Invited editorial How body composition may confound effect estimates of cardiorespiratory fitness Benno Krachler 1 and Steven D Stovitz 2 In the present issue of the European Journal of Preventive Cardiology, Salokari and co-authors studied the prognostic value of the Duke Treadmill Score (DTS) in 3936 middle-aged Finns. 1 Cardiorespiratory fitness (CRF) was determined by measurement of max- imal oxygen uptake (VO 2max ) on a bicycle ergometer rather than treadmill exercise time of the Bruce proto- col, which is used in the original DTS. The authors demonstrated that the DTS does not provide any prog- nostic information independent of its components and that CRF (the authors use the term ‘exercise capacity’) was the most important predictor of cardiovascular mortality. Converting VO 2max into metabolic equiva- lents of tasks (METs) was mentioned as a limitation since the latter is prone to confounding by body com- position. 2,3 In this editorial, we will outline the chal- lenges associated with converting individual measures of exercise capacity into (standardized) measures of CRF. Further, we will argue that the main findings of the current study are likely to remain valid, despite potential for confounding by body composition. Converting VO 2max into a measure of fitness may seem straightforward: a bigger person has a larger body and must consume more oxygen than a smaller person while doing the same activity. Thus, on the sur- face, scaling VO 2max as ml/min per kg seems appropri- ate. However, expressing fitness as VO 2max in ml/min per kg implies that in the average person VO 2max is pro- portional to total body mass. If that were true, then for every additional kilogram of body weight, VO 2max would need to increase by a fixed volume for a person’s fitness to remain unchanged (ratio scaling: mass expo- nent ¼ 1). 4 However, observations suggest that the increase in VO 2max is not a linear function of total body mass. In a study of >1100 elderly individuals we found that lean mass is a better basis for scaling VO 2max than total-body mass 3 and a meta-analysis involving >6500 participants demonstrated a pooled mass expo- nent for total body mass of only 0.7. 5 In Figure 1, we outline four people: persons A and B have similar amounts of lean mass (i.e. size of heart, lungs, muscles and other organs) but differing body compositions (i.e. proportions of body fat) and persons C and D have similar body compositions but higher body weights than persons A and B, respectively. Figure 1(a) shows the VO 2max calculations if scaled to total body mass. The fitness level of person A is high, according to both empirical scale and ratio scale, per- sons C and D have normal fitness according to ratio scale and high fitness according to empirical scale whereas person B is deemed to have low fitness. The red line is the demarcation between high and low fitness defined by the ratio scale, that is, ml/min per kg. It has to pass through the origin and the point where popu- lation means of total body mass and VO 2max intersect. The green line is the empirical mass exponent of total body mass. 5 Compared with ratio scaling, use of the empirical mass exponent (i.e. the rate of increase of fitness with increasing body weight that has been observed in real-life populations) implies a higher threshold for fitness in individuals with lower body mass. In other words, ratio scaling results in underesti- mation of fitness in heavier and overestimation in lighter individuals. Assuming that persons A and B consume the same amount of oxygen at peak perform- ance (i.e. same ml/min), the fitness level of person B (VO 2max expressed as ml/min per kg) will appear to be lower since the formula involves total body weight and person B weighs more due to larger inert fat mass. Figure 1(b) shows the same four-person scenario, but this time VO 2max is scaled to fat-free mass. Person B, freed of the handicap of inert extra weight in the form of fat, has now the same fitness level as the leaner person A. Person C, who had normal fitness 1 Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umea ˚ University, Sweden 2 Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, USA Corresponding author: Benno Krachler, Department of Public Health and Clinical Medicine Occupational and Environmental Medicine, Umea ˚ University, SE – 901 85 Umea ˚, Sweden. Email: benno.krachler@umu.se European Journal of Preventive Cardiology 2019, Vol. 26(2) 196–198 ! The European Society of Cardiology 2018 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/2047487318812507 journals.sagepub.com/home/ejpc