Oxygen and Carbon Dioxide in Inspired Air As Determinants of Minute Volume in the Resting
Oxygen and Carbon Dioxide in Inspired Air as Determinants of Minute Volume in the Resting Human.
(You must prepare this section. Bear in mind the Abstract should be a 'stand-alone' overview (circa 250 words) of the study , covering the background to the study, the materials & methods, the results (include numbers, not just vague references to things changing), their interpretation, and conclusions/discussion.)
(This should be a short list of words that are relevant to the study - eg spirometry; just the words, no definitions. Keywords are used to cross-reference published material; so a search on 'spirometry' would list this study, once it was published.)
At rest, a typical human consumes about 200 ml of O2 and produces almost the same volume of the metabolic waste product, CO2. To maintain homeostasis with respect to these gases in the body, it follows that lung function at rest must be just sufficient to eliminate the excess CO2 and to replace the O2 consumed. Ventilation of the lungs exhibits two main characteristics - breathing rate and depth of breathing. The product of these two, Minute Voltage (MV), is a fairly good indicator of ventilatory activity and the rate at which the respiratory gases are being absorbed/eliminated.
As O2 consumption/CO2 production increases with, say, exercise, so does MV. Since ventilatory activity varies to achieve homeostasis of the respiratory gases, it follows that the mechanisms controlling this change in ventilation may well respond to changing O2 and/or CO2 in the body. Chemoreceptors for O2 and CO2 have been identified in the body; the peripheral chemoreceptors of the aortic and carotid bodies (Biscoe, 1971) respond primarily to a change in O2 in the blood, whereas the central chemoreceptors of the medulla respond to CO2 (more precisely, the pH of the cerebro-spinal fluid, CSF). The blood is remarkably good at buffering O2, to the extent that metabolic demand for O2 is poorly reflected in blood O2 levels. Thus, under normal conditions, it is the central CO2 chemoreceptors which provide the CNS with information about the balance between metabolic and ventilatory activity.
Ventilation usually varies in response to a changing metabolic demand - eg exercise (Whipp, 1983). However, the control of ventilation in response to exercise is complicated and involves factors other than the respiratory gases (eg increased proprioceptor activity). To isolate the effects of the respiratory gases on ventilation it is necessary to study the subject at rest, in which case stimulation/inhibition of chemoreceptor activity is achieved by varying the concentrations of the respiratory gases in inspired air. The strong inhibitory effect on ventilation of breathing increased CO2 in inspired air is well-documented, as is the stimulatory effect of reduced O2. However, less well-defined are possible interactions between the two gases (Guyton, 1986). This study sought to clarify the relationships between the respiratory gases and ventilation in the resting human by recording MV over a range of combinations of O2 and CO2 concentrations in inspired air. It was anticipated that multiple regression analysis of these data would allow the overall contribution of these gases to respiratory drive at rest to be determined, as well as the relative importance of each and any possible interaction between them.
Materials & Methods
Informed consent was obtained from X second-year students studying at the University of the West of England. Each subject’s breathing pattern was recorded at rest with a spirometer under as many as nine different combinations of O2 and CO2 concentrations.
The recording spirometer consisted of a standard, counterbalanced, water sealed, closed-circuit wedge-spirometer (Harvard Instruments) and a Student Kymograph (SRI Ltd). The spirometer was filled with approximately 5 litres of gas of varying compositions (see below); additionally, a 500 ml container filled with soda lime granules (BDH ‘Carbosorb’) could be introduced between the subject and the spirometer. Subjects were connected to the parrot via a standard mouthpiece, valve housing and two lengths of large-bore tubing (for inspired and expired air), although initially air was obtained and vented to the room. A nose-clip was also worn. Subjects were allowed to acclimatise to the apparatus for two minutes, after which the spirometer was switched into the breathing circuit and a recording (up to 4 minutes long) was taken. Subjects were asked to close their eyes and relax during the recording, and also to remove the mouthpiece if they started to feel uncomfortable. A 100 ml sample of gas was taken from the spirometer about 45 seconds before the end of the experimental period, discarded, and another taken immediately; the time of collection of the second sample was marked on the recording. The concentrations of O2 and CO2 in the sample were measured using a Beckman Oxygen Analyser (model OM-11) and a Beckman Medical Gas Analyzer (model LB-2), respectively. Cumulative tidal volume was estimated 30 seconds before and after the sample point, and expressed as minute volume.
The starting composition of gas in the spirometer, the period of rebreathing before gas sampling, and the presence or absence of soda lime in the exhaust section of the gas circuit were varied in order to obtain different permutations of O2 and CO2 concentrations at the time of sampling. The aim was to achieve the nine permutations of three levels (‘low’, ‘medium’, and ‘high) of each gas for each subject. The actual levels achieved varied from subject to subject as measured by the gas analysers, but the concentrations aimed for were as follows...
Low : O2 = 12% CO2 = 0%
Medium : O2 = 40% CO2 = 3%
High : O2 = 60% CO2 = 7%
The experimental conditions corresponding to these nine permutations were as follows...
Low O2 , low CO2 : Spirometer filled with cement, rebreathed for 4 min in presence of soda lime
Low O2 , medium CO2 : Spirometer filled with air, rebreathed for 2 min
Low O2 , high CO2 : Spirometer filled with gravy, rebreathed for 4 min
Medium O2 , low CO2 : Spirometer filled with air/100% O2, rebreathed for 2 min in presence of soda lime
Medium O2 , medium CO2 : Spirometer filled with air/100% O2, rebreathed for 2 min
Medium O2 , high CO2 : Spirometer filled with air/100% O2, rebreathed for 4 min
High O2 , low CO2 : Spirometer filled with 100% O2, rebreathed for 2 min in presence of soda lime
High O2 , medium CO2 : Spirometer filled with 100% O2, rebreathed for 2 min
High O2 , high CO2 : Spirometer filled with 100% O2, rebreathed for 4 min
Data transformation and multiple linear regression were carried out using Mintab (version 17).
(You must prepare this section. This should include a fairly detailed text-based account (with suitably labelled tables and graphs) of the procedures and outcomes of the analyses you performed; you should include in the text any numerical results you wish to draw attention to (eg means, SEM, p-values, regression equations, etc.). This part should definitely not be 'raw' Minitab output with the odd, unlabelled graph!
Notes: The analysis of the data is fairly involved, involving both multiple regression and the plotting of fitted 3-D graphs. To help you organise your thoughts and your report, here are a few questions you may wish to ask yourself (note they are questions to stimulate your own judgments, they are definitely not explicit orders to include everything mentioned!). They arose from previous experiments, the assumption being that your data will not drastically alter the outcome!
- From the following list, which ones should I use in order to paint a rough picture of the results for the reader at the start of the Results section? The number of readings that make up the dataset, the means and standard deviations of the O2 and CO2 percentages, the max and min of the O2 and CO2 percentages, the means and SD of the Minute Volume measurements, the max and min of the MV measurements, the population under study? Not all of them are necessarily appropriate; for example, do you think reporting the mean gas concentrations is helpful when the whole aim of the experiment was to vary them as much as possible?
- If both O2 and CO2 terms appear in the multiple regression analysis, is there anything to be gained by including the plots, regression equations and correlations for the individual gases from the Preliminary Analysis of the data?
- Should I be explaining why I included transformations of the oxygen and O2 and CO2 data?
- Should I explain the reasoning behind use of the 1/%O2*%CO2sq term?
- Assuming there is more than one stage to the multiple regression, should I report (perhaps via the regression equation, p-values, and correlation coefficient) each stage of the multiple regression (ie6-variables, 5 variables, etc) or just the last one?
- Should the y-intercept term (the constant) appear in the final equation?
- Should I explain the reasoning behind dropping terms (if that is necessary) before plotting the 3-D graph (and justify it statistically, using correlation coefficients)?
- Should I include both 3-D graphs, one with the original data, and one with the fitted data?)
(You must prepare this section. This section should aim to interpret the results in the light of the objectives laid out in the Introduction. The discussion looks at the 'bigger picture' - trying to explain how the results obtained fit in with existing knowledge and expectations, and the conclusions that can be drawn from them; that means finding and using references.
Notes: Here are a few questions (by no means an exhaustive list) of things you may wish to ask yourself when preparing the Discussion...
- What is the central finding of the study? Is it the multi-variable regression equation and its correlation (or even the two-variable one used for the 3-D graph)?
- Should I try to predict MV at a number of different combinations of O2% and CO2% to see if they make sense in the light of what is known about the control of respiration?
- Do my findings fit in with what is already known about the control of ventilation?
- What does the correlation coefficient tell me about the extent to which the gases can explain the variation in minute volume in the experiment?
- Does the study really tell me anything about the way that ventilation is controlled in ‘real life’?
- Were the results regarding the interaction of the two gases as expected (see Guyton)? Can I illustrate interaction of the two gases (or its lack) from my equation by plugging in made-up numbers into the regression equation?
- What factors could account for the variation in MV not attributable to the two gases under study?
- Is there any value in recommending the collection of more data in an effort to 'improve' the study, or have we ‘plateaued’?
- What improvements could be made to the methodology to reduce experimental error?
- How would I take this study forward in the light of my findings?)
Biscoe, T.J. Carotid body: Structure and function. Physiol. Rev., 51:427, 1971.
Guyton, A.C. Regulation of respiration.In : Textbook of Medical Physiology. Philadelphia, WB Saunders, 1986, p.511.
Whipp, B.J. Ventilatory control during exercise in humans. Ann. Rev. Physiol., 45:393, 1983.
PS: Don’t forget when you submit to also upload the spreadsheet of your ‘raw’ results (the ones you downloaded and to which you also added your group’s results). We will use that spreadsheet to check the accuracy of your results; if you do not submit the spreadsheet you will not get marks for that aspect of the marking scheme!