A "Blood Relationship" Between the Overlooked Minimum Lactate Equivalent and Maximal Lactate Steady State in Trained Runners. Back to the Old Days?

Reassembling the Field-based Applicability of the Lactate Threshold for Old Age

This study aimed to investigate the applicability of the Lactate Threshold (LT) to predict maximal oxygen uptake (˙VO2max) and demarcate the boundary between the moderate- to heavy-intensity domain (HRm-h) in old age in comparison to the most utilized methods. A cross-sectional validation study was conducted. Participants aged 61 to 77 performed a familiarization procedure, an incremental maximal exercise treadmill test (CPX) for ˙VO2max determination, the Six-minute Walk Test (6MWT), and a discontinuous incremental field test for LT determination. Lower (P<0.01) internal effort was required for LT testing (76±8%HRmax) compared to 6MWT (92±9%HRmax). The application of the 6MWT reference equations overestimated ˙VO2max by 10-23%. LTs better estimated the ˙VO2max (r ≈0.90, SEE: ≈3.0] compared to the 6MWT (r=0.68, SEE=5.5). HRm-h determined by the CPX differed (20%; P=0.001) from that obtained by LT. HRm-h stratification indicated participants fall into the very light to the vigorous intensity domains. LT testing is more submaximal than the 6MWT, and is a valuable tool to estimate the ˙VO2max in older male adults. Implementation of LT testing in physical activity programs might help improving the quality of aerobic exercise training in older men.

Prediction for blood lactate during exercise using an artificial intelligence-Enabled electrocardiogram: a feasibility study

Introduction: The acquisition of blood lactate concentration (BLC) during exercise is beneficial for endurance training, yet a convenient method to measure it remains unavailable. BLC and electrocardiogram (ECG) both exhibit variations with changes in exercise intensity and duration. In this study, we hypothesized that BLC during exercise can be predicted using ECG data. Methods: Thirty-one healthy participants underwent four cardiopulmonary exercise tests, including one incremental test and three constant work rate (CWR) tests at low, moderate, and high intensity. Venous blood samples were obtained immediately after each CWR test to measure BLC. A mathematical model was constructed using 31 trios of CWR tests, which utilized a residual network combined with long short-term memory to analyze every beat of lead II ECG waveform as 2D images. An artificial neural network was used to analyze variables such as the RR interval, age, sex, and body mass index. Results: The standard deviation of the fitting error was 0.12 mmol/L for low and moderate intensities, and 0.19 mmol/L for high intensity. Weighting analysis demonstrated that ECG data, including every beat of ECG waveform and RR interval, contribute predominantly. Conclusion: By employing 2D convolution and artificial neural network-based methods, BLC during exercise can be accurately estimated non-invasively using ECG data, which has potential applications in exercise training.

A systematic review of exercise protocols applied to athymic mice in tumor-related experiments

Athymic mice are unable to produce T-cells and are then characterized as immunodeficient. This characteristic makes these animals ideal for tumor biology and xenograft research. New non-pharmacological therapeutics are required owing to the exponential increase in global oncology costs over the last 10 years and the high cancer mortality rate. In this sense, physical exercise is regarded as a relevant component of cancer treatment. However, the scientific community lacks information regarding the effect of manipulating training variables on cancer in humans, and experiments with athymic mice. Therefore, this systematic review aimed to address the exercise protocols used in tumor-related experiments using athymic mice. The PubMed, Web of Science, and Scopus databases were searched without restrictions on published data. A combination of key terms such as athymic mice, nude mice, physical activity, physical exercise, and training was used. The database search retrieved 852 studies (PubMed, 245; Web of Science, 390; and Scopus, 217). After title, abstract, and full-text screening, 10 articles were eligible. Based on the included studies, this report highlights the considerable divergences in the training variables adopted for this animal model. No studies have reported the determination of a physiological marker for intensity individualization. Future studies are recommended to explore whether invasive procedures can result in pathogenic infections in athymic mice. Moreover, time-consuming tests cannot be applied to experiments with specific characteristics such as tumor implantation. In summary, non-invasive, low-cost, and time-saving approaches can suppress these limitations and improve the welfare of these animals during experiments.

Estimation of maximal lactate steady state using the sweat lactate sensor

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.

Is the maximal lactate steady state concept really relevant to predict endurance performance?

PURPOSE

There is no convincing evidence for the idea that a high power output at the maximal lactate steady state (PO_{_MLSS}) and a high fraction of [Formula: see text]O_2max at MLSS (%[Formula: see text]O_{2_MLSS}) are decisive for endurance performance. We tested the hypotheses that (1) %[Formula: see text]O_{2_MLSS} is positively correlated with the ability to sustain a high fraction of [Formula: see text]O_2max for a given competition duration (%[Formula: see text]O_{2_TT}); (2) %[Formula: see text]O_{2_MLSS} improves the prediction of the average power output of a time trial (PO_{_TT}) in addition to [Formula: see text]O_2max and gross efficiency (GE); (3) PO_{_MLSS} improves the prediction of PO_{_TT} in addition to [Formula: see text]O_2max and GE.

METHODS

Twenty-one recreationally active participants performed stepwise incremental tests on the first and final testing day to measure GE and check for potential test-related training effects in terms of changes in the minimal lactate equivalent power output (∆PO_LEmin), 30-min constant load tests to determine MLSS, a ramp test and verification bout for [Formula: see text]O_2max, and 20-min time trials for %[Formula: see text]O_{2_TT} and PO_{_TT}. Hypothesis 1 was tested via bivariate and partial correlations between %[Formula: see text]O_{2_MLSS} and %[Formula: see text]O_{2_TT}. Multiple regression models with [Formula: see text]O_2max, GE, ∆PO_LEmin, and %[Formula: see text]O_{2_MLSS} (Hypothesis 2) or PO_{_MLSS} instead of %[Formula: see text]O_{2_MLSS} (Hypothesis 3), respectively, as predictors, and PO_{_TT} as the dependent variable were used to test the hypotheses.

RESULTS

%[Formula: see text]O_{2_MLSS} was not correlated with %[Formula: see text]O_{2_TT} (r = 0.17, p = 0.583). Neither %[Formula: see text]O_{2_MLSS} (p = 0.424) nor PO_{_MLSS} (p = 0.208) did improve the prediction of PO_{_TT} in addition to [Formula: see text]O_2max and GE.

CONCLUSION

These results challenge the assumption that PO_{_MLSS} or %[Formula: see text]O_{2_MLSS} are independent predictors of supra-MLSS PO_{_TT} and %[Formula: see text]O_{2_TT}.

Predicting Cardiorespiratory Fitness in Female Soccer Players: The Basque Female Football Cohort Study

PURPOSE

To develop gender-specific operational equations for prediction of cardiorespiratory fitness in female footballers.

METHOD

Forty-eight semiprofessional female footballers performed an intermittent progressive maximal running test for determination of fixed blood lactate concentration (FBLC) thresholds. Relationships between FBLC thresholds and the physiological responses to submaximal running were examined. Developed equations (n = 48) were compared with equations previously obtained in another investigation performed in males (n = 100).

RESULTS

Submaximal velocity associated with 90% maximal heart rate was related to FBLC thresholds (r = .76 to .79; P < .001). Predictive power (R2 = .82 to .94) of a single blood lactate concentration (BLC) sample measured at 10 or 11.5 km·h-1 was very high. A single BLC sample taken after a 5-minute running bout at 8.5 km·h-1 was related to FBLC thresholds (r = -.71; P < .001). No difference (P = .15) in the regression lines predicting FBLC thresholds from velocity associated with 90% maximal heart rate was observed between the female and male cohorts. However, regressions estimating FBLC thresholds by a single BLC sample were different (P = .002).

CONCLUSIONS

Velocity associated with 90% maximal heart rate was robustly related to FBLC thresholds and might serve for mass field testing independently of sex. BLC equations accurately predicted FBLC thresholds. However, these equations are gender-specific. This is the first study reporting operational equations to estimate the FBLC thresholds in female footballers. The use of these equations reduces the burden associated with cardiorespiratory testing. Further cross-validation studies are warranted to validate the proposed equations and establish them for mass field testing.

Physiological factors which influence the performance potential of athletes: analysis of sports medicine performance testing in Nordic combined

BACKGROUND

The sports medicine performance diagnostics include investigative procedures that supply information on the performance capacity and stamina of an athlete. This creates a foundation for a personalised training plan and enables optimised control of the training process.

METHODOLOGY

The study population consisted of 24 male Nordic combined athletes from the national German squad. They were monitored using sports medicine over a period of five winter seasons. The test speeds on the treadmill in m/s are determined at lactate values of 2, 3 and 4 mmol/l in the peripheral blood values to calculate the lactate curve.

RESULTS

The higher the test performance expressed as a percentage, the more likely it was that a top position could be achieved. The individual anaerobic threshold and the maximal oxygen uptake increased significantly with an increase in test performance expressed as a percentage. The older the athlete, the better they performed in the overall world cup. When age increased, the test speed [m/s] at lactate values of 2, 3 and 4 mmol/l also increased, along with the test performance expressed as a percentage, the maximal oxygen uptake and the individual anaerobic threshold. A higher BMI proved advantageous in terms of placement in the individual competitions.

CONCLUSION

In this study the test speed at a lactate concentration of 4 mmol/l can be recommended as a robuster, more independent from mathematical models and physiologically more valid parameter for performance diagnostics in professional athletes.

Predicting Maximal Lactate Steady State from Carminatti's Shuttle Run Test in Soccer Players

The objective of the present study was to determine the validity of Carminatti's shuttle run incremental test-T-Car derived parameters in estimating the maximal lactate steady state determined in shuttle run format. Eighteen soccer players performed a T-Car test, and several trials to determine the maximal lactate steady state. From T-Car were derived the heart rate deflection point, peak speed, maximal heart rate and parameters resulting from percentage of peak measures. The validity was accessed by Bland-Altman plots, linear regressions, and two one-sided tests of equivalence analysis. The results showed the speed at 80.4% of T-Car peak speed, the heart rate deflection point and the 91.4% of maximal heart rate were equivalent to maximal lactate steady state (Mean difference; ±90% compatibility interval; -0.8; ±1.5%, -0.4; ±1.1%, and 0.0; ±2.7%, respectively). Additionally, peak speed during the T-Car test was a stronger predictor of maximal lactate steady state (MLSS [km/h]=2.57+0.65 × sPeak; r=0.82 [90% CI; 0.62-0.92], standard error of the estimate=3.6%; 90% CI ×/÷1.4). Therefore, soccer players can use the T-Car derived parameters as a noninvasive and practical alternative to estimate the specific maximal lactate steady state for soccer.

Considerations regarding Maximal Lactate Steady State determination before redefining the gold-standard

We have read with interest the review written by Jones et al. (2019) published in a recent volume‐issue (volume 7, issue 5) of the journal. Criticisms regarding maximal lactate steady state intensity determination are to some extent correct and well‐justified. There are some aspects, however, that should be further clarified and documented before redefining the gold‐standard measure for the evaluation of endurance exercise capacity.

Effects of cardioselective beta-blockade on plasma catecholamines and performance during different forms of exercise

BACKGROUND

Beta-blockers are still frequently used in cardiovascular diseases but may negatively influence the exercise capacity. The aim of the study was to analyze the effect of beta-blockade on physical performance and plasma level of catecholamine during different forms of exercise.

METHODS

Ten prehypertensive athletes (age: 25.1±2.5 years, BMI: 24.4±2.4 kg/m2) performed repeated incremental exercise and steady-state-tests without and with the cardioselective beta-blocker bisoprolol (5mg/day). The cardiopulmonary, metabolic and the catecholamine responses were monitored.

RESULTS

Beta-blocker treatment had no effect on maximum power output (Pmax), lactate and the maximal oxygen uptake (VO2max) (Pmax: 269.0±41.5 vs. 269.0±41.5 W; lactate: 8.7±2.6 vs. 8.6±3.2 mmol/L and VO2max: 3110±482 vs. 3077±425 mL/min, respectively; P not significant). Epinephrine and norepinephrine showed a similar exponential increase to maximum load with and without beta-blockade (epinephrinemax 1.92±1.8 vs. 1.93±1.3 nmol/L; P not significant; norepinephrinemax 12.78±7.9 vs. 16.89±12.2 nmol/L; P not significant). Beta-blockade lowered heart rate (HR) and systolic blood pressure (SBP) at rest and under maximum load (ΔHRrest: 10.6±11.1 bpm, P<0.05, ΔHR-Max: 27.8±6.6 bpm, P<0.01; ΔSBPrest: 19.4±9.3 mmHg, P<0.05, ΔSBPmax: 17.7±15.3 mmHg, P<0.01). The maximum oxygen pulse was higher in the tests performed under beta-blockade (IET: ΔVO2/HR: 3.1±2.2 mL/beat, P<0.01; SST: ΔVO2/HR: 3.4±1.4 mL/beat, P<0.001).

CONCLUSIONS

Despite beta blockade and resulting differences in cardiopulmonary regulation during the exercise tests, the maximal oxygen capacity and the catecholamine concentration was similar. Higher exercise intensities (>50% Pmax) are associated with a marked increase in plasma catecholamines, which are not influenced by treatment with bisoprolol 5 mg/day.

Lactate Equivalent for Maximal Lactate Steady State Determination in Soccer

Purpose: The association between an overlooked classical Lactate Threshold (LT), named "Minimum Lactate Equivalent" (LE_min), with Maximal Lactate Steady State (MLSS) has been recently described with good MLSS prediction results in endurance-trained runners. This study aimed to determine the applicability of LE_min to predict MLSS in lower aerobic-conditioned individuals compared to well-established blood lactate-related thresholds (BLTs). Method: Fifteen soccer players [velocity at MLSS (MLSS_V) 13.2 ± 1.0 km·h^-1; coefficient of variation (CV) 7.6%] conducted a submaximal discontinuous incremental running test to determine BLTs and 3-6 constant velocity running tests to determine MLSS_V. Results: LE_min did not differ from conventional LTs (p > .05) and was 24% lower than MLSS (p < .001; ES: 3.26). Among LTs, LE_min best predicted MLSS_V (r = 0.83; p < .001; SEE = 0.59 km·h^-1). There was no statistical difference between MLSS and estimated MLSS using LE_min prediction formula (p = .99; ES: 0.001). Mean bias and limits of agreement were 0.00 ± 0.58 km·h^-1 and ±1.13 km·h^-1, respectively. LE_min best predicted MLSS_V (r = 0.92; p < .001; SEE = 0.54 km·h^-1) in the pooled data of soccer players and endurance-trained runners of the previous study (n = 28; MLSS_V range 11.2-16.5 km·h^-1; CV 9.8%). Conclusion: Results support LE_min to be one of the best single predictors of MLSS. This study is the sole study providing specific operational regression equations to estimate the impractical gold standard MLSS_V in soccer players by means of a BLT measured during a submaximal single-session test.

Applicability of Dmax Method on Heart Rate Variability to Estimate the Lactate Thresholds in Male Runners

Introduction

The purpose of this study was to evaluate the application of the Dmax method on heart rate variability (HRV) to estimate the lactate thresholds (LT), during a maximal incremental running test (MIRT).

Methods

Nineteen male runners performed two MIRTs, with the initial speed at 8 km·h^-1 and increments of 1 km·h^-1 every 3 minutes, until exhaustion. Measures of HRV and blood lactate concentrations were obtained, and lactate (LT₁ and LT₂) and HRV (HRVT_DMAX1 and HRVT_DMAX2) thresholds were identified. ANOVA with Scheffe's post hoc test, effect sizes (d), the bias ± 95% limits of agreement (LoA), standard error of the estimate (SEE), Pearson's (r), and intraclass correlation coefficient (ICC) were calculated to assess validity.

Results

No significant differences were observed between HRVT_DMAX1 and LT₁ when expressed for speed (12.1 ± 1.4 km·h^-1 and 11.2 ± 2.1 km·h^-1; p=0.55; d = 0.45; r = 0.46; bias ± LoA = 0.8 ± 3.7 km·h^-1; SEE = 1.2 km·h^-1 (95% CI, 0.9-1.9)). Significant differences were observed between HRVT_DMAX2 and LT₂ when expressed for speed (12.0 ± 1.2 km·h^-1 and 14.1 ± 2.5 km·h^-1; p=0.00; d = 1.21; r = 0.48; bias ± LoA = -1.0 ± 1.8 km·h^-1; SEE = 1.1 km·h^-1 (95% CI, 0.8-1.6)), respectively. Reproducibility values were found for the LT₁ (ICC = 0.90; bias ± LoA = -0.7 ± 2.0 km·h^-1), LT₂ (ICC = 0.97; bias ± LoA = -0.1 ± 1.1 km·h^-1), HRVT_DMAX1 (ICC = 0.48; bias ± LoA = -0.2 ± 3.4 km·h^-1), and HRVT_DMAX2 (ICC = 0.30; bias ± LoA = 0.3 ± 3.5 km·h^-1).

Conclusions

The Dmax method applied over a HRV dataset allowed the identification of LT₁ that is close to aerobic threshold, during a MIRT.

Estimation of the maximal lactate steady state in postmenopausal women

This study aimed to estimate the maximal lactate steady-state velocity (_vMLSS) from non-invasive bloodless variables and/or blood lactate-related thresholds (BL_RTs) measured during an Incremental submaximal Shuttle Test (IST), and to determine whether the addition of a Constant Velocity Test (CVT) could improve the estimation. Seventy-five postmenopausal women conducted an IST to determine several BL_RTs and bloodless variables, and two to seven CVTs to determine _vMLSS. Determined BL_RTs were conventionally used lactate threshold (LT) measured either visually (_vLT_+0.1mM) or mathematically (_vLE_min), and 0.5, 1 and 1.5 mmol·L^-1 above LT, along with fixed BL_RTs. The best single predictor of _vMLSS (7.1 ± 1.0 km·h^-1) was _vLE_min+1.5mM (R² = 0.80, P < 0.001; SEE = 0.46 km·h^-1). The combination of BL_RTs and bloodless variables improved the estimation of _vMLSS (R² = 0.85, P < 0.001; SEE = 0.38 km·h^-1). The addition of a CVT still improved the prediction of _vMLSS up to 89.2%, with lower SEE (0.32 km·h^-1). This study suggests that _vLE_min-related thresholds obtained from a single submaximal IST are accurate estimates of _vMLSS in postmenopausal women, and thus the time-consuming procedure of _vMLSS testing could be avoided. Performing an additional CVT is encouraged because it improves the prediction of _vMLSS.

Noninvasive prediction of Blood Lactate through a machine learning-based approach

We hypothesized that blood lactate concentration([Lac]_blood) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate [Lac]_blood noninvasively during constant work rate (CWR) exercise of various intensities. 31 healthy participants were recruited and each underwent 4 cardiopulmonary exercise tests: one incremental and three CWR tests (low: 35% of peak work rate for 15 min, moderate: 60% 10 min and high: 90% 4 min). At the end of each CWR test, venous blood was sampled to determine [Lac]_blood. 31 trios of CWR tests were employed to construct the mathematical model, which utilized exponential regression combined with Taylor expansion. Good fitting was achieved when the conditions of low and moderate intensity were put in one model; high-intensity in another. Standard deviation of fitting error in the former condition is 0.52; in the latter is 1.82 mmol/liter. Weighting analysis demonstrated that, besides heart rate, respiratory variables are required in the estimation of [Lac]_blood in the model of low/moderate intensity. In conclusion, by measuring noninvasive cardio-respiratory parameters, [Lac]_blood during CWR exercise can be determined with good accuracy. This should have application in endurance training and future exercise industry.