Muscle Oxygenation, A Real-Time Indicator Of Blood Lactate Levels
Assessing Real-Time Changes In Blood Lactate With NNOXX
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In a previous post titled Lactate Steady-State Training Made Easy, I discussed ways that athletes and coaches could use muscle oxygenation (SmO2) data to identify their maximum lactate steady-state in real-time, providing them with an easy-to-use method for guiding exercise intensity.
Since publishing that article, my team and I at NNOXX have created the Connect Forum, a place for users to share their data and case studies and engage in discussions. Recently, the NNOXX community has been having a lively discussion about using muscle oxygenation and nitric oxide measurements to guide zone 2 training, which inspired a recent On Human Performance article titled Zone-Based Training With The NNOXX Wearable.
In the original Zone 2 thread on the NNOXX Connect Forum, initiated by CrossFit Games athlete Brent Fikowski, one of our members shared real-time data from a ramp incremental exercise test on a treadmill. He measured his muscle oxygenation (SmO2) and nitric oxide (NO) levels with the NNOXX wearable and his heart rate and blood lactate (BLa) levels with third-party devices. The chart below presents his comprehensive time-series data:
The relationship between muscle oxygenation and blood lactate is complex and can vary depending on various factors, including the intensity and duration of exercise. Generally, as exercise intensity increases, muscle oxygenation levels decrease because the muscles require more oxygen than can be delivered. Simultaneously, blood lactate levels increase as glycolysis provides an increasing fraction of energy to power activity. Thus, we should expect to see an inverse association between these two biomarker measurements, with a strong relationship at higher exercise intensities, as was previously observed in a study titled, Muscle oxygen saturation rates coincide with lactate-based exercise threshold. To my delight, this is exactly what we found in the data.
Below, you’ll find a chart comparing the change in muscle oxygenation (ΔSmO2) from the start to finish of each exercise interval to blood lactate (BLa) levels measured immediately following each exercise interval:
The correlations between ΔSmO2 and BLa during the aforementioned ramp incremental exercise test are as follows:
Load step 1-5: -0.76 (first five sets)
Load step 3-7: -0.94 (middle five sets)
Load step 5-9: -0.93 (last five sets)
All load steps: -0.77
As you can see in the data above, the association between ΔSmO2 and BLa is much stronger during the higher-intensity exercise steps, with a nearly perfect inverse linear relationship compared to the lower-intensity steps.
Interestingly, if we shift each blood lactate measurement back one work step, the correlations between ΔSmO2 and blood lactate are significantly strengthened (-0.98 for the last five sets and -0.83 for all sets). The reason why this shifting of the time series BLa measurements to the left is necessary is that there is a time lag between lactate generation and when it accumulates in the blood (and thus when it can be measured with a blood stick), whereas SmO2 is a real-time instantaneous and continuous measurement (the NNOXX wearable measures SmO2 50x/second).
A common mistake that athletes and coaches make when taking blood lactate measurements is taking them at face value. When a blood lactate measurement is taken from an ear or finger, there is a time lag because the measurement is taken in the systemic circulation rather than at the source of lactate generation in the working muscle. Muscle oxygenation, on the other hand, provides a real-time indicator of the local muscles metabolic state, and may thus better inform pacing strategies to minimize fatigue and maximize performance.
Based on the data above and the work of others, such as Brett Kirby, Phil Batterson, and Andri Feldman, changes in muscle oxygenation levels (ΔSmO2) can reliably estimate changes in blood lactate over time.
Additionally, suppose you have enough data for a given individual. In that case, you can even predict an individual's blood lactate levels based on their SmO2 data in real-time, which is a topic I plan to cover in Decoding Biology by Evan Peikon in the coming weeks. This approach would have significant advantages over traditional blood lactate measurements because of its non-invasive, real-time nature. Thus, you can conceivably get a reliable indicator of continuous changes in lactate while running or cycling in even the most rugged environments (or while racing).
**Update**
Since the release of this article, I've conducted further research to address the question of whether ΔSmO2 and blood lactate only correlate strongly when using a given individual's data or whether these metrics correlate across a broader population. I've put five more athletes, each with varying fitness levels, through ramp-incremental exercise tests. The results, as shown in the chart below, continue to demonstrate a strong correlation between ΔSmO2 and blood lactate for all trials, reinforcing the robustness of our initial findings.
As you can see, there is still a strong inverse linear relationship between these two physiological metrics, even when comparing data between individuals of mixed sex, age, body composition, and fitness levels. It’s conceivable that if we had enough data, we could accurately predict blood lactate levels within a fraction of a mmol using nothing more than their real-time muscle oxygenation (SmO2) data and basic demographic information.