Introduction
It is well established that a high level of aerobic fitness, typically characterised by peak oxygen uptake (
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\)), is of benefit for young people with cystic fibrosis (CF). A higher
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) is associated with an improved quality of life (Hebestreit et al.
2014), reduced risk of hospitalisation for pulmonary exacerbations (Pérez et al.
2014) and reduced mortality risk (Nixon et al.
1992; Pianosi et al.
2005). As a result, regular cardiopulmonary exercise testing (CPET) is recommended by the European CF Society and endorsed by the European Respiratory Society (Hebestreit et al.
2015), to monitor changes in aerobic fitness and guide decisions concerning clinical status and therapeutic interventions.
CPET is considered the gold standard method to assess aerobic fitness, with assessment of
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) requiring the individual to provide a maximal physical effort. Factors such as excessive dyspnoea and/or a lack of motivation may cause individuals with CF to be unwilling or unable to reach volitional exhaustion and thus
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\). It has, therefore, been proposed that submaximal markers of aerobic fitness should be investigated as viable alternatives that can provide clinically useful information in such circumstances (Williams et al.
2014).
Previous research has shown the oxygen uptake efficiency slope (OUES) (Baba et al.
1996) to be a potentially useful submaximal parameter of aerobic fitness due to its high correlation with
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) in clinical populations, including adults with CF (Gruet et al.
2010). However, there are several issues that preclude the use of OUES as an alternative marker of aerobic fitness in CF. First, OUES is dependent on body size and requires allometric scaling to normalise data (Tomlinson et al.
2017)—a process that may be time consuming in clinical practice. Second, the OUES has a high level of variability [as measured by coefficients of variation (CV)], both between participants, and in terms of test–retest reproducibility in healthy adults (Sun et al.
2012b) and children (Bongers et al.
2015). Finally, the OUES is unable to discriminate aerobic fitness within children and adolescents with mild-to-moderate CF (Williams et al.
2018).
The utility of other submaximal CPET parameters in children with CF, such as oxygen uptake efficiency (OUE)—the ratio between oxygen uptake (
\(\dot {V}{{\text{O}}_2}\)) and ventilation (
\({\dot {V}_{\text{E}}}\)) [
\(\dot {V}{{\text{O}}_2}\)/
\({\dot {V}_{\text{E}}}\) (Sun et al.
2012b)]—therefore, warrants consideration. Unlike the OUES, which utilises a log-transformation of
\({\dot {V}_{\text{E}}}\) (Baba et al.
1996) to linearise the non-linear ventilatory profile often observed during incremental exercise, the OUE parameter accommodates this curvilinear relationship between
\({\dot {V}_{\text{E}}}\) and
\(\dot {V}{{\text{O}}_2}\) (Bongers et al.
2015). Furthermore, OUE has been shown to have less variability (CV) than OUES within groups of adults (39.5 vs. 14.6%) (Sun et al.
2012b) and children (32.9 vs. 10.9%) (Bongers et al.
2015) and is not dependent on body size (Sun et al.
2012b). This independence of body size, therefore, removes potential bias due to growth and the subsequent need to scale data, which may be of further benefit in a clinical setting.
Practically, OUE can be measured at any point during an incremental exercise test. However, the highest 90-second (s) plateau (oxygen uptake efficiency plateau; OUEP), which typically occurs prior to, or at, the ventilatory threshold (VT) (Bongers et al.
2015) or gas exchange threshold (GET) (Sun et al.
2012b), has been shown to be a predictor of mortality in heart failure (Sun et al.
2012a). Despite demonstrated clinical utility in cardiac populations, its role in chronic respiratory disease remains unknown. Furthermore, given that the ratio of
\({\dot {V}_{\text{E}}}\) to
\(\dot {V}{{\text{O}}_2}\) (ventilatory equivalent for oxygen) at peak exercise has been shown to be a more significant predictor of mortality in children and adolescents with CF than body mass relative
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) (Hulzebos et al.
2014), it is clear that the relationship between
\({\dot {V}_{\text{E}}}\) and
\(\dot {V}{{\text{O}}_2}\) is of clinical significance, and warrants further investigation, particularly when it is not feasible nor possible to assess
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\), e.g. due to pathophysiological or motivational reasons. Therefore, the OUE (and in particular the OUEP) has the potential to be considered submaximal measures of aerobic fitness that could be used to quantify pathophysiological and/or therapeutically induced changes. However, evidence for this utilisation of OUE is required, with recent research calling for further investigation into the prognostic properties of other OUE parameters in children and adolescents with chronic health conditions, such as CF (Bongers et al.
2015).
Therefore, the aim of this study was to explore the utility of OUE parameters, in children and adolescents with mild-to-moderate CF, as potential submaximal surrogates for \(\dot {V}{{\text{O}}_{2{\text{peak}}}}\). This is conducted first by characterising the OUE responses during CPET in children and adolescents with mild-to-moderate CF, compared with age- and sex-matched controls; second, by assessing the utility of OUE as an objective, submaximal surrogate for \(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) in this population; third, identifying the relationship between OUE parameters and disease status and severity in individuals with CF.
Discussion
In this study, whilst all OUE parameters were significantly reduced in children and adolescents with CF in the current study, results show that OUE does not provide a viable surrogate for
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) in this group. However, the novel finding of this study is that OUE appears to hold clinical utility as an independent marker of aerobic fitness, since it can differentiate between CF and CON, and holds a significant relationship with disease severity (as shown by FEV
1) in the CF group. An example is shown in Fig.
3, whereby allometrically scaled
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) was greater in individuals with CF in 16/36 (44%) age- and sex-matched pairs, but OUEP was only greater in individuals with CF in 5/36 (14%) matched pairs [and
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) and OUEP were only greater in CF in 4/36 (11%) of cases], thus indicating reduced OUE in CF, regardless of fitness status. This is further corroborated by the significant relationship between OUE (OUEP, OUE
GET) and FEV
1 (%
predicted) within the CF cohort, showing that OUE is associated with traditional clinical markers of disease severity.
For individuals with CF, a reduced aerobic fitness is a hallmark of disease progression (Orenstein and Higgins
2005) and assessment of
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) is, therefore, recommended on at least an annual basis (Hebestreit et al.
2015). However, as maximal testing may not always be possible in this patient group (due to pathophysiological and/or motivation related factors), viable submaximal measures are needed to assess aerobic fitness. Whilst submaximal physiological thresholds such as the GET are related to disease severity (Thin et al.
2002), detection rates are variable in CF [12/13; 92% (Saynor et al.
2013b)], and non-CF [45/55; 82% (Hebestreit et al.
2000)] groups and are typically dependent on knowledge of
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) to be expressed as a percentage of maximal capacity. In the present study, all OUE values were identified in the majority (94%) of participants, with OUEP identified in 100% of participants. The identification of OUEP is related to the averaging of 90 s of data and is not dependent on prior detection of the GET or RCP (to produce OUE
GET and OUE
RCP). The OUEP occurs at a submaximal point near the VT (Bongers et al.
2015) and/or GET (Sun et al.
2012b), a threshold that reportedly occurs at 50–60% of
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) in children and adolescents with CF (Bongers et al.
2014b; Saynor et al.
2014,
2016). Therefore, the exercise intensity required to generate a value for OUEP should be feasible for most children to achieve despite being unable or unwilling to exercise to exhaustion, such as those with advanced pulmonary disease, or more prone to increased levels of dyspnoea and desaturation upon exertion. The simplicity of the OUEP measure highlights how feasible a measure it may be to implement in busy clinical environments, suiting patients, researchers and clinicians alike.
In the current study, OUE variables were significantly correlated with
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) in the CON and CF groups, indicating the two variables have a medium [as defined by Cohen (
1992)] relationship (
R2 = 27% between OUEP and allometrically scaled
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) in both CF and CON). Given previous research (Williams et al.
2018) has identified differences in
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) within, and between, CF and CON groups when split by aerobic fitness tertile, a division shown to predict for mortality (Pianosi et al.
2005), it would, therefore, be anticipated that parameters of OUE would follow a similar pattern in discriminating between individuals’ of differing aerobic fitness statuses. Differences are seen within the CON group for OUEP, with the highest fitness tertile having significantly greater OUEP relative to children in the middle and lowest fitness tertiles, thus, showing that OUEP can discriminate between individuals on different fitness status. However, the same discriminatory ability is not seen for the CF group as it is only the group with the lowest aerobic fitness that is different to the group with the highest fitness (Fig.
4). Therefore, despite a relationship with
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\), the inability to discriminate between the fitness groups shows that the OUEP cannot act as a surrogate for
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\).
Of the limited research to have characterised the OUEP in youth, a large cross-sectional study of 214 healthy Dutch children identified similar mean values for OUEP (boys, 42.6 ± 4.7; girls, 42.3 ± 4.6 mL L
−1) and OUE at the VT (boys, 42.0 ± 4.6; girls, 41.9 ± 4.7 mL L
−1) to those of the CON group in the current study (Bongers et al.
2015). They also identified a stronger relationship (
r = 0.65,
p < 0.01) between the OUEP and absolute
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) than the CON group in the current study, potentially due to the higher
\({\dot {V}_{{\text{Emax}}}}\) observed in both boys and girls (80 ± 25; 71 ± 21 L min
−1 respectively) relative to the current CON group (69.2 ± 33.5 L min
−1), which may, therefore, bias the relationship between
\(\dot {V}{{\text{O}}_2}\) and
\(\dot {V}{{\text{O}}_2}\)/
\({\dot {V}_{\text{E}}}\) (OUE). However, as the current study builds upon this previous work and is the first to comprehensively examine OUE at multiple metabolic thresholds in children and adolescents with CF, only limited comparisons can be made, as no previous research has provided values against which to compare our novel data. Furthermore, the only application of OUE in clinical groups has been in adults with heart failure (Sun et al.
2012a), pulmonary hypertension (Tan et al.
2014), chronic obstructive pulmonary disease (Barron et al.
2016) and pulmonary embolism (Guo et al.
2016). However, minimal comparisons and inferences can be made against children with CF and these adult-onset, and predominantly vascular conditions.
As the current study has shown that OUEP (nor any OUE parameter) is not able to act as a surrogate measure of aerobic fitness, alternative submaximal factors must be considered. Ventilatory drive (
\({\dot {V}_{\text{E}}}\)/
\(\dot {V}{\text{C}}{{\text{O}}_2}\)) has received recent attention in predictive models of mortality (Hulzebos et al.
2014), and may be a viable candidate, given its low variability compared to
\({\dot {V}_{\text{E}}}\)/
\(\dot {V}{{\text{O}}_2}\) (Sun et al.
2002) and superior prognostic value relative to OUES in patients with heart failure (Arena et al.
2007). As such, further research should continue to explore the potential utility of this variable in individuals with CF, either as an alternative for
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\), or an independent prognostic variable. However, it is unclear whether any parameter of OUE may be of use in individuals with a more severe form of CF, or have longitudinal relevance in mild-to-moderate CF and, therefore, further research is warranted.
A number of limitations associated with the present study are worthy of comment. Primarily, this study is focused in children and adolescents with mild-to-moderate CF (FEV
1 > 40%
predicted). However, defining severity on FEV
1 alone does not account for the nutritional measures, number of exacerbations, inflammatory markers and infection statuses that also contribute towards a patient profile and definition of severity. Consequently, these results may not be applicable to those with lower lung function, a cohort for whom FEV
1 has a greater influence upon
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) (Pastre et al.
2014). Furthermore, the CON group in the current study displays a reduced level of aerobic fitness relative to previous studies investigating OUE (Bongers et al.
2015), which may explain the number of individuals with CF having a higher
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\) within age- and gender-matched pairs (Fig.
3). In addition, the lack of all participants undertaking supramaximal verification bouts (Barker et al.
2011) within CPETs could potentially influence detection of a ‘true’
\(\dot {V}{{\text{O}}_{2\hbox{max} }}\) (hence our use of
\(\dot {V}{{\text{O}}_{2{\text{peak}}}}\)). This is likely to have minimal effect, as previous work has shown that the ramp-only test elicits a ‘true’
\(\dot {V}{{\text{O}}_{2{\text{max}}}}\) in ~ 90% of healthy children (Barker et al.
2011) and ~ 80% of children with CF (Saynor et al.
2013a). Finally, when these methodological issues are considered in conjunction with our sample size, true effects may be obscured regarding the ability for OUEP to discriminate aerobic fitness. For example, the difference between middle- and low-fitness tertiles in CF revealed a
p value of 0.11, yet an ES of 0.78, thus indicating an effect is likely present but cannot be statistically confirmed. We have utilised the Sidak correction factor in this study as opposed to the more conservative Bonferroni in an attempt to alleviate the potential for Type 2 errors, yet statistical significance was not found in some comparisons and a statistical error might still have occurred. Larger clinical sample sizes would be advantageous but are not always feasible in young people who are sick.