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SIX WEEKS OF A POLARISED TRAINING INTENSITY DISTRIBUTION LEADS TO GREATER 1
PHYSIOLOGICAL AND PERFORMANCE ADAPTATIONS THAN A THRESHOLD MODEL IN TRAINED 2
CYCLISTS 3
4
5
Craig M. Neal1, Angus M. Hunter1, Lorraine Brennan2, Aifric O’Sullivan2, D. Lee Hamilton1, Giuseppe 6
De Vito3 and Stuart D.R. Galloway1 7
8
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1Health and Exercise Sciences Research Group, School of Sport, University of Stirling, SCOTLAND, 10
U.K; 2School of Agriculture and Food Science, University College Dublin, IRELAND; and 3Institute for 11
Sport and Health, University College Dublin, IRELAND. 12
13
Author contributions: CMN, AMH, GDV and SDRG conceived and planned the study, carried out data 14
collection, performed sample and data analysis and contributed to writing the manuscript. LB, AO 15
and DLH conducted sample analysis, data analysis, and contributed to writing of the manuscript. 16
17
Running head: Training intensity distribution and adaptation in cyclists 18
19
Correspondence to: 20
Dr Stuart D.R. Galloway 21
Health and Exercise Sciences Research Group 22
School of Sport 23
University of Stirling 24
Stirling, 25
SCOTLAND 26
U.K. 27
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Tel: +44 (0)1786 466494 29
Fax: +44 (0)1786 466477 30
E-mail: s.d.r.galloway@stir.ac.uk 31
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Articles in PresS. J Appl Physiol (December 20, 2012). doi:10.1152/japplphysiol.00652.2012
Copyright © 2012 by the American Physiological Society.
2
ABSTRACT 33
Aim: To investigate physiological adaptation with two endurance training periods differing in 34
intensity distribution. Methods: In a randomised cross-over fashion, separated by 4-weeks of 35
detraining, 12 male cyclists completed two 6-week training periods: (1) a polarised model 36
(6.4(±1.4)hrs.week-1; 80%, 0%, 20% of training time in low, moderate and high intensity zones); and 37
(2) a threshold model (7.5(±2.0)hrs.week-1; 57%, 43%, 0% training intensity distribution). Before and 38
after each training period, following 2 days of diet and exercise control, fasted skeletal muscle 39
biopsies were obtained for mitochondrial enzyme activity and monocarboxylate transporter 40
(MCT1/4) expression, and morning first void urine samples collected for NMR spectroscopy based 41
metabolomics analysis. Endurance performance (40km time trial), incremental exercise, peak power 42
output, and high-intensity exercise capacity (95% Wmax to exhaustion) were also assessed. Results: 43
Endurance performance, peak power output, lactate threshold, MCT4, and high-intensity exercise 44
capacity all increased over both training periods. Improvements were greater following polarised 45
than threshold for peak power output (Mean (±SEM) change of 8(±2)% vs. 3(±1)%, P<0.05), lactate 46
threshold (9(±3)% vs. 2(±4)%, P<0.05), and high-intensity exercise capacity (85(±14)% vs. 37(±14)%, 47
P<0.05). No changes in mitochondrial enzyme activities or MCT1 were observed following training. A 48
significant multi-level partial least squares-discriminant analysis model was obtained for the 49
threshold model but not the polarised model in the metabolomics analysis. Conclusion: A polarised 50
training distribution results in greater systemic adaptation over 6 weeks in already well-trained 51
cyclists. Markers of muscle metabolic adaptation are largely unchanged but metabolomics markers 52
suggest different cellular metabolic stress that requires further investigation. 53
54
Key Words: Exercise, Metabolism, Metabolomics, Skeletal Muscle 55
56
3
INTRODUCTION 57
Understanding the optimal exercise training-intensity distribution to maximise adaptation and 58
performance is important for athletes trying to gain a competitive advantage. In addition, a greater 59
understanding of the interactions between exercise intensity distribution, physiological stress, and 60
adaptation, could be important for achieving the optimal health benefits from physical activity in the 61
general population. Exercise intensity distribution is determined from the percentage of time spent 62
exercising at low (zone 1, typically <65% of peak power output (PPO), <lactate threshold (LT), 63
<2mM), moderate (zone 2, ~65-80% of PPO, between LT and lactate turn point (LTP)) and high (zone 64
3, typically >80% of PPO, >LTP, > 4mM) intensities (8, 29, 47). It has been suggested that two distinct 65
exercise training-intensity distribution models are adopted by endurance athletes (47). Firstly, the 66
polarised training model consists of a high percentage of exercise time at low exercise intensity (~75-67
80%) accompanied by little time at moderate intensity (~5-10%) with the remainder spent at high 68
intensity (~15-20%). In contrast, the second model is a threshold training distribution in which 69
moderate exercise intensity is the focus (typically 40-50% of training time) with relatively little or no 70
high intensity work, and the balance of training time spent at low intensity. 71
72
It has been suggested by Seiler (46) and Laursen (32) that adopting a polarized intensity distribution 73
may optimize adaptation to exercise while providing an acceptable level of training stress. Several 74
studies have investigated adaptation to training at different intensities with positive effects on 75
lactate threshold and performance being observed when a high proportion of training is conducted 76
at low intensities (12, 13, 26). These studies suggest that the proportion of time in zone 1 is a key 77
aspect driving endurance adaptations and performance outcomes. However, other studies (33, 57, 78
58) have observed increased peak power output and mean power sustainable during a 40km time 79
trial when high-intensity interval work (zone 3 training) is incorporated into the schedules of already 80
well-trained cyclists; i.e. when the cyclists adopted a more polarised training-intensity distribution. 81
In addition, changing the intensity distribution towards a more polarised model has been shown to 82
4
improve VO2max, running economy and running performance in a case study of an international 83
1500m runner (27). Indeed, the powerful stimulus afforded by short-term high intensity interval 84
work for promoting metabolic and performance adaptations has also been demonstrated in studies 85
on trained cyclists (51), in healthy active (52) and in sedentary (23) males and females. These studies 86
have shown significant increases in skeletal muscle oxidative capacity and mitochondrial function 87
following only a few high intensity interval exercise sessions, as well as improvements in markers of 88
endurance performance. Thus, combining a high proportion of time in zone 1 along with zone 3 89
interval work is likely to be a strong combination for optimal adaptations to training in endurance 90
athletes, but to date, no study has directly compared the adaptations induced by polarised vs. 91
threshold training models in already well-trained athletes. 92
93
An important aspect in adaptation to exercise is recovery and the ability to cope with the training 94
stress. Seiler et al., (48) identified that recovery time from high intensity training was not greater 95
than from moderate intensity training, but that recovery time from low intensity training was the 96
shortest. Their data implies that recovery from a polarised training intensity distribution would be 97
better than recovery from a threshold intensity distribution. With new technologies such as 98
metabolomics, that enable a more global overview of whole body metabolic perturbations, the 99
response to exercise training stress, adaptation and recovery can be studied in a more global 100
manner. 101
102
Metabolomics technology has in recent years provided new insights in several fields of research 103
including toxicology, pharmacology and human nutrition and can aid identification of novel 104
biomarkers (40). However, the application of metabolomics to exercise training has been 105
underutilised in human exercise studies to date. There are only two cross-sectional human studies 106
that have been published (11, 61) and both of these concluded that metabolomics is a promising 107
tool for investigation of human responses to exercise. Therefore, the purpose of the present study 108
5
was to compare the physiological adaptations and longitudinal metabolomics profile responses of 109
well-trained male cyclists to training interventions that followed both a polarised and a threshold 110
training intensity distribution. We hypothesized that the polarised training intensity distribution 111
would lead to greater adaptive responses through a greater stimulus provided by the high-intensity 112
interval exercise and the high proportion of training spent at low intensity. We also hypothesized 113
that the metabolomics profile would provide new insights into understanding the training stresses 114
induced by polarised versus threshold training intensity models in already well-trained humans. 115
116
METHODS 117
Twelve well-trained, male cyclists were recruited from two local cycling clubs. The mean (±SD) 118
characteristics of the participants were: age 37 (±6) years, body mass 76.8 (±6.6) kg, stature 178 (±6) 119
cm, peak power output (PPO) 4.7 (± 0.5) W.kg-1. Participants had been training consistently for >4 120
years and prior to entry into the study trained 7-8 hours/week (range 5-10 hours/week), with 4-5 121
training sessions per week for at least the previous 6 months. Their training intensity distribution 122
prior to entering the study was estimated to be 53% zone 1, 38% zone 2, and 9% zone 3 with a 123
training load (intensity zone x duration (min)) of 750 units. All participants were able to sustain a 124
power output above 240W for a 40 km time-trial time prior to entry into the study. Participants 125
were all competitive road cyclists but some also performed mountain biking within their training. 126
Participants provided written informed consent to take part in the study, which was approved by the 127
University Ethics Committee, in accordance with the Declaration of Helsinki. 128
129
Study Design 130
A cross-over, within-subject study design was employed. All participants were in the study for a 131
period of 29 weeks (Figure 1). This included pre-screening and habituation trials in the first 2 weeks 132
before commencing a 4 week controlled detraining period. Participants were then asked to not 133
exercise and to record all their food and fluid intake for two days prior to undertaking a baseline 134
6
testing week. Following this the participants entered the training intervention period. n=6 135
participants were assigned to complete polarised (POL) training first and n=6 assigned to complete 136
threshold (THR) training first. Participants undertook 6 weeks of training following either the POL 137
training intensity distribution (80% low intensity, 0% moderate intensity, 20% high intensity) or a 138
THR training intensity distribution (55% low intensity, 45% moderate intensity, 0% high intensity). 139
This was followed by a post-training intervention testing week. Participants then completed a 140
second 4 week controlled detraining period prior to undertaking the cross-over arm of the study in 141
which they completed a pre-training testing week, 6 weeks of training following the alternate 142
training intensity distribution, and a post-training testing week. The two 6 week training intervention 143
periods were undertaken over the winter months November-December and January-March. 144
145
In the habituation trials participants undertook at least two 40km time trial (40km TT) test rides on 146
their own bike mounted onto a CompuTrainer ergometer (RacerMate, Seattle, WA). To ensure we 147
recruited trained cyclists only riders who completed the 40km TT with a mean power output of 148
≥240W were included in the study. During the 4-week detraining periods, participants were 149
instructed to not include any threshold/tempo rides, interval sessions or races, and to ride 150
exclusively at low-intensity (zone 1). Participants completed only 4 hours/week (range 3-5 151
hours/week) of zone one training during this period. The time at which these detraining periods fell 152
during the study made this possible as it occurred during October and December-January. This 153
strategy was used to ensure that no specific adaptations from training at moderate or high intensity 154
would be gained in the 4 weeks prior to each of the study intervention periods. To determine the 155
effectiveness of the detraining period we also examined whether PPO, 40km time trial time and 156
mean power output, and high-intensity exercise capacity had all returned to baseline values before 157
beginning the second training intervention. 158
159
160
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Physiological adaptation and performance testing 161
The testing weeks included laboratory based tests that were conducted on 2 separate days (at least 162
2 days apart) before and after each training intervention period (Figure 1). Prior to the first testing 163
session participants were asked to keep a food (and activity) diary for the 2 days before each of the 164
initial testing sessions, with no exercise on the day preceding any test day. This diary was used to 165
allow them to replicate their diet and activity for the other testing weeks. Likewise, a food diary was 166
kept for the first week of training, and participants attempted to replicate their food intake as 167
closely as possible throughout the training weeks in both interventions. Participants also refrained 168
from caffeine intake in the 3 hours before each of the testing sessions. Briefly, on the first visit in 169
each testing week the participants reported to the laboratory between 7:00 and 9:00 in a rested, 170
fasted state. A first pass urine collection was obtained for metabolomics analysis, and a resting 171
skeletal muscle biopsy sample collected to assess markers of mitochondrial oxidative capacity and 172
lactate transport. The biopsy sample was obtained from the vastus lateralis using a Bard Magnum 173
Biopsy System (Bard Peripheral Vascular Inc, AZ, USA) as described by Hayot et al. (22), under local 174
anaesthesia (2% w/v Lidocaine, B Braun, Germany; 2ml per subject). Approximately 20mg of tissue 175
was collected from 1-3 extractions. The tissue was immediately frozen in liquid nitrogen and stored 176
until later analysis. 177
178
Later that day participants reported to the laboratory for a second time between 15:00 and 20:00 179
during which body mass and stature were recorded followed by an incremental cycle test to 180
determine lactate thresholds and PPO. For the incremental test, a CompuTrainer was used in 181
ergometer mode, fitted with the participants own bike. Following tyre pressure checks (120 PSI) and 182
a 10-min light warm up, the CompuTrainer was calibrated to 3.5lbs, as instructed by the 183
manufacturers for accuracy up to 500W. The test started at 100W and increased by 40W every 3-184
min, until volitional exhaustion, with the cadence remaining self-selected, but the speed being held 185
above 14mph, to ensure accurate measurements of power output. 30-s before the end of each 3-186
8
min stage, heart rate (Polar Electro, Finland) was recorded and a capillary blood sample was 187
obtained for blood lactate concentration analysis by micro-assay (LactatePro LT-1710, ArkRay Inc., 188
Kyoto, Japan). The reliability and validity of this device has been previously determined (42). The 189
lactate threshold (LT) was determined as the final point before the blood lactate concentration 190
increased distinctly from its resting concentration (1). The lactate turn point (LTP) was determined as 191
the starting point of accelerated lactate accumulation (1). Each individuals lactate profile was 192
examined independently by two people to identify lactate threshold and lactate turn point from the 193
incremental exercise tests. A typical trace would show baseline values for the initial loads, a gradual 194
increase which demarcates the zone 1 to zone 2 boundary (LT) and then following several more 195
increments in load a marked increase is noted that deviates from linearity and reflects the zone 2 to 196
zone 3 boundary (LTP). In the present study, the mean (±SD) lactate concentration at LT was 2.1(0.4) 197
mM and at LTP was 4.4(0.8) mM. This corresponded to an intensity of 64(4) % of PPO for LT, and 198
82(4) % of PPO for LTP. The PPO was assessed using the following equation: PPO = Wfinal + ([t/180] ∙ 199
40) (31); where, Wfinal = the power output of the final completed stage (W), t = the time spent in the 200
final uncompleted stage (s), 180 = the duration of each stage (s) and 40 = the increase in power 201
output between each stage (W). 202
203
Following the incremental test to exhaustion, the power was decreased to 100W and the subject 204
was asked to pedal at a self-selected cadence for 10 min. At 5-min, the CompuTrainer was re-205
calibrated to 3.5lbs. At 10-min, the power output was increased to 95% of PPO and the subject was 206
requested to maintain a speed above 14mph until volitional exhaustion to determine high intensity 207
exercise capacity. The time to fatigue achieved was recorded to the nearest second, along with the 208
peak heart rate during the test. The 95% PPO load used in the post-training testing was 95% of the 209
pre-training PPO achieved. 210
211
9
On a separate day, at least 2 days following the incremental test to exhaustion, a 40km time trial 212
was performed (Figure 1). Participants brought their bike into the laboratory at the same time of day 213
(in the afternoon) and set it up on the CompuTrainer. Following tyre checks and a 10-min light warm 214
up, the CompuTrainer was calibrated to 3.5lbs. Participants were then instructed to complete a 215
40km time trial as fast as possible. The only data the participants could see was distance completed. 216
Completion time, heart rate and mean power output were recorded. 217
218
Training interventions 219
For 6 weeks following the pre-training testing week, participants attended the laboratory on 3 days / 220
week (Monday, Wednesday and Friday) for prescribed training sessions. Training intensity was 221
prescribed in relation to the LT and the LTP obtained, and used the session goal approach (47). The 222
aim for POL training was to achieve 80% of training time in zone 1 and 20% of training time in zone 3 223
with no training time in zone 2. The aim for THR was to achieve 55% of training time in zone 1 and 224
45% of training time in zone 2, with no zone 3 training time. All laboratory training was completed 225
on the CompuTrainer, which was set-up and calibrated as previously described following a 10-min 226
light warm up. 227
228
POL training sessions consisted of 6 intervals of 4 min duration with 2 min rest periods, similar to the 229
optimal protocol for adaptation identified by the work of Stepto et al (51). The power output of the 230
6 intervals was 5-10% greater than the LTP (i.e. in zone 3) with a heart rate greater than the HR 231
corresponding to the LTP in the incremental test in all cases. During the rest periods participants 232
either stopped pedalling or pedalled backwards, and this remained consistent for every training 233
session. The minimum HR reached during the 2min recovery period was recorded. A 10-point rating 234
of perceived exertion (RPE) developed by Foster and colleagues (16, 17) was obtained at the end of 235
each training session. If the RPE, mean peak HR and mean minimum HR were decreasing over two 236
consecutive training sessions, the power output for the intervals was increased by 5-10W to 237
10
maintain a training stimulus. THR sessions included 60min at a power output half-way between the 238
LT and the LTP (i.e. in zone 2). The mean HR for the 60min session was recorded and RPE obtained at 239
the end of each training session. If the RPE and mean HR were decreasing over two consecutive 240
training sessions, the power output was increased by 5-10W to maintain a training stimulus. 241
242
The zone 1 training for both groups consisted of the warm-up and cool-down for the laboratory 243
training sessions (15-20 min∙session-1) combined with low intensity cycling on the days between the 244
laboratory training sessions. The intensity of the zone 1 training was controlled with HR, and the 245
mean HR for a zone 1 session did not exceed the value associated with the LT. Participants were 246
requested to try and maintain their HR at 5bpm below the HR corresponding to the LT at all times 247
during their zone 1 training sessions. Participants performed two to three zone 1 training sessions 248
per week on top of the three laboratory based training sessions. 249
250
Sample Analysis 251
Muscle biopsy samples were prepared for analysis of the maximal activities of citrate synthase (CS) 252
and β–hydroxyacyl-coA dehydrogenase (β-HAD). Briefly, a small piece of frozen wet muscle (4-5mg) 253
was removed from the pre- and post-training biopsy samples. The muscle samples were 254
homogenized in 0.1 M KH2PO4 and BSA and then subjected to three freeze-thaw cycles. The maximal 255
activities of CS and β-HAD were then determined on a spectrophotometer (at 37°C) using previously 256
described methods (2, 49) on an ILAB Aries analyser (Instrumentation Laboratory, Italy). Muscle 257
samples were also used for analysis of monocarboxylate transporters (MCT) 1 and 4 expression. 258
Briefly, 10-15mg of muscle tissue was scissor minced in lysis buffer (50mM Tris pH 7.5; 250mM 259
Sucrose; 1mM EDTA; 1mM EGTA; 1% Triton X-100; 1mM NaVO4; 50mM NaF; 0.50% PIC) on ice. 260
Samples were shaken for 1h (800rpm) at 4˚C, before being centrifuged for 60min at 12000g. The 261
supernatant was removed from the pellet to a fresh tube and used to determine protein 262
concentration using a DC protein assay (Bio Rad, Hertfordshire, UK). Equal amounts of protein were 263
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then boiled in laemmli sample buffer (250mM Tris-HCl, pH 6.8; 2% SDS; 10% glycerol; 0.01% 264
bromophenol blue; 5% -mercaptoethanol) and 7.5µg of protein from each sample was separated 265
on Pre-cast Criterion (Bio Rad, Hertfordshire, UK) SDS polyacrylamide gels (4-20% gradient gels) for 266
~90 mins at 150V. Proteins were transferred to a protran nitrocellulose membrane (Whatman, 267
Dassel, Germany) at 30V for 2h. Membranes were blocked in 5% BSA-TBST (TBS with 0.1% Tween-268
20) and then incubated overnight at 4˚C with the appropriate primary antibody. The primary 269
antibodies were used at the following dilutions, rabbit monoclonal GAPDH 1:5000 (14C10 Cell 270
Signalling), goat polyclonal MCT1 (C-20 Santa Cruz Biotechnology) 1:1000 and rabbit polyclonal 271
MCT4 (H-90 Santa Cruz Biotechnology) 1:1000. Following the overnight incubation the membranes 272
underwent 3 x 5min washes in TBST. The membrane was then incubated for 1h at room 273
temperature with horseradish (HRP)-linked anti-rabbit IgG (New England Biolabs, 7074; 1:1,000) or 274
anti-goat (ABCAM, Cambridge. 1:10,000) diluted in 5% BSA-TBST. The membrane was then cleared 275
of the antibody using TBST. Antibody binding was detected using enhanced chemiluminescence (GE 276
Healthcare). Molecular weight was estimated using the BioRad pre-stained Kaleidoscope molecular 277
weight standards (BioRad, Hertferdshire). In antibody test experiments GAPDH yielded a single band 278
at the 37KDa marker, whilst MCT1 and MCT4 antibodies yielded a number of bands with both 279
displaying a distinct band between the 37KDa and 50KDa. To improve antibody performance, reduce 280
non-specific bands and reduce the variability of quantifying different membranes we carried out the 281
following procedure: Prior to transfer the gels were cut at 25KDa and 50KDa molecular weight 282
markers. All the gel segments for the entire data set were transferred onto a single membrane. This 283
allowed us to visualise more clearly MCT1 and MCT4 as a band running above 37KDa and below 284
50KDa. These membranes were stripped for 30 mins at 50°C in stripping buffer (65mM Tris HCl, 2% 285
SDS vol/vol, 0.8% Mercaptoethanol vol/vol) and re-blocked followed by an overnight incubation in 286
anti-GAPDH antibody. Imaging and band quantification were carried out using a Bio Rad Bioimaging 287
Gel Doc System (Bio Rad, Hertfordshire, UK). To determine MCT1/4 the quantities for MCT1/4 were 288
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divided by the quantities for GAPDH and pre-training samples were each then normalised to 1 with 289
post training samples expressed relative to the respective pre-training data. 290
291
Urinary metabolomics analysis was performed using NMR spectroscopy. Urine samples were 292
prepared by the addition of 200 µl phosphate buffer (0.2 mol/l KH2PO4, 0.8 mol/l K2HPO4) to 500 µl 293
urine. Following centrifugation at 8000 x g for 5 minutes, 10 µl sodium trimethylsilyl [2,2,3,3-2H4] 294
proprionate (TSP) and 50 µl D2O were added to 550 µl of the supernatant. Spectra were acquired on 295
a 600 MHz Varian NMR spectrometer using the first increment of a NOESY pulse sequence at 25˚C. 296
Spectra were acquired with 16 K data points and 128 scans over a spectral width of 9 kHz. Water 297
suppression was achieved during the relaxation delay (1 s) and the mixing time (200 ms). All 1H NMR 298
urine spectra were referenced to TSP at 0.0 ppm and processed manually with Chenomx (version 6) 299
using a line broadening of 0.2 Hz. The spectra were integrated into bins consisting of spectral regions 300
of 0.04 ppm, using Chenomx (version 6). The water region (4.0 – 6.0 ppm) was excluded and the data 301
was normalised to the sum of the spectral integral. 302
303
Statistical Analyses 304
Statistical analysis was performed using SPSS version 18 (Chicago, Il, USA). A fully repeated-measures 305
ANOVA (2x2) compared the performance / physiological adaptation measures between training-306
intensity distribution models (POL and THR) and over time (pre to post training). Main effects 307
between training-intensity distribution models, over time and any interaction between these and 308
the performance / physiological adaptation measures were reported. Post-hoc analysis was 309
undertaken where significant main effects were obtained by using Paired Student’s t-tests and two-310
tailed values of P, with the Bonferroni method of adjustment to prevent type I error. Paired 311
Student’s t-tests using two-tailed values of P were also used to compare training variables at 312
baseline between POL and THR. The urinary metabolomics data were analysed using a Multivariate 313
data analyses performed using Simca-P+ software (version 11.0; Umetrics, Umeå, Sweden). Data 314
13
sets were scaled using unit variance scaling. Principal component analysis (PCA) was applied to data 315
sets to explore any trends or outliers in the data. To probe the effects of training intensity 316
distribution the data was analysed using multi-level partial least squares-discriminant analysis (PLS-317
DA) as previously used in metabolomics studies (54). 318
319
Statistical significance was accepted at P<0.05. All data in the text and Tables are expressed as Mean 320
(±SD) and in Figures as Mean (±SEM). Effect sizes for the key performance/physiological adaptation 321
measures were calculated from the mean difference (pre to post) divided by the standard deviation 322
of the baseline measure. These values were judged using the descriptors suggested by Cohen (7). 323
Effect sizes were included to highlight the magnitude of the performance/physiological adaptation 324
changes. 325
326
RESULTS 327
One participant did not complete the study, due to injury. Training adherence for the 11 remaining 328
participants was 96% and 97% for POL and THR, respectively. The total training volume was 329
significantly higher for THR than POL (Table 1, P<0.05). This was due to the nature of the study 330
design in which we attempted to match the volume of training in zone 1 between POL and THR 331
training models (Mean (±SD) zone 1 time was 313 (±65) and 283 (±76) min/week for POL and THR, 332
respectively, no significant difference). The percentage of time spent in each training intensity zone 333
(zone1:zone2:zone3) was the intended 80:0:20 distribution for POL, and was close to intended at 334
57:43:0 distribution for THR (Table 1). Body mass was not different between training periods and did 335
not change from pre to post training in either POL (76.5 ± 6.3 to 76.6 ± 6.2 kg) or THR (77.3 ± 6.7 to 336
76.5 ± 6.0 kg) training periods. 337
338
There was a main effect over time for the mean power output sustained during each of the 4 min 339
intervals in the POL training sessions (P<0.05) to maintain the training stimulus, with a significant 340
14
increase from week 1 observed by week 3 (Table 2). Due to the increase in target load there were no 341
differences over time for the peak HR reached during the sessions, the mean minimum HR following 342
the 2 minute recoveries, or the RPE rating of the session over the 6 weeks (Table 2). There was also a 343
main effect over time for the power output sustained during the 60 min threshold exercise training 344
sessions (P<0.05) to maintain the training stimulus, with an increase from week 1 observed by week 345
3 (Table 2). Due to the increase in target load there were no differences over time for the mean HR 346
sustained during the 60min ride, or the RPE rating of the session over the 6 weeks (Table 2). 347
348
Endurance Performance and Physiological Adaptation 349
There was a main effect over time for LT, LTP and for PPO (P<0.05; Figure 2). There was also a 350
significant interaction (P<0.05) with training intensity distribution model for the LT and PPO. A 351
significant increase was observed for LT power and PPO from pre to post training in POL (18 (±18) W 352
for LT and 27 (±18) W for PPO, both P<0.05) but this was not observed with the THR training model 353
(4 (±31) W for LT power and 9 (±17) W for PPO, both not significant). The effect sizes for the changes 354
in LT and PPO were both classed as moderate for the POL model but were classed as trivial and small 355
for the THR model (Table 3). The percentage change in LT and PPO from pre to post training was 356
higher in POL than THR (9 (±9) % POL vs. 2 (±14) % THR for LT, and 8 (±5) % POL vs. 3 (±4) % THR for 357
PPO, both P<0.05). 358
359
There was a main effect over time for 40km time trial mean power output (P<0.05; Figure 3). The 360
mean power output was higher from pre to post training with both POL and THR training. The 361
absolute change (Figure 3) and percentage change in the mean power output from pre to post 362
training was higher in POL than THR (8 (±8) and 4 (±6) %, respectively) but did not reach statistical 363
significance. The time to complete the 40km time trial improved by 2.3 (±2.2) min vs. 0.4 (±2.9) min 364
following POL vs. THR training, respectively. The effect size was deemed moderate for POL and small 365
for THR (Table 3). 366
15
367
There was also a main effect over time for the high-intensity exercise capacity at 95% of pre-training 368
PPO (P<0.05; Figure 3), with increases from pre to post training for both POL and THR models 369
(P<0.05). There was also an interaction effect (P<0.05) with a significantly greater percentage 370
increase from pre to post training in POL (85 (±43) %) compared with THR (37 (±47) %). 371
372
Detraining appeared to be effective with initial PPO before the first and second training 373
interventions not being significantly (p=0.94) different (359 (±31) W and 359 (±39) W, respectively). 374
The same was true for high-intensity exercise capacity which was not different (p=0.46) before the 375
first and second training interventions (286 (±60) sec and 304 (±45) sec, respectively). The 40km 376
time trial time (65 (±5) min vs. 63 (±3) min) and mean power output sustained during the time trial 377
(281 (±37) W vs. 289 (±36) W) both followed this same pattern by returning towards initial values 378
(p=0.06). Training load (intensity zone x duration (min)) dropped substantially during the detraining 379
period. The training load was reduced to 38-46% of that sustained during THR and POL, respectively. 380
381
Skeletal Muscle Analysis 382
There were no main effects over time, or with training-intensity distribution model, for the maximal 383
activities of the skeletal muscle oxidative enzymes studied. The maximal activity of CS from pre to 384
post training with POL and THR was 47 (±6) to 48 (±4) mmol∙kg wet wt-1∙min-1 and 47 (±5) to 49 (±3) 385
mmol∙kg wet wt-1∙min-1, respectively. The maximal activity of β-HAD from pre to post training was 15 386
(±2) to 15 (±2) mmol∙kg wet wt-1∙min-1 and 15 (±2) to 15 (±1) mmol∙kg wet wt-1∙min-1, with POL and 387
THR respectively. MCT1 expression was unchanged over either of the exercise training periods (12% 388
(±13%) increase for pre to post training with POL, and 10% (±13%) increase for pre to post training 389
with THR). However, MCT4 expression was increased over both training periods (Figure 4). There 390
was a 133% (±56%) increase in MCT4 total protein with POL training, and an 80% (±41%) increase in 391
16
MCT4 total protein with THR training. There was no interaction between time and training model for 392
MCT1 or MCT4 protein expression. 393
394
Urinary Metabolomics 395
Initial PCA analysis was performed and did not reveal any separation according to training intensity 396
distribution. A significant multilevel PLS-DA model was obtained for the THR training period but not 397
for the POL training (P<0.05). The NMR regions changing following the THR training period were 398
identified using the RP plot (Figure 5). The RP plot displays the sIRP and shows the discriminating 399
metabolites for the model. The metabolites responsible were identified as hippuric acid, creatinine, 400
dimethylamine, 3-methylxanthine, hypoxanthine, and an unidentified peak at 3.28 ppm. The 401
direction of change for these metabolites was identified as hippuric acid decreased post THR, 402
creatinine increased post THR, dimethylamine increased post THR, 3-methylxanthine increased post 403
THR, and hypoxanthine decreased post THR. 404
405
DISCUSSION 406
Endurance athletes have been repeatedly demonstrated to undertake a high training volume with 407
~80% of training time at low intensities (zone one) and ~20% of training time at higher intensities 408
(zones two and three, combined; (19, 35, 36, 43, 45)). Previous studies have described a 409
performance benefit from adding high-intensity training bouts into the overall training of endurance 410
athletes (33, 51, 57, 58). However, the present study demonstrates for the first time in a randomized 411
cross-over study design that adopting this training-intensity distribution leads to greater adaptations 412
over 6 weeks compared with a training-intensity distribution focussed more around moderate 413
(threshold) intensities (57% in zone one, 43% in zone two). Notably, this outcome occurs despite a 414
greater total training volume with the THR training model, and occurs in already well-trained cyclists. 415
In particular, LT, PPO and exercise capacity at 95% of pre-training PPO all improved to a greater 416
extent with POL compared with THR training. Although there was no statistically significant 417
17
difference between POL and THR for the improvement in 40km TT mean power output the effect 418
size was larger for POL and the magnitude of change was twice that observed following THR training. 419
The greater effect sizes for all of the key performance and adaptation markers with POL compared 420
with THR training provides a strong indicator that POL training is more optimal for short term 421
training adaptations to occur. The muscle enzyme activity analysis and MCT1/4 expression suggests 422
that these performance adaptations are independent of detectable differences in mitochondrial 423
oxidative capacity or differences in lactate transport/oxidation in skeletal muscle between training 424
models. However, the metabolomics analysis reveals that some markers of cellular energy stress 425
were modified with THR but not with POL. Collectively, these data provide some new insights into 426
understanding training stress and optimal intensity distribution for adaptation in already well-427
trained athletes. 428
429
It has previously been suggested that endurance athletes might not achieve optimal gains in 430
performance and/or physiological adaptation by doing too much moderate intensity training in zone 431
2 (12, 27, 34). Previous work has also shown that a group of elite runners who trained more at an 432
intensity corresponding to the LT (zone 2) had a lower performance level than a group of elite 433
runners that trained less in zone 2 and more in zone 3 (3). The evidence from the present study and 434
these previous studies suggests that a critical component for promoting adaptation is the 435
incorporation of high-intensity interval training (zone 3) sessions and reduction of moderate 436
intensity (zone 2) sessions, whilst maintaining the volume of low intensity (zone 1) sessions. While 437
we appreciate that trained athletes will incorporate all three intensity zones into their training 438
schedules and competitions, our aim was to determine the impact of high-intensity interval work vs. 439
moderate intensity continuous threshold training sessions on adaptation. It would seem that 440
reducing the emphasis on moderate intensity threshold work in place of high-intensity interval work 441
promotes greater adaptation. This may be particularly true for our cyclists who had not followed a 442
POL training model prior to entry into our study. The precise mechanisms for these beneficial effects 443
18
in already well-trained individuals are not fully understood but we do know that exercise intensity is 444
a key driver for adaptation from several short duration training studies (38, 51, 52). Recently, there 445
has been some debate about the benefits of polarised training suggesting that it helps to reduce 446
fatigue, and may be a more optimal stimulus for adaptation based on our genetic make-up and 447
ancestors activity profiles (4). 448
449
Higher training intensity (zone 3 vs. zone 2) should cause a greater increase in the activation of 450
adenosine monophosphate activated protein kinase (AMPK), as has been reported in previous 451
studies (5, 60). Indeed, in a group of well-trained cyclists, a high intensity training session involving 8 452
x 5min at 85% VO2peak caused an increase in AMPK activity and phosphorylation (6). AMPK-signalling 453
mechanisms are linked to the initiation of mitochondrial biogenesis, through the regulation of 454
peroxisome proliferator receptor-γ co-activator-1α (PGC-1α) expression and activity (41). In the 455
present study however, it seems that the differences in endurance performance and physiological 456
adaptation between POL and THR were not due to differences in mitochondrial oxidative capacity, as 457
there were no changes in the maximal activity of CS or β-HAD between training models or in 458
response to the training. The absence of a detectable change in mitochondrial oxidative capacity is 459
likely due to the skeletal muscle of the cyclists already having a high mitochondrial oxidative capacity 460
at the start of the training. This is demonstrated in the absolute magnitude of enzyme activities and 461
the performance measures attained. Any further increase in muscle oxidative capacity in already 462
well-trained skeletal muscle is likely to be small and may be too small to be detectable following a 463
short-term training intervention (30). Indeed, following high volume high intensity training in well-464
trained athletes, there was no change in the maximal activity of CS (14, 58) and β-HAD (58), despite 465
improvements in endurance performance. In contrast, studies using moderately trained individuals 466
and a similar interval training programme component to that in the POL model in the present study, 467
have found large increases in the maximal activities of CS and β-HAD of 20-30% (20, 38, 52). It is also 468
possible that an insufficient additional training stimulus could explain the lack of mitochondrial 469
19
oxidative capacity response in athletes, compared with the usually large improvements noted in 470
studies on moderately trained individuals. However, it seems likely that there may also be a ceiling 471
for adaptation in mitochondrial oxidative capacity in already well-trained skeletal muscle. Therefore, 472
in the present study it would appear that the improvements in physiological performance 473
parameters during exercise are independent of detectable changes in mitochondrial oxidative 474
capacity. 475
476
The significant increase in LT and greater change in TTE at 95% of pre-training PPO, following POL 477
compared with THR training, most likely reflects adaptations induced by the higher intensity interval 478
exercise. Since the absolute training volume at low intensity (zone 1) was closely matched these 479
differences in adaptation must come down to the training time spent in zone 2 or zone 3, with zone 480
3 proving more effective. It has been reported that the lactate transport capacity of skeletal muscle 481
is increased by training, and that MCT1 and MCT4 content are increased following high intensity 482
knee extensor exercise training over 8 weeks (39). These prior data show that intense exercise 483
influences lactate/H+ transporter expression and could explain the present LT and TTE data. 484
Interestingly, changes in intracellular lactate shuttling between type II and type I fibres, and 485
increases in capacity for lactate oxidation through upregulation of mitochondrial lactate 486
dehydrogenase and mitochondrial MCT1 (as part of a lactate oxidation complex, 21) could provide 487
an explanation for our observations. However, in the present study neither the high intensity 488
interval exercise in the POL model, nor the continuous moderate intensity session in the THR model, 489
were effective at inducing changes in total MCT1 content. Since MCT1 occupies both mitochondrial 490
and sarcolemmal domains in skeletal muscle the lack of any change in whole muscle MCT1 content 491
potentially mirrors and supports the lack of change in mitochondrial oxidative enzyme activity, due 492
to an already large training base in our participants. The lack of change in MCT1 adds support to the 493
notion that already well-trained cyclists, with high pre-intervention mitochondrial oxidative capacity, 494
will have little capacity for further mitochondrial adaptation. Furthermore, with no evidence for 495
20
mitochondrial adaptation it could be suggested that any change observed in MCT1 content would 496
then reflect sarcolemmal MCT1. On this basis our data also highlight that there are no detectable 497
sarcolemmal changes in MCT1 in already well-trained cyclists undertaking these interventions. 498
However, further work is required to investigate specific sracolemmal and mitochondrial MCT1 499
changes with training interventions to provide more insight into the precise mechanisms 500
underpinning the greater adaptations in LT and high intensity exercise capacity observed with POL 501
vs. THR. 502
503
The increase in MCT4 content in both training models is also interesting. This observation suggests 504
that the continuous moderate intensity work in the THR model, and the high intensity work in the 505
POL intervention, both provide a good stimulus to MCT4 expression. MCT4 occupies a sarcolemmal 506
domain only and is thought to largely contribute to extrusion of H+ and lactate from the cell cytosol. 507
An improved maintenance of intracellular pH and lactate concentration have both been considered 508
as factors that could delay the development of fatigue during high-intensity tasks, and as such may 509
explain our observations. However, understanding of MCT4 adaptations to exercise training is still 510
incomplete. It has been suggested that increases in MCT4 should occur in line with increases in 511
MCT1 expression, but this has not been observed in the present work. Notably, a recent review 512
highlighted that the reported responses of MCT1 and MCT4 to exercise may be influenced by the 513
timing of the post-training biopsies (53). In the present study the biopsies were obtained at least 24 514
hours following the last training session, a time at which observed changes in MCT4 may be high and 515
changes in MCT1 may be low (53). Therefore, further work remains to be done in already well-516
trained individuals to understand the relationships between training intensity, timing of tissue 517
sampling, MCT expression, and adaptations in high intensity exercise capacity. 518
519
Other contributing factors to changes in LT and TTE at 95% of pre-training PPO must also be 520
considered, and could include greater increases in buffering capacity, improved capillarity, or other 521
21
systemic cardiovascular adaptations which have all been reported to be increased to a greater 522
extent after high-intensity exercise training (10, 24, 59) and are also related to muscular exercise 523
performance/capacity (25). Alternatively, as exercise intensity increases, there is also a greater 524
recruitment of fast-twitch muscle fibres (9). Adaptations in muscle are observed to be greatest in 525
those muscle fibres that are directly activated during training (9). It has also been suggested that 526
fast-twitch muscle fibres become more fatigue resistant following high-intensity training. These 527
observations could partly explain the greater improvements in the PPO and TTE at 95% of pre-528
training PPO in POL compared with THR. 529
530
The effectiveness of a training-intensity distribution containing ~80% of total training time in zone 1 531
and ~20% in zone 3, as used for POL in the present study, has been suggested to be due not only to 532
the intensity-specific adaptations, but also to enhanced recovery (46). Therefore, the recovery 533
between training sessions could partly explain the effectiveness of POL compared with THR. It has 534
been reported that the acute recovery from a training session in zone 1 is faster than following a 535
training session in zone 2, yet the recovery following a training session in zone 3 is no different than 536
following a session in zone 2 (48). If zone 3 training leads to larger physiological adaptations 537
compared with zone 2 yet with similar recovery, this could be considered a more effective training 538
strategy. Moreover, since recovery is greater from zone 1 than zone 2 sessions, it has been 539
recommended to supplement zone 3 training with training in zone 1 (48). In the present study 540
improvements in the 40km TT and the TTE for both POL and THR suggest that overreaching did not 541
take place during either of the training interventions. This could be interpreted as recovery being a 542
minor issue. However, POL training appears to provide a stronger stimulus for physiological 543
adaptation and improvement in 40km TT performance as well as producing larger gains in high-544
intensity exercise capacity. Therefore, enhanced recovery cannot be ruled out as a potential factor 545
contributing to the greater adaptations. 546
547
22
The urinary metabolomics data are interesting since a significant model was only observed from pre 548
to post training for THR. Of course it could be argued that the metabolites of interest may reflect 549
dietary influences (caffeine intake, phytochemical intake, protein intake) as has been reported in 550
previous metabolomics and nutrition studies (54) and highlighted in a review by Gibney et al. (18). 551
However, dietary intake was controlled with participants replicating their food intake for 2 days prior 552
to the morning first pass urine sample collections. Participants did not exercise on the day before 553
collection of the samples, and the THR and POL training interventions were administered in a 554
randomized cross-over fashion. Therefore, it would seem that dietary intake would be an unlikely 555
key factor here. Alternatively, the metabolites of interest could collectively suggest differences in 556
cellular metabolic/energy stress induced by the THR training. Greater creatinine excretion would 557
normally reflect greater PCr degradation, act as a marker of glomerular filtration rate, or mirror lean 558
body mass in 24 hour urine collections (15). In morning first void urine it likely reflects changes in 559
hydration status or possibly reflects energy availability (55). In the absence of dietary influences 560
urinary dimethylamine is thought to reflect intermediary metabolism (37) while hypoxanthine 561
reflects purine nucleotide degradation which tends to be acutely lower if high-intensity sprint 562
exercise is not undertaken (50). Urinary 3-methylxanthine can be produced by demethylation of 563
theophylline in the presence of oxidising radicals (44) and increased urinary excretion of this 564
metabolite could therefore represent greater overall oxidative stress from the THR training period. 565
Changes in hippurate excretion are typically associated with gut microflora (62) and activities of gut 566
microflora may play an important role in energy metabolism and/or immune function of the whole 567
organism (28). 568
569
While the metabolomics profile change following THR, but not POL, cannot be fully explained, it may 570
provide some insight into the overall cellular metabolic / energetic stress experienced with the THR 571
training model. Greater evidence of cellular metabolic / energy stress with the THR model would 572
support the notion of longer recovery times from threshold training sessions, or may just reflect the 573
23
higher training load. Either way this greater stress was not associated with greater adaptation which 574
may suggest a maladaptive response to the THR training. These new insights provide some 575
preliminary evidence that metabolomics may be useful in tracking and identifying novel markers 576
related to training stress, adaptation and recovery. Clearly the sample size in the present study is 577
one limitation for the metabolomics analysis but future work in larger scale studies may help to 578
verify the usefulness of metabolomics profiling of training stress. 579
580
CONCLUSIONS 581
The present study therefore confirms the hypothesis that a polarised training intensity distribution 582
model is an effective strategy in already well-trained endurance athletes. A polarised training model 583
is recommended for trained cyclists wishing to maximally improve performance and physiological 584
adaptation over a short-term training period, particularly if they are currently following a threshold 585
training distribution model. There is however, much still to be understood regarding the impact of 586
endurance training periods containing different training-intensity distributions in endurance 587
athletes, and the mechanisms responsible for these effects. Therefore, this is a fruitful area for 588
future research that can contribute not only to the optimisation of endurance training programmes 589
for athletes, but also to understanding optimal ways to promote physiological adaptations to 590
exercise in the wider population. 591
592
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735
736
28
Grant funding: 737
No funding was received for this project. The study was funded jointly through the School of Sport, 738
University of Stirling, and the School of Agriculture and Food Science, University College Dublin 739
(metabolomics analyses only). 740
741
742
29
FIGURE LEGENDS 743
744
Figure 1. Study design schematic detailing the timeline for training and testing (A) and the testing 745
week measurement schedule (B). 746
747
Figure 2. Mean (±SEM) power output corresponding to the LT, LTP and PPO before (Pre) and 748
following (Post) both of the 6 week training interventions. LT: Lactate Threshold; LTP: Lactate Turn 749
Point; PPO: Peak Power Output; POL: Polarised Training Model; THR: Threshold Training Model. * 750
significantly different from Pre within a specific training model (P<0.05). 751
752
Figure 3. Mean (±SEM) 40km time trial mean power output assessed before (Pre) and following 753
(Post) both of the 6 week training interventions (A), absolute change (Δ) in mean power output 754
sustained during the 40km time trial following the training interventions (B), and time to fatigue at 755
95% of baseline peak power output assessed before (Pre) and following (Post) both of the 6 week 756
training interventions (C). POL: Polarised Training Model; THR: Threshold Training Model. * 757
Different from pre within a training model (P<0.05). † indicates significant difference between 758
training models. 759
760
Figure 4. Mean (±SEM) skeletal muscle change in total protein content from Pre to Post polarised 761
(POL) and threshold (THR) training interventions for monocarboxylate transporter protein 4 (MCT4; 762
A), and representative blots from two participants (B). 763
764
Figure 5. Mean cross model validation (CMV) prediction error (A). The dot indicates the CMV 765
prediction error estimated in terms of number of misclassifications. The variable ranks (RP) plot for 766
the model is also shown (B). 767
Table 1. Mean (±SD) details of the total training time completed per week for the polarised (POL)
and the threshold (THR) training models, the training load (intensity zone x duration (min)) and the
proportion of training time spent in zone 1, zone 2 and zone 3.
units POL THR
Total Training Time minutes / week 381 (±85) 458 (±120)*
Training load intensity zone x duration 517 (±90) 633 (±119)*
Zone 1 % of training time 80 (±4) 57 (±10)*
Zone 2 % of training time 0 (±0) 43 (±10)*
Zone 3 % of training time 20 (±4) 0 (±0)*
*Difference between POL and THR (P<0.05).
1
Table 2. Power output, heart rate (HR), and rating of perceived exertion (RPE) sustained during the laboratory training sessions for the polarised (POL, 6 x 4
min, zone 3 intensity bouts) and threshold (THR, 60 min constant zone 2 intensity bouts) training. Values are mean (±SD) from 3 laboratory training sessions
in each week during the study.
Variable Training model 1 2 3 4 5 6
Power output (W) POL 319 (±33) 321 (±34) 328 (±35)*a 331 (±37)*a337 (±35)*abc 340 (±34)*abc
Peak HR (bpm) 173 (±10) 172 (±9) 173 (±10) 173 (±9) 172 (±9) 171 (±9)
Recovery HR (bpm) 111 (±14) 111 (±10) 109 (±15) 109 (±12) 108 (±13) 108 (±14)
RPE(0-10) 7 (±1) 7 (±1) 8 (±1) 8 (±1) 8 (±1) 7 (±1)
Power output (W) THR 266 (±31) 267 (±33) 277 (±34)*a 284 (±33)*ab 288 (±33)*abc 290 (±32)*abc
HR (bpm) 158 (±12) 155 (±10) 156 (±9) 157 (±9) 159 (±8) 159 (±9)
RPE (0-10) 5 (±1) 5 (±1) 6 (±1) 6 (±1) 6 (±1) 6 (±1)
All values are different between POL and THR (P<0.05). * indicates a significant difference from week 1, a from week 2, b from week 3, c from week 4, within
each training model (P<0.01).
1
Table 3. Mean (±SD) percentage change (Δ (%)) and effect sizes for the key performance and
adaptation measures assessed before and after 6 weeks of polarised (POL) and threshold (THR)
training interventions.
Training
Model
Measure Δ
(%)
Effect Size Descriptor†
POL 40km TT MPO (W) 8 (±8) 0.57 Moderate
LT (W) 9 (±9)* 0.59 Moderate
LTP (W) 6 (±10) 0.40 Small
PPO (W) 8 (±5)* 0.77 Moderate
95% exercise capacity (s) 85 (±43)* 2.44 Large
THR 40km TT MPO (W) 4 (±6) 0.35 Small
LT (W) 2 (±14) 0.11 Trivial
LTP (W) 4 (±7) 0.34 Small
PPO(W) 3 (±4) 0.26 Small
95% exercise capacity (s) 37 (±45) 0.99 Large
MPO = mean power output; LT = lactate threshold; LTP = lactate turnpoint; PPO = peak power
output, 95% exercise capacity = time to exhaustion at 95% of pre-training PPO, †Cohen (1988). *
indicates significant difference between POL and THR training models (P<0.05).
