Periodized Aerobic Training between Thresholds Improves Submaximal Cardiorespiratory Parameters in Octogenarians

Steep ramp test protocol for preoperative risk assessment and short-term high-intensity interval training to evaluate, improve, and monitor cardiorespiratory fitness in surgical oncology.

Summary : Lifelong exercise training maintains a youthful compliance of the left ventricle (LV), whereas a year of exercise training started later in life fails to reverse LV stiffening, possibly because of accumulation of irreversible advanced glycation end products. Alagebrium is a novel drug that breaks advanced glycation end product crosslinks and improves LV stiffness in aged animals. In this study, the authors prescribed alagebrium (200 mg daily) or placebo combined with aerobic exercise training or contact control in healthy, sedentary older individuals for 1 year. The authors evaluated overall cardiac function by the use of several modalities, including invasive pressure–volume measurements, exercise testing, and cardiac MRI before and after the training. To the authors’ knowledge, this is the first study to evaluate the effects of alagebrium and exercise training in healthy aged humans. After intervention, exercise training significantly increased exercise capacity, LV mass, and LV end-diastolic volume. Conversely, alagebrium had little effect on exercise capacity or LV geometry. However, alagebrium showed a modest improvement in LV stiffness compared with placebo. This favorable effect of alagebrium on LV stiffness was most prominent in individuals with combined alagebrium and exercise training. Conclusions : Alagebrium had no effect on hemodynamics, LV geometry, or exercise capacity in healthy, previously sedentary seniors. However, it did show a modestly favorable effect on age-associated LV stiffening.1 Summary : Maximum oxygen consumption (peak VO2) and efficiency of ventilation (VE) during exercise (VE/VCO2 slope) are known to stratify adults in heart failure for 1-year survival. On the basis of adult data, a recent American Heart Association scientific statement suggested that peak VO2<50% predicted for age and sex should be considered substantial impairment in exercise performance in children with heart disease and therefore a class I indication for heart transplant listing. This single-center study examined the association of …

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Materials and methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods: Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression. Results: Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43. Conclusions: Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. Funding: No external funding was received for this work. Editor's evaluation The authors have established new formulas to predict maximum oxygen uptake for cyclists and runners based on submaximal exercise testing and anthropometric characteristics. This is an important study with a large and comprehensive dataset, which may be helpful for many exercise labs. The work is convincing, using appropriate and validated methodology in line with the current state-of-the-art, as shown by references to common exercise books. https://doi.org/10.7554/eLife.86291.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The oxygen uptake (VO2) is considered an important metric in assessing cardiorespiratory fitness, health status, or endurance performance potential (Guazzi et al., 2012). With the application of standardised procedures and interpretation protocols, during graded exercise tests (GXT), the (maximal oxygen uptake) VO2max can be established (Bentley et al., 2007). GXT is the most widely used assessment to examine the dynamic relationship between exercise and integrated physiological systems (Albouaini et al., 2007; Bentley et al., 2007). The information from GXT during cardiopulmonary exercise testing (CPET) can be applied across the spectrum of sport performance, occupational safety screening, research, and clinical diagnostics (Guazzi et al., 2017). VO2 max is often used as a boundary between severe and extreme intensity domains and by definition requires maximal effort from the tested subject (Gaesser and Poole, 1996). However, it is not always recommended or possible to undertake a test to exhaustion (Guazzi et al., 2012). For the athletes, the proximity of competition or injury history can allow submaximal testing, but not testing to exhaustion (Sassi et al., 2006). Testing that requires maximal effort may be disruptive to the training process or interfere with race performance (Coutts et al., 2007; Lamberts et al., 2011). Due to practical constraints, tests to exhaustion or peak-power-output tests are often performed only two or three times a year (Coutts et al., 2007). However, VO2 values are widely used in sport science and the decision-making process (Mann et al., 2013). VO2 is widely considered one of the major endurance performance determinants (Joyner and Coyle, 2008). Using VO2max to guide the selection process, prescribing training intensity, assessing training adaptations, or predicting race times is a common practice in high-performance sports (Bassett and Howley, 2000; Bentley et al., 2007; Hawley and Noakes, 1992; Noakes et al., 1990). VO2max is also one of the critical vital signs coordinating the function of the cardiovascular, respiratory, and muscular systems, it is an indicator of overall body health status (Kaminsky et al., 2017). Quantifying VO2max provides additional input regarding clinical decision-making, risk stratification, evaluation of therapy, and physical activity guidelines (Guazzi et al., 2012). For patients undertaking a test to exhaustion is rarely needed or possible due to health restraints or cardiac risk (Guazzi et al., 2016). For many years researchers have studied indirect methods of estimating VO2max(Sartor et al., 2013). Protocols such as the Astrand-Ryhming Test, Six-Minute Walk Test, or YMCA Step Test have been established and validated (Astrand and Ryhming, 1954; Beutner et al., 2015; Carey, 2022; Jalili et al., 2018). Moreover, estimation of the VO2 and heart rate (HR) values below the ventilatory threshold can be based on cardiorespiratory kinetics assessment using randomised changes in the work rate known as a pseudo-random binary sequences testing (Hoffmann et al., 2022). However, with the development of technology, the accessibility of laboratory testing and mobile testing improved (Montoye et al., 2020; Pritchard et al., 2021). Therefore, new opportunities to develop more precise yet simple and accessible methods and models to assess VO2max occur (Jurov et al., 2023). This appears to be especially important considering the low prediction accuracy of most of the VO2max formulae that were validated in our previous study (Wiecha et al., 2023). Recently, we have been observing the development of prediction methods with the usage of machine learning (ML) and artificial intelligence (AI) (Ashfaq et al., 2022). Both ML and AI are used in sport science as forecasting and decision-making support tools (Abut and Akay, 2015; Bobowik and Wiszomirska, 2022; Chmait and Westerbeek, 2021; Hammes et al., 2022; Rossi et al., 2021). There is growing evidence that VO2max prediction based on ML models, especially support vector ML and artificial neural network models, exhibits more robust and accurate results compared to MLR only (Abut and Akay, 2015; Ashfaq et al., 2022). Therefore, in this research, with the support of ML, we look for algorithms and prediction patterns that allow us to use values obtained during submaximal CPET and somatic measurements to estimate maximal VO2max values in male runners and cyclists. We stipulate that prediction models allow for accurate calculation of VO2max based on somatic or submaximal CPET variables. Materials and methods We have applied the development and validation of the prediction TRIPOD guidelines to conduct the study (see Supplementary Material 1TRIPOD Checklist for Prediction Model Development and Validation) (Collins et al., 2015). The study is based on retrospective data analysis from the CPET registry collected from 2013 to 2021 at the medical clinic (Sportslab, Warsaw, Poland). All CPET have been performed at the individual request of participants, as a part of regular training monitoring or performance assessment. Ethical approval Request a detailed protocol The Institutional Review Board of the Bioethical Committee at the Medical University of Warsaw (AKBE/32/2021) has approved the study protocol. The regulations of the Declaration of Helsinki were met during all parts of the study. Each study participant delivered written consent to undergo CPET and participate in the study. Derivation cohort Request a detailed protocol We selected the cohort with the use of rigorous exclusion/inclusion criteria. Due to the insufficient number of women in our database and the number of potential variables in the regression models for adequate power, we had to limit ourselves to conduct analysis in the male population only (Martens and Logan, 2021). Out of 6439 healthy, adult male cyclists and long-distance runners that undergone CPET, 4423 met the criteria as further: (1) age ≥18 years, (2) declared regular cycling or running training for ≥3 months, (3) had no extreme outliers ≤ or ≥±3 standard deviations (SD) from mean for all of the testing variables (beyond ≥±3 SD in VO2max), (4) lack of any injury, medical condition, or addiction in medical history that may affect exercise capacity, (5) not taking any medications with a modifying effect on exercise capacity, (6) maximum exertion achieved during CPET. We defined the maximum exertion in CPET as the fulfilment of the minimum six of the following criteria: (1) respiratory exchange ratio (RER) ≥1.10, (2) present VO2 plateau (growth <100 mL·min–1 in VO2 despite increased running speed or cycling power), (3) respiratory frequency (fR) ≥45 breaths·min–1, (4) declared subjective exertion intensity during CPET ≥18 in the Borg scale (Borg, 1970), (5) blood lactate concentration [La-]b ≥8 mmol·L–1, (6) growth in speed/power ≥10% of respiratory compensation point (RCP) values after exceeding the RCP, (7) peak heart rate (HRpeak) ≥15 beats·min–1 below predicted maximal heart rate (HRmax) (Lach et al., 2021). Participants’ selection procedure has been shown in Figure 1. Figure 1 Download asset Open asset Flowchart of the preliminary inclusion and exclusion process. Abbreviations: EA, endurance athlete; CPET, cardiopulmonary exercise testing; SD, standard deviation; TE, treadmill; RER, respiratory exchange ratio; VO2, oxygen uptake (mL·min−1·kg−1); [La−]b, lactate concentration (mmol·L−1); fR, breathing frequency (breaths·min−1); RCP, respiratory compensation point; HRpeak, peak heart rate (beats·min−1); HRmax, maximal heart rate (bpm). At both stages of the selection, some participants met several (>1) exclusion criteria. Somatic measurements and CPET protocols Request a detailed protocol Body mass was measured with a body composition (BC) analyser (Tanita, MC 718, Japan) with the multifrequency of 5 kHz/50 kHz/250 kHz via the bioimpedance analysis and normal testing mode. The participants’ skin was cleaned with alcohol before placing the electrodes on the skin. Prior to the test, the participants received instructions to refrain from exercising for 2 hr, consume a light meal rich in carbohydrates 2–3 hr beforehand, and maintain hydration by drinking isotonic beverages. Additionally, they were advised to abstain from medications, caffeine, and cigarettes on the day of the test. Running CPET (TE) was performed on a mechanical treadmill (h/p/Cosmos Quasar, Germany). Cycling CPET (CE) was performed on Cyclus-2 (RBM elektronik-automation GmbH, Leipzig, Germany). Hans Rudolph V2 mask (Hans Rudolph, Inc, Shawnee, KS, USA), breath-by-breath method with Cosmed Quark CPET gas exchange analysing device (Cosmed Srl, Rome, Italy), and Quark PFT Suite to Omnia 1.6 software were utilised. The gas analyser device was regularly calibrated with the reference gas (16% O2; 5% CO2) in accordance with the manufacturer’s instructions (Airgas USA, LLC, Plumsteadville, PA, USA). From 2013 to 2021, three Cosmed Quark CPET units were used. HR was measured with the Cosmed torso belt (Cosmed srl, Rome, Italy). [La-]b was measured via enzymatic-amperometric electrochemical technique with Super GL2 analyser (Müller Gerätebau GmbH, Freital, Germany). The [La-]b analyser was regularly calibrated before each measurement series. The 40 m2 indoor, air-conditioned laboratory with 20–22°C temperature and 40–60% humidity, and 100 m ASL provided the same conditions for all BC and CPET. Each CPET began with a 5 min personalised warm-up (walk or easy jog with ‘conversational’ intensity for running, easy pedalling with ‘conversational’ intensity for cycling). Then after the preparation (about 5 min), the continuous progressive step test was conducted. Due to the population diversity (training status), the running test speed started from 7 to 12 km·hr–1 with a 1% treadmill incline. The choice of initial starting speed was determined by the interview and sports results achieved. For example, those running less than 60 min at a distance of 10 km started the test at 7 km/hr, while those running 10 km for less than 35 min started the test at an initial speed of 12 km/hr. The pace increased by 1 km·hr–1 every 2 min with no change in incline. The cycling test began at 60–150 W, depending on the athletes training status. The power increased by 20–30 W every 2 min. It was recommended to maintain a constant cadence of 80–90 (repetition·min–1) during the test. The tests were terminated due to exhaustion: volitional inability to continue the activity or/and VO2 and HR plateau with increasing load or/and observed disturbance of coordination in running or/and inability to maintain the set cadence. Due to the graded protocol used, the cycling power and running speed values have been calculated as a function of time to better reflect the actual level for the test moment being determined (Kuipers et al., 1985). Before the test, after every step, and 3 min after the termination of the effort technician took a 20 µL blood sample from a fingertip. Samples were collected during the test without interrupting the effort. The samples were taken from the initial puncture. The first blood drop was collected into the swab and the second blood drop was drawn for further analysis into the capillary. VO2max was recorded as the highest value (15 s intervals) before the termination of the test. HRmax was recorded as the highest value obtained at the end of the test, without averaging. The anaerobic threshold (AT) was established with the following criteria: (1) common start of VE/VO2 and VE/VCO2 curves, (2) end-tidal partial pressure of oxygen raised constantly with the end-tidal partial pressure of carbon dioxide (Beaver et al., 1986). The was established with the following criteria: (1) PetCO2 must decrease after reaching maximal amount, (2) the presence of fast nonlinear growth in VE (second deflection), (3) the VE/VCO2 ratio achieved minimum and started to rise, and (4) a nonlinear increase in VCO2 versus VO2 (lack of linearity) (Beaver et al., 1986). The [La-]b was estimated for AT and RCP in relation to power or speed (Wiecha et al., 2022). Data analysis Request a detailed protocol Our comprehensive ML approach enables the evaluation of each formula by preliminary variables precision (at the stage of selection), then accuracy (during the model’s building) and recall (in internal validation). Individual CPET results were saved into the Excel file (Microsoft Corporation, Redmond, WA, USA) and a custom-made script was used to generate the database in Excel (Python programming). Further, mean, SD, and 95% confidence intervals (CI) were calculated. The normality of the distribution of the data was examined using the Shapiro-Wilk test and intergroup differences were calculated using the Student’s t-test for independent variables. Three-step variable selection procedures based on random forests were applied using the R package VSURF in RStudio software (R Core Team, Vienna, Austria; version 3.6.4) (Genuer et al., 2016). For each level of measurement (AT, RCP) and their combination (AT+RCP), significant variables were identified separately. The first step was dedicated to eliminate irrelevant variables from the dataset. Second step aimed to select all variables related to the response for interpretation purposes. The third step refined the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purposes (Genuer et al., 2017). Each time for variables selection, the anthropometric variables as in Tables 1–2 and the CPET parameters given in Tables 3–4 from a specific level of measurement (AT; RCP) and their combinations were visible. Table 1 Basic anthropometric characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDAge (years)36.235.6–36.98.4535.935.5–36.38.0535.534.9–36.28.14Height (cm)180.0179.6–180.56.04179.4179.1–179.76.13179.7179.2–180.26.61BM (kg)77.777.0–78.49.3577.777.3–78.19.2977.977.1–78.610.1BMI (kg·m–2)23.923.8–24.12.4324.124.0–24.22.4124.123.9–24.32.56BF (%)15.415.1–15.74.5515.515.3–15.74.5215.415.1–15.84.55FM (kg)12.211.9–12.64.6812.312.1–12.54.6512.311.9–12.74.92FFM (kg)65.565.0–66.06.4365.465.1–65.76.3165.665.1–66.16.86 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 2 Basic anthropometric characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDAge (years)37.336.6–38.09.1337.135.9–38.49.5037.636.5–38.88.46Height (cm)179.9179.4–180.46.27180.1179.2–181.06.96180.2179.4–181.06.13BM (kg)78.878.1–79.69.8079.177.7–80.510.479.878.4–81.310.9BMI (kg·m–2)24.324.1–24.62.6324.424.0–24.72.8024.624.2–25.02.96BF (%)16.415.7–17.14.9916.115.7–16.54.8116.215.5–16.84.87FM (kg)13.312.6–14.15.6613.012.6–13.45.2713.312.5–14.05.85FFM (kg)65.864.9–66.66.2565.865.4–66.36.0666.665.7–67.46.58 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 3 Cardiopulmonary exercise testing (CPET) characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)38.438.1–38.85.0138.538.3–38.74.8838.137.7–38.55.16RERAT0.870.86–0.870.040.870.86–0.870.040.870.86–0.870.04HRAT (beats·min–1)151.5150.8–152.310.3151.0150.6–151.510.8152.0151.2–152.810.8VEAT (L·min–1)79.178.1–80.012.278.377.8–78.912.077.276.3–78.212.0SPEEDAT (km·h–1)11.010.9–11.11.4511.011.0–11.11.3610.910.8–11.01.42LAAT (mmol·L–1)2.082.02–2.140.631.801.76–1.830.622.352.27–2.420.72rVO2RCP (mL·min–1·kg–1)47.547.0–48.05.8847.747.4–48.06.1547.346.8–47.86.16RERRCP1.001.00–1.000.041.001.00–1.000.041.001.00–1.000.03HRRCP (beats·min–1)173.4172.7–174.19.21173.2172.8–173.69.30174.3173.5–175.09.50VERCP (L·min–1)114.7113.5–116.015.9113.9113.1–114.616.7112.7111.4–114.016.2SPEEDRCP (km·h–1)14.013.9–14.11.7714.114.0–14.11.7013.913.8–14.11.75LARCP (mmol·L–1)4.724.63–4.821.044.404.34–4.451.044.814.69–4.931.14rVO2max (mL·min–1·kg–1)53.853.3–54.36.6754.354.0–54.66.9553.853.3–54.37.09 CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; SPEEDAT, velocity at anaerobic threshold; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; SPEEDRCP, velocity at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; rVO2max, maximal oxygen uptake relative to body mass. Table 4 Cardiopulmonary exercise testing (CPET) characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)33.032.5–33.45.8433.232.4–33.95.6833.732.9–34.55.89RERAT0.870.87–0.870.040.870.87–0.880.040.870.87–0.880.04HRAT CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; power at anaerobic threshold relative to body mass; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; power at respiratory compensation point relative to body mass; rVO2max, maximal oxygen uptake relative to body mass. selection variables were in the further only selected parameters were into multiple linear regression The data for MLR model building were randomly into that is testing, validation and of the a only significant were in the Derived are by the of mean and mean analysis was used to the model’s precision and accuracy during validation and tests to the fulfilment of MLR test the of in MLR test assessment between and test of Each model was examined the and any have not been 2 package in RStudio (R Core Team, Vienna, Austria; version version for and software version were used in was considered as the Results Somatic measurements and CPET results data of the runners models for testing, and validation are in Table while cyclists are in Table The runners of and for testing, and validation the cyclists and differences between of runners and cyclists were in BMI and between testing in all between validation only in CPET results for runners models are in Table 3 and for cyclists in Table Runners in the cohort achieved relative to body mass VO2max of in testing group and in validation group cyclists mean was and for testing, and validation to body mass oxygen uptake at anaerobic threshold in runners for ± ± and ± of in testing, and validation it was ± ± and ± of rVO2max, relative to body mass oxygen uptake at respiratory compensation point in runners for ± ± and ± of for testing, and validation while in cyclists for ± ± and ± of rVO2max, There were no significant differences in values between testing, and validation the runners and cyclists between runners and cyclists results were all significant Prediction models based on AT and RCP Full of MLR prediction models for cyclists are in Table for runners in Table The models prediction performance is as with and for cyclists from for somatic parameters to for RCP equations. For runners from for to for AT and equations. for cyclists models was the for RCP and the highest for For from for AT and to for equation. observed for cyclists was the for RCP in the validation group and the highest for while in runners the for AT and and the highest for The performance of prediction is in Figure Figure 2 Download asset Open asset of prediction for Abbreviations: maximal oxygen anaerobic threshold; RCP, respiratory compensation point; All values are in performance for running while the performance for cycling equations. performance of the prediction model for for for AT and for somatic-only equation. Table 5 VO2max prediction for cyclists. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass power at anaerobic threshold relative to body mass rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass VERCP, pulmonary ventilation at respiratory compensation point BF, body fat BM, body mass Table VO2max prediction for runners. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass SPEEDAT, velocity at anaerobic threshold FFM, fat mass VEAT, pulmonary ventilation at anaerobic threshold HRAT, heart rate at anaerobic threshold BF, body fat rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass SPEEDRCP, velocity at respiratory compensation point Models validation of each model for cyclists is in Table while for runners in Table the performance of our prediction was to that observed in the

BackgroundThe oxygen uptake efficiency slope (OUES) is considered a reliable indicator of cardiorespiratory fitness in young and clinical populations who cannot achieve maximal effort during a graded exercise test. However, OUES accuracy depends on the data points used for its calculation and it is still not clear if the submaximal OUES can accurately assess CRF in healthy young males.ObjectiveWe investigated the association between peak oxygen uptake and peak and submaximal OUES, and the agreement between submaximal OUES and peak OUES in male adolescents and young adults.MethodsIn this cross-sectional, observational study, fifty normal weight healthy participants (age 14–22 years, peak oxygen uptake 43.8 ± 7.3 mL·min−1·kg−1) performed a graded exercise test on a cycle ergometer and pulmonary gas exchange was assessed using breath-by-breath analysis. Peak oxygen uptake, and oxygen consumption at the aerobic and at the anaerobic threshold were determined as the 30-s average of the oxygen consumption values. Peak OUES (up to peak) and submaximal OUES (up to the aerobic and anaerobic thresholds) were calculated from the logarithmic relation between oxygen consumption and pulmonary ventilation.ResultsVery strong correlations were observed between peak oxygen uptake and peak OUES (r = 0.80–0.88) while fair-to-very strong correlations were observed between the peak oxygen uptake and the two submaximal OUES (r = 0.32–0.81). The level of agreement between peak OUES and OUES up to the anaerobic threshold (r = 0.89–0.93; Typical percentage error 6%; Intraclass correlation coefficient = 0.89–0.93) was greater than the one between the peak oxygen uptake with OUES up to the aerobic threshold (r = 0.39–0.56; Typical percentage error 15%; Intraclass correlation coefficient = 0.38–0.56).Conclusions. The peak OUES is a better indicator of aerobic fitness than the OUES up to the anaerobic threshold in healthy, young males. The OUES up to the anaerobic threshold is a valid alternative to peak OUES.

The present pilot study was undertaken to evaluate the efficacy of an aerobic exercise training (AET) program alone or combined with an antihypertensive agent (irbesartan) to reduce blood pressure (BP) and enhance heart rate variability (HRV) in chronic obstructive pulmonary disease patients. Twenty-one patients were randomly assigned to a double-blind treatment with exercise and placebo (n=11) or exercise and irbesartan (n=10). Subjects underwent 24 h BP monitoring and 24 h electrocardiographic recording before and after the 12-week AET. HRV was investigated using three indexes from the power spectral analysis and three indexes calculated from the time domain. The AET program consisted of exercising on a calibrated ergocycle for 30 min three times per week. Five patients in the placebo group were excluded during follow-up because they were not compliant. There was no change in 24 h systolic and diastolic BP before (130+/-14 mmHg and 70+/-3 mmHg, respectively) and after (128+/-8 mmHg and 70+/-8 mmHg, respectively) exercise training in the placebo group, whereas in the irbesartan group systolic and diastolic BP decreased from 135+/-9 mmHg and 76+/-9 mmHg to 126+/-12 mmHg and 72+/-8 mmHg, respectively (P<0.02). There were no changes in HRV parameters in either group. The present study suggests that a 12-week AET program is not associated with a significant reduction in BP or enhancement in HRV, whereas an AET program combined with irbesartan is associated with a reduction in 24 h BP.

Physical Activity and Diabetes.

Background: Despite a successful repair of tetralogy of Fallot (rToF) in childhood, residual lesions are common and can contribute to impaired exercise capacity. Although both cycle ergometer and treadmill protocols are often used interchangeably these approaches have not been directly compared. In this study we examined cardiopulmonary exercise test (CPET) measurements in rToF. Methods: Inclusion criteria were clinically stable rToF patients able to perform a cardiac magnetic resonance imaging (CMR) and two CPET studies, one on the treadmill (incremental Bruce protocol) and one on the cycle ergometer (ramped protocol), within 12 months. Demographic, surgical and clinical data; functional class; QRS duration; CMR measures; CPET data and international physical activity questionnaire (IPAQ) scores of patients were collected. Results: Fifty-seven patients were enrolled (53% male, 20.5 ± 7.8 years at CPET). CMR measurements included a right ventricle (RV) end-diastolic volume index of 119 ± 22 mL/m2, a RV ejection fraction (EF) of 55 ± 6% and a left ventricular (LV) EF of 56 ± 5%. Peak oxygen consumption (VO2)/Kg (25.5 ± 5.5 vs. 31.7 ± 6.9; p < 0.0001), VO2 at anaerobic threshold (AT) (15.3 ± 3.9 vs. 22.0 ± 4.5; p < 0.0001), peak O2 pulse (10.6 ± 3.0 vs. 12.1± 3.4; p = 0.0061) and oxygen uptake efficiency slope (OUES) (1932.2 ± 623.6 vs. 2292.0 ± 639.4; p < 0.001) were significantly lower on the cycle ergometer compared with the treadmill, differently from ventilatory efficiency (VE/VCO2) max which was significantly higher on the cycle ergometer (32.2 ± 4.5 vs. 30.4 ± 5.4; p < 0.001). Only the VE/VCO2 slope at the respiratory compensation point (RCP) was similar between the two methodologies (p = 0.150). Conclusions: The majority of CPET measurements differed according to the modality of testing, with the exception being the VE/VCO2 slope at RCP. Our data suggest that CPET parameters should be interpreted according to test type; however, these findings should be validated in larger populations and in a variety of institutions.

Traditionally, the effects of physical training in patients with chronic heart failure (CHF) are evaluated by changes in peak oxygen uptake (peak VO(2)). The assessment of peak VO(2), however, is highly dependent on the patients' motivation. The aim of the present study was to evaluate the clinical utility of effort-independent exercise variables for detecting training effects in CHF patients. In a prospective controlled trial, patients with stable CHF were allocated to an intervention group (N = 30), performing a 12-week combined cycle interval and muscle resistance training program, or a control group (N = 18) that was matched for age, gender, body composition and left ventricular ejection fraction. The following effort-independent exercise variables were evaluated: the ventilatory anaerobic threshold (VAT), oxygen uptake efficiency slope (OUES), the V(E)/VCO(2) slope and the time constant of VO(2) kinetics during recovery from submaximal constant-load exercise (tau-rec). In addition to post-training increases in peak VO(2) and peak V(E), the intervention group showed significant within and between-group improvements in VAT, OUES and tau-rec. There were no significant differences between relative improvements of the effort-independent exercise variables in the intervention group. In contrast with VAT, which could not be determined in 9% of the patients, OUES and tau-rec were determined successfully in all patients. Therefore, we conclude that OUES and tau-rec are useful in clinical practice for the assessment of training effects in CHF patients, especially in cases of poor subject effort during symptom-limited exercise testing or when patients are unable to reach a maximal exercise level.

C-Reactive Protein (CRP) is an inflammatory biomarker linked to endothelial dysfunction, resulting hypertension, and cardiovascular events. 24-hr ambulatory blood pressure monitoring (ABPM) is the gold standard for diagnosing hypertension, with recent evidence showing a superior predictive value of asleep nighttime values, in particular, for cardiovascular outcomes. PURPOSE: To assess whether circulating CRP levels differ between nighttime hypertensives and normotensives and examine the ability of exercise training to affect CRP levels. METHODS: Non-smoking, middle-to-older age, sedentary African Americans underwent an aerobic exercise training (AEXT) program for 24 weeks. Various modes of AEX were included, with intensity progressing up to 60% VO2 max. Participants underwent 6 weeks of dietary stabilization and were required to maintain a constant weight for the study’s duration. CRP and blood pressure (BP) were measured from fasted blood samples and 24-hour ABPM, respectively, before and after the AEXT program. Upon study completion, subjects were divided into groups based on average asleep ABPM values (O’Brien et al 2013) and baseline circulating CRP levels. RESULTS: At baseline, normotensive subjects had significantly lower circulating CRP than those classified as hypertensives based on nighttime systolic BP (2.95 vs 4.74 mg/L, p<.01). Although there was no difference between normotensives and hypertensives classified as at low (<1mg/L) or high (>3mg/L) risk for cardiovascular disease based on CRP levels, there was a difference in circulating CRP between groups at average risk (p<.05). CRP did not significantly decrease (p=.095), however, after 6 months of AEXT the hypertensive group experienced a significantly greater decrease in CRP than the normotensive group (p =.01). CONCLUSIONS: Our results suggests that nighttime hypertensive status may correspond to cardiovascular disease risks predicted by the classic biomarker CRP. Nocturnal SBP may be a discriminating additive factor to consider for health by those at average risk for future events by CRP levels. For both hypertensives and normotensives, AEXT may alter CRP, with hypertensives likely to see a greater effect. Supplementary studies are needed to examine the relationship between additional variables and nocturnal hypertension.

Objective Variety of processes including circadian rhythm and systemic factors affect expression of many genes in skeletal muscle during a day. Therefore, post-exercise gene expression depends on many factors: contractile activity per seas well as circadian rhythm, nerve activity, concentration of different substances in blood, feeding and fasting. In our study, we investigated specific for contractile activity changes in the transcriptome in untrained and trained (after an aerobic training programme) human skeletal muscle. The second goal was to examine effect of aerobic training on gene expression in muscle in basal state.&#x0D; Methods Seven untrained males performed the one-legged knee extension exercise (for 60 min) with the same relative intensity before and after a 2 month aerobic training programme (1 h/day, 5/week). Biopsy samples were taken at rest (basal state, 48 h after the previous exercise), 1 and 4 h after one-legged exercise from m. vastus lateralisof either leg. This approach allowed us to evaluate specific changes in the transcriptome associated with contractile activity. RNA­sequencing (84 samples in total; ~42 million reads/sample) was performed by HiSeq 2500 (Illumina).&#x0D; Results Two months aerobic training increased the aerobic capacity of the knee-extensor muscles (power at anaerobic threshold in incremental one-legged and cycling tests), the maximum rate of ADP-stimulated mitochondrial respiration in permeabilized muscle fibres and amounts of oxidative phosphorylation proteins. After one-legged exercise, expression of many genes was changed in exercised muscle (~1500) as well as in non-exercised muscle (~400). Pronounced changes in gene expression in non-exercised muscle may be associated with many factors, including circadian rhythm (result of GO analysis).&#x0D; To examine transcriptome changes specific for contractile activity, the difference in gene expression between legs was examined. In untrained muscle, one-legged exercise changed expression of ~1200 genes specific for contractile activity at each time point. Despite the same relative intensity of one-legged exercise, transcriptomic response in trained muscle was markedly lower (~300 genes) compare to untrained.&#x0D; We observed a strong overlap between transcriptomic responses (~250 genes) and particularly between enriched transcription factor binding sites in promoters of these genes in untrained and trained muscles. These sets of genes and transcription factors play the key role in adaptation of muscle to contractile activity independently on the level of muscular fitness.&#x0D; Surprisingly, 2 months aerobic training changed the expression of more than 1500 genes in basal state. Noteworthy, these genes demonstrated a small overlap (~200 genes) with genes related to specific response to acute exercise. Moreover, these genes were associated with significantly different biological processes than genes related to specific response to acute exercise.&#x0D; Conclusions Specific for contractile activity changes in the transcriptome in untrained and trained human skeletal muscle were revealed for the first time. After 2 month aerobic training, the specific transcriptome response to acute exercise become much less pronounced. A computational approach reveals common transcription factors important for adaptation of both untrained and trained muscle. We found out that adaptation of muscle to aerobic training associates not only with the transitory changes in gene expression after each exercise, but also with the marked changes in transcriptome in basal state.&#x0D; This work was supported by the Russian Science Foundation (14­15­00768).

Background: The ratio of oxygen uptake (VO2) to minute ventilation (VE) is described as the oxygen uptake efficiency slope (OUES). OUES has been suggested as a valuable submaximal cardiorespiratory index; however, its characteristics in endurance athletes remain unknown. In this study, we a) investigated OUES between different time intervals, b) assessed their prediction power for VO2peak, and c) derived new prediction equations for OUES tailored for well-trained individuals. Materials and Methods: A total of 77 male (age = 21.4 ± 4.8yrs; BMI = 22.1 ± 1.6kg·m-2; peak oxygen uptake = 4.40 ± 0.64L·min-1) and 63 female individuals (age = 23.4 ± 4.3yrs; BMI = 23.1 ± 1.6kg·m-2; peak oxygen uptake = 3.21 ± 0.48L·min-1) underwent the cycling cardiopulmonary exercise test. OUES was measured at 75%, 90%, and 100% of exercise duration. Prediction power and new models were derived with the multiple linear regression method. Results: In male subjects, OUES [mL·min-1/L·min-1] from 75% = 4.53 ± 0.90, from 90% = 4.52 ± 0.91, and from 100% = 4.41 ± 0.87. In female subjects, OUES [mL·min-1/L·min-1] from 75% = 3.50 ± 0.65, from 90% = 3.49 ± 0.62, and from 100% = 3.41 ± 0.58. OUES did not differ between time intervals in male (p = 0.65) and female individuals (p = 0.69). OUES strongly predicts peak VO2 independently from the measuring interval (β = 0.71-0.80; R 2 = 0.50-0.63). The prediction model designed for elite athletes was OUES [mL·min-1/L·min-1] = -1.54 + 2.99; BSA [m2]-0.0014; (age [in years]; sex [1 = male, 2 = female]) (R 2 = 0.36). Conclusion: OUES enables an accurate prediction of peak cardiorespiratory fitness in elite endurance athletes. OUES is a feasible alternative to maximal exercise testing. A new prediction equation should be used for highly trained individuals. Physicians should understand OUES physiology to properly assess the cardiorespiratory response to exercise in athletic cohorts.

Chronic kidney disease (CKD) patients have a shorter life expectancy than healthy individuals, from the premature development of cardiovascular disease due to traditional and non-traditional risk factors. It is therefore pertinent that management of CKD be aimed at modifying these cardiovascular disease risk factors. Exercise therapy, in particular aerobic exercise, in the end stage kidney disease population has been shown to decrease cardiovascular disease risk factors and subsequent cardiovascular disease. However, randomized exercise training trials are scarce in the stage 3-4 CKD cohort. High intensity interval training (HIIT) is a promising exercise training approach that has been shown to elicit greater improvements in cardiovascular disease risk factors than moderate intensity continuous training (MICT) in other chronic disease populations. However, HIIT has not yet been investigated in patients with CKD. This thesis comprises two sections, each containing a randomised control trial (RCT) investigating the effects of exercise training in CKD patients. Section 1 contains data from the Landmark III (LM3) trial, which investigated the effects of a nurse-led, multi-disciplinary lifestyle intervention (LI) in 187 CKD patients. Testing of both LI and standard care (control) groups occurred every 6 months for 3 years. The exercise training in the LI consisted of a combination of individualized aerobic and resistance training and encouraged patients to participate in higher intensity exercise if possible. The main study in Section 1 assesses the change in various outcome measures after 3 years of LI (Chapter 6). Section 1 also includes baseline cross-sectional analyses (Chapter 3) and 12 month sub-studies (Chapters 4 and 5). Section 2 contains the second trial that investigated the feasibility and efficacy of a 12 week HIIT program in CKD patients. Recruitment for the HIIT study was through participants who had completed the LM3 study. Chapter 1 provides an introduction to the studies, including the aims of the thesis. Topics reviewed include; pathophysiology of CKD, exercise capacity, autonomic dysfunction, haemoglobin, muscle atrophy, oxidative stress, inflammation, blood lipids and HIIT. Section 1 starts with the detailed methods of the LM3 study (Chapter 2). Further methods applicable to specific studies are reported in the relevant chapter. Chapter 3 investigates the determinants of reduced muscle strength at baseline through a cross-sectional analysis of the LM3 study. This study identified the association of oxidative stress with low muscle strength and reduced lean mass. The relationship was independent of known influences on muscle mass and strength. The feasibility and determinants of the LI group participating in higher intensity exercise training in the first 12 months of the LM3 study was investigated in Chapter 4. The findings identified that higher intensity exercise training was indeed feasible in the CKD cohort, with the number of participants reporting higher intensity exercise increasing from 20% to 43%. Reported higher intensity exercise during the LI was also associated with the greatest exercise capacity at 12 months. Furthermore, the analysis identified haemoglobin to be the greatest predictor of individuals reporting higher intensity exercise. Chapter 5 investigates the agreement between creatinine and cystatin-C estimated glomerular filtration rate (eGFR) measurements in patients completing 12 months of the LM3 study. This chapter explores whether discrepancies in the CKD Epidemiology Collaboration (CKD-EPI)cystatin-C, CKD-EPIcreatinine and modification of diet in renal disease (MDRD) eGFR equations occur after an exercise intervention. It was identified that cystatin-C based eGFR measurements were considerably lower than creatinine based eGFR measurements. Despite this discrepancy at baseline, the agreement between the different GFR estimates did not change after exercise training. Chapter 6 contains the main study from LM3, which investigates the changes in cardiorespiratory fitness, exercise capacity, functional capacity, blood biochemistry, anthropometry measures and other cardiovascular disease risk factors after 3 years of LI compared to the control group. The primary finding from this study was the ability of a long-term LI to significantly increase physical activity levels, exercise capacity and autonomic function and decrease arterial stiffness in patients with CKD. Section 2 includes a systematic review and meta-analysis of HIIT in patients with cardiometabolic diseases (Chapter 7). This review investigated the efficacy and safety of HIIT compared to MICT in individuals with a population sample of chronic disease, where poor lifestyle was considered a main contributor to the disease. The meta-analysis was performed using oxygen uptake (VO2peak or VO2maximum) as the primary outcome measure, however the influence of HIIT on all cardiovascular disease risk factors was explored. It was identified that HIIT provokes nearly twice the increase in VO2peak compared to MICT. Chapter 8 in Section 2 investigates the feasibility of HIIT and the efficacy of HIIT compared to MICT in improving cardiovascular disease risk factors. It was identified that HIIT was a feasible option for patients with CKD. However, the improvements in cardiorespiratory fitness and exercise capacity were slightly greater in the MICT than the HIIT group, although the changes were not statistically significant. Participants from the HIIT group also reported higher levels of enjoyment from the intervention, as measured by the Physical Activity Enjoyment Scale. Chapter 9 provides a summary of the outcomes from all studies. This chapter discusses how this thesis has extensively contributed to the current knowledge in exercise training in CKD patients. Based on the findings, it is recommended that a combination of aerobic and resistance training, with some higher intensity exercise if possible, be prescribed to patients with CKD to elicit the greatest health outcomes. Important factors to consider with exercise training and testing of CKD patients, such as the effects of certain medications and hydration status on physiological responses, are also explored. Further, this chapter suggests future directions for research which will extend the findings from this thesis.

The cardiovascular and behavioral adaptations associated with a 4-month program of aerobic exercise training were examined in 101 older men and women (mean age = 67 years). Subjects were randomly assigned to an Aerobic Exercise group, a Yoga and Flexibility control group, or a Waiting List control group. Prior to and following the 4-month program, subjects underwent comprehensive physiological and psychological evaluations. Physiological measures included measurement of blood pressure, lipids, bone density, and cardiorespiratory fitness including direct measurements of peak oxygen consumption (VO2) and anaerobic threshold. Psychological measures included measures of mood, psychiatric symptoms, and neuropsychological functioning. This study demonstrated that 4 months of aerobic exercise training produced an overall 11.6% improvement in peak VO2 and a 13% increase in anaerobic threshold. In contrast, the Yoga and Waiting List control groups experienced no change in cardiorespiratory fitness. Other favorable physiological changes observed among aerobic exercise participants included lower cholesterol levels, diastolic blood pressure levels, and for subjects at risk for bone fracture, a trend toward an increase in bone mineral content. Although few significant psychological changes could be attributed to aerobic exercise training, participants in the two active treatment groups perceived themselves as improving on a number of psychological and behavioral dimensions.

The aim of this study was to establish sex- and age-specific reference values for ramp cycle-ergometer and treadmill cardiopulmonary exercise testing (CPET) in patients with Tetralogy of Fallot (ToF). Despite successful surgical repair, residual pulmonary regurgitation remains common in repaired ToF (rToF), often leading to right or left ventricular dysfunction and reduced exercise capacity. CPET is a reliable tool for evaluating cardiopulmonary function. Although both treadmill and cycle ergometer protocols are used interchangeably, reference value ranges for each method in this population remain unclear. CPET data were collected from asymptomatic rToF patients who had undergone cardiac magnetic resonance imaging (CMR) and performed CPET on a treadmill or cycle ergometer between 2020 and 2024. Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ). Among 290 patients, median age at CPET was 21.6 years (15.9-29.3), and median BMI was 22.5 kg/m2 (20.0-24.9). Nearly all participants (99%) were in NYHA class I. Physical activity was classified as sedentary in 29%, moderate in 61%, and high in 9%. Median VO₂ peak was 26.4 mL/min/kg (23.0-31.5), corresponding to 72.9% predicted (62.4-83.2). Median oxygen pulse at peak was 9.9 mL/beat (8.2-12.0), with 76.9% predicted (68.0-87.5). Median VE/VCO₂ slope at the respiratory compensation point was 29.0 (26.0-32.6), and median oxygen uptake efficiency slope (OUES) was 1,792.5 mL/min/log(L/min) (1,535.0-2,181.5). VO₂ peak and percent-predicted VO₂ were significantly higher with treadmill testing in both sexes (p < 0.05); oxygen pulse was higher only in females. VE/VCO₂ slope and OUES were unaffected by modality. When stratified by age (<18 vs. ≥18 years), treadmill-related differences in VO₂ peak and percent-predicted VO₂ remained significant in both sexes ≥18 years, and in females <18 years. Oxygen pulse was significantly higher with treadmill only in females ≥18 years. VE/VCO₂ slope and OUES remained unchanged across modalities and age groups. This study provides CPET values stratified by modality and sex in a large cohort of asymptomatic rToF patients, offering valuable reference data for clinical assessment. Future studies should validate pediatric normative CPET values through prospective, inclusive, statistically powered cohorts using standardized protocols and cross-center comparability.

OBJECTIVE - To identify, the anaerobic threshold and respiratory compensation point in patients with heart failure. METHODS - The study comprised 42 Men,divided according to the functional class (FC) as follows: group I (GI) - 15 patients in FC I; group II (GII) - 15 patients in FC II; and group III (GIII) - 12 patients in FC III. Patients underwent a treadmill cardiopulmonary exercise test, where the expired gases were analyzed. RESULTS - The values for the heart rate (in bpm) at the anaerobic threshold were the following: GI, 122+/-27; GII, 117+/-17; GIII, 114+/-22. At the respiratory compensation point, the heart rates (in bpm) were as follows: GI, 145+/-33; GII, 133+/-14; GIII 123+/-22. The values for the heart rates at the respiratory compensation point in GI and GIII showed statistical difference. The values of oxygen consumption (VO2) at the anaerobic threshold were the following (in ml/kg/min): GI, 13. 6+/-3.25; GII, 10.77+/-1.89; GIII, 8.7+/-1.44 and, at the respiratory compensation point, they were as follows: GI, 19.1+/-2. 2; GII, 14.22+/-2.63; GIII, 10.27+/-1.85. CONCLUSION - Patients with stable functional class I, II, and III heart failure reached the anaerobic threshold and the respiratory compensation point at different levels of oxygen consumption and heart rate. The role played by these thresholds in physical activity for this group of patients needs to be better clarified.