Search

Abstract Coronary artery disease (CAD) remains a leading cause of death worldwide and imposes a substantial socioeconomic burden on healthcare. Improving risk stratification in clinical practice could help to combat this burden. As amino acids are biologically active metabolites whose involvement in CAD remains largely unknown, this study investigated associations between circulating amino acid levels and CAD phenotypes. A high-coverage quantitative liquid chromatography-mass spectrometry approach was applied to acquire the serum amino acids profile of age- and sex-coarsened-matched patients with CAD (n = 46, 66.9 years, 74.7% male) and healthy individuals (n = 120, 67.4 years, 74.7% male) from the COmPLETE study. Multiple linear regressions were performed to investigate associations between amino acid levels and (a) the health status (CAD vs. healthy), (b) the number of affected coronary arteries, or (c) the left ventricular ejection fraction. Regressions were adjusted for age, sex, daily physical activity, sampling, and fasting time. Urea cycle amino acids (ornithine, citrulline, homocitrulline, aspartate, and arginine) were significantly and negatively associated with CAD, the number of affected coronary arteries, and the left ventricular ejection fraction. Lysine, histidine, and the glutamine/glutamate ratio were also significantly and negatively associated with the CAD phenotypes. Overall, patients with CAD displayed lower levels of urea cycle amino acids, highlighting a potential role for urea cycle amino acid profiling in cardiovascular risk stratification. Trial registration The study was registered on https://www.clinicaltrials.gov (NCT03986892) on June 5, 2019.

Purpose: This study examines age- and sex-related physiological differences in cardiopulmonary exercise testing (CPET) between children and adults, focusing on ventilatory thresholds (VT) and oxygen uptake efficiency slope and plateau (OUES; OUEP). Methods: The cross-sectional analysis comprised 24 children (7–11 years), 20 moderately- trained (MTA) and 20 well-trained (WTA) adults (20–30 years). They performed a maximal CPET on a cycle ergometer, while respiratory responses were measured. Linear regression models analyzed differences by age and sex of the key outcome parameters VT1, VT2, OUES and OUEP. Results: Children exhibited higher absolute VO2 at VT1 (d=.66) and VT2 (d=.58) values than MTA but slightly lower VO2 at VT2 values compared to WTA (d=.35). Adults demonstrated higher OUES (MTA: d=.37; WTA: d=1.45) and OUEP (MTA: d=.81; WTA: d=.60) values than children. However, children had higher relative OUES when adjusted for body mass (OUESrel) (MTA: d=.1.80; WTA: d=.87). Males demonstrated higher values as females for VO2 at VT2 (d=.81), OUES (d=.79) and OUEP (d=.41), respectively. In contrast, females had higher VO2 at VT1 (d=.59) and VT2 (d=.44) relative to VO2peak as males. Conclusion: These findings suggest that compared to adults, children rely more on oxidative metabolism, reflected in higher ventilatory thresholds relative to their aerobic capacity. Absolute OUES and OUEP increased with age, but OUESrel values indicate that maturation influences efficiency more than body mass, reflecting underlying metabolic and physiological differences. These age- and sex-specific patterns highlight the need for further longitudinal research to clarify the roles of growth and training on these parameters.

Introduction Impaired glycaemic control is a major risk factor for cardiovascular disease, the leading cause of death worldwide (Roth et al., 2020). Reducing sedentary behaviour and increasing physical activity are key strategies for improving glycaemic control (Sparks et al., 2021). An intrinsic circadian system influences glucose metabolism, resulting in predictable 24-hour plasma glucose fluctuations, peaking during the biological night (Qian & Scheer, 2016). Relying on these fluctuations, the metabolic response might vary depending on when in the day an individual exercises. Guidelines providing evidence-based recommendations for physical activity emphasise frequency, intensity, time (duration), and type of exercise as the four foundational pillars for promoting health and well-being (DeSimone, 2019) but fail to address the potential role of exercise timing within the day. However, the established effects of the timing of behavioural factors such as sleep (Qian & Scheer, 2016) and nutrition (Wang et al., 2014), combined with evidence of diurnal variations in physical performance (Knaier et al., 2022), suggest that exercise timing might additionally influence exercise adaptations and associated health outcomes. Thus, the aim of this study was to investigate the effect of exercise timing on glycaemic control and to provide evidence to refine exercise recommendations and optimise metabolic health outcomes. Methods Thirty-nine (34% female) non-diabetic adults (mean [standard deviation] age 68 [5] years) and body mass index 24.0 [2.6] kg/m2) participated in this double-blind, randomised controlled trial. Participants were allocated to one of five groups: control group (Con) or exercise at either 8:00, 12:00, 16:00 or 20:00 o'clock (i.e. E-08, E-12, E-16, E-20), which performed two strength and one endurance session per week across 12 weeks. A two-hour oral glucose tolerance test was performed after overnight fasting before and after the 12-week intervention or control condition. After ingestion of a solution containing 75 g of dextrose, serum glucose was measured at fasting and subsequently after 10, 20, 30, 60, 90, and 120 min. Serum glucose response was quantified over 120 min by calculating the area under the curve (AUC) using the trapezoidal method. To analyse the effect of treatment over time, linear mixed effects models were used, adjusting for sex, age and body mass index. The effect sizes were calculated using the baseline standard deviation of the parameter to provide a standardised measure. Their magnitudes were classified as follows: negligible (1.2) (Hopkins et al., 2009). Results Thirty-two participants were analysed (Con: n=4, E08: n=6, E-12: n=7, E-16: n=3, E-20: n=12), with most dropouts due to missing more than two serum glucose values. Exercise adherence rate was excellent with 95% of all sessions. The exercise intervention reduced the AUC in all groups compared to Con with small to moderate effect sizes (95% confidence interval), being E-08: -0.47 (-1.31 to 0.37); E-12: -0.51, (-1.32 to 0.31); E-16: -0.54 (-1.53 to 0.46); and E-20: -0.64 (-1.38 to 0.11), respectively. Negative effect sizes (e.g., reduction in AUC) are considered beneficial for metabolic health. Yet, these effects were accompanied by considerable uncertainty. Pairwise comparisons of the exercise groups showed varying magnitudes of differences in AUC, likewise accompanied by considerable uncertainty. All comparisons including E-08 showed that AUC decreased more in E-08 than in the other groups, with effect sizes being E-12: 0.004 (-1.11 to 1.12), E-16: 0.39 (-1.20 to 1.99) and E-20: 0.16 (-0.86 to 1.17), classified as negligible or small. Similarly, comparisons including E-12 demonstrated greater reductions in AUC, with effect sizes classified as small for E-16: 0.39 (-2.00 to 1.22) and negligible for E-20: 0.15 (-1.15 to 0.85). The comparison of E-20 with E-16: 0.24 (-1.16 to 1.63) indicated a small effect size, suggesting that E-20 achieved a greater reduction in AUC. Discussion/Conclusion Exercise at any time of day has the potential to improve glycaemic control in adults. Exercise timing may have a notable influence on metabolic adaptations, as distinct patterns were observed between exercise groups. This is possibly due to the influence of circadian rhythms on metabolic mechanisms. However, further in-depth understanding of the underlying metabolic mechanisms is needed to establish a robust scientific foundation for this approach. The inclusion of time of day as a potential fifth pillar in the guidelines for physical activity remains a worthwhile consideration. References DeSimone, G. T. (2019). The tortoise factor – get FITT. ACSM's Health & Fitness Journal, 23(2), 3–4. https://doi.org/10.1249/FIT.0000000000000456 Hopkins, W. G., Marshall, S. W., Batterham, A. M., & Hanin, J. (2009). Progressive statistics for studies in sports medicine and exercise science. Medicine & Science in Sports & Exercise, 41(1), 3–12. https://doi.org/10.1249/MSS.0b013e31818cb278 Knaier, R., Qian, J., Roth, R., Infanger, D., Notter, T., Wang, W., Cajochen, C., & Scheer, F. A. (2022). Diurnal variation in maximum endurance and maximum strength performance: A systematic review and meta-analysis. Medicine & Science in Sports & Exercise, 54(1), 169–180. https://doi.org/10.1249/MSS.0000000000002773 Qian, J., & Scheer, F. A. J. L. (2016). Circadian system and glucose metabolism: Implications for physiology and disease. Trends in Endocrinology & Metabolism, 27(5), 282–293. https://doi.org/10.1016/j.tem.2016.03.005 Roth, G. A., Mensah, G. A., Johnson, C. O., Addolorato, G., Ammirati, E., Baddour, L. M., Barengo, N. C., Beaton, A. Z., Benjamin, E. J., Benziger, C. P., Bonny, A., Brauer, M., Brodmann, M., Cahill, T. J., Carapetis, J., Catapano, A. L., Chugh, S. S., Cooper, L. T., Coresh, J., … Fuster, V. (2020). Global burden of cardiovascular diseases and risk factors, 1990–2019: Update from the GBD 2019 study. Journal of the American College of Cardiology, 76(25), 2982–3021. https://doi.org/10.1016/j.jacc.2020.11.010 Sparks, J. R., Kishman, E. E., Sarzynski, M. A., Davis, J. M., Grandjean, P. W., Durstine, J. L., & Wang, X. (2021). Glycemic variability: Importance, relationship with physical activity, and the influence of exercise. Sports Medicine and Health Science, 3(4), 183–193. https://doi.org/10.1016/j.smhs.2021.09.004 Wang, J. B., Patterson, R. E., Ang, A., Emond, J. A., Shetty, N., & Arab, L. (2014). Timing of energy intake during the day is associated with the risk of obesity in adults. Journal of Human Nutrition and Dietetics, 27(s2), 255–262. https://doi.org/10.1111/jhn.12141

Abstract Background Current recommendations for physical exercise include information about the frequency, intensity, type, and duration of exercise. However, to date, there are no recommendations on what time of day one should exercise. The aim was to perform a systematic review with meta-analysis to investigate if the time of day of exercise training in intervention studies influences the degree of improvements in physical performance or health-related outcomes. Methods The databases EMBASE, PubMed, Cochrane Library, and SPORTDiscus were searched from inception to January 2023. Eligibility criteria were that the studies conducted structured endurance and/or strength training with a minimum of two exercise sessions per week for at least 2 weeks and compared exercise training between at least two different times of the day using a randomized crossover or parallel group design. Results From 14,125 screened articles, 26 articles were included in the systematic review of which seven were also included in the meta-analyses. Both the qualitative synthesis and the quantitative synthesis (i.e., meta-analysis) provide little evidence for or against the hypothesis that training at a specific time of day leads to more improvements in performance-related or health-related outcomes compared to other times. There was some evidence that there is a benefit when training and testing occur at the same time of day, mainly for performance-related outcomes. Overall, the risk of bias in most studies was high. Conclusions The current state of research provides evidence neither for nor against a specific time of the day being more beneficial, but provides evidence for larger effects when there is congruency between training and testing times. This review provides recommendations to improve the design and execution of future studies on this topic. Registration: PROSPERO (CRD42021246468).

Background Accelerometry has gained increasing popularity and yields numerous physical activity (PA) outcomes (Rowlands et al., 2019). These include traditional cut-point-based (i.e. light, moderate, and vigorous PA) and cut-point-free metrics (i.e. intensity gradient [IG] and average acceleration [AvAcc]). IG reflects the intensity distribution of PA across the day (Rowlands et al., 2018; Fairclough et al., 2019). AvAcc is a proxy for the daily volume of PA ( Rowlands et al., 2018; Fairclough et al., 2019). Cut-point-based metrics are commonly expressed in minutes per day, making their interpretation simple (Troiano et al., 2014). Yet, the measured acceleration needs to be categorised by setting population- and device-dependent cut-points to obtain these metrics (Troiano et al., 2014). Cut-point-free metrics, on the other hand, are comparable across studies, accelerometer brands (Migueles et al., 2022), and diverse populations (Rowlands et al., 2018). However, their interpretation is not easy. Besides, it is unknown how cut-point-free metrics are associated with cardiorespiratory fitness (CRF), an important health indicator in healthy individuals and patient populations with impaired CRF (Kodama et al., 2009). We thus aimed to 1) compare the association of CRF with cut-point-free metrics to that with cut-point-based metrics in a prospective cohort of healthy adults aged 20 to 89 years and patients with heart failure, and 2) provide age-, sex-, and CRF-related reference values for healthy adults. Methods The COmPLETE study was cross-sectional. Healthy individuals were recruited via unaddressed letters sent to randomly selected postal districts in the Basel area (Wagner et al., 2019). Patients with heart failure were approached as described elsewhere (Wagner et al., 2019). Subjects were asked to wear GENEActiv accelerometers on their non-dominant wrist for up to 14 days and undergo cardiopulmonary exercise testing on a cycle ergometer to determine CRF. Raw accelerometer data were processed using the R-package GGIR (Migueles et al., 2019; van Hees et al., 2013). Associations between CRF and accelerometer metrics were examined using multiple linear regression models adjusted for sex, age, and body mass index. Percentile curves were generated with Generalised Additive Models for Location, Scale, and Shape (Stasinopoulos & Rigby, 2008). Results Four hundred and sixty-three healthy adults and 67 patients with heart failure were included in the analyses. IG and AvAcc provide complementary information on PA. Both metrics were independently associated with CRF in healthy individuals. The best cut-point-free regression model (AvAcc+IG) performed similar to the best cut-point-based model (vigorous activity) and explained 73.9% and 74.2% of the variance in CRF, respectively. In patients with heart failure, IG was associated with CRF, independent of AvAcc. Cut-point-free models (IG+AvAcc, IG alone) had comparable predictive value for CRF as the best cut-point-based metric (moderate-to-vigorous activity). We produced age-, sex-, and CRF-related reference values for IG, AvAcc, moderate-to-vigorous, and vigorous activity for healthy adults. Moreover, we developed a web-based application (rawacceleration) facilitating the interpretation of cut-point-free metrics. Conclusions Cut-point-free metrics are not only more robust than cut-point-based metrics, but also have similar predictive value for CRF and, in turn, indirectly for the risk of mortality and longevity (Kodama et al., 2009; Mok et al., 2019). This may be the case in both healthy individuals and patients with heart failure. Our findings together with those of previous studies (Rowlands et al., 2018; Fairclough et al., 2019), therefore, provide a rationale that cut-point-free metrics facilitate the capture of the volume and intensity distribution of the PA profile across populations, and thus may be a viable alternative to cut-point-based metrics in describing PA. Our reference values will enhance the utility of IG and AvAcc and facilitate their interpretation. Finally, our web-based application will simplify this process and also support the translation of cut-point-free metrics into meaningful outcomes. References Fairclough, S. J., Taylor, S., Rowlands, A. V., Boddy, L. M., & Noonan, R. J. (2019) Average acceleration and intensity gradient of primary school children and associations with indicators of health and well-being. Journal of Sports Sciences, 37(18), 2159-2167. https://doi.org/10.1080/02640414.2019.1624313 Kodama, S., Saito, K., Tanaka, S., Maki, M., Yachi, Y., Asumi, M., Sugawara, A., Totsuka, K., Shimano, H., Ohashi, Y., Yamada, N., & Sone, H. (2009). Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: A meta-analysis. JAMA, 301(19), 2024-35.https://doi.org/10.1001/jama.2009.681 Migueles, J. H., Molina-Garcia, P., Torres-Lopez, L. V., Cadenas-Sanchez, C., Rowlands, A. V., Ebner-Priemer, U. W., Koch, E. D., Reif, A., & Ortega, F. B. (2022). Equivalency of four research-grade movement sensors to assess movement behaviors and its implications for population surveillance. Science Reports, 12, Article 5525. https://doi.org/10.1038/s41598-022-09469-2 Migueles, J. H., Rowlands, A. V., Huber, F., Sabia, S., & van Hees, V. T. (2019). GGIR: A research community–driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. Journal for the Measurement of Physical Behaviour, 2(3),188-96. https://doi.org/10.1123/jmpb.2018-0063 Mok, A., Khaw, K.-T., Luben, R., Wareham, N., & Brage, S. (2019). Physical activity trajectories and mortality: Population based cohort study. BMJ, 365, l2323. https://doi.org/10.1136/bmj.l2323 Rowlands, A. V., Edwardson, C. L., Davies, M. J., Khunti, K., Harrington, D. M., & Yates, T. (2018). Beyond cut points: Accelerometer metrics that capture the physical activity profile. Medicine & Science in Sports & Exercise, 50(6), 1323-32. https://doi.org/10.1249/MSS.0000000000001561 Rowlands, A. V., Fairclough, S. J., Yates, T., Edwardson, C. L., Davies, M., Munir, F., Khunti, K., & Stiles, V. H. (2019). Activity intensity, volume, and norms: Utility and interpretation of accelerometer metrics. Medicine & Science in Sports & Exercise, 51(11), 2410-2422. https://doi.org/10.1249/MSS.0000000000002047 Stasinopoulos, D. M., & Rigby, R. A. (2008). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23(7), 1 - 46. https://doi.org/10.18637/jss.v023.i07 Troiano, R. P., McClain, J. J., Brychta, R. J., & Chen, K. Y. (2014). Evolution of accelerometer methods for physical activity research. British Journal of Sports Medicine, 48(13), 1019-1023. https://doi.org/10.1136/bjsports-2014-093546 van Hees, V. T., Gorzelniak, L., Dean León, E. C., Eder, M., Pias, M., Taherian, S., Ekelung, U., Renström, F., Franks, P. W., Horsch, A., & Brage, S. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PloS one, 8(4), Article e61691. https://doi.org/10.1371/journal.pone.0061691 Wagner, J., Knaier, R., Infanger, D., Arbeev, K., Briel, M., Dieterle, T., Hanssen, H., Faude, O., Roth, R., Hinrichs, T., & Schmidt-Trucksäss, A. (2019). Functional aging in health and heart failure: The COmPLETE Study. BMC Cardiovascular Disorders, 19, Article 180. https://doi.org/10.1186/s12872-019-1164-6

Abstract Background For valid accelerometer-assessed physical activity (PA) data, several methodological aspects should be considered. We aimed to 1) visualize the applicability of absolute accelerometer cut-offs to classify PA intensity, 2) verify recommendations to measure PA over 7 days by examining inter-day variability and reactivity, 3) examine seasonal differences in PA, and 4) recommend during which 10 h day period accelerometers should be worn to capture the most PA in patients with heart failure (HEART) and healthy individuals (HEALTH). Methods Fifty-six HEART (23% female; mean age 66 ± 13 years) and 299 HEALTH (51% female; mean age 54 ± 19 years) of the COmPLETE study wore accelerometers for 14 days. Aim 1 was analyzed descriptively. Key analyses were performed using linear mixed models. Results The results yielded poor applicability of absolute cut-offs. The day of the week significantly affected PA in both groups. PA-reactivity was not present in either group. A seasonal influence on PA was only found in HEALTH. Large inter-individual variability in PA timing was present. Conclusions Our data indicated that absolute cut-offs foster inaccuracies in both populations. In HEART, Sunday and four other days included in the analyses seem sufficient to estimate PA and the consideration of seasonal differences and reactivity seems not necessary. For healthy individuals, both weekend days plus four other days should be integrated into the analyses and seasonal differences should be considered. Due to substantial inter-individual variability in PA timing, accelerometers should be worn throughout waking time. These findings may improve future PA assessment. Trial registration The COmPLETE study was registered at clinicaltrials.gov ( NCT03986892 ).

Abstract Retinal vessel phenotype is predictive for cardiovascular outcome. This cross-sectional population-based study aimed to quantify normative data and standard operating procedures for static and dynamic retinal vessel analysis. We analysed central retinal arteriolar (CRAE) and venular (CRVE) diameter equivalents, as well as retinal endothelial function, measured by flicker light‐induced maximal arteriolar (aFID) and venular (vFID) dilatation. Measurements were performed in 277 healthy individuals aged 20 to 82 years of the COmPLETE study. The mean range from the youngest compared to the oldest decade was 196 ± 13 to 166 ± 17 µm for CRAE, 220 ± 15 to 199 ± 16 µm for CRVE, 3.74 ± 2.17 to 3.79 ± 2.43% for aFID and 4.64 ± 1.85 to 3.86 ± 1.56% for vFID. Lower CRAE [estimate (95% CI): − 0.52 (− 0.61 to − 0.43)], CRVE [− 0.33 (− 0.43 to − 0.24)] and vFID [− 0.01 (− 0.26 to − 0.00)], but not aFID, were significantly associated with older age. Interestingly, higher blood pressure was associated with narrower CRAE [− 0.82 (− 1.00 to − 0.63)] but higher aFID [0.05 (0.03 to 0.07)]. Likewise, narrower CRAE were associated with a higher predicted aFID [− 0.02 (− 0.37 to − 0.01)]. We recommend use of defined standardized operating procedures and cardiovascular risk stratification based on normative data to allow for clinical implementation of retinal vessel analysis in a personalized medicine approach.

BackgroundIndividuals with a higher lifelong cardiorespiratory fitness show better vascular health with aging. Studies on fitness-related effects on endothelial function either analyzed samples with a narrow age-range or incompletely assessed endothelial responsiveness. This study aims to assess the impact of cardiorespiratory fitness on the association of brachial-arterial flow-mediated vasodilation (FMD) and low flow-mediated vasoconstriction (L-FMC) with age in healthy adults and patients with cardiovascular diseases.MethodsFMD, L-FMC and V.O2peak were prospectively measured in a population-based sample including 360 healthy adults and 99 patients with cardiovascular disease of European descend. Non-linear models were applied to assess V.O2peak-associated variations in age-related differences of endothelial function independent of classical cardiovascular risk factors.ResultsFMD was negatively associated with age in healthy adults (adjusted R2 = 0.27, partial R2 = 0.07, p < 0.001) and in cardiovascular patients (adjusted R2 = 0.29, partial R2 = 0.05, p = 002). L-FMC showed no association with age. In models predicting the change of FMD with higher age, V.O2peak accounted for 2.8% of variation in FMD (χ2(5) = 5.37, p = 0.372, s = 1.43). Thereby, V.O2peak-stratified changes of FMD started to fan out at around 30 years of age in women and 50 years of age in men, with 7–12% lower values at old age with V.O2peak ≤3rd percentile compared to V.O2peak ≥97th percentile) in both, the healthy sample and in cardiovascular patients.ConclusionThe statistical effect of cardiorespiratory fitness on the association of FMD with age independent of classical cardiovascular risk factors was small in both, healthy aging adults as well as patients with cardiovascular diseases. Its clinical significance should be assessed further.

Coronary artery disease (CAD) remains the leading cause of death worldwide. Expanding patients' metabolic phenotyping beyond clinical chemistry investigations could lead to earlier recognition of disease onset and better prevention strategies. Additionally, metabolic phenotyping, at the molecular species level, contributes to unravel the roles of metabolites in disease development. In this cross-sectional study, we investigated clinically healthy individuals (n = 116, 65% male, 70.8 ± 8.7 years) and patients with CAD (n = 54, 91% male, 67.0 ± 11.5 years) of the COmPLETE study. We applied a high-coverage quantitative liquid chromatography-mass spectrometry approach to acquire a comprehensive profile of serum acylcarnitines, free carnitine and branched-chain amino acids (BCAAs), as markers of mitochondrial health and energy homeostasis. Multivariable linear regression analyses, adjusted for confounders, were conducted to assess associations between metabolites and CAD phenotype. In total, 20 short-, medium- and long-chain acylcarnitine species, along with L-carnitine, valine and isoleucine were found to be significantly (adjusted p ≤ 0.05) and positively associated with CAD. For 17 acylcarnitine species, associations became stronger as the number of affected coronary arteries increased. This implies that circulating acylcarnitine levels reflect CAD severity and might play a role in future patients' stratification strategies. Altogether, CAD is characterized by elevated serum acylcarnitine and BCAA levels, which indicates mitochondrial imbalance between fatty acid and glucose oxidation.

Objective: The aim of this study was to analyze whether V̇O2-kinetics during cardiopulmonary exercise testing (CPET) is a useful marker for the diagnosis of heart failure (HF) and to determine which V̇O2-kinetic parameter distinguishes healthy participants and patients with HF.Methods: A total of 526 healthy participants and 79 patients with HF between 20 and 90 years of age performed a CPET. The CPET was preceded by a 3-min low-intensity warm-up and followed by a 3-min recovery bout. V̇O2-kinetics was calculated from the rest to exercise transition of the warm-up bout (on-kinetics), from the exercise to recovery transition following ramp test termination (off-kinetics) and from the initial delay of V̇O2 during the warm-up to ramp test transition (ramp-kinetics).Results: V̇O2 off-kinetics showed the highest z-score differences between healthy participants and patients with HF. Furthermore, off-kinetics was strongly associated with V̇O2peak. In contrast, ramp-kinetics and on-kinetics showed only minimal z-score differences between healthy participants and patients with HF. The best on- and off-kinetic parameters significantly improved a model to predict the disease severity. However, there was no relevant additional value of V̇O2-kinetics when V̇O2peak was part of the model.Conclusion: V̇O2 off-kinetics appears to be superior for distinguishing patients with HF and healthy participants compared with V̇O2 on-kinetics and ramp-kinetics. If V̇O2peak cannot be determined, V̇O2 off-kinetics provides an acceptable substitute. However, the additional value beyond that of V̇O2peak cannot be provided by V̇O2-kinetics.

Abstract Background Cardiovascular (CV) diseases including heart failure are the leading causes of morbidity, with age being the primary risk factor. The combination of age-related organic functional impairment and reduced physical fitness can drastically impact an individual’s healthspan. One’s lifespan can potentially be prolonged by the preservation or improvement of physical fitness. However, it remains unclear as to which biomarkers are most suitable for distinguishing between healthy aging and the impaired organ function associated with heart failure. Therefore, a comprehensive assessment of the components of physical fitness and CV function will be performed to identify the most important factors contributing to aging in relation to both health and disease. Methods This cross-sectional investigation will consist of two parts: COmPLETE-Health (C-Health) and COmPLETE-Heart (C-Heart). C-Health will examine the aging trajectories of physical fitness components and CV properties in a healthy population sample aged between 20 and 100 years (n = 490). Separately, C-Heart will assess the same markers in patients at different stages of chronic heart failure (n = 80). The primary outcome to determine the difference between C-Health and C-Heart will be cardiorespiratory fitness as measured by cardiopulmonary exercise testing on a bicycle ergometer. Secondary outcomes will include walking speed, balance, isometric strength, peak power, and handgrip strength. Physical activity as a behavioural component will be assessed objectively via accelerometry. Further, CV assessments will include pulse wave velocity; retinal, arterial, and venous diameters; brachial and retinal arterial endothelial function; carotid intima-media thickness; and systolic and diastolic function. The health distances for C-Health and C-Heart will be calculated using the methodology based on statistical (Mahalanobis) distance applied to measurements of quantitative biomarkers. Discussion This research seeks to identify physical fitness and CV biomarkers that best resemble underlying CV risk with age. Further, it will examine which physical fitness markers are impaired most in heart failure. The presented integrative approach could define new recommendations for diagnostic guidance in aging. Ultimately, this study is expected to offer a better understanding of which functional characteristics should be specifically targeted in primary and secondary prevention to achieve an optimal healthspan.

ObjectiveThis study compared the robustness of a [Formula: see text]-plateau definition and a verification-phase protocol to day-to-day and diurnal variations in determining the true [Formula: see text]. Further, the additional value of a verification-phase was investigated.MethodsEighteen adults performed six cardiorespiratory fitness tests at six different times of the day (diurnal variation) as well as a seventh test at the same time the sixth test took place (day-to-day variation). A verification-phase was performed immediately after each test, with a stepwise increase in intensity to 50%, 70%, and 105% of the peak power output.ResultsParticipants mean [Formula: see text] was 56 ± 8 mL/kg/min. Gwet's AC1 values (95% confidence intervals) for the day-to-day and diurnal variations were 0.64 (0.22, 1.00) and 0.71 (0.42, 0.99) for [Formula: see text]-plateau and for the verification-phase 0.69 (0.31, 1.00) and 0.07 (-0.38, 0.52), respectively. In 66% of the tests, performing the verification-phase added no value, while, in 32% and 2%, it added uncertain value and certain value, respectively, in the determination of [Formula: see text].ConclusionCompared to [Formula: see text]-plateau the verification-phase shows lower reliability, increases costs and only adds certain value in 2% of cases.

BackgroundAging and changing age demographics represent critical problems of our time. Physiological functions decline with age, often ending in a systemic process that contributes to numerous impairments and age-related diseases including heart failure (HF). We aimed to analyze whether differences in composite measures of physiological function [health distance (HD)], specifically physical fitness, between healthy individuals and patients with HF, can be observed.MethodsThe COmPLETE Project is a cross-sectional study of 526 healthy participants aged 20–91 years and 79 patients with stable HF. Fifty-nine biomarkers characterizing fitness (cardiovascular endurance, muscle strength, and neuromuscular coordination) and general health were assessed. We computed HDs as the Mahalanobis distance for vectors of biomarkers (all and domain-specific subsets) that quantified deviations of individuals’ biomarker profiles from “optimums” in the “reference population” (healthy participants aged

Abstract Background Wrist-worn accelerometers are increasingly used in epidemiological studies to record physical activity. The accelerometer data are usually only analyzed if the convention for compliant wear time is met (i.e. ≥ 10 h per day) but the algorithms to detect wear time have been developed based on data from hip-worn devices only and have not been tested in a free-living setting. The aim of this study was to validate the automatic wear time detection algorithms of one of the most frequently used devices in a free-living setting. Methods Sixty-eight adults wore one ActiGraph GT3X+ accelerometer on the wrist and one on the hip and additionally recorded wear times for each device separately in a diary. Monitoring phase was during three consecutive days in a free-living setting. Wear time was computed by the algorithms of Troiano and Choi and compared to the diary recordings. Results Mean wear time was over 1420 min per day for both devices on all days. Lin’s concordance correlation coefficient for the wrist-worn wear time was 0.73 (0.60; 0.82) when comparing the diary with Troiano and 0.78 (0.67; 0.86) when comparing the diary with Choi. For hip-worn devices the respective values were 0.23 (0.13; 0.33) for Troiano and 0.92 (0.88; 0.95) for Choi. Mean and standard deviation values for absolute percentage errors for wrist-worn devices were − 1.3 ± 8.1% in Troiano and 0.9 ± 7.7% in Choi. The respective values for hip-worn devices were − 17.5 ± 10% in Troiano and − 0.8 ± 4.6% in Choi. Conclusions Hip worn devices may be preferred due to their higher accuracy in physical activity measurement. Automatic wear-time detection can show high errors in individuals, but on a group level, type I, type II, and total errors are generally low when the Choi algorithm is used. In a real-life setting and participants with a high compliance, the algorithm by Choi is sufficient to distinguish wear time from non-wear time on a group level.

In competitive sports any substantial individual differences in diurnal variations in maximal performance are highly relevant. Previous studies have exclusively focused on how the time of day affects performance and disregarded the maximal individual diurnal variation of performance. Thus, the aims of this study were (1) to investigate the maximum diurnal variation in maximum oxygen uptake (VO2max), (2) to compare the diurnal variation of VO2max during the day to the day-to-day variation in VO2max, and (3) to investigate if there is a time-of-day effect on VO2max. Ten male and seven female athletes (mean VO2max: 58.2 ± 6.9 ml/kg/min) performed six maximal cardiopulmonary exercise tests including a verification-phase at six different times of the day (i.e., diurnal variation) and a seventh test at the same time the sixth test took place (i.e., day-to-day variation). The test times were 7:00, 10:00, 13:00, 16:00, 19:00, and 21:00. The order of exercise tests was the same for all participants to ensure sufficient recovery but the time of day of the first exercise test was randomized. We used paired t-tests to compare the nadir and peak of diurnal variations, day-to-day variations and the difference between diurnal and day-to-day variations. The mean difference in VO2max was 5.0 ± 1.9 ml/kg/min (95% CI: 4.1, 6.0) for the diurnal variation and 2.0 ± 1.0 ml/kg/min (95% CI: 1.5, 2.5) for the day-to-day variation. The diurnal variation was significantly higher than the day-to-day variation with a mean difference of 3.0 ± 2.1 ml/kg/min (95% CI: 1.9, 4.1). The linear mixed effects model revealed no significant differences in VO2max for any pairwise comparison between the different times of the day (all p > 0.11). This absence of a time-of-day effect is explained by the fact that peak VO2max was achieved at different times of the day by different athletes. The diurnal variations have meaningful implications for competitive sports and need to be considered by athletes. However, the results are also relevant to research. To increase signal-to-noise-ratio in intervention studies it is necessary to conduct cardiopulmonary exercise testing at the same time of the day for pre- and post-intervention exercise tests.

As ageing is a major risk factor for the development of non-communicable diseases, extending healthspan has become a medical and societal necessity. Precise lipid phenotyping that captures metabolic individuality could support healthspan extension strategies. This study applied ‘omic-scale lipid profiling to characterise sex-specific age-related differences in the serum lipidome composition of healthy humans. A subset of the COmPLETE-Health study, composed of 73 young (25.2 ± 2.6 years, 43% female) and 77 aged (73.5 ± 2.3 years, 48% female) clinically healthy individuals, was investigated, using an untargeted liquid chromatography high-resolution mass spectrometry approach. Compared to their younger counterparts, aged females and males exhibited significant higher levels in 138 and 107 lipid species representing 15 and 13 distinct subclasses, respectively. Percentage of difference ranged from 5.8% to 61.7% (females) and from 5.3% to 46.0% (males), with sphingolipid and glycerophophospholipid species displaying the greatest amplitudes. Remarkably, specific sphingolipid and glycerophospholipid species, previously described as cardiometabolically favourable, were found elevated in aged individuals. Furthermore, specific ether-glycerophospholipid and lyso-glycerophosphocholine species displayed higher levels in aged females only, revealing a more favourable lipidome evolution in females. Altogether, age determined the circulating lipidome composition, while lipid species analysis revealed additional findings that were not observed at the subclass level.

Many sports competitions take place during television prime time, a time of the day when many athletes have already exceeded their time of peak performance. We assessed the effect of different light exposure modalities on physical performance and melatonin levels in athletes during prime time. Seventy-two young, male elite athletes with a median (interquartile range) age of 23 (21; 29) years and maximum oxygen uptake (VO2max) of 63 (58; 66) ml/kg/min were randomly assigned to three different light exposure groups: bright light (BRIGHT), blue monochromatic light (BLUE), and control light (CONTROL). Each light exposure lasted 60 min and was scheduled to start 17 h after each individual's midpoint of sleep (median time: 9:17 pm). Immediately after light exposure, a 12-min time trial was performed on a bicycle ergometer. The test supervisor and participants were blinded to the light condition each participant was exposed to. The median received light intensities and peak wavelengths (photopic lx/nm) measured at eye level were 1319/545 in BRIGHT, 203/469 in BLUE, and 115/545 in CONTROL. In a multivariate analysis adjusted for individual VO2max, total work performed in 12 min did not significantly differ between the three groups. The amount of exposure to non-image forming light was positively associated with the performance gain during the time trial, defined as the ratio of the work performed in the first and last minute of the time trial, and with stronger melatonin suppression. Specifically, a tenfold increase in the exposure to melanopic light was associated with a performance gain of 8.0% (95% confidence interval: 2.6, 13.3; P = 0.004) and a melatonin decrease of −0.9 pg/ml (95% confidence interval: −1.5, −0.3; P = 0.006). Exposure to bright or blue light did not significantly improve maximum cycling performance in a 12-min all-out time trial. However, it is noteworthy that the estimated difference of 4.1 kJ between BRIGHT and CONTROL might represent an important performance advantage justifying further studies. In conclusion, we report novel evidence that evening light exposure, which strongly impacts the human circadian timing system, enables elite athletes to better maintain performance across a 12-min cycling time trial.

Objective: This study assessed the validity of a consumer activity wristband, a smartphone, and a research-grade accelerometer to measure steps in a free-living setting. Approach: Thirty healthy adults were equipped with two Garmin Vivofit (non-dominant wrist), one iPhone SE (right pants pocket), three ActiGraph wGT3X  +  (two on the hip, one on the non-dominant wrist), and one StepWatch (right ankle) and instructed to wear the devices continuously during a 3 d monitoring period. All activities of daily living were recorded in 15 min intervals in a diary. The StepWatch served as the criterion method and validity was evaluated by comparing each device with the criterion measure using mean absolute percentage errors (MAPE). Main results: The MAPE for the total step count during the 3 d monitoring period was high with a general underestimation of steps by all devices of  >20% compared to the criterion measure. The wrist-worn ActiGraph markedly overestimated steps during predominantly low active (public transport or driving, and standing) or even inactive (sitting and lying) activities of daily living. Significance: The overall high MAPE of the devices compared to the StepWatch during step-based activities, likely caused by inaccuracies during short and intermittent bouts of activity, may limit their validity in a free-living setting. [ABSTRACT FROM AUTHOR]