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<p>lable at ScienceDirect</p><p>Clinical Nutrition 41 (2022) 1491e1500</p><p>Contents lists avai</p><p>Clinical Nutrition</p><p>journal homepage: http: / /www.elsevier .com/locate/c lnu</p><p>Original article</p><p>Differences in the gut microbiome and reduced fecal butyrate in elders</p><p>with low skeletal muscle mass</p><p>Der-Sheng Han a, b, c, d, 1, Wei-Kai Wu e, f, g, 1, Po-Yu Liu g, Yu-Tang Yang g, Hsiu-Ching Hsu h,</p><p>Ching-Hua Kuo h, Ming-Shiang Wu f, g, *, Tyng-Guey Wang c, **</p><p>a Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Bei-Hu Branch, Taipei, Taiwan</p><p>b Community and Geriatric Medicine Research Center, National Taiwan University Hospital, Bei-Hu Branch, Taipei, Taiwan</p><p>c Department of Physical Medicine and Rehabilitation, National Taiwan University College of Medicine, Taipei, Taiwan</p><p>d Health Science and Wellness Center, National Taiwan University, Taipei, Taiwan</p><p>e Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan</p><p>f Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan</p><p>g Department of Internal Medicine, National Taiwan University College of Medicine, Taipei, Taiwan</p><p>h School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan</p><p>a r t i c l e i n f o</p><p>Article history:</p><p>Received 22 November 2021</p><p>Accepted 13 May 2022</p><p>Keywords:</p><p>Gut microbiome</p><p>Butyrate</p><p>Skeletal muscle mass</p><p>Sarcopenia</p><p>Gut-muscle axis</p><p>* Corresponding author. Departments of Internal M</p><p>** Corresponding author.</p><p>E-mail addresses: mingshiang@ntu.edu.tw (M.-S. W</p><p>1 Authors contributed equally.</p><p>https://doi.org/10.1016/j.clnu.2022.05.008</p><p>0261-5614/© 2022 Elsevier Ltd and European Society</p><p>s u m m a r y</p><p>Background and aims: Despite animal studies revealing a causal link between the gut microbiota and</p><p>skeletal muscle mass, the role of the gut microbiome and its metabolites in humans having low muscle</p><p>mass remains unclear.</p><p>Methods: Eighty-eight subjects older than 65 years were measured for sarcopenia-related parameters,</p><p>including body composition, grip strength, gait speed and flexibility. Participants were divided into</p><p>normal muscle mass group (NM, n ¼ 52) and low muscle mass group (LM, n ¼ 36) and fresh fecal</p><p>samples were collected for metagenome and short chain fatty acids (SCFAs) analysis.</p><p>Results: The richness and evenness of gut microbiota diversity were significantly decreased in the</p><p>subjects with low muscle mass, including observed ASVs, Shannon and Chao 1 index. A significant</p><p>difference of gut microbiota profile was noted between NM group and LM group. The Firmicutes/</p><p>Bacteroidetes ratio was significantly reduced in the LM group. A significant decrease in the abundance</p><p>of a SCFA-producer, Marvinbryantia spp., whereas a remarkable enrichment of a flavonoid degrader,</p><p>Flavonifractor spp., was observed in the LM elders. Comparing with the NM group, the fecal butyrate</p><p>significantly diminished in the LM group and correlated with skeletal muscle mass index.</p><p>Conclusions: This is the first study that demonstrates the reduced fecal butyrate in elders with low</p><p>muscle mass and highlights the associated gut microbiome changes. The identified gut microbial</p><p>features and fecal butyrate level may serve as potential biomarkers for early detection of sarcopenic</p><p>patients.</p><p>© 2022 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.</p><p>edicine, National Taiwan University College of Medicine, Taipei, Taiwan.</p><p>u), tgw@ntu.edu.tw (T.-G. Wang).</p><p>for Clinical Nutrition and Metabolism. All rights reserved.</p><p>mailto:mingshiang@ntu.edu.tw</p><p>mailto:tgw@ntu.edu.tw</p><p>http://crossmark.crossref.org/dialog/?doi=10.1016/j.clnu.2022.05.008&domain=pdf</p><p>www.sciencedirect.com/science/journal/02615614</p><p>http://www.elsevier.com/locate/clnu</p><p>https://doi.org/10.1016/j.clnu.2022.05.008</p><p>https://doi.org/10.1016/j.clnu.2022.05.008</p><p>https://doi.org/10.1016/j.clnu.2022.05.008</p><p>Key Points:</p><p>C Skeletal muscle mass in elder people is positively</p><p>correlated with increased biodiversity of gut microbiota.</p><p>C Gut microbiota in subjects with low muscle mass is</p><p>characterizedwith reduced SCFA-producer and enriched</p><p>flavonoid degrader.</p><p>C Fecal butyrate concentration, a microbiota-derived</p><p>SCFA, is significantly correlated with increased skeletal</p><p>muscle mass.</p><p>D.-S. Han, W.-K. Wu, P.-Y. Liu et al. Clinical Nutrition 41 (2022) 1491e1500</p><p>1. Introduction</p><p>Gut microbiota consists of microorganisms inhabiting the</p><p>gastrointestinal tract and is estimated to be of the same order as</p><p>human cells [1]. Gut microbiota has a tremendous impact on the</p><p>health status of the host, including the aging process [2e4]. Host-</p><p>microbe communications have been recognized to be critical in</p><p>immunity development and metabolic regulation through various</p><p>pathways [5,6]. Gut microbiota has been regarded as a neglected</p><p>endocrine organ that modulates host homeostasis by producing a</p><p>large number of bioactive molecules from colonic fermentation of</p><p>undigested foods [7,8]. For example, short-chain fatty acids (SCFAs),</p><p>such as acetate, propionate, and butyrate produced from utilization</p><p>of dietary fibers, have shown positive effects on the host, including</p><p>the improvement of skeletal muscle growth [9]. The hypothesis</p><p>that gut microbiota is associated with the mass and function of</p><p>skeletal muscle is recently coined as the gutemuscle axis [10,11].</p><p>Sarcopenia is defined as a generalized, age-related skeletal</p><p>muscle disorder characterized by the loss of muscle mass and a</p><p>reduction of muscular function. It increases the risk of detrimental</p><p>outcomes such as falls, fractures, disability, and even mortality</p><p>[12,13]. The age-related mechanisms promoting the onset of sar-</p><p>copenia include inflammation, immunosenescence, anabolic</p><p>resistance, decreased physical activity level, and increased oxida-</p><p>tive stress [14]. Gut microbiota, on the other hand, plays an</p><p>important role in the aging process by regulating energy balance,</p><p>metabolism, and inflammation that canmodulate the development</p><p>of sarcopenia [15]. Recently, Lahiri et al. showed that skeletal</p><p>muscle mass was significantly decreased in germ-free mice and</p><p>was restored by gut microbiota colonization [16]. Fielding et al. also</p><p>demonstrated that germ-free mice receiving fecal microbiota</p><p>transplantation from high-physical function human donors</p><p>exhibited greater grip strength [17]. Moreover, Okamoto et al.</p><p>demonstrated that the gutmicrobiota is indispensable to confer the</p><p>benefits of dietary fiber for improving endurance exercise by pro-</p><p>ducing SCFAs [18]. These findings supported the importance of gut</p><p>microbiota on the development of skeletal muscle mass and func-</p><p>tion and highlighted the potential mechanisms of the gutemuscle</p><p>axis [19].</p><p>Human research aimed at investigating the links between gut</p><p>microbiota and skeletal muscle mass is still limited, especially for</p><p>the elderly subjects [20]. So far, only a few studies have reported the</p><p>gut microbial compositions of sarcopenic subjects [21e23]. Ticinesi</p><p>et al. first performed shotgun metagenome sequencing of the fecal</p><p>microbiome for 5 sarcopenic and 12 non-sarcopenic subjects and</p><p>found decreased Faecalibacterium prausnitzii and depleted genes</p><p>involved in SCFAs synthesis in sarcopenic patients [20]. More</p><p>recently, Kang et al. reported reduced diversity and altered gut</p><p>microbiota among 16 sarcopenic and 11 possibly sarcopenic pa-</p><p>tients by using 16S rRNA sequencing [21]. No study has yet</p><p>1492</p><p>quantified the fecal SCFAs produced from the gut microbiota in</p><p>sarcopenic patients and the relationship between SCFAs and skel-</p><p>etal muscle mass is still unknown. Besides, other potential mech-</p><p>anistic links between gut microbiota and sarcopenia in addition to</p><p>SCFAs may exist and require to be elucidated.</p><p>Thus, in this study, we aimed to (1) compare the gut microbial</p><p>composition between normal and sarcopenic elderly; (2) analyze</p><p>the association between the fecal SCFAs and the sarcopenia-related</p><p>parameters.</p><p>2. Materials & methods</p><p>2.1. Participants</p><p>Subjects older than 65 years were recruited from the Depart-</p><p>ment of Health Check-up, National</p><p>genomic DNA extraction and 16S rRNA gene amplicon sequencing</p><p>2.9. Bioinformatic pipeline and microbiome analysis</p><p>2.10. Analysis of SCFA in fecal samples by GC–MS</p><p>3. Results</p><p>3.1. Gut microbial biodiversity is significantly reduced in elders with low muscle mass</p><p>3.2. The elders with normal and low skeletal muscle mass have distinct composition of gut microbiota</p><p>3.3. Associations of gut microbiome features with skeletal muscle mass and related parameters</p><p>3.4. Fecal butyrate is significantly decreased in subjects with low muscle mass and correlates positively with skeletal muscle m ...</p><p>4. Discussion</p><p>5. Conclusion</p><p>Author contributions</p><p>Conflict of interest</p><p>Acknowledgements</p><p>Appendix A. Supplementary data</p><p>References</p><p>Taiwan University Hospital Bei-</p><p>Hu Branch in 2019. Participants with cerebrovascular disorders,</p><p>atrial fibrillation/flutter, cardiac pacemakers, ventricular bigeminy,</p><p>severe bony deformity, neurodegenerative diseases or malignancy</p><p>were excluded. Subjects underwent measurements of height,</p><p>weight, waist circumference, body composition, grip strength,</p><p>flexibility, and gait speed. The measurements are detailed in the</p><p>following sections. The study was approved by the Research Ethical</p><p>Committee of National Taiwan University Hospital (REC no.</p><p>20160109RIND) and all participants had provided written informed</p><p>consent.</p><p>2.2. Body composition measurement</p><p>Body composition was measured by using a multi-frequency</p><p>bioimpedance analyzer InBody 720 (BIA, Biospace, Seoul, Korea).</p><p>By using the InBody 720, total and segmental impedance and phase</p><p>angle of the alternating electric current at 6 different frequencies (1,</p><p>5, 50, 250, 500, and 1000 kHz, respectively) were measured with a</p><p>tetra-polar 8-point tactile electrode system. Participants were</p><p>asked to avoid exercising, empty their bladders, and fast for 8 h</p><p>before the test. The skin and electrodes were precleaned with</p><p>specific electrolyte tissues according to the manufacturer's in-</p><p>structions. The impedance measurements were made with the</p><p>subject standing upright with his or her bare feet on foot electrodes</p><p>on the platform of the instrument with their arms away from their</p><p>trunk while gripping handles with a palm-and-thumb electrode in</p><p>each hand. All points of contact contained electrodes. Skeletal</p><p>muscle mass, bone mineral content, fat mass, and basal metabolic</p><p>rate were calculated with the instrument using the manufacturer's</p><p>proprietary software. The multi-frequency BIA was previously</p><p>validated for the accuracy of body composition estimations in older</p><p>subjects [24]. Low appendicular skeletal muscle mass index (SMI)</p><p>was defined according to the International Working Group on</p><p>Sarcopenia: SMI <7.23 kg/m2 in male and <5.67 kg/m2 in female</p><p>[25]. Pre-sarcopenia was defined as low SMI only, and sarcopenia</p><p>was defined as low SMI plus low gait speed or low grip strength</p><p>[24,26].</p><p>2.3. Grip strength</p><p>The maximal grip strength of the dominant hand was measured</p><p>using an analog isometric dynamometer (Baseline hydraulic hand</p><p>dynamometer; Fabrication Enterprises Inc., Irvington, NY) with the</p><p>subjects seated, shoulder adducted, elbow flexed at 90�, and fore-</p><p>arm in a neutral position. The subjects were asked to squeeze the</p><p>handgrip device as forcefully as possible. The highest value of 3</p><p>attempts was recorded for the analysis.</p><p>D.-S. Han, W.-K. Wu, P.-Y. Liu et al. Clinical Nutrition 41 (2022) 1491e1500</p><p>2.4. Gait speed</p><p>The usual gait speed was calculated. The subjects were</p><p>instructed to walk in a straight line at their comfortable pace on a</p><p>level surface for 7m. The time spent walking from 1.5 to 5.5m was</p><p>recorded. The average gait speed was calculated as 4/time spent</p><p>using distance in meters and time in seconds (m/s) [24].</p><p>2.5. Flexibility (sit-and-reach test)</p><p>Flexibility was measured using the modified sit-and-reach test.</p><p>The participants extended both legs forward with their feet flat on</p><p>the floor while seated at the front edge of a chair with their arms</p><p>outstretched and hands overlapped. The participants were then</p><p>instructed to reach forward toward their toes as far as possible. The</p><p>distance between the tip of the middle finger and the toes was</p><p>recorded. A positive value indicated the ability to reach past the</p><p>toes, while a negative score indicated failure to touch the toes.</p><p>2.6. Mini nutritional assessment</p><p>The MNA test was administered to identify participants with</p><p>malnutrition or at risk of malnutrition [27]. The dimensions</p><p>assessed in the MNA test encompassed anthropometric mea-</p><p>surements (body mass index, weight loss, and the circumferences</p><p>of the calves and midarms), assessment of oral intake (amount of</p><p>protein, fruit, vegetable, fluid and overall food consumption,</p><p>number of daily full meals, mode of feeding, and medication</p><p>ingestion), self-perception of nutritional and health status, and</p><p>psychomotor well-being (recent presence of psychological stress</p><p>or acute illness, neuropsychological problems, independent living,</p><p>and existence of pressure ulcer). The maximum sum score of the</p><p>full MNA test was 30 points. A score of 24 points or more was</p><p>considered adequate nutrition status, a score between 17 and 23.5</p><p>points was considered at risk of malnutrition, and a score of less</p><p>than 17 points was considered malnourished. In the present study,</p><p>we defined an MNA score of less than 24 points as risk of</p><p>malnutrition.</p><p>2.7. International physical activity questionnaire (IPAQ)</p><p>Self-reported physical activity was obtained through the Japa-</p><p>nese version of the IPAQ (the usual 7 days, short, self-administered</p><p>version) [28]. Data from the IPAQ are summed within each item (ie,</p><p>vigorous intensity, moderate intensity, andwalking) to estimate the</p><p>total amount of time spent engaged in PA per week. Total weekly PA</p><p>(MET-min/week) was estimated by adding the products of reported</p><p>time for each item by aMET value that was specific to each category</p><p>of PA. The activity levels below 383MET-min/week in male and 270</p><p>MET-min/week in female are deemed as low [29].</p><p>2.8. Fecal genomic DNA extraction and 16S rRNA gene amplicon</p><p>sequencing</p><p>Feces were sampled and processed by using our previously</p><p>published protocol [30]. The stool DNA was extracted with Mobio</p><p>PowerFecal DNA Isolation Kit and quantified by using the NanoDrop</p><p>ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham,</p><p>MA, USA). A two-step polymerase chain reaction (PCR) workflow</p><p>was performed for library preparation in accordance with proced-</p><p>ures described in the Illumina 16S sample preparation guide. The</p><p>V3eV4 region of the 16S rRNA gene was amplified using a primer</p><p>overhanging adapter (forward ¼ 50-TCGTCGGCAGCGTCAGATGTGT</p><p>ATAAGAGACAGCCTACGGGNGGCWGCAG-30 and reverse ¼ 50-GTCTC</p><p>GTGGGCTCGGAGATGTGTATAAGAGACAGGAC-TACHVGGGTATCTAAT</p><p>1493</p><p>CC-30). Dual indices and Illumina sequencing adapters were attached</p><p>through PCR by using a Nextera XT Index Kit according to the</p><p>manufacturer's instructions. After each PCR process, PCR cleanup</p><p>was performed using AMPure XP beads to purify the V3eV4</p><p>amplicon from the free primer and primer dimer. The sizes of PCR</p><p>products were verified using the Bioanalyzer DNA 1000 chip. Library</p><p>quantification was conducted for quality control before sequencing</p><p>by using the Agilent Technologies 2100 Bioanalyzer (Agilent, Santa</p><p>Clara, CA, USA). The pooled libraries were then sequenced on the</p><p>Illumina Miseq platform (Illumina, San Diego, CA, USA) with v3 re-</p><p>agents for paired-end sequencing (2 � 300 bps). The sequencing</p><p>workflow has been published previously [30].</p><p>2.9. Bioinformatic pipeline and microbiome analysis</p><p>Raw reads were processed according to the Amplicon SOP v2 of</p><p>the Microbiome Helper workflow (https://github.com/mlangill/</p><p>microbiome_helper) with QIIME2 v.2019.10 [31]. Primer sequences</p><p>were trimmed by the ‘Cutadapt’ plugin. The ‘DADA2’ plugin was</p><p>used to identify amplicon sequence variants (ASVs) from de-</p><p>multiplexed sequence files. The taxonomy assignment was done</p><p>with a Naïve Bayes Classifier trained on the SILVA 132 database of</p><p>16S rRNA genes. Read count of each sample was rarefied to the</p><p>minimal counts among all samples (39,228 reads). Alpha diversity</p><p>indices: Shannon index and species richness were calculated by</p><p>‘diversity’ and ‘specnumber’ functions, respectively. For beta di-</p><p>versity, community compositions were measured by BrayeCurtis</p><p>dissimilarity and plotted with principal coordinates analysis</p><p>(PCoA). An ADONIS (permutational multivariate analysis of variance</p><p>using distance matrices) test was used to test heterogeneity be-</p><p>tween groups. The ASVs were collapsed into the genus and species</p><p>levels for downstream analyses. Microbiome analyses were con-</p><p>ducted with R package vegan and visualized with MARco package. A</p><p>DESeq test (DESeq2 package) with a < 0.05 as significance was used</p><p>for initial feature selection</p><p>between groups. A PLS-DA analysis</p><p>(mixOmics package) was done for identifying sarcopenia-associated</p><p>bacterial genera and species. The sex-specific SMI values were</p><p>standardized using z-score normalization for males and females as</p><p>the sex-adjusted SMI score (Figure S1). The sex-adjusted SMI score</p><p><0 was corresponding to the defined low muscle mass mentioned</p><p>above. Spearman correlation analyses were performed in this study</p><p>for identifying correlations between the microbiome, sex-adjusted</p><p>SMI, other clinical parameters, and SCFAs. Selected features with</p><p>between-group differences or significant correlation then were</p><p>visualized by heatmap with the pheatmap package. A metagenome</p><p>prediction was done by PICRUSt2 [32]. Predicted genes were an-</p><p>notated by KEGG Orthology (KO); the butyryl-CoA:acetate CoA-</p><p>transferase (EC 2.3.8.3; K01034, K01035, and K19709) genes and the</p><p>corresponding ASVs were extracted for associative analysis with</p><p>skeletal muscle mass. All numerical analyses were tested using two-</p><p>tailed Student's t-tests, ManneWhitney U tests, or ANOVA tests as</p><p>deemed appropriate under a significance level of p < 0.05. Spear-</p><p>man's correlationwas used to calculate the association between two</p><p>variables. All statistics were analyzed using R software V.3.6.3 or</p><p>GraphPad Prism (V.9).</p><p>2.10. Analysis of SCFA in fecal samples by GCeMS</p><p>Detail analytical method and validation data for SCFA quantifi-</p><p>cation in fecal sample is described in our previous article [33]. In</p><p>brief, 1 g of fecal sample was lyophilized. Fecal samples before and</p><p>after lyophilization were separately weighted for calculation SCFA</p><p>content in crude and lyophilized samples. The lyophilized sample</p><p>was suspended in 5 mL of a 0.5% phosphoric acid solution con-</p><p>taining 50 mg mL�1 sodium acetate-d3 (internal standard 1; IS1)</p><p>https://github.com/mlangill/microbiome_helper</p><p>https://github.com/mlangill/microbiome_helper</p><p>D.-S. Han, W.-K. Wu, P.-Y. Liu et al. Clinical Nutrition 41 (2022) 1491e1500</p><p>and extracted with a Geno/Grinder 2010 (SPEX, Metuchen, NJ, US),</p><p>followed by sonication for 5 min. After centrifugation, 60 mL of the</p><p>supernatant were transferred into a 1.5 mL centrifuge tube and</p><p>diluted with 240 mL of a 0.5% phosphoric acid aqueous solution. An</p><p>aliquot of 300 mL of butanol were subsequently added to the solu-</p><p>tion for liquideliquid extraction (LLE) of SCFAs, and the mixture</p><p>were extracted by the Geno/Grinder 2010. Then,180 mL of the upper</p><p>organic layer were transferred into a new tube and 20 mL of butanol</p><p>containing sodium propionate-d5were added as internal standards</p><p>(IS2). All analyses were carried out on an Agilent 7890A gas chro-</p><p>matograph equipped with a MultiPurpose Sampler MPS (GERSTEL,</p><p>Mülheim an der Ruhr, Germany) that was coupled to a Pegasus GC</p><p>-TOFMS system (Leco Corporation, St. Joseph, MI, USA). A polar VF-</p><p>WAXms capillary column (30 m � 0.25 mm i.d. X 0.25 mm film</p><p>thickness) (Agilent Technologies, Santa Clara, CA) was utilized for</p><p>the separation. The helium carrier gas flow rate was set at</p><p>1 mL min�1. One microliter of the sample was injected in the split</p><p>mode at a ratio of 1:10. The oven temperature was initially held at</p><p>70 �C for 1min and then increased to 170 �C at a rate of 10 �Cmin-1,</p><p>to 240 �C at a rate of 25 �C min-1, and finally maintained at 240 �C</p><p>for 2 min (total run time 15.8 min). The temperatures of the front</p><p>inlet, transfer line, and ion source were set at 250 �C, 250 �C, and</p><p>240 �C, respectively. The electron impact ionization was 70 eV, and</p><p>the data were acquired in full scan mode with a mass range of m/z</p><p>40e550. Data acquisition and data processing were carried out</p><p>using LECO's ChromaTOF® software (Leco Corporation, St. Joseph,</p><p>MI, USA). The identification of each SCFA was confirmed by</p><p>comparing the mass spectra and retention times with those of the</p><p>in-house library.</p><p>3. Results</p><p>We recruited a total of 88 elder participants with an average age</p><p>of 71.0 years old. According to the International Working Group on</p><p>Sarcopenia [25], 36 cases had low muscle mass and 52 were</p><p>normal. Among the subjects with low muscle mass, 12 had sarco-</p><p>penia and 24 were pre-sarcopenic based on the definition reported</p><p>previously [12,24]. Comparing with the normal muscle mass group</p><p>(NM), the low muscle mass group (LM) are older and had lower</p><p>skeletal muscle mass index (SMI), BMI, fat mass, bone mass, grip</p><p>strength, flexibility, MNA score, and physical activity (Table 1).</p><p>3.1. Gut microbial biodiversity is significantly reduced in elders with</p><p>low muscle mass</p><p>In total, 4965 amplicon sequence variants (ASVs) were discov-</p><p>ered in 88 fecal samples of the elderly, ranging from 109 to 612</p><p>ASVs per sample. In the LM group, the alpha diversity of gut</p><p>Table 1</p><p>Demographic and anthropometric data of the participants.</p><p>NM LM P value</p><p>Number of participants 52 36 e</p><p>Female (%) 61.5 77.8 0.162</p><p>Age (year) 70.0 ± 4.2 72.3 ± 5.4 0.025</p><p>Skeletal Muscle Mass Index (kg/m2) 6.89 ± 1.02 5.65 ± 0.64 <0.001</p><p>BMI (kg/m2) 22.5 ± 2.2 19.7 ± 1.7 <0.001</p><p>Fat mass (kg) 14.8 ± 4.8 12.7 ± 3.6 0.021</p><p>Bone mass (kg) 2.24 ± 0.36 1.88 ± 0.34 <0.001</p><p>Grip strength (kg) 26.8 ± 8.2 21.9 ± 5.2 0.001</p><p>Gait speed (m/s) 1.11 ± 0.23 1.05 ± 0.18 0.216</p><p>Flexibility (cm) 8.3 ± 11.5 1.8 ± 13.2 0.016</p><p>Metabolic syndrome 16 (30.8%) 5 (13.9%) 0.068</p><p>MNA score 27.10 ± 1.62 25.04 ± 2.44 <0.001</p><p>Physical activity (kcal) 3698 ± 3173 1781 ± 1284 <0.001</p><p>1494</p><p>microbiota, shown by indices including the overserved ASVs,</p><p>Shannon index, and Chao 1 index, were all remarkably decreased as</p><p>compared with the NM group (Fig. 1A) and the differences remain</p><p>to be significant after adjustment for age, BMI, MNA score and</p><p>physical activity by using multiple linear regression analysis</p><p>(Table S1A). Besides, the beta diversity of gut microbiota, which</p><p>represents the compositional profile of the gut microbiome, was</p><p>also significantly different between the NM and LM groups (adonis</p><p>p ¼ 0.037) (Fig. 1B). Interestingly, both the alpha and beta diversity</p><p>of gut microbiota in the low grip strength group showed no sig-</p><p>nificant difference from the normal grip strength group (Figure S2).</p><p>When we further divided the elderly with low muscle mass to the</p><p>pre-sarcopenia and sarcopenia groups, the two groups showed no</p><p>significant difference in alpha and beta diversity (Fig. 1C-D). These</p><p>findings suggest that gut microbiota may have a stronger correla-</p><p>tion with skeletal muscle mass than the functional performance of</p><p>skeletal muscle.</p><p>3.2. The elders with normal and low skeletal muscle mass have</p><p>distinct composition of gut microbiota</p><p>Since a significant shift in the overall gut microbiome profile</p><p>was found between the NM and LM groups, we then searched in</p><p>detail for the compositional differences. At the phylum level, the</p><p>relative abundance of Firmicutes, as well as the Firmicutes/Bacter-</p><p>oidetes ratio (F/B ratio) in the NM group were significantly higher</p><p>than those of the LM group (Fig. 2A). At the family level, Bacter-</p><p>oidaceae and Fusobacteriaceae were significantly increased in the</p><p>LM group while microbial taxa such as Ruminococcacae, Pre-</p><p>votellaceae, and Akkermansiaceaewere significantly enriched in the</p><p>NM group (Fig. 2B). At lower taxa levels, the relative abundances of</p><p>24 genera and four species were identified to be significantly</p><p>different between the NM and LM groups by using DESeq analysis</p><p>(Fig. 2C). We further confirmed the differential microbial taxa by</p><p>conventional nonparametric test which is summarized in Table 2.</p><p>3.3. Associations of gut microbiome features with skeletal muscle</p><p>mass and related parameters</p><p>Specific microbial taxa such as the genera Marvinbryantia,</p><p>Ruminococcaceae UCG-10, and Akkermansia were consistently</p><p>reduced in the LM group as compared with those of the NM group</p><p>(Fig. 3A-C). In contrast, the genus Flavonifractor was robustly</p><p>increased in the LM group when comparing with the NM group</p><p>(Fig. 3D). These findings were also consistent with the microbial</p><p>features identified in each group by using Partial Least-Squares</p><p>Discriminant Analysis (PLS-DA) (Figure S3). Besides, some poten-</p><p>tial live biotherapeutics</p><p>such as Akkermansia sp., F. prausnitzii,</p><p>Subdoligranulum sp., and Barnesiella sp. were enriched in the NM</p><p>group while comparing with the LM group (Table 2). We then</p><p>performed correlation analysis for the identified microbial taxa at</p><p>the genus and species levels with sex-adjusted SMI and other</p><p>clinical parameters. We noted that the gut microbes positively</p><p>associated with SMI usually correlate favorably with structural and</p><p>functional parameters such as Basal metabolic rate (BMR), bone</p><p>mass, and flexibility. On the other hand, the gut microbes nega-</p><p>tively associated with SMI often correlate unfavorably to these</p><p>parameters. Interestingly, Akkermansia sp., a gut-barrier protecting</p><p>bacterium that was recently regarded as a potential live bio-</p><p>therapeutic for treating diabetes [19,34], showed positive correla-</p><p>tions with grip strength. Notably, two potentially pro-inflammatory</p><p>gut bacteria, Ruminococcus gnavus [35] and Fusobacterium sp. [36],</p><p>were inversely correlated with bone mass, BMR, gait speed and</p><p>flexibility, while uniquely showing positive correlations with the</p><p>body fat mass (Fig. 3E).</p><p>Fig. 1. The biodiversity of gut microbiota are different between the normal skeletal muscle mass (NM) and low skeletal muscle mass (LM) groups. (A) The alpha diversity of</p><p>gut microbiota, including observed amplicon sequencing variants (ASVs), Shannon index, and Chao1 index, were significantly decreased in the LM group as compared with the NM</p><p>group. A two-tailed Student's t-test was used for statistical analysis. (B) The beta diversity of gut microbiota displayed by principal coordinates analysis (PCoA) based on the</p><p>BrayeCurtis dissimilarity metrics showed significantly different gut microbiome profiles between the NM and LM groups. A permutational multivariate analysis of variance using</p><p>distance matrices (adonis) was used for statistical analysis. (C) The elderly subjects in the LM group were subgrouped into the pre-sarcopenia (Pre-SAR) and sarcopenia (SAR) groups</p><p>based on the functionality of skeletal muscle. The alpha diversity of gut microbiota, including ASVs, Shannon index, and Chao1 index in the Pre-SAR and SAR groups, were both</p><p>significantly lower than those in the NM group whereas no significant difference in alpha diversity was noted between the Pre-SAR and SAR groups. One-way ANOVA with Holm-</p><p>�Síd�ak post-hoc test was used for statistical analysis. (D) The beta diversity based on the BrayeCurtis dissimilarity metrics showed insignificant difference between the Pre-SAR and</p><p>SAR groups.</p><p>Fig. 2. Features of gut microbiota in the elderly significantly different between normal skeletal muscle mass (NM) and low skeletal muscle mass (LM) groups. (A) The</p><p>presence of the phylum Firmicutes and the Firmicutes/Bacteroidetes ratio were significantly higher in the NM group when comparing with the LM group. Bars represent the</p><p>mean ± S.E.M for the indicated groups. (B) The families Bacteroidaceae and Fusobacteriaceae were significantly increased in the LM group while the families Ruminococcacae,</p><p>Prevotellaceae, and Akkermansiaceae were significantly enriched in the NM group. (C) A heatmap diagram illustrating the significantly different microbial features (p < 0.05)</p><p>selected by DESeq at the genus and species levels between the NM and LM groups in the elders. * denotes a q-value < 0.05 by DESeq2.</p><p>D.-S. Han, W.-K. Wu, P.-Y. Liu et al. Clinical Nutrition 41 (2022) 1491e1500</p><p>1495</p><p>Table 2</p><p>Gut microbiome significantly different between the normal muscle mass (NM) and low muscle mass (LM) groups.</p><p>Bacterial taxa Normal group (NM) Low muscle mass (LM) P value FDR</p><p>Genus level</p><p>Marvinbryantiaa 0.035 ± 0.007% 0.006 ± 0.002% <0.001 0.0075</p><p>Family XIII UCG-001 0.036 ± 0.005% 0.011 ± 0.004% <0.001 0.0618</p><p>Flavonifractora 0.164 ± 0.041% 0.419 ± 0.083% <0.001 0.0618</p><p>Oxalobacter 0.026 ± 0.007% 0.012 ± 0.006% 0.0015 0.0618</p><p>Ruminococcaceae UCG-003 0.280 ± 0.036% 0.149 ± 0.040% 0.0016 0.0618</p><p>Subdoligranuluma 2.339 ± 0.323% 1.412 ± 0.395% 0.0017 0.0618</p><p>Ruminococcaceae UCG-010 0.057 ± 0.021% 0.003 ± 0.002% 0.0017 0.0618</p><p>Family XIII AD3011 group 0.067 ± 0.012% 0.025 ± 0.006% 0.0031 0.0979</p><p>Ruminococcaceae NK4A214 group 0.195 ± 0.047% 0.062 ± 0.031% 0.0034 0.0979</p><p>Leuconostoc 0.004 ± 0.001% 0.00 ± 0.00% 0.0044 0.0982</p><p>Christensenellaceae R-7 group 0.483 ± 0.159% 0.131 ± 0.038% 0.0045 0.0982</p><p>Dorea 0.620 ± 0.090% 0.363 ± 0.076% 0.0046 0.0982</p><p>Ruminococcaceae UCG-005 0.242 ± 0.054% 0.058 ± 0.018% 0.0068 0.1350</p><p>Sellimonas 0.015 ± 0.007% 0.047 ± 0.021% 0.0094 0.1724</p><p>Peptococcus 0.032 ± 0.011% 0.006 ± 0.004% 0.0111 0.1900</p><p>Paraprevotella 0.757 ± 0.136% 0.364 ± 0.164% 0.0128 0.2051</p><p>Lachnospiraceae UCG-010 0.209 ± 0.035% 0.152 ± 0.049% 0.0150 0.2153</p><p>Ruminococcaceae UCG-002 1.169 ± 0.168% 0.788 ± 0.232% 0.0151 0.2153</p><p>Terrisporobacter 0.011 ± 0.003% 0.002 ± 0.002% 0.0197 0.2661</p><p>Odoribacter 0.394 ± 0.088% 0.226 ± 0.065% 0.0218 0.2801</p><p>Akkermansiaa 0.986 ± 0.311% 0.402 ± 0.257% 0.0245 0.295</p><p>Lachnoclostridium 5 0.00 ± 0.00% 0.007 ± 0.005% 0.0253 0.295</p><p>Alistipes 2.501 ± 0.327% 1.700 ± 0.390% 0.0288 0.3104</p><p>Lachnospiraceae NK4A136 group 0.870 ± 0.176% 0.449 ± 0.112% 0.0300 0.3104</p><p>Ruminococcaceae UCG-014 1.042 ± 0.301% 0.495 ± 0.196% 0.0302 0.3104</p><p>[Eubacterium] coprostanoligenes group 1.421 ± 0.295% 0.566 ± 0.145% 0.0323 0.3195</p><p>Bacteroidesa 30.79 ± 1.99% 37.82 ± 2.36% 0.0337 0.3211</p><p>Ruminiclostridium 9 0.119 ± 0.023% 0.092 ± 0.024% 0.0380 0.3489</p><p>Barnesiella 0.764 ± 0.170% 0.459 ± 0.238% 0.0446 0.3954</p><p>Eggerthella 0.018 ± 0.004% 0.035 ± 0.008% 0.0494 0.4230</p><p>Species level</p><p>Bacteroides eggerthii DSM 20697 0.065 ± 0.018% 0.011 ± 0.007% 0.0094 0.7795</p><p>Lachnoclostridium phocaeense 0.00 ± 0.00% 0.0003 ± 0.0001% 0.0096 0.7795</p><p>Faecalibacterium prausnitziia 0.006 ± 0.001% 0.002 ± 0.001% 0.0225 0.9185</p><p>Gabonia massiliensis 0.001 ± 0.001% 0.00 ± 0.00% 0.0227 0.9185</p><p>Parabacteroides goldsteinii CL02T12C30 0.009 ± 0.003% 0.008 ± 0.004% 0.0392 >0.9999</p><p>Parabacteroides johnsonii CL02T12C29 0.017 ± 0.013% 0.004 ± 0.004% 0.0394 >0.9999</p><p>a The bacteria mentioned and discussed in the text and figures.</p><p>D.-S. Han, W.-K. Wu, P.-Y. Liu et al. Clinical Nutrition 41 (2022) 1491e1500</p><p>3.4. Fecal butyrate is significantly decreased in subjects with low</p><p>muscle mass and correlates positively with skeletal muscle mass</p><p>index</p><p>We then investigated whether fecal SCFAs, the principal gut</p><p>microbial fermenting products, were correlated with the skeletal</p><p>muscle mass in the 88 elderly subjects. Six common SCFAs</p><p>including acetate, propionate, butyrate, isobutyrate, valerate, and</p><p>isovalerate were quantified by using GCeMS and were compared</p><p>between the NM and LM groups. Importantly, only butyrate and</p><p>valerate concentrations in the feces showed significant differences</p><p>between the NM and LM groups (Fig. 4A). To verify the findings, we</p><p>applied a lyophilized method we developed previously to</p><p>normalize the bias caused by fecal water content and showed</p><p>consistent results [33] (Fig. 4B). Besides, fecal SCFAs of the normal</p><p>and low grip groups had no significant difference (Figure S4A),</p><p>suggesting the fecal SCFAs may be associated with muscle mass</p><p>greater than muscle function. We also noted that fecal butyrate</p><p>correlated significantly positively to the sex-adjusted SMI (p < 0.01)</p><p>(Fig. 4C) while all other SCFAs showed no significant correlations</p><p>with the sex-adjusted SMI (Figure S4B). We further performed</p><p>correlation analysis of the fecal SCFAs with other body parameters</p><p>and found that butyrate also correlated positively with flexibility,</p><p>while fecal valerate correlated with bone mass and BMR (Fig. 4D).</p><p>The positive association between fecal butyrate level and SMI re-</p><p>mains to be significant after adjustment for age, BMI, MNA score</p><p>and physical activity by using multiple linear regression analysis</p><p>1496</p><p>(Table S1B). These findings suggest that butyrate produced by the</p><p>gut fermentation of dietary fiber, exhibits a significant positive</p><p>correlation with skeletal muscle mass in the elderly. We then per-</p><p>formed functional prediction analysis of butyrate-producing ASVs</p><p>by the PICRUSt2 pipeline and extract the ASVs predicted</p><p>to contain</p><p>genes encoding butyryl-CoA:acetate CoA-transferase, an essential</p><p>enzyme responsible for butyrate production in gut fermentation.</p><p>Among the 599 ASVs predicted to be butyrate-producing bacteria,</p><p>two ASVs annotated as Odoribacter sp. and Ruminiclostridium 9 sp.</p><p>were found to be significantly increased (FDR<0.1) in the NM group</p><p>as compared with the LM group.</p><p>4. Discussion</p><p>In this study, the gutemuscle axis theory is supported by our data</p><p>that the low-muscle-mass elders had featured gut microbiome and</p><p>decreased butyrate level in their feces. Some gut microbial features,</p><p>such asMarvinbryantia, Akkermansia, Subdoligranulum, Flavonifractor</p><p>and F. prausnitzii were significantly correlated with low skeletal</p><p>muscle mass in the elders, providing a potential insight for investi-</p><p>gating microbe-mediated pathways of sarcopenia. Importantly, this</p><p>study first demonstrates that fecal butyrate is significantly correlated</p><p>with skeletal muscle mass in human, which suggests butyrate as a</p><p>biomarker relating to muscle mass for the elderly.</p><p>A recent study by Kang et al. revealed that elders having low</p><p>muscle mass had significantly lower alpha diversity in the micro-</p><p>bial communities [22]. Our results further showed similar gut</p><p>Fig. 3. Association of microbial features with skeletal muscle mass and other clinical parameters. (AeD) Comparisons of selected microbial features significantly different</p><p>between the normal muscle (NM) and low muscle mass (LM) groups. The genera Marvinbryantia, Subdoligranulum, and Akkermansia were significantly enriched in the NM group</p><p>than in the LM group. The genus of Flavonifractorwas significantly increased in the LM group as compared with the NM group. ManneWhitney U test with Benjamini and Hochberg</p><p>correction for multiple comparisons was used for the statistical analysis. Bars represent the mean ± S.E.M for the indicated groups. (E) A heatmap diagram illustrating the cor-</p><p>relations of significant microbial features selected by DESeq with clinical parameters, including the scaled skeletal muscle mass index (SMI). Microbial features positively correlating</p><p>with SMI usually have favorable correlations with other clinical parameters, whereas features inversely correlating with the scaled SMI showed unfavorable associations with other</p><p>clinical parameters. Spearman rank correlation test was used for correlating analysis, * & ** denote p < 0.05 & p < 0.01, respectively. BMI, body mass index; SMI, skeletal muscle</p><p>index; BONEM, bone mass; BMR, basic metabolic rate; FATP, fat percentage.</p><p>D.-S. Han, W.-K. Wu, P.-Y. Liu et al. Clinical Nutrition 41 (2022) 1491e1500</p><p>microbial biodiversity in elders who had low or normal grip</p><p>strength and concluded that gut microbiota may have a greater link</p><p>to the muscle mass than the muscle function. Muscle mass is</p><p>usually positively correlated with muscle strength (function).</p><p>However, comorbidities and exercise status can modify the rela-</p><p>tionship between them. With similar muscle mass, diabetes and</p><p>osteoarthritis impaired the muscle strength, and exercise enhances</p><p>it [37]. Despite there is no functional correlation, the remarkable</p><p>correlations between skeletal mass vs gut microbiota and its me-</p><p>tabolites in our findings could possibly serve as biomarker or</p><p>therapeutic target of muscle mass loss for the elder people. In</p><p>addition, this study showed a significant shift in gut microbiome</p><p>between the NM and LM groups. Specifically, the F/B ratio</p><p>decreased in the LM group, which may reflect the significantly</p><p>lower BMI in the LM elders. It is worth noting that the mean BMI of</p><p>the NM group (22.5 kg/m2) is within the normal range while that of</p><p>the LM group (19.7 kg/m2) appears to be underweight for the</p><p>elderly population [38]. Interestingly, the higher F/B ratio was</p><p>commonly regarded as a marker of gut dysbiosis in obese patients</p><p>[39]; however, in this case, it may become a protective hallmark of</p><p>favorable body composition and metabolic status in elderly people.</p><p>This finding is consistent with a recent study conducted by Bork</p><p>et al. reporting that participants with favorable metabolic status</p><p>have increased F/B ratio in a middle-aged Asian population [40]. At</p><p>the family level, our data showed that Prevotellaceae increased in</p><p>the NM group, consistent with a recent report from Fielding et al. in</p><p>which Prevotellaceae was enriched in the high physical functioning</p><p>group comparing with the low physical functioning group [17].</p><p>At a low taxonomic level, the genus of Marvinbryantia is signif-</p><p>icantly more abundant in the NM group as compared with</p><p>the LM group (FDR <0.01) by multiple analytical approaches. Mar-</p><p>vinbryantia is a cellulose-degrading bacterial genus with only</p><p>one known species named Marvinbryantia formatexigens which</p><p>1497</p><p>naturally inhabits the human gut [41]. M. formatexigens is recog-</p><p>nized as a gut acetogen being characterized by acetate generation in</p><p>the human gut and was found to boost the yield of succinate in vivo</p><p>by Gordon et al. [42]. Notably, the predominant butyrate-producing</p><p>pathway in human gut microbiota is using exogenously derived</p><p>acetate to generate butyrate via the butyryl-CoA:acetate Co-A</p><p>transferase [43], which implies the production of fecal butyratemay</p><p>rely on the generation of acetate fromM. formatexigens. Besides, the</p><p>succinate-boosting effect of M. formatexigens could induce meta-</p><p>bolic benefits by functioning as an intestinal gluconeogenic sub-</p><p>strate [44]. Therefore, we consider the reduction ofM. formatexigens</p><p>in the gut microbiota a biologically meaningful hallmark for sar-</p><p>copenic patients, though further study is still required to confirm its</p><p>causeeeeffect relationship.</p><p>In the present study, the genus of Flavonifractorwas remarkably</p><p>enriched in the LM group. Flavonifractor spp. are flavonoid-</p><p>degrading bacteria living in the human gut [45]. Flavonoids such</p><p>as quercetin are considered as senolytic agents that drive apoptosis</p><p>for the senescent cells by inhibiting AKT-, Bcl-2-, and p21/serpin-</p><p>related pro-survival pathways [46]. A recent double-blind ran-</p><p>domized clinical trial has shown that flavonoid supplement at-</p><p>tenuates oxidative stress and inflammation while improving</p><p>skeletal muscle index and mobility in elder subjects [47]. It is</p><p>plausible that the flavonoid-degrading effect of Flavonifractor spp.</p><p>may cripple the metabolic benefits brought by dietary flavonoids</p><p>for elderly people. Consistently, Gupta et al. discovered Flavoni-</p><p>fractor plautii to be highly enriched in the feces of colorectal cancer</p><p>patients which was thought to be linked with the degradation of</p><p>beneficial anticarcinogenic flavonoids [48]. Thus, Flavonifractor spp.</p><p>might be a nutritionally unfavorable gut bacteria for the aging</p><p>process and the preservation of skeletal muscle mass preservation.</p><p>The hallmark finding of this study is that the butyrate concen-</p><p>tration is significantly higher in the feces of the normal elders and is</p><p>Fig. 4. Fecal butyrate is highly associated with skeletal muscle mass among the elder subjects. (A) The quantification of fecal short-chain fatty acids (SCFAs) in the elders by</p><p>GCeMS showed significantly different butyrate and valerate levels between normal muscle (NM) and low muscle mass (LM) groups. (B) A validated method of fecal SCFAs</p><p>measurement with lyophilized fecal samples showed consistent and reproducible results. ManneWhitney U test was used for statistical group comparison. For the fecal SCFA</p><p>analysis, *, **, and *** denote FDR <0.1, FDR <0.05, and FDR <0.01, respectively. Bars represent the mean ± S.E.M for the indicated groups. (C) The fecal concentration of butyrate in</p><p>the elderly significantly correlated with the scaled SMI score (p < 0.01). (D) A heatmap diagram demonstrating the correlations between the six SCFAs with SMI and other clinical</p><p>parameters. For the heatmap diagram, * & ** denote Spearman correlation p < 0.05 & p < 0.01, respectively. BMI, body mass index; FATP, fat percentage; SMI, skeletal muscle index;</p><p>BONEM, bone mass; BMR, basic metabolic rate.</p><p>D.-S. Han, W.-K. Wu, P.-Y. Liu et al. Clinical</p><p>Nutrition 41 (2022) 1491e1500</p><p>positively correlated with skeletal muscle mass. In particular, these</p><p>findings were further confirmed by using a validated lyophilized</p><p>method of fecal SCFAs measurement which adjusts for the bias</p><p>caused by different water contents of fecal samples [33]. Impor-</p><p>tantly, this is the first study reporting human fecal SCFAs among</p><p>normal elders and sarcopenic subjects while recent animal studies</p><p>have discovered the benefits of SCFAs, especially the butyrate, on</p><p>skeletal muscle mass [9,16]. In this study, the fecal butyrate is most</p><p>significantly increased in the elders with preserved skeletal muscle</p><p>mass, whereas other fecal SCFAs, including acetate and propionate,</p><p>are unremarkable. Among the common SCFAs, the butyrate is in</p><p>particular evident in maintaining gut barrier and immunomodula-</p><p>tory functions [49]. For examples, butyrate is a unique fuel source for</p><p>mammalian colonocytes to regulate intestinal energy metabolism</p><p>[50]. Butyrate is also a specific commensal metabolite which sup-</p><p>presses colonic inflammation via GPR109a signaling [51] and en-</p><p>hances the intestinal barrier via AMPK pathway [52]. The butyrate's</p><p>gut barrier protecting effect could be important in preventing</p><p>translocation of microbe-associated molecular pattern (MAMP) into</p><p>blood stream and peripheral organs (including muscle) that may</p><p>alleviate chronic inflammation [53]. In contrast, the acetate and</p><p>propionate were reported to stimulate adipogenesis via GPCR43</p><p>signaling [54] andmight be negatively associated with muscle mass</p><p>development. Unlike other SCFAs, the butyrate has histone-</p><p>deacetylase (HDAC) inhibitory activity which can unwind DNA to</p><p>regulate intestinal macrophage and promote peripheral regulatory T</p><p>cell [55,56], enhance muscle differentiation and reduce muscle at-</p><p>rophy [57]. Our data also revealed a positive association between</p><p>1498</p><p>butyrate and Subdoligranulum, a recognized butyrate-producer [58],</p><p>and a negative association between butyrate and Bacteroides which</p><p>is consistent with a recent study showing that butyrate condition-</p><p>ally inhibits Bacteroides [59] (Figure S3C). These unique and recog-</p><p>nized effects of butyrate may allow it to be more positively</p><p>correlated with muscle mass. Our findings in the elderly people are</p><p>consistent with a recent study which integrated shotgun meta-</p><p>genome sequencing and serum SCFAs in the healthy menopause</p><p>women [60]. Interestingly, a butyrate derivative, b-hydroxy-b-</p><p>methylbutyrate (HMB), has been used to increase the performance</p><p>of the athlete in the field of sports medicine [61] and was shown to</p><p>preserve muscle mass in populations at risk of lean body mass loss,</p><p>especially in older adults [62]. Further studies are warranted to</p><p>show the clinical efficacy and mechanism of butyrate on enhancing</p><p>muscle mass and function for the elderly sarcopenic patient.</p><p>Some limitations of this study should be mentioned. First, this is</p><p>a cross-section study recruiting elder subjects receiving health</p><p>check-ups at a community hospital. Due to possible selection bias,</p><p>ethnical and geographical considerations should be taken during</p><p>the generalization process. Second, we did not collect detailed di-</p><p>etary record in this study and was not able to evaluate the contri-</p><p>bution of the fiber intake on skeletal muscle mass and the levels</p><p>fecal SCFAs. Besides, despite the differences in nutritional status</p><p>(MNA score) and level of physical activity between the two groups</p><p>were not statistically significant by multiple linear regression</p><p>analysis, the difference in muscle mass between the two groups</p><p>could also be due to malnutrition and/or physical inactivity in</p><p>addition to variations in the microbiota. Third, we did not measure</p><p>D.-S. Han, W.-K. Wu, P.-Y. Liu et al. Clinical Nutrition 41 (2022) 1491e1500</p><p>the total volume of stool and plasma SCFA, so we could not obtain</p><p>the whole volume and distribution of short chain fatty acids of each</p><p>subject. In addition, the identification of gut bacteria by using 16S</p><p>rRNA ASVs has limited taxonomic resolution, thus incurring bias in</p><p>annotating specific bacterial species or strains for the differential</p><p>sequences between groups. Further analysis by shotgun meta-</p><p>genome sequencing is necessary for acquiring more specific taxo-</p><p>nomic and functional information between groups.</p><p>5. Conclusion</p><p>The elders with low skeletal muscle mass have altered gut mi-</p><p>crobial community and lower fecal butyrate levels. Specific gut</p><p>bacteria, including SCFAs-producing Marvinbryantia sp., Akker-</p><p>mansia sp., and F. prausnitzii were enriched in the feces of normal</p><p>elders while the flavonoid-degrading Flavonifractor spp. were</p><p>increased in that with lowmuscle mass. For the first time, the fecal</p><p>butyrate level is robustly measured to be higher in normal elders</p><p>when comparing with low muscle mass elders and correlates</p><p>positively with the SMI. We believe this study provides important</p><p>translational knowledge for future clinical trials and insightful re-</p><p>sults for further investigation of the gut microbiotaemuscle axis,</p><p>especially in the low muscle mass-related clinical conditions.</p><p>Author contributions</p><p>D.S.H. conceived the study design, conducted volunteer</p><p>recruitment, collected data and drafted the manuscript; W.KW.</p><p>conceived the study design, assisted sample management, critically</p><p>analyzed the collected data and drafted the manuscript; P.Y.L.</p><p>conducted the bioinformatics analysis; Y.T.Y. assisted sample prep-</p><p>aration for microbiome sequencing and SCFA analysis; H.C.H per-</p><p>formed the fecal SCFA analysis; C.H.K asisted and supervised fecal</p><p>SCFA analysis; M.S.W. and T.G.W. supervised the study, provided</p><p>experimental resources, and critically revised the manuscript.</p><p>Conflict of interest</p><p>The authors declare that they have no conflict of interest.</p><p>Acknowledgements</p><p>The study was sponsored by the research funding from the</p><p>National Taiwan University Hospital Beihu Branch, Taipei, Taiwan;</p><p>Ministry of Science and Technology, Taiwan (MOST 107-2314-B-</p><p>002-047-MY3, 110-2321-B-001 -010 - to DS Han; MOST 108-2314-</p><p>B-002 -166 -MY3 to TG Wang; MOST 109-2314-B-002 -064 -MY3,</p><p>110-2628-B-002 -046 - to WK Wu), and the ‘Center of Precision</p><p>Medicine’ from The Featured Areas Research Center Program by the</p><p>MOST. We would like to acknowledge the service provided by the</p><p>Medical Microbiota Center of the First Core Laboratory, National</p><p>Taiwan University College of Medicine. We thank the English</p><p>editing service provided by Dr. Shu-Yi Huang from the Department</p><p>of Medical Research, National Taiwan University Hospital. The au-</p><p>thors would like to express their thanks to the staff of National</p><p>Taiwan University Hospital-Statistical Consulting Unit (NTUH-SCU)</p><p>for statistical consultation and analyses. The authors of this</p><p>manuscript certify that they comply with the ethical guidelines for</p><p>authorship and publishing in Clinical Nutrition.</p><p>Appendix A. Supplementary data</p><p>Supplementary data to this article can be found online at</p><p>https://doi.org/10.1016/j.clnu.2022.05.008.</p><p>1499</p><p>References</p><p>[1] Sender R, Fuchs S, Milo R. Revised estimates for the number of human and</p><p>bacteria cells in the body. 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in the gut microbiome and reduced fecal butyrate in elders with low skeletal muscle mass</p><p>1. 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