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Pulmonary diffusion limitation, V̇ /Q̇ mismatch and pulmonary transit time in highly trained athletes.. 1992

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PULMONARY DIFFUSION LIMITATION, VA/Q MISMATCH AND PULMONARY TRANSIT TIME IN HIGHLY TRAINED ATHLETES DURING MAXIMAL EXERCISE by SUSAN ROBERTA HOPKINS B. Med. Sci., Memorial University of Newfoundland, 1978 M.D., Memorial University of Newfoundland, 1980 M.P.E., The University of British Columbia, 1988 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Interdisciplinary Studies, Medicine/Physical Education/Zoology) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA November 1992 © Susan Roberta Hopkins, 1992 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. (Signature) Department of  Interdi:aeiplinary Studies The University of British Columbia Vancouver, Canada Date  Fehruary25. 1993 DE-6 (2/88) ABSTRACT To investigate the relationship between pulmonary diffusion limitation, ventilation- perfusion (VA/Q) mismatch, pulmonary transit times (PTT) and pulmonary gas exchange during exercise, 10 highly trained male athletes (age=26.4±4.4 years, Height=185.5±5.3 cms, Weight=78.2±8.6 kg, V 02max=5.15±0.521-min -1 ) under went exercise testing at rest (R) and 150W, 300W and maximal exercise (372±22W), corresponding to an oxygen consumption (V02) of 0.41±0.09, 2.16±0.17, 4.32±0.35 and 5.13±0.50 1-min -1 respectively, while trace amounts of six inert gases were infused via a peripheral vein. Arterial blood samples, mixed expired gas samples and metabolic data were obtained. Observed alveolar arterial difference ([A-a]D02(0)was calculated according to the alveolar gas equation. Indices of VA/Q mismatch: LogSDi and Log SDa and predicted [A-a]D02 ([A-a]DO2(p)) were derived from 50 compartment model analysis of retentions and excretions of the inert gases. Additional indices of '/A/I,) mismatch: DISPR*, DISPE and DISPR*_E and inert gas alveolar difference ([A-a]D, R(A-a)D and E(A-a)D) were obtained directly from the inert gas data. One to two weeks later, the subjects underwent first pass radionuclide angiography using a Siemens ZLC wide field of view gamma camera. Following in vitro labeling with 99mTechnecium, 5-10 ml of the subject's blood, containing 10-20 mCi of activity, were injected at rest. First pass and post-static data were obtained on an ADAC 3003 computer and cardiac output was calculated using the Stewart Hamilton equation. PTT was determined using deconvolution and centroid methods. Gated radionuclide angiography was then performed at rest, 150, and 300W. On a separate occasion, first pass cardiac outputs and pulmonary transit times were obtained at maximal exercise. Mean arterial partial pressure of 02 (Pa02) decreased significantly from rest to 150W , and from 150 to 300W to a low value of 86±9 torn, before increasing to near resting values at maximal exercise. [A-a]D02( 3) increased across each exercise levels however only the increase from 150 to 300 W was significant. The overall and perfusion- related indices of VA/Q mismatch showed a significant increase with exercise, mainly as a ii result of increasing perfusion of areas of high VA/Q [A-a]D02(0 was greater than predicted, becoming significant during heavy exercise, indicating diffusion limitation. Cardiac output increased from 6.9±0.9 1-min -1 (R) to 25.2±2.5 1-min -1 at 300W and 33.3±3.7 1-min-1 at maximal exercise. End diastolic volume increased from R to heavy exercise (p < 0.001), accompanied by a decrease in end systolic volume (p =0.05). Stroke volume and ejection fraction also increased significantly from R to 300W (p < 0.001). Deconvolution PTT decreased from 9.32±1.41 s at rest to 2.91±0.30 s during max exercise and was highly correlated with centroid PTT both at rest (r=0.99, p<0.001) and during maximal exercise (r=0.96, p<0.001). PTT during maximal exercise was significantly correlated with Pa02 (1=0.65, p<0.05) and [A-a]D02(0)_[A-a]D02(p) (r=-0.60, p<0.05). Calculated pulmonary blood volume increased during maximal exercise by 57% over resting values to over 25% of total blood volume and when corrected for body surface area correlated significantly with Pa02 (r=0.69, p<0.05). There was a significant correlation between (A-a)D, PTT, the ventilatory equivalent for CO2 and Pa02 during maximal exercise (r=0.94, p<0.01) allowing prediction of over 80% of the variance in Pa02 between subjects. These data indicate that highly trained athletes develop VA/Q mismatch accompanied by diffusion limitation during maximal exercise. Observed decrease in Pa02 during high intensity exercise is the result of a complex interaction between VA/Q mismatch, hypoventilation and diffusion limitation secondary to shortened pulmonary transit. iii TABLE OF CONTENTS Abstract^ ii List of Tables vi List of Figures^ vii List of abbreviations and symbols^ viii Acknowledgment x Introduction 1 Methods^ 6 Baseline data^ 6 Subject preparation 7 Test protocol 7 Multiple inert gas analysis^ 8 Arterial blood gases 9 Cardiac output^ 10 Pulmonary transit time 12 Data analysis 13 Results^ 14 General data^ 14 Ventilation and metabolic data^ 15 Arterial blood gases and oxygen saturation^ 16 VA/Q inequality^ 22 Diffusion disequilibrium 25 Cardiac output and cardiac volumes^ 26 Pulmonary transit time 26 Pulmonary blood volume^ 32 Discussion^ 33 Multiple inert gas analysis and exercise^ 34 MIGET data^ 35 .^. Indices of dispersion and VA/Q mismatch^ 35 Diffusion limitation 37 Effect of uncertainty in cardiac output on MIGET data^38 Maintenance of steady state conditions 40 Blood and plasma volume^ 41 Radionuclide cardiography 42 Cardiac function in athletes 43 Comparison with previous investigations^44 Effect of increasing exercise intensity on cardiac volumes^44 Pulmonary transit times^ 47 Theory 47 Pulmonary transit times and exercise in humans^49 Pulmonary blood volume^ 49 Relationship of pulmonary transit time and blood volume to pulmonary capillary transit time and blood volume^50 Arterial blood gas and metabolic data^ 51 Arterial blood measures^ 51 Mixed venous P02 52 Possible mechanisms of exercise induced hypoxemia^52 Summary of findings^ 56 References^ 57 Appendix A: Review of literature 70 Introduction^ 70 iv Respiration and the respiratory muscles^ 71 Energetics^ 72 Blood flow to the diaphragm and other respiratory muscles^72 Oxygen consumption of respiratory muscles 74 Lactate Production^ 76 Theoretical calculations of respiratory muscle VO2 and Q in humans exercising at maximal levels^ 77 Respiratory muscle fatigue 79 Histochemical properties of mammalian respiratory muscles^79 Evidence for fatigue 80 Respiratory drives^ 81 Pulmonary mechanics and expiratory flow limitation^ 83 Pressure-volume relationships^ 83 Characteristics of flow inside tubes 83 Flow-volume relationships 84 Pulmonary diffusion and gas exchange 86 Factors related to diffusion of oxygen^ 86 Factors related to diffusion of carbon dioxide 87 Factors affecting pulmonary gas exchange 88 Does the pulmonary system constrain exercise? 92 Summary^ 96 Appendix B: Methodological background^ 97 Quantitative radiocardiography 97 Red blood cell labeling 97 First pass determination of cardiac output^ 97 Gated radionucleide angiography 99 Measurement of blood volume^ 100 Pulmonary transit time^ 101 Frequency distribution of pulmonary transit times^ 102 Multiple inert gas elimination 105 Ventilation and perfusion relationships^ 105 Multiple inert gas elimination theory 107 Practical aspects^ 110 Dead space 111 Shunt^ 112 VA/Q distributions^ 113 Alveolar arterial difference 114 Appendix C: Statistical analyses and raw data^ 116 Anova tables^ 116 Metabolic data^ 116 Blood gas data 118 MIGET data for all six gases^ 121 Cardiac data 125 Regression^ 127 Appendix D: Individual Subject Data^ 131 v LIST OF TABLES ^ Table 1.^Subject descriptive data^ 14 Table 2.^Plasma volume and calculated whole blood volume - individual subject data^ 15 Table 3.^Arterial blood gases, metabolic data, transit times and MIGET summary data at rest, light, heavy and maximal exercise (i±S D)^ 18 Table 4.^Calculated mixed venous and arterio-venous 02 difference during rest, light, heavy and maximal exercise.^ 22 Table 5.^(A-a)D, R(A-a)D and E(A-a)D at rest and during light, heavy and maximal exercise (i ±SD)^ 24 Table 6.^Cardiac output, volumes and ejection fraction at rest and during, light, heavy and maximal exercise (x±- SD)^ 26 Table 7.^Pulmonary transit times by the deconvolution and centroid methods^28 Table 8.^Pulmonary blood volume at rest and during maximal exercise^32 Table 9.^Comparison of MIGET data from present study with other investigators^ 38 Table 10.^Effect of varying cardiac output on residual sum of squares and indices of dispersion^ 40 Table 11.^Cardiac output and volume indices comparison with other studies^46 Table 12.^Blood flow of respiratory muscles^ 73 Table 13. VO2 of respiratory muscles 75 Table 14.^Oxygen cost of unobstructed hyperventilation^76 Table 15.^Alveolar-arterial differences at rest and during exercise 91 Table 16.^Blood gas and cardio-respiratory data obtained at 70-100% of V02 max^ 95 vi LIST OF FIGURES Figure 1.^Pa02, PaCO2 and [A-a]DO2(o) for individual subjects during light exercise^ 19 Figure 2.^Pa02, PaCO2 and [A-4D02(o) for individual subjects during heavy exercise^ 20 Figure 3.^Pa02, PaCO2 and [A-4D02(o) for individual subjects during maximal exercise^ 21 Figure 4.^Dispersion indices at rest and during light, heavy and maximal exercise^ 24 Figure 5.^Observed and predicted alveolar-arterial 02 difference^25 Figure 6A.^Raw data and gamma univariate fit for subject one during rest^29 Figure 6B.^Frequency distribution of transit times for data in Figure 6A^29 Figure 7A.^Raw data and gamma univariate fit for subject one during maximal exercise^ 30 Figure 7A.^Frequency distribution of transit times for data in Figure 7A^30 Figure 8.^Pa02 and [A-41)02 (o-p) versus transit time ^31 Figure 9.^Maximal expiratory flow volume loop with exercise and maximum voluntary ventilation manoeuvre^ 84 Figure 10.^The relationship between end capillary P02 and mixed venous P02 for lung units of differing VA/Q^ 107 vii LIST OF ABBREVIATIONS AND SYMBOLS (A-a)D^Inert gas alveolar arterial difference area [A-a]D02 Alveolar arterial difference for oxygen (a-v)02 diff^Arterio-venous difference for oxygen DISPR* Index of dispersion for retention of MIGET gases DISPE^Index of dispersion for excretion of MIGET gases DISPR*_E Overall index of dispersion DLCO^Diffusing capacity for carbon monoxide E(A-a)D Excretion component of inert gas alveolar arterial difference area EIH^Exercise induced hypoxemia FVC Forced vital capacity FEVi^Forced expiratory flow in 1 second 1311 Iodine 131 a radioactive isotope of iodine Lambda — blood gas partition coefficient Log SDa^Standard deviation of the log normal distribution for blood flow Log SDv.^Standard deviation of the log normal distribution for ventilation MEFV Maximal expiratory flow volume MIGET^Multiple inert gas elimination technique MVV Maximum voluntary ventilation pH^Negative logarithm of hydrogen ion concentration PaO2 Arterial partial pressure of oxygen PAO2^Alveolar partial pressure of oxygen Pa/NV Retention (R) of a MIGET gas defined as the ratio of arterial to mixed venous partial pressure Pbar^barometric pressure PE partial pressure in expired gas PE/Pir^Excretion (E) of a MIGET gas defined as the ratio of mixed expired to mixed venous partial pressure viii P102^Partial pressure of inspired oxygen P ,^mixed venous partial pressure 13,02 Mixed venous partial pressure of oxygen Q^ Cardiac output or blood flow R(A-a)D^Retention component of inert gas alveolar arterial difference area RER Respiratory exchange ratio Sa02^Arterial oxygen saturation SF6 Sulfur hexafluorane 99mTc^Technetium 99 an meta stable radio-isotope of Technetium VAIQ Ventilation-perfusion ratio Vc^ Pulmonary capillary blood volume VCO2 Minute production of carbon dioxide VD^Dead space VE Minute ventilation i7E/VCO2^Ventilatory equivalent for carbon dioxide i/E/V02 Ventilatory equivalent for oxygen V02^Minute consumption of oxygen VT Tidal volume a ACKNOWLEDGMENT I would like to take this opportunity to thank everyone who helped in the preparation of this thesis: Dr. Don McKenzie, my supervisor for his helpful advice, sense of humor, support and endless proof reading, Dr. Tom Robertson, Dave Frazer, and Thien Tran, their help and patience in analyzing, programming, printing and interpreting mountains of MIGET data, Dr. Brownie Schoene for his help in providing technical support, creative financing, accommodation and beer; Dr. Rob Glenny and Ron Saxon for their enthusiastic help in data collection, Dr. Al Belzberg, and the technical staff at St. Paul's Hospital Nuclear Medicine for the excellent help with the nuclear medicine data, Barry Wiggs, for his help with deconvolution, My subjects who endured VO2 max tests, big needles, radiation and blizzards in the name of Science, My committee members Peter Hochachka, Bob Schutz and Jeremy Road for their advice, My parents, Bob and Barbara Hopkins for supporting me in the decision to return to school, and Trevor Cooper, for providing a shoulder to cry on. The financial assistance of the Medical Research Council of Canada, and the British Columbia Lung Association is also gratefully acknowledged. x INTRODUCTION Dissecting the factors that limit maximal exercise performance has been a fundamental area of investigation in exercise physiology in both human and animals. Factors such as oxygen (02) transport and the circulatory system, peripheral blood flow, and 02 diffusion and mitochondrial 02 utilization have been discussed (39). The effects of endurance type physical training on the cardiovascular and musculosketetal system are well known and include increases in cardiac output (Q), stroke volume, arterio-venous [(a-v)02] difference, plasma volume and peripheral muscle blood flow (20) all of which contribute to the increase in maximal oxygen consumption (VO2max). Similarly, effects of training on the respiratory system have been documented and include: increased respiratory muscle enzyme activities (82, 113), and increased maximal voluntary ventilation (MVV) (150) maximal ventilation (51) maximal sustainable ventilation (47, 91) and flow velocities (16). In the gas exchanging portions of the lung however, development is complete in childhood (113) and a training effect (aside from blood volume or hemoglobin alterations) on gas exchanging areas is unlikely (10, 135). As a result, the lung has attracted attention as a potential constraint to maximal performance particularly at altitude (55, 74, 161, 177, 178) and in highly trained athletes (4, 34, 35, 72, 105, 127) where a supra-normal cardiovascular system may unmask the respiratory system's limited capacity to adapt. Exercise induced hypoxemia (EIH) was first reported by Han -op (65) who made arterial blood gas measurements on patients with a variety of clinical problems as well as healthy normal subjects. Included in these measures were samples taken from a healthy Caucasian male who performed "fifteen minutes of brisk exercise consisting of arm and trunk movements and vigorous hopping about the room until quite dyspneic." He documented a decline in % saturation of arterial blood (Sa02) from 95.6% at rest to 85.5% following exercise. Sporadic reports of impaired gas exchange during exercise followed (146, 159, 185) however the lung was not widely recognized as a potential limiting factor to exercise performance as the preponderance of evidence indicated that arterial oxygenation 1 was maintained during exercise (9, 17, 156). More recently, a number of authors (35, 72, 126, 127) have confirmed the findings of Harrop (65), Rowell (146), Thompson (159) and others, stimulating interest in this avenue of exploration. Two causes of the decline in Pa02 with exercise can be outlined, based on alveolar partial pressure of oxygen (PAO2) and arterial partial pressure of oxygen (Pa02). If Pa02 is low and PAO2 is also low (< 105 torr) this suggests inadequacy of ventilation either as a result of mechanical limitation of flow or as a result of blunted respiratory drives. If Pa02 is low and PAO2 is high (>110) resulting in very wide alveolar-arterial difference ([A-a]D02) this suggests an inadequacy of gas exchange either as a result of ventilation-perfusion (VA/Q) mismatch or diffusion limitation. In fact, both of these situations have been described in the literature. Dempsey et al., (35) exercised sixteen subjects at 60-90% of VO2max and reported Pa02 of less than 60 torr associated with little or no alveolar hyperventilation. Administration of normoxic helium-oxygen mixtures improved Pa02 only to the extent that PAO2 was increased and it was concluded that the magnitude of the hyperventilatory response to exercise was a major determining factor of the hypoxemia observed. In contrast to this, Hopkins and McKenzie (72) found that alveolar P02 was high in their subjects who developed hypoxemia, with no evidence of inadequate ventilation. Both groups raised the possibility of impaired gas exchange as a possible explanation for some of the hypoxemia seen in their subjects. As exercise intensity increases, cardiac output to the working muscles also increases, in some highly trained individuals to over 40 I-min -1 (43). The time that the red blood cell spends in the pulmonary vascular bed is directly related to flow and pulmonary capillary blood volume; as flow increases, expansion of the pulmonary vascular bed by dilation and recruitment (59) becomes of paramount importance in ensuring adequate time for equilibration of gas exchange. The average resting pulmonary capillary transit time has been estimated to be - 0.75 s (80, 144) falling to about 0.3 s during exercise. These represent mean capillary transit times as a whole, and it is possible that, given regional 2 differences in pulmonary transit (70), some red cells may travel too rapidly through the pulmonary vasculature resulting in arterial hypoxemia. Transfer of a gas across the pulmonary blood gas barrier can be described by the equation (166) : 100 kA ^ax ^t • Px(t) = PAX + (P .ix - PA x).e 60^dVc • WIMWx., where Px is the partial pressure of gas x , t is time, PA and Pv are alveolar and mixed venous partial pressures of gas x, k is the diffusion coefficient of gas x, A is the cross- sectional area of diffusion, d is the thickness of the blood gas barrier, ax is the solubility of the gas x in the blood gas barrier, ox is the solubility of gas x in the blood and MW is the molecular weight of the gas x. The effect of decreasing transit time can be seen by inspection of the above equation. All other factors being equal, as transit time decreases Px will fall as a function of ct, such that when t=0 the partial pressure is equal to the mixed venous partial pressure. In the exercising athlete, the time for equilibration of oxygen is further lengthened by low mixed venous P02 (34) and a right-ward shifted oxygen- hemoglobin equilibrium curve secondary to temperature increase, high mixed venous PCO2, and low pH, which in addition to possible rapid pulmonary transit may compromise gas exchange. Two recent technological advances have made possible further detailed investigations into diffusion limitation and gas exchange in exercising athletes. The multiple inert gas elimination technique (MIGET) as it is currently in use, was developed by Wagner and co-workers (45, 167, 168) and is discussed in detail in Appendix B. It utilizes a multi- compartment model of pulmonary gas exchange calculated from data obtained from elimination of infused inert gases (usually six) based on the relationship: 3 PAX^Pc i x Pix^PiTz XX + VAN where PAx is the alveolar partial pressure, NI is the mixed venous partial pressure, Pc 'x is the end capillary partial pressure and Xx is the blood gas partition coefficient of gas x . Inert gases will approach equilibrium across the alveolus faster than oxygen, therefore if diffusion dis-equilibrium occurs, the observed [A-41)02 will exceed that predicted from inert gas exchange and will be improved by administration of 100% oxygen. Post pulmonary shunt via the bronchial and thesbian veins will not affect inert gases as they are not metabolized, however administration of 100% 02 will disproportionately increase the [A-4D02 because it will increase alveolar P02 with less effect on arterial P02 as shunted blood bypasses the gas exchanging areas. These techniques thus allow the contributing factors to [A-41)02 to be dissected. Inert gas studies in normal subjects exercising at a VO2 of about 3.01•min -1 (161) and in moderately trained individuals exercising at a VO2 of 4.01•min -1 (63) have suggested diffusion limitation, secondary to rapid pulmonary transit, as a cause of impaired gas exchange. Right heart to left heart whole lung transit time can be measured using radioisotopically labeled RBC and a frequency distribution of pulmonary transit times can be calculated (70, 71, 96). The time required for an indicator to flow past an observation point down stream from an entry point is related not only to the time it take the bolus to flow past the point but also how quickly it arrived there. In this case the gamma camera provides the observational point to observe the bolus curve derived from labeled RBC traversing the right ventricle and the output curve derived from the left ventricle. Transit time is determined by subtracting the first moment of the right ventricular curve from the first moment of the left ventricular curve. Deconvolution is a mathematical process by which a frequency 4 distribution of transit times (a transfer function, h(t)) can be derived from the input (right ventricular) and output (left ventricular) time activity curves. It was the purpose of this study to investigate pulmonary gas exchange and transit times during exercise in a population of highly trained male athletes exercising near V02 max who on the basis of high pulmonary blood flow may develop shortened pulmonary transit and diffusion limitation. 5 METHODS Baseline data Sixteen non-smoking healthy male athletes with no prior history of respiratory or cardiac disease underwent preliminary studies. After giving informed consent, a history was obtained and a physical examination was performed, seeking evidence of cardio- pulmonary abnormality. All subjects were screened for pulmonary disease with pulmonary function tests consisting of forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), peak flow rates, and 12 second maximum voluntary ventilation ( MMV) using a Medical Graphic CAD/Net 2001 metabolic cart equipped with Medical Graphics 1070 pulmonary function testing software. Maximal oxygen uptake was determined on an electronically braked cycle ergometer (Minjardt KEM-3) equipped with a racing saddle and pedals. After a 10 minute warm-up (75-100 watts), the subjects rode a progressive exercise test with the work intensity ramped at 30 watt•min -1 until they were unable to continue. Minute ventilation (VE), oxygen consumption (V02 ) and carbon dioxide production (VCO2) were measured on a breath-by-breath basis and tabulated every 15 seconds with a Medical Graphics CAD/Net 2001 Metabolic Cart equipped with Medical Graphics 2001 software. Heart rate was monitored and recorded by cardiac monitor (Lifepak 6) interfaced with the metabolic cart. VO2 max was considered to be the average of the four highest consecutive 15 second measures of oxygen uptake. These results were used to calculate a work-load which represented greater than 90% of VO2 max. Subjects were excluded from further testing unless their VO2 max was greater than 5.0 1-min -1 or 60 ml-kg - 1 .min-1 . On a separate occasion single breath carbon monoxide diffusing capacity ( DLCO) was obtained at the UBC Hospital Pulmonary Laboratory (7). 6 Ten subjects fulfilling the entry criteria were transported the following week to the Cardio-Pulmonary Laboratory at the Harborview Medical Center (Seattle Wa.). Subject Preparation Prior to the exercise test, an indwelling arterial cannula (Arrow # 20 gauge) was inserted in the right radial artery. Cannula patency was maintained by frequent flushing with normal saline to which heparin sodium (2000 u.1-1 ) had been added. Under sterile technique, and cardiac monitoring, a number 7.5F triple lumen Swan-Ganz catheter (for temperature monitoring) was introduced into the venous circulation via the left antecubital vein and positioned in the region of the superior vena cava. Cannula patency was maintained as described for the arterial cannula. A second venous line (#18 gauge) was inserted via the right antecubital vein into the peripheral venous circulation for infusion of the inert gases. The subjects were instructed to discontinue if any unusual symptoms developed. At least three physicians were present at all times during the exercise testing one of whom had primary responsibility for the subject. EKG tracing (lead II) was monitored continuously. Test protocol Subjects were seated on the bicycle ergometer previously described and connected to a respiratory circuit. This consisted of a Rudolph (2700) valve connected by large diameter heated tubing to a heated 131 Plexiglas mixing chamber which in turn was connected to a pneumotach. Breath by breath analysis of VO2 and VCO2 was obtained by a Medical Graphics 2000 system equipped with 2001 software similar to the system previously described for V02 max testing. After a ten minute rest period to allow stabilization of ventilatory data the testing protocol was started. Arterial blood gases, VE, V02, VCO2, heart rate were obtained at rest and every minute at each of the exercise levels. The exercise levels consisted of five minutes each of light (150w, mean V02 = 42% of V02 max), heavy (300w, mean V02 = 86% of V02 max) and near maximal exercise (>90% VO2 max, mean power output = 371±30 watts). Mixed expired gases and arterial blood samples 7 for inert gas analysis were collected at rest and during the last minute at each exercise level. The subjects rested approximately 5 minutes between each exercise level to allow recalibration of the instruments. Multiple inert gas analysis Six inert gases (SF6, ethane, cyclopropane, halothane, ether and acetone) dissolved in 5% dextrose (55, 63, 167, 168, 180) were infused starting 20 min before the start of the experiment, at a rate in ml•min-1 corresponding to one quarter of the expected VE in 1•min-1 (range 5 - 50 ml•min -1 ). Duplicate arterial (8 ml) and expired gas (30 ml) samples were collected in pre-heparinized glass syringes during the last minute at each exercise level and analyzed by gas chromatography. The time delay between arterial and expired samples due to the hoses and mixing chamber was calculated by dividing the mixing chamber volume by the VE and the time of collection of the expired gas sample was adjusted accordingly. Mixed venous inert gas concentrations were calculated from the Fick equation. Retention (Pa/Pv) and excretion (PE/Nr) values were used to estimate VA/Q and standard deviation of the log normal distribution of perfusion (LogSDQ. ) and ventilation (LogSDv) distribution were used as an index of VA/Q inequality (168). Retention (R(A-a)D) and excretion (E(A-a)D) components of the inert gas alveolar-arterial difference area ([A- a]D) were also derived directly from the inert gas data (42, 69). Three additional indices were derived directly from the data as described by Gale et al., (53). DISP R* analogous to log SDO and R(A-a)D, DISPE analogous to log SI:or and E(A-a)D, and DISPR*_E analogous to (A-a)D were calculated as follows: n DISPR*.E = 100 x^i=1 n 8 n I(Ri-Rhomoi) 2 i= DISPR* = 100 x^ 1 n 0\1 n I(Ehomoi-Ei)2 i= DISPE = 100 x^ 1 n where Ehomoi = Rhomoi — Xi+ V • A QT n is the number of gases, Ei and Ri represent excretions and retentions of the gas of interest and Ei is excretion corrected for dead space: Ei —^ Ei V D1 - VT Predicted values for Pa02, PaCO2 and [A-41)02, ([A-a]D02(p)) based on the derived VA/Q distribution were then calculated (174, 180). These values, calculated from inert gas data, reflect only that predicted by infra-pulmonary shunt and ventilation perfusion inequality, therefore an indirect estimate of diffusion impairment [A-4D02(o-p), is possible. It should be noted that the inert gas analysis assumes steady state and the absence of post-pulmonary shunt. Arterial blood gases The arterial samples were anaerobically collected in pre-heparinized glass syringes and were maintained in ice until the test session was complete. Each 2 ml sample was analyzed for pH, Pa02 and PaCO2 using a Corning Blood Gas/ pH Analyzer, which is calibrated daily and after each exercise test against a known standard. Resting samples and the last sample drawn had hemoglobin and hematocrit determined. Using Kelman's routines 9 (85-87) Sa02, Pa02 and PaCO2 were also corrected for arterial blood temperature measured at the superior vena cava via the Swan-Ganz catheter. Cardiac output One to two weeks later in the Department of Nuclear Medicine at St. Paul's Hospital cardiac output was measured at each exercise intensity using a combination of first pass and gated radiocardiography. Right ventricle to left ventricle first pass transit time was also measured during rest and near maximal exercise. Plasma volume was measured using 131 1 labeled albumin (RISA) (76). Ten ml of whole blood was withdrawn from the subject into a pre-heparinized syringe via an antecubital vein. The blood was labeled with 99-mTechnetium (99-mTc) using a standard commercial red blood cell (RBC) labeling kit (152) (Brookhaven National Laboratories, Upton, N.Y.). A 5-10m1 aliquot of RBC injectate containing 10-20 mCi of activity was obtained. A 20 gauge plastic cannula was inserted in the right median basilic vein and maintained patent with normal saline. The subjects were seated on the bicycle ergometer previously described in front of a Siemens ZLC 3700 gamma camera with a wide field of view, medium energy columnator. The subjects were studied in the left anterior oblique position by placing the ergometer at angle to the camera and having the subject lean forward placing his chest directly in contact with the camera and grasping the camera with the left hand. In all cases, except for one at near maximal exercise, the images obtained were of excellent technical quality and allowed good separation of the right and left ventricle. Heart rate was recorded using a Lifepak 6 monitor/defibrillator. After stabilization of heart rate to the level observed during resting level of the inert gas experiments, one half the labeled RBC were injected into the dead space of the plastic cannula and flushed into the venous circulation with a rapid bolus of 15 ml of normal saline. Data were acquired and processed on an ADAC 3003 computer (ADAC Laboratories, Sunnyvale, Ca) at 0.5 frames•second -1 during the first pass through the 10 central circulation. Without changing the position of the camera a 2 minute static image was obtained after the bolus injection. In the static projection, a large region of interest was drawn over the left ventricle using the light-pen and applied to the dynamic view. A first pass time activity curve and static or equilibrium counts were obtained. Cardiac output (Q) was calculated from the Stewart Hamilton equation: •^TB V Q = Ceq.C(t)dt where Ceq represents the static counts from the left ventricle region of interest, TBV is blood volume (from measured plasma volume and hematocrit) and C(t).dt is the area under the left ventricle first pass curve. The upstroke and down stroke of this curve are extrapolated to zero to exclude counts from the right ventricle and recirculation of blood. The remainder of RBC were then administered and the gated studies were conducted. Using the same experimental setup as described above, count data was obtained at a framing rate of 16 frames per R-R interval of the EKG and stored on the hard disk in a 64x64 pixel matrix. Data were acquired for two minutes, after the third minute of each exercise level. Gated ventricular imaging was conducted at rest, 150 watts and 300 watts. Each of the 16 images acquired was then displayed and the left ventricular region of interest was manually drawn using a light-pen. A background region of interest was drawn laterally and inferiorly to the left ventricle. Left ventricular ejection fraction was then calculated from the difference between background subtracted end-diastolic and end-systolic counts and expressed as a percentage of end-diastolic counts. Two, 5 ml samples of blood were drawn at the end of each exercise period and imaged in petri dishes for five minutes. The average background subtracted count rate for 5 ml of blood was thus obtained. The left ventricular count rate at each exercise level was corrected for loss by decay of 99mTc using standard tables and the left ventricular end- diastolic volume was obtained by dividing the left ventricular count rate by the 5 ml count rate; cardiac output was then obtained by multiplying by the ejection fraction and the 11 average heart rate during data acquisition. Correction for attenuation of counts by the chest wall for the gated studies was made by comparing the first pass cardiac output at rest with the result obtained from the resting gated study, and the results obtained during the gated studies at 150 and 300w were adjusted accordingly. Of the 40 gated determinations of cardiac output and cardiac volumes, 7 were lost for technical reasons. For the purpose of MIGET calculations, Q was calculated from a linear regression of Q vs VO2 for the remaining subjects. On a separate occasion, at least a week apart to minimize the effect of residual radioactivity, the first pass study was repeated at the near maximal exercise level. Pulmonary transit time Right ventricle to left ventricle RBC pulmonary transit times were calculated from the first pass time-activity curves (70, 96). In the static image, large regions of interest were drawn with the light pen outlining the right and left ventricle. These regions of interest were then applied to the dynamic images and time activity curves were obtained. In a similar fashion, the quality of the bolus injection was checked by drawing a region of interest in the superior vena cava. The numerical data thus obtained was then down loaded directly into a Zenith 386 PC for further processing. Each time-activity curve was fitted using linear least squares to a gamma (y) function using the method of Starmer and Clark (153) programmed into a Lotus 123 spread sheet. The transit time across the lungs was then obtained by subtracting the first moment of the right ventricular time-activity curve from that of the left ventricle (96). To confirm these results and obtain a frequency distribution of transit times (also known as a transfer function) deconvolution analysis was applied to the gamma fitted curves. This was done using Gauss 386 statistical software. The area underneath both the input (right ventricular) curve and the output (left ventricular) curve was set equal to 1. A series of fifty transfer functions was then generated with a mean transit time equal to that obtained by the subtraction of first moments, and a variance ranging from that of the input curve to that of the output curve. The transfer functions were then convoluted with the input 12 curve and ridge regression was applied to determine contribution of each output curve derived by this process to the final output curve. This process was repeated with slight alterations in the variance and mean transit times of the transfer functions until a satisfactory visual fit was obtained. Pulmonary blood volume was calculated by dividing the cardiac output in ml•second4 by the transit time. Data analysis Data were analyzed using analysis of variance for repeated measures to determine differences in blood gas, cardiac output, stroke volume, indices of dispersion and metabolic data between the rest, 150w, 300w, and near maximal exercise conditions. A similar analysis was used to determine differences in end systolic volume, end diastolic volume and ejection fraction between rest, 150w and 300w. Where overall significance was obtained, Scheffe's testing was applied post-hoc to determine were these differences occurred. Student's t test was used to evaluate changes in pulmonary transit time and pulmonary blood volume between rest and maximal exercise. Linear regression was used to determine the relationship between [A-a]D02(o-p) during maximal exercise and pulmonary transit time, and between blood volumes and Pa02 and [A-4D02(o). Multiple linear regression was used to examine the relationships between VENCO2 , PTT, (A-a)D, and Pa02. 13 RESULTS General data Subject descriptive information is given in Table 1. The high levels attained for VO2 max indicate that the subjects were highly trained males. Blood volume indices from measured plasma volume and hematocrit are given in Table 2. The subjects exhibited above normal values for plasma volume and red cell mass. Plasma volume was as high as 159% of predicted based on norms calculated from the subjects height, weight and age. Mean hematocrit was at the low end of the normal range however calculated red cell mass was also more than 130% of predicted. In all cases plasma and red cell volumes were outside the upper limit of the normal range. Mean hematocrit for the subjects was 42.6% pre-exercise and increased to 44.2% post exercise. This change was statistically significant (t=1.98, p<0.05) Table 1. Subject descriptive data Age (years) Height Weight VO2 max (cm)^(kg)^(1•min-1 ) FVC (1) FEY' (1) 1 31 188.5 89.7 6.31 7.02 6.73 2 28 180.7 67.5 4.37 5.52 4.62 3 23 193.5 79.5 5.07 6.73 6.37 4 30 181.5 81.0 5.17 5.34 4.68 5 20 188.4 82.3 5.12 6.47 5.26 6 25 185.2 76.5 5.36 7.07 6.10 7 19 183.5 68.5 4.91 6.05 5.16 8 28 185.1 77.2 4.81 5.94 5.17 9 29 176.5 68.2 4.84 5.49 4.84 10 31 192.5 92.0 5.57 6.02 4.91 Mean 26.4 185.5 78.2 5.15 6.17 5.38 ± SD 4.4 5.3 8.6 0.52 0.63 0.75 VE max MW DLCO m, (1•min-1 ) (1-min - 0 mm Hg-1 ) 218.5 186.1 240.2 201.0 211.5 199.4 209.2 212.1 186.2 201.6 206.6 16.3 230 44.70 198 41.99 257 44.98 223 42.94 219 49.74 241 49.90 204 43.47 178 194 34.19 208 43.04 215.2 43.88 23.6 4.63 14 *Subject failed to complete this portion of the testing Table 2. Plasma volume and calculated whole blood volume - individual subject data. Whole Blood Vol.(ml) ml•kg-1 % Predicted Plasma Vol.(ml) ml•kg-1 % Predicted RBC Vol. (ml) % Predicted 1 8075 90.0 152 5140 57.3 161 2934 137 2 5720 84.7 130 3621 53.6 136 2100 121 3 6959 87.5 134 4231 53.2 134 2727 133 4 6142 75.8 127 3859 47.6 133 2283 117 5 7467 90.7 145 4912 59.7 159 2556 125 6 6802 88.9 140 4249 55.5 145 2553 133 7 6759 98.7 145 4359 63.6 157 2400 135 8 6757 87.5 139 3848 49.8 131 2906 151 9 5643 82.7 130 3499 51.3 133 2144 125 10 8126 88.3 144 5143 55.9 153 2983 131 Mean 6845 87.5 139 4286 54.8 144 2559 131 ±SD 868 5.9 8 606 4.7 12 325 10 Ventilation and metabolic data Respiratory and metabolic data are given in Table 3. Statistical information in brackets refers to Scheffe's F test unless otherwise stated. Mean blood temperature measured at the superior vena cava was 36.8±0.5 °C at rest and rose significantly at each exercise level to 38.0±0.7 °C at the end of the heaviest exercise task (omnibus F(3,27)=24.3 P < 0.001).VE increased from 13.4±2.6 1-min -1 at rest to 178.1+16.3 1-min-1 at the end of maximal exercise, and was significantly less than peak values (mean=206.6 1•min-1 ) reached during the initial V02 max test (t=5.76, p<0.001). V02 increased from 0.41± 0.09 1-min-1 at rest to 2.16±0.17 1-min -1 , 4.32±0.35 1•min -1 , and 5.13±0.50 1-min-1 at light (150 watts) , heavy (300 watts) and maximal exercise respectively. VCO2 at the corresponding exercise levels was 0.36±0.10 1-min -1 , 1.85±0.11 1•min -1 , 4.39±0.35 1•min-1 and 5.97±0.681-min-1 . The respiratory exchange ratio (RER) was not significantly different between rest and light exercise (0.88E111 rest vs 0.86±0.05 light exercise F=0.06, p=NS); however it increased significantly from light to heavy exercise (F=7.74, p<0.05) to 1.02±0.11 during heavy exercise indicating that the subjects were close to their 15 anaerobic threshold during this workload. During maximal exercise RER increased significantly from heavy exercise (F=6.50, p<0.05) to 1.17±0.08. The ventilatory equivalent for carbon dioxide (VE/VCO2) decreased significantly from rest to light exercise (F=20.5, p<0.05), then increased slightly from light to heavy exercise and from heavy to maximal exercise, although VE/VCO2 at max was significantly less than resting values (F=7.6, p<0.05). The ventilatory equivalent for oxygen (VE/V02) also decreased significantly from rest to light exercise (F=18.2, p<0.05) and increased from light to heavy exercise (F=5.08, p<0.05) and from heavy to maximal exercise (F=6.26, p<0.05). VE/V02 at rest was not significantly different from maximal exercise (F=0.25, p=NS).There were modest correlations between VENCO2 and Pa02 during heavy and maximal exercise (r=0.43 and r=0.53 respectively) for these 10 subjects but they were not statistically significant. There was a similar correlation between VE/V02 and Pa02 during heavy exercise (r=0.40), again not statistically significant. Arterial blood gases and oxygen saturation Group mean blood gas values are given in Table 3 and individual subject data for Pa02, PaCO2 and [A-41)02(o) during light, heavy and maximal exercise are given in Figures 1, 2 and 3. Mean Pa02 was 98±6 torr at rest and decreased significantly (F=36.3, p<0.05) to 91±8 ton during light exercise. Pa02 declined further to 86±9 torr during heavy exercise (F=12.8 compared to rest, p<0.05) and increased to near resting levels (94±8 torr) at the end of maximal exercise. Alveolar arterial difference did not increase significantly from rest to light exercise (F=0.29, p=NS); however the increase in [A-41)02(o) from light to heavy exercise was significant (F=27.5, P<0.05). There was no further increase in [A-4D02(o) between heavy and maximal exercise (F=0.14, p=NS); therefore any improvement in Pa02 was due to improved alveolar P02. There was little evidence of hypoventilation at the end of maximal exercise as mean PAO2 calculated by the alveolar gas equation was 121±3 torr. PaCO2 was slightly depressed during the rest measurements 16 (38±3 torr), likely indicating some anxiety on the part of our subjects; it then rose to more normal levels during light exercise, before decreasing significantly during heavy and maximal exercise (F=8.4,p<0.05 and F=6.5 p<0.05 for heavy and maximal exercise vs rest). Blood pH did not change significantly from rest to light exercise, but decreased significantly from light to heavy exercise (F=7.94, p<0.05) and from heavy to maximal exercise (F=15.0, p<0.05). Sa02 decreased significantly over the exercise levels (omnibus F(3,27)= 18.5 p<0.0001) to a mean of 94.2±2.3%. There was considerable inter-subject variability in the blood gas response over the five minute exercise period at each exercise intensity (see Figures 1, 2 and 3). For example, subject six maintained Pa02 above 98 torr during heavy exercise and above 105 torr during maximal exercise. Corresponding [A-41)02(o) was less than 11 ton throughout testing and PA02 was above 110 ton at the end of heavy exercise and 115 torr at the end of maximal exercise, confirming adequate ventilation and gas exchange. In contrast to this, subject 4 developed hypoxemia ( Pa02 = 74 ton) during heavy exercise associated with an [A- 41)02(o) of 38 ton. At maximal exercise the Pa02 increased to 81 torr as a result of increasing PAO2 (123 ton) and [A-a]D02(o) widened further to 42 ton. Subject 5, also demonstrated hypoxemia during heavy exercise with Pa02 at the end of exercise of 74 torr, however there is also evidence of hypoventilation, as PaCO2 was elevated above resting levels and PA02 was 102 ton. 17 Table 3. Arterial blood gases. metabolic data. transit times and MIGET summary data at rest. light, heavy and maximal exercise (i±SD) Rest Light Exercise (150 watts) Heavy Exercise (300 watts) Maximal Exercise (371±30 watts) Heart Rate 70±10 116±9 156±8 166±8 Q (1•min-1 ) 6.9±0.9 16.1±1.5 25.3±2.5 33.3±3.7 VE (1•min-1 ) 13.4±2.6 45.5±3.5 119.3±27.3 178.1±16.3 i/EATO2 33.5±6.2 21.2±1.5 27.6±6.5 34.9±3.8 TEATCO2 38.5±6.8 24.6±1.6 27.1±5.3 30.0±2.8 V02 (1•min -1 ) 0.41±0.09 2.16±0.17 4.32±0.35 5.13±0.50 VCO2 (1•min-1 ) 0.36±0.1 1.85±0.11 4.39±0.35 5.97±0.68 RER 0.88±0.11 0.86±0.05 1.02±0.11 1.17±0.08 PAO2 (torr) 102±8 98±5 111±6 121±3 Pa02 (torr) 98±6 91±8 86±9 94±8 Sa02 (%) 97.6±0.4 96.7±0.9 95.3±1.8 94.2±2.3 [A-a]D02(o) (torr) 4±16 6±8 26±9 27±9 [A-a]D02(p) (torr) 7±8 10±4 17±7 17±3 [A-a]D02(o-p) (ton) -3±15 -4±9 9±13 10+12 PaCO2 (ton) 38±3 41±3 36±4 32±3 pH 7.44±0.03 7.41±0.03 7.34±0.05 7.24±0.06 Log SDT 1.09±0.55 0.90±0.47 1.09±0.29 1.18±0.14 Log SDa 0.38±0.20 0.44±0.12 0.64±0.17 0.78±0.11 DISPR* 2.527±2.129 2.827±2.092 4.631±2.291 5.733±1.332 DISPE 3.881±3.070 3.918±3.015 5.549±2.712 5.459±1.034 DISPR*-E 5.408±4.466 6.090±4.653 9.343±4.509 10.385±2.124 Transit time (sec) 9.32±1.41 2.91±0.30 Pulmonary blood volume (1) 1.08±0.17 1.61±0.27 18 1^2^3 ^ 4^5 Time (min) Figure 1. Pa02, PaCO2 and [A-a]D02(o) for individual subjects during light exercise. -10 0^1^2^3 ^ 4 ^ 5 Time (min) Legend: subject 1=open circle, subject 2=open square, subject 3=open diamond, subject 4=closed circle, subject 5=closed square, subject 6=closed diamond, subject 7=diagonal cross, subject 8=horizontal cross, subject 9=open triangle, subject 10=open circle with dot. 19 2^3^4^5 Time (min) Figure 2. Pa02, PaCO2 and [A-a]D02(o) for individual subjects during heavy exercise. 0^1^2^3 ^ 4 ^ 5 Time (min) Legend: subject 1=open circle, subject 2=open square, subject 3=open diamond, subject 4=closed circle, subject 5=closed square, subject 6=closed diamond, subject 7=diagonal cross, subject 8=horizontal cross, subject 9=open triangle, subject 10=open circle with dot. 20 50 40 10 0 1^2^3^4^5 Time (min) Figure 3.Pa02, PaCO2 and [A-MO2(o) during maximal exercise. 2^3^4^5 Time (min) Legend: subject 1=open circle, subject 2=open square, subject 3=open diamond, subject 4=closed circle, subject 5=closed square, subject 6=closed diamond, subject 7=diagonal cross, subject 8=horizontal cross, subject 9=open triangle, subject 10=open circle with dot. 0 21 Mixed venous P02 (NO2) and arterio-venous difference (a-i,r02diff) were calculated from the Fick equation and are given in Table 4. Mean N ,02 was 36±4 torr at rest and decreased significantly (F=17.9, P<0.05) during light exercise. A further decrease between light and heavy exercise was not statistically significant (F=2.49, p>0.05), nor was the slight increase from heavy to maximal exercise (X NO2 = 15± 6 torr heavy exercise vs 17±7 torr maximal exercise). The a-i/O2diff increased between rest and light exercise (F= 3.98, p<0.05), was the same for light and heavy exercise (71±7 and 71±9 ton respectively) and increased further (F=1.69,p>0.05) during heavy exercise to a maximum value of 77±11 torr. Table 4. Calculated mixed venous and arterio-venous 02 difference during rest. light, heavy and maximal exercise. Subject Rest NO2^(a-v)02 (torr)^(ton) 150 watts NO2^(a-v)02 (ton)^(ton) 300 watts NO2^(a-v)02 (ton)^(ton) Maximal NO2^(a-v)O2 (torr)^(torr) 1 30 59 19 77 14 68 11 77 2 40 48 21 67 24 63 19 72 3 31 67 17 78 20 71 29 68 4 36 58 19 64 13 61 19 62 5 34 65 24 62 13 61 25 65 6 43 64 24 80 18 82 12 97 7 32 73 22 76 17 77 19 83 8 40 59 18 60 14 69 15 78 9 35 68 22 72 5 88 11 86 10 35 61 17 75 8 72 7 82 SD.^36±4^62±7^20±3^71±7^15± 6^71±9^17±7^77±11 Pv- 02 = mixed venous partial pressure of oxygen, [(a-v)02] = arterio-venous difference for oxygen. '/* ^inequality Considerable difficulty was experienced in fitting the data from the present study to the MIGET model of Wagner et al., (168) as evidenced by high residual sum of squares at x± 22 rest. There was a significant improvement in the residual sum of squares during heavy and maximal exercise when compared to rest (F=3.3, and F=3.5, p<0.05) and an acceptable fit was achieved in the maximal exercise data. The mean of the log normal perfusion (mean of Q) distribution increased significantly over the exercise levels (omnibus F(3,27)=45.6, p<0.001) as did the mean of the log normal ventilation distribution (mean of V) (omnibus F(3,27)=61.2, p<0.0001). There was a non significant increase in the VA/Q heterogeneity, as measured by log SDQ from rest to light exercise; however the increase from light to heavy exercise and from heavy to maximal exercise was significant (F=6.6, p<0.05 and F=3.14, p<0.05). There was no significant change in the log SCor over the exercise levels (onmibus F(3,27)=1.35, P>0.05). Elimination of SF6 data from the analysis resulted in low residual sums of squares (4.27±2.7 at rest, which increased to a maximum value of 12.2±11.1 at max exercise) indicating adequate model fit. The results of the statistical analyses for the mean Q, mean V and logSD j were unchanged when the SF6 data were eliminated, however no statistically significant change in log SDQ was found with exercise (omnibus F(3,27)=1.12). The independent indices of VA/Q mismatch DISPR., DISP E and DISPR._E are presented in Figure 4. There was a significant increase in dispersion as measured by two of the three indices. There was a highly significant increase in DISP R* across exercise levels (omnibus F(3,27) =25.8, p<0.0001), as was the increase in DISPR._E (F(3,27)=8.45, p<0.001). The increase in DISP E was not significant (F(3,27)=2.45, p=0.09). The area under the curve described by the plot of alveolar arterial difference for the six inert gases as a function of A, (A-a)D and the retention and excretion components of the area (R(A-a)D and E(A-a)D) are given in Table 5. DISPR*, R(A-a)D and log SDQ are all measures of the perfusion distribution and therefore should be comparable. Similarly DISP E, E[A-a] and log S1D, are comparable measures of the ventilation distribution and DISPR._ E and (A-a)D are comparable overall indices of dispersion. 23 14 12 10 8 6 4 2 0 Table 5. (A-a)D, R(A-a)D and E(A-ajD at rest and during light. heavy and maximal exercise ("X ±SD). Rest 150 Watts 300 Watts Maximal Exercise (A-a)D 0.211±0.171 0.233±0.185 0.360±0.178 0.411±0.098 R(A-a)D 0.066±0.060 0.088±0.066 0.146±0.073 0.181±0.043 E(A-a)D 0.259±0.341 0.161±0.139 0.201±0.088 0.231±0.047 There was a significant increase in (A-a)D and R(A-a)D over the exercise levels (omnibus F(3,27)=9.1, p<0.001 and omnibus F(3,27)=21.0,p<0.0001) paralleling the increases in the corresponding parameters of the other methods of analysis. There was no significant change in E(A-a)D over the exercise levels (omnibus F(3,27)=1.7, p=0.64). Figure 4. Dispersion indices at rest and during light, heavy and maximal exercise.  * * * * -100^0^100^200^300^400 Work Load (watts) Legend: Closed circles = DISPR* Oa SD), open squares = DISPE (31 ± SD), open diamonds = DISPR*_E (X ± SD), * = significantly different from rest (p<0.05),** = significantly different from light exercise and rest (p<0.05). 24 40 30 -10 Diffusion disequilibrium [A-4D02 predicted on the basis of the model of Wagner et al.(164), [A-41)02(p), and that not accounted for by the inert gas analysis [A-4D02(o-p) is given in Table 3. [A- 4D02(p) is compared to observed [A-41)02 in Figure 5. [A-4D02(p) increased significantly over the exercise levels (omnibus F= 12.2, p<0.0001), paralleling the dispersion indices. There were a non-significant increases in [A-4D02(p) between rest and light exercise. The increase between light and heavy exercise was significant (F=3.7, p<0.05) and there was no further increase from heavy to maximal exercise. During heavy and maximal exercise [A-41)02(o) was 9-10 tort . greater than that predicted by the inert gas exchange (F=5.3,and F=11.4, p<0.05) suggesting diffusion limitation. Figure 5. Observed and predicted alveolar-arterial 02 difference -100^0^100^200^300^400 Work Load (watts) Legend: Open squares=predicted [A-4D02 (mean±SD), closed squares=observed [A- 4D02 (mean±SD) * = significantly different than predicted (p<0.05) 25 Cardiac output and cardiac volumes The results of the first pass and gated cardiac studies are given in Table 6. Data were processed by three independent observers and the results were averaged. The correlation was 0.90 between observers and the mean difference in cardiac output between observers was approximately 3% at each exercise level. Cardiac output rose from 7.0±0.9 1•min-1 at rest to 33.3±3.7 1-min -1 at maximal exercise, accompanied by a significant increase in ejection fraction from 0.63±0.05 % at rest to 0.76±.05% during light exercise (F=35.8 p<0.05). A further increase in ejection fraction to 0.80±0.04 % at 300 watts approached but did not reach statistical significance (F=3.2, p>0.05 compared to 150 watts). End diastolic volume increased significantly (omnibus F(2,12)=20.1, p<0.05) from rest to heavy exercise as did stroke volume (F=22.5, p<0.05). There was also a decrease in end systolic volume between rest and heavy exercise (omnibus F(2,12)=4.4, p<0.05). Table 6. Cardiac output. volumes and ejection fraction at rest and during. light, heavy and maximal exercise.(i±SD). Rest 150 watts 300 watts Maximal Q 7.0± 0.9 16.2 ± 1.5 25.3 ± 2.6 33.3 ±3.6 Heart Rate 70±10 116±9 156±8 166±8 Stroke Volume 102 ± 21 140 ± 17 163 ± 22 201±23 End Diastolic Volume 149 ± 16 184 ± 25 198 ± 22 * End Systolic Volume 55 ± 10 44 ± 12 41 ± 10 * Ejection Fraction 0.63 ± 0.04 0.76 ± 0.05 0.80 ± 0.04 * * = cardiac volumes obtained during gated study only which was not conducted during maximal exercise. Pulmonary transit time Figures 6A and 7A contain representative raw data and gamma univariate fits for regions of interest drawn over the right and left ventricles. Figure 6B and 7B show the frequency distribution of transit times (transfer function) obtained by the deconvolution 26 method for the same subject and the output curve obtained when the transfer function is convoluted with the input curve from the right ventricle. Mean transit times at rest and maximal exercise obtained by both deconvolution and centroid techniques are presented in Table 7. In one subject, (subject 10) a satisfactory fit was not obtained by deconvolution analysis and results are reported for the centroid method only in this individual. Mean transit time at rest was 9.31± 1.45 seconds by the centroid method and 9.32± 1.41 by deconvolution. These were highly correlated (r = 0.99, p<0.0001). During exercise mean transit times decreased significantly to less than 3 seconds (2.90± 0.35 centroid, 2.91± 0.30 deconvolution; t deconvolution =12.3,p<0.001) and these were also highly correlated (r=0.96, p<0.001). The mean duration of the right ventricular curve did not change significantly from rest to exercise (4.96 ± 1.37 vs 4.19 ± 0.80 seconds, t = 1.54, p = 0.1) however there was a significant decrease in the mean duration of the left ventricular curve from 14.29 ± 1.52 to 7.09 ± 1.06 seconds (t=11.2, p< 0.001). Pulmonary transit time was significantly correlated with Pa02 (Figure 8) (r=0.65, p<0.05) during maximal exercise and [A-a]l:02(o) (r=-0.59, p<0.05). There was also a significant relationship (Figure 8) between [A-a]D02(o-p) and transit time (r=-0.60, p<0.05). When multiple linear regression was used to determine the relationship between transit time, i/FATCO2, [A-a]D and Pa02 there was a highly significant relationship (R=0.94, R 2=0.88, adjusted R2=0.83, p<0.01). Thus at maximal exercise, over 80% of the variance between subjects in Pa02 can be explained on the basis of transit time, V. A/Q mismatch and the ventilatory equivalent for CO2. 27 T mon,"  fly lu en .n n r m X± D1M - I • is Rest (s) Centroid^Deconvolution Exercise (s) Centroid^Deconvolution 1 10.09 10.19 2.99 2.92 2 7.24 7.37 2.77 2.78 3 8.60 8.75 3.18 3.16 4 7.72 7.45 2.68 2.67 5 9.12 9.23 2.74 2.99 6 8.96 8.85 3.76 3.56 7 8.63 8.73 2.95 2.91 8 9.80 9.82 2.60 2.58 9 11.36 11.35 2.68 2.69 10 11.75 11.43 2.71 * Mean^9.33 ± 1.45^9.32 ± 1.41 ^ 2.90 ± 0.35 ^ 2.91 ± 0.30 ±SD * = unable to fit satisfactory deconvolution analysis 28 O O Figure 6A. Raw data and gamma univariate fit for subject one during rest 4.5 Legend: Open triangles = left ventricular counts, open diamonds = right ventricular counts. Solid lines = gamma univariate fit for the right and left ventricular curves. Figure 6B. Frequency distribution of transit times for data in Figure 6A. 0 0^5^10^15^20^25^30^35^40 Time (seconds) Legend: Solid lines = gamma univariate fit for the right ventricle and left ventricle, dashed and dotted line = frequency distribution of transit times (transfer function), dashed line = resulting output curve when the right ventricular curve is convoluted with the transfer function. 29 0Co^1 0 Figure 7A. Raw data and gamma univariate fit for subject one during maximal exercise. 0 ^ 4 ^a^12^16^20^21 Time (s) Legend: Open triangles = left ventricular counts, open diamonds = right ventricular counts. Solid lines = gamma univariate fit for the right and left ventricular curves. Figure 7B. Frequency distribution of transit times for data in Figure 7A. ° 0^5^10^15 ^ 20 Time (seconds) Legend: Solid lines = gamma univariate fit for the right ventricle and left ventricle, dashed and dotted line = frequency distribution of transit times (transfer function), dashed line = resulting output curve when the right ventricular curve is convoluted with the transfer function. 1.4 1.3 1.2 1.1 1 0.9 0.6 0.7 0.6 0.5 0.4 0.3 0.2 0.1 30 ■ ■ • Figure 8. Pa02 and [A-ajD02 (o-p) versus transit time. 110 — 105 — 100 — 95 — 90 — 85 — 80 ^ ^ 2.4^2.6 2.8^3^3.2^3.4^3.6 Transit Time (seconds) 40 — 30 • 20 — • • 1 0 — • • • 0 — • -10 — • -20 I I I I I I 2.4 2.6 2.8^3^3.2^3.4^3.6 Transit Time (seconds) 31 Pulmonary blood volume Pulmonary blood volume and pulmonary blood volume index obtained at rest and during maximal exercise are presented in Table 8. Pulmonary blood volume increased significantly during exercise (t=6.1, p<0.001) by over 50%. Pulmonary blood volume was significantly correlated with whole blood volume at rest (r=0.67, p<0.05) and there was a similar trend during exercise although the relationship did not attain statistical significance (r=0.52, p=0.06). Resting pulmonary blood volume index (pulmonary blood volume/BSA) correlated significantly with resting [A-a]D02 (r=-0.65, p<0.05). Exercising pulmonary blood volume index correlated significantly with Pa02 (r=0.69, p<0.01) and [A-a]D02(o) (r=-0.57, p<0.05). Pulmonary blood volume index correlated significantly with [A- a]D02(o-p) during exercise (r=-0.68,p<0.05), as did whole blood volume (in ml•kg -1 ) (r =-0.61, p<0.05). Table 8. Pulmonary blood volume at rest and during maximal exercise Pulmonary Blood Volume (1) Rest Pulmonary Blood Volume Index % of Total Blood Volume Maximal exercise Pulmonary^Pulmonary Blood Blood Volume (1)^Volume Index % of Total Blood Volume % increase in pulmonary blood volume 1 1.24 0.58 15 1.67 0.78 21 40 2 0.74 0.40 13 1.28 0.69 22 73 3 0.95 0.45 13 1.82 0.87 26 97 4 1.06 0.52 18 1.42 0.70 23 30 5 1.17 0.56 15 1.79 0.86 22 42 6 1.21 0.61 18 2.05 1.03 32 77 7 0.90 0.48 13 1.82 0.96 27 107 8 1.08 0.54 16 1.54 0.76 23 44 9 1.11 0.60 20 1.18 0.64 21 6 10 1.33 0.61 17 1.53 0.69 19 11 MEAN 1.08 0.53 16 1.65 0.82 24 57 ±SD 0.18 0.07 2 0.30 0.15 4 34 32 DISCUSSION Several authors have reported hypoxemia and arterial desaturation during short term heavy or maximal exercise which increases with physical conditioning (146) and is more common in highly trained athletes (184). In this population the reported incidence of hypoxemia during exercise may be as high as 52% (126). The etiology of the hypoxemia continues to attract considerable debate. Dempsey and co-workers (35) exercised sixteen highly trained athletes at 70-90% of VO2 max breathing different gas mixtures. At this exercise intensity, several subjects exhibited little alveolar hyperventilation and the authors concluded that "the magnitude of the hyperventilatory response was a major determinant of the hypoxemia seen in our athletes". The effect of hypoventilation is to reduce alveolar P02 and thus the arterial P02 without an effect on [A-a]D02. Additional evidence in support of inadequacy of ventilation as a causative factor include the observations that during MVV testing and exercise, peak expiratory flows may approach or exceed those defined by the maximal expiratory flow- volume curve (67, 78, 118) and the observation that administration of He:02 mixtures increases ventilation, decreases PaCO2, and improves arterial oxygenation, without altering [A-a])02 (35, 36, 170). Humans also exhibit entrainment to a variable extent with running, walking, rowing and cycling (13, 158) and this has been implicated as a cause of hypoxemia in galloping horses (34). Aside from the mechanical factors described above, hypoventilation could also be caused by blunted respiratory drives (26, 106, 148). Pulmonary diffusion limitation secondary to shortened pulmonary transit represents an attractive alternate hypothesis to hypoventilation as a cause of exercise induced hypoxemia. Multiple inert gas studies have shown evidence of diffusion limitation in men capable of sustaining VO2 — 4 I-min -1 , however since MIGET studies have not been made in very highly trained athletes who exhibit exercise induced hypoxemia, the arguments are largely theoretical. The consistent observation that [A-a]D02 widens with increasing exercise intensity, (35, 63, 72, 161, 182) offers indirect support to this idea. 33 Multiple inert gas analysis and exercise The multiple inert gas analysis technique is based on the observation that the retention of a gas in the blood is related to the solubility of the gas (X) and the VA/Q distribution. By using gases that bracket the solubility of 02 and CO2 and measuring arterial and mixed expired concentrations, it is possible to estimate shunt, dead space and the shape of the VA/Q distribution. It is also possible using the derived VA/Q distribution to predict the behavior of 02 and CO2. Assumptions that are made in the multiple inert gas analysis are: 1. there is steady state gas exchange, 2. the lung units are arranged in parallel and behave as independent compartments 3. ventilation and perfusion is non-pulsatile, 4. diffusion dis-equilibrium and extra-pulmonary shunt are absent. It is the exploitation of this last point that has formed the basis of detection of diffusion dis-equilibrium, as any observed [A-a]D02 that exceeds that predicted from the MIGET VA/Q distribution is likely due to diffusion limitation or extra-pulmonary shunt. The advantages of the MIGET technique are that a change in Pi02 which may alter the VA/0 distribution is not required, and that the trace amounts of gases infused are not sufficient to alter concentrations of the physiologic gases. Despite more than ten years experience with this method, studies involving humans are few, and are mostly confined to normals rather than highly trained athletes. Gledhill et al., (60) studied five male subjects at rest and during light exercise (V02 1.8 1-min -1 ) and found an increase in V A/Q as measured by log SDV and log SDQ, associated with a widening of [A-a]D02 which was improved by the breathing of a high density gas. In contrast , Derks (37) found no changes in dispersion in his subjects, again at levels of exercise less than 2.01•min-1 . The next studies, published in 1985 as companion papers by Torre-Bueno et al.(161), and Gale et al. (55), contributed substantially to knowledge of the effects of exercise and altitude on ventilation and perfusion mismatch and diffusion limitation. Nine subjects were studied at various exercise levels up to a V02 of almost 3 1•min-1 , at sea-level and simulated altitude corresponding to 5,000, 10,000 and 15,000 feet. 34 At sea-level there was a trend towards worsening of VA/Q relationships with exercise although the results did not reach statistical significance. Resting VA/Q did not increase with altitude, although the combination of exercise and altitude did produced significant deterioration in the VA/Q relationships (55). There was no evidence of diffusion dis- equilibrium at rest for any altitude although there was evidence for diffusion limitation during exercise at altitudes above 10,000 ft. There was a suggestion that, in subjects capable of higher levels of exercise, diffusion limitation might be present although small "n" hampered definitive conclusions (161). This last observation was addressed further in a paper by Hammond et al., (63) who was able to exercise moderately trained athletes to a VO2 of almost 4.0 1-min -1 . Evidence of diffusion disequilibrium was found at the highest exercise intensity (300 watts), as the average measured [A-0)02 exceeded that predicted from the derived VA/Q distribution by more than 12 ton. These results were confirmed by Bebout et al., (12) who found evidence of diffusion limitation during exercise that was worsened by altitude and improved by two weeks of acclimatization. The authors concluded that the effect of acclimatization was to lower cardiac output at any given exercise level and improve diffusion limitation through an effect on pulmonary transit time. MIGET data . Indices of dispersion and VAN mismatch Significant increases in overall indices of dispersion and in the indices of dispersion related to the blood flow distribution were observed with exercise regardless of the index used. There was little change between rest and light exercise, marked increases between light and heavy exercise and little further change between heavy and maximal exercise. No significant change was apparent in any of the indices of dispersion related to ventilation. These data indicate increasing V. A/Q mismatch predominantly related to heterogeneity of blood flow. When blood flow was examined with respect to areas of low (V. A/Q <0.1), 35 normal (0.1<VA/Q < 10) and high (VA/Q >10) VA/Q (40) it was evident that the heterogeneity in flow resulted from significantly increasing flow to areas of high V. A/Q. rather than areas of shunt or low V. A/Q . Perfusion of areas of high VA/Q accounted for over 10% of blood flow during maximal exercise compared with less than 2% at rest. At maximal exercise perfusion of areas of low V. A/Q accounted for less than 1% of blood flow and intrapulmonary shunt was not found. Similar patterns of dispersion of the perfusion indices have been reported resulting from hypoxic pulmonary vasoconstriction in lobar preparations in the dog (41) which is exacerbated in oleic acid induced pulmonary edema (40). Difficulty in interpreting these data stems from an overall right shift of the compartmental ventilation versus log VA/Q. ^as a result of an approximately twenty- fold increase in VE compared to a six-fold increase in Q. During exercise, therefore, perfusion to areas of low V. A/Q may be obscured by this shift and will be manifest by an increase in log SDO and other perfusion related indices. At rest and during exercise to a VO2 of 4.01-min -1 the mean of the Q and distribution and log SDO compare favorably with those at comparable exercise levels obtained during similar studies (12, 63, 147) (Table 9). The mean of the V distribution and log SDa are higher at all exercise levels and are due to the recovery of areas with apparently very high VA/Q distributions. In some cases the excretion of acetone (the gas of highest solubility) exceeded the retention, violating laws of mass balance. This apparent paradox can likely be explained on the basis of an complex interaction between airways heating and cooling, as it was much more pronounced at maximal exercise. Areas of very high VA/Q have been reported in many experimental situations including high frequency ventilation (68, 108, 138), and have been considered to be artifactual. No alveolar zones with such a high VA/Q seem anatomically likely, but gas exchange of soluble gases by the airways is possible (162). Gas solubility and transport is related to airways temperature, mucous temperature, water content and thickness. During high intensity exercise there will be both heating of the airways secondary to increased bronchial blood flow and increases in core 36 temperature, as well a cooling secondary to hyperventilation. It is possible that excretion of the soluble gases deposited in the airway mucosa and mucous earlier during the experiment may be enhanced by the increase in body temperature. The indices of ventilation heterogeneity E(A-a)D, DISPE and log SDv. would be most likely to be affected and do not explain the significant increases in the perfusion related indices. The increase in VA/Q heterogeneity with exercise, manifest by increasing [A- a]D02(p) is greater than reported in other studies (12, 63) and is not an effect of the higher exercise intensity achieved since [A-a]D02(p) was higher even at the submaximal workloads. The reasons for this are not apparent but may reflect an unique characteristic of this highly trained subject population. Diffusion limitation The MIGET analysis of pulmonary gas exchange accounts for V. A/Q mismatch and intra-pulmonary shunt. [A-a]1)02 (o-p) therefore represents that portion of the [A-a]D02 which is not accounted for by those factors and represents diffusion limitation or extrapulmonary shunt. A 1% extra-pulmonary shunt would result in a fall in Sa02 of about 0.7% and a decrease in Pa02 of about 7 torr accounting for most of the difference between observed and predicted values of [A-a]D02. The issue of extrapulmonary shunt therefore becomes crucial in the detection of diffusion limitation. Since extrapulmonary shunt was not measured in this study this possibility canot be dismissed. Previous work (161) during exercise at sea level and altitude has shown minimal (< 0.18 %) shunt, although these measurements are extremely difficult to make. If it is assumed that the subjects in the present study are similar with respect to extrapulmonary shunt then a 0.18% shunt in the present study would account for approximately 1 ton of the [A-a]D02 (o-p). Although the [A-a]D02(o-p) demonstrated a correlation of 0.60 with pulmonary transit time over 60% of variance between subjects is not accounted for by PTT. There are many possible explanations and making a direct comparison is fraught with difficulties. The 37 comparison is only as good as the measures used to make it. Pulmonary transit time is only an indirect indicator of pulmonary capillary transit time and therefore the relationship described above reflects uncertainty in this measure as well as uncertainty related to the detection of diffusion limitation by MIGET. Table 9. Comparison of MIGET data from present study with other investigators if02 1•min-1 Mean Q Mean V Log SD(*) Log SM DISPR* DISPE DISPR*_E Study Rest 1.00 ±0.23 1.11 ±0.17 0.63 ±0.09 1.91 ±0.99 1.28 ±0.20 0.68 ±0.23 0.38 ±0.20 0.35 ±0.05 0.28 ±0.13 1.09 ±0.55 0.42 ±0.10 0.26 ±0.04 2.53 ±2.13 1.08 ±0.29 0.68 ±0.43 3.88 ±3.07 1.07 ±0.29 0.59 ±0.27 5.41 ±4.47 1.99 ±0.51 1.16 ±0.61 p 1 2 2.46 4.21 0.44 0.90 2.83 3.92 6.09±0.62 ±1.50 ±0.12 ±0.47 ±2.09 ±3.01 ±4.65 P 3.16 3.91 0.44 0.46 1.64 1.38 2.722-3 ±0.66 ±0.83 ±0.05 ±0.05 ±0.25 ±0.17 ±0.32 1 2.79 2.98 0.34 0.31 0.93 0.82 1.48±0.69 ±0.77 ±0.07 ±0.03 ±0.28 ±0.28 ±0.54 2 2.94 7.27 0.64 1.09 4.63± 5.55 9.34±0.62 ±3.67 ±0.17 ±0.29 2.29 ±2.71 ±4.51 P >3-4.5 * * 0.55 ±0.17 0.49 ±0.20 * * * 3 3.33 3.96 0.58 0.36 1.44 0.96 2.14±0.50 ±0.61 ±0.30 ±0.09 ±0.81 ±0.47 ±1.10 2 4.44 11.82 0.78 1.18 5.73 5.45 10.39>5.0 ±0.53 ±2.90 ±0.11 ±0.14 ±1.33 ±1.03 ±2.12 p * = not reported. P= present study, 1= study of Schaffartzik et al., group 1 subjects (147), 2= Hammond et al., (63), 3= Bebout et al., (12) Effect of uncertainty in cardiac output on MIGET data Small non significant differences were noted in heart rate between measures made during the cardiac output determinations and the inert gas measures, at rest and submaximal 38 exercise. During maximal exercise, the two measures were nearly identical. On average, heart rate was 6-7 beats per minute higher during the nuclear medicine studies, resulting in an error of 12% at rest, 5.7% during light exercise and 4.4% during heavy exercise. The likely explanation , relates to small differences in posture during the two studies; during the inert gas studies the subjects were free to assume the posture that they were the most comfortable with, usually in a relaxed position holding on to the bars of the ergometer. In contrast, during the cardiac output determinations the subjects were required to hold on to the gamma camera, an awkward position at best, which likely changed their pedaling efficiency slightly. Although cardiac output was not measured simultaneously with the MIGET determinations, uncertainty of the cardiac output measurements is unlikely to affect the inert gas calculations with respect to the recovered VA/Q. ^or the predicted [A-a]D02. In theory, uncertainty in measurement of cardiac output, would be most apparent in the insoluble gases (small X ) as the mixed venous levels (P■f) of the inert gases are calculated from arterial and mixed expired (PE) levels: Pi = Pa +PEVE A.QT For highly soluble gases, X. is large and Pa dominates the right hand side of the equation. For insoluble gases such as SF6, the term (pE•E)/( )vQT) dominates the equation and uncertainty in QT will be transmitted directly to calculations of retention and excretion data .^. and from there into the calculation of the VA/Q distribution. This issue has been addressed by Wagner et al., (169) who compared recovered VA/Q distributions, residual sum of squares (model fit), [A-a]D02 (p), and Pa02 for detection of diffusion limitation in ten patients at rest using measured cardiac output and a sensitivity analysis over assumed cardiac outputs of 2 to 121•min -1 . Little effect of assumed cardiac output was observed on most of the variables of interest. Log SD(*) was insensitive 39 to large changes in cardiac output, although slightly more effect was observed on log SDv. The predicted values of Pa02, PaCO2 and [A-0)02 were also insensitive to assumed cardiac output and residual sum of squares was unaltered. These findings were also confirmed in the present study. Table 10 gives the residual sum of squares (RSS) and mean Q, mean V, logSDa and log SDV for a poorly fitting data set varying cardiac outputs from 15 to 40 1.min -1 . Table 10. Effect of varying cardiac output on residual sum of squares and indices of dispersion 1•min-1 RSS^mean of distribution mean of V distribution Log SDO Log SIDT 15 114 6.52 12.35 0.558 1.025 20 113 4.83 9.92 0.535 1.153 25 112 3.84 8.40 0.515 1.253 30 111 3.17 7.35 0.496 1.134 35 110 2.71 6.52 0.486 1.398 40 109 2.37 5.90 0.473 1.457 The mean of the ventilation and perfusion distributions are sensitive to alterations in cardiac output but are not of great importance since it is dispersion that determines VA/ Q mismatch. The indices of dispersion log SDa and log ST:07 as described by Wagner et al., (169) are relatively insensitive to changes in cardiac output; for an increase in cardiac output of over 150%, log SDa decreased by 15% while log SDI*/ increased by 14%. It can be concluded that the pattern of the VA/Q relationship recovered from inert gas data is not affected by uncertainties in cardiac output, and cannot explain the difficulties in fitting the SF6 data. Maintenance of steady state conditions Steady state conditions in the strictest sense cannot be attained at exercise levels above the anaerobic threshold and mean ventilation changed from 164.2 to 178.11-min -1 over the data collection during maximal exercise. This was an increase of 7.8%, which is 40 relatively small and unlikely to affect the results as the very high levels of blood flow and ventilation ensure rapid equilibration rates. Blood and plasma volume Methods for determination of blood and plasma volume are based on measurement of dilution of a known amount of a tracer material. The use of radiolabelled human serum albumin (RISA) is currently the accepted method for routine measurement of plasma volume in human subjects (76), although the use of albumin as an intravascular marker likely overestimates the volume of distribution by a factor of over 5% when compared to larger macromolecules such as fibrinogen (15). The RISA method for measurement of plasma volume compares favorably to measurements made with Evans blue dye, carbon monoxide gas, and 99mTc labeled erythrocytes (160), although, as would be expected from the preceding statement, RISA measurements agree closest with Evan blue dye measures which also uses albumin as the carrier molecule for the tracer. When red cell mass is not measured independently, but estimated from venous hematocrit, the accuracy of the determination is dependent on correction of venous hematocrit to whole body hematocrit (76). The subjects were both plasma volume expanded and had increased red blood cell mass. These findings have been documented in the past by a number of authors. Cross- sectional studies indicate that highly trained men and women have blood volumes that are approximately 25% larger than sedentary subjects (38, 88) with the proportionate increase in plasma volume being greater than the proportionate increase in red cell mass (21). The subjects had plasma volumes 44% greater than predicted for normal subjects although the mean value of 87.5 ml•kg-1 whole blood volume for this study compares favorably with values reported by Kjellberg et al., (88) and others (21, 38). Little is known about the mechanisms of the changes in hematological parameters, however it is postulated that increases in circulating plasma proteins are an important factor in chronic hypervolemia, as 41 are renal mechanisms involving renin-angiotensin-aldosterone, and vasopressin (31). This hypervolemia may offer advantages with respect to thermoregulation and hemodynamics and has shown a strong correlation with VO2 (31). Changes in blood volume associated with chronic endurance exercise may account for up to one half of the difference in stroke volume between trained and untrained men (73) and a decrease in plasma and blood volume with detraining has been shown to decrease both VO2 max and stroke volume in endurance athletes (32). Radionuclide cardiography First pass and gated radionuclide cardiography offer many advantages over other methods for determination of cardiac output and cardiac volumes. The method is relatively non-invasive requiring only an intravenous line and serial measurements can be made making these techniques suitable for exercise studies, particularly those involving more than one exercise level. A combination of first pass and gated determination of cardiac output was chosen for the following reasons: First pass techniques also allowed simultaneous determination of right-heart to left heart pulmonary transit times, and measurement of cardiac output at rest by both methods enabled the subsequent gated determinations to be corrected for attenuation of counts by the chest wall without on relying assumptions about ventricular geometry or depth. We used the gated technique during light and heavy exercise to minimize radiation exposure to the subjects, and first pass was only done during maximal exercise because of concern regarding motion artifact and data acquisition time. First pass radionuclide cardiography has been compared with more traditional methods of cardiac output determination both at rest and exercise and shows good agreement with values obtained by thermodilution (83), contrast ventriculography and gated equilibrium radionuclide angiography (122). Gated equilibrium radionuclide angiography, similarly has shown excellent agreement with contrast ventriculography (122, 151), direct 42 Fick method (111) and thermodilution (33). Thus both radionuclide methods can be used with confidence that they are valid means of determining cardiac output. Cardiac function in athletes The term "athletes heart" has been used to describe the alteration in cardiac structure and function which accompany regular physical conditioning. In recent years, M-Mode echocardiography and gated and first pass radionuclide angiography have allowed a more accurate picture of the effects of exercise on the heart. Maron (103) extensively reviewed 28 echocardiographic studies in athletes from a variety of athletic backgrounds. Regular athletic training produces an increase in left ventricular mass, even when corrected for body size and increases in left ventricular cavity dimension especially in endurance athletes. Left ventricular end-systolic dimension is usually increased, and many studies report increases in posterior left ventricular wall as well as ventricular septal thickness (103). Enlargement of the left atrium is also commonly reported, as is an increase in right ventricular mass. The effects of training on the heart has been examined longitudinally (94, 95, 115, 134) and the available data indicate that the nature of alterations in cardiac structure are somewhat dependent on the type of training stimulus, with strength athletes exposed to predominantly a pressure load tending to show an increase in left ventricular thickness while those exposed to endurance training, and hence a volume load, showed an increase in ventricular volume. In a longitudinal study, Rerych et al., (134) studied collegiate level swimmers with first pass radionuclide angiography and found an increase in resting stroke volume, end diastolic volume, end systolic volume and a decrease in ejection fraction after six months of swim training. During exercise, maximal cardiac output was increased predominantly as a result of increases in end diastolic volume, while ejection fraction was unchanged from pre- training levels. Fagard et al., (46) studied highly trained amateur and professional cyclists with echocardiography in both the competitive and rest seasons and compared them to recreationally active controls. During the resting season the athletes had smaller heart size 43 and significant decreases in total left ventricle end systolic diameter, due to reductions in wall thickness than in the competitive season. When compared to controls the cyclists had much higher ratios of wall thickness to internal dimensions in both the competitive season and the resting season suggesting that both volume and pressure (presumably due to isometric upper body exercise during cycling) were responsible for the increase in cardiac dimensions. Comparison with previous investigations The data from the present study are similar to those obtained by direct Fick technique, radioangiography and echocardiography (Table 11). At rest, the cardiac output was slightly higher in both the present study and the other radioangiographic study (134), but resting heart rate and cardiac output would be expected to be the most influenced by extraneous variables such as subject arousal etc. The resting stroke volume index from the present study is comparable to the mean value for the other studies cited. During exercise at a VO2 of 1.5-21•min -1 and 'T02 of 3-41•min -1 the present data again are very similar to those previously reported. During maximal exercise, although cardiac output is very similar to that obtained in the other studies, stroke volume index is higher. This may possibly be explained by the study design. Collecting clean first pass data was of paramount importance for measurement of pulmonary transit times and motion artifact was of great concern. It was elected to inject the labeled red blood cells at the end of 3 minutes of exercise and data were acquired for 150 seconds after injection. At this work intensity heart rate clearly had not fully stabilized and it may be possible that stroke volume may have fallen and heart rate risen, with redistribution of blood flow as the exercise continued, which would not be evident during the data collection. Effect of increasing exercise intensity on cardiac volumes Exercise physiology text books (20) report that stroke volume levels off with increasing exercise intensity, possibly as a result of decreased diastolic filling time and 44 reduced end diastolic volume. Indeed, some authors have reported a decrease in end diastolic volume with increasing exercise (57) while others have reported a decrease, no change or an increase in end diastolic volume (49, 124). A decrease in stroke volume with increasing exercise intensity was not found in any of the subjects, and only one subject (#9) showed a plateau in stroke volume between heavy and maximal exercise. These data would tend to support the recent observations by Gledhill et al., (61) who found increasing stroke volume with increasing exercise in endurance trained athletes without a shortening of left ventricular ejection time compared to sedentary controls, leading the authors to conclude that enhanced preload and Frank-Starling mechanism were important factors for maintenance of stroke volume in athletic subjects. 45 Table 11. Cardiac output and volume indices comparison with other studies  Exercise Cardiac End Diastolic End Systolic Stroke Study intensity Index Volume Volume Volume index Index Index mi•m-2 ml•m -2 ml•m -2 ml•m-2 Rest 3.4±0.4 73±8 27±5 51±10 present R-G 3.4±0.5 85±20 22±6 64±15 Rerych R-FP, (134) 3.0±0.9 85±14 37±11 48±15 Ginzton E (57) 2.4±0.03 38±6 Hermannsen D (66) iT02 1.5-2.0 8.0+0.7 91±13 22±7 69±8 present R-G 1•min-1 8.8±1.7 90±17 24±7 66±13 Ginzton E (57) 8.2±1.3 67±9 Ekblom F (43) iT02 3-4 12.5±1.2 98±11 21±5 81±11 present study 1•min-1 R-G 13.8±1.3 90±7 Ekblom F (43) 11.1±1.5 66±9 Hermannsen D (66) i[02 4+ 16.5±1.8 100±12 present study 1-min-1 R-FP 16.3±4.4 104±19 14±7 90±20 Rerych R-FP (134) 18±0.6 95±4 Ekblom F (43) R-FP = Radioangiography first pass method, R-G = radioangiography gated method, E= echocardiography, F = Fick method, D= Dye dilution technique. 46 Pulmonary transit times Theory The mean transit time for a well mixed indicator to flow through a specific volume at a given flow rate is described by the relationship: transit time = volume/flow. The time required for an indicator to flow past an observation point down stream from an entry point is related not only to the time it takes the bolus to flow past the point but also how quickly it arrived there. Transit time is the time that a bolus remains in a compartment if it is injected directly and instantaneously into the compartment. The first moment describes not only the time that the indicator is in the compartment but also how quickly or slowly it arrived there. The first moment therefore represents the summation of all transit times up to that point. If it were possible to deliver indicator material instantaneously into the compartment of interest the first moment would be the same as the transit time. Transit time of a compartment can be determined by subtracting the first moment of the bolus from that of the output curve derived from the compartment. In the case of pulmonary transit times, the bolus or input curve is derived from a time activity curve of the right ventricle and the output curve is derived from the left ventricle; transit time is determined by subtracting the first moment of the right ventricular curve from the first moment of the left ventricular curve. This method is referred to as the centroid method. Deconvolution is a mathematical process by which a frequency distribution of transit times (a transfer function, h(t)) can be derived from the input (right ventricular) and output (left ventricular) time activity curves. The assumption is made that since both the input curve and output curve can be described by a gamma function (153) that h(t) is also a gamma function. A series of 20 to 50 curves is then generated with mean transit times similar to that obtained by the centroid method and convoluted with the input curve producing a series of unique output curves. Ridge regression is then applied to determine the contribution of the curves obtained to the actual output curve and the final transfer function is obtained. These data when convoluted with the input curve produces a derived 47 output curve. The process is repeated until a satisfactory result is obtained. It is important to note that the transit time obtained from either method represents the delay of the bolus through pulmonary arteries, arterioles, capillaries, venules, veins, left atrium and left ventricle and does not just represent pulmonary capillary transit time. Incomplete mixing of the bolus can lead to either over or underestimation of times, therefore is preferable to measure transit time downstream from the site of injection. Other sources of error include poor bolus technique and cross contamination of time activity curves from overlying structures in the chest. Pulmonary transit times have been measured in humans and animals in a variety of experimental conditions. There is a clear gravitational difference in regional transit times with the shortest transit times at the base and longest times at the apex of the lung (70, 96). As pulmonary blood flow increases, recruitment of blood volume prevents a drastic decrease in transit time (70). In animals, very close agreement has been obtained between radioisotopic methods and dye techniques (44). Similar support for the validity of this technique has been established in humans: MacNee et al.,(96) compared pulmonary regional transit times obtained by centroid and deconvolution methods pre-operatively with those obtained infra-operatively in five patients undergoing lung resection for carcinoma. Excellent agreement between in vivo and in vitro techniques was found with the mean difference between methods less than 0.5 seconds. There were no significant differences between those results obtained by deconvolution (mean over all regions = 4.83 sec) and centroid (mean = 4.53 sec) methods. 48 Pulmonary transit times and exercise in humans Exercise results in a decrease in transit time from resting values of about 5 seconds to less than 2.5 seconds in normal subjects (77). The effect of exercise training on pulmonary transit time have also been demonstrated. Rerych et al., (134) reported an increase in pulmonary transit times in collegiate swimmers after six months of intensive exercise training, likely reflecting increases in both total blood volume and pulmonary blood volume. Resting values in that study and the present one were greater by more than a factor of two, compared to sedentary subjects. Mean pulmonary transit time during exercise was 2.8± 0.3 seconds, a result very similar to that of the present study. Pulmonary blood volume Pulmonary blood volume has also been measured during a variety of clinical situations and during maximal exercise. Radionuclide methods for determination of pulmonary blood volume have correlated closely with dye dilution techniques (44), but it is important to recognize that both techniques measure the volume of blood between the point of input of the marker and the downstream measurement. Guintini et al., (58) reported pulmonary blood volumes in patients with a variety of cardiopulmonary conditions and in 5 normal men. Pulmonary blood volume at rest was 293±50 ml•m -2 comprising approximately 10% of total blood volume and increased with mild exercise. Slightly higher resting values were obtained by Iskandrian et al., (77), with a non significant increase during exercise. Exercise training has been shown to produce an increase in pulmonary blood volume of approximately 10% at rest and 50% during maximal exercise (134) compared to pre-training values. Maximal exercise in trained individuals led to an 80% increase in pulmonary blood volume index from 465 ml•m -2 at rest to 772 ml•m-2 during maximal exercise, similar to values of 530 ml•m -2 at rest and 820 ml•m-2 obtained during the present study. 49 Relationship of pulmonary transit time and blood volume to pulmonary capillary transit time and blood volume A central issue with respect to the interpretation of the present data concerns the relationship between whole lung transit time and blood volume and pulmonary capillary transit time and blood volume, since it is the latter two factors which affect pulmonary gas exchange. Unfortunately data addressing this question are sparse and can only give an estimate of these relationships. Backmann and Hartung(8) measured whole lung blood volume in cadavers and then estimated the arterial and venous contributions by the injection of a very viscous fluid that did not enter the capillaries. They estimated that 53% of the whole lung blood volume or 270±50 ml was in the pulmonary capillaries and suggested that this was an upper limit of the measure, as small arterioles and venules would be included using this technique. In contrast to this, studies that calculate capillary blood volume from diffusing capacity (64, 171) estimate values of less than one half of that value (about 100 ml) and may underestimate the true value, as it is a functional rather than an anatomical measure. If these values for blood volume are applied to the resting data from the present study, calculated pulmonary capillary transit time would be between 1.15 and 2.34 seconds and whole lung transit times would be between 4 and 8 times greater than capillary transit times. During exercise, there are even less data on which to base calculations. Warren et al., (171) measured pulmonary capillary blood volume at 215 ml using diffusing capacity during exercise at a VO2 of greater than 4.01•min-1 . Using this information and again accepting a value of 270 ml as the upper anatomical limit of pulmonary capillary blood volume, these values would give an estimated pulmonary capillary transit time for the present study of 0.39-0.49 seconds and whole lung transit time would be some 6 to 7.5 time greater than the capillary transit time. It is important to recognize that these represent average values for transit time and because of the skewed nature of both the pulmonary and pulmonary capillary transit curves a significant portion of the cells may have very short transit times. 50 Arterial blood gas and metabolic data Arterial blood measures Our subjects exhibited Pa02 s ranging from 74 to 100 ton (mean Sa02 of 95.3%) during heavy exercise and from 81 to 109 ton (mean Sa02 of 94.2%) during maximal exercise. These values are higher than the previous values reported by this laboratory (72) and that of Dempsey et al.,(35) however it is likely that this is as a result of the different means of exercising the subjects; running in the previous studies versus cycling in the present one. Cycling was chosen as the method of exercising subjects in the present study because it facilitated the MIGET data collection, and because measurement of pulmonary transit times, cardiac output and cardiac volumes is not currently possible using other exercise forms due to motion artifact. The relatively high values for pH in the present study of 7.24±0.06, compared with the previous study value of 7.21±0.06 in a very similar group of subjects exercising at the same relative intensity, likely are as result of the smaller muscle mass involved in cycling. This factor may also contribute to the higher values for Pa02. Three subjects from the 1989 study participated in the current one: all had higher values for Sa02, Pa02 and lower values for pH than in the study involving running. No correction was made for temperature in the previous study, and it is likely that the degree of hypoxemia in those subjects was overestimated. A temperature increase of about 1°C was seen after 5 minutes of exercise at VO2 max in the current study, which may underestimate the increase in temperature seen with running. The subjects were screened for EIH during the initial VO2 max testing with a pulse oximeter and recorded a mean saturation of 92.2±2.18%. This suggest that either the exercise tests were not of sufficient duration to elicit maximal hypoxemia or that the readings of the pulse oximeter were unreliable during maximal exercise. 51 Mixed venous P02 Mixed venous blood gas data is calculated from the Fick equation and small errors in measurement of cardiac output and VO2 will be transmitted directly to the NO2 calculation. This may be more of a problem with the current study as cardiac output and VO2 were not measured simultaneously, as they were in other studies using similar methodology (63, 161). This is not likely to affect [A-a]D02(p) (63) or indicators of diffusion limitation, [A- a]D02(o-p). Bearing this in mind, the Pir02 data from the present study should be interpreted with some caution. At rest, the calculated mean NO2 is similar to values reported by several other studies employing both direct measurement and calculation from the Fick equation (28, 89, 139, 161). During light exercise, the values are similar to those reported by Cerretelli et al., (28) although they are approximately 6 torr lower than values reported by Torre-Bueno et al., (161). During heavy and maximal exercise, calculated NO2 was substantially lower than reported for normal subjects exercising at sea level, although values lower than 17 torr have been reported for subjects exercising at simulated altitude (161) and breathing hypoxic gas mixtures (139). Possible mechanisms of exercise induced hypoxemia Although marked hypoxemia was not seen in the majority of the subjects in this study the individual variation makes it possible to speculate as to causes of exercise induced hypoxemia in highly trained subjects. The data presented in this paper is consistent with hypoventilation, VAX) mismatch and diffusion limitation as viable mechanisms in the genesis of EIH, both in groups of subjects and in individuals, although clearly one mechanism may predominate over the other depending on exercise intensity and individual variability. This is perhaps best illustrated by comparing subject 4 to subject 5. Subject 5, who hypoventilated to the point of CO2 retention at 300 watts, despite a Pa02 of 74 ton, also had evidence for diffusion limitation both at 300 watts and at maximal exercise, with LA-all302(o-p) of 6 and 17 ton respectively. It is not likely that the source of this subject's 52 hypoventilation was purely mechanical in nature as PaCO2 decreased to 31 torr and PAO2 rose to 123 ton at the end of maximal exercise, indicating increased effective alveolar ventilation, when given the appropriate stimulation. Subject 4 had little evidence of hypoventilation as PaCO2 was 35 ton or less during heavy and maximal exercise, however the inert gas data suggests marked diffusion limitation with [A-a]D02(o-p) greater than 30 ton at both exercise levels. From the data in the present study, it is now possible to present an explanation of the conflicting findings of the previous study (72) and that of Dempsey et al., (35). Dempsey exercised very highly trained individuals at 70-90% of VO2 max whereas the subjects in the previous study were exercised at 100% of VO2 max. This is evidenced by similar oxygen consumption between the two groups but higher VCO2 and lower pH for the subjects in the study of Hopkins and McKenzie. It is proposed that three main mechanisms contribute to exercise induced hypoxemia. During heavy exercise, hypoxemia results from relative hypoventilation, VA/Q mismatch and diffusion limitation. During maximal exercise, diffusion limitation and VA/Q mismatch continues and may in some individuals worsen, but arterial oxygenation may improve, as ventilation increases in response to stimuli such as marked acidosis. In this study, as in the previous work, there is little evidence for hypoventilation at maximal exercise in young, highly trained subjects. All of the subjects had significantly lower values for Pa02 and higher values for PaCO2 at the heavy exercise level when compared to maximal exercise. In addition, maximal ventilation during the exercise test was significantly less than during the VO2 max determination, indicating that at least under some circumstances, our subjects were capable of higher levels of ventilation. It seems unlikely that mechanical restriction of ventilation is an important factor in these subjects and suggests that the relative hypoventilation and lower levels of arterial oxygen observed during heavy exercise may have been related to complex interactions between the work of breathing and drives to breathe. 53 The relationships between whole blood volume, pulmonary blood volume index and [A-a]DO2(o-p) suggest that in some endurance athletes with EIH that this problem could be compounded by dehydration and or cardiovascular drift which would lower blood volume and possible affect pulmonary blood volume and pulmonary transit. These results must be interpreted with caution, recognizing that due to the small sample size these subjects may not be representative of the population at large. There is no direct evidence in the present study to support this hypothesis, and there are no published papers that directly examine the effect of volume contraction on exercising pulmonary blood volume and EIH. However, a recent study in this laboratory examined the effect of administration of a single dose of furosemide on EIH using ear oximetry (163). Plasma volume was not measured directly but changes in plasma volume were estimated from changes in hematocrit. Although there was considerable intersubject variability, there was a strong correlation between the difference in the change in plasma volume between placebo and furosemide administration and the change in saturation between the two conditions (r=0.75, p<0.01). That is, the subjects who had the greatest decrease in plasma volume with furosemide and exercise also had the greatest decrease in Sa02 when compared to the placebo condition. Furosemide has many systemic effects aside from that of diuresis, and it is difficult to make firm conclusions however this would suggest that the role of plasma volume and pulmonary blood volume in the genesis of EIH is worthy of further investigation. Several authors have described alterations in a number of indirect indicators of barrier function post-exercise including increases in residual volume, decrease in diffusing capacity for carbon monoxide (DLCO), and decreases in transthoracic electrical impedance that are consistent with pulmonary edema (22, 23, 104, 129, 130). Additionally Hammond et al., (63) described widened [A-0)02 and VA/Q inequality following heavy exercise and suggested that a structural change in the lung could account for their findings. Despite this, little further investigative work has been done in this area, particularly in athletic populations. Vaughan et al., (164) failed to find an increase in lung water (measured by the 54 indicator dilution method) with sustained exercise, although an initial increase in lung water was attributed to a redistribution of blood flow, however, their subjects were exercising at a work load of less that 150 watts and a cardiac output of less that 20 1-min -1 . An interesting study by Gallagher et al., (56) examined chest radiographs in five males following a V02 max test. No evidence of pulmonary edema was found, however this study suffered from the same problem as the one of Vaughn et al., the subjects studied were normal non-athletic males who have not been shown to have evidence for inadequate gas exchange during exercise. Pulmonary capillary failure has been demonstrated in the rabbit lung at 40 mmHg of transmural pressure and it has been estimated in human lungs, during exercise of less than 4.0 1-min -1 , that transcapillary pressures of 36 mmHg would be likely, leaving little safety margin (179). The consequences of such capillary failure in humans would be increased capillary permeability at the lower end of the spectrum and frank pulmonary hemorrhage with higher pressures. Recently Schaffartzik et al., (147) have argued that sustained increases in log SDO persisting during recovery from exercise, during exercise and normobaric hypoxia is indicative of pulmonary edema. This subgroup of subjects had significant impairment of pulmonary function after exercise compared to controls and also had lower arterial saturation and pH associated with exercise. In the present study, it was possible to define two subgroups with respect to log SDQ however, there were no significant differences between groups with respect to pH, Sa02, Pa02 or other measured parameters. It is not possible to comment on the possibility of pulmonary edema in the present study as no inert gas samples were taken during recovery and the overall shift in the VA/Q distribution may obscure alterations in blood flow distribution towards zones of low VA/Q . Also the screening process used in selecting subjects excluded any who had post-exercise cough, hem optysis or decrease in pulmonary flows which could be indicative of pulmonary edema. 55 Summary of findings This paper describes VA/Q mismatch, diffusion limitation and a variable decrease in Pa02 in highly trained athletes during exercise. The decrease in Pa02 is less than previously described for similar athletic populations and may reflect the small muscle mass used in cycling exercise. 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Stuart. Hemoglobin desaturation in highly trained athletes during heavy exercise. Med. Sci. Sports Exerc. 1986;18:168-173. 185. Young, I.H., J. Woolcock. Changes in arterial blood gas tensions during unsteady- state exercise. J. Appl. Physiol. 1978;44(1):93-96. 69 APPENDIX A REVIEW OF LITERATURE' Introduction A debate of the factors providing a limit to maximal oxygen consumption (VO2max) has occupied exercise physiologists for decades. An increase in VO2 max is associated with hyperoxia and infusion of red blood cells and a decline in VO2max is observed in anemia and hypoxia. This information is generally interpreted that oxygen delivery to the working muscle provides the major limit to maximal oxygen uptake (39). In humans, increases in VO2max have been considered to be a result of both increases in cardiac output (Q) and oxygen extraction (a-v02). As the first step in the supply of oxygen to the working muscle, the respiratory system has not been thought to limit maximal performance. In most individuals, arterial oxygen tension is maintained during heavy exercise (20), and maximal capacity to ventilate during non-exercising conditions greatly exceeds observed exercise ventilation at maximal exercise (67, 118). Recently, reports of arterial hypoxemia in individuals exercising at maximal levels (35, 125, 126, 146, 159) and respiratory muscle fatigue in marathon runners after prolonged exercise (93) have focused attention on the respiratory system as a possible limiting factor to performance in individuals capable of maintaining very high levels of work. In our laboratory (72), we have documented hypoxemia with arterial P02 as low as 68 torr in athletes exercising for five minutes at VO2max. These subjects exhibited only partial respiratory compensation for the metabolic acidosis of exercise; despite a mean pH of 7.21, mean pCO2 levels of 36.4 ton, indicate only partial respiratory compensation of the acidosis. This has been observed by other authors in highly trained individuals (35) with less trained subjects showing compensatory hyperventilation to a pCO2 of approximately 30 ton (34). In resting individuals, respiratory compensation for a metabolic acidosis could result in a pCO2 as low as 15 ton (92). In highly trained athletes who have ample 70 stimulation (acidosis, hypoxia) to breathe, why are such profound disturbances in homeostasis tolerated without adequate compensation? Recently some authors (25, 34), have recognized several factors, specifically energetics, muscle fatigue, respiratory drives and gas exchange, which may limit maximal exercise performance. The following review will discuss the ways in which the respiratory system could limit aerobic performance, with particular emphasis on the respiratory system during short term, near maximal, exercise. Respiration and the respiratory muscles: During quiet breathing the primary muscle of inspiration is the diaphragm, which as it contracts, increases thoracic volume. Abdominal viscera are displaced, leading to protrusion of the anterior abdominal wall. The quadratus lumborum acts in synergy with the diaphragm, opposing the the tendency to elevate the 12th rib. The scalene muscles exhibit activity even during quiet inspiration, and act to fix or elevate the thoracic inlet. Expiration is passive, and mainly due to elastic recoil of the lungs. As ventilation increases, more of the respiratory muscle mass becomes active and at maximal exercise almost all of the muscles of the chest and back may be important. In inspiration, in addition to the muscles previously described, the sternocleidomastoid, external intercostal, serratus posterior superior and possibly portions of trapezius are recruited. Expiration becomes active, with strong contractions of the oblique and transverse muscles acting to compress the abdomen, increase infra-abdominal pressure, and displace the diaphragm upwards. In addition, the internal intercostal muscles and serratus posterior inferior act to assist expiration. The muscles of the back also have a role in respiration, acting as a counterbalance to the flexion induced by the contraction of the abdominal muscles. 71 Energetics Many investigators have not considered respiratory muscles to receive a substantial fraction of Q or V02 during exercise. At high levels of ventilation if oxygen consumption by, and blood flow to, the respiratory muscles were substantial, this could limit maximal exercise by direct competition between the working muscle and respiratory muscles. Blood flow to the diaphragm and other respiratory muscles Investigation into the blood flow and metabolism of the respiratory muscles has focused mainly on the diaphragm, however, as exercise intensity increases other muscles of respiration become progressively more important. Data collection in humans is hampered by methodology; most studies have relied on data obtained from dogs, ponies, sheep and rats. Estimates of blood flow of the various respiratory muscles are summarized in Table 12 and have been made during rest, inspiratory loads and exercise (19, 50, 98-100, 137, 140, 142). At rest, dog respiratory muscle comprising about 3-4% of body weight, receives about 1.5% of cardiac output (136, 137). During inspiratory resistive work, this value rises to greater than 10% of cardiac output. Blood flow to the diaphragm increases by 275% and 500% during mild and moderate exercise, which is roughly double the increase observed in gastrocnemius muscle (50). Intercostal muscle blood flow increases two to three times, similar to values for working skeletal muscle (50, 141). Blood flow to the respiratory muscles under work stress varies with the species investigated and type of stress applied. As outlined in the table below the resting blood flow of the diaphragm is about 150 % of intercostal muscle. During maximal exercise, these values may be higher than 260 and 130 ml•min -1 .100g -1 for diaphragm and intercostal muscle respectively. Blood flow values for the diaphragm are similar to maximal values reported for locomotor muscles in exercising dogs (239 ml•min-1 .100g-1 )(116), horses (135-237 ml-min -1 .100g -1 ) (98) and isolated working quadriceps muscle (240 ml•min -1 .100g -1 ) in humans (2). 72 Study Table 12. Blood flow of respiratory muscles Qdi (ml•min-1 ' Mg- 1 )^Qi (ml-min-1 ' 100g - 1 ) -icESD^/Z±SD Rest^Stress^Rest^Stress Reid and Johnson, 20±5 (1983)(133) Rochester and 18±7 52±30 Bettini,(1976) (140) Rochester,(1974) 22±6 (142) Robertson et al.,(1977a)(136) 8±2 207±41 10 59 Brancatisano et al., 7.3±0.8§ 9.1±1.4§ (19) 8.2±0.4* 6.2±0.4* Robertson et al.,(1977b)(137) 9±1 33±2 Fixler et al.,(1976)(48) 16±3 96±18 15±4 43±18 Manohar et al.,(1988a)(99) 12-13 151-245 7±2 119±9 Manohar,(1986)(98) 11±3 261±23 6±1 131±10 Manohar, (1990) - 30 -330 - 18 150-170 (100) Viires,(1983)(165) -14 -50 -8-9 -16-21 Comments inspiratory load dogs inspiratory load dogs dogs inspiratory load dogs inspiratory load dogs hypercapnia dogs moderate exercise dogs maximal exercise ponies maximal exercise ponies maximal exercise trained ponies cardiac tamponade dogs Qdi = diaphragmatic blood flow,^intercostal blood flow, * = internal intercostal muscle, § = external intercostal muscle. 73 Manohar et al.,(99) originally used the data obtained in their investigations in ponies to argue that the amount of cardiac output diverted to respiratory muscles during maximal exercise is relatively small, based on the measurement of diaphragm blood flow, and excluding other muscles of respiration. More recent investigations (100), in exercising ponies suggest that blood flow for the entire respiratory mass is actually much higher and may be close to 15% of the total cardiac output during maximal exercise. In humans, cardiac output has been shown to increase by 50% over resting conditions in subjects breathing through an inspiratory resistance of 50-60% of maximum inspiratory pressure (29) and by 76% in isocapnic hyperventilation at maximal sustainable ventilation >150 1-min -1 (3). No direct measures of respiratory muscle blood flow have been made. Oxygen consumption of respiratory muscles In animal models, oxygen consumption of the diaphragm has been shown to be linearly related to the work of breathing (136, 137). Like skeletal muscle, demands for increased oxygen are met by both increased flow and oxygen extraction (79, 101, 136, 137). In dogs breathing through inspiratory flow resistance, oxygen extraction becomes maximal at low work rates; increasing demand for oxygen is met by increasing blood flow (136, 137, 140). In ponies exercising at heavy and maximal levels increasing 02 demands are met by both increments in perfusion and oxygen extraction (102). Direct measures of respiratory muscle oxygen consumption have been made in dogs in a wide variety of experimental conditions (Table 13) (133, 136, 137, 140, 142). During mechanical ventilation, '102 of the diaphragm is less than 1 ml•min -1 .100 g-1 (137, 142) and doubles during quiet breathing (136, 137, 140, 142). During hypercapnic hyperventilation, oxygen consumption triples (142), although this level of hyperventilation is less than would be expected during heavy exercise. When resistance to inspiratory flow is 74 used to stress the respiratory muscles, diaphragmatic VO2 increases to about 15 ml-min -1.100g-1 (7.1-22.6) (133, 136, 140). Table 13. VO2 of respiratory muscles Study ^VO2di^VO2i^Comments (ml•min-1 -100g-1 )^(ml-min-1-100g-1) i±sd i±sd Manohar, (1986)(98) Manohar et al.,(1988b)(101) Reid and Johnson, (1983)(133) Rochester, (1974)(142) Rochester and Bettini, (1976)(140) Robertson et al., Rest 1.6 0.4 1.2±0.3 1.4±0.6 1.7 Stress 58.6 45.1 12.0±2.8 2.8±0.9 7.1±4.3 22.6 Rest 0.9 Stress 29.6 horses max exercise horses max exercise insp. flow resistance dogs hypercapnia dogs insp. flow resistance dogs insp. flow resistance 1977(136)^ dogs VO2 di = oxygen consumption diaphragm; VO2 i = oxygen consumption intercostal muscle Data during heavy and maximal exercise have been obtained in the pony (98, 101); V02 of the diaphragm reaches as high as 31 ml-min -1 -100g -1 during heavy and 45-58 ml•min-1 .100g-1 during maximal exercise. Oxygen extraction by the diaphragm increases progressively as work rate increases, but has not been shown to reach a value where it would limit 02 consumption, although observed blood flow is close to maximal. Direct measures of oxygen consumption of the diaphragm or other muscles of respiration have not been made in humans. Instead most investigators have measured the increase in VO2 which accompany an increase in VE. The estimate in the total cost of ventilation at maximal levels of exercise varies from 3% (112) to as much as 25% (54) of 75 VO2 max. This variation is in part due to different means of increasing VE: exercise, hyperventilation, CO2 and inspiratory flow resistance, as well as difficulty in obtaining steady state CO2 and accurate VO2 measures (81) . The results of these investigations are summarized in Table 14. The relationship of oxygen cost to ventilation is not a linear function but rather a series of curves (11, 109, 119) with a rapidly increasing slope as the upper limit of ventilation is approached (109). The oxygen cost of a particular ventilation varies with respiratory rate (11) and most investigators agree that healthy subjects spontaneously select the tidal volume and respiratory rate that minimizes respiratory work (110, 120). At any metabolic level the VO2 of respiratory muscles is less in trained than untrained subjects, reflecting the lower level of pulmonary ventilation for any given oxygen uptake (112). Table 14. Oxygen cost of unobstructed hyperventilation 02 cost calculation Cost of 120 Cost of 170 STUDY studied 1•min' 1 VE VE (ml•min-1 ) (ml -min - 1 ) Anholm et al., (1987)(3) 50-220 1 376).049[VE" 241 546 Bradley and Leith, (1978)(18) 103-2501 -682+8.31 VE 315 813 Shephard,(1966)(149) 90-1301 4.3VE 516 Bartlett et al., (1958)(11) 20-2001 estimated from curve -300 = 343 -900 x=753 Lactate Production No evidence of net diaphragmatic lactate production is seen in ponies during exercise at maximal levels (V02 >120 ml.kg - 1 -min -1 ) (101) even when inspiratory work of breathing is increased by laryngeal hemiplegia (102). Similar findings are observed in the 76 dog when animals are subjected to an inspiratory flow resistance (137). In low cardiac output states, net diaphragmatic lactate production is observed only when the mixed venous P02 falls below 20 ton (6). Indirect evidence suggests that other respiratory muscles may contribute to net lactate production. During cardiac tamponade, spontaneously breathing dogs have been shown to have blood lactate levels roughly double that of mechanically ventilated dogs (-7 vs 3 mmo1•1-1 )(165). In humans, Roncoroni et al.,(143) documented metabolic acidosis in almost 40 % of patients in status asthmaticus, which could not be attributed to poor gas exchange in these individuals leading to the conclusion that respiratory muscles in the presence of severe airway obstruction may increase blood lactate levels via anaerobic glycolysis. In healthy humans small (-1 mmo1•1 -1 ) increases in blood lactate have been recorded in isocapnic hyperventilation at approximately 70% of maximum breathing capacity (52). Theoretical calculations of respiratory muscle V02 and Q in humans exercising at maximal levels Difficulty in making direct measures of respiratory muscle metabolism in human subjects has restricted research in this area. It is also difficult to compare the work in different animal species because of different methods of inducing stress on the respiratory muscles (hypercapnia, inspiratory loading, maximal exercise, cardiac tamponade) as well as interspecies differences. Nonetheless a certain pattern arises: During resting conditions, oxygen consumption of the diaphragm is remarkably constant across species , and is generally less than 2 ml•min-1 -100g-1 . During maximal exercise this value increases to about 45-60 ml•min -1 .100g-1 in horses (98, 101). Data are not available for other animal species. The values for intercostal muscle are roughly half of these values (<1 and -30 ml•min-1 .100g-1 (98)) both at rest and during exercise. Diaphragmatic blood flow at rest is also similar between species (-10-30 ml•min -1 -100g-1 ) and has been reported higher than 300 ml•min -1 .100g-1 in ponies (99). Again the values for intercostal muscle blood flow 77 during resting and exercising conditions are about half (-10 and as high as 130 ml•min-1 .100g-1 ) of the values reported for the diaphragm. This information can be used to calculate theoretical values of blood flow and oxygen consumption for human respiratory muscle during maximal exercise, provided the following assumptions are made: 1. The total respiratory muscle mass in humans is - 4 kg. 2. The diaphragm is roughly 15 % of the total respiratory muscle mass or 600g. 3. The other 3400g, which includes intercostal muscle, is relatively homogeneous, allowing calculations to be made for the entire respiratory mass. 4. Meaningful comparisons can be made between animal species and humans. The pony is the mostly completely studied species and the only one in which systematic observations have been made at heavy and maximal exercise. Therefore data from these investigations are used in the following calculations. Qdi^= 260 ml•min -1 .100g -1 x 600 g^=^1560 ml•min-1 Qi^= 120 ml•min -1 .100g -1 x 3400 g^4080 ml•min-1 5640 ml-min-1 V02 di = 59 ml•min-1 .100g -1 x 600 g^354 ml•min -1 V02 i = 30 ml•min-1 .100g-1 x 3400 g^1020 ml•min-1 1374 ml•min-1 Assuming a VE of 150 1-min-1 , V02 of 4.6 1-min -1 (72) and Q of 27 1-min-1 (5) then these figures represent 30% of VO2 and 20% of Q for exercising humans at maximal work loads. Clearly these numbers are substantial and support the idea that respiratory muscles may be in direct competition with skeletal muscle for oxygen and cardiac output. The resistance of the diaphragm to lactate production has also been clearly demonstrated in a number of species and under a wide variety of experimental conditions. 78 However the diaphragm constitutes less than 20% of the total respiratory muscle mass and no data is available for the other muscles of respiration under exercising conditions. There is no reason to expect that intercostal, abdominal and other muscles of respiration should behave differently than other exercising skeletal muscle. Any increase in ventilation, rather than increasing pH and lessening the impact of metabolic acidosis, may actually worsen it through increasing respiratory work. This line of thought, while highly speculative, is not new. Otis (119) argued that a critical ventilation could be reached above which any increase in VO2 would go entirely to respiratory muscles and estimated this ventilation at 140 1•min-1 . It is logical to consider that exercise ventilation represents an optimum level of ventilation balancing respiratory muscle oxygen consumption and blood flow against the demands of skeletal muscle and pH homeostasis. The level of ventilation achieved is a compromise between supply and the cost of supplying ventilation. Respiratory muscle fatigue Respiratory muscle fatigue is commonly seen in chronic obstructive lung disease, or in acute medical situations such as adult respiratory distress syndrome, where pulmonary edema increases respiratory work. In the normal exercising human, respiratory muscles have been considered to be fatigue resistant under most conditions, however reports of decrements in respiratory muscle strength after prolonged exercise (93) and decrease in performance following respiratory work (97, 105), indicate that this is not the case. h  f m o m 11^1" The histochemical and biochemical characteristics of respiratory muscles have been investigated in both animals and humans. The diaphragm of most mammals are composed of varying percentages of the three skeletal muscle fiber types. Biopsy specimens taken during thoracotomy in humans show the composition of the diaphragm to be 55% SG, 21% FOG and 24%FG fibers (90). The oxidative capacity of diaphragm is greater than other mixed skeletal muscle, and closely resembles an intermediate between skeletal and cardiac 79 muscle (62, 114). The oxidative capacity can be trained by imposing inspiratory resistive loads (1, 82) and in induced emphysema (48). The effect of endurance exercise on the respiratory muscles is controversial, with some authors documenting an increase in the respiratory capacity of the rat diaphragm and intercostal muscles with endurance training (114, 128) and no change reported by others (53) using an almost identical protocol. After endurance exercise, depletion of muscle glycogen stores has been found in both diaphragm and intercostal muscle (62, 75, 114) as well as triglyceride depletion in the diaphragm (62).This evidence points to the diaphragm as a muscle with high aerobic capacity, relying on aerobic glycolysis and fatty acid oxidation to offset the metabolic cost of work. Other muscles of respiration have not been as extensively evaluated, but would appear to have characteristics in common with other skeletal muscle. Evidence for fatigue Muscular fatigue has been defined as a reversible reduction in force generating capacity which is relieved by rest (117). Task failure is defined as the inability to maintain or continue the force required to perform a particular task. For the respiratory system this can be defined as the failure of the respiratory muscles to generate a given pleural pressure. The unique characteristics of diaphragm muscle with ability to maintain very high oxidative capacity, renders this muscle relatively resistant to fatigue compared with skeletal muscle (172) however, as a correlate to the histochemical data, several studies have shown that high levels of ventilation cannot be maintained indefinitely (14, 24, 107). A decline in the strength of the ventilatory muscles at the end of a marathon race, with a fall in maximum inspiratory and expiratory mouth pressures and transdiaphragmatic pressures suggests that these considerations may be of practical concern (93). Reduced time to exhaustion has been observed during short-term maximal exercise after 150 minutes of maximal ventilation (105) and after inspiratory threshold loading (97). Expiratory muscle fatigue has also been reported as a result of increased expiratory work in normal subjects, persisting for up to an 80 hour after cessation of expiratory muscle loading (157). Expiratory loading also induces inspiratory muscle fatigue and these findings may have important implications for the athlete with exercise induced asthma. Taken together the histochemical and whole body information point to the respiratory system as a system that is fatigue resistant under healthy, non-exercising conditions but that will demonstrate substrate depletion and a decline in performance with levels of exercise that are not uncommon in today's active society. Respiratory drives After the preceding discussion, the advantages of a blunted ventilatory response to exercise to an athlete appear obvious. If at any given level of exercise the ventilation is reduced, the individual will be less likely to develop respiratory muscle fatigue, will require a smaller fraction of total cardiac output and V02 diverted to respiratory muscles and will experience less dyspnoea. Although controversial, most authors would agree that the peripheral chemoreceptors contribute to exercise ventilation to a substantial degree. The estimated contribution of the hypoxic ventilatory response to exercise ventilation ranges from 16 to 30% of the total VE (106, 155, 183). Resting hypoxic and hypercapnic drives are positively related (131) and also correlate with drives measured during moderate exercise (106) and with exercise ventilation (106, 132). During maximal levels of exercise the relationship between respiratory drives and exercise ventilation is less clear and it is likely that, during exercise near maximal levels, other stimuli to ventilate over-ride any contribution by hypoxic drives (35, 72). The relationship between hypercapnic drives and ventilation during maximal or very heavy exercise has not been determined. A link has been postulated between outstanding endurance performance and chemoreception (106) as some studies have shown endurance athletes to have blunted responses to hypoxia and hypercapnia, compared to other athletes and sedentary controls 81 (26, 106, 148) . It is tempting to speculate that these blunted respiratory drives may be related to exercise induced hypoxemia. Dempsey et al., (35) felt that hypoventilation was a major contributing factor to the hypoxemia seen in the athletes studied. The athlete with a blunted respiratory response to hypoxia, would ventilate less in response to the hypoxic stimulus with substantial savings in the cost of maintaining a high level of ventilation at the expense of less than optimal arterial oxygenation and presumably 02 delivery. A more recent study (72), failed to demonstrate a relationship between hypoxemia in maximal exercise and resting hypoxic drives and implicated widening alveolar-arterial differences as being the most important contributor to the hypoxemia seen in their subject population. Further investigation is needed to determine the relationship between exercising hypoxic and hypercapnic responses and hypoxemia in exercise, however it would appear that the role of respiratory drives in determining exercise ventilation is a minor one during maximal exercise, although at less intense levels of exertion, respiratory drives modify the respiratory response to submaximal exercise. 82 Pulmonary mechanics and expiratory flow limitation Pressure-volume relationships In order for air to flow through a tube a difference in pressure must exist between the two ends. In the lung, this pressure difference is generated at rest by the contraction of the diaphragm in inspiration, and by passive elastic recoil during expiration. During quiet breathing, infra-alveolar pressure becomes slightly negative with respect to atmospheric pressure during inspiration and slightly positive during expiration (± 1 torr). During exercise inspiration is aided by the external intercostal muscles and other accessory muscles of inspiration. Expiration becomes active, with contraction of the muscles of the abdominal wall and internal intercostal muscles contributing to driving pressure. Characteristics of flow inside tubes At low flow rates air flow in a tube is in smooth streams parallel to the the walls of the tube. This phenomenon is termed laminar flow and the flow rate is related to the length and diameter of the tube, the driving pressure and the viscosity of the flowing material. As flow rates increase, turbulent flow becomes more likely. The viscosity of the material becomes relatively unimportant, however driving pressure must increase to maintain flow as gas density increases. The complexity of the branching system, and irregularity of the surface in the lung makes laminar flow unlikely in much of the bronchial tree. In most parts of the lung, transitional flow predominates with eddy formation at the branch points of the bronchial tree. Airways resistance to flow can be modified by disease states and chemical agents. The effect of exercise is modest bronchodilation secondary to sympathetic nervous system stimulation. Intuitively, it seems logical that much of the resistance to flow should be located in the very small airways, however direct measures have shown that the majority of the drop 83 ••• .......... in pressure takes place over the first 10 generations of bronchial branching (176). The explanation for this is likely the large number of small airways relative to larger airways. Flow-volume relationships Maximal expiratory flow-volume (MEFV) curves have been used for many years as a clinical assessment of pulmonary function. At rest normal tidal breathing falls well within the limits defined by the MEFV curve, however in certain circumstances these limits may be approached or even exceeded (Figure 9). During progressive exercise the demands for increased ventilation are met by an increase in tidal volume, to about 50% of forced vital capacity (30, 51) and breathing frequency. Eiguis. 9. Maximal expiratory flow volume loop with exercise and maximum voluntary ventilation manoeuvre 15  ^15 MVV 10^ 10 5 5 ,•••■•.Cep^0 ^0 5 5 .2 10^ 10 15  ^15 0 1 2 3 4 5 6 0 1 2 3 4 5 6 Volume (1) A (adapted from Jensen et al., 1980 (78)) Solid line = maximal expiratory flow volume loop. Dotted line = tidal volume during maximal voluntary ventilation (A) and exercise (B) The ventilation at maximal exercise is approximately 75% of that during maximal voluntary ventilation manoeuvres (MVV) (67) and this information has been interpreted as indicating that mechanical factors do not limit ventilation during exercise. During very heavy exercise, there is some evidence to suggest that this may be inaccurate. Peak expiratory 84 flows have been measured, that approach or even exceed maximal expiratory flow measured during MEFV loops (67, 78, 118). Several explanations are possible: Firstly, during MVV measurements subjects breathe at higher lung volumes and therefore have higher expiratory flow (118); Secondly, during exercise, maximum flow may be increased by increased elastic recoil pressure; Thirdly, during exercise there may be decreased resistance to flow as a result of bronchodilation secondary to increased sympathetic or decreased parasympathetic tone (78). Finally, an increase in ventilation, alveolar P02 and arterial oxygenation is seen with the administration of He:02 mixtures during heavy exercise, and is associated with a decrease in PaCO2 (35, 36, 170). In interpreting the information gained from administration of low density gases caution must be exercised to avoid attributing all of the improvement to the effects on the MEFV curve as there will also be a reduction in the work of breathing for any given level of ventilation, with attendant interaction with respiratory muscle blood flow and metabolism. A final factor which should be considered is entrainment, the alteration of breathing frequency to be in step with exercise rhythm. One to one entrainment in galloping horses is well known (34, 121), and has been proposed as a factor causing hypoxemia in these animals (34). During exercise in humans, entrainment is much less complete and depends on the form of exercise. During walking about half of normal subjects are partially or completely entrained, increasing to about 80% during running. Entrainment during cycling is found in about 30% of subjects (13) and is apparent in oarswomen, at low work intensities (158). From the preceding arguments it is clear the respiratory mechanics have the potential to limit exercise ventilation, and by inference maximal exercise through entrainment, expiratory flow limitation or by interaction with the work of breathing. 85 Pulmonary diffusion and gas exchange In the preceding pages some of the factors that may contribute to a respiratory constraint to exercise have been discussed. Diffusion and gas exchange may interact with these factors on several levels as well as directly contributing to limitation of performance. For example, if through hypo-ventilation or mechanical limitation of flow, alveolar P02 is reduced, then the driving pressure for diffusion is also reduced and may lead to a decline in arterial oxygen content and subsequent decrease in oxygen delivery. It is not the purpose of this review to completely discuss the physics of respiratory gas exchange as it has been the subject of several excellent reviews. The reader is referred to the work of Peter Wagner (166) for a more in-depth discussion of the points discussed below. As air travels from the atmosphere to the alveolus it becomes fully saturated with water vapor and in the alveolus becomes mixed with alveolar air containing lower concentration of oxygen and a higher concentration of carbon dioxide. It is usually assumed that alveolar gas is fully saturated with water which gives a partial pressure of 47 torn at a barometric pressure of 760 torr and 37°C. Under resting conditions the gas tensions of alveolar air are approximately P02 = 105 toff, PCO2 = 40 toff, PH2O = 47 ton and PN2 = 568 ton. Once a gas has arrived at the alveolus it must pass through alveolar fluid, alveolar epithelium, epithelial basement membrane, interstitial space, capillary basement membrane and through capillary endothelium into the blood. Factors related to diffusion of oxygen The rate of combination of oxygen with hemoglobin is not instantaneous but rather occurs as a finite rate. The uptake of oxygen can be considered in two steps (145): 1. Diffusion across the alveolar membrane and plasma to the red blood cell and 2. Combination with hemoglobin. The rate of combination of hemoglobin with oxygen (given by 0) is a 86 function of hemoglobin saturation, being greatest at low saturation and approaching zero at saturations above 80% (154). The overall transfer from the alveolus to the red cell and diffusing capacity of the lung (DL) can be related by: 1 _ 1 +  1 DL DM OV c Where Vc is capillary blood volume, and DM is the diffusing capacity of the alveolar membrane. DM is close in value to Vc, therefore for 0 greater than 1, the major resistance to uptake would be that determined by combination of hemoglobin with oxygen. Most of the changes in P02 and 02 content take place in the first 0.2 second of the red blood cell transit in the pulmonary capillary when saturation is low and 8 is high, and rate is predominantly determined by DM. Later as saturation rises, 0 decreases and the chemical reaction resistance predominates. The amount of oxygen dissolved in plasma is linearly related to partial pressure and is about 0.3 m1•100 m1 -1 , less than 2% of that bound to hemoglobin and it is generally neglected in order to simplify calculations. The rate of rise in P02 is affected by simultaneous CO2 exchange: as capillary PCO2 falls the oxygen hemoglobin equilibrium curve is shifted towards the left and more oxygen is taken up by hemoglobin, delaying the rise in P02. Factors related to diffusion of carbon dioxide Although the diffusion across alveolar membrane is approximately 20 times greater for carbon dioxide than for oxygen, the diffusion rate for CO2 may be similar, or even slower, than for 02. The reason for this becomes apparent when the chemical reactions within the blood necessary for CO2 exchange are examined. Transfer of CO2 in the blood is in three forms: dissolved CO2, HCO3 -, and bound to protein (carbamino compounds), primarily hemoglobin. In arterial blood, HCO3 - is the predominant form of CO2 transport 87 (90%), with 5% bound to proteins and 5% as dissolved CO2. Plasma CO2 accounts for about 2/3 of the total CO2, with the remainder present intracellularly, bound to hemoglobin. As CO2 diffuses into the alveolar gas, it is replaced by plasma carbamino-0O2 and by the conversion of HCO3 - and H+ to CO2 and water. In the plasma, this reaction is not catalyzed by carbonic anhydrase, as it is within the red blood cell and therefore proceeds much (-104) more slowly. As HCO3 - from the plasma is consumed, it is replaced by red cell HCO3 - which is exchanged with chloride ion (chloride shift). CO2 also diffuses out of the red blood cell where is has been produced by the much more rapid carbonic anhydrase catalyzed conversion of HCO3 - . Deoxy-hemoglobin acts as a buffer for H+, as oxygen combines with hemoglobin H+ is released, which facilitates the conversion of bicarbonate to carbon dioxide and water (the Haldane effect). Although for CO2 DM is 20 times that for 02, the slowness of chemical reactions, particularly the chloride shift, with a 0/ 2 of 0.15 seconds renders the time for CO2 diffusion equilibrium, if anything, slower than that for 02. Due to the absence of carbonic anhydrase in the plasma, the plasma concentrations of CO2, H+, and HCO3 - are not in equilibrium at the end of the pulmonary transit. This leads to a post-capillary increase in pH and CO2 and a fall in HCO3 - . Factors affecting pulmonary gas exchange In the intact organism gas exchange can be modified by a number of factors. 1. Shunt: In the normal human, small amounts of blood by-pass the pulmonary capillaries and therefore do not participate in gas exchange. This includes blood from the bronchial arteries and coronary veins that drains directly into the left ventricle via the thesbian veins. Normally shunt accounts for less than 2% of cardiac output. 2. Hypoventilation: The level of alveolar 02 and CO2 is determined by a balance between supply and removal. Hypoventilation from any cause will impair pulmonary gas exchange 88 by decreasing the driving pressure across the alveolar membrane for both carbon dioxide (decreased removal) and oxygen (decreased supply). 3.Ventilation-perfusion (VA/Q) mis-match: If ventilation is abolished in a particular area of the lung then the blood passing through this portion of the lung is essentially shunted through the lung; no gas exchange takes place, and no change in gas content is observed. If on the other hand perfusion tends towards zero, the gas content of that blood will resemble that of alveolar gas. Ventilation and perfusion are not uniform throughout the lung, the effects of gravity render the bases better perfused than the apices of the lungs and the apices relatively better ventilated than the bases. (VA/Q) mis-match will tend to depress Pa02 as more blood will be poorly oxygenated. 4. Reduction of alveolar surface area: Through disease states, such as chronic obstructive pulmonary disease, alveolar surface area can be reduced thus reducing the available surface area for gas exchange. When advanced, this will be manifest in decreased diffusing capacity for CO and impaired gas exchange during exercise. 5. Diffusing capacity: The diffusing capacity of the lung can diminished by pulmonary disease. In order to produce abnormalities of gas exchange it must be reduced to 20-25% of normal. 6. Altitude: The effect of altitude is to decrease total barometric pressure and therefore the partial pressure of inspired oxygen. Some compensation is made by increasing alveolar ventilation (and decreasing alveolar CO2) however the overall effect is to decrease PAO2, and the driving pressure for oxygen across the alveolar membrane. 7. Transit time: Under normal conditions, partial pressure equilibrium is reached after about 0.25 seconds of gas exchange. The average transit time of red cells in the pulmonary capillaries is obtained by the ratio of capillary blood volume to cardiac output (Vc/Q) or about 0.75 seconds for a person at rest with a Vc of 75 ml and Q of 6000 ml•min -1 (80). During exercise, Vc may increase by a factor of 2 while cardiac output may increase to 30 1•min-1 giving an average transit time of 0.30 seconds. Cardiac output in excess of 40 89 1•min-1 has been reported in some elite athletes (43) which would reduce average transit time to 0.23 seconds using these calculations. It should be pointed out that these numbers represent average transit times, and some red cells will travel faster than these values. Capillary flow is not uniform but rather is pulsatile, which may reduce transit time further. 8. Exercise: The effects of exercise on diffusion of oxygen across the alveolar membrane can be seen in Table 15 . At rest, the normal alveolar-arterial difference is about 7 to 10 ton (60, 161, 182). During light exercise, gas exchange may improve (182), as a result of improved ventilation-perfusion relationships. As exercise intensity increases, gas exchange deteriorates and reported mean [A-a]D02 ranges from 11 to 41 ton at VO2 of 2.7 1-min-1 and above. 90 Table 15. Alveolar-arterial differences at rest and during exercise Study V02 Q PAO2 Pa02 [A-a]DO2 1-min-1 1•min-1 ton- torr torr Gledhill et al., 1978 0.29±.03 5.9±1.6 97.8±2.2 86.9±1.6 10.1±2.5 (60) Whipp and 0.32±.07 105.0±1.2 97.0±1.9 7.4±1.9 Wasserman, 1969 (182) Torre-Bueno et al., 0.35±.04 6.6±1.3 111* 102.7±10.8 8.6±5.4 1985 (161) Whipp and 1.65±.45 102.0±4.6 98.0±5.5 3.8±2.9 Wasserman,1969 (182) Gledhill et al., 1978 1.84±.07 15.5±3.1 102.9±2.2 87.5±2.7 15.5±1.6 (60) Torre-Bueno et al., 2.71±.53 21.7±3.4 113.0* 89.9±3.8 23.1±7.5 1985 (161) Whipp and 3.31±.58 108.0±6.4 97.0±3.73 10.8±3.6 Wasserman, 1969 (182) Hammond et aL, 3.97±.29 24.9±3.2 113.7* 90.7±8.2 23.0±8.0 1986 (63) Hopkins and 4.54±.45 119.0±1.5 78.0±8.6 41.0±7.7 McKenzie,1989 (72) *= Standard deviation not reported Pulmonary gas exchange has been studied using multiple inert gas techniques, at rest and during exercise at sea level and simulated altitude to 15,000 feet, allowing the contributing factors to [A-0)02 to be dissected. At sea level, ventilation-perfusion relationships worsen with exercise, with the major factor contributing to [A-4)02 being 91 VA/(5 mis-match at V02 up to 3.01•min -1 . At rest, the contribution of post-pulmonary shunt to [A-4D02 is undetectably small and does not increase with exercise, simulated altitude or exercise at simulated altitude (161). Of particular interest is the effect of exercise on the diffusion component of [A-4D02. Gale et al. (55), at exercise corresponding to a V02 greater that 3.01•min -1 , found a trend toward greater [A-4D02 than could be predicted from inert gas data, and suggested diffusion limitation was the likely cause. Similar results were obtained at V02 = 2-3 1-min -1 at simulated altitude of 5000 ft. Statistical conclusions were hampered by the small "n" of the study. In an attempt to clarify this issue, Hammond et al., (63) studied gas exchange in men at various exercise intensities up to VO2 - 4.01•min -1 (essentially maximum). During the very heavy exercise levels, [A-41)02 was measured at 23.0±8.0 torr of which 10.7±7.8 torr could be predicted by inert gas data. Administration of 100% 02 did not alter the "/A/4). relationships. They observed that [A-4D02 increased linearly with VO2. Analysis of data provided by Torre-Bueno et al., (161) yields a correlation between cardiac output and [A- MO2 of r = 0.68. As cardiac output increases pulmonary transit time will decrease from resting levels, therefore at the higher levels of exercise it is possible that some individuals will exhibit increased [A-4D02 because of shortened pulmonary transit time and diffusion dis-equilibrium. No inert gas studies have been made on highly trained athletes capable of high cardiac output, and the possibility for shortened pulmonary transit and significant arterial hypoxemia. Does the pulmonary system constrain exercise? In the preceding pages, the evidence that the pulmonary system may constrain maximal exercise performance has been explored in five areas: energetics, fatigue, respiratory drives, mechanics and diffusion. These arguments will now be applied to data obtained from several studies (Table 16) in an attempt to integrate this information. 92 Group one subjects are sedentary individuals who are unlikely to have a respiratory limitation to performance. This is borne out by the blood gas data indicating that the arterial P02 and saturation are maintained and the metabolic acidosis of exercise is relatively well compensated (pH = 7.31). At a ventilation of 80 1-min -1 the oxygen cost of breathing is about 12% of the total VO2 (from mean of Table 14) and flow volume loops are unlikely to be mechanically constraining, confirmed by maintenance of alveolar P02. Blunted respiratory drives are unlikely in sedentary subjects such as these and have not contributed to significant hypoventilation as indicated by pH and alveolar P02 and CO2 status. The observed alveolar-arterial difference is only slightly greater than that predicted from multiple inert gases therefore they are not likely to be diffusion limited (161). The predicted mean pulmonary transit time based on a capillary blood volume of 150 ml (80) is 0.41 seconds, well within that which will allow full 02 equilibration. Group Two subjects, again show minimal evidence for pulmonary constraint; Alveolar P02 is maintained as is arterial P02 (91 torr) and Sa02 (94.5 %). Hypoventilation secondary to fatigue, blunted drives or mechanics are unlikely to be a factor as evidenced by maintenance of alveolar P02, although the pH values indicate less compensation for the metabolic acidosis of exercise. In these subjects measured alveolar-arterial differences are approximately 12 ton greater than predicted for inert gas data (63) suggesting some diffusion limitation. Calculated mean pulmonary transit time is 0.36 seconds, closer to the 0.25 seconds required for full equilibration. Some interesting comparisons can be made between groups three, four and five who are all highly trained individuals exercising at a VO2 greater that 4 1.min -1 , with similar levels of ventilation and predicted cardiac output, but quite different alveolar and blood gas data. Group three is able to maintain arterial saturation above 92 % and P02 above 80 by maintaining a very high alveolar P02 in the face of widening alveolar-arterial differences to about 37 ton. VCO2 is very high in these subjects as evidenced by R values greater than 1.15 and hypocapnia to 37 torr despite this level of ventilation. Dempsey et al., (35) have 93 suggested that to maintain a PaCO2 of 30 torr in subjects with a VCO2 of 5 - 6 1•min-1 the required ventilation is — 240 1-min -1 , a level unlikely to be sustained even by highly trained individuals. Group four subjects, despite very similar ventilatory data, are unable to maintain arterial P02 and Sa02 and have extremely high alveolar-arterial differences. Inert gas data has not been obtained in these individuals but it is tempting to speculate that the trend in [A-aJDO2 due to diffusion observed at VO2 = 3.0 1-min -1 and statistically significant difference at 4.0 1-min -1 , may continue to increase at higher intensity exercise. If a cardiac output of 31 1•min -1 (43) can be assumed for these subjects, predicted mean pulmonary transit time would be less than 0.30 seconds and almost half of the RBC transit times would be less that that required for full oxygen equilibration. Why group three and four subjects, who are identical in so many respects, should behave so differently with respect to Pa02 and [A-a]D02 is unclear but perhaps some answers can be found in inspection of the Fick equation. Whole body VO2 is the product of blood flow (in this case cardiac output) and the arterio-venous difference, therefore two strategies are available to increase V02: oxygen delivery can be increased, (increase in Q) or a-v difference can be increased (increased peripheral extraction). It is possible that group three subjects despite similar V02, may have a lower cardiac output and maintain VO2 by superior peripheral extraction. The net result would be a longer pulmonary transit time and lower [A-aJDO2 due to diffusion. Group five subjects are different again, and clearly have evidence of hypoventilation as evidenced by the inability to maintain alveolar P02 greater than 108 tom, as well as a widened alveolar-arterial difference indicative of possible diffusion limitation. pH is higher and PaCO2 lower, most likely due to lower intensity exercise - 90% vs 100% of VO2 max. It is likely that in these subjects, mechanical factors may also play a role in the genesis of hypoxemia. It is also possible that at this submaximal exercise level that there may be a role for respiratory drives, which is not evident during exercise at 100% of VO2max. 94 T b e^B1 BOO •^• „.^a^•^-sibirtts I. d • t^0-100'0 of Group 1 2 3 4 5 Training status Untrained Moderately Highly Highly Highly Trained Trained Trained Trained V02 (1•min-1) 2.71 3.97 4.45 4.65 4.81 VCO2 (1•min -1 ) 2.75 4.19 5.27 5.42 4.62 R 1.05 1.04 1.18 1.17 0.96 0 (1•min -1 ) 27.1 24.9 ? ? ? PAO2 (ton) 113 114 119 119 108 Pa02 (torr) 90 91 82 71 75 [A-4D02(0)(torr) 23 23 37 48 33 [A-4D02(0(torr) 22 11 ? ? ? Sa02 (%) 95.6§ 94.5§ 93.0 89.9 91.9 PaCO2 (ton) 37 35 37 37 33 pH 7.31 7.24 7.21 7.20 7.29 n 9 8 7 5 16 § = Sa02 calculated from normogram; 1 = data from Gale et al., 1985 (55) and Torre- Bueno et al., 1985 (161); 2 = Hammond et al., 1986 (63); 3,4 = Hopkins and McKenzie, 1989 (72) and unpublished data, 5 = Dempsey et al., 1984 (35), respiratory exchange ratio; 0= cardiac output (1•min -1 ); VE = minute ventilation (1•min -1 ); PAO2 = alveolar 02 (ton); Pa02 = arterial 02 (ton); [A-4D02(0) = observed alveolar-arterial difference (ton); [A- 41)02(0 = predicted alveolar-arterial difference (ton); Sa02 = arterial hemoglobin saturation (%); PaCO2 = arterial CO2 (ton). 95 Summary This review has identified several factors by which the respiratory system may constrain exercise performance. In sedentary or moderately trained individuals, the data does not favor evidence of pulmonary limitation and it seem likely that these individuals are constrained by other factors, such as 02 delivery and extraction. In highly trained individuals exercising at high intensity, the picture is different, with falling Pa02, widening [A-4)02 and inability to maintain acid-base homeostasis suggesting a pulmonary constraint. The relative contribution of various factors is unclear, but the balance of the evidence favours mechanical limitation of ventilation and diffusion limitation at the lung as the most attractive possibilities. 96 APPENDIX B: METHODOLOGICAL BACKGROUND Quantitative radiocardiography Red blood cell labeling Labeling red blood cells with 99mTechnecium provides a stable intravascular marker that has wide spread clinical applications, particularly in blood flow measurements. Simple incubation of the erythrocytes with 99mTc does not provide a satisfactory result because of loss of radioactivity with washing of the cells (173). The addition of stannous citrate acting as a reducing agent, allows the 99mTc-pertechnetate to cross the red cell membrane, improving the stability of the label, while still providing labeling efficiency of over 90% (152). Commercial kit preparations (152) have the advantage of allowing the preparation of small quantities of labelled cells with high yields with a minimal amount of handling thus reducing the risk of contamination. The kit consists of a sterile reagent tube containing 100 units of sodium heparin, 2.6 1.4 stannous citrate (1.0 .tg stannous ion), sodium citrate and anhydrous dextrose. About 6 ml of whole blood is added to the vial followed, after mixing with 4 ml of sterile saline. After centrifuging, 2 ml of erythrocytes are withdrawn and added to a sterile vial containing 99mTc-pertechnetate and incubated for five minutes. The resulting mixture is assayed for radioactivity prior to injection for RBC label studies. First pass determination of cardiac output After injection into a flowing stream eventually all of a tracer, although diluted, will eventually pass by an observational point down-stream and the amount of indicator (given by I) can be calculated by 00 I= FoiC i(t)dt 97 where F is flow and C i(t) is the observed concentration at time t. In tracers confined to the vascular space the equilibrium concentration (Ceq) times volume of distribution (Vd) will equal the total amount of tracer injected and CO Ceq•Vd= F SC I(Odt. 0 This method can be applied to all tracers, but for radioisotopes where concentrations cannot be measured directly by an external counter it is necessary to relate concentration to count-rate by allowing for the counting efficiency of the detector for the tracer material in the volume of interest. After summation the following equation can be derived: E•Vd F_ A where E is the observed equilibrium count rate, Vd is the volume of distribution and A is the area under the first pass time activity curve after correction for re-circulation. To determine cardiac output this analysis is applied to a first pass time activity curve derived from gamma camera imaging of a region of interest in the left ventricle, following a bolus injection of 99mTc-pertechnetate label red cells. The equilibrium count rate is measured in the same region of interest after complete mixing of the tracer has occurred. Sources of error with this method arise from poor bolus technique at the time of injection, difficulties in extrapolation of the down slope of the first pass curve, dead time count losses by the detector system, and incomplete mixing of the tracer. Volume of dilution of the tracer must also be determined and can also be measured using radioisotopes (76). Determination of cardiac output by first pass radiocardiography has shown excellent correlation with other methods (83, 84) and has the advantages of being non-invasive, able to evaluate separately the right and left ventricle and provide information about pulmonary transit times. 98 Gated radionucleide angiography: Gated radionucleide angiography is based on the principle that if cardiac volumes can be determined in end-systole and end-diastole, and if heart rate is known, cardiac output can be determined from the product of stroke volume and heart rate. After injection and equilibration of labeled erythrocytes, data acquisition is performed by dividing the R-R interval into a discrete number of frames (usually 16), and using the QRS complex as a gate. A series of images are acquired that provides a dynamic picture of cardiac function. By measuring the number of counts in the left ventricle over the sixteen frames end-systolic and end-diastolic counts can be determined and left ventricular ejection fraction can then be calculated from the difference between background subtracted end-diastolic and end-systolic counts and expressed as a percentage of end-diastolic counts. Samples of blood are drawn at the end of each data acquisition and imaged in petri dishes for five minutes. The average background subtracted count rate for 5 ml of blood is obtained, and the left ventricular count rate is corrected for loss by decay of 99mTc using standard tables. Left ventricular end-diastolic volume is obtained by dividing the left ventricular count rate by the 5 ml count rate; cardiac output is obtained by multiplying the stroke volume by the average heart rate during data acquisition, usually 2 to 3 minutes. The advantage of gated studies are that sequential evaluations are possible and that dynamic information about cardiac wall motion can be obtained. The major disadvantage is that movement artifact becomes more likely as exercise intensity is increased and a correction factor must be applied to correct the ventricular counts for attenuation by the structures of the chest. This last problem can be over come for sequential studies if a first pass determination of cardiac output is performed prior to the acquisition of gated information. The ventricle depth for the gated studies is the set with the first pass information obtained at the identical workload so that the two studies agree and the result is applied to subsequent determinations of cardiac output. Gated equilibrium ventriculography has been shown to be a reliable and valid means of measuring cardiac output and ventricular volumes (111, 122, 99 123). Data obtained by this method correlates well with that obtained by contrast and first- pass ventriculography (122). Measurement of blood volume: Plasma volume can be measured relatively simply and accurately by the use of radioisotopes. Currently, the use of radioiodine ( 1311) labelled serum albumin (RISA) is the method recommended by The International Committee for Standardization in Haematology (76). Briefly, 5m1 of radioiodine labelled human serum albumin is injected intravenously and the time of injection recorded. At 10, 20 and 30 minutes from the time of the original injection 5m1 of blood is withdrawn and plasma volume is calculated as: S•D•V Pv — Po where S is the concentration of radioactivity in a prepared standard solution, D is the dilution of the standard, V is the volume of RISA injected, and 130 is the concentration of radioactivity in the sample extrapolated back to time zero, based on the radioactivity counts in the three measured samples. This method can also used with only a single sample taken at 10 minutes. Total blood volume can then be calculated from the hematocrit (Hy) and the plasma volume: PvBV —11 - 0.9H v where 0.9 is a correction factor for relating whole body hematocrit to venous hematocrit. 100 Pulmonary transit time: Theory: The mean transit time for a well mixed indicator to flow through a specific volume at a given flow rate is described by the relationship: transit time = volume/flow. The time required for an indicator to flow past an observation point down stream from an entry point is related not only to the time it take the bolus to flow past the point but also how quickly it arrived there. Transit time is the time that it takes a bolus to remain in a compartment if it is injected directly and instantaneously into the compartment. The first moment describes not only the time that the indicator is in the compartment but also how quickly or slowly it arrived there. The first moment therefore represents the summation of all transit times up to that point. If it were possible to deliver indicator material instantaneously into the compartment of interest the first moment would be the same as the transit time. Transit time of a compartment can be determined by subtracting the first moment of the bolus from that of the compartment. To measure pulmonary transit times, the bolus or input curve is derived from a labeled RBC time activity curve of the right ventricle, the output curve is derived from the left ventricle and transit time is determined by subtracting the first moment of the right ventricular curve from the first moment of the left ventricular curve. This method is referred to as the centroid method. Deconvolution is a mathematical process by which a frequency distribution of transit times (a transfer function, h(t)) can be derived from the input (right ventricular) and output (left ventricular) time activity curves. It is important to note that the transit time obtained from either method represents the the delay of the bolus through pulmonary arteries, arterioles, capillaries, venules, veins, left atrium and left ventricle and does not just represent pulmonary transit time. 101 Frequency distribution of pulmonary transit times: A frequency distribution (h(t)), such that the transformation of the input curve by the transfer function produces the output curve can be written as: i(t)*h(t) = o(t) where i(t) is the input curve, o(t) is the output curve and * represents convolution or a complex transformation. The process of determining the shape of h(t) is termed deconvolution. Several methods have been used to determine the shape of h(t). For example, o(t) = di('c)h(t-t)th where i is a variable used only for integration. If this is the case then a numerical solution can be sought by assuming that the integral can be approximated by a sum. Then the approximation of the output curve is given by: o(nAt) = /i(sAt)h(n-s+1)At) where n is an integer which varies between 1 and m (m is the number of data points in the input and output functions) At is the time interval between the two data points and s is the integer variable for the summation. Knowing o(nAt) and i(nAt) it is possible to solve for the frequency distribution of transit times. Unfortunately this method is extremely unstable in the initial part of the input curve where it is required to extrapolate towards zero. This uncertainty is then propagated throughout the solution. Fourier analysis which describes h(t) in terms of a Fourier transform (3) has also been used for calculation of transit times. 3 [o(t)] = 3 [i(t)*h(t)] = 3 [i(t)] • 3[h(t)] and solving for h(t): 102 h(t) = z [3 [0(t)] L5 [i(t)] this analysis, however also suffers from the same problem as solution one, namely propagation of error. More recently, deconvolution analysis using multiple fitting functions has been used to determine h(t). This has the advantage that knowledge of h(to) is not crucial and therefore eliminates the problem of propagation of error. The first step in this process is to fit a smooth function to i(t) and o(t). This involves elimination of noise from movement artifact during data collection, re-circulation of tracer and background radiation. Application of a gamma function to indicator dilution curves gives an excellent fit (153). The family of curves given by this model are described by C(tn )= k(tn -ta )cce -(tn -ta )/13 ' where k, a and 13 are arbitrary ,ta and to are times of appearance and nth time respectively and C(tn) is the indicator concentration at tn. This function can be linearized (see (153) for details) and then weighted least squares procedure can be used to fit the preceding equation to the input and output curves. The area under the curve can then be determined using 00 JC(t)dt = to + 13 2(a+1). 0 Since for the determination of transit times the absolute area under the curve is not important, the area under both the input curve and the output curve can be set equal to 1 (all indicator that appears in the right ventricle must at some time appear in the left ventricle). The next step is to determine many h(t)s, each with an area under the curve equal to 1, making the assumption that since both the input curve and output curve are described by a y function that h(t) is also y distributed. A family of twenty to fifty curves (ie h i (t), h2(t)...h50(t) is then generated with a mean transit time for these curves that is equal to the 103 difference between the first moments of the input and output curves and variances that range from the variance of the input curve to the variance of the output curve. Each of these curves is then convoluted with the input curve to produce a unique output curve each of which contributes to the final output curve: o(t) = i(t)*h(t) =i(t)*[hi(t) + h2(t) +^h5o(t)] Given many I n(t) it should be possible to be able to approximate h(t). If the area under h(t) is set equal to 1 then: o(t) = i(t)*[ahi(t) + bh2(t) + ch3(t)...] where a + b + c = 1 and o (t) = ai(t)*hi(t) + bi(t)*h2(t) + ci(t)*h3(t)... Multiple regression analysis is then applied to determine the contribution of each of these curves to h(t). Unfortunately, each of the generated curves are highly correlated with one another (a situation termed multicollinearity) resulting in either a highly unstable result, such as negative numbers for a,b,c,... (since i(t) = 1 and o(t) = 1 the area under each h n(t) must also equal one and a,b,c,... must add up to 1 and vary between 0 and 1). Another problem is the refusal of least squares regression software packages to complete the regression since every curve is very similar. In order to overcome these difficulties, constrained ridge regression is used to determine the relative contribution of a,b,c... to the final solution. Ridge regression acts on the assumption that the least squares estimation of the regression coefficients tends to be too large and therefore applies a shrinkage factor to the least squares estimator biasing it towards zero. The amount of shrinkage that ridge regression applies to each regression coefficient is proportional to to the coefficient's 104 variance; it is assumed that the greater the variance the less the certainty that the estimated coefficient reflects the true value. The final step in this process involves convoluting the input curve with the derived transfer function. If the resulting o(t) is the same as the measured o(t) then the transfer function must be correct and the analysis is complete. If the result is unacceptable then the entire process is repeated until a good fit is obtained. A further problem associated with deconvolution analysis revolves around the definition and quantification of a "good fit" of the derived o(t) with the actual o(t). The curves under discussion represent complex mathematical functions which are difficult to assign numerical values to determine goodness of fit. If a numerical scale is assigned, a further problem arises with quantification. For example a numerical score of 7 may fit better than 10, but how much better? The usual method relies on manual observation to determine goodness of fit. While not entirely satisfactory it should be noted that small changes in fit are associated with minimal, if any, changes in transit time. Incomplete mixing of the bolus can lead in either over or underestimation of times, therefore is is preferable to measure transit time downstream from the site of injection. Other sources of error include poor bolus technique and cross contamination of time activity curves from overlying structures in the chest. Multiple inert gas elimination Ventilation and perfusion relationships The partial pressure of 02, CO2 and N2 in any gas exchanging unit is determined by three major factors: the ventilation perfusion ratio, the composition of the inspired gases and the composition of mixed venous blood. If CO2 is the gas of interest the following equation can be derived: iTCO2 =•CO2 •K 105 where VCO2 is CO2 output, VA is alveolar ventilation, PACO2 is alveolar PCO2 and K is a constant. Similarly the loss of CO2 from the capillary blood can be described by : VCO2 = O(CiTc0 2 - Cc'co2 ) Where Q is blood flow, C\-Tc02 is mixed venous CO2, and Cc CO2 is end capillary CO2. Under steady state conditions these equations must be the same and VA •PA CO2•K = O(CVCO2 Cc ' CO2) rearranging: VA CVCO2 Cc ' CO2 PA CO2•K The effect of changing inspired oxygen concentration on arterial blood gases is determined by the ventilation perfusion ratio. For lung units with VA/Q > 1 end capillary 02 is high regardless of F102, whereas lung units with VA/Q - 0.1 end capillary 02 will increase rapidly as the F102 is increased. Those with V A/Q < .01 show little response to increasing 02 unless the inspired 02 is greater than 50%. The effect of changing the mixed venous P02 is the most marked in lung units with VA/Q between 0.1 and 1, and end capillary 02 will decrease rapidly when the mixed venous P02 falls (Figure 10). This is especially important when one considers the effect of exercise on mixed venous P02 as values of about 25 torr are not uncommon (27) in normal subjects and in athletes exercising near max (V02 - 5.0 1-min -1 , Q - 33 1-min-1 ) this can be calculated to be less than 20 ton by the Fick equation. Alterations in the inspired P02 can also alter the VA/Q relationship by causing hypoxic vasoconstriction with low F102, or collapse of low VA/Q units as 02 is absorbed with high F102 . 106 Figure 10. The relationship between end capillary P02 and mixed venous P02 for lung units of differing ,T,A/() 1 60 'E^120 80n. 40 0 VA/Q =10 VA/Q=1 .21 12(^vA/Q 0.1 .21^4kvAio =0.01 V A/Q =0.001Ar- 0^10 20 30 40 50 60 70 Mixed Venous P01 (Ton) From West, 1977 (175) Multiple inert gas elimination theory: An inert gas can be defined as one that obeys Henry's law, that is, that at constant temperature the solubility of the gas in a liquid is directly proportional to the pressure the gas exerts on the liquid. The diffusion of gas through tissues is described by Fick's law of diffusion. This states that the rate of transfer of a gas through a tissue or membrane is proportional to the difference in partial pressure of the gas on either side of the tissue. This can be written as : F = K(Pi - P2) (1) 107 Where F is flow of the gas across the membrane, K is a constant and P 1 and P2 are partial pressures of the gases on either side of the membrane. The constant K is related to the surface area that is in contact with the gas, the solubility of the gas in the tissue, and is inversely proportional to the thickness of the membrane and the square root of the molecular weight of the gas of interest (166). Fick's law when applied to a finite volume of blood (a "slug") makes several assumptions: the blood is perfectly mixed, with a homogeneous capacity for the gas, there is no axial diffusion and the slug of blood is flowing at a uniform rate. The preceding equation can be expressed in terms of diffusing capacity: Vx(t) = -Dx[PAx-Px(t)] (2) Where Vx is the flow rate and Dx is the diffusing capacity for gas x, PAx is the alveolar pressure and Px(t) is the instantaneous capillary pressure of gas x. This equation can also be expressed as a rate in change in partial pressure in pulmonary capillary blood: Vx(t) = OrPx(t)i-V4 where Px(t) is the rate of change of capillary partial pressure, Vc is the capillary blood volume and R is the solubility of the gas in blood. Diffusing capacity can also be expressed in terms of its component parts: A ^ax ^ Dx =^• , " "VMWx (3) (4) 108 where k is the diffusion coefficient of gas x in the blood-gas barrier, A is the cross-sectional area over which diffusion is occurring, d is the thickness of the blood-gas barrier, ax is the solubility of the gas x in the blood-gas barrier and MW is the molecular weight of the gas. Equation 3 and 4 can be combined with equation 2 and integrated with respect to time, giving the following: 100 kA ax^t Px(t) = PAx + (P vx- PAx)-e L 60 dVc PxVMWx_ (5) Where t = time and Pir is mixed venous Px. Although complicated, equation 5 can be used to make the following points: Absolute solubility of a gas is less important than the ratio of solubilities in blood and alveolar wall tissue, making the ratio of ax to 13x an important regulator of equilibrium rate. In the same fashion, if two gases are equal in other factors they will differ in their equilibration rates on the basis of molecular weight. The Fick principle can also be applied to an inert gas (x): V•PIx - VA•PAx = AxQ(P c ' x - Pix ) where VI is the inspired ventilation in the lung unit, VA is expired ventilation in the lung unit of interest, Pix , Pc 'x and Pv- x are inspired, end capillary and mixed venous concentrations of gas x , and Xx is the blood gas partition coefficient: xx = R x (Pbar -PH2O body temp)/100 For most inert gases Piz can be considered to be zero and and in steady state the equation simplifies to : PAX^P c ' x^Xx •^• PV x ^ Piix^X x + VA/Q 109 This analysis has considered VA/Q relationships in one lung unit or a perfectly homogeneous lung, but it can be applied to gas exchange in a heterogeneous lung divided into N compartments each representing a given^ratio by summation (69, 168): P exp x A,x ^ -^ VAi • x^j=1^(Xx + VA/ 05  E P art x PAT x Xx x + VA/ 45 where P exp x is the mixed expired partial pressure of the gas and P art x is the mixed P exp x^ P art x arterial partial pressure.^ has been termed the excretion (E) of gas x and ^ PV- x Pv-x is referred to as the retention (R) of gas x. Practical aspects Although theroretically it would be possible to apply this model to a single gas of known X , it is preferable to use six to eight gases of varying X. A gas mixture of 20% SF6 (X - 0.005), 20% cyclopropane (X - 0.5) and 60% ethane (X - 0.1) is bubbled under sterile conditions into a bag of 5% dextrose followed by injections of 2 m1.1 -1 enflurane (X - 2) , 0.5 m1-1- ldiethyl ether (A, - 12) and 6 m1.1- lacetone (X - 300). This mixture is infused into a vein and after a period of equilibration arterial blood and mixed expired gas samples are simultaneously obtained in ungreased gas tight syringes (167). The gases in the blood are extracted into the gaseous phase by the addition of helium and then analyzed using electron capture (in the case of SF6) and flame ionization (in the case of the other 110 five gases) gas chromatography. The results (Peqm - equilibrium pressure) are corrected back to the original concentrations in blood (Po) using the following equation: Vg VIPt) = Peqm • [1.0 + where k is the barometric pressure-saturated water vapour pressure/100, S is the solubility of the gas in blood (measured directly by a second equilibration) and V1 and Vg are the volume of liquid and gas in the sample respectively. Mixed venous concentrations of the gas are then calculated using the Fick equation for inert gases. Pi = Pa + PEVE XQT VE is measured in the usual fashion, QT is total cardiac output and X is obtained from the measured solubility: — Sx • 1 (Pbar-PH20) 00 Dead space Retentions (R) and Excretions (E) are calculated as described previously. An enormous amount of information can be obtained from this analysis (69, 181). The inert gas dead space (VDIG) represents ventilation that does not come into contact with perfusion and results in a decreased excretion ratio of the gas. VDIG — Rhomo Where Rhomo and Ehomo are retentions and excretions of the homogeneous lung respectively. Physiological dead space represents both inert gas dead space and that resulting from excess ventilation to a lung unit. Physiological dead space ratio can be calculated as: Rhomo-Ehomo 111 Pa PE- — VD Pa-PE Pi Pv R-E VT — P a^Pa — R Pv Where R and E are retention and excretion in the heterogeneous lung. The alveolar dead space which is physiological dead space minus inert gas dead space can also be determined directly from retention and excretion data: , Pa homo PE homo VDalv (Pa-Pa homo)^+ (PE homo-PE) VT^ Pa The effect of VA/Q heterogenity is to increase the retention and decrease the excretion of a gas. Shunt Shunt refers to that portion of blood flow that bypasses gas exchanging areas and can be divided into intra-pulmonary and extra-pulmonary shunt. The effect of either is to increase the retention of any gas without an effect on the excretion of a gas. Note that the inert gas analysis does not consider extra-pulmonary shunt. Venous admixture (Qva) includes both pure intra pulmonary shunt and that from over perfusion of lung units and can be calculated by: Qva PA - Pa Pa - PA QT PA - Pv Pi - PA Alveolar partial pressure can be calculated using mixed expired partial pressure from: PE = VDIGPA 1 VT and the preceding equation becomes: Qva R - E' where E' —^•^ 1 -— 1 - E '^VDIG T VT 112 VA/Q distributions The VA/Q distribution can be estimated from the inert gas data using a linear least squares regression with enforced smoothing (69, 168) and from this model, predicted Pa02 and PaCO2 can be calculated. Diffusion limitation for inert gases and 02 can be determined from this model. Diffusion limitation for inert gases would manifest as retention of gases with high molecular weight (SF6 and enflurane), while that of 02 would be evident as a much lower Pa02 than predicted from the model. The degree of VA/Q mismatch is generally evaluated from two types of indices of dispersion (55) one derived from the model described above and the other derived directly from the retention and excretion data. The log standard deviations of blood flow (log SDQ) and ventilation (log SDI>) are calculated as the square root of the second moment about the mean for both blood flow and ventilation (174). Normally this value is about 0.3-0.4 with 0.6 being at the upper limit of normal in young healthy subjects (181), and representing an alveolar-arterial difference of about 5 torn. In addition the curves resulting from the graph of ventilation of blood flow versus ventilation/perfusion ratio are centered on VA/ Q of 1 and do not have very high or low VA/Q, or areas of shunt. More recently Gale et al., (55) derived three indices of dispersion: DISPR* analogous to log SDQ, DISPE analogous to log SNT and DISPR*_E an overall index of dispersion. DISPR*-E 0\1 n I(Ri-Et)2 == 100 x^i 1 n 0\/ n 1(Ri-Rhomot)2 i=1 DISPR*= 100 x 113 0\1 n I(Ehomot-Et)2 i=DISPE = 100 x^1 n where Ehomoi = Rhomoi Xi • Xi+ VA QT and Ei and Ri represent excretions and retentions of the gas of interest and Et is excretion corrected for dead space: Ei — Ei V D VT These have been shown to correlate well with log SDQ and log SlDr (55), although it should be noted that DISPR unlike log SDa includes shunt (VA/Q = 0) and DISPE includes areas of dead space (VA/Q = 00) which is not true of log Spy. • Alveolar arterial difference For a distribution of ventilation and perfusion ratios the partial pressure of mixed expired gas is given by: PA — j=N y, PAj j=1 and the mixed mixed arterial partial pressure is given by : j=N IPaj*aj Pa _ j=1 j=N j=1 j=N IPAj*VAj j=1 114 As the distribution of ventilation and perfusion broadens the PAO2 and Pa02 move further away from each other; in the perfectly homogeneous lung without shunt, or VA/Q mis- match they would be equal. The alveolar-arterial difference tends to zero as A, tends to zero or infinity, and [A-a]D02 is the greatest for gases of intermediate solubility. Reducing the VA/Q has the most effect on gases of low solubility, while raising the VA/Q has the most pronounced effect on gases of high solubility (180). 115 APPENDIX C STATISTICAL ANALYSES AND RAW DATA * = significant at p < 0.05 Anova tables Metabolic data Temperature Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 4.167* Between subjects 9 2.367 0.0371 Rest vs Heavy exercise 4.167* Within subjects 30 Rest vs Maximal exercise 24.000* treatment 3 24.33 0.0001 Light vs Heavy 0 residual 27 Light vs Maximal 8.167* total 39 Heavy vs Maximal 8.167* Ventilation Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 9.280* Between subjects 9 0.085 0.9997 Rest vs Heavy exercise 101.007* Within subjects 30 Rest vs Maximal exercise 244.312* treatment 3 296.57 0.0001 Light vs Heavy 49.054* residual 27 Light vs Maximal 158.360 total 39 Heavy vs Maximal 31.140* i-Q2 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 58.312* Between subjects 9 0.032 1 Rest vs Heavy exercise 293.176* Within subjects 30 Rest vs Maximal exercise 426.225* treatment 3 524.67 0.001 Light vs Heavy 89.988* residual 27 Light vs Maximal 169.234* total 39 Heavy vs Maximal 12.410* 116 'CO2 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 25.803* Between subjects 9 0.028 1 Rest vs Heavy exercise 188.121* Within subjects 30 Rest vs Maximal exercise 364.961* treatment 3 439.65 0.0001 Light vs Heavy 74.638* residual 27 Light vs Maximal 196.680* total 39 Heavy vs Maximal 28.997* ilEic•2 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 18.191* Between subjects 9 1.064 0.42 Rest vs Heavy exercise 4.043* Within subjects 30 Rest vs Maximal exercise 0.245 treatment 3 28.248 0.0001 Light vs Heavy 5.082* residual 27 Light vs Maximal 22.657* total 39 _ Heavy vs Maximal 6.279* ilvilco2 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 20.476* Between subjects 9 0.746 0.66 Rest vs Heavy exercise 13.818* Within subjects 30 Rest vs Maximal exercise 7.616* treatment 3 23.298 0.0001 Light vs Heavy 0.653 residual 27 Light vs Maximal 3.117* total 39 _ Heavy vs Maximal 0.917 117 Respiratory exchange ratio Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.580 Between subjects 9 0.633 0.76 Rest vs Heavy exercise 6.460* Within subjects 30 Rest vs Maximal exercise 25.928* treatment 3 37.573 0.0001 Light vs Heavy 7.740* residual 27 Light vs Maximal 28.435* total 39 Heavy vs Maximal 6.504* 131gxegaLgUa Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 1.288 Between subjects 9 0.481 0.88 Rest vs Heavy exercise 15.624* Within subjects 30 Rest vs Maximal exercise 61.266* treatment 3 72.959 0.0001 Light vs Heavy 7.940* residual 27 Light vs Maximal 44.787* total 39 Heavy vs Maximal 15.012* PaCO2 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 2.908 Between subjects 9 0.941 0.51 Rest vs Heavy exercise 1.425 Within subjects 30 Rest vs Maximal exercise 14.074* treatment 3 31.565 0.0001 Light vs Heavy 8.404* residual 27 Light vs Maximal 29.777* total 39 Heavy vs Maximal 6.543* 118 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 1.435 Between subjects 9 0.577 0.83 Rest vs Heavy exercise 6.551 * Within subjects 30 Rest vs Maximal exercise 27.046* treatment 3 48.435 0.0001 Light vs Heavy 14.119* residual 27 Light vs Maximal 40.093* total 39 Heavy vs Maximal 6.976* P,122 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 3.634* Between subjects 9 4.319 0.0011 Rest vs Heavy exercise 12.774* Within subjects 30 Rest vs Maximal exercise 1.214 treatment 3 13.581 0.0001 Light vs Heavy 2.782 residual 27 Light vs Maximal 0.647 total 39 Heavy vs Maximal 6.111* 1A-AllX22(o) Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.292 Between subjects 9 1.41 0.22 Rest vs Heavy exercise 33.422* Within subjects 30 Rest vs Maximal exercise 37.938* treatment 3 65.419 0.0001 Light vs Heavy 27.647* residual 27 Light vs Maximal 31.574* total 39 Heavy vs Maximal 0.143 119 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 1.228 Between subjects 9 1.173 0.12 Rest vs Heavy exercise 7 .45* Within subjects 30 Rest vs Maximal exercise 15.915* treatment 3 18.555 0.0001 Light vs Heavy 2.629 residual 27 Light vs Maximal 8.301* total 39 Heavy vs Maximal 1.587 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 17.943* Between subjects 9 0.34 0.95 Rest vs Heavy exercise 33.802* Within subjects 30 Rest vs Maximal exercise 27.379* treatment 3 41.473 0.0001 Light vs Heavy 2.490 residual 27 Light vs Maximal 0.993 total 39 Heavy vs Maximal 0.338 fa:iX22 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 3.985* Between subjects 9 2.983 0.01 Rest vs Heavy exercise 4.075* Within subjects 30 Rest vs Maximal exercise 11.02* treatment 3 11.262 0.0001 Light vs Heavy 0.001 residual 27 Light vs Maximal 1.751 total 39 Heavy vs Maximal 1.692 120 MICiET data for all six_gases Residual sum of squares Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 1.054 Between subjects 9 2.821 0.02 Rest vs Heavy exercise 3.357* Within subjects 30 Rest vs Maximal exercise 3.466* treatment 3 4.612 0.01 Light vs Heavy 0.649 residual 27 Light vs Maximal 0.698 total 39 Heavy vs Maximal 0.001 Mean of Q distribution Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 8.065* Between subjects 9 0.133 0.98 Rest vs Heavy exercise 14.191* Within subjects 30 Rest vs Maximal exercise 44.748* treatment 3 45.611 0.0001 Light vs Heavy 0.860 residual 27 Light vs Maximal 14.818* total 39 Heavy vs Maximal 8.540* Mean of V distribution Source Degrees of freedom F- test P value Comparison Scheffe F-test Between subjects Within subjects treatment residual total 9 30 3 27 39 0.81 61.187 0.60 0.0001 Rest vs Light exercise Rest vs Heavy exercise Rest vs Maximal exercise Light vs Heavy Light vs Maximal Heavy vs Maximal 2.917 15.917* 54.555* 5.206* 32.243* 11.537* 121 Log SD Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.551 Between subjects 9 1.536 0.91 Rest vs Heavy exercise 10.258* Within subjects 30 Rest vs Maximal exercise 24.488* treatment 3 31.046 0.0001 Light vs Heavy 6.054* residual 27 Light vs Maximal 17.963* total 39 Heavy vs Maximal 3.048* Loa SD Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.579 Between subjects 9 3.61 0.005 Rest vs Heavy exercise 0.002 Within subjects 30 Rest vs Maximal exercise 0.131 treatment 3 1.347 0.28 Light vs Heavy 0.600 residual 27 Light vs Maximal 1.262 total 39 Heavy vs Maximal 0.122 ISP * Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.168 Between subjects 9 4.428 0.0013 Rest vs Heavy exercise 8.232* Within subjects 30 Rest vs Maximal exercise 19.110* treatment 3 25.757 0.001 Light vs Heavy 6.048* residual 27 Light vs Maximal 15.696* total 39 Heavy vs Maximal 2.257 122 pISP Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.001 Between subjects 9 4.094 0.001 Rest vs Heavy exercise 1.992 Within subjects 30 Rest vs Maximal exercise 1.884 treatment 3 2.453 0.089 Light vs Heavy 1.949 residual 27 Light vs Maximal 1.841 total 39 Heavy vs Maximal 0.108 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.111 Between subjects 9 3.773 0.003 Rest vs Heavy exercise 3.694* Within subjects 30 Rest vs Maximal exercise 5.909* treatment 3 8.449 0.0004 Light vs Heavy 2.524 residual 27 Light vs Maximal 4.400* total 39 Heavy vs Maximal 0.259 WA-MD Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.631 Between subjects 9 2.899 0.0136 Rest vs Heavy exercise 8.095* Within subjects 30 Rest vs Maximal exercise 16.684* treatment 3 20.998 0.001 Light vs Heavy 4.205* residual 27 Light vs Maximal 10.825* total 39 _ Heavy vs Maximal 1.536 123 RA-MD Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.517 Between subjects 9 1.709 0.1304 Rest vs Heavy exercise 0.176 Within subjects 30 Rest vs Maximal exercise 0.043 treatment 3 0.567 0.6418 Light vs Heavy 0.090 residual 27 Light vs Maximal 0.262 total 39 Heavy vs Maximal 0.045 (A-a)D Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.0780 Between subjects 9 3.809 0.0027 Rest vs Heavy exercise 3.555* Within subjects 30 Rest vs Maximal exercise 6.443* treatment 3 9.094 0.0003 Light vs Heavy 2.581 residual 27 Light vs Maximal 5.106* total 39 Heavy vs Maximal 0.426 1-A-a1DOzipl Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.745 Between subjects 9 1.326 0.2652 Rest vs Heavy exercise 7.900* Within subjects 30 Rest vs Maximal exercise 7.969* treatment 3 12.124 0.0001 Light vs Heavy 3.793* residual 27 Light vs Maximal 3.840* total 39 Heavy vs Maximal 0.0001 124 Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 0.0530 Between subjects 9 4.593 0.0013 Rest vs Heavy exercise 4.029* Within subjects 30 Rest vs Maximal exercise 4.609* treatment 3 9.679 0.0002 Light vs Heavy 5.001* residual 27 Light vs Maximal 5.646* total 39 Heavy vs Maximal 0.0200 Cardiac_dat4 Stroke volume Source Degrees of freedom F- test P value Comparison Scheffe F-test Rest vs Light exercise 8.813* Between subjects 9 0.488 0.87 Rest vs Heavy exercise 22.487* Within subjects 30 Rest vs Maximal exercise 59.456* treatment 3 62.601 0.001 Light vs Heavy 3.245* residual 27 Light vs Maximal 22.487* total 39 Heavy vs Maximal 8.813* End diastolic volume Source Degrees of freedom F- test P value Comparison Scheffe F-test Between subjects Within subjects treatment residual total 6 14 2 12 20 1.11 21.421 0.40 0.0001 Rest vs Light exercise Rest vs Heavy exercise Light vs Heavy 10.429* 20.124* 1.777 125 End ystolic volume Source Degrees of freedom F- test P value Comparison Scheffe F-test Between subjects Within subjects treatment residual total 6 14 2 12 20 2.02 4.40 0.13 0.37 Rest vs Light exercise Rest vs Heavy exercise Light vs Heavy _ 2.272 4.069* 0.26 Ejection fraction Source Degrees of freedom F- test P value Comparison Scheffe F-test Between subjects Within subjects treatment residual total 6 14 2 12 20 0.621 65.338 0.71 0.0001 Rest vs Light exercise Rest vs Heavy exercise Light vs Heavy 35.118* 59.657* 3.232 126 Regression VENCO2 vs Pa02 during heavy exercise Degrees of Freedom R^R2^Slope^Intercept 9^0.43 0.19 0.266 4.233 Source^Degrees of freedom F test^P (one tailed) Regression Residual Total 1 8 9 1.85 0.11 VE N CO2 vs Pa02 during maximal exercise Degrees of Freedom R^R2 ^ Slope^Intercept 9 ^ 0.53^0.28^0.184 12.657 Source^Degrees of freedom F test P (one tailed) Regression^1^3.18^0.06 Residual^8 Total 9 VE /V02 vs Pa02 during heavy exercise Degrees of Freedom R^R2 ^ Slope^Intercept 9^0.402^0.161^0.3 1.895 Source^Degrees of freedom F test P (one tailed) Regression^1^1.539^0.12 Residual^8 Total 9 .^. ILIILCA22 vs PaO2 during maximal exercise Degrees of Freedom R^R2 ^ Slope^Intercept 9^0.11^0.13^0.055 29.721 Source^Degrees of freedom F test P (one tailed) Regression^1^0.109^0.37 Residual^8 Total 9 127 Degrees of Freedom R^R2 ^ Slope^Intercept 9 ^ 0.648^0.420^17.9^42.243 Source^Degrees of freedom F test P (one tailed) Regression Residual Total 1 8 9 5.795^0.02 Transit, time rest centroid method vs deconvolution Degrees of Freedom R 9^0.993 R2 ^ Slope^Intercept 0.987^0.963 0.339 Source^Degrees of freedom F test P (one tailed) Regression^1^605.419^0.0001 Residual 8 Total ^9 Transit time during exercise centroid method vs deconvolution Degrees of Freedom R^R2 ^ Slope^Intercept 9^0.955 0.899^1.152^-0.432 Source^Degrees of freedom F test P (one tailed) Regression^1^72.437^0.0001 Residual 8 Total^9 Pulmonary transit time vs Pa02 at maximal exercise Pulmonary transit time vs 1A-a1D02(o) at maximal exercise Degrees of Freedom R^R2 ^ Slope^Intercept 9^0.591^0.35^-17.861^78.643 Source^Degrees of freedom F test P (one tailed) Regression^1^4.301^0.04 Residual 8 Total^9 128 Degrees of freedom F test^P (one tailed)Source 0.656 Degrees of Freedom R 9 R2 Slope Intercept Source Degrees of freedom F test^P (one tailed) 6.040 0.0201Regression Residual Total 8 9 Degrees of Freedom R^R2^Slope^Intercept 9^0.63 0.39 -23.814 78.304 Regression^1^5.283 0.025 Residual^8 Total 9 Exercising pulmonary blood index vs whole blood volume ant-kg-11 0.43 14.379^-458.816 Degrees of Freedom R^R2 ^ Slope^Intercept 9^0.651^0.423^-0.088^51.175 Source^Degrees of freedom F test P (one tailed) Regression Residual Total 1 8 9 5.871^0.02 Pulmonary transit time vs1A-a1D02_(o-p) Pulmonary blood volume index vs fA-a1D02(o) at rest Pulmonary blood volume index vs fA-a1D02(o) at maximal exercise Degrees of Freedom R^R2 ^ Slope^Intercept 9^0.569^0.34^-8.367^1024.178 Source^Degrees of freedom F test P (one tailed) Regression^1^3.84^0.04 Residual^8 Total 9 129 Pulmonary blood volume index vs Pa02 at maximal exercise Degrees of Freedom R^R2^Slope^Intercept 9^0.689 0.475 11.073 -242.825 Source^Degrees of freedom F test^P (one tailed) Regression^1^7.23 0.01 Residual^8 Total 9 Pulmonary blood volume index vs IA-alDO2 (o-p) at maximal exercise Degrees of Freedom R^R2 ^ Slope^Intercept 9^0.679^0.46^-0.058^56.446 Source^Degrees of freedom F test P (one tailed) Regression^1^6.836^0.015 Residual^8 Total 9 Multiple regression to predict Pa02 at maximal exercise Degrees of Freedom R 9^0.94 R2 0.844 Parameter Value Partial F P value Intercept -25.224 inert gas (A-a)D 28.356 5.064 0.0327 transit time 19.141 26.926 0.0012 VE/VCO2 1.714 2.503 0.0028 130 APPENDIX D: INDIVIDUAL SUBJECT DATA. *=missing data or not collected at this exercise level Subject 1. Workload (watts)^R 150 300 420 Temp. ( °C) 36.9 37.0 37.0 37.8 HCT 40 45 PaCO2 (ton) 36 36 36 31 PAO2 (torr) 89 102 112 120 Pa02 (torr) 89 96 82 85 [A-a]D02(o) (torr) 0 6 30 35 [A-a]D02(p) (ton) 5 8 10 15 [A-a]D02(o-p) (ton) -4 -2 19 20 pH 7.45 7.46 7.41 7.24 Sa02 % 97.2 97.6 96.5 93.8 13;z02 (ton) 30 19 14 11 VE (1•min -1 ) 15.7 43.4 105.4 195.4 638 2001 4191 6192 VO2 0.min -1 ) 430 1683 4316 7112 V. CO2 (1•min -1 ) 24.6 21.7 25.1 31.6 VE/V02 36.5 25.8 24.4 27.5 VE/VCO2 7.3 15.2# 26.6# 34.4 Q (1•min-1 ) * * * *End diastolic volume (ml) End systolic volume (ml) * * * * Stroke volume (ml) 126 143 186 206 Ejection fraction * * * * Transit time deconvolution (s) 10.19 2.92 Transit time centroid (s) 10.09 2.99 Pulmonary blood volume (1) 1.234 1.694 Mean residual sum of squares 99.6 69.05 94.65 33.3 0.955 2.145 2.82 4.31 Mean of a 1.045 2.79 3.46 7.575 Mean of V 0.24 0.37 0.44 0.63LogSD Q 0.42 0.80 0.47 0.91 LogSD V DISP R*-E 1.052 3.292 2.657 6.439 DISP R* 0.476 1.447 1.400 3.422 DISP E* 0.559 2.151 1.478 3.522 Inert Gas A-a area 0.039 0.122 0.087 0.237 131 Subject 2 Workload (watts) R 150 300 350 Temp. (°C) 35.5 36.7 36.7 36.9 HCT 45 48 PaCO2 (toff) 33 43 30 29 PA02 (torr) 108 102 124 123 Pa02 (toff) 88 88 87 91 [A-a]1302(o) (torr) 20 14 37 32 [A-a]D02(p) (toff) 12 9 22 16 [A-0302(o-p) (torr) 8 5 15 16 pH 7.48 7.40 7.32 7.29 Sa02 % 98.3 97.0 96.4 96.4 NO2 (toff) 40 21 24 19 VE (1•min -1 ) 11.9 44.8 133.1 175.4 V02 (1•min -1 ) 273 1867 3668 4494 VCO2 (1-min -1 ) 240 1834 4720 5431 ifEti102 43.59 24.00 36.29 39.03 49.58 24.43 28.20 32.30 VF_JVCO2 6.1 13.9 24.7 27.6 Q (1•min -1 ) End diastolic volume (ml) 132 159 190 * End systolic volume (m1) 45 43 31 * Stroke volume (ml) 87 116 158 161 Ejection fraction 0.67 0.74 0.83 * Transit time deconvolution (s) 7.37 2.78 Transit time centroid (s) 7.24 2.77 Pulmonary blood volume (1) 0.743 1.277 Mean residual sum of squares 9 37 19 22 0.89 4.53 2.26 3.98 Mean of a 1.61 5.50 5.30 8.66 Mean of V 0.64 0.47 0.77 0.67LogSD Q 1.33 0.44 1.26 1.13 LogSD V DISP R*-E 5.211 1.750 6.273 8.547 DISP R* 1.809 0.969 3.098 4.371 DISP E* 3.997 0.926 3.764 4.847 Inert Gas A-a area 0.215 0.059 0.241 0.327 132 Subject 3 Workload (watts) R 150 300 390 Temp. ( °C) 36.5 36.7 37.0 37.6 HCT 48 48 PaCO2 (torr) 38 41 35 31 PAO2 (ton) 103 92 113 124 Pa02 (torr) 98 95 91 96 [A-a]1302(o) (ton) 5 -3 22 28 [A-a]I302(p) (ton) 4 7 16 16 [A-a]D02(o-p) (ton) 2 -10 6 12 pH 7.47 7.42 7.37 7.27 Sa02 % 97.9 97.4 96.7 95.8 I3-702 (ton) 31 17 2920 VE (1.min-1 ) 17.1 45.0 177.7 195.8 V02 (I.min-1 ) 520 2393 4367 5176 VCO2 (1•min -1 ) 468 1889 4499 6657 32.9 18.8 40.7 37.8 VEJV02 36.5 23.8 39.5 29.4 VEJVCO2 Q (1.min-1 ) 6.5 14.4 21.9 34.6 End diastolic volume (m1) 121 158 178 * End systolic volume (ml) 42 36 37 * Stroke volume (ml) 79 122 140 197 Ejection fraction 0.66 0.77 0.80 * Transit time deconvolution (s) 8.75 3.16 Transit time centroid (s) 8.60 3.18 Pulmonary blood volume (1) 0.94 1.83 Mean residual sum of squares 66 67 35 72 0.92 2.16 2.62 4.73 Mean of Q 1.25 2.68 5.61 10.31 Mean of V 0.24 0.29 0.51 0.69 LogSD Q 0.82 0.65 1.29 1.16LogSD v DISP R*_E 3.213 2.283 8.303 8.071 DISP R* 1.054 0.989 3.718 4.214 DISP E* 2.469 2.511 5.283 4.381 Inert Gas A-a area 0.126 0.086 0.321 0.328 133 Subject 4 Workload (watts) Temp. (C) HCT PaCO2 (torr) PAO2 (ton) Pa02 (ton) [A-a]D02(o) (ton) [A-a]1302(p) (ton) [A-a]D02(o-p) (ton) pH Sa02 % K02 (ton) VE (1•min -1 ) V02 (1.min-1 ) VCO2 (1 min-1 ) VE/V02 VE/VCO2 Q (1•min-1 ) End diastolic volume (m1) End systolic volume (ml) Stroke volume (ml) Ejection fraction Transit time deconvolution (s) Transit time centroid (s) Pulmonary blood volume (1) Mean residual sum of squares Mean of Q Mean of V LogSD Q LogSD V DISP R*-E DISP R* DISP E* Inert Gas A-a area R 150 300 360 37.3 37.5 38.1 38.8 40 46 36 39 35 30 115 101 113 124 94 83 74 81 21 18 39 43 1 4 6 13 19 14 33 30 7.44 97.1 7.41 95.5 7.29 90.7 7.18 88.1 36 19 13 19 18.5 43.4 114.3 183.7 548 2086 4654 5178 611 1835 4895 6586 33.76 20.81 24.56 35.48 30.28 23.65 23.35 27.89 8.5 15.7# 29.0# 31.9# * * * * * * * 147 141 187 197 * * * 7.45 2.67 7.72 2.68 1.075 1.422 198 125 86 86 1.19 3.15 2.91 4.56 1.23 3.55 4.83 11.95 0.18 0.28 0.40 0.73 0.18 0.39 0.97 1.18 0.430 1.261 5.597 10.124 0.230 0.668 2.480 5.525 0.237 0.706 3.653 5.419 0.014 0.045 0.221 0.405 134 Subject 5 Workload (watts) R 150 300 400 Temp. (C) 36.7 37.0 36.8 38.3 HCT 43.0 44.0 PaCO2 (ton) 41 46 44 33 PA02 (torr) 103 94 103 122 Pa02 (ton) 99 86 74 93 [A-a]D02(o) (ton) 4 8 29 29 [A-a]D02(p) (ton) 11 12 23 15 [A-a]D02(o-p) (ton) -7 -4 6 14 pH 7.48 7.45 7.38 7.20 Sa02 % 97.9 96.8 94.8 93.5 P■;02 (ton) 34  24 2513 VE (1.min-1 ) 9.9 36.4 75.8 147.6 414 1913 3974 5291 V02 (1•min -1 ) VCO2 (1•min -1 ) 372 1621 3752 6072 23.91 19.03 19.07 27.9 VE/V02 26.61 22.46 20.2 24.31 VEIVCO2 7.6 17.8 24.4 36.0 Q (1•min -1 ) End diastolic volume (ml) 154 185 185 End systolic volume (ml) 45 33 31 * Stroke volume (m1) 108 152 154 227 Ejection fraction 0.71 0.84 0.84 * Transit time deconvolution (s) 9.23 2.99 Transit time centroid (s) 9.12 2.74 Pulmonary blood volume (1) 1.162 1.719 Mean residual sum of squares 27.1 19.7 17.2 61.9 0.91 2.38 1.99 3.38 Mean of 6 1.67 3.40 4.16 11.30 Mean of V LogSD Q 0.42 0.51 0.61 0.79 1.32 0.66 0.86 1.35 LogSD v DISP R*-E 6.791 3.634 7.120 12.974 DISP R* 2.366 1.843 3.495 6.760 DISP E* 5.017 2.128 4.307 7.292 Inert Gas A-a area 0.261 0.132 0.264 0.508 135 Subject 6 Workload (watts) R 150 300 370 Temp. (T) 37.1 37.5 37.8 39.0 HCT 43.0 42.0 PaCO2 (ton) 40 40 38 33 PAO2 (ton) 109 105 109 119 Pa02 (ton) 107 104 100 110 [A-4302(0) (ion) 2 1 9 9 [A-a]D02(p) (torr) 3 13 22 21 [A-a]D02(o-p) (torr) -1 -13 -12 -12 pH 7.41 7.39 7.30 7.20 Sa02 % 97.8 97.3 95.5 94.7 602 (ton.) 43 24 18 12 12.4 44.1 105.4 159.2 VE (1•min-1 ) 371 1938 4350 4911 V02 (1•min -1 ) VCO2 (1•min -1 ) 363 1753 4135 5171 33.42 22.76 24.23 32.42 34.16 25.16 25.49 30.79 VE/VCO2 8.2 16.3 21.9 34.5 Q (1•min -1 ) End diastolic volume (ml) 165 167 186 * End systolic volume (m1) 66 39 56 * Stroke volume (m1) 99 128 130 197 Ejection fraction 0.60 0.77 0.77 * Transit time deconvolution (s) 8.85 3.56 Transit time centroid (s) 8.96 3.76 Pulmonary blood volume (1) 1.217 2.105 Mean residual sum of squares 79.3 16.7 16.8 2.2 0.66 1.86 3.36 4.58 Mean of Q 1.43 5.03 10.11 14.19 Mean of V 0.30 0.63 0.83 0.90LogSD Q 1.65 1.39 1.22 1.16 LogSD V DISP R*-E 5.216 11.457 11.900 11.974 DISP R* 1.588 5.062 6.421 6.847 DISP E* 4.155 7.509 6.522 6.051 Inert Gas A-a area 20.9 0.214 0.442 0.462 136 Subject 7 Workload (watts) R 150 300 370 Temp. ( °C) 36.9 36.9 36.9 38.1 HCT 41.0 43.0 PaCO2 (toff) 41 44 37 32 PAO2 (toff) 107 99 114 123 Pa02 (torr) 105 98 94 106 [A-4302(o) (toff) 2 1 20 17 [A-a]D02(p) (torr) 5 10 16 19 [A-a]D02(o-p) (toff) -3 -9 3 -2 pH 7.40 7.38 7.31 7.16 Sa02 % 97.8 97.2 96.6 95.1 P;702 (toff) 32 22 17 19 15.3 49.3 105.8 184.0 VE (1•min -1 ) 454 2424 4108 4912 VO2 (1•min -1 ) 436 2165 4218 5713 VCO2 (1•min -1 ) 33.7 20.34 25.75 37.46 VE/V02 35.09 22.77 25.08 32.21 VEJVCO2 6.2 18.2 26.8# 37.6 Q (1•min-1 ) End diastolic volume (ml) 146 233 223 End systolic volume (ml) 57 67 39 Stroke volume (m1) 89 166 184 229 Ejection fraction 0.62 0.72 0.83 Transit time deconvolution (s) 8.73 2.91 Transit time centroid (s) 8.63 2.95 Pulmonary blood volume (1) 0.897 1.836 Mean residual sum of squares 190.2 88.5 18.8 15.4 1.20 1.72 3.04 4.72 Mean of Q 4.15 6.34 11.84 13.39 Mean of 0.40 0.53 0.77 0.85LogSD Q LogSD V 1.82 1.70 1.42 1.10 DISP R*-E 12.570 13.778 14.567 11.072 DISP R* 4.484 5.630 7.338 6.316 DISP E* 9.327 9.482 8.529 5.663 Inert Gas A-a area 0.513 0.550 0.574 0.437 137 Subject 8 Workload (watts) Temp. (°C) HCT PaCO2 (toff) PAO2 (toff) Pa02 (toff) [A-a]D02(o) (toff) [A-a302(p) (toff) [A-a]D02(o-p) (toff) PH Sa02 % PC/02 (toff) VE (1•min -1 ) V02 (1•min -1 ) VCO2 (1.min -1 ) VE/V02 VE/VCO2 Q (1•min -1 ) End diastolic volume (m1) End systolic volume (ml) Stroke volume (m1) Ejection fraction Transit time deconvolution (s) Transit time centroid (s) Pulmonary blood volume (1) Mean residual sum of squares Mean of Q Mean of V LogSD Q LogSD V DISP R*-E DISP R* DISP E* Inert Gas A-a area R 150 300 340 37.2 37.3 37.5 38.3 43.0 41.0 42 43 36 30 97 95 110 122 99 78 83 93 -2 17 27 29 1 20 25 20 -3 -3 2 9 7.40 97.2 7.38 94.6 7.33 95.0 7.25 94.3 40 18 1514 12.0 43.4 107.6 169.6 350 2163 4337 4673 299 1813 4218 5324 34.29 20.06 24.81 36.29 40.13 23.94 25.51 31.86 6.6 15.5 22.8 35.7 145 185 188 * 63 59 48 * 81 126 139 209 0.56 0.68 0.74 * 9.82 2.58 9.80 2.60 1.079 1.541 * 4.4 8.6 5.0 * 4.72 1.96 4.77 * 11.43 7.23 14.63 * 0.82 0.75 0.88 1.00 1.58 1.15 1.898 9.993 16.805 11.999 5.684 5.688 7.632 6.916 1.393 5.015 10.811 5.999 0.076 0.373 0.640 0.472 138 Subject 9 Workload (watts) Temp. ( °C) HCT PaCO2 (ton) PAO2 (ton) Pa02 (ton) [A-a]1302(o) (ton) [A-a]1302(p) (ton) [A-a]D02(o-p) (ton) pH Sa02 % Pv02 (ton) VE (1•min-1 ) V02 (1.min-1) VCO2 (1-min -1 ) VE/V02 VE/VCO2 6 (1•min-1 ) End diastolic volume (ml) End systolic volume (ml) Stroke volume (m1) Ejection fraction Transit time deconvolution (s) Transit time centroid (s) Pulmonary blood volume (1) Mean residual sum of squares Mean of Q Mean of V LogSD Q LogSD V DISP R*-E DISP R* DISP E* Inert Gas A-a area R 150 300 320 37.1 37.5 37.5 38.2 43.0 45.0 36 40 33 31 93 94 114 120 103 94 93 101 -10 0 21 19 26 9 24 13 -36 -8 -3 6 7.41 7.38 7.27 7.23 97.6 96.5 95.6 95.30 35 22 5 11 10.2 49.3 139.1 158.1 439 2476 5006 4749 307 1947 4915 5213 23.23 19.91 27.79 33.29 33.22 25.32 28.3 30.33 5.9 18.4 26.4# 26.4# 164 191 59 43 105 148 165 165 0.64 0.80 11.35 2.69 11.36 2.68 1.117 1.181 2.1 50.1 11.1 6.6 0.89 2.32 4.04 5.28 2.76 5.97 13.56 17.20 0.76 0.53 0.86 0.92 1.34 1.38 1.22 1.41 13.412 9.924 13.115 12.362 6.105 4.387 7.246 7.277 8.445 6.469 6.939 6.001 0.480 0.391 0.513 0.497 139 Subject 10 Workload (watts) R 150 300 390 Temp. (°C) 36.9# 37.1# 37.3# 38.2# HCT 40.0 40.0 PaCO2 (ton) 39 41 39 38 PA02 (ton) 95 92 101 113 Pa02 (ton) 96 92 80 90 [A-a]D02(o) (ton) -1 0 21 23 [A-a]D02(p) (ton) 3 11 9 18 [A-a]D02(o-p) (ton) -3 -10 12 5 pH 7.44 7.42 7.39 7.36 Sa02 % 97.5 96.9 95.2 95.10 13 ■7• 02 (ton) 35 17 8 7 12.4 48.9 106.2 182.7 VE (l min-1 ) V02 (1•min -1 ) 354 2264 4562 5569 VCO2 (1•min -1 ) 283 1791 4026 6261 35.03 21.6 23.28 32.81 VE/V02 43.82 27.3 26.38 29.18 VE/VCO2 -^-1Q (1.mm ) 7.0 16.4 28.5 33.8 End diastolic volume (m1) 165 198 235 End systolic volume (ml) 63 36 48 * Stroke volume (ml) 102 161 187 222 Ejection fraction 0.62 0.81 0.79 * Transit time deconvolution (s) 11.43 Transit time centroid (s) 11.75 2.15 Pulmonary blood volume (1) 1.352 1.211 Mean residual sum of squares 113 49.3 20.6 12.6 1.42 1.88 3.39 4.38 Mean of Q 2.08 2.59 6.53 11.83 Mean of V • 0.26 0.37 0.58 0.86LogSD Q 0.91 0.68 1.11 1.20LogSD v DISP R*-E 4.290 3.532 7.090 10.284 DISP R* 1.469 1.587 3.483 5.683 DISP E* 3.217 2.279 4.205 5.412 Inert Gas A-a area 0.171 0.130 0.275 0.418 140

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