Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Determinants of exercise capacity in cystic fibrosis patients with mild-to-moderate lung disease

BMC Pulmonary Medicine volume 14, Article number: 74 (2014) Cite this article

Abstract

Background

Adult patients with cystic fibrosis (CF) frequently have reduced exercise tolerance, which is multifactorial but mainly due to bronchial obstruction. The aim of this retrospective analysis was to determine the mechanisms responsible for exercise intolerance in patients with mild-to-moderate or severe disease.

Methods

Cardiopulmonary exercise testing with blood gas analysis at peak exercise was performed in 102 patients aged 28 ± 11 years: 48 patients had severe lung disease (FEV1 < 50%, group 1) and 54 had mild-to-moderate lung disease (FEV1 ≥ 50%, group 2). VO2 peak was measured and correlated with clinical, biological, and functional parameters.

Results

VO2 peak for all patients was 25 ± 9 mL/kg/min (65 ± 21% of the predicted value) and was < 84% of predicted in 82% of patients (100% of group 1, 65% of group 2). VO2 peak was correlated with body mass index, C-reactive protein, FEV1, FVC, RV, DLCO, VE/VCO2 peak, VD/VT, PaO2, PaCO2, P(A-a)O2, and breathing reserve. In multivariate analysis, FEV1 and overall hyperventilation during exercise were independent determinants of exercise capacity (R2 = 0.67). FEV1 was the major significant predictor of VO2 peak impairment in group 1, accounting for 31% of VO2 peak alteration, whereas excessive overall hyperventilation (reduced or absent breathing reserve and VE/VCO2) accounted for 41% of VO2 alteration in group 2.

Conclusion

Exercise limitation in adult patients with CF is largely dependent on FEV1 in patients with severe lung disease and on the magnitude of the ventilatory response to exercise in patients with mild-to-moderate lung disease.

Peer Review reports

Background

Cystic fibrosis (CF) is characterized by deterioration of nutritional status and irreversible loss of lung function [1–3]. Patients with CF often experience exertional dyspnea and have reduced maximal exercise capacity, which is an important predictor of mortality [4–7]. Regular exercise in these patients has been associated with improved aerobic exercise endurance and quality of life [4, 8]. Physical exercise requires the cardiopulmonary system to deliver oxygen to muscles in sufficient quantity to generate energy through aerobic glycolysis. There are conflicting data on the precise mechanisms underlying exercise intolerance in CF, and a number of factors have been implicated [9], including poor nutritional status, peripheral muscle dysfunction [10, 11], and especially, ventilatory limitation [12, 13]. In other studies, dysfunctional gas exchange has been shown to play a crucial role in limiting exercise performance [14–17].

Only a third of the variability in exercise capacity of CF patients can be explained by FEV1, demonstrating that resting pulmonary function tests (PFTs) alone are insufficient to explain the exercise limitation [1, 9, 13]. By comparison, cardiopulmonary exercise testing (CPET) offers a sensitive evaluation of potential physiological disturbances in cardiovascular, respiratory, peripheral, or neurosensory responses to a standardized exercise protocol [18]. Although it remains underutilized in CF [19], CPET could provide important exercise-related measures that might explain the reduced exercise performance and thus assist in CF patient management aimed at improving exercise capacity.

With this in mind, we initiated a study to determine the mechanisms responsible for exercise limitation in 102 adult CF patients with mild-to moderate or severe lung disease. The patients were subjected to CPET with blood gas analysis during exercise and the results were correlated with clinical and functional characteristics.

Methods

Patients

A total of 102 adult patients (sex ratio M:F 0.52) with CF were enrolled at four CF centers in France: Lille (75 patients), Rouen (15 patients), Dijon (5 patients), and Grenoble (7 patients). Written informed consent for participation in the study was obtained from participants. Use of the patient data was approved by the local ethics committee, and the study was considered observational and approved as such by the Institutional Review Board of the French Learned Society for Pulmonology (Société de Pneumologie de Langue Française, CEPRO 2012 009).

Clinical, nutritional, biological, PFT, and CPET data were obtained on the same day, either at diagnosis or at the routine annual evaluation, and were retrospectively collected. When patients were seen at several follow-up visits, only the data from the first visit were recorded. Hypoxemic patients did not perform CPET and were excluded from the analysis. A diagnosis of CF was obtained by sweat chloride > 60 mmol/L and the presence of CFTR gene mutations by molecular analysis. Additional characteristics recorded at the time of testing were diseases usually associated with CF, bacterial colonization, treatments, and nutritional status, including height and weight measurements and impedance analysis.

Cardiopulmonary testing

Forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), FEV1 to FVC ratio, total lung capacity (TLC), and residual volume (RV) were measured by plethysmography (Jaeger-Masterlab® cabin). Diffusing capacity of the lung for carbon monoxide (DLCO: mL CO/min/mm Hg) was adjusted for hemoglobin concentration in g/dL according to Cotes’ equation: corrected (Hb) DLCO = DLCO × (10.2 + Hb)/(1.7 × Hb). Following ATS/ERS 2005 guidelines, the lower limits of normal were set at the 5th percentile (or predicted minus 1.64 SD) of each reference population. The results are expressed as percentages of the predicted values. Predicted normal values were derived from standard equations [20–22].

PFTs and CPET were performed in an air-conditioned laboratory (22°C constant temperature), using a standardized protocol as previously described [23, 24]. The CPET protocol was the same at each center. Each patient underwent a symptom-limited incremental exercise test on an ergometric bicycle (Ergoline-Ergometrics 800®). The protocol included a warm-up period of 3 min at 20 W followed by a progressively increasing work rate (WR) in a ramp fashion and then 3 min recovery. The ramped WR increment was individualized (range, 8–30 W/min). During exercise, heart rate (HR) was monitored continuously by 12-lead ECG, and arterial oxygen saturation (SpO2) was measured by pulse oximetry (Nellcor N-395). The expired gases were analyzed with an Ergocard®, focusing on oxygen consumption (VO2), carbon dioxide production (VCO2), minute ventilation (VE), and tidal volume (VT). The oxygen pulse (VO2/HR) was calculated. Measurements of PaO2 and PaCO2 were performed on room air at rest and at peak exercise. Normal values for PaO2 were derived from [25]. Lactatemia was determined at maximal exercise. Breathing reserve (BR) was calculated as BR = (predicted maximum minute ventilation [MMV] – VE peak)/MMV, with MMV estimated from MMV = FEV1 × 40. HR peak was expressed as a percentage of maximum predicted HR, calculated as HR max = 210 – (0.65 × age). Dead space (VD/VT) was calculated according to Bohr’s equation corrected for the additional instrument dead space: VD/VT = (PaCO2 – PECO2 mean)/PaCO2 – (VD [machine]/VT) where PECO2 is the partial pressure of expired CO2. Predicted values for VO2 max were calculated from reference equations [26]. Poor motivation appeared not to be an interfering factor in our analysis, as all patients had at least one of the following: BR < 15%, peak HR > 90% of predicted, peak lactate > 7 mmol/L, peak exercise PaO2 < 55 mm Hg, and peak VE/VO2 > 35 or peak RER > 1.15 [23]. Immediately after exercise, subjects were asked to score their sense of breathlessness and muscle fatigue at peak exercise using Borg scales.

Statistical analysis

The continuous variables are reported as mean ± SD. Normal distribution of quantitative variables was tested by the Shapiro–Wilk test. Differences in FEV1 between the groups were determined with the Student’s t-test or Mann–Whitney test. Bivariate analyses were performed to study the relationships between each explanatory variable and the VO2 peak. Pearson’s or Spearman’s correlation coefficient was used for quantitative variables, and the Student’s t-test or Mann–Whitney test for qualitative variables. In addition, a multivariable linear regression with a stepwise selection at the level 0.2 was performed to identify a subset of the most important explanatory variables for the relationship with VO2 peak. In order to avoid the problem of multicolinearity which happens when the explanatory variables are highly correlated, and to obtain a parsimonious model, we adopt the following strategy: first, variables with p < 0.2 were selected and included in a Principal Component Analysis (PCA) in order to study their correlations. Then, the variables included in the multivariable regression model were selected by the results of PCA (graphic correlation circle) on the basis of their clinical pertinence. The stability of the model was assessed by a bootstrap method [27]. The bootstrap resampling method was based on 1000 replicates of the initial dataset. Multivariable regression with a stepwise selection at the level 0.2 was performed on each of these replicates. The inclusion of the variable in the final model was confirmed if this candidate variable was selected in at least 70% of these 1000 analyses. In the final model, for each variable we computed the partial R-square, coefficient, 95% confidence intervals and adjusted p-value. Final variables from the multivariate analysis were applied to each group of FEV1. All analyses were achieved with SAS software version 9.2 (SAS Institute Inc., Cary, NC). All tests were performed at the significant level at 0.05.

Results

Subjects

The demographic and clinical characteristics are shown in Table 1, and the resting PFT results are presented in Table 2. The cohort consisted of 102 CF patients with a mean age of 28 ± 11 years (range 17–67). The time from diagnosis to evaluation was 16 ± 10 years. For data analysis, the cohort was divided into two groups according to their FEV1: group 1 patients with severe lung disease (FEV1 < 50%, 48 patients) and group 2 patients with mild-to-moderate lung disease (FEV1 ≥ 50%, 54 patients). Group 1 had a significantly higher frequency of homozygosity for the CFTR ∆F 508 mutation, pancreatic insufficiency, bronchial colonization with Pseudomonas aeruginosa, and biological inflammatory syndrome, and significantly lower body mass index (BMI) and longer disease duration (data not shown).

Table 1 Demographic and clinical characteristics of the CF patients
Full size table
Table 2 Resting pulmonary function tests in CF patients classified according to FEV 1
Full size table

The patients had a range of disease severities and were recruited from four centers in France. Patients from different centers all had characteristics consistent with the French CF Registry 2009 Report [28] and showed equivalent frequencies of key CF characteristics (∆F508 mutation, exocrine pancreatic insufficiency, and colonization with P. aeruginosa), supporting the reproducibility of the results and the potential for extrapolation to other patient populations.

Resting PFT values (Table 2) were significantly more altered in group 1 than in group 2 (Table 2). As expected, three major functional abnormalities were found: obstructive syndrome (FEV1/FVC = 65 ± 15% of predicted), altered distension (RV = 176 ± 65% of predicted), and altered DLCO (68 ± 18% of predicted).

Exercise responses

Exercise cessation was mainly due to leg fatigue in combination with dyspnea (62%), whereas leg fatigue alone or dyspnea alone was observed in 17% and 21% of patients, respectively. The VO2 peak value (weight-adjusted VO2) was decreased to < 84% of predicted in 83/102 (82%) of patients (48/48 [100%] in group 1 and 35/54 [65%] in group 2) and was significantly lower in group 1 than in group 2 (Table 3).

Table 3 Cardiopulmonary exercise tests in CF patients classified according to FEV 1
Full size table

Analysis of the ventilatory response (VE peak, BR, respiratory rate (RR), VT/FVC peak) highlighted the differences according to FEV1 impairment (Table 3). Group 1 had a lower absolute value of VE at peak exercise, and a depletion of BR. Hyperventilation was due to simultaneous increases in RR and tidal volume. Impairment in pulmonary gas exchange was more severe in group 1, as shown by higher values of P(A-a)O2, VD/VT peak, and PaCO2, and lower values of PaO2. Cardiocirculatory responses were normal in group 2, but patients in group 1 showed low VO2/HR values and a significant decrease in peak HR. Four patients experienced ECG abnormalities but continued with the exercise test.

Determinants of exercise capacity

Significant correlations were observed between VO2 peak and nutritional status (BMI, lean body mass), inflammation markers (C-reactive protein [CRP], leukocytosis), resting PFT (FVC, FEV1, RV, DLCO, P(A-a)O2), and quantifiable parameters of CPET (VE peak, VE/VO2 peak, VE/VCO2 peak, BR, VD/VT peak, PaO2 peak, P(A-a)O2 peak, and HR (Table 4 and Figure 1).

Figure 1
figure 1

Correlation between VO 2 peak and FEV 1 , FVC, VE/CO 2 peak, BR, P(A-a)O 2 peak, and V D /V T peak in CF patients. VO2 peak, FEV1, FVC, and BR are expressed as percentage of predicted values. P(A-a)O2 peak is expressed as mm Hg.

Full size image
Table 4 Correlation of clinical and functional variables with VO 2 peak in CF patients
Full size table

The results of the stepwise multiple regression analysis for determinants of exercise capacity are shown in Table 5. Of the variables entered into the model (BMI, FEV1, FVC, DLCO, PaO2, VE/VCO2 peak, BR, VD/VT peak, PaO2 peak, PaCO2 peak, P(A-a)O2 peak, and lactatemia peak), only FEV1, VE/VCO2 peak, and BR were found to be independent predictors of exercise capacity (r2 = 0.67). Analysis of these three variables showed that, for group 1, 31% of the VO2 peak was explained by FEV1, whereas the major determinants of the VO2 peak in group 2 were BR , FEV1 and VE/VCO2 peak (Table 5).

Table 5 Determinants of VO 2 peak in CF patients
Full size table

Separate analysis in the cohort of Lille (75 out of the 102 patients) showed the same results: FEV1, BR and VE/CO2 were independent predictors of exercise capacity (r2 = 0.65) (data not shown).

Discussion

Our study focused on a population of 102 adults with CF who underwent CPET with blood gas analysis at peak exercise. Maximal oxygen uptake was impaired in 82% of patients and was more pronounced in patients with low FEV1. We noted a high prevalence of abnormal exercise responses in our population, including abnormal gas exchange, ventilatory and cardiocirculatory responses, and peripheral limitation. The main findings from this study are that exercise intolerance in CF is multifactorial and is correlated mainly with resting pulmonary function, nutritional status, and inflammatory status, but is also affected by the magnitude of the overall ventilatory response during exercise. Multivariate analysis revealed that bronchial obstruction plays a dominant role in patients with severe disease, whereas excessive hyperventilation during exercise was the major determinant of exercise limitation in patients with mild-to-moderate disease.

CF can be associated with abnormal gas exchange, ventilatory, cardiocirculatory, and muscular responses to exercise [3, 9, 13, 29]. In our study, these abnormalities were responsible for limiting the aerobic capacity of 82% of patients, a proportion consistent with previous studies of adult CF patients [5, 12, 30]. We did not observe a single exercise profile common to all patients, reflecting the complexity of mechanisms involved in exercise limitation in CF patients. Some patients showed abnormalities predominantly in gas exchange, others in the ventilatory response. Still others experienced exercise intolerance despite the absence of ventilatory limitation. The relative contribution of these factors differed between the two groups.

In our study, BMI and CRP levels were strongly correlated with exercise limitation, which is consistent with several studies indicating the importance of inflammatory and nutritional status in exercise limitation. Nutritional status plays a well-established role in CF exercise intolerance [31] and prognosis [32], and may be linked to the chronic inflammation observed in CF patients, which is mainly due to respiratory colonization [33]. Inflammatory markers such as CRP are also negatively associated with exercise capacity in patients with CF [7]. Moreover, inflammation is experimentally correlated with loss of muscle mass [34] and skeletal muscle weakness [10] and could explain the association observed here between CRP, lean body mass, and reduced maximal oxygen uptake.

Multivariate analysis showed that FEV1 was the most significant predictor of VO2 peak in patients with severe lung disease. This result is consistent with data from earlier studies [3, 35] and demonstrates the predominant role of ventilatory disorders in exercise limitation in severe CF patients. Additional functional parameters, such as distension, obstruction, and CO diffusion also correlated with VO2 peak, but were not independent predictors. The low BR exhibited by our population is another characteristic of the exercise response in severe CF patients. Tantisira et al. showed that the BR index (VE/maximal voluntary ventilation calculated at ventilatory threshold) was the most powerful predictor of mortality in CF patients awaiting lung transplantation [36]. This has also been observed in COPD [37] but is not common to all obstructive lung diseases. For example, McNicholl et al. reported that only 18% of severe asthma patients had ventilatory limitation due to obstructive lung function [38].

In contrast, the VO2 peak was not fully explained by FEV1 in patients with mild-to-moderate lung disease, and some patients exhibited impaired aerobic capacity despite having normal resting lung function (Figure 1). Indeed, multivariate analysis showed that two CPET parameters were the major independent determinants of VO2 peak in group 2: hyperventilation due to abnormal ventilatory control, resulting in high ventilatory equivalents (as demonstrated by VE/VO2 and VE/VCO2 peaks), and BR depletion. Exercise ventilation is regulated by numerous mechanisms, most of which remain incompletely understood [39]. Hyperventilation during exercise reflects a nonspecific response to one or more dysfunctional links in the respiratory chain, but the main cause is not known [40]. In some diseases, such as heart failure, hyperventilation is recognized as a more relevant prognostic factor than VO2 peak. The hyperventilatory response may be due to several factors, including inefficient gas exchange as reflected by P(A-a)O2 and the VD/VT ratio. Although hyperventilation is difficult to relate to other abnormalities, the strong correlation of hyperventilation with oxygen pulse and peak lactatemia suggests that central (cardiovascular) and peripheral (muscle) determinants play a role [10].

In our study, all patients underwent blood gas analysis at peak effort and we noted a high prevalence of gas exchange abnormalities during exercise. It is interesting to note that patients with identical lung function did not all show gas exchange abnormalities. This could be explained by an inadequate ventilatory response in some patients or by a high degree of ventilation-perfusion mismatch. Exercise-induced hypoxemia was common in our study and correlated with VO2 peak, workload, peak VD/VT, and dyspnea assessed by the Borg scale (results not shown). We found that P(A-a)O2 correlated well with peak VO2, highlighting the relevance of this parameter in gas exchange analysis. Other studies have examined impairment of gas exchange during exercise in CF patients. Nixon et al. showed that PETCO2 > 41 mm Hg at peak exercise is associated with a twofold higher relative risk of mortality [4]. However, PETCO2 is not a reliable marker for PaCO2 during exercise and does not allow accurate calculation of dead space [41]. Compared with PFT, CPET with blood gas analysis at peak exercise is better able to assess gas exchange abnormalities and highlight exercise hypoxemia, a recognized prognosis marker, and thus gauge the need for oxygen supplementation.

The primary limitation of our study is its retrospective nature and the possibility of missing data. Peripheral muscle strength was not assessed and might be a significant contributing factor [10]. These results should be confirmed by a prospective study.

Conclusion

In conclusion, exercise limitation in adult patients with CF correlates with respiratory function as well as nutritional and inflammatory status. This limitation is dependent on FEV1 in patients with severe disease but is mainly affected by the magnitude of the ventilatory response to exercise in patients with mild-to-moderate lung disease. CPET thus contributes to a more comprehensive understanding of exercise limitation and can assist in patient management aimed at improving exercise capacity.

References

  1. Marcotte JE, Grisdale RK, Levison H, Coates AL, Canny GJ: Multiple factors limit exercise capacity in cystic fibrosis. Pediatr Pulmonol. 1986, 2: 274-281. 10.1002/ppul.1950020505.

    Article  CAS  PubMed  Google Scholar 

  2. Boucher GP, Lands LC, Hay JA, Hornby L: Activity levels and the relationship to lung function and nutritional status in children with cystic fibrosis. Am J Phys Med Rehabil. 1997, 76: 311-315. 10.1097/00002060-199707000-00010.

    Article  CAS  PubMed  Google Scholar 

  3. Lands LC, Heigenhauser GJ, Jones NL: Analysis of factors limiting maximal exercise performance in cystic fibrosis. Clin Sci. 1992, 83: 391-397.

    Article  CAS  PubMed  Google Scholar 

  4. Nixon PA, Orenstein DM, Kelsey SF, Doershuk CF: The prognostic value of exercise testing in patients with cystic fibrosis. N Engl J Med. 1992, 327: 1785-1788. 10.1056/NEJM199212173272504.

    Article  CAS  PubMed  Google Scholar 

  5. Moorcroft AJ, Dodd ME, Webb AK: Exercise testing and prognosis in adult cystic fibrosis. Thorax. 1997, 52: 291-293. 10.1136/thx.52.3.291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Pianosi P, Leblanc J, Almudevar A: Peak oxygen uptake and mortality in children with cystic fibrosis. Thorax. 2005, 60: 50-54. 10.1136/thx.2003.008102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Van de Weert-van Leeuwen PB, Slieker MG, Hulzebos HJ, Kruitwagen CLJJ, van der Ent CK, Arets HGM: Chronic infection and inflammation affect exercise capacity in cystic fibrosis. Eur Respir J. 2012, 39: 893-898. 10.1183/09031936.00086211.

    Article  CAS  PubMed  Google Scholar 

  8. Bilton D, Dodd ME, Abbot JV, Webb AK: The benefits of exercise combined with physiotherapy in the treatment of adults with cystic fibrosis. Respir Med. 1992, 86: 507-511. 10.1016/S0954-6111(96)80012-6.

    Article  CAS  PubMed  Google Scholar 

  9. Almajed A, Lands LC: The evolution of exercise capacity and its limiting factors in Cystic Fibrosis. Paediatr Respir Rev. 2012, 13: 195-199. 10.1016/j.prrv.2012.01.001.

    Article  PubMed  Google Scholar 

  10. Troosters T, Langer D, Vrijsen B, Segers J, Wouters K, Janssens W, Gosselink R, Decramer M, Dupont L: Skeletal muscle weakness, exercise tolerance and physical activity in adults with cystic fibrosis. Eur Respir J. 2009, 33: 99-106. 10.1183/09031936.00091607.

    Article  CAS  PubMed  Google Scholar 

  11. Selvadurai HC, Allen J, Sachinwalla T, Macauley J, Blimkie CJ, Van Asperen PP: Muscle function and resting energy expenditure in female athletes with cystic fibrosis. Am J Respir Crit Care Med. 2003, 168: 1476-1480. 10.1164/rccm.200303-363OC.

    Article  PubMed  Google Scholar 

  12. Cerny FJ, Pullano TP, Cropp GJ: Cardiorespiratory adaptations to exercise in cystic fibrosis. Am Rev Respir Dis. 1982, 126: 217-220.

    CAS  PubMed  Google Scholar 

  13. Shah AR, Gozal D, Keens TG: Determinants of aerobic and anaerobic exercise performance in cystic fibrosis. Am J Respir Crit Care Med. 1998, 157: 1145-1150. 10.1164/ajrccm.157.4.9705023.

    Article  CAS  PubMed  Google Scholar 

  14. Lebecque P, Lapierre JG, Lamarre A, Coates AL: Diffusion capacity and oxygen desaturation effects on exercise in patients with cystic fibrosis. Chest. 1987, 91: 693-697. 10.1378/chest.91.5.693.

    Article  CAS  PubMed  Google Scholar 

  15. Bradley S, Solin P, Wilson J, Johns D, Walters EH, Naughton MT: Hypoxemia and hypercapnia during exercise and sleep in patients with cystic fibrosis. Chest. 1999, 116: 647-654. 10.1378/chest.116.3.647.

    Article  CAS  PubMed  Google Scholar 

  16. McKone EF, Barry SC, Fitzgerald MX, Gallagher CG: Role of arterial hypoxemia and pulmonary mechanics in exercise limitation in adults with cystic fibrosis. J Appl Physiol. 2005, 99: 1012-1018. 10.1152/japplphysiol.00475.2004.

    Article  PubMed  Google Scholar 

  17. Marcus CL, Bader D, Stabile MW, Wang CI, Osher AB, Keens TG: Supplemental oxygen and exercise performance in patients with cystic fibrosis with severe pulmonary disease. Chest. 1992, 101: 52-57. 10.1378/chest.101.1.52.

    Article  CAS  PubMed  Google Scholar 

  18. ATS/ACCP: Statement on cardiopulmonary exercise testing. Am J Respir Crit Care Med. 2003, 167: 211-277.

    Article  Google Scholar 

  19. Stevens D, Oades PJ, Armstrong N, Williams CA: A survey of exercise testing and training in UK cystic fibrosis clinics. J Cyst Fibros. 2010, 9: 302-306. 10.1016/j.jcf.2010.03.004.

    Article  CAS  PubMed  Google Scholar 

  20. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CPM, Gustafsson P, Jensen R, Johnson DC, MacIntyre N, McKay R, Navajas D, Pedersen OF, Pellegrino R, Viegi G, Wanger J: Standardisation of spirometry. Eur Respir J. 2005, 26: 319-338. 10.1183/09031936.05.00034805.

    Article  CAS  PubMed  Google Scholar 

  21. Macintyre N, Crapo RO, Viegi G, Johnson DC, van der Grinten CPM, Brusasco V, Burgos F, Casaburi R, Coates A, Enright P, Gustafsson P, Hankinson J, Jensen R, McKay R, Miller MR, Navajas D, Pedersen OF, Pellegrino R, Wanger J: Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J. 2005, 26: 720-735. 10.1183/09031936.05.00034905.

    Article  CAS  PubMed  Google Scholar 

  22. Wanger J, Clausen JL, Coates A, Pedersen OF, Brusasco V, Burgos F, Casaburi R, Crapo R, Enright P, van der Grinten CPM, Gustafsson P, Hankinson J, Jensen R, Johnson D, Macintyre N, McKay R, Miller MR, Navajas D, Pellegrino R, Viegi G: Standardisation of the measurement of lung volumes. Eur Respir J. 2005, 26: 511-522. 10.1183/09031936.05.00035005.

    Article  CAS  PubMed  Google Scholar 

  23. Aguilaniu B, Richard R, Costes F, Bart F, Martinat Y, Stach B, Aguilaniu B, Richard R, Costes F, Bart F, Martinat Y, Stach B, Denjean A, Scientific Council of the French Lung Society: [Cardiopulmonary exercise testing]. Rev Mal Respir. 2007, 24: 2S111-2S160.

    Article  CAS  PubMed  Google Scholar 

  24. Wallaert B, Talleu C, Wemeau-Stervinou L, Duhamel A, Robin S, Aguilaniu B: Reduction of maximal oxygen uptake in sarcoidosis: relationship with disease severity. Respiration. 2011, 82: 501-508. 10.1159/000330050.

    Article  CAS  PubMed  Google Scholar 

  25. Sorbini CA, Grassi V, Solinas E, Muiesan G: Arterial oxygen tension in relation to age in healthy subjects. Respiration. 1968, 25: 3-13. 10.1159/000192549.

    Article  CAS  PubMed  Google Scholar 

  26. Hansen JE, Sue DY, Wasserman K: Predicted values for clinical exercise testing. Am Rev Respir Dis. 1984, 129: S49-S55.

    Article  CAS  PubMed  Google Scholar 

  27. Sauerbrei W: The Use of Resampling Methods to Simplify Regression Models in Medical Statistics. Journal of the Royal Statistical Society: Series C (Applied Statistics). 1999, 48: 313-329. 10.1111/1467-9876.00155.

    Article  Google Scholar 

  28. Vaincre La Mucoviscidose. http://www.vaincrelamuco.org/. 2011 Rapport annuel. 2012

  29. Leroy S, Perez T, Neviere R, Aguilaniu B, Wallaert B: Determinants of dyspnea and alveolar hypoventilation during exercise in cystic fibrosis: impact of inspiratory muscle endurance. J Cyst Fibros. 2011, 10: 159-165. 10.1016/j.jcf.2010.12.006.

    Article  PubMed  Google Scholar 

  30. Godfrey S, Mearns M: Pulmonary function and response to exercise in cystic fibrosis. Arch Dis Child. 1971, 46: 144-151. 10.1136/adc.46.246.144.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gulmans VA, de Meer K, Brackel HJ, Helders PJ: Maximal work capacity in relation to nutritional status in children with cystic fibrosis. Eur Respir J. 1997, 10: 2014-2017. 10.1183/09031936.97.10092014.

    Article  CAS  PubMed  Google Scholar 

  32. Nguyen S, Leroy S, Cracowski C, Perez T, Valette M, Neviere R, Aguilaniu B, Wallaert B: Prognostic value of clinical exercise testing in adult patients with cystic fibrosis. Rev Mal Respir. 2010, 27: 219-225. 10.1016/j.rmr.2010.01.009.

    Article  CAS  PubMed  Google Scholar 

  33. Van de Weert-van Leeuwen PB, Arets HGM, van der Ent CK, Beekman JM: Infection, inflammation and exercise in cystic fibrosis. Respir Res. 2013, 14: 32-10.1186/1465-9921-14-32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Van Heeckeren AM, Tscheikuna J, Walenga RW, Konstan MW, Davis PB, Erokwu B, Haxhiu MA, Ferkol TW: Effect of Pseudomonas infection on weight loss, lung mechanics, and cytokines in mice. Am J Respir Crit Care Med. 2000, 161: 271-279. 10.1164/ajrccm.161.1.9903019.

    Article  CAS  PubMed  Google Scholar 

  35. Klijn PHC, van der Net J, Kimpen JL, Helders PJM, van der Ent CK: Longitudinal determinants of peak aerobic performance in children with cystic fibrosis. Chest. 2003, 124: 2215-2219. 10.1378/chest.124.6.2215.

    Article  PubMed  Google Scholar 

  36. Tantisira KG, Systrom DM, Ginns LC: An elevated breathing reserve index at the lactate threshold is a predictor of mortality in patients with cystic fibrosis awaiting lung transplantation. Am J Respir Crit Care Med. 2002, 165: 1629-1633. 10.1164/rccm.2105090.

    Article  PubMed  Google Scholar 

  37. Medoff BD, Oelberg DA, Kanarek DJ, Systrom DM: Breathing reserve at the lactate threshold to differentiate a pulmonary mechanical from cardiovascular limit to exercise. Chest. 1998, 113: 913-918. 10.1378/chest.113.4.913.

    Article  CAS  PubMed  Google Scholar 

  38. McNicholl DM, Megarry J, McGarvey LP, Riley MS, Heaney LG: The utility of cardiopulmonary exercise testing in difficult asthma. Chest. 2011, 139: 1117-1123. 10.1378/chest.10-2321.

    Article  PubMed  Google Scholar 

  39. Dempsey JA: Challenges for future research in exercise physiology as applied to the respiratory system. Exerc Sport Sci Rev. 2006, 34: 92-98. 10.1249/00003677-200607000-00002.

    Article  PubMed  Google Scholar 

  40. Péronnet F, Aguilaniu B: Lactic acid buffering, nonmetabolic CO2 and exercise hyperventilation: a critical reappraisal. Respir Physiol Neurobiol. 2006, 150: 4-18. 10.1016/j.resp.2005.04.005.

    Article  PubMed  Google Scholar 

  41. Lewis DA, Sietsema KE, Casaburi R, Sue DY: Inaccuracy of noninvasive estimates of VD/VT in clinical exercise testing. Chest. 1994, 106: 1476-1480. 10.1378/chest.106.5.1476.

    Article  CAS  PubMed  Google Scholar 

Pre-publication history

Download references

Acknowledgments

The authors wish to thank Anne M. O’Rourke for editing of the manuscript.

Collaborators (to be referenced in PubMed): T. Perez (Lille), L. Wémeau-Stervinou (Lille), Abderrahmane Mammar (Grenoble), J.M. Perruchini (Dijon).

Author information

Authors and Affiliations

  1. Université Lille 2 et Clinique des Maladies Respiratoires, CRCM Hôpital Calmette, CHRU Lille, France

    Jean Pastré, Anne Prévotat & Benoit Wallaert

  2. Service de Physiologie Respiratoire, CHRU Rouen, France

    Catherine Tardif

  3. Unité de Biostatistiques, CHRU Lille, France

    Carole Langlois & Alain Duhamel

Authors
  1. Jean Pastré
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anne Prévotat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Catherine Tardif
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carole Langlois
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alain Duhamel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Benoit Wallaert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benoit Wallaert.

Additional information

Competing interests

For each author, no significant competing interest exists with any companies or organisations whose products or services are mentioned in this article. The authors declare that they have no competing interests.

Authors’ contributions

Conception and design: BW, JP and AP; Analysis and interpretation: BW, JP, AP, CT, CL and AD; Drafting the manuscript for important intellectual content: BW, JP, AP, and CL. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pastré, J., Prévotat, A., Tardif, C. et al. Determinants of exercise capacity in cystic fibrosis patients with mild-to-moderate lung disease. BMC Pulm Med 14, 74 (2014). https://doi.org/10.1186/1471-2466-14-74

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1471-2466-14-74

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

Contact us