An equation to predict the maximal lactate steady state from ramp-incremental exercise test data in cycling

• DOI: 10.1016/j.jsams.2018.05.004
• Publication Date: 2018-05-23T22:11:58Z
• Authors: Danilo Iannetta; Federico Y. Fontana; Felipe Mattioni M...; Erin Calaine Inglis; Silvia Pogliaghi; Daniel A. Keir; Juan M. Murias
• Keywords: Cycling; Respiratory compensation; Incremental exercise; Lactate threshold; Time trial

The maximal lactate steady state (MLSS) represents the highest exercise intensity at which an elevated blood lactate concentration ([Lac]b) is stabilized above resting values. MLSS quantifies the boundary between the heavy-to-very-heavy intensity domains but its determination is not widely performed due to the number of trials required.This study aimed to: (i) develop a mathematical equation capable of predicting MLSS using variables measured during a single ramp-incremental cycling test and (ii) test the accuracy of the optimized mathematical equation.The predictive MLSS equation was determined by stepwise backward regression analysis of twelve independent variables measured in sixty individuals who had previously performed ramp-incremental exercise and in whom MLSS was known (MLSSobs). Next, twenty-nine different individuals were prospectively recruited to test the accuracy of the equation. These participants performed ramp-incremental exercise to exhaustion and two-to-three 30-min constant-power output cycling bouts with [Lac]b sampled at regular intervals for determination of MLSSobs. Predicted MLSS (MLSSpred) and MLSSobs in both phases of the study were compared by paired t-test, major-axis regression and Bland-Altman analysis.The predictor variables of MLSS were: respiratory compensation point (Wkg-1), peak oxygen uptake (V˙O2peak) (mlkg-1min-1) and body mass (kg). MLSSpred was highly correlated with MLSSobs (r=0.93; p<0.01). When this equation was tested on the independent group, MLSSpred was not different from MLSSobs (234±43 vs. 234±44W; SEE 4.8W; r=0.99; p<0.01).These data support the validity of the predictive MLSS equation. We advocate its use as a time-efficient alternative to traditional MLSS testing in cycling.