Estimation of maximal lactate steady state using the sweat lactate sensor

Abstract

A simple, non-invasive algorithm for maximal lactate steady state (MLSS) assessment has not been developed. We examined whether MLSS can be estimated from the sweat lactate threshold (sLT) using a novel sweat lactate sensor for healthy adults, with consideration of their exercise habits. Fifteen adults representing diverse fitness levels were recruited. Participants with/without exercise habits were defined as trained/untrained, respectively. Constant-load testing for 30 min at 110%, 115%, 120%, and 125% of sLT intensity was performed to determine MLSS. The tissue oxygenation index (TOI) of the thigh was also monitored. MLSS was not fully estimated from sLT, with 110%, 115%, 120%, and 125% of sLT in one, four, three, and seven participants, respectively. The MLSS based on sLT was higher in the trained group as compared to the untrained group. A total of 80% of trained participants had an MLSS of 120% or higher, while 75% of untrained participants had an MLSS of 115% or lower based on sLT. Furthermore, compared to untrained participants, trained participants continued constant-load exercise even if their TOI decreased below the resting baseline (P < 0.01). MLSS was successfully estimated using sLT, with 120% or more in trained participants and 115% or less in untrained participants. This suggests that trained individuals can continue exercising despite decreases in oxygen saturation in lower extremity skeletal muscles.

Figure 1

In-vitro characterization of the lactate sensor under imitated sweat environments. (A) The graph shows the corresponding calibration plots of the sensor with pH 7 under different temperature (25, 31, and 36 °C) conditions. The interference study for individual lactate (B,C). The presence of non-target electrolytes; Na, K, and Cl cause negligible interference to the response of our lactate sensors. Applied voltage = 0.16 V versus Ag/AgCl. The data were obtained from three samples.

Figure 2

Blood lactate for participants who were able to exercise at each load.

Figure 3

Blood lactate, heart rate, VO₂/kg at the MLSS in all participants (n = 15). Blood lactate (red), heart rate (gray), and oxygen uptake-adjusted weight (blue) at the MLSS in each participant. MLSS maximal lactate steady state, VO₂/kg oxygen uptake/weight.

Figure 4

Correlation between change in tissue oxygenation index (TOI) and blood lactate. The black circle represents trained participants, who showed a good correlation between ΔTOI and blood lactate level (y = −0.2859x + 3.035, r = −0.7, P < 0.01). The red triangle represents untrained participants, who showed a good correlation between ΔTOI and blood lactate (y = −0.479x + 5.2349, r = −0.8, P < 0.01). A steeper increase in blood lactate level was associated with a decrease in ΔTOI in untrained participants as compared to trained participants. ΔTOI TOI (pre-post).

Figure 5

Difference in ΔTOI between trained and untrained participants in the completed exercise. Trained participants continued constant-load exercise even if their ΔTOI decreased (mean difference: −7.4, 95% confidence interval: −11.4 to −3.4, P < 0.01). ΔTOI TOI (pre-post); *: P < 0.05.

Figure 6

Flowchart of the study protocol. NIRS near-infrared spectrometer, VO₂ oxygen uptake.

Figure 7

Sweat lactate levels during ramp exercise. HR heart rate, VO₂/W oxygen uptake/weight.

Figure 8

Imaging of the constant-load exercise. HR heart rate, VO₂/W oxygen uptake/weight, BLt blood lactate.