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A systematic review, meta-analysis and meta-regression of the effect of protein supplementation on resistance training-induced gains in muscle mass and strength in healthy adults

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A systematic review, meta-analysis and meta-regression of the effect of protein supplementation on resistance training-induced gains in muscle mass and strength in healthy adults

Abstract

Objective We performed a systematic review, meta-analysis and meta-regression to determine if dietary protein supplementation augments resistance exercise training (RET)-induced gains in muscle mass and strength. Data sources A systematic search of Medline, Embase, CINAHL and SportDiscus. Eligibility criteria Only randomised controlled trials with RET ≥6 weeks in duration and dietary protein supplementation. Design Random-effects meta-analyses and meta-regressions with four a priori determined covariates. Two-phase break point analysis was used to determine the relationship between total protein intake and changes in fat-free mass (FFM). Results Data from 49 studies with 1863 participants showed that dietary protein supplementation significantly (all p<0.05) increased changes (means (95% CI)) in: strength—one-repetition-maximum (2.49 kg (0.64, 4.33)), FFM (0.30 kg (0.09, 0.52)) and muscle size—muscle fibre cross-sectional area (CSA; 310 µm² (51, 570)) and mid-femur CSA (7.2 mm² (0.20, 14.30)) during periods of prolonged RET. The impact of protein supplementation on gains in FFM was reduced with increasing age (−0.01 kg (−0.02,–0.00), p=0.002) and was more effective in resistance-trained individuals (0.75 kg (0.09, 1.40), p=0.03). Protein supplementation beyond total protein intakes of 1.62 g/kg/day resulted in no further RET-induced gains in FFM. Summary/conclusion Dietary protein supplementation significantly enhanced changes in muscle strength and size during prolonged RET in healthy adults. Increasing age reduces and training experience increases the efficacy of protein supplementation during RET. With protein supplementation, protein intakes at amounts greater than ~1.6 g/kg/day do not further contribute RET-induced gains in FFM.
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Morton RW, etal. Br J Sports Med 2017;0:1–10. doi:10.1136/bjsports-2017-097608
ABSTRACT
Objective We performed a systematic review, meta-
analysis and meta-regression to determine if dietary
protein supplementation augments resistance exercise
training (RET)-induced gains in muscle mass and
strength.
Data sources A systematic search of Medline, Embase,
CINAHL and SportDiscus.
Eligibility criteria Only randomised controlled trials
with RET ≥6 weeks in duration and dietary protein
supplementation.
Design Random-effects meta-analyses and meta-
regressions with four a priori determined covariates. Two-
phase break point analysis was used to determine the
relationship between total protein intake and changes in
fat-free mass (FFM).
Results Data from 49 studies with 1863 participants
showed that dietary protein supplementation
significantly (all p<0.05) increased changes (means
(95% CI)) in: strength—one-repetition-maximum
(2.49 kg (0.64, 4.33)), FFM (0.30 kg (0.09, 0.52)) and
muscle size—muscle fibre cross-sectional area (CSA;
310 µm2 (51, 570)) and mid-femur CSA (7.2 mm2 (0.20,
14.30)) during periods of prolonged RET. The impact of
protein supplementation on gains in FFM was reduced
with increasing age (−0.01 kg (−0.02,–0.00), p=0.002)
and was more effective in resistance-trained individuals
(0.75 kg (0.09, 1.40), p=0.03). Protein supplementation
beyond total protein intakes of 1.62 g/kg/day resulted in
no further RET-induced gains in FFM.
Summary/conclusion Dietary protein supplementation
significantly enhanced changes in muscle strength and
size during prolonged RET in healthy adults. Increasing
age reduces and training experience increases the
efficacy of protein supplementation during RET. With
protein supplementation, protein intakes at amounts
greater than ~1.6 g/kg/day do not further contribute
RET-induced gains in FFM.
INTRODUCTION
Resistance exercise training (RET) in combination
with dietary protein supplementation is a common
practice, in athletes and recreational exercisers
alike, with the aim of enhancing RET-induced
gains in muscle mass and strength. Recognised as a
potent antisarcopenic stimulus, protein supplemen-
tation has also been advocated for ageing persons
participating in RET. Despite a large volume of
work in this area, narrative reviews1–5 and even
meta-analyses6–12 yield conflicting results as to the
actual effectiveness of protein supplementation to
enhance RET-mediated gains in muscle mass and
strength. This lack of agreement on the efficacy of
protein supplementation6–12 is likely due to the use
of divergent study inclusion criteria and inclusion
of subjects with differing: ages, training statuses,
total protein intakes, protein sources and protein
doses. Thus, an evidence-based answer to the main
question of the efficacy of protein supplementa-
tion, while previously reported,7 now appears to be
controversial.4
We conducted a meta-analysis that was more
inclusive in nature than previous meta-analyses6–12
to provide a broad, systematic and evidence-based
assessment on whether protein supplementation
can augment changes in relevant RET outcomes.
We used meta-regression to evaluate the impact
of important potentially mediating covariates that
were decided a priori to the meta-analysis. The
present meta-analysis includes more than double
the number of studies and participants than the
largest published comprehensive meta-analysis on
protein supplementation during RET to date.7ST1
We also undertook an additional rational, mech-
anism-based analysis that had the aim of answering
the following question: is there a protein intake
beyond which protein supplementation ceases to
provide a measurable benefit in increasing muscle
mass during RET? To answer this question, we
recognised that the process of muscle protein
synthesis (MPS), as the primary determinant of
muscle hypertrophy,13 shows a saturable dose-re-
sponse relationship with increasing protein intake.14
Since measures of MPS show good agreement with
hypertrophy13 we theorised that the effect of daily
protein intake on RET-induced changes in muscle
mass would show a dose-responsive relationship
but that this would ultimately plateau.
METHODS
Inclusion criteria
Any randomised controlled trials (RCTs) that
combined a RET and protein supplement interven-
tion were considered for this meta-analysis. Trials
had to be at least six weeks in duration, participants
A systematic review, meta-analysis and meta-
regression of the effect of protein supplementation
on resistance training-induced gains in muscle mass
and strength in healthyadults
Robert W Morton,1 Kevin T Murphy,1 Sean R McKellar,1 Brad J Schoenfeld,2
Menno Henselmans,3 Eric Helms,4 Alan A Aragon,5 Michaela C Devries,6
Laura Banfield,7 James W Krieger,8 Stuart M Phillips1
Review
To cite: MortonRW,
MurphyKT, McKellarSR, etal.
Br J Sports Med Published
Online First: [please include
Day Month Year]. doi:10.1136/
bjsports-2017-097608
Additional material is
published online only. To view
please visit the journal online
(http:// dx. doi. org/ 10. 1136/
bjsports- 2017- 097608).
1Department of Kinesiology,
McMaster University, Hamilton,
Canada
2Department of Health Sciences,
Lehman College of CUNY,
Bronx, New York, USA
3Bayesian Bodybuilding,
Gorinchem, Netherlands
4Sport Performance Research
Institute New Zealand, AUT
University, Auckland, New
Zealand
5California State University,
Northridge, California, USA
6Department of Kinesiology,
University of Waterloo, Waterloo,
Canada
7Health Sciences Library,
McMaster University, Hamilton,
Canada
8Weightology, LLC, Issaquah,
Washington, USA
Correspondence to
Dr Stuart M Phillips, Department
of Kinesiology, McMaster
University, 1280 Main Street,
West Hamilton, Ontario,
Canada; phillis@ mcmaster. ca
Accepted 31 May 2017
BJSM Online First, published on July 11, 2017 as 10.1136/bjsports-2017-097608
Copyright Article author (or their employer) 2017. Produced by BMJ Publishing Group Ltd under licence.
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2Morton RW, etal. Br J Sports Med 2017;0:1–10. doi:10.1136/bjsports-2017-097608
Review
had to be performing RET at least twice per week, and at least
one group had to be given a protein supplement that was not
co-ingested with other potentially hypertrophic agents (eg,
creatine, β-HMB, or testosterone-enhancing compounds). Only
trials with humans who were healthy and not energy-restricted
were accepted. Manuscripts had to be original research (not a
review or conference abstract) and be written in English.
Search strategy
A systematic search of the literature was conducted (LB) in
Medline, Embase, CINAHL and SportDiscus, current to January
2017 (see online supplementary appendix 1). As appropriate,
a combination of keywords and subject headings was used for
the following concepts: protein supplementation and resistance
training or muscle strength. The original search yielded 3056
studies. Any overlooked trials were identified by consulting
other reviews and meta-analyses on the subject and were added
in manually (17 studies). After deduplication and screening
for inclusion criteria, 155 articles were independently read/
reviewed by three authors (RWM, KTM and SRM). A total of 49
RCTs were selected for inclusion in this meta-analysis (figure 1).
Data extraction
Predetermined relevant variables from each included study
were gathered independently by three investigators (RWM,
KTM and SRM). Relevant variables included those regarding
the study design, details of the RET intervention, partici-
pant characteristics, protein supplement information, placebo/
control information, performance outcomes, body composi-
tion outcomes and any other notable information (eg, sources
of bias/conflict of interest). Where data were not presented
in table or text and authors could not be reached, data were
extracted using WebPlotDigitizer (Web Plot Digitizer, V.3.11.
Texas, USA: Ankit Rohatgi, 2017) or calculated from base-
line values and/or percentage change. Where there were any
discrepancies between the three reviewers the manuscripts were
revisited by all reviewers (RWM, KTM and SRM) and agreed
on by discussion. We also conducted a post hoc reassessment
Figure 1 PRISMA flow chart.
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Morton RW, etal. Br J Sports Med 2017;0:1–10. doi:10.1136/bjsports-2017-097608
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of 10 randomly selected studies and compared the extracted
results.15 Coder drift was <10% in all cases for each investigator
and inter-rater (RWM, KTM and SRM) reliability was excellent
(>95%).
A total of 58 different body composition and 66 perfor-
mance outcomes were extracted from the final 49 studies.16–64
Primary outcomes were limited and amalgamated to include two
different performance outcomes and four different body compo-
sition outcomes based on those most commonly reported in the
49 RCTs. Performance outcomes were: one-repetition-max-
imum strength (1RM; measured by any 1RM strength test) and
maximum voluntary contraction (MVC; measured by both isoki-
netic and/or isometric contractions using a dynamometer with
any muscle group/action). Body anthropometric and composi-
tion outcomes included: total body mass (TBM; measured by
any scale); fat-free mass (FFM) and bone-free mass (or lean mass
if FFM was not available; FFM; measured by dual-energy X-ray
absorptiometry (DXA), hydrodensitometry, or whole-body air
plethysmography (BodPod) ); fat mass (FM; measured by DXA,
hydrodensitometry and/or BodPod); muscle fibre cross-sec-
tional area (CSA;measured in any fibre subtype (I, IIa, and/or
IIx) obtained from either vastus lateralis and/or latissimus dorsi
biopsies using microscopy); and mid-femur whole muscle CSA
(mid-femur CSA, measured by MRI and/or CT).
Data syntheses
When data were reported in different units (eg, pounds vs kilo-
grams) the data were converted to metric units. In all analyses
the comparator group received an identical RET intervention
but was non-supplemented or placebo-supplemented. If a study
included a protein-supplemented group, a non-supplemented
control group and a placebo-supplemented control group that
were all part of the RET intervention, the protein-supplemented
and placebo-supplemented groups were retrieved. If a study had
multiple time points, only the preintervention and postinterven-
tion outcomes were retrieved. Where the change in SD (ΔSD)
was available it was collected alongside the preintervention and
postintervention SD. Where ΔSD was not reported, the correla-
tion coefficient (corr) for each primary outcome was calculated
according to the Cochrane Handbook for Systematic Reviews
of Interventions:65 corr = (SDpre
2 + ΔSDpost
2 SDchange
2) / (2 ×
SDpre× SDpost) and the ΔSD was then calculated as:
ΔSD = (SDpre
2
+ ΔSDpost
2 – 2 × corr × SDpre× SDpost).
The change in mean (ΔMean) and ΔSD were calculated for
each condition and uploaded to RevMan (Review Manager
(RevMan), V.5.3. Copenhagen: The Nordic Cochrane Centre,
The Cochrane Collaboration, 2014). Where studies had more
than one protein-supplemented group (eg, soy and whey),
measure of MVC (eg, isokinetic and isometric) or measure of
1RM (eg, bench press and leg press) the ΔMean and ΔSD were
independently calculated and later combined, unless other-
wise stated, using the RevMan calculator (Review Manager
(RevMan), V.5.3. Copenhagen: The Nordic Cochrane Centre,
The Cochrane Collaboration, 2014).
Meta-analyses
Random-effects meta-analyses were performed in RevMan
(Review Manager (RevMan), V.5.3. Copenhagen: The Nordic
Cochrane Centre, The Cochrane Collaboration, 2014) on the
change in each outcome. Effect sizes are presented as mean
difference (MD) with means±SD and 95% CIs for 1RM, TBM,
FFM, FM, fibre CSA and mid-femur CSA and as standardised
mean difference (SMD) and 95% CIs for MVC because it had
multiple outcomes presented on non-comparable scales (eg, N
and Nm).
Heterogeneity and risk of bias
Heterogeneity was assessed by χ2 and I2 and significance was set
at p<0.05. The internal validity of each study was determined
by domain-based evaluation to quantify risk of bias for each
study65 and was independently performed by three investigators
(RWM, KTM and SRM). The data included in the meta-analyses
were restricted to studies with less than three reported high or
unclear risk domains (predominately due to reported conflicts
of interest and lack of blinding investigators and/or participants;
(see online supplementary appendix 2)). Funnel plots were visu-
ally inspected to determine publication bias. Multiple sensitivity
analyses were performed to determine if any of the results were
influenced by the studies that were removed.
Meta-regression
In an effort to understand the sources of heterogeneity meta-re-
gressions were performed on 1RM, FFM and fibre CSA because
they were statistically significant, had considerable unexplained
heterogeneity (I2) and had a sufficient number of studies (≥10).
Meta-regression was used instead of subgroup analyses to allow
for the use of continuous covariates and to allow for the inclu-
sion of more than one covariate at a time. Four covariates were
chosen a priori to be included in our meta-regression: baseline
protein intake (g/kg/day), postexercise protein dose (g), chrono-
logical age and training status because there is evidence that
baseline protein intake,66 protein dose,14 age67 and training
status68 could influence the efficacy of protein supplementa-
tion; summarised here.4 5 These covariates were meta-regressed
individually and together in a random-effects meta-regression
model using Stata (StataCorp. 2011. Stata Statistical Software:
Release 12. College Station, Texas, USA). The random-effects
meta-regression used residual restricted maximum likelihood
to measure between-study variance (τ2) with a Knapp-Hartung
modification as recommended.69 When all four covariates were
analysed together permutation tests were performed (n=1000)
to address the issue of multiple testing by calculating adjusted
p values.70 Additional covariates were identified and individually
analysed post hoc to further explore the unexplained variance of
the effect of protein supplementation during RET on changes in
1RM and FFM. Continuous covariates were: MD in the change
in protein intake (g/day), MD in the total relative protein intake
(g/kg/day), number of repetitions/set, number of sets/exercise,
number of exercises/session, number of sessions/week, number
of weeks and total RET volume in kg: repetitions/set × sets/exer-
cise × exercises/session × sessions/week × intervention duration
in weeks. Categorical variables were: protein supplement source
(whey vs soy), sex (male vs female), type (dietary-supplement
vs RET-supplement), whole-body RET (whole-body RET vs
not whole-body RET) and RET supervision (supervised vs not
supervised). Protein supplement source was limited to soy and
whey because there were few study groups that were provided
either a casein (n=321 59 60;) or pea (n=122;) protein supplement
exclusively.
Subgroup analyses
Subgroup analyses were performed in RevMan (Review Manager
(RevMan), V.5.3. Copenhagen: The Nordic Cochrane Centre,
The Cochrane Collaboration, 2014). Subgroup analyses were
performed on changes in FFM and 1RM with training status
(untrained vs trained) as the subgroup to generate forest plots
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and neatly present training status as a categorical variable.
Subgroup analyses were also performed on changes in FFM with
age categorised into subgroups (old (>45 years) and young (<45
years)) to be presented below for the interested reader.
Breakpoint analysis
To investigate the influence of protein intake as a continuous vari-
able on individual study arms (as opposed being limited to MDs
between groups in a meta-regression) linear and segmental regres-
sions on the change in FFM (measured by DXA) were plotted
against daily and baseline protein intake. Linear and segmental
regressions were performed using GraphPad Prism (V.6, GraphPad
Software, La Jolla, California, USA) to determine models of best fit
as has been previously done in acute tracer trials measuring MPS.14
Where segmental regression was the preferred model the slope
of the second line was set to zero to determine the break point
(biphasic regression). Each group from each study that presented
daily or baseline protein intake with changes in FFM from DXA
was included. Significance was set at p<0.05 and data for the
break point is presented as mean (95% CI).
RESULTS
Participant characteristics
Participant details and outcomes are presented elsewhere
(see online supplementary table 1. A total of 49 studies from
17 countries met the inclusion criteria (figure 1). There were 10
studies in resistance-trained participants and 14 study groups in
exclusively female participants. Publications ranged from 1962
to 2016. There was a total of 1863 participants (mean±SD;
35±20 years).
RETcharacteristics
The RET characteristics are also presented elsewhere
(see online supplementary table 1). The RET interventions
lasted from 6 weeks to 52 weeks (13±8 weeks) performing
RET between 2 days and 5 days per week (3±1 days/week) with
between 1 to 14 exercises per session (7±3 exercises/session),
1 to 12 sets per exercise (4±2 sets/exercise) and anywhere
between 3 to 25 repetitions per set (9±4 repetitions/set). Four
studies used just lower-body RET, two studies used just knee
extensor RET, one study used elbow flexor RET only, and two
studies used one lower-body and one upper-body exercise only.
Protein supplementation
Details regarding the experimental (protein supplementation)
and control (placebo- or no-supplement) groups are presented
elsewhere (see online supplementary table 2). A range of 4 g to
106 g of protein was supplemented per day to the protein
group (36±30 g/day; young: 42±32 g/day; old: 20±18 g/day)
with a range of 5 g to 44 g of protein supplemented postexer-
cise on training days (24±11 g; young: 24±12 g; old: 23±10
g). Twenty-three conditions supplemented with whey protein,
3 with casein protein, 6 with soy protein, 1 with pea protein, 10
with milk or milk protein, 7 with whole food (eg, beef, yogurt,
between-meal snack) and 13 with non-specific protein blends or
blends containing multiple protein sources (eg, whey, casein, soy
and egg). In 40 studies the participants consumed part or all
of their daily protein supplement after their RET sessions. In
36 studies with 48 different conditions authors reported either
total (g/day) or relative (g/kg/day or %kcal/day) daily protein
intake preintervention and/or postintervention. There was an
increase in daily protein intake in the protein group (mean±SD;
range: 23±41 g/day; −25 g/day to 158 g/day; p=0.004) and no
change in the control group (1±14 g/day; −17 g/day to 40 g/
day; p=0.83) such that the change in daily protein intake was
significantly greater in the protein group (p=0.01). Relative
daily protein intake (g/kg/day) increased in the protein group
(pre: 1.4±0.4, post: 1.8±0.7, Δ: 0.3±0.5 g/kg/day, p=0.002)
and did not change in the control group (pre: 1.4±0.3, post:
1.3±0.3, Δ: −0.02±0.1 g/kg/day, p=0.48) such that there was
a greater change in the protein group (p<0.001). Daily energy
intake (kcal/day) was gathered from 23 studies with 29 condi-
tions and did not change with the prolonged RET and protein
supplementation nor was it significantly different between the
protein or control groups (Δ protein group: 50±293 kcal/day, Δ
control group: 70±231 kcal/day, p=0.71).
Heterogeneity and risk of bias
Significant heterogeneity was found for changes in 1RM
(χ2=53.49, I2=33%, p=0.003) and fibre CSA (χ2=30.97,
I2=68%, p=0.0006). Nine studies were removed based on risk of
bias17 18 25 26 50 63 (see online supplementary appendix 2) or publi-
cation bias assessment24 32 64 (see online supplementary figure 1).
In particular, four studies were removed from 1RM,17 26 32 50 four
from TBM,17 18 63 64 three from FM,17 18 63 five from FFM,17 18 24 63
64 three from MVC25 26 50 and one from fibre CSA.50
Sensitivity analyses
Sensitivity analysis was performed with the nine high-risk studies
mentioned above included in the outcomes they were removed
from to determine if their removal changed any of the results. The
inclusion of those studies did not influence the difference in means
or significance in 1RM, TBM, FFM or mid-femur CSA; however,
when Mitchell et al50 was included in the fibre CSA assessment
the effect of protein supplementation (310 µm2 (51, 570), p=0.02)
was eliminated (153 µm2 (−137, 443), p=0.30). This is likely
due to the small number of studies that included muscle biopsies
but may warrant caution when interpreting the effect of protein
supplementation on changes fibre CSA during RET. In no instance
did fixed-effect meta-analysis deliver a different magnitude of
effect or significance compared with random-effect meta-analysis.
Meta-analyses
Protein supplementation during prolonged RET significantly
improved gains in 1RM strength (MD: 2.49 kg (0.64, 4.33),
p=0.01; figure 2) but had no effect on MVC (SMD: 0.04
(-0.09, 0.16), p=0.54). Protein supplementation did not have
a significant effect on changes in TBM (MD: 0.11 kg (−0.23,
0.46), p=0.52) but improved changes in FFM (MD: 0.30 kg
(0.09, 0.52), p=0.007; figure 3), FM (MD: −0.41 kg (−0.70,–
0.13), p=0.005), fibre CSA (MD: 310 µm2 (51, 570), p=0.02;
see online supplementary figure 2: panel A) and mid-femur CSA
(MD: 7.2 mm2 (0.20, 14.30), p=0.04; see online supplementary
figure 2: panel B) during prolonged RET.
Meta-regression.
The results from the full model meta-regressions are presented in
table 1. When combined, baseline protein intake, protein dose,
age and training status did not explain any of the variance in the
changes in 1RM (15 studies, 1216 subjects, p=0.77) or FFM (15
studies, 642 participants, p=0.12). There were insufficient obser-
vations (<10) when all covariates were compared with the changes
in fibre CSA.
Univariate meta-regressions on changes in 1RM and FFM
following prolonged RET are also presented in table 1. None
of our covariates explained any of the heterogeneity of protein
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supplementation’s effect on changes in 1RM: baseline protein
intake (21 studies, 814 participants, p=0.59), age (27 studies,
802 participants, p=0.78), training status (28 studies, 858 partic-
ipants, p=0.40) and post-exercise protein dose (23 studies, 589
participants, p=0.13). In contrast, when the ability of protein
supplementation to affect changes in FFM was evaluated with
univariate meta-regressions, the postexercise protein dose was
the only covariate that did not influence the efficacy of protein
supplementation on changes in FFM (20 studies, 793 participants,
p=0.25) whereas baseline protein intake (22 studies, 988 partic-
ipants, p=0.045; see online supplementary figure 3: panel A),
age (25 studies, 1033 participants, p=0.02; figure 4) and training
status (26 studies, 1089 participants, p=0.03) all influenced the
effect of protein supplementation. When the effect of protein
supplementation on changes in FFM was evaluated with age strat-
ified into two subgroups the difference between old (>45; 67±7
years; MD: 0.06 (-0.14, 0.26)) and young (<45; 24±4 years; MD:
0.55 (0.30, 0.81)) participants remained significant (χ2=8.71,
I2=89%, p=0.003). There were no covariates that explained any
of the variance in the change in fibre CSA following RET: age
(10 studies, 474 participants, I2=65%, Adj. R2=-3%, p=0.50),
baseline protein intake (8studies, 384 participants, I2=43%, Adj.
R2=-44%, p=0.84), postexercise protein dose (10 studies, 270
participants, I2=77%, Adj. R2=-38%, p=0.92) and training status
(11 studies, 586 participants, I2=71%, Adj. R2=-24%, p=0.94).
Additional univariate meta-regressions are presented in else-
where (see online supplementary table 3). Only whether the RET
was whole-body (27 studies, including only 4 studies that were not
whole-body RET, I2=2%, Adj. R2=76%, p=0.01) or supervised
(28 studies, I2=5%, Adj. R2=58%, p=0.047) explained part of
the variance in the effectiveness of protein supplementation on
changes in 1RM. No other covariates explained any of the variance
associated with the efficacy of protein supplementation on changes
in 1RM or FFM.
Breakpoint analysis
Biphasic regression (42 study arms, 723 participants) explained
more variation than a linear regression between the change in
Figure 2 Forest plot of the results from a random-effects meta-analysis shown as mean difference with 95% CIs on one-repetition-maximum (1RM;
kg) in untrained and trained participants. For each study, the circle represents the mean difference of the intervention effect with the horizontal line
intersecting it as the lower and upper limits of the 95% CI. The size of each circle is indicative of the relative weight that study carried in the meta-
analysis. The rhombi represent the weighted untrained, trained and total group’s mean difference. Total: 2.49 kg (0.64, 4.33), p=0.01, untrained:
0.99 kg (−0.27, 2.25), p=0.12 and trained: 4.27 kg (0.61, 7.94), p=0.02.
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Figure 3 Forest plot of the results from a random-effects meta-analysis shown as mean difference with 95% CIs on lean or fat-free mass (FFM;
kg) in untrained and trained participants. For each study, the circle represents the mean difference of the intervention effect with the horizontal
line intersecting it as the lower and upper limits of the 95% CI. The size of each circle represents the relative weight that study carried in the meta-
analysis. The rhombi represent the weighted untrained, trained and total group’s mean difference. Total: 0.30 kg (0.09, 0.52) p=0.007, untrained:
0.15 kg (−0.02, 0.31), p=0.08 and trained: 1.05 kg (0.61, 1.50), p<0.0001.
Table 1 Meta-regression output.
Model N
1RM (kg) Fat-free mass(kg)
Coeff. (95% CI) τ2Adj. R2I2pValue N Coeff. (95% CI) τ2Adj. R2I2pValue
No covariates 28 2.49 (0.64 to 4.33) 6.05 33% 0.01 27 0.30 (0.09 to 0.52) 0.05 7% <0.01
Univariate
Baseline protein intake 21 2.85 (-8.15to 13.84) 7.82 1% 37% 0.59 22 0.64 (0.02 to 1.27) 0 100% 0% 0.045
Protein dose 23 0.13 (-0.04to 0.31) 3.16 40% 0% 0.13 20 0.02 (-0.01to 0.04) 0.09 0% 0% 0.25
Age 27 0.01 (-0.09to 0.11) 6.51 −9% 34% 0.78 25 −0.01 (-0.02 to 0.00) 0 100% 0% 0.02
Training status 28 5.77 (-2.96to 7.13) 5.77 5% 31% 0.40 26 0.75 (0.09 to 1.40) 0.03 49% 0% 0.03
All covariates 15 5.36 10% 0% 0.77 15 0 100% 0% 0.12
Baseline protein intake 15 6.40 (-11.62to 24.42) 0.43 15 −0.57 (-2.50to 1.37) 0.95
Protein dose 15 0.05 (-0.78to 0.88) 0.70 15 −0.01 (-0.07to 0.06) 0.99
Age 15 0.07 (-0.18to 0.33) 0.23 15 −0.01 (-0.02to 0.00) 0.19
Training status 15 −2.81 (-20.80to 15.17) 0.63 15 1.19 (-1.34to 2.19) 0.48
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FFM and daily protein intake (break point=1.62 (1.03, 2.20)
g/kg/day, slope=1.75, R2=0.19, df=36) and is presented as
a segmental regression despite not being statistically signifi-
cant (p=0.079;figure 5) When plotting the change in FFM
against baseline protein intake, linear regressions explained
significantly more variance than biphasic regressions in both
young (slope=−1.54 g/kg/day, R2=0.17, df=34) and old
(slope=0.16 g/kg/day, R2=0.04, df=14) participants with a
statistically significant difference between age groups (p=0.042;
see online supplementary figure 3: panel D).
DISCUSSION
This is the largest meta-analysis on interventions including
dietary protein supplementation with muscle and strength-re-
lated outcomes during prolonged RET to date. Our main
finding was that dietary protein supplementation augmented
RET-induced increases in 1RM strength (figure 2) and FFM
(figure 3). For changes in FFM, dietary protein supplementation
was more effective in resistance-trained individuals (table 1 and
figure 3), less effective with increasing chronological age (table 1
and figure 4) and did not increase beyond total protein intakes
of ~1.6 g/kg/day (figure 5). Our data show dietary protein
supplementation is both sufficient and necessary to optimise
RET adaptations in muscle mass and strength.
Previous meta-analyses6–12 have reached varying conclusions
when examining the impact of protein supplementation on
changes in lean mass or FFM and 1RM strength during RET. The
discrepancies are likely a consequence of differing study inclusion
criteria. For example, previous meta-analyses have included only
trained participants,8 only older adults,9 11 supplements containing
more than just protein,8 10 only one source of protein,8 12 shorter
RET interventions,10 12 frail/sarcopenic participants7 9 11 and/or
participants who were energy-restricted.6 7 12 Previously, the largest
comprehensive meta-analysis to date on protein supplementation
during RET included 22 studies and 680 participants7 and did
show a significant effect of protein supplementation on RET-stim-
ulated gains in strength and FFM. In agreement with this previous
report,7 and strengthening the conclusion of that same report by
including 49 studies and 1863 participants, we show that protein
supplementation augmented gains in FFM and strength with RET.
Strength
The average RET-induced increase, with all measures of 1RM
included, was 27 kg (mean±SD ; 27±22 kg22 32). Notably,
dietary protein supplementation augmented the increase in 1RM
strength by 2.49 kg (9%; figure 2;Figure 2 see online supple-
mentary figure 4), which strongly suggests that the practice of
RET is a far more potent stimulus for increasing muscle strength
than the addition of dietary protein supplementation. None of
our covariates (age, training status, postexercise protein dose or
baseline protein intake) influenced the efficacy of protein supple-
mentation on changes in 1RM strength. Improving performance
of a specific task (eg, the 1RM of an exercise) is predominately
determined by the practice of that task.71 Though protein supple-
mentation may slightly augment changes in 1RM (~9%), which
may be important for those competing in powerlifting or weight-
lifting, it is pragmatic to advocate that if an increase in 1RM is
the objective of an RET programme, a sufficient amount of work
and practice at or around the 1RM is far more influential than
protein supplementation.
Muscle mass
In addition to increasing changes in muscle strength, RET alone
(≥6; 13±8 weeks) resulted in an increase in FFM (1.1±1.2 kg ),
an increase in fibre CSA (808±) and an increase in mid-femur
CSA (52±30 mm239 65). Dietary protein supplementation
augmented the increase in FFM by 0.30 kg (27%; Figure 3;
see online supplementary figure 4), fibre CSA by 310 µm2 (38%;
see online supplementary figure 1: Panel A) and mid-femur CSA
by 7.2 mm2 (14%; see online supplementary figure 1: panel
B). The postexercise protein dose did not affect the efficacy
of protein supplementation on RET-induced changes in FFM
whereas training status (positive), age (negative) and baseline
protein intake (positive) did. Relative to untrained participants,
resistance-trained participants have a smaller potential for
muscle growth72 and an attenuated postexercise muscle protein
turnover.73 As a result, we speculate that trained persons may
have less ‘degrees of freedom’ to change with RET and therefore
have a greater need for protein supplementation to see increases
in muscle mass. Our thesis is supported by the observation of
a more consistent impact of protein supplementation on gains
Figure 4 Random-effects univariate meta-regression between age
and the mean difference in fat-free mass (FFM) between groups.
Each circle represents a study and the size of the circle reflects the
influence of that study on the model (inversely proportionate to the SE
of that study). The regression prediction is represented by the solid line
(−0.01 kg (−0.02,–0.00), p=0.02).
Figure 5 Segmental linear regression between relative total protein
intake (g/kg body mass/day) and the change in fat-free mass (ΔFFM)
measured by dual energy X-ray absorptiometry. Each circle represents a
single group from a study. Dashed arrow indicates the breakpoint=1.62
g protein/kg/day, p=0.079. Solid arrow indicates 95% CI, (1.03to 2.20).
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in FFM in resistance-trained individuals than in novice trainees
(figure 3).
Older individuals are anabolically resistant74 and require
higher per-meal protein doses to achieve similar rates of MPS,
the primary variable regulating changes in skeletal muscle
mass,75 compared with younger participants.14 The average
supplemental daily protein dose given to older participants was
surprisingly low (20±18 g/day); thus, it is perhaps not surprising
that we did not find that older individuals were responsive to
protein supplementation (Figure 4). Though age did not affect
the RET-induced change in fibre CSA, the negative effect age
had on changes in FFM leads us to speculate that even though
exercise sensitises muscle to the effect of protein ingestion,3
older persons have an increased need for higher protein intakes
to optimally respond to this effect and see gains in FFM.76
It has been theorised that the increased deviation from normal
protein intake (g/kg/day) will positively affect the RET-induced
gains in FFM.77 Contrary to this thesis, we found that a higher
prestudy protein intake actually resulted in a greater effect of
protein supplementation on changes in FFM (table 1); however,
this was likely driven by the lower mean baseline protein intake
(old: 1.2±0.2 g/kg/day, young: 1.5±0.4 g/kg/day) and daily
protein dose (old: 20±18 g/day, young: 42±32 g/day) in the
studies that included older participants (see online supplemen-
tary figure 3: panel B and D). Indeed, a sensitivity analysis that
did not include older (>45; 65±14 years) versus younger (<45;
24±4 years) individuals found that baseline protein intake had
no effect on the efficacy of protein supplementation in young
individuals (see online supplementary figure 3, panel C). In an
unadjusted meta-regression analysis, a higher baseline protein
intake in young individuals actually attenuated the change in
FFM (see online supplementary figure 3, panel D).
A goal of this meta-analysis was to deliver evidence-based recom-
mendations that could be readily translated. A crucial point is that
even though the mean baseline protein intake for the 1863 partic-
ipants was ~1.4 g protein/kg/day, which is 75% greater than the
current US/Canadian recommended dietary allowance (RDA),78
an average supplementation of ~35 g protein/day still augmented
RET-stimulated gain in FFM (figure 3) and 1RM strength
(figure 2). Thus, consuming protein at the RDA of 0.8 g protein/
kg/day appears insufficient for those who have the goal of gaining
greater strength and FFM with RET. This conclusion is emphasised
for older men79 and women80 81 wishing to obtain strength and
gain lean mass with RET and protein supplementation.
A recent retrospective analysis showed a ‘breakpoint’ for the
stimulation of MPS when ingesting an isolated protein source at
0.24 g protein/kg and 0.40 g protein/kg in younger and older
participants, respectively.14 Given the observation of a dose-re-
sponsive relationship between protein intake and MPS82–85 and
the fact that MPS is aligned with muscle hypertrophy,13 we
elected to use an identical two-segment regression approach
between total daily protein intake and changes in FFM (figure 5)
as has been done for changes in protein dose and MPS.14 Here
we provide significant insight (using 42 study arms including 723
young and old participants with protein intakes ranging from 0.9
g protein/kg/day to 2.4 g protein/kg/day) by reporting an unad-
justed plateau in RET-induced gains in FFM at 1.62 g protein/kg/
day (95% CI: 1.03 to 2.20). These results are largely in congru-
ence with previous narrative reviews that comment on the
optimal nutritional strategies to augment skeletal muscle adapta-
tion during RET.3 86 Given that the CI of this estimate spanned
from 1.03 to 2.20, it may be prudent to recommend ~2.2 g
protein/kg/d for those seeking to maximise resistance train-
ing-induced gains in FFM. Though we acknowledge that there
are limitations to this approach, we propose that these findings
are based on reasonable evidence and theory and provide a prag-
matic estimate with an incumbent error that the reader could
take into consideration.
Although the present analysis provides important and novel
data, there are limitations that we acknowledge. First, the lack of
RET research in older individuals has led to inconclusive recom-
mendations from previous meta-analyses specifically focusing on
older individuals.9 11 Indeed, in this manuscript there were only
13 studies that met our inclusion criteria in older (>45 years)
individuals and only six of those studies reported baseline protein
intakes with changes in FFM. In addition, only four studies27 29
33 45 in older individuals had participants that consumed what
we consider to be close to optimal total protein intake (~1.2 g/
kg/day to 1.6 g/kg/day) in non-exercising adults5 during or postin-
tervention provided. Furthermore, only two studies23 30 in older
individuals provided a postexercise supplemental protein dose that
we consider to be close to optimal (~35–40 g) to stimulate FFM
accretion in elderly individuals.76 Given that older adults require
more protein per day,79–81 consume less protein per day87 and that
dietary protein ingestion and RET are effective strategies to main-
tain muscle mass and function with age,67 future RET research
should focus on using higher protein doses (or potentially higher
leucine), larger sample sizes and longer interventions in ageing
populations. Second, we included a variety of additional covari-
ates into univariate meta-regressions to elucidate the variables that
may modify whether protein supplementation affects RET-induced
changes in muscle mass and strength. Such an approach is gener-
ally considered to be hypothesis generating. The only significant
findings we found were that if the RET sessions were whole-body
(adjusted R2=76%, p=0.01) or supervised (adjusted R2=58%,
p=0.047), protein supplementation was more effective at
augmenting changes in 1RM. No variable affected changes in FFM
(see online supplementary table 3). Given the relatively small effect
that protein supplementation has on changes in FFM and 1RM,
clearly other variables as a component of RET programmes are of
much greater importance. Our meta-analyses also only included
studies with participants that were at or above their energy require-
ments, which may have omitted the significant impact protein has
during periods of weight loss with RET.88 Lastly, we found that
the postexercise protein dose did not affect the efficacy of protein
supplementation on RET-induced changes in FFM. Our analysis,
and those from others,6 leads us to conclude that the specifics of
protein supplementation (eg, timing, postexercise protein dose or
protein source) play a minor, if any, role in determining RET-in-
duced gains in FFM and strength over a period of weeks. Instead,
our results indicate that a daily protein intake of ~1.6 g/kg/day,
separated into ~0.25 g/kg doses,14 is more influential on adaptive
changes with RET, at least for younger individuals.
CONCLUSION
Dietary protein supplementation augments changes in muscle
mass and strength during prolonged RET. Protein supple-
mentation is more effective at improving FFM in young or
resistance-trained individuals than in older or untrained individ-
uals. Protein supplementation is sufficient at ~1.6 g/kg/day in
healthy adults during RET. Based on limited data we observed no
overtly apparent sex-based differences but acknowledge that far
less work has been done in women than men. This analysis shows
that dietary protein supplementation can be, if protein intake
is less than 1.6 g protein/kg/day, both sufficient and necessary
to optimise RET-induced changes in FFM and 1RM strength.
However, performance of RET alone is the much more potent
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Morton RW, etal. Br J Sports Med 2017;0:1–10. doi:10.1136/bjsports-2017-097608
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stimulus, accounting, at least according to this meta-analysis, for
a substantially greater portion of the variance in RET-induced
gains in muscle mass and strength.
Summarybox
Background
There is no consensus on the efficacy of protein
supplementation during prolonged resistance exercise
training(RET).
Novel findings
Dietary protein supplementation augments changes in
fat-free mass (FFM, (0.30 kg (0.09, 0.52), p=0.007) and
one-repetition-maximum strength (2.49 kg (0.64, 4.33),
p=0.01) during prolonged RET.
Dietary protein supplementation during RET is more
effective at increasing changes in FFM in resistance-trained
individuals (0.75 kg (0.09, 1.40), p=0.03) and less effective
in older individuals (−0.01 kg (−0.02,–0.00), p=0.02).
Protein supplementation beyond a total daily protein intake
of~1.6 g/kg/day during RET provided no further benefit on
gains in muscle mass or strength.
Acknowledgements SMP thanksthe Canada Research Chairs, Canadian Institutes
for Health Research, and the Natural Science and Engineering Research Council of
Canada for their support during the completion of this work.
Contributors RWM, BJS, MH, EH, AAA, MCD, JWK and SMP contributed to the
conception and design of the study. RWM, BJS, MH, EH, AAA, MCD, LB, JWK and
SMP contributed to the development of the search strategy. LB conducted the
systematic search. RWM, KTM and SRM completed the acquisition of data. RWM
and SMP performed the data analysis. All authors assisted with the interpretation.
RWM and SMP were the principal writers of the manuscript. All authors contributed
to the drafting and revision of the final article. All authors approved the final
submitted version of the manuscript.
Competing interests SMP has received grant support, travel expenses, and
honoraria for presentations from the US National Dairy Council. This agency has
supported trials reviewed in this analysis.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement All data are available in the submitted manuscript or as
supplementary files.
© Article author(s) (or their employer(s) unless otherwise stated in the text of the
article) 2017. All rights reserved. No commercial use is permitted unless otherwise
expressly granted.
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strength in healthy adults
training-induced gains in muscle mass and
supplementation on resistance
meta-regression of the effect of protein
A systematic review, meta-analysis and
Laura Banfield, James W Krieger and Stuart M Phillips
Menno Henselmans, Eric Helms, Alan A Aragon, Michaela C Devries,
Robert W Morton, Kevin T Murphy, Sean R McKellar, Brad J Schoenfeld,
published online July 11, 2017Br J Sports Med
http://bjsm.bmj.com/content/early/2017/07/11/bjsports-2017-097608
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... kg BM -1. day -1 have been reported for resistance trained men (Morton et al., 2018) and young male body builders (Bandegan et al., 2017), while a 40 g whey protein bolus was shown to induce a ~20% higher myofibrillar fractional synthetic rate than a 20 g bolus following whole body resistance exercise typical of elite rugby training (Macnaughton et al., 2016). ...
... kg . BM -1 as optimal for healthy resistance-trained adults (Morton et al., 2018). Considering the large exercise-and collision-induced muscle damage associated with professional rugby , it may also be appropriate to investigate higher intakes of ~2.5 g . ...
... kg BM -1. day -1 in healthy resistance trained men(Morton et al., 2018), ...
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Women are the largest consumers of dietary supplements. Dietary supplements can play a role in health and performance, particularly for women. Growing evidence and innovations support the unique physiological and nutrient timing needs for women. Despite the need for more nutrition and exercise-specific research in women, initial data and known physiological differences between sexes related to the brain, respiration, bone, and muscle support new product development and evidence-based education for active women regarding the use of dietary supplements. In this narrative review, we discuss hormonal and metabolic considerations with the potential to impact nutritional recommendations for active women. We propose four potential areas of opportunity for ingredients to help support the health and well-being of active women, including: (1) body composition, (2) energy/fatigue, (3) mental health, and (4) physical health.
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Background: A dietary protein intake higher than the Recommended Dietary Allowance during an energy deficit helps to preserve lean body mass (LBM), particularly when combined with exercise. Objective: The purpose of this study was to conduct a proof-of-principle trial to test whether manipulation of dietary protein intake during a marked energy deficit in addition to intense exercise training would affect changes in body composition. Design: We used a single-blind, randomized, parallel-group prospective trial. During a 4-wk period, we provided hypoenergetic (∼40% reduction compared with requirements) diets providing 33 ± 1 kcal/kg LBM to young men who were randomly assigned (n = 20/group) to consume either a lower-protein (1.2 g · kg(-1) · d(-1)) control diet (CON) or a higher-protein (2.4 g · kg(-1) · d(-1)) diet (PRO). All subjects performed resistance exercise training combined with high-intensity interval training for 6 d/wk. A 4-compartment model assessment of body composition was made pre- and postintervention. Results: As a result of the intervention, LBM increased (P < 0.05) in the PRO group (1.2 ± 1.0 kg) and to a greater extent (P < 0.05) compared with the CON group (0.1 ± 1.0 kg). The PRO group had a greater loss of fat mass than did the CON group (PRO: -4.8 ± 1.6 kg; CON: -3.5 ± 1.4kg; P < 0.05). All measures of exercise performance improved similarly in the PRO and CON groups as a result of the intervention with no effect of protein supplementation. Changes in serum cortisol during the intervention were associated with changes in body fat (r = 0.39, P = 0.01) and LBM (r = -0.34, P = 0.03). Conclusions: Our results showed that, during a marked energy deficit, consumption of a diet containing 2.4 g protein · kg(-1) · d(-1) was more effective than consumption of a diet containing 1.2 g protein · kg(-1) · d(-1) in promoting increases in LBM and losses of fat mass when combined with a high volume of resistance and anaerobic exercise. Changes in serum cortisol were associated with changes in body fat and LBM, but did not explain much variance in either measure. This trial was registered at clinicaltrials.gov as NCT01776359.
Article
Background: Muscle mass maintenance is largely regulated by basal muscle protein synthesis rates and the ability to increase muscle protein synthesis after protein ingestion. To our knowledge, no previous studies have evaluated the impact of habituation to either low protein intake (LOW PRO) or high protein intake (HIGH PRO) on the postprandial muscle protein synthetic response. Objective: We assessed the impact of LOW PRO compared with HIGH PRO on basal and postprandial muscle protein synthesis rates after the ingestion of 25 g whey protein. Design: Twenty-four healthy, older men [age: 62 ± 1 y; body mass index (in kg/m(2)): 25.9 ± 0.4 (mean ± SEM)] participated in a parallel-group randomized trial in which they adapted to either a LOW PRO diet (0.7 g · kg(-1) · d(-1); n = 12) or a HIGH PRO diet (1.5 g · kg(-1) · d(-1); n = 12) for 14 d. On day 15, participants received primed continuous l-[ring-(2)H5]-phenylalanine and l-[1-(13)C]-leucine infusions and ingested 25 g intrinsically l-[1-(13)C]-phenylalanine- and l-[1-(13)C]-leucine-labeled whey protein. Muscle biopsies and blood samples were collected to assess muscle protein synthesis rates as well as dietary protein digestion and absorption kinetics. Results: Plasma leucine concentrations and exogenous phenylalanine appearance rates increased after protein ingestion (P < 0.01) with no differences between treatments (P > 0.05). Plasma exogenous phenylalanine availability over the 5-h postprandial period was greater after LOW PRO than after HIGH PRO (61% ± 1% compared with 56% ± 2%, respectively; P < 0.05). Muscle protein synthesis rates increased from 0.031% ± 0.004% compared with 0.039% ± 0.007%/h in the fasted state to 0.062% ± 0.005% compared with 0.057% ± 0.005%/h in the postprandial state after LOW PRO compared with HIGH PRO, respectively (P < 0.01), with no differences between treatments (P = 0.25). Conclusion: Habituation to LOW PRO (0.7 g · kg(-1) · d(-1)) compared with HIGH PRO (1.5 g · kg(-1) · d(-1)) augments the postprandial availability of dietary protein-derived amino acids in the circulation and does not lower basal muscle protein synthesis rates or increase postprandial muscle protein synthesis rates after ingestion of 25 g protein in older men. This trial was registered at clinicaltrials.gov as NCT01986842.
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Background: The current estimated average requirement (EAR) and RDA for protein of 0.66 and 0.8 g ⋅ kg(-1) ⋅ d(-1), respectively, for adults, including older men, are based on nitrogen balance data analyzed by monolinear regression. Recent studies in young men and older women that used the indicator amino acid oxidation (IAAO) technique suggest that those values may be too low. This observation is supported by 2-phase linear crossover analysis of the nitrogen balance data. Objective: The main objective of this study was to determine the protein requirement for older men by using the IAAO technique. Methods: Six men aged >65 y were studied; each individual was tested 7 times with protein intakes ranging from 0.2 to 2.0 g ⋅ kg(-1) ⋅ d(-1) in random order for a total of 42 studies. The diets provided energy at 1.5 times the resting energy expenditure and were isocaloric. Protein was consumed hourly for 8 h as an amino acid mixture with the composition of egg protein with l-[1-(13)C]phenylalanine as the indicator amino acid. The group mean protein requirement was determined by applying a mixed-effects change-point regression analysis to F(13)CO2 (label tracer oxidation in breath (13)CO2), which identified a breakpoint in F(13)CO2 in response to graded intakes of protein. Results: The estimated protein requirement and RDA for older men were 0.94 and 1.24 g ⋅ kg(-1) ⋅ d(-1), respectively, which are not different from values we published using the same method in young men and older women. Conclusions: The current intake recommendations for older adults for dietary protein of 0.66 g ⋅ kg(-1) ⋅ d(-1) for the EAR and 0.8 g ⋅ kg(-1) ⋅ d(-1) for the RDA appear to be underestimated by ∼30%. Future longer-term studies should be conducted to validate these results. This trial was registered at clinicaltrials.gov as NCT01948492.
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Hyperaminoacidemia following protein ingestion enhances the anabolic effect of resistance-type exercise by increasing the stimulation of muscle protein synthesis and attenuating the exercise-mediated increase in muscle protein breakdown rates. Although factors such as the source of protein ingested and the timing of intake relative to exercise can impact post-exercise muscle protein synthesis rates, the amount of protein ingested after exercise appears to be the key nutritional factor dictating the magnitude of the muscle protein synthetic response during post-exercise recovery. In younger adults, muscle protein synthesis rates after resistance-type exercise respond in a dose-dependent manner to ingested protein and are maximally stimulated following ingestion of ~20 g of protein. In contrast to younger adults, older adults are less sensitive to smaller doses of ingested protein (less than ~20 g) after exercise, as evidenced by an attenuated increase in muscle protein synthesis rates during post-exercise recovery. However, older muscle appears to retain the capacity to display a robust stimulation of muscle protein synthesis in response to the ingestion of greater doses of protein (~40 g), and such an amount may be required for older adults to achieve a robust stimulation of muscle protein synthesis during post-exercise recovery. The aim of this article is to discuss the current state of evidence regarding the dose-dependent relationship between dietary protein ingestion and changes in skeletal muscle protein synthesis during recovery from resistance-type exercise in older adults. We provide recommendations on the amount of protein that may be required to maximize skeletal muscle reconditioning in response to resistance-type exercise in older adults.