Whilst it is often assumed that there are linear decrements in function with increasing age, the relationship between age and physiological function in humans is complex. Interpretation of this relationship is influenced by numerous confounding factors, particularly in cross‐sectional studies. These include genetic variation and a number of lifestyle factors, of which sedentary behaviour is arguably the most critical (Blair, 2009). The crucial intersection between inactivity and biomarkers of ageing can also be demonstrated on the two most common molecular indices that have been suggested as being biomarkers of ageing. Indeed, both telomere length and methylation of DNA are sensitive to the effects of exercise (LaRocca et al. 2010; Rönn et al. 2013).

Thus, to try to define more appropriately the relationship between age and physiological function, we have attempted to ameliorate all, except genetic differences, by studying a highly and homogeneously active cohort of healthy older men and women. Furthermore, in contrast to many studies of ageing that focus on a single or limited number of systems, and to increase the probability of finding physiological markers that might predict age, a wide range of measurements encompassing cardio‐respiratory, neuromuscular, metabolic, hormonal, cognitive, and health and well‐being indices was undertaken. The data revealed a number of significant associations between age and function, although none reached the criteria for being able to predict reliably the age of a given healthy individual, as it was clear that for a given bandwidth of any function a wide age range was accommodated.

In the absence of clear evidence defining the amount of exercise necessary to combat the negative effects of inactivity, we pragmatically set standards for acceptance into the study. For men this was the ability to cycle 100 km in 6.5 h while for women the standard was set at the ability to regularly cycle 60 km in 5.5 h. The difference between sexes was necessary because of the paucity of women cyclists aged >55 years who regularly cycle 100 km. We specifically selected an age range that would: (1) represent those passing through late working life to early years of retirement, (2) straddle the ‘breakpoint’ in performance identified from the study of athletic world records (Tanaka & Seals, 2008 ) and (3) ensure that any observed associations between age and function would not be driven by simply having high performing young individuals at one extreme and poorly performing very elderly individuals at the other (Lazarus & Harridge, 2010 ). Table 2 shows that although the distance the women cycled tended to be less, it was constant across the age range for both sexes, suggesting that there was no confounding training load effect. In addition, the time spent performing light, moderate, hard and very hard activities was similar between sexes and was also constant across the age range.

To obtain a cohort of healthy active individuals who undertook vigorous and similar types of exercise, we recruited amateur, non‐competitive road cyclists. Cyclists were chosen because their activity does not give excess joint loading, requires a high degree of motor control, produces high muscle forces as well as providing significant challenges to the cardiovascular and respiratory systems. The participants were predominantly recruited from the cycling organisation Audax UK, which comprises cyclists of all ages and abilities and stages events of set distances with defined upper and lower limits of cycling speeds (∼30 and ∼15 km h −1 ). A number of indices support our contention that these were not elite athletes, which whilst of interest (Rogers et al . 1990 ; Tanaka & Seals, 2008 ) represent population outliers. For example, was generally lower (Faulkner et al . 2008 ) and body fat content higher than previously reported for elite athletes of comparable ages. Furthermore, whilst acknowledging the small scale of the present study in genetic terms, the distribution of the two most commonly studied ‘performance’ genotypes (ACE and ACTN3) were similar to that of the general population (Myerson et al . 1999 ; Yang et al . 2003 ) and not skewed to an endurance‐based phenotype (Ma et al . 2013 ).

These observations emphasise the complexity of identifying a physiological biomarker that can be used to predict the age of a healthy individual, even when a homogeneously active, healthy cohort is studied. The limitations of cross‐sectional studies on ageing are well known (Metter et al . 1999 ) and the data from the present study serve to emphasise the need for prospective, longitudinal studies to investigate the relationship between ageing and physiological function on an individual basis.

Whilst a good marker of overall health, is also sensitive to training load (in the present study it correlated weakly with self‐reported training volume), is influenced by genetic factors (Bouchard & Rankinen, 2001 ), and shows a trainability–genetic interaction (Ghosh et al . 2013 ). Nonetheless, it is surprising that within this particular cohort of individuals, where many of the other confounding factors of ageing studies have been removed, individuals of the same age could differ so markedly in function.

Having established the rationale for the study cohort, it was then important to select the criteria upon which to base an investigation of the relationship between age and physiological function. was used here because it is viewed as the gold standard of physical fitness (McArdle et al . 2014 ), is a high predictor of all cause mortality (Blair et al . 1989 ) and is a highly integrative function requiring the effective interaction of a number of key systems. The results revealed significant negative associations with age and (Fig. 1 A–C ) inferring a loss of ∼10 and 15% per decade in males and females (in ml kg −1 min −1 ), respectively. This is in agreement with the 8–15% reported in previous cross‐sectional studies of older athletic individuals (Tanaka et al . 1997 ; Wilson & Tanaka, 2000 ; Wiswell et al . 2001 ; Kusy & Zieliński, 2014 ). Indeed, the closet association between age and any of the indices measured in our study was observed for However, despite there being statistical associations between age and even this marker could not identify with any degree of accuracy the age of any given individual, which would be the requirement for any useful biomarker of ageing. A statistical association with age may be indicated by a significant P value (i.e. P < 0.05), which can be achieved with a small correlation coefficient offset by a large sample size or vice versa. However, an aim of our study was also to test whether a given physiological index could be considered as a marker of ageing and thus have predictive utility. For this we set a critical value for a correlation equivalent to five times that which would result from a random assignment of ages ( r 2 > 0.80). Post hoc analysis revealed that for all indices we were suitably powered both for the male and for the female cohorts. The phenomenon whereby a given function is significantly associated with age but has lower predictive power is highlighted graphically when the data are plotted in bandwidths of equivalent function (Fig. 1 A–D ). Here it can be seen that even when normalised to lean body mass (Fig. 1 C ) a bandwidth of 52–56 ml (kg fat‐free mass) −1 min −1 encompasses participants who span over 20 years for males (56–77 years) while females span the age range of 55–69 years. Indeed all indices measured failed to reach the power required to be used as a predictive marker of age.

In addition to physiological markers measured in the current study, a number of other markers have been suggested from which the age of an individual may be predicted; in particular, two such markers which showed promise were telomere length and DNA methylation, with the latter showing very high correlation and accuracy in a number of tissues (Horvath, 2013 ) . However, the utility of these markers must be viewed with caution because they are both sensitive to the exercise status of the subjects, and it is now known that telomere length is preserved in healthy older adults who perform vigorous exercise and is positively related to maximal aerobic exercise capacity (LaRocca et al . 2010 ). Whilst 6 months of exercise altered DNA methylation status in over 7500 individual genes, only 18 candidate genes for obesity and 21 for type 2 diabetes had CpG sites with differences in adipose tissue DNA methylation in response to exercise (LaRocca et al . 2010 ). The authors conclude that exercise induces genome‐wide changes in DNA methylation in human adipose tissue, potentially affecting adipocyte metabolism. These studies highlight further the difficulty in identifying robust markers of ageing even when sensitive molecular and genetic techniques are used.

Relationship between global physiological function and age

It is not possible within the constraints of this paper to discuss in detail each of the many indices measured in the present study. However, there are a number of noteworthy observations in regard to associations with age, the generally superior levels of function observed compared to sedentary and frail populations of the same age as well as the clinical relevance and interpretation of some of the measures reported in these healthy, high functioning individuals.

Cardiopulmonary exercise testing The data for have been discussed above, so we will consider the other indices obtained from the incremental exercise test. As expected, maximum HR was negatively associated with age, but interestingly the values were on average higher than predicted from the traditionally used estimates, such as 220 – age or 208 − 0.7*age (Tanaka et al. 2001). A decrease in VT with age has also previously been reported in the general population, although values in the current study were ∼15 ml kg−1 min−1 greater while the % at which this occurred is 5–10% lower (Paterson et al. 1999). There has been recent interest in using VT to assess the suitability for elective surgery. For example, a value of 10 ml kg−1 min−1 predicts those at risk of postoperative complications and increased length of hospital stay (Snowden et al. 2010). Extrapolating the data in the current study shows that even by 100 years of age the cyclists studied would be predicted to be comfortably above this value (24.8 and 19.2 ml kg−1 min−1 for males and females, respectively). Another marker of cardiorespiratory fitness is the rate of O 2 uptake (O 2 kinetics, τ) during a bout of submaximal exercise at a fixed power output (Rossiter et al. 1999). This has been reported to be slower in older non‐active subjects than in young individuals (DeLorey et al. 2004) while a slowing in τ of ∼16 s has been reported over a decade in older individuals with low activity levels (Bell et al. 1999). Our data from highly active individuals do not show any association with age in this parameter over the age range studied. The contention that previously observed age‐related changes are mediated by inactivity is supported by the report of non‐exercising 70‐year‐old males undertaking a 24‐week aerobic training programme improving O 2 uptake kinetics (τ = 62.2 s before vs. 31.9 s after; Babcock et al. 1994) to levels approaching those of the current study (25.2 s) and similar to those reported in young individuals (25–30 s; DeLorey et al. 2004; Murias et al. 2011). Like , both VT and τ are sensitive to training volume (Jones & Carter, 2000). In this study we found no association with VT and training load, but τ was associated with training load in both men and women.

SMI and BMD Skeletal muscle is sensitive to both mechanical and metabolic signals and, perhaps more than any other tissue, to physical inactivity. This suggests that the well‐documented declines in neuromuscular performance with age (Skelton et al. 1994; Lindle et al. 1997; Klass et al. 2008) would be highly influenced by sedentary behaviour. Other things being equal, muscle function is related to size (O'Brien et al. 2010). An age‐related loss of muscle mass is termed ‘sarcopenia’, although the precise definition of this clinical condition has been the subject of debate (Cruz‐Jentoft et al. 2010). One commonly used definition is the ‘skeletal muscle index’ derived from a DXA scan (Baumgartner et al. 1998). Although seven subjects were classified as sarcopenic (one female and six males, SMI <5.5 and 7.26 kg m−2, respectively) there was no association between age and SMI (Fig. 2A). Thus, whilst sarcopenia in a sedentary older population is associated with frailty and increased risk of falling, in this healthy population the fact that these seven sarcopenic individuals were capable of meeting the challenging exercise criteria for entry into the study does not imply frailty, or indeed impaired function, but rather appears to be related to a thin, athletic phenotype. Overall, the physiological data obtained in the present study show that this cohort comprises healthy individuals with elevated levels of function associated with their high levels of physical activity. However, one area in which the cyclists could not be distinguished from the general population was in regard to bone. Age effects for BMD were demonstrated in both males and females (Fig. 3B–D). Yet unlike other functions such as , examination of z‐scores for BMD (0.13 ± 0.85 and 0.27 ± 1.35 for male hip and lumbar spine BMD, respectively, and 0.06 ± 0.66 and 0.01 ± 1.10 for females) clearly indicate that bone strength in these exercisers is not superior to the general population. This lack of an exercise benefit is presumably explained by the relatively low loads applied to the skeleton during this type of exercise (Stewart & Hannan, 2000), which does not appear to maintain bone health above sedentary behaviour. Indeed six individuals in the current study were diagnosed as being osteoporotic.

Metabolic and endocrine function Analysis of blood samples for metabolic function provide indicators of the risk factors for cardiovascular disease and type II diabetes (Grundy et al. 1999). In the general population, regular exercise is known to induce a favourable blood lipid profile by reducing cholesterol, LDL and triglyceride levels and elevating HDL levels (Mann et al. 2014). We analysed fasting serum samples for a number of metabolic markers and found triglyceride levels within the normal clinical range (<1.7 mmol l−1) as were the values for cholesterol and LDL, although they were borderline high (cholesterol >6.2 mmol l−1 and LDL > 4.1 mmol l−1 regarded as high). As previously reported in master athletes (Wiswell et al. 2001), HDL levels were high. Insulin levels and all HOMA measurements were found to be within the normal range and similar to that reported in previous studies of older athletes (Kusy et al. 2013). Overall there was no effect of age on any of the variables measured (Table 3) and the incidences fell within normal clinically healthy ranges. Testosterone levels are typically 10‐fold higher in males than females (Harman et al. 2001; Sowers et al. 2001) a finding supported in the current study. An age‐related decline in testosterone levels in men, known as the andropause, has been reported (Harman, 2005), but interestingly was not apparent in our results. Exercise training in sedentary older men can increase testosterone levels (Hayes et al. 2013), and therefore the exercise status of the current subjects may have preserved testosterone levels into old age. Crucially, this suggests that previously reported declines in testosterone with age may also be related to declining activity levels. DHEAS levels decline from the age of 20–30 years both in males and in females. DHEAS levels in the present study were similar to those previously reported in healthy, untrained older individuals and lower than that seen in young adults (Leblhuber et al. 1993), suggesting a reduction with age, although this was not apparent in the age range studied. Likewise, the IGF/growth hormone (GH) axis has been reported to be affected by ageing, with many older people being GH/IGF‐I‐deficient (Bartke, 2008). Indeed, the sensitivity of IGF‐1 to age in highly active individuals even at a young age is reflected in the age‐correction factors applied when measuring IGF‐1 levels in young athletes as a biomarker of recombinant GH abuse (Healy et al. 2005). It is thus not surprising that in the present study IGF‐1 was also shown to decline significantly with age.

Resting cardiovascular and respiratory function As has previously been reported for endurance athletes (D'Andrea et al. 2011) the cyclists in the present study had low resting HRs and DBPs with all bar two having a DBP < 80 mmHg. There was no association with age in this parameter. A mean SBP of 134 ± 16 mmHg is in close agreement with previous reports on master athletes and its increase was associated with age in men, but not women. Although not investigated in the present study, master athletes are likely to have varying degrees of left ventricular hypertrophy, moderate cardiac enlargement and larger end diastolic volume (Seals et al. 1994; Carrick‐Ranson et al. 2014), probably related to a greater left ventricular diastolic chamber compliance, distensibility, rapid active relaxation and vigorous diastolic suction (Levine et al. 1991; Carrick‐Ranson et al. 2014). Elevated SBP may also be explained by mechanisms similar to those which cause spurious systolic hypertension in younger active individuals where raised blood pressure is attributed to an amplification of the pressure from the aorta to the brachial arteries due to an increase in the elasticity of the arteries (Mahmud & Feely, 2003). Indeed, previous studies have found that the reported age‐associated increase in aortic stiffening is attenuated in master athletes, with master athletes having aortic ‘ages’ similar to that of younger individuals (Shibata & Levine, 2011). It is possible that due to their high activity levels the subjects in the present study exhibited an increased elasticity of their arteries that, coupled with the low resting HR, could explain the elevated SBP. Lung function is one area where clinical data are typically reported as age‐predicated values, suggesting a strong association with age. The data sets from which such predictive measures are based are from the general population, but previous studies of master athletes have shown that PEF, FVC and FEV1 values are superior to those predicted, although absolute values decline with increasing age and women have lower values than men (Degens et al. 2013). The same observations are reported here, but the associations with age are not strong (Fig. 5D–F). When expressed as a percentage of that predicted based on age, height and sex it is apparent that lung function is again superior to that of the general population; in particular, FEV1 and FVC are respectively ∼10 and ∼28% greater. An FEV1/FVC below 70% is used as a clinical indicator of airway obstruction (Swanney et al. 2008). Interestingly, and in general agreement with previous studies on master athletes (Degans et al. 2013), the mean FEV1/FVC for all the cyclists in the present study was below this clinical threshold, which in the absence of other data would suggest functional impairment.

Neuromuscular function There is no denying the effect of age on neuromuscular performance, as evidenced by the declines in strength, power and athletic performance in highly trained elite master power athletes such as weightlifters and sprinters (Grassi et al. 1991; Pearson et al. 2002; Rittweger et al. 2009). This is further demonstrated in the current study, where even though activity levels were high and constant across the age range, reductions in both maximum isometric knee extensor strength and the peak power obtained during sprint cycling were apparent. These changes were not due to a decreased ability to activate the quadriceps muscle (Fig. 6D). However, the values for both knee extensor strength (Skelton et al. 1994) and peak cycling power (Pearson et al. 2006) are superior to those previously reported for similarly aged individuals. As with lower limb function, handgrip strength also was associated with age in men, but was not statistically so in women. Handgrip is a relatively simple measure that can be used clinically, with low values being predictive of all‐cause mortality (Desrosiers et al. 1995; Metter et al. 2002) and disability (Rantanen et al. 1999). Previous studies have found that grip strengths of below 33 kg in men and 21 kg in women are predictive of mobility limitation in normal weight individuals (Sallinen et al. 2010). Based on these cut‐off values none of the women in the current study were at risk of mobility limitation, although one male was (0.02 kg lower than cut‐off). Given the subjects’ exercise status and the minimal difference from the cut‐off, it is probable that this low grip strength does not signify mobility limitation, but rather reflects the low body mass of this cyclist (61.5 kg). In addition to mechanical output of muscle we were interested to investigate components of the neural system. Sarcopenia has been associated with a loss of motor units (Lexell et al. 1988). However, evidence comparing endurance runners with sedentary individuals using surface and intramuscular EMG to estimate motor unit number in the tibialis anterior suggests that exercise may be protective of the age‐related loss in motor unit number seen in non‐active individuals (Power et al. 2010). We were not able to measure this parameter in the present study, although we did measure other indices of neural function such as peroneal nerve conduction and the Hoffman reflex of the soleus muscle, the latter of which has previously been shown to be lowered in ageing (Kido et al. 2004). Our study showed no association between these parameters and age, but a wide range in values for both NCV and H max /M max were observed, as has been documented previously (Angulo‐Kinzler et al. 1998; Aagaard et al. 2002; Scaglioni et al. 2003; Klass et al. 2007). H max /M max has a reported intraclass correlation coefficient of >0.95 (Palmieri et al. 2002) and has previously been found to decline from ∼0.65 to 0.15 between 20 and 80 years of age (Kido et al. 2004). The lack of association in the current study may be due to the large inter‐subject variability in H max /M max (Fig. 6F) with the variability reported in the current study similar to that previously found in older individuals (Scaglioni et al. 2003). It is possible, however, that the lack of association in these cyclists could also be because the ageing signal is indeed less, or is masked by the more constrained age range (55–79 years) studied here as opposed to those studying wider age ranges including young individuals (20–80 years). In addition to physiological measurements, we also performed clinically relevant tasks of neuromuscular function assessing balance and functional mobility (Fig. 6G–J). There appears to be a minimal effect of age on balance, although in the male subjects EC balance did show a weak association with age. TUG is a commonly used functional assessment for older individuals (Podsiadlo & Richardson, 1991). A completion time of >15 s indicates a high risk of falling (Whitney et al. 2005) while a value greater than 9–10 s may indicate a need for interventions to improve strength and power (Bohannon, 2006). Even the oldest participants in the present study fell well below these levels with an average time of 5.6 s, which is superior to that reported for the general older population (Bohannon, 2006) and well within the norm reported for healthy young adults (Bergmann et al. 2009).