A systematic method to detect the metabolic threshold from gas exchange during incremental exercise.

Author:Dolezal, Brett A.
Position::Research article - Report
 
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Introduction

During incremental exercise there exist three domains of exercise intensity separated by two thresholds (Tschakert and Hofmann, 2013). The lower intensity domain represents aerobic muscle metabolism where the increase in carbon dioxide output ([??]C[O.sub.2]) matches the increase in oxygen uptake ([[??]O.sub.2]). Within this domain, there are small increases in blood lactate but its level remains stable.

Within the intermediate intensity domain, a sustained increase in blood lactate occurs, and C[O.sub.2] produced during cellular respiration ("metabolic C[O.sub.2]") is supplemented by "additional C[O.sub.2]" derived from bicarbonate buffering of lactic acid. This results in a disproportionate increase in [??]C[O.sub.2] relative to [[??]O.sub.2] (Cooper et al., 1992). We refer to this threshold between the lower and intermediate domains as the metabolic threshold ([[??]O.sub.2][theta]) due to the metabolic shift that defines this transition. The same physiological transition has been described as the lactate or anaerobic threshold (Wasserman and McIlroy, 1964). Finally, the higher intensity domain is defined by further lactate accumulation and a disproportionate increase in expired ventilation ([[??].sub.E]) relative to [??]C[O.sub.2] (Cooper and Storer, 2001). Notwithstanding mechanistic and semantic arguments about the nature of the transition, identification of a threshold between the lower and intermediate exercise intensity domains has known practical application and value (Myers and Ashley, 1997). Specifically, [[??]O.sub.2][theta] serves as a key parameter of aerobic function (Cooper and Storer, 2001).

As the point of demarcation between the low and intermediate exercise intensity domains, [[??]O.sub.2][theta] is a non-invasive, sub-maximal, and effort-independent marker that has proved useful in clinical cardiopulmonary exercise testing. Equations exist for reference values and their lower 95% confidence limits for [[??]O.sub.2][theta] as detected by gas exchange measures (Davis et al., 1997). Since [[??]O.sub.2][theta] is reduced in cardiovascular disease (Wasserman and Whipp, 1975), chronic pulmonary disease (Cooper, 1995), end-stage renal disease (Mayer et al., 1988), and various forms of myopathy (Inbar et al., 2001; Tirdel et al., 1998), it may be used to detect an abnormal response to exercise when patient responses are compared to these reference values. [[??]O.sub.2][theta] also provides an objective means by which to categorize relative exercise intensity. This is of value in prescribing training intensity for endurance exercise training or rehabilitation programs and for evaluating the efficacy of these programs (Coplan et al., 1986; Gibbons, 1987; Hughson and MacFarlane, 1981; Nieuwland et al., 2002). Furthermore, [[??]O.sub.2][theta] has been utilized when prioritizing patients for heart transplantation as it is an effective predictor of mortality and morbidity associated with surgery (Older et al., 1999; Older et al., 1993) and a better predictor of 6-month mortality in patients with chronic heart failure (CHF) than [[??]O.sub.2] max (Gitt et al., 2002).

Consequently, the utility of [[??]O.sub.2][theta] hinges upon its precise identification. There is a scarcity of data, however, with which to estimate the ability of laboratory personnel or computer auto-detection routines to reproducibly identify [[??]O.sub.2][theta]. Furthermore, there may be a perception that simply having a rudimentary exposure to the concepts surrounding the detection of [[??]O.sub.2][theta] is adequate for its reliable measurement. Differing methodologies used for threshold detection contribute to the confusion as well (Svedahl and MacIntosh, 2003). We are unaware of any data that compare levels of training and expertise of interpreters in the somewhat subjective identification of [[??]O.sub.2][theta]. Outstandingly, there does not seem to be a systematic approach to threshold detection wherein specific data displays, procedural steps, and decision trees are available to aid the practitioner in reliably measuring this value.

Recommended criteria to be used in selecting [[??]O.sub.2][theta] (the first threshold) are available in published reports including use of the "dual criteria" method (systematic rise in the ventilatory equivalent for oxygen, [[??].sub.E] / [[??]O.sub.2] , while the ventilatory equivalent for carbon dioxide, [[??].sub.E] / [??]C[O.sub.2], did not increase) (Caiozzo et al., 1982). The "V-slope" (Beaver et al., 1986) and modified "V-slope" (Sue et al., 1988) methods that identify [[??]O.sub.2][theta] from a plot of [??]C[O.sub.2] versus [[??]O.sub.2] (Figure 1) were not included in the analysis by Caiozzo et al. but have subsequently been shown to be superior to alternative methods of detection (Beaver et al., 1986; Sue et al., 1988). Current recommendations suggest use of a constellation of variables in selecting [[??]O.sub.2][theta]. (Boulay et al., 1984) studied different methods of threshold detection and developed a method whereby interpreters assigned a numerical value representing the confidence of each reader's choice of [[??]O.sub.2][theta]. Notably absent, however, is any systematic evaluation of the ability of laboratory personnel to correctly apply these recommendations in the reliable selection of [[??]O.sub.2][theta], specifically with respect to their expertise and experience in performing this function.

Methods

Subjects

Ten healthy, non-smoking, recreationally active men volunteered as subjects and performed the ramp cycle ergometer exercise tests described below. Six other people interpreted the exercise data and were designated as interpreters. Two were considered experienced (E1 and E2) in detection of the metabolic threshold ([[??]O.sub.2][theta]) on the basis of their previous training, research, publications, and teaching. The remaining four interpreters (N1-N4) were pulmonary fellows at various stages of training and although familiar with supervision of exercise tests and the concepts surrounding [[??]O.sub.2][theta], were considered novices in its correct detection. The MC used to acquire the gas exchange data (2900; Sensor Medics Corporation, Yorba Linda, CA) has the capability of auto-detecting [[??]O.sub.2][theta] using proprietary algorithms. The subjects gave informed consent for their participation in the study which was previously approved by the university's institutional review board.

Clinical exercise laboratories not only use laboratory personnel to identify [[??]O.sub.2][theta], but often rely upon auto-detection of the value using commercially manufactured metabolic carts (MCs) that use unknown, proprietary algorithms. Such systems are often heavily depended upon to yield a useable value for [[??]O.sub.2][theta], but are rarely systematically validated by comparison with expert human interpreters and could potentially give misleading results and conclusions. The purpose of this investigation, therefore, was to compare the variability in [[??]O.sub.2][theta] selected by interpreters of different levels of experience as well as by auto-detection algorithms employed by a commercially available metabolic cart. Implicit in this report is the expertise of the "experienced" interpreters as the standard against which detection of [[??]O.sub.2][theta] by novice interpreters and auto detection by a metabolic cart is compared. As a result of this study, we present a systematic approach to [[??]O.sub.2][theta] detection as an aid to its reliable identification.

Exercise tests

The ten exercising subjects completed three maximal leg cycling tests using a 20-watt-per-minute ramp protocol administered on non-consecutive days over a 5-day period. A calibrated electrically braked ergometer (Type 800; Ergoline, Bitz, Germany) was used for all tests. The MC measured breath-by-breath pulmonary ventilation and gas exchange throughout the warm-up and exercise phases of each test. The subjects breathed through a one-way valve with [[??].sub.E] and concentrations of [O.sub.2] and C[O.sub.2] measured downstream by a mass flow transducer and paramagnetic and near-infrared gas analyzers, respectively (Markovitz et al., 2004). The expired flow and gas concentration measurements were time aligned and corrected to standard conditions allowing the breath-by-breath calculation of oxygen uptake and carbon dioxide output.

Determination of [[??]O.sub.2]

All the interpreters were presented with randomized and coded data sets from the 30 (10 subjects x 3 trials) maximal exercise tests. These data sets included plots of the [??]C[O.sub.2] versus [[??]O.sub.2] relationship (Figure 1), as well as plots for the ventilatory equivalents for oxygen ([[??].sub.E] / [[??]O.sub.2]) and carbon dioxide ([[??].sub.E] /[??]C[O.sub.2]) versus [[??]O.sub.2](Figure 3A), and the end-tidal partial pressures for oxygen ([P.sub.ET][O.sub.2]) and carbon dioxide ( [P.sub.ET]C[O.sub.2] ) versus [[??]O.sub.2] (Figure 3B). Unlike some investigations which allowed interpreters the freedom to choose their own method for detecting [[??]O.sub.2][theta], (Gladden et al., 1985) we provided each interpreter with an outline of suggested approaches for its selection. No further information was given. The interpreters were allowed unlimited time for choosing [[??]O.sub.2] for each of the 30 plots and worked in isolation.

Briefly, it was suggested that interpreters first ascertain Vo20 using the...

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