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LETTER TO THE EDITOR
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom
Address for reprints and correspondence: J. P. Morton, Research Institute for Sport and Exercise Sciences, Liverpool John Moores Univ., 15-21 Webster St., Liverpool L3 2ET, UK
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The optimal fat-burning intensity during exercise?
Kay suggests that our understanding of the optimal percentage of 
O2max for which to oxidize fat is based largely on data concerning the respiratory exchange ratio. This is not the case, as our interpretation of data examining substrate metabolism during aerobic exercise are not based on respiratory exchange ratio measurements per se but from a range of metabolic techniques. For example, when working with undergraduate students, we frequently refer to studies employing stable isotope tracer methodology, which has in fact validated indirect calorimetry as a method to study lipid and carbohydrate utilization during exercise (15). Using the combination of these methods, Romijn et al. (15) demonstrated that in endurance-trained subjects, the maximal contribution of lipid sources to energy expenditure during aerobic exercise is observed at intensities corresponding to 65% O2max. Using tracer methodology in combination with muscle biopsy sampling, Van Loon et al. (16) obtained similar findings also in aerobically trained subjects. Given that endurance training induces shifts in substrate utilization toward lipid sources (6), it is therefore likely that for sedentary or untrained subjects (i.e., the focus of our question) that the optimal intensity at which to oxidize lipids during aerobic exercise is somewhere in the region of 55–65% O2max.
Kay is, of course, correct in that the respiratory exchange ratio does not take into account nonmetabolic CO2 production. This point, however, is only of relevance for high-intensity exercise where glycolytic flux is high, thus resulting in markedly elevated H+ production (9). Such exercise intensities are in excess of the moderate exercise intensities associated with maximal lipid oxidation rates (as evidenced by tracer/biopsy studies), and, as such, the use of indirect calorimetry to calculate maximal lipid oxidation rates (i.e., "Fatmax") is considered not to be affected by excess CO2 production (9).
Kay also states that indirect calorimetry does not account for the catabolism of protein during exercise. This statement, however, is not entirely true as the calculations of Frayn (4) (of which many studies are based) account for the process of gluconeogenesis, where the amino acid alanine is a major precursor. Moreover, we are sure that Kay is in agreement that protein contributes little to energy expenditure during aerobic exercise, even when the exercise is prolonged, as evidenced also by tracer methodology (13). Finally, we are in agreement that in order to optimally lose fat during a "training" period, an individual should undertake a combined program of both aerobic and resistance exercise. This notion of optimal training approaches, however, was not the target of our misconception.
The lactate threshold as the best predictor of endurance running performance?
The aim of this misconception question was to identify that students incorrectly perceive O2max as the most important predictor of endurance running performance. To more specifically target this misconception, we acknowledge that we may have improved the terminology of our question. For example, in retrospect, we could have rephrased the question as "From the list provided, what is the most important predictor of endurance performance?" and by also removing answer E: other as an available answer.
Kay correctly notes that the mathematical function that best describes the blood lactate response to exercise is unclear. However, what is known is that there is an intensity of exercise above which muscle lactate production exceeds lactate removal (from multiple sites) at which point blood lactate concentration is notably increased. This threshold intensity has typically been identified using a variety of experimental approaches such as the classic incremental exercise curve, maximal lactate steady state, or lactate minimum speed. Regardless of the correct mathematical function or indeed the experimental approach, for the appropriateness of undergraduates' level of understanding, we consider all of the above to fall under the umbrella of the "lactate threshold," and it was this construct that we intended to evaluate in our inventory questionnaire.
We acknowledge that a complete predictor model of endurance running performance should be multifactorial, incorporating additional indexes such as running economy, etc. (11). However, of the three indexes of O2max, running economy, and lactate threshold, we would argue that it is the velocity at the lactate threshold that is the most significant contributor to endurance running performance. Indeed, when the relationship between O2max and race time is examined in a homogenous group of runners (with similar performance times), there is little relationship between O2max and race performance (1). Kay cites several references indicating that the relationship between lactate threshold and time to exhaustion is equivocal. However, we are sure that Kay is aware that a time to exhaustion trial is neither a reliable nor, most importantly, a valid measure of performance (2).
In contrast, Farrell et al. (3) demonstrated that in distance runners, the lactate threshold was the best predictor of "performance time," accounting for 
90% of the variance in performance. Furthermore, in a 5-yr case study of an Olympic runner in which O2max did not increase but yet race time progressively improved, it was the submaximal physiological variables of running economy and lactate threshold that showed the greatest sensitivity to training, of which the latter showed the greatest improvements (10). The notion of velocity at the lactate threshold as the most important predictor of endurance running performance is physiologically reasonable given that lactate production is related to oxidative capacity (i.e., mitochondrial content and activity) of skeletal muscle (7). It is therefore not surprising that the speed associated with the lactate threshold closely equates with the natural race pace that is selected by marathon runners (3).
Finally, Kay mentions the performance-based index of "critical power" as being the single best predictor of performance. Although numerous studies have explored this concept and demonstrated changes with training (8), there does not appear to be a sound physiological rationale since it is based on a mathematical construct. Furthermore, a previous study (5) has shown critical power to be closely related to lactate-associated variables such as VOBLA. In this regard, the critical power concept appears "linked" to a lactate variable and therefore presents itself as an unexplained physiological concept.
In closing, we thank Kay for expressing an interest in our work and for further highlighting the need for precision and clarity in designing measurement tools with which to identify misconceptions in student understanding. Only then can we begin to formulate appropriate teaching strategies to enhance student learning.
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