Introduction

Sarcopenia was described as age-related loss of skeletal muscle mass (SMM) by Rosenberg [1]. Subsequently, the European Working Group on Sarcopenia in Older People recommended using the presence of low SMM plus low muscle function to diagnose sarcopenia [2]. Previous studies have reported a sarcopenia prevalence of 12.4–22.1% in community-dwelling elderly individuals [3, 4]. Sarcopenia diminishes physical ability and cardiorespiratory performance [5], leading to poor quality of life, disability and mortality [6]. Accordingly, sarcopenia leads to high health maintenance costs and has become a global health concern [7].

Among the many physiological declines induced by sarcopenia, the association between sarcopenia and cardiorespiratory fitness (CRF) should be noted because low CRF affects physical activity and cognition [8], making it difficult for affected individuals to maintain independent activities of daily living and eventually reducing quality of life [9]. Moreover, high CRF can reduce the risk of cardiovascular disease (CVD) and improve overall health outcome [10].

In previous studies, aerobic capacity was significantly reduced in the low SMM group [11]. Other studies showed that SMM was significantly correlated with aerobic capacity [12,13,14,15] and that CRF could be a potential indicator of low muscle mass [16]. However, in the above studies, not all participants were elderly, and some studies evaluated CRF directly using a cycle ergometer, which tends to generate a peak aerobic capacity that is usually 10–20% less than that measured via a treadmill [17], or indirectly through a questionnaire.

Therefore, the aim of this study was to investigate the association between CRF parameters and SMM, which plays a key role in the diagnosis of sarcopenia. In this study, CRF parameters were accurately measured with a cardiopulmonary exercise test (CPET) using a treadmill. After the CPET, the results were analyzed comprehensively after subdivision. In general, the association between sarcopenia and functional deterioration is more critical in men than in women with aging [18]. This discrepancy seems to be due to the progressive decrease in androgens including dehydroepiandrosterone sulfate and testosterone, which plays a key role in the maintenance of muscle quality and quantity because of anabolic effects. One of the mechanisms by which dehydroepiandrosterone sulfate exerts its anabolic action is to convert into testosterone rather than estradiol [19]. Thus, this research examined only men.

Materials and methods

Subjects

This study was a single-center, cross-sectional study of subjects who visited a tertiary medical institution from February 2015 to January 2017. The subjects were elderly men over 60 years old who underwent a health check-up. The subjects had sufficient cognitive ability to understand the instructions and were able to independently walk for more than 10 min. Exclusion criteria included the following: (1) history of severe cardiovascular or respiratory disease; (2) unstable angina, uncontrolled arrhythmias or orthopedic impairment that compromised exercise performance; and (3) use of medications (i.e., thyroid hormone, diuretics, testosterone, and antipsychotics) that could affect body weight or body composition. The baseline characteristics of the participants acquired through electronic medical charts included age, body mass index (BMI), medical history, and social history. None of the subjects had any cancer history, and subjects with underlying diseases, namely, hypertension, diabetes mellitus, or dyslipidemia were well-controlled by self-medication. The short-form Mini Nutritional Assessment (MNA-SF), which consists of six items and rates self-viewed aspects of nutrition [20], was used to evaluate nutritional status that could affect CRF [21]. This questionnaire is a simple and efficient tool for assessing the risk of malnutrition in the elderly. The MNA-SF has three classifications: 0–7 points, malnourished; 8–11 points, at risk for malnutrition; and 12–14 points, well-nourished. All participants provided written informed consent. This study was approved by the Institutional Review Board.

Measurement of skeletal muscle mass

Bioelectrical impedance analysis (BIA) (InBody 370®; InBody, Seoul, Korea) was used to analyze body composition. This system uses an electrical current at frequencies of 5, 50 and 250 kHz. Before the analysis, the participants were advised to maintain (1) at least 4 h of fasting, (2) a voided bladder, and (3) no exercise for 8 h to minimize measurement error [22]. None of the participants showed symptoms of dehydration such as fatigue, dizziness, dark-colored urine, and extreme thirst. Participants stood on two metallic electrodes and held metallic grip electrodes. Then, body composition was measured. Appendicular skeletal muscle mass (ASM) (kg) and skeletal muscle mass index (SMI) (kg/m2) were analyzed with this method. ASM was calculated as the sum of the muscle mass in both arms and legs. SMI was calculated by ASM divided by the square of height. Subjects were then divided into two groups based on their SMI: men with an SMI less than or equal to 7.0 kg/m2 were included in the sarcopenic group, and those with an SMI greater than 7.0 kg/m2 were included in the non-sarcopenic group. The threshold levels for group arrangements were based on the criteria of the Asian Working Group for Sarcopenia [23].

Cardiopulmonary exercise test

CPET was performed using a treadmill and a 12-channel electrocardiogram. During the entire test, exhalation gases were analyzed in real time using a respiratory gas analyzer (TrueOne®2400; Parvo Medics, UT, USA). CPET was conducted with a warm-up, exercise test, and cool-down phase. Before the test, the participants warmed-up at 1.7 mph on a 0% grade for 3 min. Then, the exercise test began with 3-min stages of walking at 1.7 mph and a 10% grade. The grade was increased by 2% every 3 min, and the speed was increased by 0.8 mph every 3 min until the treadmill reached an 18% grade and 5 mph according to the Bruce protocol [24]. After the test, the participants cooled down at 1.7 mph on a 0% grade for 3 min. All CPET parameters were measured from the beginning of warm-up until the end of the cool-down phase. The test was terminated immediately when participants requested termination; complained of chest pain, severe fatigue or dyspnea; or had an ST depression of 1 mm or more on the electrocardiogram. For the validity of indices, those with (1) a peak predicted heart rate ratio < 85%; (2) peak respiratory exchange ratio < 1; or (3) total exercise time < 6 min were excluded, because these results may indicate poor subject effort or a nonlinear relationship between oxygen consumption and work rate [17].

The results were sorted into three categories: aerobic capacity, cardiovascular response, and ventilatory response. Aerobic capacity was measured as peak oxygen consumption (VO2peak), VO2peak divided by weight (VO2peak/weight), first ventilatory threshold (VT1), VT1 divided by weight (VT1/weight) and peak metabolic equivalents (METpeak). VT1, as measured by the V-slope method, is defined as the oxygen consumption at which the lactic acid concentration in the blood is significantly increased; this is one of the key determinants of functional and exercise performance [25]. The metabolic equivalent is a measure expressing the energy cost of physical activity and is defined as the ratio of the metabolic rate during a specific physical activity to a resting metabolic rate set by an oxygen consumption of 3.5 mL/kg/min. Therefore, METpeak is calculated by dividing VO2peak/weight by 3.5 mL/kg/min.

Cardiovascular response was measured as resting systolic blood pressure (SBPrest), resting diastolic blood pressure (DBPrest), peak systolic blood pressure (SBPpeak), peak diastolic blood pressure (DBPpeak), resting heart rate (HRrest), peak heart rate (HRpeak) and peak oxygen pulse (O2pulsepeak). Oxygen pulse is defined as oxygen consumption divided by heart rate, which indirectly reflects the stroke volume [17].

Ventilatory response was measured as peak tidal volume (Vtpeak), peak minute ventilation divided by carbon dioxide output (VE/VCO2peak) and peak respiratory rate (RRpeak). VE/VCO2 indicates the amount of air required to eliminate 1 L of carbon dioxide, which is used as an index of ventilatory efficiency, and usually displays a value below 34. This measure is a powerful prognostic marker in patients with heart failure [25].

Statistical analysis

The Kolmogorov–Smirnov test was used to verify normal distribution of data. The demographic characteristics and measurements of the subjects as well as all numerical variables are expressed as the mean ± standard deviation. Pearson’s correlation analysis was performed to evaluate the relationship between SMI and CRF parameters. An independent t test was used to compare clinical characteristics, including age, BMI, MNA-SF and CRF parameters, between the two groups. In addition, multiple linear regression analyses were conducted to identify independent associations between SMI and CRF parameters, including VO2peak and O2pulsepeak. Analyses were conducted using SPSS 22.0 software (SPSS Inc., Chicago, IL, USA). Statistical significance was set at 0.05.

Results

A total of 114 subjects were assessed for eligibility (Fig. 1). Among them, five subjects fulfilled the exclusion criteria and were excluded. One subject had a severe respiratory disease history, another subject had unstable angina, and the remaining three subjects took medications that could impact body weight. As a result, 109 subjects were eligible for the study. Of these subjects, 17 were sarcopenic subjects, and 92 were non-sarcopenic subjects. After CPET was performed, 2 and 5 subjects were excluded from the sarcopenic subjects and non-sarcopenic subjects, respectively, due to validity concerns. Thus, a total of 102 subjects, consisting of 15 sarcopenic subjects and 87 non-sarcopenic subjects, ultimately participated in the study. The mean age of subjects was 65.9 ± 3.5 years. Regarding medical history, 47 subjects had a history of hypertension, 24 subjects had a history of diabetes mellitus, and 18 subjects had a history of dyslipidemia. None of the subjects had any cancer history. Other clinical characteristics of the subjects are summarized in Table 1.

Fig. 1
figure 1

Flow diagram of subjects in the study. CPET cardiopulmonary exercise test, RERpeak peak respiratory exchange ratio, predicted HRpeak peak predicted heart rate ratio

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Table 1 Clinical characteristics of the study subjects
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Correlations between SMI and CRF parameters

In the correlations between SMI and the aerobic capacity parameters, there were significant positive correlations between SMI and VO2peak (r = 0.597, p < 0.001) and between SMI and VO2peak/weight (r = 0.268, p = 0.024) (Table 2). Moreover, there were significant positive correlations between SMI and VT1 (r = 0.352, p = 0.008) and between SMI and VT1/weight (r = 0.189, p = 0.039). In the correlations between SMI and the cardiovascular response parameters, only O2pulsepeak showed a significant correlation with SMI (r = 0.558, p < 0.001). However, no correlation was found between SMI and the ventilatory response parameters including Vtpeak, VE/VCO2peak and RRpeak.

Table 2 Relationship between SMI and cardiorespiratory fitness parameters measured by a cardiopulmonary exercise test
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Comparison of clinical features between sarcopenic and non-sarcopenic groups

Fifteen subjects (14.7%) were included in the sarcopenic group, while 87 (85.3%) were included in the non-sarcopenic group (Table 3). Age, BMI, and MNA-SF showed no significant differences between the two groups. In the category of aerobic capacity, VO2peak was 1193 ± 207 mL/min in the sarcopenic group and 1430 ± 346 mL/min in the non-sarcopenic group. These results indicated significant differences in VO2peak (p < 0.001) between the two groups. In addition, the sarcopenic group had a significantly lower VO2peak/weight (17.6 ± 4.1 vs. 19.8 ± 4.4, p = 0.046) and MET peak (5.0 ± 1.2 vs. 5.7 ± 1.3, p = 0.045) than the non-sarcopenic group. In the category of cardiovascular response, O2pulsepeak in the sarcopenic group and non-sarcopenic group was 8.8 ± 1.9 mL/beat and 10.2 ± 2.5 mL/beat, respectively. There was a significant difference in O2pulsepeak between the two groups (p = 0.044). However, other cardiovascular response parameters, including SBPrest, DBPrest, SBPpeak, DBPpeak, HRrest, and HRpeak, were not significantly different between the groups. In the category of ventilatory responses, there were no significant differences in Vtpeak, VE/VCO2peak and RRpeak between the two groups.

Table 3 Comparison of clinical characteristics between two groups
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The influence of SMI on CRF

In a multiple linear regression analysis where VO2peak was a dependent variable, SMI was significantly correlated when adjusted for age and MNA-SF (β = 0.473, p < 0.001) (Table 4). In a multiple linear regression analysis where O2pulsepeak was a dependent variable, SMI was also significantly correlated (β = 0.442, p < 0.001).

Table 4 Relationship between SMI and CRF parameters based on multiple linear regression analyses
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Discussion

The purpose of this study was to identify the association between SMM and CRF in community-dwelling elderly men. In this study, 15 subjects (14.7%) were below the threshold for SMI, according to the criteria of the Asian Working Group for Sarcopenia [23]. The prevalence of sarcopenia was variable according to the methodology used to assess sarcopenia-related parameters and characteristics of the studied population such as age, sex, and ethnicity. In Asians, the prevalence of sarcopenia has been reported to range from 6.4 to 21.8% in community-dwelling elderly men with their skeletal muscle mass measured by BIA [4, 26]. Therefore, the prevalence of sarcopenic subjects in this study was similar to that in previous reports.

To the best of our knowledge, only a few studies have evaluated the relationship between SMM and CRF in older individuals, and the available studies are limited to associations with the aerobic capacities only, such as VO2peak or VT1. Therefore, the superiority of this study is that the CPET parameters were classified into aerobic capacity, cardiovascular response, and ventilatory response and were analyzed extensively. In the present study, the CPET parameters that significantly correlated with SMI were VO2peak and O2pulsepeak. These results indicated that SMM assessed using BIA was significantly related to CRF in elderly subjects.

In this study, SMI was positively correlated with VO2peak, which is consistent with the findings of previous studies. According to a previous study, VO2peak was positively correlated with femoral muscle mass [27]. In addition, SMM was decreased in people with low VO2peak [16], and there was a close relationship between SMM and aerobic capacity [13]. In particular, the intensity of aerobic exercise was inversely related to low SMM [28]. Thus, SMM was assumed to be strongly associated with VO2peak, a representative parameter reflecting aerobic capacity. Although the study sample displayed a gender difference, older women with low fat-free mass had lower levels of VO2peak [12]. Furthermore, the loss of fat-free mass played a major role in reducing VO2peak [29]. SMM can account for approximately 40% of fat-free mass [30]. Therefore, there is a close relationship between SMM and fat-free mass. Since this study used SMI, as determined through a series of calculations based on SMM, the above studies that reported the association between fat-free mass and VO2peak could also support our findings.

VT1 is an important indicator for evaluation of aerobic metabolism function [25], and because most activities of daily living are performed under VT1, quality of life can be more affected by VT1 than VO2peak [17]. Additionally, VT1 is reliable for cardiovascular mortality prognostication and represents functional disability [31]. A previous study demonstrated that VT1 was positively associated with fat-free mass, and elderly subjects with low SMM were likely to employ early utilization of anaerobic metabolism and to feel fatigue [12]. In a study of the Japanese population, the decline in VT1 with age was mainly due to a decrease in SMM [13]. Although VT1 showed a positive correlation with SMI, the difference in VT1 between the two groups in this study did not reach statistical significance. This result may be due to the relatively small sample size, which may have masked the statistical significance of the results. Another reason may be that the subject population was limited to elderly men, which does not represent the general population. Therefore, further research is required.

The oxygen pulse, which is the oxygen consumption divided by heart rate, reflects the amount of oxygen absorbed in the pulmonary blood with each heartbeat. This parameter is calculated by the product of stroke volume and the arterial-mixed venous oxygen difference and thus, provides an estimate of left ventricular stroke volume changes and peripheral oxygen extraction during exercise. It is an important clinical tool to detect severe myocardial ischemia and a powerful predictor of mortality in patients with cardiovascular diseases [32]. A previous study reported that fat-free mass was strongly related to not only VO2peak but also blood volume and stroke volume. When statistically controlling the effect of stroke volume, the relationship between fat-free mass and VO2peak was abolished. They assumed that the central circulatory factor plays an important role in the association of fat-free mass and aerobic capacity [14]. In addition, according to a recent report, O2pulsepeak was strongly associated with SMM [15]. Several studies have shown that a continuous loss of cardiomyocytes similar to the loss of skeletal muscle mass occurs with aging [33]. Although hypertrophy of the heart can be seen by the compensatory response, cardiac sarcopenia, the state of decreased cardiomyocyte numbers and increased fibrosis, likely contributes to the depressed left ventricular function [34]. A low stroke volume results in an insufficient increase in perfusion to exercising muscles, which can cause a hypoxic condition and early anaerobic metabolism and, muscle fatigue, and eventually can contribute to muscle wasting [35], via suppression of mRNA translation and subsequent protein synthesis as well as overproduction of pro-inflammatory cytokines [36]. Furthermore, the amount of SMM is a determinant of peripheral O2 extraction, which is associated with the arteriovenous O2 difference and consequently with the O2pulsepeak [37]. Thus, our results suggest that there is a bidirectional relationship between the SMM and O2pulsepeak in the elderly.

There are several possible mechanisms that may underlie the association between SMM and CRF. One of the mechanisms is likely related to peripheral oxygen utilization. Fleg and Lakatta et al. [38] found a strong relationship between SMM measured by urinary creatinine excretion and VO2peak. Since the size of the SMM was important to determine VO2peak, they assumed that the systemic basis was due to the direct association between SMM and its capacity to utilize oxygen. Physiologically, muscle wasting is characterized by inflammation and oxidative stress, and certain regulatory molecules associated with wasting are either activated or repressed [39]. In particular, the ubiquitin–proteasome system is known as the system that causes degradation of muscle proteins [40]. Moreover, muscle wasting is also correlated with lower levels of insulin-like growth factor 1 (IGF-1), which reduces protein synthesis [41]. These changes occur in both skeletal muscle and cardiomyocytes. Aerobic exercise, however, stimulates a number of pathways such as IGF-1, which can increase protein synthesis while reducing degradation [39]. These mechanisms might underlie the association between low SMM and CRF deterioration. However, further investigation is required to assess this mechanism in greater detail.

Some studies have suggested that SMM is not related to CRF. Proctor and Joyner [42] reported that body fat accumulation plays an important role in lowering VO2peak, but not in SMM loss. Moreover, in a study of the association between body composition and training characteristics, only body fat mass, not SMM, showed a correlation with race time in marathon runners [43]. A previous study also failed to detect a difference in VO2peak between sarcopenic and non-sarcopenic groups [44]. These results were likely found because the subjects were not limited to elderly men; rather, they included subjects trained in endurance running or postmenopausal women. In addition, while muscle mass was measured by BIA in this study, they measured muscle mass using dual energy X-ray absorptiometry or anthropometric measurements. These differences were thought to cause the contrasting results.

There are several limitations to this study. First, the results of this study were based on a rather small sample size that included only men and was performed in a single center. Thus, sex-related differences could not be evaluated, and selection bias might have occurred. Additionally, the imbalance in the number of subjects in the two groups might have caused statistical error. Second, because this study was designed as a cross-sectional study, causal relationships were not accurately identified. Third, the SMI was measured using BIA, the results of which can vary depending on the surrounding environment or the subject’s condition. In particular, the hydration status, such as the condition of hydric retention or dehydration, can lead to SMM over- and underestimation, respectively [45]. However, BIA has been demonstrated to be highly reliable and well-correlated with magnetic resonance imaging or dual energy X-ray absorptiometry predictions under standard conditions [46]. The authors, therefore, encouraged the subjects to maintain recommended conditions to minimize variations. Therefore, further large-scale and longitudinal studies are required to assess these limitations in greater detail.

Conclusions

In conclusion, SMI was positively correlated with VO2peak (representative parameters of aerobic capacity) and O2pulsepeak (an indirect indicator of peak stroke volume). These results demonstrated that SMM is closely associated with CRF in community-dwelling elderly men. Therefore, appropriate management for preserving SMM such as exercise training should be considered to improve an individual’s CRF.