Predicting maximal lactate steady state from lactate thresholds determined using methods based on an incremental...

Area of Science:

• Exercise Physiology
• Canine Sports Medicine
• Biostatistics

Background:

• Maximal lactate steady state (MLSS) is a key indicator of aerobic fitness, representing the highest exercise intensity sustainable without lactate accumulation.
• Accurate determination of MLSS is crucial for assessing athletic performance and training capacity in animals, particularly in research settings.
• Several methods exist to estimate MLSS, but their reliability in non-human models like Beagle dogs requires thorough investigation.

Purpose of the Study:

• To evaluate the reliability of four distinct lactate threshold (LT) methods in estimating the maximal lactate steady state (MLSS) in Beagle dogs.
• To compare the accuracy and agreement of different LT methods (visual inspection, bi-segmented linear regression, polynomial function, DMAX method) against established MLSS velocity (VMLSS).

Main Methods:

• Six male Beagle dogs underwent a standardized incremental exercise test on a treadmill with concurrent plasma lactate ([La-]) measurements.
• Lactate thresholds were determined using visual inspection (LT_V), bi-segmented linear regression (LT_BI), a polynomial function (LT_P), and the DMAX method (LT_DMAX).
• Agreement between estimated LT velocities (VLT_V, VLT_BI, VLT_P, VLT_DMAX) and VMLSS was assessed using Bland-Altman plots and ordinary least products regression, with principal component analysis used for co-relatedness.

Main Results:

• The mean [La-] at MLSS was 1.03 ± 0.24 mM.
• VMLSS showed the lowest mean bias with VLT_V and VLT_BI, indicating good agreement.
• VLT_V and VLT_BI demonstrated the narrowest limits of agreement with VMLSS, while VLT_P and VLT_DMAX showed wider limits, with VLT_DMAX underestimating MLSS.

Conclusions:

• Visual inspection (LT_V) and bi-segmented linear regression (LT_BI) are reliable and simple methods for objectively determining aerobic fitness in Beagle dogs by estimating MLSS.
• These validated methods can be practically applied in canine research and potentially in athletic training programs to monitor and enhance aerobic capacity.
• The polynomial and DMAX methods exhibited less reliability for MLSS estimation in this canine model, suggesting limitations in their application.