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Article

Sex-Specific Associations of Gut Microbiota Composition with Sarcopenia Defined by the Asian Working Group for Sarcopenia 2019 Consensus in Older Outpatients: Prospective Cross-Sectional Study in Japan

by
Daisuke Asaoka
1,2,*,
Kazuya Toda
3,
Shin Yoshimoto
2,
Noriko Katsumata
2,
Toshitaka Odamaki
2,
Noriyuki Iwabuchi
2,
Miyuki Tanaka
3,
Jin-Zhong Xiao
2,
Yuriko Nishikawa
2,
Osamu Nomura
1,
Tsutomu Takeda
4,
Akihito Nagahara
4,
Shigeo Koido
5,
Toshifumi Ohkusa
2,5 and
Nobuhiro Sato
2
1
Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan
2
Department of Microbiota Research, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
3
Innovative Research Institute, Morinaga Milk Industry Co., Ltd., Zama 252-8583, Kanagawa, Japan
4
Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
5
Division of Gastroenterology and Hepatology, Department of Internal Medicine, The Jikei University Kashiwa Hospital, 163-1 Kashiwa-shita, Kashiwa 277-0004, Chiba, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(10), 1746; https://doi.org/10.3390/nu17101746
Submission received: 5 May 2025 / Revised: 17 May 2025 / Accepted: 18 May 2025 / Published: 21 May 2025
(This article belongs to the Section Geriatric Nutrition)
Browse Figures
Versions Notes

Abstract

:
Background/Objectives: Sarcopenia (SA), an age-related impairment in skeletal muscle mass and function, is related to gut microbiota (GM) through inflammation and short-chain fatty acid (SCFA) generation. However, data on this relationship in older Japanese adults remain limited. We investigated the relationship of GM composition with SA, based on the Asian Working Group for Sarcopenia (AWGS) 2019 criteria, among elderly Japanese outpatients. Methods: Between June 2022 and January 2023, this prospective cross-sectional study enrolled 356 community-dwelling outpatients aged ≥ 65 years at the Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center. SA was determined based on the AWGS 2019 consensus criteria. GM was analyzed using 16S rRNA gene sequencing, and alpha/beta diversity, taxonomic composition, detection rates, and correlations with skeletal muscle mass index (SMI), grip strength, and gait speed were investigated. Results: Among 356 (144 males, 212 females) participants, 50 (35 males, 15 females) had SA. Differences in GM diversity and composition were primarily noted among male participants. Men with SA had lower alpha diversity and distinct beta diversity profiles. Six bacterial genera—Eubacterium I, Fusicatenibacter, Holdemanella, Unclassified Lachnospira, Enterococcus H, and Bariatricus—had lower abundances in the SA group. Several of these genera showed positive correlations with SMI, grip strength, and gait speed. Conversely, no differences in GM characteristics were seen among females. Conclusions: GM composition was associated with SA among older Japanese men. These sex-specific differences emerged consistently, highlighting the potential of microbiota-based strategies for SA prevention in older males.

1. Introduction

Sarcopenia (SA) involves age-related impairment in skeletal muscle mass (MM), muscle strength (MS), and physical function. SA is not merely a musculoskeletal disorder; it is a critical contributor to frailty, disability, and loss of independence in the elderly population. It leads to reduced mobility, diminished autonomy, and an increased risk of institutionalization and mortality. Frail individuals with SA face significantly higher risks of falls, hospitalization, functional decline, and death. The loss of muscle strength and function is closely linked to difficulties in performing activities of daily living (ADLs) and instrumental activities of daily living (IADLs) [1]. Early detection and targeted interventions are essential to mitigate its impact and preserve quality of life in older adults. Epidemiological studies estimate that 10–16% of older adults worldwide are affected by SA. However, prevalence varies depending on the population studied and the diagnostic criteria used [2]. In Japan, the Asian Working Group for Sarcopenia (AWGS) 2019 Consensus criteria is widely utilized to determine risk among individuals [3]. Risk factors for SA include physical inactivity, malnutrition, smoking, extreme sleep duration, and metabolic disorders. In terms of pathophysiology, it has been reported that age-associated imbalance between anabolic and catabolic processes on the protein synthesis pathway in skeletal muscles, a decrease in the size and number of type II muscle fibers, fat infiltration into muscles, and a decrease in the number of satellite cells [4]. The condition is associated with higher risks of falls, fractures, cognitive impairment, and mortality, as well as longer hospital stays and postoperative complications. SA interferes with walking and daily activities, predisposes to falls, and reduces social engagement. It is a major contributor to prolonged care dependence in older individuals. While SA can be mitigated through appropriate exercise and nutritional interventions, optimal nutritional strategies remain to be fully elucidated.
Recent advances in metagenomic technologies have provided further information about gut microbiota (GM) and its systemic effects. The gut-muscle axis is known as a crucial modulator of age-related muscle health, acting through pathways involving inflammation, immune regulation, energy metabolism, endocrine signaling, and insulin sensitivity [5]. GM may participate in the occurrence of SA by modulating the butyrate and bile acid synthesis metabolism, inducing chronic low-titer inflammatory reaction, promoting insulin resistance, regulating muscle synthesis metabolism, affecting the host immune system, damaging tight junctions of intestinal epithelial cells, and enhancing intestinal permeability through various pathways [6,7]. Glycine betaine, tryptophan, bile acids, and butyric acid are the most promising biomarkers among putative mediators [8,9,10]. In particular, it has been reported that GM changes with age in the elderly, and it is possible that this may affect muscle function through gut metabolites. Dysbiosis of GM can cause heightened intestinal permeability, i.e., “leaky gut,” promoting systemic inflammation via translocation of bacterial toxins and pro-inflammatory molecules. This chronic low-grade inflammation leads to SA progression [11]. Notably, SA frequently coexists with chronic disorders including diabetes, cardiovascular disease, and obesity [12,13,14]. GM can influence SA by regulating inflammation and synthesizing short-chain fatty acids (SCFAs), such as butyrate, propionate, and acetate [15,16]. These acids are key microbial metabolites that have anti-inflammatory effects and influence host energy metabolism. Consequently, improving the intestinal environment is an innovative technique for disease prevention, with probiotics and prebiotics being considered effective interventions for maintaining health.
The GM of the Japanese population is known to be strongly shaped by distinct dietary habits and genetic factors, resulting in a microbial composition that differs markedly from that of other populations. For instance, traditional Japanese dietary patterns have been shown to significantly influence the diversity and structure of the intestinal microbiota [17], with beneficial bacteria such as Bifidobacterium and Blautia commonly dominating [18]. In addition, many Japanese individuals possess genetic polymorphisms (SNPs) associated with reduced lactase activity, which may facilitate the colonization and proliferation of Bifidobacterium [19]. Given these unique dietary and genetic influences, the Japanese GM exhibits a distinctive profile. Therefore, it is of particular importance to investigate the relationship between GM and SA specifically within the Japanese population. However, despite the growing interest in this topic, the exact mechanisms underlying the association between GM and SA remain poorly understood, especially in older Japanese populations. We explored relationships of GM composition with SA among older outpatients attending a university-affiliated geriatric medical center, based on the AWGS 2019 diagnostic consensus.

2. Materials and Methods

2.1. Study Design

Between June 2022 and January 2023, this prospective cross-sectional study used fecal samples from patients aged ≥ 65 years who were outpatients at the Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center. We performed 16S rRNA-based GM analysis [20] using DNA extracted from feces and compared the alpha diversity, beta diversity, abundance, and detection rates of bacterial genera between SA and non-SA patients. Additionally, we investigated the correlations between bacterial genera and various MS indicators, including indices of MM, MS, and gait speed.
We included individuals aged 65–89 years who were assessed for SA within the past year according to the AWGS 2019 consensus criteria [3] and provided written informed consent. We excluded individuals with (i) neurological disorders such as Parkinson’s disease, Huntington’s disease, normal pressure hydrocephalus, progressive supranuclear palsy, epilepsy, multiple sclerosis, cerebral infections, or complications from head trauma; prior major depression or bipolar disorder; alcohol or other substance abuse; multiple infarctions, brain tumors, or subdural hematomas; or cognitive impairment due to vitamin B12 or folate deficiency, or neurosyphilis; (ii) severe diseases (e.g., advanced cardiovascular, hepatic, renal, gastrointestinal, or endocrine-metabolic disorders) or active gastrointestinal malignancy or inflammatory bowel disease; (iii) use of health supplements or foods containing probiotics, oligosaccharides, or dietary fiber; (iv) severely abnormal blood pressure or blood tests, severe anemia, drug or food allergies, heavy smoking or drinking, or an irregular lifestyle (e.g., inconsistent meal patterns or sleep duration); (v) use of anti-dementia drugs, psychoactive drugs, or severe diabetes mellitus requiring insulin treatment; and (vi) individuals deemed ineligible by the principal investigator.

2.2. Definition of SA and MS Indicators

SA was diagnosed based on the AWGS 2019 Consensus criteria [3]. Handgrip strength was assessed two times in both hands using a handgrip dynamometer (Toei Light Co., Ltd., Saitama, Japan), with the larger value recorded as the maximum MS. MS < 28 kg for male patients and <18 kg for female patients was considered to be reduced, in accordance with the AWGS criteria. Gait speed was manually assessed using a stopwatch and defined as slow when <1.0 m/s, as per the AWGS criteria. Lean mass and regional fat were assessed using whole-body DXA (Prodigy Advance, GE Healthcare, Tokyo, Japan), according to the standard protocol. Whole-body fat and lean mass were recorded for arms, legs, and trunk. Appendicular lean mass was calculated as the sum of the lean mass of the upper and lower limbs, and the appendicular skeletal muscle mass index (SMI) was recorded by dividing the appendicular lean mass by height squared. A low appendicular skeletal MM was considered to be SMI < 7.0 kg/m2 in male patients and <5.4 kg/m2 in female patients.

2.3. Fecal DNA Preparation and Microbiota Analysis

Fecal DNA was prepared and analyzed for microbiota as previously described with slight modifications [20]. Briefly, fecal samples were collected using the Techno Suruga stool collection kit (brush type). DNA was extracted from fecal samples, and purified DNA was suspended in 2000 µL of Tris-EDTA buffer (pH 8.0). PCR amplification and DNA sequencing of the V3–V4 region of the bacterial 16S rRNA gene were performed on the Illumina MiSeq instrument (Illumina, San Diego, CA, USA). The obtained paired-end FASTQ data from the registered data in the cohort study were trimmed and merged before selection of the amplicon sequence variants (ASVs). The classification and diversity analysis of the ASVs were performed using the QIIME2 software package, version 2022.8 (https://qiime2.org/ accessed on 29 December 2022), as described previously [9]. Taxonomic classification was performed using the naive Bayes classifier trained on Greengenes2, version 2022.10 (https://greengenes2.ucsd.edu/ accessed on 2 February 2023), with a 99% threshold for full-length sequence operational taxonomic units. After the assignment of each ASV to a bacterial species, alpha diversities were calculated using QIIME2 software. Furthermore, principal coordinate analysis based on Bray–Curtis dissimilarity was performed using R software (ver. 4.3.1) using the vegan (version 2.6-4) and ape (version 5.7-1) packages.

2.4. Statistical Analysis

We assessed 398 individuals for inclusion. After informed consent, 42 individuals who refused to undergo fecal sampling were excluded, and 356 completed the final examination and were included in FAS (Full Analysis Set). Quantitative variables are presented as mean ± standard deviation. Statistical analyses were performed using R software and EZR (ver. 4.2.2) [21]. The Mann–Whitney U test was used to analyze background information, alpha-diversity, and genera of bacteria. Fisher’s exact test was used to analyze the detection rate of bacteria genera. Permutational analysis of variance for principal coordinate analysis was conducted in R using the adonis2 function from the vegan package, version 2.6-4. Spearman’s rank correlation coefficient was employed to determine associations between gut bacterial genera and muscle-related variables.

3. Results

3.1. Baseline Characteristics

Figure 1 presents a flow chart of study participants. We assessed 398 individuals, of whom 356 met the eligibility criteria. Among these, 50 had SA, whereas 306 did not (Table 1). Of the 144 male participants, 35 had SA and 109 did not. Of the 212 females, 15 had SA and 197 did not. Males with SA were significantly older than those without SA. Both male and female SA patients had lower body mass index, SMI, grip strength, and gait speed compared to non-SA patients.
A total of 398 outpatients were enrolled, and 356 patients with gut microbiota data were investigated.

3.2. GM Analysis Between SA and Non-SA Individuals

β-diversity was significantly different between male and female participants (p = 0.0001) (Supplementary Figure S1). Therefore, subgroup analyses were conducted by sex. In male SA (n = 35) and non-SA (n = 109) groups, some indexes of α-diversity (Shannon (p = 0.004), observed features (p = 0.047), and Pielou evenness (p = 0.003)) were lower in the SA group (Figure 2a). Furthermore, the β-diversity of GM was significantly different between the two groups (p = 0.0030) (Figure 3). In contrast, for female subjects, no significant differences in either alpha or beta diversity were found between the two groups (Figure 2b and Figure 3).

3.3. Abundance of Bacterial Genera Between SA and Non-SA Patients

At the genus level, the relative abundances of six bacterial genera, Eubacterium I (p = 0.000026), Fusicatenibacter (p = 0.0132), Holdemanella (p = 0.0174), Enterococcus H (p = 0.00408), Bariatricus (p = 0.0353), and Unclassified Lachnospira (p = 0.00812), were lower in the male SA group (Figure 4). However, no significant differences were observed between female SA and non-SA groups.

3.4. Detection Rates of Bacterial Genera in SA and Non-SA Patients

Regarding the detection rates of the six bacterial genera, four of the six genera (Eubacterium I (p = 0.0002), Holdemanella (p = 0.009), Enterococcus H (p = 0.002), and Bariatricus (p = 0.01)) had decreased detection rates in the male SA group. Detection rates were not significantly different in the female group (Table 2).

3.5. Correlations Between Bacterial Genera and MM and MS

Spearman correlation analysis revealed significant positive correlations between Fusicatenibacter and Enterococcus H and grip strength and gait speed in male participants. Unclassified Lachnospira was positively correlated with grip strength, and Holdemanella was positively correlated with SMI. However, in females, no significant correlation was observed between these genera and grip strength or gait speed (Figure 5).

4. Discussion

In this prospective cross-sectional study, we assessed the association between GM composition and SA, as defined by the AWGS 2019 consensus [3], in elderly outpatients attending a university hospital clinic specializing in geriatric care. This is the first study in Japan to investigate this association in a clinical elderly population. The association between SA and GM composition differed by sex. Specifically, significant differences in GM diversity and abundance between SA and non-SA individuals were observed only in male participants, whereas such differences were not evident in females. These findings indicate a sex-specific pattern in the relationship between GM and SA, rather than sex differences in SA or GM individually.
Among male participants, the SA group exhibited lower alpha diversity compared to the non-SA group. Furthermore, the beta diversity also differed significantly between the two groups. At the genus level, six bacterial genera—Eubacterium I, Fusicatenibacter, Holdemanella, Unclassified Lachnospira, Enterococcus H, and Bariatricus—were significantly reduced in abundance in the SA group. Of these, four genera also showed lower detection rates in the male SA group. Several of these genera produce SCFAs, particularly butyrate, and may be involved in maintaining skeletal MM and function [15,16]. Conversely, no significant differences in GM diversity or composition were noted between female SA and non-SA groups. This sex disparity aligns with previous studies. For instance, Park et al. demonstrated that SMI was linked to greater alpha diversity in males, while no such difference was found in females in a large sample of middle-aged individuals [22,23]. Other studies have similarly noted sex-specific patterns in GM composition and functional pathways [24]. Testosterone production declines with age, and testosterone treatment has been suggested to have beneficial effects on muscle mass and function. The association between testosterone decline and GM involves complex interactions, particularly through microbial enzymes such as β-glucuronidase and sulfatase. A study has identified several gut bacteria with significant β-glucuronidase activity, including species from the genera Clostridium, Bacteroides, and Escherichia. These bacteria contribute to the modulation of androgen levels by regulating the balance between conjugated and free testosterone in the gut [25]. Studies have shown that microbial sulfatases are widespread among gut bacteria, particularly within the phyla Bacteroidetes and Firmicutes [26]. A decline in beneficial microbial populations or enzyme activity may lead to reduced reactivation of testosterone, contributing to lower systemic levels and associated clinical manifestations. Aging is associated with decreased activation of key signaling pathways involved in muscle growth, such as the mTOR pathway, leading to decreased muscle protein synthesis [27]. These observations suggest that sex hormones, age-related endocrine changes, or other physiological differences may underlie the observed sex differences, although further research using larger cohorts is warranted to confirm these findings.
The reduction in SCFA-producing genera in SA individuals observed in this study supports earlier reports that linked lower SCFA levels with diminished muscle function [11,15,28]. The observed reduction in SCFA-producing genera among SA individuals in this study is consistent with several prior investigations reporting GM alterations in SA. For example, Kang et al. [29] reported that butyrate-producing genera such as Lachnospira, Fusicatenibacter, Roseburia, Eubacterium, and Lachnoclostridium were significantly less abundant in individuals with SA, while Lactobacillus was more prevalent. Similarly, Ticinesi et al. [10] found that SA individuals had different fecal microbial profiles, with reduced abundances of SCFA-producing species such as Faecalibacterium prausnitzii and Roseburia inulinivorans, as well as Alistipes shahii. Lou et al. [30] reported a negative correlation between the abundance of Escherichia-Shigella and relative SMI. Furthermore, Lachnospira was positively correlated with skeletal MM. Zhang et al. [31] found that SA individuals had lower abundances of common gut bacteria such as Bacteroides, Faecalibacterium, Fusobacterium, and Prevotella, and higher abundances of pathogens such as Escherichia-Shigella and Klebsiella. They also reported impaired amino acid biosynthesis and protein processing pathways in the SA group, suggesting possible disruption of protein metabolism. Moreover, Zhou et al. [32] identified specific genera such as Blautia, Lachnospiraceae_unclassified, and Subdoligranulum as potential microbial biomarkers for SA.
These findings are further supported by a recent systematic review, which reported that multiple SCFA-producing genera—including Lachnospira, Fusicatenibacter, Roseburia, Eubacterium, Lachnoclostridium, and Slackia—were consistently reduced in individuals with age-related SA [33]. This reinforces the hypothesis that dysbiosis of SCFA-producing bacteria may play a critical role in SA pathophysiology through impaired microbial contributions to host energy and protein metabolism. In men, a reduction in short-chain fatty acid (SCFA)-producing bacteria may be associated with SA, consistent with findings reported in other countries. In contrast, in women, distinct pathogenic mechanisms may be involved, potentially arising from the combined effects of hormonal factors and the unique GM composition and dietary habits characteristic of the Japanese population.
Additionally, studies focusing on the Japanese population have also linked microbial profiles with muscle health. Sugimura et al. [34] observed that increases in Blautia and Eggerthella and decreases in Faecalibacterium were related to MS in older Japanese adults. Sugimura et al. [35] also reported positive correlations between the abundances of Blautia, Bifidobacterium, and Eisenbergiella and skeletal MM. Interestingly, another study showed that elevated Blautia levels were paradoxically linked to lower skeletal MM in older Japanese adults [36], suggesting strain- or context-specific effects. A systematic review and fecal metagenomic analysis further reported a consistent decrease in SCFA-producing bacteria and associated functional genes involved in SCFA synthesis, carotenoid and isoflavone biotransformation, and amino acid interconversion in SA individuals [37,38].
In line with previous findings, our results confirm similar microbial alterations in a Japanese clinical cohort using AWGS 2019 diagnostic criteria. In particular, Fusicatenibacter was decreased in the SA group and positively correlated with grip strength and gait speed. Previous research linked Fusicatenibacter to anaerobic performance and stool characteristics in Japanese athletes [39]. Taxonomic studies have shown that this genus produces SCFAs, which are absorbed through the colon and utilized in liver and muscle tissues as substrates for glycogen and fatty acid synthesis [40,41]. Nay et al. demonstrated that depletion of GM by antibiotics in mice led to reduced endurance and impaired muscle function, potentially because of muscle glycogen depletion [42].
Similarly, Enterococcus H was positively correlated with grip strength and gait speed, suggesting its potential involvement in host muscle metabolic pathways. Liu et al. [43] demonstrated that butyrate supplementation in SA mice restored MM and function via mTOR pathway activation. Mayer et al. [44] reported that lower diversity of butyrate-producing bacteria was related to decreased grip strength and physical performance in older adults. Collectively, these results reinforce the importance of SCFA-producing bacteria in muscle maintenance through nutrient-sensing and anabolic pathways.
Eubacterium I, the genus most strongly associated with SA in our analysis, may correspond to E. ramulus based on 16S rRNA BLAST (ver. BLAST+ 2.15.0) results. E. ramulus can metabolize dietary flavonoids, and its abundance increases following polyphenol intake [45,46]. It expresses key enzymes, such as chalcone isomerase and NADH-dependent reductases, involved in flavonoid biotransformation [47,48,49]. According to a review on the potential of flavonoids for muscle atrophy, multiple in vitro and in vivo studies have suggested that flavonoids are potential therapeutics for treating muscle atrophy [50,51]. Moreover, dietary diversity, including vegetable and fruit intake—rich sources of polyphenols—has been associated with better muscle health and reduced SA risk [52,53]. Our previous findings also support that low vegetable intake correlates with SA in patients with chronic kidney disease [54,55], possibly via reduced availability of bacteria such as E. ramulus, which metabolize beneficial compounds. Thus, microbiota-mediated polyphenol metabolism may represent an underexplored pathway in muscle preservation. Several mechanisms have been proposed through which short-chain fatty acids (SCFAs) may influence SA. First, SCFAs possess anti-inflammatory properties that may indirectly support muscle protein synthesis and maintenance by suppressing chronic inflammation [9]. In particular, butyrate has been shown to activate key signaling pathways such as MAPK and mTOR in C2C12 myoblasts, potentially promoting muscle anabolism [56]. Additionally, SCFAs have been reported to improve insulin sensitivity, which may further contribute to the enhancement of muscle protein synthesis and preservation [8].
Recent studies have shown that targeted interventions such as probiotic supplementation can mitigate these effects. For instance, Lactobacillus plantarum TWK10 has been demonstrated to improve muscle mass and strength in older adults. Bifidobacterium longum and Lactobacillus casei strains have been associated with reduced inflammation and improved muscle function in animal models [57,58]. Further investigation of the effect of probiotic administration on the symptoms of SA in a large RCT trial is warranted.
This study had several limitations. First, the sample consisted of outpatients at a single center, potentially limiting the generalizability of our findings. Second, potential confounding factors such as exercise routines, dietary patterns, occupation, education level, and marital status were not accounted for. Considering that a range of daily lifestyle behaviors has been shown to influence the risk of SA among older adults, incorporating these factors into clinical assessments and preventive strategies is essential [59]. Third, the cross-sectional design precludes causal inference between GM and SA; therefore, prospective intervention studies are warranted. Fourth, in this study, the different proportions of male and female patients in the subject. Finally, it is important to acknowledge the limitations of 16S rRNA sequencing, which offers only genus-level taxonomic resolution. To gain a more comprehensive understanding of the role of the GM in SA, the application of more advanced approaches—such as whole metagenomic and metabolomic analyses—should be emphasized. This study was conducted as a single-center cross-sectional analysis and therefore may not represent data reflective of the entire Japanese population. To address regional variability and ensure broader applicability, future research should include multicenter collaborative studies and prospective investigations directly comparing Japanese cohorts with Western populations.

5. Conclusions

This prospective cross-sectional study revealed a potential association between GM composition and SA, as defined by the AWGS 2019 criteria, in elderly outpatients in Japan. Notably, the relationship between SA and GM composition appeared to differ by sex. In males, the SA group exhibited lower alpha diversity and significant differences in beta diversity compared to the non-SA group. Additionally, six bacterial genera—Eubacterium I, Fusicatenibacter, Holdemanella, Unclassified Lachnospira, Enterococcus H, and Bariatricus—were less abundant in the SA group, and four of these exhibited lower detection rates. Several of these genera produce SCFA, particularly butyrate, and were positively correlated with MM, grip strength, and gait speed. These findings suggest that alterations in GM composition, particularly reductions in SCFA-producing bacteria, may be linked to SA in elderly males. Microbiota-targeted interventions, including dietary modifications and probiotic supplementation, could represent promising strategies for SA prevention and treatment. However, the cross-sectional study design limits causal inference, and further longitudinal and interventional studies are necessary to confirm these associations and clarify the underlying mechanisms.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/nu17101746/s1, Figure S1: Principal component analysis based on Bray Curtis (β-diversity) analysis between male and female participants.

Author Contributions

Conceptualization, D.A., S.Y. and N.K.; data curation, D.A., K.T. and N.K.; formal analysis, K.T. and T.O. (Toshitaka Odamaki); funding acquisition, N.I., M.T. and J.-Z.X.; investigation, D.A., K.T. and S.Y.; methodology, D.A., K.T., S.Y. and N.K.; project administration, D.A., Y.N., O.N. and T.T.; resources, D.A., K.T., S.Y., N.K., T.O. (Toshitaka Odamaki), O.N. and S.K.; software, K.T. and S.Y.; supervision, T.O. (Toshifumi Ohkusa) and N.S.; validation, T.T., A.N. and S.K.; visualization, D.A.; writing—original draft, D.A., K.T., S.Y., N.K. and T.O. (Toshitaka Odamaki); writing—review and editing, T.T., A.N., S.K. and T.O. (Toshitaka Odamaki); and all authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was conducted by the Department of Microbiota Research, Juntendo University Graduate School of Medicine, which received funding from Morinaga Milk Industry Co., Ltd.

Institutional Review Board Statement

The present study was conducted in accordance with the tenets of the Declaration of Helsinki. The Juntendo Tokyo Koto Geriatric Medical Center Ethics Committee ethically approved the study and study protocol (reference number G21-0020) (27 December 2021).

Informed Consent Statement

Informed consent was provided by all subjects involved in the study.

Data Availability Statement

Data supporting the findings of this study are available upon request from the corresponding author. The data is not shared publicly due to being a part of an ongoing study.

Acknowledgments

We thank the study participants for their cooperation. We also thank Mutsuko Hatanaka and Ryoko Yamaguchi for their administrative support.

Conflicts of Interest

D.A., S.Y., N.K., T.O., N.I., J-Z.X., T.O., Y.N. and N.S. are members of the Department of Microbiota Research, Juntendo University Graduate School of Medicine. K.T., S.Y., N.K., T.O., N.I., M.T. and J-Z.X. are employees of Morinaga Milk Industry Co., Ltd. O.N., T.T., A.N. and S.K. had no conflicts of interest. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

Abbreviations

The following abbreviations are used in this manuscript:
GMgut microbiota
SCFAshort-chain fatty acid
AWGSAsian Working Group for Sarcopenia
MMmuscle mass
MSmuscle strength
SMIskeletal muscle mass index
SAsarcopenia

References

  1. Wang, D.X.M.; Yao, J.; Zirek, Y.; Reijnierse, E.M.; Maier, A.B. Muscle mass, strength, and physical performance predicting activities of daily living: A meta-analysis. J. Cachexia Sarcopenia Muscle 2020, 11, 3–25. [Google Scholar] [CrossRef] [PubMed]
  2. Yuan, S.; Larsson, S.C. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism 2023, 144, 155533. [Google Scholar] [CrossRef]
  3. Chen, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Chou, M.Y.; Iijima, K.; Jang, H.C.; Kang, L.; Kim, M.; Kim, S.; et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J. Am. Med. Dir. Assoc. 2020, 21, 300–307. [Google Scholar] [CrossRef]
  4. Cho, M.-R.; Lee, S.; Song, S.-K. A Review of Sarcopenia Pathophysiology, Diagnosis, Treatment and Future Direction. J Korean Med Sci. 2022, 37, e146. [Google Scholar] [CrossRef] [PubMed]
  5. Li, W.; Sheng, R.W.; Cao, M.M.; Rui, Y.F. Exploring the Relationship Between Gut Microbiota and Sarcopenia Based on Gut-Muscle Axis. Food Sci. Nutr. 2024, 12, 8779–8792. [Google Scholar] [CrossRef]
  6. Nardone, O.M.; de Sire, R.; Petito, V.; Testa, A.; Villani, G.; Scaldaferri, F.; Castiglione, F. Inflammatory Bowel Diseases and Sarcopenia: The Role of Inflammation and Gut Microbiota in the Development of Muscle Failure. Front. Immunol. 2021, 12, 694217. [Google Scholar] [CrossRef]
  7. Prokopidis, K.; Cervo, M.M.; Gandham, A.; Scott, D. Impact of Protein Intake in Older Adults with Sarcopenia and Obesity: A Gut Microbiota Perspective. Nutrients 2020, 12, 2285. [Google Scholar] [CrossRef]
  8. Daily, J.W.; Park, S. Sarcopenia Is a Cause and Consequence of Metabolic Dysregulation in Aging Humans: Effects of Gut Dysbiosis, Glucose Dysregulation, Diet and Lifestyle. Cells 2022, 11, 338. [Google Scholar] [CrossRef] [PubMed]
  9. Prokopidis, K.; Chambers, E.; Ni Lochlainn, M.; Witard, O.C. Mechanisms Linking the Gut-Muscle Axis With Muscle Protein Metabolism and Anabolic Resistance: Implications for Older Adults at Risk of Sarcopenia. Front. Physiol. 2021, 12, 770455. [Google Scholar] [CrossRef]
  10. Ticinesi, A.; Mancabelli, L.; Tagliaferri, S.; Nouvenne, A.; Milani, C.; Del Rio, D.; Lauretani, F.; Maggio, M.G.; Ventura, M.; Meschi, T. The Gut-Muscle Axis in Older Subjects with Low Muscle Mass and Performance: A Proof of Concept Study Exploring Fecal Microbiota Composition and Function with Shotgun Metagenomics Sequencing. Int. J. Mol. Sci. 2020, 21, 8946. [Google Scholar] [CrossRef]
  11. Barry, D.J.; Wu, S.S.X.; Cooke, M.B. The Relationship Between Gut Microbiota, Muscle Mass and Physical Function in Older Individuals: A Systematic Review. Nutrients 2024, 17, 81. [Google Scholar] [CrossRef] [PubMed]
  12. Atkins, J.L.; Whincup, P.H.; Morris, R.W.; Lennon, L.T.; Papacosta, O.; Wannamethee, S.G. Sarcopenic obesity and risk of cardiovascular disease and mortality: A population-based cohort study of older men. J. Am. Geriatr. Soc. 2014, 62, 253–260. [Google Scholar] [CrossRef]
  13. Anbalagan, V.P.; Venkataraman, V.; Pradeepa, R.; Deepa, M.; Anjana, R.M.; Mohan, V. The prevalence of presarcopenia in Asian Indian individuals with and without type 2 diabetes. Diabetes Technol. Ther. 2013, 15, 768–775. [Google Scholar] [CrossRef]
  14. Shreiner, A.B.; Kao, J.Y.; Young, V.B. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 2015, 31, 69–75. [Google Scholar] [CrossRef] [PubMed]
  15. Yuan, C. Molecular mechanisms and therapeutic strategies of gut microbiota modulation in Sarcopenia (Review). Oncol. Lett. 2024, 29, 104. [Google Scholar] [CrossRef]
  16. Zhang, X.; Yang, G.; Jiang, S.; Ji, B.; Xie, W.; Li, H.; Sun, J.; Li, Y. Causal Relationship Between Gut Microbiota, Metabolites, and Sarcopenia: A Mendelian Randomization Study. J. Gerontol. A Biol. Sci. Med. Sci. 2024, 79, 173. [Google Scholar] [CrossRef] [PubMed]
  17. Moriki, D.; Koumpagioti, D.; Francino, M.P.; Rufián-Henares, J.Á.; Kalogiannis, M.; Priftis, K.N.; Douros, K. How Different Are the Influences of Mediterranean and Japanese Diets on the Gut Microbiome? Endocr. Metab. Immune Disord. Drug Targets 2024, 24, 1733–1745. [Google Scholar] [CrossRef]
  18. Nishijima, S.; Suda, W.; Oshima, K.; Kim, S.W.; Hirose, Y.; Morita, H.; Hattori, M. The gut microbiome of healthy Japanese and its microbial and functional uniqueness. DNA Res. 2016, 23, 125–133. [Google Scholar] [CrossRef]
  19. Kato, K.; Ishida, S.; Tanaka, M.; Mitsuyama, E.; Xiao, J.Z.; Odamaki, T. Association between functional lactase variants and a high abundance of Bifidobacterium in the gut of healthy Japanese people. PLoS ONE 2018, 13, e0206189. [Google Scholar] [CrossRef]
  20. Hiraku, A.; Nakata, S.; Murata, M.; Xu, C.; Mutoh, N.; Arai, S.; Odamaki, T.; Iwabuchi, N.; Tanaka, M.; Tsuno, T.; et al. Early Probiotic Supplementation of Healthy Term Infants with Bifidobacterium longum subsp. infantis M-63 Is Safe and Leads to the Development of Bifidobacterium-Predominant Gut Microbiota: A Double-Blind, Placebo-Controlled Trial. Nutrients 2023, 15, 1402–1420. [Google Scholar] [CrossRef]
  21. Kanda, Y. Investigation of the Freely Available Easy-to-Use Software “EZR” for Medical Statistics. Bone Marrow Transplant. 2013, 48, 452–458. [Google Scholar] [CrossRef] [PubMed]
  22. Park, C.H.; Lee, E.J.; Kim, H.L.; Lee, Y.T.; Yoon, K.J.; Kim, H.N. Sex-specific associations between gut microbiota and skeletal muscle mass in a population-based study. J. Cachexia Sarcopenia Muscle 2022, 13, 2908–2919. [Google Scholar] [CrossRef] [PubMed]
  23. Park, H.A.; Sung, J.; Chang, Y.; Ryu, S.; Yoon, K.J.; Kim, H.L.; Kim, H.N. Metagenomic Analysis Identifies Sex-Related Gut Microbial Functions and Bacterial Taxa Associated With Skeletal Muscle Mass. J. Cachexia Sarcopenia Muscle 2024, 16, e13636. [Google Scholar] [CrossRef]
  24. Chen, J.; Li, H.; Hird, S.M.; Chen, M.-H.; Xu, W.; Maas, K.; Cong, X. Sex differences in gut microbial development of preterm infant twins in early life: A longitudinal analysis. Front. Cell. Infect. Microbiol. 2021, 11, 671074. [Google Scholar] [CrossRef]
  25. d’Afflitto, M.; Upadhyaya, A.; Green, A.; Peiris, M. Association Between Sex Hormone Levels and Gut Microbiota Composition and Diversity-A Systematic Review. J. Clin. Gastroenterol. 2022, 56, 384–392. [Google Scholar] [CrossRef]
  26. Pant, A.; Maiti, T.K.; Mahajan, D.; Das, B. Human Gut Microbiota and Drug Metabolism. Microb. Ecol. 2023, 86, 97–111. [Google Scholar] [CrossRef] [PubMed]
  27. Tezze, C.; Sandri, M.; Tessari, P. Anabolic Resistance in the Pathogenesis of Sarcopenia in the Elderly: Role of Nutrition and Exercise in Young and Old People. Nutrients 2023, 15, 4073. [Google Scholar] [CrossRef]
  28. Mai, X.; Yang, S.; Chen, Q.; Chen, K. Gut microbial composition is altered in sarcopenia: A systematic review and meta-analysis of clinical studies. PLoS ONE 2024, 19, e0308360. [Google Scholar] [CrossRef]
  29. Kang, L.; Li, P.; Wang, D.; Wang, T.; Hao, D.; Qu, X. Alterations in intestinal microbiota diversity, composition, and function in patients with sarcopenia. Sci. Rep. 2021, 11, 4628. [Google Scholar] [CrossRef]
  30. Lou, J.; Wang, Q.; Wan, X.; Cheng, J. Changes and correlation analysis of intestinal microflora composition, inflammatory index, and skeletal muscle mass in elderly patients with sarcopenia. Geriatr. Gerontol. Int. 2023, 24, 140–146. [Google Scholar] [CrossRef]
  31. Zhang, Y.; Zhu, Y.; Guo, Q.; Wang, W.; Zhang, L. High-throughput sequencing analysis of the characteristics of the gut microbiota in aged patients with sarcopenia. Exp. Gerontol. 2023, 182, 112287. [Google Scholar] [CrossRef] [PubMed]
  32. Zhou, J.; Liu, J.; Lin, Q.; Shi, L.; Zeng, Z.; Guan, L.; Ma, Y.; Zeng, Y.; Zhong, S.; Xu, L. Characteristics of the gut microbiome and metabolic profile in elderly patients with sarcopenia. Front. Pharmacol. 2023, 14, 1279448. [Google Scholar] [CrossRef]
  33. Nikkhah, A.; Ejtahed, H.S.; Ettehad Marvasti, F.; Taghavi, M.; Pakmehr, A.; Hajipour, F.; Larijani, B. The critical role of gut microbiota dysbiosis in skeletal muscle wasting: A systematic review. J. Appl. Microbiol. 2022, 134, lxac014. [Google Scholar] [CrossRef] [PubMed]
  34. Sugimura, Y.; Yang, Y.; Kanda, A.; Mawatari, A.; Tamada, Y.; Mikami, T.; Nakaji, S.; Ihara, K. Association between Gut Microbiota and Muscle Strength in Japanese General Population of the Iwaki Health Promotion Project. Microorganisms 2024, 12, 622. [Google Scholar] [CrossRef]
  35. Sugimura, Y.; Kanda, A.; Sawada, K.; Wai, K.M.; Tanabu, A.; Ozato, N.; Midorikawa, T.; Hisada, T.; Nakaji, S.; Ihara, K. Association between Gut Microbiota and Body Composition in Japanese General Population: A Focus on Gut Microbiota and Skeletal Muscle. Int. J. Environ. Res. Public Health 2022, 19, 7464. [Google Scholar] [CrossRef]
  36. Sakaguchi, M.; Miyai, N.; Zhang, Y.; Sakamoto, Y.; Terada, K.; Utsumi, M.; Takeshita, T.; Arita, M. The gut microbiota genus Blautia is associated with skeletal muscle mass reduction in community-dwelling older Japanese adults: The Wakayama Study. Eur. Geriatr. Med. 2024, 16, 23–32. [Google Scholar] [CrossRef] [PubMed]
  37. Zhang, T.; Cheng, J.K.; Hu, Y.M. Gut microbiota as a promising therapeutic target for age-related sarcopenia. Ageing Res. Rev. 2022, 81, 101739. [Google Scholar] [CrossRef]
  38. Ticinesi, A.; Nouvenne, A.; Cerundolo, N.; Catania, P.; Prati, B.; Tana, C.; Meschi, T. Gut Microbiota, Muscle Mass and Function in Aging: A Focus on Physical Frailty and Sarcopenia. Nutrients 2019, 11, 1633. [Google Scholar] [CrossRef]
  39. Akazawa, N.; Nakamura, M.; Eda, N.; Murakami, H.; Nakagata, T.; Nanri, H.; Park, J.; Hosomi, K.; Mizuguchi, K.; Kunisawa, J.; et al. Gut microbiota alternation with training periodization and physical fitness in Japanese elite athletes. Front. Sports Act. Living 2023, 5, 1219345. [Google Scholar] [CrossRef]
  40. Takada, T.; Kurakawa, T.; Tsuji, H.; Nomoto, K. Fusicatenibacter saccharivorans gen. nov., sp. nov., isolated from human faeces. Int. J. Syst. Evol. Microbiol. 2013, 63, 3691–3696. [Google Scholar] [CrossRef]
  41. Mascolo, N.; Rajendran, V.M.; Binder, H.J. Mechanism of short-chain fatty acid uptake by apical membrane vesicles of rat distal colon. Gastroenterology 1991, 101, 331–338. [Google Scholar] [CrossRef] [PubMed]
  42. Nay, K.; Jollet, M.; Goustard, B.; Baati, N.; Vernus, B.; Pontones, M.; Lefeuvre-Orfila, L.; Bendavid, C.; Rué, O.; Mariadassou, M.; et al. Gut bacteria are critical for optimal muscle function: A potential link with glucose homeostasis. Am. J. Physiol. Metab. 2019, 317, E158–E171. [Google Scholar] [CrossRef] [PubMed]
  43. Liu, C.; Wong, P.Y.; Wang, Q.; Wong, H.Y.; Huang, T.; Cui, C.; Zhang, N.; Cheung, W.H.; Wong, R.M.Y. Short-chain fatty acids enhance muscle mass and function through the activation of mTOR signalling pathways in sarcopenic mice. J. Cachexia Sarcopenia Muscle 2024, 15, 2387–2401. [Google Scholar] [CrossRef] [PubMed]
  44. Mayer, M.H.; Woldemariam, S.; Gisinger, C.; Dorner, T.E. Association of Gut Microbiome with Muscle Mass, Muscle Strength, and Muscle Performance in Older Adults: A Systematic Review. Int. J. Environ. Res. Public Health 2024, 21, 1246. [Google Scholar] [CrossRef]
  45. Simmering, R.; Pforte, H.; Jacobasch, G.; Blaut, M. The growth of the flavonoid-degrading intestinal bacterium, Eubacterium ramulus, is stimulated by dietary flavonoids in vivo. FEMS Microbiol. Ecol. 2002, 40, 243–248. [Google Scholar] [CrossRef]
  46. Sost, M.M.; Ahles, S.; Verhoeven, J.; Verbruggen, S.; Stevens, Y.; Venema, K. A Citrus Fruit Extract High in Polyphenols Beneficially Modulates the Gut Microbiota of Healthy Human Volunteers in a Validated In Vitro Model of the Colon. Nutrients 2021, 13, 3915. [Google Scholar] [CrossRef]
  47. Thomsen, M.; Tuukkanen, A.; Dickerhoff, J.; Palm, G.J.; Kratzat, H.; Svergun, D.I.; Weisz, K.; Bornscheuer, U.T.; Hinrichs, W. Structure and catalytic mechanism of the evolutionarily unique bacterial chalcone isomerase. Acta Crystallogr. Sect. D Struct. Biol. 2015, 71, 907–917. [Google Scholar] [CrossRef]
  48. Braune, A.; Gütschow, M.; Blaut, M. An NADH-Dependent Reductase from Eubacterium ramulus Catalyzes the Stereospecific Heteroring Cleavage of Flavanones and Flavanonols. Appl. Environ. Microbiol. 2019, 85, e01233–19. [Google Scholar] [CrossRef]
  49. Paraiso, I.L.; Plagmann, L.S.; Yang, L.; Zielke, R.; Gombart, A.F.; Maier, C.S.; Sikora, A.E.; Blakemore, P.R.; Stevens, J.F. Reductive Metabolism of Xanthohumol and 8-Prenylnaringenin by the Intestinal Bacterium Eubacterium ramulus. Mol. Nutr. Food Res. 2019, 63, e1800923. [Google Scholar] [CrossRef]
  50. Li, Y.; Liu, Y.; Tan, R.; Liu, Y. Effect of flavonoids on skeletal muscle mass, strength and physical performance in middle-aged and older adults with or without Sarcopenia: A meta-analysis of randomized controlled trials. Front. Nutr. 2022, 9, 1013449. [Google Scholar] [CrossRef]
  51. Kim, C.; Hwang, J.K. Flavonoids: Nutraceutical potential for counteracting muscle atrophy. Food Sci. Biotechnol. 2020, 29, 1619–1640. [Google Scholar] [CrossRef] [PubMed]
  52. Nazri, N.S.M.; Vanoh, D.; Soo, K.L. Natural Food for Sarcopenia: A Narrative Review. Malays. J. Med. Sci. 2022, 29, 28–42. [Google Scholar] [CrossRef] [PubMed]
  53. Cailleaux, P.E.; Déchelotte, P.; Coëffier, M. Novel dietary strategies to manage sarcopenia. Curr. Opin. Clin. Nutr. Metab. Care 2024, 27, 234–243. [Google Scholar] [CrossRef] [PubMed]
  54. Inoshita, H.; Asaoka, D.; Matsuno, K.; Yanagisawa, N.; Suzuki, Y.; Miyauchi, K. Cross-Sectional Study on the Association between Dietary Patterns and Sarcopenia in Elderly Patients with Chronic Kidney Disease Receiving Conservative Treatment. Nutrients 2023, 15, 4994. [Google Scholar] [CrossRef]
  55. Asaoka, D.; Sugano, K.; Matsuno, K.; Shibata, N.; Sugiyama, H.; Endo, N.; Iwase, Y.; Tajima, M.; Sakuma, N.; Inoue, M.; et al. Association between dietary variety status and sarcopenia as defined by the Asian Working Group for Sarcopenia 2019 consensus in older outpatients at a hospital specializing in geriatric medicine: A cross sectional study with baseline data of prospective cohort study (JUSTICE TOKYO study). Biomed. Rep. 2024, 21, 123. [Google Scholar]
  56. Guan, L.; Cao, Z.; Pan, Z.; Zhao, C.; Xue, M.; Yang, F.; Chen, J. Butyrate promotes C2C12 myoblast proliferation by activating ERK/MAPK pathway. Mol. Omics 2023, 19, 552–559. [Google Scholar] [CrossRef]
  57. Lee, M.C.; Tu, Y.T.; Lee, C.C.; Tsai, S.C.; Hsu, H.Y.; Tsai, T.Y.; Liu, T.H.; Young, S.L.; Lin, J.S.; Huang, C.C. Lactobacillus plantarum TWK10 Improves Muscle Mass and Functional Performance in Frail Older Adults: A Randomized, Double-Blind Clinical Trial. Microorganisms 2021, 9, 1466. [Google Scholar] [CrossRef]
  58. Baek, J.S.; Shin, Y.J.; Ma, X.; Park, H.S.; Hwang, Y.H.; Kim, D.H. Bifidobacterium bifidum and Lactobacillus paracasei alleviate sarcopenia and cognitive impairment in aged mice by regulating gut microbiota-mediated AKT, NF-κB, and FOXO3a signaling pathways. Immun. Ageing 2023, 20, 56. [Google Scholar] [CrossRef]
  59. Tzeng, P.L.; Lin, C.Y.; Lai, T.F.; Huang, W.C.; Pien, E.; Hsueh, M.C.; Lin, K.P.; Park, J.H.; Liao, Y. Daily lifestyle behaviors and risks of sarcopenia among older adults. Arch. Public Health 2020, 78, 113. [Google Scholar] [CrossRef]
Nutrients 17 01746 g001
Figure 1. Flow chart of study participants.
Figure 1. Flow chart of study participants.
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Figure 2. Gut microbiota α-diversity analysis between sarcopenic and non-sarcopenic participants. Comparative analysis of α-diversity between sarcopenic and non-sarcopenic participants in male (a) and female (b). α-diversity (Faith_pd, Chao 1, Shannon, observed features and Pielou evenness indices) was measured of male SA (n = 35) and non-SA (n = 109) and female SA (n = 15) and non-SA (n = 197) participants. Box plots show the median, as well as the lower and upper quartiles. Whiskers represent the minimum and maximum spread. Statistical significance of differences was assessed using the Mann–Whitney U test (* p < 0.05, ** p < 0.01).
Figure 2. Gut microbiota α-diversity analysis between sarcopenic and non-sarcopenic participants. Comparative analysis of α-diversity between sarcopenic and non-sarcopenic participants in male (a) and female (b). α-diversity (Faith_pd, Chao 1, Shannon, observed features and Pielou evenness indices) was measured of male SA (n = 35) and non-SA (n = 109) and female SA (n = 15) and non-SA (n = 197) participants. Box plots show the median, as well as the lower and upper quartiles. Whiskers represent the minimum and maximum spread. Statistical significance of differences was assessed using the Mann–Whitney U test (* p < 0.05, ** p < 0.01).
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Figure 3. PCoA based on Bray–Curtis analysis between sarcopenic and non-sarcopenic participants. PCoA plots show the beta-diversity with Bray–Curtis dissimilarity. Each dot represents an individual participant. The statistical significance of differences in beta-diversity was analyzed using PERMANOVA (** p < 0.01).
Figure 3. PCoA based on Bray–Curtis analysis between sarcopenic and non-sarcopenic participants. PCoA plots show the beta-diversity with Bray–Curtis dissimilarity. Each dot represents an individual participant. The statistical significance of differences in beta-diversity was analyzed using PERMANOVA (** p < 0.01).
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Figure 4. Abundance of bacterial genera between sarcopenic and non-sarcopenic patients. Significant differences in genera present in the gut microbiota between sarcopenic and non-sarcopenic participants in males (a) and females (b). Schematic box plots show the median of relative abundance, as well as the lower and upper quartiles. Whiskers represent the minimum and maximum spread. Statistical significance of differences was assessed using the Mann–Whitney U test (* p < 0.05, ** p < 0.01).
Figure 4. Abundance of bacterial genera between sarcopenic and non-sarcopenic patients. Significant differences in genera present in the gut microbiota between sarcopenic and non-sarcopenic participants in males (a) and females (b). Schematic box plots show the median of relative abundance, as well as the lower and upper quartiles. Whiskers represent the minimum and maximum spread. Statistical significance of differences was assessed using the Mann–Whitney U test (* p < 0.05, ** p < 0.01).
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Figure 5. Correlations between bacterial genera and indices of muscle mass and strength. A heat map illustrated the Spearman rank correlation coefficients between the 6 bacterial genera relative abundance and age, skeletal muscle index (SMI), grip strength, and gait speed (* p < 0.05, ** p < 0.01).
Figure 5. Correlations between bacterial genera and indices of muscle mass and strength. A heat map illustrated the Spearman rank correlation coefficients between the 6 bacterial genera relative abundance and age, skeletal muscle index (SMI), grip strength, and gait speed (* p < 0.05, ** p < 0.01).
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Table 1. Background information of participants with and without sarcopenia.
Table 1. Background information of participants with and without sarcopenia.
All Subjects Without Sarcopenia
(n = 306)		All Subjects with Sarcopenia
(n = 50)		p-ValuesMale Subjects Without Sarcopenia
(n = 109)		Male Subjects with Sarcopenia
(n = 35)		p-ValuesFemale Subjects Without Sarcopenia
(n = 197)		Female Subjects with Sarcopenia
(n = 15)		p-Values
Age76.9 ± 6.179.5 ± 6.90.011 *76.1 ± 5.879.4 ± 6.70.004 **77.0 ± 6.278.9 ± 6.40.530
Hight (m)1.56 ± 0.101.58 ± 0.090.2421.67 ± 0.061.62 ± 0.070.002 **1.50 ± 0.061.51 ± 0.040.794
Body weight (kg)57.0 ± 11.952.8 ± 9.30.05767.3 ± 9.956.8 ± 7.40.000 **51.2 ± 8.542.9 ± 4.80.003 **
BMI23.3 ± 3.721.2 ± 2.70.000 **24.3 ± 3.421.7 ± 2.60.001 **22.8 ± 3.819.0 ± 2.40.009 **
SMI (kg/m2)6.37 ± 0.975.80 ± 0.810.000 **6.99 ± 0.906.15 ± 0.630.000 **6.07 ± 0.845.31 ± 0.650.000 **
Grip strength (kg)26.5 ± 9.024.9 ± 8.30.38334.8 ± 8.227.9 ± 7.10.000 **22.3 ± 5.719.0 ± 6.70.000 **
Walking speed (m/s)1.28 ± 0.410.96 ± 0.310.000 **1.41 ± 0.441.00 ± 0.280.000 **1.23 ± 0.380.94 ± 0.250.005 **
* p < 0.05, ** p < 0.01.
Table 2. Detection rates of bacterial genera in sarcopenic and non-sarcopenic patients.
Table 2. Detection rates of bacterial genera in sarcopenic and non-sarcopenic patients.
Detection RatioMale Non-SarcopeniaMale SarcopeniaFisher’s Exact TestFemale Non-SarcopeniaFemale SarcopeniaFisher’s Exact Test
p__Firmicutes_A|c__Clostridia_258483|o__Lachnospirales|f__Lachnospiraceae|g__Eubacterium_I61%23%0.0002 **37%27%0.581
p__Firmicutes_A|c__Clostridia_258483|o__Lachnospirales|f__Lachnospiraceae|g__Fusicatenibacter100%97%0.24397%100%1.000
p__Firmicutes_D|c__Bacilli|o__Erysipelotrichales|f__Erysipelotrichaceae|g__Holdemanella71%46%0.009 **46%33%0.427
p__Firmicutes_D|c__Bacilli|o__Lactobacillales|f__Enterococcaceae|g__Enterococcus_H_36060442%14%0.002 **28%40%0.376
p__Firmicutes_A|c__Clostridia_258483|o__Lachnospirales|f__Lachnospiraceae|__98%91%0.093 91%100%0.622
p__Firmicutes_A|c__Clostridia_258483|o__Lachnospirales|f__Lachnospiraceae|g__Bariatricus49%23%0.010 *32%20%0.401
* p < 0.05, ** p < 0.01.
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Asaoka, D.; Toda, K.; Yoshimoto, S.; Katsumata, N.; Odamaki, T.; Iwabuchi, N.; Tanaka, M.; Xiao, J.-Z.; Nishikawa, Y.; Nomura, O.; et al. Sex-Specific Associations of Gut Microbiota Composition with Sarcopenia Defined by the Asian Working Group for Sarcopenia 2019 Consensus in Older Outpatients: Prospective Cross-Sectional Study in Japan. Nutrients 2025, 17, 1746. https://doi.org/10.3390/nu17101746

AMA Style

Asaoka D, Toda K, Yoshimoto S, Katsumata N, Odamaki T, Iwabuchi N, Tanaka M, Xiao J-Z, Nishikawa Y, Nomura O, et al. Sex-Specific Associations of Gut Microbiota Composition with Sarcopenia Defined by the Asian Working Group for Sarcopenia 2019 Consensus in Older Outpatients: Prospective Cross-Sectional Study in Japan. Nutrients. 2025; 17(10):1746. https://doi.org/10.3390/nu17101746

Chicago/Turabian Style

Asaoka, Daisuke, Kazuya Toda, Shin Yoshimoto, Noriko Katsumata, Toshitaka Odamaki, Noriyuki Iwabuchi, Miyuki Tanaka, Jin-Zhong Xiao, Yuriko Nishikawa, Osamu Nomura, and et al. 2025. "Sex-Specific Associations of Gut Microbiota Composition with Sarcopenia Defined by the Asian Working Group for Sarcopenia 2019 Consensus in Older Outpatients: Prospective Cross-Sectional Study in Japan" Nutrients 17, no. 10: 1746. https://doi.org/10.3390/nu17101746

APA Style

Asaoka, D., Toda, K., Yoshimoto, S., Katsumata, N., Odamaki, T., Iwabuchi, N., Tanaka, M., Xiao, J.-Z., Nishikawa, Y., Nomura, O., Takeda, T., Nagahara, A., Koido, S., Ohkusa, T., & Sato, N. (2025). Sex-Specific Associations of Gut Microbiota Composition with Sarcopenia Defined by the Asian Working Group for Sarcopenia 2019 Consensus in Older Outpatients: Prospective Cross-Sectional Study in Japan. Nutrients, 17(10), 1746. https://doi.org/10.3390/nu17101746

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