sensitivity and specificity with ERS reference equations, its use with Munkholm et al. equations would be associated with a low false positive (type I error) risk in healthy subjects but with an increased false negative (type II error) risk in patients. On the other hand, using either the LLN 5 or LLN 2.5 with Zavorsky & Cao equations would be associated with a non-negligible risk of false negatives in both IPF and PE patients. The above discrepancies from the analysis of the same data by different predicting equations are likely due to technical rather than physiological factors. The only reference equations obtained with a single device, i.e. , Jaeger MasterScreen-PFT, the same as used in the present study, were those by Munkholm et al. [19],which were also separate for males and females. The ERS reference equations [11], without sex separation, were obtained by pooling data from three small studies, the first of which [15] using a bespoke device with chemiluminescence analyser to measure NO uptake during 10-s duration BHT, and the other two [16, 17]the same commercial device, i.e. , Medisoft Hyp’Air (Dinant, Belgium), with electrochemical cells as NO analyser and 5-s BHT. More recently, a direct comparison between the two devices commercially available in Europe, i.e. , MasterScreen-PFT, used in the present study, and Hyp’Air, showed systematic differences in DL NO (-24.0 mL·min -1 ·mmHg -1 ; 95% CI -21.7 to -26.3, P <0.0001) likely explained, at least in part, by discrepancies in V A (-0.48 L; 95% CI -0.45 to -0.52, P <0.0001) [39].The Zavorsky & Cao equations were calculated by pooling raw data from four studies obtained with different devices [15-18] and data extrapolated from Munkholm et al. [19] equations, with device and sex introduced as covariates. In a recent study in systemic sclerosis, DL NO measurements obtained with Hyp’Air device were analyzed by using ERS and Munkholm et al. equations, and the former yielded z -scores similar to the latter in males but markedly less negative in females, particularly over the lower range [40]. In our study, using MasterScreen-PFT, the DL NO z -scores of females were also markedly less negative with ERS than Munkholm et al. equations. Altogether, these findings suggest that, in subjects with IPF or PE, sex is a source of variability of measured DL NO at least as relevant as device. As expected, DL NO was negatively correlated with the CT extent of IPF and PE. There were, however, some differences related to predicting equations and disease. In the case of IPF, the number of females with severe DL NO impairment (<-4.00 z -score) was larger with Munkholm et al . than ERS or Zavorsky & Cao equations, even in cases with low-to-moderate CT abnormalities. As the majority of them had systemic sclerosis, which is primarily a vasculitis, the DL NO impairment may reflect mechanisms other than CT interstitial abnormalities [23]. Study limitations First , IPF and PE groups were not perfectly matched with control subjects for most anthropometric characteristics. However, the use of z -scores should mitigate some of the age-, sex- and size-biases [8, 21]. Second ,no CT scans were available for healthy controls for ethical reasons. Therefore, some overlap between healthy and diseased subjects might have been due to minor, clinically irrelevant, abnormalities in the former. Third , CTs of the chest were acquired in supine position and lung function studies in a sitting position and at different times from lung function studies, which might have resulted in a CT gas volume at full inspiration lower than plethysmographic TLC[41] and differences in disease time courses. However, it is unlikely that these might have differently influenced the DL NO z -scores sensitivities to CT abnormalities. Fourth , the results of the present study were not validated in an external sample, but this was deemed not necessary because it was a proof-of-concept study, not intended to establish firm thresholds of normality or severity grading. Conclusion The results of the present study show that DL CO and DL NO z -score thresholds separating subjects with significant CT-determined IPF or PE from healthy controls may differ substantially from the widely used LLN 5 (-1.645 z -score) to define functional impairment. For DL CO a grey zone of z -scores over the lower range of normality in the presence of CT-documented structural abnormalities may be explained by different physiological determinants of gas transport. For DL NO , technical factors related to reference equations and device seem to play a major role. The above differences may also affect thresholds for severity classification. Abbreviations DL CO Lung diffusing capacity for carbon monoxide DL NO Lung diffusing capacity for nitric oxide IPF Interstitial pulmonary fibrosis PE Pulmonary emphysema DM Alveolar membrane diffusive conductance GLI Global Lung Function Initiative ERS European Respiratory Society CT Computed tomography LLN 5 Lower limit of normal at 5 th percentile LLN 2.5 Lower limit of normal at 2.5 th percentile TLC Total lung capacity FEV 1 /FVC Forced expiratory volume in 1-s-to-forced vital capacity ratio BHT Breath-hold time (BHT) RO Reticular opacities HC Honeycombing TB Traction bronchiectases LAA -600 HU High-attenuation areas above -600 Hounsfield units AUROC Area under the receiver operating characteristic curve V A Alveolar volume K CO DL CO /V A V C Pulmonary capillary blood volume Declarations Supplementary information Author’s contributions GB and VB contributed to the conception and design of the study; GB enrolled the study subjects, performed pulmonary function testing, analyzed chest imaging studies, collected and analyzed the data; GB and VB interpreted the data, drafted, and wrote the manuscript; GB, SS, and VB reviewed and edited the manuscript. All the authors read and approved the final manuscript. Funding None. Conflicts of Interest None. Data availability The data that support the findings of this study are available on request from the corresponding author. Declarations Ethics approval and consent to participate This study was performed in accordance with the Declaration of Helsinki. The Regional Ethics Committee’s approval for IPF data collection was obtained for each of the two original studies [22, 23].Collection of original PE data was approved by the Comitato Etico Territoriale Liguria (No. CET - Liguria: 59/2025 - id 14300) and each subject gave written informed consent to participate in this study and to use their anonymized data. Competing interests None Author details 1 Struttura Semplice Fisiopatologia Respiratoria, Clinica Malattie Respiratorie e Allergologia, IRCCS Ospedale Policlinico San Martino, Genova, Italy 2 Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada 3 Dipartimento di Medicina Sperimentale, Università di Genova, Genova, Italy References Forster RE. Diffusion of gases across the alveolar membrane. In: AP Fishman, LE Farhi, SM Tenney, editors. Handbook of Physiology , Section 3: The Respiratory System, Vol IV: Gas Exchange. Washington DC.: American Physiological Society, 1987, p. 71-88. Epler GR, McLoud TC, Gaensler EA, Mikus JP, Carrington CB. Normal chest roentgenograms in chronic diffuse infiltrative lung disease. N Engl J Med 1978;298:934-939. Xaubet A, Agustí C, Luburich P, Roca J, Montón C, Ayuso MC, Barberá JA, Rodriguez-Roisin R. Pulmonary function tests and CT scan in the management of idiopathic pulmonary fibrosis. 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Graham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, Hallstrand TS, Kaminsky DA, McCarthy K, McCormack MC, Oropez CE, Rosenfeld M, Stanojevic S, Swanney MP, Thompson BR. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med 2019;200:e70-e88. Cotes JE, Dabbs JM, Elwood PC, Hall AM, McDonald A, Saunders MJ. Iron-deficiency anaemia: its effect on transfer factor for the lung (diffusing capacity) and ventilation and cardiac frequency during sub-maximal exercise. Clin Sci 1972;42:325-335. Hall GL, Filipow N, Ruppel G, Okitika T, Thompson B, Kirkby J, Steenbruggen I, Cooper BG, Stanojevic S; contributing GLI Network members. Official ERS technical standard: Global Lung Function Initiative reference values for static lung volumes in individuals of European ancestry. Eur Respir J 2021;57:2000289. Quanjer PH, Stanojevic S, Cole TJ, Baur X, Hall GL, Culver BH, Enright PL, Hankinson JL, Ip MS, Zheng J, Stocks J; ERS Global Lung Function Initiative. Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations. Eur Respir J 2012;40:1324-1343. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy FM, Sonka M, Buatti J, Aylward SR, Miller JV, Pieper S, Kikinis R. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging 2012;30:1323-1341. Stoel BC, Putter H, Bakker ME, Dirksen A, Stockley RA, Piitulainen E, Russi EW, Parr D, Shaker SB, Reiber JH, Stolk J. Volume correction in computed tomography densitometry for follow-up studies on pulmonary emphysema. Proc Am Thorac Soc 2008;5:919-924. Lynch DA, Austin JH, Hogg JC, Grenier PA, Kauczor HU, Bankier AA, Barr RG, Colby TV, Galvin JR, Gevenois PA, Coxson HO, Hoffman EA, Newell JD Jr, Pistolesi M, Silverman EK, Crapo JD. CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society. Radiology 2015;277:192-205. van der Lee I, Zanen P, Biesma DH, van den Bosch JM. The effect of red cell transfusion on nitric oxide diffusing capacity. Respiration 2005;72:512-516. Borland C, Patel R. Comparing in vitro nitric oxide blood uptake to its pulmonary diffusing capacity. Nitric Oxide 2024;143:29-43. Barisione G, Bacigalupo A, Brusasco C, Scanarotti C, Penco S, Bassi AM, Lamparelli T, Garlaschi A, Pellegrino R, Brusasco V. Mechanisms for reduced pulmonary diffusing capacity in haematopoietic stem-cell transplantation recipients. Respir Physiol Neurobiol 2014;194:54-61. van der Lee I, Gietema HA, Zanen P, van Klaveren RJ, Prokop M, Lammers JW, van den Bosch JM. Nitric oxide diffusing capacity versus spirometry in the early diagnosis of emphysema in smokers. Respir Med 2009;103:1892-1897. Pande JN, Gupta SP, Guleria JS. Clinical significance of the measurement of membrane diffusing capacity and pulmonary capillary blood volume. Respiration 1975;32:317-24. Oppenheimer BW, Berger KI, Hadjiangelis NP, Norman RG, Rapoport DM, Goldring RM . Membrane diffusion in diseases of the pulmonary vasculature. Respir Med 2006;100:1247-53. Farha S, Laskowski D, George D, Park MM, Tang WH, Dweik RA, Erzurum SC. Loss of alveolar membrane diffusing capacity and pulmonary capillary blood volume in pulmonary arterial hypertension. Respir Res 2013;14:6. Radtke T, de Groot Q, Haile SR, Maggi M, Hsia CCW, Dressel H. Lung diffusing capacity for nitric oxide measured by two commercial devices: a randomised crossover comparison in healthy adults. ERJ Open Res 2021;00193-2021. Radtke T, Hua-Huy T, Dressel H, Dinh-Xuan AT. Of the need to reconcile discrepancies between two different reference equations for combined single-breath D LNO - D LCO in systemic sclerosis. Eur Respir J 2019;53:1802109. Cotton DJ, Graham BL, Mink JT. Pulmonary diffusing capacity in adult cystic fibrosis: reduced positional changes are partially reversed by hyperoxia. Clin Invest Med 1990;13:82-91. Tables Table 1. Subjects’ characteristics, lung function and CT data Controls IPF PE Male/Female 43/13 27/39 36/18 Age (years) 53.9 ± 11.4 64.8 ± 13.3† 68.7 ± 9.1† Stature (cm) 172.5 ± 9.2 163.2 ± 8.6† 166.8 ± 8.4† BMI (kg·m -2 ) 25.6 ± 2.7 26.6 ± 3.2 25.5 ± 4.2 Smoking habit (C/F/N) 13/12/31 4/28/34 27/27/0 [Hb] (g·dL -1 ) 14.2 ± 0.84 13.5 ± 1.54 14.1 ± 1.71 FVC (L) (GLI z -score) 4.72 ± 1.08 0.55 ± 0.98 2.49 ± 0.72 -1.53 ± 1.33 3.24 ± 1.02 -0.69 ± 1.43 FEV 1 (L) (GLI z -score) 3.67 ± 0.82 0.49 ± 0.91 2.01 ± 0.54 -1.28 ± 1.15 1.73 ± 0.82 -2.31 ± 1.33 FEV 1 /FVC (GLI z -score) 0.78 ± 0.06 -0.12 ± 0.79 0.82 ± 0.08 0.48 ± 1.08 0.52 ± 0.13 -2.78 ± 1.24 TLC (L) (GLI z -score) 6.73 ± 1.23 0.21 ± 0.90 3.92 ± 0.96 -2.25 ± 1.42 6.33 ± 1.25 0.55 ± 1.33 DL CO (mL·min -1 ·mmHg -1 ) (GLI z -score) 30.1 ± 7.17 0.77 ± 0.98 13.2 ± 4.54 -2.73 ± 1.59 12.0 ± 4.71 -3.45 ± 1.60 K CO (mL·min -1 ·mmHg -1 ) (GLI z -score) 4.51 ± 0.63 0.12 ± 0.86 3.53 ± 0.79 -1.10 ± 1.22 2.45 ± 0.73 -2.91 ± 1.35 V A,DL CO (L) 6.67 ± 1.35 3.73 ± 0.95 4.89 ± 1.19 DL NO (mL·min -1 ·mmHg -1 ) (ERS z -score) (Munkholm et al. z -score) (Zavorsky & Cao z -score) 129.1 ± 31.1 -0.82 ± 0.70* 0.11 ± 0.93 -0.13 ± 0.79 50.3 ± 20.2 -3.10 ± 1.17 -3.44 ± 1.09† -2.84 ± 1.02 49.3 ± 20.4 -3.52 ± 1.24 -3.08 ± 1.23 -3.02 ± 1.08 V A,DL NO ( L) 6.54 ± 1.19 3.75 ± 0.91 4.95 ± 1.01 LAA -600 HU (% CT volume) NA 29 ± 16 5 ± 2 Definition of abbreviations : C/F/N = Current/Former/Never; IPF = interstitial pulmonary fibrosis; PE = pulmonary emphysema; FVC = forced vital capacity; FEV 1 = forced expiratory volume in 1-s; TLC = total lung capacity; DL CO = single-breath (11.4±0.48 s actual breath-hold time) lung diffusing capacity for carbon monoxide; K CO = DL CO /alveolar volume (V A ); DL NO = single-breath (5.52±0.49 s actual breath-hold time) lung diffusing capacity for nitric oxide; LAA <-950 HU = low attenuation areas -600 HU = high attenuation areas >-600 HU; NA = not available. Data are presented as absolute numbers or mean ± SD. *, P <0.001 vs. Munkholm et al. or Zavorsky & Cao; †, P =0.008 vs. Zavorsky & Cao. Table 2. AUROC analysis of DL CO and DL NO for IPF and PE DL CO DL NO Reference equation GLI ERS Munkholm et al. Zavorsky & Cao Interstitial pulmonary fibrosis (IPF) ≥1% AUROC ( P <0.0001) 0.99 (0.97 to 1.00) 0.98 (0.96 to 1.00) 0.99 (0.99 to 1.00) 0.99 (0.98 to 1.00) Cutoff ( z -score) -1.095 -1.900 -1.560 -1.370 Sensitivity 0.83 (0.73 to 0.90) 0.86 (0.76 to 0.93) 0.97 (0.90 to 1.00) 0.92 (0.84 to 0.97) Specificity 0.98 (0.91 to 1.00) 0.98 (0.91 to 1.00) 0.98 (0.91 to 1.00) 0.98 (0.91 to 1.00) Youden Index 0.82 0.85 0.95 0.91 Matthews Correlation Coefficient 0.81 0.86 0.95 0.90 Pulmonary emphysema (PE) ≥1% AUROC ( P <0.0001) 0.98 (0.98 to 1.00) 0.97 (0.94 to 1.00) 0.97 (0.95 to 1.00) 0.98 (0.97 to 1.00) Cutoff ( z -score) -1.180 -1.900 -1.650 -1.330 Sensitivity 0.94 (0.85 to 0.99) 0.89 (0.78 to 0.95) 0.87 (0.76 to 0.94) 0.91 (0.80 to 0.96) Specificity 0.98 (0.91 to 1.00) 0.98 (0.91 to 1.00) 0.98 (0.91 to 1.00) 0.98 (0.91 to 1.00) Youden Index 0.93 0.87 0.85 0.89 Matthews Correlation Coefficient 0.93 0.88 0.86 0.89 Definition of abbreviations : AUROC = Area Under the Receiver Operating Characteristic Curve. Other abbreviations are presented as in Table 1. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7496536","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509213672,"identity":"33ca460a-42f8-4379-afb6-f1f78ec8bdf0","order_by":0,"name":"Giovanni Barisione","email":"data:image/png;base64,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","orcid":"","institution":"IRCCS Ospedale Policlinico San Martino","correspondingAuthor":true,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Barisione","suffix":""},{"id":509213675,"identity":"f954d787-d86e-4b51-975f-bdfeea66b8f6","order_by":1,"name":"Sanja Stanojevic","email":"","orcid":"","institution":"Dalhousie University","correspondingAuthor":false,"prefix":"","firstName":"Sanja","middleName":"","lastName":"Stanojevic","suffix":""},{"id":509213677,"identity":"7cf637d5-0744-4106-af34-98fed1296d1d","order_by":2,"name":"Giovanni Brusasco","email":"","orcid":"","institution":"Università di Genova","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Brusasco","suffix":""}],"badges":[],"createdAt":"2025-08-30 16:39:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7496536/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7496536/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90474733,"identity":"1aa00e33-3e75-45a0-9726-677eecf71126","added_by":"auto","created_at":"2025-09-03 06:50:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86132,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plots (panels \u003cem\u003ea-c\u003c/em\u003e) for comparisons between \u003cem\u003ez\u003c/em\u003e-scores of lung diffusing capacity for nitric oxide (DL\u003csub\u003eNO\u003c/sub\u003e) from ERS, Munkholm \u003cem\u003eet al.\u003c/em\u003e and Zavorsky \u0026amp; Cao reference equations in healthy, interstitial pulmonary fibrosis (IPF) and pulmonary emphysema (PE) subjects grouped together as a whole. Black and white circles indicate males and females, respectively. The SD of mean difference (bias) is bounded by horizontal dashed lines included within the shaded area indicating the 95% limits of agreement, \u003cem\u003ei.e.\u003c/em\u003e, -2.168 to 1.696, -1.588 to 0.649, and -1.154 to 0.687 \u003cem\u003ez\u003c/em\u003e-scores in panels \u003cem\u003ea\u003c/em\u003e, \u003cem\u003eb\u003c/em\u003e and \u003cem\u003ec\u003c/em\u003e, respectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7496536/v1/edc03b6092242d561481bf2f.png"},{"id":90475568,"identity":"26694b93-768b-494c-b924-d6c52af4b9c1","added_by":"auto","created_at":"2025-09-03 06:58:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69624,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between DL\u003csub\u003eCO \u003c/sub\u003e\u003cem\u003ez\u003c/em\u003e-scores from GLI and CT abnormalities in IPF (panel\u003cem\u003e a\u003c/em\u003e) and PE (panel \u003cem\u003eb\u003c/em\u003e). RO+HC+TB = reticular opacities \u003cem\u003eplus\u003c/em\u003e honeycombing \u003cem\u003eplus\u003c/em\u003e traction bronchiectases; LAA\u003csub\u003e\u0026lt;-950 HU \u003c/sub\u003e= low-attenuation areas \u0026lt;-950 Hounsfield Units (HU); HAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e = high-attenuation areas \u0026gt;-600 HU. Black and white circles indicate males and females, respectively. Dashed, dotted and dot-and-dash horizontal lines correspond to -1.645, -2.50 and -4.00 \u003cem\u003ez\u003c/em\u003e-scores, respectively, as routine severity grading of DL\u003csub\u003eCO\u003c/sub\u003e impairment.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7496536/v1/04f84f3544a2200c9b9c0e36.png"},{"id":90474730,"identity":"344e3cd4-9a9f-449b-994f-be9d66625ee7","added_by":"auto","created_at":"2025-09-03 06:50:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164250,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between \u003cem\u003ez\u003c/em\u003e-scores of DL\u003csub\u003eNO \u003c/sub\u003efrom ERS (panels \u003cem\u003ea\u003c/em\u003e and \u003cem\u003ed\u003c/em\u003e), Munkholm \u003cem\u003eet al.\u003c/em\u003e (panels \u003cem\u003eb\u003c/em\u003e\u0026nbsp; and \u003cem\u003ee\u003c/em\u003e) and Zavorsky \u0026amp; Cao (panels \u003cem\u003ec\u003c/em\u003e and \u003cem\u003ef\u003c/em\u003e) and CT abnormalities in IPF and PE. Abbreviations are presented as in Figure 2.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7496536/v1/ea91d6d76b945190f11901eb.png"},{"id":90476079,"identity":"1b547522-edb5-425a-82cf-90c13901c3cd","added_by":"auto","created_at":"2025-09-03 07:06:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1270765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7496536/v1/3d9b53fe-b4f1-466d-a99e-79ad98af883a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Zone of Uncertainty in Pulmonary Function Interpretation: A Proof-of-Concept Study from Lung Diffusing Capacity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe measurement of pulmonary diffusion is the key diagnostic and monitoring tool to examine the integrity of air-blood barrier [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Traditionally, the lung diffusing capacity for carbon monoxide (DL\u003csub\u003eCO\u003c/sub\u003e) has been widely used in both interstitial pulmonary fibrosis (IPF) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and pulmonary emphysema (PE) [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and is currently recommended as a standard lung function measure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. More recently, the measurement of lung diffusing capacity for nitric oxide (DL\u003csub\u003eNO\u003c/sub\u003e) was introduced [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and subsequently standardized [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] on the premise that it is a more specific marker of alveolar membrane diffusive conductance (DM) than DL\u003csub\u003eCO\u003c/sub\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For DL\u003csub\u003eCO\u003c/sub\u003e, several predicting equations were made available over the years [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but the changes in equipment and measurement techniques made them outdated, and a new set of standardized reference equations was provided by the Global Lung Function Initiative (GLI) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For the DL\u003csub\u003eNO\u003c/sub\u003e, reference equations were provided by four small studies [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and the data of three of them [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] were eventually pooled by a European Respiratory Society (ERS) Task Force to derive predicting equations with suitable sample size [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Major limitations of this initiative were that no sex-specific separate equations were derived, and the pooled data had been obtained by different devices without robust technical standardization. Sex- and device-specific or corrected predicting equations for DL\u003csub\u003eNO\u003c/sub\u003e have been subsequently made available by two studies, using a single device [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] or including sex and device as covariates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe choice of reference equations is a crucial issue in the assessment of respiratory function, as it may impact on both definition of normality and assessment of severity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although for long time the sex-, age- and size-biased percentages of predicted were used for these purposes, the use of \u003cem\u003ez\u003c/em\u003e-score is currently recommended as an unbiased estimator of the probability for an individual to deviate from the range expected in healthy individuals [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The 5th percentile (-1.645 \u003cem\u003ez\u003c/em\u003e-score) of the reference population is currently recommended as the lower limit of normal (LLN\u003csub\u003e5\u003c/sub\u003e) for all standard lung function measurements including DL\u003csub\u003eCO\u003c/sub\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but for DL\u003csub\u003eNO\u003c/sub\u003e either LLN\u003csub\u003e5\u003c/sub\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] or the 2.5th percentile (LLN\u003csub\u003e2.5\u003c/sub\u003e, -1.960 \u003cem\u003ez\u003c/em\u003e-score) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] have been also recommended.\u003c/p\u003e\u003cp\u003eIn any case, lung function measures are a continuum and a \u003cem\u003ez\u003c/em\u003e-score below a probabilistic threshold such as the LLN\u003csub\u003e5\u003c/sub\u003e or LLN\u003csub\u003e2.5\u003c/sub\u003e does not necessarily indicate a clinically meaningful abnormality to diagnose a disease but gives the probability of a false positive diagnosis in a healthy subject [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. By converse, \u003cem\u003ez\u003c/em\u003e-scores above these thresholds do not allow to rule out disease with certainty due to the known overlap between healthy and diseased distributions, which may vary depending on various factors including nature and severity of disease, type and method of measures, and reference equations. These proposed limits of normality were based on the distribution in healthy population but not validated for the accuracy to separate diseased from healthy subjects [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe designed the present study with the primary aim to test whether the \u003cem\u003ez\u003c/em\u003e-score thresholds of DL\u003csub\u003eCO\u003c/sub\u003e and DL\u003csub\u003eNO\u003c/sub\u003e separating IPF and PE from healthy subjects may differ from LLN\u003csub\u003e5\u003c/sub\u003e or LLN\u003csub\u003e2.5\u003c/sub\u003e. A secondary aim of the study was to estimate the impact of the choice of test and predicting equations on severity stratification in relation to the extent of lung parenchymal abnormalities typical of IPF and PE. For these purposes, we retrospectively utilized the DL\u003csub\u003eCO\u003c/sub\u003e and DL\u003csub\u003eNO\u003c/sub\u003e data obtained by a single device in sixty-six subjects with IPF included in two previous studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and in fifty-four new subjects with PE, both documented by quantitative computed tomography (CT) of the chest.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis combined retrospective and prospective study included a total of 120 adults subjects of white European ancestry (Table 1). Sixty-six subjects from our previous studies [22, 23] had IPF, 43 due to usual or nonspecific interstitial pneumonia and 23 to systemic sclerosis. Fifty-four new subjects, with a smoking history \u0026ge;10 pack-years and a CT pattern consistent with mild-to-advanced PE, were consecutively enrolled between October 2022 and December 2024. Seven of them underwent preoperative pulmonary function tests for localized peripheral nodules and one for esophageal cancer; all subjects included in this group were free from history of any comorbidities potentially affecting lung diffusing capacity,\u003cem\u003e i.e.\u003c/em\u003e, congestive heart failure, liver or kidney diseases, and morbid obesity. Forty-four (~67%) subjects of the IPF group had a restrictive impairment, as defined by plethysmographic total lung capacity (TLC) \u0026lt;LLN\u003csub\u003e5\u003c/sub\u003e [8]. Forty-five (~83%) subjects of PE group had an obstructive impairment, as defined by forced expiratory volume in 1-s-to-forced vital capacity ratio (FEV\u003csub\u003e1\u003c/sub\u003e/FVC) \u0026lt;LLN\u003csub\u003e5 \u003c/sub\u003e[8] and were under treatment with long- or short-acting inhaled bronchodilators only (\u003cem\u003en\u003c/em\u003e=13) or in association with inhaled corticosteroids (\u003cem\u003en\u003c/em\u003e=23). Fifty-six asymptomatic healthy subjects, selected among health professionals and their relatives, were included as a control group. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandard lung function measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmokers were asked to refrain from smoking for 24 h prior to measurements. All subjects were tested at 160 m above sea level, following the scheduled administration of ongoing inhalation therapy if any. Lung volumes [24],spirometry [25],and standard DL\u003csub\u003eCO\u003c/sub\u003e[7] with 11.4\u0026plusmn;0.48 s (10.3 to 13.0 s) actual breath-hold time (BHT) and adjusted for effective hemoglobin concentration [26]were sequentially measured with subjects sitting in a whole-body plethysmograph (Vyntus\u0026trade; BODY Plethysmograph, Vyaire Medical, H\u0026ouml;chberg, Germany). The predicting equations used for lung volumes [27], spirometry [28], and DL\u003csub\u003eCO\u003c/sub\u003e [14] were from the GLI. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDL\u003csub\u003eNO\u003c/sub\u003e measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-breath DL\u003csub\u003eNO\u003c/sub\u003e with 5.52\u0026plusmn;0.49 s (4.60 to 6.90 s) actual BHT, was measured (MasterScreen-PFT System; Jaeger, Vyaire Medical, H\u0026ouml;chberg, Germany) at least twice at 5-min interval with subjects in a sitting posture and wearing a nose clip, as previously reported [23]. The values retained for analysis were the average of two repeatable measurements, \u003cem\u003ei.e.\u003c/em\u003e, within 17.0 mL\u0026middot;min\u003csup\u003e\u0026minus;1\u003c/sup\u003e\u0026middot;mmHg\u003csup\u003e\u0026minus;1\u003c/sup\u003e [11]. The predicting equations used for DL\u003csub\u003eNO\u003c/sub\u003e were the sex- and device-non-specific ones from Zavorsky \u003cem\u003eet al.\u003c/em\u003e [11], hereinafter referred to as ERS, sex-and device-specific from Munkholm \u003cem\u003eet al.\u003c/em\u003e[19] and sex- and device-corrected from Zavorsky \u0026amp; Cao [20]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative CT analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVolumetric CT scans of the chest (1.25-mm slice thickness, 200 mAs, 120 kVp) were obtained within 4 months before or after pulmonary function tests, during breath-hold at full inspiration, by a multi-detector row-spiral scanner (SOMATOM Emotion 6; Siemens AG Medical, Forchheim, Germany). Only scans with CT lung volume-to-TLC ratio \u0026ge;0.8 were retained for automatic quantitative three-dimensional analysis [29].For the IPF group, the overall structural abnormality was the sum of percentage areas with reticular opacities, honeycombing, and traction bronchiectases (RO+HC+TB), as in our original studies [22, 23].For the PE group, the CT images were reconstructed in contiguous submillimeter-thickness axial sections using a medium-sharp resolution reconstruction (3D Slicer 5.6.1 software; https://www.slicer.org/) [30]. The overall structural abnormality was percent voxel with low-attenuation areas \u0026lt;-950 Hounsfield Units (LAA\u003csub\u003e\u0026lt;-950 HU\u003c/sub\u003e) \u003cem\u003eplus\u003c/em\u003e high-attenuation areas \u0026gt;-600 HU (HAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e), if any. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each lung function measure, absolute value and \u003cem\u003ez\u003c/em\u003e-score were calculated. Categorical variables were compared by chi-square test with Yates correction. ANOVA, with the Holm-Sidak method for pairwise comparison testing, was used for significance testing of within group DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores by ERS, Munkholm \u003cem\u003eet al\u003c/em\u003e., and Zavorsky \u0026amp; Cao (SigmaPlot 11; 2008 Systat Software, Germany). The agreement between \u003cem\u003ez\u003c/em\u003e-scores derived from different reference equations was assessed by Bland-Altman plot (GraphPad Prism 8.4.2; GraphPad Software, San Diego, CA, USA). The accuracy of each reference equation to separate healthy from diseased subjects was determined by analysis of the area under the receiver operating characteristic curve (AUROC), Youden index, and Matthews correlation coefficient, using the presence of CT abnormalities \u0026ge;1% of total lung volume for IPF and both \u0026ge;1% and \u0026gt;6% [31] for PE as gold standard. The relationships between \u003cem\u003ez\u003c/em\u003e-scores and CT abnormalities were determined by Pearson\u0026rsquo;s correlation coefficient r. In all analyses, the acceptable Type I error was set at \u003cem\u003eP \u003c/em\u003evalue \u0026lt;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStandard lung function\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of healthy controls showed standard lung function \u003cem\u003ez\u003c/em\u003e-scores from GLI below the LLN\u003csub\u003e5\u003c/sub\u003e. By contrast, TLC was \u0026lt;LLN\u003csub\u003e5\u003c/sub\u003e in forty-four (~67%) of IPF subjects and FEV\u003csub\u003e1\u003c/sub\u003e/FVC \u0026lt;LLN\u003csub\u003e5\u003c/sub\u003e in forty-five (~83%) of PE subjects. As expected, the average absolute values and \u003cem\u003ez\u003c/em\u003e-scores of TLC were reduced in the IPF group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 \u003cem\u003evs\u003c/em\u003e. controls) and FEV\u003csub\u003e1\u003c/sub\u003e/FVC in the PE group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 \u003cem\u003evs\u003c/em\u003e. controls) whereas FVC, FEV\u003csub\u003e1\u003c/sub\u003e, DL\u003csub\u003eCO\u003c/sub\u003e and K\u003csub\u003eCO\u003c/sub\u003e were decreased in both IPF and PE groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 for all \u003cem\u003evs.\u003c/em\u003e controls).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDL\u003csub\u003eNO\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the control group, the DL\u003csub\u003eNO\u003c/sub\u003e mean \u003cem\u003ez\u003c/em\u003e-scores from Munkholm \u003cem\u003eet al.\u003c/em\u003e and Zavorsky \u0026amp; Cao did not differ significantly between each other (mean difference 0.025; 95% confidence interval [CI], -0.407 to 0.457; \u003cem\u003eP\u003c/em\u003e=0.867), while those from ERS were significantly lower than either Munkholm \u003cem\u003eet al.\u003c/em\u003e (mean difference -0.717; 95% CI, -1.486 to 0.052; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) or Zavorsky \u0026amp; Cao (mean difference -0.691; 95% CI, -1.132 to -0.251;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin the IPF group, the DL\u003csub\u003eNO\u003c/sub\u003e mean \u003cem\u003ez\u003c/em\u003e-score was significantly lower from Munkholm \u003cem\u003eet al\u003c/em\u003e. than Zavorsky \u0026amp; Cao (mean difference -0.597; 95% CI, -1.119 to -0.075; \u003cem\u003eP\u003c/em\u003e=0.008). No other significant differences were observed between mean\u003cem\u003e\u0026nbsp;z\u003c/em\u003e-scores from different predicting equations within IPF and PE groups. However, Bland-Altman plots with pooled control, IPF, and PE subjects showed poor agreement between DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores from ERS and either Munkholm \u003cem\u003eet al\u003c/em\u003e. or Zavorsky \u0026amp; Cao, due to large differences in females over the low range of values (Fig. 1). By contrast, there was a good agreement between DL\u003csub\u003eNO\u0026nbsp;\u003c/sub\u003e\u003cem\u003ez\u003c/em\u003e-scores from Munkholm \u003cem\u003eet al\u003c/em\u003e. and Zavorsky \u0026amp; Cao for both males and females.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUROC analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollectively, the DL\u003csub\u003eCO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores from GLI and the DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores from any reference equations showed excellent classification performance for both IPF and PE, with DL\u003csub\u003eCO\u003c/sub\u003e showing the highest accuracy for PE of ≥1% and \u0026gt;6% extent and DL\u003csub\u003eNO\u003c/sub\u003e from Munkholm \u003cem\u003eet al.\u003c/em\u003e for IPF (Table 2). However, the best \u003cem\u003ez\u003c/em\u003e-score thresholds separating diseased from control subjects were widely different for both DL\u003csub\u003eCO\u003c/sub\u003e (-1.095 or 13.7\u003csup\u003eth\u003c/sup\u003e percentile in IPF and -1.180 or 11.9\u003csup\u003eth\u003c/sup\u003e percentile in PE of any extent) and DL\u003csub\u003eNO\u003c/sub\u003e (-1.900 or 2.9\u003csup\u003eth\u003c/sup\u003e percentile in both IPF and PE of any extent with ERS; -1.560 or 5.9\u003csup\u003eth\u003c/sup\u003e percentile in IPF and -1.650 or 4.9\u003csup\u003eth\u003c/sup\u003e percentile and -1.665 or 4.8\u003csup\u003eth\u003c/sup\u003e percentile in PE of ≥1% and \u0026gt;6% extent, respectively, with Munkholm \u003cem\u003eet al.\u003c/em\u003e; -1.370 or 8.5\u003csup\u003eth\u003c/sup\u003e percentile in IPF and -1.330 or 9.2\u003csup\u003end\u003c/sup\u003e percentile in PE of any extent with Zavorsky \u0026amp; Cao). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructure-to-function relationships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the IPF group, the extent of RO+HC+TB was on average 29±16% of CT lung volume; in the PE group, the extent of LAA\u003csub\u003e\u0026lt;-950HU\u003c/sub\u003e was 15±11% and associated with a 5±2% HAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DL\u003csub\u003eCO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores from GLI were negatively correlated with both RO+HC+TB (r=-0.667; 95% CI, -0.783 to -0.507; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) in IPF and with LAA\u003csub\u003e\u0026lt;-950HU\u003c/sub\u003e+HAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e (r=-0.710, 95% CI, -0.822 to -0.546; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) in PE (Fig. 2). Notably, among subjects with moderate or severe DL\u003csub\u003eCO\u003c/sub\u003e impairments,\u003cem\u003e\u0026nbsp;i.e\u003c/em\u003e., \u003cem\u003ez\u003c/em\u003e-scores \u0026lt;-2.50 or \u0026lt;-4.00, females had less RO+HC+TB abnormalities than males. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DL\u003csub\u003eNO\u003c/sub\u003e\u003cem\u003e\u0026nbsp;z\u003c/em\u003e-scores from ERS were also negatively correlated with RO+HC+TB (r=-0.754; 95% CI, -0.843 to -0.627; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) in IPF but moderately with LAA\u003csub\u003e\u0026lt;-950HU\u003c/sub\u003e+HAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e (r=-0.549, 95% CI, -0.712 to -0.330; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) in PE (Fig. 3). The DL\u003csub\u003eNO\u0026nbsp;\u003c/sub\u003e\u003cem\u003ez\u003c/em\u003e-scores from Munkholm \u003cem\u003eet al.\u003c/em\u003e were negatively correlated with RO+HC+TB (r =-0.497; 95% CI, -0.659 to -0.289; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) in IPF and even more with LAA\u003csub\u003e\u0026lt;-950HU\u003c/sub\u003e+HAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e (r=-0.638; 95% CI, -0.774 to -0.466; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001) in PE. \u0026nbsp;The DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores from Zavorsky \u0026amp; Cao were negatively correlated with RO+HC+TB (r=-0.568; 95% CI, -0.712 to -0.378; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) in IPF and with LAA\u003csub\u003e\u0026lt;-950HU\u003c/sub\u003e+HAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e (r=-0.662, 95% CI, -0.790 to -0.479; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) in PE. In females, however, DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-score from either ERS (r=-0.184; 95% CI, -0.472 to 0.140; \u003cem\u003eP\u003c/em\u003e=0.262) or Zavorsky \u0026amp; Cao (r=-0.241; 95% CI, -0.517 to 0.081; \u003cem\u003eP\u003c/em\u003e=0.140) \u003cem\u003ez\u003c/em\u003e-scores were not correlated with RO+HC+TB. This was apparently due to excess of females with moderate or severe DL\u003csub\u003eNO\u003c/sub\u003e impairment despite moderate RO+HC+TB abnormalities. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main findings of the present study are that \u003cem\u003e1)\u003c/em\u003e thresholds derived using DL\u003csub\u003eCO\u003c/sub\u003e from GLI and DL\u003csub\u003eNO\u003c/sub\u003e from different reference equations were able to differentiate between CT-documented IPF or PE; \u003cem\u003e2)\u003c/em\u003e the optimal thresholds separating diseased from healthy subjects differed variably from the recommended LLN\u003csub\u003e5\u003c/sub\u003e or LLN\u003csub\u003e2.5\u003c/sub\u003e depending mainly on type of measure, \u003cem\u003ei.e.\u003c/em\u003e, DL\u003csub\u003eCO\u003c/sub\u003e or DL\u003csub\u003eNO\u003c/sub\u003e, and choice of reference equations for the latter; and \u003cem\u003e3\u003c/em\u003e) the negative correlation between extent of CT abnormalities and DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores also varied with predicting equations, thus potentially influencing severity classification. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComments on results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was designed to test the proof-of-concept that the lung function thresholds separating healthy from diseased subjects may differ from the generally assumed limits of normality, \u003cem\u003ei.e.\u003c/em\u003e, the 5\u003csup\u003eth\u003c/sup\u003e or 2.5\u003csup\u003eth\u003c/sup\u003e percentiles of normal population. For this purpose, we examined measures of gas transfer that are considered strictly linked to the lung parenchymal abnormalities typical of IPF or PE objectively demonstrable by CT of the chest. We chose standard DL\u003csub\u003eCO\u003c/sub\u003e, which is the most widely recommended and used test of gas transfer in clinical practice, and DL\u003csub\u003eNO\u003c/sub\u003e because its relative insensitivity to intrapulmonary hematocrit \u003cem\u003ein vivo\u003c/em\u003e[32] makes it a measurement likely close to a “true” alveolar membrane diffusion (DM) [33].Although previous studies have suggested that DL\u003csub\u003eNO\u003c/sub\u003e may be more sensitive than standard DL\u003csub\u003eCO\u003c/sub\u003e to lung parenchymal abnormalities [22, 23, 34, 35], some uncertainties still exist due to differences in reference equations obtained with different devices. For the purposes of the present study, this offered the opportunity to investigate the possible impact of reference equations and device. Collectively, the results support the hypothesis that using \u003cem\u003ez\u003c/em\u003e-scores thresholds of normality uniquely based on the distribution of healthy population may result in misclassification of individual subjects, possibly due to either physiological or technical reasons. For standard DL\u003csub\u003eCO\u003c/sub\u003e, the \u003cem\u003ez\u003c/em\u003e-scores thresholds best separating PE and particularly IPF from healthy subjects were surprisingly well above the recommended LLN\u003csub\u003e5\u003c/sub\u003e [8]. This was apparently due to a number of subjects with preserved DL\u003csub\u003eCO\u003c/sub\u003e despite the presence of CT abnormalities typical of IPF or PE. A physiological reason for this may be the high variability of the pulmonary capillary blood volume (V\u003csub\u003eC\u003c/sub\u003e) [36-38],which is the major determinant of DL\u003csub\u003eCO\u003c/sub\u003e [12]. Although we had DL\u003csub\u003eCO\u003c/sub\u003e also measured simultaneously with DL\u003csub\u003eNO\u003c/sub\u003e, method that allows a single-step partition of DM and V\u003csub\u003eC\u003c/sub\u003e, we did not include subcomponents for the purposes of the present study because the too many assumptions about blood NO and CO kinetic properties (θ) \u003cem\u003ein vivo\u003c/em\u003e make uncertain their calculation [12].Whatever the reason, the possible existence of a grey area of DL\u003csub\u003eCO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores \u0026lt;~14\u003csup\u003eth\u003c/sup\u003e percentile for IPF and \u0026lt;~12\u003csup\u003eth\u003c/sup\u003e percentile for PE should be carefully considered before excluding such diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor DL\u003csub\u003eNO\u003c/sub\u003e, the \u003cem\u003ez\u003c/em\u003e-score thresholds separating subjects with IPF or PE from healthy subjects were substantially different depending on reference equations, though similar between IPF and PE.\u003c/p\u003e\n\u003cp\u003eIn detail, the best threshold for DL\u003csub\u003eNO\u003c/sub\u003e from device-specific Munkholm \u003cem\u003eet al.\u003c/em\u003e were very close to the LLN\u003csub\u003e5\u003c/sub\u003e but well above the recommended LLN\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e[19], from ERS close to the recommended LLN\u003csub\u003e2.5\u003c/sub\u003e [11], and from Zavorsky \u0026amp; Cao well above the recommended LLN\u003csub\u003e5\u003c/sub\u003e and LLN\u003csub\u003e2.5\u003c/sub\u003e [17]. Therefore, while the LLN\u003csub\u003e2.5\u003c/sub\u003e seems an appropriate trade-off between sensitivity and specificity with ERS reference equations, its use with Munkholm \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003eequations would be associated with a low false positive (type I error) risk in healthy subjects but with an increased false negative (type II error) risk in patients. On the other hand, using either the LLN\u003csub\u003e5\u003c/sub\u003e or LLN\u003csub\u003e2.5\u003c/sub\u003e with Zavorsky \u0026amp; Cao equations would be associated with a non-negligible risk of false negatives in both IPF and PE patients. The above discrepancies from the analysis of the same data by different predicting equations are likely due to technical rather than physiological factors. The only reference equations obtained with a single device, \u003cem\u003ei.e.\u003c/em\u003e, Jaeger MasterScreen-PFT, the same as used in the present study, were those by Munkholm\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e[19],which were also separate for males and females. The ERS reference equations [11], without sex separation, were obtained by pooling data from three small studies, the first of which [15] using a bespoke device with chemiluminescence analyser to measure NO uptake during 10-s duration BHT, and the other two [16, 17]the same commercial device, \u003cem\u003ei.e.\u003c/em\u003e, Medisoft Hyp’Air (Dinant, Belgium), with electrochemical cells as NO analyser and 5-s BHT. More recently, a direct comparison between the two devices commercially available in Europe, \u003cem\u003ei.e.\u003c/em\u003e, MasterScreen-PFT, used in the present study, and Hyp’Air, showed systematic differences in DL\u003csub\u003eNO\u003c/sub\u003e (-24.0 mL·min\u003csup\u003e-1\u003c/sup\u003e·mmHg\u003csup\u003e-1\u003c/sup\u003e; 95% CI -21.7 to -26.3,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.0001) likely explained, at least in part, by discrepancies in V\u003csub\u003eA\u003c/sub\u003e (-0.48 L; 95% CI -0.45 to -0.52, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) [39].The Zavorsky \u0026amp; Cao equations were calculated by pooling raw data from four studies obtained with different devices [15-18] and data extrapolated from Munkholm \u003cem\u003eet al.\u003c/em\u003e [19] equations, with device and sex introduced as covariates. In a recent study in systemic sclerosis, DL\u003csub\u003eNO\u003c/sub\u003e measurements obtained with Hyp’Air device were analyzed by using ERS and Munkholm \u003cem\u003eet al.\u003c/em\u003e equations, and the former yielded \u003cem\u003ez\u003c/em\u003e-scores similar to the latter in males but markedly less negative in females, particularly over the lower range [40]. In our study, using MasterScreen-PFT, the DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores of females were also markedly less negative with ERS than Munkholm \u003cem\u003eet al.\u003c/em\u003e equations. Altogether, these findings suggest that, in subjects with IPF or PE, sex is a source of variability of measured DL\u003csub\u003eNO\u003c/sub\u003e at least as relevant as device. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs expected, DL\u003csub\u003eNO\u003c/sub\u003e was negatively correlated with the CT extent of IPF and PE. There were, however, some differences related to predicting equations and disease. In the case of IPF, the number of females with severe DL\u003csub\u003eNO\u003c/sub\u003e impairment (\u0026lt;-4.00 \u003cem\u003ez\u003c/em\u003e-score) was larger with Munkholm \u003cem\u003eet al\u003c/em\u003e. than ERS or Zavorsky \u0026amp; Cao equations, even in cases with low-to-moderate CT abnormalities. As the majority of them had systemic sclerosis, which is primarily a vasculitis, the DL\u003csub\u003eNO\u003c/sub\u003e impairment may reflect mechanisms other than CT interstitial abnormalities [23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFirst\u003c/em\u003e, IPF and PE groups were not perfectly matched with control subjects for most anthropometric characteristics. However, the use of \u003cem\u003ez\u003c/em\u003e-scores should mitigate some of the age-, sex- and size-biases [8, 21].\u003cem\u003eSecond\u003c/em\u003e,no CT scans were available for healthy controls for ethical reasons. Therefore, some overlap between healthy and diseased subjects might have been due to minor, clinically irrelevant, abnormalities in the former. \u003cem\u003eThird\u003c/em\u003e, CTs of the chest were acquired in supine position and lung function studies in a sitting position and at different times from lung function studies, which might have resulted in a CT gas volume at full inspiration lower than plethysmographic TLC[41] and differences in disease time courses. However, it is unlikely that these might have differently influenced the DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-scores sensitivities to CT abnormalities. \u003cem\u003eFourth\u003c/em\u003e, the results of the present study were not validated in an external sample, but this was deemed not necessary because it was a proof-of-concept study, not intended to establish firm thresholds of normality or severity grading.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of the present study show that DL\u003csub\u003eCO\u003c/sub\u003e and DL\u003csub\u003eNO\u003c/sub\u003e \u003cem\u003ez\u003c/em\u003e-score thresholds separating subjects with significant CT-determined IPF or PE from healthy controls may differ substantially from the widely used LLN\u003csub\u003e5\u003c/sub\u003e (-1.645 \u003cem\u003ez\u003c/em\u003e-score) to define functional impairment. For DL\u003csub\u003eCO\u003c/sub\u003e a grey zone of \u003cem\u003ez\u003c/em\u003e-scores over the lower range of normality in the presence of CT-documented structural abnormalities may be explained by different physiological determinants of gas transport. For DL\u003csub\u003eNO\u003c/sub\u003e, technical factors related to reference equations and device seem to play a major role. The above differences may also affect thresholds for severity classification.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDL\u003csub\u003eCO\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lung diffusing capacity for carbon monoxide\u003c/p\u003e\n\u003cp\u003eDL\u003csub\u003eNO\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lung diffusing capacity for nitric oxide\u003c/p\u003e\n\u003cp\u003eIPF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Interstitial pulmonary fibrosis\u003c/p\u003e\n\u003cp\u003ePE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pulmonary emphysema\u003c/p\u003e\n\u003cp\u003eDM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alveolar membrane diffusive conductance\u003c/p\u003e\n\u003cp\u003eGLI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Global Lung Function Initiative\u003c/p\u003e\n\u003cp\u003eERS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; European Respiratory Society\u003c/p\u003e\n\u003cp\u003eCT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Computed tomography\u003c/p\u003e\n\u003cp\u003eLLN\u003csub\u003e5\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lower limit of normal at 5\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n\u003cp\u003eLLN\u003csub\u003e2.5\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lower limit of normal at 2.5\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n\u003cp\u003eTLC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total lung capacity\u003c/p\u003e\n\u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Forced expiratory volume in 1-s-to-forced vital capacity ratio\u003c/p\u003e\n\u003cp\u003eBHT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Breath-hold time (BHT)\u003c/p\u003e\n\u003cp\u003eRO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Reticular opacities\u003c/p\u003e\n\u003cp\u003eHC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Honeycombing\u003c/p\u003e\n\u003cp\u003eTB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Traction bronchiectases\u003c/p\u003e\n\u003cp\u003eLAA\u003csub\u003e\u0026lt;-950 HU\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Low-attenuation areas below -950 Hounsfield units\u003c/p\u003e\n\u003cp\u003eHAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-attenuation areas above -600 Hounsfield units\u003c/p\u003e\n\u003cp\u003eAUROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eV\u003csub\u003eA\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alveolar volume\u003c/p\u003e\n\u003cp\u003eK\u003csub\u003eCO\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;DL\u003csub\u003eCO\u003c/sub\u003e/V\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eV\u003csub\u003eC\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pulmonary capillary blood volume\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGB and VB contributed to the conception and design of the study; GB enrolled the study subjects, performed pulmonary function testing, analyzed chest imaging studies, collected and analyzed the data; GB and VB interpreted the data, drafted, and wrote the manuscript; GB, SS, and VB reviewed and edited the manuscript. All the authors read and approved the final manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in accordance with the Declaration of Helsinki. The Regional Ethics Committee’s approval for IPF data collection was obtained for each of the two original studies [22, 23].Collection of original PE data was approved by the Comitato Etico Territoriale Liguria (No. CET - Liguria: 59/2025 - id 14300) and each subject gave written informed consent to participate in this study and to use their anonymized data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eStruttura Semplice Fisiopatologia Respiratoria, Clinica Malattie Respiratorie e Allergologia, IRCCS Ospedale Policlinico San Martino, Genova, Italy\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada \u003csup\u003e3\u003c/sup\u003eDipartimento di Medicina Sperimentale, Università di Genova, Genova, Italy\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eForster RE. 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Reference values for lung function in white (Caucasian) children and adults. \u003cem\u003eIn:\u003c/em\u003e Lung function. 6\u003csup\u003eth\u003c/sup\u003e edition: Blackwell Publishing, 2006, p. 333-365.\u003c/li\u003e\n\u003cli\u003eStanojevic S, Graham BL, Cooper BG, Thompson BR, Carter KW, Francis RW, Hall GL; Global Lung Function Initiative TLCO working group; Global Lung Function Initiative (GLI) TLCO. Official ERS technical standards: Global Lung Function Initiative reference values for the carbon monoxide transfer factor for Caucasians. \u003cem\u003eEur Respir J\u003c/em\u003e 2017;50:1700010.\u003c/li\u003e\n\u003cli\u003evan der Lee I, Zanen P, Stigter N, van den Bosch JM, Lammers JW. Diffusing capacity for nitric oxide: reference values and dependence on alveolar volume. \u003cem\u003eRespir Med\u003c/em\u003e 2007;101:1579-1584.\u003c/li\u003e\n\u003cli\u003eAguilaniu B, Maitre J, Gl\u0026eacute;net S, Gegout-Petit A, Gu\u0026eacute;nard H. European reference equations for CO and NO lung transfer. \u003cem\u003eEur Respir J \u003c/em\u003e2008;31:1091-1097.\u003c/li\u003e\n\u003cli\u003eZavorsky GS, Cao J, Murias JM. Reference values of pulmonary diffusing capacity for nitric oxide in an adult population. \u003cem\u003eNitric Oxide\u003c/em\u003e 2008;18:70-79.\u003c/li\u003e\n\u003cli\u003eThomas A, Hanel B, Marott JL, Buchvald F, Mortensen J, Nielsen KG. The single-breath diffusing capacity of CO and NO in healthy children of European descent. \u003cem\u003ePLoS One \u003c/em\u003e2014; 9:e113177.\u003c/li\u003e\n\u003cli\u003eMunkholm M, Marott JL, Bjerre-Kristensen L, Madsen F, Pedersen OF, Lange P, Nordestgaard BG, Mortensen J. Reference equations for pulmonary diffusing capacity of carbon monoxide and nitric oxide in adult Caucasians. \u003cem\u003eEur Respir J\u003c/em\u003e 2018;52:1500677.\u003c/li\u003e\n\u003cli\u003eZavorsky GS, Cao J. Reference equations for pulmonary diffusing capacity using segmented regression show similar predictive accuracy as GAMLSS models. \u003cem\u003eBMJ Open Respir Res\u003c/em\u003e 2022; 9:e001087.\u003c/li\u003e\n\u003cli\u003eMiller MR. Choosing and using lung function prediction equations. \u003cem\u003eEur Respir J\u003c/em\u003e 2016;48: 1535-1537.\u003c/li\u003e\n\u003cli\u003eBarisione G, Brusasco C, Garlaschi A, Baroffio M, Brusasco V. Lung diffusing capacity for nitric oxide as a marker of fibrotic changes in idiopathic interstitial pneumonias. \u003cem\u003eJ Appl Physiol \u003c/em\u003e2016;120:1029-1038.\u003c/li\u003e\n\u003cli\u003eBarisione G, Garlaschi A, Occhipinti M, Baroffio M, Pistolesi M, Brusasco V. Value of lung diffusing capacity for nitric oxide in systemic sclerosis. \u003cem\u003ePhysiol Rep\u003c/em\u003e 2019;7:e14149.\u003c/li\u003e\n\u003cli\u003eBhakta NR, McGowan A, Ramsey KA, Borg B, Kivastik J, Knight SL, Sylvester K, Burgos F, Swenson ER, McCarthy K, Cooper BG, Garc\u0026iacute;a-R\u0026iacute;o F, Skloot G, McCormack M, Mottram C, Irvin CG, Steenbruggen I, Coates AL, David A. Kaminsky DA. European Respiratory Society/American Thoracic Society technical statement: standardisation of the measurement of lung volumes, 2023 update. \u003cem\u003eEur Respir J\u003c/em\u003e 2023;62:2201519.\u003c/li\u003e\n\u003cli\u003eGraham BL, Steenbruggen I, Miller MR, Barjaktarevic IZ, Cooper BG, Hall GL, Hallstrand TS, Kaminsky DA, McCarthy K, McCormack MC, Oropez CE, Rosenfeld M, Stanojevic S, Swanney MP, Thompson BR. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement. \u003cem\u003eAm J Respir Crit Care Med \u003c/em\u003e2019;200:e70-e88.\u003c/li\u003e\n\u003cli\u003eCotes JE, Dabbs JM, Elwood PC, Hall AM, McDonald A, Saunders MJ. Iron-deficiency anaemia: its effect on transfer factor for the lung (diffusing capacity) and ventilation and cardiac frequency during sub-maximal exercise. \u003cem\u003eClin Sci\u003c/em\u003e 1972;42:325-335.\u003c/li\u003e\n\u003cli\u003eHall GL, Filipow N, Ruppel G, Okitika T, Thompson B, Kirkby J, Steenbruggen I, Cooper BG, Stanojevic S; contributing GLI Network members. Official ERS technical standard: Global Lung Function Initiative reference values for static lung volumes in individuals of European ancestry. \u003cem\u003eEur Respir J\u003c/em\u003e 2021;57:2000289.\u003c/li\u003e\n\u003cli\u003eQuanjer PH, Stanojevic S, Cole TJ, Baur X, Hall GL, Culver BH, Enright PL, Hankinson JL, Ip MS, Zheng J, Stocks J; ERS Global Lung Function Initiative. Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations. \u003cem\u003eEur Respir J\u003c/em\u003e 2012;40:1324-1343.\u003c/li\u003e\n\u003cli\u003eFedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy FM, Sonka M, Buatti J, Aylward SR, Miller JV, Pieper S, Kikinis R. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. \u003cem\u003eMagn Reson Imaging \u003c/em\u003e2012;30:1323-1341.\u003c/li\u003e\n\u003cli\u003eStoel BC, Putter H, Bakker ME, Dirksen A, Stockley RA, Piitulainen E, Russi EW, Parr D, Shaker SB, Reiber JH, Stolk J. Volume correction in computed tomography densitometry for follow-up studies on pulmonary emphysema. \u003cem\u003eProc Am Thorac Soc \u003c/em\u003e2008;5:919-924.\u003c/li\u003e\n\u003cli\u003eLynch DA, Austin JH, Hogg JC, Grenier PA, Kauczor HU, Bankier AA, Barr RG, Colby TV, Galvin JR, Gevenois PA, Coxson HO, Hoffman EA, Newell JD Jr, Pistolesi M, Silverman EK, Crapo JD. CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society. \u003cem\u003eRadiology\u003c/em\u003e 2015;277:192-205.\u003c/li\u003e\n\u003cli\u003evan der Lee I, Zanen P, Biesma DH, van den Bosch JM. The effect of red cell transfusion on nitric oxide diffusing capacity.\u003cem\u003eRespiration \u003c/em\u003e2005;72:512-516.\u003c/li\u003e\n\u003cli\u003eBorland C, Patel R. Comparing in vitro nitric oxide blood uptake to its pulmonary diffusing capacity.\u003cem\u003eNitric Oxide\u003c/em\u003e 2024;143:29-43. \u003c/li\u003e\n\u003cli\u003eBarisione G, Bacigalupo A, Brusasco C, Scanarotti C, Penco S, Bassi AM, Lamparelli T, Garlaschi A, Pellegrino R, Brusasco V. Mechanisms for reduced pulmonary diffusing capacity in haematopoietic stem-cell transplantation recipients. \u003cem\u003eRespir Physiol Neurobiol\u003c/em\u003e 2014;194:54-61.\u003c/li\u003e\n\u003cli\u003evan der Lee I, Gietema HA, Zanen P, van Klaveren RJ, Prokop M, Lammers JW, van den Bosch JM. Nitric oxide diffusing capacity versus spirometry in the early diagnosis of emphysema in smokers. \u003cem\u003eRespir Med\u003c/em\u003e 2009;103:1892-1897.\u003c/li\u003e\n\u003cli\u003ePande JN, Gupta SP, Guleria JS. Clinical significance of the measurement of membrane diffusing capacity and pulmonary capillary blood volume. \u003cem\u003eRespiration\u003c/em\u003e 1975;32:317-24.\u003c/li\u003e\n\u003cli\u003eOppenheimer BW, Berger KI, Hadjiangelis NP, Norman RG, Rapoport DM, Goldring RM\u003cem\u003e.\u003c/em\u003e Membrane diffusion in diseases of the pulmonary vasculature. \u003cem\u003eRespir Med \u003c/em\u003e2006;100:1247-53.\u003c/li\u003e\n\u003cli\u003eFarha S, Laskowski D, George D, Park MM, Tang WH, Dweik RA, Erzurum SC. Loss of alveolar membrane diffusing capacity and pulmonary capillary blood volume in pulmonary arterial hypertension. \u003cem\u003eRespir Res\u003c/em\u003e 2013;14:6.\u003c/li\u003e\n\u003cli\u003eRadtke T, de Groot Q, Haile SR, Maggi M, Hsia CCW, Dressel H. Lung diffusing capacity for nitric oxide measured by two commercial devices: a randomised crossover comparison in healthy adults. \u003cem\u003eERJ Open Res\u003c/em\u003e 2021;00193-2021.\u003c/li\u003e\n\u003cli\u003eRadtke T, Hua-Huy T, Dressel H, Dinh-Xuan AT. Of the need to reconcile discrepancies between two different reference equations for combined single-breath \u003cem\u003eD\u003c/em\u003e\u003csub\u003eLNO\u003c/sub\u003e-\u003cem\u003eD\u003c/em\u003e\u003csub\u003eLCO\u003c/sub\u003e in systemic sclerosis. \u003cem\u003eEur Respir J\u003c/em\u003e 2019;53:1802109.\u003c/li\u003e\n\u003cli\u003eCotton DJ, Graham BL, Mink JT. Pulmonary diffusing capacity in adult cystic fibrosis: reduced positional changes are partially reversed by hyperoxia. \u003cem\u003eClin Invest Med\u003c/em\u003e 1990;13:82-91.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Subjects\u0026rsquo; characteristics, lung function and CT data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIPF\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale/Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e43/13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e27/39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e36/18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e53.9 \u0026plusmn; 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e64.8 \u0026plusmn; 13.3\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e68.7 \u0026plusmn; 9.1\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStature\u0026nbsp;\u003c/strong\u003e(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e172.5 \u0026plusmn; 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e163.2 \u0026plusmn; 8.6\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e166.8 \u0026plusmn; 8.4\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e (kg\u0026middot;m\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e25.6 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e26.6 \u0026plusmn; 3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e25.5 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking habit\u0026nbsp;\u003c/strong\u003e(C/F/N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e13/12/31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e4/28/34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e27/27/0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[Hb]\u0026nbsp;\u003c/strong\u003e(g\u0026middot;dL\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e14.2 \u0026plusmn; 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e13.5 \u0026plusmn; 1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e14.1 \u0026plusmn; 1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFVC\u0026nbsp;\u003c/strong\u003e(L)\u003c/p\u003e\n \u003cp\u003e(GLI \u003cem\u003ez\u003c/em\u003e-score)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e4.72 \u0026plusmn; 1.08\u003c/p\u003e\n \u003cp\u003e0.55 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e2.49 \u0026plusmn; 0.72\u003c/p\u003e\n \u003cp\u003e-1.53 \u0026plusmn; 1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e3.24 \u0026plusmn; 1.02\u003c/p\u003e\n \u003cp\u003e-0.69 \u0026plusmn; 1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFEV\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e(L)\u003c/p\u003e\n \u003cp\u003e(GLI \u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e3.67 \u0026plusmn; 0.82\u003c/p\u003e\n \u003cp\u003e0.49 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e2.01 \u0026plusmn; 0.54\u003c/p\u003e\n \u003cp\u003e-1.28 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e1.73 \u0026plusmn; 0.82\u003c/p\u003e\n \u003cp\u003e-2.31 \u0026plusmn; 1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(GLI \u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e0.78 \u0026plusmn; 0.06\u003c/p\u003e\n \u003cp\u003e-0.12 \u0026plusmn; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e0.82 \u0026plusmn; 0.08\u003c/p\u003e\n \u003cp\u003e0.48 \u0026plusmn; 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e0.52 \u0026plusmn; 0.13\u003c/p\u003e\n \u003cp\u003e-2.78 \u0026plusmn; 1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLC\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e(L)\u003c/p\u003e\n \u003cp\u003e(GLI \u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e6.73 \u0026plusmn; 1.23\u003c/p\u003e\n \u003cp\u003e0.21 \u0026plusmn; 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e3.92 \u0026plusmn; 0.96\u003c/p\u003e\n \u003cp\u003e-2.25 \u0026plusmn; 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e6.33 \u0026plusmn; 1.25\u003c/p\u003e\n \u003cp\u003e0.55 \u0026plusmn; 1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDL\u003csub\u003eCO\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e(mL\u0026middot;min\u003csup\u003e-1\u003c/sup\u003e\u0026middot;mmHg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e(GLI \u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e30.1 \u0026plusmn; 7.17\u003c/p\u003e\n \u003cp\u003e0.77 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e13.2 \u0026plusmn; 4.54\u003c/p\u003e\n \u003cp\u003e-2.73 \u0026plusmn; 1.59 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e12.0 \u0026plusmn; 4.71\u003c/p\u003e\n \u003cp\u003e-3.45 \u0026plusmn; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003csub\u003eCO\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e(mL\u0026middot;min\u003csup\u003e-1\u003c/sup\u003e\u0026middot;mmHg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e(GLI \u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e4.51 \u0026plusmn; 0.63\u003c/p\u003e\n \u003cp\u003e0.12 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e3.53 \u0026plusmn; 0.79\u003c/p\u003e\n \u003cp\u003e-1.10 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e2.45 \u0026plusmn; 0.73\u003c/p\u003e\n \u003cp\u003e-2.91 \u0026plusmn; 1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u003csub\u003eA,DL\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eCO\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e(L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e6.67 \u0026plusmn; 1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e3.73 \u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e4.89 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDL\u003csub\u003eNO\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e(mL\u0026middot;min\u003csup\u003e-1\u003c/sup\u003e\u0026middot;mmHg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003e(ERS \u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003cp\u003e(Munkholm \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003cp\u003e(Zavorsky \u0026amp; Cao \u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e129.1 \u0026plusmn; 31.1\u003c/p\u003e\n \u003cp\u003e-0.82 \u0026plusmn; 0.70*\u003c/p\u003e\n \u003cp\u003e0.11 \u0026plusmn; 0.93\u003c/p\u003e\n \u003cp\u003e-0.13 \u0026plusmn; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e50.3 \u0026plusmn; 20.2\u003c/p\u003e\n \u003cp\u003e-3.10 \u0026plusmn; 1.17\u003c/p\u003e\n \u003cp\u003e-3.44 \u0026plusmn; 1.09\u0026dagger;\u003c/p\u003e\n \u003cp\u003e-2.84 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e49.3 \u0026plusmn; 20.4\u003c/p\u003e\n \u003cp\u003e-3.52 \u0026plusmn; 1.24\u003c/p\u003e\n \u003cp\u003e-3.08 \u0026plusmn; 1.23\u003c/p\u003e\n \u003cp\u003e-3.02 \u0026plusmn; 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u003csub\u003eA,DL\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eNO\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003eL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003e6.54 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e3.75 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e4.95 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAA\u003csub\u003e\u0026lt;-950 HU\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e(% CT volume)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e15 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.9648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e(% CT volume)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6549%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3099%;\"\u003e\n \u003cp\u003e29 \u0026plusmn; 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0704%;\"\u003e\n \u003cp\u003e5 \u0026plusmn; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eDefinition of abbreviations\u003c/em\u003e: C/F/N = Current/Former/Never; IPF = interstitial pulmonary fibrosis; PE = pulmonary emphysema; FVC = forced vital capacity; FEV\u003csub\u003e1\u003c/sub\u003e = forced expiratory volume in 1-s; TLC =\u003csub\u003e\u0026nbsp;\u003c/sub\u003etotal lung capacity; DL\u003csub\u003eCO\u003c/sub\u003e = single-breath (11.4\u0026plusmn;0.48 s actual breath-hold time) lung diffusing capacity for carbon monoxide; K\u003csub\u003eCO\u003c/sub\u003e = DL\u003csub\u003eCO\u003c/sub\u003e/alveolar volume (V\u003csub\u003eA\u003c/sub\u003e); DL\u003csub\u003eNO\u003c/sub\u003e = single-breath (5.52\u0026plusmn;0.49 s actual breath-hold time) lung diffusing capacity for nitric oxide; LAA\u003csub\u003e\u0026lt;-950 HU\u003c/sub\u003e = low attenuation areas \u0026lt;-950 Hounsfield Units (HU); CT = computed tomography; HAA\u003csub\u003e\u0026gt;-600 HU\u003c/sub\u003e = high attenuation areas \u0026gt;-600 HU; NA = not available. Data are presented as absolute numbers or mean \u0026plusmn; SD. *, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 \u003cem\u003evs.\u003c/em\u003e Munkholm \u003cem\u003eet al.\u003c/em\u003e or Zavorsky \u0026amp; Cao; \u0026dagger;, \u003cem\u003eP\u003c/em\u003e=0.008 \u003cem\u003evs.\u003c/em\u003e Zavorsky \u0026amp; Cao.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eAUROC analysis of DL\u003csub\u003eCO\u003c/sub\u003e and DL\u003csub\u003eNO\u003c/sub\u003e for IPF and PE\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"784\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDL\u003csub\u003eCO\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 378px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDL\u003csub\u003eNO\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference equation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eERS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMunkholm et al.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZavorsky \u0026amp; Cao\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 784px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterstitial pulmonary fibrosis (IPF)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026ge;1%\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUROC\u003c/strong\u003e (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.97 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.96 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.99 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.98 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutoff\u0026nbsp;\u003c/strong\u003e(\u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.095\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.900\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.560\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.370\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.83\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.73 to 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.76 to 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.97\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.90 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.84 to 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.91 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.91 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.91 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.91 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMatthews Correlation Coefficient\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 784px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulmonary emphysema (PE) \u0026ge;1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUROC\u003c/strong\u003e (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.98 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e(0.94 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e(0.95 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.97 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutoff\u0026nbsp;\u003c/strong\u003e(\u003cem\u003ez\u003c/em\u003e-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.180\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.900\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.650\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.330\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e(0.85 to 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e(0.78 to 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003cp\u003e(0.76 to 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003cp\u003e(0.80 to 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.91 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.91 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.91 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.91 to 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMatthews Correlation Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eDefinition of abbreviations\u003c/em\u003e: AUROC = Area Under the Receiver Operating Characteristic Curve. Other abbreviations are presented as in Table 1. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Carbon monoxide, Nitric oxide, Pulmonary diffusion, Z-score ","lastPublishedDoi":"10.21203/rs.3.rs-7496536/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7496536/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground The probabilistic interpretation of low lung diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO) has been recently standardized but the impact of different lower limits of normal (z-scores) on clinical assessment is not well established.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObjective To assess the uncertainty zone of DLCO and DLNO z-score interpretation in computed tomography (CT)-determined interstitial pulmonary fibrosis (IPF) and pulmonary emphysema (PE).\u003c/p\u003e\n\u003cp\u003eDesign and methods In a combined retrospective (IPF) and prospective (PE), proof-of-concept study, standard lung function, including DLCO, and single-breath DLNO were measured. Z-scores derived from Global Lung Function Initiative (GLI) for DLCO and non-specific (ERS), sex- and device-specific (Munkholm et al.) or -corrected (Zavorsky \u0026amp; Cao) DLNO reference equations, analyzed.\u003c/p\u003e\n\u003cp\u003eResults 120 adults subjects with available CT of the chest participated in the study, 66 of them with IPF and the other 54 with a CT pattern consistent with PE. 56 asymptomatic subjects served as a control group. DLCO from GLI and DLNO from any reference equations showed high sensitivity and specificity for both IPF and PE, with DLCO from GLI showing the highest diagnostic accuracy for PE and DLNO from Munkholm et al. for IPF. However, the best thresholds separating IPF and PE from asymptomatic control subjects were widely different and ranging from ~14th percentile (-1.095 z-score) for DLCO from GLI to ~3rd percentile (-1.900 z-score) for DLNO from ERS. The DLCO z-scores from GLI showed the strongest negative correlation with PE (r=-0.710, P\u0026lt;0.0001) and DLNO from ERS with IPF (r=-0.750, P\u0026lt;0.0001) but in females z-scores from ERS and Zavorsky \u0026amp; Cao were not significantly correlated with extent of IPF.\u003c/p\u003e\n\u003cp\u003eConclusion DLCO and DLNO thresholds separating subjects with IPF or PE from healthy controls may differ substantially from standard lower limits of normal of -1.645 and -1.960 z-scores. For DLCO it may be due to inhomogeneous impairment of blood-to-air barrier whereas for DLNO different devices and reference equations seem to play a major role.\u003c/p\u003e\n\u003cp\u003eClinical trial registered with https://register.clinicaltrials.gov/prs/beta/records (ClinicalTrials.gov Identifier: NCT07091838)\u003c/p\u003e","manuscriptTitle":"Zone of Uncertainty in Pulmonary Function Interpretation: A Proof-of-Concept Study from Lung Diffusing Capacity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 06:50:36","doi":"10.21203/rs.3.rs-7496536/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb44e5f7-a8fc-43ab-8329-e1a47877c4e0","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-03T06:50:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 06:50:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7496536","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7496536","identity":"rs-7496536","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}