Cardiovascular and autonomic nervous system response to graded exercise in adolescents with type 1 diabetes.

During a simulated haemorrhagic challenge, syncopal symptoms develop sooner when individuals are hyperthermic relative to normothermic. This is due, in part, to a large displacement of blood to the cutaneous circulation during hyperthermia, coupled with inadequate cutaneous vasoconstriction during the hypotensive challenge. The influence of local skin temperature on these cutaneous vasoconstrictor responses is unclear. This project tested the hypothesis that local skin temperature modulates cutaneous vasoconstriction during simulated haemorrhage in hyperthermic humans. Eight healthy participants (four men and four women; 32 ± 7 years old; 75.2 ± 10.8 kg) underwent lower-body negative pressure to presyncope while heat stressed via a water-perfused suit sufficiently to increase core temperature by 1.2 ± 0.2 °C. At forearm skin sites distal to the water-perfused suit, local skin temperature was either 35.2 ± 0.6 (mild heating) or 38.2 ± 0.2 °C (moderate heating) throughout heat stress and lower-body negative pressure, and remained at these temperatures until presyncope. The reduction in cutaneous vascular conductance during the final 90 s of lower-body negative pressure, relative to heat-stress baseline, was greatest at the mildly heated site (-10 ± 15% reduction) relative to the moderately heated site (-2 ± 12%; P = 0.05 for the magnitude of the reduction in cutaneous vascular conductance between sites), because vasoconstriction at the moderately heated site was either absent or negligible. In hyperthermic individuals, the extent of cutaneous vasoconstriction during a simulated haemorrhage can be modulated by local skin temperature. In situations where skin temperature is at least 38 °C, as is the case in soldiers operating in warm climatic conditions, a haemorrhagic insult is unlikely to be accompanied by cutaneous vasoconstriction.

AIMThe aim of this study was to assess the effects of passive heating on exercise and post‐exercise cardiac autonomic regulation.METHODSTwelve healthy young men (25±5 yrs, 23.8±1.9 kg/m2, 47.3±7.9 ml.kg−1.min−1) randomly underwent two experimental sessions: heat stress (HEAT) and control (CON). Each session composed of: a heat stress or normothermic control intervention; 7 min of cycling at 40% HRreserve (EX1); 7 min of cycling at 60% HRreserve (EX2); and 10 min of recovery in the seated position (REC). HEAT consisted of 60 min whole‐body passive heating by perfusing hot water (48°C) in a tube‐lined suit to increase core temperature (Tc) by ~1°C. CON consisted of normothermia exposure for a similar timeframe. Heart rate (HR) and finger blood pressure were continuously assessed. HR variability (HRV) was assessed using the vagal‐related RMSSD index, baroreflex sensitivity (BRS) was assessed using the sequence technique and HR recovery (HRR) was assessed through the absolute decrease of HR at 60 (HRR60s) and 300 s (HRR300s) after exercise. Tc, skin temperature (Tsk), forearm skin blood flow (SKBF) and cutaneous vascular conductance (CVC) were also continuously measured throughout.RESULTSTc, Tsk, SKBF and CVC were significantly increased in HEAT compared with CON (Tc: 38.0±0.3 vs. 37.1±0.3 °C; Tsk: 36.8±0.6 vs. 33.2±0.8 °C; SKBF: 160±96 vs. 50±34 A.U.; CVC: 2.3±1.3 vs. 0.6±0.3 A.U/mmHg; all p<0.05). During exercise, HRV and BRS were not different between trials during EX1 and EX2. However, when comparing EX1 of CON with EX2 of HS (i.e. similar absolute workloads), HRV (0.8 ± 0.4 vs. 1.9 ± 0.7 ms; p<0.01) and BRS (0.6 vs. 0.3 vs. 1.5 ± 1.0 ms/mmHg; p<0.01) were significantly reduced in HS. During recovery, HRV (1.7 ± 0.6 vs. 2.6 ± 0.9 ms, p<0.01) and BRS (6 ± 3 vs. 4 ± 3 ms/mmHg; p<0.01) were significantly reduced in HS. Furthermore, HRR60s and HRR300s were significantly reduced in HS (HRR60s = 27±9 vs. 46±10 bpm; HRR300s = 39 ± 12 vs. 61 ± 13 bpm; p<0.01).CONCLUSIONPassive heating impairs cardiac autonomic regulation during exercise at the same absolute workload and during recovery.Support or Funding InformationFAPESP 2013/04997‐0; 2013/05519‐4; 2015/15466‐0

Arterial compliance and autonomic regulation are predictors of cardiovascular disease. In adults, both are altered chronically by type 1 diabetes (T1D) and acutely by exercise; however, the effects of T1D and exercise are less clear in adolescents. We measured short-term effects of a high-intensity aerobic interval exercise session on cardiovascular and metabolic variables in normal weight adolescents with T1D or without T1D (Control). Energy expenditure (EE), heart rate variability (HRV), arterial compliance, and blood pressure (BP) were measured before exercise (baseline) and three times over 105 minutes postexercise. The T1D and control groups had similar cardiorespiratory fitness and accelerometer-measured physical activity. The T1D group had higher EE and fat oxidation throughout the trial, but postexercise changes were similar between groups. HRV transiently declined following exercise in both groups, but the T1D group had lower HRV at baseline. Among the measures of arterial compliance, the augmentation index declined postexercise while carotid-femoral pulse wave velocity and large artery elastic index remained unchanged. Central and brachial BP were unchanged following exercise until the final measurement, when a small increase occurred. However, arterial compliance and BP did not differ between groups. These results demonstrate that normal weight adolescents with T1D have impaired autonomic function and increased EE and fat oxidation compared to peers without diabetes who have similar levels of fitness and physical activity. However, acute cardiometabolic responses to exercise are normal in T1D with adequate glycemic control. Changes in arterial compliance and BP may take longer to emerge in relatively healthy adolescents with T1D.

Type 1 diabetes (T1D) is expected to cause significant changes in the serum proteome; however, few studies have systematically assessed the proteomic profile change associated with the disease. In this study, a semiquantitative spectral counting-based two dimensional liquid chromatography mass spectrometry platform was used to analyze serum samples from T1D patients and controls. In this discovery phase, significant differences were found for 21 serum proteins implicated in inflammation, oxidation, metabolic regulation, and autoimmunity. To assess the validity of these findings, six candidate proteins including adiponectin, insulin-like growth factor binding protein 2, serum amyloid protein A, C-reactive protein, myeloperoxidase, and transforming growth factor beta induced were selected for subsequent immune assays for 1139 T1D patients and 848 controls. A series of statistical analyses using cases and controls matched for age, sex, and genetic risk confirmed that T1D patients have significantly higher serum levels for four of the six proteins: adiponectin (odds ratio (OR) = 1.95, p = 10(-27)), insulin-like growth factor binding protein 2 (OR = 2.02, p < 10(-20)), C-reactive protein (OR = 1.13, p = 0.007), serum amyloid protein A (OR = 1.51, p < 10(-16)); whereas the serum levels were significantly lower in patients than controls for the two other proteins: transforming growth factor beta induced (OR = 0.74, p < 10(-5)) and myeloperoxidase (OR = 0.51, p < 10(-41)). Compared with subjects in the bottom quartile, subjects in the top quartile for adiponectin (OR = 6.29, p < 10(-37)), insulin-like growth factor binding protein 2 (OR = 7.95, p < 10(-46)), C-reactive protein (OR = 1.38, p = 0.025), serum amyloid protein A (OR = 3.36, p < 10(-16)) had the highest risk of T1D, whereas subjects in the top quartile of transforming growth factor beta induced (OR = 0.41, p < 10(-11)) and myeloperoxidase (OR = 0.10, p < 10(-43)) had the lowest risk of T1D. These findings provided valuable information on the proteomic changes in the sera of T1D patients.

To investigate the heart rate during cardio-pulmonary exercise (CPX) testing in individuals with type 1 diabetes (T1D) compared to healthy (CON) individuals. Fourteen people (seven individuals with T1D and seven CON individuals) performed a CPX test until volitional exhaustion to determine the first and second lactate turn points (LTP1 and LTP2), ventilatory thresholds (VT1 and VT2), and the heart rate turn point. For these thresholds cardio-respiratory variables and percentages of maximum heart rate, heart rate reserve, maximum oxygen uptake and oxygen uptake reserve, and maximum power output were compared between groups. Additionally, the degree and direction of the deflection of the heart rate to performance curve (kHR) were compared between groups. Individuals with T1D had similar heart rate at LTP1 (mean difference) −11, [(95% confidence interval) −27 to 4 b.min−1], at VT1 (−12, −8 to 33 b.min−1) and at LTP2 (−7, −13 to 26 b.min−1), at VT2 (−7, −13 to 28 b.min−1), and at the heart rate turn point (−5, −14 to 24 b.min−1) (p = 0.22). Heart rate expressed as percentage of maximum heart rate at LTP1, VT1, LTP2, VT2 and the heart rate turn point as well as expressed as percentages of heart rate reserve at LTP2, VT2 and the heart rate turn point was lower in individuals with T1D (p < 0.05). kHR was lower in T1D compared to CON individuals (0.11 ± 0.25 vs. 0.51 ± 0.32, p = 0.02). Our findings demonstrate that there are clear differences in the heart rate response during CPX testing in individuals with T1D compared to CON individuals. We suggest using submaximal markers to prescribe exercise intensity in people with T1D, as the heart rate at thresholds is influenced by kHR.Clinical Trial Identifier: NCT02075567 (https://clinicaltrials.gov/ct2/show/NCT02075567).

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

Objective To explore the effects of individual aerobic combined with resistance training on the blood glucose, blood lipid and total exercise capacity compared with simple aerobic training in the coronary artery disease(CAD)patients with type 2 diabetes mellitus(T2DM)and the possible mechanisms. Methods The total 60 patients of CAD and T2DM were randomly assigned to control group(n=20), aerobic training group(n=20)and combined training group(n=20). Three groups all adopted the routine medication management, health education and diet guide.The control group had no other intervention.The aerobic training group had some 60%-85% of target heart rate(HR)aerobic exercises which was set according to the cardiopulmonary exercise test(CPET). The combined training group adopted isokinetic muscle strength training based on the aerobic training group which set intensity in 11-13 according the RPE.All patients were evaluated with fasting blood-glucose(FBG), glycosylated hemoglobin(HBA1c), total cholesterol(TC), triglyceride(TG), low density lipoprotein(LDL), fat mass(FM), lean body mass(LBM), VO2max, lower limbs peak torque(PT)and balance ability before and after training.Each exercise time 30-60 min, 3 times a week, a total of 12 weeks. Results There was no significant difference among the physiology index in three groups before training(P>0.05). Aerobic training group and combined training group significantly improved compared with control group in blood glucose, blood lipid, FM, VO2max after training(P 0.05). Combined training group had great improvement in lower limbs peak torque and balance ability than the other two groups(P<0.05). Conclusions Individualized aerobic combined with resistance training can give better control of coronary risk factors in CAD-T2DM patients, and improve the muscle strength and balance ability. Key words: Coronary artery disease; Diabetes mellitus; Aerobic training; Resistance training

In children and adolescents, there are significant limitations to detecting cardiac autonomic neuropathy (CAN), an important contributor to morbidity and mortality in adults with type 1 diabetes (T1D). The analysis of heart rate variability (HRV) is one method available to detect CAN. Some evidence shows traditional linear HRV measures detect abnormalities in youth with T1D. In this study, we aimed to assess whether non-linear complexity analysis of HRV would assist identification of CAN in youth with T1D and to assess contributory factors. We studied 19 youth with T1D and 17 healthy controls. Each had an electrocardiogram recorded continuously for 10 min, at a sampling frequency of 1000 Hz. Using Labview software and an algorithm for complexity analysis, along with standard time-domain and spectral analysis, recordings of the electrocardiogram were analysed to detect differences in HRV between groups. Youth with T1D had significantly higher sample entropy than controls (P = 0.015) suggesting increased complexity in HRV, but similar detrended fluctuation analysis (P = 0.68). Youth with T1D also had increased % high frequency power (P = 0.017) and reduced mid-frequency power (P = 0.019) on spectral analysis. There were no differences in heart rate or blood pressure responses to standing, or time-domain analysis of HRV. Within the T1D group, sample entropy correlated strongly with triglycerides (r = 0.76, P = 0.001) and detrended fluctuation analysis correlated strongly with serum potassium (r = -0.86, P < 0.001). Complexity analysis of HRV, particularly using sample entropy, may aid detection of CAN in youth with T1D.

Type 2 diabetes (T2D) is associated with autonomic nervous system damage resulting in reduced heart rate variability (HRV). Limited evidence suggests yogic breathing exercises may improve indices of HRV. The purpose of this study was to evaluate the effect of two commonly used yogic breathing exercises on HRV in T2D versus an age-matched, normoglycemic (CON) population. Twelve (12) subjects with T2D (7 female, 5 male; 54.9±7.4 years) and 14 CON subjects (12 female, 2 male; 54.7±6.8 years) participated in a breathing protocol consisting of two 10-min bouts of randomly assigned uni-nostril breathing (UNB). UNB bouts were preceded and followed by 5-min periods of dual-nostril paced breathing (PB). HRV was measured by standard deviation of normal-to-normal consecutive heartbeats (SDNN), square root of the mean squared differences in successive normal heartbeats (RMSSD), and total spectral power (TP). All data (except instantaneous heart rate) were log transformed to improve normality. Within-group comparisons were analyzed using analysis of variance with repeated measures, whereas between-group comparisons were analyzed using independent-samples t-test. Between-groups comparisons revealed significant reductions in all measures of HRV at nearly all time points in T2D compared to CON. Within-group comparison demonstrated no significant effect of UNB or PB on HRV in CON. In the T2D group, however, left UNB significantly reduced mean HR (-1.2 bpm, p<0.05) as well as TP (p<0.05). In summary, neither UNB nor PB had an impact upon HRV in a healthy older population and had a minimal impact in T2D.

It is unknown if diabetes-related reductions in local skin blood flow (SkBF) and sweating (LSR) measured during passive heat stress translate into greater heat storage during exercise in the heat in individuals with type 2 diabetes (T2D) compared with nondiabetic control (CON) subjects. This study aimed to examine the effects of T2D on whole-body heat exchange during exercise in the heat. Ten adults (6 males and 4 females) with T2D and 10 adults (6 males and 4 females) without diabetes matched for age, sex, body surface area, and body surface area and aerobic fitness cycled continuously for 60 min at a fixed rate of metabolic heat production (∼370 W) in a whole-body direct calorimeter (30°C and 20% relative humidity). Upper back LSR, forearm SkBF, rectal temperature, and heart rate were measured continuously. Whole-body heat loss and changes in body heat content (ΔHb) were determined using simultaneous direct whole-body and indirect calorimetry. Whole-body heat loss was significantly attenuated from 15 min throughout the remaining exercise with the differences becoming more pronounced over time for T2D relative to CON (P = 0.004). This resulted in a significantly greater ΔHb in T2D (367 ± 35; CON, 238 ± 25 kJ, P = 0.002). No differences were measured during recovery (T2D, -79 ± 23; CON, -132 ± 23 kJ, P = 0.083). By the end of the 60-min recovery, the T2D group lost only 21% (79 kJ) of the total heat gained during exercise, whereas their nondiabetic counterparts lost in excess of 55% (131 kJ). No difference were observed in LSR, SkBF, rectal temperature or heart rate during exercise. Similarly, no differences were measured during recovery with the exception that heart rate was elevated in the T2D group relative to CON (p=0.004). Older adults with T2D have a reduced capacity to dissipate heat during exercise, resulting in a greater heat storage and therefore level of thermal strain.

This study aimed to determine the glycemic responses to cardiopulmonary exercise testing (CPET) in individuals with type 1 diabetes (T1D) and to explore the influence of starting blood glucose (BG) concentrations on subsequent CPET outcomes. This study was a retrospective, secondary analysis of pooled data from three randomized crossover trials using identical CPET protocols. During cycling, cardiopulmonary variables were measured continuously, with BG and lactate values obtained minutely via capillary earlobe sampling. Anaerobic threshold was determined using ventilatory parameters. Participants were split into (i) euglycemic ([Eu] >3.9 to ≤10.0 mmol·L-1, n = 26) and (ii) hyperglycemic ([Hyper] >10.0 mmol·L-1, n = 10) groups based on preexercise BG concentrations. Data were assessed via general linear modeling techniques and regression analyses. P values of ≤0.05 were accepted as significant. Data from 36 individuals with T1D (HbA1c, 7.3% ± 1.1% [56.0 ± 11.5 mmol·mol-1]) were included. BG remained equivalent to preexercise concentrations throughout CPET, with an overall change in BG of -0.32 ± 1.43 mmol·L-1. Hyper had higher HR at peak (+10 ± 2 bpm, P = 0.04) and during recovery (+9 ± 2 bpm, P = 0.038) as well as lower O2 pulse during the cool down period (-1.6 ± 0.04 mL per beat, P = 0.021). BG responses were comparable between glycemic groups. Higher preexercise BG led to greater lactate formation during exercise. HbA1c was inversely related to time to exhaustion (r = -0.388, P = 0.04) as well as peak power output (r = -0.355, P = 0.006) and O2 pulse (r = -0.308, P = 0.015). This study demonstrated 1) stable BG responses to CPET in patients with T1D; 2) although preexercise hyperglycemia did not influence subsequent glycemic dynamics, it did potentiate alterations in various cardiac and metabolic responses to CPET; and 3) HbA1c was a significant factor in the determination of peak performance outcomes during CPET.

Effect of exogenous ketone supplementation on cardiac energetics, function, and perfusion in type 2 diabetes, heart failure, and healthy participants- A single center, open labelled clinical study

This study was designed to identify the effects of a 12-h nicotine patch administration on cold induced vasodilation (CIVD) in healthy young chronic smokers following 16 h of abstinence from smoking. Two laser Doppler probes and temperature thermocouples were placed on the dorsal part of the distal phalanx of the middle and ring fingers of 7 smokers (>12 cigarettes/day). Following 16 h of abstinence from smoking, smokers were tested with and without administration of a 21mg transdermal nicotine patch (NicoDerm® ). Each participant's right hand was immersed in cold (~5°C) water for 40 min. Cutaneous vascular conductance (CVC) was calculated from non-invasive arterial finger blood pressure and skin blood flow and expressed as a percentage of peak CVC observed during hand skin heating to 44°C. For comparison purposes, the CIVD response of a non-smoking cohort without nicotine patch (n = 10) was also examined. Baseline CVC was similar in smokers and non-smokers (27.8 ± 12.6 CVC % peak). The initial vasoconstriction during cold-water immersion decreased skin blood flow to 4.0 ± 3.9 CVC % peak in both smokers and non-smokers. The onset of CIVD in smokers (4.5 ± 1.5 min) was delayed compared to non-smoker (3.3 ± 0.8 min, p < .05). The area under the CVC %peak-time curve during cold-water immersion averaged 1250 ± 388 CVC %peak · min in non-smokers which was larger (p < .05) than smokers with or without nicotine (789 ± 542 and 862 ± 517 CVC %peak · min, respectively). Chronic smoking impaired the CIVD response to cold-water immersion of the hand; however, the impaired CIVD response in 16 h of abstinence from smoking was not influenced by application of a 21mg transdermal nicotine patch.

Post-exercise heart rate (HR) recovery (HRR) presents a biphasic pattern, which is mediated by parasympathetic reactivation and sympathetic withdrawal. Several mechanisms regulate these post-exercise autonomic responses and thermoregulation has been proposed to play an important role. The aim of this study was to test the effects of heat stress on HRR and HR variability (HRV) after aerobic exercise in healthy subjects. Twelve healthy males (25 ± 1 years, 23.8 ± 0.5 kg/m2) performed 14 min of moderate-intensity cycling exercise (40–60% HRreserve) followed by 5 min of loadless active recovery in two conditions: heat stress (HS) and normothermia (NT). In HS, subjects dressed in a whole-body water-perfused tube-lined suit to increase internal temperature (Tc) by ~1°C. In NT, subjects did not wear the suit. HR, core and skin temperatures (Tc and Tsk), mean arterial pressure (MAP) skin blood flow (SKBF), and cutaneous vascular conductance (CVC) were measured throughout and analyzed during post-exercise recovery. HRR was assessed through calculations of HR decay after 60 and 300 s of recovery (HRR60s and HRR300s), and the short- and long-term time constants of HRR (T30 and HRRt). Post-exercise HRV was examined via calculations of RMSSD (root mean square of successive RR intervals) and RMS (root mean square residual of RR intervals). The HS protocol promoted significant thermal stress and hemodynamic adjustments during the recovery (HS-NT differences: Tc = +0.7 ± 0.3°C; Tsk = +3.2 ± 1.5°C; MAP = −12 ± 14 mmHg; SKBF = +90 ± 80 a.u; CVC = +1.5 ± 1.3 a.u./mmHg). HRR and post-exercise HRV were significantly delayed in HS (e.g., HRR60s = 27 ± 9 vs. 44 ± 12 bpm, P < 0.01; HRR300s = 39 ± 12 vs. 59 ± 16 bpm, P < 0.01). The effects of heat stress (e.g., the HS-NT differences) on HRR were associated with its effects on thermal and hemodynamic responses. In conclusion, heat stress delays HRR, and this effect seems to be mediated by an attenuated parasympathetic reactivation and sympathetic withdrawal after exercise. In addition, the impact of heat stress on HRR is related to the magnitude of the heat stress-induced thermal stress and hemodynamic changes.

Background Copious intake of soft drinks sweetened with high fructose corn syrup (HFCS) is associated with a heightened risk of cardiovascular disease, which is likely contributed to by chronic HFCS mediated endothelial dysfunction occurring secondary to decreased nitric oxide (NO) bioactivity. Acute consumption of a soft drink sweetened with HFCS does not affect cutaneous vasodilation during local heating, a functional test of NO bioactivity in the cutaneous microvasculature. However, this study was undertaken in the absence of a basal reduction in NO bioactivity, such as is speculated to occur with chronic intake of HFCS. Therefore, the present study tested the hypothesis that the cutaneous vasodilatory response to local skin heating following ischemia-reperfusion injury, which acutely reduces NO bioactivity, is attenuated following acute consumption of a caffeinated soft drink sweetened with HFCS compared to water. Methods In a randomized, counter-balanced crossover design, fourteen healthy young adults (six women) consumed 500 mL of either tap water (H2O) or a caffeinated soft drink sweetened with HFCS (Mtn. Dew®, DEW). 30 min following consumption, participants underwent 20 min of forearm ischemia and 20 min of reperfusion. Local skin heating at the forearm to 39⁰C was performed for 40 min and increased to 44⁰C for 20 min. Skin blood flow (SkBF) was measured on the dorsal forearm using laser Doppler flowmetry, blood pressure was measured using the Penaz method, and heart rate was measured via 3-lead electrocardiogram. Cutaneous vascular conductance (CVC) was calculated as the quotient of SkBF and mean arterial pressure (MAP). SkBF and CVC data during local heating to 39⁰C, which is mostly a NO mediated response, were normalized as a percentage of maximal values obtained during local skin heating to 44⁰C. Data are presented as mean ± SD. Results During local skin heating at 44⁰C, there were no differences observed between H20 and DEW trials for SkBF (H2O: 252 ± 37 PU; DEW: 240 ± 35 PU, p = 0.317), CVC (H2O: 2.6 ± 0.4 PU/mmHg; DEW: 2.4 ± 0.5 PU/mmHg, p =0.215), MAP (H2O: 96 ± 6 mmHg; DEW: 101 ± 10 mmHg, p = 0 .162), and heart rate (H2O: 59 ± 7 bpm; DEW 60 ± 10 bpm, p = 0.654). During local skin heating to 39°C, no differences were observed in absolute SkBF (H2O: 154 ± 44 PU; DEW: 152 ± 44 PU, p = 0.879), percent of maximal SkBF (H20: 60.6 ± 12.2%; DEW 62.4 ± 12.7% p = 0.272), absolute CVC (H2O: 2 ± 0.5 PU/mmHg; DEW: 1.5 ± 0.5 PU/mmHg, p = 0.368), or percent of maximal CVC (H2O): 63.8 ± 13.9%; DEW 62.6 ± 13.7% p = 0.787) in the DEW and H2O trials. However, during local heating at 39°C DEW did result in a higher MAP (H2O: 92 ± 7 mmHg; DEW: 100 ± 12 mmHg, p = 0.022) and heart rate (H2O: 56 ± 7 bpm; DEW 59 ± 8 bpm, p = 0.032) compared to H2O. Conclusion Under conditions of ischemia-reperfusion injury, these data indicate that, compared to an equivalent volume of water, consumption of 500 mL of a caffeinated soft drink sweetened with HFCS does not affect the cutaneous vasodilatory response to local heating, a mostly nitric oxide mediated response.