The Relationship Between Lactate and Ventilatory Thresholds in Runners: Validity and Reliability of Exercise Test Performance Parameters

The aims of this study were (1) to establish the best fit between ventilatory and lactate exercise performance parameters in running and (2) to explore novel alternatives to estimate the maximal aerobic speed (MAS) in well-trained runners. Twenty-two trained male athletes (V˙O2max 60.2 ± 4.3 ml·kg·min−1) completed three maximal graded exercise tests (GXT): (1) a preliminary GXT to determine individuals' MAS; (2) two experimental GXT individually adjusted by MAS to record the speed associated to the main aerobic–anaerobic transition events measured by indirect calorimetry and capillary blood lactate (CBL). Athletes also performed several 30 min constant running tests to determine the maximal lactate steady state (MLSS). Reliability analysis revealed low CV (<3.1%), low bias (<0.5 km·h−1), and high correlation (ICC > 0.91) for all determinations except V-Slope (ICC = 0.84). Validity analysis showed that LT, LT+1.0, and LT+3.0 mMol·L−1 were solid predictors of VT1 (−0.3 km·h−1; bias = 1.2; ICC = 0.90; p = 0.57), MLSS (−0.2 km·h−1; bias = 1.2; ICC = 0.84; p = 0.74), and VT2 (<0.1 km·h−1; bias = 1.3; ICC = 0.82; p = 0.9l9), respectively. MLSS was identified as a different physiological event and a midpoint between VT1 (bias = −2.0 km·h−1) and VT2 (bias = 2.3 km·h−1). MAS was accurately estimated (SEM ± 0.3 km·h−1) from peak velocity (Vpeak) attained during GXT with the equation: MASEST (km·h−1) = Vpeak (km·h−1) * 0.8348 + 2.308. Current individualized GXT protocol based on individuals' MAS was solid to determine both maximal and submaximal physiological parameters. Lactate threshold tests can be a valid and reliable alternative to VT and MLSS to identify the workloads at the transition from aerobic to anaerobic metabolism in well-trained runners. In contrast with traditional assumption, the MLSS constituted a midpoint physiological event between VT1 and VT2 in runners. The Vpeak stands out as a powerful predictor of MAS