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Increased Protein Intake Reduces Lean Body Mass Loss during Weight Loss in Athletes


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To examine the influence of dietary protein on lean body mass loss and performance during short-term hypoenergetic weight loss in athletes. In a parallel design, 20 young healthy resistance-trained athletes were examined for energy expenditure for 1 wk and fed a mixed diet (15% protein, 100% energy) in the second week followed by a hypoenergetic diet (60% of the habitual energy intake), containing either 15% (approximately 1.0 g x kg(-1)) protein (control group, n = 10; CP) or 35% (approximately 2.3 g x kg(-1)) protein (high-protein group, n = 10; HP) for 2 wk. Subjects continued their habitual training throughout the study. Total, lean body, and fat mass, performance (squat jump, maximal isometric leg extension, one-repetition maximum (1RM) bench press, muscle endurance bench press, and 30-s Wingate test) and fasting blood samples (glucose, nonesterified fatty acids (NEFA), glycerol, urea, cortisol, free testosterone, free Insulin-like growth factor-1 (IGF-1), and growth hormone), and psychologic measures were examined at the end of each of the 4 wk. Total (-3.0 +/- 0.4 and -1.5 +/- 0.3 kg for the CP and HP, respectively, P = 0.036) and lean body mass loss (-1.6 +/- 0.3 and -0.3 +/- 0.3 kg, P = 0.006) were significantly larger in the CP compared with those in the HP. Fat loss, performance, and most blood parameters were not influenced by the diet. Urea was higher in HP, and NEFA and urea showed a group x time interaction. Fatigue ratings and "worse than normal" scores on the Daily Analysis of Life Demands for Athletes were higher in HP. These results indicate that approximately 2.3 g x kg(-1) or approximately 35% protein was significantly superior to approximately 1.0 g x kg(-1) or approximately 15% energy protein for maintenance of lean body mass in young healthy athletes during short-term hypoenergetic weight loss.
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Increased Protein Intake Reduces Lean Body
Mass Loss during Weight Loss in Athletes
School of Sport and Exercise Sciences, University of Birmingham, Birmingham, UNITED KINGDOM;
Department of Agricultural and Food Sciences, ETH Zurich, Zurich, SWITZERLAND; and
English Institute of Sport,
METTLER, S., N. MITCHELL, and K. D. TIPTON. Increased Protein Intake Reduces Lean Body Mass Loss during Weight Loss in
Athletes. Med. Sci. Sports Exerc., Vol. 42, No. 2, pp. 326–337, 2010. Purpose: To examine the influence of dietary protein on lean
body mass loss and performance during short-term hypoenergetic weight loss in athletes. Methods: In a parallel design, 20 young
healthy resistance-trained athletes were examined for energy expenditure for 1 wk and fed a mixed diet (15% protein, 100% energy) in
the second week followed by a hypoenergetic diet (60% of the habitual energy intake), containing either 15% (È1.0 gIkg
) protein
(control group, n= 10; CP) or 35% (È2.3 gIkg
) protein (high-protein group, n= 10; HP) for 2 wk. Subjects continued their habitual
training throughout the study. Total, lean body, and fat mass, performance (squat jump, maximal isometric leg extension, one-repetition
maximum (1RM) bench press, muscle endurance bench press, and 30-s Wingate test) and fasting blood samples (glucose, nonesterified
fatty acids (NEFA), glycerol, urea, cortisol, free testosterone, free Insulin-like growth factor–1 (IGF-1), and growth hormone), and
psychologic measures were examined at the end of each of the 4 wk. Results: Total (j3.0 T0.4 and j1.5 T0.3 kg for the CP and HP,
respectively, P= 0.036) and lean body mass loss (j1.6 T0.3 and j0.3 T0.3 kg, P= 0.006) were significantly larger in the CP
compared with those in the HP. Fat loss, performance, and most blood parameters were not influenced by the diet. Urea was higher in
HP, and NEFA and urea showed a group time interaction. Fatigue ratings and ‘‘worse than normal’’ scores on the Daily Analysis of
Life Demands for Athletes were higher in HP. Conclusions: These results indicate that È2.3 gIkg
or È35% protein was significantly
superior to È1.0 gIkg
or È15% energy protein for maintenance of lean body mass in young healthy athletes during short-term
Making weight is a significant issue in sports
nutrition. Many athletes restrict energy intake to
achieve a certain body mass category, aesthetic
reasons or to attain a better force-to-mass ratio to improve
performance. However, a hypoenergetic diet may result in a
significant loss of lean body mass (18), perhaps leading to
compromised performance (9).
Recently, data have accumulated suggesting that in-
creased protein content of the diet, particularly in combina-
tion with exercise training, may increase weight loss and
reduce the loss of lean body mass in overweight and obese
subjects (18,19,21,24,28). This preservation of lean mass
has been attributed to the increased essential amino acid
levels, particularly leucine, provided by the protein (17).
Leucine stimulates the initiation of translation and increases
protein synthesis (1), which may help to reduce the net loss
of muscle protein.
Whereas there is ample evidence for amelioration of
lean body mass loss during hypoenergetic weight loss in
overweight and obese populations consuming high-protein
diets (18,19,21,24,28), there is little information available
on athletic populations. Clearly, the metabolic and training
status of athletic individuals differs from that of obese and
overweight, particularly sedentary, individuals. Athletes are
usually healthy and unlikely to experience metabolic
diseases, or preliminary states of diseases, which are often
apparent in inactive, obese subjects. Thus, the metabolic
situation is different and may impact the response to high-
protein hypoenergetic diets. Furthermore, initiation of a
training program may influence the response to these diets,
which may not be similar for already well-trained athletes.
Nevertheless, in the only study to date to address this issue,
Walberg et al. (40) showed that negative N balance was
substantially ameliorated with a high-protein diet compared
with a normal protein diet in a hypoenergetic situation in
weight lifters. These data do support the notion that in-
creased protein intake might ameliorate net protein balance
and reduce lean body mass loss in athletes during hypo-
energetic weight loss. However, one recent study found no
Address for correspondence: Kevin D. Tipton, Ph.D., School of Sport and
Exercise Sciences, University of Birmingham, Birmingham B15 2TT,
United Kingdom; E-mail:
Submitted for publication August 2008.
Accepted for publication June 2009.
Copyright Ó2010 by the American College of Sports Medicine
DOI: 10.1249/MSS.0b013e3181b2ef8e
by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.Copyright @ 2010
effect of increased protein or branched-chain amino acid
(BCAA) intake on lean body mass loss during hypoenergetic
weight loss in athletes (26). These results do not seem to
match those of the earlier study or fit with those from
studies on obese individuals (18,19,21,24,28). Taken
together, these limited—and apparently conflicting—data
make it difficult to form solid conclusions on the effective-
ness of high-protein intake during weight loss in athletes.
Whereas studies in obese subjects focused primarily on
health-related parameters (18,19,21,24,28), it may be consid-
ered more relevant to focus on physical performance in an
athletic population. The influence of energy restriction per se
on different aspects of performance has been examined
(9,23), but there is a paucity of information available on the
influence of protein on performance during a hypoenergetic
Therefore, the aim of the present study was to compare
the influence of a high-protein hypoenergetic diet compared
with a normal-protein hypoenergetic diet on lean body mass
loss and performance in healthy, lean, resistance-trained
Study design. The study design is summarized in
Figure 1. In a parallel design, the subjects were divided
into a control and a high-protein group. The first subjects
were randomly allocated to the groups, whereas the latter
subjects were allocated to match the groups for anthropo-
metric values and training volume. Each subject participat-
ed in a 4-wk study. The first week was used to assess
energy intake and output. In the second week, the subjects
were fed 100% of their habitual energy intake. In weeks 3
and 4, energy intake was reduced to 60% of habitual intake.
At the end of each week, body mass, body composition, and
performance were measured in a testing session. A
familiarization trial for each of the performance tests was
performed before the first week. The subjects were asked to
continue their habitual training throughout the study, and
the 4-wk time window was scheduled in a way that no
special events or stress periods in job or private life were
expected during the time a subject was in the study.
Subjects. Subjects were 18- to 40-yr-old healthy (no
known metabolic disorders as determined by health ques-
tionnaire) males with a body mass index 920 kgIm
participated in regular resistance exercise training for at
least the previous 6 months. The training had to include two
or more resistance training sessions per week. Other types
of training were not excluded and were recorded. Subjects
were recruited from local sport facilities. Twenty-two
subjects started the study. Two of them were taken out of
the study in the first study week owing to lack of adherence
to the study conditions. No subject pulled out after having
started the study. The baseline values of the 20 subjects
finishing the study are summarized in Table 1. The study
was approved by the Coventry Research Ethics Committee.
The purpose, potential risks, and benefits of the study were
explained to each subject before written informed consent
was obtained.
Detailed procedures. In the first week, the subjects
consumed their habitual diet. For 3 d, the subjects filled in a
physical activity questionnaire (4) and a food report to esti-
mate energy expenditure and energy intake. In addition, the
subjects were provided with a three-dimensional accelerom-
eter (RT3 Research Tracker;, Monrovia, CA)
and a HR monitor (Polar, Kempele, Finland). The subjects
were asked to wear the accelerometer throughout the study
except for sleeping and for training. All training sessions
(resistance and others) were recorded with the HR monitor
because the accelerometer was not thought to be ideal to
measure intensities of all types of exercise, for example, re-
sistance training. Energy expenditure of the training sessions
was estimated from training duration and mean HR (14) and
was added to the accelerometer data to get an energy ex-
penditure estimation for the entire day. Every evening before
going to bed, the subjects filled in a Daily Analysis of Life
Demands for Athletes (DALDA) questionnaire (34), a satiety
FIGURE 1—Schematic overview of the study design.
TABLE 1. Anthropometric values of the subjects.
Control (n= 10) High Protein (n= 10) P
Age (yr) 25.8 T1.7 24.7 T1.6 0.87
Body mass
(kg) 78.3 T4.3 79.9 T2.9 0.26
) 24.2 T0.9 23.4 T0.5 0.10
% body fat
17.4 T1.5 16.1 T1.6 0.91
Training sessions per week
4.9 T0.4 4.6 T0.4 0.96
) 359 T45 334 T54 0.61
Values are mean TSE.
BMI, body mass index.
Before weight loss.
All training including the testing sessions.
PROTEIN AND WEIGHT LOSS IN ATHLETES Medicine & Science in Sports & Exercise
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questionnaire, a training diary, and a general problem report.
The satiety questionnaire consisted of two 100-mm visual
analog scales anchored by the extreme values ‘‘extreme
hunger’’ at 0 mm and ‘‘very full’’ at 100 mm. The middle of
the scale was designated as the comfortable zone. One scale
was designated to rate the present satiety in the evening,
whereas the other was for estimation of the satiety during the
entire day. Subjects were asked to report fatigue and muscle
soreness ratings between 1 (not fatigue and not sore at all)
and 10 (training not possible any more) for each training
session. In the general problem report, the subjects were
asked to report any problems they felt may have impacted
training or habitual activities.
Diets. The energy and macronutrient intake of the two
groups is given in Table 2. In week 1, all subjects consumed
their habitual diet as discussed above. During week 2, all
subjects were provided with food containing 100% of their
habitual energy with a macronutrient composition of 50%
carbohydrate, 15% protein, and 35% fat. The subjects were
instructed not to eat or drink anything other than the
provided food and drinks. The only exceptions were water
and diet soft drinks that were allowed to be consumed
ad libitum. In addition, they were allowed to add salt and
pepper to spice up their food. The energy level for the first
feeding day in week 2 was estimated from the food report,
the physical activity questionnaire, the accelerometer, and
Polar data, as well as population reference and the estimated
physical activity level of the subjects. The four methods
were basically averaged. However, advantages and dis-
advantages of each method were considered, and the set
energy level may also have been slightly above or below
the mathematical value. Food protocols are known to be
prone to underreporting (22), and the physical activity
questionnaire carries the risk of overreporting, particularly
if training intensities are overrated (42). These intensity
ratings were crosschecked, for example, with HR monitor
data or training protocols, to assess their validity. In case of
discrepancies between methods and detected potential
weaknesses of a method with a specific subject, qualitative
considerations were made as well. For example, if the
physical activity questionnaire suggested a higher energy
output than all other methods and it was supported by HR
data that did not support reports of high-intensity training,
then the average of the four methods was considered to be a
slight overestimate. In such a case, the energy level would
be estimated as slightly less than the average. This level of
energy intake was then used as the starting point dur-
ing week 2. Finally, during the first few feeding days of
week 2, the subjects were advised to provide daily feedback
on the dietary energy level. When a subject reported feeling
hungry, we increased the energy content of the food
slightly. Similarly, when a subject reported an inability to
comfortably consume the provided food, subjects were
advised not to eat the excess and to report the surplus food.
We then reduced the energy content for the following days
accordingly to ensure that the energy intake for each subject
was appropriate by the end of the second week. The ab-
solute necessary adjustments were not different between
groups and amounted to 4% T4% (mean TSD) of the
originally estimated energy level.
In weeks 3 and 4, the subjects were allocated either to the
control or to the high-protein group. For both groups, the
energy was dropped to 60% of the habitual energy intake.
The relative macronutrient composition of the control diet
remained the same (i.e., a linear reduction of all three
nutrients). The high-protein diet was changed to 50%
carbohydrate, 35% protein, and 15% fat. The values in
Table 2 represent the nutrient composition of the subjects
during weeks 2, 3, and 4 considering reported noncom-
pliance. Protein intake was È1.0 g protein per kilogram
body mass per day (gIkg
) for the control group and
È2.3 gIkg
for the high-protein group. These values
would be enough to maintain lean body mass in an energy
balance situation for the control group (38) and approxi-
mately three times the US recommended daily allowance in
the protein group. Protein was distributed among the
different meals and snacks throughout the day to have a
more or less steady protein supply throughout the day.
However, the nutrient and protein intake was not explicitly
timed with the training sessions.
During weeks 2, 3, and 4, the subjects were asked to report
any foods assigned but not consumed. In addition, food items
not or only partially consumed had to be returned to the
laboratory by the subjects in order for them to be weighed.
Every attempt was made to give the subjects the confidence to
honestly report any noncompliance without any consequen-
ces. The diets were designed individually for each subject
considering individual preferences and eating patterns to
minimize noncompliance. The subjects were not informed of
group assignment. However, given the composition of the
high-protein diet, perfect blinding was not feasible. Thus, in
an attempt to blind the diets as much as possible, we attempted
to make the foods of both diets similar by providing similar
food items whenever possible, for example, by providing
‘protein’’ shakes containing fat and hardly any protein and
by providing food looking meaty or protein-rich while
containing hidden fat to the control group. At the end of the
TABLE 2. Energy and macronutrient composition of the provided diets per day.
Time Control (n= 10) High Protein (n= 10)
Energy, kJ (kJIkg
) Week 2 14,411 T977 (184 T6) 13,936 T479 (177 T6)
Week 3 8649 T603 (113 T4) 8464 T288 (108 T4)
Week 4 8583 T587 (114 T4) 8469 T281 (109 T4)
Carbohydrates, g
Week 2 428 T32 (5.4 T0.2) 415 T14 (5.3 T0.2)
Week 3 259 T18 (3.4 T0.1) 257 T9 (3.3 T0.1)
Week 4 258 T18 (3.4 T0.1) 257 T9 (3.3 T0.1)
Fat, g (gIkg
) Week 2 133 T9 (1.70 T0.05) 131 T5 (1.65 T0.06)
Week 3 82 T6 (1.06 T0.05) 31 T1 (0.40 T0.02) *
Week 4 81 T6 (1.07 T0.04) 31 T1 (0.40 T0.02) *
Protein, g (gIkg
) Week 2 128 T9 (1.64 T0.06) 125 T5 (1.58 T0.06)
Week 3 74 T4 (0.98 T0.02) 180 T6 (2.31 T0.08) *
Week 4 73 T4 (0.97 T0.02) 180 T6 (2.32 T0.08) *
Values are mean TSE.
* Significantly different from control group (PG0.001).
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study, 19 of 20 subjects guessed to be in the high-protein
group. Subjects usually reported to the laboratory to pick
up food two times per week in addition to the testing sessions
to allow for appropriate food variety and freshness.
Testing sessions. On the morning of the last day of
every week, the subjects arrived in the laboratory after an
overnight fast. Subjects were asked to have an easy evening
the night before, and they were advised not to eat and drink
anything after 2200 h until arriving in the laboratory the
next morning.
In the morning of a testing session, the subjects had to go to
the toilet first to ensure empty bladder for body weight
measurement, which was measured without shoes in light
sport clothing (shorts and T-shirt) on a laboratory scale
(CD31; OHAUS, Pine Brook, NJ) to the nearest 0.01 kg.
Subjects were asked to wear the same clothing for all tests.
Body composition was assessed by dual-energy x-ray absorp-
tiometry (DXA; QDR Discovery-C 4500; Hologic, Bedford,
MA). A fasting blood sample was taken from an antecubital
vein with an ethylene diamine tetra acetic acid (EDTA)
vacutainer (Becton Dickinson and Co, Plymouth, United
Kingdom) in a relaxed supine position. A profile of mood state
(POMS-24) questionnaire was filled in at that time, and total
mood disturbance was calculated by summing up scores on
the negative subscale and then subtracting the score on the
positive subscale (12). Afterward, the subjects were offered a
breakfast consisting of ad libitum water and three slices
of white bread (50 g per slice), butter (3 g per slice), and
apricot jam (20 g per slice) consisting of 2050 kJ, 89 g of
carbohydrates, 14 g of protein, and 9 g of fat in total. How-
ever, many subjects preferred to eat less than three slices
to feel comfortable (2.3 slices consumed on average in both
groups). In the second testing session, they were served
the amount eaten in the fist testing session, and for the third
and fourth testing sessions, they got 60% of previous energy
according to the 40% energy cut off in these weeks.
Approximately 10 min after breakfast, performance as-
sessment began. This assessment began with a general warm-
up of 5 min on the bike. The warm-up was followed by a
squat jump, a maximal isometric leg extension, a one-
repetition maximum (1RM) chest press, a chest press muscle
endurance test, and a Wingate test. Bike and machine settings
were set in the familiarization trial and held constant for all
tests. The investigator performing the laboratory testing was
not blinded in this study.
The 5-min warm-up was done at a self-selected load on a
cycle ergometer (Excalibur Sport; Lode, Groningen, the
Netherlands). The load was kept constant for all testing
sessions. The squat jump was performed on a force plate
(Kistler Instrumente AG, Winterthur, Switzerland), and the
vertical force component was recorded (BioWare version
3.2; Kistler Instrumente AG) to measure peak force (6).
Jump height was directly measured with a jump meter
(T.K.K.5406 Jump MD; Takei, Niigata, Japan). The subject
supported the hands on the hip and descended slowly down
to the crouched position (90-knee angle). The position was
controlled before every jump by the investigator, and the
subject had to hold the position for at least 2 s before
jumping as high as possible without arm support. Four easy
jumps without measurement were performed as warm-up
and to get familiar with the crouched position before the
first jump. Three serious jumps were then performed,
separated by 1 min (6). In case of a visible countermove-
ment, the jump was repeated. For jump height and peak
force, the average of the two highest jumps was used. The
average of all three jumps was used if no jump could be
To test the isometric quadriceps strength, the subject sat
in a custom-built adjustable strength testing chair (8) with
hips and knees flexed to 90-. A strap around the ankle was
attached to a strain gauge at the back of the chair. The
output of the strain gauge was amplified, converted to a
digital signal (Power 1401; CED Limited, Cambridge,
England) and sampled at 1000 Hz (Spike 2 version 5.15;
CED Limited). Three maximal contractions of 4–6 s, sepa-
rated by a 2-min recovery, were performed. The subjects
were verbally encouraged to keep up the contraction until
a maximal plateau was reached. The output signal was
smoothed with a moving average of 200 ms. The highest
200-ms average of each attempt was noted, and the average
of the two best attempts was determined to be maximal
isometric force. To convert the strain gauge output into
newtons, the strain gauge was calibrated daily. The co-
efficient of variation of the calibration factor was 0.9%.
The 1RM chest press was determined on a commercial
chest press machine (VR3 chest press; Cybex International
UK Ltd, Derbyshire, United Kingdom). A warm-up of eight
repetitions at 60% 1RM and three repetitions at 80% 1RM
separated by 1 min was made first (15). The warm-up loads
were set according to the 1RM determined in the familiar-
ization trial and were held constant for all tests. Two
minutes after the warm-up the first 1RM attempt was made.
The loads were chosen according to the perceived exertion
of the subject. The first attempt was done with 2.27–4.54 kg
(5–10 lb) less than the 1RM established in the previous
week. The resistance was increased by 2.27–4.54 kg after
a successful attempt. The 1RM was determined to the
nearest 2.27 kg (5 lb). Attempts were separated by a 3-min
recovery or longer until the subject felt recovered (15). In
84% of all testing sessions, three to four attempts were
needed to determine the 1RM. In a few cases, it was
necessary for the subject to attempt the lift only two or up
to five times.
After a recovery of at least 4 min from the final 1RM, the
load was set to 60% of the 1RM of the familiarization trial
for the chest press muscle endurance test (15). This load
was kept constant for all testing sessions even if the 1RM
changed during the study. A pacing of 60 beeps per minute
was used, and one repetition per two beeps was done
(1 beep extension, 1 beep flexion). The subjects were asked
to follow the pacing as long as possible and to continue
until failure in a slower pace if unable to follow the pacing.
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The tests were video recorded for reliable evaluation.
The tests were continuously supervised to ensure that the
subject achieved the full range of motion, and immediate
feedback was given when the range of motion was not
The 30-s Wingate test was performed on an electronically
braked ergometer (Excalibur Sport; Lode) (25) using the
official Wingate software (Wingate version 1.0.13; Lode) to
record power output and determine peak power and mean
power. A warm-up of 4 min of easy cycling was first
performed, including two 5-s full-acceleration sprints at
2 and 3 min of the warm-up. After a 3-min recovery, the
subjects started pedaling at 60 W (as a minimal resistance
is more convenient than real zero load). As soon as a
cadence of 60 revolutions per minute was stabilized, the
start command was given (31), and the subject accelerated
maximally at a torque factor of 0.7 (25) and continued to
keep cadence as high as possible to the end of the 30-s test.
Strong verbal encouragement was given throughout.
Blood analysis. Blood was centrifuged for 10 min
immediately after collection at 4-C at 1800g(Centra-CL3R;
Thermo IEC, Waltham, MA), transferred into polypropyl-
ene tubes, and frozen at j80-C until analysis. Glucose,
nonesterified fatty acids (NEFA), glycerol, and urea were
analyzed by COBAS MIRA semiautomatic analyzer (La
Roche, Basel, Switzerland). Free testosterone (IBL Hamburg,
Hamburg, Germany), cortisol (IBL Hamburg), growth
FIGURE 2—Change of body mass, fat, and lean mass from baseline
(average of the two measurements before the weight loss) to the end
of the 2-wk weight loss for the control (n= 10) and the high-protein
(n= 10) groups. Values are mean TSE. *Significant difference between
the two groups (P= 0.036). **Significant difference between the two
groups (P= 0.006).
FIGURE 3—Performance data for the control (n= 10) and the high-protein (n= 10) groups during the study. Neither was there a statistically
significant difference between the groups nor was there any significant group time effect. Squat jump height (A), jump peak force (B), 1RM chest
press (C), maximal voluntary contraction (MVC) knee extension (D), muscle endurance test chest press (E), and mean power (lower pair of lines) and
peak power (upper pair of lines) of the 30-s Wingate test (F). Values are mean TSE. r and s, Different letters indicate significant difference (PG0.05)
between time points (effect of time).
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hormone (IBL Hamburg), and free IGF-1 (DSL, Webster,
TX) were analyzed by enzyme-linked immunosorbent as-
say according to the manufacturer’s instructions. Blood pa-
rameters were analyzed in duplicate, and the average
coefficients of variation were 1.8%, 1.2%, 2.3%, 0.7%,
2.4%, 2.8%, 4.6%, and 3.3% for glucose, urea, glycerol,
NEFA, free IGF-1, free testosterone, growth hormone, and
cortisol, respectively. The mean of the duplicates was used
for statistical analysis.
Calculations and statistics. Changes in body com-
position were calculated by subtracting the average of the
week 1 and week 2 measurements (before weight loss) from
the week 4 measurement (after weight loss). Using the week
2 measurement only as pre–weight loss value instead of the
average of weeks 1 and 2 would not change the outcome
and interpretation of the data. Values that were tracked or
planned on a daily basis were averaged per subject and
week, and weekly averages were used for statistics.
Statistical analysis was performed with SAS for Win-
dows (version 8.2; SAS Institute, Inc., Cary, NC) using
ANOVA for repeated measures with Tukey adjustment.
PG0.05 was considered significant, although explicit
Pvalues are usually presented. Values are presented as
mean TSE.
The loss of total body mass, lean body mass, and fat mass
is presented in Figure 2. Total body mass was 78.3 T4.3,
78.3 T4.3, 76.5 T4.1, and 75.3 T4.0kgafterweeks1,2,
3, and 4, respectively, in the control group and 79.8 T
2.9, 79.5 T2.9, 78.5 T2.8, and 78.0 T2.9kginthehigh-
protein group. Body mass was not different between groups
(P= 0.718), but there was a significant effect of time
(PG0.001) and a group time interaction (P= 0.011).
Total body mass did not change from week 1 to week 2 in
the control (P90.999) and high-protein groups (P= 0.886).
Both groups lost the same amount of fat mass, but the
control group lost significantly more lean (P= 0.006) and
total (P= 0.036) body mass than the high-protein group.
The relative lean body mass loss in the arms, legs, trunk,
and head was 2.2% T0.7%, 2.5% T0.8%, 2.7% T0.6%, and
2.0% T1.0%, respectively, for the control group and 1.1% T
0.8%, 0.2% T0.7%, 0.4% T0.7%, and 0.8% T0.6%,
TABLE 3. Fasting blood values for the control (n= 10) and the high-protein groups (n= 10).
Week 1 Week 2 Week 3 Week 4
P(Group) P(Time) P(Group Time)
Glucose (mmolIL
) Control 5.26 T0.09 5.33 T0.08 5.14 T0.09 5.05 T0.09 0.762 G0.001 0.836
High protein 5.29 T0.11 5.31 T0.11 5.06 T0.13 4.99 T0.08
) Control 311 T31 323 T23 496 T30 430 T26 0.529 G0.001 0.005
High protein 304 T35 402 T54 379 T49 374 T33
Glycerol (KmolIL
) Control 88 T14 76 T9 108 T4 110 T14 0.714 0.054 0.443
High protein 74 T11 90 T15 96 T897T19
Urea (mmolIL
) Control 6.1 T0.4 5.9 T0.3 5.3 T0.4 5.4 T0.5 0.020 0.017 G0.001
High protein 6.6 T0.5 5.9 T0.4 8.0 T0.4 7.9 T0.5
Cortisol (ngImL
) Control 137 T15 118 T11 112 T8 123 T9 0.971 0.092 0.557
High protein 130 T21 121 T16 123 T12 119 T10
Free testosterone (KIUImL
) Control 58 T659T843T536T5 0.757 G0.001 0.259
High protein 55 T760T951T741T6
Free IGF-1 (ngImL
) Control 0.82 T0.12 0.87 T0.10 0.78 T0.09 0.75 T0.08 0.426 0.018 0.186
High protein 0.72 T0.07 0.71 T0.07 0.73 T0.08 0.67 T0.06
Growth hormone (KIUImL
) Control 2.65 T1.81 0.45 T0.37 2.43 T1.49 0.57 T0.19 0.446 0.266 0.084
High protein 2.11 T1.94 1.17 T0.95 2.27 T1.34 8.28 T5.66
Values are mean TSE. Pvalues G0.05 are in bold.
TABLE 4. Tracking of energy expenditure, satiety, fatigue, and muscle soreness ratings for the control (n= 10) and the high-protein groups (n= 10).
Week 1 Week 2 Week 3 Week 4
P(Group) P(Time) P(Group Time)
Energy expenditure
(%) Control 100 101 T1 101 T1 102 T2 0.272 0.827 0.476
High protein 100 101 T299T2 100 T2
Satiety whole day (mm) Control 60 T557T338T236T4 0.456 G0.001 0.379
High protein 62 T565T739T544T6
Satiety evening (mm) Control 62 T458T342T338T30.048 G0.001 0.706
High protein 72 T672T654T754T7
Fatigue Control 4.9 T0.5 4.8 T0.5 5.1 T0.3 5.6 T0.3 0.721 G0.001 0.009
High protein 4.3 T0.7 4.4 T0.6 6.2 T0.5 6.4 T0.5
Muscle soreness Control 4.0 T0.6 3.6 T0.5 3.6 T0.5 4.0 T0.4 0.619 0.243 0.478
High protein 4.1 T0.7 3.5 T0.5 4.5 T0.8 4.6 T0.7
Control j4.7 T1.8 j3.9 T2.4 1.6 T2.3 j1.7 T2.4 0.113 0.082 0.683
High protein 2.1 T4.1 j0.9 T2.1 3.6 T1.9 2.6 T2.2
Values are mean TSE.
Energy expenditure is expressed relative to week 1, which is set as baseline (100%).
Total mood disturbance score of the POMS questionnaire.
PROTEIN AND WEIGHT LOSS IN ATHLETES Medicine & Science in Sports & Exercise
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respectively, in the high-protein group. The relative lean
loss in the segments was statistically different between the
groups (P= 0.009) but did not differ between the
There were no statistically significant differences between
the two groups for any of the performance tests (Fig. 3).
There was a statistically significant decrease of 2.8% in peak
jump force (P= 0.011) and a 7.2% increase (P=0.044)in
muscle endurance between weeks 1 and 4 with no significant
difference between the groups. The maximal voluntary
contraction, 1RM, jump peak force, and the Wingate power
output can be corrected for body mass. However, there was
no effect of group, time, or group time for the corrected
performance values except for the 1RM. Relative to body
mass, 1RM increased significantly (PG0.001) during weeks
3 and 4 compared with weeks 1 and 2, but there was no
statistical difference between the control and high-protein
groups (data not shown).
Glucose, free testosterone, and free IGF-1 decreased,
whereas NEFA and urea increased significantly (PG0.05)
with energy restriction (effect of time). Urea was signifi-
cantly increased (P= 0.017) in the high-protein group
compared with the control group. Urea (PG0.001) and
NEFA (P= 0.005) showed a significant group time
interaction (Table 3).
The results of the energy expenditure tracking, satiety
ratings, and the muscle soreness and fatigue ratings are
presented in Table 4. There was no effect of group, time, or
group time for the number of training sessions per week,
training duration per week, and the average exercise energy
expenditure as estimated with the HR monitor (data not
shown). There was no effect of the energy restriction per se
nor was there any difference between the groups with respect
to total mood disturbance in the POMS-24 questionnaire
(Table 4). There was also no difference between the groups
in the A-part of the DALDA. In contrast, results of the B-part
of the DALDA questionnaire were influenced by the energy
restriction and showed a significant (PG0.05) increase of the
‘worse than normal’ ratings during the weight loss weeks,
including a significant (P= 0.023) group time effect,
that is, the high-protein group showed a larger rise of the
‘worse than normal’’ ratings in the weight loss weeks than
the control group (Fig. 4).
This study was designed to examine the effect of
increased dietary protein composition on lean body mass
maintenance during hypoenergetic weight loss in athletes.
We found that although fat loss was similar for both the
high-protein and the control diet groups, increased protein
intake resulted in less lean and total body mass loss. Diet
composition did not seem to impact any measured perfor-
mance parameter, and there were minimal differences, for
example, greater urea in the high-protein group, between
groups for fasting blood metabolites.
Body composition. There was a significantly reduced
loss of lean body mass with ingestion of the high-protein
diet (È2.3 gIkg
) compared with the control diet
(È1.0 gIkg
). These results may be considered similar
to those previously reported in overweight and obese
subjects (18,19,21,24,28), albeit with some notable
differences. We found no difference in fat loss between
dietary groups but a significantly elevated loss of lean body
mass in the control group, resulting in an elevated loss of
total body mass. On the other hand, in obese subjects, the
results were reversed. High protein intake resulted in greater
total body mass loss due to a larger loss of body fat (18,19).
Also in obese subjects, there was a consistently greater loss
of fat mass than lean mass, independent of the dietary
protein level (18,19,21,24,28). In our healthy, trained, lean
subjects, we found a loss of lean body mass, which was
substantially larger than the loss of fat mass, when the
control diet was consumed. However, with higher protein
intake, lean body mass loss was È20% of that of the control
group. The loss of lean body mass did not seem to be
particular to any body section but rather seemed to be fairly
consistent throughout the whole body, perhaps suggesting
that skeletal muscle and splanchnic protein may be affected
to a comparable extent.
Another important aspect to consider is the macronutri-
ent composition of the diets other than protein. Whereas
previous studies in overweight subjects increased protein in-
take at the expense of carbohydrate intake (18,19,21,24,28),
we chose to balance energy by changing fat intake.
Increasing protein intake at the expense of carbohydrates is
likely to negatively impact exercise performance and training
intensity (9,23). With respect to strength, muscle endurance,
and high-intensity performance, carbohydrate intake may
be critical if too low (9). Thus, maintenance of carbohydrate
intake would be important for this population when energy
intake is limited and when maintenance of training levels is
a critical aspect. Because dietary carbohydrate intake can
FIGURE 4—‘‘Worse than normal’’ ratings in the A-part and B-part of
the DALDA questionnaire for the control (solid lines,n= 10) and the
high-protein groups (dashed line,n= 10). In the B-part, there was a
significant group time interaction (P= 0.024). c and d, Time points
with different letters are significantly different from each other.
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reduce fat oxidation (32), maintenance of carbohydrate
intake in the high-protein group may explain the lack of
difference in fat loss between groups as was reported from
studies with obese subjects where the high protein was
balanced with carbohydrates (18,19,21,24,28). However,
differences between lean and obese subjects with respect
to the metabolic effect of high-protein meals on fuel metab-
olism and fat oxidation have been reported (16,21). There-
fore, it is not intuitively obvious which, if any, of the
variables that differ between studies (e.g., subject popula-
tion, macronutrient composition, and physical activity of
the subjects) explain the differential responses of fat and
lean mass.
Another aspect to consider is the timing of the protein
intake in relation to the training stimulus because this may
influence the impact on protein synthesis (38). We did not
explicitly advise the subjects to consume a particular
amount of protein at a particular time before, during, or
after training. However, we distributed the daily protein
load as much as possible among the different meals and
snacks so that a reasonably steady protein supply during the
day was ensured.
Previously, Walberg et al. (40) demonstrated that increased
protein intake resulted in greater N balance during weight
loss. We did not measure N balance in our study, but clearly,
greater N balance in the high-protein group would be
important for the lack of loss of lean mass that we observed.
However, it is not clear that N balance can be quantita-
tively associated with lean body mass. Although Walberg
et al. (40) found that N balance increased by as much as
on the high-protein diet, there was no increase in
fat-free mass. There seems to be an overall discrepancy
between positive N balances and body protein accretion in N
balance studies (29). Therefore, conclusions regarding protein
accretion or loss with varying diets may be qualitatively
assessed with N balance, but quantitative conclusions may be
best based on body composition measures.
In fact, if the relatively hidden information about body
composition (measured by underwater weighing) are ex-
tracted from the study of Walberg et al. (40), the results
match surprisingly well with our data. These data reveal a
similar absolute fat loss of 2.1 versus 2.0 kg in the low-
protein group (0.8 gIkg
) and the high-protein group
(1.6 gIkg
), respectively. The mean lean body mass
loss, however, was 2.7 versus 1.4 kg. This seems to be in
accordance with our data in two important aspects. The lean
body mass loss seemed to be influenced by the protein
content of the diet, and the lean body mass loss clearly
exceeded the fat loss at the lower protein level.
The influence of the dietary energy level and the amount
of energy reduction may be further parameters to consider
(5,38). Although we have attributed the differences between
groups to the obvious difference in protein intake, the
vagaries of assessing energy intake make it possible that
differences in energy intake may have influenced our
results. If energy balance was greater for the high-protein
group, then higher body weight and lean body mass in that
group may be because of energy as much as protein.
However, this explanation seems unlikely. We made
extensive attempts to control and assess diet and energy
expenditure. Our satiety ratings during week 2, that is,
when we established energy intakes, were stable, and
energy was carefully adjusted during that week to ensure
appropriate levels. Body weight did not change during
week 2. Although this time frame may be too short to detect
changes in body weight because of smaller, yet significant,
energy deficits, at the very least, it excludes large deficits
that would account for the changes noted in subsequent
weeks. In fact, if anything, the average change in body
mass during week 2 was slightly negative, albeit non-
statistically significant, for the high-protein group. Finally,
fat loss was similar for the two groups. If energy balance
was different, it is likely that fat loss would have been
greater for the control group. Thus, it seems that differ-
ences in energy balance are less likely to account for the
differences between groups than differences in protein
Energy intake may have impacted body composition in
another manner. It is possible that the drop in protein intake
with the simultaneous drop in energy intake might have
been two factors, both of which contributed to lean body
mass loss in the control group. Quevedo et al. (30) demon-
strated that N balance was negative for È10dafterare-
duction in protein intake. However, in practice, when energy
intake is restricted, protein intake almost certainly will be
reduced unless high-protein foods are specifically selected.
However, Friedlander et al. (11) noted comparable lean body
mass losses to our control group, which also exceeded fat
losses, even when the absolute protein supply was held con-
stant before and during weight loss at 1.2 gIkg
young, lean subjects.
Mourier et al. (26) found no effect of two different
nitrogen-enriched diets (high protein and high BCAA
intake) on lean body mass loss during a hypoenergetic
weight loss in athletes compared with a control group. The
reasons for discrepancies between studies are not obvious
but may be because of methodological differences influ-
encing the anabolic response of muscle.
Elevated protein intake was accomplished by different
food types in these studies. Mourier et al. (26) used soy
protein as a major protein source in the high-protein group.
Soy protein has been shown to increase protein breakdown
in animal and human experiments (3). Therefore, the high-
protein diet might not have been as effective as it could
have been with other protein sources. We relied more on
supplying animal protein sources (dairy, tuna, chicken, and
other meat proteins) to increase the protein intake in our
study. Recent data indicate that the response of muscle
anabolism to resistance exercise is greater when animal
protein sources are consumed (43). Thus, maintenance of
lean body mass may have been better in our high-protein
group owing to the type of protein ingested.
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The second nitrogen-enriched group of Mourier et al.
(26) received most of the daily nitrogen intake as BCAA
(0.9 g BCAAIkg
body mass, consisting of 76% leucine,
19% isoleucine, and 5% valine). Whereas leucine stimulates
protein synthesis (1), increased leucine levels have long
been known to decrease levels of other amino acids,
especially the other BCAA because of increased activity
of branched-chain >-keto acid dehydrogenase (13). Thus,
the high levels of BCAA from the supplementation without
additional supply of exogenous amino acids may have
limited the availability of the other (essential) amino acids
in that study (26). Amino acid availability is a key factor for
the stimulation of muscle protein synthesis; thus, muscle
anabolism may have been limited in that high-BCAA group
(26) relative to our high-protein group.
The discussion in the above paragraph is predicated pri-
marily on the notion that the higher protein intake some-
how changed muscle protein metabolism resulting in less
atrophy during hypocaloric weight loss. Because DXA
does not provide an assessment of muscle protein, we can-
not rule out the possibility that the differences in lean
body mass were due to other factors influencing DXA
methodology, for example, differences in body water.
However, there does not seem to be an obvious reason
for differences in body water with the two diets. Thus,
differences in lean body mass are likely accounted for,
at least in part, by differences in muscle protein. Walberg
et al. (40) reported a comparable result with a completely
different body composition method (underwater weighing)
with different assumptions.
Whereas no measurement of protein metabolism was made
in this study, it is intuitively satisfying to accept that provision
of excess protein in combination with the resistance exercise
may have resulted in greater magnitude of positive muscle
protein balance (2,38), either through longer or more frequent
periods of positive balance. The influence of exercise and
amino acid provision on muscle protein balance is primarily
due to the changes in muscle protein synthesis (2,29,38,43).
However, muscle protein breakdown may also have played a
role. During muscle atrophy, when amino acid availability is
low, myofibrillar proteins seem to be selectively targeted for
degradation (45). Thus, differences in lean body mass may
have been due to increased muscle protein synthesis in
response to the exercise and increased amino acid availability
and/or selective degradation of the structural proteins with
low amino acid availability.
Blood parameters. Some blood values responded to
the energy restriction, but there was no statistical difference
between the diets for most parameters. One exception was
the NEFA, which showed a group time effect (i.e., larger
increase in NEFA during weight loss in the control
compared with the high-protein group). This result was
not entirely expected because high-fat diets do not increase
resting levels of NEFA in inactive males (36). However,
this interaction may be due to the different dietary fat
intake during weeks 3 and 4 combined with training.
The other exception was the blood urea, which showed
significant group, time, and group time effects. This result
may be explained by the substantial divergent dietary protein
intake in weeks 3 and 4. The protein intake in the high-protein
group was not only relatively, but even absolutely, higher
during the two weight loss weeks compared with the
maintenance week despite the energy restriction. Because
body composition data indicate that there was no protein
accretion, but still a slight loss of lean body mass, some of the
protein must have been metabolized, causing increased urea
values (10). The time course of the urea values, therefore,
may be no real surprise (10). None of the measured anabolic
hormones showed a group or group time effect. Therefore,
the blood values cannot help elucidate the difference in lean
body mass loss between the two groups. However, this lack
of explanatory power should be put into perspective. In this
study, we measured only fasting values in the morning. It has
been shown that anabolic hormones such as insulin (39),
testosterone (35), or IGF-1 (44) respond to the dietary protein
level. Thus, given the limitations of our study design, we
cannot exclude the possibility that postprandial or whole-day
hormonal profiles may be more informative.
Moreover, we have no information regarding responses
at the muscle level. Although some anabolic signals such
as IGF-1 seem to be reasonably represented in the plasma
(27,44), there might be further autocrine or paracrine sig-
naling processes on the muscular level. In fact, the major
impact of IGF-1 is related more to the muscle than to the
blood levels (27).
Further signaling processes may be going on in the muscle
cell, for example, as a consequence of larger amino acid
availability in the high-protein diet. It has been shown that
leucine, in particular, is a potent stimulator of anabolic signal
cascades in the cell (1,20). The increased leucine could have
been a possible signal to increase protein synthesis and
therewith counterbalance to some point the catabolic
signaling of the hypoenergetic diet in the high-protein group
(20). In fact, leucine signaling has been suggested to play a
protein intake (17–20). Thus, it is possible that leucine
signaling played an important role in our results. However,
the role of leucine per se in the preservation of lean body
mass during weight loss has not yet been systematically
investigated. These possibilities need further study; measure-
ment of not only fasting blood values but also whole-day
hormonal profiles, including postprandial and postexercise
situations, as well as muscle biopsies, could contribute more
information about paracrine and other signaling on the
muscular level.
Performance. For the most part, the results of the
performance tests responded neither to the energy restriction
per se nor to the composition of the diet. These results are
similar to some, but not all, previous studies on performance
during weight loss (9). Unfortunately, the disparate subject
populations, duration of weight loss, and performance mea-
sures make it difficult to draw firm conclusions. However,
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taken together, it seems that short periods of hypoenergetic
weight loss do not result in dramatic decreases in perfor-
There was no difference in measured performance param-
eters between the control group and the high-protein group.
The jump peak force was the only performance parameter
that dropped down significantly over time. This decrease may,
at least partially, be explained by the lower body weight
toward the end of the study, where less force was needed for
the same jump height. As peak force in a squat jump increases
with increasing external loading and decreases with decreas-
ing external loading (7), a decreasing peak force in the squat
jump with the decreased body weight does not necessarily
indicate decreasing performance but rather represents an in-
evitable biomechanical effect. This supposition is supported
by the other performance tests that do not decrease signif-
icantly. In contrast, the muscle endurance improved signif-
icantly. Possible adaptation to the testing procedure cannot
be ruled out and may be the more likely explanation for this
observation. Indeed, in week 1, muscle endurance was
slightly less than in subsequent weeks. From weeks 2 to 4,
there was no statistical change in muscular endurance. Taken
together, these data support the notion that there may have
been a learning effect for this test.
However, the lack of statistical significance does not
mean that there is no effect at all. Because there was a
significant difference in lean body mass loss between the
two groups, it is feasible to speculate that performance may
be impacted but not detected by our chosen methods of
measurement. Our subjects were trained, but not elite and,
except for weight lifting, participated in somewhat varied
exercise activities. Continuing these investigations in a
more homogeneous group of elite athletes, which could be
tested in a sports-specific manner, is likely to provide more
definitive results. Potential adaptive mechanisms to the
testing sessions would likely be smaller, and the test–retest
reliability might be even better than in our less homogenous
subjects. Furthermore, accommodation to the testing may
be reduced by adoption of more preceding familiarization
trials than what we had our subjects perform. In addition,
the influence of lean body mass loss on performance may
be even more significant if periods of restricted energy
intake are repeated, and lean body mass loss potentially
accumulates, for example, during a competitive season in
sports with weight classes and long-term energy restriction
(33). However, this issue would be particularly difficult to
test in a research setting.
In summary, the performance tests do not seem to be
affected by the energy restriction or by the diet composition
in this study. Nevertheless, a small but possibly relevant,
influence of the diet composition or reduced energy intake
on performance cannot be ruled out.
Other results. The tracking of the energy expenditure
by accelerometer and HR monitor indicated that subjects
consistently did not change their physical activity level,
despite the 40% dietary energy restriction for 2 wk. This
was important information because most previous weight
loss studies have not monitored this parameter. It should be
no surprise that the satiety ratings dropped with the dietary
energy reduction; however, it might be considered surpris-
ing that satiety was not maintained in the protein group.
Whereas protein has been reported to increase satiety in the
free-living situation as well as in situations with clamped
energy (21,41), we could not detect any difference between
the groups in our study population. Leidy et al. (21)
reported a significant impact of protein on the postprandial
satiety ratings but not on the 24-h satiety. Therefore, it is
possible that we missed postprandial effects of protein
intake on satiety in our study.
Results of the psychologic assessments suggest that
higher protein intake during hypoenergetic weight loss
may negatively impact mood. There was a significant group
time effect for the fatigue rating during training sessions
as well as in the B-part of the DALDA questionnaire (i.e.,
the high-protein group reported a more pronounced increase
in the fatigue and the ‘‘worse than normal’’ ratings during
weight loss compared with the control group). There is no
obvious explanation for these results. The only difference
between groups was the fat and protein intake. To our
knowledge, there is no association of increased fatigue with
reduced fat or increased protein intake while energy and
carbohydrate intake is the same—at least to our knowledge,
such an association has never been reported. Therefore,
these potentially negative effects of the high-protein diet
with respect to well-being and fatigue should be noted.
Subjective feelings may be more relevant for performance
than lean body mass maintenance, at least in the short term.
However, it is possible that the decreased feelings of well-
being and fatigue might have counteracted any influence of
improved lean body mass maintenance on performance in
the high-protein group.
Practical implications. Our results indicate that
È2.3 gIkg
mass or È35% energy protein was sig-
nificantly superior to È1.0 gIkg
or È15% energy
protein for the maintenance of lean body mass. These levels
of intake could be considered somewhat extreme. However,
1.0 gIkg
is likely sufficient to maintain mass when
in energy balance (37) and 2.3 gIkg
is approxi-
mately three times the US recommended daily allowance.
Comparison with previous studies on overweight and
obese individuals suggests that this effect might be more
pronounced in lean trained athletes compared with obese
subjects. However, it is not possible to determine the
protein intake needed to produce the maximal positive
effect on lean body mass maintenance from our results.
More studies with different protein levels and comparing
different population groups would be needed to bring more
The practical implication of these results is that the protein
content of a hypoenergetic diet may play a crucial role.
Athletes aiming for body weight reduction while maintaining
lean body mass may be advised to keep protein intake high.
PROTEIN AND WEIGHT LOSS IN ATHLETES Medicine & Science in Sports & Exercise
by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.Copyright @ 2010
On the other hand, athletes aiming for maximizing body
weight reduction regardless of composition may wish to avoid
an elevation of protein intake during hypoenergetic weight
loss. However, maintenance of lean body mass might be the
most desirable strategy for many athletes, particularly in the
long term and/or if periods of restricted energy are repeated.
On the other hand, it should be noted that we detected slightly
but significantly reduced feelings of well-being and higher
fatigue in the high-protein group compared with the control
group. Whereas these negative results might be of less
significance compared with maintenance of lean body mass
with respect to the long-term influence on body composition
and performance, reduced well-being might indeed be
relevant for performance in the short term. In the relatively
short 2-wk duration of our study, these feelings did not seem
to impact the ability of the subjects to maintain their training
volume and intensity and thus had no measurable impact on
performance. Nonetheless, the impact of these diets on well-
being should not be ignored when planning hypoenergetic
weight loss. It should be mentioned that a high-protein diet
may sound particularly attractive to athletes compared with a
‘standard’’ diet. We blinded the diets in an attempt to avoid
the possibility that subjects in the high-protein diet would be
more motivated for the study.
In conclusion, we found a significantly reduced loss of
lean body mass with increased protein (È2.3 gIkg
compared with a normal protein diet (È1.0 gIkg
during short-term weight loss in healthy lean athletes. On
the other hand, we detected slightly but significantly
reduced feelings of well-being in the high-protein group.
Performance was affected neither by the energy restriction
nor by the diet composition in this study. Nevertheless, a
small but possibly relevant influence of the diet composi-
tion or reduced energy intake on performance cannot be
ruled out. Further studies are needed to gain more in-
formation about the dose response of the protein intake
on lean body mass loss, and future studies with a more
homogenous group of elite athletes may give more detailed
information about the influence of dietary protein and lean
body mass loss on performance.
This study was funded by UK Sports Council and the Swiss
Federal Council of Sports.
There are no conflicts of interest for any of the authors. The
results of the present study do not constitute endorsement by
American College of Sports Medicine.
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PROTEIN AND WEIGHT LOSS IN ATHLETES Medicine & Science in Sports & Exercise
... A strategy to circumvent this problem is to increase the daily intake of high-quality protein (for a review on dietary protein, see (Phillips and Van Loon, 2011)). Indeed, a high-protein diet in young, physically active volunteers effectively prevented muscle atrophy during shortterm caloric restriction (Mettler et al., 2010;Pasiakos et al., 2013;Longland et al., 2016). These observations underpin current recommendations with regard to protein intake during weight loss in athletic populations (Helms et al., 2014;Manore, 2015;Hector and Phillips, 2018). ...
... Effect sizes were reported as eta squared (η 2 ). Sample size was determined based on earlier studies investigating the effect of a high-protein hypocaloric diet on muscle wasting (primary endpoint of this study) in normal-weight subjects (Mettler et al., 2010;Pasiakos et al., 2013). ...
... Results of the current study indicate that increased protein intake fully inhibited muscle wasting in lean females during a period of caloric restriction, as was previously shown in males (Mettler et al., 2010;Pasiakos et al., 2013). Increased protein intake also preserved exercise capacity as time to exhaustion during an incremental VO 2 max test remained unchanged following the caloric restriction period. ...
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Introduction: Since low body weight is an important determinant of success in many sports such as gymnastics, martial arts and figure skating, athletes can benefit from effective weight loss strategies that preserve muscle mass and athletic performance. The present study investigates the effects of increased protein intake and exogenous ketosis on body composition, energy expenditure, exercise capacity, and perceptions of appetite and well-being during a hypocaloric diet in females. Methods: Thirty-two female recreational athletes (age: 22.2 ± .5 years; body weight: 58.3 ± .8 kg; BMI: 20.8 ± .2 kg·m−2) underwent 4 weeks of 30% caloric restriction and were randomized to receive either an increased daily amount of dietary protein (PROT, ∼2.0–2.2 g protein·kg−1·day−1), 3 × 20 g·day−1 of a ketone ester (KE), or an isocaloric placebo (PLA). Body composition was measured by DXA, resting energy expenditure (REE) by indirect calorimetry, exercise capacity during a VO2max test, appetite hormones were measured in serum, and perceptions of general well-being were evaluated via questionnaires. Results: The hypocaloric diet reduced body weight by 3.8 ± .3 kg in PLA, 3.2 ± .3 kg in KE and 2.4 ± .2 kg in PROT (Ptime<.0001). The drop in fat mass was similar between treatments (average: 2.6 ± .1 kg, Ptime<.0001), while muscle mass was only reduced in PLA and KE (average: .8 ± .2 kg, Ptime<.05), and remained preserved in PROT (Pinteraction<.01). REE [adjusted for lean mass] was reduced after caloric restriction in PLA (pre: 32.7 ± .5, post: 28.5 ± .6 kcal·day−1·kg−1) and PROT (pre: 32.9 ± 1.0, post: 28.4 ± 1.0 kcal·day−1·kg−1), but not in KE (pre: 31.8 ± .9, post: 30.4 ± .8 kcal·day−1·kg−1) (Pinteraction<.005). Furthermore, time to exhaustion during the VO2max test decreased in PLA (by 2.5 ± .7%, p < .05) but not in KE and PROT (Pinteraction<.05). Lastly, the perception of overall stress increased in PLA and PROT (p < .05), but not in KE (Pinteraction<.05). Conclusion: Increased protein intake effectively prevented muscle wasting and maintained exercise capacity during a period of caloric restriction in female recreational athletes. Furthermore, exogenous ketosis did not affect body composition, but showed its potential in weight management by preserving a drop in exercise capacity and REE and by improving overall stress parameters during a period of caloric restriction.
... There is some evidence, mainly from case-studies indicating that weight-loss with short-term (Table 1), 23,[52][53][54][55] and medium-term (Table 2) 61,62,65,66 LEA report neutral or positive effects on performance variables. Hence, some elite athletes who manipulate their body composition prior to high-priority competitions 67,70,114 when they are likely to be in top form potentially could improve their physical performance. ...
... 116 Moreover, a 40% reduction in EI over the course of 2 weeks showed retention of lean BM in resistance-trained men when following a high-protein (~2.3 g/kg/day) versus a normal protein (~1 g/kg/day) diet when training load was maintained constant. 52 As mentioned earlier, it is difficult to disentangle the direct effects of acute reduced substrate availability (e.g., skeletal muscle glycogen) with LEA/LCA, from the physiological effect of LEA-triggered mechanisms of energy preservation (e.g., altered hormonal profile, decreased muscle protein synthesis and others) on physical performance. Therefore, from a mechanistic standpoint one can speculate that LEA could be an independent modulator of skeletal muscle metabolism. ...
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Low energy availability (LEA) occurs inadvertently and purposefully in many athletes across numerous sports; and well planned, supervised periods with moderate LEA can improve body composition and power to weight ratio possibly enhancing performance in some sports. LEA however has the potential to have negative effects on a multitude of physiological and psychological systems in female and male athletes. Systems such as the endocrine, cardiovascular, metabolism, reproductive, immune, mental perception, and motivation as well as behaviors can all be impacted by severe (serious and/or prolonged or chronic) LEA. Such widely diverse effects can influence the health status, training adaptation, and performance outcomes of athletes leading to both direct changes (e.g., decreased strength and endurance) as well as indirect changes (e.g., reduced training response, increased risk of injury) in performance. To date, performance implications have not been well examined relative to LEA. Therefore, the intent of this narrative review is to characterize the effects of short-, medium-, and longterm exposure to LEA on direct and indirect sports performance outcomes. In doing so we have focused both on laboratory settings as well as descriptive athletic case-study-type experiential evidence.
... Regardless, a reduction in FM requires an energy deficit (McGuire 2011) and/ or an increase in total daily energy expenditure through physical exercise (Deighton et al. 2014); both of which can compromise fat free mass (FFM) levels. Higher energy deficits are associated with more rapid weight loss; the more pronounced the deficit, the greater the possibility to lose lean mass (Hall 2008;Mettler et al. 2010;Garthe et al. 2011). Moreover, evidence shows that a prolonged energy deficit in natural bodybuilders could also lead to hormonal imbalances, fatigue and psychological issues (Fagerberg 2018). ...
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Purpose This study evaluated the concurrent training (CT) effect in combination with either progressive energy restriction (PER) or severe energy restriction (SER) on body composition and strength-related variables in resistance-trained women. Methods Fourteen women (29.5 ± 3.8 years; 23.8 ± 2.8 kg·m⁻²) were randomly assigned to a PER (n = 7) or SER (n = 7) group. Participants performed an 8-week CT program. Pre- and post-intervention measures of fat mass (FM) and fat-free mass (FFM) were assessed by dual-energy X-ray absorptiometry and strength-related variables were assessed through 1-repetition maximum (in the squat and bench press) and countermovement jump. Results Significant reductions in FM were observed in PER and SER (Δ = − 1.7 ± 0.4 kg; P = < 0.001; ES = − 0.39 and Δ = − 1.2 ± 0.6 kg; P = 0.002; ES = − 0.20, respectively). After correcting FFM for fat-free adipose tissue (FFAT), no significant differences for this variable were found either in PER (Δ = − 0.3 ± 0.1; P = 0.071; ES = − 0.06) or in SER (Δ = − 0.2 ± 0.1; P = 0.578; ES = − 0.04). There were no significant changes in the strength-related variables. No between-group differences were found in any of the variables. Conclusion A PER has similar effects to a SER on body composition and strength in resistance-trained women performing a CT program. Given that PER is more flexible and thus may enhance dietary adherence, it might be a better alternative for FM reduction compared to SER.
... In lean, resistance-trained individuals, prior research reported that metabolic adaptations and losses of fat-free mass in response to energy restriction manifest under conditions of severe energy deficits undertaken for extended periods of time or when dietary protein intake levels are kept low (Chappell et al., 2021;Garthe et al., 2011;Mettler et al., 2010;Rohrig et al., 2017;Tinsley et al., 2019). Neither of these conditions were met in the current investigation as the energy deficit was approximately 24% for a six-week period, and protein intakes averaged 1.7 g/kg/d, which is within the recommended range for active individuals looking to build or preserve muscle mass (Jager et al., 2017). ...
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The purpose of this study was to examine the effects of intermittent versus continuous energy restriction on body composition, resting metabolic rate, and eating behaviors in resistance-trained females. Thirty-eight resistance-trained females (mean ± standard deviation age: 22.3±4.2 years) were randomized to receive either six weeks of a continuous 25% reduction in energy intake (n= 18), or one week of energy balance after every two weeks of 25% energy restriction (eight weeks total; n= 20). Participants were instructed to ingest 1.8 g protein/kilogram bodyweight per day and completed three weekly supervised resistance training sessions throughout the intervention. There were no differences between groups for changes over time in body composition, resting metabolic rate, or seven of the eight measured eating behavior variables (p > 0.05). However, a significant group-by-time interaction for disinhibition (p < 0.01) from the Three-Factor Eating Questionnaire was observed, with values (± standard error) in the continuous group increasing from 4.91 ± 0.73 to 6.17 ± 0.71, while values in the intermittent group decreased from 6.80 ± 0.68 to 6.05 ± 0.68. Thus, diet breaks do not appear to induce improvements in body composition or metabolic rate in comparison with continuous energy restriction over six weeks of dieting, but may be employed for those who desire a short-term break from an energy-restricted diet without fear of fat regain. While diet breaks may reduce the impact of prolonged energy restriction on measures of disinhibition, they also require a longer time period that may be less appealing for some individuals.
... While the most obvious solution is to seek and implement dietary programming approaches to increase caloric intake, it is important to acknowledge that the importance of dietary protein is further magnified in this scenario. Previous work has shown that during high physical demand energy deficit conditions, higher protein diets containing ≥2.2 g/kg BW/day may mitigate the loss of LBM in athletic populations [59,60] while, in some cases, actually promoting LBM gain [38]. In this regard, the UEFA expert group states that higher protein intakes, upwards of~2.4 g/kg BW/day, may be recommended during high physical demand energy deficit periods [3]. ...
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Dietary protein is required to support recovery and adaptation following exercise training. While prior research demonstrates that many athletes meet total daily protein needs, intake seems to be predominantly skewed toward the evening meal. An even distribution of protein doses of ≥0.24 g/kg BW consumed throughout the course of a day is theorized to confer greater skeletal muscle anabolism outcomes compared to a skewed pattern of intake. Protein quality is also an important dietary consideration for athletes, with the amino acid leucine seemingly serving as the primary driver of the postprandial anabolic response. The present study investigates protein consumption characteristics among a cohort of NCAA D1 soccer players and evaluates differences between male and female athletes. Athletes were instructed to complete 3-day food diaries, which were subsequently analyzed and compared to UEFA expert group-issued nutrition guidelines for soccer players. Breakfast, lunch, and dinner accounted for 81.4% of the total daily dietary protein intake. Most athletes (77.8%) ingested optimum amounts of protein at dinner but not at breakfast (11.1%) or lunch (47.2%). In addition, statistically significant sex-based differences in daily dietary protein intake, meal-specific protein amounts, and protein quality measures were detected. Findings indicate suboptimal dietary protein intake practices among the collegiate soccer athletes.
... 28 Bu durum, özellikle hızlı kilo kaybı sağlamaya çalışan ya da bilinçsiz şekilde kronik olarak enerji alımını suboptimal düzeyde sınırlayan sporcularda, yağsız vücut kitlesinin korunması ve egzersiz sonrası kas protein sentezinin indüklenmesi amacıyla protein desteğini zorunlu hale getirmektedir. 42,43 Kuvvet çalışmaları söz konusu olduğunda egzersizin indüklediği kassal büyümeyi de geliştirirler. 31 Bu nedenle diyetteki protein, kaslarda protein remodelasyonunu geliştirdiği için sporcularda toparlanma döneminde temel beslenme komponentlerinden birisi olmaktadır. ...
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30 porcular temel performans parametrelerini geliştirmeye yönelik egzersizlere antrenman programlarında yer verirler. Optimal performans düzeyi için en önemli belirleyici antrenman olmakla birlikte beslenme de çok önemli ve etkili bir komponentdir. Doğru bir beslenme planı sporcuların optimal performansın gerek-tirdiği enerji düzeyine sahip olmalarını, ideal kilolarında kalmalarını ve antrenmanın programının bütün aşamalarında kendilerini iyi hissetmelerini sağlayacaktır. İdeal bir beslenme planı oluşturmak deneyimli sporcular için bile kolay bir durum değildir. 1 Sporcu Beslenmesi ve Antrenman İlişkisi Nasıl Sağlanmalı? Ö ÖZ ZE ET T Sporda başarıyı sağlayan pek çok faktör olmakla birlikte beslenme anahtar bir rol üstlen-mektedir. Araştırma sonuçları planlı ve bilimsel bir beslenme stratejisinin sportif performansı ge-liştirdiği yönünde sonuçlar vermektedir. Sporcuya özel ve antrenman periyodizasyonuna uygun bireysel beslenme stratejisinin spor türü, performans beklentisi, hedefleri, sporcunun beslenme alış-kanlıkları ve tercihlerinin dikkate alınarak oluşturulması gerekmektedir. Sporcularda egzersiz ön-cesi optimal beslenmenin sağlanması ve egzersiz sonrası toparlanmanın desteklenmesi için yeterli düzeyde günlük enerji alımı şarttır. Total enerji ihtiyacının sağlanması kaydıyla sporcunun tercih-lerine ve toleransına göre düzenleme yapılabilir. Dengeli bir beslenme fiziksel aktivite nedeniyle artan enerji ihtiyacını karşılayabilmelidir. İyi bir beslenme, yoğun antrenman programını destek-lediği gibi kassal toparlanma ve endürans egzersizlerine olan metabolik adaptasyonu kolaylaştırır. Bu derlemede, sporcu beslenmesi ve antrenman ilişkisinin öneminden bahsedilecek ve beslenme yaklaşımları ile fizyolojik yanıtlar hakkında pratik önerilere yer verilecektir. A An na ah ht ta ar r K Ke el li im me el le er r: : Sporcular; egzersiz; beslenme durumu; spor performansı A AB BS ST TR RA AC CT T A number of factors contribute to success in sport, and diet is a key component. Research findings show that a planned scientific nutritional strategy is related to better sports performance. Individualized and periodized nutritional strategy according to training program should depend on several aspects, including the sport, the athlete's goals, the environment, and practical issues. Daily energy intake is essential in athletes, and should be timed according to training sessions in order to ensure optimal pre-workout nutrition, as well as to encourage recovery post workout. If this is not possible during the day, the intake should be tailored according to individual preference and tolerance, provided that the total daily requirements are met. Maintenance of the energy balance in individuals with increased requirements because of physical activity is important. Good nutrition assists in the ability to train intensely, as well as in muscle recovery and metabolic adaptations to endurance exercise. This review will focus on the importance of the interaction between sports nutrition and training status, and discuss practical recommendations about nutritional approaches and physiological responses. K Ke ey y W Wo or rd ds s: : Athletes; exercise; nutritional status; sports performance T Tu ur rk ki iy ye e K Kl li in ni ik kl le er ri i J J S Sp po or rt ts s M Me ed d-S Sp pe ec ci ia al l T To op pi ic cs s 2 20 01 16 6; ;2 2((3 3)):
... In energy-restricted feeding programs, it has been observed that a higher protein intake leads to a lower loss of muscle mass. For example, Mettler et al. [81] conducted a 2-week study where participants were subjected to a 40% energy restriction and divided into two groups: One group was given 1 g of protein per kg/day, while the other was given 2.3 g/kg/day. The subjects in the group that consumed less protein reported a muscle mass loss of 1.6 kg compared to 0.3 kg in the group that consumed a higher protein diet. ...
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It is estimated that three to five million sports injuries occur worldwide each year. The highest incidence is reported during competition periods with mainly affectation of the musculo-skeletal tissue. For appropriate nutritional management and correct use of nutritional supplements, it is important to individualize based on clinical effects and know the adaptive response during the rehabilitation phase after a sports injury in athletes. Therefore, the aim of this PRISMA in Exercise, Rehabilitation, Sport Medicine and Sports Science PERSiST-based systematic integrative review was to perform an update on nutritional strategies during the rehabilitation phase of musculoskeletal injuries in elite athletes. After searching the following databases: PubMed/Medline, Scopus, PEDro, and Google Scholar, a total of 18 studies met the inclusion criteria (Price Index: 66.6%). The risk of bias assessment for randomized controlled trials was performed using the RoB 2.0 tool while review articles were evaluated using the AMSTAR 2.0 items. Based on the main findings of the selected studies, nutritional strategies that benefit the rehabilitation process in injured athletes include balanced energy intake, and a high-protein and carbohydrate-rich diet. Supportive supervision should be provided to avoid low energy availability. The potential of supplementation with collagen, creatine monohydrate, omega-3 (fish oils), and vitamin D requires further research although the effects are quite promising. It is worth noting the lack of clinical research in injured athletes and the higher number of reviews in the last 10 years. After analyzing the current quantitative and non-quantitative evidence, we encourage researchers to conduct further clinical research studies evaluating doses of the discussed nutrients during the rehabilitation process to confirm findings, but also follow international guidelines at the time to review scientific literature.
... Longland et al. (2016) 12 observed greater increases in fat-free mass with higher protein intakes compared to low intakes despite both groups following a hypocaloric diet for four weeks. Samuel et al. (2010) 13 , observed greater reductions in total body mass and lean body mass in subjects consuming 1.0 g/kg compared to those consuming 2.3 g/kg. No differences in fat mass or measures of strength were reported. ...
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Introduction: Whether it is the athletic population or the general population, it is essential to understand considerations regarding the amount of protein consumed daily. Protein, often referred to as the body's building blocks, is an integral part of the diet. This macronutrient plays a vital role in developing and maintaining skeletal muscle mass. This paper aims to discuss protein intake at low (<1.2 g/kg/day), medium (1.2-2.2 g/kg), and high (>2.2 g/kg/day) levels that should be consumed with consideration of physically active populations and potential benefits or detrimental effects on body composition and performance. Methods: Searches of electronic databases PubMed and Google Scholar were undertaken to identify peer-reviewed journal articles that reported on dietary protein intake on body composition, and performance/strength. Discussion: Low protein intakes may not meet the needs of all physically active individuals. Medium and high protein intakes positively influenced body composition. Medium protein intakes can benefit strength and performance; however, these effects are not consistently reported with higher intakes. Conclusions: The minimum protein requirement for active individuals is 1.2 g/kg, and higher intakes are safe and effective for healthy, physically active individuals.
... If catabolism of muscle protein is experienced by an Olympic weightlifter or powerlifter as they attempt to ''make weight'' for competition, a compromise in force-generating capacity (Bamman et al., 1993), and thus weightlifting performance, is at least theoretically possible. To avoid this situation, consideration should be given to the amount of weight loss required and thus the specified weight category as well as nutritional strategies proven to assist with maintenance of lean body mass during weight loss, such as a relative increase in dietary protein intake (Mettler, Mitchell, & Tipton, 2010). Allocating sufficient time to achieve the specified weight-category limit without severe energy restriction will also be critical with possible consideration given to the strategic use of acute weight-loss strategies in the final 24-48 h before weigh-in. ...
... During an intermittent-fasting intervention, patients should be encouraged to consume healthy and nutrient-dense foods such as fruits, vegetables and whole-grain products to enhance their fiber and micronutrient intake. It is also recommended to consume at least 50 g of lean protein on the fast day of ADF and the 5:2 diet to help control hunger [72][73][74] and prevent excessive LBM loss [75,76]. Clinicians should regularly assess the frequency of adverse effects during the first 3 months, monitor for nutrient deficiencies and assess the continuing need for glucose-lowering, hypolipidemic and anti-hypertensive medications (potential need for dose adjustment or treatment de-escalation). ...
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Conventional hypocaloric diets, providing continuous energy restriction, are considered to be the cornerstone of dietary management of obesity. Although energy-restricted diets are overall safe, healthy, and modestly effective, their long-term adherence is difficult to accomplish. Intermittent fasting and ketogenic diets have emerged as attractive alternative dietary options for weight loss and improvement in cardiometabolic risk. Intermittent fasting is a unique dietary pattern characterized by periods of eating alternated with periods of fasting. Ketogenic diets are very low in carbohydrate, modest in protein, and high in fat. Several systematic reviews and meta-analyses of randomized controlled trials (RCTs) have reported beneficial but short-lived effects of intermittent fasting and ketogenic diets on various obesity-related health outcomes. Although for both diets, the current evidence is promising and steadily evolving, whether they are better than traditional calorie-restricted diets, whether they can safely lead to sustained weight loss and overall health benefits, and their effects on body composition, weight loss maintenance, energy intake and expenditure, diet quality, and cardiometabolic risk factors are still not unequivocally proven. The aim of the present review is to summarize the current state of evidence regarding the effects of these two popular modern diets, namely intermittent fasting and ketogenic diets. We describe the rationale and characteristics of different dietary protocols, we analyze the major mechanisms explaining their weight loss and cardiometabolic effects, and we provide a concise update on their effects on body weight and cardiometabolic risk factors, focusing on meta-analyses of RCTs. We also discuss knowledge gaps in the field of these diets, and we indicate directions for future research.
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Background: Resistance exercise leads to net muscle protein accretion through a synergistic interaction of exercise and feeding. Proteins from different sources may differ in their ability to support muscle protein accretion because of different patterns of postprandial hyperaminoacidemia. Objective: We examined the effect of consuming isonitrogenous, isoenergetic, and macronutrient-matched soy or milk beverages (18 g protein, 750 kJ) on protein kinetics and net muscle protein balance after resistance exercise in healthy young men. Our hypothesis was that soy ingestion would result in larger but transient hyperaminoacidemia compared with milk and that milk would promote a greater net balance because of lower but prolonged hyperaminoacidemia. Design: Arterial-venous amino acid balance and muscle fractional synthesis rates were measured in young men who consumed fluid milk or a soy-protein beverage in a crossover design after a bout of resistance exercise. Results: Ingestion of both soy and milk resulted in a positive net protein balance. Analysis of area under the net balance curves indicated an overall greater net balance after milk ingestion (P < 0.05). The fractional synthesis rate in muscle was also greater after milk consumption (0.10 ± 0.01%/h) than after soy consumption (0.07 ± 0.01%/h; P = 0.05). Conclusions: Milk-based proteins promote muscle protein accretion to a greater extent than do soy-based proteins when consumed after resistance exercise. The consumption of either milk or soy protein with resistance training promotes muscle mass maintenance and gains, but chronic consumption of milk proteins after resistance exercise likely supports a more rapid lean mass accrual.
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To optimize the postexercise insulin response and to increase plasma amino acid availability, we studied postexercise insulin levels after the ingestion of carbohydrate and wheat protein hydrolysate with and without free leucine and phenylalanine. After an overnight fast, eight male cyclists visited our laboratory on five occasions, during which a control drink and two different beverage compositions in two different doses were tested. After they performed a glycogen-depletion protocol, subjects received a beverage (3.5 mL · kg⁻¹) every 30 min to ensure an intake of 1.2 g · kg⁻¹ · h⁻¹ carbohydrate and 0, 0.2 or 0.4 g · kg⁻¹ · h⁻¹ protein hydrolysate (and amino acid) mixture. After the insulin response was expressed as the area under the curve, only the ingestion of the beverages containing wheat protein hydrolysate, leucine and phenylalanine resulted in a marked increase in insulin response (+52 and + 107% for the 0.2 and 0.4 g · kg⁻¹ · h⁻¹ mixtures, respectively; P < 0.05) compared with the carbohydrate-only trial). A dose-related effect existed because doubling the dose (0.2–0.4 g · kg⁻¹ · h⁻¹) led to an additional rise in insulin response (P < 0.05). Plasma leucine, phenylalanine and tyrosine concentrations showed strong correlations with the insulin response (P < 0.0001). This study provides a practical tool to markedly elevate insulin levels and plasma amino acid availability through dietary manipulation, which may be of great value in clinical nutrition, (recovery) sports drinks and metabolic research.
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Examined the psychometric properties of a modified form of S. Schacham's short version of the Profile of Mood States (POMS) for a sample of competitive athletes. The revised scale consisted of 40 adjectives that measured tension, depression, fatigue, vigor, confusion, anger, and esteem-related affect. The scale was administered to 45 female netball players immediately after competition, with the Ss instructed to respond according to how they were feeling at that point in time. Reliability coefficients (Cronbach's alphas) for the subscales ranged from .66 to .95 with a mean of .80. Validity was examined by comparing the mood states of winners and losers. All subscales, except fatigue, produced significant differences between these groups. It was concluded that this modified form of the POMS has acceptable psychometric properties for use in a sport setting. (French, Spanish, German & Italian abstracts) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Background: Ad libitum, low-carbohydrate diets decrease caloric intake and cause weight loss. It is unclear whether these effects are due to the reduced carbohydrate content of such diets or to their associated increase in protein intake. Objective: We tested the hypothesis that increasing the protein content while maintaining the carbohydrate content of the diet lowers body weight by decreasing appetite and spontaneous caloric intake. Design: Appetite, caloric intake, body weight, and fat mass were measured in 19 subjects placed sequentially on the following diets: a weight-maintaining diet (15% protein, 35% fat, and 50% carbohydrate) for 2 wk, an isocaloric diet (30% protein, 20% fat, and 50% carbohydrate) for 2 wk, and an ad libitum diet (30% protein, 20% fat, and 50% carbohydrate) for 12 wk. Blood was sampled frequently at the end of each diet phase to measure the area under the plasma concentration versus time curve (AUC) for insulin, leptin, and ghrelin. Results: Satiety was markedly increased with the isocaloric high-protein diet despite an unchanged leptin AUC. Mean (±SE) spontaneous energy intake decreased by 441 ± 63 kcal/d, body weight decreased by 4.9 ± 0.5 kg, and fat mass decreased by 3.7 ± 0.4 kg with the ad libitum, high-protein diet, despite a significantly decreased leptin AUC and increased ghrelin AUC. Conclusions: An increase in dietary protein from 15% to 30% of energy at a constant carbohydrate intake produces a sustained decrease in ad libitum caloric intake that may be mediated by increased central nervous system leptin sensitivity and results in significant weight loss. This anorexic effect of protein may contribute to the weight loss produced by low-carbohydrate diets.
Conference Paper
Evidence is accumulating that diets with reduced carbohydrates and increased levels of high quality protein are effective for weight loss. These diets appear to provide a metabolic advantage during restricted energy intake that targets increased loss of body fat while reducing loss of lean tissue and stabilizing regulations of blood glucose. We have proposed that the branched-chain amino acid leucine is a key to the metabolic advantage of a higher protein diet because of its unique roles in regulation of muscle protein synthesis, insulin signaling and glucose re-cycling via alanine. These metabolic actions of leucine require plasma and intracellular concentrations to increase above minimum levels maintained by current dietary guidelines and dietary practices in the U.S. Initial findings support use of dietary at levels above 1.5 g/kg . d during weight loss. Further, our research suggests that increased use of high quality protein at breakfast maybe important for the metabolic advantage of a higher protein diet.
This study determined the reliability and validity of a linear position transducer to measure jump performance by comparing the mean force, peak force, and time-to-peak force measurements with data obtained simultaneously with a force platform. Twenty-five men performed squat, countermovement, and drop jumps with the linear transducer connected from a waist belt and base, which were placed upon a force platform. The Pearson correlation coefficients across the 3 jumps for the mean force (r = 0.952-0.962), peak force (r = 0.861-0.934), and time-to-peak force (r = 0.924-0.995) were high, providing evidence that the linear-transducer and force-platform measurements were similar The trial-to-trial reliability of the jumps measured by the linear position transducer gave an intraclass correlation coefficient of 0.924-0.975 for mean force, 0.977-0.982 for peak force, and 0.721-0.964 for time-to-peak force. The coefficients of variation were 2.1-4.5% for mean force, 2.5-8.4% for peak force and 4.1-11.8% for time-to-peak force. Our findings showed that the calculations derived from the linear transducer were very similar to those of the force platform and hence provided evidence of the validity of this method. The data from the linear transducer were also shown to be reliable. Therefore, this method of calculating force may provide a cost-effective alternative to the force platform for measuring this variable.
A self-report inventory of sources of life-stress and symptoms of stress is described. The tool can be used to determine the nature of an athlete's response to training, particularly his/her capacity to tolerate training loads. Data are used to demonstrate the use of the inventory to determine i) training responses which are either too stressed or under-stressed, ii) the ideal amount of stress to promote the optimum level of training effort, iii) the influence of outside-of-sport stresses that interfere with the training response, iv) preliminary features of overtraining, v) reactions to jet-lag and travel fatigue, and vi) peaking responses.
Testprotokolle, Testbeschreibungen unterschiedlichster Krafttests