In search of complexity
2010; American Physiological Society; Volume: 109; Issue: 6 Linguagem: Inglês
10.1152/japplphysiol.01102.2010
ISSN8750-7587
Autores Tópico(s)Inhalation and Respiratory Drug Delivery
ResumoINVITED EDITORIALIn search of complexityBéla SukiBéla SukiDepartment of Biomedical Engineering, Boston University, Boston, MassachusettsPublished Online:01 Dec 2010https://doi.org/10.1152/japplphysiol.01102.2010This is the final version - click for previous versionMoreSectionsPDF (45 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat the last decade of intense research brought new understanding of chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). Indeed, in vitro studies at the molecular level shed light on how airway smooth muscle contracts (12) and how inflammation affects muscle function in the asthmatic airway (16). Similarly, new molecular markers and inflammatory signaling cascades have been identified that appear to be involved in the pathogenesis of COPD (19). These are great achievements of the reductionist approach; yet the results are difficult to translate into clinical practice (13).Despite the lack of immediate clinical implications, one would expect that these molecular mechanisms aid physiological understanding. However, the various molecular and cellular mechanisms identified in isolation may or may not be relevant for the in vivo situation (3, 11). The reasons are that, among others, local inflammation (4), inhaled agonist (22) and particles (5), neural tone (9), the extracellular matrix (23), and lung volume (14) could all contribute to the constriction of a single airway segment. In an airway network, flow, agonist distribution, and binding can interact in a complex manner (2), leading to stochastic fluctuations with sudden catastrophic shifts in the pattern of airway tree narrowing and hence asthma attack (21). The complexity associated with all these interacting factors produces significant fluctuations in physiological parameters at the organ level, which make it virtually impossible to predict an asthma attack or a COPD exacerbation (8).It is now well recognized that fluctuations in physiological function of various organ systems often show signs of complex behavior including long-range fractal correlations or power-law distributions of the time series of a given variable, which in turn can contain useful and clinically relevant information (10, 20). Consequently, researchers began to use stochastic and nonlinear approaches to analyze various data recordings. There is a rich literature on heart rate variability with important impact on clinical practice (1). For example, normal heart rate displays significant variability with long-range correlations and the breakdown of this behavior signifies disease or an acute emergency situation (17). Variability analysis of the respiratory system is just beginning to catch up. The first important contribution to variability in respiratory mechanical properties was from Que et al. (18) who found significant differences between short-term respiratory impedance variability in normal and asthmatic subjects. Subsequently, Frey et al. (7) reported that long-term variability of peak expiratory flow shows long-range correlations and the authors also proposed a risk predictor for asthma exacerbations. However, the results of Que et al. (18) could not be reproduced by Diba et al. (6), while the long recording required for risk prediction (7) may limit its clinical applicability. Clearly, the community should seek new methods to characterize physiological complexity that are more suitable to extract clinically useful information from respiratory data obtained over shorter time periods.A study published in this issue of the Journal of Applied Physiology by Muskulus et al. (15) represents a major step in this direction. The authors bring a number of impressive techniques to answer the question: Can short-term variability in respiratory resistance distinguish among healthy, asthmatic, and COPD subjects? The results are interesting: based on 12 min of respiratory impedance data, stochastic approaches are found not to be able to provide a clear answer, whereas nonlinear time series analysis that considers the data coming from a deterministic nonlinear system was able to reliably distinguish among the groups with relatively high specificity and sensitivity. Furthermore, the fluctuations in resistance were consistent with a power-law distribution in more than one-half of the subjects. This might explain the discrepancy between the Que et al. (18) and Diba et al. (6) studies. However, quite surprisingly, detrended fluctuation analysis suggested no correlations in the data as if the fluctuations were totally random. This seems to be at odds with the nonlinear analysis that considers the system as deterministic as well as with the long-range correlations found in peak expiratory flows (7). One should realize, however, that chaotic systems can appear unpredictable beyond a given time scale although it has not been definitively shown that the respiratory system is chaotic. Additionally, the time scales in the Muskulus et al. (15) and the Frey et al. (7) studies were orders of magnitude different and the peak flows probe the system differently than the resistance measured by the forced oscillation technique.The study by Muskulus et al. (15) makes an excellent contribution to the young field of variability and shows us that we must push the frontier ahead in search of complexity in respiratory physiology. Future efforts should aim at understanding the relation between the deterministic and stochastic approaches, extending the time window, repeating the measurements at different days, and correlating variability-derived indexes with inflammatory markers. While the relation among molecular mechanisms, environmental factors, and organ level fluctuations of measurable parameters is far from clear, variability analysis has the potential to make an immediate impact on clinical science and practice by aiding the classification of patients and providing valuable risk predictors on an individual basis that can help in evaluating treatment efficacy or disease control.DISCLOSURESNo conflicts of interest, financial or otherwise, are declared by the author.REFERENCES1. Ahmad S , Tejuja A , Newman KD , Zarychanski R , Seely AJ. Clinical review: a review and analysis of heart rate variability and the diagnosis and prognosis of infection. Crit Care 13: 232, 2009.Crossref | ISI | Google Scholar2. Amin SD , Majumdar A , Frey U , Suki B. Modeling the dynamics of airway constriction: effects of agonist transport and binding. J Appl Physiol 109: 553–563, 2010.Link | ISI | Google Scholar3. Bates JH. The multiscale manifestations of airway smooth muscle contraction in the lung. J Appl Physiol 109: 269–270, 2010.Link | ISI | Google Scholar4. Braun A , Lommatzsch M , Lewin GR , Virchow JC , Renz H. Neurotrophins: a link between airway inflammation and airway smooth muscle contractility in asthma? Int Arch Allergy Immunol 118: 163–165, 1999.Crossref | PubMed | ISI | Google Scholar5. Churg A , Wright JL. Bronchiolitis caused by occupational and ambient atmospheric particles. Semin Respir Crit Care Med 24: 577–584, 2003.Crossref | ISI | Google Scholar6. Diba C , Salome CM , Reddel HK , Thorpe CW , Toelle B , King GG. Short-term variability of airway caliber-a marker of asthma? J Appl Physiol 103: 296–304, 2007.Link | ISI | Google Scholar7. Frey U , Brodbeck T , Majumdar A , Taylor DR , Town GI , Silverman M , Suki B. Risk of severe asthma episodes predicted from fluctuation analysis of airway function. Nature 438: 667–670, 2005.Crossref | ISI | Google Scholar8. Frey U , Suki B. Complexity of chronic asthma and chronic obstructive pulmonary disease: implications for risk assessment, and disease progression and control. Lancet 372: 1088–1099, 2008.Crossref | ISI | Google Scholar9. Gleason NR , Gallos G , Zhang Y , Emala CW. The GABAA agonist muscimol attenuates induced airway constriction in guinea pigs in vivo. J Appl Physiol 106: 1257–1263, 2009.Link | ISI | Google Scholar10. Goldberger AL , Amaral LA , Hausdorff JM , Ivanov P , Peng CK , Stanley HE. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 99, Suppl 1: 2466–2472, 2002.Crossref | PubMed | ISI | Google Scholar11. Laprad AS , Szabo TL , Suki B , Lutchen KR. Tidal stretches do not modulate responsiveness of intact airways in-vitro. J Appl Physiol 109: 295–304, 2010.Link | ISI | Google Scholar12. Leguillette R , Lauzon AM. Molecular mechanics of smooth muscle contractile proteins in airway hyperresponsiveness and asthma. Proc Am Thorac Soc 5: 40–46, 2008.Crossref | PubMed | Google Scholar13. Macklem PT. The molecular-clinical divorce. Am J Respir Crit Care Med 168: 500, 2003.Crossref | ISI | Google Scholar14. Macklem PT. A theoretical analysis of the effect of airway smooth muscle load on airway narrowing. Am J Respir Crit Care Med 153: 83–89, 1996.Crossref | PubMed | ISI | Google Scholar15. Muskulus M , Slats AM , Sterk PJ , Verduyn-Lunel S. Fluctuations and determinism of respiratory impedance in asthma and chronic obstructive pulmonary disease. J Appl Physiol; doi:10.1152/japplphysiol.0144.2009.ISI | Google Scholar16. Panettieri RA. Asthma persistence versus progression: does airway smooth muscle function predict irreversible airflow obstruction? Allergy Asthma Proc 30: 103–108, 2009.Crossref | ISI | Google Scholar17. Peng CK , Havlin S , Hausdorff JM , Mietus JE , Stanley HE , Goldberger AL. Fractal mechanisms and heart rate dynamics. Long-range correlations and their breakdown with disease. J Electrocardiol 28, Suppl: 59–65, 1995.Crossref | ISI | Google Scholar18. Que CL , Kenyon CM , Olivenstein R , Macklem PT , Maksym GN. Homeokinesis and short-term variability of human airway caliber. J Appl Physiol 91: 1131–1141, 2001.Link | ISI | Google Scholar19. Stockley RA , Mannino D , Barnes PJ. Burden and pathogenesis of chronic obstructive pulmonary disease. Proc Am Thorac Soc 6: 524–526, 2009.Crossref | PubMed | Google Scholar20. Suki B. Fluctuations and power laws in pulmonary physiology. Am J Respir Crit Care Med 166: 133–137, 2002.Crossref | ISI | Google Scholar21. Venegas JG , Winkler T , Musch G , Vidal Melo MF , Layfield D , Tgavalekos N , Fischman AJ , Callahan RJ , Bellani G , Harris RS. Self-organized patchiness in asthma as a prelude to catastrophic shifts. Nature 434: 777–782, 2005.Crossref | PubMed | ISI | Google Scholar22. Wagers SS , Haverkamp HC , Bates JH , Norton RJ , Thompson-Figueroa JA , Sullivan MJ , Irvin CG. Intrinsic and antigen-induced airway hyperresponsiveness are the result of diverse physiological mechanisms. J Appl Physiol 102: 221–230, 2007.Link | ISI | Google Scholar23. Zhang W , Gunst SJ. Interactions of airway smooth muscle cells with their tissue matrix: implications for contraction. Proc Am Thorac Soc 5: 32–39, 2008.Crossref | PubMed | Google ScholarAUTHOR NOTESAddress for reprint requests and other correspondence: B. Suki, 44 Cummington St., Boston, MA 02215 (e-mail: [email protected]edu). Download PDF Previous Back to Top Next FiguresReferencesRelatedInformationCited ByEntropy Analysis for the Evaluation of Respiratory Changes Due to Asbestos Exposure and Associated Smoking27 February 2019 | Entropy, Vol. 21, No. 3Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern29 January 2016 | PLOS ONE, Vol. 11, No. 1Airflow pattern complexity during resting breathing in patients with COPD: Effect of airway obstructionRespiratory Physiology & Neurobiology, Vol. 192Airflow pattern complexity and airway obstruction in asthmaJuliana Veiga, Agnaldo J. Lopes, José M. Jansen, and Pedro L. Melo1 August 2011 | Journal of Applied Physiology, Vol. 111, No. 2 More from this issue > Volume 109Issue 6December 2010Pages 1571-1572 Copyright & PermissionsCopyright © 2010 the American Physiological Societyhttps://doi.org/10.1152/japplphysiol.01102.2010PubMed20847129History Published online 1 December 2010 Published in print 1 December 2010 Metrics
Referência(s)