Respiratory compensation and blood pH regulation during variable intensity exercise in trained versus untrained subjects

Peripheral chemoreceptor, a new player in metabolic sensing during physical exertion: a hypothetical scenario

The cardiorespiratory and metabolic response to exercise has been associated with meeting the organism's metabolic demands during physical exertion. Of note, an incremental exercise is characterized by 1) cardiodynamic phase related to cardiac output enhancement mainly determined by a positive chronotropic response, 2) ventilatory threshold one, associated with a significant contribution of cardiovascular and pulmonary ventilation, and 3) ventilatory threshold two, correlated with a tremendous increase in breathing and metabolic responses to exercise. Notably, it has been shown that the ventilatory response to exercise increases concomitantly with the release and accumulation of metabolites (i.e., lactate released from skeletal muscle). The principal peripheral chemoreceptors are the carotid bodies (CBs), allocated into the carotid bifurcation and demonstrated to respond to several stimuli, triggering autonomic and ventilatory responses. Indeed, in past and recent years, it has been shown that CB could respond to lactate in in vitro and in vivo preparations, eliciting an increase in CB activity and ventilation. However, not all evidence indicates that peripheral chemoreceptors respond to lactate. Thus, considering that CB chemoreceptors' role in lactate-dependent breathing response is not completely clear and their potential preponderance as metabolic sensors during exercise has not been thoroughly explored, the present review was focused on the possible role of CB chemoreceptors as metabolic sensors during physical exertion in a physiological context, proposing it as a new actor in exercise physiology.

Quantitatively Predicting Effects of Exercise on Pharmacokinetics of Drugs Using a Physiologically Based Pharmacokinetic Model

Exercise significantly alters human physiological functions, such as increasing cardiac output and muscle blood flow and decreasing glomerular filtration rate (GFR) and liver blood flow, thereby altering the absorption, distribution, metabolism, and excretion of drugs. In this study, we aimed to establish a database of human physiological parameters during exercise and to construct equations for the relationship between changes in each physiological parameter and exercise intensity, including cardiac output, organ blood flow (e.g., muscle blood flow and kidney blood flow), oxygen uptake, plasma pH and GFR, etc. The polynomial equation P = ΣaiHRi was used for illustrating the relationship between the physiological parameters (P) and heart rate (HR), which served as an index of exercise intensity. The pharmacokinetics of midazolam, quinidine, digoxin, and lidocaine during exercise were predicted by a whole-body physiologically based pharmacokinetic (WB-PBPK) model and the developed database of physiological parameters following administration to 100 virtual subjects. The WB-PBPK model simulation results showed that most of the observed plasma drug concentrations fell within the 5th-95th percentiles of the simulations, and the estimated peak concentrations (Cmax) and area under the curve (AUC) of drugs were also within 0.5-2.0 folds of observations. Sensitivity analysis showed that exercise intensity, exercise duration, medication time, and alterations in physiological parameters significantly affected drug pharmacokinetics and the net effect depending on drug characteristics and exercise conditions. In conclusion, the pharmacokinetics of drugs during exercise could be quantitatively predicted using the developed WB-PBPK model and database of physiological parameters. SIGNIFICANCE STATEMENT: This study simulated real-time changes of human physiological parameters during exercise in the WB-PBPK model and comprehensively investigated pharmacokinetic changes during exercise following oral and intravenous administration. Furthermore, the factors affecting pharmacokinetics during exercise were also revealed.

A flexible pH sensor based on polyaniline@oily polyurethane/polypropylene spunbonded nonwoven fabric

To fabricate a two-electrode flexible pH sensor based on polypropylene spunbonded nonwoven fabric (PP SF), oily polyurethane (OPU) was first coated on the surface of PP SF to obtain OPU/PP SF. Then, silver/silver chloride (Ag/AgCl) paste, used as the reference electrode and conductive carbon (C) paste were transferred to the OPU/PP SF surface through screen printing. Polyaniline (PANI) was deposited on the surface of the C paste to form a sensing working electrode via the electro-chemical deposition method. The results showed that the surface of the obtained PANI@OPU/PP SF flexible pH sensor (3D PANI pH sensor) presented a three-dimensional (3D) porous network structure. The 3D PANI pH sensor had good mechanical properties, an excellent Nernst response (-67.67 mV pH-1) and linearity (R2 = 0.99) in the pH range from 2.00 to 8.00 in the normal state. In the bent state, the 3D PANI pH sensor retained similar sensitivity (-68.87 mV pH-1) and linearity (R2 = 0.99). Moreover, the 3D PANI pH sensor exhibited a short response time (8 s), excellent reversibility (1.20 mV), low temperature drift (-0.0872 mV pH-1 °C-1) and long-term stability (0.83 mV h-1) in the normal state. Furthermore, the 3D PANI pH sensor can be effectively applied for pH monitoring of liquids and fruits with irregular curved surfaces. The error margin is no more than 0.16 compared to a commercial pH meter.

Time Course and Magnitude of Tolerance to the Ergogenic Effect of Caffeine on the Second Ventilatory Threshold

Pre-exercise caffeine ingestion has been shown to increase the workload at ventilatory threshold, suggesting an ergogenic effect of this stimulant on submaximal aerobic exercise. However, the time course of tolerance to the effect of caffeine on ventilatory threshold is unknown. This study aimed to determine the evolution of tolerance to the ergogenic effect of caffeine on the ventilatory threshold. Methods: Eleven participants (age 32.3 ± 4.9 yrs, height 171 ± 8 cm, body mass 66.6 ± 13.6 kg, VO_2max = 48.0 ± 3.8 mL/kg/min) took part in a longitudinal, double-blind, placebo-controlled, randomized, crossover experimental design. Each participant took part in two identical treatments: in one treatment, participants ingested a capsule containing 3 mg of caffeine per kg of body mass per day (mg/kg/day) for twenty consecutive days; in the other treatment, participants ingested a capsule filled with a placebo for the same duration and frequency. During these treatments, participants performed a maximal ramp test on a cycle ergometer three times per week and the second ventilatory threshold (VT₂) was assessed by using the ventilatory equivalents for oxygen and carbon dioxide. Results: A two-way ANOVA with repeated measures (substance × time) revealed statistically significant main effects of caffeine (p < 0.01) and time (p = 0.04) on the wattage obtained at VT₂, although there was no interaction (p = 0.09). In comparison to the placebo, caffeine increased the workload at VT₂ on days 1, 4, 6 and 15 of ingestion (p < 0.05). The size of the ergogenic effect of caffeine over the placebo on the workload at VT₂ was progressively reduced with the duration of the treatment. In addition, there were main effects of caffeine (p = 0.03) and time (p = 0.16) on VO₂ obtained at VT₂, with no interaction (p = 0.49). Specifically, caffeine increased oxygen uptake at VT₂ on days 1 and 4 (p < 0.05), with no other caffeine–placebo differences afterwards. For heart rate obtained at VT₂, there was a main effect of substance (p < 0.01), while the overall effect of time (p = 0.13) and the interaction (p = 0.22) did not reach statistical significance. Heart rate at VT₂ was higher with caffeine than with the placebo on days 1 and 4 (p < 0.05). The size of the effect of caffeine on VO₂ and heart at VT₂ tended to decline over time. Conclusion: Pre-exercise intake of 3 mg/kg/day of caffeine for twenty days enhanced the wattage obtained at VT₂ during cycling ramp tests for ~15 days of ingestion, while there was a progressive attenuation of the size of the ergogenic effect of caffeine on this performance variable. Therefore, habituation to caffeine through daily ingestion may reduce the ergogenic effect of this stimulant on aerobic exercise of submaximal intensity.

The Biology of Physiological Health

The ability to maintain health, or recover to a healthy state after disease, is an active process involving distinct adaptation mechanisms coordinating interactions between all physiological systems of an organism. Studies over the past several decades have assumed the mechanisms of health and disease are essentially inter-changeable, focusing on the elucidation of the mechanisms of disease pathogenesis to enhance health, treat disease, and increase healthspan. Here, I propose that the evolved mechanisms of health are distinct from disease pathogenesis mechanisms and suggest that we develop an understanding of the biology of physiological health. In this Perspective, I provide a definition of, a conceptual framework for, and proposed mechanisms of physiological health to complement our understanding of disease and its treatment.

Recent Progress in Electrochemical pH-Sensing Materials and Configurations for Biomedical Applications

pH-sensing materials and configurations are rapidly evolving toward exciting new applications, especially those in biomedical applications. In this review, we highlight rapid progress in electrochemical pH sensors over the past decade (2008-2018) with an emphasis on key considerations, such as materials selection, system configurations, and testing protocols. In addition to recent progress in optical pH sensors, our main focus in this review is on electromechanical pH sensors due to their significant advances, especially in biomedical applications. We summarize developments of electrochemical pH sensors that by virtue of their optimized material chemistries (from metal oxides to polymers) and geometrical features (from thin films to quantum dots) enable their adoption in biomedical applications. We further present an overview of necessary sensing standards and protocols. Standards ensure the establishment of consistent protocols, facilitating collective understanding of results and building on the current state. Furthermore, they enable objective benchmarking of various pH-sensing reports, materials, and systems, which is critical for the overall progression and development of the field. Additionally, we list critical issues in recent literary reporting and suggest various methods for objective benchmarking. pH regulation in the human body and state-of-the-art pH sensors (from ex vivo to in vivo) are compared for suitability in biomedical applications. We conclude our review by (i) identifying challenges that need to be overcome in electrochemical pH sensing and (ii) providing an outlook on future research along with insights, in which the integration of various pH sensors with advanced electronics can provide a new platform for the development of novel technologies for disease diagnostics and prevention.

The Relationship Between Lactate and Ventilatory Thresholds in Runners: Validity and Reliability of Exercise Test Performance Parameters

The aims of this study were (1) to establish the best fit between ventilatory and lactate exercise performance parameters in running and (2) to explore novel alternatives to estimate the maximal aerobic speed (MAS) in well-trained runners. Twenty-two trained male athletes ( V ˙ O_2max 60.2 ± 4.3 ml·kg·min^-1) completed three maximal graded exercise tests (GXT): (1) a preliminary GXT to determine individuals' MAS; (2) two experimental GXT individually adjusted by MAS to record the speed associated to the main aerobic-anaerobic transition events measured by indirect calorimetry and capillary blood lactate (CBL). Athletes also performed several 30 min constant running tests to determine the maximal lactate steady state (MLSS). Reliability analysis revealed low CV (<3.1%), low bias (<0.5 km·h^-1), and high correlation (ICC > 0.91) for all determinations except V-Slope (ICC = 0.84). Validity analysis showed that LT, LT+1.0, and LT+3.0 mMol·L^-1 were solid predictors of VT₁ (-0.3 km·h^-1; bias = 1.2; ICC = 0.90; p = 0.57), MLSS (-0.2 km·h^-1; bias = 1.2; ICC = 0.84; p = 0.74), and VT₂ (<0.1 km·h^-1; bias = 1.3; ICC = 0.82; p = 0.9l9), respectively. MLSS was identified as a different physiological event and a midpoint between VT₁ (bias = -2.0 km·h^-1) and VT₂ (bias = 2.3 km·h^-1). MAS was accurately estimated (SEM ± 0.3 km·h^-1) from peak velocity (V_peak) attained during GXT with the equation: MAS_EST (km·h^-1) = V_peak (km·h^-1) ^* 0.8348 + 2.308. Current individualized GXT protocol based on individuals' MAS was solid to determine both maximal and submaximal physiological parameters. Lactate threshold tests can be a valid and reliable alternative to VT and MLSS to identify the workloads at the transition from aerobic to anaerobic metabolism in well-trained runners. In contrast with traditional assumption, the MLSS constituted a midpoint physiological event between VT₁ and VT₂ in runners. The V_peak stands out as a powerful predictor of MAS.

Validity and Reliability of Ventilatory and Blood Lactate Thresholds in Well-Trained Cyclists

PURPOSE

The purpose of this study was to determine, i) the reliability of blood lactate and ventilatory-based thresholds, ii) the lactate threshold that corresponds with each ventilatory threshold (VT1 and VT2) and with maximal lactate steady state test (MLSS) as a proxy of cycling performance.

METHODS

Fourteen aerobically-trained male cyclists ([Formula: see text] 62.1±4.6 ml·kg-1·min-1) performed two graded exercise tests (50 W warm-up followed by 25 W·min-1) to exhaustion. Blood lactate, [Formula: see text] and [Formula: see text] data were collected at every stage. Workloads at VT1 (rise in [Formula: see text];) and VT2 (rise in [Formula: see text]) were compared with workloads at lactate thresholds. Several continuous tests were needed to detect the MLSS workload. Agreement and differences among tests were assessed with ANOVA, ICC and Bland-Altman. Reliability of each test was evaluated using ICC, CV and Bland-Altman plots.

RESULTS

Workloads at lactate threshold (LT) and LT+2.0 mMol·L-1 matched the ones for VT1 and VT2, respectively (p = 0.147 and 0.539; r = 0.72 and 0.80; Bias = -13.6 and 2.8, respectively). Furthermore, workload at LT+0.5 mMol·L-1 coincided with MLSS workload (p = 0.449; r = 0.78; Bias = -4.5). Lactate threshold tests had high reliability (CV = 3.4-3.7%; r = 0.85-0.89; Bias = -2.1-3.0) except for DMAX method (CV = 10.3%; r = 0.57; Bias = 15.4). Ventilatory thresholds show high reliability (CV = 1.6%-3.5%; r = 0.90-0.96; Bias = -1.8-2.9) except for RER = 1 and V-Slope (CV = 5.0-6.4%; r = 0.79; Bias = -5.6-12.4).

CONCLUSIONS

Lactate threshold tests can be a valid and reliable alternative to ventilatory thresholds to identify the workloads at the transition from aerobic to anaerobic metabolism.

Post-analysis methods for lactate threshold depend on training intensity and aerobic capacity in runners. An experimental laboratory study

CONTEXT AND OBJECTIVE

This study aimed to evaluate different mathematical post-analysis methods of determining lactate threshold in highly and lowly trained endurance runners.

DESIGN AND SETTING

Experimental laboratory study, in a tertiary-level public university hospital.

METHOD

Twenty-seven male endurance runners were divided into two training load groups: lowly trained (frequency < 4 times per week, < 6 consecutive months, training velocity ≥ 5.0 min/km) and highly trained (frequency ≥ 4 times per week, ≥ 6 consecutive months, training velocity < 5.0 min/km). The subjects performed an incremental treadmill protocol, with 1 km/h increases at each subsequent 4-minute stage. -Fingerprint -blood-lactate analysis was performed at the end of each stage. The lactate threshold (i.e. the running velocity at which blood lactate levels began to exponentially increase) was measured using three different methods: increase in blood lactate of 1 mmol/l at stages (DT1), absolute 4 mmol/l blood lactate concentration (4 mmol), and the semi-log method (semi-log). ANOVA was used to compare different lactate threshold methods and training groups.

RESULTS

Highly trained athletes showed significantly greater lactate thresholds than lowly trained runners, regardless of the calculation method used. When all the subject data were combined, DT1 and semi-log were not different, while 4 mmol was significantly lower than the other two methods. These same trends were observed when comparing lactate threshold methods in the lowly trained group. However, 4 mmol was only significantly lower than DT1 in the highly trained group.

CONCLUSION

The 4 mmol protocol did not show lactate threshold measurements comparable with DT1 and semi-log protocols among lowly trained athletes.

Maximal Sprint Power in Road Cyclists After Variable and Nonvariable High-Intensity Exercise

This study compared the sprint performance of professional cyclists after 10 minutes of variable (VAR) or nonvariable (N-VAR) high-intensity cycling with sprint performance in a rested state. Ten internationally competitive male cyclists (mean ± SD: age, 20.1 ± 1.3 years; stature, 1.81 ± 0.07 m; body weight, 69.5 ± 4.9 kg; and V[Combining Dot Above]O2peak, 72.5 ± 4.4 ml·kg·min) performed a 12-second maximal sprint in 3 conditions: (a) a rested state, (b) after 10 minutes of N-VAR cycling, and (c) after 10 minutes of VAR cycling. The intensity during the 10-minute efforts gradually increased to replicate power output observed in the final section of cycling road races. During the VAR cycling, participants performed short (2 seconds) accelerations at 80% of their sprint peak power, every 30 seconds. Average power output, cadence, and maximal heart rate (HR) during the 10-minute efforts were similar between conditions (5.3 ± 0.2 W·kg, 102 ± 1 rpm, and 93 ± 3% HRmax). Postexercise blood lactate concentration and sessional perceived exertion were also similar (8.3 ± 1.6 mmol·L, 15.4 ± 1.3 [6-20 scale]). Peak and average power output and cadence during the subsequent maximal sprint were not different between the 3 experimental conditions (p > 0.05). In conclusion, this study showed that neither the VAR nor the N-VAR 10-minute efforts ridden in this study impaired sprint performance in elite competitive cyclists.

Guidelines to Classify Subject Groups in Sport-Science Research

Purpose:

The aim of this systematic literature review was to outline the various preexperimental maximal cycle-test protocols, terminology, and performance indicators currently used to classify subject groups in sportscience research and to construct a classification system for cycling-related research.

Methods:

A database of 130 subject-group descriptions contains information on preexperimental maximal cycle-protocol designs, terminology of the subject groups, biometrical and physiological data, cycling experience, and parameters. Kolmogorov-Smirnov test, 1-way ANOVA, post hoc Bonferroni (P < .05), and trend lines were calculated on height, body mass, relative and absolute maximal oxygen consumption (VO2max), and peak power output (PPO).

Results:

During preexperimental testing, an initial workload of 100 W and a workload increase of 25 W are most frequently used. Three-minute stages provide the most reliable and valid measures of endurance performance. After obtaining data on a subject group, researchers apply various terms to define the group. To solve this complexity, the authors introduced the neutral term performance levels 1 to 5, representing untrained, recreationally trained, trained, well-trained, and professional subject groups, respectively. The most cited parameter in literature to define subject groups is relative VO2max, and therefore no overlap between different performance levels may occur for this principal parameter. Another significant cycling parameter is the absolute PPO. The description of additional physiological information and current and past cycling data is advised.

Conclusion:

This review clearly shows the need to standardize the procedure for classifying subject groups. Recommendations are formulated concerning preexperimental testing, terminology, and performance indicators.

Aerobic fitness determines whole-body fat oxidation rate during exercise in the heat

The purpose of this study was to determine whole-body fat oxidation in endurance-trained (TR) and untrained (UNTR) subjects exercising at different intensities in the heat. On 3 occasions, 10 TR cyclists and 10 UNTR healthy subjects (peak oxygen uptake = 60 ± 6 vs. 44 ± 3 mL·kg-1·min-1; p < 0.05) exercised at 40%, 60%, and 80% peak oxygen uptake in a hot, dry environment (36 °C; 25% relative humidity). To complete the same amount of work in all 3 trials, exercise duration varied (107 ± 4, 63 ± 1, and 45 ± 0 min for 40%, 60%, and 80% peak oxygen uptake, respectively). Substrate oxidation was calculated using indirect calorimetry. Blood samples were collected at the end of exercise to determine plasma epinephrine ([EPI]plasma) and norepinephrine ([NEPI]plasma) concentrations. The maximal rate of fat oxidation was achieved at 60% peak oxygen uptake for the TR group (0.41 ± 0.01 g·min-1) and at 40% peak oxygen uptake for the UNTR group (0.28 ± 0.01 g·min-1). TR subjects oxidized absolutely (g·min-1) and relatively (% of total energy expenditure) more fat than UNTR subjects at 60% and 80% peak oxygen uptake (p < 0.05). At these exercise intensities, TR subjects also had higher [NEPI]plasma concentrations than UNTR subjects (p < 0.05). In the heat, whole-body peak fat oxidation occurs at higher relative exercise intensities in TR than in UNTR subjects (60% vs. 40% peak oxygen uptake). Moreover, TR subjects oxidize more fat than UNTR subjects when exercising at moderate to high intensities (>60% peak oxygen uptake).

Restoration of blood pH between repeated bouts of high-intensity exercise: effects of various active-recovery protocols

To determine which active-recovery protocol would reduce faster the high blood H(+) and lactate concentrations produced by repeated bouts of high-intensity exercise (HIE). On three occasions, 11 moderately trained males performed 4 bouts (1.5 min) at 163% of their respiratory compensation threshold (RCT) interspersed with active-recovery: (1) 4.5 min pedalling at 24% RCT (S(HORT)); (2) 6 min at 18% RCT (M(EDIUM)); (3) 9 min at 12% RCT (L(ONG)). The total work completed during recovery was the same in all three trials. Respiratory gases and arterialized-blood samples were obtained during exercise. At the end of exercise, L(ONG) in comparison to S(HORT) and M(EDIUM) increased plasma pH (7.32 +/- 0.02 vs. approximately 7.22 +/- 0.03; P < 0.05), while reduced lactate concentration (8.5 +/- 0.9 vs. approximately 10.9 +/- 0.8 mM; P < 0.05). Ventilatory equivalent for CO(2) was higher in L(ONG) than S(HORT) and M(EDIUM) (31.4 +/- 0.5 vs. approximately 29.6 +/- 0.5; P < 0.05). Low-intensity prolonged recovery between repeated bouts of HIE maximized H(+) and lactate removal likely by enhancing CO(2) unloading.