Note: This article will be published in a forthcoming ... · lifestyle. However, exercising women...

“Within-day Energy Deficiency and Metabolic Perturbation in Male Endurance Athletes” by Torstveit et al.

International Journal of Sport Nutrition and Exercise Metabolism

© 2018 Human Kinetics, Inc.

Note: This article will be published in a forthcoming issue of

the International Journal of Sport Nutrition and Exercise

Metabolism. This article appears here in its accepted, peer-

reviewed form; it has not been copyedited, proofed, or

formatted by the publisher.

Section: Original Research

Article Title: Within-day Energy Deficiency and Metabolic Perturbation in Male Endurance

Athletes

Authors: Monica K. Torstveit1, Ida Fahrenholtz2, Thomas B. Stenqvist1, Øystein Sylta1, and

Anna Melin2

Affiliations: 1Faculty of Health and Sport Science, Institute of Public Health, Sport &

Nutrition, University of Agder, Kristiansand, Norway. 2Department of Nutrition, Exercise

and Sports, University of Copenhagen, Frederiksberg, Denmark.

Running Head: Within-day energy deficiency in male athletes

Journal: International Journal of Sport Nutrition and Exercise

Acceptance Date: January 5, 2018

©2018 Human Kinetics, Inc.

DOI: https://doi.org/10.1123/ijsnem.2017-0337

https://doi.org/10.1123/ijsnem.2017-0337




Within-day energy deficiency and metabolic perturbation in male

endurance athletes

Torstveit MK.,1 Fahrenholtz I.2, Stenqvist TB.,1 Sylta Ø.,1 Melin A.2

1Faculty of Health and Sport Science, Institute of Public Health, Sport & Nutrition,

University of Agder, Kristiansand, Norway.

2 Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg,

Denmark

Running head: Within-day energy deficiency in male athletes

Corresponding author:

Monica Klungland Torstveit,

University of Agder,

Faculty of Health and Sport Science PO. Box 422

4604 Kristiansand Norway

E-mail: [email protected]

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mailto:[email protected]




Abstract

Endurance athletes are at increased risk of relative energy deficiency associated with metabolic

perturbation and impaired health. We aimed to estimate and compare within-day energy

balance (WDEB) in male athletes with suppressed and normal resting metabolic rate (RMR)

and explore if within-day energy deficiency (WDED) is associated with endocrine markers of

energy deficiency. Thirty-one male cyclists, triathletes, and long-distance runners recruited

from regional competitive sports clubs were included. The protocol comprised measurements

of RMR by ventilated hood, and energy intake and energy expenditure to predict RMRratio

(measured RMR/predicted RMR), energy availability (EA), 24-hour energy balance (EB) and

WDEB in 1-hour intervals, assessment of body-composition by dual-energy X-ray

absorptiometry, and blood plasma analysis. Subjects were categorized as having suppressed

(RMRratio < 0.90, n=20) or normal RMR (RMRratio > 0.90, n=11). Despite no observed

differences in 24-hour EB or EA between the groups, subjects with suppressed RMR spent

more time in an energy deficit exceeding 400 kcal (20.9 [18.8 – 21.8] hours vs. 10.8 [2.5 –

16.4], P=0.023), and had larger single-hour energy deficits compared to subjects with normal

RMR (3265 ± 1963 kcal vs. -1340 ± 2439, P=0.023). Larger single-hour energy deficits were

associated with higher cortisol levels (r = -0.499, P=0.004) and a lower testosterone:cortisol

ratio (r = 0.431, P=0.015), but no associations with T3 or fasting blood glucose were observed.

In conclusion, WDED was associated with suppressed RMR and catabolic markers in male

endurance athletes.

Keywords: Energy availability, within-day energy balance, resting metabolic rate

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Introduction

A balanced diet with an appropriate energy intake supports optimal body function

(Thomas et al., 2016) and is, together with regular physical activity, the cornerstone of a healthy

lifestyle. However, exercising women and female athletes focusing on leanness, such as

endurance athletes, are reported to be at increased risk for restricted eating behavior and relative

energy deficiency related to serious health conditions that include eating disorders, premature

osteoporosis, and increased cardiovascular risk factors (De Souza et al., 2014; Mountjoy et al.,

2014; Nattiv et al., 2007). There is scientific evidence concerning the causality between relative

energy deficiency and the metabolic and endocrine perturbations related to suppressed resting

metabolic rate (RMR), subclinical and clinical menstrual dysfunction in women, and poor bone

health (Loucks et al., 1998; Loucks & Thuma, 2003). Furthermore, a growing body of evidence

suggests that energy deficiency results in an altered endocrine profile, loss of bone mass, and

suppressed RMR in male athletes (Dolan et al. 2012; Hagmar et al. 2013; Koehler et al., 2016;

Wilson et al., 2015). Nonetheless, recent position papers and reviews call for more knowledge

regarding energy deficiency and associated health- and performance variables among male

athletes (Mountjoy et al., 2014; Tenforde et al., 2016).

RMR represents the energy cost of basic physiological functions, including immunity,

reproductive function, growth, and thermoregulation (Fuqua & Rogol, 2013), which all appear

to be affected by relative energy deficiency (Mountjoy et al., 2014). When energy intake is

inadequate, energy allocation is prioritized to physiological processes essential for immediate

survival (Wade & Jones, 2004). Therefore, bodyweight and body composition may remain

within the normal range despite insufficient energy intake (Goldsmith et al., 2010; Redman et

al., 2009; Redman & Loucks, 2005). In female athletes, an RMRratio < 0.90 is widely accepted

as a surrogate marker for relative energy deficiency (De Souza et al. 2008; Gibbs et al. 2013;

Melin et al., 2015).

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Traditionally, energy status is evaluated in blocks of 24-hours as either energy balance

(EB = energy intake – total energy expenditure) or energy availability (EA) (EA = energy

intake – exercise energy expenditure (EEE) relative to fat free mass (FFM)). However, these

24-hour views of human thermodynamics have been criticized for failing to account for the

endocrine responses that act on real-time changes in energy intake and expenditure (Benardot,

2013). Within-day EB (WDEB), where energy intake and energy expenditure are assessed in

1-hour intervals may, therefore, be more appropriate (Benardot, 2013; Deutz et al., 2000).

Indeed, it has been suggested that failure to find associations between field determinations of

low EA and objective measures of energy conservation may be explained by a failure to

account for within-day energy deficiency (WDED) as a possible contributor to the metabolic

and endocrine alterations associated with relative energy deficiency

(Mountjoy et al., 2014). Published studies investigating WDED have thus so far only

assessed female athletes, where WDED has been associated with menstrual dysfunction, lower

estradiol and RMRratio and higher cortisol levels (Fahrenholtz, et al., 2017) and an unfavorable

body composition (Deutz et al., 2000).

Therefore, the aim of this study was to estimate and compare WDED, where EB is

assessed in 1-hour intervals, in male endurance athletes with suppressed and normal RMR, and

to investigate whether these comparisons deviate from the traditional 24-hour assessments.

Finally, it was of interest to explore if WDED is associated with endocrine markers of energy

deficiency in this male athletic group.

Methods

Forty-six male cyclists, triathletes, and long-distance runners were recruited to the

study through local clubs and social media in two phases (Figure 1). All subjects were

categorized as trained or well-trained (Jeukendrup et al., 2000), and at performance level 3-4

(De Pauw et al., 2013). Inclusion criteria were male, 18-50 years old, absence of disease or

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injury, maximal oxygen uptake (�̇�O2max) >55 mL.kg-1.min-1, training frequency ≥4

sessions/week during the previous year, and competing in an endurance sport at a regional or

national level. Two subjects dropped out due to personal reasons and 13 subjects were

excluded; 5 were under the age of 18, 5 were excluded due to missing data, and 3 did not follow

protocol (Figure 1). No subjects were excluded due to underreporting of energy intake

according to Black (2000). Thus, 31 subjects (67.4%) were included in the final data analysis.

The study was approved by the University Faculty Ethics Committee and registered with the

Norwegian Centre for Research Data. All subjects signed a written informed consent before

study participation.

Measurement methods

Performance and health were assessed during three non-consecutive days, followed by

four consecutive days (three weekdays and one weekend-day) of recording food consumption,

non-exercise activity thermogenesis (NEAT) and training in the subjects’ normal environment.

The test protocol was standardized for each athlete.

On the first day, determination of �̇�O2max and anthropometric measurements were

performed. On the second day, RMR and resting heart rate (HR) were assessed, a questionnaire

was completed, and the subjects received detailed instructions on how to record their energy

intake and expenditure. On the third day, blood samples were drawn and whole-body

composition was assessed. All subjects were asked to arrive in a fasted state on days two and

three, refrain from using products containing tobacco, alcohol and caffeine, and to not exceed

1 hour of low intensity exercise the day before.

Anthropometry

Height measurement was completed without shoes to the nearest 0.1 cm using a

centimeter scale affixed to the wall (Seca Optima, Seca, UK), and body weight was measured

in light clothing to the nearest 0.01 kg (InBody 720, Biospace, Seoul, Korea). Body mass index

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(BMI) was calculated as measured weight in kilograms (kg) divided by height squared in meter

(kg/m2). Body composition was measured using Dual-energy X-ray absorptiometry (DXA)

(Lunar Prodigy, EnCore v. 15, GE Medical Systems, 3030 Ohmeda Drive, Madison, Wi 53718,

USA). All measurements were completed in a fasted state between 6 and 9 a.m.

Maximal oxygen uptake

VO2max was predicted by asking the subjects to perform an incremental test until

exhaustion: cyclists and triathletes on a stationary bike (Excalibur Sport, Lode B.V.,

Groningen, the Netherlands) and runners on a treadmill (Katana Sport, Lode B.V., Groningen,

the Netherlands). Cyclists started with one minute of cycling at a power output corresponding

to 3 W/kg, and increased by 25 W/min until voluntary exhaustion or failure to maintain a

cadence ≥70 RPM. Runners started at 12 km.h-1 on a constant incline of 3°. Speed was

increased by 1 km.h-1.min-1 until exhaustion. �̇�O2max was measured using Oxycon Pro™ with

mixing chamber and 30-s sampling time (Oxycon Pro, Jaeger GmbH, Hoechberg, Germany),

using a two-way T-shape non-rebreathing valve and a nose clip (series 9015, Hans Rudolph,

Kansas, MO, USA). All systems were calibrated according to standards.

Resting metabolic rate and resting heart rate

For RMR assessment, subjects arrived at the laboratory in a fasted state by motorized

transport between 6 and 9 a.m. Subjects were instructed to minimize movement after

awakening, and rested lying down for 15 minutes before the measurements began. For a

detailed description of measurement of RMR, see table 1. The lowest obtained heart rate (HR)

during the RMR measurement was registered using a Polar V800 HR monitor (Polar Elektro

Oy, Kempele, Finland).

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Blood sampling

Fasted blood samples were drawn from a cephalic vein between 7 and 9 a.m. by a

qualified bio-technician. One 10 mL BD Vacutainer CAT (BD, Plymouth, United Kingdom)

was filled and centrifuged after at least 30 min and within 60 min. Two 1.8 mL Cryotube Vials

(Termo Fischer Science, Roskilde, Denmark) were filled with serum and frozen to -75ºC.

Blood samples were analyzed for glucose, cortisol, testosterone, and triiodothyronin (T3) at

Sorlandets Hospital in Kristiansand and Aker Hormonlab in Oslo, Norway. Reference values

based on the Norwegian laboratories standards were used: glucose (4-6 mmol.L-1); cortisol

(138-690 mmol.L-1); testosterone (18-40 y, 7.2-24 nmol.L-1; >41 y, 4.6-24 nmol.L-1); and T3

(1.2-2.7 nmol.L-1).

Energy status

Energy availability (EA) was calculated by subtracting EEE from the subjects’ daily

energy intake, relative to FFM (Nattiv et al., 2007). In order not to underestimate EA, EEE

only represented the energy attributable to training, and RMR was subtracted from EEE before

used in the EA-calculation.

An overview of the components for the WDEB calculation is presented in Table 1 and

an example of WDEB calculation is provided in Table 2, illustrating 18 hours in EB < 0 kcal,

6 hours in EB <-400 kcal, and a largest single hour deficit of 1070 kcal.

Statistics

Statistical calculations were performed using RStudio version 0.99.879 (Boston, MA,

USA) with a two-tailed significance level of < 0.05. All data sets were tested for normality and

homogeneity of variance before statistical hypothesis tests were performed. Normally

distributed data were summarized as means and standard deviations (SD), and non-normally

distributed data as median and interquartile range (IQ 25 and IQ 75 percentiles). Differences

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between subjects with suppressed RMR (RMRratio < 0.90) vs. normal RMR (RMRratio > 0.90)

were investigated using unpaired Student’s t-test for normally distributed data and the

Wilcoxon rank-sum test for non-parametric data. Pearson’s correlation coefficient and

Spearman’s rank correlation coefficient were calculated to investigate associations between

WDED variables and continuous outcomes for normally and non-normally distributed data,

respectively.

Results

Sixty-five percent of the subjects had suppressed RMR. Subjects with suppressed RMR

were older compared to subjects with normal RMR, but no differences in anthropometry,

exercise capacity, training volume (Table 3), or energy expenditure data (Table 4) between the

groups were found.

No difference in 24-hour EB or EA between the groups was observed, but subjects with

suppressed RMR spent more time in energy deficits exceeding 400 kcal (P=0.023) and had

larger single-hour energy deficits (P=0.023) compared to subjects with normal RMR (Table 4).

No difference in protein intake between subjects with normal RMR (1.8 ±0.4 g/kg/day) and

subjects with suppressed RMR (1.7 ±0.4 g/kg/day) was observed.

All subjects had fasting blood glucose, cortisol, testosterone, and T3 within the normal

range. There were no associations between WDED and glucose or T3 (Table 5). Larger single-

hour energy deficit was associated with higher cortisol (r= 0.499, P=0.004) and a lower

testesterone:cortisol ratio (r= 0.431, P=0.015). The more time spent in WDEB < 0 kcal, and

the larger the single-hour energy deficit, the lower body fat percentage (r= -0.366, P=0.043 and

r= 0.359, P=0.047, respectively). There were no associations between protein intake and any

body composition measures, although there was a tendency towards a lower fat free mass with

lower protein intake (r= -0.333, P=0.067).

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Discussion

In this group of well-trained men, 65% had suppressed RMR and, despite similar EA

and 24 hour EB compared to subjects with normal RMR, they spent more time in severe energy

deficit and had larger single-hour energy deficit, which were associated with higher cortisol

levels and a lower testosterone:cortisol ratio.

In order to account for the endocrine responses, it has been suggested that calculating

WDEB is more physiologically relevant compared to the traditional 24-hour assessment

(Benardot, 2013). The WDEB method assesses time and magnitude deviations from the

predicted EB, where ±400 kcal represent the hypothetical limits for staying in a desirable EB,

based on the predicted amount of liver glycogen, although the limits may be smaller or larger,

depending on individual factors (Benardot, 2007; Benardot 2013; Deutz et al., 2000).

Exceeding the threshold of EB below -400 kcal, could potentially accelerate catabolic

processes and compromise brain glucose availability (Benardot, 2007; Benardot 2013). This

may be reflected in endocrine alterations, such as higher cortisol levels and lower

testosterone:cortisol ratio as observed in our study, which may reduce the ability to recover

and increase the risk of overreaching and overtraining, thereby compromising athletic

performance (Banfi & Dolci, 2006). WDEB is an accumulating value that does not reset

calculations every day at midnight, thus, it is possible that a traditional 24-hour assessment of

EB or EA may mask multi-day periods with energy deficits. For instance, light training days

may have a compensatory effect on the mean 24-hour EB. Such “hidden” periods of energy

deficits may, over time, lead to serious health- and performance consequences, such as

unfavorable endocrine profile, bone loss, and suppressed RMR (Dolan et al., 2012; Koehler et

al., 2016; Wilson et al., 2015).

In an earlier study, number of hours in EB < -300 kcal was positively associated with

body fat percentage in female middle- and long-distance runners (Deutz et al., 2000),

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presumably related to both an adaptive reduction in RMR and endocrine responses that favor

muscle breakdown and fat gain (Benardot, 2007; Benardot 2013). Therefore, a restrictive

eating behavior may have the opposite of the desired effect on athletes’ body composition. This

is in contrast with our findings, where WDED (number of hours in EB < 0 kcal and largest

energy deficit) was associated with a lower body fat percentage in male athletes, and as recently

reported we found no association between WDEB and body composition in female elite

endurance athletes (Fahrenholtz et al. 2017). One explanation for conservation of fat free mass

despite hypocaloric conditions may be attributed to protein intake (Fahrenholtz et al., 2017,

Phillips & Van Loon, 2011). This could, however, not explain the findings of the present study.

The ability to compare our results with those reported by Deutz et al. (2000) is, however,

limited due to several methodological differences. For instance, Deutz et al. (2000) used 24-

hour recall to assess energy intake and energy expenditure with only one assessment day, in

contrast to our four-consecutive days of recording food- and beverage consumption as well as

objectively measured energy expenditure.

Regarding energy expenditure, some of our athletes had a considerably high NEAT,

and although not significant different, there was a trend towards a higher NEAT in the group

with suppressed RMR compared to those with normal RMR. The large NEAT may be due to

that some of the athletes were deliberately looking for ways to expend calories to maintain

leanness. Another explanation may be the fact that some of the athletes had physically active

jobs such as firefighters, carpenters, plumbers, mason workers, and ironworkers. In addition,

some athletes self-reported a physically active leisure time such as active play with their

children, which to some degree could have increased their NEAT. This information was,

however, not registered in the questionnaire, only obtained when talking to the athletes. Hence,

we can only speculate whether these factors may explain the trend towards a higher NEAT in

the group with suppressed RMR. Whether some athletes may not consider their leisure- or

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employment activities as considerably energy-demanding could be an item for future

consideration in education programs concerning how to balance energy expenditure with

adequate energy intake.

One methodological challenge may be how one distinguish between “exercise” and

“activities that contributed to NEAT”. In our study, detailed information and instructions about

the different terms were provided individually to each participant. Exercise was defined as the

athletes’ planned exercise-bouts with the aim of improving fitness/performance, while

activities that contributed to NEAT were defined as all physical activity, besides exercise.

Activities such as riding a bike to and from work (if not regarded as an exercise-bout by the

athletes), walking to the store/in the neighborhood, playing with children, and active at work

(such as working as a plumber or fireman) counted as activities that contributed to NEAT. All

athletes were instructed not to use their accelerometer (remove it physically from the body)

during their planned exercise-bouts, and to use their heart rate monitor during every exercise

bout. It is, however, complicated to control (e.g. whether athletes use their accelerometer

immediately after exercise), and we recognize that this can have a potential effect on the total

NEAT. Detailed information given to each participant in advance and during data collection

was provided to minimize such errors in the present study.

When calculating pRMR, a prediction error of 10% is expected (Cunningham, 1980),

and therefore an expected normal range of RMRratio is 0.9-1.1 (Sterling et al., 2009). A

RMRratio < 0.90 has been used as a recognized surrogate marker for energy deficiency in

females (De Souza et al., 2008; Gibbs et al., 2013; Melin et al., 2015; Scheid et al., 2009), but

more studies are needed to further investigate this relationship in males. Experimental studies

indicate that males’ reproductive system may be more resistant to energy deficiency than

females’ (Koehler et al., 2016), which may suggest a lower cut-off for RMRratio when assessing

male athletes. However, whether males’ sports performance and health consequences other

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than those related to the reproductive system are similarly sensitive to relative energy

deficiency has not yet been investigated. Additionally, both the Cunningham and the Harris-

Benedict equation have been found to significantly underestimate RMR in heavyweight male

national team rower and canoe racers (Carlsohn et al., 2011), suggesting an overestimation of

RMRratio in some athletes and an increased risk of false classification of normal RMR with a

lower RMRratio cut-off.

Strengths and limitations

To our knowledge, this is the first study analyzing WDED and associated endocrine

markers of energy deficiency in males. Other strengths of this study were inclusion of a

relatively high number of male athletes compared to previous research (Carlsohn et al., 2011;

Wilson et al., 2015), the use of valid outcome measures and that all tests followed best practice

protocols for measurements.

The results of this study should be interpreted with consideration of certain

methodological limitations. First, the data are based on a cross sectional study design, limiting

assertions of causality. Second, the WDED variables adapted from the literature leads to a high

number of correlation analysis, which may increase the risk of type 1 error. Third, a limitation

of the current study design was that the collection of data related to food consumption, NEAT,

and training occurred after the physiological assessment. Hence, we cannot be sure that these

behaviors were the cause of the results seen in the study. The reason for assessing dietary intake

and energy expenditure after the physiological testing, and not before, was exclusively

practical. Due to the fact that assessment of dietary intake and energy expenditure is

methodologically difficult, we needed to give detailed instructions to each participant and

ensure that they were all familiar with the measurement equipment and best practice

procedures. In addition, with regard to the participants total load of being a part of this project,

in combination their daily life, we chose not to invite them to the laboratory also before the

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physiological testing. Thus, we decided that the best practical solution was to include the

dietary intake and energy expenditure testing after the physiological testing was completed. It

should be noted, however, that all athletes were instructed to eat, drink and exercise “as usual”,

and the software of Dietist Net was chosen due to that it was not possible for the participants

to see any caloric calculations of their registrations neither during registration, nor afterwards.

This may have reduced the risk of under- or over reporting of food items or portions.

Based on our experiences, we recommend future research to measure dietary intake and

energy expenditure immediately before the laboratory testing in order to possibly capture a

closer correlation between dietary intake and energy expenditure and the physiological

variables of interest. Furthermore, there is a need of data that investigate the reasonable period

of time over which WBEB calculations should be conducted. We also recommend using

objective, validated methods to measure both energy intake and energy expenditure, and to

standardize when and how the equipment, such as accelerometers or heart rate monitors, should

be used. Finally, to use a registration system that identifies low compliance to the measurement

equipment, such as an accelerometer, may be of help to exclude participants not following the

test procedures from the analysis. For analysis of energy intake, we recommend the use of

Goldberg’s cut off (Black, 2000) to reduce the risk of including under-reporters.

In conclusion, we found that male endurance athletes with suppressed RMR, despite

similar 24-hour EB and EA, spent more time in energy deficits exceeding 400 kcal and had

larger single-hour energy deficits compared to those with normal RMR. WDED was associated

with higher cortisol levels and a lower testosterone:cortisol ratio. The results suggest that

assessing energy status in intervals of 24 hours may not be sufficient for detecting athletes at

risk for health-related consequences caused by energy deficiency. A continuous view on energy

status evaluated in smaller time blocks may therefore be more appropriate.

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Acknowledgements

The authors would like to thank the subjects participating in the study. We also thank the master

students in sports science at the University of Agder for assisting in the data collection.

Author contributions

The study was designed, and data was collected by MKT, ØS, and TBS; the data was analyzed

by AKM and IF, data interpretation and manuscript preparation were undertaken by TBS,

AKM, IF, and MKT. All authors approved the final version of the paper.

Declaration of funding

The study was funded by the University of Agder, Faculty of Health and Sport Sciences,

Kristiansand, Norway.

Conflicts of interest

The authors have no conflicts of interest in this study.

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Figure 1. Flowchart showing the recruitment process, dropouts and exclusion of subjects

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Table 1. Overview of methods used to calculate WDEB and RMRratio

COMPONENTS OF WITHIN-DAY ENERGY BALANCE

Component Summary of method Comments/reference

Energy intake

(EI)

Prospective weighed food and beverage record for 4 consecutive days in the

subjects’ normal environment

Digital kitchen scale: OBH Nordica 9843 Kitchen Scale Color, Taastrup,

Demark

Software program: Dietist Net, Kost och Näringsdata, Bromma, Sweden

In depth oral and written instructions

were given to the subjects

Diet induced

thermogenesis

(DIT)

Defined as 10% of EI and distributed in the hours after each meal or snack by

using the equation: 175.9·T·e-T/1.3 where T=Time and e=the base of the natural

logarithm

Reed & Hill, 1996

Non-exercise

activity

thermogenesis

(NEAT)

Subjects wore a Sensewear accelerometer (BodyMedia, Inc., Pittsburgh, PA,

175 USA) or Actigraph accelerometer (Actigraph GT3X®, Pensacola, FL,

USA) the same days as dietary intake recording

All logging was performed from the

time subjects woke up in the morning

until bedtime

Only allowed to take the logging

device off during showering,

swimming, and training

Exercise

energy

expenditure

(EEE)

Subjects recorded all training sessions with HR- monitor (Polar M400/V800)

during the same days as they recorded dietary intake as epochs of five seconds

during every training session

EEE (kcal/kg/min) = ((5.95*HRaS) + (0.23·age) + (84·1)-134)/4186.8 where

HRaS=HR above sleeping HR (beats/min) (HRaS)

Sleeping HR was estimated from a resting supine measurement during the

RMR measurement (sleep HR=0.83 * supine HR)

Crouter et al., 2008

Brage et al., 2005

Excess post-

exercise

Defined as 5% of EEE the first hour post-exercise plus 3% of EEE the second

hour post-exercise

Phelain et al., 1997

Fahrenholtz et al., 2017

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oxygen

consumption

(EPOC)

Resting

metabolic rate

(RMR)

The predicted RMR used to calculate WDEB was calculated using the

Cunningham equation

Cunningham, 1980

Sleeping

metabolic rate

(SMR)

Defined as 90% of pRMR Used instead of RMR during sleeping

hours

Within-day

energy

balance

(WDEB)

The hourly energy balance was calculated as EB = energy intake – total

energy expenditure; predicted DIT + EEE + EPOC + NEAT + RMR

In order to control for the problem of

potential underestimation of energy

requirements, the unadapted (pRMR),

instead of (mRMR) was used when

calculating total energy expenditure

The starting point for the calculation

of WDEB was at midnight on the first

day of food recording and was

calculated as follows; the mean EI of

the last daily meal/snack minus mean

total energy expenditure in the time

interval following the mean

meal/snack consumption

WDEB was calculated continuously

for the four days of registration

WDED

variables

Total hours with energy deficit (unadapted EB < 0 kcal)

Hours spent in energy deficit exceeding 400 kcal (unadapted EB < -400 kcal)

Largest single-hour energy deficit

Benardot, 2007

Benardot, 2013

COMPONENTS OF RMRratio

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Component Summary of method Comments/reference

Measured

RMR

(mRMR)

Calorimetry using a ventilated canopy hood system (Oxycon Pro, Jaeger

GmbH, Hoechberg, Germany)

Calibrated before each test according to standards

Oxygen consumption and carbon dioxide production were assessed over a 30-

min period

A 5-min steady state period defined as a coefficient of variation (CV) of less

than 10% to assess RMR was identified

Measured RMR (mRMR) was assessed using the Weir equation

Compher et al., 2006

Weir, 1990

Predicted

RMR

(pRMR)

pRMR = 500 + 22 · FFM (kg) Cunningham, 1980

Resting

metabolic rate

ratio

(RMRratio)

RMRratio = mRMR/pRMR

Suppressed RMR was defined as a RMRratio <0.90 and normal RMR as a

RMRratio >0.90

De Souza et al., 2008

Melin et al., 2015

The Cunningham equation was

chosen, since this equation has been

found to be the best predictive

equation for RMR in endurance

athletes (Thompson & Manore, 1996)

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Table 2. Example of WDEB calculation of one day for one subject.

Kcal in Kcal out

Time EI DIT EEE EPOC NEAT

SEE REE

TEE

h to h EB

00:00-01:00 0 0 0 0 0 66 0 66 40

01:00-02:00 0 0 0 0 0 66 0 1323 -26

02:00-03:00 0 0 0 0 0 66 0 199 -92

03:00-04:00 0 0 0 0 0 66 0 266 -158

04:00-05:00 0 0 0 0 0 66 0 332 -224

05:00-06:00 0 0 0 0 0 66 0 399 -290

06:00-07:00 0 0 0 0 0 66 0 465 -356

07:00-08:00 0 0 0 0 24 0 74 563 -454

08:00-09:00 242 7 0 0 169 0 74 812 -462

09:00-10:00 0 7 0 0 94 0 74 987 -637

10:00-11:00 654 24 0 0 91 0 74 1176 -172

11:00-12:00 13 22 0 0 113 0 74 1384 -369

12:00-13:00 792 38 0 0 163 0 74 1660 148

13:00-14:00 0 31 0 0 201 0 74 1966 -158

14:00-15:00 575 37 0 0 101 0 74 2178 205

15:00-16:00 0 28 0 0 162 0 74 2442 -59

16:00-17:00 0 17 721 0 0 0 74 3253 -871

17:00-18:00 278 18 0 36 99 0 74 3481 -820

18:00-19:00 0 12 0 22 142 0 74 3730 -1070

19:00-20:00 1570 55 0 0 88 0 74 3946 283

20:00-21:00 0 47 0 0 73 0 74 4140 89

21:00-22:00 259 40 0 0 79 0 74 4333 155

22:00-23:00 0 27 0 0 79 0 74 4513 -25

23:00-00:00 0 16 0 0 75 0 74 4678 -190

24-h Total 4383 426 721 58 1753 462 1258 4678 -295

Abbreviations: EI: energy intake, DIT: diet induced thermogenesis, EEE: exercise energy

expenditure, EPOC: excess post-exercise oxygen consumption, NEAT: non-exercise activity

thermogenesis, SEE: sleeping energy expenditure, REE: resting energy expenditure, TEE:

total energy expenditure, EB: energy balance.

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Table 3. Description of subjects characterized by RMRratio

All

(n=31)

Normal RMR

(n=11)

Suppressed RMR

(n=20)

P-value1

Age (years) 34.7 ± 8.1 30.8 ± 7.2 36.9 ± 7.9 0.045

Height (cm) 179.5 ± 5.3 180.4 ±

5.

2

179.1 ± 5.4 0.516

Body weight (kg) 72.0 ± 6.1 73.7 ± 6.2 71.1 ± 6.0 0.267

BMI (kg/m2) 22.3 ± 1.8 22.7 ± 2.1 22.1 ± 1.7 0.501

Body fat (kg) 8.4 (4.5 – 11.2)

11.0 (6.0 – 12.7) 8.2 (4.2 – 10.6)

0.302

Body fat (%) 11.7 ± 5.7 12.8 ± 6.1 11.1 ± 5.5 0.427

Fat free mass (kg) 63.4 ± 5.1 64.0 ± 4.7 63.1 ± 5.4 0.634

Exercise

(hours/week)

8.7 ± 3.2 9.2 ± 3.3 8.4 ± 3.2 0.515

VO2peak

(ml/kg/min)

66.4 ± 6.2 66.7 ± 8.2 66.2 ± 5.0 0.807

Data are presented as mean ± SD for normally distributed data and as median and interquartile range

(25-75) for non-normally distributed data. Abbreviations: BMI: body mass index, VO2peak: maximal

oxygen uptake. 1) Difference between subjects with normal (RMRratio> 0.9) vs. suppressed RMR

(RMRratio< 0.9).

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Table 4. Energy expenditure and within-day energy deficiency characterized by RMRratio.

All

(n=31)

Normal RMR

(n=11)

Suppressed RMR

(n=20)

P-value1

Exercise EE

(kcal/day)

678 ± 250 662 ± 283 675 ± 238 0.942

DIT (kcal/day)

228 ± 52 243 ± 66 220 ± 42 0.250

EPOC (kcal/day) 54 ± 20 55 ± 23 54 ± 19 0.920

NEAT (kcal/day) 819 (482 – 1648) 580 (374 – 1094) 1548 (557 – 1744) 0.087

pRMR (kcal/hour) 79 ± 4 79 ± 4 79 ± 5 0.741

mRMR

(kcal/hour)

69 ± 8 76 ± 8 66 ± 5 <0.001

RMRratio 0.88 ± 0.07 0.96 ± 0.05 0.83 ± 0.04 <0.001

24-hour EB*

(kcal)

-698 ± 928 -402 ± 1056 -861 ± 832 0.192

24-hour EB**

(kcal)

-914 ± 966 -463 ± 1059 -1162 ± 837 0.052

24-hour EA

(kcal/kg FFM)

39 ± 12 41 ± 11 37 ± 12 0.393

WDEB < 0 kcal

(hours/day)

22.0 (14.1 –

22.8)

14.3 (3.9 – 20.9) 22.1 (20.4 -22.8) 0.059

WDEB <-400 kcal

(hours/day)

18.8 (10.5 –

21.6)

10.8 (2.5 – 16.4) 20.9 (18.8 – 21.8) 0.023

Largest hourly

deficit (kcal)

-2582 ± 2302 -1340 ± 2439 -3265 ± 1962.9

0.023

Data are presented as mean ± SD for normally distributed data and as median and interquartile range

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(25-75) for non-normally distributed data. Abbreviations: DIT: diet induced thermogenesis, EB:

energy balance, EE: energy expenditure, EPOC: excess post-exercise oxygen consumption, mRMR:

measured resting metabolic rate, pRMR: predicted resting metabolic rate, WDEB: within-day energy

balance. 1) Difference between subjects with normal (RMRratio> 0.9) vs. suppressed RMR (RMRratio<

0.9). *using mRMR **using pRMR

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Table 5. Associations between within-day energy deficiency and markers for catabolic state

Hours with WDEB < 0

kcal

Hours with WDEB <-400

kcal

Largest hourly deficit1

r P-value

r P-value

r P-value

RMRratio -0.231 0.212 -0.242

0.190

0.335 0.065

Body fat (%) -0.366 0.043 -0.311 0.090 0.359 0.047

Cortisol

0.167 0.377

0.294 0.108 -0.499 0.004

Testosterone -0.277 0.132 -0.315 0.085

0.268 0.145

Test:cortisol -0.117 0.532 -0.235 0.204

0.413 0.016

T3

-0.104 0.577 0.032 0.864 -0.058 0.753

Glucose -0.064 0.731 -0.151 0.415 0.247 0.180

All subjects (n=31) were included in the correlation analysis. Abbreviations: BP: Blood pressure,

RMR: resting metabolic rate, Test:cortisol: the ratio between testosterone and cortisol, WDEB:

within-day energy balance. 1)Values recorded as negative numbers.

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