Received: 6 March 2024

| Revised: 23 May 2024 | Accepted: 23 May 2024

DOI: 10.14814/phy2.16100

ORIGINAL ARTICLE

Effect of resistance training and chicken meat on muscle
strength and mass and the gut microbiome of older women:
A randomized controlled trial
Masataka Uchida1| Jonguk Park2 | Shumpei Fujie1 | Koji Hosomi3 |Naoki Horii1 | Kohei Watanabe4 | Kiyoshi Sanada1 | Yasushi Shinohara1 |Kenji Mizuguchi2,5 | Jun Kunisawa3 | Motoyuki Iemitsu1 | Motohiko Miyachi61Faculty of Sport and Health Science, Ritsumeikan University, Kusatsu, Japan

2

Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan

3

Laboratory of Vaccine Materials and Laboratory of Gut Environmental System, Microbial Research Center for Health and Medicine, National
Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan

4

Laboratory of Neuromuscular Biomechanics, School of Health and Sport Sciences, Chukyo University, Toyota, Japan

5

Institute for Protein Research, Osaka University, Osaka, Japan

6

Faculty of Sport Sciences, Waseda University, Saitama, Japan

Correspondence
Motohiko Miyachi, Faculty of Sport
Sciences, Waseda University, 2-­579-­15
Mikajima, Tokorozawa, Saitama, Japan.
Email: miyachim@waseda.jp
Funding information
Ministry of Education, Culture, Sports,
Science and Technology (MEXT),
Grant/Award Number: #22H03487,#16K00944 and #20H04117; Ministry of
Health, Labour and Welfare (MHLW),
Grant/Award Number: #201709002B;
Japan Agency for Medical Researchand Development (AMED), Grant/Award Number: 22ae0121035s0102
and 22ae0121042h0002; the Ministryof Health and Welfare of Japan andPublic /Private R&D Investment
Strategic Expansion PrograM, Grant/Award Number: 20AC5004; Programsfor Bridging the gap between R&D
and the IDeal society (society 5.0)
and Generating Economic and social
value: BRiDGE, Grant/Award Number:
J. Kunisawa; Ito Foundation, Grant/Award Number: #134

Abstract
This study investigated the effects of white meat, such as chicken, intake combined with resistance training on muscle mass and strength in the elderly women,
and whether the underlying mechanism involves changes in the gut microbiota.
Ninety-­three volunteers (age 59–79 years) were randomly allocated to sedentary
control with placebo (Sed + PL) or chicken meat (Sed + HP) and resistance training with placebo (RT + PL) or chicken meat (RT + HP). Resistance training sessions were performed 3 d/week for 12 weeks using leg extensions and curls. Boiled
chicken meat (110 g, containing 22.5 g protein) was ingested 3 d/week for 12 weeks.
Maximal muscle strength and whole-­body lean mass increased significantly in the
RT + PL group compared to the Sed + HP group, and the RT + HP group showed asignificantly greater increase than the Sed + HP and RT + PL groups. Additionally,
the gut microbiota composition did not change before or after the interventions in
any of the four groups. Moreover, the individual comparison of gut bacteria using
false discovery rate-­based statistical analysis showed no alterations before or after
the interventions in the four groups. Resistance training combined with chicken
meat intake may effective have increased muscle mass and strength without drastically modifying the gut microbiota composition in elderly women.
KEYWORDS

aging, chronic resistance exercise, gut bacteria, muscle mass, muscle strength
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2024 The Author(s). Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society.
Physiological Reports. 2024;12:e16100.	﻿	https://doi.org/10.14814/phy2.16100

wileyonlinelibrary.com/journal/phy2| 1 of 14

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I N T RO DU CT ION

Muscle mass and strength decline with age. Many aging-­
associated factors contribute to decreased muscle massand strength, including imbalance in muscle protein synthesis and degradation, low physical activity, low habitual
protein intake, and attenuation of hormone secretion.
Aging-­induced loss of muscle mass and strength leads tothe developing sarcopenia (Rosenberg, 1997). Sarcopenia
is associated with physical disabilities (Baumgartner
et al., 1998) and an increased risk of falls, fractures (Walshet al., 2006), and mortality (Sanada et al., 2018), and it may
lead to diabetes (Karakelides & Nair, 2005), cardiovascular diseases (Sanada et al., 2010), and dementia (Sanada
et al., 2010). Further studies are necessary to prevent age-­
related loss of muscle mass and strength. In postmenopausal women, loss of skeletal muscle mass and functionis accelerated because of decreasing sex steroid hormone
secretion (Messier et al., 2011). Moreover, the hypertrophic response to resistance training decreases in elderly
women compared with that in elderly men (Bamman
et al., 2003).
Habitual resistance exercise is an effective method forpreventing age-­related loss of muscle mass and strength.
Resistance exercise promotes muscle protein synthesis, resulting in augmentation of muscle mass and strength (Satoet al., 2014; Watanabe, Tanimoto, et al., 2015). Therefore,
adequate daily protein intake is necessary to increase
muscle mass via resistance exercise-­induced muscle protein synthesis. The recommended dietary allowance forprotein intake in the elderly is 1.2 g/kg body weight (BW)/day (Morton et al., 2018). Furthermore, a recent study
on adults showed that an increase in total protein intake(1.6 g/kg BW/day) combined with resistance training
may promote increased lean body mass (LBM) (Morton
et al., 2018). Meat, fish, soybeans, and milk are protein-­rich sources. Previous studies have associated red meat
consumption with increased mortality due to cardiovascular disease (Schmid, 2011), cancer (Bouvard et al., 2015),
and risk of type II diabetes (Pan et al., 2011). The intakeof white meat, such as chicken, instead of red meat, hasbeen shown to be effective (Bernstein et al., 2010; Willett& Stampfer, 2013). However, the effects of chicken meatintake combined with resistance training on muscle massand strength in the elderly and the underlying mechanisms are unclear.
Several studies have revealed the effects of the gut
microbiota on muscle mass and strength (Turnbaugh
et al., 2006; Yang et al., 2017). In a cross-­
sectional
study on healthy adults and elderly with sarcopenia,
the α-­
diversity of the gut microbiota reduced withloss of muscle mass and strength (Kang et al., 2021).
Additionally, a study reported a positive correlation

UCHIDA et al.

between the Ruminococcus genus and grip strength(Kang et al., 2021). Hence, the aging-­induced loss ofmuscle mass and strength may be related to changes inthe diversity and/or composition of the gut microbiota(Kang et al., 2021). In a recent study, 12-­week resistancetraining in rats altered the β-­diversity and compositionof the gut microbiota (Castro et al., 2021). Furthermore,a study showed that chronic meat consumption inyoung adults increased the diversity and altered the gut
microbiota composition (Shi et al., 2021). However, theeffects of the gut microbiota on increased muscle massand strength in the elderly due to protein intake, suchas white meat, combined with resistance training havenot been studied, and elucidating these effects may help
propose an effective approach to improve muscle massand strength in the elderly.
Therefore, in this study, we aimed to investigate theeffects of resistance training and chicken meat intake,which increase muscle mass and strength, and their combination on the gut microbiota in elderly women. We hypothesized that the combination of resistance training
and chicken meat intake in elderly women alters the gut
microbiota, which further affects the increased muscle
mass and strength. To test this hypothesis, we conducteda randomized controlled intervention trial to examine
the effects of 12 weeks of high-­intensity resistance exercise combined with chicken meat intake on muscle mass,strength, gut microbiota, and the association among thesechanging parameters.

2
2.1

|

MATERIALS AND METHO D S| Ethical approval

All the participants voluntarily provided written informed consent before participating in the study.
This study was approved by the Ethics Committee of
Ritsumeikan University (BKC-­2018-­060) and was conducted in accordance with the Declaration of Helsinki.
The study was registered in the University Hospital
Medical Information Network Clinical Trials Registry(UMIN000038253).

2.2| ParticipantsCommunity-­dwelling elderly women were recruited fromJapan. In total, 103 participants were recruited throughadvertisements published in the local press and posters
at community health and recreation centers. Of the 103
participants, 10 individuals who were performing habitual exercise had a history of cardiovascular disease, severe

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2 of 14

liver disease, chronic renal failure, gynecological disease,joint disorder, or mental disorder; therefore, they were excluded from the study. Lastly, 93 healthy older sedentary
elderly women were included in this study.

2.3| Study design

After obtaining the informed consent, group allocation
was performed using computer-­generated random numbers. Through a randomized controlled intervention trial,
the participants were randomly divided into the following four groups: the sedentary control and placebo intake (Sed + PL) group, the sedentary control and chicken
meat intake (Sed + HP) group, the resistance training andplacebo intake (RT + PL) group, and the resistance training and chicken meat intake (RT + HP) group. The researchers who assessed each parameter were blinded to
the group allocations. Body composition, muscle mass,
muscle strength, physical activity, energy intake, and gut
microbiota were assessed before and after the 12-­week resistance training with chicken meat intake. The fecal samples and fasting blood samples were collected at least 48 h
after the last resistance exercise session to avoid the influence of acute exercise effects. All the participants were
instructed not to perform strenuous exercise or drink alcohol for at least 24 h before fecal sampling. All participants
were instructed not to eat or drink fluids, except water,
for at least 10 h before reporting to the laboratory. Serumand plasma samples were centrifuged at 1500 × g, 15 min,at 4°C immediately after collection and stored at −80°C.
The experimental room temperature was maintained at
24 ± 1°C during measurements. All the participants were
instructed to maintain their physical activity and diet during the interventions, excluding chicken meat and/or placebo intake and/or resistance training.

2.4| Chicken meat and/or placebo intake

In the Sed + HP and RT + HP groups, steamed chicken
breast meat containing an average calorie content of
164 kcal and an average protein content of 22.5 g per 110 g
serving was ingested thrice a week for 12 weeks. The
Sed + PL and RT + PL groups ingested a protein-­free carbohydrate control food diet with a similar energy content
as the high-­protein diet. The RT + PL and RT + HP groups
ingested their respective servings within 30 min of resistance exercise, whereas the Sed + HP and Sed + PL groups
took a placebo between meals. If the rate of participationin the 36 chicken meat intakes was ≤90%, the individual
was excluded from statistical analysis because of “low adherence to nutrition.”

   

2.5

| 3 of 14| High-­intensity resistance training

The RT + PL and RT + HP groups performed three setsof 10 repetitions of leg extensions and leg curls at an intensity of 70% of the one-­repetition maximum (1RM) 3 d/week on nonconsecutive days for 12 weeks (Watanabe,Sato, et al., 2015). The rest period between the sets was
2 min. Before the resistance exercise, a 3-­min walk on atreadmill and a 5-­min stretch of the lower limbs were performed for warm-­up, and a 5-­min stretch was performed
for cooldown after the resistance exercise. In the 1RM
measurement for setting the training intensity, the weightload of each participant was gradually increased, and theexercises were repeated until the participant was unable
to lift the weight, at which point the 1RM was measured.
The 1RM was assessed once every 2 weeks during theintervention period. If the rate of participation in the 36
resistance training sessions was ≤90%, the individual was
excluded from the statistical analysis because of “low adherence to training.”

2.6
2.6.1

| Measurements| Anthropometric measures

BW was measured using a body composition analyzer(RD-­801, TANITA, Tokyo, Japan). Height was measured
by height meter (WB-­510, TANITA, Tokyo, Japan). Those
measured to the nearest 0.1 kg and 0.1 cm, respectively.
Body mass index (BMI; kg/m2) was calculated using BW
and height.

2.6.2 | Body composition, muscle
mass, and strength
Body composition was determined through dual-­energyX-­
ray absorptiometry (Lunar Prodigy, GE Healthcare,
Little Chalfont, UK). Fat mass and % body fat of the total
body mass, and the LBM of arms, trunk, and legs were
measured as previously described (Watanabe, Sato,et al., 2015).
The maximum knee extension muscle strength wasevaluated as an index of muscle strength. The participants
were in a sitting position with their right leg fixed to acustom-­msade dynamometer (Takei Scientific Instruments
Co., Ltd., Niigata, Japan) equipped with a force transducer(LU-­
100KSE; Kyowa Electronic Instruments, Tokyo,
Japan) and their hip and knee joints fixed at 90°, as previously described (Watanabe et al., 2020). They were instructed to gradually increase the knee extension force
from the baseline to the maximum for over 2–3 s and then

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UCHIDA et al.

|   maintain it at the maximum force for 2 s. The maximumof the two measurements was used as the representativevalue. The participants performed triple-­leg extension
with submaximal contraction at a low workload 5 min before the measurement for warm-­up and familiarization.

2.6.3 | Physical activity, energy intake, andnutritional status
Physical activity was evaluated using a triaxial accelerometer (SHINE2; MISFIT, Kyoto, Japan). The accelerometer was attached to the left wrist of each participant.
Measurements were recorded for 2 weeks before the intervention and before the end of the intervention, and theaverage amount of physical activity for consecutive 7 days
was used as the amount of physical activity/day. Dietaryenergy intake and nutritional status were assessed using
a validated brief questionnaire containing 73 items basedon a self-­administered diet history before the intervention
and before the end of the intervention, as previously described (Kobayashi et al., 2011).

2.6.4| Gut microbiota

The fecal samples were collected using the guanidine
thiocyanate solution in collection tubes (TechnoSuruga
Laboratory Co., Ltd, Shizuoka, Japan) for analyzing the
fecal microbiota (Hosomi et al., 2017). After collection,
the samples were immediately stored at 4°C until DNA
extraction. The DNA samples were subjected to 16S ribosomal RNA (rRNA) metagenomic analysis using next-­
generation sequencing (NGS).
According to previous study, we extracted fecal DNA
using the bead-­beating method and an automatic nucleic
acid extraction system (Gene Prep Star PI-­80X; Kurabo
Industries, Ltd., Osaka, Japan) and subjected to NGS of
the 16S rRNA gene amplicon, as previously described(Hosomi et al., 2017). Sequencing of the 16S rRNA gene
amplicon in fecal DNA was based on previous study(Hosomi et al., 2017). The V3–V4 regions of the bacterial
16S rRNA were amplified using the following primers:
forward, 5′-­ T​CGT​CGG​CAG​CGT​CAG​ATG​TGT​ATA​AGC​
GAC​AGC​CTA​CGG​GNGGCWGCAG-­3′ and reverse, 5′-­GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-­3′ (Klindworth et al., 2013).
A DNA library was prepared using the Nextera kit Set
A (Illumina, San Diego, California, USA, FC-­131-­2001).
Barcoded amplicons were paired-­end sequenced using
the MiSeq system with MiSeq Reagent Kit version 3(600 cycles) chemistry (Illumina, San Diego, California,
USA, MS-­102-­3003). The sequencing data were analyzed

UCHIDA et al.

using the Quantitative Insights into Microbial Ecology(QIIME) software package (Caporaso et al., 2010) andQIIME Analysis Automating Script (Auto-­q) (10.5281/
zenodo.1439555), as previously described (Mohsen
et al., 2019). Primer sequences for the paired-­end sequencing reads were trimmed using Cutadapt Ver. 1.18 with thedefault settings. Open-­reference operational taxonomic
unit (OTU) selections and taxonomy classifications were
performed based on sequence similarity (>97%) using the
UCLUST software (Edgar, 2010) and SILVA v128 reference
sequence (Quast et al., 2013).

2.6.5| Blood sampling

Fasting total cholesterol (Total-­Cho), high-­density lipoprotein cholesterol (HDL), triglycerides (TG), and glucose (FBG) were measured using standard enzymatic
techniques based on previous studies (Watanabe, Sato,et al., 2015).

2.6.6| Statistical analyses

All values were expressed as mean ± standard deviation(SD). Statistical evaluations for each parameter were performed using a two-­way analysis of variance (ANOVA) for
repeated measures (time × group). One-­way ANOVA wasused to compare the differences in changes from baseline
to 12 weeks among the four groups. A Fisher's post hoccomparison test was performed to correct for multiple
comparisons when the analyses revealed significant differences. The p-­value of <0.05 was defined as statisticallysignificant. All statistical analyses were performed using
the StatView software (ver. 5.0; SAS Institute, Minato-­ku,
Tokyo, Japan).
The α-­diversity indices were calculated using the estimate_richness function in the R package “phyloseq”(McMurdie & Holmes, 2013). The β-­diversity index was
calculated according to the Bray–Curtis distance of thegenus-­
level data and was generated using the vegdist
function in the R package “vegan” (Oksanen et al., 2008).
Principal coordinate analysis (PCoA) was performed using
the dudi.pco function in the R package “ade4” (Dray &Dufour, 2007), and the PCoA figure was created by using
the R package “ggplot2.” The dominant bacteria from thephylum to genus level were defined as a mean bacterial
composition distribution of at least 0.05%. The Kruskal–
Wallis test was performed to compare the relative abundance of taxa before and after the intervention in eachgroup. Wilcoxon rank sum test (wilcox. test function of the“stats” R package) was performed to compare the changes
before and after intervention in the RT + PL, Sed + HP,

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4 of 14   

and RT + HP groups with those in the Sed + PL group. q
value < 0.05 was defined as statistically significant. The p-­values of the dominant bacteria were adjusted using thefalse discovery rate (FDR) method. Herein, the statisticalanalyses of the gut microbiota analyses were performed
using R (version 3.5.0).

3|

R E S U LTS

The participant flowchart is presented in Figure 1. A totalof 103 elderly women were screened and 93 participants
were enrolled in this study. Of these, 12 participants
were excluded from the study during follow-­up, and 81
completed the outcome measurements before and after
the intervention (n = 21, Sed + PL group [91.3%]; n = 22,
Sed + HP group [95.6%]; n = 20, RT + PL group [87.0%];
and n = 18, RT + HP group [78.3%]) (Figure 1).

3.1| Before intervention

No significant differences in age, height, BW, BMI, percentage of body fat, muscle mass, muscle strength, Total-­Cho, HDL, TG, FBG, or daily physical activity wereobserved among the four groups before the intervention(Table 1). However, the lower limb and whole LBM of the

FIGURE 1| 5 of 14

participants in the RT + PL group were significantly lower
than those in the RT+ HP group.
Furthermore, no significant differences in daily intake
of total energy intake or protein, fat, and carbohydrate intake, and total calorie consumption were observed among
the four groups before the intervention (Table 2).

3.2 | Participants characteristics,
muscle mass, muscle strength, andblood parameters before and after theintervention
No significant differences in BW, BMI, percentage of bodyfat, muscle mass, muscle strength, Total-­Cho, HDL, TG,
FBG, or daily physical activity were observed among thefour groups before and after the intervention (Table 1).
The change in LBM of the lower limb before and after
the intervention was significantly higher in the RT + HP
group than in the Sed + PL groups (p < 0.05, Table 3). Thechange in whole LBM was significantly higher in the
Sed + HP group compared with that in the Sed + PL groupand the RT + HP group compared with that in the RT + PL
group (p < 0.05, Table 3). Additionally, the change in muscle strength was significantly higher in the RT + PL and
RT + HP groups compared with that in the Sed + PL and
Sed + HP groups (p < 0.05, Table 3).

Flowchart showing the distribution of participants through the intervention.

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UCHIDA et al.

Abbreviations: BMI, body mass index; FBG, fasting blood glucose; HDL, high-­density lipoprotein cholesterol; LBM, lean body mass; MVC, maximum voluntary contraction; Total-­Cho, total cholesterol; TG, triglycerides.

1818.3 ± 200.3
1800.6 ± 206.4
1890.3 ± 276.2

Note: The values are expressed as mean ± SD. Sed + PL: sedentary control with placebo, Sed + HP: sedentary control with chicken meat, RT + PL: resistance training with placebo, RT + HP: resistance training withchicken meat.

95.5 ± 24.6
102.7 ± 15.1
98.9 ± 9.6

1880.7 ± 246.1
1890.3 ± 207.4

108.0 ± 28.3

1821.4 ± 243.8

FBG (mg/dL)

Total calorie consumption (kcal/day)

98.3 ± 13.0
97.5 ± 12.1
99.3 ± 9.9

1827.8 ± 188.5

101.5 ± 18.0

1758.4 ± 269.6

102.6 ± 83.9
91.3 ± 49.6
110.5 ± 51.1
103.0 ± 40.2
TG (mg/dL)

105.9 ± 52.1

101.6 ± 36.3
99.5 ± 58.7
93.0 ± 39.6

237.0 ± 29.2

84. 1 ± 13.7
86.5 ± 13.0
78.4 ± 20.4
80.0 ± 23.8
HDL (mg/dL)

75.0 ± 19.2

83.3 ± 19.2
79.0 ± 22.5

251.4 ± 34.1
241.7 ± 34.4

82.4 ± 19.8

166.0 ± 58.3
90.4 ± 39.7
168.9 ± 70.2

242.9 ± 36.3
232.3 ± 38.3
244.4 ± 49.1
Total-­Cho (mg/dL)

228.0 ± 36.7

99.5 ± 52.3
MVC (Nm)

127.9 ± 56.0

91.7 ± 53.1
127.5 ± 60.7

252.2 ± 39.6

33.1 ± 3.0

102.5 ± 68.3

15.5 ± 1.6

32.6 ± 2.9
35.9 ± 3.2
34.1 ± 3.9
Whole LBM (kg)33.9 ± 4.0

35.9 ± 3.2
34.9 ± 3.8

15.1 ± 1.6
16.9 ± 1.6

34.6 ± 3.5

14.4 ± 1.4
14.4 ± 1.3
15.6 ± 1.5

16.7 ± 1.5
16.2 ± 2.1

15.1 ± 1.9
15.2 ± 1.9

15.7 ± 2.0

Upper-­limb LBM (kg)

Lower-­limb LBM (kg)15.7 ± 2.1

15.8 ± 1.6
15.5 ± 1.7
15.4 ± 1.6

29.7 ± 6.2

16.0 ± 1.9

21.2 ± 2.8

30.1 ± 6.9
32.1 ± 8.0
32.8 ± 8.0
Body fat (%)

33.4 ± 8.0

32.1 ± 8.2
31.9 ± 7.5

21.1 ± 3.0
22.8 ± 3.1

32.4 ± 7.4

49.9 ± 7.2
49.7 ± 7.5
56.7 ± 9.5

22.6 ± 2.9

23.0 ± 3.2
22.6 ± 3.6
22.6 ± 3.6

Body weight (kg)54.3 ± 9.0

22.6 ± 3.0

54.3 ± 9.5

57.0 ± 9.5
54.6 ± 8.9
54.7 ± 8.7

After
BeforeBeforeAfter
BeforeBeforeSed + PL (N = 21)

After

RT + PL (N = 20)
Sed + HP (N = 22)

After

UCHIDA et al.

BMI (kg/m2)

TABLE 1

Participants characteristics, muscle mass, strength, blood parameters, and total calorie consumption before and after interventions.

RT + HP (N = 18)|   

3.3 | Energy and macronutrient intakes
before and after the intervention
No significant differences were observed in the daily intake and changes in the daily intake of total energy, protein, fat, and carbohydrate, and total calorie consumption
among the four groups before and after the intervention(Tables 2 and 4, respectively).

3.4 | Gut microbiota composition
before and after the intervention
To confirm the bias in the gut microbiota composition
before the intervention and the effects of each intervention, PCoA based on Bray–Curtis distance metrics was
performed, and no significant differences in the gut
microbiota composition were found before the intervention among the four groups (r2 = 0.0306, p = 0.6843,
Figure 2).
There was no significant interaction between thechanges in the gut microbiota composition before andafter the intervention among the four groups. However,significant differences were observed in the gut microbiota composition between each intervention (r2 = 0.0334,
p = 0.0009, Figure 3).
PCoA analysis of the gut microbiota composition ofeach group revealed no significant difference regarding theeffect of the intervention (Sed + PL: r2 = 0.0182, p = 0.19;
Sed + HP: r2 = 0.0091, p = 0.37; RT + PL: r2 = 0.0092,
p = 0.82; and RT + HP: r2 = 0.0328, p = 0.27; Figure 4).

3.5 | Change in the gut bacteria
before and after the intervention
Herein, we have comparatively analyzed the effects of intervention on the gut microbiota before, during, and after
the intervention. The α-­diversity indices of the observed
OTUs, Chao1, and Fisher in four groups were significantly higher before the intervention than those after theintervention, and those significant differences were also
observed in FDR analysis (Table S1). Furthermore, significant differences in multiple gut bacteria were observed
before and after the intervention among the four groups(Table S1). The changes before and after the intervention in the Sed + HP, RT + PL, and RT + HP groups werecomparatively analyzed, using the gut bacteria with anabundance ratio of >1%, with the change in the Sed + PL
group but no significant difference was observed in theα-­diversity index (Table S2). However, FDR analysis revealed no significant differences in multiple gut bacteria
among the four groups before and after the intervention

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6 of 14TABLE 2   | 7 of 14

Energy intake and macronutrient contributions before and after interventions.

Dietary energy intake(kcal/day)

Sed + PL (N = 21)

Sed + HP (N = 22)

RT + PL (N = 20)

RT + HP (N = 18)

BeforeAfter

BeforeAfter

BeforeAfter

BeforeAfter1798.6 ± 689.9

1699.9 ± 488.0

1810.3 ± 574.6

1845.5 ± 508.7

1720.7 ± 473.6

1819.4 ± 492.4

1664.3 ± 442.0

1859.9 ± 543.4

Protein intake (g/day)

79.5 ± 31.0

77.1 ± 32.7

77.5 ± 24.1

79.2 ± 24.0

76.8 ± 33.0

78.9 ± 24.7

75.7 ± 24.0

84.3 ± 29.3

Fat intake (g/day)

62.6 ± 26.7

64.0 ± 19.6

56.0 ± 17.1

65.3 ± 20.3

53.4 ± 20.1

60.1 ± 18.3

54.9 ± 17.5

66.8 ± 19.9

Carbohydrate intake(g/day)222.9 ± 102.1

195.5 ± 57.5

230.7 ± 89.3

222.3 ± 75.1

221.1 ± 69.1

226.2 ± 73.8

206.7 ± 71.6

219.3 ± 80.6

Note: The values are expressed as mean ± SD. Sed + PL: sedentary control with placebo, Sed + HP: sedentary control with chicken meat, RT + PL: resistancetraining with placebo, RT + HP: resistance training with chicken meat.

T A B L E 3 Changes in participantscharacteristics, muscle mass, muscle
strength, blood parameters, and total
calorie consumption before and after
interventions.

Body weight (kg)2

Sed + PL
(N = 21)

Sed + HP
(N = 22)

RT + PL(N = 20)

RT + HP(N = 18)−0.0 ± 1.5−0.1 ± 1.4−0.2 ± 1.4

0.2 ± 1.0

BMI (kg/m )−0.0 ± 0.7−0.1 ± 0.5−0.1 ± 0.6

0.1 ± 0.4

Body fat (%)

0.6 ± 1.2

−0.5 ± 1.5*

−0.1 ± 1.2−0.3 ± 1.5*

Upper-­limb LBM (g)−109.8 ± 288.6 97.7 ± 437.7−188.9 ± 584.9 11.2 ± 457.0

Lower-­limb LBM (g)−13.4 ± 403.2

141.3 ± 446.2

Whole LBM (g)−162.0 ± 507.5 322.5 ± 780.7* −52.1 ± 828.8

MVC (Nm)

28.4 ± 32.6

25.0 ± 39.2

77.2 ± 45.8*,**75.6 ± 54.9*,**Total-­Cho (mg/dL)−16.5 ± 26.6

−19.9 ± 18.6

−1.3 ± 33.3

−14.4 ± 23.0

HDL (mg/dL)−4.9 ± 13.1

−3.4 ± 8.0−4.9 ± 10.3

−2.4 ± 9.8

TG (mg/dL)

2.8 ± 34.9

6.5 ± 33.7

8.9 ± 46.9

11.3 ± 49.6

FBG (mg/dL)−6.5 ± 15.8

−1.8 ± 8.8

0.6 ± 7.2

−7.2 ± 19.4

Total calorie−23.5 ± 68.8
consumption (kcal/day)−6.9 ± 68.5

12.1 ± 65.6

−7.9 ± 50.4

218.3 ± 557.4

396.6 ± 518.0*
443.9 ± 710.5*,***

Note: The values are expressed as mean ± SD. Sed + PL: sedentary control with placebo, Sed + HP:
sedentary control with chicken meat, RT + PL: resistance training with placebo, RT + HP: resistancetraining with chicken meat.
Abbreviation: BMI, body mass index; FBG, fasting blood glucose; HDL, high-­density lipoproteincholesterol; LBM, lean body mass; MVC, maximum voluntary contraction; Total-­Cho, total cholesterol;
TG, triglycerides.
*p < 0.05 versus Sed + PL. **p < 0.05 versus Sed + HP. ***p < 0.05 versus RT + PL.

(Table S2). Additionally, statistical analysis based on thep value showed that the change in the Eubacterium hallii
group genera composition before and after the intervention was lower in the RT + HP group compared with thatin the Sed + PL group (Figure S1A).

3.6 | Correlation between the gut
bacteria and lower muscle mass
Change in Eubacterium hallii group genera composition before and after the intervention was significantly
negatively correlated with the changes in the low LBM(Figure S1B, r = −0.323, p = 0.003).

4|

DISC USSION

This study aimed to investigate the effects of 12 weeks
of high-­
intensity resistance exercise combined withchicken meat intake on muscle mass, muscle strength,
and gut microbiota in elderly women. Herein, high-­
intensity resistance training intervention alone increased maximal muscle strength, whereas chronic
chicken meat intake alone increased whole LBM.
Interestingly, the combination of resistance training
and chicken meat intake further increased maximal
muscle strength and whole condition LBM comparedwith those by chicken meat intake or high-­intensity resistance exercise alone, suggesting the improved effects

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UCHIDA et al.

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UCHIDA et al.

Sed + PL
(N = 21)

Sed + HP
(N = 22)

RT + PL(N = 20)

RT + HP(N = 18)

Dietary energy intake (kcal/day)−98.7 ± 600.6 35.1 ± 406.2 98.6 ± 361.5

195.6 ± 325.5

Protein intake (g/day)−2.5 ± 36.1

8.6 ± 17.9

1.8 ± 18.7

2.1 ± 25.0

Fat intake (g/day)

1.4 ± 24.7

9.3 ± 13.2

6.7 ± 16.0

11.8 ± 16.7

Carbohydrate intake (g/day)−27.4 ± 75.7

−8.4 ± 70.7

5.2 ± 55.7

12.6 ± 41.5

T A B L E 4 The changes in energy
intake and macronutrient contributions
before and after interventions.

Note: The values are expressed as mean ± SD. Sed + PL: sedentary control with placebo, Sed + HP:
sedentary control with chicken meat, RT + PL: resistance training with placebo, RT + HP: resistancetraining with chicken meat.

F I G U R E 2 Principal coordinate analysis plots of weighted UniFrac distance metrics obtained from sequencing the 16S ribosomal RNA
gene among the four groups at pre intervention. Axes represent the percentage of data explained by each coordinate dimension.

of the combined intervention on muscle mass andstrength. Additionally, the gut microbiota composition
did not change before and after these interventions in
any of the four groups, and no significant differenceswere observed among the four groups. Moreover, individual examination of the gut bacteria using FDR-­based
statistical analysis revealed no change before and after
the interventions in the four groups. Thus, these results
suggest that chronic chicken meat intake may havea combined effect with resistance training on muscle
mass and strength, without drastically altering the gut
microbiota composition in elderly women.

Red and white meats are rich in protein and very important sources of protein intake (Schmid, 2011). However,
several epidemiological studies have shown that habitualmeat intake, especially that of red meat, such as beef andpork, increases the incidence of cardiovascular diseaseand colorectal cancer (Bastide et al., 2015; Bujnowski
et al., 2011; Pham et al., 2014). Furthermore, red meat,such as pork, intake may lead to increased microbial protein fermentation in the colon and associated changes in
the gut microbiota (Zhang et al., 2020). Reportedly, redmeat intake promotes dysbiosis of the gut microbiota andthe production of metabolites that exert negative effects

2051817x, 2024, 12, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.14814/phy2.16100 by Cochrane Japan, Wiley Online Library on [18/06/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

8 of 14   | 9 of 14

F I G U R E 3 Principal coordinate analysis plots of weighted UniFrac distance metrics obtained from sequencing the 16S ribosomal RNA
gene in all groups before and after the intervention. Axes represent the percentage of data explained by each coordinate dimension.

via the oxidation of trimethylamine (TMAO) metabolized
by gut bacteria, indicating that high red meat intake negatively affects cardiovascular health (Buffa et al., 2022). In
contrast, white meat (such as poultry) does not increase
the metabolites to TMAO (Wang et al., 2019). Furthermore,
the previous study showed that the differences in the gut
microbiota composition were found among intake ofchicken meat, red meat (beef and pork), and soy protein,
and the protein intake from chicken meat increased thecomposition of beneficial Lactobacillus as compared withthat from a soy protein diet and red meat diet in rats (Zhu
et al., 2015). Moreover, the inflammatory response to thehost was declined by the chicken meat intake as compared to soy protein intake in rats (Zhu et al., 2015). Ina cross-­sectional study, a chicken-­fed population showed
increased α-­
diversity in the gut microbiota comparedwith that in a pork-­fed population (Shi et al., 2021). In
this present study, the β-­diversity of the gut microbiota
did not change dramatically by chicken intake alone or in
combination with resistance training. Moreover, no difference in the change in the β-­diversity of the gut microbiota

was found between each intervention in this present
study. Additionally, FDR analysis revealed no significant
changes in gut bacteria. Therefore, chronic chicken meatintake and resistance training did not drastically modify
the gut microbiota composition in elderly women, and habitual intake of chicken meat may not negatively affect the
gut microbiota compared with that of red meat.
A meta-­analysis of the combined effects of resistance
exercise and protein intake in the elderly showed that the
combined use of high-­protein diets was more effective inincreasing LBM and muscle strength than resistance exercise alone (Liao et al., 2017). Moreover, a recent meta-­
analysis showed that the most appropriate amount of
total protein intake for maintaining and augmenting muscle strength with resistance training is 1.5 g/kg BW/day(Tagawa et al., 2022), and the protein intake for increasingor maintaining LBM ranges from 0.5 to 3.5 g/kg BW/day(Tagawa et al., 2020). Additionally, a previous study on elderly women showed that resistance exercise or red meatintake of 160 g/day (45 g protein) alone did not increase
LBM, but their combination increased the LBM (Daly2051817x, 2024, 12, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.14814/phy2.16100 by Cochrane Japan, Wiley Online Library on [18/06/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

UCHIDA et al.

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UCHIDA et al.

F I G U R E 4 Principal coordinate analysis (PCoA) plots of weighted UniFrac distance metrics obtained from sequencing the 16S
ribosomal RNA gene before and after the intervention in each group. PCoA plots of the sedentary control with placebo (a), sedentary
control with chicken meat (b), resistance training with placebo (c), and resistance training with chicken meat (d) groups. Axes represent thepercentage of data explained by each coordinate dimension.

et al., 2014). In this study, the combination of chicken
meat intake (22.5 g protein/110 g chicken meat) and resistance exercise increased muscle mass in the lower extremities of elderly women more than either of the individualinterventions. The mean total protein intake in the combined group was 1.69 g/kg BW/day of total protein intake.
Therefore, the combination of resistance exercise andchicken meat intake increased muscle strength, suggesting that the effects of chicken meat intake may be similar
to those of other meat products.
Herein, the combination of resistance training andchicken meat intake increased muscle mass and strength

without drastically modifying the gut microbiota composition in elderly women. However, p-­value-­based statisticalanalysis of individual gut bacteria revealed a decrease in thecomposition of Eubacterium hallii group genera when intervention included chicken meat intake and resistance training alone and the composition was significantly decreased
with their combined intervention. A cross-­sectional study
on professional rugby athletes, who performed habitual resistance exercise and had high protein intake, showed thatthe β-­diversity of the gut microbiota differed in nonathletes(Clarke et al., 2014). Furthermore, Clark et al. (2014). proposed that these adaptations in athletes may be caused by

2051817x, 2024, 12, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.14814/phy2.16100 by Cochrane Japan, Wiley Online Library on [18/06/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

10 of 14   

| 11 of 14

resistance exercise training combined with high-­
proteindiet intake over a long period, resulting in the modificationof the gut microbiota in athletes. Therefore, in the case of
short-­term (12-­week) exercise and nutritional interventions,as performed in this study, the net balance of muscle protein synthesis and degradation may affect muscle hypertrophy more intensely than the changes in the gut microbiota.
Additionally, the gut microbiota of bodybuilders has showna marked decrease in the Eubacterium hallii group genera
composition compared with that in untrained adults (Janget al., 2019). Interestingly, the Eubacterium hallii group genera composition has been positively correlated with bloodC-­reactive protein levels in the elderly (Yu et al., 2018).
Age-­related chronic inflammation may be involved in muscle loss by promoting protein degradation and inhibiting
protein synthesis. Moreover, our previous study showedthat resistance training attenuated inflammatory responses
throughout systemic and various tissue levels in aged mice
(Uchida et al., 2019). In this study, changes in the relative
abundance of the Eubacterium hallii group genera were negatively correlated with changes in muscle mass. Hence, theEubacterium hallii group genera composition decreased withresistance training may affect inflammatory responses in elderly individuals, resulting in increase in muscle mass.
This study had two limitations. First, in this study, thechanges in lower limb LBM did not significantly change
the resistance training or high-­protein diet intervention
alone group as compared to the Sed + PL group, but thechanges in lower limb LBM significantly increased in the
combined intervention group as compared to the Sed + PL
group. Therefore, we believed that no effect was observedwith each intervention alone, and a significant increasein lower limb LBM was observed by the combined intervention. Second, in this study, changes in the relative
abundance of the Eubacterium hallii group genera were
negatively correlated with changes in muscle mass. Thechanges in the gut bacteria by the combined intervention
are shown in this study. Therefore, further study needs toexamine whether the gut microbiota is involved in the effect of the combined intervention.
To conclude, the combination of resistance training
and steamed chicken meat intake may be effective in increasing muscle mass and strength without drasticallymodifying the gut microbiota composition in elderly
women. Our findings indicate that chicken meat intake
during the intervention did not cause meat-­induced dysbiosis (negative effects on the gut environment), as reported
in previous studies, suggesting the positive effects of resistance training in the combined intervention.

experiments; M.U., J.P., N.H., and M.I. analyzed data;
M.I., M.U., J.P., N.H., and M.M. interpreted results ofexperiments; M.I., M.U., J.P., and N.H. prepared figures;
M.I., M.U., J.P., and M.M. drafted manuscript; M.I., M.M.,
K.S., M.U., J.P., N.H., S.F., K.H., K.W., Y.S., J.K., and K.M.
edited and revised manuscript; and M.I., M.M., K.S, M.U.,
J.P., N.H., S.F., K.H., K.W., Y.S., J.K., and K.M. approved
final version of manuscript.

AUTHOR CONTRIBUTIONS
M.I., M.M., K.S., and M.U. conceived and designed research; M.I., M.U., K.H., N H., and M.I. performed

ORCID
Masataka Uchida
Motohiko Miyachi

ACKNOWLEDGMENTS
We would like to thank Editage (www.​edita​ge.​com) for
the English language editing.
FUNDING INFORMATIONThis work was supported by the Ito Foundation, Tokyo,
Japan (#134, M. Iemitsu), Grants-­
in-­
Aid for Scientific
Research from the Ministry of Education, Culture, Sports,
Science, and Technology of Japan (KAKENHI: #22H03487
for M. Iemitsu; and #16 K00944 and #20H04117 for M.
Miyachi), the Ministry of Health, Labour and Welfare(#201709002B for M. Miyachi), the Japan Agency
for Medical Research and Development (AMED;
22ae0121035s0102 for K. Hosomi and 22ae0121042h0002
and 22ae0121035s0102 for J. Kunisawa), the Ministryof Health and Welfare of Japan and Public /Private
R&D Investment Strategic Expansion PrograM (PRISM;
20 AC5004 for J. Kunisawa), and Programs for Bridging
the gap between R&D and the IDeal society (society 5.0),
and Generating Economic and social value: BRiDGE (J.
Kunisawa).
CONFLICT OF INTEREST STATEMENT
The authors have no conflict of interest to declare. M.
Iemitsu received funding from the Ito Foundation.
DATA AVAILABILITY STATEMENT
The datasets generated in this study are available from thecorresponding author upon request.
ETHICS STATEMENT
All the participants voluntarily provided written informed
consent before participating in the study. This study
was approved by the Ethics Committee of Ritsumeikan
University (BKC-­2018-­060) and was conducted in accordance with the Declaration of Helsinki. The study was registered in the University Hospital Medical Information
Network Clinical Trials Registry (UMIN000038253).
Signed informed consent was obtained from allparticipants.
https://orcid.org/0009-0002-8670-2953
https://orcid.org/0000-0002-1146-0905

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UCHIDA et al.

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SUPPORTING INFORMATION
Additional supporting information can be found online
in the Supporting Information section at the end of thisarticle.

UCHIDA et al.

How to cite this article: Uchida, M., Park, J.,
Fujie, S., Hosomi, K., Horii, N., Watanabe, K.,
Sanada, K., Shinohara, Y., Mizuguchi, K.,
Kunisawa, J., Iemitsu, M., & Miyachi, M. (2024).
Effect of resistance training and chicken meat on
muscle strength and mass and the gut microbiomeof older women: A randomized controlled trial.
Physiological Reports, 12, e16100. https://doi.
org/10.14814/phy2.16100

2051817x, 2024, 12, Downloaded from https://physoc.onlinelibrary.wiley.com/doi/10.14814/phy2.16100 by Cochrane Japan, Wiley Online Library on [18/06/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

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