The major finding of this study is that the OSA participants had impaired walking performance and aerobic walking capacity when compared to the non-OSA adults. Our results indicate that the metabolic cost of walking was high in the OSA group compared to the control group, and by comparison, their maximal oxygen consumption was reduced. The severity of OSA, as reflected by AHI, was found to be associated with the rate of oxygen consumption at the AT, which is a robust predictor of aerobic performance exercise. There was a significant relationship between OSA severity (AHI) and the energy cost level of walking, walking distance, and net VO2 during the submaximal treadmill test. Collectively, these findings indicate that individuals with more severe OSA are more likely to expend more energy when completing activities of daily living, which may increase perceptions of fatigue. In this study, the lack of significant differences between OSA and non-OSA participants in ventilatory efficiency (VE/VO2 and VE/CO2 slope) and peak and submaximal oxygen pulse (VO2/HR is used as proxy for ventilatory and cardiac performance) suggests that the role of the cardiopulmonary response to exercise was not a limiting factor in the participants’ walking performance.
Existing literature evaluating the cardiopulmonary response to exercise in OSA patients using a motor-driven treadmill reports conflicting results. In several studies, no difference was shown in the maximal exercise capacity between healthy adults and OSA patients (Daiana Mortari et al., 2014; Flore et al., 2006). This contradictory finding may be attributed to the selection criteria for study participants (who were newly diagnosed with OSA), the presence of co-morbidities, and varying levels of OSA severity that may limit the generalizability of the results (Flore et al., 2006). In line with our findings, other studies investigating the response to exercise testing of OSA patients have shown a decrease in exercise capacity (Lin et al., 2006; Nanas et al., 2010; Ucok et al., 2009; Vanhecke et al., 2008; Vanuxem et al., 1997a). As expected, the value of the peak oxygen consumption reported in this study was within the reference range (29.6 ± 6 mL/kg/min) for the OSA population reported previously (Daiana Mortari et al., 2014). Systematic reviews showed that the reduction in VO2peak was found to be larger in non-obese patients (body mass index < 30 kg/m2) such as our participant group (Berger et al., 2019; Mendelson et al., 2018).
There are a number of etiology factors by which OSA may appear to reduce functional aerobic capacity and influence the energy cost of walking in persons with severe obstructive sleep apnea. Sleep fragmentation and daytime somnolence are known to influence aerobic capacity, which may partially contribute to the reported decline in exercise tolerance and the increase in the energy cost of walking of persons with OSA (Hong and Dimsdale, 2003; Martin, 1981; Mougin et al., 1991). A potential physiological mechanism, that were not being analyzed due methodological limitations, is decreased maximal lactate concentration and delayed lactate elimination. This has been reported previously in OSA compared to age-matched controls, which may suggest impaired glycolytic and oxidative metabolism (Vanuxem et al., 1997a). The findings of previous studies may explain the reported impairment of OSA patients’ functional aerobic capacity.
The findings of exercise intolerance in our study demonstrated by a high energy cost of walking and decreased speed and distance in OSA patients may contribute to excessive daytime somnolence. A number of observational studies have revealed diminished exercise performance among OSA patients under the influence of sleep deprivation (Aguillard et al., 1998; Martin, 1981; Van Helder and Radomski, 1989). Extreme daytime somnolence in healthy men has been shown to reduce tolerance to tasks and increase the rating of perceived exertion during exercise testing (Temesi et al., 2013). Therefore, we cannot exclude the possibility of the influence of sleep deprivation on the performance of the OSA participants in our study. Among several factors that must be considered are delayed clearance of post-exercise lactate in OSA patients and increased concentration of catecholamines. It has been reported that in OSA, the lactate threshold was attained at lower workload levels compared to age- and weight-matched controls, and an abnormal build-up of lactate was directly related to exercise capacity(Bonanni et al., 2004). These findings could be explained, at least in part, by the lower oxidative capacity at the muscle that promoting an increased reliance on anaerobic metabolism in patients with OSA. Although local muscle metabolic impairment is not clearly understood, it would support both an early onset of metabolic acidosis and a subsequent increase in perceived fatigue (Keyser, 2010). Vanuuxem et al. (Vanuxem et al., 1997a) reported a slow rate of lactate elimination, which could indicate a primary defect in oxidative metabolism secondary to the repeated events of nocturnal hypoxemia. Unfortunately, lactate concentration was not assessed in our study and would require further evaluation. Furthermore, repetitive cycles of hypoxemia and reoxygenation induce autonomic system instability, which diminishes aerobic performance through changes in the muscle bioenergetic response (Bonanni et al., 2004; Vanuxem et al., 1997b). Several studies demonstrated structural changes in the skeletal muscle fibers and bioenergetic system of the active muscle parallel to the modulation that is observed in the condition of chronic hypoxia (Beitler et al., 2014); (Sauleda et al., 2003). Abnormalities of the skeletal muscles, such as structural and bioenergetics changes in skeletal muscle fibers (Sauleda et al., 2003), which have been identified in OSA patients in a previous study using muscle biopsy, may contribute to their lower maximal exercise capacity. Lower net VO2 during submaximal exercise may in part be explained by the recruitment of glycogenolytic fibers occurring at lower levels of muscle contraction(Chwalbinska-Moneta et al., 1989). The reduced resistance to fatigue in these patients could be related to an increase in the energy cost of walking and a decrease in the workloads even after controlling for confounders such as weight, BMI, and speed in the analysis.
Previous findings related to the association between OSA severity and physical performance are generally consistent with our results. In a study by Billings et al.(Billings et al., 2016), a low level of recreational activity such as walking was correlated with greater severity of sleep apnea, defined by using the apnea-hypopnea index, especially in male and obese individuals. Our findings suggest that severity of OSA may contribute to some extent to an increase in the energy cost of walking and lower VO2 at CWR, independent of age, weight, and BMI. This is also supported by a study by Mansukhani et al.(Mansukhani et al., 2013) which offers robust evidence that the severity of sleep-related breathing was associated with the FAC.
Our study has a few limitations. First, the cross-sectional design of our study did not allow us to establish a causal relationship. Second, some of the participants in the OSA group were using continuous positive airway pressure (CPAP), which could confound our results. However, there is conflicting evidence on the effects of long-term use of CPAP on exercise capacity. It has been previously reported that nocturnal use of CPAP improves exercise tolerance and dyspnea in obese patients with OSA (Pendharkar et al., 2011). On the contrary, other studies have reported contradictory findings that CPAP treatment did not change peak oxygen consumption (VO2peak). To some extent, our findings may have been influenced by the 20% of patients who were using CPAP. Despite that, the differences between OSA and non-OSA participants in walking speed and mechanical efficiency were significant. However, future research should account for the influence of CPAP. Finally, we accounted for visceral obesity and fat distribution in this study by using weight and BMI as a confounder in the statistical analysis, yet there may be unknown confounders associated with obesity that we did not control for.