Reference values for cardiopulmonary exercise testing-derived parameters for cardiorespiratory fitness in Dutch community-dwelling 55- to 75-year-old adults

Reference values for cardiopulmonary exercise testing-derived parameters for cardiorespiratory fitness in Dutch community-dwelling 55- to 75-year-old adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reference values for cardiopulmonary exercise testing-derived parameters for cardiorespiratory fitness in Dutch community-dwelling 55- to 75-year-old adults Dax Houtkamp, Annelies L. Pool-Goudzwaard, Tim Takken, Sabrina Chettouf, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6990129/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Accurate interpretation of cardiorespiratory fitness (CRF) requires reference values that account for sex, age, and body composition. Existing reference values often lack these distinctions or exclude older adults. This study aimed to establish sex- and age-specific reference values for absolute and relative (body mass-corrected and lean body mass-corrected) CRF parameters derived from cardiopulmonary exercise testing (CPET) in Dutch community-dwelling 55- to 75-year-old adults. Methods: Cross-sectional data from 611 participants of the AMCOHF study were analysed. CRF was assessed via cycle ergometer CPET evaluating oxygen uptake (⩒O 2peak ) and work rate (WR peak ) at peak exercise, oxygen uptake at the ventilatory anaerobic threshold (⩒O 2VAT ), and oxygen uptake efficiency slope (OUES). Body mass and lean body mass were measured using dual-energy X-ray absorptiometry. Reference values stratified by sex and age were developed using generalized additive models. Prediction equations were generated using multiple linear regression . Correlations with ⩒O 2peak assessed the usefulness of ⩒O 2VAT and OUES as submaximal and effort-independent alternatives for CRF. Results: All CRF variables declined with age. ⩒O 2peak (L/min) declined quasi-linearly (females: 1.3%/year; males: 2.5%/year). Significant sex-differences were observed between all CRF-variables (absolute and body mass-corrected values: p < 0.001; lean body mass-corrected values: p < 0.05). Significant correlations were found between ⩒O 2peak and WR peak (ρ = 0.90), ⩒O 2VAT (ρ = 0.78), and OUES (ρ = 0.87). Conclusion: This study provides reference values for ⩒O 2peak , WR peak , ⩒O 2VAT , and OUES in Dutch older adults aged 55–75 years during cycle ergometer CPET, offering a unique dataset for assessing CRF and monitoring intervention effects. Sports Medicine and Kinesiology Cardiopulmonary Exercise Test Reference Values Aging Oxygen Consumption Anaerobic threshold Physical fitness Figures Figure 1 Figure 2 Introduction Low cardiorespiratory fitness (CRF) is a key predictor of adverse health outcomes in numerous chronic diseases, such as type 2 diabetes and cardiovascular disease, and all-cause mortality (Lang et al. 2024; Myers et al. 2024). CRF reflects the body's ability to transport oxygen from the lungs to muscle mitochondria to perform large muscle physical activity, thereby representing the integrated function of the cardiovascular, pulmonary, and musculoskeletal systems as a clinical vital sign (Ross et al. 2016). Moderate-to-high CRF levels are associated with lower chronic disease and mortality risks and the most significant health gains are observed when moving from the lowest fitness group to a higher level (McKinney et al. 2016; Sui et al. 2022). Identifying low CRF in older adults is especially important, as measuring a low CRF at an early stage can predict the risk of developing chronic diseases later in life (Myers et al. 2024). Moreover, a low CRF has been associated with reduced tolerance to medical treatment (e.g., perioperative risk, chemotherapy/radiation intolerance) (Levett et al. 2018). Therefore, if identified at an early stage, preventive strategies may be timely initiated to mitigate the risk of a low CRF, thereby preventing negative health outcomes. Cardiopulmonary exercise testing (CPET) is the gold standard for measuring CRF, assessing oxygen uptake (⩒O 2 ) at peak exercise (⩒O 2peak ) (Mezzani et al. 2009). Although ⩒O 2peak is the primary indicator of CRF, many older adults fail to reach a true ⩒O 2peak due to motivation and age-related limitations (Sanada et al. 2007; Wagner et al. 2020). Therefore, submaximal measures, such as the ⩒O 2 at the ventilatory anaerobic threshold (⩒O 2VAT ) that marks the onset of anaerobic metabolism, and the effort-independent oxygen uptake efficiency slope (OUES), which assesses ventilatory efficiency, seem to be good alternative CRF indicators for older adults. Both ⩒O 2VAT and OUES have demonstrated to be valid surrogates for ⩒O 2peak in older adults and can therefore be used when ⩒O 2peak is unattainable (Albouaini et al. 2007; Bongers et al. 2017). To interpret an individual’s CRF correctly, adequate sex- and age-specific reference values for CRF, corrected for anthropometric characteristics (e.g., body height, body mass, lean body mass), are required (Ross, 2003). Specifically, it has been shown that ⩒O 2peak corrected for lean body mass is the most accurate expression of CRF when available (Imboden et al. 2020). Despite its importance, valid reference values for ⩒O 2peak , WR peak , ⩒O 2VAT , and OUES in older adults remain limited, often due to a sample without older adults (Buys et al. 2015; Van de Poppe et al. 2018; van der Steeg and Takken 2021) or studies using an estimation for CRF, rather than measuring respiratory gas analysis directly, which has shown to provide errors in interpretation of CRF (Peterman et al. 2021). Lastly, criteria for a maximal effort are often set too low (i.e., respiratory exchange ratio at peak exercise (RER peak ) > 1.00), or not in line with recent suggestions to evaluate maximal effort based on age-dependent cut-off points (Wagner et al. 2020). This study aimed to establish reference values for absolute ⩒O 2peak , work rate at peak exercise (WR peak ), ⩒O 2VAT , and OUES in Dutch adults aged 55-75 years, as well as corrected for body mass and lean body mass. Reference values will be provided separately for females and males across the whole age range to examine the effects of sex and age on CRF. Males are expected to show higher values than females, and younger participants are expected to have higher values than older participants (van der Steeg and Takken 2021). Correlations between ⩒O 2peak and other CRF measures (i.e., WR peak , ⩒O 2VAT , and OUES) will also be analysed to assess their usefulness as alternatives to ⩒O 2peak . High correlations are expected between all CRF variables. Methods Experimental design This cross-sectional study utilized data from the AMersfoort COhort Study on functional decline, Healthy aging, and Frailty (AMCOHF). Ethical approval was granted by the Medical Ethical Committee Zuyderland (Heerlen, The Netherlands) under reference number NL70141.096.19 (19 September 2019) and patient inclusion and data collection started in June 2022. The trial protocol has been published elsewhere (Houtkamp et al. in press) and is registered on Open Science Framework (DOI: 10.17605/OSF.IO/RMBQV). Participants Participants were Dutch community-dwelling older adults aged 55-75 years from the city of Amersfoort, the Netherlands. Inclusion required informed consent, followed by a physician-led medical screening to rule out contraindications for maximal exercise testing. This screening included medical history and medication review, auscultation, baseline measures of blood pressure and resting electrocardiography. Comorbidities were documented using the Rockwood Frailty Index as operationalized by Collerton et al. (2012). Participants were excluded for the current study if they reported comorbidities to the physician that are likely to limit exercise capacity, including severe cardiovascular disease (e.g., ischemic heart disease, cerebrovascular disease, peripheral vascular disease, or heart failure). Participants who reported to suffer from chronic lung disease (e.g., chronic obstructive pulmonary disease or asthma) were excluded if the ratio between the forced expiratory volume in 1 s and forced vital capacity (Tiffeneau index) was < 0.70 (Mannino et al. 2007). Additionally, exclusion criteria for the AMCOHF cohort study involved active cancer, significant physical or cognitive impairment (i.e., mini mental state examination score < 23) according to previously described recommendations (Folstein et al. 1975), and having undergone surgery, cancer, chemotherapy or radiotherapy in the last 6 months. Procedures Cardiorespiratory fitness assessment CRF was assessed using incremental CPET on a cycle ergometer (Lode Corival Rehab, Lode BV, Groningen, Netherlands). Participants wore a facemask (Hans Rudolph, Kansas City, MO, USA) connected to an ergospirometry system (Metalyzer 3B, Cortex, Leipzig, Germany), calibrated for respiratory gas analysis and volume measurements. The Cortex Metalyzer 3B exhibited a measurement error of 2.85 ± 2.22% (Van Hooren et al. 2024). Forced vital capacity and forced expiratory volume in 1 second were measured before testing. Baseline cardiopulmonary values were recorded over a three-minute rest period, followed by a three-minute unloaded cycling warm-up. The work rate was then incrementally increased by 15-30 W/min according to a ramp protocol, tailored to each participant’s estimated fitness level to achieve maximal effort within 8-12 minutes (Glaab and Taube 2022). Participants maintained a pedaling frequency between 60-80 rotations/min until they reached exhaustion despite verbal encouragement or until they reached criteria for termination based on the American Thoracic Society/American College of Chest Physicians guidelines (Ross 2003). After termination of the incremental protocol, participants proceeded in a 1-minute cooldown period consisting of a constant load of 50 W. Breath-by-breath ⩒O 2 , carbon dioxide production (V̇CO 2 ), and respiratory exchange ratio (RER) were calculated and averaged over ten-second intervals. Heart rate (HR) was continuously monitored by twelve-lead electrocardiography. Our study protocol aimed to collect CRF reference data using the quality criteria for CPET standards as defined previously (Takken et al. 2019). Body composition assessment Body height and body mass were measured to the nearest 0.1 cm and 0.1 kg, respectively, and body mass index (BMI) was calculated subsequently. Lean body mass was evaluated using a fan beam dual-energy X-ray absorptiometry (DXA) device (GE Medical Systems Lunar Prodigy, Madison, Wisconsin, USA). This enables the determination of lean body mass and whole-body fat percentage and is considered highly accurate (Shepherd et al. 2017). Waist circumference was measured to compare body composition with previous studies. Data analysis For descriptive analyses, participants were divided in 5-year age groups except for the last age group (i.e., 70-75 years) to ensure enough samples. A maximal cardiorespiratory effort during CPET was defined as reaching the age-dependent cut-off points for RER peak (i.e., ≥1.10 for age group 55-59 or ≥1.06 for age group 60-75) or peak heart rate (≥92% of 208 – 0.7 × age for age group 55-59 or ≥89% for age group 60-75) (Tanaka et al. 2001; Wagner et al. 2020). The work rate at peak exercise (WR peak ) was the highest attained value. Data from other outcome variables were averaged over 30 s of exercise. Non-maximal efforts were excluded from the analysis of ⩒O 2peak and WR peak but were used for analysis of submaximal or effort-independent CRF parameters (i.e., respectively ⩒O 2VAT and OUES). The ⩒O 2VAT was determined primarily using the modified V-slope method (i.e., the point at which the linear relationship between the V̇CO 2 and ⩒O 2 changed). This point was verified using the ventilatory equivalents method (i.e., the point at which the ventilatory equivalent for oxygen and the partial end-tidal oxygen tension reached its lowest point after which it began to increase in a consistent manner, although the ventilatory equivalent for carbon dioxide and partial end tidal carbon dioxide tension remained constant). Detailed description of the methods can be found in previously described guidelines (Franssen et al. 2022). Cases with ambiguous ⩒O 2VAT were resolved by consensus with two experts (BB and DH). The oxygen uptake efficiency slope (OUES) was calculated using previously described formula (i.e., a × log 10 V̇E + b, where “a” represents the OUES and “b” the intercept) (Baba et al. 1996). Data from one minute after the start of the test up (i.e., to prevent the noisy ⩒O 2 at the start from influencing the OUES) to ⩒O 2peak . When a plateau in ⩒O 2 was detected, data up to the onset of the ⩒O 2 plateau were used to determine the OUES, in accordance with prior recommendations (Niemeijer et al. 2014). Several other cardiorespiratory parameters were determined (i.e., RER, heart rate, oxygen pulse, minute ventilation, tidal volume, and breathing frequency at peak exercise, and the slope between the minute ventilation and V̇CO 2 ). The slope between the minute ventilation and V̇CO 2 (V̇E/ V̇CO 2 -slope) was calculated up to the respiratory compensation point (RCP) which was determined using the interpretative guidelines as described before (Franssen et al. 2022). Cases with ambiguous RCP were resolved by consensus of the same two experts (BB and DH). When the RCP was not visible, the V̇E/V̇CO 2 slope was calculated using data up to peak exercise. Statistical analysis Statistical analyses were conducted using R version 4.4.2. (R Foundation for Statistical Computing, Vienna, Austria). Data are presented as mean (SD) and statistical significance was set at p < 0.05. Independent t-tests assessed sex differences, while age effects were evaluated using one-way ANOVA with Bonferroni post-hoc adjustments when needed. To assess the relationships between CPET-derived CRF variables (i.e., ⩒O 2peak , WR peak , ⩒O 2VAT , and OUES), Pearson’s correlation coefficients were calculated. In cases where the assumptions of normality were not met, Spearman’s rank coefficients were used. Correlation strength was interpreted as follows: r < 0.30 = weak, 0.30–0.59 = moderate, and ≥0.60 = strong (Cohen 2013). Reference centiles (P3, P25, P50, P75, P97) were derived using generalized additive models (GAM) which has previously shown to be the best fitting with highest predictive accuracy compared to linear or polynomial model (Mylius et al. 2019). Model performance of the GAM was assessed using adjusted R 2 . All body mass- and lean body mass-corrected CRF parameters were obtained by dividing absolute values by body mass and lean body mass. This was done to facilitate comparisons across differences in body size and body composition respectively. Descriptive statistics were calculated by five-year age groups and sex. Results A total of 661 subjects were included in the final analysis (336 females and 325 males). Participants were equally distributed across five-year intervals from 55 to 75 years, with at least 50 participants representing each age-group. A flow diagram of the study is shown in Figure 1. Participant characteristics, stratified by sex and age group and including experimental CPET results (i.e., ⩒O 2peak , WR peak , ⩒O 2VAT , and OUES) are presented in Table 1. Cardiopulmonary exercise testing-derived parameters of cardiorespiratory fitness Reference values for CPET-derived CRF parameters, corrected for body mass and estimated using GAM by sex and 5-year age groups, are presented in Table 2 for maximal CRF-variables (i.e., ⩒O 2peak and WR peak ) and in Table 3 for submaximal (i.e., ⩒O 2VAT ) and effort-independent (i.e., OUES) variables. Reference values with age as continuous variable are shown in Figure 2. Similar analyses indexed for absolute reference values and values corrected for lean body mass are available in Online Resource 1. Effects of sex and age on cardiorespiratory fitness All absolute and body mass-corrected CPET-derived CRF variables were significantly higher in males compared to females across all age groups (p < 0.001), but not when corrected for lean body mass except for WR peak (p = 0.027). Additionally, increasing age was associated with a decline in all CRF variables (p = 0.048 for ⩒O 2VAT corrected for lean body mass; all other p-values: p < 0.001). Correlations between variables of cardiorespiratory fitness Spearman correlation coefficients were calculated between all absolute CRF variables. The analyses revealed significant positive correlations between ⩒O 2peak and, respectively, WR peak (ρ = 0.90, p < 0.001), ⩒O 2VAT (ρ = 0.78, p < 0.001), and OUES (ρ = 0.87, p < 0.001), indicating strong associations between these CPET-derived variables in older adults. Reference values Prediction equations to calculate absolute and body mass-corrected CRF parameters were developed using multiple linear regression models and can be found in Table 4. Covariates include sex (0 = female, 1 = male) and three continues variables (i.e., body mass, body height, and age). Discussion This study provides reference values for maximal and submaximal CPET-derived parameters of CRF in Dutch community-dwelling older adults aged 55-75 years. In addition, results also show robust relationships between ⩒O 2peak and surrogate measures of CRF (i.e., WR peak , ⩒O 2VAT , and OUES), highlighting their potential to evaluate CRF in older adults when respiratory gas analysis is unavailable (i.e., WR peak ) or when a participant is not able to deliver a maximal effort (i.e., ⩒O 2VAT and OUES). Furthermore, results showed that sex differences between all CRF variables could mainly be attributed to differences in lean body mass, as indicated by far greater differences in absolute values or values corrected for body mass compared to CRF parameters corrected for lean body mass (Online Resource 1). Therefore, it is recommended to correct CPET-derived CRF outcomes for lean body mass if resources allow. To the authors’ knowledge, this is the first study to provide sex- and age-specific reference values for maximal (i.e., ⩒O 2peak and WR peak ) and submaximal (i.e., ⩒O 2VAT and OUES) CPET-derived parameters of CRF in absolute terms, as well as corrected for body mass and lean body mass in older adults. Currently utilized Dutch reference values do not include participants up to an age of 75 years and are not based on such a large and well-characterized cohort, nor have they been developed using GAM, which has shown to outperform linear or polynomial models (Mylius et al. 2019). Additionally, including submaximal reference data is important for assessing CRF in older adults who are unable to perform a cardiorespiratory maximal effort during CPET. ⩒O 2peak declined on average by 1.3% (0.02 L/min) per year for females and 2.5% (0.07 L/min) per year for males. This decline can partly be attributed to changes in body composition, as the decline in ⩒O 2peak was lower when corrected for body mass (females: 0.8% or 0.2 mL/kg/min per year; males: 1.5% or 0.5 mL/kg/min per year) or lean body mass (females: 0.9% or 0.4 mL/kg lean body mass/min per year; males: 1.5% or 0.7 mL/kg lean body mass/min per year). This illustrates that the decline in ⩒O 2peak in older adults is comparable to the decline of approximately one percent per year over the course of a lifetime, as has been typically described (Kenney et al. 2022). Additionally, the decline in ⩒O 2peak per year observed within the youngest age group was comparable with the decline observed in the oldest age group for females (55–60 years: 0.02 L/min or 1.1%; 70–75 years: 0.02 L/min or 1.2%) and in absolute terms for males (55–60 years: 0.08 L/min or 2.5%; 70–75 years: 0.09 L/min or 4.2%), although the relative decline (in percentage) was slightly higher in the oldest age group, highlighting a quasi-linear trend of decline. Comparison with other maximal reference data Compared to recent reference values from the United States by Kaminsky et al. (2022), this study reported significantly higher values for both ⩒O 2peak (males: +10.9 mL/kg/min; females: +11.7 mL/kg/min) as well as WR peak (males: +96 W; females: +82 W) in healthy older adults aged around 55 years (p < 0.001 for all values). These differences might partly be due to differences in exercise protocol, as this was not uniform across all data collection sites in that study. In comparison to previous Dutch references values, GAM-predicted ⩒O 2peak was slightly lower (p < 0.05) than those recently reported from the Lowlands Fitness Registry (males: 1.4 mL/kg/min; females: 1.6 mL/kg/min) around 55 years of age (van der Steeg and Takken 2021). However, these differences were substantially smaller than those observed with earlier reference values and support the potential integration of our data into the Lowlands Fitness Registry to expand the age range to 75 years. These findings highlight the importance of using context-specific reference values to ensure accurate interpretation. In accordance to these findings, GAM-predicted WR peak in this study was also significantly higher (p < 0.001) compared to previously described Dutch reference data by Van de Poppe et al. (2018), for age groups 55 (females: 187 vs 165 W; males: 293 vs 251 W) and 60 (females: 177 vs 148 W; males: 263 vs 221 W). However, data collection in that study was also not uniform across all measurements, which might explain part of these differences. The presented GAM-predicted OUES values in this study were slightly higher for females (ΔOUES: 243, p < 0.05), but not for males (ΔOUES: 158, p = 0.14) aged 55 and 60 years compared to previous reference values from Belgium (Buys et al. 2015). Although it has been well documented that ⩒O 2peak and ⩒O 2VAT decline with age, reference values in especially the highest age categories and in females are currently still lacking (van der Steeg and Takken 2021). Our findings align with previous literature demonstrating that the age-related CRF decline is partly mitigated when corrected for lean body mass (Kim et al. 2016). Furthermore, the recommendation of correcting ⩒O 2peak for lean body mass when possible is in accordance to previous suggestions (Imboden et al. 2020; Köhler et al. 2018). Nevertheless, the decline in cardiovascular capacity has been described as one of the prominent factors to contribute to a decline in CRF (Kenney et al. 2022). In contrast to the correction for body mass, sex differences between all four CRF variables seem to disappear almost completely when looking at the CRF variables corrected for lean body mass. This in accordance with previous literature demonstrating a higher fat percentage per given amount of body mass in females compared to males (Schorr et al. 2018). Strength and limitations Our study protocol met 11 out of 14 quality criteria for CPET standards (Takken et al. 2019). Another strength is that this study used recently developed cut-off points for RER peak to determine whether a maximal effort was reached (Wagner et al. 2020). These cut-off points were age-dependent, compared to a large number of studies using a RER peak cut-off of ≥1.00, potentially leading to inclusion of submaximal cardiorespiratory efforts. Furthermore, these data were collected at one laboratory setting preventing heterogeneity between multiple testing facilities done in previous studies. However, this study also has some limitations. Although people were randomly invited to participate in this study, a potential selection bias of healthy physically active people could have led to an overestimation of the CRF level of the participants. This may be substantiated by the relatively few smokers in our sample compared to the average Dutch population (van Aerde et al. 2024). Practical implications Reference values for CRF are of pivotal importance because they provide a benchmark to assess an individual’s health status, detect functional decline at an early stage, and guide personalized interventions for healthy aging. With the aging Dutch population, establishing adequate reference values for older adults is essential for clinical decision-making, for example in geriatric, cardiac, and pulmonary settings. Additionally, in sports medicine, reference data for apparently healthy older individuals are necessary, as the growing participation of master athletes in endurance events often requires medical evaluations, such as CPET, as part of the entry requirement to ensure safe participation. Applications include for example preoperative evaluations, helping to identify individuals at risk of postoperative complications, enabling targeted prehabilitation interventions to optimize surgical outcomes (Levett and Grocott 2025). Additionally, CPET-based assessments allow for early detection of a declining intrinsic capacity as proposed by the WHO. Without appropriate norm values, distinguishing between physiological aging and pathological declines in CRF remains challenging, potentially leading to inadequate risk assessment and suboptimal clinical decision-making. The inclusion of corrections for both body mass and lean body mass enhances the applicability of this dataset by reducing misclassification due to age-related changes in body composition. Additionally, establishing reference values for submaximal parameters also enhances the applicability in older populations by providing benchmarks for a good, average, or poor CRF based on sex and age. Future studies presenting reference values should provide these based on longitudinal data. By monitoring the trajectory of an individual’s CRF, potential risk factors for a declining CRF may be discovered. In addition, physical activity data should be considered to make sure no selection bias occurs. Lastly, larger databases would allow for computation of body height- and body mass-specific reference values within sex- and age-specific groups. Conclusion This study provides reference values for CRF in Dutch older adults aged between 55-75 years during cycle ergometer CPET. All CRF variables declined with age, with an average ⩒O 2peak decline of 1.3% per year for females and 2.5% for males. Sex differences were present between all absolute and body mass-corrected CRF variables, but not when corrected for lean body mass. Furthermore, WR peak , ⩒O 2VAT , and OUES can be used as practical alternative measures for ⩒O 2peak . Together, these reference values can be used to estimate an individual’s CRF for decision-making in different clinical settings. Abbreviations AMCOHF: AMersfoort COhort Study on functional decline, Healthy aging, and Frailty BF peak : breathing frequency at peak exercise BMI: body mass index CPET: cardiopulmonary exercise testing CRF: cardiorespiratory fitness DBP: diastolic blood pressure DXA: dual-energy X-ray absorptiometry GAM: generalized additive models HR: heart rate HR peak : heart rate at peak exercise O 2 -pulse peak : oxygen pulse at peak exercise OUES: oxygen uptake efficiency slope RCP: respiratory compensation point RER: respiratory exchange ratio RER peak : respiratory exchange ratio at peak exercise SBP: systolic blood pressure ⩒CO 2 : carbon dioxide production VE/VCO 2 slope: slope between the minute ventilation and carbon dioxide production up to the respiratory compensation point ⩒E peak : minute ventilation at peak exercise ⩒O 2 : oxygen uptake ⩒O 2peak : oxygen uptake at peak exercise ⩒O 2VAT : oxygen uptake at the ventilatory anaerobic threshold VT peak : tidal volume at peak exercise WR peak : work rate at peak exercise Declarations Competing interest The authors declare that there is no conflict of interest. Funding No funding was received for conducting this study. The authors have no relevant financial or non-financial interests to disclose. Author contributions Study conceptualization, study design, material preparation and analysis were performed by DH, AP, TT and BB. Data collection was performed by DH and SC. The first draft of the manuscript was written by DH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability Data generated and/or analysed during this study are available from the corresponding author upon reasonable request. References Albouaini K, Egred M, Alahmar A, Wright DJ (2007) Cardiopulmonary exercise testing and its application. 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Int Anesthesiol Clin 63(3):68-76. https://doi.org/10.1097/AIA.0000000000000481 Levett DZH, Jack S, Swart M, Carlisle J, Wilson J, Snowden C, Riley M, Danjoux G, Ward SA, Older P, Grocott MPW (2018) Perioperative cardiopulmonary exercise testing (CPET): consensus clinical guidelines on indications, organization, conduct, and physiological interpretation. Br J Anaesth 120(3):484–500. https://doi.org/10.1016/J.BJA.2017.10.020 Mannino DM, Sonia Buist A, Vollmer WM, Mannino DM (2007) Chronic obstructive pulmonary disease in the older adult: what defines abnormal lung function? Thorax 62:237–241. https://doi.org/10.1136/thx.2006.068379 McKinney J, Lithwick DJ, Morrison BN, Nazzari H, Isserow SH, Heilbron B, Krahn AD (2016) The health benefits of physical activity and cardiorespiratory fitness. BCMJ 58(3):131–137. Myers J, Cadenas-Sanchez C, Ross R, Kokkinos P (2024) The critical role of cardiorespiratory fitness in disease prevention. J Sports Med Phys Fitness 64(12):1361–1371. https://doi.org/10.23736/S0022-4707.24.16159-2 Mylius CF, Krijnen WP, van der Schans CP, Takken T (2019) Peak oxygen uptake reference values for cycle ergometry for the healthy Dutch population: data from the LowLands Fitness Registry. ERJ Open Res 5(2):00056-2018. https://doi.org/10.1183/23120541.00056-2018 Niemeijer VM, Van ’T Veer M, Schep G, Spee RF, Hoogeveen A, Kemps HMC (2014). Causes of nonlinearity of the oxygen uptake efficiency slope : a prospective study in patients with chronic heart failure. Eur J Prev Cardiol 21(3):347–353. https://doi.org/10.1177/2047487312472075 Peterman JE, Harber MP, Imboden MT, Whaley MH, Fleenor BS, Myers J, Arena R, Kaminsky LA (2021) Accuracy of Exercise-based Equations for Estimating Cardiorespiratory Fitness. Med Sci Sports Exerc 53(1):74. https://doi.org/10.1249/MSS.0000000000002435 Ross R, Blair SN, Arena R, Church TS, Despres JP, Franklin BA, Haskell WL, Kaminsky LA, Levine BD, Lavie CJ, Myers J, Niebauer J, Sallis R, Sawada SS, Sui X, Wisloff U; on behalf of the American Heart Association Physical Activity Committee of the Council on Lifestyle and Cardiometabolic Health (2016) Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association. Circulation 134(24):e653–e699. https://doi.org/10.1161/CIR.0000000000000461 Ross RM (2003) ATS/ACCP statement on cardiopulmonary exercise testing. Am J Respir Crit Care Med 167:1451. Sanada K, Kuchiki T, Miyachi M, McGrath K, Higuchi M, Ebashi H (2007) Effects of age on ventilatory threshold and peak oxygen uptake normalised for regional skeletal muscle mass in Japanese men and women aged 20-80 years. Eur J Appl Physiol 99(5):475-483. https://doi.org/10.1007/S00421-006-0375-6 Schorr M, Dichtel LE, Gerweck AV, Valera RD, Torriani M, Miller KK, Bredella MA (2018) Sex differences in body composition and association with cardiometabolic risk. Biol Sex Differ 9(1):1–10. https://doi.org/10.1186/S13293-018-0189-3/TABLES/5 Shepherd JA, Ng BK, Sommer MJ, Heymsfield SB (2017) Body composition by DXA. Bone 104:101-105. https://doi.org/10.1016/J.BONE.2017.06.010 Sui X, Sarzynski MA, Gribben N, Zhang J, Lavie CJ (2022) Cardiorespiratory Fitness and the Risk of All-Cause, Cardiovascular and Cancer Mortality in Men with Hypercholesterolemia. J Clin Med 11(17):5211. https://doi.org/10.3390/JCM11175211 Takken T, Mylius CF, Paap D, Broeders W, Hulzebos HJ, Van Brussel M, Bongers BC (2019) Reference values for cardiopulmonary exercise testing in healthy subjects–an updated systematic review. Expert Rev Cardiovasc Ther 17(6):413–426. https://doi.org/10.1080/14779072.2019.1627874 Tanaka H, Monahan KD, Seals DR (2001) Age-predicted maximal heart rate revisited. J Am Coll Cardiol. 37(1), 153–156. van Aerde M, Bommelé J, Willemsen M (2024) Smoking in the Netherlands: key statistics for 2023. https://www.trimbos.nl/aanbod/webwinkel/tri-64-011-smoking-netherlands-key-statistics-2023/. Accessed 26 May 2025 Van de Poppe DJ, Hulzebos E, Takken T, group, Low-Land Fitness Registry Study group (2018) Reference values for maximum work rate in apparently healthy Dutch/Flemish adults: data from the LowLands fitness registry. Acta Cardiol. 74(3):223-230. https://doi.org/10.1080/00015385.2018.1478763 Van der Steeg GE, Takken T (2021) Reference values for maximum oxygen uptake relative to body mass in Dutch/Flemish subjects aged 6–65 years: the LowLands Fitness Registry. Eur J Appl Physiol 121:1189-1196. https://doi.org/10.1007/s00421-021-04596-6 Van Hooren B, Souren T, Bongers BC (2024) Accuracy of respiratory gas variables, substrate, and energy use from 15 CPET systems during simulated and human exercise. Scand J Med Sci Sports 34(1):e14490. https://doi.org/10.1111/sms.14490 Wagner J, Niemeyer M, Infanger D, Hinrichs T, Streese L, Hanssen H, Myers J, Schmidt-Trucksäss A, Knaier R (2020) New data-based cutoffs for maximal exercise criteria across the lifespan. Med Sci Sports Exerc 52(9):1915–1923. https://doi.org/10.1249/MSS.0000000000002344 Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. 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Shading represents 95% confidence interval\u003c/p\u003e","description":"","filename":"Cardiorespiratoryfitnesscorrectedforbodyweightacrossage.png","url":"https://assets-eu.researchsquare.com/files/rs-6990129/v1/97dfc9707ff3fdb74d59b890.png"},{"id":85756462,"identity":"fd858e94-a32d-48cb-ad3c-af496f3d8aef","added_by":"auto","created_at":"2025-07-01 10:49:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1248399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6990129/v1/f947464e-7cd3-43bd-8d51-36b5805824d6.pdf"},{"id":85756444,"identity":"4f72f7c4-d966-46d4-9a21-e8f8c6f0bb86","added_by":"auto","created_at":"2025-07-01 10:49:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":471550,"visible":true,"origin":"","legend":"\u003cp\u003eOnline Resource 1\u003c/p\u003e","description":"","filename":"Onlineresource1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6990129/v1/695a0a86613f66882fd0d8fe.docx"},{"id":85756442,"identity":"82734f46-d476-4ce9-a85e-455baf391afa","added_by":"auto","created_at":"2025-07-01 10:49:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":49909,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6990129/v1/598694941d6238d46a197c7c.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eReference values for cardiopulmonary exercise testing-derived parameters for cardiorespiratory fitness in Dutch community-dwelling 55- to 75-year-old adults\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLow cardiorespiratory fitness (CRF) is a key predictor of adverse health outcomes in numerous chronic diseases, such as type 2 diabetes and cardiovascular disease, and all-cause mortality (Lang et al. 2024; Myers et al. 2024). CRF reflects the body\u0026apos;s ability to transport oxygen from the lungs to muscle mitochondria to perform large muscle physical activity, thereby representing the integrated function of the cardiovascular, pulmonary, and musculoskeletal systems as a clinical vital sign (Ross et al. 2016). Moderate-to-high CRF levels are associated with lower chronic disease and mortality risks and the most significant health gains are observed when moving from the lowest fitness group to a higher level (McKinney et al. 2016; Sui et al. 2022). Identifying low CRF in older adults is especially important, as measuring a low CRF at an early stage can predict the risk of developing chronic diseases later in life (Myers et al. 2024). Moreover, a low CRF has been associated with reduced tolerance to medical treatment (e.g., perioperative risk, chemotherapy/radiation intolerance) (Levett et al. 2018). Therefore, if identified at an early stage, preventive strategies may be timely initiated to mitigate the risk of a low CRF, thereby preventing negative health outcomes.\u003c/p\u003e\n\u003cp\u003eCardiopulmonary exercise testing (CPET) is the gold standard for measuring CRF, assessing oxygen uptake (⩒O\u003csub\u003e2\u003c/sub\u003e) at peak exercise (⩒O\u003csub\u003e2peak\u003c/sub\u003e) (Mezzani et al. 2009). Although ⩒O\u003csub\u003e2peak\u003c/sub\u003e is the primary indicator of CRF, many older adults fail to reach a true ⩒O\u003csub\u003e2peak\u003c/sub\u003e due to motivation and age-related limitations (Sanada et al. 2007; Wagner et al. 2020). Therefore, submaximal measures, such as the ⩒O\u003csub\u003e2\u003c/sub\u003e at the ventilatory anaerobic threshold (⩒O\u003csub\u003e2VAT\u003c/sub\u003e) that marks the onset of anaerobic metabolism, and the effort-independent oxygen uptake efficiency slope (OUES), which assesses ventilatory efficiency, seem to be good alternative CRF indicators for older adults. Both ⩒O\u003csub\u003e2VAT\u003c/sub\u003e and OUES have demonstrated to be valid surrogates for ⩒O\u003csub\u003e2peak\u003c/sub\u003e in older adults\u0026nbsp;and can therefore be used when ⩒O\u003csub\u003e2peak\u0026nbsp;\u003c/sub\u003eis unattainable (Albouaini et al. 2007; Bongers et al. 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo interpret an individual\u0026rsquo;s CRF correctly, adequate sex- and age-specific reference values for CRF, corrected for anthropometric characteristics (e.g., body height, body mass, lean body mass), are required (Ross, 2003). Specifically, it has been shown that ⩒O\u003csub\u003e2peak\u003c/sub\u003e corrected for lean body mass is the most accurate expression of CRF when available\u0026nbsp;(Imboden et al. 2020).\u0026nbsp;Despite its importance, valid reference values for ⩒O\u003csub\u003e2peak\u003c/sub\u003e, WR\u003csub\u003epeak\u003c/sub\u003e, ⩒O\u003csub\u003e2VAT\u003c/sub\u003e, and OUES in older adults remain limited, often due to a sample without older adults (Buys et al. 2015; Van de Poppe et al. 2018; van der Steeg and Takken 2021) or studies using an estimation for CRF, rather than measuring respiratory gas analysis directly, which has shown to provide errors in interpretation of CRF (Peterman et al. 2021). Lastly, criteria for a maximal effort are often set too low (i.e., respiratory exchange ratio at peak exercise (RER\u003csub\u003epeak\u003c/sub\u003e) \u0026gt; 1.00), or not in line with recent suggestions to evaluate maximal effort based on age-dependent cut-off points (Wagner et al. 2020).\u003c/p\u003e\n\u003cp\u003eThis study aimed to establish reference values for absolute ⩒O\u003csub\u003e2peak\u003c/sub\u003e,\u003csub\u003e\u0026nbsp;\u003c/sub\u003ework rate at peak exercise (WR\u003csub\u003epeak\u003c/sub\u003e),\u0026nbsp;⩒O\u003csub\u003e2VAT\u003c/sub\u003e, and OUES in Dutch adults aged 55-75 years, as well as corrected for body mass and lean body mass. Reference values will be provided separately for females and males across the whole age range to examine the effects of sex and age on CRF. Males are expected to show higher values than females, and younger participants are expected to have higher values than older participants (van der Steeg and Takken 2021). Correlations between ⩒O\u003csub\u003e2peak\u003c/sub\u003e and other CRF measures (i.e., WR\u003csub\u003epeak\u003c/sub\u003e, ⩒O\u003csub\u003e2VAT\u003c/sub\u003e, and OUES) will also be analysed to assess their usefulness as alternatives to ⩒O\u003csub\u003e2peak\u003c/sub\u003e. High correlations are expected between all CRF variables. \u003c/p\u003e\n"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eExperimental design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study utilized data from the AMersfoort COhort Study on functional decline, Healthy aging, and Frailty (AMCOHF). Ethical approval was granted by the Medical Ethical Committee Zuyderland (Heerlen, The Netherlands) under reference number NL70141.096.19 (19 September 2019) and patient inclusion and data collection started in June 2022. The trial protocol has been published elsewhere (Houtkamp et al. in press) and is registered on Open Science Framework (DOI: 10.17605/OSF.IO/RMBQV).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were Dutch community-dwelling older adults\u0026nbsp;aged 55-75 years\u0026nbsp;from the city of Amersfoort, the Netherlands. Inclusion required informed consent, followed by a physician-led medical screening to rule out contraindications for maximal exercise testing. This screening included medical history and medication review, auscultation, baseline measures of blood pressure and resting electrocardiography. Comorbidities were documented using the Rockwood Frailty Index as operationalized by\u0026nbsp;Collerton et al. (2012).\u003c/p\u003e\n\u003cp\u003eParticipants were excluded for the current study if they reported comorbidities to the physician that are likely to limit exercise capacity, including severe cardiovascular disease (e.g., ischemic heart disease, cerebrovascular disease, peripheral vascular disease, or heart failure). Participants who reported to suffer from chronic lung disease (e.g., chronic obstructive pulmonary disease or asthma) were excluded if the ratio between the forced expiratory volume in 1 s and forced vital capacity (Tiffeneau index) was \u0026lt; 0.70 (Mannino et al. 2007). Additionally, exclusion criteria for the AMCOHF cohort study involved active cancer, significant physical or cognitive impairment (i.e., mini mental state examination score \u0026lt; 23) according to previously described recommendations (Folstein et al. 1975), and having undergone surgery, cancer, chemotherapy or radiotherapy in the last 6 months.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCardiorespiratory fitness assessment\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCRF was assessed using incremental CPET on a cycle ergometer (Lode Corival Rehab, Lode BV, Groningen, Netherlands). Participants wore a facemask (Hans Rudolph, Kansas City, MO, USA) connected to an ergospirometry system (Metalyzer 3B, Cortex, Leipzig, Germany), calibrated for respiratory gas analysis and volume measurements. The Cortex Metalyzer 3B exhibited a measurement error of 2.85 \u0026plusmn; 2.22%\u0026nbsp;(Van Hooren et al. 2024). Forced vital capacity and forced expiratory volume in 1 second were measured before testing. Baseline cardiopulmonary values were recorded over a three-minute rest period, followed by a three-minute unloaded cycling warm-up. The work rate was then incrementally increased by 15-30 W/min according to a ramp protocol, tailored to each participant\u0026rsquo;s estimated fitness level to achieve maximal effort within 8-12 minutes\u0026nbsp;(Glaab and Taube 2022). Participants maintained a pedaling frequency between 60-80 rotations/min until they reached exhaustion despite verbal encouragement or until they reached criteria for termination based on the American Thoracic Society/American College of Chest Physicians guidelines\u0026nbsp;(Ross 2003). After termination of the incremental protocol, participants proceeded in a 1-minute cooldown period consisting of a constant load of 50 W. Breath-by-breath\u0026nbsp;⩒O\u003csub\u003e2\u003c/sub\u003e, carbon dioxide production (V̇CO\u003csub\u003e2\u003c/sub\u003e), and respiratory exchange ratio (RER) were calculated and averaged over ten-second intervals. Heart rate (HR) was continuously monitored by twelve-lead electrocardiography. Our study protocol aimed to collect CRF reference data using the quality criteria for CPET standards as defined previously (Takken et al. 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBody composition assessment\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBody height and body mass were measured to the nearest 0.1 cm and 0.1 kg, respectively, and body mass index (BMI) was calculated subsequently. Lean body mass was evaluated using a fan beam dual-energy X-ray absorptiometry (DXA) device (GE Medical Systems Lunar Prodigy, Madison, Wisconsin, USA). This enables the determination of lean body mass and whole-body fat percentage and is considered highly accurate (Shepherd et al. 2017). Waist circumference was measured to compare body composition with previous studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor descriptive analyses, participants were divided in 5-year age groups except for the last age group (i.e., 70-75 years) to ensure enough samples. A maximal cardiorespiratory effort during CPET was defined as reaching the age-dependent cut-off points for RER\u003csub\u003epeak\u003c/sub\u003e (i.e., \u0026ge;1.10 for age group 55-59 or \u0026ge;1.06 for age group 60-75) or peak heart rate (\u0026ge;92% of 208 \u0026ndash; 0.7 \u0026times; age for age group 55-59 or \u0026ge;89% for age group 60-75) (Tanaka et al. 2001; Wagner et al. 2020). The work rate at peak exercise (WR\u003csub\u003epeak\u003c/sub\u003e) was the highest attained value. Data from other outcome variables were averaged over 30 s of exercise. Non-maximal efforts were excluded from the analysis of\u0026nbsp;⩒O\u003csub\u003e2peak\u003c/sub\u003e and WR\u003csub\u003epeak\u003c/sub\u003e but were used for analysis of submaximal or effort-independent CRF parameters (i.e., respectively ⩒O\u003csub\u003e2VAT\u003c/sub\u003e and OUES). The ⩒O\u003csub\u003e2VAT\u003c/sub\u003e was determined primarily using the modified V-slope method (i.e., the point at which the linear relationship between the V̇CO\u003csub\u003e2\u003c/sub\u003e and ⩒O\u003csub\u003e2\u003c/sub\u003e changed). This point was verified using the ventilatory equivalents method (i.e., the point at which the ventilatory equivalent for oxygen and the partial end-tidal oxygen tension reached its lowest point after which it began to increase in a consistent manner, although the ventilatory equivalent for carbon dioxide and partial end tidal carbon dioxide tension remained constant). Detailed description of the methods can be found in previously described guidelines (Franssen et al. 2022). Cases with ambiguous ⩒O\u003csub\u003e2VAT\u003c/sub\u003e were resolved by consensus with two experts (BB and DH). The oxygen uptake efficiency slope (OUES) was calculated using previously described formula (i.e., a \u0026times; log\u003csub\u003e10\u003c/sub\u003e V̇E + b, where \u0026ldquo;a\u0026rdquo; represents the OUES and \u0026ldquo;b\u0026rdquo; the intercept) (Baba et al. 1996). Data from one minute after the start of the test up (i.e., to prevent the noisy ⩒O\u003csub\u003e2\u003c/sub\u003eat the start from influencing the OUES) to ⩒O\u003csub\u003e2peak\u003c/sub\u003e. When a plateau in\u0026nbsp;⩒O\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ewas detected, data up to the onset of the\u0026nbsp;⩒O\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eplateau were used to determine the OUES, in accordance with prior recommendations (Niemeijer et al. 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral other cardiorespiratory parameters were determined (i.e., RER, heart rate, oxygen pulse, minute ventilation, tidal volume, and breathing frequency at peak exercise, and the slope between the minute ventilation and\u0026nbsp;V̇CO\u003csub\u003e2\u003c/sub\u003e). The slope between the minute ventilation and\u0026nbsp;V̇CO\u003csub\u003e2\u003c/sub\u003e (V̇E/ V̇CO\u003csub\u003e2\u003c/sub\u003e-slope)\u0026nbsp;was calculated up to the respiratory compensation point (RCP) which was determined using the interpretative guidelines as described before\u0026nbsp;(Franssen et al. 2022). Cases with ambiguous RCP were resolved by consensus of the same two experts (BB and DH). When the RCP was not visible, the\u0026nbsp;V̇E/V̇CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eslope was calculated using data up to peak exercise.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using R version 4.4.2. (R Foundation for Statistical Computing, Vienna, Austria). Data are presented as mean (SD) and statistical significance was set at p \u0026lt; 0.05. Independent t-tests assessed sex differences, while age effects were evaluated using one-way ANOVA with Bonferroni post-hoc adjustments when needed. To assess the relationships between CPET-derived CRF variables (i.e.,\u0026nbsp;⩒O\u003csub\u003e2peak\u003c/sub\u003e, WR\u003csub\u003epeak\u003c/sub\u003e,\u0026nbsp;⩒O\u003csub\u003e2VAT\u003c/sub\u003e, and OUES), Pearson\u0026rsquo;s correlation coefficients were calculated. In cases where the assumptions of normality were not met, Spearman\u0026rsquo;s rank coefficients were used. Correlation strength was interpreted as follows: \u003cem\u003er\u003c/em\u003e \u0026lt; 0.30 = weak, 0.30\u0026ndash;0.59 = moderate, and \u0026ge;0.60 = strong (Cohen 2013). Reference centiles (P3, P25, P50, P75, P97) were derived using generalized additive models (GAM) which has previously shown to be the best fitting with highest predictive accuracy compared to linear or polynomial model (Mylius et al. 2019). Model performance of the GAM was assessed using adjusted R\u003csup\u003e2\u003c/sup\u003e. All body mass- and lean body mass-corrected CRF parameters were obtained by dividing absolute values by body mass and lean body mass. This was done to facilitate comparisons across differences in body size and body composition respectively. Descriptive statistics were calculated by five-year age groups and sex.\u003c/p\u003e\n"},{"header":"Results","content":"\u003cp\u003eA total of 661 subjects were included in the final analysis (336 females and 325 males). Participants were equally distributed across five-year intervals from 55 to 75 years, with at least 50 participants representing each age-group. A flow diagram of the study is shown in Figure 1. Participant characteristics, stratified by sex and age group and including experimental CPET results (i.e., ⩒O\u003csub\u003e2peak\u003c/sub\u003e, WR\u003csub\u003epeak\u003c/sub\u003e, ⩒O\u003csub\u003e2VAT\u003c/sub\u003e, and OUES) are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiopulmonary exercise testing-derived parameters of cardiorespiratory fitness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReference values for CPET-derived CRF parameters, corrected for body mass and estimated using GAM by sex and 5-year age groups, are presented in Table 2 for maximal CRF-variables (i.e.,\u0026nbsp;⩒O\u003csub\u003e2peak\u003c/sub\u003e and WR\u003csub\u003epeak\u003c/sub\u003e) and in Table 3 for submaximal (i.e., ⩒O\u003csub\u003e2VAT\u003c/sub\u003e) and effort-independent (i.e., OUES) variables. Reference values with age as continuous variable are shown in Figure 2. Similar analyses indexed for absolute reference values and values corrected for lean body mass are available in Online Resource 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of sex and age on cardiorespiratory fitness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll absolute and body mass-corrected CPET-derived CRF variables were significantly higher in males compared to females across all age groups (p \u0026lt; 0.001), but not when corrected for lean body mass except for WR\u003csub\u003epeak\u003c/sub\u003e (p = 0.027). Additionally, increasing age was associated with a decline in all CRF variables (p = 0.048 for ⩒O\u003csub\u003e2VAT\u003c/sub\u003e corrected for lean body mass; all other p-values: p \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations between variables of cardiorespiratory fitness\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman correlation coefficients were calculated between all absolute CRF variables. The analyses\u0026nbsp;revealed significant positive correlations between ⩒O\u003csub\u003e2peak\u003c/sub\u003e and, respectively, WR\u003csub\u003epeak\u003c/sub\u003e (\u0026rho; = 0.90, p \u0026lt; 0.001), ⩒O\u003csub\u003e2VAT\u0026nbsp;\u003c/sub\u003e(\u0026rho; = 0.78, p \u0026lt; 0.001), and OUES (\u0026rho; = 0.87, p \u0026lt; 0.001), indicating strong associations between these CPET-derived variables in older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReference values\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrediction equations to calculate absolute and body mass-corrected CRF parameters were developed using multiple linear regression models and can be found in Table 4. Covariates include sex (0 = female, 1 = male) and three continues variables (i.e., body mass, body height, and age).\u0026nbsp;\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThis study provides reference values for maximal and submaximal CPET-derived parameters of CRF in Dutch community-dwelling older adults aged 55-75 years. In addition, results also show robust relationships between ⩒O\u003csub\u003e2peak\u003c/sub\u003e and surrogate measures of CRF (i.e., WR\u003csub\u003epeak\u003c/sub\u003e, ⩒O\u003csub\u003e2VAT\u003c/sub\u003e, and OUES), highlighting their potential to evaluate CRF in older adults when respiratory gas analysis is unavailable (i.e., WR\u003csub\u003epeak\u003c/sub\u003e) or when a participant is not able to deliver a maximal effort (i.e., ⩒O\u003csub\u003e2VAT\u003c/sub\u003e and OUES). Furthermore, results showed that sex differences between all CRF variables could mainly be attributed to differences in lean body mass, as indicated by far greater differences in absolute values or values corrected for body mass compared to CRF parameters corrected for lean body mass (Online Resource 1). Therefore, it is recommended to correct CPET-derived CRF outcomes for lean body mass if resources allow.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo the authors\u0026rsquo; knowledge, this is the first study to provide sex- and age-specific reference values for maximal (i.e., ⩒O\u003csub\u003e2peak\u003c/sub\u003e and WR\u003csub\u003epeak\u003c/sub\u003e) and submaximal (i.e., ⩒O\u003csub\u003e2VAT\u003c/sub\u003e and OUES) CPET-derived parameters of CRF in absolute terms, as well as corrected for body mass and lean body mass in older adults. Currently utilized Dutch reference values do not include participants up to an age of 75 years and are not based on such a large and well-characterized cohort, nor have they been developed using GAM, which has shown to outperform linear or polynomial models (Mylius et al. 2019). Additionally, including submaximal reference data is important for assessing CRF in older adults who are unable to perform a cardiorespiratory maximal effort during CPET.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e⩒O\u003csub\u003e2peak\u003c/sub\u003e declined on average by 1.3% (0.02 L/min) per year for females and 2.5% (0.07 L/min) per year for males. This decline can partly be attributed to changes in body composition, as the decline in ⩒O\u003csub\u003e2peak\u003c/sub\u003e was lower when corrected for body mass (females: 0.8% or 0.2 mL/kg/min per year; males: 1.5% or 0.5 mL/kg/min per year) or lean body mass (females: 0.9% or 0.4 mL/kg lean body mass/min per year; males: 1.5% or 0.7 mL/kg lean body mass/min per year). This illustrates that the decline in ⩒O\u003csub\u003e2peak\u003c/sub\u003e in older adults is comparable to the decline of approximately one percent per year over the course of a lifetime, as has been typically described (Kenney et al. 2022). Additionally, the decline in ⩒O\u003csub\u003e2peak\u0026nbsp;\u003c/sub\u003eper year observed within the youngest age group was comparable with the decline observed in the oldest age group for females (55\u0026ndash;60 years: 0.02 L/min or 1.1%; 70\u0026ndash;75 years: 0.02 L/min or 1.2%) and in absolute terms for males (55\u0026ndash;60 years: 0.08 L/min or 2.5%; 70\u0026ndash;75 years: 0.09 L/min or 4.2%), although the relative decline (in percentage) was slightly higher in the oldest age group, highlighting a quasi-linear trend of decline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with other maximal reference data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to recent reference values from the United States by Kaminsky et al. (2022), this study reported significantly higher values for both ⩒O\u003csub\u003e2peak\u003c/sub\u003e (males: +10.9 mL/kg/min; females: +11.7 mL/kg/min) as well as WR\u003csub\u003epeak\u003c/sub\u003e (males: +96 W; females: +82 W) in healthy older adults aged around 55 years (p \u0026lt; 0.001 for all values). These differences might partly be due to differences in exercise protocol, as this was not uniform across all data collection sites in that study. In comparison to previous Dutch references values, GAM-predicted ⩒O\u003csub\u003e2peak\u003c/sub\u003e was slightly lower (p \u0026lt; 0.05) than those recently reported from the Lowlands Fitness Registry (males: 1.4 mL/kg/min; females: 1.6 mL/kg/min) around 55 years of age (van der Steeg and Takken 2021). However, these differences were substantially smaller than those observed with earlier reference values and support the potential integration of our data into the Lowlands Fitness Registry to expand the age range to 75 years. These findings highlight the importance of using context-specific reference values to ensure accurate interpretation. In accordance to these findings, GAM-predicted WR\u003csub\u003epeak\u003c/sub\u003e in this study was also significantly higher (p \u0026lt; 0.001) compared to previously described Dutch reference data by Van de Poppe et al. (2018), for age groups 55 (females: 187 vs 165 W; males: 293 vs 251 W) and 60 (females: 177 vs 148 W; males: 263 vs 221 W). However, data collection in that study was also not uniform across all measurements, which might explain part of these differences. The presented GAM-predicted OUES values in this study were slightly higher for females (\u0026Delta;OUES: 243, p \u0026lt; 0.05), but not for males (\u0026Delta;OUES: 158, p = 0.14) aged 55 and 60 years compared to previous reference values from Belgium (Buys et al. 2015). Although it has been well documented that ⩒O\u003csub\u003e2peak\u003c/sub\u003e and ⩒O\u003csub\u003e2VAT\u003c/sub\u003e decline with age, reference values in especially the highest age categories and in females are currently still lacking (van der Steeg and Takken 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings\u0026nbsp;align with previous literature demonstrating that the age-related CRF decline is partly mitigated when corrected for lean body mass (Kim et al. 2016). Furthermore, the recommendation of correcting ⩒O\u003csub\u003e2peak\u003c/sub\u003e for lean body mass when possible is in accordance to previous suggestions (Imboden et al. 2020; K\u0026ouml;hler et al. 2018). Nevertheless, the decline in cardiovascular capacity has been described as one of the prominent factors to contribute to a decline in CRF (Kenney et al. 2022). In contrast to the correction for body mass, sex differences between all four CRF variables seem to disappear almost completely when looking at the CRF variables corrected for lean body mass. This in accordance with previous literature demonstrating a higher fat percentage per given amount of body mass in females compared to males (Schorr et al. 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrength and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study protocol met 11 out of 14 quality criteria for CPET standards (Takken et al. 2019). Another strength is that this study used recently developed cut-off points for RER\u003csub\u003epeak\u003c/sub\u003e to determine whether a maximal effort was reached (Wagner et al. 2020). These cut-off points were age-dependent, compared to a large number of studies using a RER\u003csub\u003epeak\u003c/sub\u003e cut-off of \u0026ge;1.00, potentially leading to inclusion of submaximal cardiorespiratory efforts. Furthermore, these data were collected at one laboratory setting preventing heterogeneity between multiple testing facilities done in previous studies. However, this study also has some limitations. Although people were randomly invited to participate in this study, a potential selection bias of healthy physically active people could have led to an overestimation of the CRF level of the participants. This may be substantiated by the relatively few smokers in our sample compared to the average Dutch population (van Aerde et al. 2024). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReference values for CRF are of pivotal importance because they provide a benchmark to assess an individual\u0026rsquo;s health status, detect functional decline at an early stage, and guide personalized interventions for healthy aging. With the aging Dutch population, establishing adequate reference values for older adults is essential for clinical decision-making, for example in geriatric, cardiac, and pulmonary settings. Additionally, in sports medicine, reference data for apparently healthy older individuals are necessary, as the growing participation of master athletes in endurance events often requires medical evaluations, such as CPET, as part of the entry requirement to ensure safe participation. Applications include for example preoperative evaluations, helping to identify individuals at risk of postoperative complications, enabling targeted prehabilitation interventions to optimize surgical outcomes (Levett and Grocott 2025). Additionally, CPET-based assessments allow for early detection of a declining intrinsic capacity as proposed by the WHO. Without appropriate norm values, distinguishing between physiological aging and pathological declines in CRF remains challenging, potentially leading to inadequate risk assessment and suboptimal clinical decision-making.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe inclusion of corrections for both body mass and lean body mass enhances the applicability of this dataset by reducing misclassification due to age-related changes in body composition. Additionally, establishing reference values for submaximal parameters also enhances the applicability in older populations by providing benchmarks for a good, average, or poor CRF based on sex and age. Future studies presenting reference values should provide these based on longitudinal data. By monitoring the trajectory of an individual\u0026rsquo;s CRF, potential risk factors for a declining CRF may be discovered. In addition, physical activity data should be considered to make sure no selection bias occurs. Lastly, larger databases would allow for computation of body height- and body mass-specific reference values within sex- and age-specific groups.\u003c/p\u003e\n"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides reference values for CRF in Dutch older adults aged between 55-75 years during cycle ergometer CPET. All CRF variables declined with age, with an average ⩒O\u003csub\u003e2peak\u003c/sub\u003e decline of 1.3% per year for females and 2.5% for males. Sex differences were present between all absolute and body mass-corrected CRF variables, but not when corrected for lean body mass. Furthermore, WR\u003csub\u003epeak\u003c/sub\u003e, ⩒O\u003csub\u003e2VAT\u003c/sub\u003e, and OUES can be used as practical alternative measures for ⩒O\u003csub\u003e2peak\u003c/sub\u003e. Together, these reference values can be used to estimate an individual\u0026rsquo;s CRF for decision-making in different clinical settings.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMCOHF: AMersfoort COhort Study on functional decline, Healthy aging, and Frailty\u003c/p\u003e\n\u003cp\u003eBF\u003csub\u003epeak\u003c/sub\u003e: breathing frequency at peak exercise\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u003c/p\u003e\n\u003cp\u003eCPET: cardiopulmonary exercise testing\u003c/p\u003e\n\u003cp\u003eCRF: cardiorespiratory fitness\u003c/p\u003e\n\u003cp\u003eDBP: diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eDXA: dual-energy X-ray\u0026nbsp;absorptiometry\u003c/p\u003e\n\u003cp\u003eGAM: generalized additive models\u003c/p\u003e\n\u003cp\u003eHR: heart rate\u003c/p\u003e\n\u003cp\u003eHR\u003csub\u003epeak\u003c/sub\u003e: heart rate at peak exercise\u003c/p\u003e\n\u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e-pulse\u003csub\u003epeak\u003c/sub\u003e: oxygen pulse at peak exercise\u003c/p\u003e\n\u003cp\u003eOUES: oxygen uptake efficiency slope\u003c/p\u003e\n\u003cp\u003eRCP: respiratory compensation point\u003c/p\u003e\n\u003cp\u003eRER: respiratory exchange ratio\u003c/p\u003e\n\u003cp\u003eRER\u003csub\u003epeak\u003c/sub\u003e: respiratory exchange ratio at peak exercise\u003c/p\u003e\n\u003cp\u003eSBP: systolic blood pressure\u003c/p\u003e\n\u003cp\u003e⩒CO\u003csub\u003e2\u003c/sub\u003e:\u0026nbsp;carbon dioxide production\u003c/p\u003e\n\u003cp\u003eVE/VCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eslope: slope between the minute ventilation and carbon dioxide production up to the respiratory compensation point\u003c/p\u003e\n\u003cp\u003e⩒E\u003csub\u003epeak\u003c/sub\u003e: minute ventilation at peak exercise\u003c/p\u003e\n\u003cp\u003e⩒O\u003csub\u003e2\u003c/sub\u003e: oxygen uptake\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e⩒O\u003csub\u003e2peak\u003c/sub\u003e: oxygen uptake\u0026nbsp;at peak exercise\u003c/p\u003e\n\u003cp\u003e⩒O\u003csub\u003e2VAT\u003c/sub\u003e: oxygen uptake at the ventilatory anaerobic threshold\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVT\u003csub\u003epeak\u003c/sub\u003e: tidal volume at peak exercise\u003c/p\u003e\n\u003cp\u003eWR\u003csub\u003epeak\u003c/sub\u003e: work rate at peak exercise\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u0026nbsp;The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conceptualization, study design, material preparation and analysis were performed by DH, AP, TT and BB. Data collection was performed by DH and SC. The first draft of the manuscript was written by DH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData generated and/or analysed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbouaini K, Egred M, Alahmar A, Wright DJ (2007) Cardiopulmonary exercise testing and its application. Heart 93(10):1285\u0026ndash;1292. https://doi.org/10.1136/hrt.2007.121558\u003c/li\u003e\n\u003cli\u003eBaba R, Nagashima M, Goto M, Nagano Y, Yokota M, Tauchi N, Nishibata K (1996) Oxygen uptake efficiency slope: A new index of cardiorespiratory functional reserve derived from the relation between oxygen uptake and minute ventilation during incremental exercise. JACC 28(6):1567\u0026ndash;1572. https://doi.org/10.1016/S0735-1097(96)00412-3\u003c/li\u003e\n\u003cli\u003eBongers BC, Berkel AE, Klaase JM, Van Meeteren NL (2017) An evaluation of the validity of the pre-operative oxygen uptake efficiency slope as an indicator of cardiorespiratory fitness in elderly patients scheduled for major colorectal surgery. Anaesthesia 72(10):1206-1216. https://doi.org/10.1111/anae.14003\u003c/li\u003e\n\u003cli\u003eBuys R, Coeckelberghs E, Vanhees L, Cornelissen VA (2015) The oxygen uptake efficiency slope in 1411 Caucasian healthy men and women aged 20\u0026ndash;60 years: reference values. 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J Psychiatr Res 12(3):189\u0026ndash;198. http://www.ncbi.nlm.nih.gov/pubmed/1202204 \u003c/li\u003e\n\u003cli\u003eFranssen RFW, Eversdijk AJJ, Kuikhoven M, Klaase JM, Vogelaar FJ, Janssen-Heijnen MLG, Bongers BC (2022) Inter-observer agreement of preoperative cardiopulmonary exercise test interpretation in major abdominal surgery. BMC Anesthesiol 22(1):1\u0026ndash;11. https://doi.org/10.1186/S12871-022-01680-Y/TABLES/3\u003c/li\u003e\n\u003cli\u003eGlaab T, Taube C (2022) Practical guide to cardiopulmonary exercise testing in adults. Respir Res 23(1). https://doi.org/10.1186/S12931-021-01895-6\u003c/li\u003e\n\u003cli\u003eHoutkamp D, Chettouf S, Bongers BC, Van de Wiel A, Van Roy P, Schrama P, Beckw\u0026eacute;e D, Smeets WHAM, Bautmans I, Pool-Goudzwaard AL (in press) Study Protocol of the 10-year longitudinal Amersfoort Cohort Study on Functional decline, Healthy aging, and Frailty (AMCOHF) in a community-dwelling older population. 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Human Kinetics.\u003c/li\u003e\n\u003cli\u003eKim CH, Wheatley CM, Behnia M, Johnson BD (2016) The Effect of Aging on Relationships between Lean Body Mass and VO2max in Rowers. PloS one 11(8):e0160275. https://doi.org/10.1371/journal.pone.0160275\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;hler A, King R, Bahls M, Gro\u0026szlig; S, Steveling A, G\u0026auml;rtner S, Schipf S, Gl\u0026auml;ser S, V\u0026ouml;lzke H, Felix SB, Markus MRP, D\u0026ouml;rr M (2018) Cardiopulmonary fitness is strongly associated with body cell mass and fat-free mass: The Study of Health in Pomerania (SHIP). Scand J Med Sci Sports 28(6):1628\u0026ndash;1635. https://doi.org/10.1111/SMS.13057 \u003c/li\u003e\n\u003cli\u003eLang JJ, Prince SA, Merucci K, Cadenas-Sanchez C, Chaput JP, Fraser BJ, Manyanga T, McGrath R, Ortega FB, Singh B, Tomkinson GR (2024). Cardiorespiratory fitness is a strong and consistent predictor of morbidity and mortality among adults: an overview of meta-analyses representing over 20.9 million observations from 199 unique cohort studies. Br J Sports Med, 58(10):556\u0026ndash;566. https://doi.org/10.1136/BJSPORTS-2023-107849\u003c/li\u003e\n\u003cli\u003eLevett DZH, Grocott MPW (2025) Prehabilitation: Impact on Postoperative Outcomes. Int Anesthesiol Clin 63(3):68-76. https://doi.org/10.1097/AIA.0000000000000481\u003c/li\u003e\n\u003cli\u003eLevett DZH, Jack S, Swart M, Carlisle J, Wilson J, Snowden C, Riley M, Danjoux G, Ward SA, Older P, Grocott MPW (2018) Perioperative cardiopulmonary exercise testing (CPET): consensus clinical guidelines on indications, organization, conduct, and physiological interpretation. Br J Anaesth 120(3):484\u0026ndash;500. https://doi.org/10.1016/J.BJA.2017.10.020 \u003c/li\u003e\n\u003cli\u003eMannino DM, Sonia Buist A, Vollmer WM, Mannino DM (2007) Chronic obstructive pulmonary disease in the older adult: what defines abnormal lung function? Thorax 62:237\u0026ndash;241. https://doi.org/10.1136/thx.2006.068379\u003c/li\u003e\n\u003cli\u003eMcKinney J, Lithwick DJ, Morrison BN, Nazzari H, Isserow SH, Heilbron B, Krahn AD (2016) The health benefits of physical activity and cardiorespiratory fitness. BCMJ 58(3):131\u0026ndash;137. \u003c/li\u003e\n\u003cli\u003eMyers J, Cadenas-Sanchez C, Ross R, Kokkinos P (2024) The critical role of cardiorespiratory fitness in disease prevention. J Sports Med Phys Fitness 64(12):1361\u0026ndash;1371. https://doi.org/10.23736/S0022-4707.24.16159-2\u003c/li\u003e\n\u003cli\u003eMylius CF, Krijnen WP, van der Schans CP, Takken T (2019) Peak oxygen uptake reference values for cycle ergometry for the healthy Dutch population: data from the LowLands Fitness Registry. ERJ Open Res 5(2):00056-2018. https://doi.org/10.1183/23120541.00056-2018\u003c/li\u003e\n\u003cli\u003eNiemeijer VM, Van \u0026rsquo;T Veer M, Schep G, Spee RF, Hoogeveen A, Kemps HMC (2014). Causes of nonlinearity of the oxygen uptake efficiency slope : a prospective study in patients with chronic heart failure. Eur J Prev Cardiol 21(3):347\u0026ndash;353. https://doi.org/10.1177/2047487312472075\u003c/li\u003e\n\u003cli\u003ePeterman JE, Harber MP, Imboden MT, Whaley MH, Fleenor BS, Myers J, Arena R, Kaminsky LA (2021) Accuracy of Exercise-based Equations for Estimating Cardiorespiratory Fitness. Med Sci Sports Exerc 53(1):74. https://doi.org/10.1249/MSS.0000000000002435\u003c/li\u003e\n\u003cli\u003eRoss R, Blair SN, Arena R, Church TS, Despres JP, Franklin BA, Haskell WL, Kaminsky LA, Levine BD, Lavie CJ, Myers J, Niebauer J, Sallis R, Sawada SS, Sui X, Wisloff U; on behalf of the American Heart Association Physical Activity Committee of the Council on Lifestyle and Cardiometabolic Health (2016) Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association. Circulation 134(24):e653\u0026ndash;e699. https://doi.org/10.1161/CIR.0000000000000461\u003c/li\u003e\n\u003cli\u003eRoss RM (2003) ATS/ACCP statement on cardiopulmonary exercise testing. Am J Respir Crit Care Med 167:1451. \u003c/li\u003e\n\u003cli\u003eSanada K, Kuchiki T, Miyachi M, McGrath K, Higuchi M, Ebashi H (2007) Effects of age on ventilatory threshold and peak oxygen uptake normalised for regional skeletal muscle mass in Japanese men and women aged 20-80 years. Eur J Appl Physiol 99(5):475-483. https://doi.org/10.1007/S00421-006-0375-6\u003c/li\u003e\n\u003cli\u003eSchorr M, Dichtel LE, Gerweck AV, Valera RD, Torriani M, Miller KK, Bredella MA (2018) Sex differences in body composition and association with cardiometabolic risk. Biol Sex Differ 9(1):1\u0026ndash;10. https://doi.org/10.1186/S13293-018-0189-3/TABLES/5\u003c/li\u003e\n\u003cli\u003eShepherd JA, Ng BK, Sommer MJ, Heymsfield SB (2017) Body composition by DXA. Bone 104:101-105. https://doi.org/10.1016/J.BONE.2017.06.010\u003c/li\u003e\n\u003cli\u003eSui X, Sarzynski MA, Gribben N, Zhang J, Lavie CJ (2022) Cardiorespiratory Fitness and the Risk of All-Cause, Cardiovascular and Cancer Mortality in Men with Hypercholesterolemia. J Clin Med 11(17):5211. https://doi.org/10.3390/JCM11175211\u003c/li\u003e\n\u003cli\u003eTakken T, Mylius CF, Paap D, Broeders W, Hulzebos HJ, Van Brussel M, Bongers BC (2019) Reference values for cardiopulmonary exercise testing in healthy subjects\u0026ndash;an updated systematic review. Expert Rev Cardiovasc Ther 17(6):413\u0026ndash;426. https://doi.org/10.1080/14779072.2019.1627874\u003c/li\u003e\n\u003cli\u003eTanaka H, Monahan KD, Seals DR (2001) Age-predicted maximal heart rate revisited. J Am Coll Cardiol. 37(1), 153\u0026ndash;156.\u003c/li\u003e\n\u003cli\u003evan Aerde M, Bommel\u0026eacute; J, Willemsen M (2024) Smoking in the Netherlands: key statistics for 2023. https://www.trimbos.nl/aanbod/webwinkel/tri-64-011-smoking-netherlands-key-statistics-2023/. Accessed 26 May 2025 \u003c/li\u003e\n\u003cli\u003eVan de Poppe DJ, Hulzebos E, Takken T, group, Low-Land Fitness Registry Study group (2018) Reference values for maximum work rate in apparently healthy Dutch/Flemish adults: data from the LowLands fitness registry. Acta Cardiol. 74(3):223-230. https://doi.org/10.1080/00015385.2018.1478763 \u003c/li\u003e\n\u003cli\u003eVan der Steeg GE, Takken T (2021) Reference values for maximum oxygen uptake relative to body mass in Dutch/Flemish subjects aged 6\u0026ndash;65 years: the LowLands Fitness Registry. Eur J Appl Physiol 121:1189-1196. https://doi.org/10.1007/s00421-021-04596-6\u003c/li\u003e\n\u003cli\u003eVan Hooren B, Souren T, Bongers BC (2024) Accuracy of respiratory gas variables, substrate, and energy use from 15 CPET systems during simulated and human exercise. Scand J Med Sci Sports 34(1):e14490. https://doi.org/10.1111/sms.14490\u003c/li\u003e\n\u003cli\u003eWagner J, Niemeyer M, Infanger D, Hinrichs T, Streese L, Hanssen H, Myers J, Schmidt-Trucks\u0026auml;ss A, Knaier R (2020) New data-based cutoffs for maximal exercise criteria across the lifespan. Med Sci Sports Exerc 52(9):1915\u0026ndash;1923. https://doi.org/10.1249/MSS.0000000000002344\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SOMT University of Physiotherapy","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiopulmonary Exercise Test, Reference Values, Aging, Oxygen Consumption, Anaerobic threshold, Physical fitness","lastPublishedDoi":"10.21203/rs.3.rs-6990129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6990129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eAccurate interpretation of cardiorespiratory fitness (CRF) requires reference values that account for sex, age, and body composition. Existing reference values often lack these distinctions or exclude older adults. This study aimed to establish sex- and age-specific reference values for absolute and relative (body mass-corrected and lean body mass-corrected) CRF parameters derived from cardiopulmonary exercise testing (CPET) in Dutch community-dwelling 55- to 75-year-old adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eCross-sectional data from 611 participants of the AMCOHF study were analysed. CRF was assessed via cycle ergometer CPET evaluating oxygen uptake (⩒O\u003csub\u003e2peak\u003c/sub\u003e) and work rate (WR\u003csub\u003epeak\u003c/sub\u003e) at peak exercise, oxygen uptake at the ventilatory anaerobic threshold (⩒O\u003csub\u003e2VAT\u003c/sub\u003e), and oxygen uptake efficiency slope (OUES). Body mass and lean body mass were measured using dual-energy X-ray absorptiometry. Reference values stratified by sex and age were developed using generalized additive models. Prediction equations were generated using \u003cstrong\u003emultiple linear regression\u003c/strong\u003e. Correlations with ⩒O\u003csub\u003e2peak\u003c/sub\u003e assessed the usefulness of ⩒O\u003csub\u003e2VAT\u003c/sub\u003e and OUES as submaximal and effort-independent alternatives for CRF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAll CRF variables declined with age. ⩒O\u003csub\u003e2peak\u003c/sub\u003e (L/min) declined quasi-linearly (females: 1.3%/year; males: 2.5%/year).\u003cstrong\u003e \u003c/strong\u003eSignificant sex-differences were observed between all CRF-variables (absolute and body mass-corrected values: p \u0026lt; 0.001; lean body mass-corrected values: p \u0026lt; 0.05). Significant correlations were found between ⩒O\u003csub\u003e2peak\u003c/sub\u003e and WR\u003csub\u003epeak\u003c/sub\u003e (ρ = 0.90), ⩒O\u003csub\u003e2VAT\u003c/sub\u003e (ρ = 0.78), and OUES (ρ = 0.87).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study provides reference values for ⩒O\u003csub\u003e2peak\u003c/sub\u003e, WR\u003csub\u003epeak\u003c/sub\u003e, ⩒O\u003csub\u003e2VAT\u003c/sub\u003e, and OUES in Dutch older adults aged 55–75 years during cycle ergometer CPET, offering a unique dataset for assessing CRF and monitoring intervention effects.\u003c/p\u003e","manuscriptTitle":"Reference values for cardiopulmonary exercise testing-derived parameters for cardiorespiratory fitness in Dutch community-dwelling 55- to 75-year-old adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 10:49:30","doi":"10.21203/rs.3.rs-6990129/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67ba4406-0192-4602-9491-f1620879e0de","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50685413,"name":"Sports Medicine and Kinesiology"}],"tags":[],"updatedAt":"2025-07-01T10:49:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 10:49:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6990129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6990129","identity":"rs-6990129","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}